Implementing RNN Text Classification in MXNet

A while ago, I implemented a CNN text classification model using MXNet, which can be found here.

This time, I try to implement it in RNN (with attention).

Data

training and validation data

two txt file, the format of each line is: <label> sentence.

• <pos> This is the best movie about troubled teens since 1998's whatever.
• <neg> This 10th film in the series looks and feels tired.

config data

one label a line, the number of labels is equals to total classes.

• pos
• neg

inference data

one sentence a line, without <label>

inference data with evaluation

the format of each line is: <label> sentence, like validation file

The data is recommended to be tokenized or segmented(Chinese).

Quick start

python rnn_model.py --train path/to/train.data --validate /path/to/validate.data --config /path/to/config

python inference.py --test python/to/inference.data --config /path/to/config --checkpoint 1

python inference.py --test python/to/inference-evaluation.data --config /path/to/config --checkpoint 1 --evaluation

References

• Implementing CNN Text Classification in MXNet