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# A kernel function which improves the accuracy and interpretability of large ensembles of neural networks
We describe a new kernel (i.e. similarity function between pairs of examples) which is computed using an ensemble of neural networks. It has the following properties:
- Using it to predict test labels (via k-nearest neighbors across the training set) yields even higher accuracy than the standard ensemble inference method
of averaging predictions, once the number of networks exceeds about 100. We believe this kernel + k-NN method is the state-of-the-art for inferencing large ensembles
(although such ensembles are rarely used in practice).
- Being a similarity function, it is highly interpretable. For each test example, it allows us to visualize training examples which are deemed to have
similar features by the training process, with much greater fidelity than e.g. penultimate layer embeddings. For instance, we use this to identify the (known) fact that
~10% of the CIFAR-10 test-set examples have a near-duplicate in the training set, and to identify a failure mode.
To compute the kernel for an ensemble of n=500 models, we provide the following simple code (which can be copy-paste run in your environment).
```
import torch
import torchvision
import huggingface_hub
def normalize(logits):
logits = logits.float()
logits = logits.log_softmax(-1)
logits = (logits - logits.mean(0, keepdim=True)) / logits.std(0, keepdim=True)
return logits
def compute_kernel(logits1, logits2):
logits1 = normalize(logits1)
logits2 = normalize(logits2)
assert len(logits1) == len(logits2)
kernel = torch.zeros(logits1.shape[1], logits2.shape[1]).cuda()
for c in range(10):
logits1_cls = logits1[..., c].cuda()
logits2_cls = logits2[..., c].cuda()
corr_cls = (logits1_cls.T @ logits2_cls) / len(logits1)
kernel += corr_cls / 10
return kernel
######################################################################################
# Setup: Download CIFAR-10 labels and the outputs from 500 repeated training runs. #
######################################################################################
labels_train = torch.tensor(torchvision.datasets.CIFAR10('cifar10', train=True).targets)
labels_test = torch.tensor(torchvision.datasets.CIFAR10('cifar10', train=False).targets)
api = huggingface_hub.HfApi()
fname = 'logs_saveoutputs_main/06109e85-f5d7-4ac8-b0b0-f03542f23234/log.pt'
obj_path = api.hf_hub_download('kjj0/cifar10-multirun-logits', repo_type='dataset',
filename=fname)
obj = torch.load(obj_path, map_location='cpu')
# print(obj['code']) # Uncomment if you want to see the training code
######################################################################################
# Evaluate both the per-model and ensembled accuracy of the training outputs. #
######################################################################################
each_acc = (obj['logits'].argmax(-1) == labels_test).float().mean(1)
avg_acc = each_acc.mean()
print('average single-model accuracy \t: %.2f' % (100 * avg_acc))
ens_pred = obj['logits'].mean(0).argmax(1)
ens_acc = (ens_pred == labels_test).float().mean()
print('ensemble accuracy (%d models) \t: %.2f' % (len(obj['logits']), 100 * ens_acc))
# (n.b. averaging probabilities instead of logits makes no difference)
######################################################################################
# Evaluate the new kernel / ensemble inference method. #
######################################################################################
# use correlations between log_softmax outputs as a similarity metric for k-NN inference.
kernel = compute_kernel(obj['logits'], obj['logits_train'])
k = 3
nbrs = kernel.topk(k, dim=1)
nbr_labels = labels_train[nbrs.indices.cpu()]
pred = nbr_labels.mode(1).values
acc = (pred == labels_test).float().mean()
print('kernel accuracy (k-NN w/ k=%d) \t: %.2f' % (k, 100 * acc))
## average single-model accuracy : 93.26
## ensemble accuracy (500 models) : 94.69
## kernel accuracy (k-NN w/ k=3) : 95.01
```
The training configuration we used to generate these 500 models (i.e. the script that we re-ran 500 times with different random seeds) yields a mean accuracy of 93.26%.
If we average the predictions across those 500 models, we attain a much improved accuracy of 94.69%.
If we predict the test-set labels using our kernel applied to pairs of (train, test) examples, using k-nearest neighbors with k=3,
then we attain an even higher accuracy of 95.01%.
We include 20,000 total runs of training for the same training configuration that generated the 500 runs used in the above.
The outputs of those runs (i.e. the logits predicted by the final model on the training and test examples) can be found as the other files in `logs_saveoutputs_main`.
If we compute the kernel with all 20,000 runs instead of 500, and use a weighting scheme based on the correlation values,
then the accuracy can be futher increased to 95.53%.
Note that increasing from 500 to 20,000 does not improve the accuracy of the averaged predictions,
so with 95.53% we have reached 0.84% higher than the standard ensemble accuracy.
We additionally include outputs from three other training configurations; their kernels seem to have the same properties.
## Interpretability-type applications
### Finding similar pairs
(Below:) We rank the CIFAR-10 test-set examples by their similarity to their most similar training-set example.
We show the 601th-648th most highly ranked test examples (out of 10,000), along with their matched training examples.
Many of them turn out to be visually similar pairs.
We note that the penultimate-layer features almost entirely lack this property --
if we visualize the most similar pairs across all (test, train) pairs according to distance in penultimate feature space,
we will get not duplicates but instead just random highly confident examples which have all presumably collapsed to a similar point in space.
On the other hand, pairs which are given a high similarity score by our correlation kernel turn out to often be near-duplicates, and this holds true
for the most similar pairs even when we reduce the number of models in the ensemble down to a relatively small value like 10 or 20.
### Diagnosing failure modes
(Below:) We rank the CIFAR-10 test examples by how similar their most similar training-set example is, and then filter for cases where they have different labels.
The first (leftmost) column contains the top 8 such test examples, and then subsequent columns are their 9 nearest neighbors in the training set.
It appears that our network has difficulty seeing small objects.
### Some random examples
(Below:) We select 10 CIFAR-10 test examples at random (the first row), and display their two nearest neighbors according to the kernel (second two rows),
and the penultimate features from a single model (next two rows). The kernel yields images which are perceptually similar, whereas penultimate features
select nearly a random image of the same label.
## Open questions
* The usage of `log_softmax` in the normalization step seems to be important, especially for making the kernel work with n < 1,000 (where n is the number of networks).
But for n -> infty, it becomes less important. Why -- is it somehow removing noise?
* Via the Neural Network Gaussian Process (NNGP) theory, it is possible to compute the expectation of this kernel for untrained / newly initialized networks
(at least if the log-softmax is removed). Is there any general theory for what this kernel becomes after training (i.e., what we are seeing here)?
* This kernel is implemented as a sum of 10 correlation kernels -- one for each class. But upon inspection, each of those has dramatically worse
k-NN accuracy than their sum, at least until n becomes on the order of thousands. Why?
* Removing log-softmax, despite harming the overall accuracy as discussed earlier,
apparently increases the k-NN accuracy (and generally quality) of the individual kernels. Why??
* How does this kernel compare to [TRAK](https://arxiv.org/abs/2303.14186)
or the datamodel embeddings from [https://arxiv.org/abs/2202.00622](https://arxiv.org/abs/2202.00622)?
| kjj0/cifar10-multirun-logits | [
"license:mit",
"arxiv:2303.14186",
"arxiv:2202.00622",
"region:us"
] | 2024-01-14T07:46:15+00:00 | {"license": "mit"} | 2024-01-14T20:54:31+00:00 | [
"2303.14186",
"2202.00622"
] | [] | TAGS
#license-mit #arxiv-2303.14186 #arxiv-2202.00622 #region-us
|
# A kernel function which improves the accuracy and interpretability of large ensembles of neural networks
We describe a new kernel (i.e. similarity function between pairs of examples) which is computed using an ensemble of neural networks. It has the following properties:
- Using it to predict test labels (via k-nearest neighbors across the training set) yields even higher accuracy than the standard ensemble inference method
of averaging predictions, once the number of networks exceeds about 100. We believe this kernel + k-NN method is the state-of-the-art for inferencing large ensembles
(although such ensembles are rarely used in practice).
- Being a similarity function, it is highly interpretable. For each test example, it allows us to visualize training examples which are deemed to have
similar features by the training process, with much greater fidelity than e.g. penultimate layer embeddings. For instance, we use this to identify the (known) fact that
~10% of the CIFAR-10 test-set examples have a near-duplicate in the training set, and to identify a failure mode.
To compute the kernel for an ensemble of n=500 models, we provide the following simple code (which can be copy-paste run in your environment).
The training configuration we used to generate these 500 models (i.e. the script that we re-ran 500 times with different random seeds) yields a mean accuracy of 93.26%.
If we average the predictions across those 500 models, we attain a much improved accuracy of 94.69%.
If we predict the test-set labels using our kernel applied to pairs of (train, test) examples, using k-nearest neighbors with k=3,
then we attain an even higher accuracy of 95.01%.
We include 20,000 total runs of training for the same training configuration that generated the 500 runs used in the above.
The outputs of those runs (i.e. the logits predicted by the final model on the training and test examples) can be found as the other files in 'logs_saveoutputs_main'.
If we compute the kernel with all 20,000 runs instead of 500, and use a weighting scheme based on the correlation values,
then the accuracy can be futher increased to 95.53%.
Note that increasing from 500 to 20,000 does not improve the accuracy of the averaged predictions,
so with 95.53% we have reached 0.84% higher than the standard ensemble accuracy.
We additionally include outputs from three other training configurations; their kernels seem to have the same properties.
## Interpretability-type applications
### Finding similar pairs
(Below:) We rank the CIFAR-10 test-set examples by their similarity to their most similar training-set example.
We show the 601th-648th most highly ranked test examples (out of 10,000), along with their matched training examples.
Many of them turn out to be visually similar pairs.
!the 600-650th most similar pairs
We note that the penultimate-layer features almost entirely lack this property --
if we visualize the most similar pairs across all (test, train) pairs according to distance in penultimate feature space,
we will get not duplicates but instead just random highly confident examples which have all presumably collapsed to a similar point in space.
On the other hand, pairs which are given a high similarity score by our correlation kernel turn out to often be near-duplicates, and this holds true
for the most similar pairs even when we reduce the number of models in the ensemble down to a relatively small value like 10 or 20.
### Diagnosing failure modes
(Below:) We rank the CIFAR-10 test examples by how similar their most similar training-set example is, and then filter for cases where they have different labels.
The first (leftmost) column contains the top 8 such test examples, and then subsequent columns are their 9 nearest neighbors in the training set.
It appears that our network has difficulty seeing small objects.
!the highest-confidence failures
### Some random examples
(Below:) We select 10 CIFAR-10 test examples at random (the first row), and display their two nearest neighbors according to the kernel (second two rows),
and the penultimate features from a single model (next two rows). The kernel yields images which are perceptually similar, whereas penultimate features
select nearly a random image of the same label.
!randomly chosen test examples, with their most similar train examples
## Open questions
* The usage of 'log_softmax' in the normalization step seems to be important, especially for making the kernel work with n < 1,000 (where n is the number of networks).
But for n -> infty, it becomes less important. Why -- is it somehow removing noise?
* Via the Neural Network Gaussian Process (NNGP) theory, it is possible to compute the expectation of this kernel for untrained / newly initialized networks
(at least if the log-softmax is removed). Is there any general theory for what this kernel becomes after training (i.e., what we are seeing here)?
* This kernel is implemented as a sum of 10 correlation kernels -- one for each class. But upon inspection, each of those has dramatically worse
k-NN accuracy than their sum, at least until n becomes on the order of thousands. Why?
* Removing log-softmax, despite harming the overall accuracy as discussed earlier,
apparently increases the k-NN accuracy (and generally quality) of the individual kernels. Why??
* How does this kernel compare to TRAK
or the datamodel embeddings from URL
| [
"# A kernel function which improves the accuracy and interpretability of large ensembles of neural networks\n\nWe describe a new kernel (i.e. similarity function between pairs of examples) which is computed using an ensemble of neural networks. It has the following properties:\n\n- Using it to predict test labels (via k-nearest neighbors across the training set) yields even higher accuracy than the standard ensemble inference method\nof averaging predictions, once the number of networks exceeds about 100. We believe this kernel + k-NN method is the state-of-the-art for inferencing large ensembles\n(although such ensembles are rarely used in practice).\n- Being a similarity function, it is highly interpretable. For each test example, it allows us to visualize training examples which are deemed to have\nsimilar features by the training process, with much greater fidelity than e.g. penultimate layer embeddings. For instance, we use this to identify the (known) fact that\n~10% of the CIFAR-10 test-set examples have a near-duplicate in the training set, and to identify a failure mode.\n\nTo compute the kernel for an ensemble of n=500 models, we provide the following simple code (which can be copy-paste run in your environment).\n\n\n\nThe training configuration we used to generate these 500 models (i.e. the script that we re-ran 500 times with different random seeds) yields a mean accuracy of 93.26%.\nIf we average the predictions across those 500 models, we attain a much improved accuracy of 94.69%.\nIf we predict the test-set labels using our kernel applied to pairs of (train, test) examples, using k-nearest neighbors with k=3,\nthen we attain an even higher accuracy of 95.01%.\n\nWe include 20,000 total runs of training for the same training configuration that generated the 500 runs used in the above.\nThe outputs of those runs (i.e. the logits predicted by the final model on the training and test examples) can be found as the other files in 'logs_saveoutputs_main'.\nIf we compute the kernel with all 20,000 runs instead of 500, and use a weighting scheme based on the correlation values,\nthen the accuracy can be futher increased to 95.53%.\nNote that increasing from 500 to 20,000 does not improve the accuracy of the averaged predictions,\nso with 95.53% we have reached 0.84% higher than the standard ensemble accuracy.\n\nWe additionally include outputs from three other training configurations; their kernels seem to have the same properties.",
"## Interpretability-type applications",
"### Finding similar pairs\n\n(Below:) We rank the CIFAR-10 test-set examples by their similarity to their most similar training-set example.\nWe show the 601th-648th most highly ranked test examples (out of 10,000), along with their matched training examples.\nMany of them turn out to be visually similar pairs.\n\n!the 600-650th most similar pairs\n\nWe note that the penultimate-layer features almost entirely lack this property --\nif we visualize the most similar pairs across all (test, train) pairs according to distance in penultimate feature space,\nwe will get not duplicates but instead just random highly confident examples which have all presumably collapsed to a similar point in space.\nOn the other hand, pairs which are given a high similarity score by our correlation kernel turn out to often be near-duplicates, and this holds true\nfor the most similar pairs even when we reduce the number of models in the ensemble down to a relatively small value like 10 or 20.",
"### Diagnosing failure modes\n\n(Below:) We rank the CIFAR-10 test examples by how similar their most similar training-set example is, and then filter for cases where they have different labels.\nThe first (leftmost) column contains the top 8 such test examples, and then subsequent columns are their 9 nearest neighbors in the training set.\nIt appears that our network has difficulty seeing small objects.\n\n!the highest-confidence failures",
"### Some random examples\n\n(Below:) We select 10 CIFAR-10 test examples at random (the first row), and display their two nearest neighbors according to the kernel (second two rows),\nand the penultimate features from a single model (next two rows). The kernel yields images which are perceptually similar, whereas penultimate features\nselect nearly a random image of the same label.\n\n!randomly chosen test examples, with their most similar train examples",
"## Open questions\n\n* The usage of 'log_softmax' in the normalization step seems to be important, especially for making the kernel work with n < 1,000 (where n is the number of networks).\nBut for n -> infty, it becomes less important. Why -- is it somehow removing noise?\n* Via the Neural Network Gaussian Process (NNGP) theory, it is possible to compute the expectation of this kernel for untrained / newly initialized networks\n(at least if the log-softmax is removed). Is there any general theory for what this kernel becomes after training (i.e., what we are seeing here)?\n* This kernel is implemented as a sum of 10 correlation kernels -- one for each class. But upon inspection, each of those has dramatically worse\nk-NN accuracy than their sum, at least until n becomes on the order of thousands. Why?\n* Removing log-softmax, despite harming the overall accuracy as discussed earlier,\napparently increases the k-NN accuracy (and generally quality) of the individual kernels. Why??\n* How does this kernel compare to TRAK\nor the datamodel embeddings from URL"
] | [
"TAGS\n#license-mit #arxiv-2303.14186 #arxiv-2202.00622 #region-us \n",
"# A kernel function which improves the accuracy and interpretability of large ensembles of neural networks\n\nWe describe a new kernel (i.e. similarity function between pairs of examples) which is computed using an ensemble of neural networks. It has the following properties:\n\n- Using it to predict test labels (via k-nearest neighbors across the training set) yields even higher accuracy than the standard ensemble inference method\nof averaging predictions, once the number of networks exceeds about 100. We believe this kernel + k-NN method is the state-of-the-art for inferencing large ensembles\n(although such ensembles are rarely used in practice).\n- Being a similarity function, it is highly interpretable. For each test example, it allows us to visualize training examples which are deemed to have\nsimilar features by the training process, with much greater fidelity than e.g. penultimate layer embeddings. For instance, we use this to identify the (known) fact that\n~10% of the CIFAR-10 test-set examples have a near-duplicate in the training set, and to identify a failure mode.\n\nTo compute the kernel for an ensemble of n=500 models, we provide the following simple code (which can be copy-paste run in your environment).\n\n\n\nThe training configuration we used to generate these 500 models (i.e. the script that we re-ran 500 times with different random seeds) yields a mean accuracy of 93.26%.\nIf we average the predictions across those 500 models, we attain a much improved accuracy of 94.69%.\nIf we predict the test-set labels using our kernel applied to pairs of (train, test) examples, using k-nearest neighbors with k=3,\nthen we attain an even higher accuracy of 95.01%.\n\nWe include 20,000 total runs of training for the same training configuration that generated the 500 runs used in the above.\nThe outputs of those runs (i.e. the logits predicted by the final model on the training and test examples) can be found as the other files in 'logs_saveoutputs_main'.\nIf we compute the kernel with all 20,000 runs instead of 500, and use a weighting scheme based on the correlation values,\nthen the accuracy can be futher increased to 95.53%.\nNote that increasing from 500 to 20,000 does not improve the accuracy of the averaged predictions,\nso with 95.53% we have reached 0.84% higher than the standard ensemble accuracy.\n\nWe additionally include outputs from three other training configurations; their kernels seem to have the same properties.",
"## Interpretability-type applications",
"### Finding similar pairs\n\n(Below:) We rank the CIFAR-10 test-set examples by their similarity to their most similar training-set example.\nWe show the 601th-648th most highly ranked test examples (out of 10,000), along with their matched training examples.\nMany of them turn out to be visually similar pairs.\n\n!the 600-650th most similar pairs\n\nWe note that the penultimate-layer features almost entirely lack this property --\nif we visualize the most similar pairs across all (test, train) pairs according to distance in penultimate feature space,\nwe will get not duplicates but instead just random highly confident examples which have all presumably collapsed to a similar point in space.\nOn the other hand, pairs which are given a high similarity score by our correlation kernel turn out to often be near-duplicates, and this holds true\nfor the most similar pairs even when we reduce the number of models in the ensemble down to a relatively small value like 10 or 20.",
"### Diagnosing failure modes\n\n(Below:) We rank the CIFAR-10 test examples by how similar their most similar training-set example is, and then filter for cases where they have different labels.\nThe first (leftmost) column contains the top 8 such test examples, and then subsequent columns are their 9 nearest neighbors in the training set.\nIt appears that our network has difficulty seeing small objects.\n\n!the highest-confidence failures",
"### Some random examples\n\n(Below:) We select 10 CIFAR-10 test examples at random (the first row), and display their two nearest neighbors according to the kernel (second two rows),\nand the penultimate features from a single model (next two rows). The kernel yields images which are perceptually similar, whereas penultimate features\nselect nearly a random image of the same label.\n\n!randomly chosen test examples, with their most similar train examples",
"## Open questions\n\n* The usage of 'log_softmax' in the normalization step seems to be important, especially for making the kernel work with n < 1,000 (where n is the number of networks).\nBut for n -> infty, it becomes less important. Why -- is it somehow removing noise?\n* Via the Neural Network Gaussian Process (NNGP) theory, it is possible to compute the expectation of this kernel for untrained / newly initialized networks\n(at least if the log-softmax is removed). Is there any general theory for what this kernel becomes after training (i.e., what we are seeing here)?\n* This kernel is implemented as a sum of 10 correlation kernels -- one for each class. But upon inspection, each of those has dramatically worse\nk-NN accuracy than their sum, at least until n becomes on the order of thousands. Why?\n* Removing log-softmax, despite harming the overall accuracy as discussed earlier,\napparently increases the k-NN accuracy (and generally quality) of the individual kernels. Why??\n* How does this kernel compare to TRAK\nor the datamodel embeddings from URL"
] | [
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"passage: # A kernel function which improves the accuracy and interpretability of large ensembles of neural networks\n\nWe describe a new kernel (i.e. similarity function between pairs of examples) which is computed using an ensemble of neural networks. It has the following properties:\n\n- Using it to predict test labels (via k-nearest neighbors across the training set) yields even higher accuracy than the standard ensemble inference method\nof averaging predictions, once the number of networks exceeds about 100. We believe this kernel + k-NN method is the state-of-the-art for inferencing large ensembles\n(although such ensembles are rarely used in practice).\n- Being a similarity function, it is highly interpretable. For each test example, it allows us to visualize training examples which are deemed to have\nsimilar features by the training process, with much greater fidelity than e.g. penultimate layer embeddings. For instance, we use this to identify the (known) fact that\n~10% of the CIFAR-10 test-set examples have a near-duplicate in the training set, and to identify a failure mode.\n\nTo compute the kernel for an ensemble of n=500 models, we provide the following simple code (which can be copy-paste run in your environment).\n\n\n\nThe training configuration we used to generate these 500 models (i.e. the script that we re-ran 500 times with different random seeds) yields a mean accuracy of 93.26%.\nIf we average the predictions across those 500 models, we attain a much improved accuracy of 94.69%.\nIf we predict the test-set labels using our kernel applied to pairs of (train, test) examples, using k-nearest neighbors with k=3,\nthen we attain an even higher accuracy of 95.01%.\n\nWe include 20,000 total runs of training for the same training configuration that generated the 500 runs used in the above.\nThe outputs of those runs (i.e. the logits predicted by the final model on the training and test examples) can be found as the other files in 'logs_saveoutputs_main'.\nIf we compute the kernel with all 20,000 runs instead of 500, and use a weighting scheme based on the correlation values,\nthen the accuracy can be futher increased to 95.53%.\nNote that increasing from 500 to 20,000 does not improve the accuracy of the averaged predictions,\nso with 95.53% we have reached 0.84% higher than the standard ensemble accuracy.\n\nWe additionally include outputs from three other training configurations; their kernels seem to have the same properties.## Interpretability-type applications### Finding similar pairs\n\n(Below:) We rank the CIFAR-10 test-set examples by their similarity to their most similar training-set example.\nWe show the 601th-648th most highly ranked test examples (out of 10,000), along with their matched training examples.\nMany of them turn out to be visually similar pairs.\n\n!the 600-650th most similar pairs\n\nWe note that the penultimate-layer features almost entirely lack this property --\nif we visualize the most similar pairs across all (test, train) pairs according to distance in penultimate feature space,\nwe will get not duplicates but instead just random highly confident examples which have all presumably collapsed to a similar point in space.\nOn the other hand, pairs which are given a high similarity score by our correlation kernel turn out to often be near-duplicates, and this holds true\nfor the most similar pairs even when we reduce the number of models in the ensemble down to a relatively small value like 10 or 20.### Diagnosing failure modes\n\n(Below:) We rank the CIFAR-10 test examples by how similar their most similar training-set example is, and then filter for cases where they have different labels.\nThe first (leftmost) column contains the top 8 such test examples, and then subsequent columns are their 9 nearest neighbors in the training set.\nIt appears that our network has difficulty seeing small objects.\n\n!the highest-confidence failures### Some random examples\n\n(Below:) We select 10 CIFAR-10 test examples at random (the first row), and display their two nearest neighbors according to the kernel (second two rows),\nand the penultimate features from a single model (next two rows). The kernel yields images which are perceptually similar, whereas penultimate features\nselect nearly a random image of the same label.\n\n!randomly chosen test examples, with their most similar train examples"
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d80a8c712fe1b70935e1230231cdf3d0d7ce2d04 | **Context**
The dataset contains the Hindi and English subtitles for famous YouTube channels. This dataset was mainly created for the Hindi Language channel since the main goal was to use this dataset to build LLMs using the Hindi Language.
Data from channels in Information, Entertainment, Politics, Comedy, News, etc categories has been included in this dataset.
***Dataset Stats:***
- **58 channels**
- **103,042 total videos**
**Content**
- Video subtitles in Hindi and English
- Video metadata like duration, number of comments, likes, counts, published date
**Acknowledgements**
The source of this dataset is YouTube. The following packages were used to generate this dataset:
- [youtube-transcript-api](https://pypi.org/project/youtube-transcript-api/)
- [google-api-python-client](https://pypi.org/project/google-api-python-client/)
**Inspiration**
- Build LLMs model using Hindi
- Finetune models using Hindi for tasks like classification, summarization, translation, etc | pardeep/youtube-vidoes-transcripts-hindi-english | [
"license:odc-by",
"region:us"
] | 2024-01-14T07:47:23+00:00 | {"license": "odc-by"} | 2024-01-20T07:22:28+00:00 | [] | [] | TAGS
#license-odc-by #region-us
| Context
The dataset contains the Hindi and English subtitles for famous YouTube channels. This dataset was mainly created for the Hindi Language channel since the main goal was to use this dataset to build LLMs using the Hindi Language.
Data from channels in Information, Entertainment, Politics, Comedy, News, etc categories has been included in this dataset.
*Dataset Stats:*
- 58 channels
- 103,042 total videos
Content
- Video subtitles in Hindi and English
- Video metadata like duration, number of comments, likes, counts, published date
Acknowledgements
The source of this dataset is YouTube. The following packages were used to generate this dataset:
- youtube-transcript-api
- google-api-python-client
Inspiration
- Build LLMs model using Hindi
- Finetune models using Hindi for tasks like classification, summarization, translation, etc | [] | [
"TAGS\n#license-odc-by #region-us \n"
] | [
14
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35134b0621b1c627dbe9fe3157092de25bbff48a |
# Dataset Card for Evaluation run of abideen/NexoNimbus-MoE-2x7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [abideen/NexoNimbus-MoE-2x7B](https://huggingface.co/abideen/NexoNimbus-MoE-2x7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_abideen__NexoNimbus-MoE-2x7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T07:48:44.681252](https://huggingface.co/datasets/open-llm-leaderboard/details_abideen__NexoNimbus-MoE-2x7B/blob/main/results_2024-01-14T07-48-44.681252.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
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"mc1_stderr": 0.016801860466677157,
"mc2": 0.5305573947385855,
"mc2_stderr": 0.015330669260578354
},
"harness|arc:challenge|25": {
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},
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"acc_norm_stderr": 0.024836057868294677
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4787234042553192,
"acc_stderr": 0.029800481645628693,
"acc_norm": 0.4787234042553192,
"acc_norm_stderr": 0.029800481645628693
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47522816166883963,
"acc_stderr": 0.012754553719781753,
"acc_norm": 0.47522816166883963,
"acc_norm_stderr": 0.012754553719781753
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6985294117647058,
"acc_stderr": 0.027875982114273168,
"acc_norm": 0.6985294117647058,
"acc_norm_stderr": 0.027875982114273168
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6715686274509803,
"acc_stderr": 0.018999707383162666,
"acc_norm": 0.6715686274509803,
"acc_norm_stderr": 0.018999707383162666
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.04461272175910509,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.02812342933514278,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.02812342933514278
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8557213930348259,
"acc_stderr": 0.024845753212306046,
"acc_norm": 0.8557213930348259,
"acc_norm_stderr": 0.024845753212306046
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.03487350880197769,
"acc_norm": 0.86,
"acc_norm_stderr": 0.03487350880197769
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5662650602409639,
"acc_stderr": 0.03858158940685516,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685516
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.027539122889061456,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061456
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3598531211750306,
"mc1_stderr": 0.016801860466677157,
"mc2": 0.5305573947385855,
"mc2_stderr": 0.015330669260578354
},
"harness|winogrande|5": {
"acc": 0.8153117600631413,
"acc_stderr": 0.010905978112156888
},
"harness|gsm8k|5": {
"acc": 0.535253980288097,
"acc_stderr": 0.01373820799017732
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_abideen__NexoNimbus-MoE-2x7B | [
"region:us"
] | 2024-01-14T07:50:59+00:00 | {"pretty_name": "Evaluation run of abideen/NexoNimbus-MoE-2x7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [abideen/NexoNimbus-MoE-2x7B](https://huggingface.co/abideen/NexoNimbus-MoE-2x7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abideen__NexoNimbus-MoE-2x7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T07:48:44.681252](https://huggingface.co/datasets/open-llm-leaderboard/details_abideen__NexoNimbus-MoE-2x7B/blob/main/results_2024-01-14T07-48-44.681252.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6460879831062891,\n \"acc_stderr\": 0.032126633738886544,\n \"acc_norm\": 0.6490547896974873,\n \"acc_norm_stderr\": 0.03277080381411241,\n \"mc1\": 0.3598531211750306,\n \"mc1_stderr\": 0.016801860466677157,\n \"mc2\": 0.5305573947385855,\n \"mc2_stderr\": 0.015330669260578354\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6228668941979523,\n \"acc_stderr\": 0.014163366896192598,\n \"acc_norm\": 0.6680887372013652,\n \"acc_norm_stderr\": 0.013760988200880536\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6683927504481179,\n \"acc_stderr\": 0.004698285350019219,\n \"acc_norm\": 0.856602270464051,\n \"acc_norm_stderr\": 0.0034976171082184023\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055273,\n \"acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055273\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268542,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268542\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.03510766597959215,\n \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.03510766597959215\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047709,\n \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047709\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.02247325333276878,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.02247325333276878\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35185185185185186,\n \"acc_stderr\": 0.02911661760608301,\n \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02911661760608301\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887048,\n \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887048\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461783,\n \"acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461783\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.033981108902946366,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.033981108902946366\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n \"acc_stderr\": 0.013586619219903338,\n \"acc_norm\": 0.8250319284802043,\n \"acc_norm_stderr\": 0.013586619219903338\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.02394851290546835,\n \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.02394851290546835\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.376536312849162,\n \"acc_stderr\": 0.016204672385106603,\n \"acc_norm\": 0.376536312849162,\n \"acc_norm_stderr\": 0.016204672385106603\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7253086419753086,\n \"acc_stderr\": 0.024836057868294677,\n \"acc_norm\": 0.7253086419753086,\n \"acc_norm_stderr\": 0.024836057868294677\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47522816166883963,\n \"acc_stderr\": 0.012754553719781753,\n \"acc_norm\": 0.47522816166883963,\n \"acc_norm_stderr\": 0.012754553719781753\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6985294117647058,\n \"acc_stderr\": 0.027875982114273168,\n \"acc_norm\": 0.6985294117647058,\n \"acc_norm_stderr\": 0.027875982114273168\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162666,\n \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162666\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.02812342933514278,\n \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.02812342933514278\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n \"acc_stderr\": 0.024845753212306046,\n \"acc_norm\": 0.8557213930348259,\n \"acc_norm_stderr\": 0.024845753212306046\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n \"acc_norm_stderr\": 0.03858158940685516\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3598531211750306,\n \"mc1_stderr\": 0.016801860466677157,\n \"mc2\": 0.5305573947385855,\n \"mc2_stderr\": 0.015330669260578354\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8153117600631413,\n \"acc_stderr\": 0.010905978112156888\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.535253980288097,\n \"acc_stderr\": 0.01373820799017732\n }\n}\n```", "repo_url": "https://huggingface.co/abideen/NexoNimbus-MoE-2x7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_14T07_48_44.681252", "path": 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#region-us
|
# Dataset Card for Evaluation run of abideen/NexoNimbus-MoE-2x7B
Dataset automatically created during the evaluation run of model abideen/NexoNimbus-MoE-2x7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T07:48:44.681252(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of abideen/NexoNimbus-MoE-2x7B\n\n\n\nDataset automatically created during the evaluation run of model abideen/NexoNimbus-MoE-2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T07:48:44.681252(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of abideen/NexoNimbus-MoE-2x7B\n\n\n\nDataset automatically created during the evaluation run of model abideen/NexoNimbus-MoE-2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T07:48:44.681252(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of abideen/NexoNimbus-MoE-2x7B\n\n\n\nDataset automatically created during the evaluation run of model abideen/NexoNimbus-MoE-2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T07:48:44.681252(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]"
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] |
2f26c813ce505b067ebc395b9c67de15e1d18e8d |
# Dataset of hatakaze/旗風/旗风 (Azur Lane)
This is the dataset of hatakaze/旗風/旗风 (Azur Lane), containing 22 images and their tags.
The core tags of this character are `glasses, animal_ears, long_hair, yellow_eyes, round_eyewear, very_long_hair, twintails, braid, tail, breasts, fox_ears`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 22 | 34.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatakaze_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 22 | 18.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatakaze_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 58 | 41.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatakaze_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 22 | 30.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatakaze_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 58 | 59.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatakaze_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hatakaze_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, solo, looking_at_viewer, wide_sleeves, black_kimono, leaf, long_sleeves, hakama_short_skirt, holding, pleated_skirt, sleeves_past_wrists |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | wide_sleeves | black_kimono | leaf | long_sleeves | hakama_short_skirt | holding | pleated_skirt | sleeves_past_wrists |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:---------------|:---------------|:-------|:---------------|:---------------------|:----------|:----------------|:----------------------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/hatakaze_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T07:52:04+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T07:57:54+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of hatakaze/旗風/旗风 (Azur Lane)
=====================================
This is the dataset of hatakaze/旗風/旗风 (Azur Lane), containing 22 images and their tags.
The core tags of this character are 'glasses, animal\_ears, long\_hair, yellow\_eyes, round\_eyewear, very\_long\_hair, twintails, braid, tail, breasts, fox\_ears', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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bd14b91f1fd569fdd75488533afc9b4e52f4df3c |
# Dataset of wichita/ウィチタ/威奇塔 (Azur Lane)
This is the dataset of wichita/ウィチタ/威奇塔 (Azur Lane), containing 25 images and their tags.
The core tags of this character are `breasts, long_hair, red_hair, red_eyes, large_breasts, bangs, ponytail, hair_between_eyes, ahoge, ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 25 | 32.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wichita_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 25 | 18.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wichita_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 59 | 37.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wichita_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 25 | 28.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wichita_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 59 | 51.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wichita_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/wichita_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, black_gloves, cleavage, white_shirt, belt, black_pants, looking_at_viewer, red_rose, black_choker, black_jacket, earrings, solo, collared_shirt, formal, holding_cup, navel, black_footwear, blue_jacket, cannon, crossed_arms, full_body, grin, high_heels, indoors, night, official_alternate_costume, open_clothes, partially_unbuttoned, standing, wine_glass |
| 1 | 11 |  |  |  |  |  | 1girl, cleavage, jacket_on_shoulders, navel, solo, thighhighs, white_gloves, collarbone, epaulettes, garter_straps, hair_ribbon, looking_at_viewer, midriff, simple_background, miniskirt, open_mouth, black_bra, holding, medium_breasts, military_uniform, stomach, very_long_hair, white_background, white_jacket, white_skirt, :d, armpits, blush, bow, bra_peek |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | cleavage | white_shirt | belt | black_pants | looking_at_viewer | red_rose | black_choker | black_jacket | earrings | solo | collared_shirt | formal | holding_cup | navel | black_footwear | blue_jacket | cannon | crossed_arms | full_body | grin | high_heels | indoors | night | official_alternate_costume | open_clothes | partially_unbuttoned | standing | wine_glass | jacket_on_shoulders | thighhighs | white_gloves | collarbone | epaulettes | garter_straps | hair_ribbon | midriff | simple_background | miniskirt | open_mouth | black_bra | holding | medium_breasts | military_uniform | stomach | very_long_hair | white_background | white_jacket | white_skirt | :d | armpits | blush | bow | bra_peek |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------|:--------------|:-------|:--------------|:--------------------|:-----------|:---------------|:---------------|:-----------|:-------|:-----------------|:---------|:--------------|:--------|:-----------------|:--------------|:---------|:---------------|:------------|:-------|:-------------|:----------|:--------|:-----------------------------|:---------------|:-----------------------|:-----------|:-------------|:----------------------|:-------------|:---------------|:-------------|:-------------|:----------------|:--------------|:----------|:--------------------|:------------|:-------------|:------------|:----------|:-----------------|:-------------------|:----------|:-----------------|:-------------------|:---------------|:--------------|:-----|:----------|:--------|:------|:-----------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | | X | | | | X | | | | | X | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/wichita_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T07:52:10+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T07:59:08+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of wichita/ウィチタ/威奇塔 (Azur Lane)
=======================================
This is the dataset of wichita/ウィチタ/威奇塔 (Azur Lane), containing 25 images and their tags.
The core tags of this character are 'breasts, long\_hair, red\_hair, red\_eyes, large\_breasts, bangs, ponytail, hair\_between\_eyes, ahoge, ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
354ef33e9d68dda66f126443fbee07aee6957944 |
# Dataset of l_indomptable/ランドンターブル/不屈 (Azur Lane)
This is the dataset of l_indomptable/ランドンターブル/不屈 (Azur Lane), containing 24 images and their tags.
The core tags of this character are `blue_eyes, long_hair, multicolored_hair, white_hair, breasts, hair_bun, very_long_hair, black_hair, double_bun, bangs, hair_between_eyes, small_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 24 | 50.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_indomptable_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 24 | 23.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_indomptable_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 60 | 51.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_indomptable_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 24 | 43.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_indomptable_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 60 | 82.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_indomptable_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/l_indomptable_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, solo, looking_at_viewer, blush, open_mouth, simple_background, white_background, white_dress, white_pantyhose, gradient_hair, twintails |
| 1 | 6 |  |  |  |  |  | 1girl, navel, blush, looking_at_viewer, solo, nipples, pussy, barefoot, on_back, spread_legs, underwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | blush | open_mouth | simple_background | white_background | white_dress | white_pantyhose | gradient_hair | twintails | navel | nipples | pussy | barefoot | on_back | spread_legs | underwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:-------------|:--------------------|:-------------------|:--------------|:------------------|:----------------|:------------|:--------|:----------|:--------|:-----------|:----------|:--------------|:------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | | | | | | | | X | X | X | X | X | X | X |
| CyberHarem/l_indomptable_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T08:05:29+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T08:13:43+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of l\_indomptable/ランドンターブル/不屈 (Azur Lane)
=================================================
This is the dataset of l\_indomptable/ランドンターブル/不屈 (Azur Lane), containing 24 images and their tags.
The core tags of this character are 'blue\_eyes, long\_hair, multicolored\_hair, white\_hair, breasts, hair\_bun, very\_long\_hair, black\_hair, double\_bun, bangs, hair\_between\_eyes, small\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
98d15ce0fe02ad779bd2a04467c9caaf89fc6b72 |
# Dataset of jersey/ジャージー/泽西 (Azur Lane)
This is the dataset of jersey/ジャージー/泽西 (Azur Lane), containing 13 images and their tags.
The core tags of this character are `long_hair, red_hair, twintails, bangs, breasts, low_twintails, very_long_hair, yellow_eyes, ahoge, antenna_hair, bow, brown_eyes, crown, hair_ornament, small_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 13 | 21.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jersey_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 13 | 11.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jersey_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 36 | 27.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jersey_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 13 | 18.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jersey_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 36 | 39.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jersey_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/jersey_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, blush, solo, looking_at_viewer, open_mouth, detached_sleeves, bare_shoulders, dress, sleeveless |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | solo | looking_at_viewer | open_mouth | detached_sleeves | bare_shoulders | dress | sleeveless |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:-------------|:-------------------|:-----------------|:--------|:-------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X |
| CyberHarem/jersey_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T08:32:24+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T08:37:25+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of jersey/ジャージー/泽西 (Azur Lane)
======================================
This is the dataset of jersey/ジャージー/泽西 (Azur Lane), containing 13 images and their tags.
The core tags of this character are 'long\_hair, red\_hair, twintails, bangs, breasts, low\_twintails, very\_long\_hair, yellow\_eyes, ahoge, antenna\_hair, bow, brown\_eyes, crown, hair\_ornament, small\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
fc5bb41d385306a5931727202251189e5ebb25a2 |
# Dataset of u_96/U-96 (Azur Lane)
This is the dataset of u_96/U-96 (Azur Lane), containing 14 images and their tags.
The core tags of this character are `breasts, yellow_eyes, bangs, small_breasts, twintails, goggles_on_head, white_hair, long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 14 | 18.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_96_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 10.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_96_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 32 | 24.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_96_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 15.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_96_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 32 | 31.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_96_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/u_96_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, solo, looking_at_viewer, goggles, open_mouth, smile, open_jacket, white_one-piece_swimsuit, blue_jacket, yellow_gloves, bubble, holding, hood_down |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | goggles | open_mouth | smile | open_jacket | white_one-piece_swimsuit | blue_jacket | yellow_gloves | bubble | holding | hood_down |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:----------|:-------------|:--------|:--------------|:---------------------------|:--------------|:----------------|:---------|:----------|:------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/u_96_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T08:32:43+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T08:36:38+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of u\_96/U-96 (Azur Lane)
=================================
This is the dataset of u\_96/U-96 (Azur Lane), containing 14 images and their tags.
