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Text to vector Tool, encode text

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Text2vec

text2vec, Text to Vector.

文本向量表征工具,把文本转化为向量矩阵,是文本进行计算机处理的第一步。

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。

Guide

Question

文本向量表示咋做?文本匹配任务用哪个模型效果好?

许多NLP任务的成功离不开训练优质有效的文本表示向量。特别是文本语义匹配(Semantic Textual Similarity,如paraphrase检测、QA的问题对匹配)、文本向量检索(Dense Text Retrieval)等任务。

Solution

传统方法:基于特征的匹配

  • 基于TF-IDF、BM25、Jaccord、SimHash、LDA等算法抽取两个文本的词汇、主题等层面的特征,然后使用机器学习模型(LR, xgboost)训练分类模型
  • 优点:可解释性较好
  • 缺点:依赖人工寻找特征,泛化能力一般,而且由于特征数量的限制,模型的效果比较一般

代表模型:

  • BM25

BM25算法,通过候选句子的字段对qurey字段的覆盖程度来计算两者间的匹配得分,得分越高的候选项与query的匹配度更好,主要解决词汇层面的相似度问题。

深度方法:基于表征的匹配

  • 基于表征的匹配方式,初始阶段对两个文本各自单独处理,通过深层的神经网络进行编码(encode),得到文本的表征(embedding),再对两个表征进行相似度计算的函数得到两个文本的相似度
  • 优点:基于BERT的模型通过有监督的Fine-tune在文本表征和文本匹配任务取得了不错的性能
  • 缺点:BERT自身导出的句向量(不经过Fine-tune,对所有词向量求平均)质量较低,甚至比不上Glove的结果,因而难以反映出两个句子的语义相似度

主要原因是:

1.BERT对所有的句子都倾向于编码到一个较小的空间区域内,这使得大多数的句子对都具有较高的相似度分数,即使是那些语义上完全无关的句子对。

2.BERT句向量表示的聚集现象和句子中的高频词有关。具体来说,当通过平均词向量的方式计算句向量时,那些高频词的词向量将会主导句向量,使之难以体现其原本的语义。当计算句向量时去除若干高频词时,聚集现象可以在一定程度上得到缓解,但表征能力会下降。

代表模型:

由于2018年BERT模型在NLP界带来了翻天覆地的变化,此处不讨论和比较2018年之前的模型(如果有兴趣了解的同学,可以参考中科院开源的MatchZooMatchZoo-py)。

所以,本项目主要调研以下比原生BERT更优、适合文本匹配的向量表示模型:Sentence-BERT(2019)、BERT-flow(2020)、SimCSE(2021)、CoSENT(2022)。

深度方法:基于交互的匹配

  • 基于交互的匹配方式,则认为在最后阶段才计算文本的相似度会过于依赖文本表征的质量,同时也会丢失基础的文本特征(比如词法、句法等),所以提出尽可能早的对文本特征进行交互,捕获更基础的特征,最后在高层基于这些基础匹配特征计算匹配分数
  • 优点:基于交互的匹配模型端到端处理,效果好
  • 缺点:这类模型(Cross-Encoder)的输入要求是两个句子,输出的是句子对的相似度值,模型不会产生句子向量表示(sentence embedding),我们也无法把单个句子输入给模型。因此,对于需要文本向量表示的任务来说,这类模型并不实用

代表模型:

Cross-Encoder适用于向量检索精排。

Feature

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

Evaluation

文本匹配

  • 英文匹配数据集的评测结果:
Arch Backbone Model Name 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
SBERT xlm-roberta-base paraphrase-multilingual-MiniLM-L12-v2 84.42
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
  • 中文匹配数据集的评测结果:
Arch Backbone Model Name ATEC BQ LCQMC PAWSX STS-B Avg QPS
CoSENT hfl/chinese-macbert-base CoSENT-macbert-base 50.39 72.93 79.17 60.86 80.51 68.77 2572
CoSENT Langboat/mengzi-bert-base CoSENT-mengzi-base 50.52 72.27 78.69 12.89 80.15 58.90 2502
CoSENT bert-base-chinese CoSENT-bert-base 49.74 72.38 78.69 60.00 80.14 68.19 2653
SBERT bert-base-chinese SBERT-bert-base 46.36 70.36 78.72 46.86 66.41 61.74 1365
SBERT hfl/chinese-macbert-base SBERT-macbert-base 47.28 68.63 79.42 55.59 64.82 63.15 1948
CoSENT hfl/chinese-roberta-wwm-ext CoSENT-roberta-ext 50.81 71.45 79.31 61.56 81.13 68.85 -
SBERT hfl/chinese-roberta-wwm-ext SBERT-roberta-ext 48.29 69.99 79.22 44.10 72.42 62.80 -
  • 本项目release模型的中文匹配评测结果:
Arch Backbone Model Name ATEC BQ LCQMC PAWSX STS-B Avg QPS
Word2Vec word2vec w2v-light-tencent-chinese 20.00 31.49 59.46 2.57 55.78 33.86 10283
SBERT xlm-roberta-base paraphrase-multilingual-MiniLM-L12-v2 18.42 38.52 63.96 10.14 78.90 41.99 2371
CoSENT hfl/chinese-macbert-base text2vec-base-chinese 31.93 42.67 70.16 17.21 79.30 48.25 2572

说明:

  • 结果值均使用spearman系数
  • 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
  • CoSENT-macbert-base模型达到同级别参数量SOTA效果,是用CoSENT方法训练,运行examples/training_sup_text_matching_model.py代码可在各数据集复现结果
  • text2vec-base-chinese模型,是用CoSENT方法训练,基于MacBERT在中文STS-B数据训练得到,并在中文STS-B测试集评估达到SOTA,模型文件已经上传到huggingface的模型库shibing624/text2vec-base-chinese,中文语义匹配任务推荐使用
  • SBERT-macbert-base模型,是用SBERT方法训练,运行examples/training_sup_text_matching_model.py代码复现结果
  • paraphrase-multilingual-MiniLM-L12-v2模型名称是sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,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
  • 各预训练模型均可以通过transformers调用,如MacBERT模型:--model_name hfl/chinese-macbert-base
  • 中文匹配数据集下载链接见下方
  • 中文匹配任务实验表明,pooling最优是first_last_avg,即 SentenceModel 的EncoderType.FIRST_LAST_AVG,其与EncoderType.MEAN的方法在预测效果上差异很小
  • QPS的GPU测试环境是Tesla V100,显存32GB

Demo

Official Demo: http://42.193.145.218/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

git clone https://github.com/shibing624/text2vec.git
cd text2vec
python setup.py install

数据集

中文语义匹配数据集已经上传到huggingface datasets https://huggingface.co/datasets/shibing624/nli_zh

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}

常见中文语义匹配数据集,包含ATECBQLCQMCPAWSXSTS-B共5个任务。 可以从数据集对应的链接自行下载,也可以从百度网盘(提取码:qkt6)下载。 其中senteval_cn目录是评测数据集汇总,senteval_cn.zip是senteval目录的打包,两者下其一就好。

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, EncoderType
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",
                              encoder_type=EncoderType.FIRST_LAST_AVG)
    compute_emb(t2v_model)

    # 支持多语言的句向量模型(Sentence-BERT),英文语义匹配任务推荐,支持fine-tune继续训练
    sbert_model = SentenceModel("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
                                encoder_type=EncoderType.MEAN)
    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embeddingsnumpy.ndarray类型,shape为(sentences_size, model_embedding_size)
  • 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
  • sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2模型是Sentence-BERT的多语言句向量模型, 适用于释义(paraphrase)识别,文本匹配,通过text2vec.SentenceModelsentence-transformers库都可以调用该模型
  • 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:

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,最简示例:

from text2vec import CosentModel
m = CosentModel("bert-base-chinese")
print(m)
m.train_model(use_hf_dataset=True, num_epochs=1, output_dir="./temp")
r = m.encode(["我爱北京天安门"])
print(r)
  • 在中文STS-B数据集训练和评估MacBERT+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MacBERT+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_selfdata.py

python training_sup_text_matching_model_selfdata.py --do_train --do_predict
  • 在英文STS-B数据集训练和评估BERT+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BERT+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MacBERT+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BERT+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BERT+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

  1. 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。
  2. 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。

Contact

  • Issue(建议):GitHub issues
  • 邮件我:xuming: xuming624@qq.com
  • 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:

@software{Xu_Text2vec_Text_to,
author = {Xu, Ming},
title = {{Text2vec: Text to vector toolkit}},
url = {https://github.com/shibing624/text2vec},
version = {1.1.2}
}

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:

  • 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。

Reference

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