Pytorch Implementation of c2f-coref Model

Setup

• pip install -r requirements.txt
• ./setup_training.sh <ontonotes/path/ontonotes-release-5.0>. This assumes that you have access to OntoNotes 5.0. The preprocessed data will be included under conll_data.

Build Kernels

• python setup.py install. This will build kernel for extracting top spans implemented using the C++ interface of Pytorch.

Training

• python train.py <experiment>
• Results are stored in the log_root directory.
• For getting the result of using SpanBERT-Base and SpanBERT-Large model, use python train.py train_spanbert_base_mention and python train.py train_spanbert_large_mention
• Finetuning a SpanBERT large model on OntoNotes requires access to a 32GB GPU, while the base model can be trained in a 16GB GPU.

Performance

Model	F1 (%)
SpanBERT-base	77.5
SpanBERT-large	80.0

Acknowledgement

Many thanks to previous work https://github.com/mandarjoshi90/coref.