--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: This expenditure has financed projects in road works, energy, agriculture and water. Madam Speaker, priority allocations are being made to power generation, road networks, irrigation schemes, schools and improvement of health infrastructure. Addressing constraints in transport, energy and health and education and improving service delivery, will accord Ugandans a better quality of life. - text: interoperability, acceptance) that are not exclusively related to G2P programs and that need to be addressed to realize digital payments’ benefits. Unemployment benefits Social security contributions Labor Markets Activation measures Labor market regulations Reduced work time Wage subsidies 418 (back to the top) Sudan Social Assistance Cash-based transfers Cash transfers (conditional and unconditional) One-off cash transfers Childcare support Social pensions In-kind transfers Food, vouchers, others • The ministry of labor and social development will provide in kind support to poor households, informal workers, teachers, and casual workers (total 2,050,000 households). A total of 100,000 Bahraini will benefit from the measure (cost of BD 215 million)54 55 Social security contributions Labor Markets Activation measures Labor market regulations Reduced work time Wage subsidies 54 https://www.moh.gov.bh/COVID19/Details/3969 55 https://www.moh.gov.bh/COVID19/Details/3982 70 (back to the top) Bangladesh Social Assistance Cash-based transfers Cash transfers (conditional and unconditional) • Benefit under key safety net programs will be increased (amount not determined yet). - text: National Food and Nutrition Strategic Plan 2011-2015 55 7) Promote practices that enhance sustainable availability, accessibility and consumption of a variety of foods at household level. National Food and Nutrition Strategic Plan 2011-2015 54 5.11 Strategic Direction 11 Expanding and Developing Communication and Advocacy Support for Food and Nutrition Interventions at Various Levels. National Food and Nutrition Strategic Plan 2011-2015 18 3. - text: 13 (Deroga delle norme in materia di riconoscimento delle qualifiche professionali sanitarie) 1. 93 (Disposizioni in materia di autoservizi pubblici non di linea) 1. 4 (Disciplina delle aree sanitarie temporanee) 1. - text: Furthermore, there is a need for improvements in forecasting, distribution and funding of micronutrient commodities, as well as the provision of adequate resources to ensure universal coverage. The National Nutrition Program is also responsible for estimating the demand of nutrition commodities, such as vitamin A capsules, iron/folic acid tablets, and Mebedazole for deworming. It is therefore limited in scope to address the full spectrum of causes of undernutrition, which requires a broad coalition of multisectoral interventions. 