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faodl
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model_cca_multilabel_mpnet-65max-full-poorf10-artificial

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Model card Files
xet
Community

SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-mpnet-base-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_cca_multilabel_mpnet-65max-full-poorf10-artificial")
# Run inference
preds = model("Targeted skills audits will identify gaps in the current rural workforce and inform training investments.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 7 19.7924 100

Training Hyperparameters

  • batch_size: (8, 8)
  • num_epochs: (1, 1)
  • 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
0.0000 1 0.2267 -
0.0014 50 0.2064 -
0.0029 100 0.2078 -
0.0043 150 0.1999 -
0.0058 200 0.1965 -
0.0072 250 0.1865 -
0.0086 300 0.1831 -
0.0101 350 0.1824 -
0.0115 400 0.1696 -
0.0130 450 0.1635 -
0.0144 500 0.1685 -
0.0158 550 0.1542 -
0.0173 600 0.15 -
0.0187 650 0.1511 -
0.0202 700 0.16 -
0.0216 750 0.1413 -
0.0230 800 0.1363 -
0.0245 850 0.1527 -
0.0259 900 0.1324 -
0.0273 950 0.1274 -
0.0288 1000 0.1526 -
0.0302 1050 0.1182 -
0.0317 1100 0.1327 -
0.0331 1150 0.1291 -
0.0345 1200 0.1285 -
0.0360 1250 0.1196 -
0.0374 1300 0.1265 -
0.0389 1350 0.1167 -
0.0403 1400 0.1144 -
0.0417 1450 0.1347 -
0.0432 1500 0.1258 -
0.0446 1550 0.1332 -
0.0461 1600 0.1128 -
0.0475 1650 0.1168 -
0.0489 1700 0.1203 -
0.0504 1750 0.1042 -
0.0518 1800 0.1182 -
0.0533 1850 0.114 -
0.0547 1900 0.1139 -
0.0561 1950 0.1061 -
0.0576 2000 0.108 -
0.0590 2050 0.115 -
0.0605 2100 0.0995 -
0.0619 2150 0.1053 -
0.0633 2200 0.1227 -
0.0648 2250 0.112 -
0.0662 2300 0.1092 -
0.0677 2350 0.1136 -
0.0691 2400 0.092 -
0.0705 2450 0.099 -
0.0720 2500 0.1091 -
0.0734 2550 0.1192 -
0.0749 2600 0.1148 -
0.0763 2650 0.0921 -
0.0777 2700 0.0917 -
0.0792 2750 0.1148 -
0.0806 2800 0.1055 -
0.0820 2850 0.0943 -
0.0835 2900 0.0926 -
0.0849 2950 0.115 -
0.0864 3000 0.0928 -
0.0878 3050 0.092 -
0.0892 3100 0.0917 -
0.0907 3150 0.1149 -
0.0921 3200 0.1072 -
0.0936 3250 0.0791 -
0.0950 3300 0.0968 -
0.0964 3350 0.1018 -
0.0979 3400 0.1077 -
0.0993 3450 0.0992 -
0.1008 3500 0.0817 -
0.1022 3550 0.0955 -
0.1036 3600 0.0824 -
0.1051 3650 0.0835 -
0.1065 3700 0.101 -
0.1080 3750 0.0913 -
0.1094 3800 0.1014 -
0.1108 3850 0.0849 -
0.1123 3900 0.0855 -
0.1137 3950 0.0802 -
0.1152 4000 0.0904 -
0.1166 4050 0.0767 -
0.1180 4100 0.0829 -
0.1195 4150 0.0912 -
0.1209 4200 0.0788 -
0.1224 4250 0.0861 -
0.1238 4300 0.089 -
0.1252 4350 0.0652 -
0.1267 4400 0.0946 -
0.1281 4450 0.0819 -
0.1296 4500 0.0829 -
0.1310 4550 0.0491 -
