klue_ner_bert_model

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

• Loss: 0.0843
• Precision: 0.8839
• Recall: 0.8967
• F1: 0.8902
• Accuracy: 0.9781

Model description

KLUE BERT base is a pre-trained BERT Model on Korean Language. The developers of KLUE BERT base developed the model in the context of the development of the Korean Language Understanding Evaluation (KLUE) Benchmark.

Intended uses & limitations

How to Get Started With the Model

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/bert-base")
tokenizer = AutoTokenizer.from_pretrained("klue/bert-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
0.0638	1.0	2626	0.0807	0.8623	0.8702	0.8662	0.9747
0.0402	2.0	5252	0.0780	0.8756	0.8896	0.8825	0.9770
0.025	3.0	7878	0.0843	0.8839	0.8967	0.8902	0.9781

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.0
• Tokenizers 0.13.3