metadata

library_name: transformers
license: apache-2.0
base_model: bert-large-uncased
tags:
  - generated_from_trainer
model-index:
  - name: t5-gpt_par_bert_large_uncased_finetuned_v2
    results: []

t5-gpt_par_bert_large_uncased_finetuned_v2

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: 0.0019
eval_f1_micro: 0.9984
eval_roc_auc_micro: 1.0000
eval_accuracy: 0.9967
eval_precision_micro: 0.9990
eval_recall_micro: 0.9978
eval_f1_macro: 0.9985
eval_roc_auc_macro: 1.0000
eval_accuracy1: 0.9967
eval_precision_macro: 0.9992
eval_recall_macro: 0.9978
eval_pr_auc_micro: 1.0000
eval_pr_auc_macro: 1.0000
eval_runtime: 57.9074
eval_samples_per_second: 41.929
eval_steps_per_second: 5.25
epoch: 3.4250
step: 4158

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: 5

Framework versions

Transformers 4.48.2
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0