厨师变压器（T5）

这是由 Flax/Jax Community Week 组织的项目，TPU使用由Google赞助。

想要试一试吗？那还等什么，去Hugging Face Spaces here 吧。

小组成员

• Mehrdad Farahani（ m3hrdadfi ）
• Kartik Godawat（ dk-crazydiv ）
• Haswanth Aekula（ hassiahk ）
• Deepak Pandian（ rays2pix ）
• Nicholas Broad（ nbroad ）

数据集

RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation 。该数据集包含2,231,142个烹饪食谱（> 200万），大小为2.14 GB。它经过了更加细致的处理。

示例

{
  "NER": [
    "oyster crackers",
    "salad dressing",
    "lemon pepper",
    "dill weed",
    "garlic powder",
    "salad oil"
  ],
  "directions": [
    "Combine salad dressing mix and oil.",
    "Add dill weed, garlic powder and lemon pepper.",
    "Pour over crackers; stir to coat.",
    "Place in warm oven.",
    "Use very low temperature for 15 to 20 minutes."
  ],
  "ingredients": [
    "12 to 16 oz. plain oyster crackers",
    "1 pkg. Hidden Valley Ranch salad dressing mix",
    "1/4 tsp. lemon pepper",
    "1/2 to 1 tsp. dill weed",
    "1/4 tsp. garlic powder",
    "3/4 to 1 c. salad oil"
  ],
  "link": "www.cookbooks.com/Recipe-Details.aspx?id=648947",
  "source": "Gathered",
  "title": "Hidden Valley Ranch Oyster Crackers"
}

如何使用

# Installing requirements
pip install transformers

from transformers import FlaxAutoModelForSeq2SeqLM
from transformers import AutoTokenizer

MODEL_NAME_OR_PATH = "flax-community/t5-recipe-generation"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME_OR_PATH)

prefix = "items: "
# generation_kwargs = {
#     "max_length": 512,
#     "min_length": 64,
#     "no_repeat_ngram_size": 3,
#     "early_stopping": True,
#     "num_beams": 5,
#     "length_penalty": 1.5,
# }
generation_kwargs = {
    "max_length": 512,
    "min_length": 64,
    "no_repeat_ngram_size": 3,
    "do_sample": True,
    "top_k": 60,
    "top_p": 0.95
}


special_tokens = tokenizer.all_special_tokens
tokens_map = {
    "<sep>": "--",
    "<section>": "\n"
}
def skip_special_tokens(text, special_tokens):
    for token in special_tokens:
        text = text.replace(token, "")

    return text

def target_postprocessing(texts, special_tokens):
    if not isinstance(texts, list):
        texts = [texts]
    
    new_texts = []
    for text in texts:
        text = skip_special_tokens(text, special_tokens)

        for k, v in tokens_map.items():
            text = text.replace(k, v)

        new_texts.append(text)

    return new_texts

def generation_function(texts):
    _inputs = texts if isinstance(texts, list) else [texts]
    inputs = [prefix + inp for inp in _inputs]
    inputs = tokenizer(
        inputs, 
        max_length=256, 
        padding="max_length", 
        truncation=True, 
        return_tensors="jax"
    )

    input_ids = inputs.input_ids
    attention_mask = inputs.attention_mask

    output_ids = model.generate(
        input_ids=input_ids, 
        attention_mask=attention_mask,
        **generation_kwargs
    )
    generated = output_ids.sequences
    generated_recipe = target_postprocessing(
        tokenizer.batch_decode(generated, skip_special_tokens=False),
        special_tokens
    )
    return generated_recipe

items = [
    "macaroni, butter, salt, bacon, milk, flour, pepper, cream corn",
    "provolone cheese, bacon, bread, ginger"
]
generated = generation_function(items)
for text in generated:
    sections = text.split("\n")
    for section in sections:
        section = section.strip()
        if section.startswith("title:"):
            section = section.replace("title:", "")
            headline = "TITLE"
        elif section.startswith("ingredients:"):
            section = section.replace("ingredients:", "")
            headline = "INGREDIENTS"
        elif section.startswith("directions:"):
            section = section.replace("directions:", "")
            headline = "DIRECTIONS"
        
        if headline == "TITLE":
            print(f"[{headline}]: {section.strip().capitalize()}")
        else:
            section_info = [f"  - {i+1}: {info.strip().capitalize()}" for i, info in enumerate(section.split("--"))]
            print(f"[{headline}]:")
            print("\n".join(section_info))

    print("-" * 130)

输出：

[TITLE]: Macaroni and corn
[INGREDIENTS]:
  - 1: 2 c. macaroni
  - 2: 2 tbsp. butter
  - 3: 1 tsp. salt
  - 4: 4 slices bacon
  - 5: 2 c. milk
  - 6: 2 tbsp. flour
  - 7: 1/4 tsp. pepper
  - 8: 1 can cream corn
[DIRECTIONS]:
  - 1: Cook macaroni in boiling salted water until tender.
  - 2: Drain.
  - 3: Melt butter in saucepan.
  - 4: Blend in flour, salt and pepper.
  - 5: Add milk all at once.
  - 6: Cook and stir until thickened and bubbly.
  - 7: Stir in corn and bacon.
  - 8: Pour over macaroni and mix well.
----------------------------------------------------------------------------------------------------------------------------------
[TITLE]: Grilled provolone and bacon sandwich
[INGREDIENTS]:
  - 1: 2 slices provolone cheese
  - 2: 2 slices bacon
  - 3: 2 slices sourdough bread
  - 4: 2 slices pickled ginger
[DIRECTIONS]:
  - 1: Place a slice of provolone cheese on one slice of bread.
  - 2: Top with a slice of bacon.
  - 3: Top with a slice of pickled ginger.
  - 4: Top with the other slice of bread.
  - 5: Heat a skillet over medium heat.
  - 6: Place the sandwich in the skillet and cook until the cheese is melted and the bread is golden brown.
----------------------------------------------------------------------------------------------------------------------------------

评估

由于测试集不可用，我们将根据一个共享的测试集来评估模型。该测试集包含整个测试集的5％（= 5,000条记录），并且我们将为每个输入生成5个食谱（= 25,000条记录）。下表总结了作为我们基准的厨师变压器和RecipeNLG获得的分数。

对于每个NER（食物项）生成的5个食谱中，WER，COSIM和ROUGE指标只计算最高得分。同时，BLEU，GLEU，Meteor被设计为具有多个参考。

版权

特别感谢那些提供这些材料的人。

• Anatomy
• Chef Hat
• Moira Nazzari
• Instagram Post