| # 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: | |
| ```python | |
| 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']) | |