shibing624 / Text Classifier
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text-classifier
Text classifier and cluster. It can be applied to the fields of sentiment polarity analysis, text risk classification and so on, and it supports multiple classification algorithms.
text-classifier is a python Open Source Toolkit for text classification and text clustering. The goal is to implement text analysis algorithm, so as to achieve the use in the production environment. text-classifier has the characteristics of clear algorithm, high performance and customizable corpus.
text-classifier provides the following functions:
- Classifier
- LogisticRegression
- MultinomialNB
- KNN
- SVM
- RandomForest
- DecisionTreeClassifier
- Xgboost
- Neural Network
- Evaluate
- Precision
- Recall
- F1
- Test
- Chi-square test
- Cluster
- MiniBatchKmeans
While providing rich functions, text-classifier internal modules adhere to low coupling, model adherence to inert loading, dictionary publication, and easy to use.
demo
https://www.borntowin.cn/product/sentiment_classify
Usage
Requirements and Installation
git clone https://github.com/shibing624/text-classifier.git
pip3 install -r requirements.txt
Example Usage
- Preprocess with segment
python3 preprocess.py
- Train model
you can change model with edit config.py
and train model.
python3 train.py
- Predict with test data
python3 infer.py
Algorithm
- [x] LogisticRegression
- [x] Random Forest
- [x] Decision Tree
- [x] K-Nearest Neighbours
- [x] Naive bayes
- [x] Xgboost
- [x] Support Vector Machine(SVM)
- [x] MLP
- [x] Ensemble
- [x] Stack
- [x] Xgboost_lr
- [x] text CNN
- [x] text RNN
- [x] fasttext
- [x] HAN
- [x] Kmenas
Thanks
- SentimentPolarityAnalysis
Licence
- Apache Licence 2.0