README.md

---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: La app de BBVA está caída, pero se pide paciencia para los depósitos de mañana.
- text: Tengo un problema con un cajero automático que no me dio el dinero pero sí
    lo cargó.
- text: El chip de mi tarjeta de Banorte no funciona, hice una transferencia a mi
    tarjeta de BBVA y el cajero se quedó con ella, ¿cómo va su sábado?
- text: Evo Banco reporta un asombroso incremento del 700% en sus depósitos en un
    año y ahora ofrece la posibilidad de contratar servicios a través de WhatsApp.
- text: Los nuevos jubilados que acrediten su pensión en BBVA recibirán un regalo
    de bienvenida de $130.000.
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.8028571428571428
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| discard  | <ul><li>'Marcos informa que se puede realizar el pago de productos de BBVA a través de la Línea BBVA, cajeros automáticos, practicajas, ventanilla de sucursal o diversos comercios.'</li><li>'Se ha celebrado una reunión de alto nivel en 2024 para concretar proyectos de inversión, incluyendo la cooperación con BBVA para la construcción de un portadrones y en el ámbito turístico.'</li><li>'Diversificar es clave para alcanzar nuestros objetivos en inversiones y en la vida, descubre cómo tus decisiones financieras pueden impactar tu vida personal en este artículo.'</li></ul> |
| relevant | <ul><li>'La persona recibió un correo idéntico al que le explicaron que es una técnica de estafa que simula enviarlo desde su propia cuenta.'</li><li>'La cancelación de la cuenta se ha demorado un mes y al solicitar 200 euros para un viaje, me han cobrado 9 euros de comisión.'</li><li>'El Santander logró récords en beneficios y comisiones a los desfavorecidos bajo el ministerio del consagrado en Consumo, mientras se obsesionan con la apariencia y carecen de dignidad y principios.'</li></ul>                                                                                  |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8029   |

## 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("saraestevez/setfit-minilm-bank-tweets-processed-400")
# Run inference
preds = model("La app de BBVA está caída, pero se pide paciencia para los depósitos de mañana.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 21.6612 | 44  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| discard  | 400                   |
| relevant | 400                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1    | 0.3197        | -               |
| 0.025  | 50   | 0.2199        | -               |
| 0.05   | 100  | 0.2876        | -               |
| 0.075  | 150  | 0.2568        | -               |
| 0.1    | 200  | 0.196         | -               |
| 0.125  | 250  | 0.15          | -               |
| 0.15   | 300  | 0.1475        | -               |
| 0.175  | 350  | 0.081         | -               |
| 0.2    | 400  | 0.0441        | -               |
| 0.225  | 450  | 0.0228        | -               |
| 0.25   | 500  | 0.0017        | -               |
| 0.275  | 550  | 0.0083        | -               |
| 0.3    | 600  | 0.002         | -               |
| 0.325  | 650  | 0.0013        | -               |
| 0.35   | 700  | 0.0011        | -               |
| 0.375  | 750  | 0.0014        | -               |
| 0.4    | 800  | 0.0004        | -               |
| 0.425  | 850  | 0.0001        | -               |
| 0.45   | 900  | 0.0118        | -               |
| 0.475  | 950  | 0.0002        | -               |
| 0.5    | 1000 | 0.0012        | -               |
| 0.525  | 1050 | 0.0003        | -               |
| 0.55   | 1100 | 0.0001        | -               |
| 0.575  | 1150 | 0.0003        | -               |
| 0.6    | 1200 | 0.0001        | -               |
| 0.625  | 1250 | 0.0001        | -               |
| 0.65   | 1300 | 0.0001        | -               |
| 0.675  | 1350 | 0.0002        | -               |
| 0.7    | 1400 | 0.0197        | -               |
| 0.725  | 1450 | 0.0002        | -               |
| 0.75   | 1500 | 0.0002        | -               |
| 0.775  | 1550 | 0.0001        | -               |
| 0.8    | 1600 | 0.0004        | -               |
| 0.825  | 1650 | 0.0001        | -               |
| 0.85   | 1700 | 0.0001        | -               |
| 0.875  | 1750 | 0.0001        | -               |
| 0.9    | 1800 | 0.0001        | -               |
| 0.925  | 1850 | 0.0001        | -               |
| 0.95   | 1900 | 0.0158        | -               |
| 0.975  | 1950 | 0.0001        | -               |
| 1.0    | 2000 | 0.0001        | -               |

### Framework Versions
- Python: 3.11.0rc1
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.19.1
- 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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