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Let's go and play with text!

Project description

TextGo

TextGo is a python package to help you work with text data conveniently and efficiently. It's a powerful NLP tool, which provides various apis including text preprocessing, representation, similarity calculation, text search and classification. Besides, it supports both English and Chinese language.

Highlights

  • Support both English and Chinese languages in text preprocessing
  • Provide various text representation algorithms including BOW, TF-IDF, LDA, LSA, PCA, Word2Vec/GloVe/FastText, BERT...
  • Support fast text search based on Faiss
  • Support various text classification algorithms including FastText, XGBoost, BERT

Installing

Install and update using pip:
pip install textgo

Tips: the fasttext package needs to be installed manually as follows:

git clone https://github.com/facebookresearch/fastText.git
cd fastText-master
make
pip install .

Getting Started

1. Text preprocessing

Clean text

from textgo import Preprocess
# Chinese
tp1 = Preprocess(lang='zh')
texts1 = ["<text>自然语言处理是计算机科学领域与人工智能领域中的一个重要方向。<\text>", "??文本预处理~其实很简单!"]
ptexts1 = tp1.clean(texts1)
print(ptexts1)

Output: ['自然语言处理是计算机科学领域与人工智能领域中的一个重要方向', '文本预处理其实很简单']

# English
tp2 = Preprocess(lang='en')
texts2 = ["<text>Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language<\text>"]
ptexts2 = tp2.clean(texts2)
print(ptexts2)

Output: ['natural language processing usually shortened as nlp is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language']

Tokenize and drop stopwords

# Chinese
tokens1 = tp1.tokenize(ptexts1)
print(tokens1)

Output: [['自然语言', '处理', '计算机科学', '领域', '人工智能', '领域', '中', '重要', '方向'], ['文本', '预处理', '其实', '很', '简单']]

# English
tokens2 = tp2.tokenize(ptexts2)
print(tokens2)

Output: [['natural', 'language', 'processing', 'usually', 'shortened', 'nlp', 'branch', 'artificial', 'intelligence', 'deals', 'interaction', 'computers', 'humans', 'using', 'natural', 'language']]

Preprocess (Clean + Tokenize + Remove stopwords + Join words)

# Chinese
ptexts1 = tp1.preprocess(texts1)
print(ptexts1)

Output: ['自然语言 处理 计算机科学 领域 人工智能 领域 中 重要 方向', '文本 预处理 其实 很 简单']

# English
ptexts2 = tp2.preprocess(texts2)
print(ptexts2)

Output: ['natural language processing usually shortened nlp branch artificial intelligence deals interaction computers humans using natural language']

2. Text representation

from textgo import Embeddings
petxts = ['自然语言 处理 计算机科学 领域 人工智能 领域 中 重要 方向', '文本 预处理 其实 很 简单']
emb = Embeddings()
# BOW
bow_emb = emb.bow(ptexts)

# TF-IDF
tfidf_emb = emb.tfidf(ptexts)

# LDA
lda_emb = emb.lda(ptexts, dim=2)

# LSA
lsa_emb = emb.lsa(petxts, dim=2)

# PCA
pca_emb = emb.pca(ptexts, dim=2)

# Word2Vec
w2v_emb = emb.word2vec(ptexts, method='word2vec', model_path='model/word2vec.bin')

# GloVe
glove_emb = emb.word2vec(ptexts, method='glove', model_path='model/glove.bin')

# FastText
ft_emb = emb.word2vec(ptexts, method='fasttext', model_path='model/fasttext.bin')

# BERT
bert_emb = emb.bert(ptexts, model_path='model/bert-base-chinese')

Tips: For methods like Word2Vec and BERT, you can load the model first and then get embeddings to avoid loading model repeatedly. Take BERT For example:

emb.load_model(method="bert", model_path='model/bert-base-chinese')
bert_emb1 = emb.bert(ptexts1)
bert_emb2 = emb.bert(ptexts2)

3. Similarity calculation

Support calculating similarity/distance between texts based on text representation mentioned above. For example, we can use bert sentence embeddings to compute cosine similarity between two sentences one by one.

from textgo import TextSim
texts1 = ["她的笑渐渐变少了。","最近天气晴朗适合出去玩!"]
texts2 = ["她变得越来越不开心了。","近来总是风雨交加没法外出!"]

