Datasets:

ftopal

/

huggingface-models

like 0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-epochs15 This model is a fine-tuned version of [AKulk/wav2vec2-base-timit-epochs10](https://huggingface.co/AKulk/wav2vec2-base-timit-epochs10) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-epochs15", "results": []}]}

AKulk/wav2vec2-base-timit-epochs15

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

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us

# wav2vec2-base-timit-epochs15 This model is a fine-tuned version of AKulk/wav2vec2-base-timit-epochs10 on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-epochs5 This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-epochs5", "results": []}]}

AKulk/wav2vec2-base-timit-epochs5

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

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us

# wav2vec2-base-timit-epochs5 This model is a fine-tuned version of facebook/wav2vec2-lv-60-espeak-cv-ft on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

summarization

transformers

# summarization_fanpage128 This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on Fanpage dataset for Abstractive Summarization. It achieves the following results: - Loss: 1.5348 - Rouge1: 34.1882 - Rouge2: 15.7866 - Rougel: 25.141 - Rougelsum: 28.4882 - Gen Len: 69.3041 ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage-128") model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-fanpage-128") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/fanpage"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_fanpage128", "results": []}]}

ARTeLab/it5-summarization-fanpage

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

TAGS #transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us

# summarization_fanpage128 This model is a fine-tuned version of gsarti/it5-base on Fanpage dataset for Abstractive Summarization. It achieves the following results: - Loss: 1.5348 - Rouge1: 34.1882 - Rouge2: 15.7866 - Rougel: 25.141 - Rougelsum: 28.4882 - Gen Len: 69.3041 ## Usage ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3 More details and results in published work

summarization

transformers

# summarization_ilpost This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on IlPost dataset for Abstractive Summarization. It achieves the following results: - Loss: 1.6020 - Rouge1: 33.7802 - Rouge2: 16.2953 - Rougel: 27.4797 - Rougelsum: 30.2273 - Gen Len: 45.3175 ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-ilpost") model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-ilpost") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/ilpost"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_ilpost", "results": []}]}

ARTeLab/it5-summarization-ilpost

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

TAGS #transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/ilpost #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us

# summarization_ilpost This model is a fine-tuned version of gsarti/it5-base on IlPost dataset for Abstractive Summarization. It achieves the following results: - Loss: 1.6020 - Rouge1: 33.7802 - Rouge2: 16.2953 - Rougel: 27.4797 - Rougelsum: 30.2273 - Gen Len: 45.3175 ## Usage ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

summarization

transformers

# summarization_mlsum This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on MLSum-it for Abstractive Summarization. It achieves the following results: - Loss: 2.0190 - Rouge1: 19.3739 - Rouge2: 5.9753 - Rougel: 16.691 - Rougelsum: 16.7862 - Gen Len: 32.5268 ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum") model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-mlsum") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/mlsum-it"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_mlsum", "results": []}]}

ARTeLab/it5-summarization-mlsum

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

TAGS #transformers #pytorch #safetensors #t5 #text2text-generation #summarization #it #dataset-ARTeLab/mlsum-it #base_model-gsarti/it5-base #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us

# summarization_mlsum This model is a fine-tuned version of gsarti/it5-base on MLSum-it for Abstractive Summarization. It achieves the following results: - Loss: 2.0190 - Rouge1: 19.3739 - Rouge2: 5.9753 - Rougel: 16.691 - Rougelsum: 16.7862 - Gen Len: 32.5268 ## Usage ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3 More details and results in published work

summarization

transformers

# mbart-summarization-fanpage This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on Fanpage dataset for Abstractive Summarization. It achieves the following results: - Loss: 2.1833 - Rouge1: 36.5027 - Rouge2: 17.4428 - Rougel: 26.1734 - Rougelsum: 30.2636 - Gen Len: 75.2413 ## Usage ```python from transformers import MBartTokenizer, MBartForConditionalGeneration tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-fanpage") model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-fanpage") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/fanpage"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_fanpage4epoch", "results": []}]}

ARTeLab/mbart-summarization-fanpage

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

TAGS #transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/fanpage #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us

# mbart-summarization-fanpage This model is a fine-tuned version of facebook/mbart-large-cc25 on Fanpage dataset for Abstractive Summarization. It achieves the following results: - Loss: 2.1833 - Rouge1: 36.5027 - Rouge2: 17.4428 - Rougel: 26.1734 - Rougelsum: 30.2636 - Gen Len: 75.2413 ## Usage ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 More details and results in published work

summarization

transformers

# mbart_summarization_ilpost This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on IlPost dataset for Abstractive Summarization. It achieves the following results: - Loss: 2.3640 - Rouge1: 38.9101 - Rouge2: 21.384 - Rougel: 32.0517 - Rougelsum: 35.0743 - Gen Len: 39.8843 ## Usage ```python from transformers import MBartTokenizer, MBartForConditionalGeneration tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-ilpost") model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-ilpost") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/ilpost"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_ilpost", "results": []}]}

ARTeLab/mbart-summarization-ilpost

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

TAGS #transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/ilpost #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us

# mbart_summarization_ilpost This model is a fine-tuned version of facebook/mbart-large-cc25 on IlPost dataset for Abstractive Summarization. It achieves the following results: - Loss: 2.3640 - Rouge1: 38.9101 - Rouge2: 21.384 - Rougel: 32.0517 - Rougelsum: 35.0743 - Gen Len: 39.8843 ## Usage ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 More details and results in published work

summarization

transformers

# mbart_summarization_mlsum This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on mlsum-it for Abstractive Summarization. It achieves the following results: - Loss: 3.3336 - Rouge1: 19.3489 - Rouge2: 6.4028 - Rougel: 16.3497 - Rougelsum: 16.5387 - Gen Len: 33.5945 ## Usage ```python from transformers import MBartTokenizer, MBartForConditionalGeneration tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-mlsum") model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-mlsum") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/mlsum-it"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_mlsum", "results": []}]}

