Text2vec: Text to Vector

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Text2vec: Text to Vector

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

[2023/07/17] 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，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

[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，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，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，详见Release-v1.2.1

[2023/06/15] v1.2.0版本: 发布了中文匹配模型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

[2022/03/12] v1.1.4版本: 发布了中文匹配模型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

Guide

Features
Evaluation
Install
Usage
Contact
References

Features

文本向量表示模型

Word2Vec：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） (文件名：light_Tencent_AILab_ChineseEmbedding.bin 密码: tawe）实现词向量检索，本项目实现了句子（词向量求平均）的word2vec向量表示
SBERT(Sentence-BERT)：权衡性能和效率的句向量表示模型，训练时通过有监督训练上层分类函数，文本匹配预测时直接句子向量做余弦，本项目基于PyTorch复现了Sentence-BERT模型的训练和预测
CoSENT(Cosine Sentence)：CoSENT模型提出了一种排序的损失函数，使训练过程更贴近预测，模型收敛速度和效果比Sentence-BERT更好，本项目基于PyTorch实现了CoSENT模型的训练和预测

详细文本向量表示方法见wiki: 文本向量表示方法

Evaluation

文本匹配

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	80.12

中文匹配数据集的评测结果：

Arch	BaseModel	Model	ATEC	BQ	LCQMC	PAWSX	STS-B	Avg
SBERT	bert-base-chinese	SBERT-bert-base	46.36	70.36	78.72	46.86	66.41	61.74
SBERT	hfl/chinese-macbert-base	SBERT-macbert-base	47.28	68.63	79.42	55.59	64.82	63.15
SBERT	hfl/chinese-roberta-wwm-ext	SBERT-roberta-ext	48.29	69.99	79.22	44.10	72.42	62.80
CoSENT	bert-base-chinese	CoSENT-bert-base	49.74	72.38	78.69	60.00	79.27	68.01
CoSENT	hfl/chinese-macbert-base	CoSENT-macbert-base	50.39	72.93	79.17	60.86	79.30	68.53
CoSENT	hfl/chinese-roberta-wwm-ext	CoSENT-roberta-ext	50.81	71.45	79.31	61.56	79.96	68.61

说明：

结果评测指标：spearman系数
为评测模型能力，结果均只用该数据集的train训练，在test上评估得到的表现，没用外部数据
SBERT-macbert-base模型，是用SBert方法训练，运行examples/training_sup_text_matching_model.py代码可训练模型
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

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	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
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	3138

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
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
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
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/test_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

Demo

Official Demo: https://www.mulanai.com/product/short_text_sim/

HuggingFace Demo: https://huggingface.co/spaces/shibing624/text2vec

run example: examples/gradio_demo.py to see the demo:

python examples/gradio_demo.py

Install

pip install torch # conda install pytorch
pip install -U text2vec

or

pip install torch # conda install pytorch
pip install -r requirements.txt

git clone https://github.com/shibing624/text2vec.git
cd text2vec
pip install --no-deps .

Usage

文本向量表征

基于pretrained model计算文本向量：

>>> from text2vec import SentenceModel
>>> m = SentenceModel()
>>> m.encode("如何更换花呗绑定银行卡")
Embedding shape: (768,)

example: examples/computing_embeddings_demo.py

import sys

sys.path.append('..')
from text2vec import SentenceModel
from text2vec import Word2Vec


def compute_emb(model):
    # Embed a list of sentences
    sentences = [
        '卡',
        '银行卡',
        '如何更换花呗绑定银行卡',
        '花呗更改绑定银行卡',
        'This framework generates embeddings for each input sentence',
        'Sentences are passed as a list of string.',
        'The quick brown fox jumps over the lazy dog.'
    ]
    sentence_embeddings = model.encode(sentences)
    print(type(sentence_embeddings), sentence_embeddings.shape)

    # The result is a list of sentence embeddings as numpy arrays
    for sentence, embedding in zip(sentences, sentence_embeddings):
        print("Sentence:", sentence)
        print("Embedding shape:", embedding.shape)
        print("Embedding head:", embedding[:10])
        print()


if __name__ == "__main__":
    # 中文句向量模型(CoSENT)，中文语义匹配任务推荐，支持fine-tune继续训练
    t2v_model = SentenceModel("shibing624/text2vec-base-chinese")
    compute_emb(t2v_model)

    # 支持多语言的句向量模型（CoSENT），多语言（包括中英文）语义匹配任务推荐，支持fine-tune继续训练
    sbert_model = SentenceModel("shibing624/text2vec-base-multilingual")
    compute_emb(sbert_model)

    # 中文词向量模型(word2vec)，中文字面匹配任务和冷启动适用
    w2v_model = Word2Vec("w2v-light-tencent-chinese")
    compute_emb(w2v_model)

output:

<class 'numpy.ndarray'> (7, 768)
Sentence: 卡
Embedding shape: (768,)

Sentence: 银行卡
Embedding shape: (768,)
 ...

