--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: The monitoring and evaluation framework will track progress to deliver nutrition results, valuable lessons will be learnt, the cost effectiveness of prioritised interventions will be established, targets will be realised and the impact of nutrition interventions will be understood. Successful implementation of the Strategic Plan will therefore be dependent on the quality of data collected and reported in a timely manner. - text: ncrease the production of vital local foods Improve the trade balance for selected commodities where import substitution is economically viable - text: nsure effective communication of agricultural related priorities to international partners through formal and non-formal donor coordination meetings. Strengthen capacity of the donor coordinatio - text: In the national policy space for nutrition and food security, the National Council for Food Security, Sovereignty and Nutrition of Timor-Leste (KONSSANTIL), a government-led body, is vital in coordinating multi-sectoral responses to food security and nutrition. While it offers a unique role in shaping the country’s food and nutrition security situation, it faces some operational challenges as the government has not formally endorsed the KONSSTANTIL statute to coordinate cross-sectoral nutrition and food security programs. Also, as an effort to improve multi- sectoral coordination and add footprints to the global nutrition agenda, Timor-Leste, joined the global Scaling Up Nutrition (SUN) movement. The SUN movement secretariate at the Prime Minister’s Office has played a significant role in multi-sectoral coordination for food and nutrition security, including elaboration, positioning, and facilitating the endorsement of the statute of KONSSANTIL and the development of the SDG 2 Consolidated Action Plan for Nutrition and Food Security, a common results framework for SUN - text: 'Multi-hazard approach: A multi-hazard approach identifies and supports the implementation of solutions that address more than one hazard simultaneously. With this approach, it is possible to use the resources more efficiently to address the diverse array of climate hazards' metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.5483870967741935 name: Accuracy --- # 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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 3 classes ### 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) ### Model Labels | Label | Examples | |:--------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1.1. Food Security & Nutrition | | | 6.3.3 Awareness and use of the evidence-based / agrifood systems approach | | | 6.3.4 Effectiveness of Policy Implementation | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.5484 | ## 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("setfit_model_id") # Run inference preds = model("ncrease the production of vital local foods Improve the trade balance for selected commodities where import substitution is economically viable") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:-----| | Word count | 5 | 123.0 | 1014 | | Label | Training Sample Count | |:--------------------------------------------------------------------------|:----------------------| | 1.1. Food Security & Nutrition | 65 | | 6.3.3 Awareness and use of the evidence-based / agrifood systems approach | 32 | | 6.3.4 Effectiveness of Policy Implementation | 22 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - 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: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0019 | 1 | 0.2492 | - | | 0.0949 | 50 | 0.206 | - | | 0.1898 | 100 | 0.1261 | - | | 0.2846 | 150 | 0.1029 | - | | 0.3795 | 200 | 0.0616 | - | | 0.4744 | 250 | 0.0567 | - | | 0.5693 | 300 | 0.0559 | - | | 0.6641 | 350 | 0.0504 | - | | 0.7590 | 400 | 0.0523 | - | | 0.8539 | 450 | 0.0476 | - | | 0.9488 | 500 | 0.0513 | - | | 1.0 | 527 | - | 0.2939 | ### Framework Versions - Python: 3.12.8 - SetFit: 1.1.1 - Sentence Transformers: 3.3.1 - Transformers: 4.49.0 - PyTorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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} } ```