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