The core tags of this character are 'breasts, yellow\_eyes, bangs, small\_breasts, twintails, goggles\_on\_head, white\_hair, long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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27c4b579fe83512fad94577b5297bb5b39ce0b3c |
# Dataset of sao_martinho/サン・マルチーニョ/圣马丁号 (Azur Lane)
This is the dataset of sao_martinho/サン・マルチーニョ/圣马丁号 (Azur Lane), containing 14 images and their tags.
The core tags of this character are `breasts, large_breasts, long_hair, red_eyes, white_hair, very_long_hair, bangs, dark-skinned_female, dark_skin, wings`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 14 | 30.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sao_martinho_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 14.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sao_martinho_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 35 | 30.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sao_martinho_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 25.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sao_martinho_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 35 | 47.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sao_martinho_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sao_martinho_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bare_shoulders, center_opening, blush, navel, white_dress, cleavage, smile, breast_curtains |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | bare_shoulders | center_opening | blush | navel | white_dress | cleavage | smile | breast_curtains |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------------|:-----------------|:--------|:--------|:--------------|:-----------|:--------|:------------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/sao_martinho_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T08:32:58+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T08:36:44+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of sao\_martinho/サン・マルチーニョ/圣马丁号 (Azur Lane)
===================================================
This is the dataset of sao\_martinho/サン・マルチーニョ/圣马丁号 (Azur Lane), containing 14 images and their tags.
The core tags of this character are 'breasts, large\_breasts, long\_hair, red\_eyes, white\_hair, very\_long\_hair, bangs, dark-skinned\_female, dark\_skin, wings', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
c8613764eb1916b488c85672a645cf50c01e7f90 |
# Dataset of kersaint/ケルサン/凯尔圣 (Azur Lane)
This is the dataset of kersaint/ケルサン/凯尔圣 (Azur Lane), containing 30 images and their tags.
The core tags of this character are `breasts, long_hair, red_eyes, bangs, blonde_hair, large_breasts, very_long_hair, twintails`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 30 | 66.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kersaint_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 30 | 29.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kersaint_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 80 | 64.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kersaint_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 30 | 54.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kersaint_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 80 | 106.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kersaint_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kersaint_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | cleavage, looking_at_viewer, 1girl, pantyhose, solo, veil, white_dress, long_sleeves, simple_background, white_background, blush, closed_mouth, covered_navel, hair_between_eyes, pelvic_curtain, clothing_cutout, black_footwear, bodystocking, detached_sleeves, high_heels, thigh_strap |
| 1 | 10 |  |  |  |  |  | 1girl, cleavage, blush, midriff, navel, visor_cap, black_jacket, solo, sweat, looking_at_viewer, open_jacket, white_sports_bra, tight_pants, water_bottle, armpits, collarbone, holding_bottle, open_mouth, sunglasses, white_pants, bare_shoulders, black_footwear, long_sleeves, parted_lips, sneakers, stomach, yoga_pants |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | cleavage | looking_at_viewer | 1girl | pantyhose | solo | veil | white_dress | long_sleeves | simple_background | white_background | blush | closed_mouth | covered_navel | hair_between_eyes | pelvic_curtain | clothing_cutout | black_footwear | bodystocking | detached_sleeves | high_heels | thigh_strap | midriff | navel | visor_cap | black_jacket | sweat | open_jacket | white_sports_bra | tight_pants | water_bottle | armpits | collarbone | holding_bottle | open_mouth | sunglasses | white_pants | bare_shoulders | parted_lips | sneakers | stomach | yoga_pants |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------|:--------------------|:--------|:------------|:-------|:-------|:--------------|:---------------|:--------------------|:-------------------|:--------|:---------------|:----------------|:--------------------|:-----------------|:------------------|:-----------------|:---------------|:-------------------|:-------------|:--------------|:----------|:--------|:------------|:---------------|:--------|:--------------|:-------------------|:--------------|:---------------|:----------|:-------------|:-----------------|:-------------|:-------------|:--------------|:-----------------|:--------------|:-----------|:----------|:-------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | | X | | | X | | | X | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/kersaint_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T08:50:25+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T08:59:06+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of kersaint/ケルサン/凯尔圣 (Azur Lane)
========================================
This is the dataset of kersaint/ケルサン/凯尔圣 (Azur Lane), containing 30 images and their tags.
The core tags of this character are 'breasts, long\_hair, red\_eyes, bangs, blonde\_hair, large\_breasts, very\_long\_hair, twintails', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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920515c73ef9730cf0f10d41eed4dab0bc52ade2 |
# Dataset of abukuma/阿武隈/阿武隈 (Azur Lane)
This is the dataset of abukuma/阿武隈/阿武隈 (Azur Lane), containing 16 images and their tags.
The core tags of this character are `bangs, black_hair, hair_ornament, horns, red_eyes, ahoge, hairclip, pointy_ears, breasts, earrings, facial_mark, fang, hair_between_eyes, short_hair, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 16 | 14.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abukuma_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 16 | 12.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abukuma_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 39 | 24.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abukuma_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 16 | 13.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abukuma_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 39 | 27.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abukuma_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/abukuma_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, solo, bare_shoulders, jewelry, looking_at_viewer, sideboob, smile, thighhighs, bell, closed_mouth, cloud, detached_sleeves, medium_breasts, open_mouth, outdoors, shirt, shorts, sky |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | bare_shoulders | jewelry | looking_at_viewer | sideboob | smile | thighhighs | bell | closed_mouth | cloud | detached_sleeves | medium_breasts | open_mouth | outdoors | shirt | shorts | sky |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------------|:----------|:--------------------|:-----------|:--------|:-------------|:-------|:---------------|:--------|:-------------------|:-----------------|:-------------|:-----------|:--------|:---------|:------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/abukuma_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T08:50:31+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T08:55:09+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of abukuma/阿武隈/阿武隈 (Azur Lane)
======================================
This is the dataset of abukuma/阿武隈/阿武隈 (Azur Lane), containing 16 images and their tags.
The core tags of this character are 'bangs, black\_hair, hair\_ornament, horns, red\_eyes, ahoge, hairclip, pointy\_ears, breasts, earrings, facial\_mark, fang, hair\_between\_eyes, short\_hair, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
2fa5d26bd06febe33738c11a868845e23a76c84b |
# Dataset of hardy/ハーディ/勇敢 (Azur Lane)
This is the dataset of hardy/ハーディ/勇敢 (Azur Lane), containing 22 images and their tags.
The core tags of this character are `blonde_hair, hat, blue_eyes, short_hair, ribbon, bangs, braid, hair_bun`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 22 | 27.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hardy_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 22 | 16.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hardy_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 54 | 35.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hardy_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 22 | 24.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hardy_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 54 | 48.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hardy_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hardy_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | white_gloves, 1girl, cape, solo, rapier, epaulettes, looking_at_viewer, holding_sword, simple_background, boots, uniform, white_background, full_body, open_mouth, pleated_skirt, saber_(weapon), smile, torpedo_tubes, white_skirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | white_gloves | 1girl | cape | solo | rapier | epaulettes | looking_at_viewer | holding_sword | simple_background | boots | uniform | white_background | full_body | open_mouth | pleated_skirt | saber_(weapon) | smile | torpedo_tubes | white_skirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------|:-------|:-------|:---------|:-------------|:--------------------|:----------------|:--------------------|:--------|:----------|:-------------------|:------------|:-------------|:----------------|:-----------------|:--------|:----------------|:--------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/hardy_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:09:59+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T09:15:16+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of hardy/ハーディ/勇敢 (Azur Lane)
====================================
This is the dataset of hardy/ハーディ/勇敢 (Azur Lane), containing 22 images and their tags.
The core tags of this character are 'blonde\_hair, hat, blue\_eyes, short\_hair, ribbon, bangs, braid, hair\_bun', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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335bf8652c986eee9544305c41a19718194c4530 |
# Dataset of archerfish/アーチャーフィッシュ/射水鱼 (Azur Lane)
This is the dataset of archerfish/アーチャーフィッシュ/射水鱼 (Azur Lane), containing 14 images and their tags.
The core tags of this character are `breasts, hair_ornament, long_hair, purple_eyes, blonde_hair, star_hair_ornament, bangs, tail, one_side_up, small_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 14 | 17.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/archerfish_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 10.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/archerfish_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 35 | 20.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/archerfish_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 15.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/archerfish_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 35 | 27.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/archerfish_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/archerfish_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | looking_at_viewer, smile, 1girl, blush, star_(symbol), solo, sitting, skirt, thigh_strap, tanlines, ass, cleavage, looking_back, open_mouth, simple_background, tongue_out |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | smile | 1girl | blush | star_(symbol) | solo | sitting | skirt | thigh_strap | tanlines | ass | cleavage | looking_back | open_mouth | simple_background | tongue_out |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:--------|:--------|:----------------|:-------|:----------|:--------|:--------------|:-----------|:------|:-----------|:---------------|:-------------|:--------------------|:-------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/archerfish_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:10:01+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T09:14:35+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of archerfish/アーチャーフィッシュ/射水鱼 (Azur Lane)
================================================
This is the dataset of archerfish/アーチャーフィッシュ/射水鱼 (Azur Lane), containing 14 images and their tags.
The core tags of this character are 'breasts, hair\_ornament, long\_hair, purple\_eyes, blonde\_hair, star\_hair\_ornament, bangs, tail, one\_side\_up, small\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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5a2ef97dcafcd2b4a2399bf4d947e67daa2e3c7a |
# Dataset of curacoa/キュラソー/库拉索 (Azur Lane)
This is the dataset of curacoa/キュラソー/库拉索 (Azur Lane), containing 17 images and their tags.
The core tags of this character are `long_hair, breasts, maid_headdress, bangs, blue_eyes, large_breasts, brown_hair, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 17 | 28.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curacoa_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 17 | 15.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curacoa_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 39 | 31.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curacoa_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 17 | 24.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curacoa_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 39 | 45.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curacoa_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/curacoa_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | looking_at_viewer, smile, 1girl, dress, solo, frills, blush, juliet_sleeves, red_necktie, maid_apron, simple_background, white_apron, open_mouth, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | smile | 1girl | dress | solo | frills | blush | juliet_sleeves | red_necktie | maid_apron | simple_background | white_apron | open_mouth | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:--------|:--------|:-------|:---------|:--------|:-----------------|:--------------|:-------------|:--------------------|:--------------|:-------------|:-------------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/curacoa_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:10:23+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T09:16:54+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of curacoa/キュラソー/库拉索 (Azur Lane)
========================================
This is the dataset of curacoa/キュラソー/库拉索 (Azur Lane), containing 17 images and their tags.
The core tags of this character are 'long\_hair, breasts, maid\_headdress, bangs, blue\_eyes, large\_breasts, brown\_hair, hair\_between\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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32ba79ccef5ffbd4eac5c7302077a62f5e030638 |
# DPO Pairs
This is a preprocessed version of [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) using [Bunkatopics](https://github.com/charlesdedampierre/BunkaTopics) to extract meaningful Topics that help models converge with less data.
The objective was to create a smaller dataset than the original but buy keeping its efficiecency.To achieve this, we compared the two datasets used to train the reward model in [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs): the rejected Llama answers and the accepted ChatGPT answers from the DPO dataset.
We then conducted topic modeling on both datasets, keeping only the topics that existed in the accepted dataset but not in the rejected one. Our hypothesis is that these topics encapsulate the main differences between the two answering styles.
This method allows for quicker convergence with significantly less data (around 1/6 of the initial dataset).
See the page of the model test [here](https://huggingface.co/charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B)
# Topic Analysis
We applied the topic modeling method to both datasets, extracting 30 topics from each. These topics were characterized using the 10 most specific unigrams or bigrams. We then compared the two sets of topics (30 from each dataset) and retained those in the accepted dataset that shared fewer than 2 terms with any topic in the rejected dataset
We found the 13 distincitve following topics described by 10 terms each:
**Emotional Dynamics**: feelings, Quinn, Austin, minority women, teaching, schools, individual, personality, backgrounds, triggers.
**Global Knowledge Queries**: question, information, geography, news articles, Step, answer, capital city, pipeline system, country, analogy.
**Digital Interactions and Queries**: questions, question, PersonX, modem, answers, effect relationship, Quora, browser, answer, e-commerce.
**Business and Cybersecurity**: email, businesses, initiatives, innovation, advertising papers, spam, breaches, antivirus, payments, prospects.
**Lifestyle and Wellness**: sleep, exercise, gifts, shopping, Casey, stores, stress, headaches, options, mood.
**Wildlife Ecology**: birds, prey, animals, species, infection, nest, eggs, bacteria, insects, kitty condo.
**Environmental Science and Climate**: temperature, gases, greenhouse, emissions, perturbation, sulfur, dioxide, climate change, water, heat.
**Maritime and Mechanical Engineering**: ship, bowling, propulsion, beam width, Filing cabinet, LED, lane, containment area, lawnmower, rotors.
**Cultural and Social Dynamics**: Lindsey, museum, Kate, Rachel, Jason, Alex, Erin, conversation, Laura, exhibits.
**Political Media Analysis**: media platforms, election, politics, teenagers, elections, White House, Barack Obama, nation, Confederate, depression.
**International Relations and Policy**: cooperation, EU, nations, alliance, NATO, European Union, member states, policy, monarch, Brexit.
**Astrophysics and Physical Sciences**: electrons, km, Moon, acceleration, orbit, friction, current, asteroid, electron, collector emitter.
**Film Critique and Analysis**: movie review, film, reviewer, sentiment, critic, flaws, DVD, plot, opinion, originality.
While those topics are not domain-specific, they did not appear right away in the rejected dataset. Further research need to undersand the reason behind the prominence of those topics in the accepted dataset.
# Load Dataset
```python
dataset = load_dataset("bunkalab/topic_based_chatml_dpo_pairs")['train']
``` | bunkalab/topic_based_chatml_dpo_pairs | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2024-01-14T09:27:59+00:00 | {"language": ["en"], "license": "apache-2.0"} | 2024-01-14T15:26:46+00:00 | [] | [
"en"
] | TAGS
#language-English #license-apache-2.0 #region-us
|
# DPO Pairs
This is a preprocessed version of mlabonne/chatml_dpo_pairs using Bunkatopics to extract meaningful Topics that help models converge with less data.
The objective was to create a smaller dataset than the original but buy keeping its efficiecency.To achieve this, we compared the two datasets used to train the reward model in mlabonne/chatml_dpo_pairs: the rejected Llama answers and the accepted ChatGPT answers from the DPO dataset.
We then conducted topic modeling on both datasets, keeping only the topics that existed in the accepted dataset but not in the rejected one. Our hypothesis is that these topics encapsulate the main differences between the two answering styles.
This method allows for quicker convergence with significantly less data (around 1/6 of the initial dataset).
See the page of the model test here
# Topic Analysis
We applied the topic modeling method to both datasets, extracting 30 topics from each. These topics were characterized using the 10 most specific unigrams or bigrams. We then compared the two sets of topics (30 from each dataset) and retained those in the accepted dataset that shared fewer than 2 terms with any topic in the rejected dataset
We found the 13 distincitve following topics described by 10 terms each:
Emotional Dynamics: feelings, Quinn, Austin, minority women, teaching, schools, individual, personality, backgrounds, triggers.
Global Knowledge Queries: question, information, geography, news articles, Step, answer, capital city, pipeline system, country, analogy.
Digital Interactions and Queries: questions, question, PersonX, modem, answers, effect relationship, Quora, browser, answer, e-commerce.
Business and Cybersecurity: email, businesses, initiatives, innovation, advertising papers, spam, breaches, antivirus, payments, prospects.
Lifestyle and Wellness: sleep, exercise, gifts, shopping, Casey, stores, stress, headaches, options, mood.
Wildlife Ecology: birds, prey, animals, species, infection, nest, eggs, bacteria, insects, kitty condo.
Environmental Science and Climate: temperature, gases, greenhouse, emissions, perturbation, sulfur, dioxide, climate change, water, heat.
Maritime and Mechanical Engineering: ship, bowling, propulsion, beam width, Filing cabinet, LED, lane, containment area, lawnmower, rotors.
Cultural and Social Dynamics: Lindsey, museum, Kate, Rachel, Jason, Alex, Erin, conversation, Laura, exhibits.
Political Media Analysis: media platforms, election, politics, teenagers, elections, White House, Barack Obama, nation, Confederate, depression.
International Relations and Policy: cooperation, EU, nations, alliance, NATO, European Union, member states, policy, monarch, Brexit.
Astrophysics and Physical Sciences: electrons, km, Moon, acceleration, orbit, friction, current, asteroid, electron, collector emitter.
Film Critique and Analysis: movie review, film, reviewer, sentiment, critic, flaws, DVD, plot, opinion, originality.
While those topics are not domain-specific, they did not appear right away in the rejected dataset. Further research need to undersand the reason behind the prominence of those topics in the accepted dataset.
# Load Dataset
| [
"# DPO Pairs\n\nThis is a preprocessed version of mlabonne/chatml_dpo_pairs using Bunkatopics to extract meaningful Topics that help models converge with less data.\n\nThe objective was to create a smaller dataset than the original but buy keeping its efficiecency.To achieve this, we compared the two datasets used to train the reward model in mlabonne/chatml_dpo_pairs: the rejected Llama answers and the accepted ChatGPT answers from the DPO dataset. \nWe then conducted topic modeling on both datasets, keeping only the topics that existed in the accepted dataset but not in the rejected one. Our hypothesis is that these topics encapsulate the main differences between the two answering styles.\n\nThis method allows for quicker convergence with significantly less data (around 1/6 of the initial dataset).\n\nSee the page of the model test here",
"# Topic Analysis\n\nWe applied the topic modeling method to both datasets, extracting 30 topics from each. These topics were characterized using the 10 most specific unigrams or bigrams. We then compared the two sets of topics (30 from each dataset) and retained those in the accepted dataset that shared fewer than 2 terms with any topic in the rejected dataset\n\nWe found the 13 distincitve following topics described by 10 terms each:\n\n\nEmotional Dynamics: feelings, Quinn, Austin, minority women, teaching, schools, individual, personality, backgrounds, triggers.\n\nGlobal Knowledge Queries: question, information, geography, news articles, Step, answer, capital city, pipeline system, country, analogy.\n\nDigital Interactions and Queries: questions, question, PersonX, modem, answers, effect relationship, Quora, browser, answer, e-commerce.\n\nBusiness and Cybersecurity: email, businesses, initiatives, innovation, advertising papers, spam, breaches, antivirus, payments, prospects.\n\nLifestyle and Wellness: sleep, exercise, gifts, shopping, Casey, stores, stress, headaches, options, mood.\n\nWildlife Ecology: birds, prey, animals, species, infection, nest, eggs, bacteria, insects, kitty condo.\n\nEnvironmental Science and Climate: temperature, gases, greenhouse, emissions, perturbation, sulfur, dioxide, climate change, water, heat.\n\nMaritime and Mechanical Engineering: ship, bowling, propulsion, beam width, Filing cabinet, LED, lane, containment area, lawnmower, rotors.\n\nCultural and Social Dynamics: Lindsey, museum, Kate, Rachel, Jason, Alex, Erin, conversation, Laura, exhibits.\n\nPolitical Media Analysis: media platforms, election, politics, teenagers, elections, White House, Barack Obama, nation, Confederate, depression.\n\nInternational Relations and Policy: cooperation, EU, nations, alliance, NATO, European Union, member states, policy, monarch, Brexit.\n\nAstrophysics and Physical Sciences: electrons, km, Moon, acceleration, orbit, friction, current, asteroid, electron, collector emitter.\n\nFilm Critique and Analysis: movie review, film, reviewer, sentiment, critic, flaws, DVD, plot, opinion, originality.\n\nWhile those topics are not domain-specific, they did not appear right away in the rejected dataset. Further research need to undersand the reason behind the prominence of those topics in the accepted dataset.",
"# Load Dataset"
] | [
"TAGS\n#language-English #license-apache-2.0 #region-us \n",
"# DPO Pairs\n\nThis is a preprocessed version of mlabonne/chatml_dpo_pairs using Bunkatopics to extract meaningful Topics that help models converge with less data.\n\nThe objective was to create a smaller dataset than the original but buy keeping its efficiecency.To achieve this, we compared the two datasets used to train the reward model in mlabonne/chatml_dpo_pairs: the rejected Llama answers and the accepted ChatGPT answers from the DPO dataset. \nWe then conducted topic modeling on both datasets, keeping only the topics that existed in the accepted dataset but not in the rejected one. Our hypothesis is that these topics encapsulate the main differences between the two answering styles.\n\nThis method allows for quicker convergence with significantly less data (around 1/6 of the initial dataset).\n\nSee the page of the model test here",
"# Topic Analysis\n\nWe applied the topic modeling method to both datasets, extracting 30 topics from each. These topics were characterized using the 10 most specific unigrams or bigrams. We then compared the two sets of topics (30 from each dataset) and retained those in the accepted dataset that shared fewer than 2 terms with any topic in the rejected dataset\n\nWe found the 13 distincitve following topics described by 10 terms each:\n\n\nEmotional Dynamics: feelings, Quinn, Austin, minority women, teaching, schools, individual, personality, backgrounds, triggers.\n\nGlobal Knowledge Queries: question, information, geography, news articles, Step, answer, capital city, pipeline system, country, analogy.\n\nDigital Interactions and Queries: questions, question, PersonX, modem, answers, effect relationship, Quora, browser, answer, e-commerce.\n\nBusiness and Cybersecurity: email, businesses, initiatives, innovation, advertising papers, spam, breaches, antivirus, payments, prospects.\n\nLifestyle and Wellness: sleep, exercise, gifts, shopping, Casey, stores, stress, headaches, options, mood.\n\nWildlife Ecology: birds, prey, animals, species, infection, nest, eggs, bacteria, insects, kitty condo.\n\nEnvironmental Science and Climate: temperature, gases, greenhouse, emissions, perturbation, sulfur, dioxide, climate change, water, heat.\n\nMaritime and Mechanical Engineering: ship, bowling, propulsion, beam width, Filing cabinet, LED, lane, containment area, lawnmower, rotors.\n\nCultural and Social Dynamics: Lindsey, museum, Kate, Rachel, Jason, Alex, Erin, conversation, Laura, exhibits.\n\nPolitical Media Analysis: media platforms, election, politics, teenagers, elections, White House, Barack Obama, nation, Confederate, depression.\n\nInternational Relations and Policy: cooperation, EU, nations, alliance, NATO, European Union, member states, policy, monarch, Brexit.\n\nAstrophysics and Physical Sciences: electrons, km, Moon, acceleration, orbit, friction, current, asteroid, electron, collector emitter.\n\nFilm Critique and Analysis: movie review, film, reviewer, sentiment, critic, flaws, DVD, plot, opinion, originality.\n\nWhile those topics are not domain-specific, they did not appear right away in the rejected dataset. Further research need to undersand the reason behind the prominence of those topics in the accepted dataset.",
"# Load Dataset"
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"passage: TAGS\n#language-English #license-apache-2.0 #region-us \n# DPO Pairs\n\nThis is a preprocessed version of mlabonne/chatml_dpo_pairs using Bunkatopics to extract meaningful Topics that help models converge with less data.\n\nThe objective was to create a smaller dataset than the original but buy keeping its efficiecency.To achieve this, we compared the two datasets used to train the reward model in mlabonne/chatml_dpo_pairs: the rejected Llama answers and the accepted ChatGPT answers from the DPO dataset. \nWe then conducted topic modeling on both datasets, keeping only the topics that existed in the accepted dataset but not in the rejected one. Our hypothesis is that these topics encapsulate the main differences between the two answering styles.\n\nThis method allows for quicker convergence with significantly less data (around 1/6 of the initial dataset).\n\nSee the page of the model test here"
] | [
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] |
ef5c509c3a7e50f79890c989710dcb603a6c4bbc |
# Dataset of exeter/エクセター/埃克塞特 (Azur Lane)
This is the dataset of exeter/エクセター/埃克塞特 (Azur Lane), containing 13 images and their tags.
The core tags of this character are `breasts, green_eyes, brown_hair, long_hair, large_breasts, bangs, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 13 | 17.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/exeter_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 13 | 11.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/exeter_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 25 | 19.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/exeter_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 13 | 16.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/exeter_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 25 | 25.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/exeter_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/exeter_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, cleavage, closed_mouth, standing, white_thighhighs, black_gloves, turret, chain, full_body, hat, single_thighhigh, uneven_legwear, white_background, white_dress, aiguillette, anchor, belt, blush, elbow_gloves, hair_between_eyes, hand_on_hip, machinery, medium_breasts, red_cape, rigging, short_dress, side_slit, simple_background |
| 1 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, solo, black_dress, cleavage, flower, smile, long_sleeves, high_heels, holding_cup, indoors, jewelry, see-through, simple_background, wine_glass |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | solo | cleavage | closed_mouth | standing | white_thighhighs | black_gloves | turret | chain | full_body | hat | single_thighhigh | uneven_legwear | white_background | white_dress | aiguillette | anchor | belt | blush | elbow_gloves | hair_between_eyes | hand_on_hip | machinery | medium_breasts | red_cape | rigging | short_dress | side_slit | simple_background | black_dress | flower | long_sleeves | high_heels | holding_cup | indoors | jewelry | see-through | wine_glass |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:-----------|:---------------|:-----------|:-------------------|:---------------|:---------|:--------|:------------|:------|:-------------------|:-----------------|:-------------------|:--------------|:--------------|:---------|:-------|:--------|:---------------|:--------------------|:--------------|:------------|:-----------------|:-----------|:----------|:--------------|:------------|:--------------------|:--------------|:---------|:---------------|:-------------|:--------------|:----------|:----------|:--------------|:-------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/exeter_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:28:57+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T09:32:29+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of exeter/エクセター/埃克塞特 (Azur Lane)
========================================
This is the dataset of exeter/エクセター/埃克塞特 (Azur Lane), containing 13 images and their tags.
The core tags of this character are 'breasts, green\_eyes, brown\_hair, long\_hair, large\_breasts, bangs, hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
13dbeaafd73668d52fc3f3de35418dc4eefa9797 |
# Dataset of nachi/那智/那智 (Azur Lane)
This is the dataset of nachi/那智/那智 (Azur Lane), containing 10 images and their tags.
The core tags of this character are `bangs, breasts, hat, long_hair, large_breasts, very_long_hair, black_headwear, animal_ears, bow, pink_eyes, peaked_cap, brown_hair, floating_hair, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 10 | 20.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nachi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 10 | 10.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nachi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 21 | 19.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nachi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 10 | 17.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nachi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 21 | 30.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nachi_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nachi_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, white_shirt, black_thighhighs, blue_skirt, cleavage, pink_bra, closed_mouth, collared_shirt, heart, miniskirt, nail_polish, navel, one_eye_closed, pleated_skirt, midriff, stomach, striped, white_background, blush, bra_peek, collarbone, crop_top, dress_shirt, full_body, garter_straps, simple_background, sleeves_rolled_up, tongue_out, black_footwear, breast_pocket, crossed_legs, hand_on_hip, hand_on_own_thigh, high_heels, jewelry, partially_unbuttoned, school_uniform, sitting, standing, zettai_ryouiki |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | solo | white_shirt | black_thighhighs | blue_skirt | cleavage | pink_bra | closed_mouth | collared_shirt | heart | miniskirt | nail_polish | navel | one_eye_closed | pleated_skirt | midriff | stomach | striped | white_background | blush | bra_peek | collarbone | crop_top | dress_shirt | full_body | garter_straps | simple_background | sleeves_rolled_up | tongue_out | black_footwear | breast_pocket | crossed_legs | hand_on_hip | hand_on_own_thigh | high_heels | jewelry | partially_unbuttoned | school_uniform | sitting | standing | zettai_ryouiki |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:--------------|:-------------------|:-------------|:-----------|:-----------|:---------------|:-----------------|:--------|:------------|:--------------|:--------|:-----------------|:----------------|:----------|:----------|:----------|:-------------------|:--------|:-----------|:-------------|:-----------|:--------------|:------------|:----------------|:--------------------|:--------------------|:-------------|:-----------------|:----------------|:---------------|:--------------|:--------------------|:-------------|:----------|:-----------------------|:-----------------|:----------|:-----------|:-----------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/nachi_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:37:28+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T09:40:30+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of nachi/那智/那智 (Azur Lane)
==================================
This is the dataset of nachi/那智/那智 (Azur Lane), containing 10 images and their tags.
The core tags of this character are 'bangs, breasts, hat, long\_hair, large\_breasts, very\_long\_hair, black\_headwear, animal\_ears, bow, pink\_eyes, peaked\_cap, brown\_hair, floating\_hair, hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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05ab5e8a21c51c2ae9162b25ac2e1f2827743c6e |
# Dataset of ardent/アーデント/热心 (Azur Lane)
This is the dataset of ardent/アーデント/热心 (Azur Lane), containing 16 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, long_hair, twintails, very_long_hair, breasts, ribbon, bangs, hair_ribbon, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 16 | 17.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ardent_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 16 | 11.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ardent_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 34 | 19.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ardent_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 16 | 15.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ardent_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 34 | 26.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ardent_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ardent_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, open_mouth, full_body, long_sleeves, necktie, pleated_skirt, shoes, smile, white_background, white_shirt, school_uniform, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | blush | open_mouth | full_body | long_sleeves | necktie | pleated_skirt | shoes | smile | white_background | white_shirt | school_uniform | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------|:-------------|:------------|:---------------|:----------|:----------------|:--------|:--------|:-------------------|:--------------|:-----------------|:-------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/ardent_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:37:31+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T09:42:19+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of ardent/アーデント/热心 (Azur Lane)
======================================
This is the dataset of ardent/アーデント/热心 (Azur Lane), containing 16 images and their tags.
The core tags of this character are 'blonde\_hair, blue\_eyes, long\_hair, twintails, very\_long\_hair, breasts, ribbon, bangs, hair\_ribbon, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
2edd044faa9a82bfd105b15c92955257fe9f9273 |
# Dataset of jupiter/ジュピター/丘比特 (Azur Lane)
This is the dataset of jupiter/ジュピター/丘比特 (Azur Lane), containing 15 images and their tags.
The core tags of this character are `long_hair, blue_eyes, hair_ornament, crown, purple_hair, bangs, ahoge, bow, two_side_up, very_long_hair, black_bow, blue_hair, mini_crown, parted_bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 15 | 17.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jupiter_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 10.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jupiter_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 34 | 22.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jupiter_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 15.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jupiter_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 34 | 31.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jupiter_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/jupiter_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, white_dress, bare_shoulders, blush, sleeveless_dress, white_thighhighs, antenna_hair, simple_background, torpedo, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, solo, twintails, animal_hood, blush, sleeves_past_wrists, torpedo, full_body, hair_bow, holding, hood_up, hooded_jacket, long_sleeves, parted_lips, star_(symbol), stuffed_animal, white_bloomers, barefoot, collarbone, hair_through_headwear, hairclip, leg_warmers, loose_socks, navel, object_hug, open_jacket, rabbit_hair_ornament, ribbon, sitting, slippers, soles, yellow_jacket |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | white_dress | bare_shoulders | blush | sleeveless_dress | white_thighhighs | antenna_hair | simple_background | torpedo | white_background | twintails | animal_hood | sleeves_past_wrists | full_body | hair_bow | holding | hood_up | hooded_jacket | long_sleeves | parted_lips | star_(symbol) | stuffed_animal | white_bloomers | barefoot | collarbone | hair_through_headwear | hairclip | leg_warmers | loose_socks | navel | object_hug | open_jacket | rabbit_hair_ornament | ribbon | sitting | slippers | soles | yellow_jacket |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------------|:-----------------|:--------|:-------------------|:-------------------|:---------------|:--------------------|:----------|:-------------------|:------------|:--------------|:----------------------|:------------|:-----------|:----------|:----------|:----------------|:---------------|:--------------|:----------------|:-----------------|:-----------------|:-----------|:-------------|:------------------------|:-----------|:--------------|:--------------|:--------|:-------------|:--------------|:-----------------------|:---------|:----------|:-----------|:--------|:----------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | | X | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/jupiter_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:56:05+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T09:59:39+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of jupiter/ジュピター/丘比特 (Azur Lane)
========================================
This is the dataset of jupiter/ジュピター/丘比特 (Azur Lane), containing 15 images and their tags.
The core tags of this character are 'long\_hair, blue\_eyes, hair\_ornament, crown, purple\_hair, bangs, ahoge, bow, two\_side\_up, very\_long\_hair, black\_bow, blue\_hair, mini\_crown, parted\_bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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8b59cca2767fb4d0e22cb4db0a2be8c2a6f84aa0 |
# Dataset of bailey/ベイリー/贝利 (Azur Lane)
This is the dataset of bailey/ベイリー/贝利 (Azur Lane), containing 24 images and their tags.
The core tags of this character are `long_hair, red_hair, hair_ornament, red_eyes, side_ponytail, antenna_hair, rabbit_ears, bangs, bow, fang`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 24 | 21.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bailey_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 24 | 16.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bailey_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 52 | 30.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bailey_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 24 | 19.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bailey_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 52 | 36.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bailey_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/bailey_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, hat, skirt, blush, food-themed_hair_ornament, open_mouth, stuffed_animal, stuffed_bunny, white_thighhighs, wrist_cuffs, animal_ears, one_eye_closed |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | solo | hat | skirt | blush | food-themed_hair_ornament | open_mouth | stuffed_animal | stuffed_bunny | white_thighhighs | wrist_cuffs | animal_ears | one_eye_closed |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:------|:--------|:--------|:----------------------------|:-------------|:-----------------|:----------------|:-------------------|:--------------|:--------------|:-----------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/bailey_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:56:06+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T10:01:32+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of bailey/ベイリー/贝利 (Azur Lane)
=====================================
This is the dataset of bailey/ベイリー/贝利 (Azur Lane), containing 24 images and their tags.
The core tags of this character are 'long\_hair, red\_hair, hair\_ornament, red\_eyes, side\_ponytail, antenna\_hair, rabbit\_ears, bangs, bow, fang', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
7eacd951a98e2257c386b7ee67a7e3fd04257fc0 |
# Dataset of l_opiniatre/ルピニャート/倔强 (Azur Lane)
This is the dataset of l_opiniatre/ルピニャート/倔强 (Azur Lane), containing 38 images and their tags.
The core tags of this character are `long_hair, green_eyes, breasts, purple_hair, ahoge, glasses, bangs, very_long_hair, ribbon, bow, small_breasts, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 38 | 48.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_opiniatre_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 38 | 28.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_opiniatre_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 74 | 56.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_opiniatre_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 38 | 42.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_opiniatre_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 74 | 82.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/l_opiniatre_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/l_opiniatre_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | hair_ribbon, looking_at_viewer, red_ribbon, 1girl, cleavage, blush, purple_gloves, solo, blue_gloves, capelet, cross, semi-rimless_eyewear, white_thighhighs, black_choker |
| 1 | 9 |  |  |  |  |  | bare_shoulders, blush, looking_at_viewer, 1girl, hair_bow, hair_ornament, solo, bridal_garter, choker, frilled_bikini, navel, purple_bikini, red_bow, strapless_bikini, ass, bandeau, nail_polish, smile, stomach, thighs, blue_bikini, cross, earrings, eyewear_removed, groin, side_ponytail |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | hair_ribbon | looking_at_viewer | red_ribbon | 1girl | cleavage | blush | purple_gloves | solo | blue_gloves | capelet | cross | semi-rimless_eyewear | white_thighhighs | black_choker | bare_shoulders | hair_bow | hair_ornament | bridal_garter | choker | frilled_bikini | navel | purple_bikini | red_bow | strapless_bikini | ass | bandeau | nail_polish | smile | stomach | thighs | blue_bikini | earrings | eyewear_removed | groin | side_ponytail |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------|:--------------------|:-------------|:--------|:-----------|:--------|:----------------|:-------|:--------------|:----------|:--------|:-----------------------|:-------------------|:---------------|:-----------------|:-----------|:----------------|:----------------|:---------|:-----------------|:--------|:----------------|:----------|:-------------------|:------|:----------|:--------------|:--------|:----------|:---------|:--------------|:-----------|:------------------|:--------|:----------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | | X | | X | | X | | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/l_opiniatre_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T09:56:14+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T10:05:34+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of l\_opiniatre/ルピニャート/倔强 (Azur Lane)
=============================================
This is the dataset of l\_opiniatre/ルピニャート/倔强 (Azur Lane), containing 38 images and their tags.
