| --- |
| language: en |
| tags: |
| - token-classification |
| - named-entity-recognition |
| - ner |
| - bert |
| - conll2003 |
| datasets: |
| - conll2003 |
| metrics: |
| - seqeval |
| model-index: |
| - name: named_entity-recognition |
| results: |
| - task: |
| type: token-classification |
| dataset: |
| name: CoNLL-2003 |
| type: conll2003 |
| metrics: |
| - type: f1 |
| value: 0.9116 |
| - type: precision |
| value: 0.9041 |
| - type: recall |
| value: 0.9192 |
| --- |
| |
| # BERT Fine-Tuned for Named Entity Recognition (CoNLL-2003) |
|
|
| This model recognizes named entities in English text: **People**, **Organizations**, |
| **Locations**, and **Miscellaneous** entities. |
|
|
| ## Model Details |
|
|
| - **Base model:** bert-base-cased |
| - **Dataset:** CoNLL-2003 (14,041 training sentences from Reuters news) |
| - **Task:** Named Entity Recognition (token classification) |
| - **Framework:** PyTorch + HuggingFace Transformers |
|
|
| ## Entity Types |
|
|
| | Label | Meaning | Example | |
| |-------|---------|---------| |
| | PER | Person names | Barack Obama, Elon Musk | |
| | ORG | Organizations | Apple Inc., United Nations | |
| | LOC | Locations | New York, Mount Everest | |
| | MISC | Miscellaneous | English, FIFA World Cup | |
|
|
| ## Performance (CoNLL-2003 Test Set) |
|
|
| | Metric | Score | |
| |--------|-------| |
| | F1 Score | 0.9116 | |
| | Precision | 0.9041 | |
| | Recall | 0.9192 | |
| | Accuracy | 0.9827 | |
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import pipeline |
| |
| # Load the model |
| ner = pipeline( |
| "token-classification", |
| model="samandar1105/named_entity-recognition", |
| aggregation_strategy="simple" |
| ) |
| |
| # Run inference |
| result = ner("Elon Musk founded SpaceX in Hawthorne, California.") |
| print(result) |
| # [ |
| # {'entity_group': 'PER', 'word': 'Elon Musk', 'score': 0.998}, |
| # {'entity_group': 'ORG', 'word': 'SpaceX', 'score': 0.997}, |
| # {'entity_group': 'LOC', 'word': 'Hawthorne', 'score': 0.995}, |
| # {'entity_group': 'LOC', 'word': 'California', 'score': 0.994}, |
| # ] |
| ``` |
|
|
| ## Training Details |
|
|
| - Learning rate: 2e-5 |
| - Epochs: 4 |
| - Batch size: 16 |
| - Max sequence length: 128 |
| - Warmup ratio: 0.1 |
| - Weight decay: 0.01 |
| - Label alignment: First-subword strategy with -100 for continuation subwords |
| - Evaluation: seqeval (entity-level strict span matching) |
|
|