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_g20_multilabel_MiniLM-L12-v2_15_sample") # Run inference preds = model("13 (Deroga delle norme in materia di riconoscimento delle qualifiche professionali sanitarie) 1. 93 (Disposizioni in materia di autoservizi pubblici non di linea) 1. 4 (Disciplina delle aree sanitarie temporanee) 1.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:-----| | Word count | 3 | 93.9143 | 1651 | ### Training Hyperparameters - batch_size: (8, 8) - num_epochs: (4, 4) - 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 | 0.2185 | - | | 0.0068 | 50 | 0.1579 | - | | 0.0136 | 100 | 0.1625 | - | | 0.0204 | 150 | 0.1649 | - | | 0.0272 | 200 | 0.1511 | - | | 0.0340 | 250 | 0.1263 | - | | 0.0408 | 300 | 0.1335 | - | | 0.0476 | 350 | 0.1276 | - | | 0.0544 | 400 | 0.1143 | - | | 0.0612 | 450 | 0.1095 | - | | 0.0680 | 500 | 0.1029 | - | | 0.0748 | 550 | 0.1161 | - | | 0.0816 | 600 | 0.114 | - | | 0.0884 | 650 | 0.0945 | - | | 0.0952 | 700 | 0.0903 | - | | 0.1020 | 750 | 0.0793 | - | | 0.1088 | 800 | 0.0848 | - | | 0.1156 | 850 | 0.0802 | - | | 0.1224 | 900 | 0.0819 | - | | 0.1293 | 950 | 0.0802 | - | | 0.1361 | 1000 | 0.0879 | - | | 0.1429 | 1050 | 0.0738 | - | | 0.1497 | 1100 | 0.0737 | - | | 0.1565 | 1150 | 0.0761 | - | | 0.1633 | 1200 | 0.0715 | - | | 0.1701 | 1250 | 0.0633 | - | | 0.1769 | 1300 | 0.06 | - | | 0.1837 | 1350 | 0.06 | - | | 0.1905 | 1400 | 0.0641 | - | | 0.1973 | 1450 | 0.057 | - | | 0.2041 | 1500 | 0.0554 | - | | 0.2109 | 1550 | 0.0552 | - | | 0.2177 | 1600 | 0.0447 | - | | 0.2245 | 1650 | 0.0442 | - | | 0.2313 | 1700 | 0.0547 | - | | 0.2381 | 1750 | 0.0358 | - | | 0.2449 | 1800 | 0.0503 | - | | 0.2517 | 1850 | 0.0366 | - | | 0.2585 | 1900 | 0.0421 | - | | 0.2653 | 1950 | 0.0332 | - | | 0.2721 | 2000 | 0.0429 | - | | 0.2789 | 2050 | 0.0316 | - | | 0.2857 | 2100 | 0.0382 | - | | 0.2925 | 2150 | 0.0456 | - | | 0.2993 | 2200 | 0.0327 | - | | 0.3061 | 2250 | 0.0286 | - | | 0.3129 | 2300 | 0.0295 | - | | 0.3197 | 2350 | 0.0305 | - | | 0.3265 | 2400 | 0.0223 | - | | 0.3333 | 2450 | 0.0228 | - | | 0.3401 | 2500 | 0.0305 | - | | 0.3469 | 2550 | 0.0294 | - | | 0.3537 | 2600 | 0.0342 | - | | 0.3605 | 2650 | 0.0275 | - | | 0.3673 | 2700 | 0.0181 | - | | 0.3741 | 2750 | 0.0267 | - | | 0.3810 | 2800 | 0.0229 | - | | 0.3878 | 2850 | 0.0213 | - | | 0.3946 | 2900 | 0.0203 | - | | 0.4014 | 2950 | 0.0281 | - | | 0.4082 | 3000 | 0.025 | - | | 0.4150 | 3050 | 0.0233 | - | | 0.4218 | 3100 | 0.0306 | - | | 0.4286 | 3150 | 0.0159 | - | | 0.4354 | 3200 | 0.0246 | - | | 0.4422 | 3250 | 0.0266 | - | | 0.4490 | 3300 | 0.0242 | - | | 0.4558 | 3350 | 0.0103 | - | | 0.4626 | 3400 | 0.0191 | - | | 0.4694 | 