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| # TextAttack Model Zoo | |
| ## More details at [https://textattack.readthedocs.io/en/latest/3recipes/models.html](https://textattack.readthedocs.io/en/latest/3recipes/models.html) | |
| TextAttack includes pre-trained models for different common NLP tasks. This makes it easier for | |
| users to get started with TextAttack. It also enables a more fair comparison of attacks from | |
| the literature. | |
| All evaluation results were obtained using `textattack eval` to evaluate models on their default | |
| test dataset (test set, if labels are available, otherwise, eval/validation set). You can use | |
| this command to verify the accuracies for yourself: for example, `textattack eval --model roberta-base-mr`. | |
| The LSTM and wordCNN models' code is available in `textattack.models.helpers`. All other models are transformers | |
| imported from the [`transformers`](https://github.com/huggingface/transformers/) package. To list evaluate all | |
| TextAttack pretrained models, invoke `textattack eval` without specifying a model: `textattack eval --num-examples 1000`. | |
| All evaluations shown are on the full validation or test set up to 1000 examples. | |
| ### `LSTM` | |
| <section> | |
| - AG News (`lstm-ag-news`) | |
| - `datasets` dataset `ag_news`, split `test` | |
| - Correct/Whole: 914/1000 | |
| - Accuracy: 91.4% | |
| - IMDB (`lstm-imdb`) | |
| - `datasets` dataset `imdb`, split `test` | |
| - Correct/Whole: 883/1000 | |
| - Accuracy: 88.30% | |
| - Movie Reviews [Rotten Tomatoes] (`lstm-mr`) | |
| - `datasets` dataset `rotten_tomatoes`, split `validation` | |
| - Correct/Whole: 807/1000 | |
| - Accuracy: 80.70% | |
| - `datasets` dataset `rotten_tomatoes`, split `test` | |
| - Correct/Whole: 781/1000 | |
| - Accuracy: 78.10% | |
| - SST-2 (`lstm-sst2`) | |
| - `datasets` dataset `glue`, subset `sst2`, split `validation` | |
| - Correct/Whole: 737/872 | |
| - Accuracy: 84.52% | |
| - Yelp Polarity (`lstm-yelp`) | |
| - `datasets` dataset `yelp_polarity`, split `test` | |
| - Correct/Whole: 922/1000 | |
| - Accuracy: 92.20% | |
| </section> | |
| ### `wordCNN` | |
| <section> | |
| - AG News (`cnn-ag-news`) | |
| - `datasets` dataset `ag_news`, split `test` | |
| - Correct/Whole: 910/1000 | |
| - Accuracy: 91.00% | |
| - IMDB (`cnn-imdb`) | |
| - `datasets` dataset `imdb`, split `test` | |
| - Correct/Whole: 863/1000 | |
| - Accuracy: 86.30% | |
| - Movie Reviews [Rotten Tomatoes] (`cnn-mr`) | |
| - `datasets` dataset `rotten_tomatoes`, split `validation` | |
| - Correct/Whole: 794/1000 | |
| - Accuracy: 79.40% | |
| - `datasets` dataset `rotten_tomatoes`, split `test` | |
| - Correct/Whole: 768/1000 | |
| - Accuracy: 76.80% | |
| - SST-2 (`cnn-sst2`) | |
| - `datasets` dataset `glue`, subset `sst2`, split `validation` | |
| - Correct/Whole: 721/872 | |
| - Accuracy: 82.68% | |
| - Yelp Polarity (`cnn-yelp`) | |
| - `datasets` dataset `yelp_polarity`, split `test` | |
| - Correct/Whole: 913/1000 | |
| - Accuracy: 91.30% | |
| </section> | |
| ### `albert-base-v2` | |
| <section> | |
| - AG News (`albert-base-v2-ag-news`) | |
| - `datasets` dataset `ag_news`, split `test` | |
| - Correct/Whole: 943/1000 | |
| - Accuracy: 94.30% | |
| - CoLA (`albert-base-v2-cola`) | |
| - `datasets` dataset `glue`, subset `cola`, split `validation` | |
| - Correct/Whole: 829/1000 | |
| - Accuracy: 82.90% | |
| - IMDB (`albert-base-v2-imdb`) | |
| - `datasets` dataset `imdb`, split `test` | |
| - Correct/Whole: 913/1000 | |
| - Accuracy: 91.30% | |
