--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Strengthen macro-fiscal resilience through risk-informed public investment planning, including scenario-based budgeting and contingent financing arrangements. - text: 'finding environmentally sustainable energy solutions is central to the document. it seeks to facilitate cultural, institutional and technological change in a way that supports ''''aggressive'''' advances in energy efficiency and conservation, minimises greenhouse emissions and ultimately provides green growth. these energy efficiency and conservation goals are seen as ''''no regrets'''' mitigation actions that can have positive impacts on society and the economy, principally by reducing costs and dependency on fossil fuel imports. overall the policy propose to reduce the percentage of petroleum in the country''''s energy supply mix from the current 95 percent (does not state to what level) and increase the percentage of renewables in the energy mix with proposed targets of 11 percent by 2012, 12.5 percent by 2015 and 20 percent by 2030. six sub-policies exist to support the national energy policy, namely: - a carbon emissions trading policy developed to address jamaica''''s participation in the clean development mechanism - energy-from-waste policy - national renewable energy policy 2010-2030 - national energy from waste policy 2010-2030 - energy conservation and efficiency policy - biofuels policy' - text: 'objetivos: 1. promover la garantía del derecho a la alimentación para la población general y en especial para las personas y grupos de mayor vulnerabilidad. 2. respetar la identidad cultural, las necesidades nutricionales según el ciclo de vida y la diversidad de formas de producción, de consumo y comercialización agropecuaria, fortaleciendo los mercados locales, sin contraponerse al comercio agroalimentario internacional, favoreciéndose la producción nacional en granos básicos, frutas y vegetales. 3. promover la igualdad entre hombres y mujeres, dando las mismas posibilidades de acceso a recursos productivos, servicios y oportunidades para asumir responsabilidades y roles en la seguridad alimentaria y nutricional. 4.transformar el enfoque de las políticas públicas y sociales, para que pasen las personas de ser clientela pasiva y vulnerable que requiere de asistencia, a personas sujetos de derechos.' - text: Regulatory arrangements will be reformed to accelerate innovation in agriculture, including pilot programs, regulatory sandboxes for new inputs and services, clear intellectual property protection, and predictable approval timelines for agrochemical and digital solutions that meet safety and environmental criteria. - text: Climate-smart strategies will protect livelihoods by diversifying income sources, expanding agroforestry and drought-resistant crops, and implementing risk-transfer mechanisms that shield poor households from shocks, thereby contributing to sustained declines in poverty levels. metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: false base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 --- # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Classification head:** a OneVsRestClassifier instance - **Maximum Sequence Length:** 128 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/model_cca_multilabel_MiniLM-L12-70prop-data-augmented") # Run inference preds = model("Strengthen macro-fiscal resilience through risk-informed public investment planning, including scenario-based budgeting and contingent financing arrangements.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 69.0403 | 951 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (2, 2) - 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 | |:------:|:-----:|:-------------:|:---------------:| | 0.0001 | 1 | 0.2247 | - | | 0.0065 | 50 | 0.2105 | - | | 0.0130 | 100 | 0.1984 | - | | 0.0195 | 150 | 0.1899 | - | | 0.0260 | 200 | 0.1916 | - | | 0.0325 | 250 | 0.1769 | - | | 0.0390 | 300 | 0.1679 | - | | 0.0455 | 350 | 0.1677 | - | | 0.0520 | 400 | 0.1591 | - | | 0.0585 | 450 | 0.1521 | - | | 0.0650 | 500 | 0.1522 | - | | 0.0715 | 550 | 0.1497 | - | | 0.0780 | 600 | 0.1494 | - | | 0.0845 | 650 | 0.1457 | - | | 0.0910 | 700 | 0.1503 | - | | 0.0975 | 750 | 0.1328 | - | | 0.1040 | 800 | 0.1251 | - | | 0.1105 | 850 | 0.1395 | - | | 0.1170 | 900 | 0.1298 | - | | 0.1235 | 950 | 0.1221 | - | | 0.1300 | 1000 | 0.1313 | - | | 0.1365 | 1050 | 0.1267 | - | | 0.1429 | 1100 | 0.1367 | - | | 0.1494 | 1150 | 0.1324 | - | | 0.1559 | 1200 | 0.1201 | - | | 0.1624 | 1250 | 0.1244 | - | | 0.1689 | 1300 | 0.1231 | - | | 0.1754 | 1350 | 0.1214 | - | | 0.1819 | 1400 | 0.1098 | - | | 0.1884 | 1450 | 0.1152 | - | | 0.1949 | 1500 | 0.1149 | - | | 0.2014 | 1550 | 0.1185 | - | | 0.2079 | 1600 | 0.1123 | - | | 0.2144 | 1650 | 0.1092 | - | | 0.2209 | 1700 | 0.1097 | - | | 0.2274 | 1750 | 0.1159 | - | | 0.2339 | 1800 | 0.1076 | - | | 0.2404 | 1850 | 0.114 | - | | 0.2469 | 1900 | 0.1055 | - | | 0.2534 | 1950 | 0.1033 | - | | 0.2599 | 2000 | 0.1016 | - | | 0.2664 | 2050 | 0.1004 | - | | 0.2729 | 2100 | 0.0973 | - | | 0.2794 | 2150 | 0.1051 | - | | 0.2859 | 2200 | 0.0954 | - | | 0.2924 | 2250 | 0.0998 | - | | 0.2989 | 2300 | 0.0984 | - | | 0.3054 | 2350 | 0.0906 | - | | 0.3119 | 2400 | 0.0939 | - | | 0.3184 | 2450 | 0.1023 | - | | 0.3249 | 2500 | 0.0983 | - | | 0.3314 | 2550 | 0.0952 | - | | 0.3379 | 2600 | 0.099 | - | | 0.3444 | 2650 | 0.0994 | - | | 0.3509 | 2700 | 