3450 | 0.0237 | - | | 0.4762 | 3500 | 0.0216 | - | | 0.4830 | 3550 | 0.0103 | - | | 0.4898 | 3600 | 0.0097 | - | | 0.4966 | 3650 | 0.0158 | - | | 0.5034 | 3700 | 0.0156 | - | | 0.5102 | 3750 | 0.0152 | - | | 0.5170 | 3800 | 0.0187 | - | | 0.5238 | 3850 | 0.0129 | - | | 0.5306 | 3900 | 0.0157 | - | | 0.5374 | 3950 | 0.0161 | - | | 0.5442 | 4000 | 0.0131 | - | | 0.5510 | 4050 | 0.0119 | - | | 0.5578 | 4100 | 0.0213 | - | | 0.5646 | 4150 | 0.0086 | - | | 0.5714 | 4200 | 0.0086 | - | | 0.5782 | 4250 | 0.0121 | - | | 0.5850 | 4300 | 0.0168 | - | | 0.5918 | 4350 | 0.0147 | - | | 0.5986 | 4400 | 0.019 | - | | 0.6054 | 4450 | 0.0151 | - | | 0.6122 | 4500 | 0.0298 | - | | 0.6190 | 4550 | 0.0187 | - | | 0.6259 | 4600 | 0.013 | - | | 0.6327 | 4650 | 0.0184 | - | | 0.6395 | 4700 | 0.0249 | - | | 0.6463 | 4750 | 0.0157 | - | | 0.6531 | 4800 | 0.0081 | - | | 0.6599 | 4850 | 0.0229 | - | | 0.6667 | 4900 | 0.0227 | - | | 0.6735 | 4950 | 0.0166 | - | | 0.6803 | 5000 | 0.0222 | - | | 0.6871 | 5050 | 0.0066 | - | | 0.6939 | 5100 | 0.0135 | - | | 0.7007 | 5150 | 0.0134 | - | | 0.7075 | 5200 | 0.0134 | - | | 0.7143 | 5250 | 0.0077 | - | | 0.7211 | 5300 | 0.0106 | - | | 0.7279 | 5350 | 0.0086 | - | | 0.7347 | 5400 | 0.0169 | - | | 0.7415 | 5450 | 0.0123 | - | | 0.7483 | 5500 | 0.0085 | - | | 0.7551 | 5550 | 0.0087 | - | | 0.7619 | 5600 | 0.0143 | - | | 0.7687 | 5650 | 0.0112 | - | | 0.7755 | 5700 | 0.0185 | - | | 0.7823 | 5750 | 0.0064 | - | | 0.7891 | 5800 | 0.0077 | - | | 0.7959 | 5850 | 0.0116 | - | | 0.8027 | 5900 | 0.0063 | - | | 0.8095 | 5950 | 0.0166 | - | | 0.8163 | 6000 | 0.01 | - | | 0.8231 | 6050 | 0.0088 | - | | 0.8299 | 6100 | 0.0121 | - | | 0.8367 | 6150 | 0.0214 | - | | 0.8435 | 6200 | 0.009 | - | | 0.8503 | 6250 | 0.0133 | - | | 0.8571 | 6300 | 0.0062 | - | | 0.8639 | 6350 | 0.0077 | - | | 0.8707 | 6400 | 0.0201 | - | | 0.8776 | 6450 | 0.0163 | - | | 0.8844 | 6500 | 0.0071 | - | | 0.8912 | 6550 | 0.0138 | - | | 0.8980 | 6600 | 0.0131 | - | | 0.9048 | 6650 | 0.0126 | - | | 0.9116 | 6700 | 0.0042 | - | | 0.9184 | 6750 | 0.0152 | - | | 0.9252 | 6800 | 0.0194 | - | | 0.9320 | 6850 | 0.0068 | - | | 0.9388 | 6900 | 0.0154 | - | | 0.9456 | 6950 | 0.0077 | - | | 0.9524 | 7000 | 0.009 | - | | 0.9592 | 7050 | 0.0053 | - | | 0.9660 | 7100 | 0.0128 | - | | 0.9728 | 7150 | 0.011 | - | | 0.9796 | 7200 | 0.0039 | - | | 0.9864 | 7250 | 0.0076 | - | | 0.9932 | 7300 | 0.018 | - | | 1.0 | 7350 | 0.0215 | - | | 1.0068 | 7400 | 0.0022 | - | | 1.0136 | 7450 | 0.01 | - | | 1.0204 | 7500 | 0.0061 | - | | 1.0272 | 7550 | 0.0039 | - | | 1.0340 | 7600 | 0.0052 | - | | 1.0408 | 7650 | 0.0053 | - | | 1.0476 | 7700 | 0.0093 | - | | 1.0544 | 7750 | 0.0099 | - | | 1.0612 | 7800 | 0.0076 | - | | 1.0680 | 7850 | 0.0094 | - | | 1.0748 | 7900 | 0.0065 | - | | 