--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Extend social protection to informal workers along agrifood value chains through portable cash transfers, micro-insurance products, and inclusive retirement and disability benefits. - text: strategy for sustainable agriculture and rural development in the 2021-2030 period has been approved together with with the list of the 41 national programmes and action plans. the common goal of the strategy is of building a commodity-producing agriculture and developing agriculture based on local advantages, in the direction of modernity, with high productivity, quality, efficiency, sustainability and competitiveness, firmly ensuring national food security, making an important contribution to socio-economic stability, preventing and combating natural disasters and epidemics, protecting the environment, responding to climate change, effectively implementing international commitments on reducing greenhouse gas emissions; the strategy also aims to improve income, quality of life, role and position of people involved in agricultural production; create non-agricultural jobs to develop diversified livelihoods, reduce poverty sustainably for rural people, ensure equal development opportunities among regions; comprehensive and modern rural development associated with the process of urbanization, with synchronous infrastructure and social services and close to urban areas; the strategy looks to preserve and promote the national cultural identity; building green, clean, beautiful countryside, security and order are ensured; to develop agriculture and rural economy in association with new rural construction in the direction of highly efficient ecological agriculture, modern rural areas and civilized farmers. specific objectives are, by 2030, the gdp growth rate of agro-forestry-fishery will reach an average of 2.5 - 3 percent per year, the productivity growth rate of agricultural, forestry and fishery workers will reach an average of 5.5 - 6 percent per year. the growth rate of the export value of agro-forestry-fishery products will reach an average of 5-6 percent per year. fisheries sector is selected as the strategic production sector. - text: 'the uganda npdp works with the two overall critical variables, the population and urbanization projections and the national land use balance sheet. it provides a basis for integrating the physical and spatial with the economic and social issues of national development planning. its core elements are the pattern of human settlements, the land