metadata
base_model: klue/roberta-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: logs_rand
results: []
logs_rand
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: 0.0024
- Precision: 0.8742
- Recall: 0.8871
- F1: 0.8806
- Accuracy: 0.9992
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 57 | 0.0067 | 0.6685 | 0.6276 | 0.6474 | 0.9980 |
| No log | 2.0 | 114 | 0.0035 | 0.8286 | 0.8312 | 0.8299 | 0.9989 |
| No log | 3.0 | 171 | 0.0028 | 0.8690 | 0.8745 | 0.8717 | 0.9991 |
| No log | 4.0 | 228 | 0.0026 | 0.8693 | 0.8840 | 0.8766 | 0.9992 |
| No log | 5.0 | 285 | 0.0024 | 0.8742 | 0.8871 | 0.8806 | 0.9992 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.0.1
- Datasets 2.19.1
- Tokenizers 0.19.1