CeLLaTe-tapt-pubmedbert-tokenizer-original-baseline

This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on the Mardiyyah/TAPT_CeLLaTe2.0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.9726
• Accuracy: 0.7783
• Perplexity: 2.6448

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 3407
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Perplexity
1.1444	1.0	14	1.0112	0.7749	2.7489
1.1264	2.0	28	0.9842	0.7806	2.6757
1.118	3.0	42	0.9774	0.7810	2.6574
1.0848	4.0	56	0.9892	0.7781	2.6891
1.0813	5.0	70	0.9697	0.7861	2.6372
1.0682	6.0	84	0.9910	0.7782	2.6938
1.0576	7.0	98	0.9641	0.7807	2.6225
1.0377	8.0	112	0.9392	0.7880	2.5580
1.0206	9.0	126	0.9936	0.7765	2.7009
1.0254	10.0	140	0.9782	0.7797	2.6595
0.9976	11.0	154	0.9627	0.7833	2.6188
0.9818	12.0	168	0.9549	0.7833	2.5985
0.9706	13.0	182	0.9498	0.7855	2.5851

Framework versions

• Transformers 4.48.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.2
• Tokenizers 0.21.0