xlm-r-argumentClassification-english

• eval_loss: 0.8801
• eval_model_preparation_time: 0.0089
• eval_accuracy: 0.9064
• eval_w_accuracy: 0.3459
• eval_classification_report: {'None': {'precision': 0.027777777777777776, 'recall': 0.18, 'f1-score': 0.0481283422459893, 'support': 50.0}, 'S': {'precision': 0.9805266222423498, 'recall': 0.9339413359625339, 'f1-score': 0.9566671926779233, 'support': 16228.0}, 'A': {'precision': 0.09879518072289156, 'recall': 0.4659090909090909, 'f1-score': 0.16302186878727634, 'support': 176.0}, 'P': {'precision': 0.4457831325301205, 'recall': 0.32342657342657344, 'f1-score': 0.37487335359675783, 'support': 572.0}, 'accuracy': 0.9063784799718079, 'macro avg': {'precision': 0.3882206783182849, 'recall': 0.4758192500745495, 'f1-score': 0.38567268932698673, 'support': 17026.0}, 'weighted avg': {'precision': 0.9506490554594267, 'recall': 0.9063784799718079, 'f1-score': 0.9262493261513888, 'support': 17026.0}}
• eval_runtime: 8.9103
• eval_samples_per_second: 106.955
• eval_steps_per_second: 13.468
• step: 0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• 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