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---
base_model: kykim/albert-kor-base
tags:
- generated_from_trainer
datasets:
- nsmc
metrics:
- accuracy
model-index:
- name: electra2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: nsmc
type: nsmc
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.89276
---
<!-- 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. -->
# electra2
This model is a fine-tuned version of [kykim/albert-kor-base](https://huggingface.co/kykim/albert-kor-base) on the nsmc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3037
- Accuracy: 0.8928
## 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: 5e-06
- 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: cosine
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2815 | 1.0 | 9375 | 0.2801 | 0.8863 |
| 0.2329 | 2.0 | 18750 | 0.2705 | 0.8920 |
| 0.1949 | 3.0 | 28125 | 0.3037 | 0.8928 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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