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faodl
/
model_g20_multilabel_30sample

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Model card Files Community

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}
}
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