uses and natural resources for economic activity and the infrastructure networks which connect and service them. the strategic orientations are: strategic orientation 1: maximizing national economic growth strategic orientation 2: favoring social and regional equality. strategic orientation 3: maximizing supply of agricultural lands. strategic orientation 4: maximizing environmental sustainability strategic orientation 5: maximizing urbanization and urbanity strategic orientation 6: maximizing national and international connectivity.' - text: Mechanization investment will be integrated with soil health and water management programs, ensuring appropriate machinery selection to avoid soil compaction and water wastage. - text: The policy will publish annual import-dependency indices by commodity and translate these insights into prioritized investment in domestic production, feed safety, and regional trade integration to reduce vulnerability and improve food security. 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-v02") # Run inference preds = model("Mechanization investment will be integrated with soil health and water management programs, ensuring appropriate machinery selection to avoid soil compaction and water wastage.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 55.4334 | 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 | 0.2114 | - | | 0.0045 | 50 | 0.2069 | - | | 0.0091 | 100 | 0.2029 | - | | 0.0136 | 150 | 0.2025 | - | | 0.0181 | 200 | 0.1984 | - | | 0.0226 | 250 | 0.1848 | - | | 0.0272 | 300 | 0.1784 | - | | 0.0317 | 350 | 0.176 | - | | 0.0362 | 400 | 0.1743 | - | | 0.0408 | 450 | 0.1579 | - | | 0.0453 | 500 | 0.149 | - | | 0.0498 | 550 | 0.1532 | - | | 0.0543 | 600 | 0.1551 | - | | 0.0589 | 650 | 0.1483 | - | | 0.0634 | 700 | 0.1474 | - | | 0.0679 | 750 | 0.1444 | - | | 0.0725 | 800 | 0.1363 | - | | 0.0770 | 850 | 0.1269 | - | | 0.0815 | 900 | 0.1541 | - | | 0.0861 | 950 | 0.1256 | - | | 0.0906 | 1000 | 0.1457 | - | | 0.0951 | 1050 | 0.131 | - | | 0.0996 | 1100 | 0.1224 | - | | 0.1042 | 1150 | 0.1357 | - | | 0.1087 | 1200 | 0.1341 | - | | 0.1132 | 1250 | 0.1371 | - | | 0.1178 | 1300 | 0.1305 | - | | 0.1223 | 1350 | 0.1165 | - | | 0.1268 | 1400 | 0.1191 | - | | 0.1313 | 1450 | 0.1247 | - | | 0.1359 | 1500 | 0.1209 | - | | 0.1404 | 1550 | 0.129 | - | | 0.1449 | 1600 | 0.1161 | - | | 0.1495 | 1650 | 0.1215 | - | | 0.1540 | 1700 | 0.1213 | - | | 0.1585 | 1750 | 0.1193 | - | | 0.1630 | 1800 | 0.1126 | - | | 0.1676 | 1850 | 0.1253 | - | | 0.1721 | 1900 | 0.1135 | - | | 0.1766 | 1950 | 0.1032 | - | | 0.1812 | 2000 | 0.0998 | - | | 0.1857 | 2050 | 0.116 | - | | 0.1902 | 2100 | 0.1088 | - | | 0.1947 | 2150 | 0.104 | - | | 0.1993 | 2200 | 0.1139 | - | | 0.2038 | 2250 | 0.1084 | - | | 0.2083 | 2300 | 0.1043 | - | | 0.2129 | 2350 | 0.1149 | - | | 0.2174 | 2400 | 0.1022 | - | | 0.2219 | 2450 | 0.1106 | - | | 0.2264 | 2500 | 0.1028 | - | | 0.2310 | 2550 | 0.0986 | - | | 0.2355 | 2600 | 0.0965 | - | | 0.2400 | 2650 | 0.1047 | - | | 0.2446 | 2700 | 0.1007 | - | | 0.2491 | 2750 | 0.0979 | - | | 0.2536 | 2800 | 0.0967 | - | | 0.2582 | 2850 | 0.0999 | - | | 0.2627 | 2900 | 0.1025 | - | | 0.2672 | 2950 | 0.0938 | - | | 0.2717 | 3000 | 0.0923 | - | | 0.2763 | 3050 | 0.0885 | - | | 0.2808 | 3100 | 0.0953 | - | | 0.2853 | 3150 | 0.0931 | - | | 0.2899 | 3200 | 0.095 | - | | 0.2944 | 3250 | 0.0945 | - | | 0.2989 | 3300 | 0.0919 | - | | 0.3034 | 3350 | 0.0975 | - | | 0.3080 | 3400 | 0.0906 | - | | 0.3125 | 3450 | 0.0977 | - | | 0.3170 | 3500 | 0.0952 | - | | 0.3216 | 3550 | 0.0851 | - | | 0.3261 | 3600 | 0.0883 | - | | 0.3306 | 3650 | 0.0852 | - | | 0.3351 | 3700 | 0.082 | - | | 0.3397 | 3750 | 0.0901 | - | | 0.3442 | 3800 | 0.0778 | - | | 0.3487 | 3850 | 0.0819 | - | | 0.3533 | 3900 | 0.0804 | - | | 0.3578 | 3950 | 0.083 | - | | 0.3623 | 4000 | 0.0855 | - | | 0.3668 | 4050 | 0.0828 | - | | 0.3714 | 4100 | 0.0899 | - | | 0.3759 | 4150 | 0.0875 | - | | 0.3804 | 4200 | 0.0816 | - | | 0.3850 | 4250 | 0.09 | - | | 0.3895 | 4300 | 0.0782 | - | | 0.3940 | 4350 | 0.0831 | - | | 0.3986 | 4400 | 0.0795 | - | | 0.4031 | 4450 | 0.0807 | - | | 0.4076 | 4500 | 0.0809 | - | | 0.4121 | 4550 | 0.0763 | - | | 0.4167 | 4600 | 0.08 | - | | 0.4212 | 4650 | 0.0731 | - | | 0.4257 | 4700 | 0.0759 | - | | 0.4303 | 4750 | 0.0758 | - | | 0.4348 | 4800 | 0.0791 | - | | 0.4393 | 4850 | 0.0731 | - | | 0.4438 | 4900 | 0.0774 | - | | 0.4484 | 4950 | 0.0781 | - | | 0.4529 | 5000 | 0.0783 | - | | 0.4574 | 5050 | 0.0852 | - | | 0.4620 | 5100 | 0.0771 | - | | 0.4665 | 5150 | 0.0813 | - | | 0.4710 | 5200 | 0.0795 | - | | 0.4755 | 5250 | 0.0725 | - | | 0.4801 | 5300 | 0.0751 | - | | 0.4846 | 5350 | 0.0756 | - | | 0.4891 | 5400 | 0.0715 | - | | 0.4937 | 5450 | 0.0643 | - | | 0.4982 | 5500 | 0.0675 | - | | 0.5027 | 5550 | 0.0769 | - | | 0.5072 | 5600 | 0.0761 | - | | 0.5118 | 5650 | 0.0739 | - | | 0.5163 | 5700 | 0.0716 | - | | 0.5208 | 5750 | 0.0706 | - | | 0.5254 | 5800 | 0.0719 | - | | 0.5299 | 5850 | 0.0721 | - | | 0.5344 | 5900 | 0.068 | - | | 0.5389 | 5950 | 0.0626 | - | | 0.5435 | 6000 | 0.0679 | - | | 0.5480 | 6050 | 0.0713 | - | | 0.5525 | 6100 | 0.0692 | - | | 0.5571 | 6150 | 0.0728 | - | | 0.5616 | 6200 | 0.0622 | - | | 0.5661 | 6250 | 0.0686 | - | | 0.5707 | 6300 | 0.073 | - | | 0.5752 | 6350 | 0.0563 | - | | 0.5797 | 6400 | 0.0621 | - | | 0.5842 | 6450 | 0.0699 | - | | 0.5888 | 6500 | 0.0691 | - | | 0.5933 | 6550 | 0.0676 | - | | 0.5978 | 6600 | 0.0621 | - | | 0.6024 | 6650 | 0.0693 | - | | 0.6069 | 6700 | 0.0708 | - | | 0.6114 | 6750 | 0.0672 | - | | 0.6159 | 6800 | 0.0728 | - | | 0.6205 | 6850 | 0.0629 | - | | 0.625 | 6900 | 0.0694 | - | | 0.6295 | 6950 | 0.063 | - | | 0.6341 | 7000 | 0.0591 | - | | 0.6386 | 7050 | 0.0663 | - | | 0.6431 | 7100 | 0.0722 | - | | 0.6476 | 7150 | 0.0576 | - | | 0.6522 | 7200 | 0.0604 | - | | 0.6567 | 7250 | 0.0632 | - | | 0.6612 | 7300 | 0.0709 | - | | 0.6658 | 7350 | 0.0649 | - | | 0.6703 | 7400 | 0.0611 | - | | 0.6748 | 7450 | 0.0597 | - | | 0.6793 | 7500 | 0.0712 | - | | 0.6839 | 7550 | 0.0668 | - | | 0.6884 | 7600 | 0.0664 | - | | 0.6929 | 7650 | 0.0664 | - | | 0.6975 | 7700 | 0.0622 | - | | 0.7020 | 7750 | 0.0601 | - | | 0.7065 | 7800 | 0.0582 | - | | 0.7111 | 7850 | 0.0622 | - | | 0.7156 | 7900 | 0.0648 | - | | 0.7201 | 7950 | 0.064 | - | | 0.7246 | 8000 | 0.0624 | - | | 0.7292 | 8050 | 0.0622 | - | | 0.7337 | 8100 | 0.0596 | - | | 0.7382 | 8150 | 0.0633 | - | | 0.7428 | 8200 | 0.0532 | - | | 0.7473 | 8250 | 0.0565 | - | | 0.7518 | 8300 | 0.0724 | - | | 0.7563 | 8350 | 0.0559 | - | | 0.7609 | 8400 | 0.064 | - | | 0.7654 | 8450 | 0.0603 | - | | 0.7699 | 8500 | 0.059 | - | | 0.7745 | 8550 | 0.0543 | - | | 0.7790 | 8600 | 0.0568 | - | | 0.7835 | 8650 | 0.0638 | - | | 0.7880 | 8700 | 0.0578 | - | | 0.7926 | 8750 | 0.0692 | - | | 0.7971 | 8800 | 0.0608 | - | | 0.8016 | 8850 | 0.0652 | - | | 0.8062 | 8900 | 0.061 | - | | 0.8107 | 8950 | 0.0581 | - | | 0.8152 | 9000 | 0.0627 | - | | 0.8197 | 9050 | 0.0656 | - | | 0.8243 | 9100 | 0.0579 | - | | 0.8288 | 9150 | 0.0626 | - | | 0.8333 | 9200 | 0.0587 | - | | 0.8379 | 9250 | 0.0625 | - | | 0.8424 | 9300 | 0.051 | - | | 0.8469 | 9350 | 0.0553 | - | | 0.8514 | 9400 | 0.0507 | - | | 0.8560 | 9450 | 0.0521 | - | | 0.8605 | 9500 | 0.0548 | - | | 0.8650 | 9550 | 0.0536 | - | | 0.8696 | 9600 | 0.0517 | - | | 0.8741 | 9650 | 0.0569 | - | | 0.8786 | 9700 | 0.0572 | - | | 0.8832 | 9750 | 0.0553 | - | | 0.8877 | 9800 | 0.0567 | - | | 0.8922 | 9850 | 0.0594 | - | | 0.8967 | 9900 | 0.0598 | - | | 0.9013 | 9950 | 0.0548 | - | | 0.9058 | 10000 | 0.0563 | - | | 0.9103 | 10050 | 0.0466 | - | | 0.9149 | 10100 | 0.0561 | - | | 0.9194 | 10150 | 0.0533 | - | | 0.9239 | 10200 | 0.0569 | - | | 0.9284 | 10250 | 