0.1324 4600 0.0875 -
0.1339 4650 0.0675 -
0.1353 4700 0.0838 -
0.1367 4750 0.0637 -
0.1382 4800 0.0907 -
0.1396 4850 0.0803 -
0.1411 4900 0.06 -
0.1425 4950 0.0866 -
0.1439 5000 0.0654 -
0.1454 5050 0.0695 -
0.1468 5100 0.0723 -
0.1483 5150 0.0725 -
0.1497 5200 0.0762 -
0.1511 5250 0.0738 -
0.1526 5300 0.0732 -
0.1540 5350 0.0619 -
0.1555 5400 0.0768 -
0.1569 5450 0.0749 -
0.1583 5500 0.083 -
0.1598 5550 0.0638 -
0.1612 5600 0.0651 -
0.1627 5650 0.0633 -
0.1641 5700 0.0639 -
0.1655 5750 0.0615 -
0.1670 5800 0.0684 -
0.1684 5850 0.0539 -
0.1699 5900 0.054 -
0.1713 5950 0.0544 -
0.1727 6000 0.0532 -
0.1742 6050 0.0665 -
0.1756 6100 0.0669 -
0.1771 6150 0.0722 -
0.1785 6200 0.0581 -
0.1799 6250 0.0515 -
0.1814 6300 0.057 -
0.1828 6350 0.0509 -
0.1843 6400 0.0671 -
0.1857 6450 0.0452 -
0.1871 6500 0.0641 -
0.1886 6550 0.0746 -
0.1900 6600 0.0623 -
0.1914 6650 0.0534 -
0.1929 6700 0.0542 -
0.1943 6750 0.0576 -
0.1958 6800 0.0638 -
0.1972 6850 0.0463 -
0.1986 6900 0.0561 -
0.2001 6950 0.0789 -
0.2015 7000 0.0705 -
0.2030 7050 0.0516 -
0.2044 7100 0.0508 -
0.2058 7150 0.0537 -
0.2073 7200 0.0567 -
0.2087 7250 0.05 -
0.2102 7300 0.056 -
0.2116 7350 0.0495 -
0.2130 7400 0.0576 -
0.2145 7450 0.0574 -
0.2159 7500 0.0497 -
0.2174 7550 0.0556 -
0.2188 7600 0.0597 -
0.2202 7650 0.044 -
0.2217 7700 0.0373 -
0.2231 7750 0.0409 -
0.2246 7800 0.0532 -
0.2260 7850 0.0477 -
0.2274 7900 0.0502 -
0.2289 7950 0.0467 -
0.2303 8000 0.0507 -
0.2318 8050 0.0519 -
0.2332 8100 0.0345 -
0.2346 8150 0.052 -
0.2361 8200 0.0439 -
0.2375 8250 0.0446 -
0.2390 8300 0.049 -
0.2404 8350 0.0749 -
0.2418 8400 0.0367 -
0.2433 8450 0.0371 -
0.2447 8500 0.0631 -
0.2461 8550 0.0451 -
0.2476 8600 0.0405 -
0.2490 8650 0.0403 -
0.2505 8700 0.0501 -
0.2519 8750 0.046 -
0.2533 8800 0.0431 -
0.2548 8850 0.0474 -
0.2562 8900 0.0444 -
0.2577 8950 0.0288 -
0.2591 9000 0.0527 -
0.2605 9050 0.0434 -
0.2620 9100 0.0423 -
0.2634 9150 0.0554 -
0.2649 9200 0.0419 -
0.2663 9250 0.0465 -
0.2677 9300 0.0398 -
0.2692 9350 0.0448 -
0.2706 9400 0.0338 -
0.2721 9450 0.0545 -
0.2735 9500 0.0417 -
0.2749 9550 0.0401 -
0.2764 9600 0.0452 -
0.2778 9650 0.0403 -
0.2793 9700 0.0374 -
0.2807 9750 0.0547 -
0.2821 9800 0.0401 -
0.2836 9850 0.0381 -
0.2850 9900 0.0396 -
0.2865 9950 0.0482 -
0.2879 10000 0.0406 -
0.2893 10050 0.0454 -
0.2908 10100 0.0274 -
0.2922 10150 0.0324 -
0.2937 10200 0.0466 -
0.2951 10250 0.0322 -
0.2965 10300 0.0479 -
0.2980 10350 0.0414 -
0.2994 10400 0.0374 -
0.3008 10450 0.0383 -
0.3023 10500 0.0475 -
0.3037 10550 0.0327 -
0.3052 10600 0.0448 -
0.3066 10650 0.0507 -
0.3080 10700 0.0299 -
0.3095 10750 0.0346 -
0.3109 10800 0.0317 -
0.3124 10850 0.033 -
