shibing624/text2vec base chinese This is a CoSENT(Cosine Sentence) model: shibing624/text2vec base chinese. It maps sentences to a 768 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. Evaluation For an automated evaluation of this model, see the Evaluation Benchmark : text2vec chinese text matching task： Arch BaseModel Model ATEC BQ LCQMC PAWSX STS B SOHU dd SOHU dc Avg QPS : : : : : : : : : : : : : : : : : : : : : Word2Vec word2vec w2v light tencent chinese 20.00 31.49 59.46 2.57 55.78 55.04 20.70 35.03 23769 SBERT xlm roberta base sentence transformers/paraphrase multilingual MiniLM L12 v2 18.42 38.52 63.96 10.14 78.90 63.01 52.28 46.46 3138 Instructor hfl/chinese roberta wwm ext moka ai/m3e base 41.27 63.81 74.87 12.20 76.96 75.83 60.55 57.93 2980 CoSENT hfl/chinese macbert base shibing624/text2vec base chinese 31.93 42.67 70.16 17.21 79.30 70.27 50.42 51.61 3008 CoSENT hfl/chinese lert large GanymedeNil/text2vec large chinese 32.61 44.59 69.30 14.51 79.44 73.01 59.04 53.12 2092 CoSENT nghuyong/ernie 3.0 base zh shibing624/text2vec base chinese sentence 43.37 61.43 73.48 38.90 78.25 70.60 53.08 59.87 3089 CoSENT…
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