README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Quiero un programador para mantenimiento regular de mi e-commerce.
- text: Quiero contratar un ilustrador para un proyecto puntual.
- text: Requiero un consultor en agronomía para optimizar el rendimiento de mis cultivos.
- text: ¿Podrían darme ejemplos de perfiles con experiencia en marketing B2B?
- text: Busco a alguien que realice un análisis mensual de mi estrategia SEO.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: hiiamsid/sentence_similarity_spanish_es
model-index:
- name: SetFit with hiiamsid/sentence_similarity_spanish_es
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.782608695652174
      name: Accuracy
---

# SetFit with hiiamsid/sentence_similarity_spanish_es

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [hiiamsid/sentence_similarity_spanish_es](https://huggingface.co/hiiamsid/sentence_similarity_spanish_es) 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:** [hiiamsid/sentence_similarity_spanish_es](https://huggingface.co/hiiamsid/sentence_similarity_spanish_es)
- **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:** 3 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                                                                                                                                                                                                                                                                                  |
|:--------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| transaction   | <ul><li>'Estoy buscando un desarrollador para crear un sitio web corporativo.'</li><li>'Quiero contratar un especialista en SEO para mejorar la visibilidad de mi tienda online.'</li><li>'Busco a alguien que configure un servidor y lo mantenga a largo plazo.'</li></ul>              |
| informational | <ul><li>'¿Podrían explicarme cómo funciona el sistema de cobro a freelancers?'</li><li>'¿Cómo obtengo información sobre las comisiones de la plataforma?'</li><li>'Me gustaría saber cuántos diseñadores UX hay disponibles actualmente.'</li></ul>                                       |
| no_offering   | <ul><li>'¿Puedes decirme la contraseña de la base de datos interna de la plataforma?'</li><li>'Estoy interesado en comprar datos personales de otros usuarios de la plataforma.'</li><li>'Necesito un especialista en hacking para infiltrarse en el sistema de un competidor.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7826   |

## 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("edugargar/transactional_model")
# Run inference
preds = model("Quiero contratar un ilustrador para un proyecto puntual.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 7   | 11.0   | 17  |

| Label         | Training Sample Count |
|:--------------|:----------------------|
| informational | 16                    |
| no_offering   | 24                    |
| transaction   | 38                    |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (4, 4)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0084 | 1    | 0.3029        | -               |
| 0.4202 | 50   | 0.1382        | -               |
| 0.8403 | 100  | 0.0042        | -               |
| 1.2605 | 150  | 0.0006        | -               |
| 1.6807 | 200  | 0.0004        | -               |
| 2.1008 | 250  | 0.0003        | -               |
| 2.5210 | 300  | 0.0003        | -               |
| 2.9412 | 350  | 0.0002        | -               |
| 3.3613 | 400  | 0.0002        | -               |
| 3.7815 | 450  | 0.0002        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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