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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-epochs15
This model is a fine-tuned version of [AKulk/wav2vec2-base-timit-epochs10](https://huggingface.co/AKulk/wav2vec2-base-timit-epochs10) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-epochs15", "results": []}]} | automatic-speech-recognition | AKulk/wav2vec2-base-timit-epochs15 | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-epochs15
This model is a fine-tuned version of AKulk/wav2vec2-base-timit-epochs10 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| [
"# wav2vec2-base-timit-epochs15\n\nThis model is a fine-tuned version of AKulk/wav2vec2-base-timit-epochs10 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 5\n- total_train_batch_size: 80\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-epochs15\n\nThis model is a fine-tuned version of AKulk/wav2vec2-base-timit-epochs10 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 5\n- total_train_batch_size: 80\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.10.3"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-base-timit-epochs15\n\nThis model is a fine-tuned version of AKulk/wav2vec2-base-timit-epochs10 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 5\n- total_train_batch_size: 80\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 5\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-epochs5
This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-epochs5", "results": []}]} | automatic-speech-recognition | AKulk/wav2vec2-base-timit-epochs5 | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-epochs5
This model is a fine-tuned version of facebook/wav2vec2-lv-60-espeak-cv-ft on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| [
"# wav2vec2-base-timit-epochs5\n\nThis model is a fine-tuned version of facebook/wav2vec2-lv-60-espeak-cv-ft on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 5\n- total_train_batch_size: 80\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-epochs5\n\nThis model is a fine-tuned version of facebook/wav2vec2-lv-60-espeak-cv-ft on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 5\n- total_train_batch_size: 80\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.10.3"
] | [
56,
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] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-base-timit-epochs5\n\nThis model is a fine-tuned version of facebook/wav2vec2-lv-60-espeak-cv-ft on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 5\n- total_train_batch_size: 80\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 5\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.10.3"
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null | null | transformers |
# summarization_fanpage128
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on Fanpage dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.5348
- Rouge1: 34.1882
- Rouge2: 15.7866
- Rougel: 25.141
- Rougelsum: 28.4882
- Gen Len: 69.3041
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage-128")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-fanpage-128")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
# Citation
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
``` | {"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/fanpage"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_fanpage128", "results": []}]} | summarization | ARTeLab/it5-summarization-fanpage | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/fanpage",
"base_model:gsarti/it5-base",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# summarization_fanpage128
This model is a fine-tuned version of gsarti/it5-base on Fanpage dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.5348
- Rouge1: 34.1882
- Rouge2: 15.7866
- Rougel: 25.141
- Rougelsum: 28.4882
- Gen Len: 69.3041
## Usage
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
More details and results in published work
| [
"# summarization_fanpage128\n\nThis model is a fine-tuned version of gsarti/it5-base on Fanpage dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 1.5348\n- Rouge1: 34.1882\n- Rouge2: 15.7866\n- Rougel: 25.141\n- Rougelsum: 28.4882\n- Gen Len: 69.3041",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 3\n- eval_batch_size: 3\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# summarization_fanpage128\n\nThis model is a fine-tuned version of gsarti/it5-base on Fanpage dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 1.5348\n- Rouge1: 34.1882\n- Rouge2: 15.7866\n- Rougel: 25.141\n- Rougelsum: 28.4882\n- Gen Len: 69.3041",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 3\n- eval_batch_size: 3\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
84,
85,
3,
90,
44
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# summarization_fanpage128\n\nThis model is a fine-tuned version of gsarti/it5-base on Fanpage dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 1.5348\n- Rouge1: 34.1882\n- Rouge2: 15.7866\n- Rougel: 25.141\n- Rougelsum: 28.4882\n- Gen Len: 69.3041## Usage### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 3\n- eval_batch_size: 3\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
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null | null | transformers |
# summarization_ilpost
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on IlPost dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.6020
- Rouge1: 33.7802
- Rouge2: 16.2953
- Rougel: 27.4797
- Rougelsum: 30.2273
- Gen Len: 45.3175
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-ilpost")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-ilpost")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3 | {"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/ilpost"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_ilpost", "results": []}]} | summarization | ARTeLab/it5-summarization-ilpost | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/ilpost",
"base_model:gsarti/it5-base",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/ilpost #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# summarization_ilpost
This model is a fine-tuned version of gsarti/it5-base on IlPost dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.6020
- Rouge1: 33.7802
- Rouge2: 16.2953
- Rougel: 27.4797
- Rougelsum: 30.2273
- Gen Len: 45.3175
## Usage
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3 | [
"# summarization_ilpost\n\nThis model is a fine-tuned version of gsarti/it5-base on IlPost dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 1.6020\n- Rouge1: 33.7802\n- Rouge2: 16.2953\n- Rougel: 27.4797\n- Rougelsum: 30.2273\n- Gen Len: 45.3175",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 6\n- eval_batch_size: 6\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/ilpost #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# summarization_ilpost\n\nThis model is a fine-tuned version of gsarti/it5-base on IlPost dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 1.6020\n- Rouge1: 33.7802\n- Rouge2: 16.2953\n- Rougel: 27.4797\n- Rougelsum: 30.2273\n- Gen Len: 45.3175",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 6\n- eval_batch_size: 6\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
] | [
88,
86,
3,
90,
37
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/ilpost #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# summarization_ilpost\n\nThis model is a fine-tuned version of gsarti/it5-base on IlPost dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 1.6020\n- Rouge1: 33.7802\n- Rouge2: 16.2953\n- Rougel: 27.4797\n- Rougelsum: 30.2273\n- Gen Len: 45.3175## Usage### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 6\n- eval_batch_size: 6\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0### Framework versions\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
] | [
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null | null | transformers |
# summarization_mlsum
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on MLSum-it for Abstractive Summarization.
It achieves the following results:
- Loss: 2.0190
- Rouge1: 19.3739
- Rouge2: 5.9753
- Rougel: 16.691
- Rougelsum: 16.7862
- Gen Len: 32.5268
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-mlsum")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
# Citation
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
``` | {"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/mlsum-it"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_mlsum", "results": []}]} | summarization | ARTeLab/it5-summarization-mlsum | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/mlsum-it",
"base_model:gsarti/it5-base",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/mlsum-it #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# summarization_mlsum
This model is a fine-tuned version of gsarti/it5-base on MLSum-it for Abstractive Summarization.
It achieves the following results:
- Loss: 2.0190
- Rouge1: 19.3739
- Rouge2: 5.9753
- Rougel: 16.691
- Rougelsum: 16.7862
- Gen Len: 32.5268
## Usage
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
More details and results in published work
| [
"# summarization_mlsum\n\nThis model is a fine-tuned version of gsarti/it5-base on MLSum-it for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.0190\n- Rouge1: 19.3739\n- Rouge2: 5.9753\n- Rougel: 16.691\n- Rougelsum: 16.7862\n- Gen Len: 32.5268",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 6\n- eval_batch_size: 6\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/mlsum-it #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# summarization_mlsum\n\nThis model is a fine-tuned version of gsarti/it5-base on MLSum-it for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.0190\n- Rouge1: 19.3739\n- Rouge2: 5.9753\n- Rougel: 16.691\n- Rougelsum: 16.7862\n- Gen Len: 32.5268",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 6\n- eval_batch_size: 6\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
86,
85,
3,
90,
44
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/mlsum-it #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# summarization_mlsum\n\nThis model is a fine-tuned version of gsarti/it5-base on MLSum-it for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.0190\n- Rouge1: 19.3739\n- Rouge2: 5.9753\n- Rougel: 16.691\n- Rougelsum: 16.7862\n- Gen Len: 32.5268## Usage### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 6\n- eval_batch_size: 6\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
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null | null | transformers |
# mbart-summarization-fanpage
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on Fanpage dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 2.1833
- Rouge1: 36.5027
- Rouge2: 17.4428
- Rougel: 26.1734
- Rougelsum: 30.2636
- Gen Len: 75.2413
## Usage
```python
from transformers import MBartTokenizer, MBartForConditionalGeneration
tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-fanpage")
model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-fanpage")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
# Citation
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
``` | {"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/fanpage"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_fanpage4epoch", "results": []}]} | summarization | ARTeLab/mbart-summarization-fanpage | [
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/fanpage",
"base_model:facebook/mbart-large-cc25",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# mbart-summarization-fanpage
This model is a fine-tuned version of facebook/mbart-large-cc25 on Fanpage dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 2.1833
- Rouge1: 36.5027
- Rouge2: 17.4428
- Rougel: 26.1734
- Rougelsum: 30.2636
- Gen Len: 75.2413
## Usage
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
More details and results in published work
| [
"# mbart-summarization-fanpage\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on Fanpage dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.1833\n- Rouge1: 36.5027\n- Rouge2: 17.4428\n- Rougel: 26.1734\n- Rougelsum: 30.2636\n- Gen Len: 75.2413",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
"TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# mbart-summarization-fanpage\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on Fanpage dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.1833\n- Rouge1: 36.5027\n- Rouge2: 17.4428\n- Rougel: 26.1734\n- Rougelsum: 30.2636\n- Gen Len: 75.2413",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
79,
93,
3,
90,
43
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# mbart-summarization-fanpage\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on Fanpage dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.1833\n- Rouge1: 36.5027\n- Rouge2: 17.4428\n- Rougel: 26.1734\n- Rougelsum: 30.2636\n- Gen Len: 75.2413## Usage### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
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null | null | transformers |
# mbart_summarization_ilpost
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on IlPost dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 2.3640
- Rouge1: 38.9101
- Rouge2: 21.384
- Rougel: 32.0517
- Rougelsum: 35.0743
- Gen Len: 39.8843
## Usage
```python
from transformers import MBartTokenizer, MBartForConditionalGeneration
tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-ilpost")
model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-ilpost")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
# Citation
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
``` | {"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/ilpost"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_ilpost", "results": []}]} | summarization | ARTeLab/mbart-summarization-ilpost | [
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/ilpost",
"base_model:facebook/mbart-large-cc25",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/ilpost #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# mbart_summarization_ilpost
This model is a fine-tuned version of facebook/mbart-large-cc25 on IlPost dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 2.3640
- Rouge1: 38.9101
- Rouge2: 21.384
- Rougel: 32.0517
- Rougelsum: 35.0743
- Gen Len: 39.8843
## Usage
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
More details and results in published work
| [
"# mbart_summarization_ilpost\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on IlPost dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.3640\n- Rouge1: 38.9101\n- Rouge2: 21.384\n- Rougel: 32.0517\n- Rougelsum: 35.0743\n- Gen Len: 39.8843",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
"TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/ilpost #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# mbart_summarization_ilpost\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on IlPost dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.3640\n- Rouge1: 38.9101\n- Rouge2: 21.384\n- Rougel: 32.0517\n- Rougelsum: 35.0743\n- Gen Len: 39.8843",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
79,
91,
3,
90,
43
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/ilpost #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# mbart_summarization_ilpost\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on IlPost dataset for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 2.3640\n- Rouge1: 38.9101\n- Rouge2: 21.384\n- Rougel: 32.0517\n- Rougelsum: 35.0743\n- Gen Len: 39.8843## Usage### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
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null | null | transformers |
# mbart_summarization_mlsum
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on mlsum-it for Abstractive Summarization.