| - Movie Reviews [Rotten Tomatoes] (`albert-base-v2-mr`) | |
| - `datasets` dataset `rotten_tomatoes`, split `validation` | |
| - Correct/Whole: 882/1000 | |
| - Accuracy: 88.20% | |
| - `datasets` dataset `rotten_tomatoes`, split `test` | |
| - Correct/Whole: 851/1000 | |
| - Accuracy: 85.10% | |
| - Quora Question Pairs (`albert-base-v2-qqp`) | |
| - `datasets` dataset `glue`, subset `qqp`, split `validation` | |
| - Correct/Whole: 914/1000 | |
| - Accuracy: 91.40% | |
| - Recognizing Textual Entailment (`albert-base-v2-rte`) | |
| - `datasets` dataset `glue`, subset `rte`, split `validation` | |
| - Correct/Whole: 211/277 | |
| - Accuracy: 76.17% | |
| - SNLI (`albert-base-v2-snli`) | |
| - `datasets` dataset `snli`, split `test` | |
| - Correct/Whole: 883/1000 | |
| - Accuracy: 88.30% | |
| - SST-2 (`albert-base-v2-sst2`) | |
| - `datasets` dataset `glue`, subset `sst2`, split `validation` | |
| - Correct/Whole: 807/872 | |
| - Accuracy: 92.55%) | |
| - STS-b (`albert-base-v2-stsb`) | |
| - `datasets` dataset `glue`, subset `stsb`, split `validation` | |
| - Pearson correlation: 0.9041359738552746 | |
| - Spearman correlation: 0.8995912861209745 | |
| - WNLI (`albert-base-v2-wnli`) | |
| - `datasets` dataset `glue`, subset `wnli`, split `validation` | |
| - Correct/Whole: 42/71 | |
| - Accuracy: 59.15% | |
| - Yelp Polarity (`albert-base-v2-yelp`) | |
| - `datasets` dataset `yelp_polarity`, split `test` | |
| - Correct/Whole: 963/1000 | |
| - Accuracy: 96.30% | |
| </section> | |
| ### `bert-base-uncased` | |
| <section> | |
| - AG News (`bert-base-uncased-ag-news`) | |
| - `datasets` dataset `ag_news`, split `test` | |
| - Correct/Whole: 942/1000 | |
| - Accuracy: 94.20% | |
| - CoLA (`bert-base-uncased-cola`) | |
| - `datasets` dataset `glue`, subset `cola`, split `validation` | |
| - Correct/Whole: 812/1000 | |
| - Accuracy: 81.20% | |
| - IMDB (`bert-base-uncased-imdb`) | |
| - `datasets` dataset `imdb`, split `test` | |
| - Correct/Whole: 919/1000 | |
| - Accuracy: 91.90% | |
| - MNLI matched (`bert-base-uncased-mnli`) | |
| - `datasets` dataset `glue`, subset `mnli`, split `validation_matched` | |
| - Correct/Whole: 840/1000 | |
| - Accuracy: 84.00% | |
| - Movie Reviews [Rotten Tomatoes] (`bert-base-uncased-mr`) | |
| - `datasets` dataset `rotten_tomatoes`, split `validation` | |
| - Correct/Whole: 876/1000 | |
| - Accuracy: 87.60% | |
| - `datasets` dataset `rotten_tomatoes`, split `test` | |
| - Correct/Whole: 838/1000 | |
| - Accuracy: 83.80% | |
| - MRPC (`bert-base-uncased-mrpc`) | |
| - `datasets` dataset `glue`, subset `mrpc`, split `validation` | |
| - Correct/Whole: 358/408 | |
| - Accuracy: 87.75% | |
| - QNLI (`bert-base-uncased-qnli`) | |
| - `datasets` dataset `glue`, subset `qnli`, split `validation` | |
| - Correct/Whole: 904/1000 | |
| - Accuracy: 90.40% | |
| - Quora Question Pairs (`bert-base-uncased-qqp`) | |
| - `datasets` dataset `glue`, subset `qqp`, split `validation` | |
| - Correct/Whole: 924/1000 | |
| - Accuracy: 92.40% | |
| - Recognizing Textual Entailment (`bert-base-uncased-rte`) | |
| - `datasets` dataset `glue`, subset `rte`, split `validation` | |
| - Correct/Whole: 201/277 | |
| - Accuracy: 72.56% | |
| - SNLI (`bert-base-uncased-snli`) | |
| - `datasets` dataset `snli`, split `test` | |
| - Correct/Whole: 894/1000 | |
| - Accuracy: 89.40% | |
| - SST-2 (`bert-base-uncased-sst2`) | |
| - `datasets` dataset `glue`, subset `sst2`, split `validation` | |
| - Correct/Whole: 806/872 | |
| - Accuracy: 92.43%) | |
| - STS-b (`bert-base-uncased-stsb`) | |
