README.md

---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- text2vec
- feature-extraction
- sentence-similarity
- transformers
datasets:
- https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset
language:
- zh
metrics:
- spearmanr
library_name: transformers
---
# shibing624/text2vec-base-chinese-paraphrase
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-paraphrase.

It maps sentences to a 768 dimensional dense vector space and can be used for tasks 
like sentence embeddings, text matching or semantic search.

- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset
- base model: nghuyong/ernie-3.0-base-zh
- max_seq_length: 256
- best epoch: 3
- sentence embedding dim: 768

## Evaluation
For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)

### Release Models
- 本项目release模型的中文匹配评测结果：

| Arch       | BaseModel                         | Model                                                                                                                                             | ATEC  |  BQ   | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc |    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 |  55.04  |  20.70  |   35.03   | 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 |  63.01  |  52.28  |   46.46   | 3138  |
| Instructor | hfl/chinese-roberta-wwm-ext       | [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base)                                                                                       | 41.27 | 63.81 | 74.87 | 12.20 | 76.96 |  75.83  |  60.55  |   57.93   | 2980  |
| 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 |  70.27  |  50.42  |   51.61   | 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 |  73.01  |  59.04  |   53.12   | 2092  |
| 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 |  70.34  |  54.91  |   60.09   | 3066  |
| CoSENT     | nghuyong/ernie-3.0-base-zh        | [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase)                                 | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 |  76.70  |  63.30  | **63.08** | 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-paraphrase')
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-paraphrase')
model = BertModel.from_pretrained('shibing624/text2vec-base-chinese-paraphrase')
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-paraphrase")
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},
}
```