It achieves the following results:
- Loss: 3.3336
- Rouge1: 19.3489
- Rouge2: 6.4028
- Rougel: 16.3497
- Rougelsum: 16.5387
- Gen Len: 33.5945
## Usage
```python
from transformers import MBartTokenizer, MBartForConditionalGeneration
tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-mlsum")
model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-mlsum")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
# Citation
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
``` | {"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/mlsum-it"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_mlsum", "results": []}]} | summarization | ARTeLab/mbart-summarization-mlsum | [
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/mlsum-it",
"base_model:facebook/mbart-large-cc25",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/mlsum-it #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# mbart_summarization_mlsum
This model is a fine-tuned version of facebook/mbart-large-cc25 on mlsum-it for Abstractive Summarization.
It achieves the following results:
- Loss: 3.3336
- Rouge1: 19.3489
- Rouge2: 6.4028
- Rougel: 16.3497
- Rougelsum: 16.5387
- Gen Len: 33.5945
## Usage
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
More details and results in published work
| [
"# mbart_summarization_mlsum\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on mlsum-it for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 3.3336\n- Rouge1: 19.3489\n- Rouge2: 6.4028\n- Rougel: 16.3497\n- Rougelsum: 16.5387\n- Gen Len: 33.5945",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
"TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/mlsum-it #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# mbart_summarization_mlsum\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on mlsum-it for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 3.3336\n- Rouge1: 19.3489\n- Rouge2: 6.4028\n- Rougel: 16.3497\n- Rougelsum: 16.5387\n- Gen Len: 33.5945",
"## Usage",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
] | [
81,
91,
3,
90,
43
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/mlsum-it #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# mbart_summarization_mlsum\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on mlsum-it for Abstractive Summarization.\n\nIt achieves the following results:\n- Loss: 3.3336\n- Rouge1: 19.3489\n- Rouge2: 6.4028\n- Rougel: 16.3497\n- Rougelsum: 16.5387\n- Gen Len: 33.5945## Usage### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0### Framework versions\n\n- Transformers 4.15.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3\n\nMore details and results in published work"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PENGMENGJIE-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.9.0
- Pytorch 1.7.1+cpu
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "PENGMENGJIE-finetuned-emotion", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}}]}]} | text-classification | ASCCCCCCCC/PENGMENGJIE-finetuned-emotion | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# PENGMENGJIE-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.9.0
- Pytorch 1.7.1+cpu
- Datasets 1.17.0
- Tokenizers 0.10.3
| [
"# PENGMENGJIE-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Framework versions\n\n- Transformers 4.9.0\n- Pytorch 1.7.1+cpu\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# PENGMENGJIE-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Framework versions\n\n- Transformers 4.9.0\n- Pytorch 1.7.1+cpu\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] | [
57,
40,
6,
12,
8,
3,
90,
34
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# PENGMENGJIE-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Framework versions\n\n- Transformers 4.9.0\n- Pytorch 1.7.1+cpu\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-amazon_zh_20000
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1683
- Accuracy: 0.5224
- F1: 0.5194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.2051 | 1.0 | 2500 | 1.1717 | 0.506 | 0.4847 |
| 1.0035 | 2.0 | 5000 | 1.1683 | 0.5224 | 0.5194 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-chinese-finetuned-amazon_zh_20000", "results": []}]} | text-classification | ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000 | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-chinese-finetuned-amazon\_zh\_20000
=============================================
This model is a fine-tuned version of bert-base-chinese on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1683
* Accuracy: 0.5224
* F1: 0.5194
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.9.1
* Datasets 1.18.3
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-chinese-amazon_zh_20000
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1518
- Accuracy: 0.5092
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.196 | 1.0 | 1250 | 1.1518 | 0.5092 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-chinese-amazon_zh_20000", "results": []}]} | text-classification | ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-chinese-amazon\_zh\_20000
=========================================
This model is a fine-tuned version of bert-base-chinese on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1518
* Accuracy: 0.5092
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.9.1
* Datasets 1.18.3
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
47,
98,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-amazon_zh_20000
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3031
- Accuracy: 0.4406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.396 | 1.0 | 1250 | 1.3031 | 0.4406 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-multilingual-cased-amazon_zh_20000", "results": []}]} | text-classification | ASCCCCCCCC/distilbert-base-multilingual-cased-amazon_zh_20000 | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-multilingual-cased-amazon\_zh\_20000
====================================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3031
* Accuracy: 0.4406
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.9.1
* Datasets 1.18.3
* Tokenizers 0.10.3
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-amazon_zh_20000
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3516
- Accuracy: 0.414
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4343 | 1.0 | 1250 | 1.3516 | 0.414 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-amazon_zh_20000", "results": []}]} | text-classification | ASCCCCCCCC/distilbert-base-uncased-finetuned-amazon_zh_20000 | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-amazon\_zh\_20000
===================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3516
* Accuracy: 0.414
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.9.1
* Datasets 1.18.3
* Tokenizers 0.10.3
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
57,
98,
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] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.9.0
- Pytorch 1.7.1+cpu
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}}]}]} | text-classification | ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.9.0
- Pytorch 1.7.1+cpu
- Datasets 1.17.0
- Tokenizers 0.10.3
| [
"# distilbert-base-uncased-finetuned-clinc\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 48\n- eval_batch_size: 48\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.9.0\n- Pytorch 1.7.1+cpu\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-clinc\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 48\n- eval_batch_size: 48\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.9.0\n- Pytorch 1.7.1+cpu\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] | [
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] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# distilbert-base-uncased-finetuned-clinc\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 48\n- eval_batch_size: 48\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Framework versions\n\n- Transformers 4.9.0\n- Pytorch 1.7.1+cpu\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80.0
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | fill-mask | AT/distilroberta-base-finetuned-wikitext2 | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of distilroberta-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80.0
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| [
"# distilroberta-base-finetuned-wikitext2\n\nThis model is a fine-tuned version of distilroberta-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 80.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] | [
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"# distilroberta-base-finetuned-wikitext2\n\nThis model is a fine-tuned version of distilroberta-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 80.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] | [
56,
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"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# distilroberta-base-finetuned-wikitext2\n\nThis model is a fine-tuned version of distilroberta-base on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 80.0### Training results### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
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null | null | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | ATGdev/DialoGPT-small-harrypotter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "result", "results": []}]} | fill-mask | AVSilva/bertimbau-large-fine-tuned-md | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# result
This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
| [
"# result\n\nThis model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7458",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# result\n\nThis model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7458",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] | [
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"passage: TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# result\n\nThis model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7458## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7570
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "result", "results": []}]} | fill-mask | AVSilva/bertimbau-large-fine-tuned-sd | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# result
This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7570
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
| [
"# result\n\nThis model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7570",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# result\n\nThis model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7570",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] | [
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] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# result\n\nThis model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7570## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
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null | null | transformers |
#Tony Stark DialoGPT model | {"tags": ["conversational"]} | text-generation | AVeryRealHuman/DialoGPT-small-TonyStark | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
#Tony Stark DialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
55
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmp_znj9o4r
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmp_znj9o4r", "results": []}]} | text-classification | AWTStress/stress_classifier | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# tmp_znj9o4r
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
| [
"# tmp_znj9o4r\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# tmp_znj9o4r\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] | [
48,
37,
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] | [
"passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n# tmp_znj9o4r\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32### Training results### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# stress_score
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "stress_score", "results": []}]} | text-classification | AWTStress/stress_score | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# stress_score
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
| [