| - `datasets` dataset `glue`, subset `stsb`, split `validation` | |
| - Pearson correlation: 0.8775458937815515 | |
| - Spearman correlation: 0.8773251339980935 | |
| - WNLI (`bert-base-uncased-wnli`) | |
| - `datasets` dataset `glue`, subset `wnli`, split `validation` | |
| - Correct/Whole: 40/71 | |
| - Accuracy: 56.34% | |
| - Yelp Polarity (`bert-base-uncased-yelp`) | |
| - `datasets` dataset `yelp_polarity`, split `test` | |
| - Correct/Whole: 963/1000 | |
| - Accuracy: 96.30% | |
| </section> | |
| ### `distilbert-base-cased` | |
| <section> | |
| - CoLA (`distilbert-base-cased-cola`) | |
| - `datasets` dataset `glue`, subset `cola`, split `validation` | |
| - Correct/Whole: 786/1000 | |
| - Accuracy: 78.60% | |
| - MRPC (`distilbert-base-cased-mrpc`) | |
| - `datasets` dataset `glue`, subset `mrpc`, split `validation` | |
| - Correct/Whole: 320/408 | |
| - Accuracy: 78.43% | |
| - Quora Question Pairs (`distilbert-base-cased-qqp`) | |
| - `datasets` dataset `glue`, subset `qqp`, split `validation` | |
| - Correct/Whole: 908/1000 | |
| - Accuracy: 90.80% | |
| - SNLI (`distilbert-base-cased-snli`) | |
| - `datasets` dataset `snli`, split `test` | |
| - Correct/Whole: 861/1000 | |
| - Accuracy: 86.10% | |
| - SST-2 (`distilbert-base-cased-sst2`) | |
| - `datasets` dataset `glue`, subset `sst2`, split `validation` | |
| - Correct/Whole: 785/872 | |
| - Accuracy: 90.02%) | |
| - STS-b (`distilbert-base-cased-stsb`) | |
| - `datasets` dataset `glue`, subset `stsb`, split `validation` | |
| - Pearson correlation: 0.8421540899520146 | |
| - Spearman correlation: 0.8407155030382939 | |
| </section> | |
| ### `distilbert-base-uncased` | |
| <section> | |
| - AG News (`distilbert-base-uncased-ag-news`) | |
| - `datasets` dataset `ag_news`, split `test` | |
| - Correct/Whole: 944/1000 | |
| - Accuracy: 94.40% | |
| - CoLA (`distilbert-base-uncased-cola`) | |
| - `datasets` dataset `glue`, subset `cola`, split `validation` | |
| - Correct/Whole: 786/1000 | |
| - Accuracy: 78.60% | |
| - IMDB (`distilbert-base-uncased-imdb`) | |
| - `datasets` dataset `imdb`, split `test` | |
| - Correct/Whole: 903/1000 | |
| - Accuracy: 90.30% | |
| - MNLI matched (`distilbert-base-uncased-mnli`) | |
| - `datasets` dataset `glue`, subset `mnli`, split `validation_matched` | |
| - Correct/Whole: 817/1000 | |
| - Accuracy: 81.70% | |
| - MRPC (`distilbert-base-uncased-mrpc`) | |
| - `datasets` dataset `glue`, subset `mrpc`, split `validation` | |
| - Correct/Whole: 350/408 | |
| - Accuracy: 85.78% | |
| - QNLI (`distilbert-base-uncased-qnli`) | |
| - `datasets` dataset `glue`, subset `qnli`, split `validation` | |
| - Correct/Whole: 860/1000 | |
| - Accuracy: 86.00% | |
| - Recognizing Textual Entailment (`distilbert-base-uncased-rte`) | |
| - `datasets` dataset `glue`, subset `rte`, split `validation` | |
| - Correct/Whole: 180/277 | |
| - Accuracy: 64.98% | |
| - STS-b (`distilbert-base-uncased-stsb`) | |
| - `datasets` dataset `glue`, subset `stsb`, split `validation` | |
| - Pearson correlation: 0.8421540899520146 | |
| - Spearman correlation: 0.8407155030382939 | |
| - WNLI (`distilbert-base-uncased-wnli`) | |
| - `datasets` dataset `glue`, subset `wnli`, split `validation` | |
| - Correct/Whole: 40/71 | |
| - Accuracy: 56.34% | |
| </section> | |
| ### `roberta-base` | |
| <section> | |
| - AG News (`roberta-base-ag-news`) | |
| - `datasets` dataset `ag_news`, split `test` | |
| - Correct/Whole: 947/1000 | |
| - Accuracy: 94.70% | |
| - CoLA (`roberta-base-cola`) | |
| - `datasets` dataset `glue`, subset `cola`, split `validation` | |
