|
--- |
|
pipeline_tag: sentence-similarity |
|
license: apache-2.0 |
|
tags: |
|
- text2vec |
|
- feature-extraction |
|
- sentence-similarity |
|
- transformers |
|
- sentence-transformers |
|
language: |
|
- zh |
|
metrics: |
|
- spearmanr |
|
library_name: sentence-transformers |
|
--- |
|
# shibing624/text2vec-bge-large-chinese |
|
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-bge-large-chinese. |
|
|
|
It maps sentences to a 1024 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: https://huggingface.co/BAAI/bge-large-zh-noinstruct |
|
- max_seq_length: 256 |
|
- best epoch: 4 |
|
- sentence embedding dim: 1024 |
|
|
|
## 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 | |
|
| 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 | 3138 | |
|
| 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 | |
|
|
|
|
|
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 |
|
- `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 |
|
- `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 |
|
- `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 |
|
- 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` |
|
- 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) |
|
- 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 |
|
- 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 |
|
- QPS的GPU测试环境是Tesla V100，显存32GB |
|
|
|
模型训练实验报告：[实验报告](https://github.com/shibing624/text2vec/blob/master/docs/model_report.md) |
|
|
|
## 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-bge-large-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-bge-large-chinese') |
|
model = BertModel.from_pretrained('shibing624/text2vec-bge-large-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-bge-large-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) |
|
``` |
|
|
|
|
|
## Full Model Architecture |
|
``` |
|
CoSENT( |
|
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: ErnieModel |
|
(1): Pooling({'word_embedding_dimension': 1024, '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 https://huggingface.co/BAAI/bge-large-zh-noinstruct 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. |
|
|
|
|
|
## 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}, |
|
} |
|
``` |
