--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9263600176912871 - name: Recall type: recall value: 0.9372413021590782 - name: F1 type: f1 value: 0.9317688928432408 - name: Accuracy type: accuracy value: 0.9834781641698572 --- # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0611 - Precision: 0.9264 - Recall: 0.9372 - F1: 0.9318 - Accuracy: 0.9835 ## 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: 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2565 | 1.0 | 878 | 0.0714 | 0.9014 | 0.9214 | 0.9113 | 0.9797 | | 0.0503 | 2.0 | 1756 | 0.0622 | 0.9248 | 0.9309 | 0.9278 | 0.9828 | | 0.0312 | 3.0 | 2634 | 0.0611 | 0.9264 | 0.9372 | 0.9318 | 0.9835 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1