xlm-r-argumentClassification-basque

This model is a fine-tuned version of fromdeath2morning/xlm-r-argumentClassification-basque on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.6626
• eval_model_preparation_time: 0.0123
• eval_accuracy: 0.9248
• eval_w_accuracy: 0.3346
• eval_classification_report: {'None': {'precision': 0.078125, 'recall': 0.1, 'f1-score': 0.08771929824561403, 'support': 50.0}, 'S': {'precision': 0.9726655774789493, 'recall': 0.9538452058171062, 'f1-score': 0.9631634621367681, 'support': 16228.0}, 'A': {'precision': 0.09888357256778309, 'recall': 0.3522727272727273, 'f1-score': 0.15442092154420922, 'support': 176.0}, 'P': {'precision': 0.4750593824228028, 'recall': 0.34965034965034963, 'f1-score': 0.4028197381671702, 'support': 572.0}, 'accuracy': 0.9248208622107366, 'macro avg': {'precision': 0.4061833831173838, 'recall': 0.43894207068504576, 'f1-score': 0.4020308550234404, 'support': 17026.0}, 'weighted avg': {'precision': 0.9442887769791003, 'recall': 0.9248208622107366, 'f1-score': 0.9334073535117557, 'support': 17026.0}}
• eval_runtime: 11.0109
• eval_samples_per_second: 86.551
• eval_steps_per_second: 10.898
• step: 0

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3.0
• mixed_precision_training: Native AMP

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2