| - Correct/Whole: 857/1000 | |
| - Accuracy: 85.70% | |
| - IMDB (`roberta-base-imdb`) | |
| - `datasets` dataset `imdb`, split `test` | |
| - Correct/Whole: 941/1000 | |
| - Accuracy: 94.10% | |
| - Movie Reviews [Rotten Tomatoes] (`roberta-base-mr`) | |
| - `datasets` dataset `rotten_tomatoes`, split `validation` | |
| - Correct/Whole: 899/1000 | |
| - Accuracy: 89.90% | |
| - `datasets` dataset `rotten_tomatoes`, split `test` | |
| - Correct/Whole: 883/1000 | |
| - Accuracy: 88.30% | |
| - MRPC (`roberta-base-mrpc`) | |
| - `datasets` dataset `glue`, subset `mrpc`, split `validation` | |
| - Correct/Whole: 371/408 | |
| - Accuracy: 91.18% | |
| - QNLI (`roberta-base-qnli`) | |
| - `datasets` dataset `glue`, subset `qnli`, split `validation` | |
| - Correct/Whole: 917/1000 | |
| - Accuracy: 91.70% | |
| - Recognizing Textual Entailment (`roberta-base-rte`) | |
| - `datasets` dataset `glue`, subset `rte`, split `validation` | |
| - Correct/Whole: 217/277 | |
| - Accuracy: 78.34% | |
| - SST-2 (`roberta-base-sst2`) | |
| - `datasets` dataset `glue`, subset `sst2`, split `validation` | |
| - Correct/Whole: 820/872 | |
| - Accuracy: 94.04%) | |
| - STS-b (`roberta-base-stsb`) | |
| - `datasets` dataset `glue`, subset `stsb`, split `validation` | |
| - Pearson correlation: 0.906067852162708 | |
| - Spearman correlation: 0.9025045272903051 | |
| - WNLI (`roberta-base-wnli`) | |
| - `datasets` dataset `glue`, subset `wnli`, split `validation` | |
| - Correct/Whole: 40/71 | |
| - Accuracy: 56.34% | |
| </section> | |
| ### `xlnet-base-cased` | |
| <section> | |
| - CoLA (`xlnet-base-cased-cola`) | |
| - `datasets` dataset `glue`, subset `cola`, split `validation` | |
| - Correct/Whole: 800/1000 | |
| - Accuracy: 80.00% | |
| - IMDB (`xlnet-base-cased-imdb`) | |
| - `datasets` dataset `imdb`, split `test` | |
| - Correct/Whole: 957/1000 | |
| - Accuracy: 95.70% | |
| - Movie Reviews [Rotten Tomatoes] (`xlnet-base-cased-mr`) | |
| - `datasets` dataset `rotten_tomatoes`, split `validation` | |
| - Correct/Whole: 908/1000 | |
| - Accuracy: 90.80% | |
| - `datasets` dataset `rotten_tomatoes`, split `test` | |
| - Correct/Whole: 876/1000 | |
| - Accuracy: 87.60% | |
| - MRPC (`xlnet-base-cased-mrpc`) | |
| - `datasets` dataset `glue`, subset `mrpc`, split `validation` | |
| - Correct/Whole: 363/408 | |
| - Accuracy: 88.97% | |
| - Recognizing Textual Entailment (`xlnet-base-cased-rte`) | |
| - `datasets` dataset `glue`, subset `rte`, split `validation` | |
| - Correct/Whole: 196/277 | |
| - Accuracy: 70.76% | |
| - STS-b (`xlnet-base-cased-stsb`) | |
| - `datasets` dataset `glue`, subset `stsb`, split `validation` | |
| - Pearson correlation: 0.883111673280641 | |
| - Spearman correlation: 0.8773439961182335 | |
| - WNLI (`xlnet-base-cased-wnli`) | |
| - `datasets` dataset `glue`, subset `wnli`, split `validation` | |
| - Correct/Whole: 41/71 | |
| - Accuracy: 57.75% | |
| </section> | |
| # More details on TextAttack models (details on NLP task, output type, SOTA on paperswithcode; model card on huggingface): | |
| <section> | |
| Fine-tuned Model | NLP Task | Input type | Output Type | paperswithcode.com SOTA | huggingface.co Model Card | |
| ------------------------------|-----------------------------|------------------------------|-----------------------------|------------------------------|------------------------------------- | |
| albert-base-v2-CoLA | linguistic acceptability | single sentences | binary (1=acceptable/ 0=unacceptable) | <sub><sup>https://paperswithcode.com/sota/linguistic-acceptability-on-cola </sub></sup> | <sub><sup>https://huggingface.co./textattack/albert-base-v2-CoLA </sub></sup> | |
