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---
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"
---
<!-- 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. -->
# 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 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
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](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:
- 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
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