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model_cca_multilabel_mpnet-75max-full-poorf10-artificial-expanded-desc

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-75max-full-poorf10-artificial-expanded-desc")
# Run inference
preds = model("We will create a monthly market information digest to support price transparency and policy responses.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 7 19.7347 97

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 0.2027 -
0.0013 50 0.1991 -
0.0026 100 0.2007 -
0.0039 150 0.1891 -
0.0052 200 0.1988 -
0.0065 250 0.1838 -
0.0078 300 0.1797 -
0.0091 350 0.1842 -
0.0104 400 0.1721 -
0.0116 450 0.1687 -
0.0129 500 0.1567 -
0.0142 550 0.1661 -
0.0155 600 0.1551 -
0.0168 650 0.146 -
0.0181 700 0.1391 -
0.0194 750 0.1483 -
0.0207 800 0.1428 -
0.0220 850 0.138 -
0.0233 900 0.1549 -
0.0246 950 0.1406 -
0.0259 1000 0.1408 -
0.0272 1050 0.1346 -
0.0285 1100 0.1278 -
0.0298 1150 0.1248 -
0.0311 1200 0.1219 -
0.0324 1250 0.1328 -
0.0336 1300 0.1242 -
0.0349 1350 0.1298 -
0.0362 1400 0.1167 -
0.0375 1450 0.1325 -
0.0388 1500 0.1254 -
0.0401 1550 0.1229 -
0.0414 1600 0.1142 -
0.0427 1650 0.1262 -
0.0440 1700 0.1141 -
0.0453 1750 0.1028 -
0.0466 1800 0.1202 -
0.0479 1850 0.1013 -
0.0492 1900 0.1027 -
0.0505 1950 0.1252 -
0.0518 2000 0.114 -
0.0531 2050 0.1034 -
0.0544 2100 0.1222 -
0.0556 2150 0.1266 -
0.0569 2200 0.1185 -
0.0582 2250 0.1079 -
0.0595 2300 0.1133 -
0.0608 2350 0.104 -
0.0621 2400 0.0937 -
0.0634 2450 0.1021 -
0.0647 2500 0.1007 -
0.0660 2550 0.0989 -
0.0673 2600 0.1154 -
0.0686 2650 0.0955 -
0.0699 2700 0.109 -
0.0712 2750 0.1057 -
0.0725 2800 0.0993 -
0.0738 2850 0.0938 -
0.0751 2900 0.101 -
0.0764 2950 0.1147 -
0.0776 3000 0.083 -
0.0789 3050 0.1096 -
0.0802 3100 0.0873 -
0.0815 3150 0.1006 -
0.0828 3200 0.1009 -
0.0841 3250 0.0826 -
0.0854 3300 0.0987 -
0.0867 3350 0.0832 -
0.0880 3400 0.0992 -
0.0893 3450 0.0899 -
0.0906 3500 0.0991 -
0.0919 3550 0.0993 -
0.0932 3600 0.0943 -
0.0945 3650 0.0991 -
0.0958 3700 0.087 -
0.0971 3750 0.0843 -
0.0984 3800 0.0907 -
0.0997 3850 0.0979 -
0.1009 3900 0.0935 -
0.1022 3950 0.0917 -
0.1035 4000 0.1051 -
0.1048 4050 0.0932 -
0.1061 4100 0.0842 -
0.1074 4150 0.0947 -
0.1087 4200 0.0835 -
0.1100 4250 0.088 -
0.1113 4300 0.0866 -
0.1126 4350 0.0908 -
0.1139 4400 0.0789 -
0.1152 4450 0.088 -
0.1165 4500 0.0961 -
0.1178 4550 0.0856 -
0.1191 4600 0.0861 -
0.1204 4650 0.0851 -
0.1217 4700 0.0778 -
0.1229 4750 0.0913 -
0.1242 4800 0.0786 -
0.1255 4850 0.0798 -
0.1268 4900 0.0832 -
0.1281 4950 0.0788 -
0.1294 5000 0.0621 -
0.1307 5050 0.0804 -
0.1320 5100 0.0705 -
0.1333 5150 0.0813 -
0.1346 5200 0.0652 -
0.1359 5250 0.0765 -
0.1372 5300 0.0729 -
