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fine-tuned-NLI-idk-mrc-nli-drop-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: 0.0610
- Accuracy: 0.9777
- F1: 0.9777
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 |
|---|---|---|---|---|---|
| 1.0581 | 0.5 | 39 | 0.6855 | 0.5079 | 0.3506 |
| 0.7217 | 1.0 | 78 | 0.2164 | 0.9293 | 0.9292 |
| 0.4239 | 1.5 | 117 | 0.1141 | 0.9686 | 0.9686 |
| 0.1448 | 2.0 | 156 | 0.0929 | 0.9660 | 0.9660 |
| 0.1448 | 2.5 | 195 | 0.0677 | 0.9777 | 0.9777 |
| 0.103 | 3.0 | 234 | 0.0933 | 0.9751 | 0.9751 |
| 0.0826 | 3.5 | 273 | 0.0723 | 0.9764 | 0.9764 |
| 0.0598 | 4.0 | 312 | 0.0610 | 0.9777 | 0.9777 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
