---
base_model: pysentimiento/robertuito-sentiment-analysis
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Aquest text és valid per a un cercador de tràmits d'un ajuntament
- text: Aquest text és ofensiu o violent o negatiu o inapropiat per a un cercador
    de tràmits d'un ajuntament
- text: Aquest text és valid per a un cercador de tràmits d'un ajuntament
- text: Aquest text és valid per a un cercador de tràmits d'un ajuntament
- text: Aquest text és ofensiu o violent o negatiu o inapropiat per a un cercador
    de tràmits d'un ajuntament
inference: true
---

# SetFit with pysentimiento/robertuito-sentiment-analysis

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) 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:** [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis)
- **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
<!-- - **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                                                                                                                                                                                                                                                                                                                                               |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>"Aquest text és ofensiu o violent o negatiu o inapropiat per a un cercador de tràmits d'un ajuntament"</li><li>"Aquest text és ofensiu o violent o negatiu o inapropiat per a un cercador de tràmits d'un ajuntament"</li><li>"Aquest text és ofensiu o violent o negatiu o inapropiat per a un cercador de tràmits d'un ajuntament"</li></ul> |
| 1     | <ul><li>"Aquest text és valid per a un cercador de tràmits d'un ajuntament"</li><li>"Aquest text és valid per a un cercador de tràmits d'un ajuntament"</li><li>"Aquest text és valid per a un cercador de tràmits d'un ajuntament"</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("adriansanz/sentimentv2")
# Run inference
preds = model("Aquest text és valid per a un cercador de tràmits d'un ajuntament")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 12  | 15.0   | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 20                    |
| 1     | 20                    |

### Training Hyperparameters
- batch_size: (16, 16)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0189 | 1    | 0.2722        | -               |
| 0.9434 | 50   | 0.0004        | -               |
| 1.8868 | 100  | 0.0003        | -               |
| 2.8302 | 150  | 0.0002        | -               |
| 3.7736 | 200  | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- 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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