| bert-base-uncased-CoLA | linguistic acceptability | single sentences | binary (1=acceptable/ 0=unacceptable) | none yet | <sub><sup>https://huggingface.co./textattack/bert-base-uncased-CoLA </sub></sup> | |
| distilbert-base-cased-CoLA | linguistic acceptability | single sentences | binary (1=acceptable/ 0=unacceptable) | <sub><sup> https://paperswithcode.com/sota/linguistic-acceptability-on-cola </sub></sup> | <sub><sup>https://huggingface.co./textattack/distilbert-base-cased-CoLA </sub></sup> | |
| distilbert-base-uncased-CoLA | linguistic acceptability | single sentences | binary (1=acceptable/ 0=unacceptable) | <sub><sup> https://paperswithcode.com/sota/linguistic-acceptability-on-cola </sub></sup> | <sub><sup>https://huggingface.co./textattack/distilbert-base-uncased-CoLA </sub></sup> | |
| roberta-base-CoLA | linguistic acceptability | single sentences | binary (1=acceptable/ 0=unacceptable) | <sub><sup> https://paperswithcode.com/sota/linguistic-acceptability-on-cola </sub></sup> | <sub><sup> https://huggingface.co./textattack/roberta-base-CoLA </sub></sup> | |
| xlnet-base-cased-CoLA | linguistic acceptability | single sentences | binary (1=acceptable/ 0=unacceptable) | <sub><sup> https://paperswithcode.com/sota/linguistic-acceptability-on-cola </sub></sup> | <sub><sup>https://huggingface.co./textattack/xlnet-base-cased-CoLA </sub></sup> | |
| albert-base-v2-RTE | natural language inference | sentence pairs (1 premise and 1 hypothesis) | binary(0=entailed/1=not entailed) | <sub><sup> https://paperswithcode.com/sota/natural-language-inference-on-rte </sub></sup> | <sub><sup> https://huggingface.co./textattack/albert-base-v2-RTE </sub></sup> | |
| albert-base-v2-snli | natural language inference | sentence pairs | accuracy (0=entailment, 1=neutral,2=contradiction) | none yet | <sub><sup> https://huggingface.co./textattack/albert-base-v2-snli </sub></sup> | |
| albert-base-v2-WNLI | natural language inference | sentence pairs | binary | <sub><sup> https://paperswithcode.com/sota/natural-language-inference-on-wnli </sub></sup> | <sub><sup> https://huggingface.co./textattack/albert-base-v2-WNLI</sub></sup> | |
| bert-base-uncased-MNLI | natural language inference | sentence pairs (1 premise and 1 hypothesis) | accuracy (0=entailment, 1=neutral,2=contradiction) | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-MNLI </sub></sup> | |
| bert-base-uncased-QNLI | natural language inference | question/answer pairs | binary (1=unanswerable/ 0=answerable) | none yet |<sub><sup> https://huggingface.co./textattack/bert-base-uncased-QNLI </sub></sup> | |
| bert-base-uncased-RTE | natural language inference | sentence pairs (1 premise and 1 hypothesis) | binary(0=entailed/1=not entailed) | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-RTE </sub></sup> | |
| bert-base-uncased-snli | natural language inference | sentence pairs | accuracy (0=entailment, 1=neutral,2=contradiction) | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-snli </sub></sup> | |
| bert-base-uncased-WNLI | natural language inference | sentence pairs | binary | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-WNLI </sub></sup> | |
| distilbert-base-cased-snli | natural language inference | sentence pairs | accuracy (0=entailment, 1=neutral,2=contradiction) | none yet | <sub><sup> https://huggingface.co./textattack/distilbert-base-cased-snli </sub></sup> | |