"# stress_score\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# stress_score\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] | [
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"passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n# stress_score\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32### Training results### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4812
- Wer: 0.3557
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4668 | 4.0 | 500 | 1.3753 | 0.9895 |
| 0.6126 | 8.0 | 1000 | 0.4809 | 0.4350 |
| 0.2281 | 12.0 | 1500 | 0.4407 | 0.4033 |
| 0.1355 | 16.0 | 2000 | 0.4590 | 0.3765 |
| 0.0923 | 20.0 | 2500 | 0.4754 | 0.3707 |
| 0.0654 | 24.0 | 3000 | 0.4719 | 0.3557 |
| 0.0489 | 28.0 | 3500 | 0.4812 | 0.3557 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | automatic-speech-recognition | Pinwheel/wav2vec2-base-timit-demo-colab | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4812
* Wer: 0.3557
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] | [
56,
130,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
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null | null | null |
#FashionMNIST
PyTorch Quick Start | {"tags": ["image-classification", "pytorch", "huggingpics", "some_thing"], "metrics": ["accuracy"], "private": false} | image-classification | Ab0/foo-model | [
"pytorch",
"image-classification",
"huggingpics",
"some_thing",
"model-index",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#pytorch #image-classification #huggingpics #some_thing #model-index #region-us
|
#FashionMNIST
PyTorch Quick Start | [] | [
"TAGS\n#pytorch #image-classification #huggingpics #some_thing #model-index #region-us \n"
] | [
28
] | [
"passage: TAGS\n#pytorch #image-classification #huggingpics #some_thing #model-index #region-us \n"
] | [
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null | null | transformers | # BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis
These are different BERT models (BERT Arabic models are initialized from [AraBERT](https://huggingface.co/aubmindlab/bert-large-arabertv02)) fine-tuned on the [Algerian Dialect Sentiment Analysis](https://huggingface.co/datasets/Abdou/dz-sentiment-yt-comments) dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set:
| Model Version | No. of Parameters | Training Time | F1-Score | Accuracy |
| ------------------- | ----------------- | -------------- | -------- | -------- |
| LSTM | ~4 M | 3 min | 0.7399 | 0.7445 |
| Bi-LSTM | ~4.3 M | 6 min 35 s | 0.7380 | 0.7437 |
| [BERT Base](https://huggingface.co/bert-base-uncased) | ~109.5 M | 33 min 20 s | 0.6979 | 0.7500 |
| [BERT Large](https://huggingface.co/bert-large-uncased) | ~335.1 M | 1 h 50 min | 0.6976 | 0.7484 |
| [BERT Arabic Mini](https://huggingface.co/Abdou/arabert-mini-algerian) | ~11.6 M | 2 min 40 s | 0.7057 | 0.7527 |
| [BERT Arabic Medium](https://huggingface.co/Abdou/arabert-medium-algerian) | ~42.1 M | 11 min 25 s | 0.7521 | 0.7860 |
| [BERT Arabic Base](https://huggingface.co/Abdou/arabert-base-algerian) | ~110.6 M | 34 min 19 s | 0.7688 | 0.8002 |
| **[BERT Arabic Large](https://huggingface.co/Abdou/arabert-large-algerian)** | **~336.7 M** | **1 h 53 min** | **0.7838** | **0.8174** |
# Citation
If you find our work useful, please cite it as follows:
```bibtex
@article{2023,
title={Sentiment Analysis on Algerian Dialect with Transformers},
author={Zakaria Benmounah and Abdennour Boulesnane and Abdeladim Fadheli and Mustapha Khial},
journal={Applied Sciences},
volume={13},
number={20},
pages={11157},
year={2023},
month={Oct},
publisher={MDPI AG},
DOI={10.3390/app132011157},
ISSN={2076-3417},
url={http://dx.doi.org/10.3390/app132011157}
}
```
| {"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["Abdou/dz-sentiment-yt-comments"], "metrics": ["f1", "accuracy"]} | text-classification | Abdou/arabert-base-algerian | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ar",
"dataset:Abdou/dz-sentiment-yt-comments",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis
=============================================================
These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set:
If you find our work useful, please cite it as follows:
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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] |
null | null | transformers | # BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis
These are different BERT models (BERT Arabic models are initialized from [AraBERT](https://huggingface.co/aubmindlab/bert-large-arabertv02)) fine-tuned on the [Algerian Dialect Sentiment Analysis](https://huggingface.co/datasets/Abdou/dz-sentiment-yt-comments) dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set:
| Model Version | No. of Parameters | Training Time | F1-Score | Accuracy |
| ------------------- | ----------------- | -------------- | -------- | -------- |
| LSTM | ~4 M | 3 min | 0.7399 | 0.7445 |
| Bi-LSTM | ~4.3 M | 6 min 35 s | 0.7380 | 0.7437 |
| [BERT Base](https://huggingface.co/bert-base-uncased) | ~109.5 M | 33 min 20 s | 0.6979 | 0.7500 |
| [BERT Large](https://huggingface.co/bert-large-uncased) | ~335.1 M | 1 h 50 min | 0.6976 | 0.7484 |
| [BERT Arabic Mini](https://huggingface.co/Abdou/arabert-mini-algerian) | ~11.6 M | 2 min 40 s | 0.7057 | 0.7527 |
| [BERT Arabic Medium](https://huggingface.co/Abdou/arabert-medium-algerian) | ~42.1 M | 11 min 25 s | 0.7521 | 0.7860 |
| [BERT Arabic Base](https://huggingface.co/Abdou/arabert-base-algerian) | ~110.6 M | 34 min 19 s | 0.7688 | 0.8002 |
| **[BERT Arabic Large](https://huggingface.co/Abdou/arabert-large-algerian)** | **~336.7 M** | **1 h 53 min** | **0.7838** | **0.8174** |
# Citation
If you find our work useful, please cite it as follows:
```bibtex
@article{2023,
title={Sentiment Analysis on Algerian Dialect with Transformers},
author={Zakaria Benmounah and Abdennour Boulesnane and Abdeladim Fadheli and Mustapha Khial},
journal={Applied Sciences},
volume={13},
number={20},
pages={11157},
year={2023},
month={Oct},
publisher={MDPI AG},
DOI={10.3390/app132011157},
ISSN={2076-3417},
url={http://dx.doi.org/10.3390/app132011157}
}
```
| {"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["Abdou/dz-sentiment-yt-comments"], "metrics": ["f1", "accuracy"]} | text-classification | Abdou/arabert-large-algerian | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ar",
"dataset:Abdou/dz-sentiment-yt-comments",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis
=============================================================
These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set:
If you find our work useful, please cite it as follows:
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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] |
null | null | transformers | # BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis
These are different BERT models (BERT Arabic models are initialized from [AraBERT](https://huggingface.co/aubmindlab/bert-large-arabertv02)) fine-tuned on the [Algerian Dialect Sentiment Analysis](https://huggingface.co/datasets/Abdou/dz-sentiment-yt-comments) dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set:
| Model Version | No. of Parameters | Training Time | F1-Score | Accuracy |
| ------------------- | ----------------- | -------------- | -------- | -------- |
| LSTM | ~4 M | 3 min | 0.7399 | 0.7445 |
| Bi-LSTM | ~4.3 M | 6 min 35 s | 0.7380 | 0.7437 |
| [BERT Base](https://huggingface.co/bert-base-uncased) | ~109.5 M | 33 min 20 s | 0.6979 | 0.7500 |
| [BERT Large](https://huggingface.co/bert-large-uncased) | ~335.1 M | 1 h 50 min | 0.6976 | 0.7484 |
| [BERT Arabic Mini](https://huggingface.co/Abdou/arabert-mini-algerian) | ~11.6 M | 2 min 40 s | 0.7057 | 0.7527 |
| [BERT Arabic Medium](https://huggingface.co/Abdou/arabert-medium-algerian) | ~42.1 M | 11 min 25 s | 0.7521 | 0.7860 |
| [BERT Arabic Base](https://huggingface.co/Abdou/arabert-base-algerian) | ~110.6 M | 34 min 19 s | 0.7688 | 0.8002 |
| **[BERT Arabic Large](https://huggingface.co/Abdou/arabert-large-algerian)** | **~336.7 M** | **1 h 53 min** | **0.7838** | **0.8174** |
# Citation
If you find our work useful, please cite it as follows:
```bibtex
@article{2023,
title={Sentiment Analysis on Algerian Dialect with Transformers},
author={Zakaria Benmounah and Abdennour Boulesnane and Abdeladim Fadheli and Mustapha Khial},
journal={Applied Sciences},
volume={13},
number={20},
pages={11157},
year={2023},
month={Oct},
publisher={MDPI AG},
DOI={10.3390/app132011157},
ISSN={2076-3417},
url={http://dx.doi.org/10.3390/app132011157}
}
```
| {"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["Abdou/dz-sentiment-yt-comments"], "metrics": ["f1", "accuracy"]} | text-classification | Abdou/arabert-medium-algerian | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ar",
"dataset:Abdou/dz-sentiment-yt-comments",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis
=============================================================
These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set:
If you find our work useful, please cite it as follows:
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers | # BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis
These are different BERT models (BERT Arabic models are initialized from [AraBERT](https://huggingface.co/aubmindlab/bert-large-arabertv02)) fine-tuned on the [Algerian Dialect Sentiment Analysis](https://huggingface.co/datasets/Abdou/dz-sentiment-yt-comments) dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set:
| Model Version | No. of Parameters | Training Time | F1-Score | Accuracy |
| ------------------- | ----------------- | -------------- | -------- | -------- |
| LSTM | ~4 M | 3 min | 0.7399 | 0.7445 |
| Bi-LSTM | ~4.3 M | 6 min 35 s | 0.7380 | 0.7437 |
| [BERT Base](https://huggingface.co/bert-base-uncased) | ~109.5 M | 33 min 20 s | 0.6979 | 0.7500 |
| [BERT Large](https://huggingface.co/bert-large-uncased) | ~335.1 M | 1 h 50 min | 0.6976 | 0.7484 |
| [BERT Arabic Mini](https://huggingface.co/Abdou/arabert-mini-algerian) | ~11.6 M | 2 min 40 s | 0.7057 | 0.7527 |
| [BERT Arabic Medium](https://huggingface.co/Abdou/arabert-medium-algerian) | ~42.1 M | 11 min 25 s | 0.7521 | 0.7860 |
| [BERT Arabic Base](https://huggingface.co/Abdou/arabert-base-algerian) | ~110.6 M | 34 min 19 s | 0.7688 | 0.8002 |
| **[BERT Arabic Large](https://huggingface.co/Abdou/arabert-large-algerian)** | **~336.7 M** | **1 h 53 min** | **0.7838** | **0.8174** |
# Citation
If you find our work useful, please cite it as follows:
```bibtex
@article{2023,
title={Sentiment Analysis on Algerian Dialect with Transformers},
author={Zakaria Benmounah and Abdennour Boulesnane and Abdeladim Fadheli and Mustapha Khial},
journal={Applied Sciences},
volume={13},
number={20},
pages={11157},
year={2023},
month={Oct},
publisher={MDPI AG},
DOI={10.3390/app132011157},
ISSN={2076-3417},
url={http://dx.doi.org/10.3390/app132011157}
}
```
| {"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["Abdou/dz-sentiment-yt-comments"], "metrics": ["f1", "accuracy"]} | text-classification | Abdou/arabert-mini-algerian | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ar",
"dataset:Abdou/dz-sentiment-yt-comments",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis
=============================================================
These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set:
If you find our work useful, please cite it as follows:
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | null | Model details available [here](https://github.com/awasthiabhijeet/PIE) | {} | null | AbhijeetA/PIE | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| Model details available here | [] | [
"TAGS\n#region-us \n"
] | [
6
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null | null | transformers |
#HarryPotter DialoGPT Model | {"tags": ["conversational"]} | text-generation | AbhinavSaiTheGreat/DialoGPT-small-harrypotter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#HarryPotter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
## Petrained Model BERT: base model (cased)
BERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this [paper](https://arxiv.org/abs/1810.04805) and first released in this [repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference between english and English.
