TextAttack Model Zoo

February 14, 2021 · View on GitHub

More details at 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 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`

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%

`wordCNN`

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%

`albert-base-v2`

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%

`bert-base-uncased`

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%

`distilbert-base-cased`

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

`distilbert-base-uncased`

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%

`roberta-base`

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%

`xlnet-base-cased`

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%

More details on TextAttack models (details on NLP task, output type, SOTA on paperswithcode; model card on huggingface):

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)	_{^{https://paperswithcode.com/sota/linguistic-acceptability-on-cola}}	_{^{https://huggingface.co/textattack/albert-base-v2-CoLA}}
bert-base-uncased-CoLA	linguistic acceptability	single sentences	binary (1=acceptable/ 0=unacceptable)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-CoLA}}
distilbert-base-cased-CoLA	linguistic acceptability	single sentences	binary (1=acceptable/ 0=unacceptable)	_{^{https://paperswithcode.com/sota/linguistic-acceptability-on-cola}}	_{^{https://huggingface.co/textattack/distilbert-base-cased-CoLA}}
distilbert-base-uncased-CoLA	linguistic acceptability	single sentences	binary (1=acceptable/ 0=unacceptable)	_{^{https://paperswithcode.com/sota/linguistic-acceptability-on-cola}}	_{^{https://huggingface.co/textattack/distilbert-base-uncased-CoLA}}
roberta-base-CoLA	linguistic acceptability	single sentences	binary (1=acceptable/ 0=unacceptable)	_{^{https://paperswithcode.com/sota/linguistic-acceptability-on-cola}}	_{^{https://huggingface.co/textattack/roberta-base-CoLA}}
xlnet-base-cased-CoLA	linguistic acceptability	single sentences	binary (1=acceptable/ 0=unacceptable)	_{^{https://paperswithcode.com/sota/linguistic-acceptability-on-cola}}	_{^{https://huggingface.co/textattack/xlnet-base-cased-CoLA}}
albert-base-v2-RTE	natural language inference	sentence pairs (1 premise and 1 hypothesis)	binary(0=entailed/1=not entailed)	_{^{https://paperswithcode.com/sota/natural-language-inference-on-rte}}	_{^{https://huggingface.co/textattack/albert-base-v2-RTE}}
albert-base-v2-snli	natural language inference	sentence pairs	accuracy (0=entailment, 1=neutral,2=contradiction)	none yet	_{^{https://huggingface.co/textattack/albert-base-v2-snli}}
albert-base-v2-WNLI	natural language inference	sentence pairs	binary	_{^{https://paperswithcode.com/sota/natural-language-inference-on-wnli}}	_{^{https://huggingface.co/textattack/albert-base-v2-WNLI}}
bert-base-uncased-MNLI	natural language inference	sentence pairs (1 premise and 1 hypothesis)	accuracy (0=entailment, 1=neutral,2=contradiction)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-MNLI}}
bert-base-uncased-QNLI	natural language inference	question/answer pairs	binary (1=unanswerable/ 0=answerable)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-QNLI}}
bert-base-uncased-RTE	natural language inference	sentence pairs (1 premise and 1 hypothesis)	binary(0=entailed/1=not entailed)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-RTE}}
bert-base-uncased-snli	natural language inference	sentence pairs	accuracy (0=entailment, 1=neutral,2=contradiction)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-snli}}
bert-base-uncased-WNLI	natural language inference	sentence pairs	binary	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-WNLI}}
distilbert-base-cased-snli	natural language inference	sentence pairs	accuracy (0=entailment, 1=neutral,2=contradiction)	none yet	_{^{https://huggingface.co/textattack/distilbert-base-cased-snli}}
distilbert-base-uncased-MNLI	natural language inference	sentence pairs (1 premise and 1 hypothesis)	accuracy (0=entailment,1=neutral, 2=contradiction)	none yet	_{^{https://huggingface.co/textattack/distilbert-base-uncased-MNLI}}
distilbert-base-uncased-RTE	natural language inference	sentence pairs (1 premise and 1 hypothesis)	binary(0=entailed/1=not entailed)	_{^{https://paperswithcode.com/sota/natural-language-inference-on-rte}}	_{^{https://huggingface.co/textattack/distilbert-base-uncased-RTE}}
