Update metadata

README.md

@@ -7,6 +7,12 @@ metrics:
 model-index:
 - name: xlm-roberta-base-finetuned-recipe-all
   results: []
+widget:
+- text: "1 sheet of frozen puff pastry (thawed)"
+- text: "1/2 teaspoon fresh thyme, minced"
+- text: "2-3 medium tomatoes"
+- text: "1 petit oignon rouge"
+
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -14,25 +20,50 @@ should probably proofread and complete it, then remove this comment. -->
 
 # xlm-roberta-base-finetuned-recipe-all
 
-This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the recipe ingredient [NER dataset](https://github.com/cosylabiiit/recipe-knowledge-mining) from the paper [A Named Entity Based Approach to Model Recipes](https://arxiv.org/abs/2004.12184) (using both the `gk` and `ar` datasets).
+
+
+
 It achieves the following results on the evaluation set:
 - Loss: 0.1169
 - F1: 0.9672
 
+On the test set it obtains an F1 of 0.9615, slightly above the CRF used in the paper.
+
 ## Model description
 
-More information needed
+Predicts tag of each token in an ingredient string.
+
+| Tag | Significance | Example |
+| --- | --- | --- |
+| NAME | Name of Ingredient | salt, pepper |
+| STATE | Processing State of Ingredient. | ground, thawed |
+| UNIT | Measuring unit(s). | gram, cup |
+| QUANTITY | Quantity associated with the unit(s). | 1, 1 1/2 , 2-4 |
+| SIZE | Portion sizes mentioned. | small, large |
+| TEMP | Temperature applied prior to cooking. | hot, frozen |
+| DF (DRY/FRESH) | Fresh otherwise as mentioned. | dry, fresh |
 
 ## Intended uses & limitations
 
-More information needed
+* Only trained on ingredient strings.
+* Tags subtokens; tag should be propagated to whole word
+* Works best with pre-tokenisation splitting of symbols (such as parentheses) and numbers (e.g. 50g -> 50 g)
+* Typically only detects the first ingredient if there are multiple.
+* Only trained on two American English data sources
+* Tags TEMP and DF have very few training data.
 
 ## Training and evaluation data
 
-More information needed
+Both the `ar` (AllRecipes.com) and `gk` (FOOD.com) datasets obtained from the TSVs from the authors' [repository](https://github.com/cosylabiiit/recipe-knowledge-mining).
 
 ## Training procedure
 
+
+It follows the overall procedure from Chapter 4 of [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098103231/) by Tunstall, von Wera and Wolf.
+
+See the [training notebook](https://github.com/EdwardJRoss/nlp_transformers_exercises/blob/master/notebooks/ch4-ner-recipe-stanford-crf.ipynb) for details.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: