---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: ': Nuestro Plan de Bacheo  continúa acabando con los huecos de los diversos
    sectores de nuestro municipio. Estuvimos interviniendo la Av. Ppal. de  y la Calle
    El Rocío de .'
- text: buenos días un cordial saludo es para preguntar como puedo hacer para adquirir
    otro plan ya q no tengo papeles del codificador la dueña lo vendío y se fue del
    país y no pude contactarla mas no me entregó documentos todo esta legal pero quiero
    ponerlo a mi nombre
- text: Si los empresarios facturan sus ventas a precio internacional (Dólares), entonces
    porque no le exigirnos salarios con valor internacional?. Osea el salario mínimo
    desde 400$ al cambio! Unos 11 millones de BS Soberanos!. Lo que es igual no es
    trampa!.
- text: Coño cuál juego de la violencia Henry,aquí la violencia viene de un solo lado,en
    El Tocuyo y Carora cazaron a esos muchachos como animales
- text: Una vez más vuelvo y digo . COMO ODIO SENTIRME DOMINADA X EL DOLAR  nojoda
    si no tengo una mierda de esa entonces no comemos mis hijos y yo  tengo unas ganas
    de quemar con todo y persona el malnacido que solo está exigiendo verdes para
    venderte comida
pipeline_tag: text-classification
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: 1.0
      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>'Federer, Nadal y Djoković han gobernado con mano de hierro el tenis mundial en esta era. Se va el primero, el que empezó a instalar la tiranía. No somos conscientes de lo que se va con Roger. Afortunados de poder vivir uno de los momentos del deporte más gloriosos. Sin dudas.'</li><li>'Nuestro primer viaje deportivo mayerlingaranguren ! El primero de muchos...         en Distrito Federal,…'</li><li>'Por nuestro país y el futuro de nuestros hijos.'</li></ul>        |
| 1     | <ul><li>'¡Aquí estoy!, la depresión y tristeza por problemas económicos no me va a matar. Viviré a pesar de los 3 $ que como ingeniero jubilado me pagan mensual el gobierno. Ofrezco mis servicios en impermeabilización de techos y platabandas. Me disculpan que lo haga por este medio.'</li><li>'.: Remontarse a los precios de diciembre de 2017 generará más desempleo'</li><li>'Tengo media hora intentando comprar $ por banesco y no hay disponible en la mesa de cambio'</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("setfit_model_id")
# Run inference
preds = model("Coño cuál juego de la violencia Henry,aquí la violencia viene de un solo lado,en El Tocuyo y Carora cazaron a esos muchachos como animales")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 30.0686 | 76  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 122                   |
| 1     | 53                    |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (0.0001, 0.0001)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0018 | 1    | 0.408         | -               |
| 0.0894 | 50   | 0.0144        | -               |
| 0.1789 | 100  | 0.0002        | -               |
| 0.2683 | 150  | 0.0           | -               |
| 0.3578 | 200  | 0.0           | -               |
| 0.4472 | 250  | 0.0           | -               |
| 0.5367 | 300  | 0.0           | -               |
| 0.6261 | 350  | 0.0           | -               |
| 0.7156 | 400  | 0.0           | -               |
| 0.8050 | 450  | 0.0           | -               |
| 0.8945 | 500  | 0.0           | -               |
| 0.9839 | 550  | 0.0           | -               |
| 1.0733 | 600  | 0.0           | -               |
| 1.1628 | 650  | 0.0           | -               |
| 1.2522 | 700  | 0.0           | -               |
| 1.3417 | 750  | 0.0           | -               |
| 1.4311 | 800  | 0.0           | -               |
| 1.5206 | 850  | 0.0           | -               |
| 1.6100 | 900  | 0.0           | -               |
| 1.6995 | 950  | 0.0           | -               |
| 1.7889 | 1000 | 0.0           | -               |
| 1.8784 | 1050 | 0.0           | -               |
| 1.9678 | 1100 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.15.0
- Tokenizers: 0.15.0

## 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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