Update README.md

README.md

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 ---
+pipeline_tag: sentence-similarity
 license: apache-2.0
+tags:
+- text2vec
+- feature-extraction
+- sentence-similarity
+- transformers
+datasets:
+- shibing624/nli_zh
+language:
+- zh
+metrics:
+- bleu
+library_name: transformers
 ---
+# shibing624/text2vec-base-chinese-nli
+This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-nli.
+
+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
+- base model: nghuyong/ernie-3.0-base-zh
+- max_seq_length = 256
+- best epoch: 2
+
+## Evaluation
+For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
+
+- 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-nli](https://huggingface.co/shibing624/text2vec-base-chinese-nli)                                               | 51.26 | 68.72 | 79.13 | 34.28 | 80.70 |   **62.81**   | 3066  |
+
+
+## Usage (text2vec)
+Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
+
+```
+pip install -U text2vec
+```
+
+Then you can use the model like this:
+
+```python
+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')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: 
+
+First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+Install transformers:
+```
+pip install transformers
+```
+
+Then load model and predict:
+```python
+from transformers import BertTokenizer, BertModel
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    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')
+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')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Usage (sentence-transformers)
+[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.
+
+Install sentence-transformers:
+```
+pip install -U sentence-transformers
+```
+
+Then load model and predict:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+m = SentenceTransformer("shibing624/text2vec-base-chinese-nli")
+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)
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+## Full Model Architecture
+```
+CoSENT(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
+)
+```
+## Citing & Authors
+This model was trained by [text2vec](https://github.com/shibing624/text2vec). 
+        
+If you find this model helpful, feel free to cite:
+```bibtex 
+@software{text2vec,
+  author = {Ming Xu},
+  title = {text2vec: A Tool for Text to Vector},
+  year = {2023},
+  url = {https://github.com/shibing624/text2vec},
+}
+```