The core tags of this character are 'long\_hair, green\_eyes, breasts, purple\_hair, ahoge, glasses, bangs, very\_long\_hair, ribbon, bow, small\_breasts, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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bde353ba321e90f2176e2434e17d083380fcdaac | # Dataset Card for "araproje_mmlu_en_conf_mgpt_nearestscore_true_y"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_mmlu_en_conf_mgpt_nearestscore_true_y | [
"region:us"
] | 2024-01-14T10:02:06+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "validation", "num_bytes": 130579.0, "num_examples": 250}], "download_size": 79213, "dataset_size": 130579.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:02:09+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_mmlu_en_conf_mgpt_nearestscore_true_y"
More Information needed | [
"# Dataset Card for \"araproje_mmlu_en_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_mmlu_en_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed"
] | [
6,
34
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_en_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed"
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c86c43546bf1590e95d2cc0593152fc55d78e824 | # Dataset Card for "araproje_mmlu_en_conf_mgpt_nearestscore_true_x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_mmlu_en_conf_mgpt_nearestscore_true_x | [
"region:us"
] | 2024-01-14T10:02:10+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "validation", "num_bytes": 130579.0, "num_examples": 250}], "download_size": 79223, "dataset_size": 130579.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:02:12+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_mmlu_en_conf_mgpt_nearestscore_true_x"
More Information needed | [
"# Dataset Card for \"araproje_mmlu_en_conf_mgpt_nearestscore_true_x\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_mmlu_en_conf_mgpt_nearestscore_true_x\"\n\nMore Information needed"
] | [
6,
34
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c0c5cd40cc3b514615ab73260f9660a4544c9f9d | # Dataset Card for "araproje_mmlu_en_conf_mgpt_nearestscore_true"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_mmlu_en_conf_mgpt_nearestscore_true | [
"region:us"
] | 2024-01-14T10:02:13+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "validation", "num_bytes": 130579.0, "num_examples": 250}], "download_size": 79132, "dataset_size": 130579.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:02:15+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_mmlu_en_conf_mgpt_nearestscore_true"
More Information needed | [
"# Dataset Card for \"araproje_mmlu_en_conf_mgpt_nearestscore_true\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_mmlu_en_conf_mgpt_nearestscore_true\"\n\nMore Information needed"
] | [
6,
32
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"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_en_conf_mgpt_nearestscore_true\"\n\nMore Information needed"
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d07091fcadcaf1f5d214f849e5f1bac0aa6611ea | # Dataset Card for "araproje_mmlu_tr_conf_gpt2_nearestscore_true_y"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_mmlu_tr_conf_gpt2_nearestscore_true_y | [
"region:us"
] | 2024-01-14T10:04:19+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "validation", "num_bytes": 137404.0, "num_examples": 250}], "download_size": 83939, "dataset_size": 137404.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:04:23+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_mmlu_tr_conf_gpt2_nearestscore_true_y"
More Information needed | [
"# Dataset Card for \"araproje_mmlu_tr_conf_gpt2_nearestscore_true_y\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_mmlu_tr_conf_gpt2_nearestscore_true_y\"\n\nMore Information needed"
] | [
6,
35
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_tr_conf_gpt2_nearestscore_true_y\"\n\nMore Information needed"
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ae484fd785c2b9e5a1dbd1a6bd85cda47f60f153 | # Dataset Card for "araproje_mmlu_tr_conf_gpt2_nearestscore_true_x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_mmlu_tr_conf_gpt2_nearestscore_true_x | [
"region:us"
] | 2024-01-14T10:04:25+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "validation", "num_bytes": 137404.0, "num_examples": 250}], "download_size": 83805, "dataset_size": 137404.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:04:26+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_mmlu_tr_conf_gpt2_nearestscore_true_x"
More Information needed | [
"# Dataset Card for \"araproje_mmlu_tr_conf_gpt2_nearestscore_true_x\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_mmlu_tr_conf_gpt2_nearestscore_true_x\"\n\nMore Information needed"
] | [
6,
35
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_tr_conf_gpt2_nearestscore_true_x\"\n\nMore Information needed"
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936a90253f0615b3e8b0d23e05c4af3f0ecdb260 | # Dataset Card for "araproje_mmlu_tr_conf_gpt2_nearestscore_true"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_mmlu_tr_conf_gpt2_nearestscore_true | [
"region:us"
] | 2024-01-14T10:04:28+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "validation", "num_bytes": 137404.0, "num_examples": 250}], "download_size": 83939, "dataset_size": 137404.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:04:29+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_mmlu_tr_conf_gpt2_nearestscore_true"
More Information needed | [
"# Dataset Card for \"araproje_mmlu_tr_conf_gpt2_nearestscore_true\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_mmlu_tr_conf_gpt2_nearestscore_true\"\n\nMore Information needed"
] | [
6,
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22054c099646b942710e7ff552b79119857db6e6 | # Dataset Card for "araproje_hellaswag_tr_conf_gpt2_nearestscore_true"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_hellaswag_tr_conf_gpt2_nearestscore_true | [
"region:us"
] | 2024-01-14T10:06:58+00:00 | {"dataset_info": {"features": [{"name": "ind", "dtype": "int32"}, {"name": "activity_label", "dtype": "string"}, {"name": "ctx_a", "dtype": "string"}, {"name": "ctx_b", "dtype": "string"}, {"name": "ctx", "dtype": "string"}, {"name": "endings", "sequence": "string"}, {"name": "source_id", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "split_type", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 162703.0, "num_examples": 250}], "download_size": 87144, "dataset_size": 162703.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:07:00+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_hellaswag_tr_conf_gpt2_nearestscore_true"
More Information needed | [
"# Dataset Card for \"araproje_hellaswag_tr_conf_gpt2_nearestscore_true\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_hellaswag_tr_conf_gpt2_nearestscore_true\"\n\nMore Information needed"
] | [
6,
34
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_hellaswag_tr_conf_gpt2_nearestscore_true\"\n\nMore Information needed"
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0b5117d3891aba6465bed03cd5f8bdfe0290ffc7 | # Dataset Card for "araproje_arc_en_conf_mgpt_nearestscore_true_y"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_arc_en_conf_mgpt_nearestscore_true_y | [
"region:us"
] | 2024-01-14T10:09:48+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 80031.0, "num_examples": 250}], "download_size": 46799, "dataset_size": 80031.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:09:50+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_arc_en_conf_mgpt_nearestscore_true_y"
More Information needed | [
"# Dataset Card for \"araproje_arc_en_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_arc_en_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed"
] | [
6,
34
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"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_en_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed"
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515f8b42f01c57f7180ab5e6b3f7a986eaa065ad | # Dataset Card for "araproje_arc_en_conf_mgpt_nearestscore_true_x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_arc_en_conf_mgpt_nearestscore_true_x | [
"region:us"
] | 2024-01-14T10:09:53+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 80031.0, "num_examples": 250}], "download_size": 46916, "dataset_size": 80031.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:09:59+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_arc_en_conf_mgpt_nearestscore_true_x"
More Information needed | [
"# Dataset Card for \"araproje_arc_en_conf_mgpt_nearestscore_true_x\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_arc_en_conf_mgpt_nearestscore_true_x\"\n\nMore Information needed"
] | [
6,
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7ac3917b84a00ad9131b5a71e240e878a1f09a8b | # Dataset Card for "araproje_arc_en_conf_mgpt_nearestscore_true"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_arc_en_conf_mgpt_nearestscore_true | [
"region:us"
] | 2024-01-14T10:10:02+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 80031.0, "num_examples": 250}], "download_size": 46813, "dataset_size": 80031.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:10:05+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_arc_en_conf_mgpt_nearestscore_true"
More Information needed | [
"# Dataset Card for \"araproje_arc_en_conf_mgpt_nearestscore_true\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_arc_en_conf_mgpt_nearestscore_true\"\n\nMore Information needed"
] | [
6,
32
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_en_conf_mgpt_nearestscore_true\"\n\nMore Information needed"
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2f0bb505a81c75c8e0b32f0d3c989863b85e50e1 | # Dataset Card for "araproje_arc_tr_conf_gpt2_nearestscore_true_y"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_arc_tr_conf_gpt2_nearestscore_true_y | [
"region:us"
] | 2024-01-14T10:11:06+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 86423.0, "num_examples": 250}], "download_size": 50655, "dataset_size": 86423.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:11:08+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_arc_tr_conf_gpt2_nearestscore_true_y"
More Information needed | [
"# Dataset Card for \"araproje_arc_tr_conf_gpt2_nearestscore_true_y\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_arc_tr_conf_gpt2_nearestscore_true_y\"\n\nMore Information needed"
] | [
6,
35
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_tr_conf_gpt2_nearestscore_true_y\"\n\nMore Information needed"
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6061861e39e93e77807668fe4f14c128788e1638 | # Dataset Card for "araproje_arc_tr_conf_gpt2_nearestscore_true_x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_arc_tr_conf_gpt2_nearestscore_true_x | [
"region:us"
] | 2024-01-14T10:11:11+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 86423.0, "num_examples": 250}], "download_size": 50724, "dataset_size": 86423.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:11:13+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_arc_tr_conf_gpt2_nearestscore_true_x"
More Information needed | [
"# Dataset Card for \"araproje_arc_tr_conf_gpt2_nearestscore_true_x\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_arc_tr_conf_gpt2_nearestscore_true_x\"\n\nMore Information needed"
] | [
6,
35
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_tr_conf_gpt2_nearestscore_true_x\"\n\nMore Information needed"
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e9de7d62f6428047dd7d2f1b7a03e8f308bb457b | # Dataset Card for "araproje_arc_tr_conf_gpt2_nearestscore_true"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ibranze/araproje_arc_tr_conf_gpt2_nearestscore_true | [
"region:us"
] | 2024-01-14T10:11:16+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 86423.0, "num_examples": 250}], "download_size": 50681, "dataset_size": 86423.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-14T10:11:17+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "araproje_arc_tr_conf_gpt2_nearestscore_true"
More Information needed | [
"# Dataset Card for \"araproje_arc_tr_conf_gpt2_nearestscore_true\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"araproje_arc_tr_conf_gpt2_nearestscore_true\"\n\nMore Information needed"
] | [
6,
33
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] |
8104069faa6579b509248e184d53e6aada977914 |
# Dataset of le_mars/ル・マルス/勒马尔 (Azur Lane)
This is the dataset of le_mars/ル・マルス/勒马尔 (Azur Lane), containing 24 images and their tags.
The core tags of this character are `brown_hair, short_hair, blue_eyes, bow, breasts, ahoge, green_eyes, hair_bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 24 | 30.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/le_mars_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 24 | 18.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/le_mars_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 48 | 33.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/le_mars_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 24 | 26.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/le_mars_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 48 | 48.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/le_mars_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/le_mars_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | looking_at_viewer, 1girl, solo, black_gloves, fingerless_gloves, shorts, smile, white_background, bare_shoulders, blush, cannon, full_body, hair_ornament, holding_weapon, machinery, navel, rigging, thighhighs, turret, bangs, closed_mouth, dark-skinned_female, simple_background, standing, sword |
| 1 | 6 |  |  |  |  |  | double_bun, looking_at_viewer, open_mouth, smile, 1girl, blue_bikini, solo, ;d, antenna_hair, ass, blush, hood, innertube, one-piece_tan, one_eye_closed, small_breasts, torpedo, water, barefoot, beachball, blue_sky, cloud, day, official_alternate_costume, outdoors, polka_dot_bikini, wrist_scrunchie |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | 1girl | solo | black_gloves | fingerless_gloves | shorts | smile | white_background | bare_shoulders | blush | cannon | full_body | hair_ornament | holding_weapon | machinery | navel | rigging | thighhighs | turret | bangs | closed_mouth | dark-skinned_female | simple_background | standing | sword | double_bun | open_mouth | blue_bikini | ;d | antenna_hair | ass | hood | innertube | one-piece_tan | one_eye_closed | small_breasts | torpedo | water | barefoot | beachball | blue_sky | cloud | day | official_alternate_costume | outdoors | polka_dot_bikini | wrist_scrunchie |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:-------|:---------------|:--------------------|:---------|:--------|:-------------------|:-----------------|:--------|:---------|:------------|:----------------|:-----------------|:------------|:--------|:----------|:-------------|:---------|:--------|:---------------|:----------------------|:--------------------|:-----------|:--------|:-------------|:-------------|:--------------|:-----|:---------------|:------|:-------|:------------|:----------------|:-----------------|:----------------|:----------|:--------|:-----------|:------------|:-----------|:--------|:------|:-----------------------------|:-----------|:-------------------|:------------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | | | X | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/le_mars_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T10:11:26+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T10:18:02+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of le\_mars/ル・マルス/勒马尔 (Azur Lane)
=========================================
This is the dataset of le\_mars/ル・マルス/勒马尔 (Azur Lane), containing 24 images and their tags.
The core tags of this character are 'brown\_hair, short\_hair, blue\_eyes, bow, breasts, ahoge, green\_eyes, hair\_bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
57b8f84260ff6580093d5d8b2c87e6ce23a09392 |
# Dataset of nagatsuki/長月/长月 (Azur Lane)
This is the dataset of nagatsuki/長月/长月 (Azur Lane), containing 21 images and their tags.
The core tags of this character are `animal_ears, long_hair, brown_hair, dog_ears, purple_eyes, hair_ornament, tail, dog_tail, crescent_hair_ornament, fang, hat, ribbon, school_hat, hairclip, side_ponytail, bangs, dog_girl, very_long_hair, yellow_headwear, bow, hair_between_eyes, hair_bow, candy_hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 21 | 24.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagatsuki_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 21 | 14.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagatsuki_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 50 | 30.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagatsuki_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 21 | 22.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagatsuki_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 50 | 42.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagatsuki_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nagatsuki_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | blush, open_mouth, 1girl, crescent, smile, solo, looking_at_viewer, blue_shirt, kindergarten_uniform, long_sleeves, pantyhose, blue_skirt, school_uniform |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | open_mouth | 1girl | crescent | smile | solo | looking_at_viewer | blue_shirt | kindergarten_uniform | long_sleeves | pantyhose | blue_skirt | school_uniform |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------|:-----------|:--------|:-------|:--------------------|:-------------|:-----------------------|:---------------|:------------|:-------------|:-----------------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/nagatsuki_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T10:11:31+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T10:23:39+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of nagatsuki/長月/长月 (Azur Lane)
======================================
This is the dataset of nagatsuki/長月/长月 (Azur Lane), containing 21 images and their tags.
The core tags of this character are 'animal\_ears, long\_hair, brown\_hair, dog\_ears, purple\_eyes, hair\_ornament, tail, dog\_tail, crescent\_hair\_ornament, fang, hat, ribbon, school\_hat, hairclip, side\_ponytail, bangs, dog\_girl, very\_long\_hair, yellow\_headwear, bow, hair\_between\_eyes, hair\_bow, candy\_hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
c6f81191b41e1f772229e4a84cf21d4572901ed0 |
# Dataset of chaser/チェイサー/追赶者 (Azur Lane)
This is the dataset of chaser/チェイサー/追赶者 (Azur Lane), containing 11 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, breasts, large_breasts, long_hair, bangs, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 11 | 13.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chaser_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 11 | 8.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chaser_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 24 | 14.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chaser_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 11 | 12.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chaser_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 24 | 20.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chaser_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/chaser_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, cleavage, dress, open_mouth, holding, simple_background, white_background, closed_mouth, full_body, long_sleeves, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | solo | cleavage | dress | open_mouth | holding | simple_background | white_background | closed_mouth | full_body | long_sleeves | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:-----------|:--------|:-------------|:----------|:--------------------|:-------------------|:---------------|:------------|:---------------|:-------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/chaser_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T10:12:17+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T10:14:55+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of chaser/チェイサー/追赶者 (Azur Lane)
=======================================
This is the dataset of chaser/チェイサー/追赶者 (Azur Lane), containing 11 images and their tags.
The core tags of this character are 'blonde\_hair, blue\_eyes, breasts, large\_breasts, long\_hair, bangs, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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7b1f7e74116063ea5bfab83697e17b3d6bbdb0c2 |
# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3](https://huggingface.co/HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T10:22:49.140128](https://huggingface.co/datasets/open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca-v3/blob/main/results_2024-01-14T10-22-49.140128.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"acc_stderr": 0.03248977876941496,
"acc_norm": 0.6279979276428014,
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"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.6128599699123188,
"mc2_stderr": 0.01559006777346825
},
"harness|arc:challenge|25": {
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"acc_norm": 0.6629692832764505,
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},
"harness|hellaswag|10": {
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"acc_norm": 0.8453495319657439,
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},
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},
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},
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},
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},
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},
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},
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},
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},
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"acc_norm": 0.7721518987341772,
"acc_norm_stderr": 0.027303484599069443
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.03126580522513713,
"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7633587786259542,
"acc_stderr": 0.03727673575596913,
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"harness|hendrycksTest-international_law|5": {
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"acc_stderr": 0.03896878985070417,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070417
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.0401910747255735,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.0401910747255735
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6993865030674846,
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"acc_norm": 0.6993865030674846,
"acc_norm_stderr": 0.03602511318806771
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"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.4642857142857143,
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"harness|hendrycksTest-management|5": {
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"acc_norm": 0.8058252427184466,
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"harness|hendrycksTest-marketing|5": {
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"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.02250903393707781
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"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.67,
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},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8135376756066411,
"acc_stderr": 0.013927751372001505,
"acc_norm": 0.8135376756066411,
"acc_norm_stderr": 0.013927751372001505
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6965317919075145,
"acc_stderr": 0.02475241196091721,
"acc_norm": 0.6965317919075145,
"acc_norm_stderr": 0.02475241196091721
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3318435754189944,
"acc_stderr": 0.015748421208187303,
"acc_norm": 0.3318435754189944,
"acc_norm_stderr": 0.015748421208187303
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7124183006535948,
"acc_stderr": 0.02591780611714716,
"acc_norm": 0.7124183006535948,
"acc_norm_stderr": 0.02591780611714716
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6945337620578779,
"acc_stderr": 0.02616058445014045,
"acc_norm": 0.6945337620578779,
"acc_norm_stderr": 0.02616058445014045
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6882716049382716,
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"acc_norm": 0.6882716049382716,
"acc_norm_stderr": 0.025773111169630457
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"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.46808510638297873,
"acc_stderr": 0.029766675075873866,
"acc_norm": 0.46808510638297873,
"acc_norm_stderr": 0.029766675075873866
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"harness|hendrycksTest-professional_law|5": {
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"acc_norm_stderr": 0.012719949543032199
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"harness|hendrycksTest-professional_medicine|5": {
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"acc_stderr": 0.02946513363977613,
"acc_norm": 0.6213235294117647,
"acc_norm_stderr": 0.02946513363977613
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_stderr": 0.01920660684882536,
"acc_norm": 0.6568627450980392,
"acc_norm_stderr": 0.01920660684882536
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.04494290866252091,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.04494290866252091
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7183673469387755,
"acc_stderr": 0.02879518557429129,
"acc_norm": 0.7183673469387755,
"acc_norm_stderr": 0.02879518557429129
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8159203980099502,
"acc_stderr": 0.02740385941078685,
"acc_norm": 0.8159203980099502,
"acc_norm_stderr": 0.02740385941078685
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.03487350880197771,
"acc_norm": 0.86,
"acc_norm_stderr": 0.03487350880197771
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727668,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727668
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.6128599699123188,
"mc2_stderr": 0.01559006777346825
},
"harness|winogrande|5": {
"acc": 0.7758484609313339,
"acc_stderr": 0.011720400740774099
},
"harness|gsm8k|5": {
"acc": 0.25549658832448824,
"acc_stderr": 0.012013462405460069
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Citation [optional]
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca-v3 | [
"region:us"
] | 2024-01-14T10:25:11+00:00 | {"pretty_name": "Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3", "dataset_summary": "Dataset automatically created during the evaluation run of model [HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3](https://huggingface.co/HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca-v3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T10:22:49.140128](https://huggingface.co/datasets/open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca-v3/blob/main/results_2024-01-14T10-22-49.140128.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6208194474020036,\n \"acc_stderr\": 0.03248977876941496,\n \"acc_norm\": 0.6279979276428014,\n \"acc_norm_stderr\": 0.03316548494950365,\n \"mc1\": 0.4418604651162791,\n \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.6128599699123188,\n \"mc2_stderr\": 0.01559006777346825\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6339590443686007,\n \"acc_stderr\": 0.01407722310847014,\n \"acc_norm\": 0.6629692832764505,\n \"acc_norm_stderr\": 0.013813476652902274\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6611232822146983,\n \"acc_stderr\": 0.0047236053769369145,\n \"acc_norm\": 0.8453495319657439,\n \"acc_norm_stderr\": 0.0036083220651418834\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062946,\n \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062946\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224469,\n \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224469\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.040824829046386284,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.040824829046386284\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3915343915343915,\n \"acc_stderr\": 0.025138091388851105,\n \"acc_norm\": 0.3915343915343915,\n \"acc_norm_stderr\": 0.025138091388851105\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7354838709677419,\n \"acc_stderr\": 0.02509189237885928,\n \"acc_norm\": 0.7354838709677419,\n \"acc_norm_stderr\": 0.02509189237885928\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494562,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494562\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.025033870583015178,\n \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.025033870583015178\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647078,\n \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647078\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606649,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606649\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8275229357798165,\n \"acc_stderr\": 0.016197807956848043,\n \"acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.016197807956848043\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069443,\n \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069443\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070417,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070417\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 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#region-us
|
# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3
Dataset automatically created during the evaluation run of model HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T10:22:49.140128(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3\n\n\n\nDataset automatically created during the evaluation run of model HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T10:22:49.140128(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3\n\n\n\nDataset automatically created during the evaluation run of model HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T10:22:49.140128(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3\n\n\n\nDataset automatically created during the evaluation run of model HenryJJ/dolphin-2.6-mistral-7b-dpo-orca-v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T10:22:49.140128(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]"
] | [
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1463f11dd6850215f1515ca927d4dd05612a18c8 |
# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [FelixChao/NarutoDolphin-7B](https://huggingface.co/FelixChao/NarutoDolphin-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_FelixChao__NarutoDolphin-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T10:24:57.162360](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__NarutoDolphin-7B/blob/main/results_2024-01-14T10-24-57.162360.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6306583942825644,
"acc_stderr": 0.03252627508388141,
"acc_norm": 0.632276909104878,
"acc_norm_stderr": 0.03317986227116511,
"mc1": 0.40514075887392903,
"mc1_stderr": 0.01718561172775337,
"mc2": 0.5912860013096678,
"mc2_stderr": 0.015586868131613507
},
"harness|arc:challenge|25": {
"acc": 0.6220136518771331,
"acc_stderr": 0.014169664520303098,
"acc_norm": 0.6382252559726962,
"acc_norm_stderr": 0.014041957945038083
},
"harness|hellaswag|10": {
"acc": 0.6542521410077674,
"acc_stderr": 0.0047463946133845325,
"acc_norm": 0.841665006970723,
"acc_norm_stderr": 0.0036430875292137216
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7105263157894737,
"acc_stderr": 0.03690677986137282,
"acc_norm": 0.7105263157894737,
"acc_norm_stderr": 0.03690677986137282
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237101,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237101
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6528301886792452,
"acc_stderr": 0.029300101705549652,
"acc_norm": 0.6528301886792452,
"acc_norm_stderr": 0.029300101705549652
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7430555555555556,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5895953757225434,
"acc_stderr": 0.03750757044895536,
"acc_norm": 0.5895953757225434,
"acc_norm_stderr": 0.03750757044895536
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.04858083574266345,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.04858083574266345
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.548936170212766,
"acc_stderr": 0.03252909619613197,
"acc_norm": 0.548936170212766,
"acc_norm_stderr": 0.03252909619613197
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.43859649122807015,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.43859649122807015,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5103448275862069,
"acc_stderr": 0.04165774775728763,
"acc_norm": 0.5103448275862069,
"acc_norm_stderr": 0.04165774775728763
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.025279850397404904,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.025279850397404904
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.023287665127268545,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.023287665127268545
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_stderr": 0.035158955511656986,
"acc_norm": 0.4827586206896552,
"acc_norm_stderr": 0.035158955511656986
},
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"harness|gsm8k|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_FelixChao__NarutoDolphin-7B | [
"region:us"
] | 2024-01-14T10:27:14+00:00 | {"pretty_name": "Evaluation run of FelixChao/NarutoDolphin-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [FelixChao/NarutoDolphin-7B](https://huggingface.co/FelixChao/NarutoDolphin-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FelixChao__NarutoDolphin-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T10:24:57.162360](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__NarutoDolphin-7B/blob/main/results_2024-01-14T10-24-57.162360.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6306583942825644,\n \"acc_stderr\": 0.03252627508388141,\n \"acc_norm\": 0.632276909104878,\n \"acc_norm_stderr\": 0.03317986227116511,\n \"mc1\": 0.40514075887392903,\n \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5912860013096678,\n \"mc2_stderr\": 0.015586868131613507\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.014169664520303098,\n \"acc_norm\": 0.6382252559726962,\n \"acc_norm_stderr\": 0.014041957945038083\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6542521410077674,\n \"acc_stderr\": 0.0047463946133845325,\n \"acc_norm\": 0.841665006970723,\n \"acc_norm_stderr\": 0.0036430875292137216\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137282,\n \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137282\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6528301886792452,\n \"acc_stderr\": 0.029300101705549652,\n \"acc_norm\": 0.6528301886792452,\n \"acc_norm_stderr\": 0.029300101705549652\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895536,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895536\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.03252909619613197,\n \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.03252909619613197\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268545,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268545\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 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#region-us
|
# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-7B
Dataset automatically created during the evaluation run of model FelixChao/NarutoDolphin-7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T10:24:57.162360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/NarutoDolphin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T10:24:57.162360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/NarutoDolphin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T10:24:57.162360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/NarutoDolphin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T10:24:57.162360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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99cdf17ce9b89e217dc48bce370955ef5b1badad |
# Dataset of emanuele_pessagno/エマヌエーレ・ペッサーニョ/埃曼努埃尔·佩萨格诺 (Azur Lane)
This is the dataset of emanuele_pessagno/エマヌエーレ・ペッサーニョ/埃曼努埃尔·佩萨格诺 (Azur Lane), containing 13 images and their tags.
The core tags of this character are `long_hair, pink_hair, breasts, pink_eyes, bangs, hairband, hair_between_eyes, large_breasts, purple_eyes, ahoge, bow, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 13 | 21.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emanuele_pessagno_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 13 | 10.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emanuele_pessagno_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 28 | 21.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emanuele_pessagno_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 13 | 18.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emanuele_pessagno_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 28 | 32.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emanuele_pessagno_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/emanuele_pessagno_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, solo, looking_at_viewer, blush, cleavage, frills, long_sleeves, white_dress |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | blush | cleavage | frills | long_sleeves | white_dress |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:-----------|:---------|:---------------|:--------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X |
| CyberHarem/emanuele_pessagno_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T10:32:05+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T10:35:28+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of emanuele\_pessagno/エマヌエーレ・ペッサーニョ/埃曼努埃尔·佩萨格诺 (Azur Lane)
==================================================================
This is the dataset of emanuele\_pessagno/エマヌエーレ・ペッサーニョ/埃曼努埃尔·佩萨格诺 (Azur Lane), containing 13 images and their tags.
The core tags of this character are 'long\_hair, pink\_hair, breasts, pink\_eyes, bangs, hairband, hair\_between\_eyes, large\_breasts, purple\_eyes, ahoge, bow, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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bd1926e07818a06bc46e711a2dcfc39bd7d5932f |
# Dataset of u_410/U-410 (Azur Lane)
This is the dataset of u_410/U-410 (Azur Lane), containing 13 images and their tags.
The core tags of this character are `breasts, grey_hair, red_eyes, long_hair, medium_breasts, mole, mole_under_eye, bangs, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 13 | 20.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 13 | 12.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 31 | 23.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 13 | 18.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 31 | 33.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/u_410_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | looking_at_viewer, 1girl, bare_shoulders, solo, black_one-piece_swimsuit, iron_cross, red_gloves, underboob, choker, leg_tattoo, smile, thighs, cross_necklace, holding, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | 1girl | bare_shoulders | solo | black_one-piece_swimsuit | iron_cross | red_gloves | underboob | choker | leg_tattoo | smile | thighs | cross_necklace | holding | simple_background | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:-----------------|:-------|:---------------------------|:-------------|:-------------|:------------|:---------|:-------------|:--------|:---------|:-----------------|:----------|:--------------------|:-------------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/u_410_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T10:32:09+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T10:36:36+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of u\_410/U-410 (Azur Lane)
===================================
This is the dataset of u\_410/U-410 (Azur Lane), containing 13 images and their tags.
The core tags of this character are 'breasts, grey\_hair, red\_eyes, long\_hair, medium\_breasts, mole, mole\_under\_eye, bangs, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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25c2673f71080904fb3d71e0c58a562db21cef29 |
# Dataset Card for Evaluation run of macadeliccc/Orca-SOLAR-4x10.7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [macadeliccc/Orca-SOLAR-4x10.7b](https://huggingface.co/macadeliccc/Orca-SOLAR-4x10.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_macadeliccc__Orca-SOLAR-4x10.7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T10:39:27.836739](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__Orca-SOLAR-4x10.7b/blob/main/results_2024-01-14T10-39-27.836739.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.673209053846357,
"acc_stderr": 0.03136589520436306,
"acc_norm": 0.6739418750343522,
"acc_norm_stderr": 0.032009470403028546,
"mc1": 0.48225214198286415,
"mc1_stderr": 0.017492470843075356,
"mc2": 0.6453861832315595,
"mc2_stderr": 0.015356815946066372
},
"harness|arc:challenge|25": {
"acc": 0.6527303754266212,
"acc_stderr": 0.013913034529620455,
"acc_norm": 0.6851535836177475,
"acc_norm_stderr": 0.013572657703084948
},
"harness|hellaswag|10": {
"acc": 0.6820354511053575,
"acc_stderr": 0.004647338877642187,
"acc_norm": 0.867755427205736,
"acc_norm_stderr": 0.0033806414709899313
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5703703703703704,
"acc_stderr": 0.042763494943765995,
"acc_norm": 0.5703703703703704,
"acc_norm_stderr": 0.042763494943765995
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.75,
"acc_stderr": 0.03523807393012047,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03523807393012047
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6943396226415094,
"acc_stderr": 0.028353298073322666,
"acc_norm": 0.6943396226415094,
"acc_norm_stderr": 0.028353298073322666
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
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"harness|gsm8k|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_macadeliccc__Orca-SOLAR-4x10.7b | [
"region:us"
] | 2024-01-14T10:41:42+00:00 | {"pretty_name": "Evaluation run of macadeliccc/Orca-SOLAR-4x10.7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [macadeliccc/Orca-SOLAR-4x10.7b](https://huggingface.co/macadeliccc/Orca-SOLAR-4x10.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_macadeliccc__Orca-SOLAR-4x10.7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T10:39:27.836739](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__Orca-SOLAR-4x10.7b/blob/main/results_2024-01-14T10-39-27.836739.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.673209053846357,\n \"acc_stderr\": 0.03136589520436306,\n \"acc_norm\": 0.6739418750343522,\n \"acc_norm_stderr\": 0.032009470403028546,\n \"mc1\": 0.48225214198286415,\n \"mc1_stderr\": 0.017492470843075356,\n \"mc2\": 0.6453861832315595,\n \"mc2_stderr\": 0.015356815946066372\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620455,\n \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.013572657703084948\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6820354511053575,\n \"acc_stderr\": 0.004647338877642187,\n \"acc_norm\": 0.867755427205736,\n \"acc_norm_stderr\": 0.0033806414709899313\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03523807393012047,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03523807393012047\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 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#region-us
|
# Dataset Card for Evaluation run of macadeliccc/Orca-SOLAR-4x10.7b
Dataset automatically created during the evaluation run of model macadeliccc/Orca-SOLAR-4x10.7b on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T10:39:27.836739(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of macadeliccc/Orca-SOLAR-4x10.7b\n\n\n\nDataset automatically created during the evaluation run of model macadeliccc/Orca-SOLAR-4x10.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T10:39:27.836739(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of macadeliccc/Orca-SOLAR-4x10.7b\n\n\n\nDataset automatically created during the evaluation run of model macadeliccc/Orca-SOLAR-4x10.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T10:39:27.836739(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of macadeliccc/Orca-SOLAR-4x10.7b\n\n\n\nDataset automatically created during the evaluation run of model macadeliccc/Orca-SOLAR-4x10.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T10:39:27.836739(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]"
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] |
fed2732cc0142bc7cec4e2f0a0a413b913b2ead5 |
# Dataset of bush/ブッシュ/布什 (Azur Lane)
This is the dataset of bush/ブッシュ/布什 (Azur Lane), containing 12 images and their tags.
The core tags of this character are `hat, purple_eyes, purple_hair, short_hair, hair_ornament, hairclip, bangs, beret`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 12 | 7.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bush_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 12 | 6.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bush_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 24 | 11.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bush_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 12 | 7.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bush_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 24 | 13.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bush_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/bush_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, jacket, looking_at_viewer, solo, blush, smile, white_background, long_sleeves, open_mouth, shoes, simple_background, collarbone, holding, hood, white_footwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | jacket | looking_at_viewer | solo | blush | smile | white_background | long_sleeves | open_mouth | shoes | simple_background | collarbone | holding | hood | white_footwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------------------|:-------|:--------|:--------|:-------------------|:---------------|:-------------|:--------|:--------------------|:-------------|:----------|:-------|:-----------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/bush_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T11:05:27+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T11:07:42+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of bush/ブッシュ/布什 (Azur Lane)
===================================
This is the dataset of bush/ブッシュ/布什 (Azur Lane), containing 12 images and their tags.
The core tags of this character are 'hat, purple\_eyes, purple\_hair, short\_hair, hair\_ornament, hairclip, bangs, beret', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
d69fd1b9c4ddce3570db8783c16cab2c9eb97ad9 |
# Dataset of dace/デイス/鲦鱼 (Azur Lane)
This is the dataset of dace/デイス/鲦鱼 (Azur Lane), containing 14 images and their tags.
The core tags of this character are `blue_eyes, breasts, pink_hair, medium_breasts, ponytail, bow, fang, hair_bow, bangs, long_hair, black_bow, hair_ornament, parted_bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 14 | 14.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dace_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 8.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dace_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 31 | 18.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dace_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 12.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dace_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 31 | 27.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dace_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/dace_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, blush, open_mouth, solo, looking_at_viewer, navel, one-piece_swimsuit, bare_shoulders, elbow_gloves, :d, leotard, simple_background, toeless_legwear, black_gloves, highleg, star_(symbol), straddling, torpedo, watercraft, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | open_mouth | solo | looking_at_viewer | navel | one-piece_swimsuit | bare_shoulders | elbow_gloves | :d | leotard | simple_background | toeless_legwear | black_gloves | highleg | star_(symbol) | straddling | torpedo | watercraft | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------------|:-------|:--------------------|:--------|:---------------------|:-----------------|:---------------|:-----|:----------|:--------------------|:------------------|:---------------|:----------|:----------------|:-------------|:----------|:-------------|:-------------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/dace_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T11:05:28+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T11:09:27+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of dace/デイス/鲦鱼 (Azur Lane)
==================================
This is the dataset of dace/デイス/鲦鱼 (Azur Lane), containing 14 images and their tags.
The core tags of this character are 'blue\_eyes, breasts, pink\_hair, medium\_breasts, ponytail, bow, fang, hair\_bow, bangs, long\_hair, black\_bow, hair\_ornament, parted\_bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
2b9b3441cc5df6764d7ebf9de843065d7765a276 |
# Dataset of hornet_ii/ホーネットII/大黄蜂II (Azur Lane)
This is the dataset of hornet_ii/ホーネットII/大黄蜂II (Azur Lane), containing 23 images and their tags.
The core tags of this character are `blonde_hair, long_hair, bangs, breasts, green_eyes, large_breasts, twintails, very_long_hair, hat, black_headwear, cowboy_hat, sidelocks`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 23 | 30.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_ii_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 23 | 19.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_ii_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 58 | 40.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_ii_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 23 | 27.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_ii_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 58 | 51.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_ii_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hornet_ii_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, bikini_top_only, black_bikini, cleavage, short_shorts, black_gloves, fingerless_gloves, looking_at_viewer, midriff, solo, black_shorts, blush, shrug_(clothing), black_thighhighs, grin, navel, standing, boots, belt, cowboy_shot, one_eye_closed, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bikini_top_only | black_bikini | cleavage | short_shorts | black_gloves | fingerless_gloves | looking_at_viewer | midriff | solo | black_shorts | blush | shrug_(clothing) | black_thighhighs | grin | navel | standing | boots | belt | cowboy_shot | one_eye_closed | simple_background | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------------|:---------------|:-----------|:---------------|:---------------|:--------------------|:--------------------|:----------|:-------|:---------------|:--------|:-------------------|:-------------------|:-------|:--------|:-----------|:--------|:-------|:--------------|:-----------------|:--------------------|:-------------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/hornet_ii_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T11:06:43+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T11:10:51+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of hornet\_ii/ホーネットII/大黄蜂II (Azur Lane)
===============================================
This is the dataset of hornet\_ii/ホーネットII/大黄蜂II (Azur Lane), containing 23 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, bangs, breasts, green\_eyes, large\_breasts, twintails, very\_long\_hair, hat, black\_headwear, cowboy\_hat, sidelocks', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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542b566e1bb8df76d594b57f631a7e19de2a298b |
# Dataset Card for Evaluation run of Abhinav7/NeuralPipe-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Abhinav7/NeuralPipe-7B-slerp](https://huggingface.co/Abhinav7/NeuralPipe-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T11:22:42.602148](https://huggingface.co/datasets/open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp/blob/main/results_2024-01-14T11-22-42.602148.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
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},
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"acc_norm_stderr": 0.02495418432487991
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7138263665594855,
"acc_stderr": 0.025670259242188933,
"acc_norm": 0.7138263665594855,
"acc_norm_stderr": 0.025670259242188933
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7469135802469136,
"acc_stderr": 0.024191808600712995,
"acc_norm": 0.7469135802469136,
"acc_norm_stderr": 0.024191808600712995
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
"acc_stderr": 0.02980873964223777,
"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47196870925684486,
"acc_stderr": 0.012750151802922438,
"acc_norm": 0.47196870925684486,
"acc_norm_stderr": 0.012750151802922438
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6911764705882353,
"acc_stderr": 0.02806499816704009,
"acc_norm": 0.6911764705882353,
"acc_norm_stderr": 0.02806499816704009
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.018926082916083383,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.018926082916083383
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.746938775510204,
"acc_stderr": 0.027833023871399673,
"acc_norm": 0.746938775510204,
"acc_norm_stderr": 0.027833023871399673
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.025870646766169136,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.025870646766169136
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.43084455324357407,
"mc1_stderr": 0.017335272475332366,
"mc2": 0.5982418830210784,
"mc2_stderr": 0.01515275893598861
},
"harness|winogrande|5": {
"acc": 0.797947908445146,
"acc_stderr": 0.011285013754047436
},
"harness|gsm8k|5": {
"acc": 0.6929492039423806,
"acc_stderr": 0.01270568572313171
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp | [
"region:us"
] | 2024-01-14T11:25:01+00:00 | {"pretty_name": "Evaluation run of Abhinav7/NeuralPipe-7B-slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [Abhinav7/NeuralPipe-7B-slerp](https://huggingface.co/Abhinav7/NeuralPipe-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T11:22:42.602148](https://huggingface.co/datasets/open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp/blob/main/results_2024-01-14T11-22-42.602148.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6447169263171724,\n \"acc_stderr\": 0.03211493893533018,\n \"acc_norm\": 0.6450175117328331,\n \"acc_norm_stderr\": 0.03277128130072703,\n \"mc1\": 0.43084455324357407,\n \"mc1_stderr\": 0.017335272475332366,\n \"mc2\": 0.5982418830210784,\n \"mc2_stderr\": 0.01515275893598861\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6467576791808873,\n \"acc_stderr\": 0.013967822714840055,\n \"acc_norm\": 0.674061433447099,\n \"acc_norm_stderr\": 0.013697432466693252\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6692889862577176,\n \"acc_stderr\": 0.004695076629884537,\n \"acc_norm\": 0.8611830312686716,\n \"acc_norm_stderr\": 0.003450488042965012\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6538461538461539,\n \"acc_stderr\": 0.02412112541694119,\n \"acc_norm\": 0.6538461538461539,\n \"acc_norm_stderr\": 0.02412112541694119\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.029837962388291932,\n \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.029837962388291932\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233494,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233494\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n \"acc_stderr\": 0.02280138253459754,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.02280138253459754\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8326947637292464,\n \"acc_stderr\": 0.013347327202920332,\n \"acc_norm\": 0.8326947637292464,\n \"acc_norm_stderr\": 0.013347327202920332\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.02386800326250011,\n \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.02386800326250011\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.358659217877095,\n \"acc_stderr\": 0.016040454426164474,\n \"acc_norm\": 0.358659217877095,\n \"acc_norm_stderr\": 0.016040454426164474\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n \"acc_stderr\": 0.025670259242188933,\n \"acc_norm\": 0.7138263665594855,\n \"acc_norm_stderr\": 0.025670259242188933\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47196870925684486,\n \"acc_stderr\": 0.012750151802922438,\n \"acc_norm\": 0.47196870925684486,\n \"acc_norm_stderr\": 0.012750151802922438\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 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#region-us
|
# Dataset Card for Evaluation run of Abhinav7/NeuralPipe-7B-slerp
Dataset automatically created during the evaluation run of model Abhinav7/NeuralPipe-7B-slerp on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T11:22:42.602148(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Abhinav7/NeuralPipe-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Abhinav7/NeuralPipe-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T11:22:42.602148(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Abhinav7/NeuralPipe-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Abhinav7/NeuralPipe-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T11:22:42.602148(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Abhinav7/NeuralPipe-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Abhinav7/NeuralPipe-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T11:22:42.602148(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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] |
d9fa0f22444cdacd366b8596557f19ecf043542a |
# Dataset of painleve/パンルヴェ/伴尔维 (Azur Lane)
This is the dataset of painleve/パンルヴェ/伴尔维 (Azur Lane), containing 29 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, long_hair, breasts, large_breasts, bangs, very_long_hair, hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 29 | 47.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/painleve_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 29 | 23.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/painleve_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 69 | 48.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/painleve_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 29 | 39.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/painleve_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 69 | 73.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/painleve_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/painleve_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 19 |  |  |  |  |  | 1girl, looking_at_viewer, solo, tiara, white_gloves, dress, blush, closed_mouth, elbow_gloves |
| 1 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, ponytail, bare_shoulders, hair_ornament, solo, black_choker, blush, ass, parted_lips, sports_bra, yoga_pants, black_pants, sweat, from_behind, indoors, looking_back, window |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | tiara | white_gloves | dress | blush | closed_mouth | elbow_gloves | ponytail | bare_shoulders | hair_ornament | black_choker | ass | parted_lips | sports_bra | yoga_pants | black_pants | sweat | from_behind | indoors | looking_back | window |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------|:---------------|:--------|:--------|:---------------|:---------------|:-----------|:-----------------|:----------------|:---------------|:------|:--------------|:-------------|:-------------|:--------------|:--------|:--------------|:----------|:---------------|:---------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/painleve_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T11:25:39+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T11:32:13+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of painleve/パンルヴェ/伴尔维 (Azur Lane)
=========================================
This is the dataset of painleve/パンルヴェ/伴尔维 (Azur Lane), containing 29 images and their tags.
The core tags of this character are 'blonde\_hair, blue\_eyes, long\_hair, breasts, large\_breasts, bangs, very\_long\_hair, hairband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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ee802a7a3b51d96363bc424b44f8679b5c9e27fa |
# Dataset of bellona/ベローナ/司战女神 (Azur Lane)
This is the dataset of bellona/ベローナ/司战女神 (Azur Lane), containing 52 images and their tags.
The core tags of this character are `breasts, short_hair, hair_between_eyes, large_breasts, bangs, purple_eyes, grey_hair, maid_headdress, purple_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 52 | 72.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bellona_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 52 | 36.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bellona_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 129 | 79.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bellona_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 52 | 61.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bellona_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 129 | 118.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bellona_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/bellona_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 26 |  |  |  |  |  | 1girl, solo, looking_at_viewer, underboob_cutout, black_dress, smile, official_alternate_costume, white_gloves, closed_mouth, simple_background, white_background, standing, white_apron, juliet_sleeves, maid_apron |
| 1 | 7 |  |  |  |  |  | crop_top, midriff, navel, 1girl, long_sleeves, smile, white_background, cowboy_shot, looking_at_viewer, pants, simple_background, solo, stomach, collarbone, sweater, denim, holding, standing, white_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | underboob_cutout | black_dress | smile | official_alternate_costume | white_gloves | closed_mouth | simple_background | white_background | standing | white_apron | juliet_sleeves | maid_apron | crop_top | midriff | navel | long_sleeves | cowboy_shot | pants | stomach | collarbone | sweater | denim | holding | white_shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------------|:--------------|:--------|:-----------------------------|:---------------|:---------------|:--------------------|:-------------------|:-----------|:--------------|:-----------------|:-------------|:-----------|:----------|:--------|:---------------|:--------------|:--------|:----------|:-------------|:----------|:--------|:----------|:--------------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | | | X | | | | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/bellona_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T11:25:41+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T11:42:07+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of bellona/ベローナ/司战女神 (Azur Lane)
========================================
This is the dataset of bellona/ベローナ/司战女神 (Azur Lane), containing 52 images and their tags.
The core tags of this character are 'breasts, short\_hair, hair\_between\_eyes, large\_breasts, bangs, purple\_eyes, grey\_hair, maid\_headdress, purple\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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0055800b84d1147a52d40acbcaa6fc69e5f66395 |
<h1 align="center">🌸 Synthetic Haiku Prompts 🌸</h1>
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/60107b385ac3e86b3ea4fc34/nmz7lvu64BytxDvPMm1C5.png" alt="Banner for a dataset card featuring a fusion of digital and traditional Japanese elements. The design includes stylized digital prompts and haikus within text bubbles and on digital screens, set against a backdrop of delicate cherry blossoms and a serene Japanese landscape. The color scheme is dominated by soft pastel pink tones, creating a harmonious blend of modern technology and classical poetry aesthetics." width="500">
</p>
<p align="center"><em>In data's embrace,<br>Synthetic haiku wishes bloom,<br>
Code-born poetry.<br>
</em></p>
# Dataset Card for Synthetic Haiku Prompts
[<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel)
## Dataset Details
This is a dataset of synthetic prompts that aims to replicate user requests to a chat model for a haiku about a given topic. The data was generated using the [distilabel](https://github.com/argilla-io/distilabel) library using [teknium](https://huggingface.co/teknium)'s [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model. The prompts were generated from a seed list of terms and an adapted version of the [SELF-INSTRUCT](https://arxiv.org/abs/2212.10560) papers prompting strategy.
