This repository contains experiments for defining custom models in allenNLP and integrating them with the command line tools provided by the allenNLP framework.

Currently, this repository includes:

• A simple Sequence classification model (custom/models.py) with a configurable Seq2Vec encoder (from the ones provided in allennlp/modules/seq2vec_encoders (https://github.com/allenai/allennlp/tree/master/allennlp/modules/seq2vec_encoders))
• A simple jsonl reader (custom/readers.py) with configurable input and label fields
• A simple csv/tsv reader (custom/readers.py) with simple configurable parameters
• A predictor to be used for the Sequence classifier (custom/predictors.py)
• A script setting up the predictor and wrapping a call to main() from the command line module of allenNLP
• A folder with experiment definitions (experiments/definitions), including a simple BagOfEmbeddingClassifier for a really small subset of the Amazon Reviews dataset (data)

Assuming you have already installed allenNLP following https://github.com/allenai/allennlp, you can run the following

• Train: python -m custom.run train experiments/definitions/sequence_boe_classifier.json -s experiments/output/boe
• Predict: python -m custom.run predict experiments/output/boe/model.tar.gz data/amazon_reviews_video_games_5-1000.json.dev(of course this should be done with a different set of data, not used for training/evaluating the model, this is just for demonstration purposes