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jankrepl avatar jankrepl commented on May 26, 2024 1

Thanks for the reply:) Yeh, providing a couple of examples and hoping the model gets it is probably the most straightforward solution.

As I mentioned, my intuition would be to go for

  1. Having a prompt prefix where one simply copy pastes the definitions and then just continues with the standard prompt

In other words, one would let the user define a CSV table

name definition
A Definition of label A
B Definition of label B
C Definition of label C

and then introduced an option in the CLI e.g. --label-definition-path that would point at the table.

Finally the jinja2 template would contain something like

{% if labels %}
Below are definitions of all labels:
{% for label in labels %}
Text:
"""
label: {{ label.name }}
definition: {{ label.definition }}
"""
{% endfor %}
{% endif %}

{# Now one could have the examples section #}

This would not be compatible with the current labels CLI argument that is a comma separated list of label names (without definitions).

Anyway, I understand that this feature might not be that relevant. I just happen to be dealing with a dataset where the labels need to be defined. Closing the issue.

Thanks for the help:)

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koaning avatar koaning commented on May 26, 2024 1

I'm re-opening this issue because it occurs to me as fair feedback and I'd like to have this ticket around as a reminder.

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koaning avatar koaning commented on May 26, 2024

Have you seen the setting for the --example-path? Here's an example of what you might send along. It's not exactly what you're asking for, but have you tried it? It might be enough.

There's certainly some prompt engineering that you could do here though, which is stuff we're putting on our roadmap. If you've used a custom template with better results I'd be all ears.

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koaning avatar koaning commented on May 26, 2024

The recipes in this repository have since moved to Prodigy and are being maintained there. They will soon even get an upgrade with the advent of spacy-llm support, which features better prompts and multiple LLM providers. That is why we've opted to archive this repo, which is also why I'm closing all the issues.

You can learn more by checking out the large language models section on the docs.

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