0.3138 10900 0.0351 -
0.3152 10950 0.0324 -
0.3167 11000 0.0401 -
0.3181 11050 0.0308 -
0.3196 11100 0.0314 -
0.3210 11150 0.0317 -
0.3224 11200 0.0352 -
0.3239 11250 0.0314 -
0.3253 11300 0.0278 -
0.3268 11350 0.0413 -
0.3282 11400 0.0272 -
0.3296 11450 0.0424 -
0.3311 11500 0.0316 -
0.3325 11550 0.0351 -
0.3340 11600 0.0332 -
0.3354 11650 0.0295 -
0.3368 11700 0.0251 -
0.3383 11750 0.027 -
0.3397 11800 0.0306 -
0.3412 11850 0.0332 -
0.3426 11900 0.0308 -
0.3440 11950 0.0269 -
0.3455 12000 0.0354 -
0.3469 12050 0.0231 -
0.3484 12100 0.0341 -
0.3498 12150 0.0299 -
0.3512 12200 0.0224 -
0.3527 12250 0.0238 -
0.3541 12300 0.026 -
0.3555 12350 0.0336 -
0.3570 12400 0.0366 -
0.3584 12450 0.0305 -
0.3599 12500 0.0362 -
0.3613 12550 0.0202 -
0.3627 12600 0.0219 -
0.3642 12650 0.021 -
0.3656 12700 0.0395 -
0.3671 12750 0.031 -
0.3685 12800 0.0234 -
0.3699 12850 0.0374 -
0.3714 12900 0.0214 -
0.3728 12950 0.0307 -
0.3743 13000 0.0283 -
0.3757 13050 0.0284 -
0.3771 13100 0.0311 -
0.3786 13150 0.0206 -
0.3800 13200 0.0322 -
0.3815 13250 0.0255 -
0.3829 13300 0.0275 -
0.3843 13350 0.0301 -
0.3858 13400 0.0366 -
0.3872 13450 0.033 -
0.3887 13500 0.0159 -
0.3901 13550 0.0327 -
0.3915 13600 0.0229 -
0.3930 13650 0.0333 -
0.3944 13700 0.0192 -
0.3959 13750 0.0272 -
0.3973 13800 0.0173 -
0.3987 13850 0.0257 -
0.4002 13900 0.0187 -
0.4016 13950 0.0235 -
0.4031 14000 0.0223 -
0.4045 14050 0.0212 -
0.4059 14100 0.0235 -
0.4074 14150 0.0268 -
0.4088 14200 0.0282 -
0.4102 14250 0.0211 -
0.4117 14300 0.0207 -
0.4131 14350 0.0175 -
0.4146 14400 0.0267 -
0.4160 14450 0.0246 -
0.4174 14500 0.0266 -
0.4189 14550 0.021 -
0.4203 14600 0.028 -
0.4218 14650 0.0229 -
0.4232 14700 0.0216 -
0.4246 14750 0.04 -
0.4261 14800 0.0233 -
0.4275 14850 0.0256 -
0.4290 14900 0.0216 -
0.4304 14950 0.0296 -
0.4318 15000 0.0168 -
0.4333 15050 0.0215 -
0.4347 15100 0.0135 -
0.4362 15150 0.0158 -
0.4376 15200 0.02 -
0.4390 15250 0.0302 -
0.4405 15300 0.0242 -
0.4419 15350 0.0255 -
0.4434 15400 0.0145 -
0.4448 15450 0.0161 -
0.4462 15500 0.0238 -
0.4477 15550 0.0083 -
0.4491 15600 0.0213 -
0.4506 15650 0.0241 -
0.4520 15700 0.0253 -
0.4534 15750 0.0196 -
0.4549 15800 0.0285 -
0.4563 15850 0.0225 -
0.4578 15900 0.0262 -
0.4592 15950 0.017 -
0.4606 16000 0.0251 -
0.4621 16050 0.0212 -
0.4635 16100 0.023 -
0.4649 16150 0.0173 -
0.4664 16200 0.0355 -
0.4678 16250 0.0205 -
0.4693 16300 0.0114 -
0.4707 16350 0.0157 -
0.4721 16400 0.0304 -
0.4736 16450 0.0163 -
0.4750 16500 0.0208 -
0.4765 16550 0.0124 -
0.4779 16600 0.0327 -
0.4793 16650 0.0228 -
0.4808 16700 0.0161 -
0.4822 16750 0.0217 -
0.4837 16800 0.0151 -
0.4851 16850 0.0255 -
0.4865 16900 0.0283 -
