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Model: shibing624/text2vec-bge-large-chinese Source: Original Platform
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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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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- sentence-transformers
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- onnx
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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: sentence-transformers
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---
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# shibing624/text2vec-bge-large-chinese
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This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-bge-large-chinese.
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It maps sentences to a 1024 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: https://huggingface.co/BAAI/bge-large-zh-noinstruct
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- max_seq_length: 256
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- best epoch: 4
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- sentence embedding dim: 1024
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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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| 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 | 3138 |
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| CoSENT | BAAI/bge-large-zh-noinstruct | [shibing624/text2vec-bge-large-chinese](https://huggingface.co/shibing624/text2vec-bge-large-chinese) | 38.41 | 61.34 | 71.72 | 35.15 | 76.44 | 71.81 | 63.15 | 59.72 | 844 |
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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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- `shibing624/text2vec-base-multilingual`模型，是用CoSENT方法训练，基于`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`用人工挑选后的多语言STS数据集[shibing624/nli-zh-all/text2vec-base-multilingual-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset)训练得到，并在中英文测试集评估相对于原模型效果有提升，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，多语言语义匹配任务推荐使用
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- `shibing624/text2vec-bge-large-chinese`模型，是用CoSENT方法训练，基于`BAAI/bge-large-zh-noinstruct`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)训练得到，并在中文测试集评估相对于原模型效果有提升，在短文本区分度上提升明显，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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- `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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- 各预训练模型均可以通过transformers调用，如MacBERT模型：`--model_name hfl/chinese-macbert-base` 或者roberta模型：`--model_name uer/roberta-medium-wwm-chinese-cluecorpussmall`
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- 为测评模型的鲁棒性，加入了未训练过的SOHU测试集，用于测试模型的泛化能力；为达到开箱即用的实用效果，使用了搜集到的各中文匹配数据集，数据集也上传到HF datasets[链接见下方](#数据集)
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- 中文匹配任务实验表明，pooling最优是`EncoderType.FIRST_LAST_AVG`和`EncoderType.MEAN`，两者预测效果差异很小
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- 中文匹配评测结果复现，可以下载中文匹配数据集到`examples/data`，运行 [tests/model_spearman.py](https://github.com/shibing624/text2vec/blob/master/tests/model_spearman.py) 代码复现评测结果
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- QPS的GPU测试环境是Tesla V100，显存32GB
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模型训练实验报告：[实验报告](https://github.com/shibing624/text2vec/blob/master/docs/model_report.md)
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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-bge-large-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-bge-large-chinese')
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model = BertModel.from_pretrained('shibing624/text2vec-bge-large-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-bge-large-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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## 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: ErnieModel
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(1): Pooling({'word_embedding_dimension': 1024, '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 https://huggingface.co/BAAI/bge-large-zh-noinstruct 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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## 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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```
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40
config.json
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{
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"_name_or_path": "shibing624/text2vec-bge-large-chinese",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"directionality": "bidi",
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
|
||||||
|
"0": "LABEL_0"
|
||||||
|
},
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 4096,
|
||||||
|
"label2id": {
|
||||||
|
"LABEL_0": 0
|
||||||
|
},
|
||||||
|
"layer_norm_eps": 1e-12,
|
||||||
|
"max_position_embeddings": 512,
|
||||||
|
"model_type": "bert",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 24,
|
||||||
|
"output_past": true,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"pooler_fc_size": 768,
|
||||||
|
"pooler_num_attention_heads": 12,
|
||||||
|
"pooler_num_fc_layers": 3,
|
||||||
|
"pooler_size_per_head": 128,
|
||||||
|
"pooler_type": "first_token_transform",
|
||||||
|
"position_embedding_type": "absolute",
|
||||||
|
"torch_dtype": "float32",
|
||||||
|
"transformers_version": "4.31.0",
|
||||||
|
"type_vocab_size": 2,
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 21128
|
||||||
|
}
|
||||||
2
eval_results.txt
