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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: WHO and UNICEF has recommended that a child should receive the minimum dietary |
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diversity (MDD) of foods and beverages from at least five out of eight defined |
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food groups to maintain proper growth and development during this critical period |
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19 . In Timor-Leste, 35.3% received minimum dietary diversity (MDD) 4 . On the |
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other hand, the proportion of children 6-23 months receiving MDD has been on the |
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upward rise (28% in 2013 to 35.3% in 2020) although it is still low. Food group |
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diversity is associated with improved linear growth in young children20 . A diet |
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lacking in diversity can increase the risk of micronutrient deficiencies, which |
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may have a damaging effect on 47.0% 81.7% 93.4% 75.2% 30.7% 57.5% 62.3% 50.2% |
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% TLDHS 2003 TLDHS 2010 TLFNS 2013 TLFNS |
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2016 46.8% 64.2% TLFNS 2020 Early Initiation (1 hour) Exclusive breastfeeding |
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(0-5 months) 20NATIONAL HEALTH SECTOR NUTRITION STRATEGIC PLAN 2022-2026 children’s |
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physical and cognitive development21 . Consequently, TLFNS 2020 reported that |
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a very high proportion of children 6-23 months had consumed grains, roots, and |
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tubers (97.5%) and breast milk (90.6%), as well as vitamin A-rich fruits and vegetables |
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(71.5%). Consumption of dairy products (0.8%) was low, while consumption of flesh |
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foods (23.1%) and legumes or nuts (31.0%) was also relatively low. The 2020 survey |
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reported that 19.1% of children 6-23 months consumed sugar sweetened beverages, |
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31.0% consumed sweet or savoury junk foods, while 20.0% did not consume any fruits |
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or vegetables and 35.9% consumed no eggs or flesh foods. |
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- text: Climate Risk and Vulnerability Baseline. One of the key roles of the NAP process |
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is to develop a common evidence base on CC that can be referenced by stakeholders |
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in various documents, including strategies and project proposals. Therefore, climate |
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risk and vulnerability assessments shall be summarized and updated on a periodical |
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basis to underlie the development of the NAP and the list of m |
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- text: 'Agriculture in Armenia has always been remarkable with the high level of |
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climate risks (hail damage, frost damage, drought, etc.). As it is already mentioned, |
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agriculture has suffered losses from natural disasters worth of AMD 110 billion |
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during the recent 6 years. Climate risks in Armenia are a serious problem since |
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there are no clearly formed such state, political or institutional mechanisms, |
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the application of which would make it possible to noticeably mitigate the existing |
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risks. Due to the lack of such mechanisms, the mechanism of full assessment of |
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the agricultural losses does not work too, as well as the risks are not assessed |
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in advance. In this context, to reduce the agricultural risks, to introduce loss |
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compensation mechanisms in a systemized way, and to provide sustainable income |
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levels for economic entities, it is necessary to address the critical issue of |
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agricultural risk insurance. ' |
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- text: 'Strategy 6.3: Strengthen monitoring, evaluation and surveillance systems |
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for routine information sharing and data utilization at all levels Activities |
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Stakeholder Conduct bi-annual nutrition M&E coordination meetings. ND, M&ED, INS |
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Collaborate with HIS Department (HISD) and M&E Department MOH to conduct routine |
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nutrition data quality assessments and audits (RDQA). ND, HISD, M&ED, INS In collaboration |
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with HISD MOH and M&E Department, train M&E officers, DPHO nutrition, nutrition |
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focal points and Municipality Health Services on data management (collection analyses, |
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interpreting and reporting) at all levels. ND, HISD, M&ED, INS Develop and disseminate |
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the Nutrition M&E Plan. ND, M&ED Strengthen the nutrition information system within |
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the HMIS by integrating key nutrition indicators and databases. ND, HISD, M&ED |
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Establish and scale up a nutrition surveillance system for real time monitoring |
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at all levels. ND, M&ED, INS Conduct mid-term and end-term evaluation of the nutrition |
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strategic plan. ND, HISD, M&ED, INS Conduct a food and nutrition survey every |
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5 years. ND, HISD, M&ED, INS Conduct knowledge attitude and practices (KAP) survey |
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on nutrition. ND, HISD, M&ED, HPD, INS Liaise with HMIS to introduce real-time |
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data collection linked to DHIS2. ND, HISD, M&ED Periodic publishing of nutrition |
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bulletin/report ND, HISD, M&ED Develop and regularly review nutrition indicators |
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monitoring and evaluation guideline. ND, HMIS, M&ED, INS ' |
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- text: Provision 1 - Access to safe nutritious food for all The package will be aimed |
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at ending hunger and all forms of malnutrition and reduce the incidence of non-communicable |
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diseases, enabling all people to be nourished and healthy. This suggests that |
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all people at all times have access to sufficient quantities of affordable and |
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safe foo |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: false |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a OneVsRestClassifier instance |
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- **Maximum Sequence Length:** 512 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("faodl/setfit-paraphrase-mpnet-base-v2-5ClassesDesc-multilabel") |
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# Run inference |
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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") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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<!-- |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:---------|:-----| |
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| Word count | 7 | 123.3475 | 1014 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0028 | 1 | 0.3314 | - | |
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| 0.0709 | 50 | 0.2212 | - | |
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| 0.1418 | 100 | 0.1679 | - | |
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| 0.2128 | 150 | 0.1224 | - | |
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| 0.2837 | 200 | 0.0782 | - | |
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| 0.3546 | 250 | 0.0889 | - | |
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| 0.4255 | 300 | 0.0765 | - | |
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| 0.4965 | 350 | 0.0591 | - | |
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| 0.5674 | 400 | 0.0511 | - | |
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| 0.6383 | 450 | 0.0364 | - | |
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| 0.7092 | 500 | 0.0454 | - | |
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| 0.7801 | 550 | 0.0327 | - | |
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| 0.8511 | 600 | 0.0237 | - | |
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| 0.9220 | 650 | 0.024 | - | |
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| 0.9929 | 700 | 0.0216 | - | |
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### Framework Versions |
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- Python: 3.11.11 |
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- SetFit: 1.1.1 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.50.2 |
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- PyTorch: 2.6.0+cu124 |
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- Datasets: 3.5.0 |
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- Tokenizers: 0.21.1 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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