ARTeLab/mbart-summarization-mlsum

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

TAGS #transformers #pytorch #safetensors #mbart #text2text-generation #summarization #it #dataset-ARTeLab/mlsum-it #base_model-facebook/mbart-large-cc25 #autotrain_compatible #endpoints_compatible #has_space #region-us

# mbart_summarization_mlsum This model is a fine-tuned version of facebook/mbart-large-cc25 on mlsum-it for Abstractive Summarization. It achieves the following results: - Loss: 3.3336 - Rouge1: 19.3489 - Rouge2: 6.4028 - Rougel: 16.3497 - Rougelsum: 16.5387 - Gen Len: 33.5945 ## Usage ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 More details and results in published work

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # PENGMENGJIE-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.9.0 - Pytorch 1.7.1+cpu - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "PENGMENGJIE-finetuned-emotion", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}}]}]}

ASCCCCCCCC/PENGMENGJIE-finetuned-emotion

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

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# PENGMENGJIE-finetuned-emotion This model is a fine-tuned version of distilbert-base-uncased on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.9.0 - Pytorch 1.7.1+cpu - Datasets 1.17.0 - Tokenizers 0.10.3

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-chinese-finetuned-amazon_zh_20000 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1683 - Accuracy: 0.5224 - F1: 0.5194 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.2051 | 1.0 | 2500 | 1.1717 | 0.506 | 0.4847 | | 1.0035 | 2.0 | 5000 | 1.1683 | 0.5224 | 0.5194 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-chinese-finetuned-amazon_zh_20000", "results": []}]}

ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000

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

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

bert-base-chinese-finetuned-amazon\_zh\_20000 ============================================= This model is a fine-tuned version of bert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.1683 * Accuracy: 0.5224 * F1: 0.5194 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.9.1 * Datasets 1.18.3 * Tokenizers 0.10.3

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-chinese-amazon_zh_20000 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1518 - Accuracy: 0.5092 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.196 | 1.0 | 1250 | 1.1518 | 0.5092 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-chinese-amazon_zh_20000", "results": []}]}

ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000

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

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

distilbert-base-chinese-amazon\_zh\_20000 ========================================= This model is a fine-tuned version of bert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.1518 * Accuracy: 0.5092 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.9.1 * Datasets 1.18.3 * Tokenizers 0.10.3

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-multilingual-cased-amazon_zh_20000 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3031 - Accuracy: 0.4406 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.396 | 1.0 | 1250 | 1.3031 | 0.4406 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-multilingual-cased-amazon_zh_20000", "results": []}]}

ASCCCCCCCC/distilbert-base-multilingual-cased-amazon_zh_20000

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

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-multilingual-cased-amazon\_zh\_20000 ==================================================== This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.3031 * Accuracy: 0.4406 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.9.1 * Datasets 1.18.3 * Tokenizers 0.10.3

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-amazon_zh_20000 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3516 - Accuracy: 0.414 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4343 | 1.0 | 1250 | 1.3516 | 0.414 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-amazon_zh_20000", "results": []}]}

ASCCCCCCCC/distilbert-base-uncased-finetuned-amazon_zh_20000

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

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-amazon\_zh\_20000 =================================================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.3516 * Accuracy: 0.414 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.9.1 * Datasets 1.18.3 * Tokenizers 0.10.3

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.9.0 - Pytorch 1.7.1+cpu - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}}]}]}

ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc

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

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of distilbert-base-uncased on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.9.0 - Pytorch 1.7.1+cpu - Datasets 1.17.0 - Tokenizers 0.10.3

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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]}

AT/distilroberta-base-finetuned-wikitext2

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

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

# distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of distilroberta-base on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

text-generation

transformers

#Harry Potter DialoGPT Model

{"tags": ["conversational"]}

ATGdev/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

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. --> # result This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7458 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

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

AVSilva/bertimbau-large-fine-tuned-md

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

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

# result This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7458 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

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. --> # result This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

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

AVSilva/bertimbau-large-fine-tuned-sd

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

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

# result This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

text-generation

transformers

#Tony Stark DialoGPT model

{"tags": ["conversational"]}

AVeryRealHuman/DialoGPT-small-TonyStark

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

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

#Tony Stark DialoGPT model

text-classification

transformers

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # tmp_znj9o4r This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmp_znj9o4r", "results": []}]}

AWTStress/stress_classifier

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

TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us

# tmp_znj9o4r This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0

text-classification

transformers

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # stress_score This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "stress_score", "results": []}]}

AWTStress/stress_score

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

TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us

# stress_score This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4812 - Wer: 0.3557 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4668 | 4.0 | 500 | 1.3753 | 0.9895 | | 0.6126 | 8.0 | 1000 | 0.4809 | 0.4350 | | 0.2281 | 12.0 | 1500 | 0.4407 | 0.4033 | | 0.1355 | 16.0 | 2000 | 0.4590 | 0.3765 | | 0.0923 | 20.0 | 2500 | 0.4754 | 0.3707 | | 0.0654 | 24.0 | 3000 | 0.4719 | 0.3557 | | 0.0489 | 28.0 | 3500 | 0.4812 | 0.3557 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}

Pinwheel/wav2vec2-base-timit-demo-colab

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

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us

wav2vec2-base-timit-demo-colab ============================== This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4812 * Wer: 0.3557 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.0+cu111 * Datasets 1.13.3 * Tokenizers 0.10.3

image-classification

#FashionMNIST PyTorch Quick Start

{"tags": ["image-classification", "pytorch", "huggingpics", "some_thing"], "metrics": ["accuracy"], "private": false}

Ab0/foo-model

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

TAGS #pytorch #image-classification #huggingpics #some_thing #model-index #region-us

