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Coreference resolution with different higher-order inference methods; implemented in PyTorch.

License: Apache License 2.0

Python 98.80% Shell 1.20%

coreference-resolution nlp pytorch

coref-hoi's Introduction

NLP4J

The NLP4J project provides a NLP toolkit for JVM languages. This project is under the Apache 2 license and is currently developed by the NLP Research Group at Emory University. Please join our forum to get notifications about new releases and give your feedback about this project.

Latest release: 1.1.2 (06/29/2016).
Release notes.
Google groups.

Quick Start

Components

Tokenization.
Morphological analysis.
Part-of-speech tagging.
Named entity recognition.
Dependency parsing.
Semantic role labeling (coming soon).
Sentiment analysis (coming soon).
Word2Vec & Struct2Vec (coming soon).

Supplements

English lexica and models (hosted in bitbucket).
Data format.

coref-hoi's People

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shtoshni bahaisi mangoes988 rshnk73 ondfa chc1015

coref-hoi's Issues

Out of memory

Hi,

I use bert-base-cased model as pretrained model and I use Tesla K40m GPU to train. The memory is 11G. During training, I always get the "CUDA out of memory" error. Can I ask how many memory it will use? Or there are some tricks in the code?

Thanks a lot.

train on bert base

Hello, may I ask if this model can also get good results on BERT base? I have trianed on bert base with c2f mode, but get a result about 64 F1

train on spanbert large, but get F1 1 point lower than presented in paprer

Hi,

I use spanbert large model with default parameters in config file, and I get Avg F1 78.27, lower than Avg.F1 79.9 in paper.

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