This dataset was primarily constructed as part of a broader project to explore the extent to which open models and Direct Preference Optimization (DPO) can be used to generate synthetic data that can be used to effectively cultivate desired behavior in language models (in this case the ability to write haikus). The project is a WIP and is primarily a learning exercise for the author, but the dataset is being released in the hopes that it may be useful to others. You can also find the code used to generate the dataset [here](https://github.com/davanstrien/haiku-dpo). The main dataset for this project is at [davanstrien/haiku_dpo](https://huggingface.co/datasets/davanstrien/haiku_dpo).
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** Daniel van Strien
- **Language(s) (NLP):** English (synthetically generated)
- **License:** CC-BY-4.0
### Dataset Sources
- **Repository:** https://github.com/davanstrien/haiku-dpo
## Uses
### Direct Use
This dataset can be used to generate haikus about a given topic. The prompts are used as part of a wider project that uses these prompts as seeds to generate haikus.
### Out-of-Scope Use
This dataset is primarily intended for my own and others' learning. You could use it for other purposes but before doing this, I would suggest you validate the prompts to ensure that they are suitable for your use case.
## Dataset Structure
This dataset has one split and a single configuration. A single row of the dataset looks like this:
```python
{'instructions': 'Can you compose a haiku about the serenity of mountain peaks?'}
```
## Dataset Creation
This dataset was constructed using the [distilabel](https://github.com/argilla-io/distilabel) library. It used a slightly modified version of the approach outlined in the SELF-INSTRUCT paper. The application description used was:
```python
application_description = (
"An AI assistant adept at writing Haikus. "
"It expects complete suggestions from users providing details of the kind of haiku they want. "
"The AI assistant will help users write haikus about particular topics and is willing to accept requests related to a specific subject or object or a more abstract request"
"based on an emotion, theme or vibe."
)
```
The main difference between this approach and the SELF-INSTRUCT approach is that I reformulated the task description to be more specific to the haiku generation task i.e. not asking for prompts to include step-by-step instructions. The following task description was used:
```python
"# Task Description
Develop {{ num_instructions }} user queries that can be received by the given AI application and applicable to the provided context. Emphasize diversity in verbs and linguistic structures within the model's textual capabilities.
# Criteria for Queries
Incorporate a diverse range of verbs, avoiding repetition.
Ensure queries are compatible with AI model's text generation functions and are limited to 1-2 sentences.
Design queries to be self-contained and standalone.
# AI Application
{{ application_description }}
# Context
{{ input }}
```
### Curation Rationale
This dataset was created as part of a larger effort to create a DPO dataset aimed at making LLMs better at writing haikus. This dataset is shared separately since it could be used independently of the other dataset.
#### Data Collection and Processing
No human annotators were used in the creation of this dataset. The original seed prompts were created by Daniel van Strien with help from ChatGPT-4 (used via the web interface). The actual prompts were created by [tekium](https://huggingface.co/teknium)'s [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model.
#### Personal and Sensitive Information
It is very unlikely that this dataset contains any personal or sensitive information, but if you find any prompts that you believe to be harmful, please open a discussion and I will remove them from the dataset.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Whilst I have not found any harmful prompts in the dataset, I have not manually validated all of the prompts. If you find any prompts which you believe to be harmful, please open a discussion and I will remove them from the dataset.
### Recommendations
The original seed prompts used to generate this dataset are by no means comprehensive, and the dataset is likely to be biased toward the topics covered by the seed prompts. If you would like to see more prompts about a particular topic, please open a discussion and I will add them to the seed list. In general, I focused on prompts that were more geared towards "traditional" haiku topics i.e. the natural world and the impermanence of life. If you want to use these prompts to generate a dataset of haikus about other topics, you may want to consider adding prompts that are more relevant to those topics.
## Citation
I have zero expectation that this dataset will be cited, but if you do use it in your work, please cite it as follows:
**BibTeX:**
```bibtex
@misc{vanstrien2024synthetichaikuprompts,
author = {van Strien, Daniel},
title = {Synthetic Haiku Prompts},
year = {2024},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/davanstrien/haiku_prompts}}
}
```
## Glossary
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
- DPO/Direct Preference Optimization: Introduced in [*Direct Preference Optimization: Your Language Model is Secretly a Reward Model*](https://huggingface.co/papers/2305.18290)
- SELF-INSTRUCT: A prompting strategy introduced in [*Self-Instruct: Aligning Language Model with Self Generated Instructions*](https://huggingface.co/papers/2212.10560)
## Dataset Card Authors
[davanstrien](https://huggingface.co/davanstrien)
## Dataset Card Contact
[davanstrien](https://huggingface.co/davanstrien) | davanstrien/haiku_prompts | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"poetry",
"haiku",
"synthetic",
"distilabel",
"arxiv:2212.10560",
"arxiv:2305.18290",
"region:us"
] | 2024-01-14T11:35:13+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "pretty_name": "Synthetic Haiku Prompts", "dataset_info": {"features": [{"name": "instructions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 280969, "num_examples": 4303}], "download_size": 95440, "dataset_size": 280969}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["poetry", "haiku", "synthetic", "distilabel"]} | 2024-01-15T16:26:38+00:00 | [
"2212.10560",
"2305.18290"
] | [
"en"
] | TAGS
#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #poetry #haiku #synthetic #distilabel #arxiv-2212.10560 #arxiv-2305.18290 #region-us
|
<h1 align="center"> Synthetic Haiku Prompts </h1>
<p align="center">
<img src="URL alt="Banner for a dataset card featuring a fusion of digital and traditional Japanese elements. The design includes stylized digital prompts and haikus within text bubbles and on digital screens, set against a backdrop of delicate cherry blossoms and a serene Japanese landscape. The color scheme is dominated by soft pastel pink tones, creating a harmonious blend of modern technology and classical poetry aesthetics." width="500">
</p>
<p align="center"><em>In data's embrace,<br>Synthetic haiku wishes bloom,<br>
Code-born poetry.<br>
</em></p>
# Dataset Card for Synthetic Haiku Prompts
<img src="URL alt="Built with Distilabel" width="200" height="32"/>
## Dataset Details
This is a dataset of synthetic prompts that aims to replicate user requests to a chat model for a haiku about a given topic. The data was generated using the distilabel library using teknium's OpenHermes-2.5-Mistral-7B model. The prompts were generated from a seed list of terms and an adapted version of the SELF-INSTRUCT papers prompting strategy.
This dataset was primarily constructed as part of a broader project to explore the extent to which open models and Direct Preference Optimization (DPO) can be used to generate synthetic data that can be used to effectively cultivate desired behavior in language models (in this case the ability to write haikus). The project is a WIP and is primarily a learning exercise for the author, but the dataset is being released in the hopes that it may be useful to others. You can also find the code used to generate the dataset here. The main dataset for this project is at davanstrien/haiku_dpo.
### Dataset Description
- Curated by: Daniel van Strien
- Language(s) (NLP): English (synthetically generated)
- License: CC-BY-4.0
### Dataset Sources
- Repository: URL
## Uses
### Direct Use
This dataset can be used to generate haikus about a given topic. The prompts are used as part of a wider project that uses these prompts as seeds to generate haikus.
### Out-of-Scope Use
This dataset is primarily intended for my own and others' learning. You could use it for other purposes but before doing this, I would suggest you validate the prompts to ensure that they are suitable for your use case.
## Dataset Structure
This dataset has one split and a single configuration. A single row of the dataset looks like this:
## Dataset Creation
This dataset was constructed using the distilabel library. It used a slightly modified version of the approach outlined in the SELF-INSTRUCT paper. The application description used was:
The main difference between this approach and the SELF-INSTRUCT approach is that I reformulated the task description to be more specific to the haiku generation task i.e. not asking for prompts to include step-by-step instructions. The following task description was used:
### Curation Rationale
This dataset was created as part of a larger effort to create a DPO dataset aimed at making LLMs better at writing haikus. This dataset is shared separately since it could be used independently of the other dataset.
#### Data Collection and Processing
No human annotators were used in the creation of this dataset. The original seed prompts were created by Daniel van Strien with help from ChatGPT-4 (used via the web interface). The actual prompts were created by tekium's OpenHermes-2.5-Mistral-7B model.
#### Personal and Sensitive Information
It is very unlikely that this dataset contains any personal or sensitive information, but if you find any prompts that you believe to be harmful, please open a discussion and I will remove them from the dataset.
## Bias, Risks, and Limitations
Whilst I have not found any harmful prompts in the dataset, I have not manually validated all of the prompts. If you find any prompts which you believe to be harmful, please open a discussion and I will remove them from the dataset.
### Recommendations
The original seed prompts used to generate this dataset are by no means comprehensive, and the dataset is likely to be biased toward the topics covered by the seed prompts. If you would like to see more prompts about a particular topic, please open a discussion and I will add them to the seed list. In general, I focused on prompts that were more geared towards "traditional" haiku topics i.e. the natural world and the impermanence of life. If you want to use these prompts to generate a dataset of haikus about other topics, you may want to consider adding prompts that are more relevant to those topics.
I have zero expectation that this dataset will be cited, but if you do use it in your work, please cite it as follows:
BibTeX:
## Glossary
- DPO/Direct Preference Optimization: Introduced in *Direct Preference Optimization: Your Language Model is Secretly a Reward Model*
- SELF-INSTRUCT: A prompting strategy introduced in *Self-Instruct: Aligning Language Model with Self Generated Instructions*
## Dataset Card Authors
davanstrien
## Dataset Card Contact
davanstrien | [
"# Dataset Card for Synthetic Haiku Prompts\n\n<img src=\"URL alt=\"Built with Distilabel\" width=\"200\" height=\"32\"/>",
"## Dataset Details\n\nThis is a dataset of synthetic prompts that aims to replicate user requests to a chat model for a haiku about a given topic. The data was generated using the distilabel library using teknium's OpenHermes-2.5-Mistral-7B model. The prompts were generated from a seed list of terms and an adapted version of the SELF-INSTRUCT papers prompting strategy.\n\nThis dataset was primarily constructed as part of a broader project to explore the extent to which open models and Direct Preference Optimization (DPO) can be used to generate synthetic data that can be used to effectively cultivate desired behavior in language models (in this case the ability to write haikus). The project is a WIP and is primarily a learning exercise for the author, but the dataset is being released in the hopes that it may be useful to others. You can also find the code used to generate the dataset here. The main dataset for this project is at davanstrien/haiku_dpo.",
"### Dataset Description\n\n\n\n- Curated by: Daniel van Strien\n- Language(s) (NLP): English (synthetically generated)\n- License: CC-BY-4.0",
"### Dataset Sources \n\n- Repository: URL",
"## Uses",
"### Direct Use\n\nThis dataset can be used to generate haikus about a given topic. The prompts are used as part of a wider project that uses these prompts as seeds to generate haikus.",
"### Out-of-Scope Use\n\nThis dataset is primarily intended for my own and others' learning. You could use it for other purposes but before doing this, I would suggest you validate the prompts to ensure that they are suitable for your use case.",
"## Dataset Structure\n\nThis dataset has one split and a single configuration. A single row of the dataset looks like this:",
"## Dataset Creation\n\nThis dataset was constructed using the distilabel library. It used a slightly modified version of the approach outlined in the SELF-INSTRUCT paper. The application description used was: \n\n\n\n\nThe main difference between this approach and the SELF-INSTRUCT approach is that I reformulated the task description to be more specific to the haiku generation task i.e. not asking for prompts to include step-by-step instructions. The following task description was used:",
"### Curation Rationale\n\nThis dataset was created as part of a larger effort to create a DPO dataset aimed at making LLMs better at writing haikus. This dataset is shared separately since it could be used independently of the other dataset.",
"#### Data Collection and Processing\n\nNo human annotators were used in the creation of this dataset. The original seed prompts were created by Daniel van Strien with help from ChatGPT-4 (used via the web interface). The actual prompts were created by tekium's OpenHermes-2.5-Mistral-7B model.",
"#### Personal and Sensitive Information\n\nIt is very unlikely that this dataset contains any personal or sensitive information, but if you find any prompts that you believe to be harmful, please open a discussion and I will remove them from the dataset.",
"## Bias, Risks, and Limitations\n\n\n\nWhilst I have not found any harmful prompts in the dataset, I have not manually validated all of the prompts. If you find any prompts which you believe to be harmful, please open a discussion and I will remove them from the dataset.",
"### Recommendations\n\nThe original seed prompts used to generate this dataset are by no means comprehensive, and the dataset is likely to be biased toward the topics covered by the seed prompts. If you would like to see more prompts about a particular topic, please open a discussion and I will add them to the seed list. In general, I focused on prompts that were more geared towards \"traditional\" haiku topics i.e. the natural world and the impermanence of life. If you want to use these prompts to generate a dataset of haikus about other topics, you may want to consider adding prompts that are more relevant to those topics. \n\nI have zero expectation that this dataset will be cited, but if you do use it in your work, please cite it as follows:\n\nBibTeX:",
"## Glossary \n\n\n\n- DPO/Direct Preference Optimization: Introduced in *Direct Preference Optimization: Your Language Model is Secretly a Reward Model*\n- SELF-INSTRUCT: A prompting strategy introduced in *Self-Instruct: Aligning Language Model with Self Generated Instructions*",
"## Dataset Card Authors\n\ndavanstrien",
"## Dataset Card Contact\n\ndavanstrien"
] | [
"TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #poetry #haiku #synthetic #distilabel #arxiv-2212.10560 #arxiv-2305.18290 #region-us \n",
"# Dataset Card for Synthetic Haiku Prompts\n\n<img src=\"URL alt=\"Built with Distilabel\" width=\"200\" height=\"32\"/>",
"## Dataset Details\n\nThis is a dataset of synthetic prompts that aims to replicate user requests to a chat model for a haiku about a given topic. The data was generated using the distilabel library using teknium's OpenHermes-2.5-Mistral-7B model. The prompts were generated from a seed list of terms and an adapted version of the SELF-INSTRUCT papers prompting strategy.\n\nThis dataset was primarily constructed as part of a broader project to explore the extent to which open models and Direct Preference Optimization (DPO) can be used to generate synthetic data that can be used to effectively cultivate desired behavior in language models (in this case the ability to write haikus). The project is a WIP and is primarily a learning exercise for the author, but the dataset is being released in the hopes that it may be useful to others. You can also find the code used to generate the dataset here. The main dataset for this project is at davanstrien/haiku_dpo.",
"### Dataset Description\n\n\n\n- Curated by: Daniel van Strien\n- Language(s) (NLP): English (synthetically generated)\n- License: CC-BY-4.0",
"### Dataset Sources \n\n- Repository: URL",
"## Uses",
"### Direct Use\n\nThis dataset can be used to generate haikus about a given topic. The prompts are used as part of a wider project that uses these prompts as seeds to generate haikus.",
"### Out-of-Scope Use\n\nThis dataset is primarily intended for my own and others' learning. You could use it for other purposes but before doing this, I would suggest you validate the prompts to ensure that they are suitable for your use case.",
"## Dataset Structure\n\nThis dataset has one split and a single configuration. A single row of the dataset looks like this:",
"## Dataset Creation\n\nThis dataset was constructed using the distilabel library. It used a slightly modified version of the approach outlined in the SELF-INSTRUCT paper. The application description used was: \n\n\n\n\nThe main difference between this approach and the SELF-INSTRUCT approach is that I reformulated the task description to be more specific to the haiku generation task i.e. not asking for prompts to include step-by-step instructions. The following task description was used:",
"### Curation Rationale\n\nThis dataset was created as part of a larger effort to create a DPO dataset aimed at making LLMs better at writing haikus. This dataset is shared separately since it could be used independently of the other dataset.",
"#### Data Collection and Processing\n\nNo human annotators were used in the creation of this dataset. The original seed prompts were created by Daniel van Strien with help from ChatGPT-4 (used via the web interface). The actual prompts were created by tekium's OpenHermes-2.5-Mistral-7B model.",
"#### Personal and Sensitive Information\n\nIt is very unlikely that this dataset contains any personal or sensitive information, but if you find any prompts that you believe to be harmful, please open a discussion and I will remove them from the dataset.",
"## Bias, Risks, and Limitations\n\n\n\nWhilst I have not found any harmful prompts in the dataset, I have not manually validated all of the prompts. If you find any prompts which you believe to be harmful, please open a discussion and I will remove them from the dataset.",
"### Recommendations\n\nThe original seed prompts used to generate this dataset are by no means comprehensive, and the dataset is likely to be biased toward the topics covered by the seed prompts. If you would like to see more prompts about a particular topic, please open a discussion and I will add them to the seed list. In general, I focused on prompts that were more geared towards \"traditional\" haiku topics i.e. the natural world and the impermanence of life. If you want to use these prompts to generate a dataset of haikus about other topics, you may want to consider adding prompts that are more relevant to those topics. \n\nI have zero expectation that this dataset will be cited, but if you do use it in your work, please cite it as follows:\n\nBibTeX:",
"## Glossary \n\n\n\n- DPO/Direct Preference Optimization: Introduced in *Direct Preference Optimization: Your Language Model is Secretly a Reward Model*\n- SELF-INSTRUCT: A prompting strategy introduced in *Self-Instruct: Aligning Language Model with Self Generated Instructions*",
"## Dataset Card Authors\n\ndavanstrien",
"## Dataset Card Contact\n\ndavanstrien"
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"passage: TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #poetry #haiku #synthetic #distilabel #arxiv-2212.10560 #arxiv-2305.18290 #region-us \n# Dataset Card for Synthetic Haiku Prompts\n\n<img src=\"URL alt=\"Built with Distilabel\" width=\"200\" height=\"32\"/>## Dataset Details\n\nThis is a dataset of synthetic prompts that aims to replicate user requests to a chat model for a haiku about a given topic. The data was generated using the distilabel library using teknium's OpenHermes-2.5-Mistral-7B model. The prompts were generated from a seed list of terms and an adapted version of the SELF-INSTRUCT papers prompting strategy.\n\nThis dataset was primarily constructed as part of a broader project to explore the extent to which open models and Direct Preference Optimization (DPO) can be used to generate synthetic data that can be used to effectively cultivate desired behavior in language models (in this case the ability to write haikus). The project is a WIP and is primarily a learning exercise for the author, but the dataset is being released in the hopes that it may be useful to others. You can also find the code used to generate the dataset here. The main dataset for this project is at davanstrien/haiku_dpo.### Dataset Description\n\n\n\n- Curated by: Daniel van Strien\n- Language(s) (NLP): English (synthetically generated)\n- License: CC-BY-4.0### Dataset Sources \n\n- Repository: URL## Uses### Direct Use\n\nThis dataset can be used to generate haikus about a given topic. The prompts are used as part of a wider project that uses these prompts as seeds to generate haikus.### Out-of-Scope Use\n\nThis dataset is primarily intended for my own and others' learning. You could use it for other purposes but before doing this, I would suggest you validate the prompts to ensure that they are suitable for your use case.",
"passage: ## Dataset Structure\n\nThis dataset has one split and a single configuration. A single row of the dataset looks like this:## Dataset Creation\n\nThis dataset was constructed using the distilabel library. It used a slightly modified version of the approach outlined in the SELF-INSTRUCT paper. The application description used was: \n\n\n\n\nThe main difference between this approach and the SELF-INSTRUCT approach is that I reformulated the task description to be more specific to the haiku generation task i.e. not asking for prompts to include step-by-step instructions. The following task description was used:### Curation Rationale\n\nThis dataset was created as part of a larger effort to create a DPO dataset aimed at making LLMs better at writing haikus. This dataset is shared separately since it could be used independently of the other dataset.#### Data Collection and Processing\n\nNo human annotators were used in the creation of this dataset. The original seed prompts were created by Daniel van Strien with help from ChatGPT-4 (used via the web interface). The actual prompts were created by tekium's OpenHermes-2.5-Mistral-7B model.#### Personal and Sensitive Information\n\nIt is very unlikely that this dataset contains any personal or sensitive information, but if you find any prompts that you believe to be harmful, please open a discussion and I will remove them from the dataset.## Bias, Risks, and Limitations\n\n\n\nWhilst I have not found any harmful prompts in the dataset, I have not manually validated all of the prompts. If you find any prompts which you believe to be harmful, please open a discussion and I will remove them from the dataset."
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6c75be6edfbdfd1a565f2a682e34f28e4f022917 | # Dataset Card for Dataset Name
This is a reduced variation of the truthful_qa dataset (https://huggingface.co/datasets/truthful_qa), modified to associate boolean values with the given answers, with a correct answer as a reference.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
TruthfulQA:
@misc{lin2021truthfulqa,
title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
author={Stephanie Lin and Jacob Hilton and Owain Evans},
year={2021},
eprint={2109.07958},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | nmarafo/truthful_qa_TrueFalse | [
"task_categories:table-question-answering",
"language:en",
"license:apache-2.0",
"arxiv:2109.07958",
"region:us"
] | 2024-01-14T11:40:36+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["table-question-answering"]} | 2024-01-14T12:01:51+00:00 | [
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"en"
] | TAGS
#task_categories-table-question-answering #language-English #license-apache-2.0 #arxiv-2109.07958 #region-us
| # Dataset Card for Dataset Name
This is a reduced variation of the truthful_qa dataset (URL modified to associate boolean values with the given answers, with a correct answer as a reference.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
TruthfulQA:
@misc{lin2021truthfulqa,
title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
author={Stephanie Lin and Jacob Hilton and Owain Evans},
year={2021},
eprint={2109.07958},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
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"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\nTruthfulQA:\n@misc{lin2021truthfulqa,\n title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},\n author={Stephanie Lin and Jacob Hilton and Owain Evans},\n year={2021},\n eprint={2109.07958},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n\n\nBibTeX:\n\n\n\nAPA:",
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"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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] |
2789948812658161e99db2bff82bb1fc28943ac1 |
# Dataset of pompeo_magno/ポンペオ・マーニョ/庞培·马格诺 (Azur Lane)
This is the dataset of pompeo_magno/ポンペオ・マーニョ/庞培·马格诺 (Azur Lane), containing 10 images and their tags.
The core tags of this character are `bangs, pink_hair, long_hair, hat, blunt_bangs, green_eyes, ribbon, two_side_up, yellow_eyes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 10 | 16.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pompeo_magno_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 10 | 8.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pompeo_magno_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 25 | 19.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pompeo_magno_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 10 | 14.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pompeo_magno_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 25 | 28.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pompeo_magno_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/pompeo_magno_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, long_sleeves, solo, open_mouth, white_dress, barefoot, beret, black_ribbon, bow, hair_ribbon, lying, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | solo | open_mouth | white_dress | barefoot | beret | black_ribbon | bow | hair_ribbon | lying | simple_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:-------------|:--------------|:-----------|:--------|:---------------|:------|:--------------|:--------|:--------------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/pompeo_magno_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T11:41:05+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T11:47:34+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of pompeo\_magno/ポンペオ・マーニョ/庞培·马格诺 (Azur Lane)
=====================================================
This is the dataset of pompeo\_magno/ポンペオ・マーニョ/庞培·马格诺 (Azur Lane), containing 10 images and their tags.
The core tags of this character are 'bangs, pink\_hair, long\_hair, hat, blunt\_bangs, green\_eyes, ribbon, two\_side\_up, yellow\_eyes, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
1cfe2486214f143906e3efee03daf06114df714a |
# Dataset of haguro/羽黒/羽黑 (Azur Lane)
This is the dataset of haguro/羽黒/羽黑 (Azur Lane), containing 11 images and their tags.
The core tags of this character are `black_hair, hair_ornament, red_eyes, bangs, earrings, hairclip, breasts, ear_piercing, multicolored_hair, hair_between_eyes, streaked_hair, ponytail, white_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 11 | 16.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 11 | 8.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 28 | 19.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 11 | 13.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 28 | 28.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/haguro_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | midriff, 1girl, black_choker, crop_top, jewelry, navel, solo, black_shirt, looking_at_viewer, piercing, short_sleeves, pleated_skirt, belt, black_serafuku, black_skirt, stomach, black_nails, black_sailor_collar, blush, closed_mouth, collarbone, holding, nail_polish, purple_neckerchief, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | midriff | 1girl | black_choker | crop_top | jewelry | navel | solo | black_shirt | looking_at_viewer | piercing | short_sleeves | pleated_skirt | belt | black_serafuku | black_skirt | stomach | black_nails | black_sailor_collar | blush | closed_mouth | collarbone | holding | nail_polish | purple_neckerchief | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------|:--------|:---------------|:-----------|:----------|:--------|:-------|:--------------|:--------------------|:-----------|:----------------|:----------------|:-------|:-----------------|:--------------|:----------|:--------------|:----------------------|:--------|:---------------|:-------------|:----------|:--------------|:---------------------|:----------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/haguro_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T11:41:17+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T11:45:16+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of haguro/羽黒/羽黑 (Azur Lane)
===================================
This is the dataset of haguro/羽黒/羽黑 (Azur Lane), containing 11 images and their tags.
The core tags of this character are 'black\_hair, hair\_ornament, red\_eyes, bangs, earrings, hairclip, breasts, ear\_piercing, multicolored\_hair, hair\_between\_eyes, streaked\_hair, ponytail, white\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
789cedf394c999d699742a00ac28bfe460d8ab35 |
This dataset comprises a collection of traditional Tajik names, sourced from the [Catalogue of Tajik National Names](https://kumitaizabon.tj/tg/catalogue-of-tajik-national-names). It includes a variety of names along with their translations and descriptions. The dataset is designed to serve as a resource for linguistic and cultural studies, providing insights into naming conventions and practices in Tajik culture.
Columns:
1. `Number`: A unique identifier for each name entry.
2. `Tajik`: The name in the Tajik language.
3. `Russian`: The Russian transliteration of the name.
4. `English`: The English transliteration of the name.
5. `Description`: A category indicating the gender association of the name, with possible values being 'Male' or 'Female'.
The dataset is suitable for researchers and enthusiasts interested in Central Asian cultures, linguistics, and gender studies in naming practices. | sobir-hf/tajik-names | [
"size_categories:1K<n<10K",
"language:tg",
"license:mit",
"tajik",
"names",
"region:us"
] | 2024-01-14T11:44:33+00:00 | {"language": ["tg"], "license": "mit", "size_categories": ["1K<n<10K"], "pretty_name": "Tajik National Names", "tags": ["tajik", "names"]} | 2024-01-14T12:17:35+00:00 | [] | [
"tg"
] | TAGS
#size_categories-1K<n<10K #language-Tajik #license-mit #tajik #names #region-us
|
This dataset comprises a collection of traditional Tajik names, sourced from the Catalogue of Tajik National Names. It includes a variety of names along with their translations and descriptions. The dataset is designed to serve as a resource for linguistic and cultural studies, providing insights into naming conventions and practices in Tajik culture.
Columns:
1. 'Number': A unique identifier for each name entry.
2. 'Tajik': The name in the Tajik language.
3. 'Russian': The Russian transliteration of the name.
4. 'English': The English transliteration of the name.
5. 'Description': A category indicating the gender association of the name, with possible values being 'Male' or 'Female'.
The dataset is suitable for researchers and enthusiasts interested in Central Asian cultures, linguistics, and gender studies in naming practices. | [] | [
"TAGS\n#size_categories-1K<n<10K #language-Tajik #license-mit #tajik #names #region-us \n"
] | [
34
] | [
"passage: TAGS\n#size_categories-1K<n<10K #language-Tajik #license-mit #tajik #names #region-us \n"
] | [
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ecf21c2885972560c663f0c7d13b63d928bc589f |
# Dataset Card for Evaluation run of AI-B/UTENA-7B-NSFW-V2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [AI-B/UTENA-7B-NSFW-V2](https://huggingface.co/AI-B/UTENA-7B-NSFW-V2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AI-B__UTENA-7B-NSFW-V2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T11:48:04.187010](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-B__UTENA-7B-NSFW-V2/blob/main/results_2024-01-14T11-48-04.187010.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc1_stderr": 0.01638797677964794,
"mc2": 0.47807391011550315,
"mc2_stderr": 0.014833615164608181
},
"harness|arc:challenge|25": {
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},
"harness|hellaswag|10": {
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},
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},
"harness|hendrycksTest-business_ethics|5": {
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"harness|hendrycksTest-sociology|5": {
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"acc_stderr": 0.025196929874827058,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.025196929874827058
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.029547741687640044,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640044
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3243574051407589,
"mc1_stderr": 0.01638797677964794,
"mc2": 0.47807391011550315,
"mc2_stderr": 0.014833615164608181
},
"harness|winogrande|5": {
"acc": 0.7868981846882399,
"acc_stderr": 0.011508957690722759
},
"harness|gsm8k|5": {
"acc": 0.42380591357088704,
"acc_stderr": 0.013611632008810366
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_AI-B__UTENA-7B-NSFW-V2 | [
"region:us"
] | 2024-01-14T11:50:22+00:00 | {"pretty_name": "Evaluation run of AI-B/UTENA-7B-NSFW-V2", "dataset_summary": "Dataset automatically created during the evaluation run of model [AI-B/UTENA-7B-NSFW-V2](https://huggingface.co/AI-B/UTENA-7B-NSFW-V2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AI-B__UTENA-7B-NSFW-V2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T11:48:04.187010](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-B__UTENA-7B-NSFW-V2/blob/main/results_2024-01-14T11-48-04.187010.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.638083652271864,\n \"acc_stderr\": 0.03246467430851539,\n \"acc_norm\": 0.6431039350752417,\n \"acc_norm_stderr\": 0.03311589246690635,\n \"mc1\": 0.3243574051407589,\n \"mc1_stderr\": 0.01638797677964794,\n \"mc2\": 0.47807391011550315,\n \"mc2_stderr\": 0.014833615164608181\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6015358361774744,\n \"acc_stderr\": 0.014306946052735565,\n \"acc_norm\": 0.6331058020477816,\n \"acc_norm_stderr\": 0.0140841331181043\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6462856004779924,\n \"acc_stderr\": 0.004771447244095128,\n \"acc_norm\": 0.8454491137223661,\n \"acc_norm_stderr\": 0.003607372606295101\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.042320736951515885\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316092,\n \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316092\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.47058823529411764,\n \"acc_stderr\": 0.04966570903978529,\n \"acc_norm\": 0.47058823529411764,\n \"acc_norm_stderr\": 0.04966570903978529\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.032321469162244675,\n \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.032321469162244675\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192118,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192118\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894444,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894444\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7451612903225806,\n \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.7451612903225806,\n \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602354,\n \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602354\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.03017680828897434,\n \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.03017680828897434\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8275229357798165,\n \"acc_stderr\": 0.01619780795684805,\n \"acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.01619780795684805\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728742,\n \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728742\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8020434227330779,\n \"acc_stderr\": 0.014248873549217578,\n \"acc_norm\": 0.8020434227330779,\n \"acc_norm_stderr\": 0.014248873549217578\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2335195530726257,\n \"acc_stderr\": 0.014149575348976267,\n \"acc_norm\": 0.2335195530726257,\n \"acc_norm_stderr\": 0.014149575348976267\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.024954184324879905,\n \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.024954184324879905\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7331189710610932,\n \"acc_stderr\": 0.025122637608816646,\n \"acc_norm\": 0.7331189710610932,\n \"acc_norm_stderr\": 0.025122637608816646\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7253086419753086,\n \"acc_stderr\": 0.024836057868294677,\n \"acc_norm\": 0.7253086419753086,\n \"acc_norm_stderr\": 0.024836057868294677\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.02982074719142248,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.02982074719142248\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4556714471968709,\n \"acc_stderr\": 0.012719949543032209,\n \"acc_norm\": 0.4556714471968709,\n \"acc_norm_stderr\": 0.012719949543032209\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389844,\n \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389844\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360375,\n \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360375\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n \"acc_stderr\": 0.025196929874827058,\n \"acc_norm\": 0.8507462686567164,\n \"acc_norm_stderr\": 0.025196929874827058\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3243574051407589,\n \"mc1_stderr\": 0.01638797677964794,\n \"mc2\": 0.47807391011550315,\n \"mc2_stderr\": 0.014833615164608181\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.011508957690722759\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.42380591357088704,\n \"acc_stderr\": 0.013611632008810366\n }\n}\n```", "repo_url": "https://huggingface.co/AI-B/UTENA-7B-NSFW-V2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_14T11_48_04.187010", 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{"split": "latest", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_biology_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_chemistry_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_computer_science_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": 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"harness_hendrycksTest_high_school_government_and_politics_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_macroeconomics_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_mathematics_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_microeconomics_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_physics_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_psychology_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["**/details_harness|winogrande|5_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-14T11-48-04.187010.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_14T11_48_04.187010", "path": ["results_2024-01-14T11-48-04.187010.parquet"]}, {"split": "latest", "path": ["results_2024-01-14T11-48-04.187010.parquet"]}]}]} | 2024-01-14T11:50:43+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of AI-B/UTENA-7B-NSFW-V2
Dataset automatically created during the evaluation run of model AI-B/UTENA-7B-NSFW-V2 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T11:48:04.187010(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of AI-B/UTENA-7B-NSFW-V2\n\n\n\nDataset automatically created during the evaluation run of model AI-B/UTENA-7B-NSFW-V2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T11:48:04.187010(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of AI-B/UTENA-7B-NSFW-V2\n\n\n\nDataset automatically created during the evaluation run of model AI-B/UTENA-7B-NSFW-V2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T11:48:04.187010(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AI-B/UTENA-7B-NSFW-V2\n\n\n\nDataset automatically created during the evaluation run of model AI-B/UTENA-7B-NSFW-V2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T11:48:04.187010(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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9d411092d6b327e6c6aa4dee5515eeb40e8df28b |
# Dataset Card for Evaluation run of Weyaxi/Cosmosis-3x34B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Weyaxi/Cosmosis-3x34B](https://huggingface.co/Weyaxi/Cosmosis-3x34B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__Cosmosis-3x34B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T11:59:17.025888](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Cosmosis-3x34B/blob/main/results_2024-01-14T11-59-17.025888.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"acc_norm": 0.7725855380923361,
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"mc1_stderr": 0.017463793867168103,
"mc2": 0.6382238408380394,
"mc2_stderr": 0.01475552588950266
},
"harness|arc:challenge|25": {
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},
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},
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"harness|hendrycksTest-astronomy|5": {
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},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.04309118709946458,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.04309118709946458
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8408163265306122,
"acc_stderr": 0.02342097206916635,
"acc_norm": 0.8408163265306122,
"acc_norm_stderr": 0.02342097206916635
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8855721393034826,
"acc_stderr": 0.022509345325101706,
"acc_norm": 0.8855721393034826,
"acc_norm_stderr": 0.022509345325101706
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.93,
"acc_stderr": 0.025643239997624294,
"acc_norm": 0.93,
"acc_norm_stderr": 0.025643239997624294
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5662650602409639,
"acc_stderr": 0.03858158940685515,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685515
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8713450292397661,
"acc_stderr": 0.025679342723276908,
"acc_norm": 0.8713450292397661,
"acc_norm_stderr": 0.025679342723276908
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4663402692778458,
"mc1_stderr": 0.017463793867168103,
"mc2": 0.6382238408380394,
"mc2_stderr": 0.01475552588950266
},
"harness|winogrande|5": {
"acc": 0.8413575374901342,
"acc_stderr": 0.010267936243028214
},
"harness|gsm8k|5": {
"acc": 0.7225170583775588,
"acc_stderr": 0.01233344758104755
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Weyaxi__Cosmosis-3x34B | [
"region:us"
] | 2024-01-14T12:01:27+00:00 | {"pretty_name": "Evaluation run of Weyaxi/Cosmosis-3x34B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/Cosmosis-3x34B](https://huggingface.co/Weyaxi/Cosmosis-3x34B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__Cosmosis-3x34B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T11:59:17.025888](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Cosmosis-3x34B/blob/main/results_2024-01-14T11-59-17.025888.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7691798340940261,\n \"acc_stderr\": 0.027910883477876437,\n \"acc_norm\": 0.7725855380923361,\n \"acc_norm_stderr\": 0.02844764712553433,\n \"mc1\": 0.4663402692778458,\n \"mc1_stderr\": 0.017463793867168103,\n \"mc2\": 0.6382238408380394,\n \"mc2_stderr\": 0.01475552588950266\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6655290102389079,\n \"acc_stderr\": 0.013787460322441377,\n \"acc_norm\": 0.697098976109215,\n \"acc_norm_stderr\": 0.013428241573185347\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6569408484365664,\n \"acc_stderr\": 0.004737608340163403,\n \"acc_norm\": 0.851822346146186,\n \"acc_norm_stderr\": 0.003545499169558051\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.9078947368421053,\n \"acc_stderr\": 0.02353268597044349,\n \"acc_norm\": 0.9078947368421053,\n \"acc_norm_stderr\": 0.02353268597044349\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8150943396226416,\n \"acc_stderr\": 0.02389335183446432,\n \"acc_norm\": 0.8150943396226416,\n \"acc_norm_stderr\": 0.02389335183446432\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9027777777777778,\n \"acc_stderr\": 0.02477451625044016,\n \"acc_norm\": 0.9027777777777778,\n \"acc_norm_stderr\": 0.02477451625044016\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7572254335260116,\n \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.7572254335260116,\n \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7957446808510639,\n \"acc_stderr\": 0.026355158413349417,\n \"acc_norm\": 0.7957446808510639,\n \"acc_norm_stderr\": 0.026355158413349417\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6140350877192983,\n \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.6140350877192983,\n \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.034165204477475494,\n \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.034165204477475494\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.701058201058201,\n \"acc_stderr\": 0.023577604791655802,\n \"acc_norm\": 0.701058201058201,\n \"acc_norm_stderr\": 0.023577604791655802\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5873015873015873,\n \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.5873015873015873,\n \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9064516129032258,\n \"acc_stderr\": 0.016565754668270972,\n \"acc_norm\": 0.9064516129032258,\n \"acc_norm_stderr\": 0.016565754668270972\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6354679802955665,\n \"acc_stderr\": 0.0338640574606209,\n \"acc_norm\": 0.6354679802955665,\n \"acc_norm_stderr\": 0.0338640574606209\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8727272727272727,\n \"acc_stderr\": 0.02602465765165619,\n \"acc_norm\": 0.8727272727272727,\n \"acc_norm_stderr\": 0.02602465765165619\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9343434343434344,\n \"acc_stderr\": 0.017646526677233335,\n \"acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.017646526677233335\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.8205128205128205,\n \"acc_stderr\": 0.019457390787681803,\n \"acc_norm\": 0.8205128205128205,\n \"acc_norm_stderr\": 0.019457390787681803\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.43703703703703706,\n \"acc_stderr\": 0.030242862397654002,\n \"acc_norm\": 0.43703703703703706,\n \"acc_norm_stderr\": 0.030242862397654002\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8697478991596639,\n \"acc_stderr\": 0.021863258494852118,\n \"acc_norm\": 0.8697478991596639,\n \"acc_norm_stderr\": 0.021863258494852118\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289694,\n \"acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289694\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9155963302752294,\n \"acc_stderr\": 0.011918819327334879,\n \"acc_norm\": 0.9155963302752294,\n \"acc_norm_stderr\": 0.011918819327334879\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6620370370370371,\n \"acc_stderr\": 0.03225941352631295,\n \"acc_norm\": 0.6620370370370371,\n \"acc_norm_stderr\": 0.03225941352631295\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089678,\n \"acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089678\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065494,\n \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065494\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n \"acc_stderr\": 0.02737309550054019,\n \"acc_norm\": 0.7892376681614349,\n \"acc_norm_stderr\": 0.02737309550054019\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8854961832061069,\n \"acc_stderr\": 0.027927473753597446,\n \"acc_norm\": 0.8854961832061069,\n \"acc_norm_stderr\": 0.027927473753597446\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.03038159675665167,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.03038159675665167\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.9223300970873787,\n \"acc_stderr\": 0.026501440784762752,\n \"acc_norm\": 0.9223300970873787,\n \"acc_norm_stderr\": 0.026501440784762752\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n \"acc_stderr\": 0.01700436856813234,\n \"acc_norm\": 0.9273504273504274,\n \"acc_norm_stderr\": 0.01700436856813234\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9054916985951469,\n \"acc_stderr\": 0.01046101533819307,\n \"acc_norm\": 0.9054916985951469,\n \"acc_norm_stderr\": 0.01046101533819307\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.838150289017341,\n \"acc_stderr\": 0.019829299214925416,\n \"acc_norm\": 0.838150289017341,\n \"acc_norm_stderr\": 0.019829299214925416\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7720670391061453,\n \"acc_stderr\": 0.014030149950805097,\n \"acc_norm\": 0.7720670391061453,\n \"acc_norm_stderr\": 0.014030149950805097\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8594771241830066,\n \"acc_stderr\": 0.019899435463539946,\n \"acc_norm\": 0.8594771241830066,\n \"acc_norm_stderr\": 0.019899435463539946\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8360128617363344,\n \"acc_stderr\": 0.021029576464662695,\n \"acc_norm\": 0.8360128617363344,\n \"acc_norm_stderr\": 0.021029576464662695\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8796296296296297,\n \"acc_stderr\": 0.01810541409432967,\n \"acc_norm\": 0.8796296296296297,\n \"acc_norm_stderr\": 0.01810541409432967\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.6560283687943262,\n \"acc_stderr\": 0.02833801742861133,\n \"acc_norm\": 0.6560283687943262,\n \"acc_norm_stderr\": 0.02833801742861133\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6160365058670143,\n \"acc_stderr\": 0.01242158783313423,\n \"acc_norm\": 0.6160365058670143,\n \"acc_norm_stderr\": 0.01242158783313423\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.8308823529411765,\n \"acc_stderr\": 0.022770868010113018,\n \"acc_norm\": 0.8308823529411765,\n \"acc_norm_stderr\": 0.022770868010113018\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.8202614379084967,\n \"acc_stderr\": 0.01553374508338279,\n \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.01553374508338279\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8408163265306122,\n \"acc_stderr\": 0.02342097206916635,\n \"acc_norm\": 0.8408163265306122,\n \"acc_norm_stderr\": 0.02342097206916635\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.93,\n \"acc_stderr\": 0.025643239997624294,\n \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.025643239997624294\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 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#region-us
|
