Push model using huggingface_hub.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 384,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Poke de Salmão com Cream Cheese
+- text: Podrão da esquina
+- text: Pizza Marguerita
+- text: Nhoque ao sugo
+- text: Pizza Portuguesa
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) 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](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 8 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label       | Examples                                                                                                   |
+|:------------|:-----------------------------------------------------------------------------------------------------------|
+| Lanche      | <ul><li>'X-Tudo completo com bacon'</li><li>'Hamburguer artesanal'</li><li>'Sanduíche de frango'</li></ul> |
+| Japonesa    | <ul><li>'Barca de Sushi 40 peças'</li><li>'Temaki de Salmão'</li><li>'Sashimi variado'</li></ul>           |
+| Brasileira  | <ul><li>'Feijoada completa'</li><li>'Prato Feito de Carne'</li><li>'Arroz, feijão e bife'</li></ul>        |
+| Pizza/Massa | <ul><li>'Pizza de Calabresa'</li><li>'Pizza Portuguesa'</li><li>'Macarrão a Bolonhesa'</li></ul>           |
+| Sobremesa   | <ul><li>'Petit Gateau com sorvete'</li><li>'Bolo de Chocolate'</li><li>'Açaí 500ml com granola'</li></ul>  |
+| Bebida      | <ul><li>'Coca-Cola Zero'</li><li>'Guaraná Antartica'</li><li>'Suco de Laranja Natural'</li></ul>           |
+| Petiscos    | <ul><li>'Batata Frita com cheddar'</li><li>'Fritas grande'</li><li>'Porção de Mandioca'</li></ul>          |
+| Árabe       | <ul><li>'Esfiha de Carne'</li><li>'Kibe frito'</li><li>'Esfiha de Queijo'</li></ul>                        |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("JoaoVitorr/food-classification-model")
+# Run inference
+preds = model("Nhoque ao sugo")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## 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
+```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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
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+}

config_sentence_transformers.json

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+  },
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+  "prompts": {
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+    "document": ""
+  },
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+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "Bebida",
+    "Brasileira",
+    "Japonesa",
+    "Lanche",
+    "Petiscos",
+    "Pizza/Massa",
+    "Sobremesa",
+    "\u00c1rabe"
+  ]
+}

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model_head.pkl

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modules.json

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sentence_bert_config.json

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special_tokens_map.json

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