ts = TextSim(lang='zh', method='bert', model_path='model/bert-base-chinese')
sim = ts.similarity(texts1, texts2, mutual=False)
print(sim)

Output: [0.9143135, 0.7350756]

Besides, we can also calculate similarity between each sentences among two datasets by setting mutual=True.

sim = ts.similarity(texts1, texts2, mutual=True)
print(sim)

Output: array([[0.9143138 , 0.772496 ], [0.704296 , 0.73507595]], dtype=float32)

4. Text search

It also supports searching query text in a large text database based on cosine similarity or euclidean distance. It provides two kinds of implementation: the normal one which is suitable for small dataset and the optimized one which is based on Faiss and suitable for large dataset.

from textgo import TextSim
# query texts
texts1 = ["A soccer game with multiple males playing."]
# database
texts2 = ["Some men are playing a sport.", "A man is driving down a lonely road.", "A happy woman in a fairy costume holds an umbrella."]
ts = TextSim(lang='en', method='word2vec', model_path='model/word2vec.bin')

Normal search

res = ts.get_similar_res(texts1, texts2, metric='cosine', threshold=0.5, topn=2)
print(res)

Output: [[(0, 'Some men are playing a sport.', 0.828474), (1, 'A man is driving down a lonely road.', 0.60927737)]]

Fast search

ts.build_index(texts2, metric='cosine')
res = ts.search(texts1, threshold=0.5, topn=2)
print(res)

Output: [[(0, 'Some men are playing a sport.', 0.828474), (1, 'A man is driving down a lonely road.', 0.60927737)]]

5. Text classification

from textgo import Preprocess
from sklearn.model_selection import train_test_split

# Prepare data
X = [text1, text2, ... textn]
y = [label1, label2, ... labeln]

FastText

from textgo.classifier import FastText
ft = FastText()

# preprocess
tp = Preprocess(lang='en')
X = tp.preprocess(X)

# train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=3)

# train
model = ft.train(X_train, y_train, output_path, epoch=10)

# evaluate
classification_report, acc = ft.evaluate(X_test, y_test, model)

# predict
predpro, predclass = ft.predict(X_test, model)

XGBoost

from textgo.classifier import XGBoost
xgb = XGBoost()

# preprocess
tp = Preprocess(lang='en')
X = tp.preprocess(X)


# get features
from textgo import Embeddings
import numpy as np
emb = Embeddings()
tfidf_emb = emb.tfidf(X)
lda_emb = emb.lda(X, dim=10)
X = np.concatenate((tfidf_emb,lda_emb),axis=1)

# train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=3)

# train
model = xgb.train(X_train, y_train, output_path=output_path, num_rounds=50)

# evaluate
classification_report, acc = xgb.evaluate(X_test, y_test, model)

# predict
predpro, predclass = xgb.predict(X_test, model)

Bert

from textgo.classifier import Bert
bert = Bert()

# preprocess
tp = Preprocess(lang='en')
X = tp.clean(X) # BERT has its own tokenizer, so we don't need to tokenize.

# train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=3)

# train
args = { 
            "max_len":128,                        # max length of input text string
            "batch_size":8,                       # size of batch
            "num_labels":len(set(y_train)),       # num of classes
            "learning_rate":2e-5,                 # learning rate
            "epochs":10,                          # num of training epochs
            "evaluation_steps":12,                # evaluate every num of steps
            "val_metric":"val_loss",              # metric to choose best model, default "val_loss"
            "val_threshold":0.8,                  # threshold to choose best model
            "pretrained_model":"path1",           # pretrained model path
            "output_dir":"path2"                  # output model path/dir
            } 
model, tokenizer, training_stats = bert.train(X_train, y_train, args)

# evaluate
classification_report, acc, loss = bert.evaluate(X_test, y_test, model=model, tokenizer=tokenizer, batch_size=args['batch_size'],max_len=args['max_len'],num_labels=args['num_labels'])

# predict
predpro, predclass = bert.predict(X_test, model=model, tokenizer=tokenizer, batch_size=args['batch_size'],max_len=args['max_len'],num_labels=args['num_labels'])

LICENSE

TextGo is MIT-licensed.

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