| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: bert-finetuned-ner |
| 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. --> |
|
|
| # bert-finetuned-ner |
|
|
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2481 |
| - Model Preparation Time: 0.0021 |
|
|
| ```ipython |
| precision recall f1-score support |
| |
| B-COMMENT 0.88 0.90 0.89 767 |
| B-NAME 0.91 0.91 0.91 1050 |
| B-QTY 0.99 0.99 0.99 836 |
| B-RANGE_END 0.93 1.00 0.96 13 |
| B-UNIT 0.99 0.99 0.99 706 |
| I-COMMENT 0.93 0.96 0.95 1499 |
| I-NAME 0.92 0.84 0.88 572 |
| OTHER 0.88 0.82 0.85 439 |
| |
| accuracy 0.93 5882 |
| macro avg 0.93 0.93 0.93 5882 |
| weighted avg 0.93 0.93 0.93 5882 |
| ``` |
|
|
| ## Usage |
|
|
| ```ipython |
| In [40]: from transformers import pipeline |
| |
| In [41]: |
| |
| In [41]: classifier = pipeline("token-classification", "AXKuhta/bert-finetuned-ner") |
| Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 199/199 [00:00<00:00, 9724.08it/s] |
| |
| In [42]: classifier.tokenizer.tokenize("mozzarella") |
| Out[42]: ['mozzarella'] |
| |
| In [43]: classifier("1 pound broccoli", aggregation_strategy="simple") |
| Out[43]: |
| [{'entity_group': 'QTY', |
| 'score': np.float32(0.9997178), |
| 'word': '1', |
| 'start': 0, |
| 'end': 1}, |
| {'entity_group': 'UNIT', |
| 'score': np.float32(0.99991345), |
| 'word': 'pound', |
| 'start': 2, |
| 'end': 7}, |
| {'entity_group': 'NAME', |
| 'score': np.float32(0.999385), |
| 'word': 'broccoli', |
| 'start': 8, |
| 'end': 16}] |
| ``` |
|
|
| ## Training procedure |
|
|
| See bert_finetune_fin.ipynb |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 4e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - 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: 6 |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.6.2 |
| - Pytorch 2.11.0+cu126 |
| - Datasets 3.6.0 |
| - Tokenizers 0.22.2 |
| |
