Updated model card

README.md

@@ -1,36 +1,74 @@
 ---
+language:
+- es
 tags:
 - generated_from_trainer
+- recipe-generation
 model-index:
 - name: gastronomia_para_to2
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Model description
 
-# gastronomia_para_to2
-
-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.
+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
 
-## Team members
+## Contributors
 
 - Julián Cendrero ([jucendrero](https://huggingface.co/jucendrero))
 - Silvia Duque ([silBERTa](https://huggingface.co/silBERTa))
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+## 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
 
@@ -66,3 +104,8 @@ The following hyperparameters were used during training:
 - 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).
+