distilbert-base-uncased-WNLI	natural language inference	sentence pairs	binary	_{^{https://paperswithcode.com/sota/natural-language-inference-on-wnli}}	_{^{https://huggingface.co/textattack/distilbert-base-uncased-WNLI}}
roberta-base-QNLI	natural language inference	question/answer pairs	binary (1=unanswerable/ 0=answerable)	_{^{https://paperswithcode.com/sota/natural-language-inference-on-qnli}}	_{^{https://huggingface.co/textattack/roberta-base-QNLI}}
roberta-base-RTE	natural language inference	sentence pairs (1 premise and 1 hypothesis)	binary(0=entailed/1=not entailed)	_{^{https://paperswithcode.com/sota/natural-language-inference-on-rte}}	_{^{https://huggingface.co/textattack/roberta-base-RTE}}
roberta-base-WNLI	natural language inference	sentence pairs	binary	_{^{https://paperswithcode.com/sota/natural-language-inference-on-wnli}}	https://huggingface.co/textattack/roberta-base-WNLI
xlnet-base-cased-RTE	natural language inference	sentence pairs (1 premise and 1 hypothesis)	binary(0=entailed/1=not entailed)	_{^{https://paperswithcode.com/sota/}}natural-language-inference-on-rte	_{^{https://huggingface.co/textattack/xlnet-base-cased-RTE}}
xlnet-base-cased-WNLI	natural language inference	sentence pairs	binary	none yet	_{^{https://huggingface.co/textattack/xlnet-base-cased-WNLI}}
albert-base-v2-QQP	paraphase similarity	question pairs	binary (1=similar/0=not similar)	_{^{https://paperswithcode.com/sota/question-answering-on-quora-question-pairs}}	_{^{https://huggingface.co/textattack/albert-base-v2-QQP}}
bert-base-uncased-QQP	paraphase similarity	question pairs	binary (1=similar/0=not similar)	_{^{https://paperswithcode.com/sota/question-answering-on-quora-question-pairs}}	_{^{https://huggingface.co/textattack/bert-base-uncased-QQP}}
distilbert-base-uncased-QNLI	question answering/natural language inference	question/answer pairs	binary (1=unanswerable/ 0=answerable)	_{^{https://paperswithcode.com/sota/natural-language-inference-on-qnli}}	_{^{https://huggingface.co/textattack/distilbert-base-uncased-QNLI}}
distilbert-base-cased-QQP	question answering/paraphase similarity	question pairs	binary (1=similar/ 0=not similar)	_{^{https://paperswithcode.com/sota/question-answering-on-quora-question-pairs}}	_{^{https://huggingface.co/textattack/distilbert-base-cased-QQP}}
albert-base-v2-STS-B	semantic textual similarity	sentence pairs	similarity (0.0 to 5.0)	_{^{https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark}}	_{^{https://huggingface.co/textattack/albert-base-v2-STS-B}}
bert-base-uncased-MRPC	semantic textual similarity	sentence pairs	binary (1=similar/0=not similar)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-MRPC}}
bert-base-uncased-STS-B	semantic textual similarity	sentence pairs	similarity (0.0 to 5.0)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-STS-B}}
distilbert-base-cased-MRPC	semantic textual similarity	sentence pairs	binary (1=similar/0=not similar)	_{^{https://paperswithcode.com/sota/semantic-textual-similarity-on-mrpc}}	_{^{https://huggingface.co/textattack/distilbert-base-cased-MRPC}}
distilbert-base-cased-STS-B	semantic textual similarity	sentence pairs	similarity (0.0 to 5.0)	_{^{https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark}}	_{^{https://huggingface.co/textattack/distilbert-base-cased-STS-B}}
distilbert-base-uncased-MRPC	semantic textual similarity	sentence pairs	binary (1=similar/0=not similar)	_{^{https://paperswithcode.com/sota/semantic-textual-similarity-on-mrpc}}	_{^{https://huggingface.co/textattack/distilbert-base-uncased-MRPC}}
roberta-base-MRPC	semantic textual similarity	sentence pairs	binary (1=similar/0=not similar)	_{^{https://paperswithcode.com/sota/semantic-textual-similarity-on-mrpc}}	_{^{https://huggingface.co/textattack/roberta-base-MRPC}}
roberta-base-STS-B	semantic textual similarity	sentence pairs	similarity (0.0 to 5.0)	_{^{https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark}}	_{^{https://huggingface.co/textattack/roberta-base-STS-B}}
xlnet-base-cased-MRPC	semantic textual similarity	sentence pairs	binary (1=similar/0=not similar)	_{^{https://paperswithcode.com/sota/semantic-textual-similarity-on-mrpc}}	_{^{https://huggingface.co/textattack/xlnet-base-cased-MRPC}}
