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ellarose
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test-trainer-alternate

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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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 LogisticRegression 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

Model Labels

Label Examples
subject
  • 'vidourle river (france)'
  • 'knockout kings 2000 (game)'
  • 'social practice (art)'
genre/form
  • 'hadith stories'
  • 'discographies'
  • 'dance drama'

Evaluation

Metrics

Label Accuracy
all 0.9556

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("ellarose/test-trainer-alternate")
# Run inference
preds = model("prison fiction")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 2.415 10
Label Training Sample Count
subject 500
genre/form 500

Training Hyperparameters

  • batch_size: (40, 40)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.3379 -
0.0040 50 0.3311 -
0.0080 100 0.3642 -
0.0120 150 0.3077 -
0.0160 200 0.2542 -
0.0200 250 0.233 -
0.0240 300 0.23 -
0.0279 350 0.2247 -
0.0319 400 0.2009 -
0.0359 450 0.2354 -
0.0399 500 0.1823 -
0.0439 550 0.2048 -
0.0479 600 0.1546 -
0.0519 650 0.1363 -
0.0559 700 0.1031 -
0.0599 750 0.0668 -
0.0639 800 0.1156 -
0.0679 850 0.0435 -
0.0719 900 0.0495 -
0.0758 950 0.046 -
0.0798 1000 0.0424 -
0.0838 1050 0.1312 -
0.0878 1100 0.0246 -
0.0918 1150 0.0273 -
0.0958 1200 0.0075 -
0.0998 1250 0.0203 -
0.1038 1300 0.0073 -
0.1078 1350 0.0328 -
0.1118 1400 0.0274 -
0.1158 1450 0.0042 -
0.1198 1500 0.0494 -
0.1238 1550 0.0413 -
0.1277 1600 0.0036 -
0.1317 1650 0.0329 -
0.1357 1700 0.0168 -
0.1397 1750 0.0028 -
0.1437 1800 0.0227 -
0.1477 1850 0.002 -
0.1517 1900 0.0121 -
0.1557 1950 0.0018 -
0.1597 2000 0.0019 -
0.1637 2050 0.001 -
0.1677 2100 0.0009 -
0.1717 2150 0.0012 -
0.1756 2200 0.0007 -
0.1796 2250 0.001 -
0.1836 2300 0.0008 -
0.1876 2350 0.0009 -
0.1916 2400 0.001 -
0.1956 2450 0.0009 -
0.1996 2500 0.0247 -
0.2036 2550 0.0007 -
0.2076 2600 0.0008 -
0.2116 2650 0.0008 -
0.2156 2700 0.0006 -
0.2196 2750 0.0023 -
0.2236 2800 0.0007 -
0.2275 2850 0.0004 -
0.2315 2900 0.0054 -
0.2355 2950 0.0007 -
0.2395 3000 0.0004 -
0.2435 3050 0.0007 -
0.2475 3100 0.0244 -
0.2515 3150 0.0243 -
0.2555 3200 0.0005 -
0.2595 3250 0.0004 -
0.2635 3300 0.0004 -
0.2675 3350 0.0004 -
0.2715 3400 0.0004 -
0.2754 3450 0.0482 -
0.2794 3500 0.0004 -
