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Keras-TextClassification

yongzhuo/Keras-TextClassification

Chinese text classification models based on Keras

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

30views this month

1.8k stars397 forksLast push Jun 17, 2024 Python MIT

Decision brief

Chinese-focused text classification models using Keras.

Good fit when

  • Requires Chinese text classification for tasks like multi-label, sentence similarity analysis.
  • Needs a variety of neural net architectures including FastText, CNNs, RNNs and Transformers for flexibility.

Avoid when

  • Does not cater to non-Chinese language datasets effectively due to its Chinese-specific models.
  • Avoid if you seek a tool with extensive support beyond text classification like NER or POS tagging.
Requirements:
Min 4 GB RAM; Python environment must be prepared for running Keras.; Supports multiple model types requiring different levels of computation resources.

Observed Jul 16, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Dormant (824d since push)
As of Sep 20, 2026
Provenance
Not a fork · Personal account
As of Sep 20, 2026
Security (OSV)
No lockfile
As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install Keras-TextClassification
PyPI

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Provides multiple neural network architectures for text classification tasks including multi-label and sentence similarity analysis. Models include FastText, CNNs (CharCNN, TextCNN), RNNs, Transformers, BERT, XLNet, and more.

Capability facts

Languages
python

Source: github.language · Sep 20, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 20, 2026)

```python step2: download and unzip the dir of 'data.rar', 地址: 链接:https://pan.baidu.com/s/
Source link

Tags

README

Install(安装)

For agents

This page has a .md twin and JSON over the API.

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