--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Government-led initiatives have introduced tailored insurance products that mitigate the financial risks faced by smallholder farmers exposed to climate-induced hazards such as droughts and floods. - text: "National Food and Nutrition Strategic Plan 2011-2015\n\n53\n\n\n\n5.10.7\ \ Resource allocation and generation \n\nThe resources required for monitoring\ \ and evaluation of nutrition intervention should \nnormally be built into the\ \ cost of the intervention programmes." - text: 'COVID-19: The Development Program for Drinking Water Supply and Sanitation Systems of the Kyrgyz Republic until 2026 was approved. The Program is aimed at increasing the provision of drinking water of standard quality, improving the health and quality of life of the population of the republic, reducing the harmful effects on the environment through the construction, reconstruction, and modernization of drinking water supply and sanitation systems.' - text: "Objectives of this project are \nto develop socio-economic infrastructure\ \ in the rural sector, expand road \ntransportation network, conduct rural employment\ \ activities, and build \n\n\n\n 227\n\nlocal level’s institutional capacity." - text: "Housing and Community Amenities \n \n\n133." 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_30sample") # Run inference preds = model("Housing and Community Amenities 133.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 41.0925 | 506 | ### 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 | 0.2661 | - | | 0.0068 | 50 | 0.1923 | - | | 0.0136 | 100 | 0.1856 | - | | 0.0204 | 150 | 0.1927 | - | | 0.0272 | 200 | 0.1708 | - | | 0.0340 | 250 | 0.1706 | - | | 0.0408 | 300 | 0.156 | - | | 0.0476 | 350 | 0.1597 | - | | 0.0544 | 400 | 0.149 | - | | 0.0612 | 450 | 0.1488 | - | | 0.0680 | 500 | 0.1375 | - | | 0.0748 | 550 | 0.1234 | - | | 0.0816 | 600 | 0.1339 | - | | 0.0884 | 650 | 0.126 | - | | 0.0952 | 700 | 0.1347 | - | | 0.1020 | 750 | 0.1323 | - | | 0.1088 | 800 | 0.1159 | - | | 0.1156 | 850 | 0.1236 | - | | 0.1224 | 900 | 0.1218 | - | | 0.1293 | 950 | 0.1323 | - | | 0.1361 | 1000 | 0.1258 | - | | 0.1429 | 1050 | 0.1206 | - | | 0.1497 | 1100 | 0.1127 | - | | 0.1565 | 1150 | 0.1211 | - | | 0.1633 | 1200 | 0.1234 | - | | 0.1701 | 1250 | 0.1178 | - | | 0.1769 | 1300 | 0.1009 | - | | 0.1837 | 1350 | 0.11 | - | | 0.1905 | 1400 | 0.1103 | - | | 0.1973 | 1450 | 0.1015 | - | | 0.2041 | 1500 | 0.0926 | - | | 0.2109 | 1550 | 0.099 | - | | 0.2177 | 1600 | 0.1079 | - | | 0.2245 | 1650 | 0.0979 | - | | 0.2313 | 1700 | 0.1001 | - | | 0.2381 | 1750 | 0.1039 | - | | 0.2449 | 1800 | 0.0838 | - | | 0.2517 | 1850 | 0.0941 | - | | 0.2585 | 1900 | 0.0929 | - | | 0.2653 | 1950 | 0.0851 | - | | 0.2721 | 2000 | 0.0956 | - | | 0.2789 | 2050 | 0.075 | - | | 0.2857 | 2100 | 0.1067 | - | | 0.2925 | 2150 | 0.0891 | - | | 0.2993 | 2200 | 0.0939 | - | | 0.3061 | 2250 | 0.0908 | - | | 0.3129 | 2300 | 0.0847 | - | | 0.3197 | 2350 | 0.0812 | - | | 0.3265 | 2400 | 0.0918 | - | | 0.3333 | 2450 | 0.0935 | - | | 0.3401 | 2500 | 0.0792 | - | | 0.3469 | 2550 | 0.0669 | - | | 0.3537 | 2600 | 0.0883 | - | | 0.3605 | 2650 | 0.0829 | - | | 0.3673 | 2700 | 0.0656 | - | | 0.3741 | 2750 | 0.0752 | - | | 0.3810 | 2800 | 0.0825 | - | | 0.3878 | 2850 | 0.0813 | - | | 0.3946 | 2900 | 0.0852 | - | | 0.4014 | 2950 | 0.0903 | - | | 0.4082 | 3000 | 0.0902 | - | | 0.4150 | 3050 | 0.0739 | - | | 0.4218 | 3100 | 0.0786 | - | | 0.4286 | 3150 | 0.083 | - | | 0.4354 | 3200 | 0.0648 | - | | 0.4422 | 3250 | 0.0704 | - | | 0.4490 | 3300 | 0.0798 | - | | 0.4558 | 3350 | 0.0651 | - | | 0.4626 | 3400 | 0.0705 | - | | 0.4694 | 3450 | 0.0653 | - | | 0.4762 | 3500 | 0.0767 | - | | 0.4830 | 3550 | 0.0747 | - | | 0.4898 | 3600 | 0.0738 | - | | 0.4966 | 3650 | 0.055 | - | | 0.5034 | 3700 | 0.0741 | - | | 0.5102 | 3750 | 0.0688 | - | | 0.5170 | 3800 | 0.0699 | - | | 0.5238 | 3850 | 0.0787 | - | | 0.5306 | 3900 | 0.0673 | - | | 0.5374 | 3950 | 0.0629 | - | | 0.5442 | 4000 | 0.0639 | - | | 0.5510 | 4050 | 0.0809 | - | | 0.5578 | 4100 | 0.0694 | - | | 0.5646 | 4150 | 0.0696 | - | | 0.5714 | 4200 | 0.0577 | - | | 0.5782 | 4250 | 0.0707 | - | | 0.5850 | 4300 | 0.0542 | - | | 0.5918 | 4350 | 0.0541 | - | | 0.5986 | 4400 | 0.0462 | - | | 0.6054 | 4450 | 0.0675 | - | | 0.6122 | 4500 | 0.0561 | - | | 0.6190 | 4550 | 0.056 | - | | 0.6259 | 4600 | 0.0556 | - | | 0.6327 | 4650 | 0.0552 | - | | 0.6395 | 4700 | 0.0566 | - | | 0.6463 | 4750 | 0.0578 | - | | 0.6531 | 4800 | 0.0488 | - | | 0.6599 | 4850 | 0.0419 | - | | 0.6667 | 4900 | 0.0485 | - | | 0.6735 | 4950 | 0.0477 | - | | 0.6803 | 5000 | 0.0566 | - | | 0.6871 | 5050 | 0.0571 | - | | 0.6939 | 5100 | 0.0531 | - | | 0.7007 | 5150 | 0.0563 | - | | 0.7075 | 5200 | 0.0452 | - | | 0.7143 | 5250 | 0.0459 | - | | 0.7211 | 5300 | 0.039 | - | | 0.7279 | 5350 | 0.0382 | - | | 0.7347 | 5400 | 0.0679 | - | | 0.7415 | 5450 | 0.0465 | - | | 0.7483 | 5500 | 0.0493 | - | | 0.7551 | 5550 | 0.0489 | - | | 0.7619 | 5600 | 0.0443 | - | | 0.7687 | 5650 | 0.0591 | - | | 0.7755 | 5700 | 0.0441 | - | | 0.7823 | 5750 | 0.0501 | - | | 0.7891 | 5800 | 0.0497 | - | | 0.7959 | 5850 | 0.0543 | - | | 0.8027 | 5900 | 0.05 | - | | 0.8095 | 5950 | 0.0449 | - | | 0.8163 | 6000 | 0.0432 | - | | 0.8231 | 6050 | 0.0491 | - | | 0.8299 | 6100 | 0.0507 | - | | 0.8367 | 6150 | 0.0405 | - | | 0.8435 | 6200 | 0.0426 | - | | 0.8503 | 6250 | 0.0528 | - | | 0.8571 | 6300 | 0.0428 | - | | 0.8639 | 6350 | 0.0534 | - | | 0.8707 | 6400 | 0.0512 | - | | 0.8776 | 6450 | 0.049 | - | | 0.8844 | 6500 | 0.0386 | - | | 0.8912 | 6550 | 0.0468 | - | | 0.8980 | 6600 | 0.0505 | - | | 0.9048 | 6650 | 0.0538 | - | | 0.9116 | 6700 | 0.0484 | - | | 0.9184 | 6750 | 0.044 | - | | 0.9252 | 6800 | 0.0431 | - | | 0.9320 | 6850 | 0.0456 | - | | 0.9388 | 6900 | 0.0342 | - | | 0.9456 | 6950 | 0.0445 | - | | 0.9524 | 7000 | 0.0499 | - | | 0.9592 | 7050 | 0.0589 | - | | 0.9660 | 7100 | 0.0409 | - | | 0.9728 | 7150 | 0.04 | - | | 0.9796 | 7200 | 0.0443 | - | | 0.9864 | 7250 | 0.0373 | - | | 0.9932 | 7300 | 0.0306 | - | | 1.0 | 7350 | 0.0303 | - | | 1.0068 | 7400 | 0.0317 | - | | 1.0136 | 7450 | 0.0364 | - | | 1.0204 | 7500 | 0.0349 | - | | 1.0272 | 7550 | 0.0388 | - | | 1.0340 | 7600 | 0.0466 | - | | 1.0408 | 7650 | 0.0334 | - | | 1.0476 | 7700 | 0.0512 | - | | 1.0544 | 7750 | 0.0413 | - | | 1.0612 | 7800 | 0.0399 | - | | 1.0680 | 7850 | 0.0412 | - | | 1.0748 | 7900 | 0.0341 | - | | 1.0816 | 7950 | 0.0395 | - | | 1.0884 | 8000 | 0.045 | - | | 1.0952 | 8050 | 0.0385 | - | | 1.1020 | 8100 | 0.038 | - | | 1.1088 | 8150 | 0.0376 | - | | 1.1156 | 8200 | 0.0434 | - | | 1.1224 | 8250 | 0.0323 | - | | 1.1293 | 8300 | 0.0364 | - | | 1.1361 | 8350 | 0.033 | - | | 1.1429 | 8400 | 0.025 | - | | 1.1497 | 8450 | 0.0461 | - | | 1.1565 | 8500 | 0.033 | - | | 1.1633 | 8550 | 0.0317 | - | | 1.1701 | 8600 | 0.047 | - | | 1.1769 | 8650 | 0.0344 | - | | 1.1837 | 8700 | 0.0388 | - | | 1.1905 | 8750 | 0.0359 | - | | 1.1973 | 8800 | 0.0429 | - | | 1.2041 | 8850 | 0.0355 | - | | 1.2109 | 8900 | 0.0421 | - | | 1.2177 | 8950 | 0.0351 | - | | 1.2245 | 9000 | 0.0359 | - | | 1.2313 | 9050 | 0.035 | - | | 1.2381 | 9100 | 0.0331 | - | | 1.2449 | 9150 | 0.0337 | - | | 1.2517 | 9200 | 0.0376 | - | | 1.2585 | 9250 | 0.0366 | - | | 1.2653 | 9300 | 0.0369 | - | | 1.2721 | 9350 | 0.0353 | - | | 1.2789 | 9400 | 0.0439 | - | | 1.2857 | 9450 | 0.0439 | - | | 1.2925 | 9500 | 0.0288 | - | | 1.2993 | 9550 | 0.0404 | - | | 1.3061 | 9600 | 0.0355 | - | | 1.3129 | 9650 | 0.0375 | - | | 1.3197 | 9700 | 0.0452 | - | | 1.3265 | 9750 | 0.0408 | - | | 1.3333 | 