返回值embeddings是numpy.ndarray类型，shape为(sentences_size, model_embedding_size)，三个模型任选一种即可，推荐用第一个。
shibing624/text2vec-base-chinese模型是CoSENT方法在中文STS-B数据集训练得到的，模型已经上传到huggingface的模型库shibing624/text2vec-base-chinese，是text2vec.SentenceModel指定的默认模型，可以通过上面示例调用，或者如下所示用transformers库调用，模型自动下载到本机路径：~/.cache/huggingface/transformers
w2v-light-tencent-chinese是通过gensim加载的Word2Vec模型，使用腾讯词向量Tencent_AILab_ChineseEmbedding.tar.gz计算各字词的词向量，句子向量通过单词词向量取平均值得到，模型自动下载到本机路径：~/.text2vec/datasets/light_Tencent_AILab_ChineseEmbedding.bin

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.

example: examples/use_origin_transformers_demo.py

import os
import torch
from transformers import AutoTokenizer, AutoModel

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


# 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 = AutoTokenizer.from_pretrained('shibing624/text2vec-base-chinese')
model = AutoModel.from_pretrained('shibing624/text2vec-base-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, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)

Usage (sentence-transformers)

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:

from sentence_transformers import SentenceTransformer

m = SentenceTransformer("shibing624/text2vec-base-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)

`Word2Vec`词向量

提供两种Word2Vec词向量，任选一个：

轻量版腾讯词向量百度云盘-密码:tawe 或谷歌云盘，二进制文件，111M，是简化后的高频143613个词，每个词向量还是200维（跟原版一样），运行程序，自动下载到 ~/.text2vec/datasets/light_Tencent_AILab_ChineseEmbedding.bin
腾讯词向量-官方全量, 6.78G放到： ~/.text2vec/datasets/Tencent_AILab_ChineseEmbedding.txt，腾讯词向量主页：https://ai.tencent.com/ailab/nlp/zh/index.html 词向量下载地址：https://ai.tencent.com/ailab/nlp/en/download.html 更多查看腾讯词向量介绍-wiki

下游任务

1. 句子相似度计算

example: examples/semantic_text_similarity_demo.py

import sys

sys.path.append('..')
from text2vec import Similarity

# Two lists of sentences
sentences1 = ['如何更换花呗绑定银行卡',
              'The cat sits outside',
              'A man is playing guitar',
              'The new movie is awesome']

sentences2 = ['花呗更改绑定银行卡',
              'The dog plays in the garden',
              'A woman watches TV',
              'The new movie is so great']

sim_model = Similarity()
for i in range(len(sentences1)):
    for j in range(len(sentences2)):
        score = sim_model.get_score(sentences1[i], sentences2[j])
        print("{} \t\t {} \t\t Score: {:.4f}".format(sentences1[i], sentences2[j], score))

output:

如何更换花呗绑定银行卡 		 花呗更改绑定银行卡 		 Score: 0.9477
如何更换花呗绑定银行卡 		 The dog plays in the garden 		 Score: -0.1748
如何更换花呗绑定银行卡 		 A woman watches TV 		 Score: -0.0839
如何更换花呗绑定银行卡 		 The new movie is so great 		 Score: -0.0044
The cat sits outside 		 花呗更改绑定银行卡 		 Score: -0.0097
The cat sits outside 		 The dog plays in the garden 		 Score: 0.1908
The cat sits outside 		 A woman watches TV 		 Score: -0.0203
The cat sits outside 		 The new movie is so great 		 Score: 0.0302
A man is playing guitar 		 花呗更改绑定银行卡 		 Score: -0.0010
A man is playing guitar 		 The dog plays in the garden 		 Score: 0.1062
A man is playing guitar 		 A woman watches TV 		 Score: 0.0055
A man is playing guitar 		 The new movie is so great 		 Score: 0.0097
The new movie is awesome 		 花呗更改绑定银行卡 		 Score: 0.0302
The new movie is awesome 		 The dog plays in the garden 		 Score: -0.0160
The new movie is awesome 		 A woman watches TV 		 Score: 0.1321
The new movie is awesome 		 The new movie is so great 		 Score: 0.9591

