README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- Precision_micro
- Precision_weighted
- Precision_samples
- Recall_micro
- Recall_weighted
- Recall_samples
- F1-Score
- accuracy
widget:
- text: To support the traditional knowledge and adaptive capacity of indigenous peoples
    in the face of climate change, we aim to establish 50 community-based adaptation
    projects led by indigenous peoples by 2030, focusing on the sustainable management
    of natural resources and the preservation of cultural practices.
- text: Measures related to climate change are incorporated into national policies,
    strategies and plans. In this regard, mechanisms are also promoted to increase
    capacity for effective planning and management in relation to climate change.
    SDG No. 14 (Marine life). Adaptation. There is a link between the Coastal Marine
    Resources sector in the measures proposed in this document and the indicators
    of this SDG regarding the sustainable management and conservation of marine and
    coastal ecosystems to achieve an increase in their climate resilience. SDG No.
- text: ' Pathways with higher demand for food, feed, and water, more resource-intensive
    consumption and production, and more limited technological improvements in agriculture
    yields result in higher risks from water scarcity in drylands, land degradation,
    and food insecurity 1. This means that communities that rely on agriculture for
    their livelihoods are at risk of losing their crops and experiencing food shortages
    due to climate change.'
- text: The population aged 60 years and above is projected to increase from almost
    one million (988,000) in 2000 to over six million (6,319,000) by 2050. The female
    aged population will continue to grow faster and will increasingly be far higher
    than the male population for the advanced ages. Policies addressing the needs
    of the elderly will have to take the sex structure of the aged population into
    consideration.
- text: Indigenous peoples who choose or are forced to migrate away from their traditional
    lands often face double discrimination as both migrants and as indigenous peoples.
    Indigenous peoples may be more vulnerable to irregular migration such as trafficking
    and smuggling, owing to sudden displacement by a climactic event, limited legal
    migration options and limited opportunities to make informed choices. Deforestation,
    particularly in developing countries, is pushing indigenous families to migrate
    to cities for economic reasons, often ending up in urban slums.
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: Precision_micro
      value: 0.7762237762237763
      name: Precision_Micro
    - type: Precision_weighted
      value: 0.7968800430338892
      name: Precision_Weighted
    - type: Precision_samples
      value: 0.7762237762237763
      name: Precision_Samples
    - type: Recall_micro
      value: 0.7762237762237763
      name: Recall_Micro
    - type: Recall_weighted
      value: 0.7762237762237763
      name: Recall_Weighted
    - type: Recall_samples
      value: 0.7762237762237763
      name: Recall_Samples
    - type: F1-Score
      value: 0.7762237762237763
      name: F1-Score
    - type: accuracy
      value: 0.7762237762237763
      name: Accuracy
---

# 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 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 384 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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)

## Evaluation

### Metrics
| Label   | Precision_Micro | Precision_Weighted | Precision_Samples | Recall_Micro | Recall_Weighted | Recall_Samples | F1-Score | Accuracy |
|:--------|:----------------|:-------------------|:------------------|:-------------|:----------------|:---------------|:---------|:---------|
| **all** | 0.7762          | 0.7969             | 0.7762            | 0.7762       | 0.7762          | 0.7762         | 0.7762   | 0.7762   |

## 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_target")
# Run inference
preds = model("To support the traditional knowledge and adaptive capacity of indigenous peoples in the face of climate change, we aim to establish 50 community-based adaptation projects led by indigenous peoples by 2030, focusing on the sustainable management of natural resources and the preservation of cultural practices.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 15  | 70.8675 | 238 |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0012 | 1    | 0.3493        | -               |
| 0.0602 | 50   | 0.2285        | -               |
| 0.1205 | 100  | 0.1092        | -               |
| 0.1807 | 150  | 0.1348        | -               |
| 0.2410 | 200  | 0.0365        | -               |
| 0.3012 | 250  | 0.0052        | -               |
| 0.3614 | 300  | 0.0012        | -               |
| 0.4217 | 350  | 0.0031        | -               |
| 0.4819 | 400  | 0.0001        | -               |
| 0.5422 | 450  | 0.0011        | -               |
| 0.6024 | 500  | 0.0001        | -               |
| 0.6627 | 550  | 0.0001        | -               |
| 0.7229 | 600  | 0.0001        | -               |
| 0.7831 | 650  | 0.0002        | -               |
| 0.8434 | 700  | 0.0001        | -               |
| 0.9036 | 750  | 0.0001        | -               |
| 0.9639 | 800  | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.25.1
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.13.3

## 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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