Borchmann / ac-tagger

AC-Tagger

Requirements

Python 2.7 and PyTorch (http://pytorch.org/).

The model is implemented and tested on PyTorch version 0.3.1 (http://pytorch.org/docs/0.3.1/).

Hardware Requirements

The model is fast on a GPU unit with CUDA + cuDNN deep learning libraries.

Data Requirements

First you need to obtain word embeddings.

For English, we use 100-dimensions Glove embeddings (https://nlp.stanford.edu/projects/glove/).

The preprocessed version of the embeddings can be downloaded from the following link: https://goo.gl/8D87oP

For German, we obtain and utilize the 64-dimensions German embeddings of https://arxiv.org/abs/1603.01360.

The preprocessed version of the embeddings can be downloaded from the following link: https://goo.gl/U8dQAJ

Running Configurations

All configurations are manually set via the config.py file.

Training Instructions

python tagger.py train <path to save model>

Example:

> mkdir ./saved_models
> python tagger.py train ./saved_models/

Testing Instructions

python tagger.py test <path to restore model> <input file path> <output file path>

Example:

> python tagger.py test ./saved_models/ ./data/dev.raw ./saved_models/dev.predicted
> python tagger.py test ./saved_models/ ./data/test.raw ./saved_models/test.predicted

License

MIT license.