argha9177/bert-squad-qa

BERT SQuAD Question Answering Model

A fine-tuned version of bert-base-uncased on SQuAD v1.1 for extractive question answering.

This model finds answer spans directly within a provided context paragraph. It does not generate new text — the answer must exist in the context.

Model Performance

Evaluated on 1000 examples from the SQuAD v1.1 validation set:

Metric	Score
Exact Match (EM)	61.20
F1 Score	76.25

How to Use

from transformers import pipeline

qa = pipeline("question-answering", model="argha9177/bert-squad-qa")

result = qa(
    question="What is the capital of France?",
    context="France is a country in Western Europe. Its capital city is Paris."
)
print(result)
# {'answer': 'Paris', 'score': 0.98, 'start': 58, 'end': 63}

Input Format

• question: The question to answer (string)
• context: The paragraph containing the answer (string)
• The answer must exist verbatim within the context
• Max combined input length: 384 tokens
• Longer contexts are handled automatically via sliding window (stride=128)

Training Details

Parameter	Value
Base model	bert-base-uncased
Dataset	rajpurkar/squad (v1.1)
Training samples	8000
Epochs	2
Batch size	16
Learning rate	3e-05
Max length	384
Doc stride	128
Warmup ratio	0.1
Optimizer	AdamW with linear LR decay