lylylylylyly / SimpleFSRE

A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction

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