## Pretained Model Description
BERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives:
- Masked language modeling (MLM)
- Next sentence prediction (NSP)
## Fine-tuned Model Description: BERT fine-tuned Cola
The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK.
By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable
## How to use ?
###### Directly with a pipeline for a text-classification NLP task
```python
from transformers import pipeline
cola = pipeline('text-classification', model='Abirate/bert_fine_tuned_cola')
cola("Tunisia is a beautiful country")
[{'label': 'acceptable', 'score': 0.989352285861969}]
```
###### Breaking down all the steps (Tokenization, Modeling, Postprocessing)
```python
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
import tensorflow as tf
import numpy as np
tokenizer = AutoTokenizer.from_pretrained('Abirate/bert_fine_tuned_cola')
model = TFAutoModelForSequenceClassification.from_pretrained("Abirate/bert_fine_tuned_cola")
text = "Tunisia is a beautiful country."
encoded_input = tokenizer(text, return_tensors='tf')
#The logits
output = model(encoded_input)
#Postprocessing
probas_output = tf.math.softmax(tf.squeeze(output['logits']), axis = -1)
class_preds = np.argmax(probas_output, axis = -1)
#Predicting the class acceptable or not acceptable
model.config.id2label[class_preds]
#Result
'acceptable'
``` | {} | text-classification | Abirate/bert_fine_tuned_cola | [
"transformers",
"tf",
"bert",
"text-classification",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #tf #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Petrained Model BERT: base model (cased)
BERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English.
## Pretained Model Description
BERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives:
- Masked language modeling (MLM)
- Next sentence prediction (NSP)
## Fine-tuned Model Description: BERT fine-tuned Cola
The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK.
By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable
## How to use ?
###### Directly with a pipeline for a text-classification NLP task
###### Breaking down all the steps (Tokenization, Modeling, Postprocessing)
| [
"## Petrained Model BERT: base model (cased)\nBERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English.",
"## Pretained Model Description\nBERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives:\n\n- Masked language modeling (MLM)\n- Next sentence prediction (NSP)",
"## Fine-tuned Model Description: BERT fine-tuned Cola\nThe pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK. \n\nBy fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable",
"## How to use ?",
"###### Directly with a pipeline for a text-classification NLP task",
"###### Breaking down all the steps (Tokenization, Modeling, Postprocessing)"
] | [
"TAGS\n#transformers #tf #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Petrained Model BERT: base model (cased)\nBERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English.",
"## Pretained Model Description\nBERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives:\n\n- Masked language modeling (MLM)\n- Next sentence prediction (NSP)",
"## Fine-tuned Model Description: BERT fine-tuned Cola\nThe pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK. \n\nBy fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable",
"## How to use ?",
"###### Directly with a pipeline for a text-classification NLP task",
"###### Breaking down all the steps (Tokenization, Modeling, Postprocessing)"
] | [
48,
76,
96,
126,
5,
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] | [
"passage: TAGS\n#transformers #tf #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Petrained Model BERT: base model (cased)\nBERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English.## Pretained Model Description\nBERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives:\n\n- Masked language modeling (MLM)\n- Next sentence prediction (NSP)## Fine-tuned Model Description: BERT fine-tuned Cola\nThe pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK. \n\nBy fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable## How to use ?###### Directly with a pipeline for a text-classification NLP task###### Breaking down all the steps (Tokenization, Modeling, Postprocessing)"
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null | null | transformers |
# jeff's 100% authorized brain scan | {"tags": ["conversational"]} | text-generation | AccurateIsaiah/DialoGPT-small-jefftastic | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# jeff's 100% authorized brain scan | [
"# jeff's 100% authorized brain scan"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# jeff's 100% authorized brain scan"
] | [
51,
10
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# jeff's 100% authorized brain scan"
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null | null | transformers |
# Mozark's Brain Uploaded to Hugging Face | {"tags": ["conversational"]} | text-generation | AccurateIsaiah/DialoGPT-small-mozark | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mozark's Brain Uploaded to Hugging Face | [
"# Mozark's Brain Uploaded to Hugging Face"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mozark's Brain Uploaded to Hugging Face"
] | [
51,
13
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mozark's Brain Uploaded to Hugging Face"
] | [
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null | null | transformers |
# Mozark's Brain Uploaded to Hugging Face but v2 | {"tags": ["conversational"]} | text-generation | AccurateIsaiah/DialoGPT-small-mozarkv2 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mozark's Brain Uploaded to Hugging Face but v2 | [
"# Mozark's Brain Uploaded to Hugging Face but v2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mozark's Brain Uploaded to Hugging Face but v2"
] | [
51,
16
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mozark's Brain Uploaded to Hugging Face but v2"
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null | null | transformers |
# Un Filtered brain upload of sinclair | {"tags": ["conversational"]} | text-generation | AccurateIsaiah/DialoGPT-small-sinclair | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Un Filtered brain upload of sinclair | [
"# Un Filtered brain upload of sinclair"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Un Filtered brain upload of sinclair"
] | [
51,
9
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Un Filtered brain upload of sinclair"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2128
- Accuracy: 0.928
- F1: 0.9280
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8151 | 1.0 | 250 | 0.3043 | 0.907 | 0.9035 |
| 0.24 | 2.0 | 500 | 0.2128 | 0.928 | 0.9280 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.928, "name": "Accuracy"}, {"type": "f1", "value": 0.9280065074208208, "name": "F1"}]}]}]} | text-classification | ActivationAI/distilbert-base-uncased-finetuned-emotion | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2128
* Accuracy: 0.928
* F1: 0.9280
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] | [
67,
98,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-anli_r3` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [anli](https://huggingface.co/datasets/anli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-anli_r3", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["anli"]} | text-classification | AdapterHub/bert-base-uncased-pf-anli_r3 | [
"adapter-transformers",
"bert",
"text-classification",
"en",
"dataset:anli",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #en #dataset-anli #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-anli_r3' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the anli dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-anli_r3' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the anli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #en #dataset-anli #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-anli_r3' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the anli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
34,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #en #dataset-anli #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-anli_r3' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the anli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-art` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [art](https://huggingface.co/datasets/art/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-art", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["art"]} | null | AdapterHub/bert-base-uncased-pf-art | [
"adapter-transformers",
"bert",
"en",
"dataset:art",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #en #dataset-art #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-art' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the art dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-art' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the art dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #en #dataset-art #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-art' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the art dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
28,
78,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #en #dataset-art #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-art' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the art dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-boolq` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/boolq](https://adapterhub.ml/explore/qa/boolq/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-boolq", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:qa/boolq", "adapter-transformers"], "datasets": ["boolq"]} | text-classification | AdapterHub/bert-base-uncased-pf-boolq | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:qa/boolq",
"en",
"dataset:boolq",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-qa/boolq #en #dataset-boolq #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-boolq' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the qa/boolq dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-boolq' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/boolq dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-qa/boolq #en #dataset-boolq #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-boolq' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/boolq dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
42,
82,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-qa/boolq #en #dataset-boolq #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-boolq' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/boolq dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-cola` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [lingaccept/cola](https://adapterhub.ml/explore/lingaccept/cola/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-cola", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:lingaccept/cola", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-cola | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:lingaccept/cola",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-lingaccept/cola #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-cola' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-cola' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-lingaccept/cola #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-cola' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
82,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-lingaccept/cola #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-cola' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-commonsense_qa` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/csqa](https://adapterhub.ml/explore/comsense/csqa/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-commonsense_qa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapterhub:comsense/csqa", "adapter-transformers"], "datasets": ["commonsense_qa"]} | null | AdapterHub/bert-base-uncased-pf-commonsense_qa | [
"adapter-transformers",
"bert",
"adapterhub:comsense/csqa",
"en",
"dataset:commonsense_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #adapterhub-comsense/csqa #en #dataset-commonsense_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-commonsense_qa' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-commonsense_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #adapterhub-comsense/csqa #en #dataset-commonsense_qa #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-commonsense_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
41,
86,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #adapterhub-comsense/csqa #en #dataset-commonsense_qa #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-commonsense_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-comqa` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [com_qa](https://huggingface.co/datasets/com_qa/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-comqa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["com_qa"]} | question-answering | AdapterHub/bert-base-uncased-pf-comqa | [
"adapter-transformers",
"bert",
"question-answering",
"en",
"dataset:com_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #en #dataset-com_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-comqa' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the com_qa dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-comqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the com_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #en #dataset-com_qa #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-comqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the com_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
36,
82,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #en #dataset-com_qa #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-comqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the com_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-conll2000` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [chunk/conll2000](https://adapterhub.ml/explore/chunk/conll2000/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-conll2000", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:chunk/conll2000", "adapter-transformers"], "datasets": ["conll2000"]} | token-classification | AdapterHub/bert-base-uncased-pf-conll2000 | [
"adapter-transformers",
"bert",
"token-classification",
"adapterhub:chunk/conll2000",
"en",
"dataset:conll2000",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #adapterhub-chunk/conll2000 #en #dataset-conll2000 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-conll2000' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-conll2000' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #adapterhub-chunk/conll2000 #en #dataset-conll2000 #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-conll2000' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
46,
85,
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45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #adapterhub-chunk/conll2000 #en #dataset-conll2000 #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-conll2000' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-conll2003` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ner/conll2003](https://adapterhub.ml/explore/ner/conll2003/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-conll2003", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:ner/conll2003", "adapter-transformers"], "datasets": ["conll2003"]} | token-classification | AdapterHub/bert-base-uncased-pf-conll2003 | [
"adapter-transformers",
"bert",
"token-classification",
"adapterhub:ner/conll2003",
"en",
"dataset:conll2003",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #adapterhub-ner/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #adapterhub-ner/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
45,
84,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #adapterhub-ner/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-conll2003_pos` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [pos/conll2003](https://adapterhub.ml/explore/pos/conll2003/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-conll2003_pos", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:pos/conll2003", "adapter-transformers"], "datasets": ["conll2003"]} | token-classification | AdapterHub/bert-base-uncased-pf-conll2003_pos | [
"adapter-transformers",
"bert",
"token-classification",
"adapterhub:pos/conll2003",
"en",
"dataset:conll2003",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #adapterhub-pos/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003_pos' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003_pos' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #adapterhub-pos/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003_pos' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
45,
86,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #adapterhub-pos/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003_pos' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-copa` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/copa](https://adapterhub.ml/explore/comsense/copa/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-copa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapterhub:comsense/copa", "adapter-transformers"]} | null | AdapterHub/bert-base-uncased-pf-copa | [
"adapter-transformers",
"bert",
"adapterhub:comsense/copa",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #adapterhub-comsense/copa #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-copa' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-copa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #adapterhub-comsense/copa #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-copa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
