README.md

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----
-pipeline_tag: sentence-similarity
-license: apache-2.0
-tags:
-- text2vec
-- feature-extraction
-- sentence-similarity
-- transformers
----
-# 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：
-
-| Model Name | ATEC | BQ | LCQMC | PAWSX | STS-B | Avg | QPS |
-| :---- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
-| w2v-light-tencent-chinese | 20.00 | 31.49 | 59.46 | 2.57 | 55.78 | 33.86 | 10283 |
-| paraphrase-multilingual-MiniLM-L12-v2 | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 | 41.99 | 2371 |
-| 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:
-
-```
-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')
-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')
-model = BertModel.from_pretrained('shibing624/text2vec-base-chinese')
-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, max 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")
-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': 128, '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 = {Xu Ming},
-  title = {text2vec: A Tool for Text to Vector},
-  year = {2022},
-  url = {https://github.com/shibing624/text2vec},
-}
-```

special_tokens_map.json

@@ -1 +0,0 @@
-{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}