Text2vec

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。

- 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 |



**Full Changelog**: https://github.com/shibing624/text2vec/compare/1.1.3...1.1.4

add word2vec tencent light embeddings file: light_Tencent_AILab_ChineseEmbedding.bin

**Full Changelog**: https://github.com/shibing624/text2vec/compare/1.1.2...1.1.3

add dataset of nli_zh

重写了CoSENT, SentenceBERT模型的训练和预测代码：
1. 句子匹配模型训练逻辑继承基类SentenceModel，
2. 新增train_model, eval_model, 代码结构更清晰，
3. 预测均使用基类的encode实现。

0.1.3, new CoSENT model.