0.4880 16950 0.0192 -
0.4894 17000 0.0217 -
0.4909 17050 0.02 -
0.4923 17100 0.0296 -
0.4937 17150 0.0263 -
0.4952 17200 0.0196 -
0.4966 17250 0.019 -
0.4981 17300 0.0185 -
0.4995 17350 0.018 -
0.5009 17400 0.0146 -
0.5024 17450 0.0144 -
0.5038 17500 0.0143 -
0.5053 17550 0.0179 -
0.5067 17600 0.0213 -
0.5081 17650 0.022 -
0.5096 17700 0.0136 -
0.5110 17750 0.012 -
0.5125 17800 0.0148 -
0.5139 17850 0.0189 -
0.5153 17900 0.0209 -
0.5168 17950 0.0191 -
0.5182 18000 0.0155 -
0.5196 18050 0.0223 -
0.5211 18100 0.0172 -
0.5225 18150 0.0147 -
0.5240 18200 0.0205 -
0.5254 18250 0.0196 -
0.5268 18300 0.018 -
0.5283 18350 0.0123 -
0.5297 18400 0.0146 -
0.5312 18450 0.0154 -
0.5326 18500 0.0099 -
0.5340 18550 0.0113 -
0.5355 18600 0.0191 -
0.5369 18650 0.0161 -
0.5384 18700 0.0113 -
0.5398 18750 0.0236 -
0.5412 18800 0.021 -
0.5427 18850 0.0107 -
0.5441 18900 0.021 -
0.5456 18950 0.0213 -
0.5470 19000 0.028 -
0.5484 19050 0.0164 -
0.5499 19100 0.0197 -
0.5513 19150 0.0074 -
0.5528 19200 0.0108 -
0.5542 19250 0.0118 -
0.5556 19300 0.013 -
0.5571 19350 0.0215 -
0.5585 19400 0.0124 -
0.5600 19450 0.0163 -
0.5614 19500 0.01 -
0.5628 19550 0.0188 -
0.5643 19600 0.019 -
0.5657 19650 0.0075 -
0.5672 19700 0.0168 -
0.5686 19750 0.0073 -
0.5700 19800 0.0151 -
0.5715 19850 0.0236 -
0.5729 19900 0.0197 -
0.5743 19950 0.0207 -
0.5758 20000 0.0106 -
0.5772 20050 0.0137 -
0.5787 20100 0.0155 -
0.5801 20150 0.0118 -
0.5815 20200 0.0231 -
0.5830 20250 0.0186 -
0.5844 20300 0.0139 -
0.5859 20350 0.0183 -
0.5873 20400 0.0136 -
0.5887 20450 0.0139 -
0.5902 20500 0.0131 -
0.5916 20550 0.014 -
0.5931 20600 0.021 -
0.5945 20650 0.0172 -
0.5959 20700 0.016 -
0.5974 20750 0.0136 -
0.5988 20800 0.0144 -
0.6003 20850 0.0142 -
0.6017 20900 0.0148 -
0.6031 20950 0.0197 -
0.6046 21000 0.0081 -
0.6060 21050 0.0088 -
0.6075 21100 0.0216 -
0.6089 21150 0.0231 -
0.6103 21200 0.0182 -
0.6118 21250 0.0132 -
0.6132 21300 0.0104 -
0.6147 21350 0.0107 -
0.6161 21400 0.0051 -
0.6175 21450 0.0131 -
0.6190 21500 0.0118 -
0.6204 21550 0.0122 -
0.6219 21600 0.0154 -
0.6233 21650 0.0138 -
0.6247 21700 0.0197 -
0.6262 21750 0.0159 -
0.6276 21800 0.0101 -
0.6290 21850 0.0105 -
0.6305 21900 0.0108 -
0.6319 21950 0.0098 -
0.6334 22000 0.013 -
0.6348 22050 0.0188 -
0.6362 22100 0.008 -
0.6377 22150 0.0159 -
0.6391 22200 0.0211 -
0.6406 22250 0.0128 -
0.6420 22300 0.0136 -
0.6434 22350 0.0152 -
0.6449 22400 0.0105 -
0.6463 22450 0.0129 -
0.6478 22500 0.0119 -
0.6492 22550 0.0177 -
0.6506 22600 0.0085 -
0.6521 22650 0.0119 -
0.6535 22700 0.0033 -
0.6550 22750 0.0115 -
0.6564 22800 0.0068 -
0.6578 22850 0.0241 -
0.6593 22900 0.0135 -
0.6607 22950 0.0134 -
