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Evaluation of several non-personally developed pre-trained text semantic similarity models on SimCLUE.

License: GNU General Public License v3.0

Python 100.00%
semantic-similarity semantic-similarity-measures text-similarity

text-similarity-model-evaluation's Introduction

text-similarity-model-evaluation

Evaluation of several non-personally developed pre-trained text semantic similarity models on SimCLUE ( a Chinese text semantic similarity test set ) .

Dataset

SimCLUE: https://github.com/CLUEbenchmark/SimCLUE

List of integrated datasets:

  • LCQMC
  • AFQMC
  • OPPO Xiaobu
  • PKU-Paraphrase-Bank
  • Chinese-STS-B
  • Chinese-MNLI
  • Chinese-SNLI
  • OCNLI
  • CINLID

Result

model_name f1 / auc / speed (test) f1 / auc / speed (dev)
sentence-transformers/
distiluse-base-multilingual-cased-v2
64.7 / 71.4 / 163.85 66.6 / 74.8 / 163.70
sentence-transformers/
paraphrase-multilingual-MiniLM-L12-v2
68.2 / 76.6 / 345.99 68.7 / 78.9 / 407.74
sentence-transformers/
paraphrase-multilingual-mpnet-base-v2
67.9 / 76.9 / 145.30 68.3 / 79.2 / 145.78
symanto/
sn-xlm-roberta-base-snli-mnli-anli-xnli
70.0 / 78.0 / 148.80 70.7 / 79.6 / 145.59
DMetaSoul/
sbert-chinese-general-v2
62.6 / 92.3 / 110.68 63.7 / 92.7 / 111.24
DMetaSoul/
sbert-chinese-general-v2-distill
61.5 / 82.0 / 313.89 63.2 / 84.1 / 300.35

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