9800 | 0.0369 | - | | 1.3401 | 9850 | 0.0337 | - | | 1.3469 | 9900 | 0.0294 | - | | 1.3537 | 9950 | 0.0341 | - | | 1.3605 | 10000 | 0.0356 | - | | 1.3673 | 10050 | 0.0394 | - | | 1.3741 | 10100 | 0.0387 | - | | 1.3810 | 10150 | 0.0276 | - | | 1.3878 | 10200 | 0.0345 | - | | 1.3946 | 10250 | 0.037 | - | | 1.4014 | 10300 | 0.0272 | - | | 1.4082 | 10350 | 0.0341 | - | | 1.4150 | 10400 | 0.033 | - | | 1.4218 | 10450 | 0.0517 | - | | 1.4286 | 10500 | 0.0297 | - | | 1.4354 | 10550 | 0.0388 | - | | 1.4422 | 10600 | 0.0312 | - | | 1.4490 | 10650 | 0.0283 | - | | 1.4558 | 10700 | 0.0287 | - | | 1.4626 | 10750 | 0.0319 | - | | 1.4694 | 10800 | 0.0343 | - | | 1.4762 | 10850 | 0.033 | - | | 1.4830 | 10900 | 0.0444 | - | | 1.4898 | 10950 | 0.0239 | - | | 1.4966 | 11000 | 0.0294 | - | | 1.5034 | 11050 | 0.0313 | - | | 1.5102 | 11100 | 0.0344 | - | | 1.5170 | 11150 | 0.0304 | - | | 1.5238 | 11200 | 0.0339 | - | | 1.5306 | 11250 | 0.0342 | - | | 1.5374 | 11300 | 0.0291 | - | | 1.5442 | 11350 | 0.0301 | - | | 1.5510 | 11400 | 0.0309 | - | | 1.5578 | 11450 | 0.0346 | - | | 1.5646 | 11500 | 0.0406 | - | | 1.5714 | 11550 | 0.034 | - | | 1.5782 | 11600 | 0.0273 | - | | 1.5850 | 11650 | 0.0316 | - | | 1.5918 | 11700 | 0.0404 | - | | 1.5986 | 11750 | 0.0295 | - | | 1.6054 | 11800 | 0.0385 | - | | 1.6122 | 11850 | 0.0373 | - | | 1.6190 | 11900 | 0.0384 | - | | 1.6259 | 11950 | 0.0307 | - | | 1.6327 | 12000 | 0.0222 | - | | 1.6395 | 12050 | 0.0257 | - | | 1.6463 | 12100 | 0.0313 | - | | 1.6531 | 12150 | 0.0293 | - | | 1.6599 | 12200 | 0.0312 | - | | 1.6667 | 12250 | 0.0299 | - | | 1.6735 | 12300 | 0.0284 | - | | 1.6803 | 12350 | 0.042 | - | | 1.6871 | 12400 | 0.031 | - | | 1.6939 | 12450 | 0.0295 | - | | 1.7007 | 12500 | 0.0339 | - | | 1.7075 | 12550 | 0.0385 | - | | 1.7143 | 12600 | 0.0355 | - | | 1.7211 | 12650 | 0.0291 | - | | 1.7279 | 12700 | 0.0366 | - | | 1.7347 | 12750 | 0.0337 | - | | 1.7415 | 12800 | 0.0268 | - | | 1.7483 | 12850 | 0.0373 | - | | 1.7551 | 12900 | 0.0404 | - | | 1.7619 | 12950 | 0.025 | - | | 1.7687 | 13000 | 0.0282 | - | | 1.7755 | 13050 | 0.0282 | - | | 1.7823 | 13100 | 0.0341 | - | | 1.7891 | 13150 | 0.0338 | - | | 1.7959 | 13200 | 0.0342 | - | | 1.8027 | 13250 | 0.035 | - | | 1.8095 | 13300 | 0.0399 | - | | 1.8163 | 13350 | 0.035 | - | | 1.8231 | 13400 | 0.0367 | - | | 1.8299 | 13450 | 0.0294 | - | | 1.8367 | 13500 | 0.0382 | - | | 1.8435 | 13550 | 0.0261 | - | | 1.8503 | 13600 | 0.0301 | - | | 1.8571 | 13650 | 0.0258 | - | | 1.8639 | 13700 | 0.0301 | - | | 1.8707 | 13750 | 0.0306 | - | | 1.8776 | 13800 | 0.0242 | - | | 1.8844 | 13850 | 0.0258 | - | | 1.8912 | 13900 | 0.0296 | - | | 1.8980 | 13950 | 0.0338 | - | | 1.9048 | 14000 | 0.0315 | - | | 1.9116 | 14050 | 0.0282 | - | | 1.9184 | 14100 | 0.0325 | - | | 1.9252 | 14150 | 0.0286 | - | | 1.9320 | 14200 | 0.0355 | - | | 1.9388 | 14250 | 0.0317 | - | | 1.9456 | 14300 | 0.0314 | - | | 1.9524 | 14350 | 0.031 | - | | 1.9592 | 14400 | 0.03 | - | | 1.9660 | 14450 | 0.0262 | - | | 1.9728 | 14500 | 0.0275 | - | | 1.9796 | 14550 | 0.0356 | - | | 1.9864 | 14600 | 0.0369 | - | | 1.9932 | 14650 | 0.0364 | - | | 2.0 | 14700 | 0.0344 | - | | 2.0068 | 14750 | 0.0248 | - | | 2.0136 | 14800 | 0.0273 | - | | 2.0204 | 14850 | 0.0282 | - | | 2.0272 | 14900 | 0.023 | - | | 2.0340 | 14950 | 0.0278 | - | | 2.0408 | 15000 | 0.0355 | - | | 2.0476 | 15050 | 0.0258 | - | | 2.0544 | 15100 | 0.0258 | - | | 2.0612 | 15150 | 0.0322 | - | | 2.0680 | 15200 | 0.0266 | - | | 2.0748 | 15250 | 0.0279 | - | | 2.0816 | 15300 | 0.0282 | - | | 2.0884 | 15350 | 0.0289 | - | | 2.0952 | 15400 | 0.024 | - | | 2.1020 | 15450 | 0.0268 | - | | 2.1088 | 15500 | 0.0348 | - | | 2.1156 | 15550 | 0.0281 | - | | 2.1224 | 15600 | 0.0282 | - | | 2.1293 | 15650 | 0.0218 | - | | 2.1361 | 15700 | 0.0201 | - | | 2.1429 | 15750 | 0.0207 | - | | 2.1497 | 15800 | 0.0308 | - | | 2.1565 | 15850 | 0.0261 | - | | 2.1633 | 15900 | 0.0292 | - | | 2.1701 | 15950 | 0.0308 | - | | 2.1769 | 16000 | 0.0298 | - | | 2.1837 | 16050 | 0.0308 | - | | 2.1905 | 16100 | 0.0359 | - | | 2.1973 | 16150 | 0.0265 | - | | 2.2041 | 16200 | 0.0351 | - | | 2.2109 | 16250 | 0.0223 | - | | 2.2177 | 16300 | 0.0322 | - | | 2.2245 | 16350 | 0.0261 | - | | 2.2313 | 16400 | 0.0206 | - | | 2.2381 | 16450 | 0.0384 | - | | 2.2449 | 16500 | 0.0381 | - | | 2.2517 | 16550 | 0.0238 | - | | 2.2585 | 16600 | 0.0261 | - | | 2.2653 | 16650 | 0.0323 | - | | 2.2721 | 16700 | 0.0296 | - | | 2.2789 | 16750 | 0.0256 | - | | 2.2857 | 16800 | 0.0287 | - | | 2.2925 | 16850 | 0.0272 | - | | 2.2993 | 16900 | 0.0285 | - | | 2.3061 | 16950 | 0.0245 | - | | 2.3129 | 17000 | 0.0299 | - | | 2.3197 | 17050 | 0.0193 | - | | 2.3265 | 17100 | 0.0234 | - | | 2.3333 | 17150 | 0.0308 | - | | 2.3401 | 17200 | 0.0239 | - | | 2.3469 | 17250 | 0.0309 | - | | 2.3537 | 17300 | 0.0331 | - | | 2.3605 | 17350 | 0.0316 | - | | 2.3673 | 17400 | 0.0292 | - | | 2.3741 | 17450 | 0.0337 | - | | 2.3810 | 17500 | 0.0338 | - | | 2.3878 | 17550 | 0.0288 | - | | 2.3946 | 17600 | 0.031 | - | | 2.4014 | 17650 | 0.0251 | - | | 2.4082 | 17700 | 0.0288 | - | | 