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ignaciosg
/
blueCarbon

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

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A MultiOutputClassifier 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("ignaciosg/blueCarbon")
# Run inference
preds = model("blue carbon is vital aspect of climate change mitigation, which necessitates the identification of stocks and drivers for implementing mitigation strategies. however, reclamation may be among the most invasive forms, and the question of its influence has not been addressed well in blue carbon research. therefore, the effects of reclamation on carbon stocks and the interaction of crucial drivers from reclamation time areas were evaluated in the liaohe river delta and compared with natural reserves . carbon stocks based on invest model were lower than preexisting conditions . one way analysis of variance showed that average carbon stocks accumulated 55 years after reclamation and reached the lowest value in 85 years. the interaction analysis of dominant drivers affecting carbon showed the difference between reclaimed areas and reserves regarding potential effect pathways. in the 1930s and 1960s reclamation time areas, crop yield and industrial output determined blue carbon by changing no3 and ap. in the 1990s reclamation time area, population density played an important role. in defining the impact of vegetation cover on carbon within the reserves, the distance to the coast and residence were significant factors. this study demonstrated that coastal")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 105 229.475 432

Training Hyperparameters

  • batch_size: (1, 1)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.0006155918397454662
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • max_length: 1000
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0000 1 0.1819 -
0.0011 50 0.201 -
0.0023 100 0.3533 -
0.0034 150 0.0788 -
0.0046 200 0.1445 -
0.0057 250 0.1584 -
0.0069 300 0.3425 -
0.0080 350 0.1203 -
0.0092 400 0.2045 -
0.0103 450 0.0287 -
0.0115 500 0.1784 -
0.0126 550 0.2521 -
0.0138 600 0.1285 -
0.0149 650 0.2292 -
0.0161 700 0.0943 -
0.0172 750 0.1753 -
0.0184 800 0.3433 -
0.0195 850 0.262 -
0.0207 900 0.1097 -
0.0218 950 0.0015 -
0.0230 1000 0.5522 -
0.0241 1050 0.5939 -
0.0253 1100 0.1134 -
0.0264 1150 0.1258 -
0.0276 1200 0.0146 -
0.0287 1250 0.0467 -
0.0299 1300 0.3501 -
0.0310 1350 0.291 -
0.0322 1400 0.0569 -
0.0333 1450 0.0812 -
0.0345 1500 0.3397 -
0.0356 1550 0.1664 -
0.0368 1600 0.3841 -
0.0379 1650 0.1659 -
0.0391 1700 0.0809 -
0.0402 1750 0.3604 -
0.0414 1800 0.0056 -
