CeLLaTe-tapt-pubmedbert-tokenizer-adapted-spanmask

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: 2.3029
• Accuracy: 0.5761
• Perplexity: 10.0034

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
3.0089	1.0	14	2.6409	0.5507	14.0253
2.7347	2.0	28	2.4055	0.5698	11.0835
2.6256	3.0	42	2.4142	0.5723	11.1810
2.5855	4.0	56	2.3657	0.5720	10.6515
2.5716	5.0	70	2.3596	0.5713	10.5871
2.5212	6.0	84	2.3339	0.5698	10.3177
2.4673	7.0	98	2.3420	0.5751	10.4017
2.4827	8.0	112	2.2801	0.5824	9.7779
2.4762	9.0	126	2.2619	0.5839	9.6009
2.457	10.0	140	2.2566	0.5845	9.5509
2.4403	11.0	154	2.3328	0.5761	10.3072
2.3889	12.0	168	2.2309	0.5902	9.3085
2.3968	13.0	182	2.2488	0.5802	9.4764
2.3618	14.0	196	2.2365	0.5814	9.3607
2.3805	15.0	210	2.2719	0.5776	9.6975
2.348	16.0	224	2.3000	0.5791	9.9740
2.3436	17.0	238	2.2701	0.5824	9.6807

Framework versions

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