Awesome

GRIT: Generative Role-ﬁller Transformers for Document-level Event Entity Extraction (link) EACL'21

<p align='center'> <img src='figs/task.png' width="350px"> </p>

Dependencies

Python 3.6.10
Transformers: transformers 2.4.1 installed from source.
Pytorch-Lightning==0.7.1
Pytorch==1.4.0
seqeval

Dataset

./data/muc, refer to ./data/muc/README.md for details

Eval

import eval_ceaf, from eval import eval_ceaf, read eval.py for details
eval on preds.out python eval.py --pred_file model_gtt/preds_s_t.out
run simple test case: python test_cases_eval.py

GRIT model

<p align='center'> <img src='figs/architecture.png' width="800px"> </p>

The encoder-decoder model (code is written based on hugging face transformers/examples/ner/3ee431d
How to run: see README in model_gtt

Citation

If you use materials in this repo helpful, please cite.