bertin-roberta-fine-tuned-text-classification-SL-data-augmentation-ds

This model is a fine-tuned version of bertin-project/bertin-roberta-base-spanish on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 1.9552
• eval_f1: 0.6062
• eval_recall: 0.5982
• eval_accuracy: 0.5982
• eval_precision: 0.6312
• eval_runtime: 15.886
• eval_samples_per_second: 99.647
• eval_steps_per_second: 6.232
• epoch: 6.0
• step: 2772

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3