1.0816 | 7950 | 0.0083 | - | | 1.0884 | 8000 | 0.007 | - | | 1.0952 | 8050 | 0.0056 | - | | 1.1020 | 8100 | 0.0112 | - | | 1.1088 | 8150 | 0.0087 | - | | 1.1156 | 8200 | 0.0055 | - | | 1.1224 | 8250 | 0.0051 | - | | 1.1293 | 8300 | 0.0096 | - | | 1.1361 | 8350 | 0.0038 | - | | 1.1429 | 8400 | 0.0055 | - | | 1.1497 | 8450 | 0.0051 | - | | 1.1565 | 8500 | 0.01 | - | | 1.1633 | 8550 | 0.0058 | - | | 1.1701 | 8600 | 0.0112 | - | | 1.1769 | 8650 | 0.003 | - | | 1.1837 | 8700 | 0.0094 | - | | 1.1905 | 8750 | 0.0069 | - | | 1.1973 | 8800 | 0.0131 | - | | 1.2041 | 8850 | 0.0089 | - | | 1.2109 | 8900 | 0.0061 | - | | 1.2177 | 8950 | 0.0109 | - | | 1.2245 | 9000 | 0.008 | - | | 1.2313 | 9050 | 0.0122 | - | | 1.2381 | 9100 | 0.0081 | - | | 1.2449 | 9150 | 0.0014 | - | | 1.2517 | 9200 | 0.0046 | - | | 1.2585 | 9250 | 0.0049 | - | | 1.2653 | 9300 | 0.0147 | - | | 1.2721 | 9350 | 0.0105 | - | | 1.2789 | 9400 | 0.0126 | - | | 1.2857 | 9450 | 0.0031 | - | | 1.2925 | 9500 | 0.0039 | - | | 1.2993 | 9550 | 0.0038 | - | | 1.3061 | 9600 | 0.0047 | - | | 1.3129 | 9650 | 0.0037 | - | | 1.3197 | 9700 | 0.0103 | - | | 1.3265 | 9750 | 0.0007 | - | | 1.3333 | 9800 | 0.0053 | - | | 1.3401 | 9850 | 0.0018 | - | | 1.3469 | 9900 | 0.0057 | - | | 1.3537 | 9950 | 0.0044 | - | | 1.3605 | 10000 | 0.0109 | - | | 1.3673 | 10050 | 0.0056 | - | | 1.3741 | 10100 | 0.0081 | - | | 1.3810 | 10150 | 0.008 | - | | 1.3878 | 10200 | 0.0081 | - | | 1.3946 | 10250 | 0.0033 | - | | 1.4014 | 10300 | 0.0055 | - | | 1.4082 | 10350 | 0.0019 | - | | 1.4150 | 10400 | 0.0033 | - | | 1.4218 | 10450 | 0.0033 | - | | 1.4286 | 10500 | 0.0058 | - | | 1.4354 | 10550 | 0.0047 | - | | 1.4422 | 10600 | 0.0068 | - | | 1.4490 | 10650 | 0.0052 | - | | 1.4558 | 10700 | 0.0033 | - | | 1.4626 | 10750 | 0.001 | - | | 1.4694 | 10800 | 0.0101 | - | | 1.4762 | 10850 | 0.0011 | - | | 1.4830 | 10900 | 0.008 | - | | 1.4898 | 10950 | 0.0038 | - | | 1.4966 | 11000 | 0.0033 | - | | 1.5034 | 11050 | 0.0031 | - | | 1.5102 | 11100 | 0.0107 | - | | 1.5170 | 11150 | 0.004 | - | | 1.5238 | 11200 | 0.0009 | - | | 1.5306 | 11250 | 0.0034 | - | | 1.5374 | 11300 | 0.0033 | - | | 1.5442 | 11350 | 0.0011 | - | | 1.5510 | 11400 | 0.0081 | - | | 1.5578 | 11450 | 0.0025 | - | | 1.5646 | 11500 | 0.0065 | - | | 1.5714 | 11550 | 0.0069 | - | | 1.5782 | 11600 | 0.0053 | - | | 1.5850 | 11650 | 0.0031 | - | | 1.5918 | 11700 | 0.0059 | - | | 1.5986 | 11750 | 0.006 | - | | 1.6054 | 11800 | 0.0007 | - | | 1.6122 | 11850 | 0.0027 | - | | 1.6190 | 11900 | 0.003 | - | | 1.6259 | 11950 | 0.0052 | - | | 1.6327 | 12000 | 0.0065 | - | | 1.6395 | 12050 | 0.0032 | - | | 1.6463 | 12100 | 0.0054 | - | | 1.6531 | 12150 | 0.0063 | - | | 1.6599 | 12200 | 0.0155 | - | | 1.6667 | 12250 | 0.0105 | - | | 1.6735 | 12300 | 0.0067 | - | | 1.6803 | 12350 | 0.0034 | - | | 1.6871 | 12400 | 0.0076 | - | | 1.6939 | 12450 | 0.0042 | - | | 1.7007 | 12500 | 0.003 | - | | 1.7075 | 12550 | 0.0096 | - | | 1.7143 | 12600 | 0.0054 | - | | 1.7211 | 12650 | 0.005 | - | | 1.7279 | 12700 | 0.0039 | - | | 1.7347 | 12750 | 0.0061 | - | | 1.7415 | 12800 | 0.0027 | - | | 1.7483 | 12850 | 0.0033 | - | | 1.7551 | 12900 | 0.0028 | - | | 1.7619 | 12950 | 0.0038 | - | | 1.7687 | 13000 | 0.0083 | - | | 1.7755 | 13050 | 0.0074 | - | | 1.7823 | 13100 | 0.0015 | - | | 1.7891 | 13150 | 0.0037 | - | | 1.7959 | 13200 | 0.0041 | - | | 1.8027 | 13250 | 0.0007 | - | | 1.8095 | 13300 | 0.0046 | - | | 1.8163 | 13350 | 0.0007 | - | | 1.8231 | 13400 | 0.0019 | - | | 1.8299 | 13450 | 0.0051 | - | | 1.8367 | 13500 | 0.0007 | - | | 1.8435 | 13550 | 0.0013 | - | | 1.8503 | 13600 | 0.0045 | - | | 1.8571 | 13650 | 0.0006 | - | | 1.8639 | 13700 | 0.0028 | - | | 1.8707 | 13750 | 0.0028 | - | | 1.8776 | 13800 | 0.001 | - | | 1.8844 | 13850 | 0.001 | - | | 1.8912 | 13900 | 0.0075 | - | | 1.8980 | 13950 | 0.0041 | - | | 1.9048 | 14000 | 0.0115 | - | | 1.9116 | 14050 | 0.0007 | - | | 1.9184 | 14100 | 0.0069 | - | | 1.9252 | 14150 | 0.0017 | - | | 1.9320 | 14200 | 0.005 | - | | 1.9388 | 14250 | 0.0028 | - | | 1.9456 | 14300 | 0.0029 | - | | 1.9524 | 14350 | 0.0052 | - | | 1.9592 | 14400 | 0.0023 | - | | 1.9660 | 14450 | 0.0046 | - | | 1.9728 | 14500 | 0.001 | - | | 1.9796 | 14550 | 0.0009 | - | | 1.9864 | 14600 | 0.0059 | - | | 1.9932 | 14650 | 0.0075 | - | | 2.0 | 14700 | 0.003 | - | | 2.0068 | 14750 | 0.0088 | - | | 2.0136 | 14800 | 0.0073 | - | | 2.0204 | 14850 | 0.0023 | - | | 2.0272 | 14900 | 0.0104 | - | | 2.0340 | 14950 | 0.0024 | - | | 2.0408 | 15000 | 0.0059 | - | | 2.0476 | 15050 | 0.0041 | - | | 2.0544 | 15100 | 0.0079 | - | | 2.0612 | 15150 | 0.0011 | - | | 2.0680 | 15200 | 0.0038 | - | | 2.0748 | 15250 | 0.0009 | - | | 2.0816 | 15300 | 0.0057 | - | | 2.0884 | 15350 | 0.0025 | - | | 2.0952 | 15400 | 0.0033 | - | | 2.1020 | 15450 | 0.0093 | - | | 2.1088 | 15500 | 0.0006 | - | | 2.1156 | 15550 | 0.0024 | - | | 2.1224 | 15600 | 0.0044 | - | | 2.1293 | 15650 | 0.0069 | - | | 2.1361 | 15700 | 0.0051 | - | | 2.1429 | 15750 | 0.008 | - | | 2.1497 | 15800 | 0.0047 | - | | 2.1565 | 15850 | 0.0012 | - | | 2.1633 | 15900 | 0.001 | - | | 2.1701 | 15950 | 0.0019 | - | | 2.1769 | 16000 | 0.0024 | - | | 2.1837 | 16050 | 0.0066 | - | | 2.1905 | 16100 | 0.0025 | - | | 2.1973 | 16150 | 0.0037 | - | | 2.2041 | 16200 | 0.0033 | - | | 2.2109 | 16250 | 0.0023 | - | | 2.2177 | 16300 | 0.0013 | - | | 2.2245 | 16350 | 0.0033 | - | | 2.2313 | 16400 | 0.0029 | - | | 2.2381 | 16450 | 0.0038 | - | | 2.2449 | 16500 | 0.0015 | - | | 2.2517 | 16550 | 0.0007 | - | | 2.2585 | 16600 | 0.0031 | - | | 2.2653 | 16650 | 0.0061 | - | | 2.2721 | 16700 | 0.0011 | - | | 2.2789 | 16750 | 0.0049 | - | | 2.2857 | 16800 | 0.0012 | - | | 2.2925 | 16850 | 0.0036 | - | | 2.2993 | 16900 | 0.004 | - | | 2.3061 | 16950 | 0.0005 | - | | 2.3129 | 17000 | 0.0019 | - | | 2.3197 | 17050 | 0.003 | - | | 2.3265 | 17100 | 0.0006 | - | | 2.3333 | 17150 | 0.0009 | - | | 2.3401 | 17200 | 0.0013 | - | | 2.3469 | 17250 | 0.0018 | - | | 2.3537 | 17300 | 0.0007 | - | | 2.3605 | 17350 | 0.001 | - | | 2.3673 | 17400 | 0.0054 | - | | 2.3741 | 17450 | 0.0004 | - | | 2.3810 | 17500 | 0.0028 | - | | 2.3878 | 17550 | 0.0005 | - | | 2.3946 | 17600 | 0.0003 | - | | 2.4014 | 17650 | 0.0004 | - | | 2.4082 | 17700 | 0.0031 | - | | 2.4150 | 17750 | 0.0004 | - | | 2.4218 | 17800 | 0.0013 | - | | 2.4286 | 17850 | 0.0017 | - | | 2.4354 | 17900 | 0.0013 | - | | 2.4422 | 17950 | 0.0025 | - | | 2.4490 | 18000 | 0.0004 | - | | 2.4558 | 18050 | 0.0029 | - | | 2.4626 | 18100 | 0.0023 | - | | 2.4694 | 18150 | 0.0027 | - | | 2.4762 | 18200 | 0.0015 | - | | 2.4830 | 18250 | 0.0006 | - | | 2.4898 | 18300 | 0.0024 | - | | 2.4966 | 18350 | 0.0021 | - | | 2.5034 | 18400 | 0.0005 | - | | 2.5102 | 18450 | 0.0004 | - | | 2.5170 | 18500 | 0.0042 | - | | 2.5238 | 18550 | 0.0005 | - | | 2.5306 | 18600 | 0.0012 | - | | 2.5374 | 18650 | 0.005 | - | | 2.5442 | 18700 | 0.0032 | - | | 2.5510 | 18750 | 0.0079 | - | | 2.5578 | 18800 | 0.001 | - | | 2.5646 | 18850 | 0.0008 | - | | 2.5714 | 18900 | 0.0042 | - | | 2.5782 | 18950 | 0.001 | - | | 2.5850 | 19000 | 0.001 | - | | 2.5918 | 19050 | 0.0009 | - | | 2.5986 | 19100 | 0.0003 | - | | 2.6054 | 19150 | 0.0003 | - | | 2.6122 | 19200 | 0.0003 | - | | 2.6190 | 19250 | 0.0035 | - | | 2.6259 | 19300 | 0.0006 | - | | 2.6327 | 19350 | 0.0035 | - | | 2.6395 | 19400 | 0.0003 | - | | 2.6463 | 19450 | 0.0021 | - | | 2.6531 | 19500 | 0.0005 | - | | 2.6599 | 19550 | 0.004 | - | | 2.6667 | 19600 | 0.0023 | - | | 2.6735 | 19650 | 0.0006 | - | | 2.6803 | 19700 | 0.004 | - | | 2.6871 | 19750 | 0.0015 | - | | 2.6939 | 19800 | 0.0008 | - | | 2.7007 | 19850 | 0.0022 | - | | 2.7075 | 19900 | 0.001 | - | | 2.7143 | 19950 | 0.0007 | - | | 2.7211 | 20000 | 0.0013 | - | | 2.7279 | 20050 | 0.0004 | - | | 2.7347 | 20100 | 0.001 | - | | 2.7415 | 20150 | 0.0013 | - | | 2.7483 | 20200 | 0.0004 | - | | 2.7551 | 20250 | 0.0035 | - | | 2.7619 | 20300 | 0.0006 | - | | 2.7687 | 20350 | 0.001 | - | | 2.7755 | 20400 | 0.0003 | - | | 2.7823 | 20450 | 0.0006 | - | | 2.7891 | 20500 | 0.0012 | - | | 2.7959 | 20550 | 0.0003 | - | | 2.8027 | 20600 | 0.0031 | - | | 2.8095 | 20650 | 0.0005 | - | | 2.8163 | 20700 | 0.0008 | - | | 2.8231 | 20750 | 0.0006 | - | | 2.8299 | 20800 | 0.0005 | - | | 2.8367 | 20850 | 0.0004 | - | | 2.8435 | 20900 | 0.0002 | - | | 2.8503 | 20950 | 0.0011 | - | | 2.8571 | 21000 | 0.0002 | - | | 