# 649453932/bert-chinese-text-classification-pytorch

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4,425 stars · 926 forks · Python · MIT

## Links

- GitHub: https://github.com/649453932/Bert-Chinese-Text-Classification-Pytorch
- awesome-repositories: https://awesome-repositories.com/repository/649453932-bert-chinese-text-classification-pytorch.md

## Description

This project is a PyTorch-based Chinese text classification framework. It provides a transformer-based pipeline designed to categorize Chinese language sequences into predefined labels using deep learning models.

The implementation supports both BERT and ERNIE language models for processing and tagging complex Chinese text. These models are used to perform tasks such as sentiment analysis and general text categorization.

The system utilizes transformer-based text encoding and attention-weighted sequence pooling to convert raw characters into document vectors. It employs pre-trained model fine-tuning and cross-entropy loss optimization to adapt the models to specific classification tasks.

## Tags

### Artificial Intelligence & ML

- [Chinese Text Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/chinese-text-classifiers.md) — Provides a deep learning framework to categorize Chinese text into predefined labels with high accuracy. ([source](https://github.com/649453932/bert-chinese-text-classification-pytorch#readme))
- [BERT Text Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/bert-text-classifiers.md) — Provides a transformer-based architecture for understanding and labeling Chinese language sequences using BERT.
- [Chinese Language Models](https://awesome-repositories.com/f/artificial-intelligence-ml/chinese-language-models.md) — Integrates the ERNIE knowledge-enhanced language model for processing and classifying complex Chinese text.
- [Chinese Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/chinese-natural-language-processing.md) — Applies transformer-based models for the analysis and organization of large sets of Chinese text.
- [PyTorch Tensor Operations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries/pytorch-tensor-operations.md) — Uses PyTorch tensor operations to execute mathematical computations on multi-dimensional arrays for gradient-based learning.
- [Classification Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/fine-tuned-model-deployment/classification-fine-tuning.md) — Adapts pre-trained transformer models to specific Chinese text classification tasks through gradient-based fine-tuning.
- [Transformer-Based NLP Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-based-nlp-libraries.md) — Implements a transformer-based NLP pipeline to convert raw Chinese text into categorical labels.
- [Transformer Encoders](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-encoders.md) — Utilizes transformer encoders to convert raw Chinese characters into dense contextual vector representations.
- [Attention Pooling Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-pooling-mechanisms.md) — Implements attention-weighted sequence pooling to convert raw Chinese characters into document vectors.
- [Cross-Entropy Loss Functions](https://awesome-repositories.com/f/artificial-intelligence-ml/prediction-visualization/loss-function-calculators/binary-cross-entropy-calculators/cross-entropy-loss-functions.md) — Employs cross-entropy loss optimization to measure prediction error and update model weights during training.
- [Chinese Sentiment Analyzers](https://awesome-repositories.com/f/artificial-intelligence-ml/sentiment-analysis-tools/lexicon-based-sentiment-analyzers/chinese-sentiment-analyzers.md) — Supports the identification of emotional tone in Chinese text by recognizing positive or negative sentiment.

### Education & Learning Resources

- [Text Classification Architectures](https://awesome-repositories.com/f/education-learning-resources/architecture-reference-implementations/deep-learning-architectures/text-classification-architectures.md) — Implements BERT and ERNIE architectures within PyTorch for automated category assignment to Chinese text.

### Scientific & Mathematical Computing

- [Softmax Normalization](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/statistics-probability/probability-distributions/softmax-normalization.md) — Maps high-dimensional model outputs to normalized probability distributions across classification labels using softmax.