| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conll2003 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: bert-ner-conll2003 |
| results: |
| - task: |
| type: token-classification |
| name: Token Classification |
| dataset: |
| name: conll2003 |
| type: conll2003 |
| config: conll2003 |
| split: validation |
| args: conll2003 |
| metrics: |
| - type: precision |
| value: 0.9414244508542268 |
| name: Precision |
| - type: recall |
| value: 0.9493231905134802 |
| name: Recall |
| - type: f1 |
| value: 0.9453573218960619 |
| name: F1 |
| - type: accuracy |
| value: 0.9865601220074031 |
| name: Accuracy |
| --- |
| |
| <!-- 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-ner-conll2003 |
|
|
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0631 |
| - Precision: 0.9414 |
| - Recall: 0.9493 |
| - F1: 0.9454 |
| - Accuracy: 0.9866 |
|
|
| ## Model description |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForTokenClassification |
| from transformers import pipeline |
| |
| tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") |
| model = AutoModelForTokenClassification.from_pretrained("PassbyGrocer/bert-ner-conll2003") |
| |
| nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
| example = "My name is Wolfgang and I live in Berlin." |
| |
| ner_results = nlp(example) |
| print(ner_results) |
| |
| ``` |
|
|
| ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0844 | 1.0 | 878 | 0.0693 | 0.9029 | 0.9201 | 0.9114 | 0.9806 | |
| | 0.0216 | 2.0 | 1756 | 0.0559 | 0.9340 | 0.9444 | 0.9391 | 0.9854 | |
| | 0.0206 | 3.0 | 2634 | 0.0569 | 0.9436 | 0.9447 | 0.9442 | 0.9863 | |
| | 0.0141 | 4.0 | 3512 | 0.0634 | 0.9369 | 0.9488 | 0.9428 | 0.9860 | |
| | 0.0176 | 5.0 | 4390 | 0.0631 | 0.9414 | 0.9493 | 0.9454 | 0.9866 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.46.1 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.1 |
|
|