#FashionMNIST PyTorch Quick Start

text-classification

transformers

# BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis These are different BERT models (BERT Arabic models are initialized from [AraBERT](https://huggingface.co/aubmindlab/bert-large-arabertv02)) fine-tuned on the [Algerian Dialect Sentiment Analysis](https://huggingface.co/datasets/Abdou/dz-sentiment-yt-comments) dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: | Model Version | No. of Parameters | Training Time | F1-Score | Accuracy | | ------------------- | ----------------- | -------------- | -------- | -------- | | LSTM | ~4 M | 3 min | 0.7399 | 0.7445 | | Bi-LSTM | ~4.3 M | 6 min 35 s | 0.7380 | 0.7437 | | [BERT Base](https://huggingface.co/bert-base-uncased) | ~109.5 M | 33 min 20 s | 0.6979 | 0.7500 | | [BERT Large](https://huggingface.co/bert-large-uncased) | ~335.1 M | 1 h 50 min | 0.6976 | 0.7484 | | [BERT Arabic Mini](https://huggingface.co/Abdou/arabert-mini-algerian) | ~11.6 M | 2 min 40 s | 0.7057 | 0.7527 | | [BERT Arabic Medium](https://huggingface.co/Abdou/arabert-medium-algerian) | ~42.1 M | 11 min 25 s | 0.7521 | 0.7860 | | [BERT Arabic Base](https://huggingface.co/Abdou/arabert-base-algerian) | ~110.6 M | 34 min 19 s | 0.7688 | 0.8002 | | **[BERT Arabic Large](https://huggingface.co/Abdou/arabert-large-algerian)** | **~336.7 M** | **1 h 53 min** | **0.7838** | **0.8174** | # Citation If you find our work useful, please cite it as follows: ```bibtex @article{2023, title={Sentiment Analysis on Algerian Dialect with Transformers}, author={Zakaria Benmounah and Abdennour Boulesnane and Abdeladim Fadheli and Mustapha Khial}, journal={Applied Sciences}, volume={13}, number={20}, pages={11157}, year={2023}, month={Oct}, publisher={MDPI AG}, DOI={10.3390/app132011157}, ISSN={2076-3417}, url={http://dx.doi.org/10.3390/app132011157} } ```

{"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["Abdou/dz-sentiment-yt-comments"], "metrics": ["f1", "accuracy"]}

Abdou/arabert-base-algerian

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

TAGS #transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us

BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis ============================================================= These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows:

text-classification

transformers

# BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis These are different BERT models (BERT Arabic models are initialized from [AraBERT](https://huggingface.co/aubmindlab/bert-large-arabertv02)) fine-tuned on the [Algerian Dialect Sentiment Analysis](https://huggingface.co/datasets/Abdou/dz-sentiment-yt-comments) dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: | Model Version | No. of Parameters | Training Time | F1-Score | Accuracy | | ------------------- | ----------------- | -------------- | -------- | -------- | | LSTM | ~4 M | 3 min | 0.7399 | 0.7445 | | Bi-LSTM | ~4.3 M | 6 min 35 s | 0.7380 | 0.7437 | | [BERT Base](https://huggingface.co/bert-base-uncased) | ~109.5 M | 33 min 20 s | 0.6979 | 0.7500 | | [BERT Large](https://huggingface.co/bert-large-uncased) | ~335.1 M | 1 h 50 min | 0.6976 | 0.7484 | | [BERT Arabic Mini](https://huggingface.co/Abdou/arabert-mini-algerian) | ~11.6 M | 2 min 40 s | 0.7057 | 0.7527 | | [BERT Arabic Medium](https://huggingface.co/Abdou/arabert-medium-algerian) | ~42.1 M | 11 min 25 s | 0.7521 | 0.7860 | | [BERT Arabic Base](https://huggingface.co/Abdou/arabert-base-algerian) | ~110.6 M | 34 min 19 s | 0.7688 | 0.8002 | | **[BERT Arabic Large](https://huggingface.co/Abdou/arabert-large-algerian)** | **~336.7 M** | **1 h 53 min** | **0.7838** | **0.8174** | # Citation If you find our work useful, please cite it as follows: ```bibtex @article{2023, title={Sentiment Analysis on Algerian Dialect with Transformers}, author={Zakaria Benmounah and Abdennour Boulesnane and Abdeladim Fadheli and Mustapha Khial}, journal={Applied Sciences}, volume={13}, number={20}, pages={11157}, year={2023}, month={Oct}, publisher={MDPI AG}, DOI={10.3390/app132011157}, ISSN={2076-3417}, url={http://dx.doi.org/10.3390/app132011157} } ```

{"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["Abdou/dz-sentiment-yt-comments"], "metrics": ["f1", "accuracy"]}

Abdou/arabert-large-algerian

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

TAGS #transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us

BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis ============================================================= These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows:

text-classification

transformers

# BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis These are different BERT models (BERT Arabic models are initialized from [AraBERT](https://huggingface.co/aubmindlab/bert-large-arabertv02)) fine-tuned on the [Algerian Dialect Sentiment Analysis](https://huggingface.co/datasets/Abdou/dz-sentiment-yt-comments) dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: | Model Version | No. of Parameters | Training Time | F1-Score | Accuracy | | ------------------- | ----------------- | -------------- | -------- | -------- | | LSTM | ~4 M | 3 min | 0.7399 | 0.7445 | | Bi-LSTM | ~4.3 M | 6 min 35 s | 0.7380 | 0.7437 | | [BERT Base](https://huggingface.co/bert-base-uncased) | ~109.5 M | 33 min 20 s | 0.6979 | 0.7500 | | [BERT Large](https://huggingface.co/bert-large-uncased) | ~335.1 M | 1 h 50 min | 0.6976 | 0.7484 | | [BERT Arabic Mini](https://huggingface.co/Abdou/arabert-mini-algerian) | ~11.6 M | 2 min 40 s | 0.7057 | 0.7527 | | [BERT Arabic Medium](https://huggingface.co/Abdou/arabert-medium-algerian) | ~42.1 M | 11 min 25 s | 0.7521 | 0.7860 | | [BERT Arabic Base](https://huggingface.co/Abdou/arabert-base-algerian) | ~110.6 M | 34 min 19 s | 0.7688 | 0.8002 | | **[BERT Arabic Large](https://huggingface.co/Abdou/arabert-large-algerian)** | **~336.7 M** | **1 h 53 min** | **0.7838** | **0.8174** | # Citation If you find our work useful, please cite it as follows: ```bibtex @article{2023, title={Sentiment Analysis on Algerian Dialect with Transformers}, author={Zakaria Benmounah and Abdennour Boulesnane and Abdeladim Fadheli and Mustapha Khial}, journal={Applied Sciences}, volume={13}, number={20}, pages={11157}, year={2023}, month={Oct}, publisher={MDPI AG}, DOI={10.3390/app132011157}, ISSN={2076-3417}, url={http://dx.doi.org/10.3390/app132011157} } ```

