- Relation Extraction task is one of the task of Korean Language Understanding Evaluation(KLUE) Benchmark.
- Relation extraction can be defined as multiclass classification task for relationship between subject entity and object entity.
- Classes are such as
no_relation,per:employee_of,org:founded_by... totaling 30 labels. - This repo contains custom fine-tuning method utilizing monologg's R-BERT Implementation.
- Custom punctuations with Pororo NER has been added to the dataset prior to the model's training.
- If you want to refer to the experimentation note such as punctuation method of the entity, please refer to the blog post
RBERT structure can also be used on Code Clone Detection Task and Natural Language Inference Task.
| Argument | type | Default | Explanation |
|---|---|---|---|
| batch_size | int | 32 | batch size for training and inferece |
| num_folds | int | 5 | number of fold for Stratified KFold |
| num_train_epochs | int | 5 | number of epochs for training |
| loss | str | focalloss | loss function |
| gamma | float | 1.0 | focalloss's gamma value |
| optimizer | str | adamp | optimizer for training |
| scheduler | str | get_cosine_schedule_with_warmup | learning rate scheduler |
| learning_rate | float | 0.00005 | initial learning rate |
| weight_decay | float | 0.01 | Loss function's weight decay, preventing overfit |
| warmup_step | int | 500 | |
| debug | bool | false | debug with CPU device for better error representation |
| dropout_rate | float | 0.1 | |
| save_steps | int | 100 | number of steps for saving the model |
| evaluation_steps | int | 100 | number of step until the evaluation |
| metric_for_best_model | str | eval/loss | the metric for determining which is the best model |
| load_best_model_at_end | bool | True |
- monologg's R-BERT Implementation in Pytorch
- Enriching Pre-trained Language Model with Entity Information for Relation Classification
- KLUE: Korean Language Understanding Evaluation
- KLUE RE (Relation Extraction) Dataset / CC BY-SA
- Pororo's NER Documentation
GPU : Tesla V100 32GB
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