0.2834 3550 0.0005 -
0.2874 3600 0.0005 -
0.2914 3650 0.0007 -
0.2954 3700 0.0063 -
0.2994 3750 0.0043 -
0.3034 3800 0.0005 -
0.3074 3850 0.0366 -
0.3114 3900 0.0245 -
0.3154 3950 0.0242 -
0.3194 4000 0.0003 -
0.3234 4050 0.0007 -
0.3273 4100 0.0123 -
0.3313 4150 0.0004 -
0.3353 4200 0.0007 -
0.3393 4250 0.0238 -
0.3433 4300 0.0002 -
0.3473 4350 0.0238 -
0.3513 4400 0.0003 -
0.3553 4450 0.0224 -
0.3593 4500 0.0006 -
0.3633 4550 0.0005 -
0.3673 4600 0.0004 -
0.3713 4650 0.0025 -
0.3752 4700 0.0003 -
0.3792 4750 0.0218 -
0.3832 4800 0.001 -
0.3872 4850 0.0004 -
0.3912 4900 0.0004 -
0.3952 4950 0.0161 -
0.3992 5000 0.0008 -
0.4032 5050 0.0024 -
0.4072 5100 0.0003 -
0.4112 5150 0.0002 -
0.4152 5200 0.0005 -
0.4192 5250 0.0021 -
0.4232 5300 0.0235 -
0.4271 5350 0.0035 -
0.4311 5400 0.0007 -
0.4351 5450 0.0007 -
0.4391 5500 0.0217 -
0.4431 5550 0.0006 -
0.4471 5600 0.0054 -
0.4511 5650 0.002 -
0.4551 5700 0.0013 -
0.4591 5750 0.0026 -
0.4631 5800 0.0051 -
0.4671 5850 0.0003 -
0.4711 5900 0.0003 -
0.4750 5950 0.0119 -
0.4790 6000 0.0011 -
0.4830 6050 0.0253 -
0.4870 6100 0.0244 -
0.4910 6150 0.0002 -
0.4950 6200 0.0002 -
0.4990 6250 0.0002 -
0.5030 6300 0.0167 -
0.5070 6350 0.0002 -
0.5110 6400 0.0003 -
0.5150 6450 0.0012 -
0.5190 6500 0.003 -
0.5230 6550 0.0003 -
0.5269 6600 0.0003 -
0.5309 6650 0.0006 -
0.5349 6700 0.0026 -
0.5389 6750 0.0004 -
0.5429 6800 0.0001 -
0.5469 6850 0.0002 -
0.5509 6900 0.0003 -
0.5549 6950 0.0028 -
0.5589 7000 0.0022 -
0.5629 7050 0.0007 -
0.5669 7100 0.0004 -
0.5709 7150 0.0002 -
0.5749 7200 0.0001 -
0.5788 7250 0.0122 -
0.5828 7300 0.0017 -
0.5868 7350 0.0001 -
0.5908 7400 0.0002 -
0.5948 7450 0.0001 -
0.5988 7500 0.0003 -
0.6028 7550 0.0011 -
0.6068 7600 0.0002 -
0.6108 7650 0.0003 -
0.6148 7700 0.0001 -
0.6188 7750 0.0001 -
0.6228 7800 0.0001 -
0.6267 7850 0.0002 -
0.6307 7900 0.0149 -
0.6347 7950 0.0106 -
0.6387 8000 0.0015 -
0.6427 8050 0.0001 -
0.6467 8100 0.0009 -
0.6507 8150 0.0015 -
0.6547 8200 0.0306 -
0.6587 8250 0.0054 -
0.6627 8300 0.0011 -
0.6667 8350 0.0003 -
0.6707 8400 0.0001 -
0.6747 8450 0.0024 -
0.6786 8500 0.0001 -
0.6826 8550 0.0001 -
0.6866 8600 0.0001 -
0.6906 8650 0.0072 -
0.6946 8700 0.0001 -
0.6986 8750 0.0002 -
0.7026 8800 0.0001 -
0.7066 8850 0.0243 -
0.7106 8900 0.0001 -
0.7146 8950 0.0001 -
0.7186 9000 0.0001 -
0.7226 9050 0.0001 -
0.7265 9100 0.0001 -
0.7305 9150 0.0001 -
0.7345 9200 0.0008 -
0.7385 9250 0.021 -
0.7425 9300 0.0229 -
