TextAttack Model Zoo

TextAttack is model-agnostic - meaning it can run attacks on models implemented in any deep learning framework. Model objects must be able to take a string (or list of strings) and return an output that can be processed by the goal function. For example, machine translation models take a list of strings as input and produce a list of strings as output. Classification and entailment models return an array of scores. As long as the user’s model meets this specification, the model is fit to use with TextAttack.

To help users, 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.

Available Models

TextAttack Models

TextAttack has two build-in model types, a 1-layer bidirectional LSTM with a hidden state size of 150 (lstm), and a WordCNN with 3 window sizes (3, 4, 5) and 100 filters for the window size (cnn). Both models set dropout to 0.3 and use a base of the 200-dimensional GLoVE embeddings.

transformers Models

Along with the lstm and cnn, you can theoretically fine-tune any model based in the huggingface transformers repo. Just type the model name (like bert-base-cased) and it will be automatically loaded.

Here are some models from transformers that have worked well for us:

  • bert-base-uncased and bert-base-cased

  • distilbert-base-uncased and distilbert-base-cased

  • albert-base-v2

  • roberta-base

  • xlnet-base-cased

Evaluation Results of Available Models

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%

How we have trained the TextAttack Models

  • By Oct 2020, TextAttack provides users with 82 pre-trained TextAttack models, including word-level LSTM, word-level CNN, BERT, and other transformer based models pre-trained on various datasets provided by HuggingFace.

  • Since TextAttack is integrated with the https://github.com/huggingface/nlp/ library, it can automatically load the test or validation data set for the corresponding pre-trained model. While the literature has mainly focused on classification and entailment, TextAttack’s pretrained models enable research on the robustness of models across all GLUE tasks.

  • We host all TextAttack Models at huggingface Model Hub: https://huggingface.co/textattack

Training details for each TextAttack Model

All of our models have model cards on the HuggingFace model hub. So for now, the easiest way to figure this out is as follows:

  • Please Go to our page on the model hub: https://huggingface.co/textattack

  • Find the model you’re looking for and select its page, for instance: https://huggingface.co/textattack/roberta-base-imdb

  • Scroll down to the end of the page, looking for model card section. Here it is the details of the model training for that specific TextAttack model.

  • BTW: For each of our transformers, we selected the best out of a grid search over a bunch of possible hyperparameters. So the model training hyperparemeter actually varies from model to model.

More details on TextAttack fine-tuned NLP models (details on target NLP task, input type, output type, SOTA results 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