Datasets:

ftopal

/

huggingface-models

like 0

token-classification

transformers

# CAMeLBERT-MSA POS-MSA Model ## Model description **CAMeLBERT-MSA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model. For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf/10.5555/1621804.1621808) dataset . Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT). ## Intended uses You can use the CAMeLBERT-MSA POS-MSA model as part of the transformers pipeline. This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon. #### How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa') >>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع' >>> pos(text) [{'entity': 'noun', 'score': 0.9999764, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.99991846, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.9998356, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.99368894, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.9999426, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.9999339, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.99996775, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.99996895, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.99990183, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.9999347, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.99931145, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```

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CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# CAMeLBERT-MSA POS-MSA Model ## Model description CAMeLBERT-MSA POS-MSA Model is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the CAMeLBERT-MSA model. For the fine-tuning, we used the PATB dataset . Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* Our fine-tuning code can be found here. ## Intended uses You can use the CAMeLBERT-MSA POS-MSA model as part of the transformers pipeline. This model will also be available in CAMeL Tools soon. #### How to use To use the model with a transformers pipeline: *Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually.

fill-mask

transformers

# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks ## Model description **CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* This model card describes **CAMeLBERT-MSA-quarter** (`bert-base-arabic-camelbert-msa-quarter`), a model pre-trained on a quarter of the full MSA dataset. ||Model|Variant|Size|#Word| |-|-|:-:|-:|-:| ||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B| ||`bert-base-arabic-camelbert-ca`|CA|6GB|847M| ||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B| ||`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B| ||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B| |✔|`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B| ||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B| ||`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M| ## Intended uses You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT). #### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter') >>> unmasker("الهدف من الحياة هو [MASK] .") [{'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]', 'score': 0.17437894642353058, 'token': 3696, 'token_str': 'الحياة'}, {'sequence': '[CLS] الهدف من الحياة هو النجاح. [SEP]', 'score': 0.042852893471717834, 'token': 6232, 'token_str': 'النجاح'}, {'sequence': '[CLS] الهدف من الحياة هو البقاء. [SEP]', 'score': 0.030925093218684196, 'token': 9331, 'token_str': 'البقاء'}, {'sequence': '[CLS] الهدف من الحياة هو الحب. [SEP]', 'score': 0.02964409440755844, 'token': 3088, 'token_str': 'الحب'}, {'sequence': '[CLS] الهدف من الحياة هو الكمال. [SEP]', 'score': 0.028030086308717728, 'token': 17188, 'token_str': 'الكمال'}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter') model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter') text = "مرحبا يا عالم." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AutoTokenizer, TFAutoModel tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter') model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter') text = "مرحبا يا عالم." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data - MSA (Modern Standard Arabic) - [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11) - [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus) - [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osian) - [Arabic Wikipedia](https://archive.org/details/arwiki-20190201) - The unshuffled version of the Arabic [OSCAR corpus](https://oscar-corpus.com/) ## Training procedure We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training. We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. ### Preprocessing - After extracting the raw text from each corpus, we apply the following pre-processing. - We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297). - We also remove lines without any Arabic characters. - We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools). - Finally, we split each line into sentences with a heuristics-based sentence segmenter. - We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers). - We do not lowercase letters nor strip accents. ### Pre-training - The model was trained on a single cloud TPU (`v3-8`) for one million steps in total. - The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. - The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. - We use whole word masking and a duplicate factor of 10. - We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens. - We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1. - The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results - We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. - We fine-tune and evaluate the models using 12 dataset. - We used Hugging Face's transformers to fine-tune our CAMeLBERT models. - We used transformers `v3.1.0` along with PyTorch `v1.5.1`. - The fine-tuning was done by adding a fully connected linear layer to the last hidden state. - We use \\(F_{1}\\) score as a metric for all tasks. - Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT). ### Results | Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | NER | ANERcorp | MSA | 80.8% | 67.9% | 74.1% | 82.4% | 82.0% | 82.1% | 82.6% | 80.8% | | POS | PATB (MSA) | MSA | 98.1% | 97.8% | 97.7% | 98.3% | 98.2% | 98.3% | 98.2% | 98.2% | | | ARZTB (EGY) | DA | 93.6% | 92.3% | 92.7% | 93.6% | 93.6% | 93.7% | 93.6% | 93.6% | | | Gumar (GLF) | DA | 97.3% | 97.7% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | | SA | ASTD | MSA | 76.3% | 69.4% | 74.6% | 76.9% | 76.0% | 76.8% | 76.7% | 75.3% | | | ArSAS | MSA | 92.7% | 89.4% | 91.8% | 93.0% | 92.6% | 92.5% | 92.5% | 92.3% | | | SemEval | MSA | 69.0% | 58.5% | 68.4% | 72.1% | 70.7% | 72.8% | 71.6% | 71.2% | | DID | MADAR-26 | DA | 62.9% | 61.9% | 61.8% | 62.6% | 62.0% | 62.8% | 62.0% | 62.2% | | | MADAR-6 | DA | 92.5% | 91.5% | 92.2% | 91.9% | 91.8% | 92.2% | 92.1% | 92.0% | | | MADAR-Twitter-5 | MSA | 75.7% | 71.4% | 74.2% | 77.6% | 78.5% | 77.3% | 77.7% | 76.2% | | | NADI | DA | 24.7% | 17.3% | 20.1% | 24.9% | 24.6% | 24.6% | 24.9% | 23.8% | | Poetry | APCD | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | ### Results (Average) | | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | Variant-wise-average^{[[1]](#footnote-1)} | MSA | 82.1% | 75.7% | 80.1% | 83.4% | 83.0% | 83.3% | 83.2% | 82.3% | | | DA | 74.4% | 72.1% | 72.9% | 74.2% | 74.0% | 74.3% | 74.1% | 73.9% | | | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | | Macro-Average | ALL | 78.7% | 74.7% | 77.1% | 79.2% | 79.0% | 79.2% | 79.1% | 78.6% | <a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant. ## Acknowledgements This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC). ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```

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CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks ================================================================== Model description ----------------- CAMeLBERT is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes CAMeLBERT-MSA-quarter ('bert-base-arabic-camelbert-msa-quarter'), a model pre-trained on a quarter of the full MSA dataset. Intended uses ------------- You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here. #### How to use You can use this model directly with a pipeline for masked language modeling: *Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually. Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: Training data ------------- * MSA (Modern Standard Arabic) + The Arabic Gigaword Fifth Edition + Abu El-Khair Corpus + OSIAN corpus + Arabic Wikipedia + The unshuffled version of the Arabic OSCAR corpus Training procedure ------------------ We use the original implementation released by Google for pre-training. We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. ### Preprocessing * After extracting the raw text from each corpus, we apply the following pre-processing. * We first remove invalid characters and normalize white spaces using the utilities provided by the original BERT implementation. * We also remove lines without any Arabic characters. * We then remove diacritics and kashida using CAMeL Tools. * Finally, we split each line into sentences with a heuristics-based sentence segmenter. * We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using HuggingFace's tokenizers. * We do not lowercase letters nor strip accents. ### Pre-training * The model was trained on a single cloud TPU ('v3-8') for one million steps in total. * The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. * The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. * We use whole word masking and a duplicate factor of 10. * We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens. * We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1. * The optimizer used is Adam with a learning rate of 1e-4, \(\beta\_{1} = 0.9\) and \(\beta\_{2} = 0.999\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. Evaluation results ------------------ * We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. * We fine-tune and evaluate the models using 12 dataset. * We used Hugging Face's transformers to fine-tune our CAMeLBERT models. * We used transformers 'v3.1.0' along with PyTorch 'v1.5.1'. * The fine-tuning was done by adding a fully connected linear layer to the last hidden state. * We use \(F\_{1}\) score as a metric for all tasks. * Code used for fine-tuning is available here. ### Results ### Results (Average) [1]: Variant-wise-average refers to average over a group of tasks in the same language variant. Acknowledgements ---------------- This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).

text-classification

transformers

# CAMeLBERT MSA SA Model ## Model description **CAMeLBERT MSA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model. For the fine-tuning, we used the [ASTD](https://aclanthology.org/D15-1299.pdf), [ArSAS](http://lrec-conf.org/workshops/lrec2018/W30/pdf/22_W30.pdf), and [SemEval](https://aclanthology.org/S17-2088.pdf) datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT). ## Intended uses You can use the CAMeLBERT MSA SA model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component (*recommended*) or as part of the transformers pipeline. #### How to use To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component: ```python >>> from camel_tools.sentiment import SentimentAnalyzer >>> sa = SentimentAnalyzer("CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment") >>> sentences = ['أنا بخير', 'أنا لست بخير'] >>> sa.predict(sentences) >>> ['positive', 'negative'] ``` You can also use the SA model directly with a transformers pipeline: ```python >>> from transformers import pipeline >>> sa = pipeline('sentiment-analysis', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment') >>> sentences = ['أنا بخير', 'أنا لست بخير'] >>> sa(sentences) [{'label': 'positive', 'score': 0.9616648554801941}, {'label': 'negative', 'score': 0.9779177904129028}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```

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CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# CAMeLBERT MSA SA Model ## Model description CAMeLBERT MSA SA Model is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* Our fine-tuning code can be found here. ## Intended uses You can use the CAMeLBERT MSA SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. #### How to use To use the model with the CAMeL Tools SA component: You can also use the SA model directly with a transformers pipeline: *Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually.

fill-mask

transformers

# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks ## Model description **CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* This model card describes **CAMeLBERT-MSA-sixteenth** (`bert-base-arabic-camelbert-msa-sixteenth`), a model pre-trained on a sixteenth of the full MSA dataset. ||Model|Variant|Size|#Word| |-|-|:-:|-:|-:| ||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B| ||`bert-base-arabic-camelbert-ca`|CA|6GB|847M| ||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B| ||`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B| ||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B| ||`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B| ||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B| |✔|`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M| ## Intended uses You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT). #### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') >>> unmasker("الهدف من الحياة هو [MASK] .") [{'sequence': '[CLS] الهدف من الحياة هو التغيير. [SEP]', 'score': 0.08320745080709457, 'token': 7946, 'token_str': 'التغيير'}, {'sequence': '[CLS] الهدف من الحياة هو التعلم. [SEP]', 'score': 0.04305094853043556, 'token': 12554, 'token_str': 'التعلم'}, {'sequence': '[CLS] الهدف من الحياة هو العمل. [SEP]', 'score': 0.0417640283703804, 'token': 2854, 'token_str': 'العمل'}, {'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]', 'score': 0.041371218860149384, 'token': 3696, 'token_str': 'الحياة'}, {'sequence': '[CLS] الهدف من الحياة هو المعرفة. [SEP]', 'score': 0.039794355630874634, 'token': 7344, 'token_str': 'المعرفة'}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') text = "مرحبا يا عالم." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AutoTokenizer, TFAutoModel tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') text = "مرحبا يا عالم." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data - MSA (Modern Standard Arabic) - [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11) - [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus) - [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osian) - [Arabic Wikipedia](https://archive.org/details/arwiki-20190201) - The unshuffled version of the Arabic [OSCAR corpus](https://oscar-corpus.com/) ## Training procedure We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training. We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. ### Preprocessing - After extracting the raw text from each corpus, we apply the following pre-processing. - We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297). - We also remove lines without any Arabic characters. - We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools). - Finally, we split each line into sentences with a heuristics-based sentence segmenter. - We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers). - We do not lowercase letters nor strip accents. ### Pre-training - The model was trained on a single cloud TPU (`v3-8`) for one million steps in total. - The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. - The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. - We use whole word masking and a duplicate factor of 10. - We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens. - We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1. - The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results - We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. - We fine-tune and evaluate the models using 12 dataset. - We used Hugging Face's transformers to fine-tune our CAMeLBERT models. - We used transformers `v3.1.0` along with PyTorch `v1.5.1`. - The fine-tuning was done by adding a fully connected linear layer to the last hidden state. - We use \\(F_{1}\\) score as a metric for all tasks. - Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT). ### Results | Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | NER | ANERcorp | MSA | 80.8% | 67.9% | 74.1% | 82.4% | 82.0% | 82.1% | 82.6% | 80.8% | | POS | PATB (MSA) | MSA | 98.1% | 97.8% | 97.7% | 98.3% | 98.2% | 98.3% | 98.2% | 98.2% | | | ARZTB (EGY) | DA | 93.6% | 92.3% | 92.7% | 93.6% | 93.6% | 93.7% | 93.6% | 93.6% | | | Gumar (GLF) | DA | 97.3% | 97.7% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | | SA | ASTD | MSA | 76.3% | 69.4% | 74.6% | 76.9% | 76.0% | 76.8% | 76.7% | 75.3% | | | ArSAS | MSA | 92.7% | 89.4% | 91.8% | 93.0% | 92.6% | 92.5% | 92.5% | 92.3% | | | SemEval | MSA | 69.0% | 58.5% | 68.4% | 72.1% | 70.7% | 72.8% | 71.6% | 71.2% | | DID | MADAR-26 | DA | 62.9% | 61.9% | 61.8% | 62.6% | 62.0% | 62.8% | 62.0% | 62.2% | | | MADAR-6 | DA | 92.5% | 91.5% | 92.2% | 91.9% | 91.8% | 92.2% | 92.1% | 92.0% | | | MADAR-Twitter-5 | MSA | 75.7% | 71.4% | 74.2% | 77.6% | 78.5% | 77.3% | 77.7% | 76.2% | | | NADI | DA | 24.7% | 17.3% | 20.1% | 24.9% | 24.6% | 24.6% | 24.9% | 23.8% | | Poetry | APCD | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | ### Results (Average) | | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | Variant-wise-average^{[[1]](#footnote-1)} | MSA | 82.1% | 75.7% | 80.1% | 83.4% | 83.0% | 83.3% | 83.2% | 82.3% | | | DA | 74.4% | 72.1% | 72.9% | 74.2% | 74.0% | 74.3% | 74.1% | 73.9% | | | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | | Macro-Average | ALL | 78.7% | 74.7% | 77.1% | 79.2% | 79.0% | 79.2% | 79.1% | 78.6% | <a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant. ## Acknowledgements This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC). ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```

{"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]}

CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks ================================================================== Model description ----------------- CAMeLBERT is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes CAMeLBERT-MSA-sixteenth ('bert-base-arabic-camelbert-msa-sixteenth'), a model pre-trained on a sixteenth of the full MSA dataset. Intended uses ------------- You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here. #### How to use You can use this model directly with a pipeline for masked language modeling: *Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually. Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: Training data ------------- * MSA (Modern Standard Arabic) + The Arabic Gigaword Fifth Edition + Abu El-Khair Corpus + OSIAN corpus + Arabic Wikipedia + The unshuffled version of the Arabic OSCAR corpus Training procedure ------------------ We use the original implementation released by Google for pre-training. We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. ### Preprocessing * After extracting the raw text from each corpus, we apply the following pre-processing. * We first remove invalid characters and normalize white spaces using the utilities provided by the original BERT implementation. * We also remove lines without any Arabic characters. * We then remove diacritics and kashida using CAMeL Tools. * Finally, we split each line into sentences with a heuristics-based sentence segmenter. * We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using HuggingFace's tokenizers. * We do not lowercase letters nor strip accents. ### Pre-training * The model was trained on a single cloud TPU ('v3-8') for one million steps in total. * The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. * The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. * We use whole word masking and a duplicate factor of 10. * We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens. * We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1. * The optimizer used is Adam with a learning rate of 1e-4, \(\beta\_{1} = 0.9\) and \(\beta\_{2} = 0.999\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. Evaluation results ------------------ * We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. * We fine-tune and evaluate the models using 12 dataset. * We used Hugging Face's transformers to fine-tune our CAMeLBERT models. * We used transformers 'v3.1.0' along with PyTorch 'v1.5.1'. * The fine-tuning was done by adding a fully connected linear layer to the last hidden state. * We use \(F\_{1}\) score as a metric for all tasks. * Code used for fine-tuning is available here. ### Results ### Results (Average) [1]: Variant-wise-average refers to average over a group of tasks in the same language variant. Acknowledgements ---------------- This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).

