| --- |
| license: apache-2.0 |
| pipeline_tag: sentence-similarity |
| tags: |
| - Sentence Transformers |
| - sentence-similarity |
| - sentence-transformers |
| datasets: |
| - shibing624/nli_zh |
| language: |
| - zh |
| library_name: sentence-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 | 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) | 43.37 | 61.43 | 73.48 | 38.90 | 78.25 | 70.60 | 53.08 | 59.87 | 3089 | |
| | 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 | |
| | CoSENT | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) | 32.39 | 50.33 | 65.64 | 32.56 | 74.45 | 68.88 | 51.17 | 53.67 | 4004 | |
|
|
|
|
| 说明： |
| - 结果评测指标：spearman系数 |
| - `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行[examples/training_sup_text_matching_model.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model.py)代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 |
| - `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)训练得到，并在中文各NLI测试集评估达到较好效果，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 |
| - `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset)，数据集相对于[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 |
| - `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`模型是用SBERT训练，是`paraphrase-MiniLM-L12-v2`模型的多语言版本，支持中文、英文等 |
| - `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 |
|
|
| ## 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, 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") |
| 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) |
| ``` |
|
|
| ## Model speed up |
|
|
|
|
| | Model | ATEC | BQ | LCQMC | PAWSX | STSB | |
| |------------------------------------------------------------------------------------------------------------------------------|-------------------|-------------------|------------------|------------------|------------------| |
| | shibing624/text2vec-base-chinese (fp32, baseline) | 0.31928 | 0.42672 | 0.70157 | 0.17214 | 0.79296 | |
| | shibing624/text2vec-base-chinese (onnx-O4, [#29](https://huggingface.co/shibing624/text2vec-base-chinese/discussions/29)) | 0.31928 | 0.42672 | 0.70157 | 0.17214 | 0.79296 | |
| | shibing624/text2vec-base-chinese (ov, [#27](https://huggingface.co/shibing624/text2vec-base-chinese/discussions/27)) | 0.31928 | 0.42672 | 0.70157 | 0.17214 | 0.79296 | |
| | shibing624/text2vec-base-chinese (ov-qint8, [#30](https://huggingface.co/shibing624/text2vec-base-chinese/discussions/30)) | 0.30778 (-3.60%) | 0.43474 (+1.88%) | 0.69620 (-0.77%) | 0.16662 (-3.20%) | 0.79396 (+0.13%) | |
|
|
| In short: |
| 1. ✅ shibing624/text2vec-base-chinese (onnx-O4), ONNX Optimized to [O4](https://huggingface.co/docs/optimum/en/onnxruntime/usage_guides/optimization) does not reduce performance, but gives a [~2x speedup](https://sbert.net/docs/sentence_transformer/usage/efficiency.html#benchmarks) on GPU. |
| 2. ✅ shibing624/text2vec-base-chinese (ov), OpenVINO does not reduce performance, but gives a 1.12x speedup on CPU. |
| 3. 🟡 shibing624/text2vec-base-chinese (ov-qint8), int8 quantization with OV incurs a small performance hit on some tasks, and a tiny performance gain on others, when quantizing with [Chinese STSB](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt). Additionally, it results in a [4.78x speedup](https://sbert.net/docs/sentence_transformer/usage/efficiency.html#benchmarks) on CPU. |
|
|
| - usage: shibing624/text2vec-base-chinese (onnx-O4), for gpu |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer( |
| "shibing624/text2vec-base-chinese", |
| backend="onnx", |
| model_kwargs={"file_name": "model_O4.onnx"}, |
| ) |
| embeddings = model.encode(["如何更换花呗绑定银行卡", "花呗更改绑定银行卡", "你是谁"]) |
| print(embeddings.shape) |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| ``` |
|
|
|
|
| - usage: shibing624/text2vec-base-chinese (ov), for cpu |
| ```python |
| # pip install 'optimum[openvino]' |
| |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer( |
| "shibing624/text2vec-base-chinese", |
| backend="openvino", |
| ) |
| |
| embeddings = model.encode(["如何更换花呗绑定银行卡", "花呗更改绑定银行卡", "你是谁"]) |
| print(embeddings.shape) |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| ``` |
|
|
| - usage: shibing624/text2vec-base-chinese (ov-qint8), for cpu |
| ```python |
| # pip install optimum |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer( |
| "shibing624/text2vec-base-chinese", |
| backend="onnx", |
| model_kwargs={"file_name": "model_qint8_avx512_vnni.onnx"}, |
| ) |
| embeddings = model.encode(["如何更换花呗绑定银行卡", "花呗更改绑定银行卡", "你是谁"]) |
| print(embeddings.shape) |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| ``` |
|
|
|
|
| ## 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}) |
| ) |
| ``` |
|
|
| ## Intended uses |
|
|
| Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures |
| the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. |
|
|
| By default, input text longer than 256 word pieces is truncated. |
|
|
|
|
| ## Training procedure |
|
|
| ### Pre-training |
|
|
| We use the pretrained [`hfl/chinese-macbert-base`](https://huggingface.co/hfl/chinese-macbert-base) model. |
| Please refer to the model card for more detailed information about the pre-training procedure. |
|
|
| ### Fine-tuning |
|
|
| We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each |
| possible sentence pairs from the batch. |
| We then apply the rank loss by comparing with true pairs and false pairs. |
|
|
| #### Hyper parameters |
|
|
| - training dataset: https://huggingface.co/datasets/shibing624/nli_zh |
| - max_seq_length: 128 |
| - best epoch: 5 |
| - sentence embedding dim: 768 |
| |
| |
| |
| ## 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}, |
| } |
| ``` |
