Ruri: Japanese General Text Embeddings Notes: v3 models are out! We recommend using the following v3 models going forward. ID Param. Max Len. Avg. JMTEB cl nagoya/ruri v3 30m 37M 8192 74.51 cl nagoya/ruri v3 70m 70M 8192 75.48 cl nagoya/ruri v3 130m 132M 8192 76.55 cl nagoya/ruri v3 310m 315M 8192 77.24 Usage First install the Sentence Transformers library: Then you can load this model and run inference. Benchmarks JMTEB Evaluated with JMTEB. Model Param. Avg. Retrieval STS Classfification Reranking Clustering PairClassification : : : : : : : : : : : : : : : : : : cl nagoya/sup simcse ja base 111M 68.56 49.64 82.05 73.47 91.83 51.79 62.57 cl nagoya/sup simcse ja large 337M 66.51 37.62 83.18 73.73 91.48 50.56 62.51 cl nagoya/unsup simcse ja base 111M 65.07 40.23 78.72 73.07 91.16 44.77 62.44 cl nagoya/unsup simcse ja large 337M 66.27 40.53 80.56 74.66 90.95 48.41 62.49 pkshatech/GLuCoSE base ja 133M 70.44 59.02 78.71 76.82 91.90 49.78 66.39 sentence transformers/LaBSE 472M 64.70 40.12 76.56 72.66 91.63 44.88 62.33 intfloat/multilingual e5 small 118M 69.52 67.27 80.07 67.62 93.03 46.91 62.19 intfloat/multilingual e5 base 278M 70.12 68.21 79.84 69.30 92.85 48.26 62.26 intfloat/multili…
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