Roberta Large Squad2

🚀 Roberta large model for QA (SQuAD 2.0)

This model leverages the roberta-large architecture to address question-answering tasks, trained on the SQuAD 2.0 dataset.

🚀 Quick Start

This Roberta large model is designed for question-answering tasks. It's trained on the SQuAD 2.0 dataset and can be easily integrated into your projects.

✨ Features

• Model Type: Utilizes the roberta-large architecture.
• Training Data: Trained on the SQuAD 2.0 dataset.
• Functionality: Capable of handling question-answering tasks effectively.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

# Load model & tokenizer
roberta_model = AutoModelForQuestionAnswering.from_pretrained('navteca/roberta-large-squad2')
roberta_tokenizer = AutoTokenizer.from_pretrained('navteca/roberta-large-squad2')

# Get predictions
nlp = pipeline('question-answering', model=roberta_model, tokenizer=roberta_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,
#}

📄 License

This project is licensed under the MIT license.