CeLLaTe-tapt-pubmedbert-tokenizer-adapted-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: 1.1076
• Accuracy: 0.7643
• Perplexity: 3.0272

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
1.3684	1.0	14	1.2141	0.7641	3.3674
1.3406	2.0	28	1.1774	0.7648	3.2459
1.3246	3.0	42	1.1310	0.7689	3.0988
1.2929	4.0	56	1.1271	0.7675	3.0866
1.2489	5.0	70	1.1542	0.7641	3.1716
1.2216	6.0	84	1.0947	0.7733	2.9882
1.1987	7.0	98	1.0540	0.7750	2.8691
1.2006	8.0	112	1.0942	0.7706	2.9868
1.1659	9.0	126	1.0899	0.7701	2.9741
1.1546	10.0	140	1.0596	0.7757	2.8851
1.1216	11.0	154	1.0536	0.7735	2.8680
1.1113	12.0	168	1.0336	0.7779	2.8111
1.0872	13.0	182	1.0709	0.7716	2.9179
1.1014	14.0	196	1.0786	0.7746	2.9406
1.0882	15.0	210	1.0788	0.7727	2.9413
1.0683	16.0	224	1.0398	0.7796	2.8287
1.0612	17.0	238	1.0620	0.7712	2.8920

Framework versions

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