Update README.md

README.md

@@ -7,8 +7,24 @@ tags:
 - sentence-similarity
 - transformers
 ---
-# shibing624/text2vec
-This is a CoSENT(Cosine Sentence) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+# 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](https://github.com/shibing624/text2vec)
+
+- chinese text matching task：
+
+| Arch | Backbone | Model Name | ATEC | BQ | LCQMC | PAWSX | STS-B | Avg | QPS |
+| :-- | :--- | :---- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
+| Word2Vec | word2vec | w2v-light-tencent-chinese | 20.00 | 31.49 | 59.46 | 2.57 | 55.78 | 33.86 | 10283 |
+| SBERT | xlm-roberta-base | paraphrase-multilingual-MiniLM-L12-v2 | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 | 41.99 | 2371 |
+| CoSENT | hfl/chinese-macbert-base | text2vec-base-chinese | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 | **48.25** | 2572 |
+
 
 ## Usage (text2vec)
 Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
@@ -86,8 +102,6 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
-## Evaluation Results
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
 
 ## Full Model Architecture
 ```