0.0425 1850 0.3335 -
0.0437 1900 0.0005 -
0.0448 1950 0.1624 -
0.0460 2000 0.8162 -
0.0471 2050 0.0097 -
0.0483 2100 0.2561 -
0.0494 2150 0.0003 -
0.0506 2200 0.4198 -
0.0517 2250 0.0002 -
0.0529 2300 0.2793 -
0.0540 2350 0.6288 -
0.0552 2400 0.6944 -
0.0563 2450 0.7394 -
0.0575 2500 0.011 -
0.0586 2550 0.8041 -
0.0598 2600 0.0041 -
0.0609 2650 0.2446 -
0.0621 2700 0.2759 -
0.0632 2750 0.151 -
0.0644 2800 0.0651 -
0.0655 2850 0.0026 -
0.0666 2900 0.0845 -
0.0678 2950 0.7541 -
0.0689 3000 0.0993 -
0.0701 3050 0.7355 -
0.0712 3100 0.6959 -
0.0724 3150 0.1687 -
0.0735 3200 0.2048 -
0.0747 3250 0.0906 -
0.0758 3300 0.0582 -
0.0770 3350 0.9064 -
0.0781 3400 0.8038 -
0.0793 3450 0.2515 -
0.0804 3500 0.0196 -
0.0816 3550 0.0081 -
0.0827 3600 0.8483 -
0.0839 3650 0.0651 -
0.0850 3700 0.8224 -
0.0862 3750 0.2872 -
0.0873 3800 0.0506 -
0.0885 3850 0.6795 -
0.0896 3900 0.0126 -
0.0908 3950 0.5083 -
0.0919 4000 0.0215 -
0.0931 4050 0.8133 -
0.0942 4100 0.1534 -
0.0954 4150 0.2397 -
0.0965 4200 0.8576 -
0.0977 4250 0.0554 -
0.0988 4300 0.1018 -
0.1000 4350 0.3324 -
0.1011 4400 0.0221 -
0.1023 4450 0.0516 -
0.1034 4500 0.796 -
0.1046 4550 0.0903 -
0.1057 4600 0.1979 -
0.1069 4650 0.9194 -
0.1080 4700 0.2556 -
0.1092 4750 0.7224 -
0.1103 4800 0.0012 -
0.1115 4850 0.5042 -
0.1126 4900 0.5732 -
0.1138 4950 0.1041 -
0.1149 5000 0.0247 -
0.1161 5050 0.0265 -
0.1172 5100 0.0126 -
0.1184 5150 0.0098 -
0.1195 5200 0.0386 -
0.1207 5250 0.001 -
0.1218 5300 0.9248 -
0.1230 5350 0.4783 -
0.1241 5400 0.1841 -
0.1253 5450 0.4721 -
0.1264 5500 0.0601 -
0.1276 5550 0.0073 -
0.1287 5600 0.0028 -
0.1298 5650 0.012 -
0.1310 5700 0.0451 -
0.1321 5750 0.0125 -
0.1333 5800 0.5423 -
0.1344 5850 0.7545 -
0.1356 5900 0.0158 -
0.1367 5950 0.1388 -
0.1379 6000 0.0136 -
0.1390 6050 0.0043 -
0.1402 6100 0.4147 -
0.1413 6150 0.0503 -
0.1425 6200 0.0347 -
0.1436 6250 0.0465 -
0.1448 6300 0.0086 -
0.1459 6350 0.8752 -
0.1471 6400 0.5546 -
0.1482 6450 0.0348 -
0.1494 6500 0.0853 -
0.1505 6550 0.6107 -
0.1517 6600 0.005 -
0.1528 6650 0.3526 -
0.1540 6700 0.2429 -
0.1551 6750 0.6727 -
0.1563 6800 0.0019 -
0.1574 6850 0.6662 -
0.1586 6900 0.0068 -
0.1597 6950 0.0117 -
0.1609 7000 0.4718 -
0.1620 7050 0.0072 -
0.1632 7100 0.8174 -
0.1643 7150 0.0094 -
0.1655 7200 0.0241 -
0.1666 7250 0.1359 -
0.1678 7300 0.0528 -
0.1689 7350 0.0184 -
0.1701 7400 0.2204 -
0.1712 7450 0.3476 -
0.1724 7500 0.1153 -
0.1735 7550 0.0717 -
0.1747 7600 0.022 -
0.1758 7650 0.0311 -
0.1770 7700 0.4385 -
0.1781 7750 0.4274 -
0.1793 7800 0.4994 -
0.1804 7850 0.2518 -
0.1816 7900 0.8652 -
0.1827 7950 0.0019 -
0.1839 8000 0.01 -
0.1850 8050 0.0129 -
0.1862 8100 0.0001 -
0.1873 8150 0.0005 -
