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m-aliabbas1
/
medicare_idrak_ab

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
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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
decline
  • 'no i am not registered with medicare for part a or part b'
  • "no i'm not in"
  • "thank you very much but i don't think i will need that"
provide_age
  • 'my age is 36'
  • "i'm 62 years old"
  • '49'
complain_calls
  • 'stop disrupting my life with these calls'
  • 'your constant calls are causing me distress'
  • 'same as i was last time you called'
already
  • 'i already charged it should be good to go'
  • 'already took care of it no worries'
  • 'got the app installed already'
Not_Interested
  • "oh but i'm not interested"
  • "no i don't want to talk i don't want to talk"
  • "i'm not interested in making any decisions right now"
DNC
  • 'i asked you to stop calling me'
  • "i've had enough no more calls"
  • 'would you take me off your list please'
where_get_number
  • "i never provided my number to you what's going on"
  • 'who is responsible for sharing my number with you'
  • "what's the source of my contact details in your database"
language_barrier
  • "speak espanol i'm lost"
  • "sorry i'm not speaking english"
  • 'no english'
answering_machine
  • 'this is the voicemail system record your message after the beep'
  • 'this is the voicemail speak your message after the beep'
  • 'you have reached a law source number that is no longer in service please check the number you have dialed'
BUSY
  • "i'm engaged in a conference call can we talk later"
  • "right now i'm not able to talk right now bye"
  • "i don't have time"
where_are_you_calling_from
  • 'is the philippines where your organization is based'
  • 'where can i find your headquarters'
  • "can you confirm if you're in canada"
scam
  • "that's none of your business"
  • "i don't think i'm going to say it"
  • 'scammers are clever what measures do you have to counteract them'
affirmation
  • 'yeah you are'
  • "for sure that's correct"
  • 'i have secured both medicare part a and part b coverage'
transfer_request
  • 'i want to discuss this with someone higher up'
  • 'transfer my call to your superior please'
  • 'i require assistance from your manager immediately'
abusive
  • 'what the fuck you calling me for'
  • 'and you rather the fucking guy you fucked up'
  • 'crikey'
calling_about
  • 'why are you getting in touch with me'
  • "what's the main subject of discussion in this call"
  • "what's the rationale behind this call"
GreetBack
  • "what's crackin' how you been"
  • "i'm doing good how are you doing"
  • "hi i'm fine how's your day been so far"
say_again
  • 'can you please say that again'
  • 'sorry i need you to repeat that'
  • 'what was that can you repeat it'
sorry_greeting
  • "to be honest i'm feeling a bit down"
  • "i'm not really in a great mood"
  • "i'm not feeling very joyful right now"
not_decision_maker
  • 'decisions like this require a different approach'
  • "decisions of this nature aren't mine to make"
  • 'decisions in this regard are not mine to make'
hold_a_sec
  • 'please stay on the line while i check'
  • 'i need to consult with my colleague hold on'
  • "i'll be right back don't disconnect"
interested
  • 'your topic has piqued my curiosity do continue'
  • 'go'
  • "i'm all ears talk"
greetings
  • "i'm doing great thank you"
  • "hi there hope you're having a splendid day"
  • 'rest well and recharge good night'
who_are_you
  • 'start by telling me who you are'
  • 'can you tell me your given name'
  • "what's your name and position"
can_you_email
  • 'can you send an email with the instructions'
  • 'can you email me the contract terms'
  • 'is email the preferred way to receive updates'
DNQ
  • "i'm not the right fit for this"
  • "i'm not the right person for this"
  • "it's not the right fit for me"
are_you_bot
  • 'is this interaction with an automated system'
  • 'is this interaction with a human or a bot'
  • 'is this interaction with a robot or human'
other
  • 'i love exploring the different neighborhoods of our city each has its own charm'
  • 'no i have to buy a car'
  • 'i was just reading an article about the latest technological innovations'
weather
  • "how's the climate today"
  • 'tell me what the weather is like'
  • "how's the weather in the morning"

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("m-aliabbas1/medicare_idrak_ab")
# Run inference
preds = model("35")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 7.3794 109
Label Training Sample Count
BUSY 528
DNC 585
DNQ 96
GreetBack 224
Not_Interested 497
abusive 145
affirmation 306
already 70
answering_machine 316
are_you_bot 205
calling_about 147
can_you_email 116
complain_calls 65
decline 455
greetings 82
hold_a_sec 79
interested 94
language_barrier 163
not_decision_maker 83
other 56
provide_age 355
say_again 83
scam 110
sorry_greeting 102
transfer_request 73
weather 129
where_are_you_calling_from 250
where_get_number 127
who_are_you 221

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 40
  • 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
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0000 1 0.2366 -
0.0017 50 0.1785 -
0.0035 100 0.1604 -
0.0052 150 0.1877 -
0.0069 200 0.1209 -
0.0087 250 0.161 -
0.0104 300 0.1261 -
0.0121 350 0.153 -
0.0139 400 0.1333 -
0.0156 450 0.0675 -