fill-mask

transformers

# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks ## Model description **CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* This model card describes **CAMeLBERT-MSA** (`bert-base-arabic-camelbert-msa`), a model pre-trained on the entire MSA dataset. ||Model|Variant|Size|#Word| |-|-|:-:|-:|-:| ||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B| ||`bert-base-arabic-camelbert-ca`|CA|6GB|847M| ||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B| |✔|`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B| ||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B| ||`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B| ||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B| ||`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M| ## Intended uses You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT). #### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-msa') >>> unmasker("الهدف من الحياة هو [MASK] .") [{'sequence': '[CLS] الهدف من الحياة هو العمل. [SEP]', 'score': 0.08507660031318665, 'token': 2854, 'token_str': 'العمل'}, {'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]', 'score': 0.058905381709337234, 'token': 3696, 'token_str': 'الحياة'}, {'sequence': '[CLS] الهدف من الحياة هو النجاح. [SEP]', 'score': 0.04660581797361374, 'token': 6232, 'token_str': 'النجاح'}, {'sequence': '[CLS] الهدف من الحياة هو الربح. [SEP]', 'score': 0.04156001657247543, 'token': 12413, 'token_str': 'الربح'}, {'sequence': '[CLS] الهدف من الحياة هو الحب. [SEP]', 'score': 0.03534102067351341, 'token': 3088, 'token_str': 'الحب'}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa') model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa') text = "مرحبا يا عالم." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AutoTokenizer, TFAutoModel tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa') model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa') text = "مرحبا يا عالم." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data - MSA (Modern Standard Arabic) - [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11) - [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus) - [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osian) - [Arabic Wikipedia](https://archive.org/details/arwiki-20190201) - The unshuffled version of the Arabic [OSCAR corpus](https://oscar-corpus.com/) ## Training procedure We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training. We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. ### Preprocessing - After extracting the raw text from each corpus, we apply the following pre-processing. - We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297). - We also remove lines without any Arabic characters. - We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools). - Finally, we split each line into sentences with a heuristics-based sentence segmenter. - We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers). - We do not lowercase letters nor strip accents. ### Pre-training - The model was trained on a single cloud TPU (`v3-8`) for one million steps in total. - The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. - The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. - We use whole word masking and a duplicate factor of 10. - We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens. - We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1. - The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results - We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. - We fine-tune and evaluate the models using 12 dataset. - We used Hugging Face's transformers to fine-tune our CAMeLBERT models. - We used transformers `v3.1.0` along with PyTorch `v1.5.1`. - The fine-tuning was done by adding a fully connected linear layer to the last hidden state. - We use \\(F_{1}\\) score as a metric for all tasks. - Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT). ### Results | Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | NER | ANERcorp | MSA | 80.8% | 67.9% | 74.1% | 82.4% | 82.0% | 82.1% | 82.6% | 80.8% | | POS | PATB (MSA) | MSA | 98.1% | 97.8% | 97.7% | 98.3% | 98.2% | 98.3% | 98.2% | 98.2% | | | ARZTB (EGY) | DA | 93.6% | 92.3% | 92.7% | 93.6% | 93.6% | 93.7% | 93.6% | 93.6% | | | Gumar (GLF) | DA | 97.3% | 97.7% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | | SA | ASTD | MSA | 76.3% | 69.4% | 74.6% | 76.9% | 76.0% | 76.8% | 76.7% | 75.3% | | | ArSAS | MSA | 92.7% | 89.4% | 91.8% | 93.0% | 92.6% | 92.5% | 92.5% | 92.3% | | | SemEval | MSA | 69.0% | 58.5% | 68.4% | 72.1% | 70.7% | 72.8% | 71.6% | 71.2% | | DID | MADAR-26 | DA | 62.9% | 61.9% | 61.8% | 62.6% | 62.0% | 62.8% | 62.0% | 62.2% | | | MADAR-6 | DA | 92.5% | 91.5% | 92.2% | 91.9% | 91.8% | 92.2% | 92.1% | 92.0% | | | MADAR-Twitter-5 | MSA | 75.7% | 71.4% | 74.2% | 77.6% | 78.5% | 77.3% | 77.7% | 76.2% | | | NADI | DA | 24.7% | 17.3% | 20.1% | 24.9% | 24.6% | 24.6% | 24.9% | 23.8% | | Poetry | APCD | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | ### Results (Average) | | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | Variant-wise-average^{[[1]](#footnote-1)} | MSA | 82.1% | 75.7% | 80.1% | 83.4% | 83.0% | 83.3% | 83.2% | 82.3% | | | DA | 74.4% | 72.1% | 72.9% | 74.2% | 74.0% | 74.3% | 74.1% | 73.9% | | | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | | Macro-Average | ALL | 78.7% | 74.7% | 77.1% | 79.2% | 79.0% | 79.2% | 79.1% | 78.6% | <a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant. ## Acknowledgements This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC). ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```

{"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]}

CAMeL-Lab/bert-base-arabic-camelbert-msa

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks ================================================================== Model description ----------------- CAMeLBERT is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes CAMeLBERT-MSA ('bert-base-arabic-camelbert-msa'), a model pre-trained on the entire MSA dataset. Intended uses ------------- You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here. #### How to use You can use this model directly with a pipeline for masked language modeling: *Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually. Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: Training data ------------- * MSA (Modern Standard Arabic) + The Arabic Gigaword Fifth Edition + Abu El-Khair Corpus + OSIAN corpus + Arabic Wikipedia + The unshuffled version of the Arabic OSCAR corpus Training procedure ------------------ We use the original implementation released by Google for pre-training. We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. ### Preprocessing * After extracting the raw text from each corpus, we apply the following pre-processing. * We first remove invalid characters and normalize white spaces using the utilities provided by the original BERT implementation. * We also remove lines without any Arabic characters. * We then remove diacritics and kashida using CAMeL Tools. * Finally, we split each line into sentences with a heuristics-based sentence segmenter. * We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using HuggingFace's tokenizers. * We do not lowercase letters nor strip accents. ### Pre-training * The model was trained on a single cloud TPU ('v3-8') for one million steps in total. * The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. * The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. * We use whole word masking and a duplicate factor of 10. * We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens. * We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1. * The optimizer used is Adam with a learning rate of 1e-4, \(\beta\_{1} = 0.9\) and \(\beta\_{2} = 0.999\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. Evaluation results ------------------ * We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. * We fine-tune and evaluate the models using 12 dataset. * We used Hugging Face's transformers to fine-tune our CAMeLBERT models. * We used transformers 'v3.1.0' along with PyTorch 'v1.5.1'. * The fine-tuning was done by adding a fully connected linear layer to the last hidden state. * We use \(F\_{1}\) score as a metric for all tasks. * Code used for fine-tuning is available here. ### Results ### Results (Average) [1]: Variant-wise-average refers to average over a group of tasks in the same language variant. Acknowledgements ---------------- This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).

fill-mask

transformers

## JavaBERT A BERT-like model pretrained on Java software code. ### Training Data The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A ```bert-base-uncased``` tokenizer is used by this model. ### Training Objective A MLM (Masked Language Model) objective was used to train this model. ### Usage ```python from transformers import pipeline pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT') output = pipe(CODE) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code. ```

{"language": ["java", "code"], "license": "apache-2.0", "widget": [{"text": "public [MASK] isOdd(Integer num){if (num % 2 == 0) {return \"even\";} else {return \"odd\";}}"}]}

CAUKiel/JavaBERT-uncased

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

## JavaBERT A BERT-like model pretrained on Java software code. ### Training Data The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A tokenizer is used by this model. ### Training Objective A MLM (Masked Language Model) objective was used to train this model. ### Usage

fill-mask

transformers

# Model Card for JavaBERT A BERT-like model pretrained on Java software code. # Model Details ## Model Description A BERT-like model pretrained on Java software code. - **Developed by:** Christian-Albrechts-University of Kiel (CAUKiel) - **Shared by [Optional]:** Hugging Face - **Model type:** Fill-Mask - **Language(s) (NLP):** en - **License:** Apache-2.0 - **Related Models:** A version of this model using an uncased tokenizer is available at [CAUKiel/JavaBERT-uncased](https://huggingface.co/CAUKiel/JavaBERT-uncased). - **Parent Model:** BERT - **Resources for more information:** - [Associated Paper](https://arxiv.org/pdf/2110.10404.pdf) # Uses ## Direct Use Fill-Mask ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. { see paper= word something) # Training Details ## Training Data The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A ```bert-base-cased``` tokenizer is used by this model. ## Training Procedure ### Training Objective A MLM (Masked Language Model) objective was used to train this model. ### Preprocessing More information needed. ### Speeds, Sizes, Times More information needed. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed. ### Factors ### Metrics More information needed. ## Results More information needed. # Model Examination More information needed. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed. - **Hours used:** More information needed. - **Cloud Provider:** More information needed. - **Compute Region:** More information needed. - **Carbon Emitted:** More information needed. # Technical Specifications [optional] ## Model Architecture and Objective More information needed. ## Compute Infrastructure More information needed. ### Hardware More information needed. ### Software More information needed. # Citation **BibTeX:** More information needed. **APA:** More information needed. # Glossary [optional] More information needed. # More Information [optional] More information needed. # Model Card Authors [optional] Christian-Albrechts-University of Kiel (CAUKiel) in collaboration with Ezi Ozoani and the team at Hugging Face # Model Card Contact More information needed. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import pipeline pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT') output = pipe(CODE) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code. ``` </details>

{"language": ["code"], "license": "apache-2.0", "widget": [{"text": "public [MASK] isOdd(Integer num) {if (num % 2 == 0) {return \"even\";} else {return \"odd\";}}"}]}

CAUKiel/JavaBERT

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #bert #fill-mask #code #arxiv-2110.10404 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Model Card for JavaBERT A BERT-like model pretrained on Java software code. # Model Details ## Model Description A BERT-like model pretrained on Java software code. - Developed by: Christian-Albrechts-University of Kiel (CAUKiel) - Shared by [Optional]: Hugging Face - Model type: Fill-Mask - Language(s) (NLP): en - License: Apache-2.0 - Related Models: A version of this model using an uncased tokenizer is available at CAUKiel/JavaBERT-uncased. - Parent Model: BERT - Resources for more information: - Associated Paper # Uses ## Direct Use Fill-Mask ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. { see paper= word something) # Training Details ## Training Data The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A tokenizer is used by this model. ## Training Procedure ### Training Objective A MLM (Masked Language Model) objective was used to train this model. ### Preprocessing More information needed. ### Speeds, Sizes, Times More information needed. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed. ### Factors ### Metrics More information needed. ## Results More information needed. # Model Examination More information needed. # Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: More information needed. - Hours used: More information needed. - Cloud Provider: More information needed. - Compute Region: More information needed. - Carbon Emitted: More information needed. # Technical Specifications [optional] ## Model Architecture and Objective More information needed. ## Compute Infrastructure More information needed. ### Hardware More information needed. ### Software More information needed. BibTeX: More information needed. APA: More information needed. # Glossary [optional] More information needed. # More Information [optional] More information needed. # Model Card Authors [optional] Christian-Albrechts-University of Kiel (CAUKiel) in collaboration with Ezi Ozoani and the team at Hugging Face # Model Card Contact More information needed. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> </details>

translation

transformers

This model translate from English to Khmer. It is the pure fine-tuned version of MarianMT model en-zh. This is the result after 30 epochs of pure fine-tuning of khmer language. ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/en-km") tokenizer = AutoTokenizer.from_pretrained("CLAck/en-km") # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it # We used the one coming from the initial model # This tokenizer is used to tokenize the input sentence tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') # These special tokens are needed to reproduce the original tokenizer tokenizer_en.add_tokens(["<2zh>", "<2khm>"], special_tokens=True) sentence = "The cat is on the table" # This token is needed to identify the target language input_sentence = "<2khm> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ```

{"tags": ["translation"]}

CLAck/en-km

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #marian #text2text-generation #translation #autotrain_compatible #endpoints_compatible #region-us

This model translate from English to Khmer. It is the pure fine-tuned version of MarianMT model en-zh. This is the result after 30 epochs of pure fine-tuning of khmer language. ### Example

translation

transformers

This is a finetuning of a MarianMT pretrained on English-Chinese. The target language pair is English-Vietnamese. The first phase of training (mixed) is performed on a dataset containing both English-Chinese and English-Vietnamese sentences. The second phase of training (pure) is performed on a dataset containing only English-Vietnamese sentences. ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/en-vi") tokenizer = AutoTokenizer.from_pretrained("CLAck/en-vi") # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it # We used the one coming from the initial model # This tokenizer is used to tokenize the input sentence tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') # These special tokens are needed to reproduce the original tokenizer tokenizer_en.add_tokens(["<2zh>", "<2vi>"], special_tokens=True) sentence = "The cat is on the table" # This token is needed to identify the target language input_sentence = "<2vi> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` ### Training results MIXED | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 26.2407 | | 2.0 | 32.6016 | | 3.0 | 35.4060 | | 4.0 | 36.6737 | | 5.0 | 37.3774 | PURE | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 37.3169 | | 2.0 | 37.4407 | | 3.0 | 37.6696 | | 4.0 | 37.8765 | | 5.0 | 38.0105 |

{"language": ["en", "vi"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]}

CLAck/en-vi

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #marian #text2text-generation #translation #en #vi #dataset-ALT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

This is a finetuning of a MarianMT pretrained on English-Chinese. The target language pair is English-Vietnamese. The first phase of training (mixed) is performed on a dataset containing both English-Chinese and English-Vietnamese sentences. The second phase of training (pure) is performed on a dataset containing only English-Vietnamese sentences. ### Example ### Training results MIXED PURE

translation

transformers

This model is pretrained on Chinese and Indonesian languages, and fine-tuned on Indonesian language. ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/indo-mixed") tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-mixed") # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it # We used the one coming from the initial model # This tokenizer is used to tokenize the input sentence tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') # These special tokens are needed to reproduce the original tokenizer tokenizer_en.add_tokens(["<2zh>", "<2indo>"], special_tokens=True) sentence = "The cat is on the table" # This token is needed to identify the target language input_sentence = "<2indo> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` ### Training results MIXED | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 24.2579 | | 2.0 | 30.6287 | | 3.0 | 34.4417 | | 4.0 | 36.2577 | | 5.0 | 37.3488 | FINETUNING | Epoch | Bleu | |:-----:|:-------:| | 6.0 | 34.1676 | | 7.0 | 35.2320 | | 8.0 | 36.7110 | | 9.0 | 37.3195 | | 10.0 | 37.9461 |

{"language": ["en", "id"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]}

CLAck/indo-mixed

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #marian #text2text-generation #translation #en #id #dataset-ALT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

