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this year we published the massive text embedding benchmarks um which was kind of like a follow-up work which were more put a bit more broadly in perspective so we collected different tasks like where you can use embedding models and you can use them for clustering you can use them for by text mining meaning finding sentence with the same meaning in different languages you can use them for retrieval for a semantic textual similarity for summarization for text classification for pair classification and for re-ranking and so we so so this is like a project that started also really long time ago so when I published the sentence break paper sentence from former papers it should get results on STS and sentiva which was like the defactor standard in embedding evaluation but if you really use it it didnt perform that well and also the original sentence model didnt perform that well as universal sentence encoder even such that The Benchmark on STS showed the opposite and so over the years wi