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

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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:

• —Loss: 1.2144
• —Accuracy: 0.7569
• —Perplexity: 3.3682

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
• —trainbatchsize: 32
• —evalbatchsize: 32
• —seed: 3407
• —gradientaccumulationsteps: 2
• —totaltrainbatch_size: 64
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_ratio: 0.06
• —num_epochs: 100
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

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