metadata
license: apache-2.0
pipeline_tag: question-answering
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'])