CeLLaTe-tapt-pubmedbert-tokenizer-original-baseline-combinedData

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

• Loss: 0.9904
• Accuracy: 0.7745
• Perplexity: 2.6923

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
1.1406	1.0	28	1.0306	0.7735	2.8028
1.1214	2.0	56	0.9953	0.7771	2.7055
1.1017	3.0	84	0.9777	0.7752	2.6582
1.0798	4.0	112	0.9868	0.7746	2.6826
1.0642	5.0	140	0.9592	0.7816	2.6096
1.0604	6.0	168	0.9888	0.7742	2.6880
1.0397	7.0	196	0.9889	0.7761	2.6884
1.039	8.0	224	0.9815	0.7780	2.6683
1.0295	9.0	252	0.9945	0.7729	2.7035
1.0088	10.0	280	0.9534	0.7820	2.5945
1.0021	11.0	308	0.9779	0.7746	2.6590
0.9926	12.0	336	0.9840	0.7751	2.6750
0.9761	13.0	364	0.9873	0.7769	2.6840
0.9724	14.0	392	0.9602	0.7809	2.6121
0.9665	15.0	420	0.9978	0.7726	2.7123

Framework versions

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