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EXP_2_MULTI_DISEASE_ANATOMY_FULL-microsoft-BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext – AI Model by irebil | AlphaNeural AI
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EXP_2_MULTI_DISEASE_ANATOMY_FULL-microsoft-BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
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transformers
safetensors
bert
token-classification
generated_from_trainer
microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
finetune
mit
endpoints_compatible
us
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EXP_4_MULTI_DISEASE_ANATOMY_FULL-microsoft-BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
This model is a fine-tuned version of
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.1025
Precision: 0.7177
Recall: 0.7110
F1: 0.7143
Accuracy: 0.9718
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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
0.1365
1.0
220
0.1269
0.6794
0.6365
0.6572
0.9678
0.1028
2.0
440
0.1061
0.7142
0.6852
0.6994
0.9709
0.0937
3.0
660
0.1025
0.7177
0.7110
0.7143
0.9718
Framework versions
Transformers 4.57.2
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1
