MetaNER-ICL

• An implementation for ACL 2023 paper Learning In-context Learning for Named Entity Recognition

Quick links

• Environment
• Pre-training
• In-context Learning
• Model Evaluation
• Fewshot Fine-tuning
• License

Environment

conda create -n metaner python=3.9.0
conda activate metaner
bash env.sh

Pre-training

The pre-trained models are in huggingface: metaner and metaner-base

The pre-trained dataset is in huggingface

We use one A100-80g to pre-train the t5-v1_1-large and you can run:

python pretrain.py --plm google/t5-v1_1-large --do_train --per_device_train_batch_size 8 --learning_rate 5e-5 \
--logging_step 1000 \
--output_dir plm/metaner \
--evaluation_strategy steps \
--do_eval \
--per_device_eval_batch_size 32 \
--metric_for_best_model f1 \
--eval_steps 10000 \
--max_steps 500000 \
--save_steps 10000 \
--lr_scheduler_type constant_with_warmup \
--warmup_steps 10000 \
--save_total_limit 50 \
--remove_unused_columns False \
--dataset pretrain_data 

The pretraining dataset should be putted in path pretrain_data/

pretrain_data/
├── ICL_train.json
├── ICL_dev.json
├── label2id.json
├── code2name.json
└── lmtrain.json

where ICL_train.json and ICL_dev.json are the NER dataset from wikipedia and wikidata, label2id.json is used for ner pre-training, code2name.json is the wikidata code and label name mapping file, lmtrain.json is used for pseudo extraction language modeling task.

In-context Learning

You can run:

python predictor.py --output_dir tmp/conll03/metaner-icl \
--plm plm/metaner \
--formatsconfig config/formats/metaner.yaml \
--testset data/conll03 \
--do_predict \
--remove_unused_columns False \
--shot_num 5 \
--per_device_eval_batch_size 16

The result will be in output_dir. You can change the shot_num for different shot and testset for different dataset.

For different pre-trained model, you should change plm and formatsconfig.

For t5 model, you can change the formatsconfig to config/formats/t5.yaml. For gpt/opt model, you can change the formatsconfig to config/formats/gpt.yaml/config/formats/opt.yaml.

Model Evaluation

You can run:

python getresult.py -modelpath tmp/conll03/metaner-icl

Note that due to possible variations in the order of demonstrations, there may be a slight performance difference, approximately 1%.

Fewshot Fine-tuning

run:

python finetuning.py \
--dataset data/conll03 \
--shot 5 \
--plm plm/metaner \
--formatsconfig config/formats/finetune/t5.yaml \
--output_dir tmp/conll03/metaner-ft \
--do_train \
--per_device_train_batch_size 4 \
--save_strategy no \
--num_train_epochs 200 \
--learning_rate 1e-4 \
--save_total_limit 1 \
--warmup_ratio 0.06 \
--remove_unused_columns False 

The result will be in output_dir. You can change the shot for different shot and dataset for different dataset.

For different pre-trained model, you should change plm and formatsconfig.

For t5/metaner model, the formatsconfig should be config/formats/finetune/t5.yaml. For gpt model, it should be config/formats/finetune/t5.yaml.

For prediction, you can run predictor.py and set context_num is -1:

python predictor.py --output_dir tmp/conll03/metaner-ft \
--plm plm/metaner \
--formatsconfig config/formats/finetune/t5.yaml \
--testset data/conll03 \
--do_predict \
--remove_unused_columns False \
--shot_num 5 \
--context_num -1 \
--per_device_eval_batch_size 16

License

The code is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License for Noncommercial use only. Any commercial use should get formal permission first.

Shield:

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.