metadata
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ko_roberta_small_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: Precision
type: precision
value: 0.6827303934512807
- name: Recall
type: recall
value: 0.7253980500806622
- name: F1
type: f1
value: 0.703417786090801
- name: Accuracy
type: accuracy
value: 0.9403969397937687
ko_roberta_small_model
This model is a fine-tuned version of hyeonseo/ko_roberta_small_model on the klue dataset. It achieves the following results on the evaluation set:
- Loss: 0.1864
- Precision: 0.6827
- Recall: 0.7254
- F1: 0.7034
- Accuracy: 0.9404
Model description
More information needed
Intended uses & limitations
More information needed
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2341 | 1.0 | 1313 | 0.2069 | 0.6516 | 0.6999 | 0.6749 | 0.9336 |
| 0.16 | 2.0 | 2626 | 0.1864 | 0.6827 | 0.7254 | 0.7034 | 0.9404 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3