Xlm Roberta Base Finetuned Recipe All

🚀 xlm-roberta-base-finetuned-recipe-all

This model is a fine - tuned version of xlm - roberta - base, designed to predict tags for each token in ingredient strings, achieving high F1 scores on evaluation and test sets.

🚀 Quick Start

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 (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.

✨ Features

• Predicts tag of each token in an ingredient string.
• Achieves high F1 scores on evaluation and test sets.

📚 Documentation

Model description

Predicts tag of each token in an ingredient string.

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

Framework versions

• Transformers 4.16.2
• Pytorch 1.9.1
• Datasets 1.18.4
• Tokenizers 0.11.6

📄 License

This model is released under the MIT license.