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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
text2vec, text to vector. 文本向量表征工具，把文本转化为向量矩阵，实现了Word2Vec、RankBM25、Sentence-BERT、CoSENT等文本表征、文本相似度计算模型，开箱即用。
| Date | Stars |
|---|---|
| 2026-07-24 | 4975 |
| 2026-07-25 | 4976 |
| 2026-07-28 | 4976 |
| 2026-07-30 | 4976 |
| 2026-08-08 | 4978 |
| 2026-08-17 | 4976 |
| 2026-08-20 | 4977 |
| 2026-08-21 | 4976 |
| 2026-08-23 | 4977 |
| 2026-08-24 | 4974 |
| 2026-08-27 | 4975 |
| 2026-08-30 | 4975 |
| 2026-08-31 | 4974 |
| 2026-09-01 | 4974 |
| 2026-09-02 | 4974 |
| 2026-09-04 | 4973 |
| 2026-09-06 | 4974 |
| 2026-09-08 | 4973 |
| 2026-09-13 | 4974 |
| 2026-09-14 | 4975 |
| 2026-09-19 | 4975 |
| 2026-09-20 | 4975 |
Today
— stars today
This week
+1 stars this week
This month
-1 stars this month
Momentum
0.0
growth rate 0.02%/day
[**🇨🇳中文**](https://github.com/shibing624/text2vec/blob/master/README.md) | [**🌐English**](https://github.com/shibing624/text2vec/blob/master/README_EN.md) | [**📖文档/Docs**](https://github.com/shibing624/text2vec/wiki) | [**🤖模型/Models**](https://huggingface.co/shibing624)
<div align="center">
<a href="https://github.com/shibing624/text2vec">
<img src="https://github.com/shibing624/text2vec/blob/master/docs/t2v-logo.png" height="150" alt="Logo">
</a>
</div>
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# Text2vec: Text to Vector
[](https://badge.fury.io/py/text2vec)
[](https://pepy.tech/project/text2vec)
[](CONTRIBUTING.md)
[](LICENSE)
[](requirements.txt)
[](https://github.com/shibing624/text2vec/issues)
[](#Contact)
**Text2vec**: Text to Vector, Get Sentence Embeddings. 文本向量化，把文本(包括词、句子、段落)表征为向量矩阵。
**text2vec**实现了Word2Vec、RankBM25、BERT、Sentence-BERT、CoSENT等多种文本表征、文本相似度计算模型，并在文本语义匹配（相似度计算）任务上比较了各模型的效果。
### News
[2023/09/20] v1.2.9版本: 支持多卡推理（多进程实现多GPU、多CPU推理），新增命令行工具（CLI），可以脚本执行批量文本向量化，详见[Release-v1.2.9](https://github.com/shibing624/text2vec/releases/tag/1.2.9)
[2023/09/03] v1.2.4版本: 支持FlagEmbedding模型训练，发布了中文匹配模型[shibing624/text2vec-bge-large-chinese](https://huggingface.co/shibing624/text2vec-bge-large-chinese)，用CoSENT方法监督训练，基于`BAAI/bge-large-zh-noinstruct`用中文匹配数据集训练得到，并在中文测试集评估相对于原模型效果有提升，短文本区分度上提升明显，详见[Release-v1.2.4](https://github.com/shibing624/text2vec/releases/tag/1.2.4)
[2023/07/17] v1.2.2版本: 支持多卡训练，发布了多语言匹配模型[shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual)，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，详见[Release-v1.2.2](https://github.com/shibing624/text2vec/releases/tag/1.2.2)
[2023/06/19] v1.2.1版本: 更新了中文匹配模型`shibing624/text2vec-base-chinese-nli`为新版[shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence)，针对CoSENT的loss计算对排序敏感特点，人工挑选并整理出高质量的有相关性排序的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)，在各评估集表现相对之前有提升；发布了适用于s2p的中文匹配模型[shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase)，详见[Release-v1.2.1](https://github.com/shibing624/text2vec/releases/tag/1.2.1)
[2023/06/15] v1.2.0版本: 发布了中文匹配模型[shibing624/text2vec-base-chinese-nli](https://huggingface.co/shibing624/text2vec-base-chinese-nli)，基于`nghuyong/ernie-3.0-base-zh`模型，使用了中文NLI数据集[shibing624/nli_zh](https://huggingface.co/datasets/shibing624/nli_zh)全部语料训练的CoSENT文本匹配模型，在各评估集表现提升明显，详见[Release-v1.2.0](https://github.com/shibing624/text2vec/releases/tag/1.2.0)
[2022/03/12] v1.1.4版本: 发布了中文匹配模型[shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese)，基于中文STS训练集训练的CoSENT匹配模型。详见[Release-v1.1.4](https://github.com/shibing624/text2vec/releases/tag/1.1.4)
**Guide**
- [Features](#Features)
- [Evaluation](#Evaluation)
- [Install](#install)
- [Usage](#usage)
- [Contact](#Contact)
- [References](#references)
## Features
### 文本向量表示模型
- [Word2Vec](https://github.com/shibing624/text2vec/blob/master/text2vec/word2vec.py)：通过腾讯AI Lab开源的大规模高质量中文[词向量数据（800万中文词轻量版）](https://pan.baidu.com/s/1La4U4XNFe8s5BJqxPQpeiQ) (文件名：light_Tencent_AILab_ChineseEmbedExcerpt of 42,650 characters
Read on GitHub
Ming Xu (徐明) · @tencent · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b10d35d32800a492, topic:embeddings, topic:sentence-embeddings, readme:sentence embeddings
matched fp:b10d35d32800a492, topic:nlp