0.1885 8200 0.0199 -
0.1896 8250 0.1489 -
0.1908 8300 0.0016 -
0.1919 8350 0.5111 -
0.1931 8400 0.807 -
0.1942 8450 0.1489 -
0.1953 8500 0.29 -
0.1965 8550 0.0001 -
0.1976 8600 0.0043 -
0.1988 8650 0.0041 -
0.1999 8700 0.3061 -
0.2011 8750 0.0221 -
0.2022 8800 0.801 -
0.2034 8850 0.2316 -
0.2045 8900 0.2784 -
0.2057 8950 0.0957 -
0.2068 9000 0.611 -
0.2080 9050 0.7529 -
0.2091 9100 0.0565 -
0.2103 9150 0.0114 -
0.2114 9200 0.2864 -
0.2126 9250 0.1954 -
0.2137 9300 0.7993 -
0.2149 9350 0.0501 -
0.2160 9400 0.0051 -
0.2172 9450 0.6012 -
0.2183 9500 0.0131 -
0.2195 9550 0.0157 -
0.2206 9600 0.0606 -
0.2218 9650 0.9143 -
0.2229 9700 0.0001 -
0.2241 9750 0.0021 -
0.2252 9800 0.0004 -
0.2264 9850 0.0498 -
0.2275 9900 0.0021 -
0.2287 9950 0.8591 -
0.2298 10000 0.2218 -
0.2310 10050 0.0065 -
0.2321 10100 0.0924 -
0.2333 10150 0.8866 -
0.2344 10200 0.0004 -
0.2356 10250 0.1434 -
0.2367 10300 0.0118 -
0.2379 10350 0.025 -
0.2390 10400 0.8472 -
0.2402 10450 0.0352 -
0.2413 10500 0.0105 -
0.2425 10550 0.0025 -
0.2436 10600 0.0042 -
0.2448 10650 0.3461 -
0.2459 10700 0.0314 -
0.2471 10750 0.1411 -
0.2482 10800 0.0006 -
0.2494 10850 0.0013 -
0.2505 10900 0.894 -
0.2517 10950 0.9961 -
0.2528 11000 0.9908 -
0.2540 11050 0.836 -
0.2551 11100 0.8847 -
0.2563 11150 0.8493 -
0.2574 11200 0.5851 -
0.2585 11250 0.9502 -
0.2597 11300 0.8396 -
0.2608 11350 0.1942 -
0.2620 11400 0.9298 -
0.2631 11450 0.742 -
0.2643 11500 0.8624 -
0.2654 11550 0.5423 -
0.2666 11600 0.8576 -
0.2677 11650 0.8042 -
0.2689 11700 0.7447 -
0.2700 11750 0.5319 -
0.2712 11800 0.451 -
0.2723 11850 0.4115 -
0.2735 11900 0.6772 -
0.2746 11950 0.4701 -
0.2758 12000 0.6101 -
0.2769 12050 0.4914 -
0.2781 12100 0.653 -
0.2792 12150 0.6205 -
0.2804 12200 0.651 -
0.2815 12250 0.2223 -
0.2827 12300 0.7124 -
0.2838 12350 0.6502 -
0.2850 12400 0.5812 -
0.2861 12450 0.6483 -
0.2873 12500 0.7335 -
0.2884 12550 0.239 -
0.2896 12600 0.6499 -
0.2907 12650 0.4453 -
0.2919 12700 0.7152 -
0.2930 12750 0.5551 -
0.2942 12800 0.6034 -
0.2953 12850 0.5714 -
0.2965 12900 0.5867 -
0.2976 12950 0.4249 -
0.2988 13000 0.7262 -
0.2999 13050 0.542 -
0.3011 13100 0.5301 -
0.3022 13150 0.7503 -
0.3034 13200 0.6918 -
0.3045 13250 0.5352 -
0.3057 13300 0.6065 -
0.3068 13350 0.373 -
0.3080 13400 0.7648 -
0.3091 13450 0.2762 -
0.3103 13500 0.708 -
0.3114 13550 0.1481 -
0.3126 13600 0.7231 -
0.3137 13650 0.6023 -
0.3149 13700 0.7021 -
0.3160 13750 0.5843 -
0.3172 13800 0.7361 -
0.3183 13850 0.7844 -
0.3195 13900 0.51 -
0.3206 13950 0.506 -
0.3218 14000 0.3072 -
0.3229 14050 0.5854 -
0.3240 14100 0.3553 -
0.3252 14150 0.6827 -
0.3263 14200 0.5342 -
0.3275 14250 0.6887 -
0.3286 14300 0.6007 -
