bert-base-uncased-ag-news-finetuned

This model is a fine-tuned version of bert-base-uncased on the ag_news dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.2589
• eval_accuracy: 0.9424
• eval_f1(weighted): 0.9425
• eval_precision(weighted): 0.9427
• eval_recall(weighted): 0.9424
• eval_runtime: 73.6126
• eval_samples_per_second: 326.031
• eval_steps_per_second: 6.792
• epoch: 1.0
• step: 2000

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: 48
• eval_batch_size: 48
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Framework versions

• Transformers 4.34.1
• Pytorch 2.0.1+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1