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All Projects → jiegzhan → Multi Class Text Classification Cnn

jiegzhan / Multi Class Text Classification Cnn

Licence: apache-2.0
Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.

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tensorflowconvolutional-neural-networkscnntext-classificationembeddings

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Project: Classify Kaggle Consumer Finance Complaints

Highlights:

  • This is a multi-class text classification (sentence classification) problem.
  • The purpose of this project is to classify Kaggle Consumer Finance Complaints into 11 classes.
  • The model was built with Convolutional Neural Network (CNN) and Word Embeddings on Tensorflow.

Data: Kaggle Consumer Finance Complaints

  • Input: consumer_complaint_narrative

    • Example: "someone in north Carolina has stolen my identity information and has purchased items including XXXX cell phones thru XXXX on XXXX/XXXX/2015. A police report was filed as soon as I found out about it on XXXX/XXXX/2015. A investigation from XXXX is under way thru there fraud department and our local police department.\n"

  • Output: product

    • Example: Credit reporting

Train:

  • Command: python3 train.py training_data.file parameters.json
  • Example: python3 train.py ./data/consumer_complaints.csv.zip ./parameters.json

A directory will be created during training, and the trained model will be saved in this directory.

Predict:

Provide the model directory (created when running train.py) and new data to predict.py.

  • Command: python3 predict.py ./trained_model_directory/ new_data.file
  • Example: python3 predict.py ./trained_model_1479757124/ ./data/small_samples.json

Reference:

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