0.0484 | - | | 0.9330 | 10300 | 0.0563 | - | | 0.9375 | 10350 | 0.0597 | - | | 0.9420 | 10400 | 0.0556 | - | | 0.9466 | 10450 | 0.0542 | - | | 0.9511 | 10500 | 0.0528 | - | | 0.9556 | 10550 | 0.0652 | - | | 0.9601 | 10600 | 0.0541 | - | | 0.9647 | 10650 | 0.0581 | - | | 0.9692 | 10700 | 0.0529 | - | | 0.9737 | 10750 | 0.0497 | - | | 0.9783 | 10800 | 0.0591 | - | | 0.9828 | 10850 | 0.055 | - | | 0.9873 | 10900 | 0.0464 | - | | 0.9918 | 10950 | 0.0529 | - | | 0.9964 | 11000 | 0.0562 | - | | 1.0009 | 11050 | 0.0508 | - | | 1.0054 | 11100 | 0.0447 | - | | 1.0100 | 11150 | 0.0493 | - | | 1.0145 | 11200 | 0.0526 | - | | 1.0190 | 11250 | 0.0476 | - | | 1.0236 | 11300 | 0.0469 | - | | 1.0281 | 11350 | 0.0465 | - | | 1.0326 | 11400 | 0.0484 | - | | 1.0371 | 11450 | 0.0492 | - | | 1.0417 | 11500 | 0.0518 | - | | 1.0462 | 11550 | 0.0554 | - | | 1.0507 | 11600 | 0.0533 | - | | 1.0553 | 11650 | 0.0558 | - | | 1.0598 | 11700 | 0.0517 | - | | 1.0643 | 11750 | 0.0536 | - | | 1.0688 | 11800 | 0.051 | - | | 1.0734 | 11850 | 0.0512 | - | | 1.0779 | 11900 | 0.0531 | - | | 1.0824 | 11950 | 0.0496 | - | | 1.0870 | 12000 | 0.0471 | - | | 1.0915 | 12050 | 0.0492 | - | | 1.0960 | 12100 | 0.0444 | - | | 1.1005 | 12150 | 0.0441 | - | | 1.1051 | 12200 | 0.053 | - | | 1.1096 | 12250 | 0.048 | - | | 1.1141 | 12300 | 0.044 | - | | 1.1187 | 12350 | 0.0482 | - | | 1.1232 | 12400 | 0.0486 | - | | 1.1277 | 12450 | 0.0475 | - | | 1.1322 | 12500 | 0.0484 | - | | 1.1368 | 12550 | 0.0512 | - | | 1.1413 | 12600 | 0.0534 | - | | 1.1458 | 12650 | 0.0532 | - | | 1.1504 | 12700 | 0.0422 | - | | 1.1549 | 12750 | 0.0487 | - | | 1.1594 | 12800 | 0.0489 | - | | 1.1639 | 12850 | 0.0558 | - | | 1.1685 | 12900 | 0.0506 | - | | 1.1730 | 12950 | 0.0497 | - | | 1.1775 | 13000 | 0.0593 | - | | 1.1821 | 13050 | 0.0476 | - | | 1.1866 | 13100 | 0.0435 | - | | 1.1911 | 13150 | 0.0553 | - | | 1.1957 | 13200 | 0.0527 | - | | 1.2002 | 13250 | 0.0535 | - | | 1.2047 | 13300 | 0.0469 | - | | 1.2092 | 13350 | 0.054 | - | | 1.2138 | 13400 | 0.0476 | - | | 1.2183 | 13450 | 0.0507 | - | | 1.2228 | 13500 | 0.0497 | - | | 1.2274 | 13550 | 0.0519 | - | | 1.2319 | 13600 | 0.0444 | - | | 1.2364 | 13650 | 0.0516 | - | | 1.2409 | 13700 | 0.0494 | - | | 1.2455 | 13750 | 0.0425 | - | | 1.25 | 13800 | 0.0487 | - | | 1.2545 | 13850 | 0.0509 | - | | 1.2591 | 13900 | 0.0523 | - | | 1.2636 | 13950 | 0.0446 | - | | 1.2681 | 14000 | 0.0478 | - | | 1.2726 | 14050 | 0.0439 | - | | 1.2772 | 14100 | 0.0518 | - | | 1.2817 | 14150 | 0.0505 | - | | 1.2862 | 14200 | 0.0497 | - | | 1.2908 | 14250 | 0.0554 | - | | 1.2953 | 14300 | 0.0482 | - | | 1.2998 | 14350 | 0.0427 | - | | 1.3043 | 14400 | 0.05 | - | | 1.3089 | 14450 | 0.0477 | - | | 1.3134 | 14500 | 0.0463 | - | | 1.3179 | 14550 | 0.0448 | - | | 1.3225 | 14600 | 0.045 | - | | 1.3270 | 14650 | 0.0437 | - | | 1.3315 | 14700 | 0.0467 | - | | 1.3361 | 14750 | 0.0435 | - | | 1.3406 | 14800 | 0.043 | - | | 1.3451 | 14850 | 0.0486 | - | | 1.3496 | 14900 | 0.049 | - | | 1.3542 | 14950 | 0.0471 | - | | 1.3587 | 15000 | 0.0455 | - | | 1.3632 | 15050 | 0.0428 | - | | 1.3678 | 15100 | 0.0425 | - | | 1.3723 | 15150 | 0.0465 | - | | 1.3768 | 15200 | 0.0452 | - | | 1.3813 | 15250 | 0.0487 | - | | 1.3859 | 15300 | 0.045 | - | | 1.3904 | 15350 | 0.0431 | - | | 1.3949 | 15400 | 0.0512 | - | | 1.3995 | 15450 | 0.0411 | - | | 1.4040 | 15500 | 0.0495 | - | | 1.4085 | 15550 | 0.0406 | - | | 1.4130 | 15600 | 0.0445 | - | | 1.4176 | 15650 | 0.0496 | - | | 1.4221 | 15700 | 0.0497 | - | | 1.4266 | 15750 | 0.0466 | - | | 1.4312 | 15800 | 0.0479 | - | | 1.4357 | 15850 | 0.0448 | - | | 1.4402 | 15900 | 0.0453 | - | | 1.4447 | 15950 | 0.0486 | - | | 1.4493 | 16000 | 0.0519 | - | | 1.4538 | 16050 | 0.06 | - | | 1.4583 | 16100 | 0.045 | - | | 1.4629 | 16150 | 0.0421 | - | | 1.4674 | 16200 | 0.0501 | - | | 1.4719 | 16250 | 0.0525 | - | | 1.4764 | 16300 | 0.0444 | - | | 1.4810 | 16350 | 0.0422 | - | | 1.4855 | 16400 | 0.0428 | - | | 1.4900 | 16450 | 0.0398 | - | | 1.4946 | 16500 | 0.0412 | - | | 1.4991 | 16550 | 0.0482 | - | | 1.5036 | 16600 | 0.046 | - | | 1.5082 | 16650 | 0.0502 | - | | 1.5127 | 16700 | 0.048 | - | | 1.5172 | 16750 | 0.0447 | - | | 1.5217 | 16800 | 0.0419 | - | | 1.5263 | 16850 | 0.0429 | - | | 1.5308 | 16900 | 0.0533 | - | | 1.5353 | 16950 | 0.0482 | - | | 1.5399 | 17000 | 0.0519 | - | | 1.5444 | 17050 | 0.0503 | - | | 1.5489 | 17100 | 0.0432 | - | | 1.5534 | 17150 | 0.0388 | - | | 1.5580 | 17200 | 0.0537 | - | | 1.5625 | 17250 | 0.0477 | - | | 1.5670 | 17300 | 0.0444 | - | | 1.5716 | 17350 | 0.0407 | - | | 1.5761 | 17400 | 0.0463 | - | | 1.5806 | 17450 | 0.0417 | - | | 1.5851 | 17500 | 0.0403 | - | | 1.5897 | 17550 | 0.0481 | - | | 1.5942 | 17600 | 0.0485 | - | | 1.5987 | 17650 | 0.0462 | - | | 1.6033 | 17700 | 0.0383 | - | | 1.6078 | 17750 | 0.0429 | - | | 1.6123 | 17800 | 0.0413 | - | | 1.6168 | 17850 | 0.0421 | - | | 1.6214 | 17900 | 0.0409 | - | | 1.6259 | 17950 | 0.0436 | - | | 1.6304 | 18000 | 0.0468 | - | | 1.6350 | 18050 | 0.0446 | - | | 1.6395 | 18100 | 0.0389 | - | | 1.6440 | 18150 | 0.0443 | - | | 1.6486 | 18200 | 0.0489 | - | | 1.6531 | 18250 | 0.0489 | - | | 1.6576 | 18300 | 0.0498 | - | | 1.6621 | 18350 | 0.044 | - | | 1.6667 | 18400 | 0.0392 | - | | 1.6712 | 18450 | 0.0441 | - | | 1.6757 | 18500 | 0.0429 | - | | 1.6803 | 18550 | 0.0369 | - | | 1.6848 | 18600 | 0.0409 | - | | 1.6893 | 18650 | 0.0496 | - | | 1.6938 | 18700 | 0.052 | - | | 1.6984 | 18750 | 0.0377 | - | | 1.7029 | 18800 | 0.0403 | - | | 1.7074 | 18850 | 0.0473 | - | | 1.7120 | 18900 | 0.0474 | - | | 1.7165 | 18950 | 0.0447 | - | | 1.7210 | 19000 | 0.0498 | - | | 1.7255 | 19050 | 0.0427 | - | | 1.7301 | 19100 | 0.0454 | - | | 1.7346 | 19150 | 0.0478 | - | | 1.7391 | 19200 | 0.0438 | - | | 1.7437 | 19250 | 0.0378 | - | | 1.7482 | 19300 | 0.0442 | - | | 1.7527 | 19350 | 0.0453 | - | | 1.7572 | 19400 | 0.0407 | - | | 1.7618 | 19450 | 0.0487 | - | | 1.7663 | 19500 | 0.0408 | - | | 1.7708 | 19550 | 0.0444 | - | | 1.7754 | 19600 | 0.0426 | - | | 1.7799 | 19650 | 0.044 | - | | 1.7844 | 19700 | 0.04 | - | | 1.7889 | 19750 | 0.0484 | - | | 1.7935 | 19800 | 0.0478 | - | | 1.7980 | 19850 | 0.0495 | - | | 1.8025 | 19900 | 0.037 | - | | 1.8071 | 19950 | 0.0472 | - | | 1.8116 | 20000 | 0.0411 | - | | 1.8161 | 20050 | 0.0368 | - | | 1.8207 | 20100 | 0.042 | - | | 1.8252 | 20150 | 0.0359 | - | | 1.8297 | 20200 | 0.0452 | - | | 1.8342 | 20250 | 0.0504 | - | | 1.8388 | 20300 | 0.0513 | - | | 1.8433 | 20350 | 0.048 | - | | 1.8478 | 20400 | 0.0497 | - | | 1.8524 | 20450 | 0.0408 | - | | 1.8569 | 20500 | 0.0448 | - | | 1.8614 | 20550 | 0.0411 | - | | 1.8659 | 20600 | 0.0401 | - | | 1.8705 | 20650 | 0.0445 | - | | 1.875 | 20700 | 0.0468 | - | | 1.8795 | 20750 | 0.0488 | - | | 1.8841 | 20800 | 0.0401 | - | | 1.8886 | 20850 | 0.0408 | - | | 1.8931 | 20900 | 0.0384 | - | | 1.8976 | 20950 | 0.0496 | - | | 1.9022 | 21000 | 0.0436 | - | | 1.9067 | 21050 | 0.0434 | - | | 1.9112 | 21100 | 0.041 | - | | 1.9158 | 21150 | 0.0452 | - | | 1.9203 | 21200 | 0.0454 | - | | 1.9248 | 21250 | 0.0447 | - | | 1.9293 | 21300 | 0.0476 | - | | 1.9339 | 21350 | 0.0399 | - | | 1.9384 | 21400 | 0.0397 | - | | 1.9429 | 21450 | 0.0399 | - | | 1.9475 | 21500 | 0.0462 | - | | 1.9520 | 21550 | 0.0452 | - | | 1.9565 | 21600 | 0.046 | - | | 1.9611 | 21650 | 0.0368 | - | | 1.9656 | 21700 | 0.043 | - | | 1.9701 | 21750 | 0.0413 | - | | 1.9746 | 21800 | 0.0459 | - | | 1.9792 | 21850 | 0.0442 | - | | 1.9837 | 21900 | 0.0381 | - | | 1.9882 | 21950 | 0.0411 | - | | 1.9928 | 22000 | 0.0519 | - | | 1.9973 | 22050 | 0.0445 | - | ### Framework Versions - Python: 3.12.12 - SetFit: 1.1.3 - Sentence Transformers: 5.1.2 - 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} } ```