Text2vec

text2vec --input_file input.txt --output_file out.csv --batch_size 128 --multi_gpu True



**Full Changelog**: https://github.com/shibing624/text2vec/compare/1.2.8...1.2.9

1. 支持多卡推理（多进程实现多GPU和多CPU推理），`text2vec`支持多卡推理(计算文本向量): [examples/computing_embeddings_multi_gpu_demo.py](https://github.com/shibing624/text2vec/blob/master/examples/computing_embeddings_multi_gpu_demo.py)

2. 新增命令行工具（CLI），可以无需代码开发批量获取文本向量：
zsh
pip install text2vec -U
text2vec --input_file input.txt --output_file out.csv --batch_size 16



**Full Changelog**: https://github.com/shibing624/text2vec/compare/1.2.4...1.2.8

1. 实现了BGE微调训练方法 ，支持自定义样本集训练 https://github.com/shibing624/text2vec/blob/master/examples/training_bge_model_mydata.py ；支持构建训练样本集 https://github.com/shibing624/text2vec/blob/master/examples/data/build_zh_bge_dataset.py ；支持使用C-MTEB评估 https://github.com/shibing624/text2vec/blob/master/tests/eval_C-MTEB.py
2. 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，相较于原模型在短文本区分度上提升明显。

**Full Changelog**: https://github.com/shibing624/text2vec/compare/1.2.3...1.2.4

v1.2.2版本

- 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

英文匹配数据集的评测结果：


| Arch | BaseModel | Model | English-STS-B |
|:-------|:------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|:-------------:|
| GloVe | glove | Avg_word_embeddings_glove_6B_300d | 61.77 |
| BERT | bert-base-uncased | BERT-base-cls | 20.29 |
| BERT | bert-base-uncased | BERT-base-first_last_avg | 59.04 |
| BERT | bert-base-uncased | BERT-base-first_last_avg-whiten(NLI) | 63.65 |
| SBERT | sentence-transformers/bert-base-nli-mean-tokens | SBERT-base-nli-cls | 73.65 |
| SBERT | sentence-transformers/bert-base-nli-mean-tokens | SBERT-base-nli-first_last_avg | 77.96 |
| CoSENT | bert-base-uncased | CoSENT-base-first_last_avg | 69.93 |
| CoSENT | sentence-transformers/bert-base-nli-mean-tokens | CoSENT-base-nli-first_last_avg | 79.68 |
| CoSENT | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) | 80.12 |


- 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 |
| 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
- `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








**Full Changelog**: https://github.com/shibing624/text2vec/compare/1.2.1...1.2.2

- 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[shibing624/nli-zh-all](https://huggingface.co/datasets/shibing624/nli-zh-all)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)



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 |
| 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 |


- 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，SOHU数据集 [https://huggingface.co/datasets/shibing624/sts-sohu2021](https://huggingface.co/datasets/shibing624/sts-sohu2021)


**Full Changelog**: https://github.com/shibing624/text2vec/compare/1.2.0...1.2.1

- 发布了中文匹配模型[shibing624/text2vec-base-chinese-nli](https://huggingface.co/shibing624/text2vec-base-chinese-nli)，基于ERNIE-3.0-base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。
- 发布了2个中文NLI数据集：shibing624/snli-zh 和 shibing624/nli-zh-all

- 本项目release模型的中文匹配评测结果：

| Arch | BaseModel | Model | ATEC | BQ | LCQMC | PAWSX | STS-B | 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 | 33.86 | 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 | 41.99 | 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 | 48.25 | 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 | 48.08 | 2092 |
| CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-nli](https://huggingface.co/shibing624/text2vec-base-chinese-nli) | 51.26 | 68.72 | 79.13 | 34.28 | 80.70 | **62.81** | 3066 |



- 本项目release的数据集：

| Dataset | Introduce | Download Link |
|:----------------------|:--------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| shibing624/nli-zh-all | 中文语义匹配数据合集，整合了文本推理，相似，摘要，问答，指令微调等任务的820万高质量数据，并转化为匹配格式数据集 | [https://huggingface.co/datasets/shibing624/nli-zh-all](https://huggingface.co/datasets/shibing624/nli-zh-all) |
| shibing624/snli-zh | 中文SNLI和MultiNLI数据集，翻译自英文SNLI和MultiNLI | [https://huggingface.co/datasets/shibing624/snli-zh](https://huggingface.co/datasets/shibing624/snli-zh) |
| shibing624/nli_zh | 中文语义匹配数据集，整合了中文ATEC、BQ、LCQMC、PAWSX、STS-B共5个任务的数据集 | [https://huggingface.co/datasets/shibing624/nli_zh](https://huggingface.co/datasets/shibing624/nli_zh) </br> or </br> [百度网盘(提取码:qkt6)](https://pan.baidu.com/s/1d6jSiU1wHQAEMWJi7JJWCQ) </br> or </br> [github](https://github.com/shibing624/text2vec/releases/download/1.1.2/senteval_cn.zip) </br> |


- 基于更大数据集shibing624/nli-zh-all的CoSENT匹配模型在训练中。

**Full Changelog**: https://github.com/shibing624/text2vec/compare/1.1.8...1.2.0