0.7465 9350 0.0001 -
0.7505 9400 0.002 -
0.7545 9450 0.0008 -
0.7585 9500 0.0225 -
0.7625 9550 0.0001 -
0.7665 9600 0.0041 -
0.7705 9650 0.0012 -
0.7745 9700 0.0034 -
0.7784 9750 0.0011 -
0.7824 9800 0.0008 -
0.7864 9850 0.0101 -
0.7904 9900 0.0039 -
0.7944 9950 0.0001 -
0.7984 10000 0.0005 -
0.8024 10050 0.0011 -
0.8064 10100 0.0025 -
0.8104 10150 0.0001 -
0.8144 10200 0.0003 -
0.8184 10250 0.0002 -
0.8224 10300 0.0002 -
0.8263 10350 0.0001 -
0.8303 10400 0.0007 -
0.8343 10450 0.0005 -
0.8383 10500 0.0005 -
0.8423 10550 0.0001 -
0.8463 10600 0.0206 -
0.8503 10650 0.0023 -
0.8543 10700 0.0001 -
0.8583 10750 0.0001 -
0.8623 10800 0.0001 -
0.8663 10850 0.0001 -
0.8703 10900 0.0001 -
0.8743 10950 0.0002 -
0.8782 11000 0.0007 -
0.8822 11050 0.0025 -
0.8862 11100 0.0001 -
0.8902 11150 0.0001 -
0.8942 11200 0.0001 -
0.8982 11250 0.0001 -
0.9022 11300 0.0047 -
0.9062 11350 0.0001 -
0.9102 11400 0.0002 -
0.9142 11450 0.0001 -
0.9182 11500 0.0013 -
0.9222 11550 0.0011 -
0.9261 11600 0.0001 -
0.9301 11650 0.001 -
0.9341 11700 0.0145 -
0.9381 11750 0.0001 -
0.9421 11800 0.0156 -
0.9461 11850 0.0001 -
0.9501 11900 0.0016 -
0.9541 11950 0.0001 -
0.9581 12000 0.0011 -
0.9621 12050 0.002 -
0.9661 12100 0.0001 -
0.9701 12150 0.0004 -
0.9741 12200 0.0007 -
0.9780 12250 0.0014 -
0.9820 12300 0.0255 -
0.9860 12350 0.004 -
0.9900 12400 0.002 -
0.9940 12450 0.0146 -
0.9980 12500 0.0008 -
1.0 12525 - 0.0588
1.0020 12550 0.0175 -
1.0060 12600 0.0001 -
1.0100 12650 0.0006 -
1.0140 12700 0.0002 -
1.0180 12750 0.0149 -
1.0220 12800 0.0001 -
1.0259 12850 0.0001 -
1.0299 12900 0.0001 -
1.0339 12950 0.003 -
1.0379 13000 0.0003 -
1.0419 13050 0.0254 -
1.0459 13100 0.0001 -
1.0499 13150 0.0001 -
1.0539 13200 0.0001 -
1.0579 13250 0.0001 -
1.0619 13300 0.0003 -
1.0659 13350 0.0244 -
1.0699 13400 0.0001 -
1.0739 13450 0.0001 -
1.0778 13500 0.0175 -
1.0818 13550 0.0002 -
1.0858 13600 0.0002 -
1.0898 13650 0.0001 -
1.0938 13700 0.0001 -
1.0978 13750 0.0002 -
1.1018 13800 0.0001 -
1.1058 13850 0.0001 -
1.1098 13900 0.0001 -
1.1138 13950 0.0005 -
1.1178 14000 0.0001 -
1.1218 14050 0.0001 -
1.1257 14100 0.0002 -
1.1297 14150 0.0001 -
1.1337 14200 0.0002 -
1.1377 14250 0.0008 -
1.1417 14300 0.0001 -
1.1457 14350 0.0001 -
1.1497 14400 0.0013 -
1.1537 14450 0.0001 -
1.1577 14500 0.0001 -
1.1617 14550 0.0004 -
1.1657 14600 0.0001 -
1.1697 14650 0.0001 -
1.1737 14700 0.001 -
1.1776 14750 0.0156 -
1.1816 14800 0.0001 -
1.1856 14850 0.0003 -
