metadata
base_model: klue/roberta-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: logs
results: []
logs
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: 0.0008
- Precision: 0.9900
- Recall: 0.995
- F1: 0.9925
- Accuracy: 0.9999
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: 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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 44 | 0.0039 | 0.9701 | 0.975 | 0.9726 | 0.9991 |
| No log | 2.0 | 88 | 0.0018 | 0.8744 | 0.94 | 0.9060 | 0.9995 |
| No log | 3.0 | 132 | 0.0011 | 0.9559 | 0.975 | 0.9653 | 0.9998 |
| No log | 4.0 | 176 | 0.0008 | 0.9900 | 0.995 | 0.9925 | 0.9999 |
| No log | 5.0 | 220 | 0.0007 | 0.9803 | 0.995 | 0.9876 | 0.9999 |
| No log | 6.0 | 264 | 0.0007 | 0.9851 | 0.995 | 0.9900 | 0.9999 |
| No log | 7.0 | 308 | 0.0007 | 0.9900 | 0.995 | 0.9925 | 0.9999 |
| No log | 8.0 | 352 | 0.0007 | 0.9803 | 0.995 | 0.9876 | 0.9999 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.0.1
- Datasets 2.19.1
- Tokenizers 0.19.1