NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit

Introduction

NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. It is built on PyTorch. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.

Support tasks

• Binary-class text classifcation
• Multi-class text classification
• Multi-label text classification
• Hiearchical (multi-label) text classification (HMC)

Support text encoders

• TextCNN (Kim, 2014)
• RCNN (Lai et al., 2015)
• TextRNN (Liu et al., 2016)
• FastText (Joulin et al., 2016)
• VDCNN (Conneau et al., 2016)
• DPCNN (Johnson and Zhang, 2017)
• AttentiveConvNet (Yin and Schutze, 2017)
• DRNN (Wang, 2018)
• Region embedding (Qiao et al., 2018)
• Transformer encoder (Vaswani et al., 2017)
• Star-Transformer encoder (Guo et al., 2019)

Requirement

• Python 3
• PyTorch 0.4+
• Numpy 1.14.3+

System Architecture

Usage

Training

python train.py conf/train.json

Detail configurations and explanations see Configuration.

The training info will be outputted in standard output and log.logger_file.

Evaluation

python eval.py conf/train.json

• if eval.is_flat = false, hierarchical evaluation will be outputted.
• eval.model_dir is the model to evaluate.
• data.test_json_files is the input text file to evaluate.

The evaluation info will be outputed in eval.dir.

Prediction

python predict.py conf/train.json data/predict.json 

• predict.json should be of json format, while each instance has a dummy label like "其他" or any other label in label map.
• eval.model_dir is the model to predict.
• eval.top_k is the number of labels to output.
• eval.threshold is the probability threshold.

The predict info will be outputed in predict.txt.

Input Data Format

JSON example:

{
    "doc_label": ["Computer--MachineLearning--DeepLearning", "Neuro--ComputationalNeuro"],
    "doc_token": ["I", "love", "deep", "learning"],
    "doc_keyword": ["deep learning"],
    "doc_topic": ["AI", "Machine learning"]
}

"doc_keyword" and "doc_topic" are optional.

Performance

0. Dataset

• RCV1: Lewis et al., 2004
• Yelp: Yelp

1. Compare with state-of-the-art

• HR-DGCNN: Peng et al., 2018
• HMCN: Wehrmann et al., 2018

2. Different text encoders

3. Hierarchical vs Flat

Acknowledgement

Some public codes are referenced by our toolkit:

• https://pytorch.org/docs/stable/
• https://github.com/jadore801120/attention-is-all-you-need-pytorch/
• https://github.com/Hsuxu/FocalLoss-PyTorch
• https://github.com/Shawn1993/cnn-text-classification-pytorch
• https://github.com/ailias/Focal-Loss-implement-on-Tensorflow/
• https://github.com/brightmart/text_classification
• https://github.com/NLPLearn/QANet
• https://github.com/huggingface/pytorch-pretrained-BERT

Update

• 2019-04-29, init version