0.3298 14350 0.4573 -
0.3309 14400 0.5979 -
0.3321 14450 0.5328 -
0.3332 14500 0.6814 -
0.3344 14550 0.6207 -
0.3355 14600 0.8189 -
0.3367 14650 0.5794 -
0.3378 14700 0.3987 -
0.3390 14750 0.5281 -
0.3401 14800 0.652 -
0.3413 14850 0.6811 -
0.3424 14900 0.3334 -
0.3436 14950 0.565 -
0.3447 15000 0.4956 -
0.3459 15050 0.7289 -
0.3470 15100 0.6103 -
0.3482 15150 0.4173 -
0.3493 15200 0.2138 -
0.3505 15250 0.893 -
0.3516 15300 0.5385 -
0.3528 15350 0.6386 -
0.3539 15400 0.7168 -
0.3551 15450 0.1189 -
0.3562 15500 0.3046 -
0.3574 15550 0.4776 -
0.3585 15600 0.7062 -
0.3597 15650 0.0972 -
0.3608 15700 0.4485 -
0.3620 15750 0.5843 -
0.3631 15800 0.5656 -
0.3643 15850 0.5682 -
0.3654 15900 0.416 -
0.3666 15950 0.2427 -
0.3677 16000 0.4942 -
0.3689 16050 0.4734 -
0.3700 16100 0.7099 -
0.3712 16150 0.5899 -
0.3723 16200 0.3502 -
0.3735 16250 0.3448 -
0.3746 16300 0.6606 -
0.3758 16350 0.5239 -
0.3769 16400 0.6872 -
0.3781 16450 0.2828 -
0.3792 16500 0.6973 -
0.3804 16550 0.6628 -
0.3815 16600 0.6429 -
0.3827 16650 0.4321 -
0.3838 16700 0.6626 -
0.3850 16750 0.5044 -
0.3861 16800 0.7683 -
0.3872 16850 0.6687 -
0.3884 16900 0.5821 -
0.3895 16950 0.6572 -
0.3907 17000 0.9609 -
0.3918 17050 0.0123 -
0.3930 17100 0.5649 -
0.3941 17150 0.1006 -
0.3953 17200 0.003 -
0.3964 17250 0.278 -
0.3976 17300 0.8632 -
0.3987 17350 0.5101 -
0.3999 17400 0.8753 -
0.4010 17450 0.3195 -
0.4022 17500 0.9436 -
0.4033 17550 0.9388 -
0.4045 17600 0.0097 -
0.4056 17650 0.6898 -
0.4068 17700 0.035 -
0.4079 17750 0.4828 -
0.4091 17800 0.1888 -
0.4102 17850 0.0354 -
0.4114 17900 0.0008 -
0.4125 17950 0.2885 -
0.4137 18000 0.0624 -
0.4148 18050 0.5545 -
0.4160 18100 0.5317 -
0.4171 18150 0.0207 -
0.4183 18200 0.0228 -
0.4194 18250 0.0168 -
0.4206 18300 0.0935 -
0.4217 18350 0.8391 -
0.4229 18400 0.0005 -
0.4240 18450 0.7018 -
0.4252 18500 0.0137 -
0.4263 18550 0.0053 -
0.4275 18600 0.0307 -
0.4286 18650 0.0127 -
0.4298 18700 0.2351 -
0.4309 18750 0.0047 -
0.4321 18800 0.0114 -
0.4332 18850 0.0153 -
0.4344 18900 0.3732 -
0.4355 18950 0.77 -
0.4367 19000 0.1298 -
0.4378 19050 0.7064 -
0.4390 19100 0.0 -
0.4401 19150 0.0044 -
0.4413 19200 0.7627 -
0.4424 19250 0.556 -
0.4436 19300 0.2105 -
0.4447 19350 0.8194 -
0.4459 19400 0.027 -
0.4470 19450 0.9308 -
0.4482 19500 0.0194 -
0.4493 19550 0.0144 -
0.4505 19600 0.584 -
0.4516 19650 0.0042 -
0.4527 19700 0.1354 -
0.4539 19750 0.2151 -
0.4550 19800 0.0006 -
0.4562 19850 0.3085 -
0.4573 19900 0.0543 -
0.4585 19950 0.0178 -
0.4596 20000 0.418 -
0.4608 20050 0.019 -
0.4619 20100 0.0001 -
0.4631 20150 0.5443 -
0.4642 20200 0.5111 -
0.4654 20250 0.0594 -
0.4665 20300 0.0086 -
0.4677 20350 0.0064 -
