Update README.md

README.md

@@ -7,11 +7,11 @@ tags:
 - sentence-similarity
 - transformers
 datasets:
-- shibing624/nli_zh
+- https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset
 language:
 - zh
 metrics:
-- bleu
+- spearmanr
 library_name: transformers
 ---
 # shibing624/text2vec-base-chinese-sentence
@@ -20,23 +20,28 @@ This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-senten
 It maps sentences to a 768 dimensional dense vector space and can be used for tasks 
 like sentence embeddings, text matching or semantic search.
 
-- using all 5 tasks' datasets, dataset: https://huggingface.co/datasets/shibing624/nli_zh
+- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset
 - base model: nghuyong/ernie-3.0-base-zh
-- max_seq_length = 256
+- max_seq_length: 256
 - best epoch: 3
+- sentence embedding dim: 768
 
 ## Evaluation
 For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
 
+### Release Models
 - 本项目release模型的中文匹配评测结果：
 
-| Arch | BaseModel                    | Model                                                                                                                                             | ATEC  |  BQ   | LCQMC | PAWSX | STS-B |    Avg    |  QPS  |
-| :-- |:-----------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:---------:|:-----:|
-| Word2Vec | word2vec                     | [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html)                                                                    | 20.00 | 31.49 | 59.46 | 2.57  | 55.78 |   33.86   | 23769 |
-| SBERT | xlm-roberta-base             | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 |   41.99   | 3138  |
-| CoSENT | hfl/chinese-macbert-base     | [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese)                                                       | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 | 48.25 | 3008  |
-| CoSENT | hfl/chinese-lert-large       | [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese)                                                   | 32.61 | 44.59 | 69.30 | 14.51 | 79.44 |   48.08   | 2092  |
-| CoSENT | nghuyong/ernie-3.0-base-zh   | [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence)                                               | 51.26 | 68.72 | 79.13 | 34.28 | 80.70 |   **62.81**   | 3066  |
+| Arch       | BaseModel                         | Model                                                                                                                                             | ATEC  |  BQ   | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc |    Avg    |  QPS  |
+|:-----------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|:-------:|:---------:|:-----:|
+| Word2Vec   | word2vec                          | [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html)                                                                    | 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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/shibing624/text2vec-base-chinese-sentence)                                     | 51.26 | 68.72 | 79.13 | 34.28 | 80.70 |  70.34  |  54.91  |   60.09   | 3066  |
+| CoSENT     | nghuyong/ernie-3.0-base-zh        | [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase)                                 | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 |  76.70  |  63.30  | **63.08** | 3066  |
+
 
 
 ## Usage (text2vec)