MTEB is a benchmark that spans 8 embedding tasks covering a total of 56 datasets and 112 languages. The 8 task types are Bitext mining, Classification, Clustering, Pair Classification, Reranking, Retrieval, Semantic Textual Similarity and Summarisation. The 56 datasets contain varying text lengths and they are grouped into three categories: Sentence to sentence, Paragraph to paragraph, and Sentence to paragraph.
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ASSIN (Avaliação de Similaridade Semântica e INferência textual) is a dataset with semantic similarity score and entailment annotations. It was used in a shared task in the PROPOR 2016 conference.
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ASSIN2 (Avaliação de Similaridade Semântica e Inferência Textual) is the second edition of a workshop that evaluates Semantic Textual Similarity (STS) and Textual Entailment Recognition (RTE). It was held in conjunction with STIL 2019. Let’s break it down: