ukraine-war-pov
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2307
- Accuracy: 0.9125
- F1: 0.9125
- Precision: 0.9129
- Recall: 0.9125
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: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.3378 | 1.0 | 1360 | 0.3154 | 0.852 | 0.8508 | 0.8633 | 0.852 |
0.3481 | 2.0 | 2720 | 0.2672 | 0.8955 | 0.8954 | 0.8975 | 0.8955 |
0.2835 | 3.0 | 4080 | 0.2708 | 0.905 | 0.9044 | 0.9148 | 0.905 |
0.2975 | 4.0 | 5440 | 0.2364 | 0.911 | 0.9110 | 0.9116 | 0.911 |
0.2419 | 5.0 | 6800 | 0.2307 | 0.9125 | 0.9125 | 0.9129 | 0.9125 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
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