0.0975 | - | | 0.3574 | 2750 | 0.0871 | - | | 0.3639 | 2800 | 0.0969 | - | | 0.3704 | 2850 | 0.0845 | - | | 0.3769 | 2900 | 0.1007 | - | | 0.3834 | 2950 | 0.0887 | - | | 0.3899 | 3000 | 0.0807 | - | | 0.3964 | 3050 | 0.0859 | - | | 0.4029 | 3100 | 0.0826 | - | | 0.4094 | 3150 | 0.0784 | - | | 0.4159 | 3200 | 0.0851 | - | | 0.4224 | 3250 | 0.0834 | - | | 0.4288 | 3300 | 0.0922 | - | | 0.4353 | 3350 | 0.0862 | - | | 0.4418 | 3400 | 0.0856 | - | | 0.4483 | 3450 | 0.0848 | - | | 0.4548 | 3500 | 0.0735 | - | | 0.4613 | 3550 | 0.0752 | - | | 0.4678 | 3600 | 0.0881 | - | | 0.4743 | 3650 | 0.0836 | - | | 0.4808 | 3700 | 0.0808 | - | | 0.4873 | 3750 | 0.0963 | - | | 0.4938 | 3800 | 0.0816 | - | | 0.5003 | 3850 | 0.0809 | - | | 0.5068 | 3900 | 0.0833 | - | | 0.5133 | 3950 | 0.0852 | - | | 0.5198 | 4000 | 0.0788 | - | | 0.5263 | 4050 | 0.0742 | - | | 0.5328 | 4100 | 0.0693 | - | | 0.5393 | 4150 | 0.0856 | - | | 0.5458 | 4200 | 0.072 | - | | 0.5523 | 4250 | 0.0805 | - | | 0.5588 | 4300 | 0.0741 | - | | 0.5653 | 4350 | 0.0845 | - | | 0.5718 | 4400 | 0.0753 | - | | 0.5783 | 4450 | 0.0814 | - | | 0.5848 | 4500 | 0.0691 | - | | 0.5913 | 4550 | 0.0823 | - | | 0.5978 | 4600 | 0.0847 | - | | 0.6043 | 4650 | 0.0714 | - | | 0.6108 | 4700 | 0.0879 | - | | 0.6173 | 4750 | 0.0711 | - | | 0.6238 | 4800 | 0.0697 | - | | 0.6303 | 4850 | 0.0741 | - | | 0.6368 | 4900 | 0.0771 | - | | 0.6433 | 4950 | 0.0837 | - | | 0.6498 | 5000 | 0.0743 | - | | 0.6563 | 5050 | 0.0755 | - | | 0.6628 | 5100 | 0.0739 | - | | 0.6693 | 5150 | 0.0816 | - | | 0.6758 | 5200 | 0.0782 | - | | 0.6823 | 5250 | 0.0755 | - | | 0.6888 | 5300 | 0.0712 | - | | 0.6953 | 5350 | 0.0639 | - | | 0.7018 | 5400 | 0.0694 | - | | 0.7083 | 5450 | 0.0806 | - | | 0.7147 | 5500 | 0.071 | - | | 0.7212 | 5550 | 0.0707 | - | | 0.7277 | 5600 | 0.0751 | - | | 0.7342 | 5650 | 0.0724 | - | | 0.7407 | 5700 | 0.0688 | - | | 0.7472 | 5750 | 0.067 | - | | 0.7537 | 5800 | 0.0718 | - | | 0.7602 | 5850 | 0.0681 | - | | 0.7667 | 5900 | 0.0694 | - | | 0.7732 | 5950 | 0.0693 | - | | 0.7797 | 6000 | 0.0731 | - | | 0.7862 | 6050 | 0.0626 | - | | 0.7927 | 6100 | 0.0691 | - | | 0.7992 | 6150 | 0.0711 | - | | 0.8057 | 6200 | 0.0627 | - | | 0.8122 | 6250 | 0.0726 | - | | 0.8187 | 6300 | 0.068 | - | | 0.8252 | 6350 | 0.0766 | - | | 0.8317 | 6400 | 0.0617 | - | | 0.8382 | 6450 | 0.0671 | - | | 0.8447 | 6500 | 0.0645 | - | | 0.8512 | 6550 | 0.0722 | - | | 0.8577 | 6600 | 0.0751 | - | | 0.8642 | 6650 | 0.0591 | - | | 0.8707 | 6700 | 0.0664 | - | | 0.8772 | 6750 | 0.0735 | - | | 0.8837 | 6800 | 0.0709 | - | | 0.8902 | 6850 | 0.0632 | - | | 0.8967 | 6900 | 0.0679 | - | | 0.9032 | 6950 | 0.0596 | - | | 0.9097 | 7000 | 0.0676 | - | | 0.9162 | 7050 | 0.066 | - | | 0.9227 | 7100 | 0.069 | - | | 0.9292 | 7150 | 0.0615 | - | | 0.9357 | 7200 | 0.0579 | - | | 0.9422 | 7250 | 0.0576 | - | | 0.9487 | 7300 | 0.0558 | - | | 0.9552 | 7350 | 0.0556 | - | | 0.9617 | 7400 | 0.0637 | - | | 0.9682 | 7450 | 0.0615 | - | | 0.9747 | 7500 | 0.0677 | - | | 0.9812 | 7550 | 0.0584 | - | | 0.9877 | 7600 | 0.0661 | - | | 0.9942 | 7650 | 0.0583 | - | | 1.0006 | 7700 | 0.0639 | - | | 1.0071 | 7750 | 0.0598 | - | | 1.0136 | 7800 | 0.0586 | - | | 1.0201 | 7850 | 0.055 | - | | 1.0266 | 7900 | 0.0636 | - | | 1.0331 | 7950 | 0.0623 | - | | 1.0396 | 8000 | 0.0661 | - | | 1.0461 | 8050 | 0.0633 | - | | 1.0526 | 8100 | 0.056 | - | | 1.0591 | 8150 | 0.0555 | - | | 1.0656 | 8200 | 0.0608 | - | | 1.0721 | 8250 | 0.0491 | - | | 1.0786 | 8300 | 0.0592 | - | | 1.0851 | 8350 | 0.0645 | - | | 1.0916 | 8400 | 0.0553 | - | | 1.0981 | 8450 | 0.0547 | - | | 1.1046 | 8500 | 0.0494 | - | | 1.1111 | 8550 | 0.0594 | - | | 1.1176 | 8600 | 0.058 | - | | 1.1241 | 8650 | 0.0589 | - | | 1.1306 | 8700 | 0.0552 | - | | 1.1371 | 8750 | 0.0554 | - | | 1.1436 | 8800 | 0.0566 | - | | 1.1501 | 8850 | 0.0558 | - | | 1.1566 | 8900 | 0.0596 | - | | 1.1631 | 8950 | 0.0551 | - | | 1.1696 | 9000 | 0.061 | - | | 1.1761 | 9050 | 0.0689 | - | | 1.1826 | 9100 | 0.0565 | - | | 1.1891 | 9150 | 0.0581 | - | | 1.1956 | 9200 | 0.0606 | - | | 1.2021 | 9250 | 0.057 | - | | 1.2086 | 9300 | 0.0577 | - | | 1.2151 | 9350 | 0.0629 | - | | 1.2216 | 9400 | 0.0592 | - | | 1.2281 | 9450 | 0.0547 | - | | 1.2346 | 9500 | 0.0606 | - | | 1.2411 | 9550 | 0.0588 | - | | 1.2476 | 9600 | 0.0581 | - | | 1.2541 | 9650 | 0.0624 | - | | 1.2606 | 9700 | 0.0589 | - | | 1.2671 | 9750 | 0.0646 | - | | 1.2736 | 9800 | 0.0559 | - | | 1.2801 | 9850 | 0.0594 | - | | 1.2865 | 9900 | 0.0586 | - | | 1.2930 | 9950 | 0.0552 | - | | 1.2995 | 10000 | 0.0513 | - | | 1.3060 | 10050 | 0.0565 | - | | 1.3125 | 10100 | 0.0626 | - | | 1.3190 | 10150 | 0.0483 | - | | 1.3255 | 10200 | 0.0643 | - | | 1.3320 | 10250 | 0.0524 | - | | 1.3385 | 10300 | 0.0559 | - | | 1.3450 | 10350 | 0.0589 | - | | 1.3515 | 10400 | 0.0562 | - | | 1.3580 | 10450 | 0.0592 | - | | 1.3645 | 10500 | 0.047 | - | | 1.3710 | 10550 | 0.0531 | - | | 1.3775 | 10600 | 0.0506 | - | | 1.3840 | 10650 | 0.0579 | - | | 1.3905 | 10700 | 0.0569 | - | | 1.3970 | 10750 | 0.0579 | - | | 1.4035 | 10800 | 0.0504 | - | | 1.4100 | 10850 | 0.0547 | - | | 1.4165 | 10900 | 0.0497 | - | | 1.4230 | 10950 | 0.0533 | - | | 1.4295 | 11000 | 0.0488 | - | | 1.4360 | 11050 | 0.0537 | - | | 1.4425 | 11100 | 0.0544 | - | | 1.4490 | 11150 | 0.0548 | - | | 1.4555 | 11200 | 0.0475 | - | | 1.4620 | 11250 | 0.0519 | - | | 1.4685 | 11300 | 0.0568 | - | | 1.4750 | 11350 | 0.0567 | - | | 1.4815 | 11400 | 0.0473 | - | | 1.4880 | 11450 | 0.0535 | - | | 