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model_outputs
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: 0.5523
- F1: 0.7649
- Recall: 0.7481
- Accuracy: 0.9208
- Precision: 0.8203
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 2800
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Accuracy | Precision |
|---|---|---|---|---|---|---|---|
| 0.6476 | 9.86 | 700 | 0.4384 | 0.6886 | 0.6906 | 0.8971 | 0.7746 |
| 0.0936 | 19.72 | 1400 | 0.4654 | 0.7344 | 0.7215 | 0.9103 | 0.8153 |
| 0.0292 | 29.58 | 2100 | 0.5578 | 0.7475 | 0.7234 | 0.9077 | 0.7881 |
| 0.0191 | 39.44 | 2800 | 0.5523 | 0.7649 | 0.7481 | 0.9208 | 0.8203 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
