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---
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: 'Enhancing climate change resilience in the Benguela current fisheries system
(regional project: Angola, Namibia and South Africa). The project aims to build
resilience and reduce vulnerability of the Benguela Current marine fisheries systems
to climate change through strengthened adaptive capacity and implementation of
participatory and integrated adaptive strategies in order to ensure food and livelihood
security. Fisheries. Agriculture and food security. Total project cost (US $ million):
16.520. Implementing GEF agency: FAO.'
- 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
co2_eq_emissions:
emissions: 268.4261122496047
source: codecarbon
training_type: fine-tuning
---
# 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
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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("GIZ/vulnerability_multilabel_v2")
# Run inference
preds = model("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.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 61.2897 | 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 | 0.2095 | - |
| 0.2084 | 1000 | 0.0307 | 0.1211 |
| 0.4168 | 2000 | 0.0165 | 0.1275 |
| 0.6251 | 3000 | 0.0085 | 0.131 |
| 0.8335 | 4000 | 0.0317 | 0.1171 |
### Framework Versions
- Python: 3.9.5
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Datasets: 2.3.0
- Tokenizers: 0.19.1
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.155 kg of CO2
- **Hours Used**: 1.08 hours
### 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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