| distilbert-base-uncased-MNLI | natural language inference | sentence pairs (1 premise and 1 hypothesis) | accuracy (0=entailment,1=neutral, 2=contradiction) | none yet | <sub><sup> https://huggingface.co./textattack/distilbert-base-uncased-MNLI </sub></sup> | |
| distilbert-base-uncased-RTE | natural language inference | sentence pairs (1 premise and 1 hypothesis) | binary(0=entailed/1=not entailed) | <sub><sup> https://paperswithcode.com/sota/natural-language-inference-on-rte </sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-uncased-RTE</sub></sup> | |
| distilbert-base-uncased-WNLI | natural language inference | sentence pairs | binary | <sub><sup> https://paperswithcode.com/sota/natural-language-inference-on-wnli </sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-uncased-WNLI </sub></sup> | |
| roberta-base-QNLI | natural language inference | question/answer pairs | binary (1=unanswerable/ 0=answerable) | <sub><sup> https://paperswithcode.com/sota/natural-language-inference-on-qnli </sub></sup> | <sub><sup> https://huggingface.co./textattack/roberta-base-QNLI </sub></sup> | |
| roberta-base-RTE | natural language inference | sentence pairs (1 premise and 1 hypothesis) | binary(0=entailed/1=not entailed) | <sub><sup> https://paperswithcode.com/sota/natural-language-inference-on-rte </sub></sup> | <sub><sup> https://huggingface.co./textattack/roberta-base-RTE</sub></sup> | |
| roberta-base-WNLI | natural language inference | sentence pairs | binary | <sub><sup> https://paperswithcode.com/sota/natural-language-inference-on-wnli </sub></sup> | https://huggingface.co./textattack/roberta-base-WNLI </sub></sup> | |
| xlnet-base-cased-RTE | natural language inference | sentence pairs (1 premise and 1 hypothesis) | binary(0=entailed/1=not entailed) | <sub><sup> https://paperswithcode.com/sota/ </sub></sup>natural-language-inference-on-rte | <sub><sup> https://huggingface.co./textattack/xlnet-base-cased-RTE </sub></sup> | |
| xlnet-base-cased-WNLI | natural language inference | sentence pairs | binary | none yet | <sub><sup> https://huggingface.co./textattack/xlnet-base-cased-WNLI </sub></sup> | |
| albert-base-v2-QQP | paraphase similarity | question pairs | binary (1=similar/0=not similar) | <sub><sup> https://paperswithcode.com/sota/question-answering-on-quora-question-pairs </sub></sup> | <sub><sup> https://huggingface.co./textattack/albert-base-v2-QQP</sub></sup> | |
| bert-base-uncased-QQP | paraphase similarity | question pairs | binary (1=similar/0=not similar) | <sub><sup> https://paperswithcode.com/sota/question-answering-on-quora-question-pairs </sub></sup> | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-QQP </sub></sup> | |
| distilbert-base-uncased-QNLI | question answering/natural language inference | question/answer pairs | binary (1=unanswerable/ 0=answerable) | <sub><sup> https://paperswithcode.com/sota/natural-language-inference-on-qnli </sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-uncased-QNLI </sub></sup> | |
| distilbert-base-cased-QQP | question answering/paraphase similarity | question pairs | binary (1=similar/ 0=not similar) | <sub><sup> https://paperswithcode.com/sota/question-answering-on-quora-question-pairs </sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-cased-QQP </sub></sup> | |
| albert-base-v2-STS-B | semantic textual similarity | sentence pairs | similarity (0.0 to 5.0) | <sub><sup> https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark </sub></sup> | <sub><sup> https://huggingface.co./textattack/albert-base-v2-STS-B </sub></sup> | |
