---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Perché poi ci si mettono anche i «puristi» delle diverse parlate.
- text: '"C''è stato un momento molto difficile.'
- text: 'Casaleggio [Gianroberto Casaleggio] è ormai isolato; più o meno tutti i suoi
    uomini hanno fatto il salto dall’altra parte - cosa che lui potrebbe aver capito
    (starebbe, ci dicono, escogitando rimedi che potremo vedere solo in seguito: candidare
    una donna premier?).'
- text: Ma c’è pure il calendario che aiuta un rinvio a gennaio di una legge che Renzi
    [Matteo Renzi] è comunque determinato a portare a casa, altamente osteggiata però
    dai s... continua
- text: Quindi è perfettamente inutile lavorare i festivi per chi ha famiglia o per
    chi si vuol divertire».
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/distiluse-base-multilingual-cased-v1
model-index:
- name: SetFit with sentence-transformers/distiluse-base-multilingual-cased-v1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.5753938484621155
      name: Accuracy
---

# SetFit with sentence-transformers/distiluse-base-multilingual-cased-v1

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/distiluse-base-multilingual-cased-v1](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) 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/distiluse-base-multilingual-cased-v1](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1)
- **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'È stato neutralizzato da un poliziotto in borghese e agli agenti ha subito urlato il suo odio per gli stranieri, ma si sta valutando anche se sia affetto da problemi mentali.'</li><li>'Tenore di vita elevato rispetto ai compensi dichiarati: è il caso di 1.731 segnalazioni su persone con beni di lusso ma stipendi nella norma, o con magre buste paga a fronte di maxi mutui.'</li><li>'«Se avessimo paura di perdere non ci presenteremmo alle elezioni.'</li></ul>                                                                                                                                                                                                                                                       |
| 1     | <ul><li>'Poi, gli uomini della Digos di Milano all’alba del primo di luglio le avevano tirato giù la porta di casa a Inzago, e se l’erano portata via insieme al marito Sergio [Sergio Sergio] e a Marianna [Marianna Sergio].'</li><li>"Laureata in storia greca [Daniela Bellingeri], è approdata al giornale nel 1989, diventando subito l'interlocutore cui affidarsi, anche, in certi casi, aggrapparsi, quando arrivavano richieste di pezzi impossibili a ore impossibili."</li><li>'I promotori sono tredici organizzazioni di varie nazioni che si sono già riunite a Roma durante il sinodo del 2014 e danno vita al "Global Network of Rainbow Catholic", una rete internazionale di gay, lesbiche e trans cattolici.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5754   |

## 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("fede-m/FGSDI_final_setfit_fold_0")
# Run inference
preds = model("\"C'è stato un momento molto difficile.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 39.3862 | 139 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 45                    |
| 1     | 245                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0028 | 1    | 0.5452        | -               |
| 0.1377 | 50   | 0.2129        | -               |
| 0.2755 | 100  | 0.0364        | -               |
| 0.4132 | 150  | 0.0077        | -               |
| 0.5510 | 200  | 0.0017        | -               |
| 0.6887 | 250  | 0.0015        | -               |
| 0.8264 | 300  | 0.001         | -               |
| 0.9642 | 350  | 0.0009        | -               |

### 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}
}
```

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