| | --- |
| | 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: |
| |
|
| | ```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']) |
