--- license: cc-by-4.0 base_model: dicta-il/dictabert tags: - generated_from_trainer datasets: - nemo_corpus metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: nemo_corpus type: nemo_corpus config: flat_token split: validation args: flat_token metrics: - name: Precision type: precision value: 0.8606811145510835 - name: Recall type: recall value: 0.852760736196319 - name: F1 type: f1 value: 0.8567026194144837 - name: Accuracy type: accuracy value: 0.9786301369863014 --- # bert-finetuned-ner This model is a fine-tuned version of [dicta-il/dictabert](https://huggingface.co/dicta-il/dictabert) on the nemo_corpus dataset. It achieves the following results on the evaluation set: - Loss: 0.1102 - Precision: 0.8607 - Recall: 0.8528 - F1: 0.8567 - Accuracy: 0.9786 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2884 | 1.0 | 618 | 0.1202 | 0.8182 | 0.8006 | 0.8093 | 0.9733 | | 0.0896 | 2.0 | 1236 | 0.1081 | 0.8298 | 0.8374 | 0.8336 | 0.9771 | | 0.0548 | 3.0 | 1854 | 0.1102 | 0.8607 | 0.8528 | 0.8567 | 0.9786 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cpu - Datasets 2.15.0 - Tokenizers 0.15.0