0.1385 5350 0.0737 -
0.1398 5400 0.076 -
0.1411 5450 0.0871 -
0.1424 5500 0.0704 -
0.1437 5550 0.0695 -
0.1449 5600 0.0656 -
0.1462 5650 0.0582 -
0.1475 5700 0.0638 -
0.1488 5750 0.0775 -
0.1501 5800 0.0653 -
0.1514 5850 0.0774 -
0.1527 5900 0.0596 -
0.1540 5950 0.0716 -
0.1553 6000 0.0604 -
0.1566 6050 0.0849 -
0.1579 6100 0.066 -
0.1592 6150 0.0755 -
0.1605 6200 0.0739 -
0.1618 6250 0.064 -
0.1631 6300 0.067 -
0.1644 6350 0.0652 -
0.1657 6400 0.0651 -
0.1669 6450 0.0599 -
0.1682 6500 0.0702 -
0.1695 6550 0.0559 -
0.1708 6600 0.074 -
0.1721 6650 0.0593 -
0.1734 6700 0.0804 -
0.1747 6750 0.0635 -
0.1760 6800 0.0617 -
0.1773 6850 0.0664 -
0.1786 6900 0.0677 -
0.1799 6950 0.0503 -
0.1812 7000 0.043 -
0.1825 7050 0.0559 -
0.1838 7100 0.0549 -
0.1851 7150 0.0473 -
0.1864 7200 0.0624 -
0.1877 7250 0.0537 -
0.1889 7300 0.0598 -
0.1902 7350 0.0519 -
0.1915 7400 0.0513 -
0.1928 7450 0.0766 -
0.1941 7500 0.0551 -
0.1954 7550 0.0488 -
0.1967 7600 0.0604 -
0.1980 7650 0.0515 -
0.1993 7700 0.0588 -
0.2006 7750 0.05 -
0.2019 7800 0.0486 -
0.2032 7850 0.0548 -
0.2045 7900 0.0497 -
0.2058 7950 0.0502 -
0.2071 8000 0.0556 -
0.2084 8050 0.0554 -
0.2097 8100 0.0557 -
0.2109 8150 0.0481 -
0.2122 8200 0.0484 -
0.2135 8250 0.0474 -
0.2148 8300 0.04 -
0.2161 8350 0.0527 -
0.2174 8400 0.0627 -
0.2187 8450 0.048 -
0.2200 8500 0.0487 -
0.2213 8550 0.0606 -
0.2226 8600 0.0441 -
0.2239 8650 0.0543 -
0.2252 8700 0.039 -
0.2265 8750 0.0588 -
0.2278 8800 0.0451 -
0.2291 8850 0.054 -
0.2304 8900 0.0405 -
0.2317 8950 0.0466 -
0.2329 9000 0.0588 -
0.2342 9050 0.052 -
0.2355 9100 0.0453 -
0.2368 9150 0.0445 -
0.2381 9200 0.0417 -
0.2394 9250 0.0517 -
0.2407 9300 0.0478 -
0.2420 9350 0.0474 -
0.2433 9400 0.0506 -
0.2446 9450 0.0416 -
0.2459 9500 0.0462 -
0.2472 9550 0.0412 -
0.2485 9600 0.0456 -
0.2498 9650 0.0433 -
0.2511 9700 0.0409 -
0.2524 9750 0.045 -
0.2537 9800 0.0415 -
0.2550 9850 0.0281 -
0.2562 9900 0.0453 -
0.2575 9950 0.0555 -
0.2588 10000 0.0537 -
0.2601 10050 0.0431 -
0.2614 10100 0.0332 -
0.2627 10150 0.0416 -
0.2640 10200 0.0377 -
0.2653 10250 0.0368 -
0.2666 10300 0.0336 -
0.2679 10350 0.0393 -
0.2692 10400 0.0391 -
0.2705 10450 0.0401 -
0.2718 10500 0.0341 -
0.2731 10550 0.0336 -
0.2744 10600 0.0375 -
0.2757 10650 0.0308 -
0.2770 10700 0.045 -
0.2782 10750 0.0301 -
0.2795 10800 0.0427 -
0.2808 10850 0.0317 -
0.2821 10900 0.0349 -
0.2834 10950 0.0419 -
0.2847 11000 0.0325 -
0.2860 11050 0.0317 -
0.2873 11100 0.0309 -
0.2886 11150 0.0369 -
0.2899 11200 0.0384 -
0.2912 11250 0.0317 -
0.2925 11300 0.0319 -
0.2938 11350 0.0409 -
0.2951 11400 0.0319 -
0.2964 11450 0.0424 -
0.2977 11500 0.0268 -
0.2990 11550 0.0452 -
0.3002 11600 0.0367 -
0.3015 11650 0.0374 -
0.3028 11700 0.0382 -