1.4945 | 11500 | 0.0531 | - | | 1.5010 | 11550 | 0.0567 | - | | 1.5075 | 11600 | 0.0529 | - | | 1.5140 | 11650 | 0.0544 | - | | 1.5205 | 11700 | 0.0612 | - | | 1.5270 | 11750 | 0.055 | - | | 1.5335 | 11800 | 0.0474 | - | | 1.5400 | 11850 | 0.0572 | - | | 1.5465 | 11900 | 0.0484 | - | | 1.5530 | 11950 | 0.0553 | - | | 1.5595 | 12000 | 0.0519 | - | | 1.5660 | 12050 | 0.0565 | - | | 1.5724 | 12100 | 0.0466 | - | | 1.5789 | 12150 | 0.0502 | - | | 1.5854 | 12200 | 0.0525 | - | | 1.5919 | 12250 | 0.054 | - | | 1.5984 | 12300 | 0.0556 | - | | 1.6049 | 12350 | 0.0515 | - | | 1.6114 | 12400 | 0.0476 | - | | 1.6179 | 12450 | 0.0579 | - | | 1.6244 | 12500 | 0.0567 | - | | 1.6309 | 12550 | 0.0551 | - | | 1.6374 | 12600 | 0.0518 | - | | 1.6439 | 12650 | 0.0508 | - | | 1.6504 | 12700 | 0.0503 | - | | 1.6569 | 12750 | 0.0484 | - | | 1.6634 | 12800 | 0.0531 | - | | 1.6699 | 12850 | 0.0553 | - | | 1.6764 | 12900 | 0.0588 | - | | 1.6829 | 12950 | 0.0547 | - | | 1.6894 | 13000 | 0.0587 | - | | 1.6959 | 13050 | 0.0562 | - | | 1.7024 | 13100 | 0.0558 | - | | 1.7089 | 13150 | 0.0559 | - | | 1.7154 | 13200 | 0.0547 | - | | 1.7219 | 13250 | 0.059 | - | | 1.7284 | 13300 | 0.053 | - | | 1.7349 | 13350 | 0.0532 | - | | 1.7414 | 13400 | 0.0552 | - | | 1.7479 | 13450 | 0.0443 | - | | 1.7544 | 13500 | 0.058 | - | | 1.7609 | 13550 | 0.0503 | - | | 1.7674 | 13600 | 0.0499 | - | | 1.7739 | 13650 | 0.0478 | - | | 1.7804 | 13700 | 0.0569 | - | | 1.7869 | 13750 | 0.052 | - | | 1.7934 | 13800 | 0.0458 | - | | 1.7999 | 13850 | 0.0551 | - | | 1.8064 | 13900 | 0.0567 | - | | 1.8129 | 13950 | 0.0511 | - | | 1.8194 | 14000 | 0.0546 | - | | 1.8259 | 14050 | 0.058 | - | | 1.8324 | 14100 | 0.0539 | - | | 1.8389 | 14150 | 0.0544 | - | | 1.8454 | 14200 | 0.061 | - | | 1.8519 | 14250 | 0.0521 | - | | 1.8583 | 14300 | 0.046 | - | | 1.8648 | 14350 | 0.0494 | - | | 1.8713 | 14400 | 0.0604 | - | | 1.8778 | 14450 | 0.0543 | - | | 1.8843 | 14500 | 0.0522 | - | | 1.8908 | 14550 | 0.0533 | - | | 1.8973 | 14600 | 0.0469 | - | | 1.9038 | 14650 | 0.0525 | - | | 1.9103 | 14700 | 0.0516 | - | | 1.9168 | 14750 | 0.0485 | - | | 1.9233 | 14800 | 0.0601 | - | | 1.9298 | 14850 | 0.0487 | - | | 1.9363 | 14900 | 0.0496 | - | | 1.9428 | 14950 | 0.0529 | - | | 1.9493 | 15000 | 0.054 | - | | 1.9558 | 15050 | 0.0431 | - | | 1.9623 | 15100 | 0.0449 | - | | 1.9688 | 15150 | 0.0602 | - | | 1.9753 | 15200 | 0.0447 | - | | 1.9818 | 15250 | 0.0506 | - | | 1.9883 | 15300 | 0.0503 | - | | 1.9948 | 15350 | 0.0515 | - | ### Framework Versions - Python: 3.12.12 - SetFit: 1.1.3 - Sentence Transformers: 5.1.1 - Transformers: 4.57.1 - PyTorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.22.1 ## 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} } ```