This model is pretrained on Chinese and Indonesian languages, and fine-tuned on Indonesian language. ### Example ### Training results MIXED FINETUNING

translation

transformers

Pure fine-tuning version of MarianMT en-zh on Indonesian Language ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/indo-pure") tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-pure") # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it # We used the one coming from the initial model # This tokenizer is used to tokenize the input sentence tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') # These special tokens are needed to reproduce the original tokenizer tokenizer_en.add_tokens(["<2zh>", "<2indo>"], special_tokens=True) sentence = "The cat is on the table" # This token is needed to identify the target language input_sentence = "<2indo> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` ### Training results | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 15.9336 | | 2.0 | 28.0175 | | 3.0 | 31.6603 | | 4.0 | 33.9151 | | 5.0 | 35.0472 | | 6.0 | 35.8469 | | 7.0 | 36.1180 | | 8.0 | 36.6018 | | 9.0 | 37.1973 | | 10.0 | 37.2738 |

{"language": ["en", "id"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]}

CLAck/indo-pure

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #marian #text2text-generation #translation #en #id #dataset-ALT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

Pure fine-tuning version of MarianMT en-zh on Indonesian Language ### Example ### Training results

translation

transformers

This is a finetuning of a MarianMT pretrained on Chinese-English. The target language pair is Vietnamese-English. ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/vi-en") tokenizer = AutoTokenizer.from_pretrained("CLAck/vi-en") sentence = your_vietnamese_sentence # This token is needed to identify the source language input_sentence = "<2vi> " + sentence translated = model.generate(**tokenizer(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` ### Training results | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 21.3180 | | 2.0 | 26.8012 | | 3.0 | 29.3578 | | 4.0 | 31.5178 | | 5.0 | 32.8740 |

{"language": ["en", "vi"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]}

CLAck/vi-en

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #marian #text2text-generation #translation #en #vi #dataset-ALT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

This is a finetuning of a MarianMT pretrained on Chinese-English. The target language pair is Vietnamese-English. ### Example ### Training results

fill-mask

transformers

# MedRoBERTa.nl ## Description This model is a RoBERTa-based model pre-trained from scratch on Dutch hospital notes sourced from Electronic Health Records. The model is not fine-tuned. All code used for the creation of MedRoBERTa.nl can be found at https://github.com/cltl-students/verkijk_stella_rma_thesis_dutch_medical_language_model. ## Intended use The model can be fine-tuned on any type of task. Since it is a domain-specific model trained on medical data, it is meant to be used on medical NLP tasks for Dutch. ## Data The model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres. The training data was anonymized before starting the pre-training procedure. ## Privacy By anonymizing the training data we made sure the model did not learn any representative associations linked to names. Apart from the training data, the model's vocabulary was also anonymized. This ensures that the model can not predict any names in the generative fill-mask task. ## Authors Stella Verkijk, Piek Vossen ## Reference Paper: Verkijk, S. & Vossen, P. (2022) MedRoBERTa.nl: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.

{"language": "nl", "license": "mit"}

CLTL/MedRoBERTa.nl

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #nl #doi-10.57967/hf/0960 #license-mit #autotrain_compatible #endpoints_compatible #region-us

# URL ## Description This model is a RoBERTa-based model pre-trained from scratch on Dutch hospital notes sourced from Electronic Health Records. The model is not fine-tuned. All code used for the creation of URL can be found at URL ## Intended use The model can be fine-tuned on any type of task. Since it is a domain-specific model trained on medical data, it is meant to be used on medical NLP tasks for Dutch. ## Data The model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres. The training data was anonymized before starting the pre-training procedure. ## Privacy By anonymizing the training data we made sure the model did not learn any representative associations linked to names. Apart from the training data, the model's vocabulary was also anonymized. This ensures that the model can not predict any names in the generative fill-mask task. ## Authors Stella Verkijk, Piek Vossen ## Reference Paper: Verkijk, S. & Vossen, P. (2022) URL: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.

token-classification

transformers

# Early-modern Dutch NER (General Letters) ## Description This is a fine-tuned NER model for early-modern Dutch United East India Company (VOC) letters based on XLM-R_base [(Conneau et al., 2020)](https://aclanthology.org/2020.acl-main.747/). The model identifies *locations*, *persons*, *organisations*, but also *ships* as well as derived forms of locations and religions. ## Intended uses and limitations This model was fine-tuned (trained, validated and tested) on a single source of data, the General Letters (Generale Missiven). These letters span a large variety of Dutch, as they cover the largest part of the 17th and 18th centuries, and have been extended with editorial notes between 1960 and 2017. As the model was only fine-tuned on this data however, it may perform less well on other texts from the same period. ## How to use The model can run on raw text through the *token-classification* pipeline: ``` from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("CLTL/gm-ner-xlmrbase") model = AutoModelForTokenClassification.from_pretrained("CLTL/gm-ner-xlmrbase") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Batavia heeft om advies gevraagd." ner_results = nlp(example) print(ner_results) ``` This outputs a list of entities with their character offsets in the input text: ``` [{'entity': 'B-LOC', 'score': 0.99739265, 'index': 1, 'word': '▁Bata', 'start': 0, 'end': 4}, {'entity': 'I-LOC', 'score': 0.5373179, 'index': 2, 'word': 'via', 'start': 4, 'end': 7}] ``` ## Training data and tagset The model was fine-tuned on the General Letters [GM-NER](https://github.com/cltl/voc-missives/tree/master/data/ner/datasplit_all_standard) dataset, with the following tagset: | tag | description | notes | | --- | ----------- | ----- | | LOC | locations | | | LOCderiv | derived forms of locations | by derivation, e.g. *Bandanezen*, or composition, e.g. *Javakoffie* | | ORG | organisations | includes forms derived by composition, e.g. *Compagnieszaken* | PER | persons | | RELderiv | forms related to religion | merges religion names (*Christendom*), derived forms (*christenen*) and composed forms (*Christen-orangkay*) | | SHP | ships | The base text for this dataset is OCR text that has been partially corrected. The text is clean overall but errors remain. ## Training procedure The model was fine-tuned with [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), using [this script](https://github.com/huggingface/transformers/blob/master/examples/legacy/token-classification/run_ner.py). Non-default training parameters are: * training batch size: 16 * max sequence length: 256 * number of epochs: 4 -- loading the best checkpoint model by loss at the end, with checkpoints every 200 steps * (seed: 1) ## Evaluation ### Metric * entity-level F1 ### Results | overall | 92.7 | | --- | ----------- | | LOC | 95.8 | | LOCderiv | 92.7 | | ORG | 92.5 | | PER | 86.2 | | RELderiv | 90.7 | | SHP | 81.6 | ## Reference The model and fine-tuning data presented here were developed as part of: ```bibtex @inproceedings{arnoult-etal-2021-batavia, title = "Batavia asked for advice. Pretrained language models for Named Entity Recognition in historical texts.", author = "Arnoult, Sophie I. and Petram, Lodewijk and Vossen, Piek", booktitle = "Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature", month = nov, year = "2021", address = "Punta Cana, Dominican Republic (online)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.latechclfl-1.3", pages = "21--30" } ```

{"language": "nl", "license": "apache-2.0", "tags": ["dighum"], "pipeline_tag": "token-classification"}

CLTL/gm-ner-xlmrbase

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #xlm-roberta #token-classification #dighum #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

Early-modern Dutch NER (General Letters) ======================================== Description ----------- This is a fine-tuned NER model for early-modern Dutch United East India Company (VOC) letters based on XLM-R\_base (Conneau et al., 2020). The model identifies *locations*, *persons*, *organisations*, but also *ships* as well as derived forms of locations and religions. Intended uses and limitations ----------------------------- This model was fine-tuned (trained, validated and tested) on a single source of data, the General Letters (Generale Missiven). These letters span a large variety of Dutch, as they cover the largest part of the 17th and 18th centuries, and have been extended with editorial notes between 1960 and 2017. As the model was only fine-tuned on this data however, it may perform less well on other texts from the same period. How to use ---------- The model can run on raw text through the *token-classification* pipeline: This outputs a list of entities with their character offsets in the input text: Training data and tagset ------------------------ The model was fine-tuned on the General Letters GM-NER dataset, with the following tagset: tag: LOC, description: locations, notes: tag: LOCderiv, description: derived forms of locations, notes: by derivation, e.g. *Bandanezen*, or composition, e.g. *Javakoffie* tag: ORG, description: organisations, notes: includes forms derived by composition, e.g. *Compagnieszaken* tag: PER, description: persons, notes: tag: RELderiv, description: forms related to religion, notes: merges religion names (*Christendom*), derived forms (*christenen*) and composed forms (*Christen-orangkay*) tag: SHP, description: ships, notes: The base text for this dataset is OCR text that has been partially corrected. The text is clean overall but errors remain. Training procedure ------------------ The model was fine-tuned with xlm-roberta-base, using this script. Non-default training parameters are: * training batch size: 16 * max sequence length: 256 * number of epochs: 4 -- loading the best checkpoint model by loss at the end, with checkpoints every 200 steps * (seed: 1) Evaluation ---------- ### Metric * entity-level F1 ### Results Reference --------- The model and fine-tuning data presented here were developed as part of:

text-classification

transformers

# A-PROOF ICF-domains Classification ## Description A fine-tuned multi-label classification model that detects 9 [WHO-ICF](https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health) domains in clinical text in Dutch. The model is based on a pre-trained Dutch medical language model ([link to be added]()), a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. ## ICF domains The model can detect 9 domains, which were chosen due to their relevance to recovery from COVID-19: ICF code | Domain | name in repo ---|---|--- b440 | Respiration functions | ADM b140 | Attention functions | ATT d840-d859 | Work and employment | BER b1300 | Energy level | ENR d550 | Eating | ETN d450 | Walking | FAC b455 | Exercise tolerance functions | INS b530 | Weight maintenance functions | MBW b152 | Emotional functions | STM ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import MultiLabelClassificationModel model = MultiLabelClassificationModel( 'roberta', 'CLTL/icf-domains', use_cuda=False, ) example = 'Nu sinds 5-6 dagen progressieve benauwdheidsklachten (bij korte stukken lopen al kortademig), terwijl dit eerder niet zo was.' predictions, raw_outputs = model.predict([example]) ``` The predictions look like this: ``` [[1, 0, 0, 0, 0, 1, 1, 0, 0]] ``` The indices of the multi-label stand for: ``` [ADM, ATT, BER, ENR, ETN, FAC, INS, MBW, STM] ``` In other words, the above prediction corresponds to assigning the labels ADM, FAC and INS to the example sentence. The raw outputs look like this: ``` [[0.51907885 0.00268032 0.0030862 0.03066113 0.00616694 0.64720929 0.67348498 0.0118863 0.0046311 ]] ``` For this model, the threshold at which the prediction for a label flips from 0 to 1 is **0.5**. ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 - Threshold: 0.5 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). ### Sentence-level | | ADM | ATT | BER | ENR | ETN | FAC | INS | MBW | STM |---|---|---|---|---|---|---|---|---|--- precision | 0.98 | 0.98 | 0.56 | 0.96 | 0.92 | 0.84 | 0.89 | 0.79 | 0.70 recall | 0.49 | 0.41 | 0.29 | 0.57 | 0.49 | 0.71 | 0.26 | 0.62 | 0.75 F1-score | 0.66 | 0.58 | 0.35 | 0.72 | 0.63 | 0.76 | 0.41 | 0.70 | 0.72 support | 775 | 39 | 54 | 160 | 382 | 253 | 287 | 125 | 181 ### Note-level | | ADM | ATT | BER | ENR | ETN | FAC | INS | MBW | STM |---|---|---|---|---|---|---|---|---|--- precision | 1.0 | 1.0 | 0.66 | 0.96 | 0.95 | 0.84 | 0.95 | 0.87 | 0.80 recall | 0.89 | 0.56 | 0.44 | 0.70 | 0.72 | 0.89 | 0.46 | 0.87 | 0.87 F1-score | 0.94 | 0.71 | 0.50 | 0.81 | 0.82 | 0.86 | 0.61 | 0.87 | 0.84 support | 231 | 27 | 34 | 92 | 165 | 95 | 116 | 64 | 94 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-domains

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #region-us

A-PROOF ICF-domains Classification ================================== Description ----------- A fine-tuned multi-label classification model that detects 9 WHO-ICF domains in clinical text in Dutch. The model is based on a pre-trained Dutch medical language model (link to be added), a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. ICF domains ----------- The model can detect 9 domains, which were chosen due to their relevance to recovery from COVID-19: ICF code: b440, Domain: Respiration functions, name in repo: ADM ICF code: b140, Domain: Attention functions, name in repo: ATT ICF code: d840-d859, Domain: Work and employment, name in repo: BER ICF code: b1300, Domain: Energy level, name in repo: ENR ICF code: d550, Domain: Eating, name in repo: ETN ICF code: d450, Domain: Walking, name in repo: FAC ICF code: b455, Domain: Exercise tolerance functions, name in repo: INS ICF code: b530, Domain: Weight maintenance functions, name in repo: MBW ICF code: b152, Domain: Emotional functions, name in repo: STM Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The predictions look like this: The indices of the multi-label stand for: In other words, the above prediction corresponds to assigning the labels ADM, FAC and INS to the example sentence. The raw outputs look like this: For this model, the threshold at which the prediction for a label flips from 0 to 1 is 0.5. Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 * Threshold: 0.5 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). ### Sentence-level ### Note-level Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Respiration Functioning Levels (ICF b440) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing respiration functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about respiration functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 4 | No problem with respiration, and/or respiratory rate is normal (EWS: 9-20). 3 | Shortness of breath in exercise (saturation &ge;90), and/or respiratory rate is slightly increased (EWS: 21-30). 2 | Shortness of breath in rest (saturation &ge;90), and/or respiratory rate is fairly increased (EWS: 31-35). 1 | Needs oxygen at rest or during exercise (saturation &lt;90), and/or respiratory rate &gt;35. 0 | Mechanical ventilation is needed. The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-adm', use_cuda=False, ) example = 'Nu sinds 5-6 dagen progressieve benauwdheidsklachten (bij korte stukken lopen al kortademig), terwijl dit eerder niet zo was.' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 2.26 ``` The raw outputs look like this: ``` [[2.26074648]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.48 | 0.37 mean squared error | 0.55 | 0.34 root mean squared error | 0.74 | 0.58 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-adm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Respiration Functioning Levels (ICF b440) ============================================================== Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing respiration functions. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about respiration functions in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 0.48 Sentence-level: mean squared error, Note-level: 0.55 Sentence-level: root mean squared error, Note-level: 0.74 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Attention Functioning Levels (ICF b140) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing attention functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about attention functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 4 | No problem with concentrating / directing / holding / dividing attention. 3 | Slight problem with concentrating / directing / holding / dividing attention for a longer period of time or for complex tasks. 2 | Can concentrate / direct / hold / divide attention only for a short time. 1 | Can barely concentrate / direct / hold / divide attention. 0 | Unable to concentrate / direct / hold / divide attention. The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-att', use_cuda=False, ) example = 'Snel afgeleid, moeite aandacht te behouden.' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 2.89 ``` The raw outputs look like this: ``` [[2.89226103]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.99 | 1.03 mean squared error | 1.35 | 1.47 root mean squared error | 1.16 | 1.21 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-att