# Dataset Card for Evaluation run of Weyaxi/Cosmosis-3x34B
Dataset automatically created during the evaluation run of model Weyaxi/Cosmosis-3x34B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T11:59:17.025888(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Weyaxi/Cosmosis-3x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Cosmosis-3x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T11:59:17.025888(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Weyaxi/Cosmosis-3x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Cosmosis-3x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T11:59:17.025888(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/Cosmosis-3x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Cosmosis-3x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T11:59:17.025888(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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688f7995dc128513f70b4d08b5c0e4d727641f49 |
# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-10B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [FelixChao/NarutoDolphin-10B](https://huggingface.co/FelixChao/NarutoDolphin-10B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_FelixChao__NarutoDolphin-10B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T12:12:30.168914](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__NarutoDolphin-10B/blob/main/results_2024-01-14T12-12-30.168914.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc2": 0.5912860013096678,
"mc2_stderr": 0.015586868131613507
},
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},
"harness|hellaswag|10": {
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},
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"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302505,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302505
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.710204081632653,
"acc_stderr": 0.02904308868330433,
"acc_norm": 0.710204081632653,
"acc_norm_stderr": 0.02904308868330433
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8109452736318408,
"acc_stderr": 0.027686913588013014,
"acc_norm": 0.8109452736318408,
"acc_norm_stderr": 0.027686913588013014
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.038786267710023595,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.038786267710023595
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7953216374269005,
"acc_stderr": 0.03094445977853321,
"acc_norm": 0.7953216374269005,
"acc_norm_stderr": 0.03094445977853321
},
"harness|truthfulqa:mc|0": {
"mc1": 0.40514075887392903,
"mc1_stderr": 0.01718561172775337,
"mc2": 0.5912860013096678,
"mc2_stderr": 0.015586868131613507
},
"harness|winogrande|5": {
"acc": 0.7750591949486977,
"acc_stderr": 0.011735043564126735
},
"harness|gsm8k|5": {
"acc": 0.5943896891584534,
"acc_stderr": 0.013524848894462115
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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### Curation Rationale
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#### Data Collection and Processing
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_FelixChao__NarutoDolphin-10B | [
"region:us"
] | 2024-01-14T12:14:45+00:00 | {"pretty_name": "Evaluation run of FelixChao/NarutoDolphin-10B", "dataset_summary": "Dataset automatically created during the evaluation run of model [FelixChao/NarutoDolphin-10B](https://huggingface.co/FelixChao/NarutoDolphin-10B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FelixChao__NarutoDolphin-10B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T12:12:30.168914](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__NarutoDolphin-10B/blob/main/results_2024-01-14T12-12-30.168914.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6306583942825644,\n \"acc_stderr\": 0.03252627508388141,\n \"acc_norm\": 0.632276909104878,\n \"acc_norm_stderr\": 0.03317986227116511,\n \"mc1\": 0.40514075887392903,\n \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5912860013096678,\n \"mc2_stderr\": 0.015586868131613507\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.014169664520303098,\n \"acc_norm\": 0.6382252559726962,\n \"acc_norm_stderr\": 0.014041957945038083\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6542521410077674,\n \"acc_stderr\": 0.0047463946133845325,\n \"acc_norm\": 0.841665006970723,\n \"acc_norm_stderr\": 0.0036430875292137216\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137282,\n \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137282\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6528301886792452,\n \"acc_stderr\": 0.029300101705549652,\n \"acc_norm\": 0.6528301886792452,\n \"acc_norm_stderr\": 0.029300101705549652\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895536,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895536\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.03252909619613197,\n \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.03252909619613197\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268545,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268545\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124484,\n \"acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124484\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758723,\n \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758723\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396993,\n \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396993\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.03068473711513536,\n \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.03068473711513536\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763744,\n \"acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763744\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8220183486238533,\n \"acc_stderr\": 0.016399436366612907,\n \"acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.016399436366612907\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640773,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640773\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.03487825168497892,\n \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.03487825168497892\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n \"acc_stderr\": 0.01374079725857982,\n \"acc_norm\": 0.8199233716475096,\n \"acc_norm_stderr\": 0.01374079725857982\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.708092485549133,\n \"acc_stderr\": 0.02447699407624734,\n \"acc_norm\": 0.708092485549133,\n \"acc_norm_stderr\": 0.02447699407624734\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3642458100558659,\n \"acc_stderr\": 0.0160943387684746,\n \"acc_norm\": 0.3642458100558659,\n \"acc_norm_stderr\": 0.0160943387684746\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n \"acc_stderr\": 0.026082700695399662,\n \"acc_norm\": 0.6977491961414791,\n \"acc_norm_stderr\": 0.026082700695399662\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.02563082497562135,\n \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.02563082497562135\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44328552803129073,\n \"acc_stderr\": 0.012687818419599923,\n \"acc_norm\": 0.44328552803129073,\n \"acc_norm_stderr\": 0.012687818419599923\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6519607843137255,\n \"acc_stderr\": 0.019270998708223977,\n \"acc_norm\": 0.6519607843137255,\n \"acc_norm_stderr\": 0.019270998708223977\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.710204081632653,\n \"acc_stderr\": 0.02904308868330433,\n \"acc_norm\": 0.710204081632653,\n \"acc_norm_stderr\": 0.02904308868330433\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n \"acc_stderr\": 0.027686913588013014,\n \"acc_norm\": 0.8109452736318408,\n \"acc_norm_stderr\": 0.027686913588013014\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7953216374269005,\n \"acc_stderr\": 0.03094445977853321,\n \"acc_norm\": 0.7953216374269005,\n \"acc_norm_stderr\": 0.03094445977853321\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40514075887392903,\n \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5912860013096678,\n \"mc2_stderr\": 0.015586868131613507\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7750591949486977,\n \"acc_stderr\": 0.011735043564126735\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5943896891584534,\n \"acc_stderr\": 0.013524848894462115\n }\n}\n```", "repo_url": "https://huggingface.co/FelixChao/NarutoDolphin-10B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", 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{"split": "latest", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_biology_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_chemistry_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_computer_science_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": 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"harness_hendrycksTest_high_school_government_and_politics_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_macroeconomics_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_mathematics_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": 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["**/details_harness|truthfulqa:mc|0_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["**/details_harness|winogrande|5_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-14T12-12-30.168914.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_14T12_12_30.168914", "path": ["results_2024-01-14T12-12-30.168914.parquet"]}, {"split": "latest", "path": ["results_2024-01-14T12-12-30.168914.parquet"]}]}]} | 2024-01-14T12:15:06+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-10B
Dataset automatically created during the evaluation run of model FelixChao/NarutoDolphin-10B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T12:12:30.168914(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-10B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/NarutoDolphin-10B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T12:12:30.168914(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-10B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/NarutoDolphin-10B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T12:12:30.168914(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of FelixChao/NarutoDolphin-10B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/NarutoDolphin-10B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T12:12:30.168914(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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440236fb2384eeab89b92263b7b87d5fe0b3d2a4 | # Dataset Card for "vietnamese-retrieval-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | thanhdath/vietnamese-retrieval-v2 | [
"region:us"
] | 2024-01-14T12:15:06+00:00 | {"dataset_info": {"features": [{"name": "query_id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "positive_passages", "list": [{"name": "docid", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "negative_passages", "list": [{"name": "docid", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 6347713728, "num_examples": 574167}], "download_size": 2987843540, "dataset_size": 6347713728}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-14T12:22:46+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "vietnamese-retrieval-v2"
More Information needed | [
"# Dataset Card for \"vietnamese-retrieval-v2\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"vietnamese-retrieval-v2\"\n\nMore Information needed"
] | [
6,
20
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"vietnamese-retrieval-v2\"\n\nMore Information needed"
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46f68ae1897088c7ddd17402d776a980469c77d9 |
# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboardMerge
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [dhanushreddy29/BrokenKeyboardMerge](https://huggingface.co/dhanushreddy29/BrokenKeyboardMerge) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboardMerge",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T12:28:57.888363](https://huggingface.co/datasets/open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboardMerge/blob/main/results_2024-01-14T12-28-57.888363.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5820084848111404,
"acc_stderr": 0.033007700619969375,
"acc_norm": 0.5876752361456778,
"acc_norm_stderr": 0.03370856721497056,
"mc1": 0.3769889840881273,
"mc1_stderr": 0.01696551757893035,
"mc2": 0.520009813591209,
"mc2_stderr": 0.01568688657303073
},
"harness|arc:challenge|25": {
"acc": 0.5639931740614335,
"acc_stderr": 0.014491225699230916,
"acc_norm": 0.5972696245733788,
"acc_norm_stderr": 0.01433223630679015
},
"harness|hellaswag|10": {
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"acc_stderr": 0.0048201660022530795,
"acc_norm": 0.8124875522804222,
"acc_norm_stderr": 0.0038952463204527657
},
"harness|hendrycksTest-abstract_algebra|5": {
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},
"harness|hendrycksTest-anatomy|5": {
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},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.03925523381052932,
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},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
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"acc_norm": 0.58,
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},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6490566037735849,
"acc_stderr": 0.02937364625323469,
"acc_norm": 0.6490566037735849,
"acc_norm_stderr": 0.02937364625323469
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.6597222222222222,
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},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.33,
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
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"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5317919075144508,
"acc_stderr": 0.03804749744364764,
"acc_norm": 0.5317919075144508,
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},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.04389869956808778
},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768077
},
"harness|hendrycksTest-conceptual_physics|5": {
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},
"harness|hendrycksTest-econometrics|5": {
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},
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},
"harness|hendrycksTest-elementary_mathematics|5": {
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"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.27,
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},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.82,
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"mc2": 0.520009813591209,
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"harness|winogrande|5": {
"acc": 0.7868981846882399,
"acc_stderr": 0.011508957690722747
},
"harness|gsm8k|5": {
"acc": 0.25928733889310085,
"acc_stderr": 0.012071405369905504
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboardMerge | [
"region:us"
] | 2024-01-14T12:31:15+00:00 | {"pretty_name": "Evaluation run of dhanushreddy29/BrokenKeyboardMerge", "dataset_summary": "Dataset automatically created during the evaluation run of model [dhanushreddy29/BrokenKeyboardMerge](https://huggingface.co/dhanushreddy29/BrokenKeyboardMerge) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboardMerge\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T12:28:57.888363](https://huggingface.co/datasets/open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboardMerge/blob/main/results_2024-01-14T12-28-57.888363.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5820084848111404,\n \"acc_stderr\": 0.033007700619969375,\n \"acc_norm\": 0.5876752361456778,\n \"acc_norm_stderr\": 0.03370856721497056,\n \"mc1\": 0.3769889840881273,\n \"mc1_stderr\": 0.01696551757893035,\n \"mc2\": 0.520009813591209,\n \"mc2_stderr\": 0.01568688657303073\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5639931740614335,\n \"acc_stderr\": 0.014491225699230916,\n \"acc_norm\": 0.5972696245733788,\n \"acc_norm_stderr\": 0.01433223630679015\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6292571200955985,\n \"acc_stderr\": 0.0048201660022530795,\n \"acc_norm\": 0.8124875522804222,\n \"acc_norm_stderr\": 0.0038952463204527657\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6490566037735849,\n \"acc_stderr\": 0.02937364625323469,\n \"acc_norm\": 0.6490566037735849,\n \"acc_norm_stderr\": 0.02937364625323469\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5317919075144508,\n \"acc_stderr\": 0.03804749744364764,\n \"acc_norm\": 0.5317919075144508,\n \"acc_norm_stderr\": 0.03804749744364764\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3684210526315789,\n \"acc_stderr\": 0.04537815354939391,\n \"acc_norm\": 0.3684210526315789,\n \"acc_norm_stderr\": 0.04537815354939391\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.02510742548113729,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.02510742548113729\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n \"acc_stderr\": 0.040735243221471255,\n \"acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.040735243221471255\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7161290322580646,\n \"acc_stderr\": 0.025649381063029265,\n \"acc_norm\": 0.7161290322580646,\n \"acc_norm_stderr\": 0.025649381063029265\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.39901477832512317,\n \"acc_stderr\": 0.034454876862647144,\n \"acc_norm\": 0.39901477832512317,\n \"acc_norm_stderr\": 0.034454876862647144\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7424242424242424,\n \"acc_stderr\": 0.03115626951964683,\n \"acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.03115626951964683\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5307692307692308,\n \"acc_stderr\": 0.025302958890850154,\n \"acc_norm\": 0.5307692307692308,\n \"acc_norm_stderr\": 0.025302958890850154\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.027940457136228405,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.027940457136228405\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.0322529423239964,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.0322529423239964\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7376146788990826,\n \"acc_stderr\": 0.01886188502153473,\n \"acc_norm\": 0.7376146788990826,\n \"acc_norm_stderr\": 0.01886188502153473\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252336,\n \"acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252336\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7696078431372549,\n \"acc_stderr\": 0.02955429260569507,\n \"acc_norm\": 0.7696078431372549,\n 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#region-us
|
# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboardMerge
Dataset automatically created during the evaluation run of model dhanushreddy29/BrokenKeyboardMerge on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T12:28:57.888363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboardMerge\n\n\n\nDataset automatically created during the evaluation run of model dhanushreddy29/BrokenKeyboardMerge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T12:28:57.888363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboardMerge\n\n\n\nDataset automatically created during the evaluation run of model dhanushreddy29/BrokenKeyboardMerge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T12:28:57.888363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboardMerge\n\n\n\nDataset automatically created during the evaluation run of model dhanushreddy29/BrokenKeyboardMerge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T12:28:57.888363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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1588e143af96f49e0c7c2bb2cb69759e5a7b5bca |
# Dataset Card for MS COCO Karpathy in German language
This dataset contains captions that were machine translated using [opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de).
## Dataset Details
### Dataset Description
- **Curated by:** {{ curators | default("[More Information Needed]", true)}}
- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
- **License:** {{ license | default("[More Information Needed]", true)}}
### Dataset Sources
The processed [MS COCO datasets](https://cocodataset.org/#download) (Karpathy Split) in this repo are based on the following sources:
| Type | MD5 | URL |
|------------|----------------------------------|-----------------------------------------------------------------------------------------------|
| Train | aa31ac474cf6250ebb81d18348a07ed8 | https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json |
| Validation | b273847456ef5580e33713b1f7de52a0 | https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json |
| Test | 3ff34b0ef2db02d01c37399f6a2a6cd1 | https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json |
MS COCO:
- **Download:** https://cocodataset.org/#download
- **Paper:** http://arxiv.org/abs/1405.0312
## Dataset Creation
This dataset was generated by processing the annotations via [opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de).
| Jotschi/coco-karpathy-opus-de | [
"task_categories:text-generation",
"task_categories:image-to-text",
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"size_categories:n<650k",
"source_datasets:mscoco",
"language:de",
"coco",
"mscoco",
"german",
"arxiv:1405.0312",
"region:us"
] | 2024-01-14T12:38:08+00:00 | {"annotations_creators": ["machine-generated"], "language": ["de"], "size_categories": ["n<650k"], "source_datasets": ["mscoco"], "task_categories": ["text-generation", "image-to-text", "text-to-image"], "pretty_name": "MS COCO Karpathy in german", "license_name": "cc-by-4.0", "license_link": "https://creativecommons.org/licenses/by/4.0/legalcode", "tags": ["coco", "mscoco", "german"]} | 2024-01-14T13:10:49+00:00 | [
"1405.0312"
] | [
"de"
] | TAGS
#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<650k #source_datasets-mscoco #language-German #coco #mscoco #german #arxiv-1405.0312 #region-us
| Dataset Card for MS COCO Karpathy in German language
====================================================
This dataset contains captions that were machine translated using opus-mt-en-de.
Dataset Details
---------------
### Dataset Description
* Curated by: {{ curators | default("", true)}}
* Language(s) (NLP): {{ language | default("", true)}}
* License: {{ license | default("", true)}}
### Dataset Sources
The processed MS COCO datasets (Karpathy Split) in this repo are based on the following sources:
Type: Train, MD5: aa31ac474cf6250ebb81d18348a07ed8, URL: URL
Type: Validation, MD5: b273847456ef5580e33713b1f7de52a0, URL: URL
Type: Test, MD5: 3ff34b0ef2db02d01c37399f6a2a6cd1, URL: URL
MS COCO:
* Download: URL
* Paper: URL
Dataset Creation
----------------
This dataset was generated by processing the annotations via opus-mt-en-de.
| [
"### Dataset Description\n\n\n* Curated by: {{ curators | default(\"\", true)}}\n* Language(s) (NLP): {{ language | default(\"\", true)}}\n* License: {{ license | default(\"\", true)}}",
"### Dataset Sources\n\n\nThe processed MS COCO datasets (Karpathy Split) in this repo are based on the following sources:\n\n\nType: Train, MD5: aa31ac474cf6250ebb81d18348a07ed8, URL: URL\nType: Validation, MD5: b273847456ef5580e33713b1f7de52a0, URL: URL\nType: Test, MD5: 3ff34b0ef2db02d01c37399f6a2a6cd1, URL: URL\n\n\nMS COCO:\n\n\n* Download: URL\n* Paper: URL\n\n\nDataset Creation\n----------------\n\n\nThis dataset was generated by processing the annotations via opus-mt-en-de."
] | [
"TAGS\n#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<650k #source_datasets-mscoco #language-German #coco #mscoco #german #arxiv-1405.0312 #region-us \n",
"### Dataset Description\n\n\n* Curated by: {{ curators | default(\"\", true)}}\n* Language(s) (NLP): {{ language | default(\"\", true)}}\n* License: {{ license | default(\"\", true)}}",
"### Dataset Sources\n\n\nThe processed MS COCO datasets (Karpathy Split) in this repo are based on the following sources:\n\n\nType: Train, MD5: aa31ac474cf6250ebb81d18348a07ed8, URL: URL\nType: Validation, MD5: b273847456ef5580e33713b1f7de52a0, URL: URL\nType: Test, MD5: 3ff34b0ef2db02d01c37399f6a2a6cd1, URL: URL\n\n\nMS COCO:\n\n\n* Download: URL\n* Paper: URL\n\n\nDataset Creation\n----------------\n\n\nThis dataset was generated by processing the annotations via opus-mt-en-de."
] | [
97,
56,
165
] | [
"passage: TAGS\n#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<650k #source_datasets-mscoco #language-German #coco #mscoco #german #arxiv-1405.0312 #region-us \n### Dataset Description\n\n\n* Curated by: {{ curators | default(\"\", true)}}\n* Language(s) (NLP): {{ language | default(\"\", true)}}\n* License: {{ license | default(\"\", true)}}### Dataset Sources\n\n\nThe processed MS COCO datasets (Karpathy Split) in this repo are based on the following sources:\n\n\nType: Train, MD5: aa31ac474cf6250ebb81d18348a07ed8, URL: URL\nType: Validation, MD5: b273847456ef5580e33713b1f7de52a0, URL: URL\nType: Test, MD5: 3ff34b0ef2db02d01c37399f6a2a6cd1, URL: URL\n\n\nMS COCO:\n\n\n* Download: URL\n* Paper: URL\n\n\nDataset Creation\n----------------\n\n\nThis dataset was generated by processing the annotations via opus-mt-en-de."
] | [
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] |
e92d4976a315449c947645576bc5343043604c29 |
# Dataset of suffren/シュフラン/絮弗伦 (Azur Lane)
This is the dataset of suffren/シュフラン/絮弗伦 (Azur Lane), containing 25 images and their tags.
The core tags of this character are `long_hair, breasts, bangs, horns, blue_eyes, large_breasts, white_hair, very_long_hair, fang, grey_hair, twintails, two_side_up`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 25 | 41.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suffren_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 25 | 23.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suffren_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 64 | 49.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suffren_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 25 | 36.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suffren_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 64 | 71.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suffren_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/suffren_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, solo, smile, blush, gloves, arm_up, holding, thighhighs, white_dress, armored_boots, gauntlets, grey_eyes, hair_ornament, see-through, shoulder_armor, simple_background, tail, thighs, white_background |
| 1 | 13 |  |  |  |  |  | 1girl, solo, blush, open_mouth, long_sleeves, looking_at_viewer, navel, hoodie, pleated_skirt, miniskirt, blue_skirt, cleavage, hair_ribbon, hood_down, stomach, ahoge, belt, black_bikini, black_thighhighs, hair_bow, thighs, underwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | open_mouth | solo | smile | blush | gloves | arm_up | holding | thighhighs | white_dress | armored_boots | gauntlets | grey_eyes | hair_ornament | see-through | shoulder_armor | simple_background | tail | thighs | white_background | long_sleeves | navel | hoodie | pleated_skirt | miniskirt | blue_skirt | cleavage | hair_ribbon | hood_down | stomach | ahoge | belt | black_bikini | black_thighhighs | hair_bow | underwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------|:-------|:--------|:--------|:---------|:---------|:----------|:-------------|:--------------|:----------------|:------------|:------------|:----------------|:--------------|:-----------------|:--------------------|:-------|:---------|:-------------------|:---------------|:--------|:---------|:----------------|:------------|:-------------|:-----------|:--------------|:------------|:----------|:--------|:-------|:---------------|:-------------------|:-----------|:------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | X | X | | X | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/suffren_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T12:43:21+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T12:49:02+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of suffren/シュフラン/絮弗伦 (Azur Lane)
========================================
This is the dataset of suffren/シュフラン/絮弗伦 (Azur Lane), containing 25 images and their tags.
The core tags of this character are 'long\_hair, breasts, bangs, horns, blue\_eyes, large\_breasts, white\_hair, very\_long\_hair, fang, grey\_hair, twintails, two\_side\_up', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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d03648b950969217a6f31e81f01a4702f1e5a9bd |
# Dataset Card for Visual Genome Annotations in German language
This dataset contains captions that were machine translated using [opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de).
## Dataset Details
### Dataset Description
- **Curated by:** {{ curators | default("[More Information Needed]", true)}}
- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
- **License:** {{ license | default("[More Information Needed]", true)}}
### Dataset Sources
The processed [Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html) captions in this repo are based on the following sources:
* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/visual_genome/vg_caption.json)
Visual Genome:
- **Download:** https://homes.cs.washington.edu/~ranjay/visualgenome/index.html
- **Paper:** https://link.springer.com/article/10.1007/s11263-016-0981-7
## Dataset Creation
This dataset was generated by processing the annotations via [opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de).
| Jotschi/visual_genome-opus-de | [
"task_categories:text-generation",
"task_categories:image-to-text",
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"size_categories:n<820k",
"source_datasets:visual_genome",
"language:de",
"visual_genome",
"german",
"region:us"
] | 2024-01-14T12:54:49+00:00 | {"annotations_creators": ["machine-generated"], "language": ["de"], "size_categories": ["n<820k"], "source_datasets": ["visual_genome"], "task_categories": ["text-generation", "image-to-text", "text-to-image"], "pretty_name": "Visual Genome in german", "license_name": "cc-by-4.0", "license_link": "https://creativecommons.org/licenses/by/4.0/legalcode", "tags": ["visual_genome", "german"]} | 2024-01-14T13:03:37+00:00 | [] | [
"de"
] | TAGS
#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<820k #source_datasets-visual_genome #language-German #visual_genome #german #region-us
|
# Dataset Card for Visual Genome Annotations in German language
This dataset contains captions that were machine translated using opus-mt-en-de.
## Dataset Details
### Dataset Description
- Curated by: {{ curators | default("", true)}}
- Language(s) (NLP): {{ language | default("", true)}}
- License: {{ license | default("", true)}}
### Dataset Sources
The processed Visual Genome captions in this repo are based on the following sources:
* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (URL
Visual Genome:
- Download: URL
- Paper: URL
## Dataset Creation
This dataset was generated by processing the annotations via opus-mt-en-de.
| [
"# Dataset Card for Visual Genome Annotations in German language\n\nThis dataset contains captions that were machine translated using opus-mt-en-de.",
"## Dataset Details",
"### Dataset Description\n\n- Curated by: {{ curators | default(\"\", true)}}\n- Language(s) (NLP): {{ language | default(\"\", true)}}\n- License: {{ license | default(\"\", true)}}",
"### Dataset Sources\n\nThe processed Visual Genome captions in this repo are based on the following sources:\n\n* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (URL\n\nVisual Genome:\n\n- Download: URL\n- Paper: URL",
"## Dataset Creation\n\nThis dataset was generated by processing the annotations via opus-mt-en-de."
] | [
"TAGS\n#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<820k #source_datasets-visual_genome #language-German #visual_genome #german #region-us \n",
"# Dataset Card for Visual Genome Annotations in German language\n\nThis dataset contains captions that were machine translated using opus-mt-en-de.",
"## Dataset Details",
"### Dataset Description\n\n- Curated by: {{ curators | default(\"\", true)}}\n- Language(s) (NLP): {{ language | default(\"\", true)}}\n- License: {{ license | default(\"\", true)}}",
"### Dataset Sources\n\nThe processed Visual Genome captions in this repo are based on the following sources:\n\n* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (URL\n\nVisual Genome:\n\n- Download: URL\n- Paper: URL",
"## Dataset Creation\n\nThis dataset was generated by processing the annotations via opus-mt-en-de."
] | [
90,
37,
4,
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] | [
"passage: TAGS\n#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<820k #source_datasets-visual_genome #language-German #visual_genome #german #region-us \n# Dataset Card for Visual Genome Annotations in German language\n\nThis dataset contains captions that were machine translated using opus-mt-en-de.## Dataset Details### Dataset Description\n\n- Curated by: {{ curators | default(\"\", true)}}\n- Language(s) (NLP): {{ language | default(\"\", true)}}\n- License: {{ license | default(\"\", true)}}### Dataset Sources\n\nThe processed Visual Genome captions in this repo are based on the following sources:\n\n* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (URL\n\nVisual Genome:\n\n- Download: URL\n- Paper: URL## Dataset Creation\n\nThis dataset was generated by processing the annotations via opus-mt-en-de."
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] |
3851c84dbc1397b8a30e27f098f4a019722b2cd9 |
# Dataset of vincennes/ヴィンセンス/文森斯 (Azur Lane)
This is the dataset of vincennes/ヴィンセンス/文森斯 (Azur Lane), containing 17 images and their tags.
The core tags of this character are `blue_eyes, long_hair, blue_hair, bangs, twintails, hair_ornament, breasts, sidelocks, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 17 | 11.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vincennes_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 17 | 9.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vincennes_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 28 | 15.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vincennes_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 17 | 11.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vincennes_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 28 | 18.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vincennes_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/vincennes_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | blush, 1girl, looking_at_viewer, solo, white_background, full_body, long_sleeves, skirt, black_jacket, black_thighhighs, simple_background, closed_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | 1girl | looking_at_viewer | solo | white_background | full_body | long_sleeves | skirt | black_jacket | black_thighhighs | simple_background | closed_mouth |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:-------------------|:------------|:---------------|:--------|:---------------|:-------------------|:--------------------|:---------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/vincennes_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T13:02:18+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T13:05:55+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of vincennes/ヴィンセンス/文森斯 (Azur Lane)
===========================================
This is the dataset of vincennes/ヴィンセンス/文森斯 (Azur Lane), containing 17 images and their tags.
The core tags of this character are 'blue\_eyes, long\_hair, blue\_hair, bangs, twintails, hair\_ornament, breasts, sidelocks, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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38fb5e6ffbcfbe3ae0a54a1d6ae801e9cdc397c0 |
# Dataset of u_73/U-73 (Azur Lane)
This is the dataset of u_73/U-73 (Azur Lane), containing 15 images and their tags.
The core tags of this character are `long_hair, red_eyes, black_hair, breasts, bangs, hat, very_long_hair, one_side_up`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 15 | 14.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_73_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 11.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_73_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 31 | 20.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_73_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 13.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_73_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 31 | 22.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_73_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/u_73_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | looking_at_viewer, smile, 1girl, blush, solo, jacket, navel, swimsuit, black_thighhighs, fingerless_gloves, holding, skirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | smile | 1girl | blush | solo | jacket | navel | swimsuit | black_thighhighs | fingerless_gloves | holding | skirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:--------|:--------|:-------|:---------|:--------|:-----------|:-------------------|:--------------------|:----------|:--------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/u_73_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T13:02:20+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T13:06:07+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of u\_73/U-73 (Azur Lane)
=================================
This is the dataset of u\_73/U-73 (Azur Lane), containing 15 images and their tags.
The core tags of this character are 'long\_hair, red\_eyes, black\_hair, breasts, bangs, hat, very\_long\_hair, one\_side\_up', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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0.0315636545419693,
0.21211880445480347,
-0.03636613115668297,
-0.01574229821562767,
-0.19566787779331207,
0.05434812232851982,
-0.007541419006884098,
-0.10589005053043365,
-0.14510110020637512
] |
cc327c8c9494d7ba7bfdf3a90ea79661b3e6d2ca |
# Dataset Card for Evaluation run of AI-B/UTENA-7B-V3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [AI-B/UTENA-7B-V3](https://huggingface.co/AI-B/UTENA-7B-V3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AI-B__UTENA-7B-V3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T13:18:55.824915](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-B__UTENA-7B-V3/blob/main/results_2024-01-14T13-18-55.824915.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.647960372928455,
"acc_stderr": 0.03225433281918251,
"acc_norm": 0.6510088264996041,
"acc_norm_stderr": 0.03289983967691888,
"mc1": 0.3708690330477356,
"mc1_stderr": 0.016909693580248818,
"mc2": 0.5364232527046322,
"mc2_stderr": 0.01504213226474297
},
"harness|arc:challenge|25": {
"acc": 0.6271331058020477,
"acc_stderr": 0.014131176760131167,
"acc_norm": 0.659556313993174,
"acc_norm_stderr": 0.013847460518892978
},
"harness|hellaswag|10": {
"acc": 0.6607249551882095,
"acc_stderr": 0.0047249566658799725,
"acc_norm": 0.8570005974905397,
"acc_norm_stderr": 0.0034935679140932906
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249386,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249386
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
"acc_stderr": 0.03232146916224468,
"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224468
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42328042328042326,
"acc_stderr": 0.025446365634406786,
"acc_norm": 0.42328042328042326,
"acc_norm_stderr": 0.025446365634406786
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"harness|hendrycksTest-formal_logic|5": {
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"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm_stderr": 0.024472243840895518
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
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"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm_stderr": 0.02381447708659356
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.0302839955258844,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.0302839955258844
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"harness|hendrycksTest-high_school_physics|5": {
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"acc_stderr": 0.0395802723112157,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.0395802723112157
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8366972477064221,
"acc_stderr": 0.015848255806501534,
"acc_norm": 0.8366972477064221,
"acc_norm_stderr": 0.015848255806501534
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"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5416666666666666,
"acc_stderr": 0.03398110890294636,
"acc_norm": 0.5416666666666666,
"acc_norm_stderr": 0.03398110890294636
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"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8059071729957806,
"acc_stderr": 0.02574490253229092,
"acc_norm": 0.8059071729957806,
"acc_norm_stderr": 0.02574490253229092
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
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"harness|hendrycksTest-international_law|5": {
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"acc_norm_stderr": 0.03849856098794088
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"acc_norm": 0.7777777777777778,
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"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm_stderr": 0.03291099578615769
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"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.5178571428571429,
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},
"harness|hendrycksTest-management|5": {
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"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.02093019318517933,
"acc_norm": 0.8846153846153846,
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},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.8084291187739464,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7167630057803468,
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"acc_norm": 0.7167630057803468,
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3564245810055866,
"acc_stderr": 0.016018239710513405,
"acc_norm": 0.3564245810055866,
"acc_norm_stderr": 0.016018239710513405
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7483660130718954,
"acc_stderr": 0.024848018263875192,
"acc_norm": 0.7483660130718954,
"acc_norm_stderr": 0.024848018263875192
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7491961414790996,
"acc_stderr": 0.024619771956697168,
"acc_norm": 0.7491961414790996,
"acc_norm_stderr": 0.024619771956697168
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7376543209876543,
"acc_stderr": 0.024477222856135114,
"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.024477222856135114
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4858156028368794,
"acc_stderr": 0.02981549448368206,
"acc_norm": 0.4858156028368794,
"acc_norm_stderr": 0.02981549448368206
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.455019556714472,
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"acc_norm": 0.455019556714472,
"acc_norm_stderr": 0.012718456618701768
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.028418208619406755,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.028418208619406755
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6715686274509803,
"acc_stderr": 0.018999707383162673,
"acc_norm": 0.6715686274509803,
"acc_norm_stderr": 0.018999707383162673
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
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"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7551020408163265,
"acc_stderr": 0.027529637440174923,
"acc_norm": 0.7551020408163265,
"acc_norm_stderr": 0.027529637440174923
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8606965174129353,
"acc_stderr": 0.024484487162913973,
"acc_norm": 0.8606965174129353,
"acc_norm_stderr": 0.024484487162913973
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.029547741687640044,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640044
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3708690330477356,
"mc1_stderr": 0.016909693580248818,
"mc2": 0.5364232527046322,
"mc2_stderr": 0.01504213226474297
},
"harness|winogrande|5": {
"acc": 0.8026835043409629,
"acc_stderr": 0.011185026389050376
},
"harness|gsm8k|5": {
"acc": 0.5420773313115997,
"acc_stderr": 0.013723629649844075
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_AI-B__UTENA-7B-V3 | [
"region:us"
] | 2024-01-14T13:10:22+00:00 | {"pretty_name": "Evaluation run of AI-B/UTENA-7B-V3", "dataset_summary": "Dataset automatically created during the evaluation run of model [AI-B/UTENA-7B-V3](https://huggingface.co/AI-B/UTENA-7B-V3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AI-B__UTENA-7B-V3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T13:18:55.824915](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-B__UTENA-7B-V3/blob/main/results_2024-01-14T13-18-55.824915.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.647960372928455,\n \"acc_stderr\": 0.03225433281918251,\n \"acc_norm\": 0.6510088264996041,\n \"acc_norm_stderr\": 0.03289983967691888,\n \"mc1\": 0.3708690330477356,\n \"mc1_stderr\": 0.016909693580248818,\n \"mc2\": 0.5364232527046322,\n \"mc2_stderr\": 0.01504213226474297\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.014131176760131167,\n \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892978\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6607249551882095,\n \"acc_stderr\": 0.0047249566658799725,\n \"acc_norm\": 0.8570005974905397,\n \"acc_norm_stderr\": 0.0034935679140932906\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249386,\n \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249386\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406786,\n \"acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406786\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7548387096774194,\n \"acc_stderr\": 0.024472243840895518,\n \"acc_norm\": 0.7548387096774194,\n \"acc_norm_stderr\": 0.024472243840895518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659356,\n \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659356\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857413,\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857413\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8366972477064221,\n \"acc_stderr\": 0.015848255806501534,\n \"acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.015848255806501534\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8059071729957806,\n \"acc_stderr\": 0.02574490253229092,\n \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.02574490253229092\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3564245810055866,\n \"acc_stderr\": 0.016018239710513405,\n \"acc_norm\": 0.3564245810055866,\n \"acc_norm_stderr\": 0.016018239710513405\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.024848018263875192,\n \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.024848018263875192\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7491961414790996,\n \"acc_stderr\": 0.024619771956697168,\n \"acc_norm\": 0.7491961414790996,\n \"acc_norm_stderr\": 0.024619771956697168\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.455019556714472,\n \"acc_stderr\": 0.012718456618701768,\n \"acc_norm\": 0.455019556714472,\n \"acc_norm_stderr\": 0.012718456618701768\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174923,\n \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174923\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3708690330477356,\n \"mc1_stderr\": 0.016909693580248818,\n \"mc2\": 0.5364232527046322,\n \"mc2_stderr\": 0.01504213226474297\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8026835043409629,\n \"acc_stderr\": 0.011185026389050376\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5420773313115997,\n \"acc_stderr\": 0.013723629649844075\n }\n}\n```", "repo_url": "https://huggingface.co/AI-B/UTENA-7B-V3", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_14T13_08_02.429354", "path": ["**/details_harness|arc:challenge|25_2024-01-14T13-08-02.429354.parquet"]}, {"split": "2024_01_14T13_18_55.824915", "path": ["**/details_harness|arc:challenge|25_2024-01-14T13-18-55.824915.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-14T13-18-55.824915.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_14T13_08_02.429354", "path": ["**/details_harness|gsm8k|5_2024-01-14T13-08-02.429354.parquet"]}, {"split": "2024_01_14T13_18_55.824915", "path": ["**/details_harness|gsm8k|5_2024-01-14T13-18-55.824915.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-14T13-18-55.824915.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_14T13_08_02.429354", "path": ["**/details_harness|hellaswag|10_2024-01-14T13-08-02.429354.parquet"]}, {"split": "2024_01_14T13_18_55.824915", "path": ["**/details_harness|hellaswag|10_2024-01-14T13-18-55.824915.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-14T13-18-55.824915.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_14T13_08_02.429354", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-14T13-08-02.429354.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T13-08-02.429354.parquet", 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["results_2024-01-14T13-08-02.429354.parquet"]}, {"split": "2024_01_14T13_18_55.824915", "path": ["results_2024-01-14T13-18-55.824915.parquet"]}, {"split": "latest", "path": ["results_2024-01-14T13-18-55.824915.parquet"]}]}]} | 2024-01-14T13:21:15+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of AI-B/UTENA-7B-V3
Dataset automatically created during the evaluation run of model AI-B/UTENA-7B-V3 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T13:18:55.824915(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of AI-B/UTENA-7B-V3\n\n\n\nDataset automatically created during the evaluation run of model AI-B/UTENA-7B-V3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T13:18:55.824915(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of AI-B/UTENA-7B-V3\n\n\n\nDataset automatically created during the evaluation run of model AI-B/UTENA-7B-V3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T13:18:55.824915(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AI-B/UTENA-7B-V3\n\n\n\nDataset automatically created during the evaluation run of model AI-B/UTENA-7B-V3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T13:18:55.824915(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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e5c10415d5c8b1238e65ff808139f911a3816286 |
# Dataset Card for Visual Genome Annotations in Simple English
This dataset contains captions that were rephrased into simple english so that a young child would understand it.
## Dataset Details
### Dataset Description
- **Curated by:** {{ curators | default("[More Information Needed]", true)}}
- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
- **License:** {{ license | default("[More Information Needed]", true)}}
### Dataset Sources
The processed [Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html) captions in this repo are based on the following sources:
* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/visual_genome/vg_caption.json)
Visual Genome:
- **Download:** https://homes.cs.washington.edu/~ranjay/visualgenome/index.html
- **Paper:** https://link.springer.com/article/10.1007/s11263-016-0981-7
## Dataset Creation
This dataset was generated by processing the annotations via [Mistal7B](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-AWQ).
Prompt used:
```
Rewrite the sentence " + caption + " for a 3 to 4 year old child. Give only one simple sentence. Don't use the word see. Give only a single answer.
```
A filter was applied to only store captions which matched the common output format. A best effort filter was applied to reduce the chance of including multiple example sentences in the output.
### Curation Rationale
This dataset is useful for experiments with small LLMs which have only a reduced corpus. The dataset is suitable to be used for LAVIS experiments (QFormer Training) with a finetuned TinyStories 33M LLM.
| Jotschi/visual_genome-simple-en | [
"task_categories:text-generation",
"task_categories:image-to-text",
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"size_categories:n<820k",
"source_datasets:visual_genome",
"language:en",
"visual_genome",
"simple-english",
"region:us"
] | 2024-01-14T13:12:08+00:00 | {"annotations_creators": ["machine-generated"], "language": ["en"], "size_categories": ["n<820k"], "source_datasets": ["visual_genome"], "task_categories": ["text-generation", "image-to-text", "text-to-image"], "pretty_name": "Visual Genome in Simple English", "license_name": "cc-by-4.0", "license_link": "https://creativecommons.org/licenses/by/4.0/legalcode", "tags": ["visual_genome", "simple-english"]} | 2024-01-14T13:16:08+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<820k #source_datasets-visual_genome #language-English #visual_genome #simple-english #region-us
|
# Dataset Card for Visual Genome Annotations in Simple English
This dataset contains captions that were rephrased into simple english so that a young child would understand it.