0.0174 500 0.0623 -
0.0191 550 0.1324 -
0.0208 600 0.0481 -
0.0226 650 0.0894 -
0.0243 700 0.0484 -
0.0260 750 0.0683 -
0.0278 800 0.1025 -
0.0295 850 0.028 -
0.0312 900 0.0218 -
0.0330 950 0.0078 -
0.0347 1000 0.0682 -
0.0364 1050 0.0094 -
0.0382 1100 0.0836 -
0.0399 1150 0.0858 -
0.0417 1200 0.0115 -
0.0434 1250 0.0738 -
0.0451 1300 0.009 -
0.0469 1350 0.044 -
0.0486 1400 0.059 -
0.0503 1450 0.0271 -
0.0521 1500 0.1249 -
0.0538 1550 0.0032 -
0.0555 1600 0.0897 -
0.0573 1650 0.0758 -
0.0590 1700 0.0573 -
0.0607 1750 0.0063 -
0.0625 1800 0.011 -
0.0642 1850 0.005 -
0.0659 1900 0.0545 -
0.0677 1950 0.0216 -
0.0694 2000 0.0059 -
0.0712 2050 0.0043 -
0.0729 2100 0.0109 -
0.0746 2150 0.0049 -
0.0764 2200 0.012 -
0.0781 2250 0.0012 -
0.0798 2300 0.0284 -
0.0816 2350 0.0089 -
0.0833 2400 0.0023 -
0.0850 2450 0.0234 -
0.0868 2500 0.0463 -
0.0885 2550 0.0647 -
0.0902 2600 0.0578 -
0.0920 2650 0.0119 -
0.0937 2700 0.0562 -
0.0955 2750 0.0009 -
0.0972 2800 0.0573 -
0.0989 2850 0.0042 -
0.1007 2900 0.0028 -
0.1024 2950 0.0048 -
0.1041 3000 0.1124 -
0.1059 3050 0.0022 -
0.1076 3100 0.0033 -
0.1093 3150 0.0029 -
0.1111 3200 0.0281 -
0.1128 3250 0.0474 -
0.1145 3300 0.0059 -
0.1163 3350 0.0198 -
0.1180 3400 0.128 -
0.1198 3450 0.0092 -
0.1215 3500 0.0023 -
0.1232 3550 0.044 -
0.1250 3600 0.0333 -
0.1267 3650 0.0014 -
0.1284 3700 0.0019 -
0.1302 3750 0.0514 -
0.1319 3800 0.0004 -
0.1336 3850 0.0022 -
0.1354 3900 0.0012 -
0.1371 3950 0.0598 -
0.1388 4000 0.0013 -
0.1406 4050 0.0597 -
0.1423 4100 0.0004 -
0.1440 4150 0.0038 -
0.1458 4200 0.0523 -
0.1475 4250 0.0481 -
0.1493 4300 0.1062 -
0.1510 4350 0.0033 -
0.1527 4400 0.0007 -
0.1545 4450 0.0002 -
0.1562 4500 0.0009 -
0.1579 4550 0.0021 -
0.1597 4600 0.0013 -
0.1614 4650 0.0012 -
0.1631 4700 0.0012 -
0.1649 4750 0.0016 -
0.1666 4800 0.0002 -
0.1683 4850 0.0005 -
0.1701 4900 0.0039 -
0.1718 4950 0.0013 -
0.1736 5000 0.0022 -
0.1753 5050 0.0006 -
0.1770 5100 0.002 -
0.1788 5150 0.0004 -
0.1805 5200 0.0009 -
0.1822 5250 0.0004 -
0.1840 5300 0.0006 -
0.1857 5350 0.0107 -
0.1874 5400 0.0002 -
0.1892 5450 0.0006 -
0.1909 5500 0.0017 -
0.1926 5550 0.0049 -
0.1944 5600 0.0006 -
0.1961 5650 0.0138 -
0.1978 5700 0.011 -
0.1996 5750 0.0042 -
0.2013 5800 0.0017 -
0.2031 5850 0.0011 -
0.2048 5900 0.0103 -
0.2065 5950 0.0008 -
0.2083 6000 0.0615 -
0.2100 6050 0.0539 -
0.2117 6100 0.0016 -
0.2135 6150 0.0005 -
0.2152 6200 0.0004 -
0.2169 6250 0.0296 -
0.2187 6300 0.0003 -
0.2204 6350 0.0023 -
0.2221 6400 0.0306 -
0.2239 6450 0.0496 -
0.2256 6500 0.0433 -
0.2274 6550 0.0005 -
0.2291 6600 0.0109 -
0.2308 6650 0.0354 -
0.2326 6700 0.0007 -
0.2343 6750 0.0003 -
0.2360 6800 0.0006 -
0.2378 6850 0.0002 -
0.2395 6900 0.0014 -
0.2412 6950 0.0005 -
0.2430 7000 0.0002 -
