Create README.md

README.md

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+# 📝 Question Answers Roberta Model
+
+This repository demonstrates how to **fine-tune** and **quantize** the [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) model for Question Answering using a sample dataset from Hugging Face Hub.
+
+---
+
+## 🚀 Model Overview
+- **Base Model:** `deepset/roberta-base-squad2`
+- **Task:** Extractive Question Answering  
+- **Precision:** Supports FP32, FP16 (half-precision), and INT8 (quantized)
+- **Dataset:** [`squad`](https://huggingface.co/datasets/squad) — Stanford Question Answering Dataset (Hugging Face Datasets)
+
+---
+
+## 📦 Dataset Used
+We use the **`squad`** dataset from Hugging Face:
+```bash
+pip install datasets
+```
+# Dataset
+```Pyhton
+from datasets import load_dataset
+
+dataset = load_dataset("squad")
+```
+
+# Load Model & Tokenizer:
+
+```python
+
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, TrainingArguments, Trainer
+from datasets import load_dataset
+
+model = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2")
+tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2")
+dataset = load_dataset("squad")
+```
+
+# ✅ Results
+Feature	Benefit
+FP16 Fine-Tuning -	Faster Training + Lower Memory
+INT8 Quantization -	Smaller Model + Fast Inference
+Dataset -	Stanford QA Dataset (SQuAD)