ner_model

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

• eval_loss: 1.0047
• eval_model_preparation_time: 0.0051
• eval_precision: 0.0553
• eval_recall: 0.1916
• eval_f1: 0.0858
• eval_accuracy: 0.6607
• eval_runtime: 4.1414
• eval_samples_per_second: 227.217
• eval_steps_per_second: 28.493
• step: 0

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: 8
• 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
• num_epochs: 3

Framework versions

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