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Attention Functioning Levels (ICF b140) ============================================================ Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing attention functions. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about attention functions in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 0.99 Sentence-level: mean squared error, Note-level: 1.35 Sentence-level: root mean squared error, Note-level: 1.16 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Work and Employment Functioning Levels (ICF d840-d859) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing work and employment functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about work and employment functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 4 | Can work/study fully (like when healthy). 3 | Can work/study almost fully. 2 | Can work/study only for about 50\%, or can only work at home and cannot go to school / office. 1 | Work/study is severely limited. 0 | Cannot work/study. The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-ber', use_cuda=False, ) example = 'Fysiek zwaar werk is niet mogelijk, maar administrative taken zou zij wel aan moeten kunnen.' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 2.41 ``` The raw outputs look like this: ``` [[2.40793037]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 1.56 | 1.49 mean squared error | 3.06 | 2.85 root mean squared error | 1.75 | 1.69 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-ber

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Work and Employment Functioning Levels (ICF d840-d859) =========================================================================== Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing work and employment functions. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about work and employment functions in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 1.56 Sentence-level: mean squared error, Note-level: 3.06 Sentence-level: root mean squared error, Note-level: 1.75 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Energy Levels (ICF b1300) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing energy level. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about energy level in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 4 | No problem with the energy level. 3 | Slight fatigue that causes mild limitations. 2 | Moderate fatigue; the patient gets easily tired from light activities or needs a long time to recover after an activity. 1 | Severe fatigue; the patient is capable of very little. 0 | Very severe fatigue; unable to do anything and mostly lays in bed. The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-enr', use_cuda=False, ) example = 'Al jaren extreme vermoeidheid overdag, valt overdag in slaap tijdens school- en werkactiviteiten en soms zelfs tijdens een gesprek.' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 1.98 ``` The raw outputs look like this: ``` [[1.97520316]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.48 | 0.43 mean squared error | 0.49 | 0.42 root mean squared error | 0.70 | 0.65 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-enr

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Energy Levels (ICF b1300) ============================================== Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing energy level. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about energy level in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 0.48 Sentence-level: mean squared error, Note-level: 0.49 Sentence-level: root mean squared error, Note-level: 0.70 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Eating Functioning Levels (ICF d550) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing eating functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about eating functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 4 | Can eat independently (in culturally acceptable ways), good intake, eats according to her/his needs. 3 | Can eat independently but with adjustments, and/or somewhat reduced intake (&gt;75% of her/his needs), and/or good intake can be achieved with proper advice. 2 | Reduced intake, and/or stimulus / feeding modules / nutrition drinks are needed (but not tube feeding / TPN). 1 | Intake is severely reduced (&lt;50% of her/his needs), and/or tube feeding / TPN is needed. 0 | Cannot eat, and/or fully dependent on tube feeding / TPN. The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-etn', use_cuda=False, ) example = 'Sondevoeding is geïndiceerd' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 0.89 ``` The raw outputs look like this: ``` [[0.8872931]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.59 | 0.50 mean squared error | 0.65 | 0.47 root mean squared error | 0.81 | 0.68 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-etn

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Eating Functioning Levels (ICF d550) ========================================================= Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing eating functions. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about eating functions in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 0.59 Sentence-level: mean squared error, Note-level: 0.65 Sentence-level: root mean squared error, Note-level: 0.81 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Walking Functioning Levels (ICF d550) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing walking functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about walking functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 5 | Patient can walk independently anywhere: level surface, uneven surface, slopes, stairs. 4 | Patient can walk independently on level surface but requires help on stairs, inclines, uneven surface; or, patient can walk independently, but the walking is not fully normal. 3 | Patient requires verbal supervision for walking, without physical contact. 2 | Patient needs continuous or intermittent support of one person to help with balance and coordination. 1 | Patient needs firm continuous support from one person who helps carrying weight and with balance. 0 | Patient cannot walk or needs help from two or more people; or, patient walks on a treadmill. The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-fac', use_cuda=False, ) example = 'kan nog goed traplopen, maar flink ingeleverd aan conditie na Corona' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 4.2 ``` The raw outputs look like this: ``` [[4.20903111]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.70 | 0.66 mean squared error | 0.91 | 0.93 root mean squared error | 0.95 | 0.96 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-fac

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Walking Functioning Levels (ICF d550) ========================================================== Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing walking functions. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about walking functions in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 0.70 Sentence-level: mean squared error, Note-level: 0.91 Sentence-level: root mean squared error, Note-level: 0.95 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Exercise Tolerance Functioning Levels (ICF b455) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about exercise tolerance functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 5 | MET&gt;6. Can tolerate jogging, hard exercises, running, climbing stairs fast, sports. 4 | 4&le;MET&le;6. Can tolerate walking / cycling at a brisk pace, considerable effort (e.g. cycling from 16 km/h), heavy housework. 3 | 3&le;MET&lt;4. Can tolerate walking / cycling at a normal pace, gardening, exercises without equipment. 2 | 2&le;MET&lt;3. Can tolerate walking at a slow to moderate pace, grocery shopping, light housework. 1 | 1&le;MET&lt;2. Can tolerate sitting activities. 0 | 0&le;MET&lt;1. Can physically tolerate only recumbent activities. The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-ins', use_cuda=False, ) example = 'kan nog goed traplopen, maar flink ingeleverd aan conditie na Corona' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 3.13 ``` The raw outputs look like this: ``` [[3.1300993]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.69 | 0.61 mean squared error | 0.80 | 0.64 root mean squared error | 0.89 | 0.80 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-ins

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Exercise Tolerance Functioning Levels (ICF b455) ===================================================================== Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance functions. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about exercise tolerance functions in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 0.69 Sentence-level: mean squared error, Note-level: 0.80 Sentence-level: root mean squared error, Note-level: 0.89 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Weight Maintenance Functioning Levels (ICF b530) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing weight maintenance functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about weight maintenance functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 4 | Healthy weight, no unintentional weight loss or gain, SNAQ 0 or 1. 3 | Some unintentional weight loss or gain, or lost a lot of weight but gained some of it back afterwards. 2 | Moderate unintentional weight loss or gain (more than 3 kg in the last month), SNAQ 2. 1 | Severe unintentional weight loss or gain (more than 6 kg in the last 6 months), SNAQ &ge; 3. 0 | Severe unintentional weight loss or gain (more than 6 kg in the last 6 months) and admitted to ICU. The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-mbw', use_cuda=False, ) example = 'Tijdens opname >10 kg afgevallen.' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 1.95 ``` The raw outputs look like this: ``` [[1.95429301]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.81 | 0.60 mean squared error | 0.83 | 0.56 root mean squared error | 0.91 | 0.75 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-mbw

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Weight Maintenance Functioning Levels (ICF b530) ===================================================================== Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing weight maintenance functions. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about weight maintenance functions in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 0.81 Sentence-level: mean squared error, Note-level: 0.83 Sentence-level: root mean squared error, Note-level: 0.91 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

# Regression Model for Emotional Functioning Levels (ICF b152) ## Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing emotional functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about emotional functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. ## Functioning levels Level | Meaning ---|--- 4 | No problem with emotional functioning: emotions are appropriate, well regulated, etc. 3 | Slight problem with emotional functioning: irritable, gloomy, etc. 2 | Moderate problem with emotional functioning: negative emotions, such as fear, anger, sadness, etc. 1 | Severe problem with emotional functioning: intense negative emotions, such as fear, anger, sadness, etc. 0 | Flat affect, apathy, unstable, inappropriate emotions. The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. ## Intended uses and limitations - The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. ## How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-stm', use_cuda=False, ) example = 'Naarmate het somatische beeld een herstellende trend laat zien, valt op dat patient zich depressief en suicidaal uit.' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 1.60 ``` The raw outputs look like this: ``` [[1.60418844]] ``` ## Training data - The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. - The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). ## Training procedure The default training parameters of Simple Transformers were used, including: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 8 ## Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.76 | 0.68 mean squared error | 1.03 | 0.87 root mean squared error | 1.01 | 0.93 ## Authors and references ### Authors Jenia Kim, Piek Vossen ### References TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}

CLTL/icf-levels-stm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us

Regression Model for Emotional Functioning Levels (ICF b152) ============================================================ Description ----------- A fine-tuned regression model that assigns a functioning level to Dutch sentences describing emotional functions. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about emotional functions in clinical text in Dutch, use the icf-domains classification model. Functioning levels ------------------ The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. Intended uses and limitations ----------------------------- * The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). * The model was fine-tuned with the Simple Transformers library. This library is based on Transformers but the model cannot be used directly with Transformers 'pipeline' and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. How to use ---------- To generate predictions with the model, use the Simple Transformers library: The prediction on the example is: The raw outputs look like this: Training data ------------- * The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. * The annotation guidelines used for the project can be found here. Training procedure ------------------ The default training parameters of Simple Transformers were used, including: * Optimizer: AdamW * Learning rate: 4e-5 * Num train epochs: 1 * Train batch size: 8 Evaluation results ------------------ The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). Sentence-level: mean absolute error, Note-level: 0.76 Sentence-level: mean squared error, Note-level: 1.03 Sentence-level: root mean squared error, Note-level: 1.01 Authors and references ---------------------- ### Authors Jenia Kim, Piek Vossen ### References TBD

text-classification

transformers

emilyalsentzer/Bio_ClinicalBERT with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing." Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John M Bernabei, Peter D Galer, Nina J Ghosn, Adam S Greenblatt, Tara Jennings, Alana Kornspun, Catherine V Kulick-Soper, Jal M Panchal, Akash R Pattnaik, Brittany H Scheid, Danmeng Wei, Micah Weitzman, Ramya Muthukrishnan, Joongwon Kim, Brian Litt, Colin A Ellis, Dan Roth, Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing, Journal of the American Medical Informatics Association, 2022;, ocac018, https://doi.org/10.1093/jamia/ocac018 Bio_ClinicalBERT_for_seizureFreedom_classification classifies patients has having seizures or being seizure free using the HPI and/or Interval History paragraphs from a medical note.

{}

CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us

emilyalsentzer/Bio_ClinicalBERT with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing." Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John M Bernabei, Peter D Galer, Nina J Ghosn, Adam S Greenblatt, Tara Jennings, Alana Kornspun, Catherine V Kulick-Soper, Jal M Panchal, Akash R Pattnaik, Brittany H Scheid, Danmeng Wei, Micah Weitzman, Ramya Muthukrishnan, Joongwon Kim, Brian Litt, Colin A Ellis, Dan Roth, Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing, Journal of the American Medical Informatics Association, 2022;, ocac018, URL Bio_ClinicalBERT_for_seizureFreedom_classification classifies patients has having seizures or being seizure free using the HPI and/or Interval History paragraphs from a medical note.

question-answering

transformers

RoBERTa-base with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing." Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John M Bernabei, Peter D Galer, Nina J Ghosn, Adam S Greenblatt, Tara Jennings, Alana Kornspun, Catherine V Kulick-Soper, Jal M Panchal, Akash R Pattnaik, Brittany H Scheid, Danmeng Wei, Micah Weitzman, Ramya Muthukrishnan, Joongwon Kim, Brian Litt, Colin A Ellis, Dan Roth, Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing, Journal of the American Medical Informatics Association, 2022;, ocac018, https://doi.org/10.1093/jamia/ocac018 RoBERTa_for_seizureFrequency_QA performs extractive question answering to identify a patient's seizure freedom and/or date of last seizure using the HPI and/or Interval History paragraphs from a medical note.

{}

CNT-UPenn/RoBERTa_for_seizureFrequency_QA

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us

RoBERTa-base with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing." Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John M Bernabei, Peter D Galer, Nina J Ghosn, Adam S Greenblatt, Tara Jennings, Alana Kornspun, Catherine V Kulick-Soper, Jal M Panchal, Akash R Pattnaik, Brittany H Scheid, Danmeng Wei, Micah Weitzman, Ramya Muthukrishnan, Joongwon Kim, Brian Litt, Colin A Ellis, Dan Roth, Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing, Journal of the American Medical Informatics Association, 2022;, ocac018, URL RoBERTa_for_seizureFrequency_QA performs extractive question answering to identify a patient's seizure freedom and/or date of last seizure using the HPI and/or Interval History paragraphs from a medical note.

fill-mask

transformers

# XLM-Align **Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment** (ACL-2021, [paper](https://arxiv.org/pdf/2106.06381.pdf), [github](https://github.com/CZWin32768/XLM-Align)) XLM-Align is a pretrained cross-lingual language model that supports 94 languages. See details in our [paper](https://arxiv.org/pdf/2106.06381.pdf). ## Example ``` model = = AutoModel.from_pretrained("CZWin32768/xlm-align") ``` ## Evaluation Results XTREME cross-lingual understanding tasks: | Model | POS | NER | XQuAD | MLQA | TyDiQA | XNLI | PAWS-X | Avg | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| | XLM-R_base | 75.6 | 61.8 | 71.9 / 56.4 | 65.1 / 47.2 | 55.4 / 38.3 | 75.0 | 84.9 | 66.4 | | XLM-Align | **76.0** | **63.7** | **74.7 / 59.0** | **68.1 / 49.8** | **62.1 / 44.8** | **76.2** | **86.8** | **68.9** | ## MD5 ``` b9d214025837250ede2f69c9385f812c config.json 6005db708eb4bab5b85fa3976b9db85b pytorch_model.bin bf25eb5120ad92ef5c7d8596b5dc4046 sentencepiece.bpe.model eedbd60a7268b9fc45981b849664f747 tokenizer.json ``` ## About Contact: chizewen\@outlook.com BibTeX: ``` @article{xlmalign, title={Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment}, author={Zewen Chi and Li Dong and Bo Zheng and Shaohan Huang and Xian-Ling Mao and Heyan Huang and Furu Wei}, journal={arXiv preprint arXiv:2106.06381}, year={2021} } ```

{}

CZWin32768/xlm-align

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #arxiv-2106.06381 #autotrain_compatible #endpoints_compatible #region-us

XLM-Align ========= Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (ACL-2021, paper, github) XLM-Align is a pretrained cross-lingual language model that supports 94 languages. See details in our paper. Example ------- Evaluation Results ------------------ XTREME cross-lingual understanding tasks: MD5 --- About ----- Contact: chizewen@URL BibTeX:

summarization

transformers

# Paper Title Generator Generates titles for computer science papers given an abstract. The model is a BERT2BERT Encoder-Decoder using the official `bert-base-uncased` checkpoint as initialization for the encoder and decoder. It was fine-tuned on 318,500 computer science papers posted on arXiv.org between 2007 and 2022 and achieved a 26.3% Rouge2 F1-Score on held-out validation data. **Live Demo:** [https://paper-titles.ey.r.appspot.com/](https://paper-titles.ey.r.appspot.com/)

{"language": ["en"], "license": "apache-2.0", "tags": ["summarization"], "datasets": ["arxiv_dataset"], "metrics": ["rouge"], "widget": [{"text": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."}]}

Callidior/bert2bert-base-arxiv-titlegen

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #en #dataset-arxiv_dataset #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# Paper Title Generator Generates titles for computer science papers given an abstract. The model is a BERT2BERT Encoder-Decoder using the official 'bert-base-uncased' checkpoint as initialization for the encoder and decoder. It was fine-tuned on 318,500 computer science papers posted on URL between 2007 and 2022 and achieved a 26.3% Rouge2 F1-Score on held-out validation data. Live Demo: URL.r.URL

text-generation

transformers

A PyTorch GPT-2 model trained on hansard from 2019-01-01 to 2020-06-01 For more information see: https://github.com/CallumRai/Hansard/