2.4150 | 17750 | 0.0249 | - | | 2.4218 | 17800 | 0.0281 | - | | 2.4286 | 17850 | 0.0284 | - | | 2.4354 | 17900 | 0.0268 | - | | 2.4422 | 17950 | 0.0303 | - | | 2.4490 | 18000 | 0.0233 | - | | 2.4558 | 18050 | 0.0297 | - | | 2.4626 | 18100 | 0.0265 | - | | 2.4694 | 18150 | 0.0306 | - | | 2.4762 | 18200 | 0.0286 | - | | 2.4830 | 18250 | 0.0278 | - | | 2.4898 | 18300 | 0.0254 | - | | 2.4966 | 18350 | 0.0278 | - | | 2.5034 | 18400 | 0.0257 | - | | 2.5102 | 18450 | 0.0272 | - | | 2.5170 | 18500 | 0.0297 | - | | 2.5238 | 18550 | 0.0262 | - | | 2.5306 | 18600 | 0.0309 | - | | 2.5374 | 18650 | 0.0259 | - | | 2.5442 | 18700 | 0.0212 | - | | 2.5510 | 18750 | 0.026 | - | | 2.5578 | 18800 | 0.0252 | - | | 2.5646 | 18850 | 0.0228 | - | | 2.5714 | 18900 | 0.0304 | - | | 2.5782 | 18950 | 0.0278 | - | | 2.5850 | 19000 | 0.0263 | - | | 2.5918 | 19050 | 0.0305 | - | | 2.5986 | 19100 | 0.0315 | - | | 2.6054 | 19150 | 0.0288 | - | | 2.6122 | 19200 | 0.0221 | - | | 2.6190 | 19250 | 0.022 | - | | 2.6259 | 19300 | 0.0299 | - | | 2.6327 | 19350 | 0.0302 | - | | 2.6395 | 19400 | 0.0282 | - | | 2.6463 | 19450 | 0.0308 | - | | 2.6531 | 19500 | 0.0306 | - | | 2.6599 | 19550 | 0.0327 | - | | 2.6667 | 19600 | 0.0284 | - | | 2.6735 | 19650 | 0.0185 | - | | 2.6803 | 19700 | 0.0248 | - | | 2.6871 | 19750 | 0.0212 | - | | 2.6939 | 19800 | 0.0254 | - | | 2.7007 | 19850 | 0.0276 | - | | 2.7075 | 19900 | 0.027 | - | | 2.7143 | 19950 | 0.0261 | - | | 2.7211 | 20000 | 0.0307 | - | | 2.7279 | 20050 | 0.0225 | - | | 2.7347 | 20100 | 0.0189 | - | | 2.7415 | 20150 | 0.0325 | - | | 2.7483 | 20200 | 0.0304 | - | | 2.7551 | 20250 | 0.0351 | - | | 2.7619 | 20300 | 0.0274 | - | | 2.7687 | 20350 | 0.0318 | - | | 2.7755 | 20400 | 0.0266 | - | | 2.7823 | 20450 | 0.0211 | - | | 2.7891 | 20500 | 0.0388 | - | | 2.7959 | 20550 | 0.0245 | - | | 2.8027 | 20600 | 0.0307 | - | | 2.8095 | 20650 | 0.0346 | - | | 2.8163 | 20700 | 0.0251 | - | | 2.8231 | 20750 | 0.0289 | - | | 2.8299 | 20800 | 0.0338 | - | | 2.8367 | 20850 | 0.0228 | - | | 2.8435 | 20900 | 0.0248 | - | | 2.8503 | 20950 | 0.0176 | - | | 2.8571 | 21000 | 0.0277 | - | | 2.8639 | 21050 | 0.0312 | - | | 2.8707 | 21100 | 0.0271 | - | | 2.8776 | 21150 | 0.0251 | - | | 2.8844 | 21200 | 0.0253 | - | | 2.8912 | 21250 | 0.0304 | - | | 2.8980 | 21300 | 0.0321 | - | | 2.9048 | 21350 | 0.0223 | - | | 2.9116 | 21400 | 0.0269 | - | | 2.9184 | 21450 | 0.0326 | - | | 2.9252 | 21500 | 0.0226 | - | | 2.9320 | 21550 | 0.0347 | - | | 2.9388 | 21600 | 0.0223 | - | | 2.9456 | 21650 | 0.0256 | - | | 2.9524 | 21700 | 0.0256 | - | | 2.9592 | 21750 | 0.0322 | - | | 2.9660 | 21800 | 0.0281 | - | | 2.9728 | 21850 | 0.0318 | - | | 2.9796 | 21900 | 0.0279 | - | | 2.9864 | 21950 | 0.0303 | - | | 2.9932 | 22000 | 0.0349 | - | | 3.0 | 22050 | 0.0254 | - | | 3.0068 | 22100 | 0.0185 | - | | 3.0136 | 22150 | 0.0241 | - | | 3.0204 | 22200 | 0.0285 | - | | 3.0272 | 22250 | 0.0257 | - | | 3.0340 | 22300 | 0.0247 | - | | 3.0408 | 22350 | 0.023 | - | | 3.0476 | 22400 | 0.0335 | - | | 3.0544 | 22450 | 0.0302 | - | | 3.0612 | 22500 | 0.0249 | - | | 3.0680 | 22550 | 0.029 | - | | 3.0748 | 22600 | 0.0312 | - | | 3.0816 | 22650 | 0.0303 | - | | 3.0884 | 22700 | 0.0225 | - | | 3.0952 | 22750 | 0.0271 | - | | 3.1020 | 22800 | 0.0275 | - | | 3.1088 | 22850 | 0.0264 | - | | 3.1156 | 22900 | 0.0202 | - | | 3.1224 | 22950 | 0.0247 | - | | 3.1293 | 23000 | 0.0292 | - | | 3.1361 | 23050 | 0.0235 | - | | 3.1429 | 23100 | 0.019 | - | | 3.1497 | 23150 | 0.0247 | - | | 3.1565 | 23200 | 0.0219 | - | | 3.1633 | 23250 | 0.0217 | - | | 3.1701 | 23300 | 0.0236 | - | | 3.1769 | 23350 | 0.0223 | - | | 3.1837 | 23400 | 0.0237 | - | | 3.1905 | 23450 | 0.0307 | - | | 3.1973 | 23500 | 0.0275 | - | | 3.2041 | 23550 | 0.0192 | - | | 3.2109 | 23600 | 0.0198 | - | | 3.2177 | 23650 | 0.0322 | - | | 3.2245 | 23700 | 0.0195 | - | | 3.2313 | 23750 | 0.019 | - | | 3.2381 | 23800 | 0.0266 | - | | 3.2449 | 23850 | 0.0287 | - | | 3.2517 | 23900 | 0.0205 | - | | 3.2585 | 23950 | 0.025 | - | | 3.2653 | 24000 | 0.0282 | - | | 3.2721 | 24050 | 0.0261 | - | | 3.2789 | 24100 | 0.0275 | - | | 3.2857 | 24150 | 0.0273 | - | | 3.2925 | 24200 | 0.0195 | - | | 3.2993 | 24250 | 0.0265 | - | | 3.3061 | 24300 | 0.0276 | - | | 3.3129 | 24350 | 0.0277 | - | | 3.3197 | 24400 | 0.0224 | - | | 3.3265 | 24450 | 0.0231 | - | | 3.3333 | 24500 | 0.0275 | - | | 3.3401 | 24550 | 0.0333 | - | | 3.3469 | 24600 | 0.0181 | - | | 3.3537 | 24650 | 0.0266 | - | | 3.3605 | 24700 | 0.0268 | - | | 3.3673 | 24750 | 0.0177 | - | | 3.3741 | 24800 | 0.0185 | - | | 3.3810 | 24850 | 0.023 | - | | 3.3878 | 24900 | 0.0281 | - | | 3.3946 | 24950 | 0.0202 | - | | 3.4014 | 25000 | 0.0206 | - | | 3.4082 | 25050 | 0.0224 | - | | 3.4150 | 25100 | 0.0275 | - | | 3.4218 | 25150 | 0.0272 | - | | 3.4286 | 25200 | 0.0221 | - | | 3.4354 | 25250 | 0.0259 | - | | 3.4422 | 25300 | 0.0244 | - | | 3.4490 | 25350 | 0.034 | - | | 3.4558 | 25400 | 0.0258 | - | | 3.4626 | 25450 | 0.0271 | - | | 3.4694 | 25500 | 0.0291 | - | | 3.4762 | 25550 | 0.0204 | - | | 3.4830 | 25600 | 0.0248 | - | | 3.4898 | 25650 | 