| bert-base-uncased-MRPC | semantic textual similarity | sentence pairs | binary (1=similar/0=not similar) | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-MRPC </sub></sup> | |
| bert-base-uncased-STS-B | semantic textual similarity | sentence pairs | similarity (0.0 to 5.0) | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-STS-B </sub></sup> | |
| distilbert-base-cased-MRPC | semantic textual similarity | sentence pairs | binary (1=similar/0=not similar) | <sub><sup> https://paperswithcode.com/sota/semantic-textual-similarity-on-mrpc </sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-cased-MRPC </sub></sup> | |
| distilbert-base-cased-STS-B | semantic textual similarity | sentence pairs | similarity (0.0 to 5.0) | <sub><sup> https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark </sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-cased-STS-B </sub></sup> | |
| distilbert-base-uncased-MRPC | semantic textual similarity | sentence pairs | binary (1=similar/0=not similar) | <sub><sup> https://paperswithcode.com/sota/semantic-textual-similarity-on-mrpc </sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-uncased-MRPC</sub></sup> | |
| roberta-base-MRPC | semantic textual similarity | sentence pairs | binary (1=similar/0=not similar) | <sub><sup> https://paperswithcode.com/sota/semantic-textual-similarity-on-mrpc </sub></sup> | <sub><sup> https://huggingface.co./textattack/roberta-base-MRPC </sub></sup> | |
| roberta-base-STS-B | semantic textual similarity | sentence pairs | similarity (0.0 to 5.0) | <sub><sup> https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark </sub></sup> | <sub><sup> https://huggingface.co./textattack/roberta-base-STS-B </sub></sup> | |
| xlnet-base-cased-MRPC | semantic textual similarity | sentence pairs | binary (1=similar/0=not similar) | <sub><sup> https://paperswithcode.com/sota/semantic-textual-similarity-on-mrpc </sub></sup> | <sub><sup> https://huggingface.co./textattack/xlnet-base-cased-MRPC </sub></sup> | |
| xlnet-base-cased-STS-B | semantic textual similarity | sentence pairs | similarity (0.0 to 5.0) | <sub><sup> https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark </sub></sup> | <sub><sup> https://huggingface.co./textattack/xlnet-base-cased-STS-B </sub></sup> | |
| albert-base-v2-imdb | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/albert-base-v2-imdb </sub></sup> | |
| albert-base-v2-rotten-tomatoes | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/albert-base-v2-rotten-tomatoes </sub></sup> | |
| albert-base-v2-SST-2 | sentiment analysis | phrases | accuracy (0.0000 to 1.0000) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary </sub></sup> | <sub><sup> https://huggingface.co./textattack/albert-base-v2-SST-2 </sub></sup> | |
| albert-base-v2-yelp-polarity | sentiment analysis | yelp reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/albert-base-v2-yelp-polarity </sub></sup> | |
| bert-base-uncased-imdb | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-imdb </sub></sup> | |
| bert-base-uncased-rotten-tomatoes | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-rotten-tomatoes </sub></sup> | |
| bert-base-uncased-SST-2 | sentiment analysis | phrases | accuracy (0.0000 to 1.0000) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary </sub></sup> | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-SST-2 </sub></sup> | |
| bert-base-uncased-yelp-polarity | sentiment analysis | yelp reviews | binary (1=good/0=bad) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-yelp-binary </sub></sup> | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-yelp-polarity </sub></sup> | |
