bert_finetuned_ner_trial

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

bert_finetuned_ner_trial

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

• Loss: 0.0996
• Loc: {'precision': 0.8850174216027874, 'recall': 0.9477611940298507, 'f1': 0.9153153153153153, 'number': 134000}
• Misc: {'precision': 0.44144144144144143, 'recall': 0.5764705882352941, 'f1': 0.5, 'number': 42500}
• Org: {'precision': 0.8516746411483254, 'recall': 0.9128205128205128, 'f1': 0.8811881188118811, 'number': 97500}
• Per: {'precision': 0.9636363636363636, 'recall': 0.9578313253012049, 'f1': 0.9607250755287009, 'number': 166000}
• Overall Precision: 0.8527
• Overall Recall: 0.9080
• Overall F1: 0.8795
• Overall Accuracy: 0.9696

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

Training results

Framework versions

• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.2