1.1896 14900 0.0045 -
1.1936 14950 0.0011 -
1.1976 15000 0.0015 -
1.2016 15050 0.0017 -
1.2056 15100 0.017 -
1.2096 15150 0.0001 -
1.2136 15200 0.0011 -
1.2176 15250 0.0003 -
1.2216 15300 0.0001 -
1.2255 15350 0.0001 -
1.2295 15400 0.0005 -
1.2335 15450 0.0144 -
1.2375 15500 0.0001 -
1.2415 15550 0.0001 -
1.2455 15600 0.0191 -
1.2495 15650 0.0001 -
1.2535 15700 0.0001 -
1.2575 15750 0.0001 -
1.2615 15800 0.0001 -
1.2655 15850 0.0008 -
1.2695 15900 0.0005 -
1.2735 15950 0.013 -
1.2774 16000 0.0001 -
1.2814 16050 0.0201 -
1.2854 16100 0.0008 -
1.2894 16150 0.0001 -
1.2934 16200 0.0001 -
1.2974 16250 0.0001 -
1.3014 16300 0.0001 -
1.3054 16350 0.0 -
1.3094 16400 0.0118 -
1.3134 16450 0.0253 -
1.3174 16500 0.0001 -
1.3214 16550 0.0012 -
1.3253 16600 0.0017 -
1.3293 16650 0.0001 -
1.3333 16700 0.0094 -
1.3373 16750 0.0001 -
1.3413 16800 0.0243 -
1.3453 16850 0.049 -
1.3493 16900 0.0001 -
1.3533 16950 0.0247 -
1.3573 17000 0.0001 -
1.3613 17050 0.0001 -
1.3653 17100 0.0001 -
1.3693 17150 0.0246 -
1.3733 17200 0.0001 -
1.3772 17250 0.0245 -
1.3812 17300 0.0012 -
1.3852 17350 0.0001 -
1.3892 17400 0.0001 -
1.3932 17450 0.0001 -
1.3972 17500 0.0001 -
1.4012 17550 0.0001 -
1.4052 17600 0.0002 -
1.4092 17650 0.0001 -
1.4132 17700 0.0001 -
1.4172 17750 0.0039 -
1.4212 17800 0.0012 -
1.4251 17850 0.0043 -
1.4291 17900 0.0001 -
1.4331 17950 0.0001 -
1.4371 18000 0.0001 -
1.4411 18050 0.0001 -
1.4451 18100 0.0043 -
1.4491 18150 0.0023 -
1.4531 18200 0.0001 -
1.4571 18250 0.0002 -
1.4611 18300 0.0001 -
1.4651 18350 0.0001 -
1.4691 18400 0.0001 -
1.4731 18450 0.0099 -
1.4770 18500 0.0001 -
1.4810 18550 0.0001 -
1.4850 18600 0.03 -
1.4890 18650 0.0001 -
1.4930 18700 0.0014 -
1.4970 18750 0.0 -
1.5010 18800 0.0246 -
1.5050 18850 0.0001 -
1.5090 18900 0.0001 -
1.5130 18950 0.0001 -
1.5170 19000 0.0001 -
1.5210 19050 0.0001 -
1.5250 19100 0.0001 -
1.5289 19150 0.0001 -
1.5329 19200 0.0032 -
1.5369 19250 0.0001 -
1.5409 19300 0.0001 -
1.5449 19350 0.0001 -
1.5489 19400 0.025 -
1.5529 19450 0.0028 -
1.5569 19500 0.0028 -
1.5609 19550 0.0001 -
1.5649 19600 0.005 -
1.5689 19650 0.0024 -
1.5729 19700 0.0001 -
1.5768 19750 0.0 -
1.5808 19800 0.0001 -
1.5848 19850 0.0001 -
1.5888 19900 0.0001 -
1.5928 19950 0.0001 -
1.5968 20000 0.0001 -
1.6008 20050 0.0001 -
1.6048 20100 0.0001 -
1.6088 20150 0.0001 -
1.6128 20200 0.0 -
1.6168 20250 0.0001 -
1.6208 20300 0.0001 -
1.6248 20350 0.0028 -
1.6287 20400 0.0019 -
