--- library_name: transformers license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: [] --- # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9345 - Recall: 0.9512 - F1: 0.9428 - Accuracy: 0.9870 ## 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 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 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0735 | 1.0 | 1756 | 0.0654 | 0.9003 | 0.9349 | 0.9173 | 0.9827 | | 0.0331 | 2.0 | 3512 | 0.0679 | 0.9240 | 0.9426 | 0.9332 | 0.9851 | | 0.0214 | 3.0 | 5268 | 0.0604 | 0.9345 | 0.9512 | 0.9428 | 0.9870 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Tokenizers 0.20.3