Roberta Base Squad2

🚀 Roberta base model for QA (SQuAD 2.0)

This model leverages the Roberta architecture to address question - answering tasks on the SQuAD 2.0 dataset, offering accurate and efficient answers.

🚀 Quick Start

This model uses [roberta - base](https://huggingface.co/roberta - base) and has been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD - explorer/) dataset. It is designed for question - answering tasks.

✨ Features

• Model Type: Roberta base model adapted for question - answering.
• Training Data: [SQuAD 2.0](https://rajpurkar.github.io/SQuAD - explorer/) dataset.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

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

📚 Documentation

The trained model can be used as demonstrated in the code example above. It can take a question and a context as input and return an answer along with relevant metadata such as the start and end positions in the context and a confidence score.

📄 License

This model is released under the MIT license.