2.8639 | 21050 | 0.0033 | - | | 2.8707 | 21100 | 0.0024 | - | | 2.8776 | 21150 | 0.0004 | - | | 2.8844 | 21200 | 0.0002 | - | | 2.8912 | 21250 | 0.0029 | - | | 2.8980 | 21300 | 0.0004 | - | | 2.9048 | 21350 | 0.0003 | - | | 2.9116 | 21400 | 0.0024 | - | | 2.9184 | 21450 | 0.0027 | - | | 2.9252 | 21500 | 0.0003 | - | | 2.9320 | 21550 | 0.0006 | - | | 2.9388 | 21600 | 0.0002 | - | | 2.9456 | 21650 | 0.0021 | - | | 2.9524 | 21700 | 0.0011 | - | | 2.9592 | 21750 | 0.0006 | - | | 2.9660 | 21800 | 0.0002 | - | | 2.9728 | 21850 | 0.0004 | - | | 2.9796 | 21900 | 0.0008 | - | | 2.9864 | 21950 | 0.0028 | - | | 2.9932 | 22000 | 0.0004 | - | | 3.0 | 22050 | 0.0002 | - | | 3.0068 | 22100 | 0.0002 | - | | 3.0136 | 22150 | 0.0026 | - | | 3.0204 | 22200 | 0.0002 | - | | 3.0272 | 22250 | 0.0004 | - | | 3.0340 | 22300 | 0.0005 | - | | 3.0408 | 22350 | 0.0005 | - | | 3.0476 | 22400 | 0.0022 | - | | 3.0544 | 22450 | 0.0006 | - | | 3.0612 | 22500 | 0.0009 | - | | 3.0680 | 22550 | 0.0004 | - | | 3.0748 | 22600 | 0.0002 | - | | 3.0816 | 22650 | 0.0003 | - | | 3.0884 | 22700 | 0.0002 | - | | 3.0952 | 22750 | 0.0002 | - | | 3.1020 | 22800 | 0.0002 | - | | 3.1088 | 22850 | 0.0041 | - | | 3.1156 | 22900 | 0.0014 | - | | 3.1224 | 22950 | 0.0019 | - | | 3.1293 | 23000 | 0.0023 | - | | 3.1361 | 23050 | 0.0003 | - | | 3.1429 | 23100 | 0.0005 | - | | 3.1497 | 23150 | 0.0003 | - | | 3.1565 | 23200 | 0.0009 | - | | 3.1633 | 23250 | 0.0023 | - | | 3.1701 | 23300 | 0.0003 | - | | 3.1769 | 23350 | 0.0002 | - | | 3.1837 | 23400 | 0.0003 | - | | 3.1905 | 23450 | 0.0003 | - | | 3.1973 | 23500 | 0.0015 | - | | 3.2041 | 23550 | 0.0002 | - | | 3.2109 | 23600 | 0.0004 | - | | 3.2177 | 23650 | 0.0004 | - | | 3.2245 | 23700 | 0.0009 | - | | 3.2313 | 23750 | 0.0002 | - | | 3.2381 | 23800 | 0.0003 | - | | 3.2449 | 23850 | 0.0002 | - | | 3.2517 | 23900 | 0.0001 | - | | 3.2585 | 23950 | 0.0003 | - | | 3.2653 | 24000 | 0.0002 | - | | 3.2721 | 24050 | 0.0019 | - | | 3.2789 | 24100 | 0.0002 | - | | 3.2857 | 24150 | 0.0002 | - | | 3.2925 | 24200 | 0.0002 | - | | 3.2993 | 24250 | 0.0002 | - | | 3.3061 | 24300 | 0.0003 | - | | 3.3129 | 24350 | 0.0007 | - | | 3.3197 | 24400 | 0.0009 | - | | 3.3265 | 24450 | 0.0006 | - | | 3.3333 | 24500 | 0.0003 | - | | 3.3401 | 24550 | 0.0008 | - | | 3.3469 | 24600 | 0.0007 | - | | 3.3537 | 24650 | 0.0003 | - | | 3.3605 | 24700 | 0.0002 | - | | 3.3673 | 24750 | 0.0001 | - | | 3.3741 | 24800 | 0.0001 | - | | 3.3810 | 24850 | 0.0002 | - | | 3.3878 | 24900 | 0.0009 | - | | 3.3946 | 24950 | 0.0005 | - | | 3.4014 | 25000 | 0.0001 | - | | 3.4082 | 25050 | 0.0003 | - | | 3.4150 | 25100 | 0.0001 | - | | 3.4218 | 25150 | 0.0002 | - | | 3.4286 | 25200 | 0.0002 | - | | 3.4354 | 25250 | 0.0003 | - | | 3.4422 | 25300 | 0.0002 | - | | 3.4490 | 25350 | 