0.3041 11750 0.0338 -
0.3054 11800 0.0385 -
0.3067 11850 0.0332 -
0.3080 11900 0.029 -
0.3093 11950 0.0416 -
0.3106 12000 0.0288 -
0.3119 12050 0.0418 -
0.3132 12100 0.0314 -
0.3145 12150 0.0476 -
0.3158 12200 0.0401 -
0.3171 12250 0.0329 -
0.3184 12300 0.0331 -
0.3197 12350 0.0228 -
0.3210 12400 0.0347 -
0.3222 12450 0.027 -
0.3235 12500 0.0273 -
0.3248 12550 0.0273 -
0.3261 12600 0.0219 -
0.3274 12650 0.0352 -
0.3287 12700 0.0391 -
0.3300 12750 0.0286 -
0.3313 12800 0.0277 -
0.3326 12850 0.0296 -
0.3339 12900 0.0314 -
0.3352 12950 0.0236 -
0.3365 13000 0.0347 -
0.3378 13050 0.0365 -
0.3391 13100 0.03 -
0.3404 13150 0.0234 -
0.3417 13200 0.0303 -
0.3430 13250 0.0387 -
0.3442 13300 0.0254 -
0.3455 13350 0.0329 -
0.3468 13400 0.0351 -
0.3481 13450 0.0227 -
0.3494 13500 0.0197 -
0.3507 13550 0.0267 -
0.3520 13600 0.0263 -
0.3533 13650 0.0319 -
0.3546 13700 0.0257 -
0.3559 13750 0.0281 -
0.3572 13800 0.0332 -
0.3585 13850 0.0272 -
0.3598 13900 0.0268 -
0.3611 13950 0.0223 -
0.3624 14000 0.0205 -
0.3637 14050 0.0247 -
0.3650 14100 0.0322 -
0.3662 14150 0.022 -
0.3675 14200 0.0303 -
0.3688 14250 0.0199 -
0.3701 14300 0.0282 -
0.3714 14350 0.0286 -
0.3727 14400 0.0295 -
0.3740 14450 0.0234 -
0.3753 14500 0.0325 -
0.3766 14550 0.0192 -
0.3779 14600 0.0245 -
0.3792 14650 0.0207 -
0.3805 14700 0.029 -
0.3818 14750 0.0225 -
0.3831 14800 0.0218 -
0.3844 14850 0.0215 -
0.3857 14900 0.0229 -
0.3870 14950 0.0299 -
0.3882 15000 0.0282 -
0.3895 15050 0.0314 -
0.3908 15100 0.016 -
0.3921 15150 0.0252 -
0.3934 15200 0.0257 -
0.3947 15250 0.0336 -
0.3960 15300 0.0243 -
0.3973 15350 0.018 -
0.3986 15400 0.0248 -
0.3999 15450 0.0172 -
0.4012 15500 0.028 -
0.4025 15550 0.0134 -
0.4038 15600 0.0238 -
0.4051 15650 0.0203 -
0.4064 15700 0.0326 -
0.4077 15750 0.0222 -
0.4090 15800 0.0217 -
0.4102 15850 0.0241 -
0.4115 15900 0.026 -
0.4128 15950 0.033 -
0.4141 16000 0.022 -
0.4154 16050 0.0193 -
0.4167 16100 0.0186 -
0.4180 16150 0.016 -
0.4193 16200 0.0212 -
0.4206 16250 0.019 -
0.4219 16300 0.0199 -
0.4232 16350 0.0242 -
0.4245 16400 0.0124 -
0.4258 16450 0.0256 -
0.4271 16500 0.0144 -
0.4284 16550 0.0259 -
0.4297 16600 0.0286 -
0.4310 16650 0.0277 -
0.4323 16700 0.0226 -
0.4335 16750 0.0246 -
0.4348 16800 0.0218 -
0.4361 16850 0.0131 -
0.4374 16900 0.0272 -
0.4387 16950 0.0169 -
0.4400 17000 0.0171 -
0.4413 17050 0.0139 -
0.4426 17100 0.0256 -
0.4439 17150 0.0159 -
0.4452 17200 0.0216 -
0.4465 17250 0.0185 -
0.4478 17300 0.021 -
0.4491 17350 0.0147 -
0.4504 17400 0.017 -
0.4517 17450 0.0126 -
0.4530 17500 0.0175 -
0.4543 17550 0.0169 -
0.4555 17600 0.0231 -
0.4568 17650 0.0161 -
0.4581 17700 0.0162 -
0.4594 17750 0.0208 -
0.4607 17800 0.0256 -
0.4620 17850 0.0251 -
