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jiegzhan

multi-class-text-classification-cnn-rnn

Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.

TutorialsML/AI fundamentalsPython
Open on GitHub
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Created 2016-10-28 · Updated 2026-09-24 · #16285 today
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jiegzhan/multi-class-text-classification-cnn-rnn is ranked 16,285 with 601 stars.

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jiegzhan/multi-class-text-classification-cnn-rnn is ranked 16,285 among these 8 peers; microsoft/samples-for-ai is ranked 16,288.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguagePython
CategoryTutorials
SubcategoryML/AI fundamentals
Stars601
Forks256
Contributors4
Created date2016-10-28
Updated date2026-09-24
Stars in 1 day0
Stars in 7 days0
Rank16,285
Previous rank16,075
Rank change-210
7-day stars rank15,451
Previous 7-day stars rank15,461
7-day stars rank change10
Total stars rank16,770
Previous total stars rank16,744
Total stars rank change-26
CountryUSA
README summary

Project: Classify Kaggle San Francisco Crime Description Highlights Multi-class text classification (sentence classification) problem. Classify Kaggle San Francisco Crime **Descript** into 39 **Category** labels. Hybrid **TextCNN + GRU** model implemented with **TensorFlow 2. Reference Implement a CNN for text classification in TensorFlow

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jiegzhan/multi-class-text-classification-cnn-rnn has 601 stars and 256 forks.

What is the repository rank?

jiegzhan/multi-class-text-classification-cnn-rnn is ranked 16,285.

What language and category are recorded?

jiegzhan/multi-class-text-classification-cnn-rnn has 601 stars, uses Python and belongs to Tutorials.

Which repository is closest in rank?

jiegzhan/multi-class-text-classification-cnn-rnn is compared with microsoft/samples-for-ai, ranked 16,288 with 601 stars.

README

Project: Classify Kaggle San Francisco Crime Description

Highlights

  • Multi-class text classification (sentence classification) problem.
  • Classify Kaggle San Francisco Crime Descript into 39 Category labels.
  • Hybrid TextCNN + GRU model implemented with TensorFlow 2.x Keras.

Data: Kaggle San Francisco Crime

  • Input: Descript
  • Output: Category

Examples:

Descript Category
GRAND THEFT FROM LOCKED AUTO LARCENY/THEFT
POSSESSION OF NARCOTICS PARAPHERNALIA DRUG/NARCOTIC

Setup

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Training data is included at ./data/train.csv.zip. Prediction sample data is at ./data/small_samples.csv.

Train

python3 train.py ./data/train.csv.zip ./training_config.json

Artifacts are written to ./trained_results_/:

  • saved_model/ — exported SavedModel for inference
  • best_model.keras — best validation checkpoint (primary load path for predict)
  • words_index.json — vocabulary mapping
  • labels.json — class labels
  • trained_parameters.json — hyperparameters and sequence length

Predict

python3 predict.py ./trained_results_/ ./data/small_samples.csv

Predictions are saved to ./predicted_results_/predictions_all.csv.

Reference

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