0.4688 20400 0.0577 -
0.4700 20450 0.0712 -
0.4711 20500 0.0271 -
0.4723 20550 0.5118 -
0.4734 20600 0.1834 -
0.4746 20650 0.0116 -
0.4757 20700 0.0052 -
0.4769 20750 0.7975 -
0.4780 20800 0.3037 -
0.4792 20850 0.0264 -
0.4803 20900 0.6911 -
0.4815 20950 0.008 -
0.4826 21000 0.0041 -
0.4838 21050 0.0379 -
0.4849 21100 0.0033 -
0.4861 21150 0.0297 -
0.4872 21200 0.0147 -
0.4884 21250 0.0001 -
0.4895 21300 0.0047 -
0.4907 21350 0.0247 -
0.4918 21400 0.0059 -
0.4930 21450 0.5724 -
0.4941 21500 0.3113 -
0.4953 21550 0.0026 -
0.4964 21600 0.835 -
0.4976 21650 0.0007 -
0.4987 21700 0.029 -
0.4999 21750 0.707 -
0.5010 21800 0.0211 -
0.5022 21850 0.0071 -
0.5033 21900 0.0009 -
0.5045 21950 0.0319 -
0.5056 22000 0.2219 -
0.5068 22050 0.0244 -
0.5079 22100 0.0341 -
0.5091 22150 0.0372 -
0.5102 22200 0.3981 -
0.5114 22250 0.0627 -
0.5125 22300 0.0559 -
0.5137 22350 0.5366 -
0.5148 22400 0.6952 -
0.5159 22450 0.0504 -
0.5171 22500 0.5098 -
0.5182 22550 0.6538 -
0.5194 22600 0.0015 -
0.5205 22650 0.0005 -
0.5217 22700 0.0974 -
0.5228 22750 0.009 -
0.5240 22800 0.6559 -
0.5251 22850 0.026 -
0.5263 22900 0.0049 -
0.5274 22950 0.0104 -
0.5286 23000 0.7918 -
0.5297 23050 0.0007 -
0.5309 23100 0.0015 -
0.5320 23150 0.2873 -
0.5332 23200 0.002 -
0.5343 23250 0.0067 -
0.5355 23300 0.2943 -
0.5366 23350 0.0029 -
0.5378 23400 0.0 -
0.5389 23450 0.0727 -
0.5401 23500 0.0084 -
0.5412 23550 0.0 -
0.5424 23600 0.0054 -
0.5435 23650 0.0004 -
0.5447 23700 0.5525 -
0.5458 23750 0.0251 -
0.5470 23800 0.0269 -
0.5481 23850 0.7426 -
0.5493 23900 0.0016 -
0.5504 23950 0.8143 -
0.5516 24000 0.5158 -
0.5527 24050 0.0047 -
0.5539 24100 0.0067 -
0.5550 24150 0.0 -
0.5562 24200 0.0045 -
0.5573 24250 0.0021 -
0.5585 24300 0.0012 -
0.5596 24350 0.3501 -
0.5608 24400 0.0101 -
0.5619 24450 0.0008 -
0.5631 24500 0.0112 -
0.5642 24550 0.0148 -
0.5654 24600 0.2246 -
0.5665 24650 0.1538 -
0.5677 24700 0.0001 -
0.5688 24750 0.0001 -
0.5700 24800 0.1296 -
0.5711 24850 0.0101 -
0.5723 24900 0.0032 -
0.5734 24950 0.0714 -
0.5746 25000 0.0 -
0.5757 25050 0.0886 -
0.5769 25100 0.0003 -
0.5780 25150 0.0041 -
0.5792 25200 0.0151 -
0.5803 25250 0.0099 -
0.5814 25300 0.0008 -
0.5826 25350 0.028 -
0.5837 25400 0.1064 -
0.5849 25450 0.0373 -
0.5860 25500 0.5589 -
0.5872 25550 0.2522 -
0.5883 25600 0.8553 -
0.5895 25650 0.0004 -
0.5906 25700 0.6575 -
0.5918 25750 0.0034 -
0.5929 25800 0.7313 -
0.5941 25850 0.8363 -
0.5952 25900 0.0156 -
0.5964 25950 0.0044 -
0.5975 26000 0.1387 -
0.5987 26050 0.0487 -
0.5998 26100 0.001 -
0.6010 26150 0.0004 -
0.6021 26200 0.0071 -
0.6033 26250 0.0012 -
0.6044 26300 0.021 -
0.6056 26350 0.0212 -
0.6067 26400 0.8472 -
0.6079 26450 0.5686 -