{}

CallumRai/HansardGPT2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

A PyTorch GPT-2 model trained on hansard from 2019-01-01 to 2020-06-01 For more information see: URL

summarization

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. --> # mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0393 - Rouge1: 17.2936 - Rouge2: 8.0678 - Rougel: 16.8129 - Rougelsum: 16.9991 ## 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: 5.6e-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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 6.6665 | 1.0 | 1209 | 3.2917 | 13.912 | 5.595 | 13.2984 | 13.4171 | | 3.8961 | 2.0 | 2418 | 3.1711 | 16.2845 | 8.6033 | 15.5509 | 15.7383 | | 3.5801 | 3.0 | 3627 | 3.0917 | 17.316 | 8.122 | 16.697 | 16.773 | | 3.4258 | 4.0 | 4836 | 3.0583 | 16.1347 | 7.7829 | 15.6475 | 15.7804 | | 3.3154 | 5.0 | 6045 | 3.0573 | 17.5918 | 8.7349 | 17.0537 | 17.2216 | | 3.2438 | 6.0 | 7254 | 3.0479 | 17.2294 | 8.0383 | 16.8141 | 16.9858 | | 3.2024 | 7.0 | 8463 | 3.0377 | 17.2918 | 8.139 | 16.8178 | 16.9671 | | 3.1745 | 8.0 | 9672 | 3.0393 | 17.2936 | 8.0678 | 16.8129 | 16.9991 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]}

CalvinHuang/mt5-small-finetuned-amazon-en-es

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

mt5-small-finetuned-amazon-en-es ================================ This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.0393 * Rouge1: 17.2936 * Rouge2: 8.0678 * Rougel: 16.8129 * Rougelsum: 16.9991 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: 5.6e-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: 8 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

text-generation

transformers

# MaamiBot

{"tags": ["conversational"]}

Camzure/MaamiBot-test

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# MaamiBot

text-generation

transformers

# Jesse (Breaking Bad) DialoGPT Model

{"tags": ["conversational"]}

Canadiancaleb/DialoGPT-small-jesse

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Jesse (Breaking Bad) DialoGPT Model

text-generation

transformers

# Walter (Breaking Bad) DialoGPT Model

{"tags": ["conversational"]}

Canadiancaleb/DialoGPT-small-walter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Walter (Breaking Bad) DialoGPT Model

text-classification

transformers

# capreolus/bert-base-msmarco ## Model description BERT-Base model (`google/bert_uncased_L-12_H-768_A-12`) fine-tuned on the MS MARCO passage classification task. It is intended to be used as a `ForSequenceClassification` model; see the [Capreolus BERT-MaxP implementation](https://github.com/capreolus-ir/capreolus/blob/master/capreolus/reranker/TFBERTMaxP.py) for a usage example. This corresponds to the BERT-Base model used to initialize BERT-MaxP and PARADE variants in [PARADE: Passage Representation Aggregation for Document Reranking](https://arxiv.org/abs/2008.09093) by Li et al. It was converted from the released [TFv1 checkpoint](https://zenodo.org/record/3974431/files/vanilla_bert_base_on_MSMARCO.tar.gz). Please cite the PARADE paper if you use these weights.

{}

Capreolus/bert-base-msmarco

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #jax #bert #text-classification #arxiv-2008.09093 #autotrain_compatible #endpoints_compatible #region-us

# capreolus/bert-base-msmarco ## Model description BERT-Base model ('google/bert_uncased_L-12_H-768_A-12') fine-tuned on the MS MARCO passage classification task. It is intended to be used as a 'ForSequenceClassification' model; see the Capreolus BERT-MaxP implementation for a usage example. This corresponds to the BERT-Base model used to initialize BERT-MaxP and PARADE variants in PARADE: Passage Representation Aggregation for Document Reranking by Li et al. It was converted from the released TFv1 checkpoint. Please cite the PARADE paper if you use these weights.

text-classification

transformers

# capreolus/electra-base-msmarco ## Model description ELECTRA-Base model (`google/electra-base-discriminator`) fine-tuned on the MS MARCO passage classification task. It is intended to be used as a `ForSequenceClassification` model, but requires some modification since it contains a BERT classification head rather than the standard ELECTRA classification head. See the [TFElectraRelevanceHead](https://github.com/capreolus-ir/capreolus/blob/master/capreolus/reranker/TFBERTMaxP.py) in the Capreolus BERT-MaxP implementation for a usage example. This corresponds to the ELECTRA-Base model used to initialize PARADE (ELECTRA) in [PARADE: Passage Representation Aggregation for Document Reranking](https://arxiv.org/abs/2008.09093) by Li et al. It was converted from the released [TFv1 checkpoint](https://zenodo.org/record/3974431/files/vanilla_electra_base_on_MSMARCO.tar.gz). Please cite the PARADE paper if you use these weights.

{}

Capreolus/electra-base-msmarco

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #electra #text-classification #arxiv-2008.09093 #autotrain_compatible #endpoints_compatible #region-us

# capreolus/electra-base-msmarco ## Model description ELECTRA-Base model ('google/electra-base-discriminator') fine-tuned on the MS MARCO passage classification task. It is intended to be used as a 'ForSequenceClassification' model, but requires some modification since it contains a BERT classification head rather than the standard ELECTRA classification head. See the TFElectraRelevanceHead in the Capreolus BERT-MaxP implementation for a usage example. This corresponds to the ELECTRA-Base model used to initialize PARADE (ELECTRA) in PARADE: Passage Representation Aggregation for Document Reranking by Li et al. It was converted from the released TFv1 checkpoint. Please cite the PARADE paper if you use these weights.

text-classification

transformers

# Master Thesis ## Predictive Value of Sentiment Analysis from Headlines for Crude Oil Prices ### Understanding and Exploiting Deep Learning-based Sentiment Analysis from News Headlines for Predicting Price Movements of WTI Crude Oil The focus of this thesis deals with the task of research and development of state-of-the-art sentiment analysis methods, which can potentially provide helpful quantification of news that can be used to assess the future price movements of crude oil. CrudeBERT is a pre-trained NLP model to analyze sentiment of news headlines relevant to crude oil. It was developed by fine tuning [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/pdf/1908.10063.pdf).  Performing sentiment analysis on the news regarding a specific asset requires domain adaptation. Domain adaptation requires training data made up of examples with text and its associated polarity of sentiment. The experiments show that pre-trained deep learning-based sentiment analysis can be further fine-tuned, and the conclusions of these experiments are as follows: * Deep learning-based sentiment analysis models from the general financial world such as FinBERT are of little or hardly any significance concerning the price development of crude oil. The reason behind this is a lack of domain adaptation of the sentiment. Moreover, the polarity of sentiment cannot be generalized and is highly dependent on the properties of its target. * The properties of crude oil prices are, according to the literature, determined by changes in supply and demand. News can convey information about these direction changes and can broadly be identified through query searches and serve as a foundation for creating a training dataset to perform domain adaptation. For this purpose, news headlines tend to be rich enough in content to provide insights into supply and demand changes. Even when significantly reducing the number of headlines to more reputable sources. * Domain adaptation can be achieved to some extend by analyzing the properties of the target through literature review and creating a corresponding training dataset to fine-tune the model. For example, considering supply and demand changes regarding crude oil seems to be a suitable component for a domain adaptation. In order to advance sentiment analysis applications in the domain of crude oil, this paper presents CrudeBERT. In general, sentiment analysis of headlines from crude oil through CrudeBERT could be a viable source of insight for the price behaviour of WTI crude oil. However, further research is required to see if CrudeBERT can serve as beneficial for predicting oil prices. For this matter, the codes and the thesis is made publicly available on [GitHub] (https://github.com/Captain-1337/Master-Thesis).

{}

Captain-1337/CrudeBERT

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #arxiv-1908.10063 #autotrain_compatible #endpoints_compatible #region-us

# Master Thesis ## Predictive Value of Sentiment Analysis from Headlines for Crude Oil Prices ### Understanding and Exploiting Deep Learning-based Sentiment Analysis from News Headlines for Predicting Price Movements of WTI Crude Oil The focus of this thesis deals with the task of research and development of state-of-the-art sentiment analysis methods, which can potentially provide helpful quantification of news that can be used to assess the future price movements of crude oil. CrudeBERT is a pre-trained NLP model to analyze sentiment of news headlines relevant to crude oil. It was developed by fine tuning FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. !CrudeBERT comparison_white_2 Performing sentiment analysis on the news regarding a specific asset requires domain adaptation. Domain adaptation requires training data made up of examples with text and its associated polarity of sentiment. The experiments show that pre-trained deep learning-based sentiment analysis can be further fine-tuned, and the conclusions of these experiments are as follows: * Deep learning-based sentiment analysis models from the general financial world such as FinBERT are of little or hardly any significance concerning the price development of crude oil. The reason behind this is a lack of domain adaptation of the sentiment. Moreover, the polarity of sentiment cannot be generalized and is highly dependent on the properties of its target. * The properties of crude oil prices are, according to the literature, determined by changes in supply and demand. News can convey information about these direction changes and can broadly be identified through query searches and serve as a foundation for creating a training dataset to perform domain adaptation. For this purpose, news headlines tend to be rich enough in content to provide insights into supply and demand changes. Even when significantly reducing the number of headlines to more reputable sources. * Domain adaptation can be achieved to some extend by analyzing the properties of the target through literature review and creating a corresponding training dataset to fine-tune the model. For example, considering supply and demand changes regarding crude oil seems to be a suitable component for a domain adaptation. In order to advance sentiment analysis applications in the domain of crude oil, this paper presents CrudeBERT. In general, sentiment analysis of headlines from crude oil through CrudeBERT could be a viable source of insight for the price behaviour of WTI crude oil. However, further research is required to see if CrudeBERT can serve as beneficial for predicting oil prices. For this matter, the codes and the thesis is made publicly available on [GitHub] (URL

text2text-generation

transformers

**mt5-spanish-memmories-analysis** **// ES** Este es un trabajo en proceso. Este modelo aún es solo un punto de control inicial que mejoraré en los próximos meses. El objetivo es proporcionar un modelo capaz de, utilizando una combinación de tareas del modelo mT5, comprender los recuerdos y proporcionar una interacción útil para las personas con alzeimer o personas como mi propio abuelo que escribió sus recuerdos, pero ahora es solo un libro en la estantería. por lo que este modelo puede hacer que esos recuerdos parezcan "vivos". Pronto (si aún no está cargado) cargaré un cuaderno de **Google Colaboratory con una aplicación visual** que al usar este modelo proporcionará toda la interacción necesaria y deseada con una interfaz fácil de usar. **LINK APLICACIÓN (sobre él se actualizará la versión):** https://drive.google.com/drive/folders/1ewGcxxCYHHwhHhWtGlLiryZfV8wEAaBa?usp=sharing -> Debe descargarse la carpeta "memorium" del enlace y subirse a Google Drive sin incluir en ninguna otra carpeta (directamente en "Mi unidad"). -> A continuación se podrá abrir la app, encontrada dentro de dicha carpeta "memorium" con nombre "APP-Memorium" (el nombre puede incluir además un indicador de versión). -> Si haciendo doble click en el archivo de la app no permite abrirla, debe hacerse pulsando el botón derecho sobre el archivo y seleccionar "Abrir con", "Conectar más aplicaciones", y a continuación escoger Colaboratory (se pedirá instalar). Completada la instalación (tiempo aproximado: 2 minutos) se podrá cerrar la ventana de instalación para volver a visualizar la carpeta donde se encuentra el fichero de la app, que de ahora en adelante se podrá abrir haciendo doble click. -> Se podrán añadir memorias en la carpeta "perfiles" como se indica en la aplicación en el apartado "crear perfil". **// EN** This is a work in process. This model is just an initial checkpoint yet that I will be improving the following months. **APP LINK (it will contain the latest version):** https://drive.google.com/drive/folders/1ewGcxxCYHHwhHhWtGlLiryZfV8wEAaBa?usp=sharing -> The folder "memorium" must be downloaded and then uploaded to Google Drive at "My Drive", NOT inside any other folder. The aim is to provide a model able to, using a mixture of mT5 model's tasks, understand memories and provide an interaction useful for people with alzeimer or people like my own grandfather who wrote his memories but it is now just a book in the shelf, so this model can make those memories seem 'alive'. I will soon (if it is´t uploaded by now) upload a **Google Colaboratory notebook with a visual App** that using this model will provide all the needed and wanted interaction with an easy-to-use Interface.

{}

CarlosPR/mt5-spanish-memmories-analysis

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

mt5-spanish-memmories-analysis // ES Este es un trabajo en proceso. Este modelo aún es solo un punto de control inicial que mejoraré en los próximos meses. El objetivo es proporcionar un modelo capaz de, utilizando una combinación de tareas del modelo mT5, comprender los recuerdos y proporcionar una interacción útil para las personas con alzeimer o personas como mi propio abuelo que escribió sus recuerdos, pero ahora es solo un libro en la estantería. por lo que este modelo puede hacer que esos recuerdos parezcan "vivos". Pronto (si aún no está cargado) cargaré un cuaderno de Google Colaboratory con una aplicación visual que al usar este modelo proporcionará toda la interacción necesaria y deseada con una interfaz fácil de usar. LINK APLICACIÓN (sobre él se actualizará la versión): URL -> Debe descargarse la carpeta "memorium" del enlace y subirse a Google Drive sin incluir en ninguna otra carpeta (directamente en "Mi unidad"). -> A continuación se podrá abrir la app, encontrada dentro de dicha carpeta "memorium" con nombre "APP-Memorium" (el nombre puede incluir además un indicador de versión). -> Si haciendo doble click en el archivo de la app no permite abrirla, debe hacerse pulsando el botón derecho sobre el archivo y seleccionar "Abrir con", "Conectar más aplicaciones", y a continuación escoger Colaboratory (se pedirá instalar). Completada la instalación (tiempo aproximado: 2 minutos) se podrá cerrar la ventana de instalación para volver a visualizar la carpeta donde se encuentra el fichero de la app, que de ahora en adelante se podrá abrir haciendo doble click. -> Se podrán añadir memorias en la carpeta "perfiles" como se indica en la aplicación en el apartado "crear perfil". // EN This is a work in process. This model is just an initial checkpoint yet that I will be improving the following months. APP LINK (it will contain the latest version): URL -> The folder "memorium" must be downloaded and then uploaded to Google Drive at "My Drive", NOT inside any other folder. The aim is to provide a model able to, using a mixture of mT5 model's tasks, understand memories and provide an interaction useful for people with alzeimer or people like my own grandfather who wrote his memories but it is now just a book in the shelf, so this model can make those memories seem 'alive'. I will soon (if it is´t uploaded by now) upload a Google Colaboratory notebook with a visual App that using this model will provide all the needed and wanted interaction with an easy-to-use Interface.

