| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-cased |
| tags: |
| - generated_from_trainer |
| - token-classification |
| - ner |
| - bert |
| datasets: |
| - eriktks/conll2003 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: bert-finetuned-ner |
| results: [] |
| language: |
| - en |
| --- |
| |
|
|
| # BERT fine-tuned for Named Entity Recognition (CoNLL-2003) |
|
|
| A fine-tuned version of [`bert-base-cased`](https://huggingface.co/bert-base-cased) |
| for Named Entity Recognition (NER), trained on the CoNLL-2003 English dataset as part |
| of working through the [Hugging Face LLM Course](https://huggingface.co/learn/llm-course), |
| Chapter 7. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0599 |
| - Precision: 0.9319 |
| - Recall: 0.9507 |
| - F1: 0.9412 |
| - Accuracy: 0.9867 |
|
|
| ## Model details |
|
|
| | Attribute | Value | |
| |------------------|-------------------------------| |
| | Base model | `bert-base-cased` | |
| | Architecture | Transformer Encoder (BERT) | |
| | Task | Token Classification (NER) | |
| | Training dataset | CoNLL-2003 (English) | |
| | Training epochs | 3 | |
| | Learning rate | 2e-5 | |
| | Weight decay | 0.01 | |
| | Hardware | Google Colab (T4 GPU) | |
|
|
| ## Entity types |
|
|
| The model recognises four entity types in IOB2 format: |
|
|
| | Label | Description | |
| |--------|---------------| |
| | PER | Person | |
| | ORG | Organisation | |
| | LOC | Location | |
| | MISC | Miscellaneous | |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import pipeline |
| |
| ner = pipeline( |
| "token-classification", |
| model="AlexStamp/bert-finetuned-ner", |
| aggregation_strategy="simple" |
| ) |
| |
| ner("Alexis works at CERN in Switzerland.") |
| ``` |
|
|
| ## Training procedure |
|
|
| Fine-tuning was performed using the Hugging Face `Trainer` API with |
| `DataCollatorForTokenClassification` and evaluated using the `seqeval` |
| library, which computes entity-level F1 — stricter than token-level accuracy |
| since the entire entity span must be correctly identified. |
|
|
| ### 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0759 | 1.0 | 1756 | 0.0651 | 0.8905 | 0.9310 | 0.9103 | 0.9812 | |
| | 0.0355 | 2.0 | 3512 | 0.0681 | 0.9321 | 0.9473 | 0.9397 | 0.9853 | |
| | 0.0224 | 3.0 | 5268 | 0.0599 | 0.9319 | 0.9507 | 0.9412 | 0.9867 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.12.0 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
| |
| ## Limitations |
| |
| - Trained on English news wire text (Reuters corpus); may generalise poorly |
| to other domains or languages |
| - `bert-base-cased` is case-sensitive by design, which is appropriate for NER |
| but means casing errors in input text can degrade performance |
| |
| ## Notes |
| |
| This model was trained as a portfolio exercise. The base model choice |
| (`bert-base-cased` over `bert-base-uncased`) is deliberate — NER is |
| case-sensitive since capitalisation is a strong signal for entity detection. |