1.6327 20450 0.0115 -
1.6367 20500 0.0029 -
1.6407 20550 0.0035 -
1.6447 20600 0.0005 -
1.6487 20650 0.0007 -
1.6527 20700 0.0374 -
1.6567 20750 0.0064 -
1.6607 20800 0.004 -
1.6647 20850 0.0009 -
1.6687 20900 0.0 -
1.6727 20950 0.0017 -
1.6766 21000 0.0001 -
1.6806 21050 0.0001 -
1.6846 21100 0.0001 -
1.6886 21150 0.0083 -
1.6926 21200 0.0001 -
1.6966 21250 0.0001 -
1.7006 21300 0.0001 -
1.7046 21350 0.0009 -
1.7086 21400 0.0001 -
1.7126 21450 0.0001 -
1.7166 21500 0.0015 -
1.7206 21550 0.0001 -
1.7246 21600 0.0001 -
1.7285 21650 0.0001 -
1.7325 21700 0.0007 -
1.7365 21750 0.0001 -
1.7405 21800 0.0213 -
1.7445 21850 0.0007 -
1.7485 21900 0.0029 -
1.7525 21950 0.0007 -
1.7565 22000 0.0025 -
1.7605 22050 0.0002 -
1.7645 22100 0.0173 -
1.7685 22150 0.0012 -
1.7725 22200 0.0027 -
1.7764 22250 0.0009 -
1.7804 22300 0.0013 -
1.7844 22350 0.024 -
1.7884 22400 0.0043 -
1.7924 22450 0.0001 -
1.7964 22500 0.0001 -
1.8004 22550 0.0109 -
1.8044 22600 0.0001 -
1.8084 22650 0.0002 -
1.8124 22700 0.0246 -
1.8164 22750 0.0001 -
1.8204 22800 0.0002 -
1.8244 22850 0.0248 -
1.8283 22900 0.0001 -
1.8323 22950 0.0119 -
1.8363 23000 0.0254 -
1.8403 23050 0.1233 -
1.8443 23100 0.0003 -
1.8483 23150 0.0001 -
1.8523 23200 0.0 -
1.8563 23250 0.0 -
1.8603 23300 0.0001 -
1.8643 23350 0.0001 -
1.8683 23400 0.0001 -
1.8723 23450 0.0001 -
1.8762 23500 0.0001 -
1.8802 23550 0.0017 -
1.8842 23600 0.0 -
1.8882 23650 0.0001 -
1.8922 23700 0.0 -
1.8962 23750 0.0001 -
1.9002 23800 0.0055 -
1.9042 23850 0.0001 -
1.9082 23900 0.0003 -
1.9122 23950 0.0002 -
1.9162 24000 0.0007 -
1.9202 24050 0.0011 -
1.9242 24100 0.0001 -
1.9281 24150 0.0001 -
1.9321 24200 0.0001 -
1.9361 24250 0.0001 -
1.9401 24300 0.0192 -
1.9441 24350 0.0001 -
1.9481 24400 0.0008 -
1.9521 24450 0.0 -
1.9561 24500 0.0004 -
1.9601 24550 0.0001 -
1.9641 24600 0.0001 -
1.9681 24650 0.0001 -
1.9721 24700 0.0001 -
1.9760 24750 0.0013 -
1.9800 24800 0.0129 -
1.9840 24850 0.0024 -
1.9880 24900 0.0015 -
1.9920 24950 0.0196 -
1.9960 25000 0.0164 -
2.0 25050 0.002 0.0769
2.0040 25100 0.0001 -
2.0080 25150 0.0156 -
2.0120 25200 0.0 -
2.0160 25250 0.0169 -
2.0200 25300 0.0023 -
2.0240 25350 0.0001 -
2.0279 25400 0.0 -
2.0319 25450 0.001 -
2.0359 25500 0.0152 -
2.0399 25550 0.0001 -
2.0439 25600 0.001 -
2.0479 25650 0.0001 -
2.0519 25700 0.002 -
2.0559 25750 0.0006 -
2.0599 25800 0.0031 -
2.0639 25850 0.0016 -
2.0679 25900 0.0 -
2.0719 25950 0.0001 -
2.0758 26000 0.0001 -