## Dataset Details
### Dataset Description
- Curated by: {{ curators | default("", true)}}
- Language(s) (NLP): {{ language | default("", true)}}
- License: {{ license | default("", true)}}
### Dataset Sources
The processed Visual Genome captions in this repo are based on the following sources:
* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (URL
Visual Genome:
- Download: URL
- Paper: URL
## Dataset Creation
This dataset was generated by processing the annotations via Mistal7B.
Prompt used:
A filter was applied to only store captions which matched the common output format. A best effort filter was applied to reduce the chance of including multiple example sentences in the output.
### Curation Rationale
This dataset is useful for experiments with small LLMs which have only a reduced corpus. The dataset is suitable to be used for LAVIS experiments (QFormer Training) with a finetuned TinyStories 33M LLM.
| [
"# Dataset Card for Visual Genome Annotations in Simple English\n\nThis dataset contains captions that were rephrased into simple english so that a young child would understand it.",
"## Dataset Details",
"### Dataset Description\n\n- Curated by: {{ curators | default(\"\", true)}}\n- Language(s) (NLP): {{ language | default(\"\", true)}}\n- License: {{ license | default(\"\", true)}}",
"### Dataset Sources\n\nThe processed Visual Genome captions in this repo are based on the following sources:\n\n* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (URL\n\nVisual Genome:\n\n- Download: URL\n- Paper: URL",
"## Dataset Creation\n\nThis dataset was generated by processing the annotations via Mistal7B.\n\n\nPrompt used:\n\n\n\nA filter was applied to only store captions which matched the common output format. A best effort filter was applied to reduce the chance of including multiple example sentences in the output.",
"### Curation Rationale\n\nThis dataset is useful for experiments with small LLMs which have only a reduced corpus. The dataset is suitable to be used for LAVIS experiments (QFormer Training) with a finetuned TinyStories 33M LLM."
] | [
"TAGS\n#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<820k #source_datasets-visual_genome #language-English #visual_genome #simple-english #region-us \n",
"# Dataset Card for Visual Genome Annotations in Simple English\n\nThis dataset contains captions that were rephrased into simple english so that a young child would understand it.",
"## Dataset Details",
"### Dataset Description\n\n- Curated by: {{ curators | default(\"\", true)}}\n- Language(s) (NLP): {{ language | default(\"\", true)}}\n- License: {{ license | default(\"\", true)}}",
"### Dataset Sources\n\nThe processed Visual Genome captions in this repo are based on the following sources:\n\n* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (URL\n\nVisual Genome:\n\n- Download: URL\n- Paper: URL",
"## Dataset Creation\n\nThis dataset was generated by processing the annotations via Mistal7B.\n\n\nPrompt used:\n\n\n\nA filter was applied to only store captions which matched the common output format. A best effort filter was applied to reduce the chance of including multiple example sentences in the output.",
"### Curation Rationale\n\nThis dataset is useful for experiments with small LLMs which have only a reduced corpus. The dataset is suitable to be used for LAVIS experiments (QFormer Training) with a finetuned TinyStories 33M LLM."
] | [
92,
38,
4,
56,
66,
66,
62
] | [
"passage: TAGS\n#task_categories-text-generation #task_categories-image-to-text #task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<820k #source_datasets-visual_genome #language-English #visual_genome #simple-english #region-us \n# Dataset Card for Visual Genome Annotations in Simple English\n\nThis dataset contains captions that were rephrased into simple english so that a young child would understand it.## Dataset Details### Dataset Description\n\n- Curated by: {{ curators | default(\"\", true)}}\n- Language(s) (NLP): {{ language | default(\"\", true)}}\n- License: {{ license | default(\"\", true)}}### Dataset Sources\n\nThe processed Visual Genome captions in this repo are based on the following sources:\n\n* 941425b651f50cdb1a6f0673eaab6260 vg_caption.json (URL\n\nVisual Genome:\n\n- Download: URL\n- Paper: URL## Dataset Creation\n\nThis dataset was generated by processing the annotations via Mistal7B.\n\n\nPrompt used:\n\n\n\nA filter was applied to only store captions which matched the common output format. A best effort filter was applied to reduce the chance of including multiple example sentences in the output.### Curation Rationale\n\nThis dataset is useful for experiments with small LLMs which have only a reduced corpus. The dataset is suitable to be used for LAVIS experiments (QFormer Training) with a finetuned TinyStories 33M LLM."
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1cfc3af9dfd102776942d2a5224ff134db0af11f |
# Dataset Card for Evaluation run of Weyaxi/Helion-4x34B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Weyaxi/Helion-4x34B](https://huggingface.co/Weyaxi/Helion-4x34B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__Helion-4x34B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T13:23:45.843719](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Helion-4x34B/blob/main/results_2024-01-14T13-23-45.843719.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_stderr": 0.027825032662237632,
"acc_norm": 0.7733690955948024,
"acc_norm_stderr": 0.028359676428301124,
"mc1": 0.4724602203182375,
"mc1_stderr": 0.017476930190712187,
"mc2": 0.6391431988345577,
"mc2_stderr": 0.014739254450901405
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.013796182947785562,
"acc_norm": 0.697098976109215,
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},
"harness|hellaswag|10": {
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"acc_norm": 0.8528181637124079,
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},
"harness|hendrycksTest-abstract_algebra|5": {
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},
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},
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},
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},
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},
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},
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},
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},
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_law|5": {
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"acc_norm_stderr": 0.015643069911273344
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"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.04350271442923243
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.8448979591836735,
"acc_stderr": 0.0231747988612186,
"acc_norm": 0.8448979591836735,
"acc_norm_stderr": 0.0231747988612186
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"harness|hendrycksTest-sociology|5": {
"acc": 0.8955223880597015,
"acc_stderr": 0.021628920516700643,
"acc_norm": 0.8955223880597015,
"acc_norm_stderr": 0.021628920516700643
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"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.93,
"acc_stderr": 0.025643239997624294,
"acc_norm": 0.93,
"acc_norm_stderr": 0.025643239997624294
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"harness|hendrycksTest-virology|5": {
"acc": 0.572289156626506,
"acc_stderr": 0.038515976837185335,
"acc_norm": 0.572289156626506,
"acc_norm_stderr": 0.038515976837185335
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"harness|hendrycksTest-world_religions|5": {
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"acc_stderr": 0.025679342723276908,
"acc_norm": 0.8713450292397661,
"acc_norm_stderr": 0.025679342723276908
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"harness|truthfulqa:mc|0": {
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"mc1_stderr": 0.017476930190712187,
"mc2": 0.6391431988345577,
"mc2_stderr": 0.014739254450901405
},
"harness|winogrande|5": {
"acc": 0.8437253354380426,
"acc_stderr": 0.010205351791873504
},
"harness|gsm8k|5": {
"acc": 0.7225170583775588,
"acc_stderr": 0.012333447581047546
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Weyaxi__Helion-4x34B | [
"region:us"
] | 2024-01-14T13:25:54+00:00 | {"pretty_name": "Evaluation run of Weyaxi/Helion-4x34B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/Helion-4x34B](https://huggingface.co/Weyaxi/Helion-4x34B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__Helion-4x34B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T13:23:45.843719](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Helion-4x34B/blob/main/results_2024-01-14T13-23-45.843719.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7699592649917206,\n \"acc_stderr\": 0.027825032662237632,\n \"acc_norm\": 0.7733690955948024,\n \"acc_norm_stderr\": 0.028359676428301124,\n \"mc1\": 0.4724602203182375,\n \"mc1_stderr\": 0.017476930190712187,\n \"mc2\": 0.6391431988345577,\n \"mc2_stderr\": 0.014739254450901405\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6646757679180887,\n \"acc_stderr\": 0.013796182947785562,\n \"acc_norm\": 0.697098976109215,\n \"acc_norm_stderr\": 0.013428241573185349\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6577375024895439,\n \"acc_stderr\": 0.004734972668299616,\n \"acc_norm\": 0.8528181637124079,\n \"acc_norm_stderr\": 0.0035356302890914575\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.9078947368421053,\n \"acc_stderr\": 0.02353268597044349,\n \"acc_norm\": 0.9078947368421053,\n \"acc_norm_stderr\": 0.02353268597044349\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8150943396226416,\n \"acc_stderr\": 0.023893351834464324,\n \"acc_norm\": 0.8150943396226416,\n \"acc_norm_stderr\": 0.023893351834464324\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9027777777777778,\n \"acc_stderr\": 0.02477451625044016,\n \"acc_norm\": 0.9027777777777778,\n \"acc_norm_stderr\": 0.02477451625044016\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5686274509803921,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.5686274509803921,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7914893617021277,\n \"acc_stderr\": 0.02655698211783874,\n \"acc_norm\": 0.7914893617021277,\n \"acc_norm_stderr\": 0.02655698211783874\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.034165204477475494,\n \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.034165204477475494\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.6904761904761905,\n \"acc_stderr\": 0.023809523809523867,\n \"acc_norm\": 0.6904761904761905,\n \"acc_norm_stderr\": 0.023809523809523867\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9,\n \"acc_stderr\": 0.017066403719657255,\n \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.017066403719657255\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6354679802955665,\n \"acc_stderr\": 0.0338640574606209,\n \"acc_norm\": 0.6354679802955665,\n \"acc_norm_stderr\": 0.0338640574606209\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8727272727272727,\n \"acc_stderr\": 0.02602465765165619,\n \"acc_norm\": 0.8727272727272727,\n \"acc_norm_stderr\": 0.02602465765165619\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9292929292929293,\n \"acc_stderr\": 0.01826310542019949,\n \"acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.01826310542019949\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.019348070174396985,\n \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.019348070174396985\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03029677128606732,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03029677128606732\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8739495798319328,\n \"acc_stderr\": 0.02155962312121393,\n \"acc_norm\": 0.8739495798319328,\n \"acc_norm_stderr\": 0.02155962312121393\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.5033112582781457,\n \"acc_stderr\": 0.04082393379449654,\n \"acc_norm\": 0.5033112582781457,\n \"acc_norm_stderr\": 0.04082393379449654\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9137614678899083,\n \"acc_stderr\": 0.012035597300116248,\n \"acc_norm\": 0.9137614678899083,\n \"acc_norm_stderr\": 0.012035597300116248\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6574074074074074,\n \"acc_stderr\": 0.032365852526021574,\n \"acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.032365852526021574\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073322,\n \"acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073322\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.9071729957805907,\n \"acc_stderr\": 0.018889750550956715,\n \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.018889750550956715\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n \"acc_stderr\": 0.02737309550054019,\n \"acc_norm\": 0.7892376681614349,\n \"acc_norm_stderr\": 0.02737309550054019\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8854961832061069,\n \"acc_stderr\": 0.027927473753597446,\n \"acc_norm\": 0.8854961832061069,\n \"acc_norm_stderr\": 0.027927473753597446\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.03038159675665167,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.03038159675665167\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.9320388349514563,\n \"acc_stderr\": 0.024919959142514478,\n \"acc_norm\": 0.9320388349514563,\n \"acc_norm_stderr\": 0.024919959142514478\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n \"acc_stderr\": 0.016534627684311357,\n \"acc_norm\": 0.9316239316239316,\n \"acc_norm_stderr\": 0.016534627684311357\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n },\n 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#region-us
|
# Dataset Card for Evaluation run of Weyaxi/Helion-4x34B
Dataset automatically created during the evaluation run of model Weyaxi/Helion-4x34B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T13:23:45.843719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Weyaxi/Helion-4x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Helion-4x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T13:23:45.843719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Weyaxi/Helion-4x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Helion-4x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T13:23:45.843719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/Helion-4x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Helion-4x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T13:23:45.843719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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] |
3e4f8c55c55d908fcdc3a3a118126cabce6e3b09 |
# Dataset of stremitelny/ストレミテルヌイ/神速 (Azur Lane)
This is the dataset of stremitelny/ストレミテルヌイ/神速 (Azur Lane), containing 15 images and their tags.
The core tags of this character are `red_eyes, white_hair, long_hair, hair_between_eyes, bangs, antenna_hair, hat, ahoge, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 15 | 22.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stremitelny_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 13.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stremitelny_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 38 | 28.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stremitelny_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 20.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stremitelny_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 38 | 41.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stremitelny_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/stremitelny_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, solo, long_sleeves, looking_at_viewer, blush, open_mouth, white_coat, :d, black_pantyhose, fur-trimmed_coat, simple_background, white_background, white_headwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | long_sleeves | looking_at_viewer | blush | open_mouth | white_coat | :d | black_pantyhose | fur-trimmed_coat | simple_background | white_background | white_headwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:--------------------|:--------|:-------------|:-------------|:-----|:------------------|:-------------------|:--------------------|:-------------------|:-----------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/stremitelny_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T13:31:03+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T13:35:33+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of stremitelny/ストレミテルヌイ/神速 (Azur Lane)
==============================================
This is the dataset of stremitelny/ストレミテルヌイ/神速 (Azur Lane), containing 15 images and their tags.
The core tags of this character are 'red\_eyes, white\_hair, long\_hair, hair\_between\_eyes, bangs, antenna\_hair, hat, ahoge, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
60cc109811fd4dc6960ed95028b5c9f697894c47 |
# Dataset of stanly/スタンリー/斯坦利 (Azur Lane)
This is the dataset of stanly/スタンリー/斯坦利 (Azur Lane), containing 15 images and their tags.
The core tags of this character are `long_hair, purple_eyes, pink_hair, headband, hair_between_eyes, hairband, bangs, pink_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 15 | 14.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stanly_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 9.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stanly_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 34 | 17.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stanly_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 12.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stanly_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 34 | 21.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stanly_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/stanly_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, looking_at_viewer, jacket, solo, smile, blush, single_thighhigh, necktie, white_background, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | jacket | solo | smile | blush | single_thighhigh | necktie | white_background | simple_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:---------|:-------|:--------|:--------|:-------------------|:----------|:-------------------|:--------------------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/stanly_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T13:31:06+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T13:34:59+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of stanly/スタンリー/斯坦利 (Azur Lane)
=======================================
This is the dataset of stanly/スタンリー/斯坦利 (Azur Lane), containing 15 images and their tags.
The core tags of this character are 'long\_hair, purple\_eyes, pink\_hair, headband, hair\_between\_eyes, hairband, bangs, pink\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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4efb8da236046b1043272931e15f8450e75d205c |
# Downloading this Options Dataset
This document will guide you through the steps to download the Merval options dataset from Hugging Face Datasets.
To start, you'll need to install Hugging Face's `datasets` library if you haven't done so already.
You can do this using the following pip command:
```python
!pip install datasets
```
Here's the Python code to load the Merval equity dataset from Hugging Face Datasets and convert it into a pandas DataFrame:
```python
from datasets import load_dataset
import pandas as pd
id = "gauss314/opciones"
data = load_dataset(id)
df = pd.DataFrame(data['train'][:])
``` | gauss314/opciones | [
"task_categories:tabular-classification",
"task_categories:tabular-regression",
"license:apache-2.0",
"Merval",
"options",
"region:us"
] | 2024-01-14T13:38:42+00:00 | {"license": "apache-2.0", "task_categories": ["tabular-classification", "tabular-regression"], "pretty_name": "Merval historical options data, for deep learning and machine learning tests", "tags": ["Merval", "options"]} | 2024-01-14T13:46:43+00:00 | [] | [] | TAGS
#task_categories-tabular-classification #task_categories-tabular-regression #license-apache-2.0 #Merval #options #region-us
|
# Downloading this Options Dataset
This document will guide you through the steps to download the Merval options dataset from Hugging Face Datasets.
To start, you'll need to install Hugging Face's 'datasets' library if you haven't done so already.
You can do this using the following pip command:
Here's the Python code to load the Merval equity dataset from Hugging Face Datasets and convert it into a pandas DataFrame:
| [
"# Downloading this Options Dataset\n\nThis document will guide you through the steps to download the Merval options dataset from Hugging Face Datasets. \n\nTo start, you'll need to install Hugging Face's 'datasets' library if you haven't done so already. \nYou can do this using the following pip command:\n\n\n\nHere's the Python code to load the Merval equity dataset from Hugging Face Datasets and convert it into a pandas DataFrame:"
] | [
"TAGS\n#task_categories-tabular-classification #task_categories-tabular-regression #license-apache-2.0 #Merval #options #region-us \n",
"# Downloading this Options Dataset\n\nThis document will guide you through the steps to download the Merval options dataset from Hugging Face Datasets. \n\nTo start, you'll need to install Hugging Face's 'datasets' library if you haven't done so already. \nYou can do this using the following pip command:\n\n\n\nHere's the Python code to load the Merval equity dataset from Hugging Face Datasets and convert it into a pandas DataFrame:"
] | [
44,
105
] | [
"passage: TAGS\n#task_categories-tabular-classification #task_categories-tabular-regression #license-apache-2.0 #Merval #options #region-us \n# Downloading this Options Dataset\n\nThis document will guide you through the steps to download the Merval options dataset from Hugging Face Datasets. \n\nTo start, you'll need to install Hugging Face's 'datasets' library if you haven't done so already. \nYou can do this using the following pip command:\n\n\n\nHere's the Python code to load the Merval equity dataset from Hugging Face Datasets and convert it into a pandas DataFrame:"
] | [
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] |
a57e4b4f7cb4cf05e08b45946620572875aa88b3 |
# Dataset of bearn/ベアルン/贝亚恩 (Azur Lane)
This is the dataset of bearn/ベアルン/贝亚恩 (Azur Lane), containing 13 images and their tags.
The core tags of this character are `bangs, breasts, small_breasts, multicolored_hair, short_hair, horns, black_hair, glasses, grey_hair, blunt_bangs, grey_eyes, purple_hair, streaked_hair, two-tone_hair, blue_eyes, hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 13 | 14.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bearn_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 13 | 8.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bearn_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 23 | 14.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bearn_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 13 | 12.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bearn_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 23 | 20.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bearn_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/bearn_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, solo, monocle, bare_shoulders, holding, simple_background, black_gloves, blush, closed_mouth, covered_navel, long_sleeves, thighhighs, dress, full_body, jacket, off_shoulder, swimsuit, thigh_boots, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | monocle | bare_shoulders | holding | simple_background | black_gloves | blush | closed_mouth | covered_navel | long_sleeves | thighhighs | dress | full_body | jacket | off_shoulder | swimsuit | thigh_boots | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:----------|:-----------------|:----------|:--------------------|:---------------|:--------|:---------------|:----------------|:---------------|:-------------|:--------|:------------|:---------|:---------------|:-----------|:--------------|:-------------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/bearn_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T13:46:10+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T13:49:14+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of bearn/ベアルン/贝亚恩 (Azur Lane)
=====================================
This is the dataset of bearn/ベアルン/贝亚恩 (Azur Lane), containing 13 images and their tags.
The core tags of this character are 'bangs, breasts, small\_breasts, multicolored\_hair, short\_hair, horns, black\_hair, glasses, grey\_hair, blunt\_bangs, grey\_eyes, purple\_hair, streaked\_hair, two-tone\_hair, blue\_eyes, hairband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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769b1b78c9dd7814175057cb4e9b759a0664e1e3 |
# Dataset Card for Evaluation run of FelixChao/MathDolphin-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [FelixChao/MathDolphin-7B](https://huggingface.co/FelixChao/MathDolphin-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_FelixChao__MathDolphin-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T13:48:07.624647](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__MathDolphin-7B/blob/main/results_2024-01-14T13-48-07.624647.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
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},
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},
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},
"harness|winogrande|5": {
"acc": 0.8121546961325967,
"acc_stderr": 0.010977481103435091
},
"harness|gsm8k|5": {
"acc": 0.6785443517816527,
"acc_stderr": 0.012864471384836703
}
}
```
## Dataset Details
### Dataset Description
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_FelixChao__MathDolphin-7B | [
"region:us"
] | 2024-01-14T13:50:28+00:00 | {"pretty_name": "Evaluation run of FelixChao/MathDolphin-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [FelixChao/MathDolphin-7B](https://huggingface.co/FelixChao/MathDolphin-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FelixChao__MathDolphin-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T13:48:07.624647](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__MathDolphin-7B/blob/main/results_2024-01-14T13-48-07.624647.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.65315875756261,\n \"acc_stderr\": 0.03196302131707709,\n \"acc_norm\": 0.6538202133223454,\n \"acc_norm_stderr\": 0.03261743110455684,\n \"mc1\": 0.3708690330477356,\n \"mc1_stderr\": 0.01690969358024882,\n \"mc2\": 0.5291514968771067,\n \"mc2_stderr\": 0.015285199336849235\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6279863481228669,\n \"acc_stderr\": 0.014124597881844461,\n \"acc_norm\": 0.658703071672355,\n \"acc_norm_stderr\": 0.01385583128749773\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6622186815375424,\n \"acc_stderr\": 0.004719870074967248,\n \"acc_norm\": 0.8549093806014738,\n \"acc_norm_stderr\": 0.0035147239847366034\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03523807393012047,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03523807393012047\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.02783491252754407,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.02783491252754407\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.025467149045469553,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.025467149045469553\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n \"acc_stderr\": 0.023664216671642514,\n \"acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.023664216671642514\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479049,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479049\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9222797927461139,\n \"acc_stderr\": 0.019321805557223168,\n \"acc_norm\": 0.9222797927461139,\n \"acc_norm_stderr\": 0.019321805557223168\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971118,\n \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971118\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948482,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948482\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887034,\n \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887034\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8284313725490197,\n \"acc_stderr\": 0.02646056956124064,\n \"acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.02646056956124064\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7130044843049327,\n \"acc_stderr\": 0.03036037971029195,\n \"acc_norm\": 0.7130044843049327,\n \"acc_norm_stderr\": 0.03036037971029195\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.03192193448934724,\n \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.03192193448934724\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n \"acc_stderr\": 0.021586494001281372,\n \"acc_norm\": 0.8760683760683761,\n \"acc_norm_stderr\": 0.021586494001281372\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.023267528432100174,\n \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.023267528432100174\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3854748603351955,\n \"acc_stderr\": 0.01627792703963819,\n \"acc_norm\": 0.3854748603351955,\n \"acc_norm_stderr\": 0.01627792703963819\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.025058503316958143,\n \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.025058503316958143\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n \"acc_stderr\": 0.025403832978179604,\n \"acc_norm\": 0.7234726688102894,\n \"acc_norm_stderr\": 0.025403832978179604\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.023468429832451152,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.023468429832451152\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n \"acc_stderr\": 0.012752858346533133,\n \"acc_norm\": 0.47392438070404175,\n \"acc_norm_stderr\": 0.012752858346533133\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462937,\n \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462937\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3708690330477356,\n \"mc1_stderr\": 0.01690969358024882,\n \"mc2\": 0.5291514968771067,\n \"mc2_stderr\": 0.015285199336849235\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8121546961325967,\n \"acc_stderr\": 0.010977481103435091\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6785443517816527,\n \"acc_stderr\": 0.012864471384836703\n }\n}\n```", "repo_url": "https://huggingface.co/FelixChao/MathDolphin-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_14T13_48_07.624647", 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#region-us
|
# Dataset Card for Evaluation run of FelixChao/MathDolphin-7B
Dataset automatically created during the evaluation run of model FelixChao/MathDolphin-7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T13:48:07.624647(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of FelixChao/MathDolphin-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/MathDolphin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T13:48:07.624647(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of FelixChao/MathDolphin-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/MathDolphin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T13:48:07.624647(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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181,
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40,
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of FelixChao/MathDolphin-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/MathDolphin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T13:48:07.624647(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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] |
6740f433dd642d55cb44d91d69d09bd4f0bb3e5a |
# Dataset of asuka/飛鳥/飞鸟 (Azur Lane)
This is the dataset of asuka/飛鳥/飞鸟 (Azur Lane), containing 368 images and their tags.
The core tags of this character are `breasts, ponytail, brown_eyes, ribbon, large_breasts, hair_ribbon, black_hair, brown_hair, white_ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 368 | 446.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 368 | 270.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 875 | 566.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 368 | 396.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 875 | 781.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/asuka_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, open_mouth, :d, blush, striped_bikini, navel, red_scarf, simple_background, white_background |
| 1 | 10 |  |  |  |  |  | 1girl, navel, solo, striped_bikini, cleavage, front-tie_top, looking_at_viewer, blush, side-tie_bikini_bottom, multicolored_stripes, open_mouth, red_scarf, white_background, smile, multicolored_clothes, simple_background |
| 2 | 9 |  |  |  |  |  | cleavage, looking_at_viewer, 1girl, cloud, day, open_mouth, outdoors, solo, blue_sky, beach, navel, side-tie_bikini_bottom, smile, ocean, striped_bikini, blush, long_hair |
| 3 | 30 |  |  |  |  |  | school_uniform, 1girl, solo, sweater_vest, black_thighhighs, dual_wielding, plaid_skirt, red_scarf, katana, necktie, smile, looking_at_viewer, blush |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | cleavage | open_mouth | :d | blush | striped_bikini | navel | red_scarf | simple_background | white_background | front-tie_top | side-tie_bikini_bottom | multicolored_stripes | smile | multicolored_clothes | cloud | day | outdoors | blue_sky | beach | ocean | long_hair | school_uniform | sweater_vest | black_thighhighs | dual_wielding | plaid_skirt | katana | necktie |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:-------------|:-----|:--------|:-----------------|:--------|:------------|:--------------------|:-------------------|:----------------|:-------------------------|:-----------------------|:--------|:-----------------------|:--------|:------|:-----------|:-----------|:--------|:--------|:------------|:-----------------|:---------------|:-------------------|:----------------|:--------------|:---------|:----------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | X | X | X | | X | X | X | | | | | X | | X | | X | X | X | X | X | X | X | | | | | | | |
| 3 | 30 |  |  |  |  |  | X | X | X | | | | X | | | X | | | | | | X | | | | | | | | | X | X | X | X | X | X | X |
| CyberHarem/asuka_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T13:51:17+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T15:01:19+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of asuka/飛鳥/飞鸟 (Azur Lane)
==================================
This is the dataset of asuka/飛鳥/飞鸟 (Azur Lane), containing 368 images and their tags.
The core tags of this character are 'breasts, ponytail, brown\_eyes, ribbon, large\_breasts, hair\_ribbon, black\_hair, brown\_hair, white\_ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
f8c198f1bb9a37e9c683b9a6f96d37fa5de91e79 | # Dataset Card
- **Homepage: https://kaistai.github.io/prometheus-vision/**
- **Repository: https://github.com/kaistAI/prometheus-vision**
- **Paper: https://arxiv.org/abs/2401.06591**
- **Point of Contact: [email protected]**
### Dataset summary
Perception Collection is the first multi-modal feedback dataset that could be used to train an evaluator VLM. Perception Collection includes 15K fine-grained criteria that determine the crucial aspect for each instance.
### Languages
English
## Dataset Structure
* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.
* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.
* output: The output that the evaluator VLM should generate. It includes the feedback and score decision divided by a phrase ```[RESULT]```.
* orig```_```instruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.
* orig```_```response: The response to be evaluated.
* orig```_```reference```_```answer: A reference answer to the orig```_```instruction.
* orig```_```criteria: The score criteria used to evaluate the orig```_``` response.
* orig```_```score1```_```description: A description of when to give a score of 1 to the orig```_```response.
* orig```_```score2```_```description: A description of when to give a score of 2 to the orig```_```response.
* orig```_```score3```_```description: A description of when to give a score of 3 to the orig```_```response.
* orig```_```score4```_```description: A description of when to give a score of 4 to the orig```_```response.
* orig```_```score5```_```description: A description of when to give a score of 5 to the orig```_```response.
* orig```_```feedback: A feedback that critiques the orig```_```response.
* orig```_```score: An integer between 1 and 5 given to the orig```_```response.
In our paper, we trained the input using the following prompt format (already processed in the 'instruction'):
```
###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, image and a score rubric representing an evaluation criterion is given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)\"
4. Please do not generate any other opening, closing, and explanations.
###The instruction to evaluate:
{orig_instruction}
###Response to evaluate:
{orig_response}
###Reference Answer (Score 5):
{orig_reference_answer}
###Score Rubrics:
[{orig_criteria}]
Score 1: {orig_score1_description}
Score 2: {orig_score2_description}
Score 3: {orig_score3_description}
Score 4: {orig_score4_description}
Score 5: {orig_score5_description}
###Feedback:
```
### Data Splits
| name | train |
|-------------------|------:|
|Perception-Collection|150,108|
### Citation Information
If you find the following dataset helpful, please consider citing our paper!
```bibtex
@misc{lee2024prometheusvision,
title={Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation},
author={Seongyun Lee and Seungone Kim and Sue Hyun Park and Geewook Kim and Minjoon Seo},
year={2024},
eprint={2401.06591},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | kaist-ai/Perception-Collection | [
"task_categories:visual-question-answering",
"task_categories:text2text-generation",
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"arxiv:2401.06591",
"region:us"
] | 2024-01-14T14:05:16+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["visual-question-answering", "text2text-generation", "image-to-text"]} | 2024-01-15T12:52:11+00:00 | [
"2401.06591"
] | [
"en"
] | TAGS
#task_categories-visual-question-answering #task_categories-text2text-generation #task_categories-image-to-text #size_categories-100K<n<1M #language-English #license-cc-by-4.0 #arxiv-2401.06591 #region-us
| Dataset Card
============
* Homepage: URL
* Repository: URL
* Paper: URL
* Point of Contact: seongyun@URL
### Dataset summary
Perception Collection is the first multi-modal feedback dataset that could be used to train an evaluator VLM. Perception Collection includes 15K fine-grained criteria that determine the crucial aspect for each instance.
!plot
### Languages
English
Dataset Structure
-----------------
* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.
* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.
* output: The output that the evaluator VLM should generate. It includes the feedback and score decision divided by a phrase .
* originstruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.
* origresponse: The response to be evaluated.
* origreferenceanswer: A reference answer to the originstruction.
* origcriteria: The score criteria used to evaluate the orig response.
* origscore1description: A description of when to give a score of 1 to the origresponse.
* origscore2description: A description of when to give a score of 2 to the origresponse.
* origscore3description: A description of when to give a score of 3 to the origresponse.
* origscore4description: A description of when to give a score of 4 to the origresponse.
* origscore5description: A description of when to give a score of 5 to the origresponse.
* origfeedback: A feedback that critiques the origresponse.
* origscore: An integer between 1 and 5 given to the origresponse.
In our paper, we trained the input using the following prompt format (already processed in the 'instruction'):
### Data Splits
If you find the following dataset helpful, please consider citing our paper!
| [
"### Dataset summary\n\n\nPerception Collection is the first multi-modal feedback dataset that could be used to train an evaluator VLM. Perception Collection includes 15K fine-grained criteria that determine the crucial aspect for each instance.\n!plot",
"### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\n* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.\n* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.\n* output: The output that the evaluator VLM should generate. It includes the feedback and score decision divided by a phrase .\n* originstruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.\n* origresponse: The response to be evaluated.\n* origreferenceanswer: A reference answer to the originstruction.\n* origcriteria: The score criteria used to evaluate the orig response.\n* origscore1description: A description of when to give a score of 1 to the origresponse.\n* origscore2description: A description of when to give a score of 2 to the origresponse.\n* origscore3description: A description of when to give a score of 3 to the origresponse.\n* origscore4description: A description of when to give a score of 4 to the origresponse.\n* origscore5description: A description of when to give a score of 5 to the origresponse.\n* origfeedback: A feedback that critiques the origresponse.\n* origscore: An integer between 1 and 5 given to the origresponse.\n\n\nIn our paper, we trained the input using the following prompt format (already processed in the 'instruction'):",
"### Data Splits\n\n\n\nIf you find the following dataset helpful, please consider citing our paper!"
] | [
"TAGS\n#task_categories-visual-question-answering #task_categories-text2text-generation #task_categories-image-to-text #size_categories-100K<n<1M #language-English #license-cc-by-4.0 #arxiv-2401.06591 #region-us \n",
"### Dataset summary\n\n\nPerception Collection is the first multi-modal feedback dataset that could be used to train an evaluator VLM. Perception Collection includes 15K fine-grained criteria that determine the crucial aspect for each instance.\n!plot",
"### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\n* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.\n* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.\n* output: The output that the evaluator VLM should generate. It includes the feedback and score decision divided by a phrase .\n* originstruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.\n* origresponse: The response to be evaluated.\n* origreferenceanswer: A reference answer to the originstruction.\n* origcriteria: The score criteria used to evaluate the orig response.\n* origscore1description: A description of when to give a score of 1 to the origresponse.\n* origscore2description: A description of when to give a score of 2 to the origresponse.\n* origscore3description: A description of when to give a score of 3 to the origresponse.\n* origscore4description: A description of when to give a score of 4 to the origresponse.\n* origscore5description: A description of when to give a score of 5 to the origresponse.\n* origfeedback: A feedback that critiques the origresponse.\n* origscore: An integer between 1 and 5 given to the origresponse.\n\n\nIn our paper, we trained the input using the following prompt format (already processed in the 'instruction'):",
"### Data Splits\n\n\n\nIf you find the following dataset helpful, please consider citing our paper!"
] | [
80,
53,
371,
21
] | [
"passage: TAGS\n#task_categories-visual-question-answering #task_categories-text2text-generation #task_categories-image-to-text #size_categories-100K<n<1M #language-English #license-cc-by-4.0 #arxiv-2401.06591 #region-us \n### Dataset summary\n\n\nPerception Collection is the first multi-modal feedback dataset that could be used to train an evaluator VLM. Perception Collection includes 15K fine-grained criteria that determine the crucial aspect for each instance.\n!plot### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\n* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.\n* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.\n* output: The output that the evaluator VLM should generate. It includes the feedback and score decision divided by a phrase .\n* originstruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.\n* origresponse: The response to be evaluated.\n* origreferenceanswer: A reference answer to the originstruction.\n* origcriteria: The score criteria used to evaluate the orig response.\n* origscore1description: A description of when to give a score of 1 to the origresponse.\n* origscore2description: A description of when to give a score of 2 to the origresponse.\n* origscore3description: A description of when to give a score of 3 to the origresponse.\n* origscore4description: A description of when to give a score of 4 to the origresponse.\n* origscore5description: A description of when to give a score of 5 to the origresponse.\n* origfeedback: A feedback that critiques the origresponse.\n* origscore: An integer between 1 and 5 given to the origresponse.\n\n\nIn our paper, we trained the input using the following prompt format (already processed in the 'instruction'):"
] | [
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] |
288a3244f0e58d97a7b3cc9f33ff420dfebb6588 |
# Dataset of constellation/コンステレーション/星座 (Azur Lane)
This is the dataset of constellation/コンステレーション/星座 (Azur Lane), containing 15 images and their tags.
The core tags of this character are `long_hair, blue_hair, breasts, large_breasts, very_long_hair, animal_ears, hair_between_eyes, blue_eyes, bangs, fake_animal_ears, purple_eyes, rabbit_ears`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 15 | 23.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constellation_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 11.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constellation_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 37 | 24.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constellation_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 20.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constellation_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 37 | 35.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constellation_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/constellation_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, playboy_bunny, solo, wrist_cuffs, bare_shoulders, thigh_strap, white_leotard, white_pantyhose, cleavage, detached_collar, official_alternate_costume, strapless_leotard, full_body, navel_cutout, simple_background, smile, blush, bowtie, closed_mouth, highleg_leotard, holding, light_blue_hair, sitting, standing |
| 1 | 5 |  |  |  |  |  | 1girl, bare_shoulders, halo, looking_at_viewer, solo, white_dress, full_body, no_shoes, sideboob, elbow_gloves, from_side, simple_background, white_background, ass, blush, cleavage, feet, knee_up, legs, light_blue_hair, parted_lips, sitting, stirrup_legwear, toes, white_gloves, white_thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | playboy_bunny | solo | wrist_cuffs | bare_shoulders | thigh_strap | white_leotard | white_pantyhose | cleavage | detached_collar | official_alternate_costume | strapless_leotard | full_body | navel_cutout | simple_background | smile | blush | bowtie | closed_mouth | highleg_leotard | holding | light_blue_hair | sitting | standing | halo | white_dress | no_shoes | sideboob | elbow_gloves | from_side | white_background | ass | feet | knee_up | legs | parted_lips | stirrup_legwear | toes | white_gloves | white_thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:----------------|:-------|:--------------|:-----------------|:--------------|:----------------|:------------------|:-----------|:------------------|:-----------------------------|:--------------------|:------------|:---------------|:--------------------|:--------|:--------|:---------|:---------------|:------------------|:----------|:------------------|:----------|:-----------|:-------|:--------------|:-----------|:-----------|:---------------|:------------|:-------------------|:------|:-------|:----------|:-------|:--------------|:------------------|:-------|:---------------|:-------------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | | X | | | | X | | | | X | | X | | X | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/constellation_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:07:46+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:11:35+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of constellation/コンステレーション/星座 (Azur Lane)
=================================================
This is the dataset of constellation/コンステレーション/星座 (Azur Lane), containing 15 images and their tags.
The core tags of this character are 'long\_hair, blue\_hair, breasts, large\_breasts, very\_long\_hair, animal\_ears, hair\_between\_eyes, blue\_eyes, bangs, fake\_animal\_ears, purple\_eyes, rabbit\_ears', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
a54ac9aab90f371085615a38026d4681fa128b0b |
# Dataset of eagle/イーグル/鹰 (Azur Lane)
This is the dataset of eagle/イーグル/鹰 (Azur Lane), containing 10 images and their tags.
The core tags of this character are `breasts, bangs, large_breasts, long_hair, hairband, grey_hair, yellow_eyes, braid`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 10 | 14.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eagle_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 10 | 7.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eagle_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 24 | 16.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eagle_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 10 | 12.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eagle_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 24 | 24.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eagle_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/eagle_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, black_bra, black_pantyhose, closed_mouth, pencil_skirt, white_shirt, black_skirt, holding, necklace, bra_peek, collarbone, miniskirt, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | looking_at_viewer | solo | black_bra | black_pantyhose | closed_mouth | pencil_skirt | white_shirt | black_skirt | holding | necklace | bra_peek | collarbone | miniskirt | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------------------|:-------|:------------|:------------------|:---------------|:---------------|:--------------|:--------------|:----------|:-----------|:-----------|:-------------|:------------|:----------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/eagle_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:07:53+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:11:12+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of eagle/イーグル/鹰 (Azur Lane)
===================================
This is the dataset of eagle/イーグル/鹰 (Azur Lane), containing 10 images and their tags.
The core tags of this character are 'breasts, bangs, large\_breasts, long\_hair, hairband, grey\_hair, yellow\_eyes, braid', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
cf9ee7fe2837dc9340f7647f3c07c46a5656a8d5 |
# Dataset of curlew/カーリュー/杓鹬 (Azur Lane)
This is the dataset of curlew/カーリュー/杓鹬 (Azur Lane), containing 10 images and their tags.
The core tags of this character are `bangs, blue_eyes, braid, breasts, long_hair, purple_hair, large_breasts, bow, hair_bow, maid_headdress, single_braid, sidelocks`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 10 | 13.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curlew_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 10 | 9.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curlew_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 17 | 14.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curlew_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 10 | 12.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curlew_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 17 | 17.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/curlew_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/curlew_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | looking_at_viewer, juliet_sleeves, maid_apron, frills, 1girl, cannon, full_body, holding, machinery, parted_lips, rigging, shoes, sitting, turret, white_apron, 2girls, black_dress, black_footwear, chair, cleavage, flower, indoors, petals, solo_focus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | juliet_sleeves | maid_apron | frills | 1girl | cannon | full_body | holding | machinery | parted_lips | rigging | shoes | sitting | turret | white_apron | 2girls | black_dress | black_footwear | chair | cleavage | flower | indoors | petals | solo_focus |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:-----------------|:-------------|:---------|:--------|:---------|:------------|:----------|:------------|:--------------|:----------|:--------|:----------|:---------|:--------------|:---------|:--------------|:-----------------|:--------|:-----------|:---------|:----------|:---------|:-------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/curlew_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:07:55+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:12:03+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of curlew/カーリュー/杓鹬 (Azur Lane)
======================================
This is the dataset of curlew/カーリュー/杓鹬 (Azur Lane), containing 10 images and their tags.
The core tags of this character are 'bangs, blue\_eyes, braid, breasts, long\_hair, purple\_hair, large\_breasts, bow, hair\_bow, maid\_headdress, single\_braid, sidelocks', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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ac7fb05c8b23d90217daefac02dec435d9994358 | # Dataset Card
- **Homepage: https://kaistai.github.io/prometheus-vision/**
- **Repository: https://github.com/kaistAI/prometheus-vision**
- **Paper: https://arxiv.org/abs/2401.06591**
- **Point of Contact: [email protected]**
### Dataset summary
Perception-Bench is a benchmark for evaluating the long-form response of a VLM (Vision Language Model) across various domains of images, and it is a held-out test
set of the [Perception-Collection](https://huggingface.co/datasets/kaist-ai/Perception-Collection)
### Languages
English
## Dataset Structure
* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.
* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.
* orig```_```instruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.
* orig```_```reference```_```answer: A reference answer to the orig```_```instruction.
* orig```_```criteria: The score criteria used to evaluate the orig```_``` response.
* orig```_```score1```_```description: A description of when to give a score of 1 to the orig```_```response.
* orig```_```score2```_```description: A description of when to give a score of 2 to the orig```_```response.
* orig```_```score3```_```description: A description of when to give a score of 3 to the orig```_```response.
* orig```_```score4```_```description: A description of when to give a score of 4 to the orig```_```response.
