xlm-roberta-large

This model is a fine-tuned version of xlm-roberta-large on the None dataset.

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Date	Loc	Org	Per	Price	Product	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	100	0.0445	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}	{'precision': 0.8984375, 'recall': 0.9274193548387096, 'f1': 0.9126984126984127, 'number': 124}	{'precision': 0.8448275862068966, 'recall': 0.8305084745762712, 'f1': 0.8376068376068375, 'number': 59}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 70}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79}	{'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13}	0.9406	0.9479	0.9442	0.9859

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0