| --- |
| 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 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # 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 |
| |