| cnn-imdb | sentiment analysis | movie reviews | binary (1=good/0=bad) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-imdb </sub></sup> | none | |
| cnn-mr | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | none | |
| cnn-sst2 | sentiment analysis | phrases | accuracy (0.0000 to 1.0000) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary </sub></sup> | none | |
| cnn-yelp | sentiment analysis | yelp reviews | binary (1=good/0=bad) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-yelp-binary </sub></sup> | none | |
| distilbert-base-cased-SST-2 | sentiment analysis | phrases | accuracy (0.0000 to 1.0000) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary </sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-cased-SST-2 </sub></sup> | |
| distilbert-base-uncased-imdb | sentiment analysis | movie reviews | binary (1=good/0=bad) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-imdb</sub></sup> | <sub><sup> https://huggingface.co./textattack/distilbert-base-uncased-imdb </sub></sup> | |
| distilbert-base-uncased-rotten-tomatoes | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/distilbert-base-uncased-rotten-tomatoes </sub></sup> | |
| lstm-imdb | sentiment analysis | movie reviews | binary (1=good/0=bad) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-imdb </sub></sup> | none | |
| lstm-mr | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | none | |
| lstm-sst2 | sentiment analysis | phrases | accuracy (0.0000 to 1.0000) | none yet | none | |
| lstm-yelp | sentiment analysis | yelp reviews | binary (1=good/0=bad) | none yet | none | |
| roberta-base-imdb | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/roberta-base-imdb </sub></sup> | |
| roberta-base-rotten-tomatoes | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/roberta-base-rotten-tomatoes </sub></sup> | |
| roberta-base-SST-2 | sentiment analysis | phrases | accuracy (0.0000 to 1.0000) | <sub><sup> https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary </sub></sup> | <sub><sup> https://huggingface.co./textattack/roberta-base-SST-2 </sub></sup> | |
| xlnet-base-cased-imdb | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/xlnet-base-cased-imdb </sub></sup> | |
| xlnet-base-cased-rotten-tomatoes | sentiment analysis | movie reviews | binary (1=good/0=bad) | none yet | <sub><sup> https://huggingface.co./textattack/xlnet-base-cased-rotten-tomatoes </sub></sup> | |
| albert-base-v2-ag-news | text classification | news articles | news category | none yet | <sub><sup> https://huggingface.co./textattack/albert-base-v2-ag-news </sub></sup> | |
| bert-base-uncased-ag-news | text classification | news articles | news category | none yet | <sub><sup> https://huggingface.co./textattack/bert-base-uncased-ag-news </sub></sup> | |
| cnn-ag-news | text classification | news articles | news category | <sub><sup> https://paperswithcode.com/sota/text-classification-on-ag-news </sub></sup> | none | |
| distilbert-base-uncased-ag-news | text classification | news articles | news category | none yet | <sub><sup> https://huggingface.co./textattack/distilbert-base-uncased-ag-news </sub></sup> | |
| lstm-ag-news | text classification | news articles | news category | <sub><sup> https://paperswithcode.com/sota/text-classification-on-ag-news </sub></sup> | none | |
| roberta-base-ag-news | text classification | news articles | news category | none yet | <sub><sup> https://huggingface.co./textattack/roberta-base-ag-news </sub></sup> | |
| </section> | |