---
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：

| 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:

```
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/cosent](https://github.com/shibing624/text2vec/tree/master/text2vec/cosent). 
        
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},
}
```