xlnet-base-cased-STS-B	semantic textual similarity	sentence pairs	similarity (0.0 to 5.0)	_{^{https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark}}	_{^{https://huggingface.co/textattack/xlnet-base-cased-STS-B}}
albert-base-v2-imdb	sentiment analysis	movie reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/albert-base-v2-imdb}}
albert-base-v2-rotten-tomatoes	sentiment analysis	movie reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/albert-base-v2-rotten-tomatoes}}
albert-base-v2-SST-2	sentiment analysis	phrases	accuracy (0.0000 to 1.0000)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary}}	_{^{https://huggingface.co/textattack/albert-base-v2-SST-2}}
albert-base-v2-yelp-polarity	sentiment analysis	yelp reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/albert-base-v2-yelp-polarity}}
bert-base-uncased-imdb	sentiment analysis	movie reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-imdb}}
bert-base-uncased-rotten-tomatoes	sentiment analysis	movie reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-rotten-tomatoes}}
bert-base-uncased-SST-2	sentiment analysis	phrases	accuracy (0.0000 to 1.0000)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary}}	_{^{https://huggingface.co/textattack/bert-base-uncased-SST-2}}
bert-base-uncased-yelp-polarity	sentiment analysis	yelp reviews	binary (1=good/0=bad)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-yelp-binary}}	_{^{https://huggingface.co/textattack/bert-base-uncased-yelp-polarity}}
cnn-imdb	sentiment analysis	movie reviews	binary (1=good/0=bad)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-imdb}}	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)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary}}	none
cnn-yelp	sentiment analysis	yelp reviews	binary (1=good/0=bad)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-yelp-binary}}	none
distilbert-base-cased-SST-2	sentiment analysis	phrases	accuracy (0.0000 to 1.0000)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary}}	_{^{https://huggingface.co/textattack/distilbert-base-cased-SST-2}}
distilbert-base-uncased-imdb	sentiment analysis	movie reviews	binary (1=good/0=bad)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-imdb}}	_{^{https://huggingface.co/textattack/distilbert-base-uncased-imdb}}
distilbert-base-uncased-rotten-tomatoes	sentiment analysis	movie reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/distilbert-base-uncased-rotten-tomatoes}}
lstm-imdb	sentiment analysis	movie reviews	binary (1=good/0=bad)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-imdb}}	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	_{^{https://huggingface.co/textattack/roberta-base-imdb}}
roberta-base-rotten-tomatoes	sentiment analysis	movie reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/roberta-base-rotten-tomatoes}}
roberta-base-SST-2	sentiment analysis	phrases	accuracy (0.0000 to 1.0000)	_{^{https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary}}	_{^{https://huggingface.co/textattack/roberta-base-SST-2}}
xlnet-base-cased-imdb	sentiment analysis	movie reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/xlnet-base-cased-imdb}}
xlnet-base-cased-rotten-tomatoes	sentiment analysis	movie reviews	binary (1=good/0=bad)	none yet	_{^{https://huggingface.co/textattack/xlnet-base-cased-rotten-tomatoes}}
albert-base-v2-ag-news	text classification	news articles	news category	none yet	_{^{https://huggingface.co/textattack/albert-base-v2-ag-news}}
bert-base-uncased-ag-news	text classification	news articles	news category	none yet	_{^{https://huggingface.co/textattack/bert-base-uncased-ag-news}}
cnn-ag-news	text classification	news articles	news category	_{^{https://paperswithcode.com/sota/text-classification-on-ag-news}}	none
distilbert-base-uncased-ag-news	text classification	news articles	news category	none yet	_{^{https://huggingface.co/textattack/distilbert-base-uncased-ag-news}}
lstm-ag-news	text classification	news articles	news category	_{^{https://paperswithcode.com/sota/text-classification-on-ag-news}}	none
roberta-base-ag-news	text classification	news articles	news category	none yet	_{^{https://huggingface.co/textattack/roberta-base-ag-news}}