{"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["Abdou/dz-sentiment-yt-comments"], "metrics": ["f1", "accuracy"]}

Abdou/arabert-medium-algerian

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

TAGS #transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us

BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis ============================================================= These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows:

text-classification

transformers

# BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis These are different BERT models (BERT Arabic models are initialized from [AraBERT](https://huggingface.co/aubmindlab/bert-large-arabertv02)) fine-tuned on the [Algerian Dialect Sentiment Analysis](https://huggingface.co/datasets/Abdou/dz-sentiment-yt-comments) dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: | Model Version | No. of Parameters | Training Time | F1-Score | Accuracy | | ------------------- | ----------------- | -------------- | -------- | -------- | | LSTM | ~4 M | 3 min | 0.7399 | 0.7445 | | Bi-LSTM | ~4.3 M | 6 min 35 s | 0.7380 | 0.7437 | | [BERT Base](https://huggingface.co/bert-base-uncased) | ~109.5 M | 33 min 20 s | 0.6979 | 0.7500 | | [BERT Large](https://huggingface.co/bert-large-uncased) | ~335.1 M | 1 h 50 min | 0.6976 | 0.7484 | | [BERT Arabic Mini](https://huggingface.co/Abdou/arabert-mini-algerian) | ~11.6 M | 2 min 40 s | 0.7057 | 0.7527 | | [BERT Arabic Medium](https://huggingface.co/Abdou/arabert-medium-algerian) | ~42.1 M | 11 min 25 s | 0.7521 | 0.7860 | | [BERT Arabic Base](https://huggingface.co/Abdou/arabert-base-algerian) | ~110.6 M | 34 min 19 s | 0.7688 | 0.8002 | | **[BERT Arabic Large](https://huggingface.co/Abdou/arabert-large-algerian)** | **~336.7 M** | **1 h 53 min** | **0.7838** | **0.8174** | # Citation If you find our work useful, please cite it as follows: ```bibtex @article{2023, title={Sentiment Analysis on Algerian Dialect with Transformers}, author={Zakaria Benmounah and Abdennour Boulesnane and Abdeladim Fadheli and Mustapha Khial}, journal={Applied Sciences}, volume={13}, number={20}, pages={11157}, year={2023}, month={Oct}, publisher={MDPI AG}, DOI={10.3390/app132011157}, ISSN={2076-3417}, url={http://dx.doi.org/10.3390/app132011157} } ```

{"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["Abdou/dz-sentiment-yt-comments"], "metrics": ["f1", "accuracy"]}

Abdou/arabert-mini-algerian

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

TAGS #transformers #pytorch #bert #text-classification #ar #dataset-Abdou/dz-sentiment-yt-comments #license-mit #autotrain_compatible #endpoints_compatible #region-us

BERT Models Fine-tuned on Algerian Dialect Sentiment Analysis ============================================================= These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows:

Model details available [here](https://github.com/awasthiabhijeet/PIE)

{}

AbhijeetA/PIE

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

TAGS #region-us

Model details available here

text-generation

transformers

#HarryPotter DialoGPT Model

{"tags": ["conversational"]}

AbhinavSaiTheGreat/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

#HarryPotter DialoGPT Model

text-classification

transformers

## Petrained Model BERT: base model (cased) BERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this [paper](https://arxiv.org/abs/1810.04805) and first released in this [repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference between english and English. ## Pretained Model Description BERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives: - Masked language modeling (MLM) - Next sentence prediction (NSP) ## Fine-tuned Model Description: BERT fine-tuned Cola The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK.  By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable ## How to use ? ###### Directly with a pipeline for a text-classification NLP task ```python from transformers import pipeline cola = pipeline('text-classification', model='Abirate/bert_fine_tuned_cola') cola("Tunisia is a beautiful country") [{'label': 'acceptable', 'score': 0.989352285861969}] ``` ###### Breaking down all the steps (Tokenization, Modeling, Postprocessing) ```python from transformers import AutoTokenizer, TFAutoModelForSequenceClassification import tensorflow as tf import numpy as np tokenizer = AutoTokenizer.from_pretrained('Abirate/bert_fine_tuned_cola') model = TFAutoModelForSequenceClassification.from_pretrained("Abirate/bert_fine_tuned_cola") text = "Tunisia is a beautiful country." encoded_input = tokenizer(text, return_tensors='tf') #The logits output = model(encoded_input) #Postprocessing probas_output = tf.math.softmax(tf.squeeze(output['logits']), axis = -1) class_preds = np.argmax(probas_output, axis = -1) #Predicting the class acceptable or not acceptable model.config.id2label[class_preds] #Result 'acceptable' ```

{}

Abirate/bert_fine_tuned_cola

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

TAGS #transformers #tf #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us

## Petrained Model BERT: base model (cased) BERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. ## Pretained Model Description BERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives: - Masked language modeling (MLM) - Next sentence prediction (NSP) ## Fine-tuned Model Description: BERT fine-tuned Cola The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK.  By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable ## How to use ? ###### Directly with a pipeline for a text-classification NLP task ###### Breaking down all the steps (Tokenization, Modeling, Postprocessing)

text-generation

transformers

# jeff's 100% authorized brain scan

{"tags": ["conversational"]}

AccurateIsaiah/DialoGPT-small-jefftastic

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

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

# jeff's 100% authorized brain scan

text-generation

transformers

# Mozark's Brain Uploaded to Hugging Face

{"tags": ["conversational"]}

AccurateIsaiah/DialoGPT-small-mozark

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

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

# Mozark's Brain Uploaded to Hugging Face

text-generation

transformers

# Mozark's Brain Uploaded to Hugging Face but v2

{"tags": ["conversational"]}

AccurateIsaiah/DialoGPT-small-mozarkv2

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

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

# Mozark's Brain Uploaded to Hugging Face but v2

text-generation

transformers

# Un Filtered brain upload of sinclair

{"tags": ["conversational"]}

AccurateIsaiah/DialoGPT-small-sinclair

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

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

# Un Filtered brain upload of sinclair

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2128 - Accuracy: 0.928 - F1: 0.9280 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8151 | 1.0 | 250 | 0.3043 | 0.907 | 0.9035 | | 0.24 | 2.0 | 500 | 0.2128 | 0.928 | 0.9280 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.928, "name": "Accuracy"}, {"type": "f1", "value": 0.9280065074208208, "name": "F1"}]}]}]}

