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| title: FoodExtract Fine-tuned LLM Structued Data Extractor v1 | |
| emoji: 📝➡️🍟 | |
| colorFrom: green | |
| colorTo: blue | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| """ | |
| Fine-tuned Gemma 3 270M to extract food and drink items from raw text. | |
| Input can be any form of real text (mostly focused on shorter image caption-like texts): | |
| ``` | |
| A truly eclectic and mouth-watering feast is laid out on the table, featuring savory favorites like crispy fried chicken, | |
| a perfectly seared steak, and loaded tacos, complete with a side of creamy mayonnaise. To balance the heavier mains, | |
| a vibrant assortment of fresh fruit sits nearby, including a crisp red apple, a tropical pineapple, and a scattering of | |
| sweet cherries. Thirst-quenching options complete this extravagant spread, with a classic iced latte, an earthy matcha latte, | |
| and a simple, refreshing glass of milk ready to be enjoyed. | |
| ``` | |
| And output will be a formatted string such as the following: | |
| ``` | |
| food_or_drink: 1 | |
| tags: fi, re | |
| foods: tacos,red apple, pineapple, cherries, fried chicken, steak, mayonnaise | |
| drinks: iced latte, matcha latte, milk | |
| ``` | |
| The tags map to the following items: | |
| ``` | |
| tags_dict = {'np': 'nutrition_panel', | |
| 'il': 'ingredient list', | |
| 'me': 'menu', | |
| 're': 'recipe', | |
| 'fi': 'food_items', | |
| 'di': 'drink_items', | |
| 'fa': 'food_advertistment', | |
| 'fp': 'food_packaging'} | |
| ``` | |
| * You can see walkthrough step by step code details at: https://www.learnhuggingface.com/notebooks/hugging_face_llm_full_fine_tune_tutorial | |
| * See the fine-tuning dataset: https://huggingface.co/datasets/mrdbourke/FoodExtract-1k | |
| * See the fine-tuned model: https://huggingface.co/mrdbourke/FoodExtract-gemma-3-270m-fine-tune-v1 | |
| """ | |