This repository is a replicate implementation based on paper PUnifiedNER:a Prompt-Based NER System Jointly Crossing Multiple NER Datasets (Accepted to the main conference of AAAI2023)
Other datasets can be found under ./ner_datasets/
Tested Python 3.6, and requiring the following packages, which are available via PIP:
- Required: numpy >= 1.19.5
- Required: scikit-learn >= 0.21.1
- Required: pandas >= 1.1.5
- Required: torch >= 1.9.0
- Required: transformers >= 4.8.2
- Required: datasets>=1.14.0
- Required: nltk>=3.6.5
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Prefix language model adapatation: sh_lm_adaption_1e-4_multi_gpus.sh
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Multi-dataset PUnifiedNER tuning: sh_train_all_multi_gpus.sh
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Test set evaluation: sh_eval_{dataset_name}.sh
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Quick demo using Gradio: PUnifiedNER_Demo_Gradio.ipynb
