A small language model project based on Gemma, designed for structured food and drink extraction from text descriptions.
Task
Given a text description, the model is intended to return structured output in the following format:
food_or_drink: 0 or 1
tags:
foods:
drinks:
Base Model
Gemma 3 270M
Dataset
FoodExtract-1k
Intended Use
This project demonstrates:
dataset preparation
prompt/response formatting
train/test splitting
Hugging Face pipelines
supervised fine-tuning workflow with TRL
evaluation of predictions against test labels
model packaging and Hugging Face Hub deployment
Example
Input:
A plate contains grilled chicken, rice, and broccoli.
Expected structured output:
food_or_drink: 1
tags: fi
foods: grilled chicken, rice, broccoli
drinks:
Limitations
This is an educational model project. Model quality depends on training completion, dataset quality, and hardware used for fine-tuning.
Author
Christopher Dameron
Runs of cdameron1024 gemma-food-drink-extractor on huggingface.co
2
Total runs
0
24-hour runs
-3
3-day runs
-27
7-day runs
-26
30-day runs
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