README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Plasta
- text: 203 terminada
- text: habitación 294 limpia
- text: ¡Hola, cómo va todo!
- text: Quiero ver el estado de la incidencia que reporté en la Calle Mayor de Triana.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: jaimevera1107/all-MiniLM-L6-v2-similarity-es
---

# Input Classifier

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [jaimevera1107/all-MiniLM-L6-v2-similarity-es](https://huggingface.co/jaimevera1107/all-MiniLM-L6-v2-similarity-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:** [jaimevera1107/all-MiniLM-L6-v2-similarity-es](https://huggingface.co/jaimevera1107/all-MiniLM-L6-v2-similarity-es)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** 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                                                                                                                                                             |
|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| request      | <ul><li>'lencería necesaria'</li><li>'material necesario para hoy'</li><li>'terminé la habitación 234'</li></ul>                                                     |
| conversation | <ul><li>'buena noche'</li><li>'Qué pasa, tío, ¿todo bien?'</li><li>'Buenas, ¿cómo va la cosa?!'</li></ul>                                                            |
| help         | <ul><li>'ayuda por favor'</li><li>'Ayuda que no sé que puedo hacer'</li><li>'Hola, que puedo hacer'</li></ul>                                                        |
| censorship   | <ul><li>'Eres un completo inútil, no sirves para nada'</li><li>'Siempre diciendo estupideces, mejor cállate'</li><li>'Tu sola existencia es una vergüenza'</li></ul> |

## 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("monentiadev/es-input-classifier")
# Run inference
preds = model("Hola")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 5.0723 | 38  |

| Label        | Training Sample Count |
|:-------------|:----------------------|
| censorship   | 407                   |
| conversation | 137                   |
| help         | 274                   |
| request      | 552                   |

### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (3, 3)
- 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
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0023 | 1    | 0.3161        | -               |
| 0.1166 | 50   | 0.2857        | -               |
| 0.2331 | 100  | 0.2158        | -               |
| 0.3497 | 150  | 0.1581        | -               |
| 0.4662 | 200  | 0.0878        | -               |
| 0.5828 | 250  | 0.0299        | -               |
| 0.6993 | 300  | 0.0124        | -               |
| 0.8159 | 350  | 0.0083        | -               |
| 0.9324 | 400  | 0.006         | -               |
| 1.0490 | 450  | 0.0038        | -               |
| 1.1655 | 500  | 0.0027        | -               |
| 1.2821 | 550  | 0.0027        | -               |
| 1.3986 | 600  | 0.0017        | -               |
| 1.5152 | 650  | 0.0016        | -               |
| 1.6317 | 700  | 0.0013        | -               |
| 1.7483 | 750  | 0.0012        | -               |
| 1.8648 | 800  | 0.0012        | -               |
| 1.9814 | 850  | 0.001         | -               |
| 2.0979 | 900  | 0.001         | -               |
| 2.2145 | 950  | 0.0011        | -               |
| 2.3310 | 1000 | 0.0009        | -               |
| 2.4476 | 1050 | 0.0008        | -               |
| 2.5641 | 1100 | 0.0009        | -               |
| 2.6807 | 1150 | 0.0008        | -               |
| 2.7972 | 1200 | 0.0008        | -               |
| 2.9138 | 1250 | 0.0007        | -               |

### Framework Versions
- Python: 3.10.0
- SetFit: 1.1.2
- Sentence Transformers: 5.0.0
- Transformers: 4.53.1
- PyTorch: 2.7.1+cu126
- Datasets: 2.19.2
- Tokenizers: 0.21.2

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