---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Ciboire de sacréfice de verrat de colon
- text: Verrat de cibolac d'estique de cibouleau
- text: esti
- text: Câlique de cossin
- text: estique!
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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:** 5 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                                                                                                                      |
|:-----------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| sacre composé doux           | <ul><li>'Saint-cimonaque de sainte-viarge'</li><li>'Cibolac de cibouleau'</li><li>'câline de bines!'</li></ul>                |
| sacre ponctuation intense    | <ul><li>'sacrament!'</li><li>'siboire!'</li><li>'câlisse!'</li></ul>                                                          |
| sacre composé intense        | <ul><li>'Ciboire de ciarge'</li><li>"ciboire de viarge de bout d'crisse"</li><li>"sacrement d'tarbarnak de câlisse"</li></ul> |
| sacre ponctuation doux       | <ul><li>'tabarouette!'</li><li>'cibole'</li><li>'baptême'</li></ul>                                                           |
|  "sacre ponctuation intense" | <ul><li>'sti!'</li></ul>                                                                                                      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("POBonin/setfit-quebec-profanity-classifier")
# Run inference
preds = model("esti")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 2.2653 | 6   |

| Label                     | Training Sample Count |
|:--------------------------|:----------------------|
| sacre ponctuation intense | 12                    |
| sacre ponctuation doux    | 12                    |
| sacre composé intense     | 12                    |
| sacre composé doux        | 12                    |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.0001
- 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: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0175 | 1    | 0.2373        | -               |
| 0.8772 | 50   | 0.2157        | 0.0794          |
| 1.7544 | 100  | 0.0818        | 0.0061          |
| 2.6316 | 150  | 0.0014        | 0.0069          |
| 3.5088 | 200  | 0.0004        | 0.0086          |
| 4.3860 | 250  | 0.0003        | 0.0057          |
| 5.2632 | 300  | 0.0003        | 0.0103          |
| 6.1404 | 350  | 0.0002        | 0.0092          |
| 7.0175 | 400  | 0.0002        | 0.0169          |

### Framework Versions
- Python: 3.12.10
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu126
- Datasets: 2.19.1
- Tokenizers: 0.21.1

## 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}
}
```

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