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• generated_from_trainer model-index:
• name: t5-finetuned results: []

t5-finetuned

• Loss: 0.2728

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.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: 3

Training results

Framework versions

• Transformers 4.46.3
• Pytorch 2.0.1+cu117
• Datasets 3.1.0
• Tokenizers 0.20.4 =======

• generated_from_trainer model-index:
• name: t5-finetuned results: []

t5-finetuned

• Loss: 0.2728

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.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: 3

Training results

Framework versions

• Transformers 4.46.3
• Pytorch 2.0.1+cu117
• Datasets 3.1.0
• Tokenizers 0.20.4

a25bc89 (Upload fine-tuned T5 model)