bert-finetuned-for-medical-ner

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bert-finetuned-for-medical-ner

This model is a fine-tuned version of google-bert/bert-base-uncased on the PLODv2-filtered dataset. It achieves the following results on the evaluation set:

• Loss: 0.1344
• Accuracy: 0.9402
• Precision: 0.8342
• Recall: 0.9029
• F1: 0.8672

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: 8
• eval_batch_size: 2
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2000
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1