Normal file
2
eval_results.txt
Normal file
@@ -0,0 +1,2 @@
|
|||||||
|
eval_pearson = 0.5551856961372815
|
||||||
|
eval_spearman = 0.5470897314949114
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:7e6b8b127408144cb6b627895f2da831ef0bb6b232c2f8bf6f6bed47133454d9
|
||||||
|
size 1302134568
|
||||||
40
onnx/config.json
Normal file
40
onnx/config.json
Normal file
@@ -0,0 +1,40 @@
|
|||||||
|
{
|
||||||
|
"_attn_implementation_autoset": true,
|
||||||
|
"architectures": [
|
||||||
|
"BertModel"
|
||||||
|
],
|
||||||
|
"attention_probs_dropout_prob": 0.1,
|
||||||
|
"bos_token_id": 0,
|
||||||
|
"classifier_dropout": null,
|
||||||
|
"directionality": "bidi",
|
||||||
|
"eos_token_id": 2,
|
||||||
|
"hidden_act": "gelu",
|
||||||
|
"hidden_dropout_prob": 0.1,
|
||||||
|
"hidden_size": 1024,
|
||||||
|
"id2label": {
|
||||||
|
"0": "LABEL_0"
|
||||||
|
},
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 4096,
|
||||||
|
"label2id": {
|
||||||
|
"LABEL_0": 0
|
||||||
|
},
|
||||||
|
"layer_norm_eps": 1e-12,
|
||||||
|
"max_position_embeddings": 512,
|
||||||
|
"model_type": "bert",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 24,
|
||||||
|
"output_past": true,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"pooler_fc_size": 768,
|
||||||
|
"pooler_num_attention_heads": 12,
|
||||||
|
"pooler_num_fc_layers": 3,
|
||||||
|
"pooler_size_per_head": 128,
|
||||||
|
"pooler_type": "first_token_transform",
|
||||||
|
"position_embedding_type": "absolute",
|
||||||
|
"torch_dtype": "float32",
|
||||||
|
"transformers_version": "4.51.3",
|
||||||
|
"type_vocab_size": 2,
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 21128
|
||||||
|
}
|
||||||
3
onnx/model.onnx
Normal file
3
onnx/model.onnx
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:15714229468fee8abaf63499d512fa49ebfa2f8089a60c8823e65ec3ea19404b
|
||||||
|
size 1298440934
|
||||||
37
onnx/special_tokens_map.json
Normal file
37
onnx/special_tokens_map.json
Normal file
@@ -0,0 +1,37 @@
|
|||||||
|
{
|
||||||
|
"cls_token": {
|
||||||
|
"content": "[CLS]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"mask_token": {
|
||||||
|
"content": "[MASK]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "[PAD]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"sep_token": {
|
||||||
|
"content": "[SEP]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "[UNK]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
21292
onnx/tokenizer.json
Normal file
21292
onnx/tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
65
onnx/tokenizer_config.json
Normal file
65
onnx/tokenizer_config.json
Normal file
@@ -0,0 +1,65 @@
|
|||||||
|
{
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "[PAD]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"100": {
|
||||||
|
"content": "[UNK]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"101": {
|
||||||
|
"content": "[CLS]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"102": {
|
||||||
|
"content": "[SEP]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"103": {
|
||||||
|
"content": "[MASK]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"clean_up_tokenization_spaces": true,
|
||||||
|
"cls_token": "[CLS]",
|
||||||
|
"do_basic_tokenize": true,
|
||||||
|
"do_lower_case": true,
|
||||||
|
"extra_special_tokens": {},
|
||||||
|
"mask_token": "[MASK]",
|
||||||
|
"max_length": 256,
|
||||||
|
"model_max_length": 512,
|
||||||
|
"never_split": null,
|
||||||
|
"pad_to_multiple_of": null,
|
||||||
|
"pad_token": "[PAD]",
|
||||||
|
"pad_token_type_id": 0,
|
||||||
|
"padding_side": "right",
|
||||||
|
"sep_token": "[SEP]",
|
||||||
|
"stride": 0,
|
||||||
|
"strip_accents": null,
|
||||||
|
"tokenize_chinese_chars": true,
|
||||||
|
"tokenizer_class": "BertTokenizer",
|
||||||
|
"truncation_side": "right",
|
||||||
|
"truncation_strategy": "longest_first",
|
||||||
|
"unk_token": "[UNK]"
|
||||||
|
}
|
||||||
21128
onnx/vocab.txt
Normal file
21128
onnx/vocab.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
pytorch_model.bin
Normal file
3
pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9a15a7e95aa1c02184301c06d8bfd93921b9a007515e8f864432d19ffbd5e500
|
||||||
|
size 1302221545
|
||||||
7
special_tokens_map.json
Normal file
7
special_tokens_map.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"cls_token": "[CLS]",
|
||||||
|
"mask_token": "[MASK]",
|
||||||
|
"pad_token": "[PAD]",
|
||||||
|
"sep_token": "[SEP]",
|
||||||
|
"unk_token": "[UNK]"
|
||||||
|
}
|
||||||
21292
tokenizer.json
Normal file
21292
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
15
tokenizer_config.json
Normal file
15
tokenizer_config.json
Normal file
@@ -0,0 +1,15 @@
|
|||||||
|
{
|
||||||
|
"clean_up_tokenization_spaces": true,
|
||||||
|
"cls_token": "[CLS]",
|
||||||
|
"do_basic_tokenize": true,
|
||||||
|
"do_lower_case": true,
|
||||||
|
"mask_token": "[MASK]",
|
||||||
|
"model_max_length": 512,
|
||||||
|
"never_split": null,
|
||||||
|
"pad_token": "[PAD]",
|
||||||
|
"sep_token": "[SEP]",
|
||||||
|
"strip_accents": null,
|
||||||
|
"tokenize_chinese_chars": true,
|
||||||
|
"tokenizer_class": "BertTokenizer",
|
||||||
|
"unk_token": "[UNK]"
|
||||||
|
}
|
||||||
6
training_progress_scores.csv
Normal file
6
training_progress_scores.csv
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
global_step,train_loss,eval_spearman,eval_pearson
|
||||||
|
6217,1.187222957611084,0.5114652838626846,0.4941802088748416
|
||||||
|
12434,0.5630741119384766,0.529277507678798,0.5185274121904764
|
||||||
|
18651,0.4950772523880005,0.4936530600465713,0.4916064648614785
|
||||||
|
24868,0.06126270815730095,0.5470897314949114,0.5551856961372815
|
||||||
|
31085,0.09893279522657394,0.5114652838626846,0.5117443179915746
|
||||||
|
Reference in New Issue
Block a user