metadata
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: roberta-base-finetuned-ynat
results: []
roberta-base-finetuned-ynat
This model is a fine-tuned version of klue/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3938
- F1: 0.8672
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: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.3162 | 0.56 | 50 | 0.4069 | 0.8610 |
| 0.276 | 1.11 | 100 | 0.4176 | 0.8587 |
| 0.2706 | 1.67 | 150 | 0.4036 | 0.8631 |
| 0.2941 | 2.22 | 200 | 0.4232 | 0.8590 |
| 0.2778 | 2.78 | 250 | 0.3994 | 0.8623 |
| 0.2575 | 3.33 | 300 | 0.3979 | 0.8628 |
| 0.2389 | 3.89 | 350 | 0.4008 | 0.8652 |
| 0.2258 | 4.44 | 400 | 0.3950 | 0.8653 |
| 0.2097 | 5.0 | 450 | 0.3938 | 0.8672 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0