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: Violence from intimate partners and male family members can escalate during
    emergencies. This tends to increase as the crisis worsens, and men have lost their
    jobs and status – particularly in communities with traditional gender roles, and
    where family violence is normalised
- text: Expand livelihood protection policies that assist vulnerable, low-income individuals
    to recover from damages associated with extreme weather events; provide support
    and protection for internally displaced persons, persons displaced across borders
    and host communities;. By 2026, draw up disaster recovery plans for all 22 municipalities
    with resource inventories, first response measures and actions (including on logistics)
    concerning humanitarian post-disaster needs.
- text: recurrent droughts, (decrease in amount of rainfall from 550 to 400mm in the
    highlands), changes in seasonality that had resulted frequent crop failure, massive
    death of livestock, genetic erosion, extinction of endemic species, degradation
    of habitats and disequilibria in the ecosystem structure and function. The impact
    of climate change is manifested in recurrent droughts, desertification, sea level
    rise and increase in sea water temperature, depletion of ground water, widespread
    land degradation, and emergence of climate sensitive diseases.
- 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: Overcoming Poverty. Colombia, as a developing country, faces major socioeconomic
    challenges. According to the official figures of DANE, by 2014, the percentage
    of people in multidimensional poverty situation was 21.9% (this figure rises to
    44.1% if we take into account only the rural population). For the same year, 28.5%
    of the population was found in a situation of monetary poverty (41.4% of the population
    in the case of the villages and rural centers scattered).
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: 0.7972027972027972
      name: Precision_Micro
    - type: Precision_weighted
      value: 0.8053038510784989
      name: Precision_Weighted
    - type: Precision_samples
      value: 0.7972027972027972
      name: Precision_Samples
    - type: Recall_micro
      value: 0.7972027972027972
      name: Recall_Micro
    - type: Recall_weighted
      value: 0.7972027972027972
      name: Recall_Weighted
    - type: Recall_samples
      value: 0.7972027972027972
      name: Recall_Samples
    - type: F1-Score
      value: 0.7972027972027972
      name: F1-Score
    - type: accuracy
      value: 0.7972027972027972
      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** | 0.7972          | 0.8053             | 0.7972            | 0.7972       | 0.7972          | 0.7972         | 0.7972   | 0.7972   |

## 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("Violence from intimate partners and male family members can escalate during emergencies. This tends to increase as the crisis worsens, and men have lost their jobs and status – particularly in communities with traditional gender roles, and where family violence is normalised")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 15  | 71.9518 | 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    | 0.2559        | -               |
| 0.0602 | 50   | 0.2509        | -               |
| 0.1205 | 100  | 0.2595        | -               |
| 0.1807 | 150  | 0.0868        | -               |
| 0.2410 | 200  | 0.0302        | -               |
| 0.3012 | 250  | 0.0024        | -               |
| 0.3614 | 300  | 0.0225        | -               |
| 0.4217 | 350  | 0.0007        | -               |
| 0.4819 | 400  | 0.0004        | -               |
| 0.5422 | 450  | 0.0003        | -               |
| 0.6024 | 500  | 0.0002        | -               |
| 0.6627 | 550  | 0.0005        | -               |
| 0.7229 | 600  | 0.0319        | -               |
| 0.7831 | 650  | 0.0001        | -               |
| 0.8434 | 700  | 0.0104        | -               |
| 0.9036 | 750  | 0.0003        | -               |
| 0.9639 | 800  | 0.0009        | -               |

### 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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