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saraestevez
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setfit-minilm-bank-tweets-processed-100

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
setfit-minilm-bank-tweets-processed-100
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---
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: Para saber si un negocio va a funcionar, es necesario realizar un estudio
    de mercado, valorar la economía local durante un año, considerar la afluencia
    de personas y la ubicación, así como determinar el tamaño de la inversión.
- text: Apoyo la opinión de Tyrexito y también reclamo al Banco Sabadell por sus comisiones.
- text: Los resultados del Banco Sabadell impulsan al IBEX 35.
- text: Aunque no pude retirar el bono de festividad en el cajero, ING y AKBANK rechazaron
    mis quejas, pero tras anunciar una denuncia, me transfirieron el dinero en una
    hora; si tienes razón, no te rindas.
- text: El Gobierno presentará al nuevo gobernador del Banco de España en una Comisión
    del Congreso este jueves.
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: 0.7594202898550725
      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
|:---------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| relevant | <ul><li>'Caixa y BBVA son los prestamistas que se benefician de la garantía en caso de impago en la financiación de inversiones, algo común también en las sociedades que avalan a comunidades autónomas.'</li><li>'El IBEX supera los 11.200 puntos gracias al impulso de la banca, liderado por Banco Sabadell con una subida del 2,05%.'</li><li>'Nuevo caso de phishing relacionado con ING, registrado el 16 de julio de 2024, con la URL /www.ingseguridad-app.com/es/login.'</li></ul> |
| discard  | <ul><li>'El BBVA también tiene un mal servicio, ya que no aceptan billetes de 2.000 ni de 1.000 de San Martín, obligando a hacer largas filas tanto para cambiar como para depositar.'</li><li>'Merhaba, yaşadığınız deneyim için üzgünüz; Garanti BBVA ATM konum bilgilerini paylaşırsanız gerekli kontrolleri hızlıca yapacağız.'</li><li>'En la gasolinera sobre Constituyentes, mi tarjeta de crédito fue denegada y no me hicieron cargo en la aplicación.'</li></ul>                    |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7594   |

## 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-100")
# Run inference
preds = model("Los resultados del Banco Sabadell impulsan al IBEX 35.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 22.0   | 41  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| discard  | 100                   |
| relevant | 100                   |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1    | 0.3931        | -               |
| 0.0396 | 50   | 0.2501        | -               |
| 0.0792 | 100  | 0.2471        | -               |
| 0.1188 | 150  | 0.1991        | -               |
| 0.1584 | 200  | 0.0902        | -               |
| 0.1979 | 250  | 0.0218        | -               |
| 0.2375 | 300  | 0.0055        | -               |
| 0.2771 | 350  | 0.0026        | -               |
| 0.3167 | 400  | 0.0013        | -               |
| 0.3563 | 450  | 0.0005        | -               |
| 0.3959 | 500  | 0.0005        | -               |
| 0.4355 | 550  | 0.001         | -               |
| 0.4751 | 600  | 0.0003        | -               |
| 0.5146 | 650  | 0.0003        | -               |
| 0.5542 | 700  | 0.0001        | -               |
| 0.5938 | 750  | 0.0003        | -               |
| 0.6334 | 800  | 0.0003        | -               |
| 0.6730 | 850  | 0.0004        | -               |
| 0.7126 | 900  | 0.0002        | -               |
| 0.7522 | 950  | 0.0001        | -               |
| 0.7918 | 1000 | 0.0001        | -               |
| 0.8314 | 1050 | 0.0001        | -               |
| 0.8709 | 1100 | 0.0002        | -               |
| 0.9105 | 1150 | 0.0002        | -               |
| 0.9501 | 1200 | 0.0002        | -               |
| 0.9897 | 1250 | 0.0           | -               |

### 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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