ActivationAI/distilbert-base-uncased-finetuned-emotion

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

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-emotion ========================================= This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2128 * Accuracy: 0.928 * F1: 0.9280 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.0+cu111 * Datasets 1.16.1 * Tokenizers 0.10.3

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-anli_r3` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [anli](https://huggingface.co/datasets/anli/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-anli_r3", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["anli"]}

AdapterHub/bert-base-uncased-pf-anli_r3

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

TAGS #adapter-transformers #bert #text-classification #en #dataset-anli #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-anli_r3' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the anli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-art` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [art](https://huggingface.co/datasets/art/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-art", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["art"]}

AdapterHub/bert-base-uncased-pf-art

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

TAGS #adapter-transformers #bert #en #dataset-art #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-art' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the art dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-boolq` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/boolq](https://adapterhub.ml/explore/qa/boolq/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-boolq", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:qa/boolq", "adapter-transformers"], "datasets": ["boolq"]}

AdapterHub/bert-base-uncased-pf-boolq

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-qa/boolq #en #dataset-boolq #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-boolq' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the qa/boolq dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-cola` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [lingaccept/cola](https://adapterhub.ml/explore/lingaccept/cola/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-cola", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:lingaccept/cola", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-cola

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-lingaccept/cola #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-cola' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-commonsense_qa` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/csqa](https://adapterhub.ml/explore/comsense/csqa/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-commonsense_qa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapterhub:comsense/csqa", "adapter-transformers"], "datasets": ["commonsense_qa"]}

AdapterHub/bert-base-uncased-pf-commonsense_qa

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

TAGS #adapter-transformers #bert #adapterhub-comsense/csqa #en #dataset-commonsense_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-commonsense_qa' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-comqa` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [com_qa](https://huggingface.co/datasets/com_qa/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-comqa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["com_qa"]}

AdapterHub/bert-base-uncased-pf-comqa

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

TAGS #adapter-transformers #bert #question-answering #en #dataset-com_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-comqa' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the com_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-conll2000` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [chunk/conll2000](https://adapterhub.ml/explore/chunk/conll2000/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-conll2000", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:chunk/conll2000", "adapter-transformers"], "datasets": ["conll2000"]}

AdapterHub/bert-base-uncased-pf-conll2000

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

TAGS #adapter-transformers #bert #token-classification #adapterhub-chunk/conll2000 #en #dataset-conll2000 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-conll2000' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-conll2003` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ner/conll2003](https://adapterhub.ml/explore/ner/conll2003/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-conll2003", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:ner/conll2003", "adapter-transformers"], "datasets": ["conll2003"]}

AdapterHub/bert-base-uncased-pf-conll2003

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

TAGS #adapter-transformers #bert #token-classification #adapterhub-ner/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-conll2003_pos` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [pos/conll2003](https://adapterhub.ml/explore/pos/conll2003/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-conll2003_pos", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:pos/conll2003", "adapter-transformers"], "datasets": ["conll2003"]}

AdapterHub/bert-base-uncased-pf-conll2003_pos

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

TAGS #adapter-transformers #bert #token-classification #adapterhub-pos/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-conll2003_pos' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-copa` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/copa](https://adapterhub.ml/explore/comsense/copa/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-copa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapterhub:comsense/copa", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-copa

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

TAGS #adapter-transformers #bert #adapterhub-comsense/copa #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-copa' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-cosmos_qa` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/cosmosqa](https://adapterhub.ml/explore/comsense/cosmosqa/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-cosmos_qa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapterhub:comsense/cosmosqa", "adapter-transformers"], "datasets": ["cosmos_qa"]}

AdapterHub/bert-base-uncased-pf-cosmos_qa

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

TAGS #adapter-transformers #bert #adapterhub-comsense/cosmosqa #en #dataset-cosmos_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-cosmos_qa' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-cq` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/cq](https://adapterhub.ml/explore/qa/cq/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-cq", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapterhub:qa/cq", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-cq

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

TAGS #adapter-transformers #bert #question-answering #adapterhub-qa/cq #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-cq' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the qa/cq dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-drop` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [drop](https://huggingface.co/datasets/drop/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-drop", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["drop"]}

AdapterHub/bert-base-uncased-pf-drop

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

TAGS #adapter-transformers #bert #question-answering #en #dataset-drop #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-drop' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the drop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-duorc_p` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-duorc_p", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["duorc"]}

AdapterHub/bert-base-uncased-pf-duorc_p

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

TAGS #adapter-transformers #bert #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_p' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-duorc_s` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-duorc_s", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["duorc"]}

AdapterHub/bert-base-uncased-pf-duorc_s

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

TAGS #adapter-transformers #bert #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-duorc_s' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-emo` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [emo](https://huggingface.co/datasets/emo/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-emo", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["emo"]}

AdapterHub/bert-base-uncased-pf-emo

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

TAGS #adapter-transformers #bert #text-classification #en #dataset-emo #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-emo' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the emo dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-emotion` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [emotion](https://huggingface.co/datasets/emotion/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-emotion", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["emotion"]}

AdapterHub/bert-base-uncased-pf-emotion

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

TAGS #adapter-transformers #bert #text-classification #en #dataset-emotion #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-emotion' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the emotion dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-fce_error_detection` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ged/fce](https://adapterhub.ml/explore/ged/fce/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-fce_error_detection", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:ged/fce", "adapter-transformers"], "datasets": ["fce_error_detection"]}