句子余弦相似度值score范围是[-1, 1]，值越大越相似。

2. 文本匹配搜索

一般在文档候选集中找与query最相似的文本，常用于QA场景的问句相似匹配、文本相似检索等任务。

example: examples/semantic_search_demo.py

import sys

sys.path.append('..')
from text2vec import SentenceModel, cos_sim, semantic_search

embedder = SentenceModel()

# Corpus with example sentences
corpus = [
    '花呗更改绑定银行卡',
    '我什么时候开通了花呗',
    'A man is eating food.',
    'A man is eating a piece of bread.',
    'The girl is carrying a baby.',
    'A man is riding a horse.',
    'A woman is playing violin.',
    'Two men pushed carts through the woods.',
    'A man is riding a white horse on an enclosed ground.',
    'A monkey is playing drums.',
    'A cheetah is running behind its prey.'
]
corpus_embeddings = embedder.encode(corpus)

# Query sentences:
queries = [
    '如何更换花呗绑定银行卡',
    'A man is eating pasta.',
    'Someone in a gorilla costume is playing a set of drums.',
    'A cheetah chases prey on across a field.']

for query in queries:
    query_embedding = embedder.encode(query)
    hits = semantic_search(query_embedding, corpus_embeddings, top_k=5)
    print("\n\n======================\n\n")
    print("Query:", query)
    print("\nTop 5 most similar sentences in corpus:")
    hits = hits[0]  # Get the hits for the first query
    for hit in hits:
        print(corpus[hit['corpus_id']], "(Score: {:.4f})".format(hit['score']))

output:

Query: 如何更换花呗绑定银行卡
Top 5 most similar sentences in corpus:
花呗更改绑定银行卡 (Score: 0.9477)
我什么时候开通了花呗 (Score: 0.3635)
A man is eating food. (Score: 0.0321)
A man is riding a horse. (Score: 0.0228)
Two men pushed carts through the woods. (Score: 0.0090)

======================
Query: A man is eating pasta.
Top 5 most similar sentences in corpus:
A man is eating food. (Score: 0.6734)
A man is eating a piece of bread. (Score: 0.4269)
A man is riding a horse. (Score: 0.2086)
A man is riding a white horse on an enclosed ground. (Score: 0.1020)
A cheetah is running behind its prey. (Score: 0.0566)

======================
Query: Someone in a gorilla costume is playing a set of drums.
Top 5 most similar sentences in corpus:
A monkey is playing drums. (Score: 0.8167)
A cheetah is running behind its prey. (Score: 0.2720)
A woman is playing violin. (Score: 0.1721)
A man is riding a horse. (Score: 0.1291)
A man is riding a white horse on an enclosed ground. (Score: 0.1213)

======================
Query: A cheetah chases prey on across a field.
Top 5 most similar sentences in corpus:
A cheetah is running behind its prey. (Score: 0.9147)
A monkey is playing drums. (Score: 0.2655)
A man is riding a horse. (Score: 0.1933)
A man is riding a white horse on an enclosed ground. (Score: 0.1733)
A man is eating food. (Score: 0.0329)

下游任务支持库

similarities库[推荐]

文本相似度计算和文本匹配搜索任务，推荐使用 similarities库，兼容本项目release的 Word2vec、SBERT、Cosent类语义匹配模型，还支持字面维度相似度计算、匹配搜索算法，支持文本、图像。

安装： pip install -U similarities

句子相似度计算：

from similarities import Similarity

m = Similarity()
r = m.similarity('如何更换花呗绑定银行卡', '花呗更改绑定银行卡')
print(f"similarity score: {float(r)}")  # similarity score: 0.855146050453186

Models

CoSENT model

CoSENT（Cosine Sentence）文本匹配模型，在Sentence-BERT上改进了CosineRankLoss的句向量方案

Network structure:

Training:

Inference:

CoSENT 监督模型

训练和预测CoSENT模型：

在中文STS-B数据集训练和评估CoSENT模型

example: examples/training_sup_text_matching_model.py

cd examples
python training_sup_text_matching_model.py --model_arch cosent --do_train --do_predict --num_epochs 10 --model_name hfl/chinese-macbert-base --output_dir ./outputs/STS-B-cosent

在蚂蚁金融匹配数据集ATEC上训练和评估CoSENT模型

支持这些中文匹配数据集的使用：'ATEC', 'STS-B', 'BQ', 'LCQMC', 'PAWSX'，具体参考HuggingFace datasets https://huggingface.co/datasets/shibing624/nli_zh

python training_sup_text_matching_model.py --task_name ATEC --model_arch cosent --do_train --do_predict --num_epochs 10 --model_name hfl/chinese-macbert-base --output_dir ./outputs/ATEC-cosent

