| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: huggingface-course/bert-finetuned-ner |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: ner_model_output |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # ner_model_output |
|
|
| This model is a fine-tuned version of [huggingface-course/bert-finetuned-ner](https://huggingface.co/huggingface-course/bert-finetuned-ner) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2567 |
| - Precision: 0.6421 |
| - Recall: 0.6747 |
| - F1: 0.6580 |
| - Accuracy: 0.9242 |
|
|
| ## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.4135 | 1.0 | 1756 | 0.3290 | 0.5395 | 0.5106 | 0.5246 | 0.8946 | |
| | 0.2314 | 2.0 | 3512 | 0.2702 | 0.6515 | 0.6254 | 0.6382 | 0.9213 | |
| | 0.1599 | 3.0 | 5268 | 0.2567 | 0.6421 | 0.6747 | 0.6580 | 0.9242 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.14.1 |
| - Pytorch 2.13.0+cu130 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
| |
