Awesome
PromptNER
Code for PromptNER: Prompt Locating and Typing for Named Entity Recognition, accepted at ACL 2023.
<p float="left"> <img src="./assets/comparison.png" width="28.7%" /> <img src="./assets/overview.png" width="69%" /> </p>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.
Setup
conda create -n promptner python=3.8
conda activate promptner
pip install -r requirements.txt
Usage
This repository is under construction. We will further detail the usage of the code and the data.
Data
For the data, please refer to data/README.md.
Training
- 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
- Low-resource Setting:
- Wiki Warmup Pretrain: <a name="wiki-warmup-pretrain"></a>
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
Evaluation
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