* orig```_```score5```_```description: A description of when to give a score of 5 to the orig```_```response.
### Data Splits
| name | test |
|-------------------|------:|
|Perception-Bench|500|
### Citation Information
If you find the following benchmark helpful, please consider citing our paper!
```bibtex
@misc{lee2024prometheusvision,
title={Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation},
author={Seongyun Lee and Seungone Kim and Sue Hyun Park and Geewook Kim and Minjoon Seo},
year={2024},
eprint={2401.06591},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | kaist-ai/Perception-Bench | [
"task_categories:visual-question-answering",
"task_categories:text2text-generation",
"task_categories:image-to-text",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"arxiv:2401.06591",
"region:us"
] | 2024-01-14T14:09:06+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "task_categories": ["visual-question-answering", "text2text-generation", "image-to-text"]} | 2024-01-15T14:25:01+00:00 | [
"2401.06591"
] | [
"en"
] | TAGS
#task_categories-visual-question-answering #task_categories-text2text-generation #task_categories-image-to-text #size_categories-n<1K #language-English #license-cc-by-4.0 #arxiv-2401.06591 #region-us
| Dataset Card
============
* Homepage: URL
* Repository: URL
* Paper: URL
* Point of Contact: seongyun@URL
### Dataset summary
Perception-Bench is a benchmark for evaluating the long-form response of a VLM (Vision Language Model) across various domains of images, and it is a held-out test
set of the Perception-Collection
!plot
### Languages
English
Dataset Structure
-----------------
* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.
* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.
* originstruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.
* origreferenceanswer: A reference answer to the originstruction.
* origcriteria: The score criteria used to evaluate the orig response.
* origscore1description: A description of when to give a score of 1 to the origresponse.
* origscore2description: A description of when to give a score of 2 to the origresponse.
* origscore3description: A description of when to give a score of 3 to the origresponse.
* origscore4description: A description of when to give a score of 4 to the origresponse.
* origscore5description: A description of when to give a score of 5 to the origresponse.
### Data Splits
If you find the following benchmark helpful, please consider citing our paper!
| [
"### Dataset summary\n\n\nPerception-Bench is a benchmark for evaluating the long-form response of a VLM (Vision Language Model) across various domains of images, and it is a held-out test\nset of the Perception-Collection\n!plot",
"### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\n* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.\n* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.\n* originstruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.\n* origreferenceanswer: A reference answer to the originstruction.\n* origcriteria: The score criteria used to evaluate the orig response.\n* origscore1description: A description of when to give a score of 1 to the origresponse.\n* origscore2description: A description of when to give a score of 2 to the origresponse.\n* origscore3description: A description of when to give a score of 3 to the origresponse.\n* origscore4description: A description of when to give a score of 4 to the origresponse.\n* origscore5description: A description of when to give a score of 5 to the origresponse.",
"### Data Splits\n\n\n\nIf you find the following benchmark helpful, please consider citing our paper!"
] | [
"TAGS\n#task_categories-visual-question-answering #task_categories-text2text-generation #task_categories-image-to-text #size_categories-n<1K #language-English #license-cc-by-4.0 #arxiv-2401.06591 #region-us \n",
"### Dataset summary\n\n\nPerception-Bench is a benchmark for evaluating the long-form response of a VLM (Vision Language Model) across various domains of images, and it is a held-out test\nset of the Perception-Collection\n!plot",
"### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\n* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.\n* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.\n* originstruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.\n* origreferenceanswer: A reference answer to the originstruction.\n* origcriteria: The score criteria used to evaluate the orig response.\n* origscore1description: A description of when to give a score of 1 to the origresponse.\n* origscore2description: A description of when to give a score of 2 to the origresponse.\n* origscore3description: A description of when to give a score of 3 to the origresponse.\n* origscore4description: A description of when to give a score of 4 to the origresponse.\n* origscore5description: A description of when to give a score of 5 to the origresponse.",
"### Data Splits\n\n\n\nIf you find the following benchmark helpful, please consider citing our paper!"
] | [
78,
56,
267,
20
] | [
"passage: TAGS\n#task_categories-visual-question-answering #task_categories-text2text-generation #task_categories-image-to-text #size_categories-n<1K #language-English #license-cc-by-4.0 #arxiv-2401.06591 #region-us \n### Dataset summary\n\n\nPerception-Bench is a benchmark for evaluating the long-form response of a VLM (Vision Language Model) across various domains of images, and it is a held-out test\nset of the Perception-Collection\n!plot### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\n* image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.\n* instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.\n* originstruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.\n* origreferenceanswer: A reference answer to the originstruction.\n* origcriteria: The score criteria used to evaluate the orig response.\n* origscore1description: A description of when to give a score of 1 to the origresponse.\n* origscore2description: A description of when to give a score of 2 to the origresponse.\n* origscore3description: A description of when to give a score of 3 to the origresponse.\n* origscore4description: A description of when to give a score of 4 to the origresponse.\n* origscore5description: A description of when to give a score of 5 to the origresponse.### Data Splits\n\n\n\nIf you find the following benchmark helpful, please consider citing our paper!"
] | [
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6b50b29008545fb4d4c5729e00da18a196b6d2fd |
# Dataset Card for Evaluation run of PSanni/MPOMixtral-8x7B-Instruct-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [PSanni/MPOMixtral-8x7B-Instruct-v0.1](https://huggingface.co/PSanni/MPOMixtral-8x7B-Instruct-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PSanni__MPOMixtral-8x7B-Instruct-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T14:23:30.207507](https://huggingface.co/datasets/open-llm-leaderboard/details_PSanni__MPOMixtral-8x7B-Instruct-v0.1/blob/main/results_2024-01-14T14-23-30.207507.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
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"mc2": 0.665216588266765,
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},
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},
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},
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"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.791907514450867,
"acc_stderr": 0.021855255263421795,
"acc_norm": 0.791907514450867,
"acc_norm_stderr": 0.021855255263421795
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4558659217877095,
"acc_stderr": 0.01665722942458631,
"acc_norm": 0.4558659217877095,
"acc_norm_stderr": 0.01665722942458631
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8006535947712419,
"acc_stderr": 0.02287581699346408,
"acc_norm": 0.8006535947712419,
"acc_norm_stderr": 0.02287581699346408
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7845659163987139,
"acc_stderr": 0.023350225475471442,
"acc_norm": 0.7845659163987139,
"acc_norm_stderr": 0.023350225475471442
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8240740740740741,
"acc_stderr": 0.021185893615225153,
"acc_norm": 0.8240740740740741,
"acc_norm_stderr": 0.021185893615225153
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5106382978723404,
"acc_stderr": 0.02982074719142244,
"acc_norm": 0.5106382978723404,
"acc_norm_stderr": 0.02982074719142244
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5443285528031291,
"acc_stderr": 0.012719949543032226,
"acc_norm": 0.5443285528031291,
"acc_norm_stderr": 0.012719949543032226
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7867647058823529,
"acc_stderr": 0.024880971512294254,
"acc_norm": 0.7867647058823529,
"acc_norm_stderr": 0.024880971512294254
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7647058823529411,
"acc_stderr": 0.01716058723504635,
"acc_norm": 0.7647058823529411,
"acc_norm_stderr": 0.01716058723504635
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.043091187099464585,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.043091187099464585
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7836734693877551,
"acc_stderr": 0.026358916334904028,
"acc_norm": 0.7836734693877551,
"acc_norm_stderr": 0.026358916334904028
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8756218905472637,
"acc_stderr": 0.023335401790166323,
"acc_norm": 0.8756218905472637,
"acc_norm_stderr": 0.023335401790166323
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.89,
"acc_stderr": 0.03144660377352203,
"acc_norm": 0.89,
"acc_norm_stderr": 0.03144660377352203
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8654970760233918,
"acc_stderr": 0.026168221344662297,
"acc_norm": 0.8654970760233918,
"acc_norm_stderr": 0.026168221344662297
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5152998776009792,
"mc1_stderr": 0.017495304473187902,
"mc2": 0.665216588266765,
"mc2_stderr": 0.014619883028401507
},
"harness|winogrande|5": {
"acc": 0.8255722178374112,
"acc_stderr": 0.010665187902498428
},
"harness|gsm8k|5": {
"acc": 0.5852918877937832,
"acc_stderr": 0.013570623842304504
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_PSanni__MPOMixtral-8x7B-Instruct-v0.1 | [
"region:us"
] | 2024-01-14T14:25:44+00:00 | {"pretty_name": "Evaluation run of PSanni/MPOMixtral-8x7B-Instruct-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [PSanni/MPOMixtral-8x7B-Instruct-v0.1](https://huggingface.co/PSanni/MPOMixtral-8x7B-Instruct-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PSanni__MPOMixtral-8x7B-Instruct-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T14:23:30.207507](https://huggingface.co/datasets/open-llm-leaderboard/details_PSanni__MPOMixtral-8x7B-Instruct-v0.1/blob/main/results_2024-01-14T14-23-30.207507.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7021378898937154,\n \"acc_stderr\": 0.03050401556178155,\n \"acc_norm\": 0.7057392541812927,\n \"acc_norm_stderr\": 0.031097861836160572,\n \"mc1\": 0.5152998776009792,\n \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.665216588266765,\n \"mc2_stderr\": 0.014619883028401507\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6800341296928327,\n \"acc_stderr\": 0.013631345807016193,\n \"acc_norm\": 0.7098976109215017,\n \"acc_norm_stderr\": 0.013261573677520764\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.690300736904999,\n \"acc_stderr\": 0.004614246282055377,\n \"acc_norm\": 0.8795060744871539,\n \"acc_norm_stderr\": 0.0032487292211528865\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6888888888888889,\n \"acc_stderr\": 0.0399926287661772,\n \"acc_norm\": 0.6888888888888889,\n \"acc_norm_stderr\": 0.0399926287661772\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7960526315789473,\n \"acc_stderr\": 0.0327900040631005,\n \"acc_norm\": 0.7960526315789473,\n \"acc_norm_stderr\": 0.0327900040631005\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7622641509433963,\n \"acc_stderr\": 0.02619980880756193,\n \"acc_norm\": 0.7622641509433963,\n \"acc_norm_stderr\": 0.02619980880756193\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.03309615177059006,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.03309615177059006\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.033450369167889904,\n \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.033450369167889904\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6851063829787234,\n \"acc_stderr\": 0.03036358219723817,\n \"acc_norm\": 0.6851063829787234,\n \"acc_norm_stderr\": 0.03036358219723817\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5877192982456141,\n \"acc_stderr\": 0.04630653203366596,\n \"acc_norm\": 0.5877192982456141,\n \"acc_norm_stderr\": 0.04630653203366596\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947558,\n \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947558\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.47354497354497355,\n \"acc_stderr\": 0.025715239811346758,\n \"acc_norm\": 0.47354497354497355,\n \"acc_norm_stderr\": 0.025715239811346758\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8387096774193549,\n \"acc_stderr\": 0.020923327006423294,\n \"acc_norm\": 0.8387096774193549,\n \"acc_norm_stderr\": 0.020923327006423294\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5960591133004927,\n \"acc_stderr\": 0.03452453903822033,\n \"acc_norm\": 0.5960591133004927,\n \"acc_norm_stderr\": 0.03452453903822033\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8585858585858586,\n \"acc_stderr\": 0.024825909793343336,\n \"acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.024825909793343336\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9585492227979274,\n \"acc_stderr\": 0.01438543285747646,\n \"acc_norm\": 0.9585492227979274,\n \"acc_norm_stderr\": 0.01438543285747646\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.7205128205128205,\n \"acc_stderr\": 0.022752388839776823,\n \"acc_norm\": 0.7205128205128205,\n \"acc_norm_stderr\": 0.022752388839776823\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.029869605095316908,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.029869605095316908\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.773109243697479,\n \"acc_stderr\": 0.027205371538279472,\n \"acc_norm\": 0.773109243697479,\n \"acc_norm_stderr\": 0.027205371538279472\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.4768211920529801,\n \"acc_stderr\": 0.04078093859163083,\n \"acc_norm\": 0.4768211920529801,\n \"acc_norm_stderr\": 0.04078093859163083\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8715596330275229,\n \"acc_stderr\": 0.014344977542914318,\n \"acc_norm\": 0.8715596330275229,\n \"acc_norm_stderr\": 0.014344977542914318\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5740740740740741,\n \"acc_stderr\": 0.033723432716530624,\n \"acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.033723432716530624\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.02485747808025045,\n \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02485747808025045\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8776371308016878,\n \"acc_stderr\": 0.02133174182974679,\n \"acc_norm\": 0.8776371308016878,\n \"acc_norm_stderr\": 0.02133174182974679\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n \"acc_stderr\": 0.030636591348699813,\n \"acc_norm\": 0.7040358744394619,\n \"acc_norm_stderr\": 0.030636591348699813\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.0349814938546247,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.0349814938546247\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8842975206611571,\n \"acc_stderr\": 0.02919980245562281,\n \"acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.02919980245562281\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.803680981595092,\n \"acc_stderr\": 0.031207970394709218,\n \"acc_norm\": 0.803680981595092,\n \"acc_norm_stderr\": 0.031207970394709218\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.034926064766237906,\n \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.034926064766237906\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n \"acc_stderr\": 0.019119892798924974,\n \"acc_norm\": 0.905982905982906,\n \"acc_norm_stderr\": 0.019119892798924974\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8722860791826309,\n \"acc_stderr\": 0.011935626313999878,\n \"acc_norm\": 0.8722860791826309,\n \"acc_norm_stderr\": 0.011935626313999878\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.791907514450867,\n \"acc_stderr\": 0.021855255263421795,\n \"acc_norm\": 0.791907514450867,\n \"acc_norm_stderr\": 0.021855255263421795\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4558659217877095,\n \"acc_stderr\": 0.01665722942458631,\n \"acc_norm\": 0.4558659217877095,\n \"acc_norm_stderr\": 0.01665722942458631\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8006535947712419,\n \"acc_stderr\": 0.02287581699346408,\n \"acc_norm\": 0.8006535947712419,\n \"acc_norm_stderr\": 0.02287581699346408\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7845659163987139,\n \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n 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#region-us
|
# Dataset Card for Evaluation run of PSanni/MPOMixtral-8x7B-Instruct-v0.1
Dataset automatically created during the evaluation run of model PSanni/MPOMixtral-8x7B-Instruct-v0.1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T14:23:30.207507(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
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- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
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## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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## Glossary [optional]
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## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of PSanni/MPOMixtral-8x7B-Instruct-v0.1\n\n\n\nDataset automatically created during the evaluation run of model PSanni/MPOMixtral-8x7B-Instruct-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T14:23:30.207507(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of PSanni/MPOMixtral-8x7B-Instruct-v0.1\n\n\n\nDataset automatically created during the evaluation run of model PSanni/MPOMixtral-8x7B-Instruct-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T14:23:30.207507(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PSanni/MPOMixtral-8x7B-Instruct-v0.1\n\n\n\nDataset automatically created during the evaluation run of model PSanni/MPOMixtral-8x7B-Instruct-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T14:23:30.207507(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]"
] | [
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] |
61a82414fa150f2923ad3515473e79681797fbe2 |
This repository contains importance matrix datasets for use with the improved quantization methods recently added to `llama.cpp`.
The importance matrix has been computed using `wiki.train.raw` as training data.
Hope the file names are self-explanatory.
To use, after cloning this repo, for e.g. Mixtral-8x7B and `Q4_K_M` quantization, use
```
./quantize --imatrix path_to_repo/mixtral-8x7b.imatrix path_to_model ggml-model-q4k-m.gguf Q4_K_M
```
| ikawrakow/imatrix-from-wiki-train | [
"license:apache-2.0",
"region:us"
] | 2024-01-14T14:27:02+00:00 | {"license": "apache-2.0"} | 2024-01-14T15:11:22+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
This repository contains importance matrix datasets for use with the improved quantization methods recently added to 'URL'.
The importance matrix has been computed using 'URL' as training data.
Hope the file names are self-explanatory.
To use, after cloning this repo, for e.g. Mixtral-8x7B and 'Q4_K_M' quantization, use
| [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] | [
14
] | [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
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] |
2f2332989ebc1a0b262f84f3366d91590b0f9849 |
# Dataset of radford/ラドフォード/拉德福特 (Azur Lane)
This is the dataset of radford/ラドフォード/拉德福特 (Azur Lane), containing 16 images and their tags.
The core tags of this character are `pink_hair, blue_eyes, long_hair, ribbon, bow, hair_bow, hair_ribbon, ponytail, symbol-shaped_pupils`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 16 | 14.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/radford_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 16 | 9.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/radford_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 34 | 20.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/radford_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 16 | 13.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/radford_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 34 | 26.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/radford_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/radford_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | looking_at_viewer, 1girl, blush, solo, skirt, lollipop, navel, midriff, smile, belt, open_mouth, heart, holding, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | 1girl | blush | solo | skirt | lollipop | navel | midriff | smile | belt | open_mouth | heart | holding | simple_background | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:--------|:-------|:--------|:-----------|:--------|:----------|:--------|:-------|:-------------|:--------|:----------|:--------------------|:-------------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/radford_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:29:21+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:32:56+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of radford/ラドフォード/拉德福特 (Azur Lane)
==========================================
This is the dataset of radford/ラドフォード/拉德福特 (Azur Lane), containing 16 images and their tags.
The core tags of this character are 'pink\_hair, blue\_eyes, long\_hair, ribbon, bow, hair\_bow, hair\_ribbon, ponytail, symbol-shaped\_pupils', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
373dab56a041184b72b88335a003956b41b294a5 |
# Dataset of san_jacinto/サン・ジャシント/圣哈辛托 (Azur Lane)
This is the dataset of san_jacinto/サン・ジャシント/圣哈辛托 (Azur Lane), containing 17 images and their tags.
The core tags of this character are `breasts, large_breasts, purple_eyes, bangs, short_hair, white_hair, animal_ears, fake_animal_ears, rabbit_ears, bow, tail`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 17 | 26.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/san_jacinto_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 17 | 13.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/san_jacinto_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 38 | 26.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/san_jacinto_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 17 | 22.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/san_jacinto_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 38 | 40.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/san_jacinto_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/san_jacinto_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | 1girl, looking_at_viewer, solo, smile, bare_shoulders, playboy_bunny, black_leotard, pantyhose, blush, sideboob, sitting, closed_mouth, detached_collar, wrist_cuffs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | smile | bare_shoulders | playboy_bunny | black_leotard | pantyhose | blush | sideboob | sitting | closed_mouth | detached_collar | wrist_cuffs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------|:-----------------|:----------------|:----------------|:------------|:--------|:-----------|:----------|:---------------|:------------------|:--------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/san_jacinto_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:29:30+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:33:13+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of san\_jacinto/サン・ジャシント/圣哈辛托 (Azur Lane)
=================================================
This is the dataset of san\_jacinto/サン・ジャシント/圣哈辛托 (Azur Lane), containing 17 images and their tags.
The core tags of this character are 'breasts, large\_breasts, purple\_eyes, bangs, short\_hair, white\_hair, animal\_ears, fake\_animal\_ears, rabbit\_ears, bow, tail', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
15f59839236255155a8d12d20082d10cb5a5711b |
# Dataset of carabiniere/カラビニエーレ/龙骑兵 (Azur Lane)
This is the dataset of carabiniere/カラビニエーレ/龙骑兵 (Azur Lane), containing 12 images and their tags.
The core tags of this character are `blonde_hair, purple_eyes, breasts, bangs, curly_hair, hair_between_eyes, hat, short_hair, hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 12 | 16.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carabiniere_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 12 | 9.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carabiniere_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 26 | 18.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carabiniere_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 12 | 14.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carabiniere_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 26 | 26.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carabiniere_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/carabiniere_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, blush, flower, white_dress, holding, solo_focus, umbrella |
| 1 | 6 |  |  |  |  |  | 1girl, epaulettes, long_sleeves, white_gloves, garter_straps, saber_(weapon), solo, chick, closed_mouth, holding, looking_at_viewer, multicolored_cape, sheathed, single_thighhigh, animal_on_head, belt, bicorne, black_cape, black_headwear, black_jacket, black_skirt, blue_headwear, cannon, full_body, gun, knee_boots, turret, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | blush | flower | white_dress | holding | solo_focus | umbrella | epaulettes | long_sleeves | white_gloves | garter_straps | saber_(weapon) | solo | chick | closed_mouth | multicolored_cape | sheathed | single_thighhigh | animal_on_head | belt | bicorne | black_cape | black_headwear | black_jacket | black_skirt | blue_headwear | cannon | full_body | gun | knee_boots | turret | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:---------|:--------------|:----------|:-------------|:-----------|:-------------|:---------------|:---------------|:----------------|:-----------------|:-------|:--------|:---------------|:--------------------|:-----------|:-------------------|:-----------------|:-------|:----------|:-------------|:-----------------|:---------------|:--------------|:----------------|:---------|:------------|:------|:-------------|:---------|:-------------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/carabiniere_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:38:57+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:43:14+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of carabiniere/カラビニエーレ/龙骑兵 (Azur Lane)
==============================================
This is the dataset of carabiniere/カラビニエーレ/龙骑兵 (Azur Lane), containing 12 images and their tags.
The core tags of this character are 'blonde\_hair, purple\_eyes, breasts, bangs, curly\_hair, hair\_between\_eyes, hat, short\_hair, hairband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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561f491efe3dd67e079e7a318f65f50712fce8c8 |
# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.5
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.5](https://huggingface.co/andysalerno/openchat-nectar-0.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_andysalerno__openchat-nectar-0.5",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T14:46:05.051264](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.5/blob/main/results_2024-01-14T14-46-05.051264.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc2_stderr": 0.015319650015486606
},
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},
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},
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"harness|hendrycksTest-high_school_us_history|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_andysalerno__openchat-nectar-0.5 | [
"region:us"
] | 2024-01-14T14:48:31+00:00 | {"pretty_name": "Evaluation run of andysalerno/openchat-nectar-0.5", "dataset_summary": "Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.5](https://huggingface.co/andysalerno/openchat-nectar-0.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_andysalerno__openchat-nectar-0.5\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T14:46:05.051264](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.5/blob/main/results_2024-01-14T14-46-05.051264.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.656162636449764,\n \"acc_stderr\": 0.03183140048341385,\n \"acc_norm\": 0.6568859855566402,\n \"acc_norm_stderr\": 0.03248609595939316,\n \"mc1\": 0.3574051407588739,\n \"mc1_stderr\": 0.01677659967672941,\n \"mc2\": 0.5215263336816769,\n \"mc2_stderr\": 0.015319650015486606\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6305460750853242,\n \"acc_stderr\": 0.014104578366491892,\n \"acc_norm\": 0.6672354948805461,\n \"acc_norm_stderr\": 0.013769863046192309\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6392152957578172,\n \"acc_stderr\": 0.004792467255899766,\n \"acc_norm\": 0.835291774546903,\n \"acc_norm_stderr\": 0.003701589571274316\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.032081157507886836,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.032081157507886836\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.04692008381368909,\n \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.04692008381368909\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483016,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483016\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945627,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945627\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3851851851851852,\n \"acc_stderr\": 0.02967090612463088,\n \"acc_norm\": 0.3851851851851852,\n \"acc_norm_stderr\": 0.02967090612463088\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.02995382389188704,\n \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.02995382389188704\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8532110091743119,\n \"acc_stderr\": 0.015173141845126255,\n \"acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.015173141845126255\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579647,\n \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579647\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n \"acc_stderr\": 0.030636591348699824,\n \"acc_norm\": 0.7040358744394619,\n \"acc_norm_stderr\": 0.030636591348699824\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932262\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7543352601156069,\n \"acc_stderr\": 0.023176298203992,\n \"acc_norm\": 0.7543352601156069,\n \"acc_norm_stderr\": 0.023176298203992\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27039106145251396,\n \"acc_stderr\": 0.01485499393801009,\n \"acc_norm\": 0.27039106145251396,\n \"acc_norm_stderr\": 0.01485499393801009\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.02463004897982478,\n \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.02463004897982478\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7561728395061729,\n \"acc_stderr\": 0.023891879541959603,\n \"acc_norm\": 0.7561728395061729,\n \"acc_norm_stderr\": 0.023891879541959603\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.475177304964539,\n \"acc_stderr\": 0.02979071924382972,\n \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.02979071924382972\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4934810951760104,\n \"acc_stderr\": 0.012769150688867503,\n \"acc_norm\": 0.4934810951760104,\n \"acc_norm_stderr\": 0.012769150688867503\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7279411764705882,\n \"acc_stderr\": 0.02703304115168146,\n \"acc_norm\": 0.7279411764705882,\n \"acc_norm_stderr\": 0.02703304115168146\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069443,\n \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069443\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.02752963744017493,\n \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.02752963744017493\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3574051407588739,\n \"mc1_stderr\": 0.01677659967672941,\n \"mc2\": 0.5215263336816769,\n \"mc2_stderr\": 0.015319650015486606\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047986\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6815769522365428,\n \"acc_stderr\": 0.012832225723075413\n }\n}\n```", "repo_url": "https://huggingface.co/andysalerno/openchat-nectar-0.5", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_14T14_46_05.051264", "path": ["**/details_harness|arc:challenge|25_2024-01-14T14-46-05.051264.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-14T14-46-05.051264.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_14T14_46_05.051264", "path": ["**/details_harness|gsm8k|5_2024-01-14T14-46-05.051264.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-14T14-46-05.051264.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_14T14_46_05.051264", "path": ["**/details_harness|hellaswag|10_2024-01-14T14-46-05.051264.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-14T14-46-05.051264.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_14T14_46_05.051264", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T14-46-05.051264.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T14-46-05.051264.parquet", 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#region-us
|
# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.5
Dataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.5 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T14:46:05.051264(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.5\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T14:46:05.051264(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.5\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T14:46:05.051264(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.5\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T14:46:05.051264(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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] |
a770c37d5f32150de99ea297d2d1e9448298c809 |
# Dataset of magdeburg/マクデブルク/马格德堡 (Azur Lane)
This is the dataset of magdeburg/マクデブルク/马格德堡 (Azur Lane), containing 15 images and their tags.
The core tags of this character are `black_hair, horns, long_hair, breasts, multicolored_hair, red_eyes, bangs, hair_between_eyes, red_hair, large_breasts, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 15 | 20.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/magdeburg_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 12.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/magdeburg_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 35 | 25.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/magdeburg_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 18.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/magdeburg_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 35 | 35.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/magdeburg_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/magdeburg_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, solo, navel, open_mouth, smile, looking_at_viewer, black_bikini, blush, nail_polish, thighhighs, cleavage, cloud, o-ring_bikini, outdoors, see-through, sky, tied_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | navel | open_mouth | smile | looking_at_viewer | black_bikini | blush | nail_polish | thighhighs | cleavage | cloud | o-ring_bikini | outdoors | see-through | sky | tied_shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:-------------|:--------|:--------------------|:---------------|:--------|:--------------|:-------------|:-----------|:--------|:----------------|:-----------|:--------------|:------|:-------------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/magdeburg_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:51:17+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:55:01+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of magdeburg/マクデブルク/马格德堡 (Azur Lane)
============================================
This is the dataset of magdeburg/マクデブルク/马格德堡 (Azur Lane), containing 15 images and their tags.
The core tags of this character are 'black\_hair, horns, long\_hair, breasts, multicolored\_hair, red\_eyes, bangs, hair\_between\_eyes, red\_hair, large\_breasts, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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28f622caad074d9730f577b24a82fec5e03cdd62 |
# Dataset of andrea_doria/アンドレア・ドーリア/安德烈亚·多利亚 (Azur Lane)
This is the dataset of andrea_doria/アンドレア・ドーリア/安德烈亚·多利亚 (Azur Lane), containing 11 images and their tags.
The core tags of this character are `breasts, drill_hair, large_breasts, long_hair, bangs, ahoge, yellow_eyes, brown_eyes, very_long_hair, brown_hair, hair_intakes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 11 | 18.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/andrea_doria_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 11 | 9.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/andrea_doria_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 31 | 22.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/andrea_doria_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 11 | 15.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/andrea_doria_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 31 | 34.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/andrea_doria_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/andrea_doria_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, smile, cleavage, blush, solo, closed_mouth, green_dress, black_pantyhose, indoors, standing, window |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | looking_at_viewer | smile | cleavage | blush | solo | closed_mouth | green_dress | black_pantyhose | indoors | standing | window |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:--------|:-----------|:--------|:-------|:---------------|:--------------|:------------------|:----------|:-----------|:---------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/andrea_doria_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:51:19+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:55:18+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of andrea\_doria/アンドレア・ドーリア/安德烈亚·多利亚 (Azur Lane)
========================================================
This is the dataset of andrea\_doria/アンドレア・ドーリア/安德烈亚·多利亚 (Azur Lane), containing 11 images and their tags.
The core tags of this character are 'breasts, drill\_hair, large\_breasts, long\_hair, bangs, ahoge, yellow\_eyes, brown\_eyes, very\_long\_hair, brown\_hair, hair\_intakes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
f19283a231f8c2adcea48920a32fa26a7620596d |
# Dataset of z26/Z26 (Azur Lane)
This is the dataset of z26/Z26 (Azur Lane), containing 13 images and their tags.
The core tags of this character are `long_hair, pink_hair, purple_eyes, very_long_hair, twintails, bangs, hair_between_eyes, hair_ornament, hat, black_headwear, mask_on_head, sidelocks, breasts, fang, peaked_cap, small_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 13 | 15.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z26_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 13 | 9.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z26_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 32 | 19.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z26_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 13 | 14.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z26_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 32 | 27.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z26_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/z26_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, sleep_mask, solo, black_shirt, collarbone, short_sleeves, simple_background, white_background, holding, off-shoulder_shirt, short_shorts, :d, blush, crop_top, full_body, hairclip, navel, no_shoes, open_mouth, stuffed_toy, white_socks |
| 1 | 6 |  |  |  |  |  | 1girl, black_skirt, looking_at_viewer, midriff, miniskirt, navel, solo, boots, capelet, hair_flaps, pleated_skirt, red_gloves, suspender_skirt, ahoge, closed_mouth, full_body, simple_background, socks, v-shaped_eyebrows, white_background, armpits, black_shirt, buttons, crop_top_overhang, frown, outstretched_arm, revealing_clothes, sleeveless_shirt, stomach, thigh_strap, thighs, undershirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | looking_at_viewer | sleep_mask | solo | black_shirt | collarbone | short_sleeves | simple_background | white_background | holding | off-shoulder_shirt | short_shorts | :d | blush | crop_top | full_body | hairclip | navel | no_shoes | open_mouth | stuffed_toy | white_socks | black_skirt | midriff | miniskirt | boots | capelet | hair_flaps | pleated_skirt | red_gloves | suspender_skirt | ahoge | closed_mouth | socks | v-shaped_eyebrows | armpits | buttons | crop_top_overhang | frown | outstretched_arm | revealing_clothes | sleeveless_shirt | stomach | thigh_strap | thighs | undershirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------------|:-------------|:-------|:--------------|:-------------|:----------------|:--------------------|:-------------------|:----------|:---------------------|:---------------|:-----|:--------|:-----------|:------------|:-----------|:--------|:-----------|:-------------|:--------------|:--------------|:--------------|:----------|:------------|:--------|:----------|:-------------|:----------------|:-------------|:------------------|:--------|:---------------|:--------|:--------------------|:----------|:----------|:--------------------|:--------|:-------------------|:--------------------|:-------------------|:----------|:--------------|:---------|:-------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | | X | X | | | X | X | | | | | | | X | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/z26_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T14:51:19+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T14:55:27+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of z26/Z26 (Azur Lane)
==============================
This is the dataset of z26/Z26 (Azur Lane), containing 13 images and their tags.
The core tags of this character are 'long\_hair, pink\_hair, purple\_eyes, very\_long\_hair, twintails, bangs, hair\_between\_eyes, hair\_ornament, hat, black\_headwear, mask\_on\_head, sidelocks, breasts, fang, peaked\_cap, small\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
9e48ba6b3eda5d1715621a299d7d9ab7140d8068 | # Promoter Sequences for Various plant species
The data in this dataset has the promoter sequences for **241 different plant species** and has been used for the pretraining step of [`Florabert`](https://huggingface.co/Gurveer05/FloraBERT). It has been created by processing the raw fasta files and the gff3 / gff files from [`Ensembl`](https://plants.ensembl.org/) and [`Refseq`](https://www.ncbi.nlm.nih.gov/refseq/).
*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.
The data has been split into train and test data (90-10 split). In all, there are ~ 10 million sequences across the split files. The steps followed to obtain this data are available in this [`Github Repository`](https://github.com/gurveervirk/florabert). | Gurveer05/plant-promoter-sequences | [
"size_categories:10M<n<100M",
"biology",
"region:us"
] | 2024-01-14T15:03:50+00:00 | {"size_categories": ["10M<n<100M"], "tags": ["biology"]} | 2024-01-14T17:26:56+00:00 | [] | [] | TAGS
#size_categories-10M<n<100M #biology #region-us
| # Promoter Sequences for Various plant species
The data in this dataset has the promoter sequences for 241 different plant species and has been used for the pretraining step of 'Florabert'. It has been created by processing the raw fasta files and the gff3 / gff files from 'Ensembl' and 'Refseq'.
*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.
The data has been split into train and test data (90-10 split). In all, there are ~ 10 million sequences across the split files. The steps followed to obtain this data are available in this 'Github Repository'. | [
"# Promoter Sequences for Various plant species\n\nThe data in this dataset has the promoter sequences for 241 different plant species and has been used for the pretraining step of 'Florabert'. It has been created by processing the raw fasta files and the gff3 / gff files from 'Ensembl' and 'Refseq'.\n*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.\n\nThe data has been split into train and test data (90-10 split). In all, there are ~ 10 million sequences across the split files. The steps followed to obtain this data are available in this 'Github Repository'."
] | [
"TAGS\n#size_categories-10M<n<100M #biology #region-us \n",
"# Promoter Sequences for Various plant species\n\nThe data in this dataset has the promoter sequences for 241 different plant species and has been used for the pretraining step of 'Florabert'. It has been created by processing the raw fasta files and the gff3 / gff files from 'Ensembl' and 'Refseq'.\n*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.\n\nThe data has been split into train and test data (90-10 split). In all, there are ~ 10 million sequences across the split files. The steps followed to obtain this data are available in this 'Github Repository'."
] | [
21,
168
] | [
"passage: TAGS\n#size_categories-10M<n<100M #biology #region-us \n# Promoter Sequences for Various plant species\n\nThe data in this dataset has the promoter sequences for 241 different plant species and has been used for the pretraining step of 'Florabert'. It has been created by processing the raw fasta files and the gff3 / gff files from 'Ensembl' and 'Refseq'.\n*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.\n\nThe data has been split into train and test data (90-10 split). In all, there are ~ 10 million sequences across the split files. The steps followed to obtain this data are available in this 'Github Repository'."
] | [
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] |
6f3fb8fcd91ea065cafa081377328757f91641e2 |
# Dataset of friedrich_eckoldt/Z16 (Azur Lane)
This is the dataset of friedrich_eckoldt/Z16 (Azur Lane), containing 11 images and their tags.
The core tags of this character are `black_hair, multicolored_hair, red_eyes, streaked_hair, bangs, breasts, long_hair, white_hair, horns, x-shaped_pupils, symbol-shaped_pupils, two-tone_hair, hair_between_eyes, v-shaped_eyebrows`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 11 | 15.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/friedrich_eckoldt_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 11 | 8.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/friedrich_eckoldt_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 26 | 17.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/friedrich_eckoldt_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 11 | 12.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/friedrich_eckoldt_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 26 | 26.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/friedrich_eckoldt_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/friedrich_eckoldt_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, solo, navel, black_jacket, bare_shoulders, crop_top, long_sleeves, midriff, stomach, black_thighhighs, iron_cross, off_shoulder, standing, thigh_strap, white_panties, cowboy_shot, open_mouth, red_gloves, simple_background, skindentation, thighs, white_shirt, :d, black_footwear, black_gloves, full_body, open_jacket, sharp_teeth, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | navel | black_jacket | bare_shoulders | crop_top | long_sleeves | midriff | stomach | black_thighhighs | iron_cross | off_shoulder | standing | thigh_strap | white_panties | cowboy_shot | open_mouth | red_gloves | simple_background | skindentation | thighs | white_shirt | :d | black_footwear | black_gloves | full_body | open_jacket | sharp_teeth | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------|:---------------|:-----------------|:-----------|:---------------|:----------|:----------|:-------------------|:-------------|:---------------|:-----------|:--------------|:----------------|:--------------|:-------------|:-------------|:--------------------|:----------------|:---------|:--------------|:-----|:-----------------|:---------------|:------------|:--------------|:--------------|:-------------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/friedrich_eckoldt_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T15:08:50+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T15:11:51+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of friedrich\_eckoldt/Z16 (Azur Lane)
=============================================
This is the dataset of friedrich\_eckoldt/Z16 (Azur Lane), containing 11 images and their tags.
The core tags of this character are 'black\_hair, multicolored\_hair, red\_eyes, streaked\_hair, bangs, breasts, long\_hair, white\_hair, horns, x-shaped\_pupils, symbol-shaped\_pupils, two-tone\_hair, hair\_between\_eyes, v-shaped\_eyebrows', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
155539f0708c88ca3d1554453ee7d95a900abf85 |
# Dataset of louisville/ルイビル/路易斯维尔 (Azur Lane)
This is the dataset of louisville/ルイビル/路易斯维尔 (Azur Lane), containing 18 images and their tags.
The core tags of this character are `breasts, long_hair, hair_over_one_eye, large_breasts, blue_eyes, braid, bow, animal_ears, fake_animal_ears, hair_ornament, rabbit_ears, pink_hair, huge_breasts, very_long_hair, blue_bow, purple_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 18 | 28.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/louisville_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 18 | 16.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/louisville_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 46 | 35.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/louisville_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 18 | 25.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/louisville_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 46 | 51.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/louisville_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/louisville_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | bare_shoulders, bowtie, cleavage, detached_collar, playboy_bunny, 1girl, looking_at_viewer, white_gloves, blue_leotard, solo, blush, white_pantyhose, official_alternate_costume, strapless_leotard, holding_tray, breast_rest |
| 1 | 5 |  |  |  |  |  | 1girl, cleavage, long_sleeves, solo, dress, looking_at_viewer, white_gloves, white_thighhighs, blush, frills, garter_straps, simple_background, white_background, bangs, clothes_lift, full_body, lifted_by_self, skirt, smile, white_panties |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | bare_shoulders | bowtie | cleavage | detached_collar | playboy_bunny | 1girl | looking_at_viewer | white_gloves | blue_leotard | solo | blush | white_pantyhose | official_alternate_costume | strapless_leotard | holding_tray | breast_rest | long_sleeves | dress | white_thighhighs | frills | garter_straps | simple_background | white_background | bangs | clothes_lift | full_body | lifted_by_self | skirt | smile | white_panties |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------|:---------|:-----------|:------------------|:----------------|:--------|:--------------------|:---------------|:---------------|:-------|:--------|:------------------|:-----------------------------|:--------------------|:---------------|:--------------|:---------------|:--------|:-------------------|:---------|:----------------|:--------------------|:-------------------|:--------|:---------------|:------------|:-----------------|:--------|:--------|:----------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | | | X | | | X | X | X | | X | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/louisville_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T15:29:53+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T15:34:10+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of louisville/ルイビル/路易斯维尔 (Azur Lane)
============================================
This is the dataset of louisville/ルイビル/路易斯维尔 (Azur Lane), containing 18 images and their tags.
The core tags of this character are 'breasts, long\_hair, hair\_over\_one\_eye, large\_breasts, blue\_eyes, braid, bow, animal\_ears, fake\_animal\_ears, hair\_ornament, rabbit\_ears, pink\_hair, huge\_breasts, very\_long\_hair, blue\_bow, purple\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
aadc3836f337cb17823e07dd83cb7d3773af9903 |
# Dataset of matchless/マッチレス/无敌 (Azur Lane)
This is the dataset of matchless/マッチレス/无敌 (Azur Lane), containing 16 images and their tags.
The core tags of this character are `purple_hair, hat, purple_eyes, short_hair, bangs, breasts, beret, black_headwear, ribbon, hair_ornament, rabbit_hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 16 | 18.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matchless_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 16 | 10.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matchless_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 35 | 22.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matchless_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 16 | 16.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matchless_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 35 | 29.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matchless_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/matchless_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, bare_shoulders, blush, white_dress, looking_at_viewer, smile, solo, choker, collarbone, pink_bow, white_footwear, bag, holding_food, ice_cream_cone, lifebuoy, shoes, tongue_out, bench, bird, brick_floor, closed_mouth, military_hat, off_shoulder, sitting, torpedo_tubes |
| 1 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bare_shoulders, black_gloves, blush, smile, open_mouth, sleeveless_shirt, pink_skirt, star_(symbol), white_shirt, kneehighs, white_socks, ;d, full_body, mole, one_eye_closed |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | blush | white_dress | looking_at_viewer | smile | solo | choker | collarbone | pink_bow | white_footwear | bag | holding_food | ice_cream_cone | lifebuoy | shoes | tongue_out | bench | bird | brick_floor | closed_mouth | military_hat | off_shoulder | sitting | torpedo_tubes | black_gloves | open_mouth | sleeveless_shirt | pink_skirt | star_(symbol) | white_shirt | kneehighs | white_socks | ;d | full_body | mole | one_eye_closed |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:--------------|:--------------------|:--------|:-------|:---------|:-------------|:-----------|:-----------------|:------|:---------------|:-----------------|:-----------|:--------|:-------------|:--------|:-------|:--------------|:---------------|:---------------|:---------------|:----------|:----------------|:---------------|:-------------|:-------------------|:-------------|:----------------|:--------------|:------------|:--------------|:-----|:------------|:-------|:-----------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/matchless_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-14T15:30:03+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-14T15:33:31+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of matchless/マッチレス/无敌 (Azur Lane)
=========================================
This is the dataset of matchless/マッチレス/无敌 (Azur Lane), containing 16 images and their tags.
