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Model: mrdbourke/FoodExtract-gemma-3-270m-fine-tune-v2 Source: Original Platform
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README.md
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README.md
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---
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base_model: google/gemma-3-270m-it
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library_name: transformers
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model_name: checkpoint_models
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tags:
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- generated_from_trainer
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- sft
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- trl
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license: gemma
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---
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# FoodExtract-v2
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This is a food and drink extraction language model built on [Gemma 3 270M](https://huggingface.co/google/gemma-3-270m-it).
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Given raw text, it's designed to:
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1. Classify the text into food or drink (e.g. "a photo of a dog" = not food or drink, "a photo of a pizza" = food or drink).
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2. Tag the text with one or more tags (see tags_dict below).
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3. Extract the edible food-related items as a list.
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4. Extract the edible drink-related items as a list.
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For example, the input text might be:
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```
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British Breakfast with baked beans, fried eggs, black pudding, sausages, bacon, mushrooms, a cup of tea and toast and fried tomatoes
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```
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And the model will generate:
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```
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food_or_drink: 1
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tags: fi, di
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foods: British Breakfast, baked beans, fried eggs, black pudding, sausages, bacon, mushrooms, toast, fried tomatoes
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drinks: tea
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```
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This model can be used for filtering a large image caption (e.g. [DataComp-1B](https://huggingface.co/datasets/UCSC-VLAA/Recap-DataComp-1B)) text dataset for food and drink related items.
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## Dataset
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The model was trained on the [FoodExtract-135k](https://huggingface.co/datasets/mrdbourke/FoodExtract-135k) dataset.
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This dataset contains 135,000 samples of raw text and JSON output pairs of structured food extractions provided by `gpt-oss-120b`.
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For example, a raw image caption input might be:
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```
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another optional quest takes place on windfall island during the night time play the song of passing a number of times and each time, glance towards the sky
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```
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And the `gpt-oss-120b` generated output (JSON) would be:
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```
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{'is_food_or_drink': 'false', 'tags': [], 'food_items': [], 'drink_items': []}
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```
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This is condensed to:
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```
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food_or_drink: 0\ntags: \nfoods: \ndrinks:
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```
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### Tags dictionary mapping
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These tags are designed for fast filtering.
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For example, the model can assign a certain tag based on what's in the raw text and then we can filter for "ingredient list" items.
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```
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tags_dict = {'np': 'nutrition_panel',
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'il': 'ingredient list',
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'me': 'menu',
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're': 'recipe',
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'fi': 'food_items',
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'di': 'drink_items',
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'fa': 'food_advertistment',
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'fp': 'food_packaging'}
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```
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## Helper functions
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The model is trained to output a condensed version of the structured data.
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We do this so the model can generate less tokens (e.g. it doesn't have to generate JSON outputs).
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The following functions help to condense and uncondense raw text outputs/inputs into the desired structure.
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```python
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def condense_output(original_output):
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'''Helper function to condense a given FoodExtract string.
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Example input: {'is_food_or_drink': True, 'tags': ['fi'], 'food_items': ['cape gooseberries', 'mulberry', 'chilli powder', 'flathead lobster', 'hoisin sauce', 'duck leg', 'chestnuts', 'raw quail', 'duck breast', 'rogan josh curry sauce', 'brown rice', 'dango'], 'drink_items': []}
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Example output: food_or_drink: 1\ntags: fi\nfoods: cape gooseberries, mulberry, chilli powder, flathead lobster, hoisin sauce, duck leg, chestnuts, raw quail, duck breast, rogan josh curry sauce, brown rice, dango\ndrinks:'''
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condensed_output_string_base = '''food_or_drink: <is_food_or_drink>
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tags: <output_tags>
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foods: <food_items>
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drinks: <drink_items>'''
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is_food_or_drink = str(1) if str(original_output["is_food_or_drink"]).lower() == "true" else str(0)
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tags = ", ".join(original_output["tags"]) if len(original_output["tags"]) > 0 else ""
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foods = ", ".join(original_output["food_items"]) if len(original_output["food_items"]) > 0 else ""
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drinks = ", ".join(original_output["drink_items"]) if len(original_output["drink_items"]) > 0 else ""
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condensed_output_string_formatted = condensed_output_string_base.replace("<is_food_or_drink>", is_food_or_drink).replace("<output_tags>", tags).replace("<food_items>", foods).replace("<drink_items>", drinks)
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return condensed_output_string_formatted.strip()
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def uncondense_output(condensed_output):
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'''Helper to go from condensed output to uncondensed output.
