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README.md

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Deep Sequence Classification

Generic library for training models for deep neural networks for text sequence classification tasks.

Usage:

  • Create a json file config.json (default name) using the template in config.json.sample and specify the parameters for your training.

  • Best strategy is to save the training and test files in vector format in advance and then give their paths in data_vectors parameter in the file.

  • Train a model:

python model.py --config config_multitask.json --verbose 1
  • Resume training from saved weights:
python model.py --config config_multitask.json --verbose 1 --weights output/models/model_multi_brnn_multitask_h2-45.h5 --base_epochs 45

Preprocessing:

  • Currently, we support the preprocessing for the following file formats:
<ROOT><DOC>
<DOCNO> DOCUMENT 1 </DOCNO>
     For <TIME TYPE="DATE:DATE">six years</TIME> , <ENTITY TYPE="PERSON">Shubhanshu A. B. Mishra</ENTITY> has made several programming projects after being inspired by <ENTITY TYPE="PERSON">Linus Torvalds</ENTITY>, a very renowned programmer.
</DOC></ROOT>
  • Each file can contain multiple DOCNO.
  • The dir structure consists of many folders of data split for cross validation. It is as follows:
data/
data/CV_files
data/CV_files/1/file1.xml
data/CV_files/1/file2.xml
data/CV_files/1/file3.xml
...

data/CV_files/5/file1.xml
data/CV_files/5/file2.xml

Supports:

  • Boundary and Category Detection
  • Simple RNN and Bidirectional RNN
  • Multi task sequence learning (Boundary + Category trained using same model)
  • CNN + BRNN

Coming Up:

Use Cases:

  • Named Entity Recognition
  • POS Tagging
  • Dependency Parsing

Author:

  • Shubhanshu Mishra

Dependencies:

  • Theano
  • Keras
  • BeautifulSoup (with lxml)
  • numpy
  • lxml (requires libxml2, libxslt and libxml2-dev)

Install theano and keras using the following commands:

pip install --user --upgrade --no-deps git+git://github.com/Theano/Theano.git
pip install --user --upgrade --no-deps git+git://github.com/fchollet/keras.git

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Deep neural network based model for sequence to sequence classification

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