---
language:
  - en
tags:
  - question-answering
  - transformers
  - bert
  - squad
license: apache-2.0
datasets:
  - squad
model-name: bert-base-uncased-finetuned-squad
library_name: transformers
---

# BERT-Base Uncased Fine-Tuned on SQuAD

## Overview

This repository contains a **BERT-Base Uncased** model fine-tuned on the **SQuAD (Stanford Question Answering Dataset)** for **Question Answering (QA) tasks**. The model has been fine-tuned for **2 epochs**, making it suitable for extracting answers from given contexts by predicting start and end token positions.

## The Model predicts 2 probabilities among all the tokens in the vocab , One indicating the start token and the other indicating the end token, Then the answer between both these tokens are extracted.

## Model Details

- **Model Type**: BERT-Base Uncased
- **Fine-Tuning Dataset**: SQuAD (Stanford Question Answering Dataset)
- **Number of Epochs**: 2
- **Task**: Question Answering
- **Base Model**: [BERT-Base Uncased](https://huggingface.co./bert-base-uncased)

---

## Usage

### How to Load the Model

You can load the model using the `transformers` library from Hugging Face:

```python
from transformers import BertForQuestionAnswering, BertTokenizer

# Load the tokenizer and model
tokenizer = BertTokenizer.from_pretrained("Abdo36/Bert-SquAD-QA")
model = BertForQuestionAnswering.from_pretrained("Abdo36/Bert-SquAD-QA")

context = "BERT is a method of pre-training language representations."
question = "What is BERT?"

inputs = tokenizer.encode_plus(question, context, return_tensors="pt")

# Perform inference
outputs = model(**inputs)
start_scores = outputs.start_logits
end_scores = outputs.end_logits

# Extract answer
start_index = start_scores.argmax()
end_index = end_scores.argmax()
answer = tokenizer.decode(inputs["input_ids"][0][start_index:end_index + 1])

print("Answer:", answer)
```

## Citation

If you use this model in your research, please cite the original BERT paper:

```bibtex
@article{devlin2018bert,
  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}
```