The core tags of this character are 'purple\_hair, hat, purple\_eyes, short\_hair, bangs, breasts, beret, black\_headwear, ribbon, hair\_ornament, rabbit\_hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
3531d9a7a8a01c1619b297a509fb9485b37d80ae |
This is a unfiltered dataset of images scraped from the internet for the Detective Conan character Masumi Sera | 234bcn/masumi_sera_images | [
"language:en",
"region:us"
] | 2024-01-14T15:41:22+00:00 | {"language": ["en"], "pretty_name": "Masumi Sera Dataset for AI"} | 2024-01-15T18:38:20+00:00 | [] | [
"en"
] | TAGS
#language-English #region-us
|
This is a unfiltered dataset of images scraped from the internet for the Detective Conan character Masumi Sera | [] | [
"TAGS\n#language-English #region-us \n"
] | [
10
] | [
"passage: TAGS\n#language-English #region-us \n"
] | [
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3d1bfe3ed7a68e99e2780155b14d38956b76d5ea | # Dataset Card for "real-toxicity-prompts_first_5K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Leogrin/real-toxicity-prompts_first_5K | [
"region:us"
] | 2024-01-14T15:48:57+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "filename", "dtype": "string"}, {"name": "begin", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "challenging", "dtype": "bool"}, {"name": "prompt", "struct": [{"name": "text", "dtype": "string"}, {"name": "profanity", "dtype": "float64"}, {"name": "sexually_explicit", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "flirtation", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "toxicity", "dtype": "float64"}]}, {"name": "continuation", "struct": [{"name": "text", "dtype": "string"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "toxicity", "dtype": "float64"}, {"name": "profanity", "dtype": "float64"}, {"name": "sexually_explicit", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "flirtation", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 1701249, "num_examples": 5000}], "download_size": 1566036, "dataset_size": 1701249}} | 2024-01-14T15:48:59+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "real-toxicity-prompts_first_5K"
More Information needed | [
"# Dataset Card for \"real-toxicity-prompts_first_5K\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"real-toxicity-prompts_first_5K\"\n\nMore Information needed"
] | [
6,
24
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"real-toxicity-prompts_first_5K\"\n\nMore Information needed"
] | [
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dfccbf5d5206330d074a0d31e1a564b68bf6a4eb |
# DeliData
This is a README that outlines key fields and characteristics of the DeliData corpus.
For full description of how we collected DeliData, as well as possible applications, please refer to the original
paper [link](#citation).
# Data Fields
###### group_id
Unique identifier of the group chat
###### message_id
Message identifier. System messages will have an id of -1, however all participant messages' ids are unique.
###### message_type
INITIAL - indicating the cards presented and aliases of participants;
SUBMIT - indicating that a participant has pressed the Submit Solution button
MESSAGE - noting a chat entry
###### origin
The alias of the participant who submitted a message/solution
###### original_text
Original text as said in the collected conversation;
For INITIAL type, contains the list of participants and cards presented.
For SUBMIT type, contains the cards submitted
###### clean_text
Normalised message, with applied tokenisation, and masking of special tokens. Special tokens are considered solution
mentions, which are masked with < CARD > and participant mentions which are masked with < MENTION >
###### annotation_type
A record from the first level of DeliAnnotation. Can be Probing, Non-probing deliberation, or None. For more details,
please refer to the DeliData paper.
###### annotation_target
A record from the second level of DeliAnnotation. Can be Moderation, Reasoning, Solution, Agree, or Disagree. For more
details, please refer to the DeliData paper.
###### annotation_additional
A record from the third level of DeliAnnotation. Can be partial_solution, complete_solution, specific_referee,
solution_summary, or consider_opposite. For more details, please refer to the DeliData paper.
###### team_performance
An approximation of team performance, based on user submissions, and solution mentions. Range [0-1], where 1 indicates
each participant selecting the correct solution.
###### performance_change
Change of performance based compared to the previous utterance
###### sol_tracker_message
Extracted solution from the current message
###### sol_tracker_all
Up-to-date "state-of-mind" for each of the participants, i.e. an approximation of what each participant think the
correct solution is at given timestep. This is based on initial solutions, submitted solutions, and solution mentions.
team_performance value is calculated based on this column
### Citation
**DeliData A dataset for deliberation in multi-party problem solving (https://delibot.xyz/delidata)**
@article{karadzhov2023delidata,
title={DeliData: A dataset for deliberation in multi-party problem solving},
author={Karadzhov, Georgi and Stafford, Tom and Vlachos, Andreas},
journal={Proceedings of the ACM on Human-Computer Interaction},
volume={7},
number={CSCW2},
pages={1--25},
year={2023},
publisher={ACM New York, NY, USA}
}
| gkaradzhov/DeliData | [
"license:cc-by-4.0",
"region:us"
] | 2024-01-14T16:08:40+00:00 | {"license": "cc-by-4.0"} | 2024-01-14T16:10:42+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
|
# DeliData
This is a README that outlines key fields and characteristics of the DeliData corpus.
For full description of how we collected DeliData, as well as possible applications, please refer to the original
paper link.
# Data Fields
###### group_id
Unique identifier of the group chat
###### message_id
Message identifier. System messages will have an id of -1, however all participant messages' ids are unique.
###### message_type
INITIAL - indicating the cards presented and aliases of participants;
SUBMIT - indicating that a participant has pressed the Submit Solution button
MESSAGE - noting a chat entry
###### origin
The alias of the participant who submitted a message/solution
###### original_text
Original text as said in the collected conversation;
For INITIAL type, contains the list of participants and cards presented.
For SUBMIT type, contains the cards submitted
###### clean_text
Normalised message, with applied tokenisation, and masking of special tokens. Special tokens are considered solution
mentions, which are masked with < CARD > and participant mentions which are masked with < MENTION >
###### annotation_type
A record from the first level of DeliAnnotation. Can be Probing, Non-probing deliberation, or None. For more details,
please refer to the DeliData paper.
###### annotation_target
A record from the second level of DeliAnnotation. Can be Moderation, Reasoning, Solution, Agree, or Disagree. For more
details, please refer to the DeliData paper.
###### annotation_additional
A record from the third level of DeliAnnotation. Can be partial_solution, complete_solution, specific_referee,
solution_summary, or consider_opposite. For more details, please refer to the DeliData paper.
###### team_performance
An approximation of team performance, based on user submissions, and solution mentions. Range [0-1], where 1 indicates
each participant selecting the correct solution.
###### performance_change
Change of performance based compared to the previous utterance
###### sol_tracker_message
Extracted solution from the current message
###### sol_tracker_all
Up-to-date "state-of-mind" for each of the participants, i.e. an approximation of what each participant think the
correct solution is at given timestep. This is based on initial solutions, submitted solutions, and solution mentions.
team_performance value is calculated based on this column
DeliData A dataset for deliberation in multi-party problem solving (URL
@article{karadzhov2023delidata,
title={DeliData: A dataset for deliberation in multi-party problem solving},
author={Karadzhov, Georgi and Stafford, Tom and Vlachos, Andreas},
journal={Proceedings of the ACM on Human-Computer Interaction},
volume={7},
number={CSCW2},
pages={1--25},
year={2023},
publisher={ACM New York, NY, USA}
}
| [
"# DeliData\n\nThis is a README that outlines key fields and characteristics of the DeliData corpus.\nFor full description of how we collected DeliData, as well as possible applications, please refer to the original\npaper link.",
"# Data Fields",
"###### group_id\n\nUnique identifier of the group chat",
"###### message_id\n\nMessage identifier. System messages will have an id of -1, however all participant messages' ids are unique.",
"###### message_type\n\nINITIAL - indicating the cards presented and aliases of participants;\n\nSUBMIT - indicating that a participant has pressed the Submit Solution button\n\nMESSAGE - noting a chat entry",
"###### origin\n\nThe alias of the participant who submitted a message/solution",
"###### original_text\n\nOriginal text as said in the collected conversation;\n\nFor INITIAL type, contains the list of participants and cards presented.\n\nFor SUBMIT type, contains the cards submitted",
"###### clean_text\n\nNormalised message, with applied tokenisation, and masking of special tokens. Special tokens are considered solution\nmentions, which are masked with < CARD > and participant mentions which are masked with < MENTION >",
"###### annotation_type\n\nA record from the first level of DeliAnnotation. Can be Probing, Non-probing deliberation, or None. For more details,\nplease refer to the DeliData paper.",
"###### annotation_target\n\nA record from the second level of DeliAnnotation. Can be Moderation, Reasoning, Solution, Agree, or Disagree. For more\ndetails, please refer to the DeliData paper.",
"###### annotation_additional\n\nA record from the third level of DeliAnnotation. Can be partial_solution, complete_solution, specific_referee,\nsolution_summary, or consider_opposite. For more details, please refer to the DeliData paper.",
"###### team_performance\n\nAn approximation of team performance, based on user submissions, and solution mentions. Range [0-1], where 1 indicates\neach participant selecting the correct solution.",
"###### performance_change\n\nChange of performance based compared to the previous utterance",
"###### sol_tracker_message\n\nExtracted solution from the current message",
"###### sol_tracker_all\n\nUp-to-date \"state-of-mind\" for each of the participants, i.e. an approximation of what each participant think the\ncorrect solution is at given timestep. This is based on initial solutions, submitted solutions, and solution mentions.\nteam_performance value is calculated based on this column\n\nDeliData A dataset for deliberation in multi-party problem solving (URL\n\n @article{karadzhov2023delidata,\n title={DeliData: A dataset for deliberation in multi-party problem solving},\n author={Karadzhov, Georgi and Stafford, Tom and Vlachos, Andreas},\n journal={Proceedings of the ACM on Human-Computer Interaction},\n volume={7},\n number={CSCW2},\n pages={1--25},\n year={2023},\n publisher={ACM New York, NY, USA}\n }"
] | [
"TAGS\n#license-cc-by-4.0 #region-us \n",
"# DeliData\n\nThis is a README that outlines key fields and characteristics of the DeliData corpus.\nFor full description of how we collected DeliData, as well as possible applications, please refer to the original\npaper link.",
"# Data Fields",
"###### group_id\n\nUnique identifier of the group chat",
"###### message_id\n\nMessage identifier. System messages will have an id of -1, however all participant messages' ids are unique.",
"###### message_type\n\nINITIAL - indicating the cards presented and aliases of participants;\n\nSUBMIT - indicating that a participant has pressed the Submit Solution button\n\nMESSAGE - noting a chat entry",
"###### origin\n\nThe alias of the participant who submitted a message/solution",
"###### original_text\n\nOriginal text as said in the collected conversation;\n\nFor INITIAL type, contains the list of participants and cards presented.\n\nFor SUBMIT type, contains the cards submitted",
"###### clean_text\n\nNormalised message, with applied tokenisation, and masking of special tokens. Special tokens are considered solution\nmentions, which are masked with < CARD > and participant mentions which are masked with < MENTION >",
"###### annotation_type\n\nA record from the first level of DeliAnnotation. Can be Probing, Non-probing deliberation, or None. For more details,\nplease refer to the DeliData paper.",
"###### annotation_target\n\nA record from the second level of DeliAnnotation. Can be Moderation, Reasoning, Solution, Agree, or Disagree. For more\ndetails, please refer to the DeliData paper.",
"###### annotation_additional\n\nA record from the third level of DeliAnnotation. Can be partial_solution, complete_solution, specific_referee,\nsolution_summary, or consider_opposite. For more details, please refer to the DeliData paper.",
"###### team_performance\n\nAn approximation of team performance, based on user submissions, and solution mentions. Range [0-1], where 1 indicates\neach participant selecting the correct solution.",
"###### performance_change\n\nChange of performance based compared to the previous utterance",
"###### sol_tracker_message\n\nExtracted solution from the current message",
"###### sol_tracker_all\n\nUp-to-date \"state-of-mind\" for each of the participants, i.e. an approximation of what each participant think the\ncorrect solution is at given timestep. This is based on initial solutions, submitted solutions, and solution mentions.\nteam_performance value is calculated based on this column\n\nDeliData A dataset for deliberation in multi-party problem solving (URL\n\n @article{karadzhov2023delidata,\n title={DeliData: A dataset for deliberation in multi-party problem solving},\n author={Karadzhov, Georgi and Stafford, Tom and Vlachos, Andreas},\n journal={Proceedings of the ACM on Human-Computer Interaction},\n volume={7},\n number={CSCW2},\n pages={1--25},\n year={2023},\n publisher={ACM New York, NY, USA}\n }"
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"passage: TAGS\n#license-cc-by-4.0 #region-us \n# DeliData\n\nThis is a README that outlines key fields and characteristics of the DeliData corpus.\nFor full description of how we collected DeliData, as well as possible applications, please refer to the original\npaper link.# Data Fields###### group_id\n\nUnique identifier of the group chat###### message_id\n\nMessage identifier. System messages will have an id of -1, however all participant messages' ids are unique.###### message_type\n\nINITIAL - indicating the cards presented and aliases of participants;\n\nSUBMIT - indicating that a participant has pressed the Submit Solution button\n\nMESSAGE - noting a chat entry###### origin\n\nThe alias of the participant who submitted a message/solution###### original_text\n\nOriginal text as said in the collected conversation;\n\nFor INITIAL type, contains the list of participants and cards presented.\n\nFor SUBMIT type, contains the cards submitted###### clean_text\n\nNormalised message, with applied tokenisation, and masking of special tokens. Special tokens are considered solution\nmentions, which are masked with < CARD > and participant mentions which are masked with < MENTION >###### annotation_type\n\nA record from the first level of DeliAnnotation. Can be Probing, Non-probing deliberation, or None. For more details,\nplease refer to the DeliData paper.###### annotation_target\n\nA record from the second level of DeliAnnotation. Can be Moderation, Reasoning, Solution, Agree, or Disagree. For more\ndetails, please refer to the DeliData paper.###### annotation_additional\n\nA record from the third level of DeliAnnotation. Can be partial_solution, complete_solution, specific_referee,\nsolution_summary, or consider_opposite. For more details, please refer to the DeliData paper.###### team_performance\n\nAn approximation of team performance, based on user submissions, and solution mentions. Range [0-1], where 1 indicates\neach participant selecting the correct solution.###### performance_change\n\nChange of performance based compared to the previous utterance"
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0354f80bb4095c3e18800966ad1b6a296cfcc530 | model: https://huggingface.co/sentence-transformers/clip-ViT-L-14 # 1.71GB | teamtom/25000_word_emb_large | [
"license:apache-2.0",
"region:us"
] | 2024-01-14T16:10:20+00:00 | {"license": "apache-2.0"} | 2024-01-14T16:12:52+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| model: URL # 1.71GB | [
"# 1.71GB"
] | [
"TAGS\n#license-apache-2.0 #region-us \n",
"# 1.71GB"
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d4f20ff2896edaf78b384691615f3b083f27a18d | # Promoter Sequences for Maize NAM lines
The data in this dataset has the promoter sequences for **26 Maize NAM lines** and has been used for the finetuning step of [`Florabert`](https://huggingface.co/Gurveer05/FloraBERT). It has been created by processing the raw fasta files and the gff3 files from [`MaizeGDB`](https://www.maizegdb.org/) for the 26 NAM lines.
*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.
The data has been split into train and test data (70-30 split). In all, there are ~ 1 million sequences across the files. The steps followed to obtain this data are available in this [`Github Repository`](https://github.com/gurveervirk/florabert). | Gurveer05/maize-promoter-sequences | [
"size_categories:1M<n<10M",
"biology",
"region:us"
] | 2024-01-14T16:24:21+00:00 | {"size_categories": ["1M<n<10M"], "tags": ["biology"]} | 2024-01-14T17:13:53+00:00 | [] | [] | TAGS
#size_categories-1M<n<10M #biology #region-us
| # Promoter Sequences for Maize NAM lines
The data in this dataset has the promoter sequences for 26 Maize NAM lines and has been used for the finetuning step of 'Florabert'. It has been created by processing the raw fasta files and the gff3 files from 'MaizeGDB' for the 26 NAM lines.
*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.
The data has been split into train and test data (70-30 split). In all, there are ~ 1 million sequences across the files. The steps followed to obtain this data are available in this 'Github Repository'. | [
"# Promoter Sequences for Maize NAM lines\n\nThe data in this dataset has the promoter sequences for 26 Maize NAM lines and has been used for the finetuning step of 'Florabert'. It has been created by processing the raw fasta files and the gff3 files from 'MaizeGDB' for the 26 NAM lines.\n*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.\n\nThe data has been split into train and test data (70-30 split). In all, there are ~ 1 million sequences across the files. The steps followed to obtain this data are available in this 'Github Repository'."
] | [
"TAGS\n#size_categories-1M<n<10M #biology #region-us \n",
"# Promoter Sequences for Maize NAM lines\n\nThe data in this dataset has the promoter sequences for 26 Maize NAM lines and has been used for the finetuning step of 'Florabert'. It has been created by processing the raw fasta files and the gff3 files from 'MaizeGDB' for the 26 NAM lines.\n*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.\n\nThe data has been split into train and test data (70-30 split). In all, there are ~ 1 million sequences across the files. The steps followed to obtain this data are available in this 'Github Repository'."
] | [
21,
165
] | [
"passage: TAGS\n#size_categories-1M<n<10M #biology #region-us \n# Promoter Sequences for Maize NAM lines\n\nThe data in this dataset has the promoter sequences for 26 Maize NAM lines and has been used for the finetuning step of 'Florabert'. It has been created by processing the raw fasta files and the gff3 files from 'MaizeGDB' for the 26 NAM lines.\n*samtools* and *bedtools* have been used to extract the promoter sequences from these that are 1kb upstream of the sequence.\n\nThe data has been split into train and test data (70-30 split). In all, there are ~ 1 million sequences across the files. The steps followed to obtain this data are available in this 'Github Repository'."
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531f2de26dada8bcc16f1f3f049baf3cae916dff |
# A classics data set for use with mistral-7b-v0.1
This dataset was used for a fine-tune of Mistral 7b base model. It contains 1,640 Q/A pairs on Greek & Roman history.
The dataset was generated via Mixtral-8x7b Instruct v01, run over 512 token-length chunks of vol's 2&3 of Will Durants' 13 vol **Story of Civilization** (*Life of Greece* and *Caesar & Christ*).
Training data was formatted with [INST] and [/INST] delimiting instructions:
```bash
{"text": "Q: \"Why did many Greeks come to resent Rome's 'liberation' and 'peacekeeping' efforts, such as forbidding class war and interfering in disputes, despite Rome having given Greece freedom from previous conflicts?\"\nA: Many Greeks came to resent Rome's \"liberation\" and \"peacekeeping\" efforts due to several reasons. First, after the Romans had given Greece freedom...(blah blah blah)...interfering in their domestic affairs, and ultimately"}
``` | wmmarcellino/mistral-7b-v0.1-GreeceRome-v0.1 | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2024-01-14T16:25:18+00:00 | {"language": ["en"], "license": "apache-2.0"} | 2024-01-14T16:29:07+00:00 | [] | [
"en"
] | TAGS
#language-English #license-apache-2.0 #region-us
|
# A classics data set for use with mistral-7b-v0.1
This dataset was used for a fine-tune of Mistral 7b base model. It contains 1,640 Q/A pairs on Greek & Roman history.
The dataset was generated via Mixtral-8x7b Instruct v01, run over 512 token-length chunks of vol's 2&3 of Will Durants' 13 vol Story of Civilization (*Life of Greece* and *Caesar & Christ*).
Training data was formatted with [INST] and [/INST] delimiting instructions:
| [
"# A classics data set for use with mistral-7b-v0.1\nThis dataset was used for a fine-tune of Mistral 7b base model. It contains 1,640 Q/A pairs on Greek & Roman history.\nThe dataset was generated via Mixtral-8x7b Instruct v01, run over 512 token-length chunks of vol's 2&3 of Will Durants' 13 vol Story of Civilization (*Life of Greece* and *Caesar & Christ*).\n\nTraining data was formatted with [INST] and [/INST] delimiting instructions:"
] | [
"TAGS\n#language-English #license-apache-2.0 #region-us \n",
"# A classics data set for use with mistral-7b-v0.1\nThis dataset was used for a fine-tune of Mistral 7b base model. It contains 1,640 Q/A pairs on Greek & Roman history.\nThe dataset was generated via Mixtral-8x7b Instruct v01, run over 512 token-length chunks of vol's 2&3 of Will Durants' 13 vol Story of Civilization (*Life of Greece* and *Caesar & Christ*).\n\nTraining data was formatted with [INST] and [/INST] delimiting instructions:"
] | [
18,
131
] | [
"passage: TAGS\n#language-English #license-apache-2.0 #region-us \n# A classics data set for use with mistral-7b-v0.1\nThis dataset was used for a fine-tune of Mistral 7b base model. It contains 1,640 Q/A pairs on Greek & Roman history.\nThe dataset was generated via Mixtral-8x7b Instruct v01, run over 512 token-length chunks of vol's 2&3 of Will Durants' 13 vol Story of Civilization (*Life of Greece* and *Caesar & Christ*).\n\nTraining data was formatted with [INST] and [/INST] delimiting instructions:"
] | [
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] |
1a47923fb183e14b2674f79c54d0ab1cf223a2e0 |
To watch a video on how this dataset was created, watch the following videos:
Are words free?:
* https://youtu.be/Utg_D-yQB_E?si=FKp_QZ4PbKesiDrn
Replacing Chatgpt 3.5 turbo workflows with Openchat:
* https://youtu.be/DNKepnKuZns?si=bleufaiGdwGdrueK
| russellbal/dictionary-openchat-3.5-0106 | [
"license:wtfpl",
"region:us"
] | 2024-01-14T17:02:26+00:00 | {"license": "wtfpl"} | 2024-01-14T18:00:23+00:00 | [] | [] | TAGS
#license-wtfpl #region-us
|
To watch a video on how this dataset was created, watch the following videos:
Are words free?:
* URL
Replacing Chatgpt 3.5 turbo workflows with Openchat:
* URL
| [] | [
"TAGS\n#license-wtfpl #region-us \n"
] | [
14
] | [
"passage: TAGS\n#license-wtfpl #region-us \n"
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42150099223f14cf78c039bdd1e80e85da97cc04 |
# Alpaca Hindi Small
This is a synthesized dataset created by translation of alpaca dataset from English to Hindi language. | QuantumMik/alpaca_hindi_small | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:hi",
"license:apache-2.0",
"region:us"
] | 2024-01-14T17:06:44+00:00 | {"language": ["hi"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "text-generation"]} | 2024-01-16T16:02:58+00:00 | [] | [
"hi"
] | TAGS
#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-Hindi #license-apache-2.0 #region-us
|
# Alpaca Hindi Small
This is a synthesized dataset created by translation of alpaca dataset from English to Hindi language. | [
"# Alpaca Hindi Small\n\nThis is a synthesized dataset created by translation of alpaca dataset from English to Hindi language."
] | [
"TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-Hindi #license-apache-2.0 #region-us \n",
"# Alpaca Hindi Small\n\nThis is a synthesized dataset created by translation of alpaca dataset from English to Hindi language."
] | [
53,
28
] | [
"passage: TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-Hindi #license-apache-2.0 #region-us \n# Alpaca Hindi Small\n\nThis is a synthesized dataset created by translation of alpaca dataset from English to Hindi language."
] | [
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7f4e1788c0add94483c53185193c582bb1c2c4c2 |
# Dataset Card for Evaluation run of Felladrin/Llama-68M-Chat-v1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Felladrin/Llama-68M-Chat-v1](https://huggingface.co/Felladrin/Llama-68M-Chat-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Felladrin__Llama-68M-Chat-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T17:25:12.605913](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__Llama-68M-Chat-v1/blob/main/results_2024-01-14T17-25-12.605913.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc2_stderr": 0.015727848850119193
},
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},
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},
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},
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Felladrin__Llama-68M-Chat-v1 | [
"region:us"
] | 2024-01-14T17:27:02+00:00 | {"pretty_name": "Evaluation run of Felladrin/Llama-68M-Chat-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [Felladrin/Llama-68M-Chat-v1](https://huggingface.co/Felladrin/Llama-68M-Chat-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Felladrin__Llama-68M-Chat-v1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T17:25:12.605913](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__Llama-68M-Chat-v1/blob/main/results_2024-01-14T17-25-12.605913.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2518558528274769,\n \"acc_stderr\": 0.030387282193610175,\n \"acc_norm\": 0.25203959947439164,\n \"acc_norm_stderr\": 0.031196164528136557,\n \"mc1\": 0.2741738066095471,\n \"mc1_stderr\": 0.015616518497219376,\n \"mc2\": 0.4726841055154348,\n \"mc2_stderr\": 0.015727848850119193\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.1885665529010239,\n \"acc_stderr\": 0.011430897647675815,\n \"acc_norm\": 0.23293515358361774,\n \"acc_norm_stderr\": 0.012352507042617405\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.27693686516630156,\n \"acc_stderr\": 0.004465704810893541,\n \"acc_norm\": 0.28271260705038836,\n \"acc_norm_stderr\": 0.004493975527386726\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.22962962962962963,\n \"acc_stderr\": 0.03633384414073461,\n \"acc_norm\": 0.22962962962962963,\n \"acc_norm_stderr\": 0.03633384414073461\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21132075471698114,\n \"acc_stderr\": 0.025125766484827845,\n \"acc_norm\": 0.21132075471698114,\n \"acc_norm_stderr\": 0.025125766484827845\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2152777777777778,\n \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.2152777777777778,\n \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.30057803468208094,\n \"acc_stderr\": 0.03496101481191181,\n \"acc_norm\": 0.30057803468208094,\n \"acc_norm_stderr\": 0.03496101481191181\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.1829787234042553,\n \"acc_stderr\": 0.025276041000449966,\n \"acc_norm\": 0.1829787234042553,\n \"acc_norm_stderr\": 0.025276041000449966\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n \"acc_stderr\": 0.03892431106518754,\n \"acc_norm\": 0.21929824561403508,\n \"acc_norm_stderr\": 0.03892431106518754\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.20689655172413793,\n \"acc_stderr\": 0.03375672449560554,\n \"acc_norm\": 0.20689655172413793,\n \"acc_norm_stderr\": 0.03375672449560554\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.022569897074918417,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.022569897074918417\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15079365079365079,\n \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3161290322580645,\n \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.3161290322580645,\n \"acc_norm_stderr\": 0.02645087448904277\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114468,\n \"acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114468\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.03401506715249039,\n \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.03401506715249039\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.3383838383838384,\n \"acc_stderr\": 0.03371124142626302,\n \"acc_norm\": 0.3383838383838384,\n \"acc_norm_stderr\": 0.03371124142626302\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.33678756476683935,\n \"acc_stderr\": 0.03410780251836184,\n \"acc_norm\": 0.33678756476683935,\n \"acc_norm_stderr\": 0.03410780251836184\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.34102564102564104,\n \"acc_stderr\": 0.02403548967633507,\n \"acc_norm\": 0.34102564102564104,\n \"acc_norm_stderr\": 0.02403548967633507\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.3445378151260504,\n \"acc_stderr\": 0.030868682604121633,\n \"acc_norm\": 0.3445378151260504,\n \"acc_norm_stderr\": 0.030868682604121633\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.23853211009174313,\n \"acc_stderr\": 0.01827257581023187,\n \"acc_norm\": 0.23853211009174313,\n \"acc_norm_stderr\": 0.01827257581023187\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.030190282453501947,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.030190282453501947\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.21076233183856502,\n \"acc_stderr\": 0.027373095500540193,\n \"acc_norm\": 0.21076233183856502,\n \"acc_norm_stderr\": 0.027373095500540193\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.25190839694656486,\n \"acc_stderr\": 0.03807387116306086,\n \"acc_norm\": 0.25190839694656486,\n \"acc_norm_stderr\": 0.03807387116306086\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.24793388429752067,\n \"acc_stderr\": 0.03941897526516303,\n \"acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.03941897526516303\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.2331288343558282,\n \"acc_stderr\": 0.033220157957767414,\n \"acc_norm\": 0.2331288343558282,\n \"acc_norm_stderr\": 0.033220157957767414\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.1875,\n \"acc_stderr\": 0.0370468111477387,\n \"acc_norm\": 0.1875,\n \"acc_norm_stderr\": 0.0370468111477387\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.22330097087378642,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.22330097087378642,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.19658119658119658,\n \"acc_stderr\": 0.02603538609895129,\n \"acc_norm\": 0.19658119658119658,\n \"acc_norm_stderr\": 0.02603538609895129\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2835249042145594,\n \"acc_stderr\": 0.01611731816683228,\n \"acc_norm\": 0.2835249042145594,\n \"acc_norm_stderr\": 0.01611731816683228\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24134078212290502,\n \"acc_stderr\": 0.014310999547961438,\n \"acc_norm\": 0.24134078212290502,\n \"acc_norm_stderr\": 0.014310999547961438\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351298,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351298\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2990353697749196,\n \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.2990353697749196,\n \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.24691358024691357,\n \"acc_stderr\": 0.02399350170904211,\n \"acc_norm\": 0.24691358024691357,\n \"acc_norm_stderr\": 0.02399350170904211\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.2553191489361702,\n \"acc_stderr\": 0.02601199293090201,\n \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.02601199293090201\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24511082138200782,\n \"acc_stderr\": 0.010986307870045517,\n \"acc_norm\": 0.24511082138200782,\n \"acc_norm_stderr\": 0.010986307870045517\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.029896163033125478,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.029896163033125478\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.017630827375148383,\n \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.017630827375148383\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.03831305140884603,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.03831305140884603\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.23673469387755103,\n \"acc_stderr\": 0.02721283588407316,\n \"acc_norm\": 0.23673469387755103,\n \"acc_norm_stderr\": 0.02721283588407316\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n \"acc_norm_stderr\": 0.030147775935409224\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.25301204819277107,\n \"acc_stderr\": 0.033844291552331346,\n \"acc_norm\": 0.25301204819277107,\n \"acc_norm_stderr\": 0.033844291552331346\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2741738066095471,\n \"mc1_stderr\": 0.015616518497219376,\n \"mc2\": 0.4726841055154348,\n \"mc2_stderr\": 0.015727848850119193\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5430149960536701,\n \"acc_stderr\": 0.01400038676159829\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/Felladrin/Llama-68M-Chat-v1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_14T17_25_12.605913", "path": ["**/details_harness|arc:challenge|25_2024-01-14T17-25-12.605913.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-14T17-25-12.605913.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_14T17_25_12.605913", "path": ["**/details_harness|gsm8k|5_2024-01-14T17-25-12.605913.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-14T17-25-12.605913.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_14T17_25_12.605913", "path": ["**/details_harness|hellaswag|10_2024-01-14T17-25-12.605913.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-14T17-25-12.605913.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_14T17_25_12.605913", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T17-25-12.605913.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T17-25-12.605913.parquet", 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#region-us
|
# Dataset Card for Evaluation run of Felladrin/Llama-68M-Chat-v1
Dataset automatically created during the evaluation run of model Felladrin/Llama-68M-Chat-v1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T17:25:12.605913(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Felladrin/Llama-68M-Chat-v1\n\n\n\nDataset automatically created during the evaluation run of model Felladrin/Llama-68M-Chat-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T17:25:12.605913(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"# Dataset Card for Evaluation run of Felladrin/Llama-68M-Chat-v1\n\n\n\nDataset automatically created during the evaluation run of model Felladrin/Llama-68M-Chat-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T17:25:12.605913(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Felladrin/Llama-68M-Chat-v1\n\n\n\nDataset automatically created during the evaluation run of model Felladrin/Llama-68M-Chat-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T17:25:12.605913(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]"
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be9393c81659b176efda35dfa5291d38920ff500 |
# Dataset Card for Evaluation run of AA051611/limb
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [AA051611/limb](https://huggingface.co/AA051611/limb) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AA051611__limb",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T17:31:13.154923](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051611__limb/blob/main/results_2024-01-14T17-31-13.154923.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc1_stderr": 0.017142825728496767,
"mc2": 0.5836669238966421,
"mc2_stderr": 0.01521191071011394
},
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},
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},
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},
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_AA051611__limb | [
"region:us"
] | 2024-01-14T17:33:21+00:00 | {"pretty_name": "Evaluation run of AA051611/limb", "dataset_summary": "Dataset automatically created during the evaluation run of model [AA051611/limb](https://huggingface.co/AA051611/limb) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AA051611__limb\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-14T17:31:13.154923](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051611__limb/blob/main/results_2024-01-14T17-31-13.154923.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7173948628205344,\n \"acc_stderr\": 0.029795425890422344,\n \"acc_norm\": 0.7228232912878558,\n \"acc_norm_stderr\": 0.030359217292974663,\n \"mc1\": 0.3990208078335373,\n \"mc1_stderr\": 0.017142825728496767,\n \"mc2\": 0.5836669238966421,\n \"mc2_stderr\": 0.01521191071011394\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5921501706484642,\n \"acc_stderr\": 0.014361097288449712,\n \"acc_norm\": 0.6348122866894198,\n \"acc_norm_stderr\": 0.0140702655192688\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6357299342760406,\n \"acc_stderr\": 0.0048024139199326675,\n \"acc_norm\": 0.8307110137422824,\n \"acc_norm_stderr\": 0.0037424055874098806\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6814814814814815,\n \"acc_stderr\": 0.04024778401977108,\n \"acc_norm\": 0.6814814814814815,\n \"acc_norm_stderr\": 0.04024778401977108\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.0315469804508223,\n \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.0315469804508223\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7849056603773585,\n \"acc_stderr\": 0.025288394502891363,\n \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.025288394502891363\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.03309615177059007,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.03309615177059007\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.0351494255126744,\n \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.0351494255126744\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287533,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287533\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.723404255319149,\n \"acc_stderr\": 0.029241883869628813,\n \"acc_norm\": 0.723404255319149,\n \"acc_norm_stderr\": 0.029241883869628813\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7034482758620689,\n \"acc_stderr\": 0.03806142687309992,\n \"acc_norm\": 0.7034482758620689,\n \"acc_norm_stderr\": 0.03806142687309992\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.626984126984127,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\": 0.626984126984127,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8451612903225807,\n \"acc_stderr\": 0.020579287326583227,\n \"acc_norm\": 0.8451612903225807,\n \"acc_norm_stderr\": 0.020579287326583227\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.034991131376767445,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.034991131376767445\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8363636363636363,\n \"acc_stderr\": 0.02888787239548795,\n \"acc_norm\": 0.8363636363636363,\n \"acc_norm_stderr\": 0.02888787239548795\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9141414141414141,\n \"acc_stderr\": 0.019960225563172885,\n \"acc_norm\": 0.9141414141414141,\n \"acc_norm_stderr\": 0.019960225563172885\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078898,\n \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078898\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.764102564102564,\n \"acc_stderr\": 0.021525965407408726,\n \"acc_norm\": 0.764102564102564,\n \"acc_norm_stderr\": 0.021525965407408726\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.4111111111111111,\n \"acc_stderr\": 0.029999923508706682,\n \"acc_norm\": 0.4111111111111111,\n \"acc_norm_stderr\": 0.029999923508706682\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7899159663865546,\n \"acc_stderr\": 0.026461398717471874,\n \"acc_norm\": 0.7899159663865546,\n \"acc_norm_stderr\": 0.026461398717471874\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.45695364238410596,\n \"acc_stderr\": 0.04067325174247443,\n \"acc_norm\": 0.45695364238410596,\n \"acc_norm_stderr\": 0.04067325174247443\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8880733944954129,\n \"acc_stderr\": 0.013517352714958792,\n \"acc_norm\": 0.8880733944954129,\n \"acc_norm_stderr\": 0.013517352714958792\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6342592592592593,\n \"acc_stderr\": 0.03284738857647206,\n \"acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.03284738857647206\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8921568627450981,\n \"acc_stderr\": 0.021770522281368394,\n \"acc_norm\": 0.8921568627450981,\n \"acc_norm_stderr\": 0.021770522281368394\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8818565400843882,\n \"acc_stderr\": 0.02101105265987846,\n \"acc_norm\": 0.8818565400843882,\n \"acc_norm_stderr\": 0.02101105265987846\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7399103139013453,\n \"acc_stderr\": 0.029442495585857476,\n \"acc_norm\": 0.7399103139013453,\n \"acc_norm_stderr\": 0.029442495585857476\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.032178294207446306,\n \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.032178294207446306\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8842975206611571,\n \"acc_stderr\": 0.0291998024556228,\n \"acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.0291998024556228\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n \"acc_stderr\": 0.03520703990517963,\n \"acc_norm\": 0.8425925925925926,\n \"acc_norm_stderr\": 0.03520703990517963\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8098159509202454,\n \"acc_stderr\": 0.03083349114628123,\n \"acc_norm\": 0.8098159509202454,\n \"acc_norm_stderr\": 0.03083349114628123\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5446428571428571,\n \"acc_stderr\": 0.04726835553719098,\n \"acc_norm\": 0.5446428571428571,\n \"acc_norm_stderr\": 0.04726835553719098\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.03288180278808628,\n \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.03288180278808628\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9188034188034188,\n \"acc_stderr\": 0.017893784904018543,\n \"acc_norm\": 0.9188034188034188,\n \"acc_norm_stderr\": 0.017893784904018543\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263714,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263714\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.896551724137931,\n \"acc_stderr\": 0.0108904525446915,\n \"acc_norm\": 0.896551724137931,\n \"acc_norm_stderr\": 0.0108904525446915\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.02326752843210017,\n \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.02326752843210017\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42569832402234636,\n \"acc_stderr\": 0.016536829648997112,\n \"acc_norm\": 0.42569832402234636,\n \"acc_norm_stderr\": 0.016536829648997112\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8169934640522876,\n \"acc_stderr\": 0.022140767512880973,\n \"acc_norm\": 0.8169934640522876,\n \"acc_norm_stderr\": 0.022140767512880973\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7813504823151125,\n \"acc_stderr\": 0.02347558141786111,\n \"acc_norm\": 0.7813504823151125,\n \"acc_norm_stderr\": 0.02347558141786111\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8117283950617284,\n \"acc_stderr\": 0.02175186606081587,\n \"acc_norm\": 0.8117283950617284,\n \"acc_norm_stderr\": 0.02175186606081587\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5283687943262412,\n \"acc_stderr\": 0.029779450957303062,\n \"acc_norm\": 0.5283687943262412,\n \"acc_norm_stderr\": 0.029779450957303062\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.529986962190352,\n \"acc_stderr\": 0.012747248967079058,\n \"acc_norm\": 0.529986962190352,\n \"acc_norm_stderr\": 0.012747248967079058\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.025767252010855946,\n \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.025767252010855946\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.7630718954248366,\n \"acc_stderr\": 0.01720166216978978,\n \"acc_norm\": 0.7630718954248366,\n \"acc_norm_stderr\": 0.01720166216978978\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7836734693877551,\n \"acc_stderr\": 0.026358916334904028,\n \"acc_norm\": 0.7836734693877551,\n \"acc_norm_stderr\": 0.026358916334904028\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8706467661691543,\n \"acc_stderr\": 0.023729830881018526,\n \"acc_norm\": 0.8706467661691543,\n \"acc_norm_stderr\": 0.023729830881018526\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.95,\n \"acc_stderr\": 0.021904291355759033,\n \"acc_norm\": 0.95,\n \"acc_norm_stderr\": 0.021904291355759033\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015577,\n \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015577\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3990208078335373,\n \"mc1_stderr\": 0.017142825728496767,\n \"mc2\": 0.5836669238966421,\n \"mc2_stderr\": 0.01521191071011394\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.797947908445146,\n \"acc_stderr\": 0.01128501375404745\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.55420773313116,\n \"acc_stderr\": 0.013691305174506698\n }\n}\n```", "repo_url": "https://huggingface.co/AA051611/limb", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_14T17_31_13.154923", "path": ["**/details_harness|arc:challenge|25_2024-01-14T17-31-13.154923.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-14T17-31-13.154923.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_14T17_31_13.154923", "path": ["**/details_harness|gsm8k|5_2024-01-14T17-31-13.154923.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-14T17-31-13.154923.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_14T17_31_13.154923", "path": ["**/details_harness|hellaswag|10_2024-01-14T17-31-13.154923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-14T17-31-13.154923.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_14T17_31_13.154923", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T17-31-13.154923.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T17-31-13.154923.parquet", 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#region-us
|
# Dataset Card for Evaluation run of AA051611/limb
Dataset automatically created during the evaluation run of model AA051611/limb on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-14T17:31:13.154923(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of AA051611/limb\n\n\n\nDataset automatically created during the evaluation run of model AA051611/limb on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T17:31:13.154923(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of AA051611/limb\n\n\n\nDataset automatically created during the evaluation run of model AA051611/limb on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-14T17:31:13.154923(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AA051611/limb\n\n\n\nDataset automatically created during the evaluation run of model AA051611/limb on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T17:31:13.154923(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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