AdapterHub/bert-base-uncased-pf-fce_error_detection

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

TAGS #adapter-transformers #bert #token-classification #adapterhub-ged/fce #en #dataset-fce_error_detection #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-fce_error_detection' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the ged/fce dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-hellaswag` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/hellaswag](https://adapterhub.ml/explore/comsense/hellaswag/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-hellaswag", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapterhub:comsense/hellaswag", "adapter-transformers"], "datasets": ["hellaswag"]}

AdapterHub/bert-base-uncased-pf-hellaswag

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

TAGS #adapter-transformers #bert #adapterhub-comsense/hellaswag #en #dataset-hellaswag #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-hellaswag' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-hotpotqa` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [hotpot_qa](https://huggingface.co/datasets/hotpot_qa/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-hotpotqa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["hotpot_qa"]}

AdapterHub/bert-base-uncased-pf-hotpotqa

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

TAGS #adapter-transformers #bert #question-answering #en #dataset-hotpot_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-hotpotqa' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the hotpot_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-imdb` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/imdb](https://adapterhub.ml/explore/sentiment/imdb/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-imdb", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sentiment/imdb", "adapter-transformers"], "datasets": ["imdb"]}

AdapterHub/bert-base-uncased-pf-imdb

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-sentiment/imdb #en #dataset-imdb #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-imdb' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the sentiment/imdb dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-mit_movie_trivia` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ner/mit_movie_trivia](https://adapterhub.ml/explore/ner/mit_movie_trivia/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mit_movie_trivia", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:ner/mit_movie_trivia", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-mit_movie_trivia

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

TAGS #adapter-transformers #bert #token-classification #adapterhub-ner/mit_movie_trivia #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-mit_movie_trivia' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-mnli` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mnli", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:nli/multinli", "adapter-transformers"], "datasets": ["multi_nli"]}

AdapterHub/bert-base-uncased-pf-mnli

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-nli/multinli #en #dataset-multi_nli #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-mnli' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the nli/multinli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-mrpc` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sts/mrpc](https://adapterhub.ml/explore/sts/mrpc/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mrpc", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sts/mrpc", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-mrpc

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-sts/mrpc #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-mrpc' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the sts/mrpc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-multirc` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [rc/multirc](https://adapterhub.ml/explore/rc/multirc/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-multirc", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "adapterhub:rc/multirc", "bert", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-multirc

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-rc/multirc #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-multirc' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the rc/multirc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-newsqa` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [newsqa](https://huggingface.co/datasets/newsqa/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-newsqa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["newsqa"]}

AdapterHub/bert-base-uncased-pf-newsqa

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

TAGS #adapter-transformers #bert #question-answering #en #dataset-newsqa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-newsqa' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the newsqa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-pmb_sem_tagging` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [semtag/pmb](https://adapterhub.ml/explore/semtag/pmb/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-pmb_sem_tagging", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:semtag/pmb", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-pmb_sem_tagging

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

TAGS #adapter-transformers #bert #token-classification #adapterhub-semtag/pmb #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-pmb_sem_tagging' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the semtag/pmb dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-qnli` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/qnli](https://adapterhub.ml/explore/nli/qnli/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-qnli", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:nli/qnli", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-qnli

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-nli/qnli #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-qnli' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the nli/qnli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-qqp` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sts/qqp](https://adapterhub.ml/explore/sts/qqp/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-qqp", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "adapter-transformers", "adapterhub:sts/qqp", "bert"]}

AdapterHub/bert-base-uncased-pf-qqp

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-sts/qqp #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-qqp' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the sts/qqp dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-quail` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [quail](https://huggingface.co/datasets/quail/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-quail", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["quail"]}

AdapterHub/bert-base-uncased-pf-quail

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

TAGS #adapter-transformers #bert #en #dataset-quail #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-quail' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the quail dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-quartz` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [quartz](https://huggingface.co/datasets/quartz/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-quartz", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["quartz"]}

AdapterHub/bert-base-uncased-pf-quartz

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

TAGS #adapter-transformers #bert #en #dataset-quartz #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the quartz dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-quoref` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [quoref](https://huggingface.co/datasets/quoref/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-quoref", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapter-transformers"], "datasets": ["quoref"]}

AdapterHub/bert-base-uncased-pf-quoref

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

TAGS #adapter-transformers #bert #question-answering #en #dataset-quoref #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-quoref' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the quoref dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-race` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [rc/race](https://adapterhub.ml/explore/rc/race/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-race", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["adapterhub:rc/race", "bert", "adapter-transformers"], "datasets": ["race"]}

AdapterHub/bert-base-uncased-pf-race

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

TAGS #adapter-transformers #bert #adapterhub-rc/race #en #dataset-race #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-race' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the rc/race dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-record` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [rc/record](https://adapterhub.ml/explore/rc/record/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-record", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:rc/record", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-record

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-rc/record #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-record' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the rc/record dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-rotten_tomatoes` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/rotten_tomatoes](https://adapterhub.ml/explore/sentiment/rotten_tomatoes/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-rotten_tomatoes", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sentiment/rotten_tomatoes", "adapter-transformers"], "datasets": ["rotten_tomatoes"]}

AdapterHub/bert-base-uncased-pf-rotten_tomatoes

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-sentiment/rotten_tomatoes #en #dataset-rotten_tomatoes #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-rotten_tomatoes' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-rte` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/rte](https://adapterhub.ml/explore/nli/rte/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-rte", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:nli/rte", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-rte

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-nli/rte #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-rte' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the nli/rte dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-scicite` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [scicite](https://huggingface.co/datasets/scicite/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-scicite", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["scicite"]}

AdapterHub/bert-base-uncased-pf-scicite

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

TAGS #adapter-transformers #bert #text-classification #en #dataset-scicite #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-scicite' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the scicite dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-scitail` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/scitail](https://adapterhub.ml/explore/nli/scitail/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-scitail", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:nli/scitail", "adapter-transformers"], "datasets": ["scitail"]}