在自有中文数据集上训练模型

example: examples/training_sup_text_matching_model_mydata.py

单卡训练：

CUDA_VISIBLE_DEVICES=0 python training_sup_text_matching_model_mydata.py --do_train --do_predict

多卡训练：

CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node 2  training_sup_text_matching_model_mydata.py --do_train --do_predict --output_dir outputs/STS-B-text2vec-macbert-v1 --batch_size 64 --fp16 --data_parallel

训练集格式参考examples/data/STS-B/STS-B.valid.data

sentence1   sentence2   label
一个女孩在给她的头发做发型。	一个女孩在梳头。	2
一群男人在海滩上踢足球。	一群男孩在海滩上踢足球。	3
一个女人在测量另一个女人的脚踝。	女人测量另一个女人的脚踝。	5

label可以是0，1标签，0代表两个句子不相似，1代表相似；也可以是0-5的评分，评分越高，表示两个句子越相似。模型都能支持。

在英文STS-B数据集训练和评估CoSENT模型

example: examples/training_sup_text_matching_model_en.py

cd examples
python training_sup_text_matching_model_en.py --model_arch cosent --do_train --do_predict --num_epochs 10 --model_name bert-base-uncased  --output_dir ./outputs/STS-B-en-cosent

CoSENT 无监督模型

在英文NLI数据集训练CoSENT模型，在STS-B测试集评估效果

example: examples/training_unsup_text_matching_model_en.py

cd examples
python training_unsup_text_matching_model_en.py --model_arch cosent --do_train --do_predict --num_epochs 10 --model_name bert-base-uncased --output_dir ./outputs/STS-B-en-unsup-cosent

Sentence-BERT model

Sentence-BERT文本匹配模型，表征式句向量表示方案

Network structure:

Training:

Inference:

SentenceBERT 监督模型

在中文STS-B数据集训练和评估SBERT模型

example: examples/training_sup_text_matching_model.py

cd examples
python training_sup_text_matching_model.py --model_arch sentencebert --do_train --do_predict --num_epochs 10 --model_name hfl/chinese-macbert-base --output_dir ./outputs/STS-B-sbert

在英文STS-B数据集训练和评估SBERT模型

example: examples/training_sup_text_matching_model_en.py

cd examples
python training_sup_text_matching_model_en.py --model_arch sentencebert --do_train --do_predict --num_epochs 10 --model_name bert-base-uncased --output_dir ./outputs/STS-B-en-sbert

SentenceBERT 无监督模型

在英文NLI数据集训练SBERT模型，在STS-B测试集评估效果

example: examples/training_unsup_text_matching_model_en.py

cd examples
python training_unsup_text_matching_model_en.py --model_arch sentencebert --do_train --do_predict --num_epochs 10 --model_name bert-base-uncased --output_dir ./outputs/STS-B-en-unsup-sbert

BERT-Match model

BERT文本匹配模型，原生BERT匹配网络结构，交互式句向量匹配模型

Network structure:

Training and inference:

训练脚本同上examples/training_sup_text_matching_model.py。

模型蒸馏（Model Distillation）

由于text2vec训练的模型可以使用sentence-transformers库加载，此处复用其模型蒸馏方法distillation。

模型降维，参考dimensionality_reduction.py使用PCA对模型输出embedding降维，可减少milvus等向量检索数据库的存储压力，还能轻微提升模型效果。
模型蒸馏，参考model_distillation.py使用蒸馏方法，将Teacher大模型蒸馏到更少layers层数的student模型中，在权衡效果的情况下，可大幅提升模型预测速度。

模型部署

提供两种部署模型，搭建服务的方法： 1）基于Jina搭建gRPC服务【推荐】；2）基于FastAPI搭建原生Http服务。

Jina服务

采用C/S模式搭建高性能服务，支持docker云原生，gRPC/HTTP/WebSocket，支持多个模型同时预测，GPU多卡处理。

安装： pip install jina
启动服务：

example: examples/jina_server_demo.py

from jina import Flow

port = 50001
f = Flow(port=port).add(
    uses='jinahub://Text2vecEncoder',
    uses_with={'model_name': 'shibing624/text2vec-base-chinese'}
)

with f:
    # backend server forever
    f.block()

