# 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>