
# krumeto/setfit-recipe-classifer

This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for classification how difficult is a given recipe. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Usage

To use this model for inference, first install the SetFit library:

```bash
python -m pip install setfit
```

You can then run inference as follows:

```python
from setfit import SetFitModel

# Download from Hub and run inference
model = SetFitModel.from_pretrained("krumeto/setfit-recipe-classifer")
complicated_recipe = """Ingredients:
4 ounces pancetta, diced into 1/4 inch cubes
2 1/2 to 3 pounds veal shanks (4 to 6 pieces 2 to 3 inches thick)
1/2 cup diced onion
1/2 cup diced celery
1/2 cup diced carrot
3 garlic cloves , minced
1 1/2 cups canned chopped tomatoes
1 1/2 cups chicken broth
1/2 cup dry white wine
1 bay leaf
1 sprig fresh thyme
salt
freshly ground black pepper
all-purpose flour for dredging
2 tablespoons unsalted butter
2 tablespoons extra-virgin olive oil
4 3-inch strips of lemon zest

Directions:

Preheat oven to 375°F.
Heat the olive oil over medium heat in a large Dutch oven.
Cook pancetta until browned and crisp.
Remove pancetta with a slotted spoon and transfer to a paper towel-lined plate.
Season veal shanks with salt and pepper and dredge in flour.
Cook the veal until browned on all sides, working in batches if necessary, then transfer to a plate.
Add the onion, celery, carrot, garlic, and a pinch of salt to the Dutch oven and cook until softened.
Stir in the tomatoes, chicken broth, dry white wine, bay leaf, and thyme sprig.
Return the veal shanks and pancetta to the Dutch oven and bring the liquid to a simmer.
Cover the pot and place it in the oven to braise for 2-2 1/2 hours, until the veal is very tender.
Serve with gremolata and garnish with lemon zest strips.
Note: To make gremolata, finely chop 2 tablespoons fresh parsley, 1 tablespoon grated lemon zest, and 1 garlic clove. Mix together and sprinkle over the osso buco before serving."""

# Run inference
preds = model([complicated_recipe])
```

## BibTeX entry and citation info

```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```

{"license": "apache-2.0", "tags": ["setfit", "sentence-transformers", "text-classification"], "pipeline_tag": "text-classification"}
sentence-transformers pytorch bert setfit text-classification arxiv:2209.11055 license:apache-2.0 region:us
TAGS
#sentence-transformers #pytorch #bert #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us 


# krumeto/setfit-recipe-classifer

This is a SetFit model that can be used for classification how difficult is a given recipe. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows:



## BibTeX entry and citation info



# krumeto/setfit-recipe-classifer

This is a SetFit model that can be used for classification how difficult is a given recipe. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows: ## BibTeX entry and citation info
TAGS
#sentence-transformers #pytorch #bert #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us 
 # krumeto/setfit-recipe-classifer

This is a SetFit model that can be used for classification how difficult is a given recipe. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows: ## BibTeX entry and citation info
TAGS
#sentence-transformers #pytorch #bert #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us 
# krumeto/setfit-recipe-classifer

This is a SetFit model that can be used for classification how difficult is a given recipe. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows:## BibTeX entry and citation info