Deberta V3 Large Squad2

🚀 Deberta v3 large model for QA (SQuAD 2.0)

This is a fine - tuned [deberta - v3 - large](https://huggingface.co/microsoft/deberta - v3 - large) model using the SQuAD2.0 dataset. It's designed for the question - answering task, trained on question - answer pairs including unanswerable questions.

✨ Features

• Fine - tuned Model: Based on [deberta - v3 - large](https://huggingface.co/microsoft/deberta - v3 - large), fine - tuned with the SQuAD2.0 dataset.
• Question - Answering Task: Trained on question - answer pairs, capable of handling unanswerable questions.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

# Load model & tokenizer
deberta_model = AutoModelForQuestionAnswering.from_pretrained('navteca/deberta-v3-large-squad2')
deberta_tokenizer = AutoTokenizer.from_pretrained('navteca/deberta-v3-large-squad2')

# Get predictions
nlp = pipeline('question-answering', model=deberta_model, tokenizer=deberta_tokenizer)

result = nlp({
    'question': 'How many people live in Berlin?',
    'context': 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'
})

print(result)

#{
#  "answer": "3,520,031"
#  "end": 36,
#  "score": 0.96186668,
#  "start": 27,
#}

📚 Documentation

Training Data

The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD - explorer/) dataset. It can be used for the question - answering task.

Model Performance

📄 License

This project is under the MIT license.

👨‍💻 Author

deepset