--- license: apache-2.0 base_model: distilbert/distilbert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-NER results: [] --- # bert-base-NER This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2957 - Precision: 0.4291 - Recall: 0.4305 - F1: 0.4298 - Accuracy: 0.9095 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 56 | 0.3666 | 0.2951 | 0.0309 | 0.0559 | 0.8777 | | No log | 2.0 | 112 | 0.3045 | 0.4456 | 0.3791 | 0.4096 | 0.9088 | | No log | 3.0 | 168 | 0.2957 | 0.4291 | 0.4305 | 0.4298 | 0.9095 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2