Update README.md

README.md

@@ -14,8 +14,8 @@ metrics:
 - bleu
 library_name: transformers
 ---
-# shibing624/text2vec-base-chinese-nli
-This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-nli.
+# shibing624/text2vec-base-chinese-sentence
+This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-sentence.
 
 It maps sentences to a 768 dimensional dense vector space and can be used for tasks 
 like sentence embeddings, text matching or semantic search.
@@ -36,7 +36,7 @@ For an automated evaluation of this model, see the *Evaluation Benchmark*: [text
 | 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-nli](https://huggingface.co/shibing624/text2vec-base-chinese-nli)                                               | 51.26 | 68.72 | 79.13 | 34.28 | 80.70 |   **62.81**   | 3066  |
+| 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  |
 
 
 ## Usage (text2vec)
@@ -52,7 +52,7 @@ Then you can use the model like this:
 from text2vec import SentenceModel
 sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
 
-model = SentenceModel('shibing624/text2vec-base-chinese-nli')
+model = SentenceModel('shibing624/text2vec-base-chinese-sentence')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -79,8 +79,8 @@ def mean_pooling(model_output, attention_mask):
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 
 # Load model from HuggingFace Hub
-tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese-nli')
-model = BertModel.from_pretrained('shibing624/text2vec-base-chinese-nli')
+tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese-sentence')
+model = BertModel.from_pretrained('shibing624/text2vec-base-chinese-sentence')
 sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -107,7 +107,7 @@ Then load model and predict:
 ```python
 from sentence_transformers import SentenceTransformer
 
-m = SentenceTransformer("shibing624/text2vec-base-chinese-nli")
+m = SentenceTransformer("shibing624/text2vec-base-chinese-sentence")
 sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
 
 sentence_embeddings = m.encode(sentences)