JoaoVitorr/food-classification-model

SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

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.

Model Details

Model Description

• Model Type: SetFit
• Sentence Transformer body: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 128 tokens
• Number of Classes: 8 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
Lanche	'X-Tudo completo com bacon' 'Hamburguer artesanal' 'Sanduíche de frango'
Japonesa	'Barca de Sushi 40 peças' 'Temaki de Salmão' 'Sashimi variado'
Brasileira	'Feijoada completa' 'Prato Feito de Carne' 'Arroz, feijão e bife'
Pizza/Massa	'Pizza de Calabresa' 'Pizza Portuguesa' 'Macarrão a Bolonhesa'
Sobremesa	'Petit Gateau com sorvete' 'Bolo de Chocolate' 'Açaí 500ml com granola'
Bebida	'Coca-Cola Zero' 'Guaraná Antartica' 'Suco de Laranja Natural'
Petiscos	'Batata Frita com cheddar' 'Fritas grande' 'Porção de Mandioca'
Árabe	'Esfiha de Carne' 'Kibe frito' 'Esfiha de Queijo'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("JoaoVitorr/food-classification-model")
# Run inference
preds = model("Nhoque ao sugo")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	2.84	6

Label	Training Sample Count
Bebida	9
Brasileira	9
Japonesa	9
Lanche	9
Petiscos	10
Pizza/Massa	9
Sobremesa	9
Árabe	11

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 20
• body_learning_rate: (1e-05, 1e-05)
• head_learning_rate: 0.01
• loss: CosineSimilarityLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.1
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0053	1	0.291	-
0.2660	50	0.2175	-
0.5319	100	0.1953	-
0.7979	150	0.1689	-

Framework Versions

• Python: 3.12.12
• SetFit: 1.1.3
• Sentence Transformers: 5.1.2
• Transformers: 4.57.1
• PyTorch: 2.8.0+cu126
• Datasets: 4.0.0
• Tokenizers: 0.22.1

Citation

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}
}