0.2447 7050 0.0394 -
0.2464 7100 0.0006 -
0.2482 7150 0.0005 -
0.2499 7200 0.0002 -
0.2516 7250 0.0017 -
0.2534 7300 0.0004 -
0.2551 7350 0.0018 -
0.2569 7400 0.0184 -
0.2586 7450 0.0003 -
0.2603 7500 0.0515 -
0.2621 7550 0.0003 -
0.2638 7600 0.0013 -
0.2655 7650 0.0609 -
0.2673 7700 0.0017 -
0.2690 7750 0.0003 -
0.2707 7800 0.0011 -
0.2725 7850 0.0016 -
0.2742 7900 0.003 -
0.2759 7950 0.1212 -
0.2777 8000 0.0001 -
0.2794 8050 0.0004 -
0.2812 8100 0.0003 -
0.2829 8150 0.0608 -
0.2846 8200 0.0002 -
0.2864 8250 0.0003 -
0.2881 8300 0.0022 -
0.2898 8350 0.0052 -
0.2916 8400 0.0003 -
0.2933 8450 0.0001 -
0.2950 8500 0.0007 -
0.2968 8550 0.0336 -
0.2985 8600 0.0071 -
0.3002 8650 0.0002 -
0.3020 8700 0.0002 -
0.3037 8750 0.0107 -
0.3054 8800 0.0006 -
0.3072 8850 0.002 -
0.3089 8900 0.001 -
0.3107 8950 0.0002 -
0.3124 9000 0.0002 -
0.3141 9050 0.0021 -
0.3159 9100 0.0545 -
0.3176 9150 0.0007 -
0.3193 9200 0.0152 -
0.3211 9250 0.0003 -
0.3228 9300 0.0005 -
0.3245 9350 0.053 -
0.3263 9400 0.0031 -
0.3280 9450 0.0002 -
0.3297 9500 0.0002 -
0.3315 9550 0.0002 -
0.3332 9600 0.0009 -
0.3350 9650 0.0023 -
0.3367 9700 0.0011 -
0.3384 9750 0.0003 -
0.3402 9800 0.0003 -
0.3419 9850 0.0005 -
0.3436 9900 0.0004 -
0.3454 9950 0.0028 -
0.3471 10000 0.0016 -
0.3488 10050 0.0008 -
0.3506 10100 0.001 -
0.3523 10150 0.0005 -
0.3540 10200 0.0002 -
0.3558 10250 0.0002 -
0.3575 10300 0.0003 -
0.3593 10350 0.0003 -
0.3610 10400 0.0009 -
0.3627 10450 0.0001 -
0.3645 10500 0.0001 -
0.3662 10550 0.0002 -
0.3679 10600 0.0003 -
0.3697 10650 0.0002 -
0.3714 10700 0.0006 -
0.3731 10750 0.0042 -
0.3749 10800 0.0005 -
0.3766 10850 0.0009 -
0.3783 10900 0.0604 -
0.3801 10950 0.0002 -
0.3818 11000 0.0013 -
0.3835 11050 0.0001 -
0.3853 11100 0.0005 -
0.3870 11150 0.0007 -
0.3888 11200 0.0002 -
0.3905 11250 0.0001 -
0.3922 11300 0.0006 -
0.3940 11350 0.0593 -
0.3957 11400 0.0007 -
0.3974 11450 0.0001 -
0.3992 11500 0.0003 -
0.4009 11550 0.0647 -
0.4026 11600 0.0001 -
0.4044 11650 0.0001 -
0.4061 11700 0.0001 -
0.4078 11750 0.0003 -
0.4096 11800 0.0002 -
0.4113 11850 0.0128 -
0.4131 11900 0.0015 -
0.4148 11950 0.0002 -
0.4165 12000 0.0004 -
0.4183 12050 0.0003 -
0.4200 12100 0.0001 -
0.4217 12150 0.0003 -
0.4235 12200 0.0006 -
0.4252 12250 0.0205 -
0.4269 12300 0.0004 -
0.4287 12350 0.0002 -
0.4304 12400 0.0001 -
0.4321 12450 0.0002 -
0.4339 12500 0.0025 -
0.4356 12550 0.0002 -
0.4373 12600 0.0002 -
0.4391 12650 0.0102 -
0.4408 12700 0.0001 -
0.4426 12750 0.0002 -
0.4443 12800 0.0003 -
0.4460 12850 0.0002 -
0.4478 12900 0.0003 -
0.4495 12950 0.0003 -
0.4512 13000 0.0007 -
0.4530 13050 0.0001 -
0.4547 13100 0.0002 -
0.4564 13150 0.0002 -
0.4582 13200 0.0004 -
0.4599 13250 0.0002 -
0.4616 13300 0.0001 -
0.4634 13350 0.0001 -
