{"url":"/dataset/e-snli","name":"e-SNLI","full_name":null,"description_markdown":"e-SNLI is used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets.\r\n\r\nSource: [e-SNLI: Natural Language Inference with Natural Language Explanations](/paper/e-snli-natural-language-inference-with)\r\nImage Source: [https://arxiv.org/pdf/1812.01193v2.pdf](https://arxiv.org/pdf/1812.01193v2.pdf)","description_withheld":null,"homepage":"https://github.com/OanaMariaCamburu/e-SNLI","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/e-snli-natural-language-inference-with","title":"e-SNLI: Natural Language Inference with Natural Language Explanations","first_author":"Oana-Maria Camburu","url":null},"license":{"name":"Custom","url":"https://github.com/OanaMariaCamburu/e-SNLI"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Text Classification","url":"/task/text-classification","datasets_with_task":"/datasets/task/text-classification"},{"name":"Natural Language Inference","url":"/task/natural-language-inference","datasets_with_task":"/datasets/task/natural-language-inference"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["e-SNLI"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/esnli/esnli","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/esnli","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/esnli","frameworks":["tf","jax"]},{"repo":"https://github.com/OanaMariaCamburu/e-SNLI","url":"https://github.com/OanaMariaCamburu/e-SNLI","frameworks":["pytorch"]}],"num_papers_in_archive":139,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/natural-language-inference-on-e-snli","task":"Natural Language Inference","dataset_variant":"e-SNLI","rows":3,"metrics":["BLEU","Accuracy"],"first_row_in_archive_order":{"model":"ExplainThenPredictAttention (e-InferSent Bi-LSTM + Attention)","paper":"/paper/e-snli-natural-language-inference-with","metrics":{"Accuracy":"81.71","BLEU":"27.58"},"code_links":[{"title":"OanaMariaCamburu/e-SNLI","url":"https://github.com/OanaMariaCamburu/e-SNLI"},{"title":"qtli/eib","url":"https://github.com/qtli/eib"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/first-train-to-generate-then-generate-to","title":"First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI","date":"2024-12-12","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/e-snli-natural-language-inference-with","title":"e-SNLI: Natural Language Inference with Natural Language Explanations","date":"2018-12-04","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}