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Mardiyyah/
CeLLaTe-tapt-pubmedbert-tokenizer-adapted_v2_lr-2e5

Name: CeLLaTe-tapt-pubmedbert-tokenizer-adapted_v2_lr-2e5
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CeLLaTe-tapt-pubmedbert-tokenizer-adapted_v2_lr-2e5

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:

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The following hyperparameters were used during training:

learning_rate: 2e-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 Loss	Epoch	Step	Validation Loss	Accuracy	Perplexity
1.3593	1.0	14	1.2054	0.7589	3.3382
1.3738	2.0	28	1.2002	0.7611	3.3209
1.3468	3.0	42	1.1980	0.7585	3.3135
1.3319	4.0	56	1.2181	0.7557	3.3808
1.3085	5.0	70	1.2109	0.7581	3.3565
1.2994	6.0	84	1.2024	0.7545	3.3279
1.2836	7.0	98	1.1483	0.7640	3.1530
1.2806	8.0	112	1.2058	0.7517	3.3393
1.254	9.0	126	1.1481	0.7611	3.1522
1.247	10.0	140	1.1739	0.7567	3.2345
1.2627	11.0	154	1.1571	0.7559	3.1807
1.2227	12.0	168	1.1721	0.7587	3.2287
1.2235	13.0	182	1.2401	0.7522	3.4560
1.2163	14.0	196	1.1925	0.7567	3.2953