| --- |
| language: en |
| tags: |
| - question-answering |
| - bert |
| - squad |
| - extractive-qa |
| datasets: |
| - squad |
| metrics: |
| - exact_match |
| - f1 |
| model-index: |
| - name: Question_Answering |
| results: |
| - task: |
| type: question-answering |
| dataset: |
| name: SQuAD v1.1 |
| type: squad |
| metrics: |
| - type: exact_match |
| value: 81.0501 |
| - type: f1 |
| value: 88.5526 |
| --- |
| |
| # BERT Fine-Tuned on SQuAD (Extractive Question Answering) |
|
|
| This model extracts answers to questions directly from a provided text passage. |
| It was fine-tuned from `bert-base-cased` on the SQuAD v1.1 dataset. |
|
|
| ## How It Works |
|
|
| Given a **context** (a paragraph of text) and a **question**, the model finds |
| and returns the exact span of text in the context that answers the question. |
|
|
| ## Performance |
|
|
| | Metric | Score | |
| |--------|-------| |
| | Exact Match | 81.05% | |
| | F1 Score | 88.55% | |
|
|
| *(Evaluated on SQuAD v1.1 validation set — 10,570 examples)* |
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import pipeline |
| |
| qa = pipeline( |
| "question-answering", |
| model="samandar1105/Question_Answering" |
| ) |
| |
| result = qa( |
| question="Who designed the Eiffel Tower?", |
| context="The Eiffel Tower was designed by Gustave Eiffel and built between 1887 and 1889 in Paris." |
| ) |
| print(result) |
| # {'answer': 'Gustave Eiffel', 'score': 0.99, 'start': 31, 'end': 45} |
| ``` |
|
|
| ## Training Details |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Base model | bert-base-cased | |
| | Dataset | SQuAD v1.1 (87,599 train / 10,570 val) | |
| | Learning rate | 2e-5 | |
| | Epochs | 3 | |
| | Batch size | 16 | |
| | Max sequence length | 384 | |
| | Stride | 128 | |
| | Framework | PyTorch + HuggingFace Transformers | |
|
|