0.4633 17900 0.0327 -
0.4646 17950 0.0226 -
0.4659 18000 0.02 -
0.4672 18050 0.0253 -
0.4685 18100 0.011 -
0.4698 18150 0.0249 -
0.4711 18200 0.0216 -
0.4724 18250 0.0203 -
0.4737 18300 0.022 -
0.4750 18350 0.0142 -
0.4763 18400 0.0101 -
0.4775 18450 0.0164 -
0.4788 18500 0.0223 -
0.4801 18550 0.017 -
0.4814 18600 0.024 -
0.4827 18650 0.0169 -
0.4840 18700 0.0169 -
0.4853 18750 0.0187 -
0.4866 18800 0.0219 -
0.4879 18850 0.0168 -
0.4892 18900 0.0181 -
0.4905 18950 0.0166 -
0.4918 19000 0.0186 -
0.4931 19050 0.0233 -
0.4944 19100 0.0332 -
0.4957 19150 0.013 -
0.4970 19200 0.0201 -
0.4983 19250 0.0276 -
0.4995 19300 0.0186 -
0.5008 19350 0.0223 -
0.5021 19400 0.0106 -
0.5034 19450 0.024 -
0.5047 19500 0.0193 -
0.5060 19550 0.0171 -
0.5073 19600 0.0143 -
0.5086 19650 0.0273 -
0.5099 19700 0.0184 -
0.5112 19750 0.0169 -
0.5125 19800 0.0166 -
0.5138 19850 0.011 -
0.5151 19900 0.0138 -
0.5164 19950 0.0116 -
0.5177 20000 0.0136 -
0.5190 20050 0.02 -
0.5203 20100 0.0119 -
0.5215 20150 0.0134 -
0.5228 20200 0.0191 -
0.5241 20250 0.0144 -
0.5254 20300 0.0196 -
0.5267 20350 0.0114 -
0.5280 20400 0.0155 -
0.5293 20450 0.0151 -
0.5306 20500 0.0184 -
0.5319 20550 0.0153 -
0.5332 20600 0.0113 -
0.5345 20650 0.0125 -
0.5358 20700 0.0141 -
0.5371 20750 0.0177 -
0.5384 20800 0.0109 -
0.5397 20850 0.0185 -
0.5410 20900 0.0192 -
0.5423 20950 0.0229 -
0.5435 21000 0.0174 -
0.5448 21050 0.0187 -
0.5461 21100 0.0111 -
0.5474 21150 0.0066 -
0.5487 21200 0.0139 -
0.5500 21250 0.0186 -
0.5513 21300 0.015 -
0.5526 21350 0.0183 -
0.5539 21400 0.0132 -
0.5552 21450 0.0187 -
0.5565 21500 0.0267 -
0.5578 21550 0.0161 -
0.5591 21600 0.0072 -
0.5604 21650 0.0104 -
0.5617 21700 0.0066 -
0.5630 21750 0.0213 -
0.5643 21800 0.0093 -
0.5655 21850 0.018 -
0.5668 21900 0.0134 -
0.5681 21950 0.0164 -
0.5694 22000 0.0263 -
0.5707 22050 0.0077 -
0.5720 22100 0.0192 -
0.5733 22150 0.0128 -
0.5746 22200 0.0065 -
0.5759 22250 0.0217 -
0.5772 22300 0.0147 -
0.5785 22350 0.0112 -
0.5798 22400 0.0199 -
0.5811 22450 0.0144 -
0.5824 22500 0.0112 -
0.5837 22550 0.0159 -
0.5850 22600 0.0122 -
0.5863 22650 0.0165 -
0.5876 22700 0.0149 -
0.5888 22750 0.016 -
0.5901 22800 0.0205 -
0.5914 22850 0.0121 -
0.5927 22900 0.0192 -
0.5940 22950 0.0075 -
0.5953 23000 0.0118 -
0.5966 23050 0.0211 -
0.5979 23100 0.0158 -
0.5992 23150 0.0148 -
0.6005 23200 0.0164 -
0.6018 23250 0.0104 -
0.6031 23300 0.0097 -
0.6044 23350 0.008 -
0.6057 23400 0.0143 -
0.6070 23450 0.0158 -
0.6083 23500 0.0102 -
0.6096 23550 0.0178 -
0.6108 23600 0.0116 -
0.6121 23650 0.0083 -
0.6134 23700 0.0118 -
0.6147 23750 0.0178 -
0.6160 23800 0.0089 -
0.6173 23850 0.0087 -
0.6186 23900 0.0154 -
0.6199 23950 0.013 -
0.6212 24000 0.0068 -