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Example input: food_or_drink: 1\ntags: fi\nfoods: cape gooseberries, mulberry, chilli powder, flathead lobster, hoisin sauce, duck leg, chestnuts, raw quail, duck breast, rogan josh curry sauce, brown rice, dango\ndrinks:
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Example output: {'is_food_or_drink': True, 'tags': ['fi'], 'food_items': ['cape gooseberries', 'mulberry', 'chilli powder', 'flathead lobster', 'hoisin sauce', 'duck leg', 'chestnuts', 'raw quail', 'duck breast', 'rogan josh curry sauce', 'brown rice', 'dango'], 'drink_items': []}
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'''
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condensed_list = condensed_output.split("\n")
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condensed_dict_base = {
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"is_food_or_drink": "",
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"tags": [],
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"food_items": [],
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"drink_items": []
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}
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# Set values to defaults
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food_or_drink_item = None
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tags_item = None
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foods_item = None
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drinks_item = None
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# Extract items from condensed_list
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for item in condensed_list:
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if "food_or_drink:" in item.strip():
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food_or_drink_item = item
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if "tags:" in item:
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tags_item = item
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if "foods:" in item:
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foods_item = item
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if "drinks:" in item:
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drinks_item = item
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if food_or_drink_item:
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is_food_or_drink_bool = True if food_or_drink_item.replace("food_or_drink: ", "").strip() == "1" else False
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else:
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is_food_or_drink_bool = None
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if tags_item:
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tags_list = [item.replace("tags: ", "").replace("tags:", "").strip() for item in tags_item.split(", ")]
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tags_list = [item for item in tags_list if item] # Filter for empty items
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else:
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tags_list = []
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if foods_item:
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foods_list = [item.replace("foods:", "").replace("foods: ", "").strip() for item in foods_item.split(", ")]
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foods_list = [item for item in foods_list if item] # Filter for empty items
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else:
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foods_list = []
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if drinks_item:
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drinks_list = [item.replace("drinks:", "").replace("drinks: ", "").strip() for item in drinks_item.split(", ")]
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drinks_list = [item for item in drinks_list if item] # Filter for empty items
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else:
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drinks_list = []
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condensed_dict_base["is_food_or_drink"] = is_food_or_drink_bool
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condensed_dict_base["tags"] = tags_list
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condensed_dict_base["food_items"] = foods_list
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condensed_dict_base["drink_items"] = drinks_list
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return condensed_dict_base
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````
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## Quick start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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MODEL_PATH = "mrdbourke/FoodExtract-gemma-3-270m-fine-tune-v2"
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# Load the model into a pipeline
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loaded_model = AutoModelForCausalLM.from_pretrained(
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pretrained_model_name_or_path=MODEL_PATH,
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dtype="auto",
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device_map="auto",
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attn_implementation="eager"
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)
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# Load the tokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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pretrained_model_name_or_path=MODEL_PATH,
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)
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# Create model pipeline
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loaded_model_pipeline = pipeline("text-generation",
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model=loaded_model,
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tokenizer=tokenizer)
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# Create a sample to predict on
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input_text = "A plate with bacon, eggs and toast on it"
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input_text_user = [{'content': input_text, 'role': 'user'}]
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# Apply the chat template
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input_prompt = loaded_model_pipeline.tokenizer.apply_chat_template(conversation=input_text_user,
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tokenize=False,
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add_generation_prompt=True)
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# Let's run the default model on our input
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default_outputs = loaded_model_pipeline(text_inputs=input_prompt,
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max_new_tokens=256)
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# View the outputs
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print(f"[INFO] Test sample input:\n{input_prompt}\n")
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print(f"[INFO] Fine-tuned model output:\n{default_outputs[0]['generated_text'][len(input_prompt):]}\n")
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```
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You should see an output similar to:
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```
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[INFO] Test sample input:
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<bos><start_of_turn>user
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A plate with bacon, eggs and toast on it<end_of_turn>
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<start_of_turn>model
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[INFO] Fine-tuned model output:
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food_or_drink: 1
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tags: fi
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foods: bacon, eggs, toast
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drinks:
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```
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## Training procedure
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This model was trained with SFT (Supervised Fine-Tuning) via Hugging Face's TRL library.
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See the full training walkthrough at: https://www.learnhuggingface.com/notebooks/hugging_face_llm_full_fine_tune_tutorial
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## Citations
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* Reference for structured data extraction was taken from the paper (tk- paper link to the paper which used a small qwen model to extract structured data from webpages)
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