SimpleFSRE
The code of the short paper "A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction". This paper has been accepted to Findings of ACL2022. You can find the main results (username is liuyang00) in the paper on FewRel 1.0 competition on CodaLab competition websit: FewRel 1.0 Competition
We will release the paper link after camera ready.
Environments
python 3
PyTorch 1.7.1
transformers 4.6.0
Datasets and Models
You can find the training and validation data here: FewRel 1.0 data. For the test data, you can easily download from FewRel 1.0 competition website: https://competitions.codalab.org/competitions/27980
We release our trained models using BERT and CP as backend models respectively at Google Drive. The file structure as below:
--BERT
--nodropPrototype-nodropRelation-lr-1e-5
--CP
--nodropPrototype-nodropRelation-lr-9e-6
--nodropPrototype-nodropRelation-lr-5e-6
You can reproduce our result in the paper with models in BERT/nodropPrototype-nodropRelation-lr-1e-5 and CP/nodropPrototype-nodropRelation-lr-5e-6. We also provide the trained model with a different learning rate for CP in CP/--nodropPrototype-nodropRelation-lr-9e-6 for extra reference.
Code
Put all data in the data folder, CP pretrained model in the CP_model folder (you can download CP model from https://github.com/thunlp/RE-Context-or-Names/tree/master/pretrain or Google Drive), and then you can simply use three scripts: run_train.sh, run_eval.sh, run_submit.sh for train, evaluation and test.
Train
Set the corresponding parameter values in the script, and then run:
sh run_train.sh
Some explanations of the parameters in the script:
--pretrain_ckpt
the path for the BERT-base-uncased
--backend_model
bert or cp, select one backend model
Evaluation
Set the corresponding parameter values in the script, and then run:
sh run_eval.sh
Some explanations of the parameters in the script:
--test_iter
1000, the evaluation iteration
--load_ckpt
the path of the trained model
Test
Set the corresponding parameter values in the script, and then run:
sh run_submit.sh
Some explanations of the parameters in the script:
--test_output
the path to save the prediction file
Results
BERT on FewRel 1.0
5-way-1-shot | 5-way-5-shot | 10-way-1-shot | 10-way-5-shot | |
---|---|---|---|---|
Val | 91.29 | 94.05 | 86.09 | 89.68 |
Test | 94.42 | 96.37 | 90.73 | 93.47 |
CP on FewRel 1.0
5-way-1-shot | 5-way-5-shot | 10-way-1-shot | 10-way-5-shot | |
---|---|---|---|---|
Val | 96.21 | 97.07 | 93.38 | 95.11 |
Test | 96.63 | 97.93 | 94.94 | 96.39 |