0.0225 | - | | 3.4966 | 25700 | 0.0347 | - | | 3.5034 | 25750 | 0.0243 | - | | 3.5102 | 25800 | 0.031 | - | | 3.5170 | 25850 | 0.024 | - | | 3.5238 | 25900 | 0.0199 | - | | 3.5306 | 25950 | 0.0278 | - | | 3.5374 | 26000 | 0.0318 | - | | 3.5442 | 26050 | 0.0267 | - | | 3.5510 | 26100 | 0.027 | - | | 3.5578 | 26150 | 0.0191 | - | | 3.5646 | 26200 | 0.0233 | - | | 3.5714 | 26250 | 0.0239 | - | | 3.5782 | 26300 | 0.0203 | - | | 3.5850 | 26350 | 0.0243 | - | | 3.5918 | 26400 | 0.0246 | - | | 3.5986 | 26450 | 0.0233 | - | | 3.6054 | 26500 | 0.0364 | - | | 3.6122 | 26550 | 0.0273 | - | | 3.6190 | 26600 | 0.0269 | - | | 3.6259 | 26650 | 0.0206 | - | | 3.6327 | 26700 | 0.0316 | - | | 3.6395 | 26750 | 0.023 | - | | 3.6463 | 26800 | 0.0257 | - | | 3.6531 | 26850 | 0.0263 | - | | 3.6599 | 26900 | 0.0218 | - | | 3.6667 | 26950 | 0.0257 | - | | 3.6735 | 27000 | 0.0228 | - | | 3.6803 | 27050 | 0.0256 | - | | 3.6871 | 27100 | 0.0239 | - | | 3.6939 | 27150 | 0.0225 | - | | 3.7007 | 27200 | 0.0294 | - | | 3.7075 | 27250 | 0.0187 | - | | 3.7143 | 27300 | 0.02 | - | | 3.7211 | 27350 | 0.0261 | - | | 3.7279 | 27400 | 0.0201 | - | | 3.7347 | 27450 | 0.0253 | - | | 3.7415 | 27500 | 0.0265 | - | | 3.7483 | 27550 | 0.0303 | - | | 3.7551 | 27600 | 0.0239 | - | | 3.7619 | 27650 | 0.0246 | - | | 3.7687 | 27700 | 0.0249 | - | | 3.7755 | 27750 | 0.023 | - | | 3.7823 | 27800 | 0.0237 | - | | 3.7891 | 27850 | 0.0197 | - | | 3.7959 | 27900 | 0.0268 | - | | 3.8027 | 27950 | 0.0246 | - | | 3.8095 | 28000 | 0.029 | - | | 3.8163 | 28050 | 0.0248 | - | | 3.8231 | 28100 | 0.0275 | - | | 3.8299 | 28150 | 0.0241 | - | | 3.8367 | 28200 | 0.027 | - | | 3.8435 | 28250 | 0.0252 | - | | 3.8503 | 28300 | 0.0245 | - | | 3.8571 | 28350 | 0.0241 | - | | 3.8639 | 28400 | 0.0264 | - | | 3.8707 | 28450 | 0.0233 | - | | 3.8776 | 28500 | 0.0319 | - | | 3.8844 | 28550 | 0.0236 | - | | 3.8912 | 28600 | 0.0277 | - | | 3.8980 | 28650 | 0.0178 | - | | 3.9048 | 28700 | 0.0209 | - | | 3.9116 | 28750 | 0.0263 | - | | 3.9184 | 28800 | 0.0236 | - | | 3.9252 | 28850 | 0.0216 | - | | 3.9320 | 28900 | 0.0209 | - | | 3.9388 | 28950 | 0.0283 | - | | 3.9456 | 29000 | 0.0307 | - | | 3.9524 | 29050 | 0.0276 | - | | 3.9592 | 29100 | 0.0277 | - | | 3.9660 | 29150 | 0.031 | - | | 3.9728 | 29200 | 0.0304 | - | | 3.9796 | 29250 | 0.0332 | - | | 3.9864 | 29300 | 0.0277 | - | | 3.9932 | 29350 | 0.0233 | - | | 4.0 | 29400 | 0.0237 | - | ### 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: 0.21.1 ## 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} } ```