0.6225 24050 0.015 -
0.6238 24100 0.0125 -
0.6251 24150 0.0183 -
0.6264 24200 0.0186 -
0.6277 24250 0.0093 -
0.6290 24300 0.0112 -
0.6303 24350 0.0133 -
0.6316 24400 0.0085 -
0.6328 24450 0.0122 -
0.6341 24500 0.0094 -
0.6354 24550 0.0104 -
0.6367 24600 0.0195 -
0.6380 24650 0.0156 -
0.6393 24700 0.0151 -
0.6406 24750 0.0109 -
0.6419 24800 0.0064 -
0.6432 24850 0.0211 -
0.6445 24900 0.008 -
0.6458 24950 0.0093 -
0.6471 25000 0.0054 -
0.6484 25050 0.0157 -
0.6497 25100 0.0103 -
0.6510 25150 0.0107 -
0.6523 25200 0.0131 -
0.6536 25250 0.0119 -
0.6548 25300 0.014 -
0.6561 25350 0.012 -
0.6574 25400 0.011 -
0.6587 25450 0.0082 -
0.6600 25500 0.014 -
0.6613 25550 0.0131 -
0.6626 25600 0.0093 -
0.6639 25650 0.012 -
0.6652 25700 0.006 -
0.6665 25750 0.0132 -
0.6678 25800 0.0177 -
0.6691 25850 0.011 -
0.6704 25900 0.0113 -
0.6717 25950 0.0155 -
0.6730 26000 0.0083 -
0.6743 26050 0.0168 -
0.6756 26100 0.0152 -
0.6768 26150 0.0162 -
0.6781 26200 0.0101 -
0.6794 26250 0.0217 -
0.6807 26300 0.0055 -
0.6820 26350 0.0123 -
0.6833 26400 0.0082 -
0.6846 26450 0.0102 -
0.6859 26500 0.0124 -
0.6872 26550 0.0211 -
0.6885 26600 0.0104 -
0.6898 26650 0.0161 -
0.6911 26700 0.0157 -
0.6924 26750 0.0158 -
0.6937 26800 0.0119 -
0.6950 26850 0.0153 -
0.6963 26900 0.013 -
0.6976 26950 0.0071 -
0.6988 27000 0.0085 -
0.7001 27050 0.0078 -
0.7014 27100 0.015 -
0.7027 27150 0.0048 -
0.7040 27200 0.0037 -
0.7053 27250 0.009 -
0.7066 27300 0.0042 -
0.7079 27350 0.0094 -
0.7092 27400 0.0076 -
0.7105 27450 0.0034 -
0.7118 27500 0.0161 -
0.7131 27550 0.0062 -
0.7144 27600 0.0092 -
0.7157 27650 0.0084 -
0.7170 27700 0.0098 -
0.7183 27750 0.0084 -
0.7196 27800 0.0121 -
0.7208 27850 0.0148 -
0.7221 27900 0.0051 -
0.7234 27950 0.0173 -
0.7247 28000 0.0107 -
0.7260 28050 0.0082 -
0.7273 28100 0.0102 -
0.7286 28150 0.0088 -
0.7299 28200 0.0133 -
0.7312 28250 0.0142 -
0.7325 28300 0.0145 -
0.7338 28350 0.0182 -
0.7351 28400 0.0088 -
0.7364 28450 0.0057 -
0.7377 28500 0.0027 -
0.7390 28550 0.0136 -
0.7403 28600 0.0093 -
0.7416 28650 0.0157 -
0.7428 28700 0.0129 -
0.7441 28750 0.015 -
0.7454 28800 0.0129 -
0.7467 28850 0.0057 -
0.7480 28900 0.0103 -
0.7493 28950 0.0099 -
0.7506 29000 0.0123 -
0.7519 29050 0.0072 -
0.7532 29100 0.0158 -
0.7545 29150 0.0149 -
0.7558 29200 0.0061 -
0.7571 29250 0.0161 -
0.7584 29300 0.012 -
0.7597 29350 0.0181 -
0.7610 29400 0.0174 -
0.7623 29450 0.0057 -
0.7636 29500 0.0058 -
0.7649 29550 0.0013 -
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0.9318 36000 0.0083 -
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0.9991 38600 0.0037 -

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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