metadata
tags:
- generated_from_trainer
datasets:
- nsmc
metrics:
- accuracy
model-index:
- name: kcbert-base-finetuned
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: nsmc
type: nsmc
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8978
kcbert-base-finetuned
This model is a fine-tuned version of beomi/kcbert-base on the nsmc dataset. It achieves the following results on the evaluation set:
- Loss: 0.6977
- Accuracy: 0.8978
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 |
|---|---|---|---|---|
| 0.152 | 1.0 | 9375 | 0.3803 | 0.8880 |
| 0.1741 | 2.0 | 18750 | 0.3669 | 0.892 |
| 0.105 | 3.0 | 28125 | 0.5072 | 0.8975 |
| 0.054 | 4.0 | 37500 | 0.6541 | 0.8966 |
| 0.0302 | 5.0 | 46875 | 0.6977 | 0.8978 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3