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Mardiyyah/
CeLLaTe-tapt-pubmedbert-tokenizer-adapted-wwmask

Name: CeLLaTe-tapt-pubmedbert-tokenizer-adapted-wwmask
Rating: 2.5 (74 reviews)

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CeLLaTe-tapt-pubmedbert-tokenizer-adapted-wwmask

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: 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 Loss	Epoch	Step	Validation Loss	Accuracy	Perplexity
1.4325	1.0	14	1.2906	0.7533	3.6351
1.4038	2.0	28	1.2569	0.7560	3.5146
1.3795	3.0	42	1.2207	0.7535	3.3895
1.3515	4.0	56	1.1725	0.7632	3.2301
1.3173	5.0	70	1.1807	0.7625	3.2565
1.271	6.0	84	1.1901	0.7561	3.2874
1.2655	7.0	98	1.1605	0.7615	3.1915
1.2681	8.0	112	1.1396	0.7630	3.1255
1.2362	9.0	126	1.1118	0.7709	3.0397
1.2196	10.0	140	1.1419	0.7619	3.1328
1.2191	11.0	154	1.1508	0.7569	3.1606
1.1892	12.0	168	1.1398	0.7642	3.1263
1.1819	13.0	182	1.1509	0.7616	3.1612
1.1469	14.0	196	1.1664	0.7601	3.2103