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fine-tuned-NLI-indonli_mnli_idkmrc-nli-with-xlm-roberta-large
This model is a fine-tuned version of xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4177
- Accuracy: 0.8624
- F1: 0.8628
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.4676 | 0.5 | 1647 | 0.4202 | 0.8406 | 0.8414 |
| 0.4363 | 1.0 | 3294 | 0.3928 | 0.8514 | 0.8523 |
| 0.3831 | 1.5 | 4941 | 0.3954 | 0.8576 | 0.8580 |
| 0.3601 | 2.0 | 6588 | 0.3774 | 0.8605 | 0.8617 |
| 0.3164 | 2.5 | 8235 | 0.3820 | 0.8657 | 0.8656 |
| 0.301 | 3.0 | 9882 | 0.3819 | 0.8652 | 0.8655 |
| 0.2521 | 3.5 | 11529 | 0.4150 | 0.8643 | 0.8650 |
| 0.2616 | 4.0 | 13176 | 0.4177 | 0.8624 | 0.8628 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