32,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #adapterhub-comsense/copa #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-copa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-cosmos_qa` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/cosmosqa](https://adapterhub.ml/explore/comsense/cosmosqa/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-cosmos_qa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapterhub:comsense/cosmosqa", "adapter-transformers"], "datasets": ["cosmos_qa"]} | null | AdapterHub/bert-base-uncased-pf-cosmos_qa | [
"adapter-transformers",
"bert",
"adapterhub:comsense/cosmosqa",
"en",
"dataset:cosmos_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #adapterhub-comsense/cosmosqa #en #dataset-cosmos_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-cosmos_qa' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-cosmos_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #adapterhub-comsense/cosmosqa #en #dataset-cosmos_qa #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-cosmos_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
41,
86,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #adapterhub-comsense/cosmosqa #en #dataset-cosmos_qa #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-cosmos_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-cq` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/cq](https://adapterhub.ml/explore/qa/cq/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-cq", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapterhub:qa/cq", "adapter-transformers"]} | question-answering | AdapterHub/bert-base-uncased-pf-cq | [
"adapter-transformers",
"bert",
"question-answering",
"adapterhub:qa/cq",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #adapterhub-qa/cq #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-cq' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the qa/cq dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-cq' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/cq dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #adapterhub-qa/cq #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-cq' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/cq dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #adapterhub-qa/cq #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-cq' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/cq dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-drop` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [drop](https://huggingface.co/datasets/drop/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-drop", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["drop"]} | question-answering | AdapterHub/bert-base-uncased-pf-drop | [
"adapter-transformers",
"bert",
"question-answering",
"en",
"dataset:drop",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #en #dataset-drop #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-drop' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the drop dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-drop' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the drop dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #en #dataset-drop #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-drop' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the drop dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
34,
79,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #en #dataset-drop #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-drop' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the drop dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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] |
null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-duorc_p` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-duorc_p", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["duorc"]} | question-answering | AdapterHub/bert-base-uncased-pf-duorc_p | [
"adapter-transformers",
"bert",
"question-answering",
"en",
"dataset:duorc",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_p' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_p' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_p' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
35,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_p' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-duorc_s` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-duorc_s", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["duorc"]} | question-answering | AdapterHub/bert-base-uncased-pf-duorc_s | [
"adapter-transformers",
"bert",
"question-answering",
"en",
"dataset:duorc",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_s' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_s' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_s' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
35,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_s' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-emo` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [emo](https://huggingface.co/datasets/emo/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-emo", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["emo"]} | text-classification | AdapterHub/bert-base-uncased-pf-emo | [
"adapter-transformers",
"bert",
"text-classification",
"en",
"dataset:emo",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #en #dataset-emo #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-emo' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the emo dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-emo' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the emo dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #en #dataset-emo #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-emo' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the emo dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
33,
78,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #en #dataset-emo #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-emo' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the emo dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-emotion` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [emotion](https://huggingface.co/datasets/emotion/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-emotion", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["emotion"]} | text-classification | AdapterHub/bert-base-uncased-pf-emotion | [
"adapter-transformers",
"bert",
"text-classification",
"en",
"dataset:emotion",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #en #dataset-emotion #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-emotion' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the emotion dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-emotion' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the emotion dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #en #dataset-emotion #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-emotion' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the emotion dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
34,
79,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #en #dataset-emotion #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-emotion' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the emotion dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-fce_error_detection` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ged/fce](https://adapterhub.ml/explore/ged/fce/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-fce_error_detection", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:ged/fce", "adapter-transformers"], "datasets": ["fce_error_detection"]} | token-classification | AdapterHub/bert-base-uncased-pf-fce_error_detection | [
"adapter-transformers",
"bert",
"token-classification",
"adapterhub:ged/fce",
"en",
"dataset:fce_error_detection",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #adapterhub-ged/fce #en #dataset-fce_error_detection #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-fce_error_detection' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the ged/fce dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-fce_error_detection' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ged/fce dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #adapterhub-ged/fce #en #dataset-fce_error_detection #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-fce_error_detection' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ged/fce dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
48,
87,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #adapterhub-ged/fce #en #dataset-fce_error_detection #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-fce_error_detection' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ged/fce dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-hellaswag` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/hellaswag](https://adapterhub.ml/explore/comsense/hellaswag/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-hellaswag", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapterhub:comsense/hellaswag", "adapter-transformers"], "datasets": ["hellaswag"]} | null | AdapterHub/bert-base-uncased-pf-hellaswag | [
"adapter-transformers",
"bert",
"adapterhub:comsense/hellaswag",
"en",
"dataset:hellaswag",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #adapterhub-comsense/hellaswag #en #dataset-hellaswag #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-hellaswag' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-hellaswag' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #adapterhub-comsense/hellaswag #en #dataset-hellaswag #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-hellaswag' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
40,
85,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #adapterhub-comsense/hellaswag #en #dataset-hellaswag #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-hellaswag' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-hotpotqa` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [hotpot_qa](https://huggingface.co/datasets/hotpot_qa/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-hotpotqa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["hotpot_qa"]} | question-answering | AdapterHub/bert-base-uncased-pf-hotpotqa | [
"adapter-transformers",
"bert",
"question-answering",
"en",
"dataset:hotpot_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #en #dataset-hotpot_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-hotpotqa' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the hotpot_qa dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-hotpotqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the hotpot_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #en #dataset-hotpot_qa #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-hotpotqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the hotpot_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
84,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #en #dataset-hotpot_qa #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-hotpotqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the hotpot_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-imdb` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/imdb](https://adapterhub.ml/explore/sentiment/imdb/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-imdb", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sentiment/imdb", "adapter-transformers"], "datasets": ["imdb"]} | text-classification | AdapterHub/bert-base-uncased-pf-imdb | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:sentiment/imdb",
"en",
"dataset:imdb",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-sentiment/imdb #en #dataset-imdb #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-imdb' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the sentiment/imdb dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-imdb' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/imdb dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sentiment/imdb #en #dataset-imdb #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-imdb' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/imdb dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
43,
82,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sentiment/imdb #en #dataset-imdb #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-imdb' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/imdb dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-mit_movie_trivia` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ner/mit_movie_trivia](https://adapterhub.ml/explore/ner/mit_movie_trivia/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mit_movie_trivia", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:ner/mit_movie_trivia", "adapter-transformers"]} | token-classification | AdapterHub/bert-base-uncased-pf-mit_movie_trivia | [
"adapter-transformers",
"bert",
"token-classification",
"adapterhub:ner/mit_movie_trivia",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #adapterhub-ner/mit_movie_trivia #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-mit_movie_trivia' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-mit_movie_trivia' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #adapterhub-ner/mit_movie_trivia #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-mit_movie_trivia' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
41,
90,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #adapterhub-ner/mit_movie_trivia #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-mit_movie_trivia' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-mnli` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mnli", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:nli/multinli", "adapter-transformers"], "datasets": ["multi_nli"]} | text-classification | AdapterHub/bert-base-uncased-pf-mnli | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:nli/multinli",
"en",
"dataset:multi_nli",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-nli/multinli #en #dataset-multi_nli #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-mnli' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the nli/multinli dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-mnli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/multinli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/multinli #en #dataset-multi_nli #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-mnli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/multinli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
46,
84,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/multinli #en #dataset-multi_nli #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-mnli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/multinli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-mrpc` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sts/mrpc](https://adapterhub.ml/explore/sts/mrpc/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mrpc", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sts/mrpc", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-mrpc | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:sts/mrpc",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-sts/mrpc #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-mrpc' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the sts/mrpc dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-mrpc' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/mrpc dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sts/mrpc #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-mrpc' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/mrpc dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sts/mrpc #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-mrpc' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/mrpc dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-multirc` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [rc/multirc](https://adapterhub.ml/explore/rc/multirc/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-multirc", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "adapterhub:rc/multirc", "bert", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-multirc | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:rc/multirc",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-rc/multirc #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-multirc' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the rc/multirc dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-multirc' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/multirc dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-rc/multirc #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-multirc' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/multirc dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
36,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-rc/multirc #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-multirc' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/multirc dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-newsqa` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [newsqa](https://huggingface.co/datasets/newsqa/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-newsqa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["newsqa"]} | question-answering | AdapterHub/bert-base-uncased-pf-newsqa | [
"adapter-transformers",
"bert",
"question-answering",
"en",
"dataset:newsqa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #en #dataset-newsqa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-newsqa' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the newsqa dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-newsqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the newsqa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #en #dataset-newsqa #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-newsqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the newsqa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
35,
81,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #en #dataset-newsqa #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-newsqa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the newsqa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-pmb_sem_tagging` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [semtag/pmb](https://adapterhub.ml/explore/semtag/pmb/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-pmb_sem_tagging", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:semtag/pmb", "adapter-transformers"]} | token-classification | AdapterHub/bert-base-uncased-pf-pmb_sem_tagging | [
