--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: token_classification_NER results: [] --- # token_classification_NER This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2840 - Precision: 0.5572 - Recall: 0.3930 - F1: 0.4609 - Accuracy: 0.9470 ## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2875 | 0.6047 | 0.2651 | 0.3686 | 0.9398 | | No log | 2.0 | 426 | 0.2644 | 0.5878 | 0.3133 | 0.4087 | 0.9431 | | 0.181 | 3.0 | 639 | 0.2795 | 0.5697 | 0.3485 | 0.4324 | 0.9459 | | 0.181 | 4.0 | 852 | 0.2831 | 0.5650 | 0.3707 | 0.4477 | 0.9467 | | 0.0548 | 5.0 | 1065 | 0.2840 | 0.5572 | 0.3930 | 0.4609 | 0.9470 | ### Framework versions - Transformers 4.53.3 - Pytorch 2.9.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4