metadata
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- accuracy
model-index:
- name: klue_nli_roberta_base_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
config: nli
split: validation
args: nli
metrics:
- name: Accuracy
type: accuracy
value: 0.8653333333333333
klue_nli_roberta_base_model
This model is a fine-tuned version of klue/roberta-base on the klue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6867
- Accuracy: 0.8653
Model description
Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.
Intended uses & limitations
How to use
NOTE: Use BertTokenizer instead of RobertaTokenizer. (AutoTokenizer will load BertTokenizer)
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")
Training and evaluation data
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|
| 0.5988 | 1.0 | 782 | 0.4378 | 0.8363 |
| 0.2753 | 2.0 | 1564 | 0.4169 | 0.851 |
| 0.1735 | 3.0 | 2346 | 0.5267 | 0.8607 |
| 0.0956 | 4.0 | 3128 | 0.6275 | 0.8683 |
| 0.0708 | 5.0 | 3910 | 0.6867 | 0.8653 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3