README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Implementing the reform required strong support from all ministries involved.
    A major effort was required to present the conceptual change to car importers,
    politicians and the public. A great deal was also invested in public relations
    to describe the benefits of the tax, which by many was perceived as yet another
    attempt to increase tax revenues. A number of the most popular car models’ prices
    were about to increase – mostly large family, luxury and sport cars – but for
    many models, the retail price was actually reduced.
- text: Facilitate transition of workers from the informal to the formal economy.
    This will target the promotion and facilitation of access to SP programs such
    as employment and entrepreneurship opportunities, social security schemes, social
    services, and insurance systems.
- text: environmental and climate awareness, public participation of youth organizations
    and different local actors, teacher training in environmental education for climate
    change, training and technical assistance for projects that allow communities
    and citizens to access and acquire knowledge of environmental issues and of climate
    change. The process of institutionalization of environmental education and culture
    as public policy will promote the consolidation of comprehensive regulatory frameworks,
    the incorporation of the environmental and climate dimension into educational
    and cultural policies, the training of technical management teams and policy design.
- text: SocialSecurityandCommunityDevelopment • Financially sound National Insurance
    Services (NIS). • Extensive public assistance programmes for indigent and economically
    disadvantaged persons. • Rich cultural heritage.
- text: Incorporate a mechanism for monitoring and reviewing marine protected areas
    management plans involving local populations;. Adopt a law to regulate marine
    spatial planning by 2022 and/or revision and adaptation of the current basic law
    of territorial planning and urban planning to include maritime spatial planning
    (a tool that allows the zoning of activities to be developed at sea; law defining
    the use of maritime space and maritime spatial planning);.
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/all-mpnet-base-v2
---

# SetFit with sentence-transformers/all-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 18 classes
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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)

## 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("leavoigt/vulnerability_multilabel_updated")
# Run inference
preds = model("SocialSecurityandCommunityDevelopment • Financially sound National Insurance Services (NIS). • Extensive public assistance programmes for indigent and economically disadvantaged persons. • Rich cultural heritage.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 21  | 72.7143 | 238 |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 0)
- max_steps: -1
- sampling_strategy: undersampling
- 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: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0006 | 1    | 0.3244        | -               |
| 0.6309 | 1000 | 0.0331        | 0.1204          |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.3.0
- 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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