Deberta V3 Xsmall Squad2

🚀 Deberta-v3-xsmall-squad2

This model is designed for question - answering tasks, leveraging the SQuAD dataset to provide accurate answers to various questions.

🚀 Quick Start

Inference

from transformers import pipeline

qa = pipeline("question-answering", model="nlpconnect/deberta-v3-xsmall-squad2")

result = qa(context="My name is Sarah and I live in London", question="Where do I live?")

✨ Features

What is SQuAD?

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset. It consists of questions posed by crowd - workers on a set of Wikipedia articles. For each question, the answer is a text segment, or span, from the corresponding reading passage. Some questions might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions. These unanswerable questions are written adversarially by crowd - workers to look similar to answerable ones. To perform well on SQuAD2.0, systems must not only answer questions when possible but also determine when no answer is supported by the paragraph and refrain from answering.

💻 Usage Examples

Basic Usage

from transformers import pipeline

qa = pipeline("question-answering", model="nlpconnect/deberta-v3-xsmall-squad2")

result = qa(context="My name is Sarah and I live in London", question="Where do I live?")

📚 Documentation

Accuracy

squad_v2 = {'exact': 79.392,
   'f1': 82.674}
   
squad = {'exact': 84.925,
   'f1': 91.620}

📄 License

This project is licensed under the Apache-2.0 license.

📦 Model Information

📊 Results

Task: Question Answering (squad_v2)

Task: Question Answering (squad)