setfit safetensors xlm-roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base model-index region:us
---
base_model: FacebookAI/xlm-roberta-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Banco Sabadell confirma el día de pago de pensiones para jubilados en agosto.
- text: Banco Santander supera resistencias y avanza hacia máximos anuales, lo que
    tiene implicaciones para los inversores.
- text: Abre una cuenta online gratuita en BBVA, domicilia tu nómina durante 12 meses
    y recibe 250€ usando el código 90030031951793.
- text: MyInvestor tiene una grave falta de oferta en acciones individuales y sus
    comisiones son peores que las de ING en ese mismo ámbito.
- text: Los recicladores están durmiendo en la vereda del BBVA y el fin de semana
    dentro del cajero, mientras la seguridad parece ausente.
inference: true
model-index:
- name: SetFit with FacebookAI/xlm-roberta-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.7138461538461538
      name: Accuracy
---

# SetFit with FacebookAI/xlm-roberta-base

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) 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:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 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>'Las negociaciones para el Banco de España avanzan rápidamente y el Congreso convocará el jueves la comisión de economía para anunciar los nombres pactados, con Conthe como la candidata más firme a gobernadora.'</li><li>'Depósitos y seguros son aspectos fundamentales para atraer clientes y potenciar el negocio de Caixabank en la segunda mitad del año.'</li><li>'El Banco Santander ofrece 400€ al cambiar tu nómina a su cuenta en línea, eliminando las comisiones bancarias.'</li></ul> |
| relevant | <ul><li>'Nuevo caso de phishing relacionado con Evobanco registrado el 13 de julio de 2024.'</li><li>'El Banco Sabadell ofrece depósitos a plazo fijo con un interés del 2,5% TAE a 1 año y 3% TAE a 6 meses, lo cual es una buena opción.'</li><li>'Estoy en Abanca porque no me cobran comisiones, de lo contrario ya los habría dejado.'</li></ul>                                                                                                                                                         |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7138   |

## 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-xlm-bank-tweets-processed-80")
# Run inference
preds = model("Banco Sabadell confirma el día de pago de pensiones para jubilados en agosto.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 21.0437 | 36  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| discard  | 80                    |
| relevant | 80                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0025 | 1    | 0.4924        | -               |
| 0.125  | 50   | 0.2519        | -               |
| 0.25   | 100  | 0.186         | -               |
| 0.375  | 150  | 0.188         | -               |
| 0.5    | 200  | 0.0504        | -               |
| 0.625  | 250  | 0.0412        | -               |
| 0.75   | 300  | 0.0147        | -               |
| 0.875  | 350  | 0.0517        | -               |
| 1.0    | 400  | 0.0162        | -               |

### 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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