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The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences. Annotators were instructed to judge the relation between sentences given that they describe the same event. Each pair is labeled as “entailment”, “neutral”, “contradiction” or “-”, where “-” indicates that an agreement could not be reached. Source: Breaking NLI Systemswith Sentences that Require Simple Lexical Inferences

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. Source: e-SNLI: Natural Language Inference with Natural Language Explanations Image Source: https://arxiv.org/pdf/1812.01193v2.pdf

Visual Entailment (VE) consists of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text. SNLI-VE is a dataset for VE which is based on the Stanford Natural Language Inference corpus and Flickr30k dataset. Source: https://github.com/necla-ml/SNLI-VE

Chaos NLI is a Natural Language Inference (NLI) dataset with 100 annotations per example (for a total of 464,500 annotations) for some existing data points in the development sets of SNLI, MNLI, and Abductive NLI. The dataset provides additional labels for NLI annotations that reflect the distribution of human annotators, instead of picking the majority label as the gold standard label. Source: ChaosNLI Github Repository

e-SNLI-VE is a large VL (vision-language) dataset with NLEs (natural language explanations) with over 430k instances for which the explanations rely on the image content. It has been built by merging the explanations from e-SNLI and the image-sentence pairs from SNLI-VE.

JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer from large monolingual or multilingual pretrained models. While our work, along with previous work, shows that transfer from these models to low-resource languages that are unrelated to languages in their training set is not very effective, we would expect stronger results from transfer to creoles. Indeed, our experiments show considerably better results from few-shot learning of JamPatoisNLI than for such unrelated languages, and help us begin to understand how the unique relationship between creoles and their high-resource base languages affect cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring premises and expert-written hypotheses, is a step towards steering research into a traditionally underserved language and a useful benchmark for understanding cross-lingual NLP.

Translated SNLI Dataset in Marathi A translated version of the SNLI dataset in Marathi, designed for Semantic Textual Similarity (STS) tasks. The translations were generated using the model aryaumesh/english-to-marathi. #### Translation Quality Metrics: - BERT Precision: 88.57% - BERT Recall: 88.60% - BERT F1: 88.58% - TransQuest Score: 0.72