---
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: Quin és el percentatge de bonificació per a les famílies monoparentals o nombroses?
- text: Salut, tanque's
- text: Quin és el tema principal de l'informe previ?
- text: Quin és el destinatari de la sol·licitud de canvi d'ubicació?
- text: Què es necessita per obtenir una placa de gual?
inference: true
model-index:
- name: SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9978448275862069
      name: Accuracy
---

# 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 -->
<!-- - **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                                                                                                                                                                                                   |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'Bona nit, com estàs?'</li><li>'Ei, què tal tot?'</li><li>'Hola, com està el temps?'</li></ul>                                                                                                     |
| 0     | <ul><li>'Quin és el propòsit de la llicència administrativa?'</li><li>'Quin és el benefici de les subvencions per als infants?'</li><li>"Què acredita el certificat d'empadronament col·lectiu?"</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9978   |

## 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/greetings-v2")
# Run inference
preds = model("Salut, tanque's")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 9.8187 | 23  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 100                   |
| 1     | 60                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0012 | 1    | 0.2127        | -               |
| 0.0581 | 50   | 0.1471        | -               |
| 0.1163 | 100  | 0.0168        | -               |
| 0.1744 | 150  | 0.001         | -               |
| 0.2326 | 200  | 0.0004        | -               |
| 0.2907 | 250  | 0.0002        | -               |
| 0.3488 | 300  | 0.0001        | -               |
| 0.4070 | 350  | 0.0001        | -               |
| 0.4651 | 400  | 0.0001        | -               |
| 0.5233 | 450  | 0.0001        | -               |
| 0.5814 | 500  | 0.0001        | -               |
| 0.6395 | 550  | 0.0001        | -               |
| 0.6977 | 600  | 0.0001        | -               |
| 0.7558 | 650  | 0.0           | -               |
| 0.8140 | 700  | 0.0           | -               |
| 0.8721 | 750  | 0.0           | -               |
| 0.9302 | 800  | 0.0           | -               |
| 0.9884 | 850  | 0.0           | -               |
| 1.0465 | 900  | 0.0           | -               |
| 1.1047 | 950  | 0.0           | -               |
| 1.1628 | 1000 | 0.0           | -               |
| 1.2209 | 1050 | 0.0           | -               |
| 1.2791 | 1100 | 0.0           | -               |
| 1.3372 | 1150 | 0.0           | -               |
| 1.3953 | 1200 | 0.0           | -               |
| 1.4535 | 1250 | 0.0           | -               |
| 1.5116 | 1300 | 0.0           | -               |
| 1.5698 | 1350 | 0.0           | -               |
| 1.6279 | 1400 | 0.0           | -               |
| 1.6860 | 1450 | 0.0           | -               |
| 1.7442 | 1500 | 0.0           | -               |
| 1.8023 | 1550 | 0.0           | -               |
| 1.8605 | 1600 | 0.0           | -               |
| 1.9186 | 1650 | 0.0           | -               |
| 1.9767 | 1700 | 0.0           | -               |
| 2.0349 | 1750 | 0.0           | -               |
| 2.0930 | 1800 | 0.0           | -               |
| 2.1512 | 1850 | 0.0           | -               |
| 2.2093 | 1900 | 0.0           | -               |
| 2.2674 | 1950 | 0.0           | -               |
| 2.3256 | 2000 | 0.0           | -               |
| 2.3837 | 2050 | 0.0           | -               |
| 2.4419 | 2100 | 0.0           | -               |
| 2.5    | 2150 | 0.0           | -               |
| 2.5581 | 2200 | 0.0           | -               |
| 2.6163 | 2250 | 0.0           | -               |
| 2.6744 | 2300 | 0.0           | -               |
| 2.7326 | 2350 | 0.0           | -               |
| 2.7907 | 2400 | 0.0           | -               |
| 2.8488 | 2450 | 0.0           | -               |
| 2.9070 | 2500 | 0.0           | -               |
| 2.9651 | 2550 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Datasets: 3.1.0
- Tokenizers: 0.19.1

## 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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