238 lines
13 KiB
Markdown
238 lines
13 KiB
Markdown
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---
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license: apache-2.0
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pipeline_tag: sentence-similarity
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tags:
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- Sentence Transformers
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- sentence-similarity
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- sentence-transformers
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datasets:
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- shibing624/nli_zh
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language:
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- zh
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library_name: sentence-transformers
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---
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# shibing624/text2vec-base-chinese
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This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese.
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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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## 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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- chinese text matching task：
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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) | 43.37 | 61.43 | 73.48 | 38.90 | 78.25 | 70.60 | 53.08 | 59.87 | 3089 |
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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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| 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 |
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说明：
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- 结果评测指标：spearman系数
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- `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
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- `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
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- `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
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- `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
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- `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
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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')
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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')
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model = BertModel.from_pretrained('shibing624/text2vec-base-chinese')
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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")
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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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## Model speed up
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| Model | ATEC | BQ | LCQMC | PAWSX | STSB |
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|------------------------------------------------------------------------------------------------------------------------------|-------------------|-------------------|------------------|------------------|------------------|
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| shibing624/text2vec-base-chinese (fp32, baseline) | 0.31928 | 0.42672 | 0.70157 | 0.17214 | 0.79296 |
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| 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 |
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| 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 |
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| 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%) |
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In short:
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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.
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2. ✅ shibing624/text2vec-base-chinese (ov), OpenVINO does not reduce performance, but gives a 1.12x speedup on CPU.
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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.
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- usage: shibing624/text2vec-base-chinese (onnx-O4), for gpu
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer(
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"shibing624/text2vec-base-chinese",
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backend="onnx",
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model_kwargs={"file_name": "model_O4.onnx"},
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)
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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"])
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print(embeddings.shape)
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similarities = model.similarity(embeddings, embeddings)
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print(similarities)
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```
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- usage: shibing624/text2vec-base-chinese (ov), for cpu
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```python
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# pip install 'optimum[openvino]'
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer(
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"shibing624/text2vec-base-chinese",
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backend="openvino",
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)
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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"])
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print(embeddings.shape)
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similarities = model.similarity(embeddings, embeddings)
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print(similarities)
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```
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- usage: shibing624/text2vec-base-chinese (ov-qint8), for cpu
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```python
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# pip install optimum
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer(
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"shibing624/text2vec-base-chinese",
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backend="onnx",
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model_kwargs={"file_name": "model_qint8_avx512_vnni.onnx"},
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)
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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"])
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print(embeddings.shape)
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similarities = model.similarity(embeddings, embeddings)
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print(similarities)
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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': 128, '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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## Intended uses
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Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
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the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
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By default, input text longer than 256 word pieces is truncated.
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## Training procedure
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### Pre-training
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We use the pretrained [`hfl/chinese-macbert-base`](https://huggingface.co/hfl/chinese-macbert-base) model.
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Please refer to the model card for more detailed information about the pre-training procedure.
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### Fine-tuning
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We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each
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possible sentence pairs from the batch.
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We then apply the rank loss by comparing with true pairs and false pairs.
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#### Hyper parameters
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- training dataset: https://huggingface.co/datasets/shibing624/nli_zh
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- max_seq_length: 128
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- best epoch: 5
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- sentence embedding dim: 768
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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 = {Xu Ming},
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title = {text2vec: A Tool for Text to Vector},
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year = {2022},
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url = {https://github.com/shibing624/text2vec},
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}
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```
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