t5-gpt_par_bert_large_uncased_finetuned

This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.0051
• eval_f1_micro: 0.9975
• eval_roc_auc_micro: 0.9998
• eval_accuracy: 0.9954
• eval_precision_micro: 0.9987
• eval_recall_micro: 0.9963
• eval_f1_macro: 0.9974
• eval_roc_auc_macro: 0.9998
• eval_accuracy1: 0.9954
• eval_precision_macro: 0.9987
• eval_recall_macro: 0.9962
• eval_runtime: 60.252
• eval_samples_per_second: 39.501
• eval_steps_per_second: 4.946
• epoch: 2.1882
• step: 2604

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 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: 7

Framework versions

• Transformers 4.48.2
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0