This repository demonstrates how to
fine-tune
and
quantize
the
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
— Stanford Question Answering Dataset (Hugging Face Datasets)
📦 Dataset Used
We use the
squad
dataset from Hugging Face:
pip install datasets
Dataset
from datasets import load_dataset
dataset = load_dataset("squad")
Load Model & Tokenizer:
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)
Runs of AventIQ-AI Question-Answers-Roberta-Model on huggingface.co
12
Total runs
0
24-hour runs
1
3-day runs
3
7-day runs
11
30-day runs
More Information About Question-Answers-Roberta-Model huggingface.co Model
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Question-Answers-Roberta-Model install url in huggingface.co: