---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- accuracy
model-index:
- name: kcbert-base-finetuned
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: ynat
metrics:
- name: Accuracy
type: accuracy
value: 0.8329856154606347
---
<!-- 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. -->
# kcbert-base-finetuned
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7393
- Accuracy: 0.8330
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4612 | 1.0 | 2855 | 0.5216 | 0.8143 |
| 0.3061 | 2.0 | 5710 | 0.5130 | 0.8248 |
| 0.2129 | 3.0 | 8565 | 0.6062 | 0.8257 |
| 0.1337 | 4.0 | 11420 | 0.7393 | 0.8330 |
| 0.0653 | 5.0 | 14275 | 0.8651 | 0.8302 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3