AdapterHub/bert-base-uncased-pf-scitail

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-nli/scitail #en #dataset-scitail #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-scitail' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the nli/scitail dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-sick` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/sick](https://adapterhub.ml/explore/nli/sick/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-sick", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "adapter-transformers", "bert", "adapterhub:nli/sick"], "datasets": ["sick"]}

AdapterHub/bert-base-uncased-pf-sick

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-nli/sick #en #dataset-sick #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-sick' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the nli/sick dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-snli` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [snli](https://huggingface.co/datasets/snli/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-snli", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["snli"]}

AdapterHub/bert-base-uncased-pf-snli

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

TAGS #adapter-transformers #bert #text-classification #en #dataset-snli #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-snli' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the snli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-social_i_qa` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [social_i_qa](https://huggingface.co/datasets/social_i_qa/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-social_i_qa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["social_i_qa"]}

AdapterHub/bert-base-uncased-pf-social_i_qa

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

TAGS #adapter-transformers #bert #en #dataset-social_i_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-social_i_qa' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-squad` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/squad1](https://adapterhub.ml/explore/qa/squad1/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-squad", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapterhub:qa/squad1", "adapter-transformers"], "datasets": ["squad"]}

AdapterHub/bert-base-uncased-pf-squad

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

TAGS #adapter-transformers #bert #question-answering #adapterhub-qa/squad1 #en #dataset-squad #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-squad' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the qa/squad1 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-squad_v2` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/squad2](https://adapterhub.ml/explore/qa/squad2/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-squad_v2", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapterhub:qa/squad2", "adapter-transformers"], "datasets": ["squad_v2"]}

AdapterHub/bert-base-uncased-pf-squad_v2

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

TAGS #adapter-transformers #bert #question-answering #adapterhub-qa/squad2 #en #dataset-squad_v2 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-squad_v2' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the qa/squad2 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-sst2` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/sst-2](https://adapterhub.ml/explore/sentiment/sst-2/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-sst2", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sentiment/sst-2", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-sst2

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-sentiment/sst-2 #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-sst2' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-stsb` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sts/sts-b](https://adapterhub.ml/explore/sts/sts-b/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-stsb", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:sts/sts-b", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-stsb

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-sts/sts-b #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-stsb' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the sts/sts-b dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-swag` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [swag](https://huggingface.co/datasets/swag/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-swag", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapter-transformers"], "datasets": ["swag"]}

AdapterHub/bert-base-uncased-pf-swag

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

TAGS #adapter-transformers #bert #en #dataset-swag #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-swag' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the swag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-trec` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [trec](https://huggingface.co/datasets/trec/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-trec", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["trec"]}

AdapterHub/bert-base-uncased-pf-trec

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

TAGS #adapter-transformers #bert #text-classification #en #dataset-trec #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-trec' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the trec dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-ud_deprel` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [deprel/ud_ewt](https://adapterhub.ml/explore/deprel/ud_ewt/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-ud_deprel", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:deprel/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]}

AdapterHub/bert-base-uncased-pf-ud_deprel

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

TAGS #adapter-transformers #bert #token-classification #adapterhub-deprel/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-ud_deprel' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-ud_en_ewt` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [dp/ud_ewt](https://adapterhub.ml/explore/dp/ud_ewt/) dataset and includes a prediction head for dependency parsing. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-ud_en_ewt", source="hf", set_active=True) ``` ## Architecture & Training This adapter was trained using adapter-transformer's example script for dependency parsing. See https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples/dependency-parsing. ## Evaluation results Scores achieved by dependency parsing adapters on the test set of UD English EWT after training: | Model | UAS | LAS | | --- | --- | --- | | `bert-base-uncased` | 91.74 | 89.15 | | `roberta-base` | 91.43 | 88.43 | ## Citation <!-- Add some description here -->

{"language": ["en"], "tags": ["bert", "adapterhub:dp/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]}

AdapterHub/bert-base-uncased-pf-ud_en_ewt

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

TAGS #adapter-transformers #bert #adapterhub-dp/ud_ewt #en #dataset-universal_dependencies #region-us

Adapter 'AdapterHub/bert-base-uncased-pf-ud\_en\_ewt' for bert-base-uncased =========================================================================== An adapter for the 'bert-base-uncased' model that was trained on the dp/ud\_ewt dataset and includes a prediction head for dependency parsing. This adapter was created for usage with the adapter-transformers library. Usage ----- First, install 'adapter-transformers': *Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More* Now, the adapter can be loaded and activated like this: Architecture & Training ----------------------- This adapter was trained using adapter-transformer's example script for dependency parsing. See URL Evaluation results ------------------ Scores achieved by dependency parsing adapters on the test set of UD English EWT after training: Model: 'bert-base-uncased', UAS: 91.74, LAS: 89.15 Model: 'roberta-base', UAS: 91.43, LAS: 88.43

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-ud_pos` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [pos/ud_ewt](https://adapterhub.ml/explore/pos/ud_ewt/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-ud_pos", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapterhub:pos/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]}

AdapterHub/bert-base-uncased-pf-ud_pos

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

TAGS #adapter-transformers #bert #token-classification #adapterhub-pos/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-ud_pos' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-wic` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [wordsence/wic](https://adapterhub.ml/explore/wordsence/wic/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-wic", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapterhub:wordsence/wic", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-wic

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

TAGS #adapter-transformers #bert #text-classification #adapterhub-wordsence/wic #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-wic' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the wordsence/wic dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-wikihop` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/wikihop](https://adapterhub.ml/explore/qa/wikihop/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-wikihop", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "bert", "adapterhub:qa/wikihop", "adapter-transformers"]}

AdapterHub/bert-base-uncased-pf-wikihop

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

TAGS #adapter-transformers #bert #question-answering #adapterhub-qa/wikihop #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-wikihop' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the qa/wikihop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-winogrande` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [comsense/winogrande](https://adapterhub.ml/explore/comsense/winogrande/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-winogrande", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["bert", "adapterhub:comsense/winogrande", "adapter-transformers"], "datasets": ["winogrande"]}

