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---
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}
}
```
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