该模型预测方法（executor）已经上传到JinaHub，里面包括docker、k8s部署方法。

调用服务：

from jina import Client
from docarray import Document, DocumentArray

port = 50001

c = Client(port=port)

data = ['如何更换花呗绑定银行卡',
        '花呗更改绑定银行卡']
print("data:", data)
print('data embs:')
r = c.post('/', inputs=DocumentArray([Document(text='如何更换花呗绑定银行卡'), Document(text='花呗更改绑定银行卡')]))
print(r.embeddings)

批量调用方法见example: examples/jina_client_demo.py

FastAPI服务

安装： pip install fastapi uvicorn
启动服务：

example: examples/fastapi_server_demo.py

cd examples
python fastapi_server_demo.py

调用服务：

curl -X 'GET' \
  'http://0.0.0.0:8001/emb?q=hello' \
  -H 'accept: application/json'

Dataset

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
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
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 or 百度网盘(提取码:qkt6) or github
shibing624/sts-sohu2021	中文语义匹配数据集，2021搜狐校园文本匹配算法大赛数据集	https://huggingface.co/datasets/shibing624/sts-sohu2021
ATEC	中文ATEC数据集，蚂蚁金服Q-Qpair数据集	ATEC
BQ	中文BQ(Bank Question)数据集，银行Q-Qpair数据集	BQ
LCQMC	中文LCQMC(large-scale Chinese question matching corpus)数据集，Q-Qpair数据集	LCQMC
PAWSX	中文PAWS(Paraphrase Adversaries from Word Scrambling)数据集，Q-Qpair数据集	PAWSX
STS-B	中文STS-B数据集，中文自然语言推理数据集，从英文STS-B翻译为中文的数据集	STS-B

常用英文匹配数据集：

英文匹配数据集：multi_nli: https://huggingface.co/datasets/multi_nli
英文匹配数据集：snli: https://huggingface.co/datasets/snli
https://huggingface.co/datasets/metaeval/cnli
https://huggingface.co/datasets/mteb/stsbenchmark-sts
https://huggingface.co/datasets/JeremiahZ/simcse_sup_nli
https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7

数据集使用示例：

pip install datasets

from datasets import load_dataset

dataset = load_dataset("shibing624/nli_zh", "STS-B") # ATEC or BQ or LCQMC or PAWSX or STS-B
print(dataset)
print(dataset['test'][0])

output:

DatasetDict({
    train: Dataset({
        features: ['sentence1', 'sentence2', 'label'],
        num_rows: 5231
    })
    validation: Dataset({
        features: ['sentence1', 'sentence2', 'label'],
        num_rows: 1458
    })
    test: Dataset({
        features: ['sentence1', 'sentence2', 'label'],
        num_rows: 1361
    })
})
{'sentence1': '一个女孩在给她的头发做发型。', 'sentence2': '一个女孩在梳头。', 'label': 2}

Contact

Issue(建议)：
邮件我：xuming: [email protected]
微信我：加我微信号：xuming624, 备注：姓名-公司-NLP 进NLP交流群。

Citation

如果你在研究中使用了text2vec，请按如下格式引用：

APA:

Xu, M. Text2vec: Text to vector toolkit (Version 1.1.2) [Computer software]. https://github.com/shibing624/text2vec

BibTeX:

@misc{Text2vec,
  author = {Ming Xu},
  title = {Text2vec: Text to vector toolkit},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/shibing624/text2vec}},
}

License

授权协议为 The Apache License 2.0，可免费用做商业用途。请在产品说明中附加text2vec的链接和授权协议。

Contribute

项目代码还很粗糙，如果大家对代码有所改进，欢迎提交回本项目，在提交之前，注意以下两点：

在tests添加相应的单元测试
使用python -m pytest -v来运行所有单元测试，确保所有单测都是通过的

之后即可提交PR。

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examples		examples
tests		tests
text2vec		text2vec
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CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
README_EN.md		README_EN.md
requirements.txt		requirements.txt
setup.py		setup.py

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Text2vec: Text to Vector

News

Features

文本向量表示模型

Evaluation

英文匹配数据集的评测结果：

中文匹配数据集的评测结果：

Release Models

Demo

Install

Usage

文本向量表征

Usage (HuggingFace Transformers)

Usage (sentence-transformers)

Word2Vec词向量

下游任务

1. 句子相似度计算

2. 文本匹配搜索

下游任务支持库

Models

CoSENT model

CoSENT 监督模型

CoSENT 无监督模型

Sentence-BERT model

SentenceBERT 监督模型

SentenceBERT 无监督模型

BERT-Match model

模型蒸馏（Model Distillation）

模型部署

Jina服务

FastAPI服务

Dataset

Contact

Citation

License

Contribute

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