Text_classification

Overview

The repository implements the common algorithms for multi-class text classification.
Note that it’s just prototypes for experimental purposes only

• Word or char level representation: chi-square+tfidf, word2vec, glove, fasttext, elmo, bert, or concated one
• Model: CNN, BiLSTM, Self-attention,C-LSTM, RCNN, Capsule, HAN, SVM, XGBoost
• Multi task learning: for more than one multi_labels

Dependencies

pip install -r requirements.txt

• Python 3.6
• Tensorflow 1.12.0

Usage

python run_classifier.py

• in config.py, set the new_data=True, -> generate the ./data/*.tf_record -> utilize config.py parameters
• in config.py, set the new_data=False, -> utilize the data from ./data/*.tf_record -> utilize config.json parameters

Pretrained

• word2vec Chinese pretrained download
• fasttext Chinese pretrained download
• bert Chinese pretrained download from google
• tips: make sure the text use the similar preprocessing trick like segmentation as the pretrained material
• create a word2vec pretrained model reference

Purpose

• The classification is used to clarify the damaged part and damage type from vehicles comments

Evaluation

• Check in tensorboard: tensorboard --logdir=./outputs
• Due to we have too many categories of labels (ca. 500 class for 100,000 examples), and they are not equally important, so we don’t use Macro- evaluation. And the Micro- precision/recall/F1 is the same for multi-label classification. So we check the accuracy and weighted F1.

Ignored property

• Sometimes in one sample, more than one label are valid
• Some labels have hierarchy relationship
• imbalance issue: weighted loss, data argument, anomaly detection, upsampling and downsampling

Todo

• Multi-task learning
• Multi-label classification