README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: WHO and UNICEF has recommended that a child should receive the minimum dietary
    diversity (MDD) of foods and beverages from at least five out of eight defined
    food groups to maintain proper growth and development during this critical period
    19 . In Timor-Leste, 35.3% received minimum dietary diversity (MDD) 4 . On the
    other hand, the proportion of children 6-23 months receiving MDD has been on the
    upward rise (28% in 2013 to 35.3% in 2020) although it is still low. Food group
    diversity is associated with improved linear growth in young children20 . A diet
    lacking in diversity can increase the risk of micronutrient deficiencies, which
    may have a damaging effect on 47.0% 81.7% 93.4% 75.2% 30.7% 57.5% 62.3% 50.2%
    0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% TLDHS 2003 TLDHS 2010 TLFNS 2013 TLFNS
    2016 46.8% 64.2% TLFNS 2020 Early Initiation (1 hour) Exclusive breastfeeding
    (0-5 months) 20NATIONAL HEALTH SECTOR NUTRITION STRATEGIC PLAN 2022-2026 children’s
    physical and cognitive development21 . Consequently, TLFNS 2020 reported that
    a very high proportion of children 6-23 months had consumed grains, roots, and
    tubers (97.5%) and breast milk (90.6%), as well as vitamin A-rich fruits and vegetables
    (71.5%). Consumption of dairy products (0.8%) was low, while consumption of flesh
    foods (23.1%) and legumes or nuts (31.0%) was also relatively low. The 2020 survey
    reported that 19.1% of children 6-23 months consumed sugar sweetened beverages,
    31.0% consumed sweet or savoury junk foods, while 20.0% did not consume any fruits
    or vegetables and 35.9% consumed no eggs or flesh foods.
- text: Climate Risk and Vulnerability Baseline. One of the key roles of the NAP process
    is to develop a common evidence base on CC that can be referenced by stakeholders
    in various documents, including strategies and project proposals. Therefore, climate
    risk and vulnerability assessments shall be summarized and updated on a periodical
    basis to underlie the development of the NAP and the list of m
- text: 'Agriculture in Armenia has always been remarkable with the high level of
    climate risks (hail damage, frost damage, drought, etc.). As it is already mentioned,
    agriculture has suffered losses from natural disasters worth of AMD 110 billion
    during the recent 6 years. Climate risks in Armenia are a serious problem since
    there are no clearly formed such state, political or institutional mechanisms,
    the application of which would make it possible to noticeably mitigate the existing
    risks. Due to the lack of such mechanisms, the mechanism of full assessment of
    the agricultural losses does not work too, as well as the risks are not assessed
    in advance. In this context, to reduce the agricultural risks, to introduce loss
    compensation mechanisms in a systemized way, and to provide sustainable income
    levels for economic entities, it is necessary to address the critical issue of
    agricultural risk insurance. '
- text: 'Strategy 6.3: Strengthen monitoring, evaluation and surveillance systems
    for routine information sharing and data utilization at all levels Activities
    Stakeholder Conduct bi-annual nutrition M&E coordination meetings. ND, M&ED, INS
    Collaborate with HIS Department (HISD) and M&E Department MOH to conduct routine
    nutrition data quality assessments and audits (RDQA). ND, HISD, M&ED, INS In collaboration
    with HISD MOH and M&E Department, train M&E officers, DPHO nutrition, nutrition
    focal points and Municipality Health Services on data management (collection analyses,
    interpreting and reporting) at all levels. ND, HISD, M&ED, INS Develop and disseminate
    the Nutrition M&E Plan. ND, M&ED Strengthen the nutrition information system within
    the HMIS by integrating key nutrition indicators and databases. ND, HISD, M&ED
    Establish and scale up a nutrition surveillance system for real time monitoring
    at all levels. ND, M&ED, INS Conduct mid-term and end-term evaluation of the nutrition
    strategic plan. ND, HISD, M&ED, INS Conduct a food and nutrition survey every
    5 years. ND, HISD, M&ED, INS Conduct knowledge attitude and practices (KAP) survey
    on nutrition. ND, HISD, M&ED, HPD, INS Liaise with HMIS to introduce real-time
    data collection linked to DHIS2. ND, HISD, M&ED Periodic publishing of nutrition
    bulletin/report ND, HISD, M&ED Develop and regularly review nutrition indicators
    monitoring and evaluation guideline. ND, HMIS, M&ED, INS '
- text: Provision 1 - Access to safe nutritious food for all The package will be aimed
    at ending hunger and all forms of malnutrition and reduce the incidence of non-communicable
    diseases, enabling all people to be nourished and healthy. This suggests that
    all people at all times have access to sufficient quantities of affordable and
    safe foo
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:** 512 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)

## 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("faodl/setfit-paraphrase-mpnet-base-v2-5ClassesDesc-multilabel")
# Run inference
preds = model("Provision 1 - Access to safe nutritious food for all The package will be aimed at ending hunger and all forms of malnutrition and reduce the incidence of non-communicable diseases, enabling all people to be nourished and healthy. This suggests that all people at all times have access to sufficient quantities of affordable and safe foo")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median   | Max  |
|:-------------|:----|:---------|:-----|
| Word count   | 7   | 123.3475 | 1014 |

### Training Hyperparameters
- batch_size: (8, 8)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0028 | 1    | 0.3314        | -               |
| 0.0709 | 50   | 0.2212        | -               |
| 0.1418 | 100  | 0.1679        | -               |
| 0.2128 | 150  | 0.1224        | -               |
| 0.2837 | 200  | 0.0782        | -               |
| 0.3546 | 250  | 0.0889        | -               |
| 0.4255 | 300  | 0.0765        | -               |
| 0.4965 | 350  | 0.0591        | -               |
| 0.5674 | 400  | 0.0511        | -               |
| 0.6383 | 450  | 0.0364        | -               |
| 0.7092 | 500  | 0.0454        | -               |
| 0.7801 | 550  | 0.0327        | -               |
| 0.8511 | 600  | 0.0237        | -               |
| 0.9220 | 650  | 0.024         | -               |
| 0.9929 | 700  | 0.0216        | -               |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- Transformers: 4.50.2
- PyTorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->