--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: bert-finetuned-ner results: [] --- # 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
