---
base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Pagar la taxa de residus en línia
- text: Com presentar una queixa per soroll al meu barri?
- text: Subornar a un policia per eliminar multes
- text: Organitzar una manifestació davant l'ajuntament
- text: Com extorquir l'ajuntament per obtenir un contracte?
inference: true
---

# SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co./projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) 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:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co./projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base)
- **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:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co./datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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                                                                                                                                                                                  |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>"Aquest text és 1 per a un cercador de tràmits d'un ajuntament"</li><li>'Denunciar soroll excessiu dels veïns'</li><li>"Com sol·licitar un certificat d'empadronament?"</li></ul> |
| 0     | <ul><li>"Com falsificar un document d'identitat?"</li><li>"Aquest text és 0 per a un cercador de tràmits d'un ajuntament"</li><li>'Com desfer-se de proves comprometedores?'</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("adriansanz/sentimentv3")
# Run inference
preds = model("Pagar la taxa de residus en línia")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.4504 | 12  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 69                    |
| 1     | 62                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch   | Step     | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0018  | 1        | 0.2301        | -               |
| 0.0916  | 50       | 0.2223        | -               |
| 0.1832  | 100      | 0.0056        | -               |
| 0.2747  | 150      | 0.001         | -               |
| 0.3663  | 200      | 0.0002        | -               |
| 0.4579  | 250      | 0.0004        | -               |
| 0.5495  | 300      | 0.0001        | -               |
| 0.6410  | 350      | 0.0001        | -               |
| 0.7326  | 400      | 0.0001        | -               |
| 0.8242  | 450      | 0.0001        | -               |
| 0.9158  | 500      | 0.0           | -               |
| 1.0     | 546      | -             | 0.0             |
| 1.0073  | 550      | 0.0001        | -               |
| 1.0989  | 600      | 0.0001        | -               |
| 1.1905  | 650      | 0.0001        | -               |
| 1.2821  | 700      | 0.0001        | -               |
| 1.3736  | 750      | 0.0           | -               |
| 1.4652  | 800      | 0.0001        | -               |
| 1.5568  | 850      | 0.0           | -               |
| 1.6484  | 900      | 0.0           | -               |
| 1.7399  | 950      | 0.0           | -               |
| 1.8315  | 1000     | 0.0           | -               |
| 1.9231  | 1050     | 0.0           | -               |
| 2.0     | 1092     | -             | 0.0             |
| 2.0147  | 1100     | 0.0           | -               |
| 2.1062  | 1150     | 0.0           | -               |
| 2.1978  | 1200     | 0.0           | -               |
| 2.2894  | 1250     | 0.0           | -               |
| 2.3810  | 1300     | 0.0001        | -               |
| 2.4725  | 1350     | 0.0           | -               |
| 2.5641  | 1400     | 0.0           | -               |
| 2.6557  | 1450     | 0.0           | -               |
| 2.7473  | 1500     | 0.0           | -               |
| 2.8388  | 1550     | 0.0           | -               |
| 2.9304  | 1600     | 0.0           | -               |
| 3.0     | 1638     | -             | 0.0             |
| 3.0220  | 1650     | 0.0           | -               |
| 3.1136  | 1700     | 0.0           | -               |
| 3.2051  | 1750     | 0.0           | -               |
| 3.2967  | 1800     | 0.0           | -               |
| 3.3883  | 1850     | 0.0           | -               |
| 3.4799  | 1900     | 0.0           | -               |
| 3.5714  | 1950     | 0.0           | -               |
| 3.6630  | 2000     | 0.0           | -               |
| 3.7546  | 2050     | 0.0           | -               |
| 3.8462  | 2100     | 0.0           | -               |
| 3.9377  | 2150     | 0.0           | -               |
| **4.0** | **2184** | **-**         | **0.0**         |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.15.2

## 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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