"adapter-transformers",
"bert",
"token-classification",
"adapterhub:semtag/pmb",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #adapterhub-semtag/pmb #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-pmb_sem_tagging' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the semtag/pmb dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-pmb_sem_tagging' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the semtag/pmb dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #adapterhub-semtag/pmb #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-pmb_sem_tagging' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the semtag/pmb dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
38,
88,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #adapterhub-semtag/pmb #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-pmb_sem_tagging' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the semtag/pmb dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-qnli` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/qnli](https://adapterhub.ml/explore/nli/qnli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-qnli", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:nli/qnli", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-qnli | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:nli/qnli",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-nli/qnli #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-qnli' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the nli/qnli dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-qnli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/qnli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/qnli #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-qnli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/qnli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
38,
85,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/qnli #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-qnli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/qnli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-qqp` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sts/qqp](https://adapterhub.ml/explore/sts/qqp/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-qqp", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "adapter-transformers", "adapterhub:sts/qqp", "bert"]} | text-classification | AdapterHub/bert-base-uncased-pf-qqp | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:sts/qqp",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-sts/qqp #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-qqp' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the sts/qqp dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-qqp' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/qqp dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sts/qqp #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-qqp' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/qqp dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sts/qqp #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-qqp' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/qqp dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-quail` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [quail](https://huggingface.co/datasets/quail/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-quail", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["quail"]} | null | AdapterHub/bert-base-uncased-pf-quail | [
"adapter-transformers",
"bert",
"en",
"dataset:quail",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #en #dataset-quail #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-quail' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the quail dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-quail' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quail dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #en #dataset-quail #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-quail' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quail dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
29,
80,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #en #dataset-quail #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-quail' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quail dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-quartz` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [quartz](https://huggingface.co/datasets/quartz/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-quartz", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["quartz"]} | null | AdapterHub/bert-base-uncased-pf-quartz | [
"adapter-transformers",
"bert",
"en",
"dataset:quartz",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #en #dataset-quartz #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the quartz dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quartz dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #en #dataset-quartz #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quartz dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
29,
80,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #en #dataset-quartz #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quartz dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-quoref` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [quoref](https://huggingface.co/datasets/quoref/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-quoref", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["quoref"]} | question-answering | AdapterHub/bert-base-uncased-pf-quoref | [
"adapter-transformers",
"bert",
"question-answering",
"en",
"dataset:quoref",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #en #dataset-quoref #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-quoref' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the quoref dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-quoref' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quoref dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #en #dataset-quoref #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-quoref' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quoref dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
35,
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"passage: TAGS\n#adapter-transformers #bert #question-answering #en #dataset-quoref #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-quoref' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the quoref dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-race` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [rc/race](https://adapterhub.ml/explore/rc/race/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-race", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["adapterhub:rc/race", "bert", "adapter-transformers"], "datasets": ["race"]} | null | AdapterHub/bert-base-uncased-pf-race | [
"adapter-transformers",
"bert",
"adapterhub:rc/race",
"en",
"dataset:race",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #adapterhub-rc/race #en #dataset-race #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-race' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the rc/race dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-race' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/race dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #adapterhub-rc/race #en #dataset-race #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-race' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/race dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
35,
81,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #adapterhub-rc/race #en #dataset-race #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-race' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/race dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-record` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [rc/record](https://adapterhub.ml/explore/rc/record/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-record", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:rc/record", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-record | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:rc/record",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-rc/record #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-record' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the rc/record dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-record' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/record dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-rc/record #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-record' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/record dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
36,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-rc/record #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-record' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the rc/record dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-rotten_tomatoes` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/rotten_tomatoes](https://adapterhub.ml/explore/sentiment/rotten_tomatoes/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-rotten_tomatoes", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sentiment/rotten_tomatoes", "adapter-transformers"], "datasets": ["rotten_tomatoes"]} | text-classification | AdapterHub/bert-base-uncased-pf-rotten_tomatoes | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:sentiment/rotten_tomatoes",
"en",
"dataset:rotten_tomatoes",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-sentiment/rotten_tomatoes #en #dataset-rotten_tomatoes #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-rotten_tomatoes' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-rotten_tomatoes' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sentiment/rotten_tomatoes #en #dataset-rotten_tomatoes #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-rotten_tomatoes' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
51,
90,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sentiment/rotten_tomatoes #en #dataset-rotten_tomatoes #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-rotten_tomatoes' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-rte` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/rte](https://adapterhub.ml/explore/nli/rte/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-rte", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:nli/rte", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-rte | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:nli/rte",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-nli/rte #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-rte' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the nli/rte dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-rte' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/rte dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/rte #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-rte' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/rte dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
36,
81,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/rte #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-rte' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/rte dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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] |
null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-scicite` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [scicite](https://huggingface.co/datasets/scicite/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-scicite", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["scicite"]} | text-classification | AdapterHub/bert-base-uncased-pf-scicite | [
"adapter-transformers",
"bert",
"text-classification",
"en",
"dataset:scicite",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #en #dataset-scicite #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-scicite' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the scicite dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-scicite' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the scicite dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #en #dataset-scicite #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-scicite' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the scicite dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
34,
80,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #en #dataset-scicite #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-scicite' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the scicite dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-scitail` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/scitail](https://adapterhub.ml/explore/nli/scitail/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-scitail", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:nli/scitail", "adapter-transformers"], "datasets": ["scitail"]} | text-classification | AdapterHub/bert-base-uncased-pf-scitail | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:nli/scitail",
"en",
"dataset:scitail",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-nli/scitail #en #dataset-scitail #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-scitail' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the nli/scitail dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-scitail' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/scitail dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/scitail #en #dataset-scitail #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-scitail' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/scitail dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
43,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/scitail #en #dataset-scitail #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-scitail' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/scitail dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-sick` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/sick](https://adapterhub.ml/explore/nli/sick/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-sick", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "adapter-transformers", "bert", "adapterhub:nli/sick"], "datasets": ["sick"]} | text-classification | AdapterHub/bert-base-uncased-pf-sick | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:nli/sick",
"en",
"dataset:sick",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-nli/sick #en #dataset-sick #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-sick' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the nli/sick dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-sick' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/sick dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/sick #en #dataset-sick #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-sick' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/sick dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
43,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-nli/sick #en #dataset-sick #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-sick' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the nli/sick dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-snli` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [snli](https://huggingface.co/datasets/snli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-snli", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["snli"]} | text-classification | AdapterHub/bert-base-uncased-pf-snli | [
"adapter-transformers",
"bert",
"text-classification",
"en",
"dataset:snli",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #en #dataset-snli #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-snli' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the snli dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-snli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the snli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #en #dataset-snli #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-snli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the snli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
35,
82,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #en #dataset-snli #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-snli' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the snli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-social_i_qa` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [social_i_qa](https://huggingface.co/datasets/social_i_qa/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-social_i_qa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["social_i_qa"]} | null | AdapterHub/bert-base-uncased-pf-social_i_qa | [
"adapter-transformers",
"bert",
"en",
"dataset:social_i_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #en #dataset-social_i_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-social_i_qa' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-social_i_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #en #dataset-social_i_qa #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-social_i_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
32,
86,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #en #dataset-social_i_qa #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-social_i_qa' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-squad` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/squad1](https://adapterhub.ml/explore/qa/squad1/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-squad", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapterhub:qa/squad1", "adapter-transformers"], "datasets": ["squad"]} | question-answering | AdapterHub/bert-base-uncased-pf-squad | [
"adapter-transformers",
"bert",
"question-answering",
"adapterhub:qa/squad1",
"en",
