readme: ---
  language:
  - es
  tags:
  - generated_from_trainer
  - recipe-generation
  
  widget:
    - text: "<RECIPE_START> <INPUT_START> salmón <NEXT_INPUT> zumo de naranja <NEXT_INPUT> aceite de oliva <NEXT_INPUT> sal <NEXT_INPUT> pimienta <INPUT_END> <INGR_START>"
    - text: "<RECIPE_START> <INPUT_START> harina <NEXT_INPUT> azúcar <NEXT_INPUT> huevos <NEXT_INPUT> chocolate <NEXT_INPUT> levadura Royal <INPUT_END> <INGR_START>"
  inference:
    parameters:
      top_k: 50
      top_p: 0.92  
      do_sample: True
      num_return_sequences: 3
      max_new_tokens: 100
    
  ---
  
  # Model description
  
  This model is a fine-tuned version of [flax-community/gpt-2-spanish](https://huggingface.co/flax-community/gpt-2-spanish) on a custom dataset (not publicly available). The dataset is made of crawled data from 3 Spanish cooking websites and it contains approximately ~50000 recipes. 
  It achieves the following results on the evaluation set:
  - Loss: 0.5796
  
  ## Contributors
  
  - Julián Cendrero ([jucendrero](https://huggingface.co/jucendrero))
  - Silvia Duque ([silBERTa](https://huggingface.co/silBERTa))
  
  ## How to use it
  
  ```python
  from transformers import AutoTokenizer, AutoModelForCausalLM
  
  model_checkpoint = 'gastronomia-para-to2/gastronomia_para_to2'
  tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
  model = AutoModelForCausalLM.from_pretrained(model_checkpoint)
  ```
  
  The tokenizer makes use of the following special tokens to indicate the structure of the recipe:
  
  ```python
  special_tokens = [
  '<INPUT_START>',
  '<NEXT_INPUT>',
  '<INPUT_END>',
  '<TITLE_START>',
  '<TITLE_END>',
  '<INGR_START>',
  '<NEXT_INGR>',
  '<INGR_END>',
  '<INSTR_START>',
  '<NEXT_INSTR>',
  '<INSTR_END>',
  '<RECIPE_START>',
  '<RECIPE_END>']
  ```
  
  The input should be of the form:
  
  ```python
  <RECIPE_START> <INPUT_START> ingredient_1 <NEXT_INPUT> ingredient_2 <NEXT_INPUT> ... <NEXT_INPUT> ingredient_n <INPUT_END> <INGR_START>
  ```
  
  We are using the following configuration to generate recipes, but feel free to change parameters as needed:
  
  ```python
  tokenized_input = tokenizer(input, return_tensors='pt')
  output = model.generate(**tokenized_input,
                            max_length=600,
                            do_sample=True,
                            top_p=0.92,
                            top_k=50,
                            num_return_sequences=3)
  pre_output = tokenizer.decode(output[0], skip_special_tokens=False)
  ```
  
  The recipe ends where the \<RECIPE_END\> special token appears for the first time.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 0.6213        | 1.0   | 5897  | 0.6214          |
  | 0.5905        | 2.0   | 11794 | 0.5995          |
  | 0.5777        | 3.0   | 17691 | 0.5893          |
  | 0.574         | 4.0   | 23588 | 0.5837          |
  | 0.5553        | 5.0   | 29485 | 0.5807          |
  | 0.5647        | 6.0   | 35382 | 0.5796          |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu102
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  
  ## References
  The list of special tokens used for generation recipe structure has been taken from:
  [RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation](https://www.aclweb.org/anthology/2020.inlg-1.4.pdf).
  
  