klue_ner_roberta_model

This model is a fine-tuned version of klue/roberta-base on the klue dataset. It achieves the following results on the evaluation set:

Loss: 0.0487
Precision: 0.9546
Recall: 0.9557
F1: 0.9552
Accuracy: 0.9884

Model description

Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.

Intended uses & limitations

How to use

NOTE: Use BertTokenizer instead of RobertaTokenizer. (AutoTokenizer will load BertTokenizer)

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0449	1.0	2626	0.0601	0.9361	0.9176	0.9267	0.9830
0.0262	2.0	5252	0.0469	0.9484	0.9510	0.9497	0.9874
0.0144	3.0	7878	0.0487	0.9546	0.9557	0.9552	0.9884

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

chunwoolee0
/

klue_ner_roberta_model