"dataset:squad",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #adapterhub-qa/squad1 #en #dataset-squad #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-squad' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the qa/squad1 dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-squad' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/squad1 dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #adapterhub-qa/squad1 #en #dataset-squad #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-squad' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/squad1 dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
44,
84,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #adapterhub-qa/squad1 #en #dataset-squad #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-squad' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/squad1 dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-squad_v2` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/squad2](https://adapterhub.ml/explore/qa/squad2/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-squad_v2", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapterhub:qa/squad2", "adapter-transformers"], "datasets": ["squad_v2"]} | question-answering | AdapterHub/bert-base-uncased-pf-squad_v2 | [
"adapter-transformers",
"bert",
"question-answering",
"adapterhub:qa/squad2",
"en",
"dataset:squad_v2",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #adapterhub-qa/squad2 #en #dataset-squad_v2 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-squad_v2' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the qa/squad2 dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-squad_v2' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/squad2 dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #adapterhub-qa/squad2 #en #dataset-squad_v2 #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-squad_v2' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/squad2 dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
47,
87,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #adapterhub-qa/squad2 #en #dataset-squad_v2 #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-squad_v2' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/squad2 dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-sst2` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/sst-2](https://adapterhub.ml/explore/sentiment/sst-2/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-sst2", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sentiment/sst-2", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-sst2 | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:sentiment/sst-2",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-sentiment/sst-2 #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-sst2' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-sst2' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sentiment/sst-2 #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-sst2' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
38,
84,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sentiment/sst-2 #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-sst2' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-stsb` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sts/sts-b](https://adapterhub.ml/explore/sts/sts-b/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-stsb", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sts/sts-b", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-stsb | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:sts/sts-b",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-sts/sts-b #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-stsb' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the sts/sts-b dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-stsb' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/sts-b dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sts/sts-b #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-stsb' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/sts-b dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
39,
86,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-sts/sts-b #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-stsb' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the sts/sts-b dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-swag` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [swag](https://huggingface.co/datasets/swag/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-swag", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["swag"]} | null | AdapterHub/bert-base-uncased-pf-swag | [
"adapter-transformers",
"bert",
"en",
"dataset:swag",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #en #dataset-swag #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-swag' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the swag dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-swag' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the swag dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #en #dataset-swag #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-swag' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the swag dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
29,
80,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #en #dataset-swag #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-swag' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the swag dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-trec` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [trec](https://huggingface.co/datasets/trec/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-trec", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["trec"]} | text-classification | AdapterHub/bert-base-uncased-pf-trec | [
"adapter-transformers",
"bert",
"text-classification",
"en",
"dataset:trec",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #en #dataset-trec #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-trec' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the trec dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-trec' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the trec dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #en #dataset-trec #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-trec' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the trec dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
34,
79,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #en #dataset-trec #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-trec' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the trec dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-ud_deprel` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [deprel/ud_ewt](https://adapterhub.ml/explore/deprel/ud_ewt/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-ud_deprel", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:deprel/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]} | token-classification | AdapterHub/bert-base-uncased-pf-ud_deprel | [
"adapter-transformers",
"bert",
"token-classification",
"adapterhub:deprel/ud_ewt",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #adapterhub-deprel/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-ud_deprel' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-ud_deprel' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #adapterhub-deprel/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-ud_deprel' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
51,
89,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #adapterhub-deprel/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-ud_deprel' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-ud_en_ewt` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [dp/ud_ewt](https://adapterhub.ml/explore/dp/ud_ewt/) dataset and includes a prediction head for dependency parsing.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-ud_en_ewt", source="hf", set_active=True)
```
## Architecture & Training
This adapter was trained using adapter-transformer's example script for dependency parsing.
See https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples/dependency-parsing.
## Evaluation results
Scores achieved by dependency parsing adapters on the test set of UD English EWT after training:
| Model | UAS | LAS |
| --- | --- | --- |
| `bert-base-uncased` | 91.74 | 89.15 |
| `roberta-base` | 91.43 | 88.43 |
## Citation
<!-- Add some description here --> | {"language": ["en"], "tags": ["bert", "adapterhub:dp/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]} | null | AdapterHub/bert-base-uncased-pf-ud_en_ewt | [
"adapter-transformers",
"bert",
"adapterhub:dp/ud_ewt",
"en",
"dataset:universal_dependencies",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#adapter-transformers #bert #adapterhub-dp/ud_ewt #en #dataset-universal_dependencies #region-us
| Adapter 'AdapterHub/bert-base-uncased-pf-ud\_en\_ewt' for bert-base-uncased
===========================================================================
An adapter for the 'bert-base-uncased' model that was trained on the dp/ud\_ewt dataset and includes a prediction head for dependency parsing.
This adapter was created for usage with the adapter-transformers library.
Usage
-----
First, install 'adapter-transformers':
*Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More*
Now, the adapter can be loaded and activated like this:
Architecture & Training
-----------------------
This adapter was trained using adapter-transformer's example script for dependency parsing.
See URL
Evaluation results
------------------
Scores achieved by dependency parsing adapters on the test set of UD English EWT after training:
Model: 'bert-base-uncased', UAS: 91.74, LAS: 89.15
Model: 'roberta-base', UAS: 91.43, LAS: 88.43
| [] | [
"TAGS\n#adapter-transformers #bert #adapterhub-dp/ud_ewt #en #dataset-universal_dependencies #region-us \n"
] | [
36
] | [
"passage: TAGS\n#adapter-transformers #bert #adapterhub-dp/ud_ewt #en #dataset-universal_dependencies #region-us \n"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-ud_pos` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [pos/ud_ewt](https://adapterhub.ml/explore/pos/ud_ewt/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-ud_pos", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:pos/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]} | token-classification | AdapterHub/bert-base-uncased-pf-ud_pos | [
"adapter-transformers",
"bert",
"token-classification",
"adapterhub:pos/ud_ewt",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #adapterhub-pos/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-ud_pos' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-ud_pos' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #adapterhub-pos/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-ud_pos' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
49,
85,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #adapterhub-pos/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-ud_pos' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-wic` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [wordsence/wic](https://adapterhub.ml/explore/wordsence/wic/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-wic", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:wordsence/wic", "adapter-transformers"]} | text-classification | AdapterHub/bert-base-uncased-pf-wic | [
"adapter-transformers",
"bert",
"text-classification",
"adapterhub:wordsence/wic",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #adapterhub-wordsence/wic #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-wic' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the wordsence/wic dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-wic' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the wordsence/wic dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #adapterhub-wordsence/wic #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-wic' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the wordsence/wic dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
81,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #adapterhub-wordsence/wic #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-wic' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the wordsence/wic dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-wikihop` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/wikihop](https://adapterhub.ml/explore/qa/wikihop/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-wikihop", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "bert", "adapterhub:qa/wikihop", "adapter-transformers"]} | question-answering | AdapterHub/bert-base-uncased-pf-wikihop | [
"adapter-transformers",
"bert",
"question-answering",
"adapterhub:qa/wikihop",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #question-answering #adapterhub-qa/wikihop #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-wikihop' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the qa/wikihop dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-wikihop' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/wikihop dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #question-answering #adapterhub-qa/wikihop #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-wikihop' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/wikihop dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
83,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #question-answering #adapterhub-qa/wikihop #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-wikihop' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the qa/wikihop dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-winogrande` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/winogrande](https://adapterhub.ml/explore/comsense/winogrande/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-winogrande", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["bert", "adapterhub:comsense/winogrande", "adapter-transformers"], "datasets": ["winogrande"]} | null | AdapterHub/bert-base-uncased-pf-winogrande | [
"adapter-transformers",
"bert",
"adapterhub:comsense/winogrande",
"en",
"dataset:winogrande",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #adapterhub-comsense/winogrande #en #dataset-winogrande #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-winogrande' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-winogrande' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #adapterhub-comsense/winogrande #en #dataset-winogrande #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-winogrande' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
40,
85,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #adapterhub-comsense/winogrande #en #dataset-winogrande #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-winogrande' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-wnut_17` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [wnut_17](https://huggingface.co/datasets/wnut_17/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-wnut_17", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "bert", "adapter-transformers"], "datasets": ["wnut_17"]} | token-classification | AdapterHub/bert-base-uncased-pf-wnut_17 | [
"adapter-transformers",
"bert",
"token-classification",
"en",
"dataset:wnut_17",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #token-classification #en #dataset-wnut_17 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-wnut_17' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the wnut_17 dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-wnut_17' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the wnut_17 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #token-classification #en #dataset-wnut_17 #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-wnut_17' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the wnut_17 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
84,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #token-classification #en #dataset-wnut_17 #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-wnut_17' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the wnut_17 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/bert-base-uncased-pf-yelp_polarity` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [yelp_polarity](https://huggingface.co/datasets/yelp_polarity/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-yelp_polarity", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["yelp_polarity"]} | text-classification | AdapterHub/bert-base-uncased-pf-yelp_polarity | [
"adapter-transformers",
"bert",
"text-classification",
"en",
"dataset:yelp_polarity",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #bert #text-classification #en #dataset-yelp_polarity #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/bert-base-uncased-pf-yelp_polarity' for bert-base-uncased
An adapter for the 'bert-base-uncased' model that was trained on the yelp_polarity dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/bert-base-uncased-pf-yelp_polarity' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the yelp_polarity dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #bert #text-classification #en #dataset-yelp_polarity #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/bert-base-uncased-pf-yelp_polarity' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the yelp_polarity dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
38,
88,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #bert #text-classification #en #dataset-yelp_polarity #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/bert-base-uncased-pf-yelp_polarity' for bert-base-uncased\n\nAn adapter for the 'bert-base-uncased' model that was trained on the yelp_polarity dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/bioASQyesno` for facebook/bart-base
An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [qa/bioasq](https://adapterhub.ml/explore/qa/bioasq/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("facebook/bart-base")
adapter_name = model.load_adapter("AdapterHub/bioASQyesno", source="hf", set_active=True)
```
## Architecture & Training
Trained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset.
