---
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 |