A transformers based transfer learning framework for named entity recognition (NER).
Clone the repository and run the requirements file:
git clone https://github.com/suamin/t2ner.git
cd t2ner
pip install -r requirements
Download the NER data of interest and convert it into CoNLL format. Example datasets are provided in data folder (GermEval 2014, CoNLL-2002). Then, preprocess the CoNLL formatted data:
python t2ner/preprocess.py \
--data_dir data/ner \
--output_dir data/processed \
--model_name_or_path bert-base-multilingual-cased \
--model_type bert \
--max_len 128 \
--overwrite_output_dir \
--languages es,nl
To run an experiment:
python t2ner/run.py \
--exp_type ner \
--base_json configs/base.json \
--exp_json configs/ner.json
If you find our framework useful, please consider citing:
@inproceedings{amin-neumann-2021-t2ner, title = "{T}2{NER}: Transformers based Transfer Learning Framework for Named Entity Recognition", author = "Amin, Saadullah and Neumann, G{\"u}nter", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.25", doi = "10.18653/v1/2021.eacl-demos.25", pages = "212--220" }
Also, check our follow-up work using T2NER for few-shot cross-lingual de-identification of clinical texts:
@inproceedings{amin-etal-2022-shot, title = "Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts", author = "Amin, Saadullah and Pokaratsiri Goldstein, Noon and Kelly Wixted, Morgan and Garcia-Rudolph, Alejandro and Mart{\'\i}nez-Costa, Catalina and Neumann, G{\"u}nter", booktitle = "Proceedings of the 21st Workshop on Biomedical Language Processing", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.bionlp-1.20", doi = "10.18653/v1/2022.bionlp-1.20", pages = "200--211" }
The algorithmic components of the framework largely follow Transfer-Learning-Library and Dassl.pytorch, if you find T2NER useful, please also consider citing these works.