text2vec-base-chinese

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shibing624/text2vec-base-chinese

This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese.

It maps sentences to a 768 dimensional dense vector space and can be used for tasks

like sentence embeddings, text matching or semantic search.

Evaluation

For an automated evaluation of this model, see the *Evaluation Benchmark*: text2vec

chinese text matching task：

Arch BaseModel Model ATEC BQ LCQMC PAWSX STS-B SOHU-dd SOHU-dc Avg QPS

:----------- :---------------------------------- :-------------------------------------------------------------------------------------------------------------------------------------------------- :-----: :-----: :-----: :-----: :-----: :-------: :-------: :---------: :-----:

Word2Vec word2vec w2v-light-tencent-chinese 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 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 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 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 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 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 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 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，数据集相对于shibing624/nli-zh-all/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

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 模型是用SBERT训练，是 paraphrase-MiniLM-L12-v2 模型的多语言版本，支持中文、英文等

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

Usage (text2vec)

Using this model becomes easy when you have text2vec installed:

Then you can use the model like this:

Usage (HuggingFace Transformers)

Without 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:

Then load model and predict:

Usage (sentence-transformers)

sentence-transformers is a popular library to compute dense vector representations for sentences.

Install sentence-transformers:

Then load model and predict:

Full Model Architecture

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 hfl/chinese-macbert-base 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.

Hyper parameters

training dataset: https://huggingface.co/datasets/shibing624/nli zh

max seq length: 128

best epoch: 5

sentence embedding dim: 768

Citing & Authors

This model was trained by text2vec.

If you find this model helpful, feel free to cite: