TextMatch
TextMatch is a semantic matching model library for QA & text search … It’s easy to train models and to export representation vectors.
Let’s Run examples !
test models List
train models List
TODO
（1）knn
（2）dssm
（3）实体识别
（4）文本纠错
样例：
git clone https://github.com/MachineLP/TextMatch
cd TextMatch
pip install -r requirements.txt
export PYTHONPATH=${PYTHONPATH}:../TextMatch
python examples/text_search.py
examples/text_search.py
import sys
from textmatch.models.text_embedding.model_factory_sklearn import ModelFactory
if __name__ == '__main__':
# doc
doc_dict = {"0":"我去玉龙雪山并且喜欢玉龙雪山玉龙雪山", "1":"我在玉龙雪山并且喜欢玉龙雪山", "2":"我在九寨沟", "3":"你好"}
# query
query = "我在九寨沟,很喜欢"
# 模型工厂，选择需要的模型加到列表中: 'bow', 'tfidf', 'ngram_tfidf', 'bert', 'albert', 'w2v'
mf = ModelFactory( match_models=['bow', 'tfidf', 'ngram_tfidf'] )
# 模型处理初始化
mf.init(words_dict=doc_dict, update=True)
# query 与 doc的相似度
search_res = mf.predict(query)
print ('search_res>>>>>', search_res)
# search_res>>>>> {'bow': [('0', 0.2773500981126146), ('1', 0.5303300858899106), ('2', 0.8660254037844388), ('3', 0.0)], 'tfidf': [('0', 0.2201159065358879), ('1', 0.46476266418455736), ('2', 0.8749225357988296), ('3', 0.0)], 'ngram_tfidf': [('0', 0.035719486884261346), ('1', 0.09654705406841395), ('2', 0.9561288696241232), ('3', 0.0)]}
# query的embedding
query_emb = mf.predict_emb(query)
print ('query_emb>>>>>', query_emb)
'''
pre_emb>>>>> {'bow': array([1., 0., 0., 1., 1., 0., 1., 0.]), 'tfidf': array([0.61422608, 0. , 0. , 0.4842629 , 0.4842629 ,
0. , 0.39205255, 0. ]), 'ngram_tfidf': array([0. , 0. , 0.37156534, 0.37156534, 0. ,
0. , 0. , 0.29294639, 0. , 0.37156534,
0.37156534, 0. , 0. , 0.37156534, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.29294639, 0.37156534, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ])}
'''
样例：
git clone https://github.com/MachineLP/TextMatch
cd TextMatch
export PYTHONPATH=${PYTHONPATH}:../TextMatch
python tests/tools_test/faiss_test.py
tests/tools_test/faiss_test.py
import sys
import json
import time
import faiss
import numpy as np
from faiss import normalize_L2
from textmatch.config.constant import Constant as const
from textmatch.core.text_embedding import TextEmbedding
from textmatch.tools.decomposition.pca import PCADecomposition
from textmatch.tools.faiss.faiss import FaissSearch
test_dict = {"id0": "其实事物发展有自己的潮流和规律",
"id1": "当你身处潮流之中的时候，要紧紧抓住潮流的机会",
"id2": "想办法脱颖而出，即使没有成功，也会更加洞悉时代的脉搏",
"id3": "收获珍贵的知识和经验。而如果潮流已经退去",
"id4": "这个时候再去往这个方向上努力，只会收获迷茫与压抑",
"id5": "对时代、对自己都没有什么帮助",
"id6": "但是时代的浪潮犹如海滩上的浪花，总是一浪接着一浪，只要你站在海边，身处这个行业之中，下一个浪潮很快又会到来。你需要敏感而又深刻地去观察，略去那些浮躁的泡沫，抓住真正潮流的机会，奋力一搏，不管成败，都不会遗憾。",
"id7": "其实事物发展有自己的潮流和规律",
"id8": "当你身处潮流之中的时候，要紧紧抓住潮流的机会" }
if __name__ == '__main__':
# ['bow', 'tfidf', 'ngram_tfidf', 'bert']
# ['bow', 'tfidf', 'ngram_tfidf', 'bert', 'w2v']
# text_embedding = TextEmbedding( match_models=['bow', 'tfidf', 'ngram_tfidf', 'w2v'], words_dict=test_dict )
text_embedding = TextEmbedding( match_models=['bow', 'tfidf', 'ngram_tfidf', 'w2v'], words_dict=None, update=False )
feature_list = []
for sentence in test_dict.values():
pre = text_embedding.predict(sentence)
feature = np.concatenate([pre[model] for model in ['bow', 'tfidf', 'ngram_tfidf', 'w2v']], axis=0)
feature_list.append(feature)
pca = PCADecomposition(n_components=8)
data = np.array( feature_list )
pca.fit( data )
res = pca.transform( data )
print('res>>', res)
pre = text_embedding.predict("潮流和规律")
feature = np.concatenate([pre[model] for model in ['bow', 'tfidf', 'ngram_tfidf', 'w2v']], axis=0)
test = pca.transform( [feature] )
faiss_search = FaissSearch( res, sport_mode=False )
faiss_res = faiss_search.predict( test )
print( "faiss_res:", faiss_res )
'''
faiss kmeans result times 8.0108642578125e-05
faiss_res: [{0: 0.7833399, 7: 0.7833399, 3: 0.63782495}]
'''
faiss_search = FaissSearch( res, sport_mode=True )
faiss_res = faiss_search.predict( test )
print( "faiss_res:", faiss_res )
'''
faiss kmeans result times 3.266334533691406e-05
faiss_res: [{0: 0.7833399, 7: 0.7833399, 3: 0.63782495}]
'''
run train_model/ (train embedding(bow/tfidf/ngram tfidf/bert/albert… train classifer))
git clone https://github.com/MachineLP/TextMatch
cd TextMatch
pip install -r requirements.txt
export PYTHONPATH=${PYTHONPATH}:../TextMatch
python train_model/train_bow.py (文本embedding)
python train_model/train_tfidf.py (文本embedding)
python train_model/train_ngram_tfidf.py (文本embedding)
python train_model/train_bert.py (文本embedding)
python train_model/train_albert.py (文本embedding)
python train_model/train_w2v.py (文本embedding)
python train_model/train_dssm.py (文本embedding)
python train_model/train_lr_classifer.py (文本分类)
python train_model/train_gbdt_classifer.py (文本分类)
python train_model/train_gbdlr_classifer.py (文本分类)
python train_model/train_lgb_classifer.py (文本分类)
python train_model/train_xgb_classifer.py (文本分类)
python train_model/train_dnn_classifer.py (文本分类)
run tests/core_test （qa/文本embedding）
git clone https://github.com/MachineLP/TextMatch
cd TextMatch
pip install -r requirements.txt
export PYTHONPATH=${PYTHONPATH}:../TextMatch
python tests/core_test/qa_match_test.py
python tests/core_test/text_embedding_test.py
run tests/models_test （模型测试）
git clone https://github.com/MachineLP/TextMatch
cd TextMatch
pip install -r requirements.txt
export PYTHONPATH=${PYTHONPATH}:../TextMatch
python tests/models_test/bm25_test.py (bm25)
python tests/models_test/edit_sim_test.py (编辑距离)
python tests/models_test/jaccard_sim_test.py (jaccard)
python tests/models_test/bow_sklearn_test.py (bow)
python tests/models_test/tf_idf_sklearn_test.py (tf_idf)
python tests/models_test/ngram_tf_idf_sklearn_test.py (ngram_tf_idf)
python tests/models_test/w2v_test.py (w2v)
python tests/models_test/albert_test.py (bert)
run tests/ml_test （机器学习模型测试）
git clone https://github.com/MachineLP/TextMatch
cd TextMatch
pip install -r requirements.txt
export PYTHONPATH=${PYTHONPATH}:../TextMatch
python tests/ml_test/lr_test.py (lr)
python tests/ml_test/gbdt_test.py (gbdt)
python tests/ml_test/gbdt_lr_test.py (gbdt_lr)
python tests/ml_test/lgb_test.py (lgb)
python tests/ml_test/xgb_test.py (xgb)
git clone https://github.com/MachineLP/TextMatch
cd TextMatch
pip install -r requirements.txt
export PYTHONPATH=${PYTHONPATH}:../TextMatch
python tests/tools_test/kmeans_test.py (kmeans)
python tests/tools_test/dbscan_test.py (dbscan)
python tests/tools_test/pca_test.py (pca)
python tests/tools_test/faiss_test.py (faiss)
git clone https://github.com/MachineLP/TextMatch
cd TextMatch
pip install -r requirements.txt
cd tests/tools_test
python generate_word_cloud.py
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