QA-SQuAD-BERT

A BERT-based model fine-tuned on SQuAD v1.1 for extractive QA

Model Description

This model is based on bert-base-uncased and was fine-tuned on the SQuAD v1.1 dataset for extractive question answering. It takes a question and a context passage as input and predicts the span of text in the passage that most likely answers the question.

The model was trained using the Hugging Face 🤗 Transformers library.

Intended Uses & Limitations

Intended Uses

Extractive question answering on Wikipedia-style passages.
As a downstream component in information retrieval pipelines.
Educational purposes or experimentation with fine-tuning on QA tasks.

Limitations

The model may not generalize well to out-of-domain datasets.
It does not handle unanswerable questions (not trained on SQuAD v2.0).
It may produce incorrect or misleading answers if context is ambiguous.

Training Details

Base model: bert-base-uncased
Dataset: SQuAD v1.1
Epochs: 3
Batch size: 8
Learning rate: 2e-5
Optimizer: AdamW
Max length: 384
Hardware used: Colab/GPU T4

Evaluation Results

The model was evaluated on the SQuAD v1.1 development set using the standard metrics: Exact Match (EM) and F1.

Metric	Score
Exact Match	82.7
F1	87.0039

How to Use

You can load this model using the pipeline API:

from transformers import pipeline

qa_pipeline = pipeline("question-answering", model="tmt3103/SQuAD_BERT")
result = qa_pipeline({
    "context": "Hugging Face is creating a tool that democratizes AI.",
    "question": "What is Hugging Face creating?"
})
print(result)

tmt3103
/

SQuAD_BERT