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Achyudh Ram 951df4ada5 Neural Document Classification (#159 )

* Add ReutersTrainer, ReutersEvaluator options in Factory classes

* Add Reuters to Kim-CNN command line arguments

* Fix SST dataset path according to changes in Kim-CNN args

The dataset path in args.py was made to point at the dataset folder rather than dataset/SST folder. Hence SST folder was added to paths in the SST dataset class

* Add Reuters dataset class, and support in __main__

* Add Reuters dataset trainers and evaluators

* Remove debug print statement in reuters_evaluator

* Fix rounding bug in reuters_trainer and reuters_evaluator

* Add LSTM for baseline text classification measurements

* Add eval metrics for lstm_baseline

* Set batch_first param in lstm_baseline

* Remove onnx args from lstm_baseline

* Pack padded sequences in LSTM_baseline

* Add TensorBoardX support for Reuters trainer

* Add Arxiv Academic Paper Dataset (AAPD)

* Add Hidden Bottleneck Layer to BiLSTM

* Fix packing of padded tensors in Reuters

* Add cmdline args for Hidden Bottleneck Layer for BiLSTM

* Include pre-padding lengths in AAPD dataset

* Remove duplication of preprocessing code in AAPD

* Remove batch_size condition in ReutersTrainer

* Add ignore_lengths option to ReutersTrainer and ReutersEvaluator

* Add AAPDCharQuantized and ReutersCharQuantized

* Rename Reuters_hierarchical to ReutersHierarchical

* Add CharacterCNN for document classification

* Update README.md for CharacterCNN

* Fix table in README.md for CharacterCNN

* Add AAPDHierarchical for HAN

* Update HAN for changes in Reuters dataset endpoints

* Fix bug in CharCNN when running on CPU

* Add AAPD dataset support for KimCNN

* Fix dataset paths for SST-1

* Fix dimensions of FC1 in CharCNN

* Add model checkpointing for Reuters based on F1

* Refactor LSTM baseline __main__

* Add precision, recall and F1 to Reuters evaluator

* Checkpoint only at the end of an epoch for ReutersTrainer

Add detailed log printing for dev evaluations

* Fix log_template and dev_log_template in ReutersTrainer

* Add IMDB dataset

* Add support for single_label datasets in ReutersTrainer

* Add support for IMDB dataset in lstm_baseline and lstm_reg

2018-11-10 23:07:11 -05:00

__init__.py

Baseline LSTM implementation (#150 )

2018-10-11 03:04:23 -04:00

__main__.py

Neural Document Classification (#159 )

2018-11-10 23:07:11 -05:00

args.py

Neural Document Classification (#159 )

2018-11-10 23:07:11 -05:00

model.py

Replication of STOA for Reuters Dataset (#152 )

2018-10-26 19:10:19 -04:00

README.md

Baseline LSTM implementation (#150 )

2018-10-11 03:04:23 -04:00

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