---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: soy quien busca
- text: adios, buenas tardes
- text: no se encuentra
- text: yo le puedo pasar el mensaje
- text: quizas funcione
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: 0.9111111111111111
      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:** 18 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                                                                                                                             |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------|
| 14    | <ul><li>'tengo otro prestamo activo'</li><li>'mi historial esta mal'</li><li>'tengo credito con otra financiera'</li></ul>           |
| 11    | <ul><li>'hable mas fuerte'</li><li>'se oye muy lejos'</li><li>'se corta'</li></ul>                                                   |
| 15    | <ul><li>'ahorita voy manejando, hablame luego'</li><li>'ahorita no puedo atenderte, estoy ocupado'</li><li>'voy manejando'</li></ul> |
| 7     | <ul><li>'ya fallecio'</li><li>'ya no esta con nosotros'</li><li>'el ya no vive'</li></ul>                                            |
| 4     | <ul><li>'adios, buenas noches'</li><li>'bueno, gracias, adios'</li><li>'listo, hasta luego'</li></ul>                                |
| 10    | <ul><li>'si, quiero saber'</li><li>'si, digame rapido'</li><li>'te escucho'</li></ul>                                                |
| 12    | <ul><li>'no, joven, muchas gracias'</li><li>'no, oiga, gracias'</li><li>'no, por ahora paso, gracias'</li></ul>                      |
| 17    | <ul><li>'bueno, diga'</li><li>'si'</li><li>'si, diga'</li></ul>                                                                      |
| 3     | <ul><li>'si, a ver de que se trata'</li><li>'tal vez si'</li><li>'esta bien, envialo'</li></ul>                                      |
| 5     | <ul><li>'no corresponde ese numero'</li><li>'esta llamando al numero equivocado'</li><li>'aqui no vive esa persona'</li></ul>        |
| 8     | <ul><li>'¿me da la direccion de sus oficinas?'</li><li>'yo no les di mi telefono'</li><li>'yo no le di mis datos a nadie'</li></ul>  |
| 0     | <ul><li>'soy su hermana'</li><li>'esta bajo tratamiento'</li><li>'se siente mal'</li></ul>                                           |
| 16    | <ul><li>'¿quien me llama?'</li><li>'¿de que empresa llaman?'</li><li>'¿quien es?'</li></ul>                                          |
| 1     | <ul><li>'habla el senor'</li><li>'con ella habla'</li><li>'si aqui habla'</li></ul>                                                  |
| 6     | <ul><li>'un momento por favor'</li><li>'deja le hablo'</li><li>'permiteme un segundo, no me cuelgues'</li></ul>                      |
| 2     | <ul><li>'¿con quien quiere hablar?'</li><li>'¿quien busca?'</li><li>'¿a quien esta buscando?'</li></ul>                              |
| 9     | <ul><li>'no esten chingando'</li><li>'es la quinta vez que me marcan hoy'</li><li>'¡que no entiendes que no!'</li></ul>              |
| 13    | <ul><li>'salio a la tienda, no tarda'</li><li>'ahorita no esta, anda de viaje'</li><li>'anda trabajando'</li></ul>                   |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9111   |

## 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("setfit_model_id")
# Run inference
preds = model("soy quien busca")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 3.9018 | 11  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 32                    |
| 1     | 18                    |
| 2     | 11                    |
| 3     | 18                    |
| 4     | 18                    |
| 5     | 22                    |
| 6     | 9                     |
| 7     | 12                    |
| 8     | 40                    |
| 9     | 11                    |
| 10    | 33                    |
| 11    | 13                    |
| 12    | 48                    |
| 13    | 8                     |
| 14    | 36                    |
| 15    | 13                    |
| 16    | 18                    |
| 17    | 37                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0010 | 1    | 0.3888        | -               |
| 0.0504 | 50   | 0.211         | -               |
| 0.1007 | 100  | 0.1344        | -               |
| 0.1511 | 150  | 0.0742        | -               |
| 0.2014 | 200  | 0.0484        | -               |
| 0.2518 | 250  | 0.0387        | -               |
| 0.3021 | 300  | 0.0264        | -               |
| 0.3525 | 350  | 0.0183        | -               |
| 0.4028 | 400  | 0.0135        | -               |
| 0.4532 | 450  | 0.0115        | -               |
| 0.5035 | 500  | 0.0082        | -               |
| 0.5539 | 550  | 0.0083        | -               |
| 0.6042 | 600  | 0.0073        | -               |
| 0.6546 | 650  | 0.009         | -               |
| 0.7049 | 700  | 0.0067        | -               |
| 0.7553 | 750  | 0.0075        | -               |
| 0.8056 | 800  | 0.0085        | -               |
| 0.8560 | 850  | 0.0073        | -               |
| 0.9063 | 900  | 0.0065        | -               |
| 0.9567 | 950  | 0.0076        | -               |
| 1.0    | 993  | -             | 0.0437          |

### Framework Versions
- Python: 3.12.12
- SetFit: 1.1.3
- Sentence Transformers: 5.2.2
- Transformers: 4.44.2
- PyTorch: 2.9.0+cu126
- Datasets: 4.0.0
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->