metadata
base_model: klue/roberta-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sentence_classification
results: []
sentence_classification
This model is a fine-tuned version of klue/roberta-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0744
- Precision: 0.9707
- Recall: 0.97
- F1: 0.9698
- Accuracy: 0.97
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-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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 18 | 0.1791 | 0.9524 | 0.95 | 0.9497 | 0.95 |
| No log | 2.0 | 36 | 0.0987 | 0.9707 | 0.97 | 0.9698 | 0.97 |
| No log | 3.0 | 54 | 0.0958 | 0.9808 | 0.98 | 0.9800 | 0.98 |
| No log | 4.0 | 72 | 0.0839 | 0.9808 | 0.98 | 0.9797 | 0.98 |
| No log | 5.0 | 90 | 0.0744 | 0.9707 | 0.97 | 0.9698 | 0.97 |
| No log | 6.0 | 108 | 0.0793 | 0.9707 | 0.97 | 0.9698 | 0.97 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1