metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - f1
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

xlm-roberta-base-finetuned-recipe-all

This model is a fine-tuned version of xlm-roberta-base on the recipe ingredient NER dataset from the paper A Named Entity Based Approach to Model Recipes (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

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

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

Both the ar (AllRecipes.com) and gk (FOOD.com) datasets obtained from the TSVs from the authors' repository.

Training procedure

It follows the overall procedure from Chapter 4 of Natural Language Processing with Transformers by Tunstall, von Wera and Wolf.

See the training notebook for details.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.2529	1.0	331	0.1303	0.9592
0.1164	2.0	662	0.1224	0.9640
0.0904	3.0	993	0.1156	0.9671
0.0585	4.0	1324	0.1169	0.9672

Framework versions

Transformers 4.16.2
Pytorch 1.9.1
Datasets 1.18.4
Tokenizers 0.11.6