README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: The infrastructure requirement for collection based on the targets and projections
    made is presented in Table 12.22. A total of about 149,000 km length and 8,660km
    length of sewers are required for urban and rural communities, respectively by
    2047. In addition, a little over 4 million facilities in urban areas and about
    853,000 facilities in rural areas will be required to meet on-site sanitation
    needs by 2033 nationwide.
- text: The population of the Republic of Congo is among the most vulnerable, insofar
    as it has limited room for adaptation, mainly due to poverty. Maintaining the
    services provided by natural ecosystems (forests, savannas, hydrological basins,
    etc.) is essential to ensure future development relays, limit the impacts of climate
    change and offer possibilities for adaptation to the most vulnerable groups, including
    are part of women and young people of all socio-cultural categories of urban and
    rural centers.
- text: Finally, and from the generation of spaces for the exchange of experiences
    and good practices, specialists in the subject,. In terms of raising awareness,
    a discussion was held to integrate the gender perspective into the climate change
    agenda, which included views from government management and science. The discussion
    was aimed at the general public and had the participation of youth organizations
    that work to raise awareness and sensitize in the fight against climate change.
- text: 'Over the past period, even the Party and the Government of Lao PDR were aware
    of the importance of and have paid attention to gender role.  However, the status
    of women in Lao PDR in many fields is not equal to that of men, and women were
    still taken advantage of in many forms. Hence, in order to ensure that peoples
    of all gender and ages and all social strata are able to participate in the process
    and receive the benefits from the development in a comprehensive, inclusive and
    fair manner, the National Green Growth Strategy of the Lao PDR has identified  gender
    role/protection and promotion of the advancement of women activities to be an
    important focus of the green growth and will particularly focus on: '
- text: Construction of pipelines and connection to existing ones to transmit water
    to demand centres. Reduce water loss during transmission by investing on telemetric
    monitoring systems. Enhance conjunctive groundwater-surface water use. Agriculture.
    Improve genetic characteristics of the livestock breed such as Musi breed. Improve
    livestock diet through supplementary feeding. A switch to crops with the following
    traits:. Drought resistant. Tolerant to high temperatures. Short maturity. Health.
    Public education and malaria campaigns. Malaria Strategy. Control of Diarrhoeal
    Diseases
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---

# SetFit with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 17 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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)

## 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("peter2000/setfit-vulnerability-groups")
# Run inference
preds = model("The infrastructure requirement for collection based on the targets and projections made is presented in Table 12.22. A total of about 149,000 km length and 8,660km length of sewers are required for urban and rural communities, respectively by 2047. In addition, a little over 4 million facilities in urban areas and about 853,000 facilities in rural areas will be required to meet on-site sanitation needs by 2033 nationwide.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 15  | 71.2316 | 164 |

### 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
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0011 | 1    | 0.3006        | -               |
| 0.0526 | 50   | 0.2232        | -               |
| 0.1053 | 100  | 0.1670        | -               |
| 0.1579 | 150  | 0.1202        | -               |
| 0.2105 | 200  | 0.0935        | -               |
| 0.2632 | 250  | 0.0862        | -               |
| 0.3158 | 300  | 0.0626        | -               |
| 0.3684 | 350  | 0.0664        | -               |
| 0.4211 | 400  | 0.0555        | -               |
| 0.4737 | 450  | 0.0528        | -               |
| 0.5263 | 500  | 0.0543        | -               |
| 0.5789 | 550  | 0.0501        | -               |
| 0.6316 | 600  | 0.0535        | -               |
| 0.6842 | 650  | 0.0465        | -               |
| 0.7368 | 700  | 0.0468        | -               |
| 0.7895 | 750  | 0.0470        | -               |
| 0.8421 | 800  | 0.0421        | -               |
| 0.8947 | 850  | 0.0379        | -               |
| 0.9474 | 900  | 0.0475        | -               |
| 1.0    | 950  | 0.0449        | -               |

### Framework Versions
- Python: 3.12.12
- SetFit: 1.2.0
- Sentence Transformers: 6.1.0
- Transformers: 5.17.0
- PyTorch: 2.14.0+cu130
- Datasets: 5.0.1
- Tokenizers: 0.23.2

## 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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