setfit safetensors xlm-roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 region:us
---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Dominica is striving for multi-sectoral and multi-level adaptation across
    all segments of society, giving particular consideration to vulnerable groups
    - the poor, disabled, elderly and Kalinago community; as well as gender disparities.
    Recognising the threats posed by climate change, Dominica has over the last two
    decades, undertaken a number of initiatives to respond to this threat. The adaptation
    component has been revised to incorporate updated information on regional climate
    change projections and impacts on Caribbean SIDS.
- text: They live in geographical regions and ecosystems that are the most vulnerable
    to climate change. These include polar regions, humid tropical forests, high mountains,
    small islands, coastal regions, and arid and semi-arid lands, among others. The
    impacts of climate change in such regions have strong implications for the ecosystem-based
    livelihoods on which many indigenous peoples depend. Moreover, in some regions
    such as the Pacific, the very existence of many indigenous territories is under
    threat from rising sea levels that not only pose a grave threat to indigenous
    peoples’ livelihoods but also to their cultures and ways of life.
- text: Seek to increase urban resilience by developing master plans for rainwater
    drainage, improving and extending drainage infrastructure, and implementing flood
    management systems in vulnerable areas. Adaptive capacity of agro- silvo- pastoral
    production and promotion of blue economy.
- text: As the average annual precipitation across the country is expected to decline
    2.6-3.4% by 2025 and 5.9-6.3% by 2050 this will result direct yield response.
    As described by PACE experiment59 on the Pastures and Climate Extremes using a
    factorial combination of elevated temperature (ambient +3°C) and winter/spring
    extreme drought (60% rainfall reduction) resulted in productivity declines of
    up to 73%. Functional group identity was not an important predictor of yield response
    to drought.
- text: Poor rural households in marginal territories that have a low productive potential
    and/or that are far from markets and infrastructure are highly vulnerable to climate-change
    impacts and could easily fall into poverty-environment traps 9. This means that
    communities that are already struggling economically and geographically isolated
    are at greater risk of experiencing the negative impacts of climate change on
    their agricultural livelihoods.
inference: false
---

# SetFit with sentence-transformers/paraphrase-multilingual-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-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 18 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("leavoigt/vulnerability_multilabel_v2")
# Run inference
preds = model("Seek to increase urban resilience by developing master plans for rainwater drainage, improving and extending drainage infrastructure, and implementing flood management systems in vulnerable areas. Adaptive capacity of agro- silvo- pastoral production and promotion of blue economy.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 61.3809 | 164 |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1    | 0.25          | -               |
| 0.2084 | 1000 | 0.0461        | 0.1223          |
| 0.4168 | 2000 | 0.0169        | 0.1294          |
| 0.6251 | 3000 | 0.032         | 0.121           |
| 0.8335 | 4000 | 0.023         | 0.1172          |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.36.2
- PyTorch: 2.4.0+cu121
- Datasets: 2.10.0
- 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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