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model_g20_multilabel_30sample

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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model_g20_multilabel_30sample / README.md
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metadata
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


      53




      5.10.7 Resource allocation and generation 


      The resources required for monitoring and evaluation of nutrition
      intervention should 

      normally 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 
      to develop socio-economic infrastructure in the rural sector, expand road 
      transportation network, conduct rural employment activities, and build 



       227

      local level’s institutional capacity.
  - text: |-
      Housing and Community Amenities 
       

      133.
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 model that can be used for Text Classification. This SetFit model uses 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 with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

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 

@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}
}