bert-biobert

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

• Loss: 0.1159
• Precision: 0.9239
• Recall: 0.9548
• F1: 0.9391
• Accuracy: 0.9698

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: Use 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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.5326	1.0	612	0.1450	0.9196	0.9350	0.9272	0.9642
0.175	2.0	1224	0.1159	0.9239	0.9548	0.9391	0.9698

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3