Skip to content

Latest commit

 

History

History

lstm_baseline

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
README.md
README.md
 
 
__init__.py
__init__.py
 
 
__main__.py
__main__.py
 
 
args.py
args.py
 
 
model.py
model.py
 
 

README.md

lstm_baseline

Implementation of a standard LSTM using PyTorch and Torchtext for text classification baseline measurements.

Model Type

  • rand: All words are randomly initialized and then modified during training.
  • static: A model with pre-trained vectors from word2vec. All words -- including the unknown ones that are initialized with zero -- are kept static and only the other parameters of the model are learned.
  • non-static: Same as above but the pretrained vectors are fine-tuned for each task.

Quick Start

To run the model on Reuters dataset on static, just run the following from the Castor working directory.

python -m lstm_baseline --mode static

Dataset

We experiment the model on the following datasets.

  • Reuters dataset - ModApte splits

Settings

Adadelta is used for training.

TODO

  • Support ONNX export. Currently throws a ONNX export failed (Couldn't export Python operator forward_flattened_wrapper) exception.
  • Add dataset results with different hyperparameters
  • Parameters tuning