AlexBeneath/text-generation-test

MyQA Model

This model is designed for question answering tasks based on provided text documents.

Model Description

This model can analyze the contents of a text document and generate answers to questions posed by the user. It is built on the [base model type, e.g., BERT, RoBERTa, etc.] architecture and is fine-tuned for the task of question answering.

Intended Use

• —Task Type: Question Answering
• —Use Cases:
• —Answering questions based on the content of documents.
• —Assisting with information retrieval from text sources.
• —Providing summaries or key information extracted from documents.

How to Use

You can use this model with the Hugging Face Transformers library as follows:

from transformers import pipeline

# Load the question-answering pipeline
qa_pipeline = pipeline("question-answering", model="username/myqa")  # Replace with your model path

# Example document
context = """Your text document content here."""
question = "What is the main topic of the document?"

# Generate answer
result = qa_pipeline(question=question, context=context)

# Print the answer
print(result['answer'])