We spend a fortune on manual labeling for our classification models. I’m looking at using the Instructor library to automate this by forcing an LLM to output labels in a strict categorical format. Does the validation logic handle multi-label classification well? I need to ensure the LLM only picks from a predefined list of enums without inventing new tags.
3 answers
Using the Instructor library for synthetic data or labeling is a game changer because of Enum support. You can define your labels as a Python Enum within a Pydantic model, and the library will ensure the LLM strictly adheres to those choices. If the model tries to hallucinate a new category, the Pydantic validation fails, and the library prompts a retry. We used this for sentiment analysis on 50,000 product reviews. By providing clear descriptions within the Pydantic "Field" attributes, we actually achieved an agreement rate with human labelers of over 92%. It effectively turns the LLM into a high-speed, structured labeling engine that follows your domain-specific taxonomy perfectly.
Have you tried using the Instructor library for extracting relationships, like "Subject-Predicate-Object" triples? I’ve found simple classification easy, but nested structures sometimes make the LLM lose the plot during the validation loop.
The best part is that you can add custom validators to the Instructor library. I added a check to ensure the "confidence score" the LLM gives is always between 0 and 1.
That's a great use case, Janet. Adding business logic directly into the validation layer is exactly why this approach beats standard prompting.
Wayne, I've handled nested triples by using the List type in Pydantic. The key is to keep the nested models relatively flat. If the relationship is too complex, I split it into two calls. First, extract the entities, and then use a second call with this library to define the relationships between those specific entities. This "chaining" approach with typed outputs makes the whole machine learning pipeline much more predictable than one giant, unstructured prompt.