0.6622 23000 0.0109 -
0.6636 23050 0.0151 -
0.6650 23100 0.0106 -
0.6665 23150 0.0125 -
0.6679 23200 0.007 -
0.6694 23250 0.0171 -
0.6708 23300 0.0108 -
0.6722 23350 0.0163 -
0.6737 23400 0.0196 -
0.6751 23450 0.0054 -
0.6766 23500 0.0068 -
0.6780 23550 0.0157 -
0.6794 23600 0.0183 -
0.6809 23650 0.0153 -
0.6823 23700 0.0143 -
0.6837 23750 0.0072 -
0.6852 23800 0.0168 -
0.6866 23850 0.0157 -
0.6881 23900 0.0056 -
0.6895 23950 0.0196 -
0.6909 24000 0.0094 -
0.6924 24050 0.0107 -
0.6938 24100 0.0177 -
0.6953 24150 0.0143 -
0.6967 24200 0.0088 -
0.6981 24250 0.0148 -
0.6996 24300 0.0171 -
0.7010 24350 0.0079 -
0.7025 24400 0.0171 -
0.7039 24450 0.0161 -
0.7053 24500 0.0066 -
0.7068 24550 0.0142 -
0.7082 24600 0.0139 -
0.7097 24650 0.0122 -
0.7111 24700 0.0188 -
0.7125 24750 0.008 -
0.7140 24800 0.0142 -
0.7154 24850 0.0114 -
0.7169 24900 0.0104 -
0.7183 24950 0.0204 -
0.7197 25000 0.0137 -
0.7212 25050 0.0096 -
0.7226 25100 0.0075 -
0.7241 25150 0.0143 -
0.7255 25200 0.0095 -
0.7269 25250 0.0068 -
0.7284 25300 0.0092 -
0.7298 25350 0.01 -
0.7313 25400 0.0064 -
0.7327 25450 0.0066 -
0.7341 25500 0.023 -
0.7356 25550 0.0137 -
0.7370 25600 0.0062 -
0.7384 25650 0.0105 -
0.7399 25700 0.0043 -
0.7413 25750 0.0137 -
0.7428 25800 0.0097 -
0.7442 25850 0.0124 -
0.7456 25900 0.0112 -
0.7471 25950 0.0101 -
0.7485 26000 0.0149 -
0.7500 26050 0.0111 -
0.7514 26100 0.006 -
0.7528 26150 0.0126 -
0.7543 26200 0.0122 -
0.7557 26250 0.0049 -
0.7572 26300 0.0126 -
0.7586 26350 0.0133 -
0.7600 26400 0.0035 -
0.7615 26450 0.018 -
0.7629 26500 0.0175 -
0.7644 26550 0.0068 -
0.7658 26600 0.0079 -
0.7672 26650 0.0084 -
0.7687 26700 0.014 -
0.7701 26750 0.0113 -
0.7716 26800 0.0153 -
0.7730 26850 0.0251 -
0.7744 26900 0.0102 -
0.7759 26950 0.0135 -
0.7773 27000 0.0079 -
0.7788 27050 0.0081 -
0.7802 27100 0.0055 -
0.7816 27150 0.0014 -
0.7831 27200 0.0134 -
0.7845 27250 0.0058 -
0.7860 27300 0.0071 -
0.7874 27350 0.0045 -
0.7888 27400 0.0067 -
0.7903 27450 0.0125 -
0.7917 27500 0.0094 -
0.7931 27550 0.0129 -
0.7946 27600 0.0096 -
0.7960 27650 0.0032 -
0.7975 27700 0.0061 -
0.7989 27750 0.0054 -
0.8003 27800 0.0121 -
0.8018 27850 0.0124 -
0.8032 27900 0.0065 -
0.8047 27950 0.0035 -
0.8061 28000 0.012 -
0.8075 28050 0.0168 -
0.8090 28100 0.0107 -
0.8104 28150 0.0085 -
0.8119 28200 0.0075 -
0.8133 28250 0.0114 -
0.8147 28300 0.0134 -
0.8162 28350 0.0082 -
0.8176 28400 0.0118 -
0.8191 28450 0.0094 -
0.8205 28500 0.0073 -
0.8219 28550 0.0069 -
0.8234 28600 0.0155 -
0.8248 28650 0.011 -
0.8263 28700 0.0091 -
0.8277 28750 0.0042 -
0.8291 28800 0.0095 -
0.8306 28850 0.0155 -
0.8320 28900 0.0195 -
0.8335 28950 0.0094 -
0.8349 29000 0.0084 -
