---
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: 0.7739130434782608
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
### 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 | - 'Nuevo caso de phishing relacionado con Abanca, registrado el 23 de julio de 2024, con la URL: /www.inicio-abanca.com/es/WELE200M_Logon_Ini.aspx.'
- 'Una alumna que trabajó en Bancomer reveló un esquema de robo en el que dos cajeros afirmaban que un cliente había depositado mil pesos en un pago de dos mil y se quedaban con la mitad cada uno.'
- 'Las previsiones de crecimiento de España para 2024 han mejorado según diversas organizaciones, con estimaciones que oscilan entre el 1,8% y el 2,4%, impulsadas por turismo, exportaciones y trabajadores extranjeros.'
|
| discard | - 'Banco Santander ofrece una cuenta en línea sin comisiones y un bono de 400€ por domiciliar tu nómina.'
- 'El BBVA fue el banco que peor me trató al tener que contratar productos innecesarios para conseguir mi primera hipoteca de funcionario.'
- 'CaixaBank se destaca como líder del sector bancario gracias a su sólido crecimiento y eficiencia operativa, convirtiéndose en una opción atractiva para inversores.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7739 |
## 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-200")
# Run inference
preds = model("Los resultados del Banco Sabadell impulsan al IBEX 35.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 21.3275 | 41 |
| Label | Training Sample Count |
|:---------|:----------------------|
| discard | 200 |
| relevant | 200 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.4199 | - |
| 0.0100 | 50 | 0.3357 | - |
| 0.0199 | 100 | 0.3198 | - |
| 0.0299 | 150 | 0.2394 | - |
| 0.0398 | 200 | 0.2411 | - |
| 0.0498 | 250 | 0.2277 | - |
| 0.0597 | 300 | 0.1876 | - |
| 0.0697 | 350 | 0.1481 | - |
| 0.0796 | 400 | 0.1533 | - |
| 0.0896 | 450 | 0.0145 | - |
| 0.0995 | 500 | 0.0113 | - |
| 0.1095 | 550 | 0.0045 | - |
| 0.1194 | 600 | 0.0201 | - |
| 0.1294 | 650 | 0.0008 | - |
| 0.1393 | 700 | 0.0003 | - |
| 0.1493 | 750 | 0.0003 | - |
| 0.1592 | 800 | 0.0003 | - |
| 0.1692 | 850 | 0.0001 | - |
| 0.1791 | 900 | 0.0001 | - |
| 0.1891 | 950 | 0.0001 | - |
| 0.1990 | 1000 | 0.0001 | - |
| 0.2090 | 1050 | 0.0001 | - |
| 0.2189 | 1100 | 0.0002 | - |
| 0.2289 | 1150 | 0.0001 | - |
| 0.2388 | 1200 | 0.0001 | - |
| 0.2488 | 1250 | 0.0001 | - |
| 0.2587 | 1300 | 0.0 | - |
| 0.2687 | 1350 | 0.0001 | - |
| 0.2786 | 1400 | 0.0001 | - |
| 0.2886 | 1450 | 0.0001 | - |
| 0.2985 | 1500 | 0.0 | - |
| 0.3085 | 1550 | 0.0001 | - |
| 0.3184 | 1600 | 0.0 | - |
| 0.3284 | 1650 | 0.0 | - |
| 0.3383 | 1700 | 0.0 | - |
| 0.3483 | 1750 | 0.0001 | - |
| 0.3582 | 1800 | 0.0 | - |
| 0.3682 | 1850 | 0.0 | - |
| 0.3781 | 1900 | 0.0 | - |
| 0.3881 | 1950 | 0.0 | - |
| 0.3980 | 2000 | 0.0 | - |
| 0.4080 | 2050 | 0.0 | - |
| 0.4179 | 2100 | 0.0 | - |
| 0.4279 | 2150 | 0.0 | - |
| 0.4378 | 2200 | 0.0 | - |
| 0.4478 | 2250 | 0.0 | - |
| 0.4577 | 2300 | 0.0 | - |
| 0.4677 | 2350 | 0.0 | - |
| 0.4776 | 2400 | 0.0 | - |
| 0.4876 | 2450 | 0.0 | - |
| 0.4975 | 2500 | 0.0 | - |
| 0.5075 | 2550 | 0.0 | - |
| 0.5174 | 2600 | 0.0 | - |
| 0.5274 | 2650 | 0.0 | - |
| 0.5373 | 2700 | 0.0 | - |
| 0.5473 | 2750 | 0.0 | - |
| 0.5572 | 2800 | 0.0 | - |
| 0.5672 | 2850 | 0.0 | - |
| 0.5771 | 2900 | 0.0 | - |
| 0.5871 | 2950 | 0.0 | - |
| 0.5970 | 3000 | 0.0 | - |
| 0.6070 | 3050 | 0.0 | - |
| 0.6169 | 3100 | 0.0 | - |
| 0.6269 | 3150 | 0.0 | - |
| 0.6368 | 3200 | 0.0 | - |
| 0.6468 | 3250 | 0.0 | - |
| 0.6567 | 3300 | 0.0 | - |
| 0.6667 | 3350 | 0.0 | - |
| 0.6766 | 3400 | 0.0 | - |
| 0.6866 | 3450 | 0.0 | - |
| 0.6965 | 3500 | 0.0 | - |
| 0.7065 | 3550 | 0.0 | - |
| 0.7164 | 3600 | 0.0 | - |
| 0.7264 | 3650 | 0.0 | - |
| 0.7363 | 3700 | 0.0 | - |
| 0.7463 | 3750 | 0.0 | - |
| 0.7562 | 3800 | 0.0 | - |
| 0.7662 | 3850 | 0.0 | - |
| 0.7761 | 3900 | 0.0 | - |
| 0.7861 | 3950 | 0.0 | - |
| 0.7960 | 4000 | 0.0 | - |
| 0.8060 | 4050 | 0.0 | - |
| 0.8159 | 4100 | 0.0 | - |
| 0.8259 | 4150 | 0.0 | - |
| 0.8358 | 4200 | 0.0 | - |
| 0.8458 | 4250 | 0.0 | - |
| 0.8557 | 4300 | 0.0 | - |
| 0.8657 | 4350 | 0.0 | - |
| 0.8756 | 4400 | 0.0 | - |
| 0.8856 | 4450 | 0.0 | - |
| 0.8955 | 4500 | 0.0 | - |
| 0.9055 | 4550 | 0.0 | - |
| 0.9154 | 4600 | 0.0 | - |
| 0.9254 | 4650 | 0.0 | - |
| 0.9353 | 4700 | 0.0 | - |
| 0.9453 | 4750 | 0.0 | - |
| 0.9552 | 4800 | 0.0 | - |
| 0.9652 | 4850 | 0.0 | - |
| 0.9751 | 4900 | 0.0 | - |
| 0.9851 | 4950 | 0.0 | - |
| 0.9950 | 5000 | 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}
}
```