https://pypi.org/project/seqtag/

BiLSTM + CRF for sequence tagging

This is adapted from guillaumegenthial 's original implementation and is made configurable and easy to adapt and use.

Requirements:

This code is tested with all tensorflow versions from 1.3.0 to 1.10.0.

tensorflow is not included in setup.py since, it will remove tensorflow-gpu. Separately install tensorflow by following https://www.tensorflow.org/install/ for this module to work.

Download the 300 dimnesional glove vectors from https://nlp.stanford.edu/projects/glove/

Installation:

The stable version can be installed by running "pip install seqtag"

How to Use:

Create a training directory with train.txt and valid.txt (test.txt is optional) set the config parameters as expected in a configuration file.

An example of the configuration file can be found at https://github.com/bedapudi6788/seqtag/blob/master/example_config.json

Training:

from seqtag import trainer

trainer.train(config_path = 'path_to_config_json')

Running Predictions:

from seqtag import predictor

model = predictor.load_model(path_to_config.json)

predictor.predict(model, ['I', 'am', 'Batman'])

['O', 'O', 'B-PER']

For an usage example take a look at https://github.com/bedapudi6788/Deep-Segmentation/ . seqtag is used for sentence segmentation in this repo.