Code for PromptNER: Prompt Locating and Typing for Named Entity Recognition, accepted at ACL 2023.

The left figure show a comparison of the type-oriented (a) and span-oriented (b) prompt learning with the proposed PromptNER (c). C, N and M denote the number of entity types, words and prompts, respectively; The right figure shows an overview of PromptNER. The left part describes the model’s inference process and the right part describes the dynamic template filling mechanism during training.

conda create -n promptner python=3.8
conda activate promptner
pip install -r requirements.txt

This repository is under construction. We will further detail the usage of the code and the data.

For the data, please refer to data/README.md.

1. Supervised Setting:

• PromptNER[BERT] on ACE2004:

python prompt4ner.py train --config configs/bert/ace2004.conf

• PromptNER[RoBERTa] on ACE2005:

python prompt4ner.py train --config configs/roberta/ace2005.conf

2. Low-resource Setting:

• Wiki Warmup Pretrain:

python prompt4ner.py train --config configs/few_shot/wiki_pretrain/pretrain.conf

• In-domain Setting:

python prompt4ner.py train --config configs/few_shot/in_domain/conll2003_sampling.conf

• Cross-domain Setting:

First train PromptNER on source domain (CoNLL 2003) based on the pre-trained model from Wiki Warmup Pretrain:

python prompt4ner.py train  --config configs/few_shot/cross_domain/source.conf

Then train PromptNER on target domain (MIT Movie) using the model from the previous step:

python prompt4ner.py train --config configs/few_shot/cross_domain/mm.conf

For evaluation, please first replace the model_path@configs/eval.conf with the path of the trained model. Then run:

python prompt4ner.py eval --config configs/eval.conf