--- 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 ### 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-augmented") # 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") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:-----| | Word count | 6 | 93.5916 | 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 | |:------:|:----:|:-------------:|:---------------:| | 0.0010 | 1 | 0.3063 | - | | 0.0524 | 50 | 0.2204 | - | | 0.1047 | 100 | 0.1689 | - | | 0.1571 | 150 | 0.1464 | - | | 0.2094 | 200 | 0.1236 | - | | 0.2618 | 250 | 0.1088 | - | | 0.3141 | 300 | 0.0649 | - | | 0.3665 | 350 | 0.0697 | - | | 0.4188 | 400 | 0.0395 | - | | 0.4712 | 450 | 0.052 | - | | 0.5236 | 500 | 0.0263 | - | | 0.5759 | 550 | 0.0376 | - | | 0.6283 | 600 | 0.0307 | - | | 0.6806 | 650 | 0.022 | - | | 0.7330 | 700 | 0.0162 | - | | 0.7853 | 750 | 0.012 | - | | 0.8377 | 800 | 0.0135 | - | | 0.8901 | 850 | 0.0173 | - | | 0.9424 | 900 | 0.0171 | - | | 0.9948 | 950 | 0.0117 | - | ### 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} } ```