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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ko_roberta_small_model
This model is a fine-tuned version of [hyeonseo/ko_roberta_small_model](https://huggingface.co/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