our_awesome_BERT_model

This model is a fine-tuned version of distilbert/distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0567
• Precision: 0.7594
• Recall: 0.7909
• F1: 0.7748
• Accuracy: 0.9825

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
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1072	1.0	784	0.0602	0.7686	0.7360	0.7520	0.9807
0.0271	2.0	1568	0.0567	0.7594	0.7909	0.7748	0.9825

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.0+cpu
• Datasets 2.19.0
• Tokenizers 0.15.2