## Evaluation results
Achieved 75% accuracy on the test dataset of bioASQ 8b dataset.
## Citation
<!-- Add some description here --> | {"tags": ["adapterhub:qa/bioasq", "adapter-transformers", "bart"]} | null | AdapterHub/bioASQyesno | [
"adapter-transformers",
"bart",
"adapterhub:qa/bioasq",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#adapter-transformers #bart #adapterhub-qa/bioasq #region-us
|
# Adapter 'AdapterHub/bioASQyesno' for facebook/bart-base
An adapter for the 'facebook/bart-base' model that was trained on the qa/bioasq dataset.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
Trained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset.
## Evaluation results
Achieved 75% accuracy on the test dataset of bioASQ 8b dataset.
| [
"# Adapter 'AdapterHub/bioASQyesno' for facebook/bart-base\n\nAn adapter for the 'facebook/bart-base' model that was trained on the qa/bioasq dataset.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nTrained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset.",
"## Evaluation results\n\nAchieved 75% accuracy on the test dataset of bioASQ 8b dataset."
] | [
"TAGS\n#adapter-transformers #bart #adapterhub-qa/bioasq #region-us \n",
"# Adapter 'AdapterHub/bioASQyesno' for facebook/bart-base\n\nAn adapter for the 'facebook/bart-base' model that was trained on the qa/bioasq dataset.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nTrained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset.",
"## Evaluation results\n\nAchieved 75% accuracy on the test dataset of bioASQ 8b dataset."
] | [
22,
62,
57,
49,
25
] | [
"passage: TAGS\n#adapter-transformers #bart #adapterhub-qa/bioasq #region-us \n# Adapter 'AdapterHub/bioASQyesno' for facebook/bart-base\n\nAn adapter for the 'facebook/bart-base' model that was trained on the qa/bioasq dataset.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nTrained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset.## Evaluation results\n\nAchieved 75% accuracy on the test dataset of bioASQ 8b dataset."
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null | null | adapter-transformers |
# Adapter `hSterz/narrativeqa` for facebook/bart-base
An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [qa/narrativeqa](https://adapterhub.ml/explore/qa/narrativeqa/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("facebook/bart-base")
adapter_name = model.load_adapter("hSterz/narrativeqa", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapterhub:qa/narrativeqa", "adapter-transformers", "bart"], "datasets": ["narrativeqa"]} | null | AdapterHub/narrativeqa | [
"adapter-transformers",
"bart",
"adapterhub:qa/narrativeqa",
"dataset:narrativeqa",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#adapter-transformers #bart #adapterhub-qa/narrativeqa #dataset-narrativeqa #region-us
|
# Adapter 'hSterz/narrativeqa' for facebook/bart-base
An adapter for the 'facebook/bart-base' model that was trained on the qa/narrativeqa dataset.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'hSterz/narrativeqa' for facebook/bart-base\n\nAn adapter for the 'facebook/bart-base' model that was trained on the qa/narrativeqa dataset.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #bart #adapterhub-qa/narrativeqa #dataset-narrativeqa #region-us \n",
"# Adapter 'hSterz/narrativeqa' for facebook/bart-base\n\nAn adapter for the 'facebook/bart-base' model that was trained on the qa/narrativeqa dataset.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
31,
61,
57,
5,
4
] | [
"passage: TAGS\n#adapter-transformers #bart #adapterhub-qa/narrativeqa #dataset-narrativeqa #region-us \n# Adapter 'hSterz/narrativeqa' for facebook/bart-base\n\nAn adapter for the 'facebook/bart-base' model that was trained on the qa/narrativeqa dataset.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training## Evaluation results"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-anli_r3` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [anli](https://huggingface.co/datasets/anli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-anli_r3", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["anli"]} | text-classification | AdapterHub/roberta-base-pf-anli_r3 | [
"adapter-transformers",
"roberta",
"text-classification",
"en",
"dataset:anli",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #en #dataset-anli #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-anli_r3' for roberta-base
An adapter for the 'roberta-base' model that was trained on the anli dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/roberta-base-pf-anli_r3' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the anli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #roberta #text-classification #en #dataset-anli #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-anli_r3' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the anli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
35,
73,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #roberta #text-classification #en #dataset-anli #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-anli_r3' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the anli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-art` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [art](https://huggingface.co/datasets/art/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-art", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["art"]} | null | AdapterHub/roberta-base-pf-art | [
"adapter-transformers",
"roberta",
"en",
"dataset:art",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #en #dataset-art #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-art' for roberta-base
An adapter for the 'roberta-base' model that was trained on the art dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/roberta-base-pf-art' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the art dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #roberta #en #dataset-art #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-art' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the art dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
29,
68,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #roberta #en #dataset-art #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-art' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the art dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-boolq` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/boolq](https://adapterhub.ml/explore/qa/boolq/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-boolq", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:qa/boolq", "adapter-transformers"], "datasets": ["boolq"]} | text-classification | AdapterHub/roberta-base-pf-boolq | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:qa/boolq",
"en",
"dataset:boolq",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-qa/boolq #en #dataset-boolq #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-boolq' for roberta-base
An adapter for the 'roberta-base' model that was trained on the qa/boolq dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/roberta-base-pf-boolq' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the qa/boolq dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #roberta #text-classification #adapterhub-qa/boolq #en #dataset-boolq #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-boolq' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the qa/boolq dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
43,
72,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #roberta #text-classification #adapterhub-qa/boolq #en #dataset-boolq #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-boolq' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the qa/boolq dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-cola` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [lingaccept/cola](https://adapterhub.ml/explore/lingaccept/cola/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-cola", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:lingaccept/cola", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-cola | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:lingaccept/cola",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-lingaccept/cola #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-cola' for roberta-base
An adapter for the 'roberta-base' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/roberta-base-pf-cola' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #roberta #text-classification #adapterhub-lingaccept/cola #en #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-cola' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
38,
72,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #roberta #text-classification #adapterhub-lingaccept/cola #en #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-cola' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-commonsense_qa` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/csqa](https://adapterhub.ml/explore/comsense/csqa/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-commonsense_qa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` | {"language": ["en"], "tags": ["roberta", "adapterhub:comsense/csqa", "adapter-transformers"], "datasets": ["commonsense_qa"]} | null | AdapterHub/roberta-base-pf-commonsense_qa | [
"adapter-transformers",
"roberta",
"adapterhub:comsense/csqa",
"en",
"dataset:commonsense_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #adapterhub-comsense/csqa #en #dataset-commonsense_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-commonsense_qa' for roberta-base
An adapter for the 'roberta-base' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/roberta-base-pf-commonsense_qa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #roberta #adapterhub-comsense/csqa #en #dataset-commonsense_qa #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-commonsense_qa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
42,
76,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #roberta #adapterhub-comsense/csqa #en #dataset-commonsense_qa #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-commonsense_qa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-comqa` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [com_qa](https://huggingface.co/datasets/com_qa/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-comqa", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["com_qa"]} | question-answering | AdapterHub/roberta-base-pf-comqa | [
"adapter-transformers",
"roberta",
"question-answering",
"en",
"dataset:com_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #en #dataset-com_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-comqa' for roberta-base
An adapter for the 'roberta-base' model that was trained on the com_qa dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/roberta-base-pf-comqa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the com_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #roberta #question-answering #en #dataset-com_qa #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-comqa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the com_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
37,
72,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #roberta #question-answering #en #dataset-com_qa #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-comqa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the com_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-conll2000` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [chunk/conll2000](https://adapterhub.ml/explore/chunk/conll2000/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-conll2000", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:chunk/conll2000", "adapter-transformers"], "datasets": ["conll2000"]} | token-classification | AdapterHub/roberta-base-pf-conll2000 | [
"adapter-transformers",
"roberta",
"token-classification",
"adapterhub:chunk/conll2000",
"en",
"dataset:conll2000",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #adapterhub-chunk/conll2000 #en #dataset-conll2000 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-conll2000' for roberta-base
An adapter for the 'roberta-base' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/roberta-base-pf-conll2000' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #roberta #token-classification #adapterhub-chunk/conll2000 #en #dataset-conll2000 #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-conll2000' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
47,
75,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #roberta #token-classification #adapterhub-chunk/conll2000 #en #dataset-conll2000 #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-conll2000' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-conll2003` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [ner/conll2003](https://adapterhub.ml/explore/ner/conll2003/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-conll2003", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` | {"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:ner/conll2003", "adapter-transformers"], "datasets": ["conll2003"]} | token-classification | AdapterHub/roberta-base-pf-conll2003 | [
"adapter-transformers",
"roberta",
"token-classification",
"adapterhub:ner/conll2003",
"en",
"dataset:conll2003",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #adapterhub-ner/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-conll2003' for roberta-base
An adapter for the 'roberta-base' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_
Now, the adapter can be loaded and activated like this:
## Architecture & Training
The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.
## Evaluation results
Refer to the paper for more information on results.
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
| [
"# Adapter 'AdapterHub/roberta-base-pf-conll2003' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
"TAGS\n#adapter-transformers #roberta #token-classification #adapterhub-ner/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-conll2003' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.",
"## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
46,
74,
57,
30,
45
] | [
"passage: TAGS\n#adapter-transformers #roberta #token-classification #adapterhub-ner/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-conll2003' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training\n\nThe training code for this adapter is available at URL\nIn particular, training configurations for all tasks can be found here.## Evaluation results\n\nRefer to the paper for more information on results.\n\nIf you use this adapter, please cite our paper \"What to Pre-Train on? Efficient Intermediate Task Selection\":"
] | [
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