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--- |
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pipeline_tag: sentence-similarity |
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license: apache-2.0 |
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tags: |
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- text2vec |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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datasets: |
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- https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset |
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language: |
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- zh |
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metrics: |
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- spearmanr |
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library_name: transformers |
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--- |
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# shibing624/text2vec-base-chinese-paraphrase |
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This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-paraphrase. |
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It maps sentences to a 768 dimensional dense vector space and can be used for tasks |
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like sentence embeddings, text matching or semantic search. |
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- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset |
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- base model: nghuyong/ernie-3.0-base-zh |
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- max_seq_length: 256 |
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- best epoch: 3 |
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- sentence embedding dim: 768 |
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## Evaluation |
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For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec) |
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### Release Models |
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- 本项目release模型的中文匹配评测结果： |
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| Arch | BaseModel | Model | ATEC | BQ | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc | Avg | QPS | |
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|:-----------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|:-------:|:---------:|:-----:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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## Usage (text2vec) |
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Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed: |
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``` |
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pip install -U text2vec |
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``` |
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Then you can use the model like this: |
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```python |
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from text2vec import SentenceModel |
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sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡'] |
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model = SentenceModel('shibing624/text2vec-base-chinese-paraphrase') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) |
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Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: |
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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. |
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Install transformers: |
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``` |
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pip install transformers |
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``` |
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Then load model and predict: |
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```python |
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from transformers import BertTokenizer, BertModel |
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import torch |
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# Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] # First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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# Load model from HuggingFace Hub |
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tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese-paraphrase') |
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model = BertModel.from_pretrained('shibing624/text2vec-base-chinese-paraphrase') |
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sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡'] |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, mean pooling. |
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Usage (sentence-transformers) |
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[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences. |
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Install sentence-transformers: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then load model and predict: |
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```python |
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from sentence_transformers import SentenceTransformer |
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m = SentenceTransformer("shibing624/text2vec-base-chinese-paraphrase") |
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sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡'] |
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sentence_embeddings = m.encode(sentences) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Full Model Architecture |
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``` |
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CoSENT( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True}) |
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) |
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``` |
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## Citing & Authors |
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This model was trained by [text2vec](https://github.com/shibing624/text2vec). |
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If you find this model helpful, feel free to cite: |
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```bibtex |
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@software{text2vec, |
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author = {Ming Xu}, |
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title = {text2vec: A Tool for Text to Vector}, |
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year = {2023}, |
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url = {https://github.com/shibing624/text2vec}, |
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} |
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``` |
