metadata
tags:
- generated_from_trainer
datasets:
- nsmc
metrics:
- accuracy
- f1
model-index:
- name: nsmc_roberta_base_model
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: 0.91174
- name: F1
type: f1
value: 0.9117155392338556
nsmc_roberta_base_model
This model is a fine-tuned version of klue/roberta-base on the nsmc dataset. It achieves the following results on the evaluation set:
- Loss: 0.2570
- Accuracy: 0.9117
- F1: 0.9117
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2501 | 1.0 | 2344 | 0.2306 | 0.9072 | 0.9072 |
| 0.1805 | 2.0 | 4688 | 0.2306 | 0.9112 | 0.9112 |
| 0.1313 | 3.0 | 7032 | 0.2570 | 0.9117 | 0.9117 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3