2.0798 26050 0.0159 -
2.0838 26100 0.0005 -
2.0878 26150 0.0001 -
2.0918 26200 0.0001 -
2.0958 26250 0.0006 -
2.0998 26300 0.0008 -
2.1038 26350 0.0 -
2.1078 26400 0.0 -
2.1118 26450 0.0183 -
2.1158 26500 0.0001 -
2.1198 26550 0.0167 -
2.1238 26600 0.0001 -
2.1277 26650 0.0014 -
2.1317 26700 0.0003 -
2.1357 26750 0.0014 -
2.1397 26800 0.0001 -
2.1437 26850 0.0001 -
2.1477 26900 0.0011 -
2.1517 26950 0.0 -
2.1557 27000 0.0001 -
2.1597 27050 0.0001 -
2.1637 27100 0.0007 -
2.1677 27150 0.0001 -
2.1717 27200 0.0 -
2.1756 27250 0.0001 -
2.1796 27300 0.0005 -
2.1836 27350 0.0 -
2.1876 27400 0.0002 -
2.1916 27450 0.0001 -
2.1956 27500 0.0001 -
2.1996 27550 0.025 -
2.2036 27600 0.0001 -
2.2076 27650 0.0001 -
2.2116 27700 0.0001 -
2.2156 27750 0.0001 -
2.2196 27800 0.0001 -
2.2236 27850 0.0001 -
2.2275 27900 0.0001 -
2.2315 27950 0.0 -
2.2355 28000 0.0001 -
2.2395 28050 0.0001 -
2.2435 28100 0.0245 -
2.2475 28150 0.0001 -
2.2515 28200 0.0251 -
2.2555 28250 0.0001 -
2.2595 28300 0.0497 -
2.2635 28350 0.0002 -
2.2675 28400 0.0487 -
2.2715 28450 0.0703 -
2.2754 28500 0.0248 -
2.2794 28550 0.0001 -
2.2834 28600 0.0245 -
2.2874 28650 0.0004 -
2.2914 28700 0.0001 -
2.2954 28750 0.0001 -
2.2994 28800 0.0 -
2.3034 28850 0.0002 -
2.3074 28900 0.0489 -
2.3114 28950 0.0245 -
2.3154 29000 0.0002 -
2.3194 29050 0.0001 -
2.3234 29100 0.0001 -
2.3273 29150 0.0247 -
2.3313 29200 0.0003 -
2.3353 29250 0.0001 -
2.3393 29300 0.0001 -
2.3433 29350 0.0246 -
2.3473 29400 0.0246 -
2.3513 29450 0.0246 -
2.3553 29500 0.0001 -
2.3593 29550 0.0001 -
2.3633 29600 0.0001 -
2.3673 29650 0.0246 -
2.3713 29700 0.0 -
2.3752 29750 0.0246 -
2.3792 29800 0.0001 -
2.3832 29850 0.0001 -
2.3872 29900 0.0001 -
2.3912 29950 0.0002 -
2.3952 30000 0.0248 -
2.3992 30050 0.0002 -
2.4032 30100 0.0001 -
2.4072 30150 0.0001 -
2.4112 30200 0.0001 -
2.4152 30250 0.0001 -
2.4192 30300 0.0001 -
2.4232 30350 0.0245 -
2.4271 30400 0.0001 -
2.4311 30450 0.0001 -
2.4351 30500 0.0001 -
2.4391 30550 0.0 -
2.4431 30600 0.0001 -
2.4471 30650 0.0001 -
2.4511 30700 0.0001 -
2.4551 30750 0.0001 -
2.4591 30800 0.0001 -
2.4631 30850 0.0002 -
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  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.14
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.0
  • PyTorch: 2.4.0
  • Datasets: 2.21.0
  • 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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