text-generation

transformers

# Harry potter DialoGPT Model

{"tags": ["conversational"]}

CasualHomie/DialoGPT-small-harrypotter

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

automatic-speech-recognition

transformers

# Cdial/Hausa_xlsr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): - Loss: 0.275118 - Wer: 0.329955 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Hausa train.tsv, dev.tsv, invalidated.tsv, reported.tsv and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000096 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 500 | 5.175900 | 2.750914 | 1.000000 | | 1000 | 1.028700 | 0.338649 | 0.497999 | | 1500 | 0.332200 | 0.246896 | 0.402241 | | 2000 | 0.227300 | 0.239640 | 0.395839 | | 2500 | 0.175000 | 0.239577 | 0.373966 | | 3000 | 0.140400 | 0.243272 | 0.356095 | | 3500 | 0.119200 | 0.263761 | 0.365164 | | 4000 | 0.099300 | 0.265954 | 0.353428 | | 4500 | 0.084400 | 0.276367 | 0.349693 | | 5000 | 0.073700 | 0.282631 | 0.343825 | | 5500 | 0.068000 | 0.282344 | 0.341158 | | 6000 | 0.064500 | 0.281591 | 0.342491 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/Hausa_xlsr --dataset mozilla-foundation/common_voice_8_0 --config ha --split test ```

{"language": ["ha"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ha", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Cdial/Hausa_xlsr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ha"}, "metrics": [{"type": "wer", "value": 0.20614541257934219, "name": "Test WER"}, {"type": "cer", "value": 0.04358048053214061, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ha"}, "metrics": [{"type": "wer", "value": 0.20614541257934219, "name": "Test WER"}, {"type": "cer", "value": 0.04358048053214061, "name": "Test CER"}]}]}]}

Cdial/hausa-asr

2022-03-02T23:29:04+00:00

TAGS #transformers #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ha #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

Cdial/Hausa\_xlsr ================= This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): * Loss: 0.275118 * Wer: 0.329955 Model description ----------------- "facebook/wav2vec2-xls-r-300m" was finetuned. Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- Training data - Common voice Hausa URL, URL, URL, URL and URL Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 Training procedure ------------------ For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000096 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 13 * gradient\_accumulation\_steps: 2 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.18.3 * Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'

text-generation

transformers

# Cedille AI Cedille is a project to bring large language models to non-English languages. ## fr-boris Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) codebase. Boris was trained on around 78B tokens of French text from the [C4](https://huggingface.co/datasets/c4) dataset. We started training from GPT-J, which has been trained on [The Pile](https://pile.eleuther.ai/). As a consequence the model still has good performance in English language. Boris makes use of the unmodified GPT-2 tokenizer. Boris is named after the great French writer [Boris Vian](https://en.wikipedia.org/wiki/Boris_Vian). # How do I test Cedille? For the time being, the easiest way to test the model is to use our [publicly accessible playground](https://en.cedille.ai/). Cedille is a relatively large model and running it in production can get expensive. Consider contacting us for API access at [email protected]. ## 📊 Cedille paper Our paper is out now! https://arxiv.org/abs/2202.03371 Thanks for citing our work if you make use of Cedille ```bibtex @misc{muller2022cedille, title={Cedille: A large autoregressive French language model}, author={Martin M{\"{u}}ller and Florian Laurent}, year={2022}, eprint={2202.03371}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Contact us For any custom development please contact us at [email protected]. ## Links * [Official website](https://en.cedille.ai/) * [Blog](https://en.cedille.ai/blog) * [GitHub](https://github.com/coteries/cedille-ai) * [Twitter](https://twitter.com/CedilleAI)

{"language": "fr", "license": "mit", "tags": ["pytorch", "causal-lm"], "datasets": ["c4"]}

Cedille/fr-boris

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gptj #text-generation #causal-lm #fr #dataset-c4 #arxiv-2202.03371 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us

# Cedille AI Cedille is a project to bring large language models to non-English languages. ## fr-boris Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase. Boris was trained on around 78B tokens of French text from the C4 dataset. We started training from GPT-J, which has been trained on The Pile. As a consequence the model still has good performance in English language. Boris makes use of the unmodified GPT-2 tokenizer. Boris is named after the great French writer Boris Vian. # How do I test Cedille? For the time being, the easiest way to test the model is to use our publicly accessible playground. Cedille is a relatively large model and running it in production can get expensive. Consider contacting us for API access at hello@URL. ## Cedille paper Our paper is out now! URL Thanks for citing our work if you make use of Cedille ## Contact us For any custom development please contact us at hello@URL. ## Links * Official website * Blog * GitHub * Twitter

transformers

# ALBERT Base Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0008838834765 - Batch Size: 960 - Warmup ratio: 0.00625 - Warmup steps: 53333.33333 - Goal steps: 8533333.333 - Total steps: 3650000 - Total training time (aprox): 70.4 days. ## Training loss 

{"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]}

dccuchile/albert-base-spanish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #albert #pretraining #spanish #OpenCENIA #es #dataset-large_spanish_corpus #endpoints_compatible #region-us

# ALBERT Base Spanish This is an ALBERT model trained on a big spanish corpora. The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0008838834765 - Batch Size: 960 - Warmup ratio: 0.00625 - Warmup steps: 53333.33333 - Goal steps: 8533333.333 - Total steps: 3650000 - Total training time (aprox): 70.4 days. ## Training loss !URL

transformers

# ALBERT Large Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.000625 - Batch Size: 512 - Warmup ratio: 0.003125 - Warmup steps: 12500 - Goal steps: 4000000 - Total steps: 1450000 - Total training time (aprox): 42 days. ## Training loss 

{"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]}

dccuchile/albert-large-spanish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #albert #pretraining #spanish #OpenCENIA #es #dataset-large_spanish_corpus #endpoints_compatible #region-us

# ALBERT Large Spanish This is an ALBERT model trained on a big spanish corpora. The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.000625 - Batch Size: 512 - Warmup ratio: 0.003125 - Warmup steps: 12500 - Goal steps: 4000000 - Total steps: 1450000 - Total training time (aprox): 42 days. ## Training loss !URL

transformers

# ALBERT Tiny Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.00125 - Batch Size: 2048 - Warmup ratio: 0.0125 - Warmup steps: 125000 - Goal steps: 10000000 - Total steps: 8300000 - Total training time (aprox): 58.2 days ## Training loss 

{"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]}

dccuchile/albert-tiny-spanish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #albert #pretraining #spanish #OpenCENIA #es #dataset-large_spanish_corpus #endpoints_compatible #region-us

# ALBERT Tiny Spanish This is an ALBERT model trained on a big spanish corpora. The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.00125 - Batch Size: 2048 - Warmup ratio: 0.0125 - Warmup steps: 125000 - Goal steps: 10000000 - Total steps: 8300000 - Total training time (aprox): 58.2 days ## Training loss !URL

transformers

# ALBERT XLarge Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0003125 - Batch Size: 128 - Warmup ratio: 0.00078125 - Warmup steps: 6250 - Goal steps: 8000000 - Total steps: 2775000 - Total training time (aprox): 64.2 days. ## Training loss 

{"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]}

dccuchile/albert-xlarge-spanish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #albert #pretraining #spanish #OpenCENIA #es #dataset-large_spanish_corpus #endpoints_compatible #region-us

# ALBERT XLarge Spanish This is an ALBERT model trained on a big spanish corpora. The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0003125 - Batch Size: 128 - Warmup ratio: 0.00078125 - Warmup steps: 6250 - Goal steps: 8000000 - Total steps: 2775000 - Total training time (aprox): 64.2 days. ## Training loss !URL

transformers

# ALBERT XXLarge Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0003125 - Batch Size: 128 - Warmup ratio: 0.00078125 - Warmup steps: 3125 - Goal steps: 4000000 - Total steps: 1650000 - Total training time (aprox): 70.7 days. ## Training loss 

{"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]}

dccuchile/albert-xxlarge-spanish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #albert #pretraining #spanish #OpenCENIA #es #dataset-large_spanish_corpus #endpoints_compatible #region-us

# ALBERT XXLarge Spanish This is an ALBERT model trained on a big spanish corpora. The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0003125 - Batch Size: 128 - Warmup ratio: 0.00078125 - Warmup steps: 3125 - Goal steps: 4000000 - Total steps: 1650000 - Total training time (aprox): 70.7 days. ## Training loss !URL

fill-mask

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-recipe-1 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: 3.0641 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 3 | 3.2689 | | No log | 2.0 | 6 | 3.0913 | | No log | 3.0 | 9 | 3.0641 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-recipe-1", "results": []}]}

CennetOguz/distilbert-base-uncased-finetuned-recipe-1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-recipe-1 ========================================== 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: 3.0641 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: 256 * eval\_batch\_size: 256 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.2+cu102 * Datasets 1.18.3 * Tokenizers 0.11.0

fill-mask

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-recipe 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: 2.9488 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 3 | 3.2689 | | No log | 2.0 | 6 | 3.0913 | | No log | 3.0 | 9 | 3.0641 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-recipe", "results": []}]}

CennetOguz/distilbert-base-uncased-finetuned-recipe

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-recipe ======================================== 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: 2.9488 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: 256 * eval\_batch\_size: 256 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.2+cu102 * Datasets 1.18.3 * Tokenizers 0.11.0

text-generation

transformers

# Lego Batman DialoGPT Model

{"tags": ["conversational"]}

Chae/botman

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Lego Batman DialoGPT Model

text-generation

transformers

# Model trained on F.R.I.E.N.D.S dialogue

{"tags": ["conversational"]}

Chakita/Friends

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

# Model trained on F.R.I.E.N.D.S dialogue

fill-mask

transformers

Kannada BERT model finetuned on a news corpus --- language: - kn thumbnail: tags: - Masked Language model - Autocomplete license: mit datasets: - custom data set of Kannada news ---

{}

Chakita/KNUBert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Kannada BERT model finetuned on a news corpus --- language: - kn thumbnail: tags: - Masked Language model - Autocomplete license: mit datasets: - custom data set of Kannada news ---

fill-mask

transformers

RoBERTa model trained on Kannada news corpus.

{"tags": ["masked-lm", "fill-in-the-blanks"]}

Chakita/KROBERT

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #masked-lm #fill-in-the-blanks #autotrain_compatible #endpoints_compatible #region-us

RoBERTa model trained on Kannada news corpus.

fill-mask

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. --> # Kalbert This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on a kannada news dataset. It achieves the following results on the evaluation set: - Loss: 1.5324 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.5835 | 1.0 | 3953 | 1.7985 | | 1.6098 | 2.0 | 7906 | 1.7434 | | 1.5266 | 3.0 | 11859 | 1.6934 | | 1.5179 | 4.0 | 15812 | 1.6665 | | 1.5459 | 5.0 | 19765 | 1.6135 | | 1.5511 | 6.0 | 23718 | 1.6002 | | 1.5209 | 7.0 | 27671 | 1.5657 | | 1.5413 | 8.0 | 31624 | 1.5578 | | 1.4828 | 9.0 | 35577 | 1.5465 | | 1.4651 | 10.0 | 39530 | 1.5451 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Kalbert", "results": []}]}

Chakita/Kalbert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #albert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us

Kalbert ======= This model is a fine-tuned version of ai4bharat/indic-bert on a kannada news dataset. It achieves the following results on the evaluation set: * Loss: 1.5324 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: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.25.1 * Pytorch 1.13.0+cu116 * Datasets 2.8.0 * Tokenizers 0.13.2

fill-mask

transformers

RoBERTa model trained on OSCAR Kannada corpus.

{"tags": ["masked-lm", "fill-in-the-blanks"]}

Chakita/KannadaBERT

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #masked-lm #fill-in-the-blanks #autotrain_compatible #endpoints_compatible #region-us

RoBERTa model trained on OSCAR Kannada corpus.

text-generation

transformers

#help why did i feed this bot the bee movie

{"tags": ["conversational"]}

Chalponkey/DialoGPT-small-Barry

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#help why did i feed this bot the bee movie

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

ChaseBread/DialoGPT-small-harrypotter

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

## Model based on [Ko-GPT-Trinity 1.2B (v0.5)](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5) ## Example ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained( "CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper", revision="punct_wrapper-related_words-overfit", # or punct_wrapper-related_words-minevalloss bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", ) model = AutoModelForCausalLM.from_pretrained( "CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper", revision="punct_wrapper-related_words-overfit", # or punct_wrapper-related_words-minevalloss pad_token_id=tokenizer.eos_token_id, ).to(device="cuda") model.eval() prompt = "석양이 보이는 경치" wrapped_prompt = f"@{prompt}@<usr>\n" with torch.no_grad(): tokens = tokenizer.encode(wrapped_prompt, return_tensors="pt").to(device="cuda") gen_tokens = model.generate( tokens, max_length=64, repetition_penalty=2.0, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, top_k=16, top_p=0.8, ) generated = tokenizer.decode(gen_tokens[0][len(tokens[0]):]) print(generated) # 해가 지고 있을 무렵 # 나는 석양을 보러 간다 # 붉은 하늘과 하얀 구름이 나를 반겨줄 것 같아서리 # 하지만 내가 본 해는 저물어만 가고 # 구름마저 자취를 감춘 어둠만이 남아있을 뿐이네 # 내가 탄 배는 보이지도 않고 ```

{"language": ["ko"], "license": "cc-by-nc-sa-4.0", "tags": ["gpt2"]}

CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper

2022-03-02T23:29:04+00:00

TAGS #gpt2 #ko #license-cc-by-nc-sa-4.0 #region-us

## Model based on Ko-GPT-Trinity 1.2B (v0.5) ## Example

question-answering

transformers

This question answering model was fine tuned to detect negation expressions How to use: question: negation context: That is not safe! Answer: not question: negation context: Weren't we going to go to the moon? Answer: Weren't

{}

Ching/negation_detector

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us

This question answering model was fine tuned to detect negation expressions How to use: question: negation context: That is not safe! Answer: not question: negation context: Weren't we going to go to the moon? Answer: Weren't

text-generation

transformers

Donald Trump DialoGPT Model built by following tutorial by [Ruolin Zheng](https://youtu.be/Rk8eM1p_xgM). The data used for training was 2020 presidential debate. More work is needed to optimize it. I don't have access to larger VRAM.

