xlm-r-argumentClassification-hindi

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

• eval_loss: 0.6166
• eval_model_preparation_time: 0.0071
• eval_accuracy: 0.9231
• eval_w_accuracy: 0.2732
• eval_classification_report: {'None': {'precision': 0.04666666666666667, 'recall': 0.14, 'f1-score': 0.07, 'support': 50.0}, 'S': {'precision': 0.9689883706389896, 'recall': 0.9550160216909046, 'f1-score': 0.9619514617342189, 'support': 16228.0}, 'A': {'precision': 0.10362694300518134, 'recall': 0.22727272727272727, 'f1-score': 0.1423487544483986, 'support': 176.0}, 'P': {'precision': 0.34475806451612906, 'recall': 0.29895104895104896, 'f1-score': 0.3202247191011236, 'support': 572.0}, 'accuracy': 0.9230588511688007, 'macro avg': {'precision': 0.36601001120674165, 'recall': 0.4053099494786702, 'f1-score': 0.37363123382093527, 'support': 17026.0}, 'weighted avg': {'precision': 0.9363630075728294, 'recall': 0.9230588511688007, 'f1-score': 0.9293004957789066, 'support': 17026.0}}
• eval_runtime: 9.1554
• eval_samples_per_second: 104.092
• eval_steps_per_second: 13.107
• 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