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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: 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) -->
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### 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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