{"tags": ["conversational"]}

Chiuchiyin/DialoGPT-small-Donald

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Donald Trump DialoGPT Model built by following tutorial by Ruolin Zheng. The data used for training was 2020 presidential debate. More work is needed to optimize it. I don't have access to larger VRAM.

text-generation

transformers

# CMJS DialoGPT Model

{"tags": ["conversational"]}

ChrisVCB/DialoGPT-medium-cmjs

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# CMJS DialoGPT Model

text-generation

transformers

# Eddie Jones DialoGPT Model

{"tags": ["conversational"]}

ChrisVCB/DialoGPT-medium-ej

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Eddie Jones DialoGPT Model

depth-estimation

# MADNet Keras MADNet is a deep stereo depth estimation model. Its key defining features are: 1. It has a light-weight architecture which means it has low latency. 2. It supports self-supervised training, so it can be conveniently adapted in the field with no training data. 3. It's a stereo depth model, which means it's capable of high accuracy. The MADNet weights in this repository were trained using a Tensorflow 2 / Keras implementation of the original code. The model was created using the Keras Functional API, which enables the following features: 1. Good optimization. 2. High level Keras methods (.fit, .predict and .evaluate). 3. Little boilerplate code. 4. Decent support from external packages (like Weights and Biases). 5. Callbacks. The weights provided were either trained on the 2012 / 2015 kitti stereo dataset or flyingthings-3d dataset. The weights of the pretrained models from the original paper (tf1_conversion_kitti.h5 and tf1_conversion_synthetic.h5) are provided in tensorflow 2 format. The TF1 weights help speed up fine-tuning, but its recommended to use either synthetic.h5 (trained on flyingthings-3d) or kitti.h5 (trained on 2012 and 2015 kitti stereo datasets). **Abstract**: Deep convolutional neural networks trained end-to-end are the undisputed state-of-the-art methods to regress dense disparity maps directly from stereo pairs. However, such methods suffer from notable accuracy drops when exposed to scenarios significantly different from those seen in the training phase (e.g.real vs synthetic images, indoor vs outdoor, etc). As it is unlikely to be able to gather enough samples to achieve effective training/ tuning in any target domain, we propose to perform unsupervised and continuous online adaptation of a deep stereo network in order to preserve its accuracy independently of the sensed environment. However, such a strategy can be extremely demanding regarding computational resources and thus not enabling real-time performance. Therefore, we address this side effect by introducing a new lightweight, yet effective, deep stereo architecture Modularly ADaptive Network (MADNet) and by developing Modular ADaptation (MAD), an algorithm to train independently only sub-portions of our model. By deploying MADNet together with MAD we propose the first ever realtime self-adaptive deep stereo system. ## Usage Instructions See the accompanying codes readme for details on how to perform training and inferencing with the model: [madnet-deep-stereo-with-keras](https://github.com/ChristianOrr/madnet-deep-stereo-with-keras). ## Training ### TF1 Kitti and TF1 Synthetic Training details for the TF1 weights are available in the supplementary material (at the end) of this paper: [Real-time self-adaptive deep stereo](https://arxiv.org/abs/1810.05424) ### Synthetic The synthetic model was finetuned using the tf1 synthetic weights. It was trained on the flyingthings-3d dataset with the following parameters: - Steps: 1.5 million - Learning Rate: 0.0001 - Decay Rate: 0.999 - Minimum Learning Rate Cap: 0.000001 - Batch Size: 1 - Optimizer: Adam - Image Height: 480 - Image Width: 640 ### Kitti The kitti model was finetuned using the synthetic weights. Tensorboard events file is available in the logs directory. It was trained on the 2012 and 2015 kitti stereo dataset with the following parameters: - Steps: 0.5 million - Learning Rate: 0.0001 - Decay Rate: 0.999 - Minimum Learning Rate Cap: 0.0000001 - Batch Size: 1 - Optimizer: Adam - Image Height: 480 - Image Width: 640 ## BibTeX entry and citation info ```bibtex @InProceedings{Tonioni_2019_CVPR, author = {Tonioni, Alessio and Tosi, Fabio and Poggi, Matteo and Mattoccia, Stefano and Di Stefano, Luigi}, title = {Real-time self-adaptive deep stereo}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2019} } ``` ```bibtex @article{Poggi2021continual, author={Poggi, Matteo and Tonioni, Alessio and Tosi, Fabio and Mattoccia, Stefano and Di Stefano, Luigi}, title={Continual Adaptation for Deep Stereo}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, year={2021} } ``` ```bibtex @InProceedings{MIFDB16, author = "N. Mayer and E. Ilg and P. Hausser and P. Fischer and D. Cremers and A. Dosovitskiy and T. Brox", title = "A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation", booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)", year = "2016", note = "arXiv:1512.02134", url = "http://lmb.informatik.uni-freiburg.de/Publications/2016/MIFDB16" } ``` ```bibtex @INPROCEEDINGS{Geiger2012CVPR, author = {Andreas Geiger and Philip Lenz and Raquel Urtasun}, title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite}, booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2012} } ``` ```bibtex @INPROCEEDINGS{Menze2015CVPR, author = {Moritz Menze and Andreas Geiger}, title = {Object Scene Flow for Autonomous Vehicles}, booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2015} } ```

{"license": "apache-2.0", "tags": ["vision", "deep-stereo", "depth-estimation", "Tensorflow2", "Keras"], "datasets": ["flyingthings-3d", "kitti"]}

ChristianOrr/madnet_keras

2022-03-02T23:29:04+00:00

TAGS #tensorboard #vision #deep-stereo #depth-estimation #Tensorflow2 #Keras #dataset-flyingthings-3d #dataset-kitti #arxiv-1810.05424 #license-apache-2.0 #region-us

# MADNet Keras MADNet is a deep stereo depth estimation model. Its key defining features are: 1. It has a light-weight architecture which means it has low latency. 2. It supports self-supervised training, so it can be conveniently adapted in the field with no training data. 3. It's a stereo depth model, which means it's capable of high accuracy. The MADNet weights in this repository were trained using a Tensorflow 2 / Keras implementation of the original code. The model was created using the Keras Functional API, which enables the following features: 1. Good optimization. 2. High level Keras methods (.fit, .predict and .evaluate). 3. Little boilerplate code. 4. Decent support from external packages (like Weights and Biases). 5. Callbacks. The weights provided were either trained on the 2012 / 2015 kitti stereo dataset or flyingthings-3d dataset. The weights of the pretrained models from the original paper (tf1_conversion_kitti.h5 and tf1_conversion_synthetic.h5) are provided in tensorflow 2 format. The TF1 weights help speed up fine-tuning, but its recommended to use either synthetic.h5 (trained on flyingthings-3d) or kitti.h5 (trained on 2012 and 2015 kitti stereo datasets). Abstract: Deep convolutional neural networks trained end-to-end are the undisputed state-of-the-art methods to regress dense disparity maps directly from stereo pairs. However, such methods suffer from notable accuracy drops when exposed to scenarios significantly different from those seen in the training phase (e.g.real vs synthetic images, indoor vs outdoor, etc). As it is unlikely to be able to gather enough samples to achieve effective training/ tuning in any target domain, we propose to perform unsupervised and continuous online adaptation of a deep stereo network in order to preserve its accuracy independently of the sensed environment. However, such a strategy can be extremely demanding regarding computational resources and thus not enabling real-time performance. Therefore, we address this side effect by introducing a new lightweight, yet effective, deep stereo architecture Modularly ADaptive Network (MADNet) and by developing Modular ADaptation (MAD), an algorithm to train independently only sub-portions of our model. By deploying MADNet together with MAD we propose the first ever realtime self-adaptive deep stereo system. ## Usage Instructions See the accompanying codes readme for details on how to perform training and inferencing with the model: madnet-deep-stereo-with-keras. ## Training ### TF1 Kitti and TF1 Synthetic Training details for the TF1 weights are available in the supplementary material (at the end) of this paper: Real-time self-adaptive deep stereo ### Synthetic The synthetic model was finetuned using the tf1 synthetic weights. It was trained on the flyingthings-3d dataset with the following parameters: - Steps: 1.5 million - Learning Rate: 0.0001 - Decay Rate: 0.999 - Minimum Learning Rate Cap: 0.000001 - Batch Size: 1 - Optimizer: Adam - Image Height: 480 - Image Width: 640 ### Kitti The kitti model was finetuned using the synthetic weights. Tensorboard events file is available in the logs directory. It was trained on the 2012 and 2015 kitti stereo dataset with the following parameters: - Steps: 0.5 million - Learning Rate: 0.0001 - Decay Rate: 0.999 - Minimum Learning Rate Cap: 0.0000001 - Batch Size: 1 - Optimizer: Adam - Image Height: 480 - Image Width: 640 ## BibTeX entry and citation info

transformers

# IndoBERT (Indonesian BERT Model) ## Model description ELECTRA is a new method for self-supervised language representation learning. This repository contains the pre-trained Electra Base model (tensorflow 1.15.0) trained in a Large Indonesian corpus (~16GB of raw text | ~2B indonesian words). IndoELECTRA is a pre-trained language model based on ELECTRA architecture for the Indonesian Language. This model is base version which use electra-base config. ## Intended uses & limitations #### How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ChristopherA08/IndoELECTRA") model = AutoModel.from_pretrained("ChristopherA08/IndoELECTRA") tokenizer.encode("hai aku mau makan.") [2, 8078, 1785, 2318, 1946, 18, 4] ``` ## Training procedure The training of the model has been performed using Google's original Tensorflow code on eight core Google Cloud TPU v2. We used a Google Cloud Storage bucket, for persistent storage of training data and models.

{"language": "id", "datasets": ["oscar"]}

ChristopherA08/IndoELECTRA

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #electra #pretraining #id #dataset-oscar #endpoints_compatible #region-us

# IndoBERT (Indonesian BERT Model) ## Model description ELECTRA is a new method for self-supervised language representation learning. This repository contains the pre-trained Electra Base model (tensorflow 1.15.0) trained in a Large Indonesian corpus (~16GB of raw text | ~2B indonesian words). IndoELECTRA is a pre-trained language model based on ELECTRA architecture for the Indonesian Language. This model is base version which use electra-base config. ## Intended uses & limitations #### How to use ## Training procedure The training of the model has been performed using Google's original Tensorflow code on eight core Google Cloud TPU v2. We used a Google Cloud Storage bucket, for persistent storage of training data and models.

text-generation

transformers

# Harry Potter DialoGPT MOdel

{"tags": ["conversational"]}

Chuah/DialoGPT-small-harrypotter

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

text-generation

transformers

# Dr. Fauci DialoGPT Model

{"tags": ["conversational"]}

ChukSamuels/DialoGPT-small-Dr.FauciBot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Dr. Fauci DialoGPT Model

copied from boris

{}

Cilan/dalle-knockoff

2022-03-02T23:29:04+00:00

TAGS #region-us

copied from boris

transformers

## Japanese ELECTRA-small We provide a Japanese **ELECTRA-Small** model, as described in [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). Our pretraining process employs subword units derived from the [Japanese Wikipedia](https://dumps.wikimedia.org/jawiki/latest), using the [Byte-Pair Encoding](https://www.aclweb.org/anthology/P16-1162.pdf) method and building on an initial tokenization with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd). For optimal performance, please take care to set your MeCab dictionary appropriately. ## How to use the discriminator in `transformers` ``` from transformers import BertJapaneseTokenizer, ElectraForPreTraining tokenizer = BertJapaneseTokenizer.from_pretrained('Cinnamon/electra-small-japanese-discriminator', mecab_kwargs={"mecab_option": "-d /usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd"}) model = ElectraForPreTraining.from_pretrained('Cinnamon/electra-small-japanese-discriminator') ```

{"language": "ja", "license": "apache-2.0"}

Cinnamon/electra-small-japanese-discriminator

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #electra #pretraining #ja #license-apache-2.0 #endpoints_compatible #region-us

## Japanese ELECTRA-small We provide a Japanese ELECTRA-Small model, as described in ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. Our pretraining process employs subword units derived from the Japanese Wikipedia, using the Byte-Pair Encoding method and building on an initial tokenization with mecab-ipadic-NEologd. For optimal performance, please take care to set your MeCab dictionary appropriately. ## How to use the discriminator in 'transformers'

fill-mask

transformers

## Japanese ELECTRA-small We provide a Japanese **ELECTRA-Small** model, as described in [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). Our pretraining process employs subword units derived from the [Japanese Wikipedia](https://dumps.wikimedia.org/jawiki/latest), using the [Byte-Pair Encoding](https://www.aclweb.org/anthology/P16-1162.pdf) method and building on an initial tokenization with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd). For optimal performance, please take care to set your MeCab dictionary appropriately. ``` # ELECTRA-small generator usage from transformers import BertJapaneseTokenizer, ElectraForMaskedLM tokenizer = BertJapaneseTokenizer.from_pretrained('Cinnamon/electra-small-japanese-generator', mecab_kwargs={"mecab_option": "-d /usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd"}) model = ElectraForMaskedLM.from_pretrained('Cinnamon/electra-small-japanese-generator') ```

{"language": "ja"}

Cinnamon/electra-small-japanese-generator

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #electra #fill-mask #ja #autotrain_compatible #endpoints_compatible #region-us

## Japanese ELECTRA-small We provide a Japanese ELECTRA-Small model, as described in ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. Our pretraining process employs subword units derived from the Japanese Wikipedia, using the Byte-Pair Encoding method and building on an initial tokenization with mecab-ipadic-NEologd. For optimal performance, please take care to set your MeCab dictionary appropriately.

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

Ciruzzo/DialoGPT-small-harrypotter

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

text-generation

transformers

# RickBot built for [Chai](https://chai.ml/) Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)

{"tags": ["conversational"]}

ClaudeCOULOMBE/RickBot

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

# RickBot built for Chai Make your own here

zero-shot-classification

transformers

ETH Zeroshot

{"datasets": ["multi_nli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "ETH", "candidate_labels": "Location & Address, Employment, Organizational, Name, Service, Studies, Science", "hypothesis_template": "This is {}."}]}

ClaudeYang/awesome_fb_model

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bart #text-classification #zero-shot-classification #dataset-multi_nli #autotrain_compatible #endpoints_compatible #region-us

ETH Zeroshot

text-generation

# My Awesome Model

{"tags": ["conversational"]}

ClydeWasTaken/DialoGPT-small-joshua

2022-03-02T23:29:04+00:00

TAGS #conversational #region-us

# My Awesome Model

text-generation

transformers

# Cartman DialoGPT Model

{"tags": ["conversational"]}

CodeDanCode/CartmenBot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Cartman DialoGPT Model

text-generation

transformers

# SouthPark Kyle Bot

{"tags": ["conversational"]}

CodeDanCode/SP-KyleBot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# SouthPark Kyle Bot

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

CoderBoy432/DialoGPT-small-harrypotter

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

text-generation

transformers

Chat with the model: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-marxbot") model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-marxbot") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("MarxBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

{"tags": ["conversational"]}

CoderEFE/DialoGPT-marxbot

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

Chat with the model:

text-classification

transformers

# bart-faithful-summary-detector ## Model description A BART (base) model trained to classify whether a summary is *faithful* to the original article. See our [paper in NAACL'21](https://www.seas.upenn.edu/~sihaoc/static/pdf/CZSR21.pdf) for details. ## Usage Concatenate a summary and a source document as input (note that the summary needs to be the **first** sentence). Here's an example usage (with PyTorch) ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CogComp/bart-faithful-summary-detector") model = AutoModelForSequenceClassification.from_pretrained("CogComp/bart-faithful-summary-detector") article = "Ban Ki-Moon was re-elected for a second term by the UN General Assembly, unopposed and unanimously, on 21 June 2011." bad_summary = "Ban Ki-moon was elected for a second term in 2007." good_summary = "Ban Ki-moon was elected for a second term in 2011." bad_pair = tokenizer(text=bad_summary, text_pair=article, return_tensors='pt') good_pair = tokenizer(text=good_summary, text_pair=article, return_tensors='pt') bad_score = model(**bad_pair) good_score = model(**good_pair) print(good_score[0][:, 1] > bad_score[0][:, 1]) # True, label mapping: "0" -> "Hallucinated" "1" -> "Faithful" ``` ### BibTeX entry and citation info ```bibtex @inproceedings{CZSR21, author = {Sihao Chen and Fan Zhang and Kazoo Sone and Dan Roth}, title = {{Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection}}, booktitle = {NAACL}, year = {2021} } ```

{"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["text-classification", "bart", "xsum"], "datasets": ["xsum"], "thumbnail": "https://cogcomp.seas.upenn.edu/images/logo.png", "widget": [{"text": "<s> Ban Ki-moon was elected for a second term in 2007. </s></s> Ban Ki-Moon was re-elected for a second term by the UN General Assembly, unopposed and unanimously, on 21 June 2011."}, {"text": "<s> Ban Ki-moon was elected for a second term in 2011. </s></s> Ban Ki-Moon was re-elected for a second term by the UN General Assembly, unopposed and unanimously, on 21 June 2011."}]}