AdapterHub/bert-base-uncased-pf-winogrande

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

TAGS #adapter-transformers #bert #adapterhub-comsense/winogrande #en #dataset-winogrande #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-winogrande' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-wnut_17` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [wnut_17](https://huggingface.co/datasets/wnut_17/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-wnut_17", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "bert", "adapter-transformers"], "datasets": ["wnut_17"]}

AdapterHub/bert-base-uncased-pf-wnut_17

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

TAGS #adapter-transformers #bert #token-classification #en #dataset-wnut_17 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-wnut_17' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the wnut_17 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/bert-base-uncased-pf-yelp_polarity` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [yelp_polarity](https://huggingface.co/datasets/yelp_polarity/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-yelp_polarity", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "bert", "adapter-transformers"], "datasets": ["yelp_polarity"]}

AdapterHub/bert-base-uncased-pf-yelp_polarity

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

TAGS #adapter-transformers #bert #text-classification #en #dataset-yelp_polarity #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/bert-base-uncased-pf-yelp_polarity' for bert-base-uncased An adapter for the 'bert-base-uncased' model that was trained on the yelp_polarity dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/bioASQyesno` for facebook/bart-base An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [qa/bioasq](https://adapterhub.ml/explore/qa/bioasq/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("facebook/bart-base") adapter_name = model.load_adapter("AdapterHub/bioASQyesno", source="hf", set_active=True) ``` ## Architecture & Training Trained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset. ## Evaluation results Achieved 75% accuracy on the test dataset of bioASQ 8b dataset. ## Citation <!-- Add some description here -->

{"tags": ["adapterhub:qa/bioasq", "adapter-transformers", "bart"]}

AdapterHub/bioASQyesno

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

TAGS #adapter-transformers #bart #adapterhub-qa/bioasq #region-us

# Adapter 'AdapterHub/bioASQyesno' for facebook/bart-base An adapter for the 'facebook/bart-base' model that was trained on the qa/bioasq dataset. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training Trained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset. ## Evaluation results Achieved 75% accuracy on the test dataset of bioASQ 8b dataset.

adapter-transformers

# Adapter `hSterz/narrativeqa` for facebook/bart-base An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [qa/narrativeqa](https://adapterhub.ml/explore/qa/narrativeqa/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("facebook/bart-base") adapter_name = model.load_adapter("hSterz/narrativeqa", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->

{"tags": ["adapterhub:qa/narrativeqa", "adapter-transformers", "bart"], "datasets": ["narrativeqa"]}

AdapterHub/narrativeqa

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

TAGS #adapter-transformers #bart #adapterhub-qa/narrativeqa #dataset-narrativeqa #region-us

# Adapter 'hSterz/narrativeqa' for facebook/bart-base An adapter for the 'facebook/bart-base' model that was trained on the qa/narrativeqa dataset. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training ## Evaluation results

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-anli_r3` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [anli](https://huggingface.co/datasets/anli/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-anli_r3", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["anli"]}

AdapterHub/roberta-base-pf-anli_r3

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

TAGS #adapter-transformers #roberta #text-classification #en #dataset-anli #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-anli_r3' for roberta-base An adapter for the 'roberta-base' model that was trained on the anli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-art` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [art](https://huggingface.co/datasets/art/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-art", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["art"]}

AdapterHub/roberta-base-pf-art

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

TAGS #adapter-transformers #roberta #en #dataset-art #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-art' for roberta-base An adapter for the 'roberta-base' model that was trained on the art dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-boolq` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/boolq](https://adapterhub.ml/explore/qa/boolq/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-boolq", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:qa/boolq", "adapter-transformers"], "datasets": ["boolq"]}

AdapterHub/roberta-base-pf-boolq

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

TAGS #adapter-transformers #roberta #text-classification #adapterhub-qa/boolq #en #dataset-boolq #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-boolq' for roberta-base An adapter for the 'roberta-base' model that was trained on the qa/boolq dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-cola` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [lingaccept/cola](https://adapterhub.ml/explore/lingaccept/cola/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-cola", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:lingaccept/cola", "adapter-transformers"]}

AdapterHub/roberta-base-pf-cola

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

TAGS #adapter-transformers #roberta #text-classification #adapterhub-lingaccept/cola #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-cola' for roberta-base An adapter for the 'roberta-base' model that was trained on the lingaccept/cola dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-commonsense_qa` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/csqa](https://adapterhub.ml/explore/comsense/csqa/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-commonsense_qa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapterhub:comsense/csqa", "adapter-transformers"], "datasets": ["commonsense_qa"]}

AdapterHub/roberta-base-pf-commonsense_qa

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

TAGS #adapter-transformers #roberta #adapterhub-comsense/csqa #en #dataset-commonsense_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-commonsense_qa' for roberta-base An adapter for the 'roberta-base' model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-comqa` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [com_qa](https://huggingface.co/datasets/com_qa/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-comqa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["com_qa"]}

AdapterHub/roberta-base-pf-comqa

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

TAGS #adapter-transformers #roberta #question-answering #en #dataset-com_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-comqa' for roberta-base An adapter for the 'roberta-base' model that was trained on the com_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-conll2000` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [chunk/conll2000](https://adapterhub.ml/explore/chunk/conll2000/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-conll2000", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:chunk/conll2000", "adapter-transformers"], "datasets": ["conll2000"]}

AdapterHub/roberta-base-pf-conll2000

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

TAGS #adapter-transformers #roberta #token-classification #adapterhub-chunk/conll2000 #en #dataset-conll2000 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-conll2000' for roberta-base An adapter for the 'roberta-base' model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-conll2003` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [ner/conll2003](https://adapterhub.ml/explore/ner/conll2003/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-conll2003", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:ner/conll2003", "adapter-transformers"], "datasets": ["conll2003"]}

AdapterHub/roberta-base-pf-conll2003

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

TAGS #adapter-transformers #roberta #token-classification #adapterhub-ner/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-conll2003' for roberta-base An adapter for the 'roberta-base' model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. ## Usage First, install 'adapter-transformers': _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_ Now, the adapter can be loaded and activated like this: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":