0.8363 29050 0.0126 -
0.8378 29100 0.0148 -
0.8392 29150 0.0093 -
0.8407 29200 0.0044 -
0.8421 29250 0.0121 -
0.8435 29300 0.0132 -
0.8450 29350 0.009 -
0.8464 29400 0.0097 -
0.8478 29450 0.0059 -
0.8493 29500 0.0192 -
0.8507 29550 0.0093 -
0.8522 29600 0.011 -
0.8536 29650 0.0153 -
0.8550 29700 0.0157 -
0.8565 29750 0.0113 -
0.8579 29800 0.0062 -
0.8594 29850 0.008 -
0.8608 29900 0.007 -
0.8622 29950 0.0099 -
0.8637 30000 0.0059 -
0.8651 30050 0.0103 -
0.8666 30100 0.0115 -
0.8680 30150 0.0155 -
0.8694 30200 0.0104 -
0.8709 30250 0.0073 -
0.8723 30300 0.0112 -
0.8738 30350 0.0059 -
0.8752 30400 0.0069 -
0.8766 30450 0.0109 -
0.8781 30500 0.0111 -
0.8795 30550 0.0074 -
0.8810 30600 0.012 -
0.8824 30650 0.0057 -
0.8838 30700 0.0106 -
0.8853 30750 0.0014 -
0.8867 30800 0.0147 -
0.8882 30850 0.0119 -
0.8896 30900 0.0071 -
0.8910 30950 0.0033 -
0.8925 31000 0.0013 -
0.8939 31050 0.0128 -
0.8954 31100 0.0151 -
0.8968 31150 0.016 -
0.8982 31200 0.0107 -
0.8997 31250 0.0094 -
0.9011 31300 0.0074 -
0.9025 31350 0.0082 -
0.9040 31400 0.0079 -
0.9054 31450 0.011 -
0.9069 31500 0.013 -
0.9083 31550 0.0092 -
0.9097 31600 0.0092 -
0.9112 31650 0.011 -
0.9126 31700 0.0061 -
0.9141 31750 0.0043 -
0.9155 31800 0.0114 -
0.9169 31850 0.0105 -
0.9184 31900 0.0017 -
0.9198 31950 0.0039 -
0.9213 32000 0.0308 -
0.9227 32050 0.0108 -
0.9241 32100 0.0098 -
0.9256 32150 0.0112 -
0.9270 32200 0.0062 -
0.9285 32250 0.0074 -
0.9299 32300 0.0115 -
0.9313 32350 0.0134 -
0.9328 32400 0.0087 -
0.9342 32450 0.0114 -
0.9357 32500 0.0066 -
0.9371 32550 0.0112 -
0.9385 32600 0.0045 -
0.9400 32650 0.0056 -
0.9414 32700 0.0137 -
0.9429 32750 0.0123 -
0.9443 32800 0.0054 -
0.9457 32850 0.0083 -
0.9472 32900 0.0037 -
0.9486 32950 0.0099 -
0.9501 33000 0.0055 -
0.9515 33050 0.01 -
0.9529 33100 0.0082 -
0.9544 33150 0.0082 -
0.9558 33200 0.0054 -
0.9572 33250 0.0087 -
0.9587 33300 0.0099 -
0.9601 33350 0.0104 -
0.9616 33400 0.0062 -
0.9630 33450 0.0065 -
0.9644 33500 0.0046 -
0.9659 33550 0.0136 -
0.9673 33600 0.002 -
0.9688 33650 0.0058 -
0.9702 33700 0.0048 -
0.9716 33750 0.0071 -
0.9731 33800 0.0064 -
0.9745 33850 0.0061 -
0.9760 33900 0.0202 -
0.9774 33950 0.0116 -
0.9788 34000 0.0091 -
0.9803 34050 0.0061 -
0.9817 34100 0.0144 -
0.9832 34150 0.0066 -
0.9846 34200 0.0048 -
0.9860 34250 0.0064 -
0.9875 34300 0.0055 -
0.9889 34350 0.0144 -
0.9904 34400 0.0011 -
0.9918 34450 0.0049 -
0.9932 34500 0.0131 -
0.9947 34550 0.013 -
0.9961 34600 0.0041 -
0.9976 34650 0.0074 -
0.9990 34700 0.0062 -

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 

@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}
}
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