CogComp/bart-faithful-summary-detector

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bart #text-classification #xsum #en #dataset-xsum #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# bart-faithful-summary-detector ## Model description A BART (base) model trained to classify whether a summary is *faithful* to the original article. See our paper in NAACL'21 for details. ## Usage Concatenate a summary and a source document as input (note that the summary needs to be the first sentence). Here's an example usage (with PyTorch) ### BibTeX entry and citation info

fill-mask

transformers

# roberta-temporal-predictor A RoBERTa-base model that is fine-tuned on the [The New York Times Annotated Corpus](https://catalog.ldc.upenn.edu/LDC2008T19) to predict temporal precedence of two events. This is used as the ``temporality prediction'' component in our ROCK framework for reasoning about commonsense causality. See our [paper](https://arxiv.org/abs/2202.00436) for more details. # Usage You can directly use this model for filling-mask tasks, as shown in the example widget. However, for better temporal inference, it is recommended to symmetrize the outputs as $$ P(E_1 \prec E_2) = \frac{1}{2} (f(E_1,E_2) + f(E_2,E_1)) $$ where ``f(E_1,E_2)`` denotes the predicted probability for ``E_1`` to occur preceding ``E_2``. For simplicity, we implement the following TempPredictor class that incorporate this symmetrization automatically. Below is an example usage for the ``TempPredictor`` class: ```python from transformers import (RobertaForMaskedLM, RobertaTokenizer) from src.temp_predictor import TempPredictor TORCH_DEV = "cuda:0" # change as needed tp_roberta_ft = src.TempPredictor( model=RobertaForMaskedLM.from_pretrained("CogComp/roberta-temporal-predictor"), tokenizer=RobertaTokenizer.from_pretrained("CogComp/roberta-temporal-predictor"), device=TORCH_DEV ) E1 = "The man turned on the faucet." E2 = "Water flows out." t12 = tp_roberta_ft(E1, E2, top_k=5) print(f"P('{E1}' before '{E2}'): {t12}") ``` # BibTeX entry and citation info ```bib @misc{zhang2022causal, title={Causal Inference Principles for Reasoning about Commonsense Causality}, author={Jiayao Zhang and Hongming Zhang and Dan Roth and Weijie J. Su}, year={2022}, eprint={2202.00436}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"license": "mit", "widget": [{"text": "The man turned on the faucet <mask> water flows out."}, {"text": "The woman received her pension <mask> she retired."}]}

CogComp/roberta-temporal-predictor

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #arxiv-2202.00436 #license-mit #autotrain_compatible #endpoints_compatible #region-us

# roberta-temporal-predictor A RoBERTa-base model that is fine-tuned on the The New York Times Annotated Corpus to predict temporal precedence of two events. This is used as the ''temporality prediction'' component in our ROCK framework for reasoning about commonsense causality. See our paper for more details. # Usage You can directly use this model for filling-mask tasks, as shown in the example widget. However, for better temporal inference, it is recommended to symmetrize the outputs as $$ P(E_1 \prec E_2) = \frac{1}{2} (f(E_1,E_2) + f(E_2,E_1)) $$ where ''f(E_1,E_2)'' denotes the predicted probability for ''E_1'' to occur preceding ''E_2''. For simplicity, we implement the following TempPredictor class that incorporate this symmetrization automatically. Below is an example usage for the ''TempPredictor'' class: # BibTeX entry and citation info

feature-extraction

transformers

해당 모델은 [해당 사이트](https://huggingface.co/gpt2-medium)에서 가져온 모델입니다. 해당 모델은 [Teachable NLP](https://ainize.ai/teachable-nlp) 서비스에서 사용됩니다.

{}

ComCom/gpt2-large

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us

해당 모델은 해당 사이트에서 가져온 모델입니다. 해당 모델은 Teachable NLP 서비스에서 사용됩니다.

feature-extraction

transformers

해당 모델은 [해당 사이트](https://huggingface.co/gpt2-medium)에서 가져온 모델입니다. 해당 모델은 [Teachable NLP](https://ainize.ai/teachable-nlp) 서비스에서 사용됩니다.

{}

ComCom/gpt2-medium

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us

해당 모델은 해당 사이트에서 가져온 모델입니다. 해당 모델은 Teachable NLP 서비스에서 사용됩니다.

feature-extraction

transformers

해당 모델은 [해당 사이트](https://huggingface.co/gpt2)에서 가져온 모델입니다. 해당 모델은 [Teachable NLP](https://ainize.ai/teachable-nlp) 서비스에서 사용됩니다.

{}

ComCom/gpt2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us

해당 모델은 해당 사이트에서 가져온 모델입니다. 해당 모델은 Teachable NLP 서비스에서 사용됩니다.

text-generation

transformers

# neurotitle-rugpt3-small Model based on [ruGPT-3](https://huggingface.co/sberbank-ai) for generating scientific paper titles. Trained on [All NeurIPS (NIPS) Papers](https://www.kaggle.com/rowhitswami/nips-papers-1987-2019-updated) dataset. Use exclusively as a crazier alternative to SCIgen. ## Made with Cometrain AlphaML & AutoCode This model was automatically fine-tuned using the Cometrain AlphaML framework and tested with CI/CD pipeline made by Cometrain AutoCode ## Cometrain AlphaML command ```shell $ cometrain create --name neurotitle --model auto --task task_0x2231.txt --output transformers ``` ## Use with Transformers ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model="CometrainResearch/neurotitle-rugpt3-small") generator("BERT:", max_length=50) ```

{"language": ["ru", "en"], "license": "mit", "tags": ["Cometrain AutoCode", "Cometrain AlphaML"], "datasets": ["All-NeurIPS-Papers-Scraper"], "widget": [{"text": "NIPSE:", "example_title": "NIPS"}, {"text": "Learning CNN", "example_title": "Learning CNN"}, {"text": "ONNX:", "example_title": "ONNX"}, {"text": "BERT:", "example_title": "BERT"}], "inference": {"parameters": {"temperature": 0.9}}}

cometrain/neurotitle-rugpt3-small

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #Cometrain AutoCode #Cometrain AlphaML #ru #en #dataset-All-NeurIPS-Papers-Scraper #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# neurotitle-rugpt3-small Model based on ruGPT-3 for generating scientific paper titles. Trained on All NeurIPS (NIPS) Papers dataset. Use exclusively as a crazier alternative to SCIgen. ## Made with Cometrain AlphaML & AutoCode This model was automatically fine-tuned using the Cometrain AlphaML framework and tested with CI/CD pipeline made by Cometrain AutoCode ## Cometrain AlphaML command ## Use with Transformers

text-generation

transformers

# Rick DialoGPT Model

{"tags": ["conversational"]}

Connor/DialoGPT-small-rick

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Rick DialoGPT Model

text-generation

transformers

#enlightened GPT model

{"tags": ["conversational"]}

Connorvr/BrightBot-small

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#enlightened GPT model

text-generation

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. --> # model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown 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: 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: 3.0 ### Training results ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "model", "results": []}]}

Connorvr/TeachingGen

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# model This model is a fine-tuned version of gpt2 on an unknown 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: 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: 3.0 ### Training results ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6

@inproceedings{wan2020bringing, title={Bringing Old Photos Back to Life}, author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={2747--2757}, year={2020} } @article{wan2020old, title={Old Photo Restoration via Deep Latent Space Translation}, author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang}, journal={arXiv preprint arXiv:2009.07047}, year={2020} }

{"language": ["en"], "license": "mit", "tags": ["image_restoration", "superresolution"], "thumbnail": "https://github.com/Nick-Harvey/for_my_abuela/blob/master/cuban_large.jpg"}

Coolhand/Abuela

2022-03-02T23:29:04+00:00

TAGS #image_restoration #superresolution #en #license-mit #region-us

@inproceedings{wan2020bringing, title={Bringing Old Photos Back to Life}, author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={2747--2757}, year={2020} } @article{wan2020old, title={Old Photo Restoration via Deep Latent Space Translation}, author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang}, journal={arXiv preprint arXiv:2009.07047}, year={2020} }

text-generation

transformers

# Atakan DialoGPT Model

{"tags": ["conversational"]}

CopymySkill/DialoGPT-medium-atakan

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Atakan DialoGPT Model

text-generation

transformers

#DiabloGPT Captain Price (Extended)

{"tags": ["conversational"]}

Corvus/DialoGPT-medium-CaptainPrice-Extended

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#DiabloGPT Captain Price (Extended)

text-generation

transformers

# Captain Price DialoGPT Model

{"tags": ["conversational"]}

Corvus/DialoGPT-medium-CaptainPrice

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Captain Price DialoGPT Model

text-classification

transformers

### Description A Multi-label text classification model trained on a customer feedback data using DistilBert. Possible labels are: - Delivery (delivery status, time of arrival, etc.) - Return (return confirmation, return label requests, etc.) - Product (quality, complaint, etc.) - Monetary (pending transactions, refund, etc.) ### Usage ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_mlc_v7_distil") model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_mlc_v7_distil") ```

{"language": "en", "license": "mit", "tags": ["multi-label"], "widget": [{"text": "I would like to return these pants and shoes"}]}

CouchCat/ma_mlc_v7_distil

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #text-classification #multi-label #en #license-mit #autotrain_compatible #endpoints_compatible #region-us

### Description A Multi-label text classification model trained on a customer feedback data using DistilBert. Possible labels are: - Delivery (delivery status, time of arrival, etc.) - Return (return confirmation, return label requests, etc.) - Product (quality, complaint, etc.) - Monetary (pending transactions, refund, etc.) ### Usage

token-classification

transformers

### Description A Named Entity Recognition model trained on a customer feedback data using DistilBert. Possible labels are: - PRD: for certain products - BRND: for brands ### Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_ner_v6_distil") model = AutoModelForTokenClassification.from_pretrained("CouchCat/ma_ner_v6_distil") ```

{"language": "en", "license": "mit", "tags": ["ner"], "widget": [{"text": "These shoes from Adidas fit quite well"}]}

CouchCat/ma_ner_v6_distil

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #token-classification #ner #en #license-mit #autotrain_compatible #endpoints_compatible #region-us

### Description A Named Entity Recognition model trained on a customer feedback data using DistilBert. Possible labels are: - PRD: for certain products - BRND: for brands ### Usage

token-classification

transformers

### Description A Named Entity Recognition model trained on a customer feedback data using DistilBert. Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples: - PROD: for certain products - BRND: for brands - PERS: people names The following tags are simply in place to help better categorize the previous tags - MATR: relating to materials, e.g. cloth, leather, seam, etc. - TIME: time related entities - MISC: any other entity that might skew the results ### Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_ner_v7_distil") model = AutoModelForTokenClassification.from_pretrained("CouchCat/ma_ner_v7_distil") ```

{"language": "en", "license": "mit", "tags": ["ner"], "widget": [{"text": "These shoes I recently bought from Tommy Hilfiger fit quite well. The shirt, however, has got a hole"}]}

CouchCat/ma_ner_v7_distil

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #token-classification #ner #en #license-mit #autotrain_compatible #endpoints_compatible #region-us

### Description A Named Entity Recognition model trained on a customer feedback data using DistilBert. Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples: - PROD: for certain products - BRND: for brands - PERS: people names The following tags are simply in place to help better categorize the previous tags - MATR: relating to materials, e.g. cloth, leather, seam, etc. - TIME: time related entities - MISC: any other entity that might skew the results ### Usage

text-classification

transformers

### Description A Sentiment Analysis model trained on customer feedback data using DistilBert. Possible sentiments are: * negative * neutral * positive ### Usage ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_sa_v7_distil") model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_sa_v7_distil") ```

{"language": "en", "license": "mit", "tags": ["sentiment-analysis"], "widget": [{"text": "I am disappointed in the terrible quality of my dress"}]}

CouchCat/ma_sa_v7_distil

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #text-classification #sentiment-analysis #en #license-mit #autotrain_compatible #endpoints_compatible #region-us

### Description A Sentiment Analysis model trained on customer feedback data using DistilBert. Possible sentiments are: * negative * neutral * positive ### Usage

text-generation

Arthur Morgan DialoGPT Model

{"tags": ["conversational"]}

Coyotl/DialoGPT-test-last-arthurmorgan

2022-03-02T23:29:04+00:00

TAGS #conversational #region-us

Arthur Morgan DialoGPT Model

text-generation

transformers

# Arthur Morgan DialoGPT Model

{"tags": ["conversational"]}

Coyotl/DialoGPT-test2-arthurmorgan

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Arthur Morgan DialoGPT Model

text-generation

# DialoGPT Arthur Morgan

{"tags": ["conversational"]}

Coyotl/DialoGPT-test3-arthurmorgan

2022-03-02T23:29:04+00:00

TAGS #conversational #region-us

# DialoGPT Arthur Morgan

text-generation

transformers

@Piglin Talks Harry Potter

{"tags": ["conversational"]}

CracklesCreeper/Piglin-Talks-Harry-Potter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

@Piglin Talks Harry Potter

feature-extraction

sentence-transformers

# A model.

{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"}

Craig/mGqFiPhu

2022-03-02T23:29:04+00:00

TAGS #sentence-transformers #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us

# A model.

feature-extraction

sentence-transformers

# sentence-transformers/paraphrase-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This is a clone of the original model, with `pipeline_tag` metadata changed to `feature-extraction`, so it can just return the embedded vector. Otherwise it is unchanged. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-MiniLM-L6-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"}

Craig/paraphrase-MiniLM-L6-v2

2022-03-02T23:29:04+00:00

TAGS #sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us

# sentence-transformers/paraphrase-MiniLM-L6-v2 This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This is a clone of the original model, with 'pipeline_tag' metadata changed to 'feature-extraction', so it can just return the embedded vector. Otherwise it is unchanged. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Full Model Architecture ## Citing & Authors This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

text-classification

transformers

# Model Finetuned from BERT-base for - Problem type: Multi-class Classification - Model ID: 25805800 ## Validation Metrics - Loss: 0.4422711133956909 - Accuracy: 0.8615328555811976 - Macro F1: 0.8642434650461513 - Micro F1: 0.8615328555811976 - Weighted F1: 0.8617743626671308 - Macro Precision: 0.8649112225076049 - Micro Precision: 0.8615328555811976 - Weighted Precision: 0.8625407179375096 - Macro Recall: 0.8640777539828228 - Micro Recall: 0.8615328555811976 - Weighted Recall: 0.8615328555811976 ## Usage ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Crasher222/kaggle-comp-test") tokenizer = AutoTokenizer.from_pretrained("Crasher222/kaggle-comp-test") inputs = tokenizer("I am in love with you", return_tensors="pt") outputs = model(**inputs) ```

{"language": "en", "tags": "autonlp", "datasets": ["Crasher222/autonlp-data-kaggle-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 60.744727079482495}

Crasher222/kaggle-comp-test

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #autonlp #en #dataset-Crasher222/autonlp-data-kaggle-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us

# Model Finetuned from BERT-base for - Problem type: Multi-class Classification - Model ID: 25805800 ## Validation Metrics - Loss: 0.4422711133956909 - Accuracy: 0.8615328555811976 - Macro F1: 0.8642434650461513 - Micro F1: 0.8615328555811976 - Weighted F1: 0.8617743626671308 - Macro Precision: 0.8649112225076049 - Micro Precision: 0.8615328555811976 - Weighted Precision: 0.8625407179375096 - Macro Recall: 0.8640777539828228 - Micro Recall: 0.8615328555811976 - Weighted Recall: 0.8615328555811976 ## Usage

text-generation

transformers

hello

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CrisLeaf/generador-de-historias-de-tolkien

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

hello