0.0004 | - | | 3.4558 | 25400 | 0.0005 | - | | 3.4626 | 25450 | 0.0005 | - | | 3.4694 | 25500 | 0.0002 | - | | 3.4762 | 25550 | 0.0003 | - | | 3.4830 | 25600 | 0.0001 | - | | 3.4898 | 25650 | 0.0003 | - | | 3.4966 | 25700 | 0.0006 | - | | 3.5034 | 25750 | 0.0002 | - | | 3.5102 | 25800 | 0.0003 | - | | 3.5170 | 25850 | 0.0005 | - | | 3.5238 | 25900 | 0.0002 | - | | 3.5306 | 25950 | 0.0003 | - | | 3.5374 | 26000 | 0.0002 | - | | 3.5442 | 26050 | 0.0004 | - | | 3.5510 | 26100 | 0.0001 | - | | 3.5578 | 26150 | 0.0001 | - | | 3.5646 | 26200 | 0.0002 | - | | 3.5714 | 26250 | 0.0001 | - | | 3.5782 | 26300 | 0.0005 | - | | 3.5850 | 26350 | 0.0002 | - | | 3.5918 | 26400 | 0.0001 | - | | 3.5986 | 26450 | 0.0001 | - | | 3.6054 | 26500 | 0.0003 | - | | 3.6122 | 26550 | 0.0002 | - | | 3.6190 | 26600 | 0.0002 | - | | 3.6259 | 26650 | 0.0001 | - | | 3.6327 | 26700 | 0.0001 | - | | 3.6395 | 26750 | 0.0001 | - | | 3.6463 | 26800 | 0.0005 | - | | 3.6531 | 26850 | 0.0001 | - | | 3.6599 | 26900 | 0.0002 | - | | 3.6667 | 26950 | 0.0001 | - | | 3.6735 | 27000 | 0.0001 | - | | 3.6803 | 27050 | 0.0002 | - | | 3.6871 | 27100 | 0.0002 | - | | 3.6939 | 27150 | 0.0001 | - | | 3.7007 | 27200 | 0.0001 | - | | 3.7075 | 27250 | 0.0002 | - | | 3.7143 | 27300 | 0.0002 | - | | 3.7211 | 27350 | 0.0001 | - | | 3.7279 | 27400 | 0.0008 | - | | 3.7347 | 27450 | 0.0002 | - | | 3.7415 | 27500 | 0.0008 | - | | 3.7483 | 27550 | 0.0005 | - | | 3.7551 | 27600 | 0.0002 | - | | 3.7619 | 27650 | 0.0003 | - | | 3.7687 | 27700 | 0.0002 | - | | 3.7755 | 27750 | 0.0007 | - | | 3.7823 | 27800 | 0.0003 | - | | 3.7891 | 27850 | 0.0001 | - | | 3.7959 | 27900 | 0.0006 | - | | 3.8027 | 27950 | 0.0002 | - | | 3.8095 | 28000 | 0.0001 | - | | 3.8163 | 28050 | 0.0001 | - | | 3.8231 | 28100 | 0.0002 | - | | 3.8299 | 28150 | 0.0001 | - | | 3.8367 | 28200 | 0.0001 | - | | 3.8435 | 28250 | 0.0004 | - | | 3.8503 | 28300 | 0.0001 | - | | 3.8571 | 28350 | 0.0001 | - | | 3.8639 | 28400 | 0.0001 | - | | 3.8707 | 28450 | 0.0005 | - | | 3.8776 | 28500 | 0.0004 | - | | 3.8844 | 28550 | 0.0001 | - | | 3.8912 | 28600 | 0.0002 | - | | 3.8980 | 28650 | 0.0002 | - | | 3.9048 | 28700 | 0.0003 | - | | 3.9116 | 28750 | 0.0001 | - | | 3.9184 | 28800 | 0.0002 | - | | 3.9252 | 28850 | 0.0001 | - | | 3.9320 | 28900 | 0.0001 | - | | 3.9388 | 28950 | 0.0002 | - | | 3.9456 | 29000 | 0.0002 | - | | 3.9524 | 29050 | 0.0001 | - | | 3.9592 | 29100 | 0.0001 | - | | 3.9660 | 29150 | 0.0002 | - | | 3.9728 | 29200 | 0.0002 | - | | 3.9796 | 29250 | 0.0003 | - | | 3.9864 | 29300 | 0.0001 | - | | 3.9932 | 29350 | 0.0007 | - | | 4.0 | 29400 | 0.0007 | - | ### Framework Versions - Python: 3.11.13 - SetFit: 1.1.2 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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} } ```