I want to fine-tune a model on SageMaker to automatically answer questions based on a given context. I’ve heard about using datasets like SQuAD, but as I understand it, those are extractive, meaning the answer has to be a specific line or passage from the context. Instead, I’m looking to implement an abstractive approach, where the model can generate answers by synthesizing information from multiple parts of the context and applying reasoning.
Hello,
Choose the Right Pretrained Model
Use a model designed for sequence-to-sequence tasks, such as T5 (Text-to-Text Transfer Transformer) or BART (Bidirectional and Auto-Regressive Transformer). These models are particularly well-suited for generating natural language responses.
2. Dataset Selection
Look for datasets suitable for abstractive question answering, such as:
NarrativeQA: Requires generating summaries or answers from long narrative texts.
HotpotQA: Includes questions that demand reasoning across multiple contexts.
Custom Dataset: If no dataset matches your requirements, create your own, ensuring it contains questions, corresponding contexts, and human-generated abstractive answers.
3. Dataset Preparation
Format your dataset to pair questions with their contexts and reference answers.
Structure the data to guide the model effectively, ensuring contexts are detailed and answers are representative of the reasoning expected.
4. Preprocess the Data
Tokenize the input text and reference answers according to the tokenizer of the chosen model.
Ensure the length of input sequences does not exceed the model’s maximum token limit.
5. Fine-Tuning on SageMaker
Environment Setup: Configure SageMaker with the appropriate compute instance type (e.g., GPU-based instances like ml.p3 or ml.g4).
Data Handling: Upload your prepared dataset to an S3 bucket to make it accessible for SageMaker.
Training Script: Create a script that leverages a library like Hugging Face’s transformers to load the model and dataset, configure hyperparameters, and fine-tune the model.
6. Hyperparameter Tuning
Experiment with parameters such as learning rate, batch size, and the number of training epochs to optimize performance.
Consider using SageMaker’s hyperparameter optimization feature for automated tuning.
7. Validation and Evaluation
Split the dataset into training, validation, and test sets.
During training, monitor metrics such as BLEU, ROUGE, or METEOR to assess the quality of generated answers.
8. Deploy the Fine-Tuned Model
Once training is complete, deploy the model as an endpoint on SageMaker.
Use the endpoint for inference, where you can input a question and context to receive an abstractive answer.
9. Post-Deployment Testing
Test the endpoint with various questions and contexts to validate the quality of the generated answers. ForAgentsOnly
Gather user feedback and use it to iteratively refine the model if needed.
10. Maintenance and Updates
Regularly monitor the model’s performance and update it with new data or improved configurations to maintain relevance and accuracy.
Best Regards