0.6090 26500 0.0721 -
0.6102 26550 0.0235 -
0.6113 26600 0.0 -
0.6125 26650 0.0098 -
0.6136 26700 0.3805 -
0.6148 26750 0.0525 -
0.6159 26800 0.0139 -
0.6171 26850 0.0011 -
0.6182 26900 0.0013 -
0.6194 26950 0.0058 -
0.6205 27000 0.0581 -
0.6217 27050 0.477 -
0.6228 27100 0.0073 -
0.6240 27150 0.0033 -
0.6251 27200 0.0082 -
0.6263 27250 0.0028 -
0.6274 27300 0.0001 -
0.6286 27350 0.0265 -
0.6297 27400 0.097 -
0.6309 27450 0.2339 -
0.6320 27500 0.5429 -
0.6332 27550 0.3859 -
0.6343 27600 0.0116 -
0.6355 27650 0.0006 -
0.6366 27700 0.0018 -
0.6378 27750 0.0197 -
0.6389 27800 0.0085 -
0.6401 27850 0.0 -
0.6412 27900 0.0141 -
0.6424 27950 0.1121 -
0.6435 28000 0.0123 -
0.6446 28050 0.3018 -
0.6458 28100 0.7669 -
0.6469 28150 0.6745 -
0.6481 28200 0.4283 -
0.6492 28250 0.0237 -
0.6504 28300 0.8327 -
0.6515 28350 0.1052 -
0.6527 28400 0.4264 -
0.6538 28450 0.6714 -
0.6550 28500 0.0039 -
0.6561 28550 0.0065 -
0.6573 28600 0.0178 -
0.6584 28650 0.3817 -
0.6596 28700 0.0584 -
0.6607 28750 0.0217 -
0.6619 28800 0.0019 -
0.6630 28850 0.4605 -
0.6642 28900 0.0049 -
0.6653 28950 0.0011 -
0.6665 29000 0.569 -
0.6676 29050 0.0 -
0.6688 29100 0.0874 -
0.6699 29150 0.5388 -
0.6711 29200 0.4093 -
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0.9193 40000 0.4475 -
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0.9216 40100 0.0077 -
0.9227 40150 0.0668 -
0.9239 40200 0.0036 -
0.9250 40250 0.0032 -
0.9262 40300 0.035 -
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0.9285 40400 0.0 -
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0.9319 40550 0.0 -
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0.9377 40800 0.2938 -
0.9388 40850 0.0059 -
0.9400 40900 0.0646 -
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0.9423 41000 0.001 -
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0.9492 41300 0.0 -
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0.9653 42000 0.0128 -
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0.9710 42250 0.0039 -
0.9721 42300 0.0001 -
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0.9859 42900 0.1647 -
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0.9882 43000 0.0006 -
0.9894 43050 0.0008 -
0.9905 43100 0.0 -
0.9917 43150 0.1409 -
0.9928 43200 0.0004 -
0.9940 43250 0.0006 -
0.9951 43300 0.0634 -
0.9963 43350 0.1843 -
0.9974 43400 0.0133 -
0.9986 43450 0.2553 -
0.9997 43500 0.0005 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.5.1
  • Transformers: 4.38.2
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.18.0
  • Tokenizers: 0.15.2

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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