This repo contains the PyTorch code for paper Position-aware Attention and Supervised Data Improve Slot Filling.
The TACRED dataset: Details on the TAC Relation Extraction Dataset can be found on this dataset website.
- Python 3 (tested on 3.6.2)
- PyTorch (tested on 1.0.0)
- unzip, wget (for downloading only)
First, download and unzip GloVe vectors from the Stanford website, with:
chmod x download.sh; ./download.sh
Then prepare vocabulary and initial word vectors with:
python prepare_vocab.py dataset/tacred dataset/vocab --glove_dir dataset/glove
This will write vocabulary and word vectors as a numpy matrix into the dir dataset/vocab
.
Train a position-aware attention RNN model with:
python train.py --data_dir dataset/tacred --vocab_dir dataset/vocab --id 00 --info "Position-aware attention model"
Use --topn N
to finetune the top N word vectors only. The script will do the preprocessing automatically (word dropout, entity masking, etc.).
Train an LSTM model with:
python train.py --data_dir dataset/tacred --vocab_dir dataset/vocab --no-attn --id 01 --info "LSTM model"
Model checkpoints and logs will be saved to ./saved_models/00
.
Run evaluation on the test set with:
python eval.py saved_models/00 --dataset test
This will use the best_model.pt
by default. Use --model checkpoint_epoch_10.pt
to specify a model checkpoint file. Add --out saved_models/out/test1.pkl
to write model probability output to files (for ensemble, etc.).
Please see the example script ensemble.sh
.
All work contained in this package is licensed under the Apache License, Version 2.0. See the included LICENSE file.