0.4651 13400 0.0001 -
0.4669 13450 0.0002 -
0.4686 13500 0.0007 -
0.4703 13550 0.0023 -
0.4721 13600 0.0004 -
0.4738 13650 0.0001 -
0.4755 13700 0.0002 -
0.4773 13750 0.0001 -
0.4790 13800 0.0001 -
0.4807 13850 0.0002 -
0.4825 13900 0.0003 -
0.4842 13950 0.027 -
0.4859 14000 0.0002 -
0.4877 14050 0.0001 -
0.4894 14100 0.0002 -
0.4911 14150 0.0003 -
0.4929 14200 0.0001 -
0.4946 14250 0.0001 -
0.4964 14300 0.0002 -
0.4981 14350 0.0001 -
0.4998 14400 0.0002 -
0.5016 14450 0.0004 -
0.5033 14500 0.0001 -
0.5050 14550 0.0085 -
0.5068 14600 0.0008 -
0.5085 14650 0.0001 -
0.5102 14700 0.0001 -
0.5120 14750 0.0001 -
0.5137 14800 0.044 -
0.5154 14850 0.0001 -
0.5172 14900 0.0001 -
0.5189 14950 0.0001 -
0.5207 15000 0.0002 -
0.5224 15050 0.0001 -
0.5241 15100 0.0001 -
0.5259 15150 0.0003 -
0.5276 15200 0.003 -
0.5293 15250 0.0027 -
0.5311 15300 0.0001 -
0.5328 15350 0.0003 -
0.5345 15400 0.0003 -
0.5363 15450 0.0002 -
0.5380 15500 0.0004 -
0.5397 15550 0.0002 -
0.5415 15600 0.0001 -
0.5432 15650 0.0001 -
0.5449 15700 0.0002 -
0.5467 15750 0.0108 -
0.5484 15800 0.0001 -
0.5502 15850 0.0002 -
0.5519 15900 0.0001 -
0.5536 15950 0.0014 -
0.5554 16000 0.0001 -
0.5571 16050 0.0003 -
0.5588 16100 0.0008 -
0.5606 16150 0.0333 -
0.5623 16200 0.0018 -
0.5640 16250 0.0002 -
0.5658 16300 0.0002 -
0.5675 16350 0.0001 -
0.5692 16400 0.0001 -
0.5710 16450 0.0003 -
0.5727 16500 0.0001 -
0.5745 16550 0.0073 -
0.5762 16600 0.0012 -
0.5779 16650 0.0002 -
0.5797 16700 0.0001 -
0.5814 16750 0.0022 -
0.5831 16800 0.0003 -
0.5849 16850 0.0002 -
0.5866 16900 0.0001 -
0.5883 16950 0.0019 -
0.5901 17000 0.0003 -
0.5918 17050 0.0001 -
0.5935 17100 0.0003 -
0.5953 17150 0.0001 -
0.5970 17200 0.0001 -
0.5988 17250 0.0167 -
0.6005 17300 0.0002 -
0.6022 17350 0.0001 -
0.6040 17400 0.0001 -
0.6057 17450 0.0242 -
0.6074 17500 0.0015 -
0.6092 17550 0.0009 -
0.6109 17600 0.0001 -
0.6126 17650 0.0001 -
0.6144 17700 0.0001 -
0.6161 17750 0.0001 -
0.6178 17800 0.0001 -
0.6196 17850 0.0113 -
0.6213 17900 0.0001 -
0.6230 17950 0.0005 -
0.6248 18000 0.0017 -
0.6265 18050 0.0001 -
0.6283 18100 0.0001 -
0.6300 18150 0.0003 -
0.6317 18200 0.0001 -
0.6335 18250 0.0004 -
0.6352 18300 0.0001 -
0.6369 18350 0.0001 -
0.6387 18400 0.0021 -
0.6404 18450 0.0001 -
0.6421 18500 0.0002 -
0.6439 18550 0.0006 -
0.6456 18600 0.0001 -
0.6473 18650 0.0001 -
0.6491 18700 0.0003 -
0.6508 18750 0.0001 -
0.6526 18800 0.0001 -
0.6543 18850 0.0002 -
0.6560 18900 0.001 -
0.6578 18950 0.0002 -
0.6595 19000 0.0047 -
0.6612 19050 0.0001 -
0.6630 19100 0.0001 -
0.6647 19150 0.0002 -
0.6664 19200 0.0001 -
0.6682 19250 0.0001 -
0.6699 19300 0.0064 -
0.6716 19350 0.0001 -
0.6734 19400 0.0001 -
0.6751 19450 0.0001 -
0.6768 19500 0.0001 -
0.6786 19550 0.0001 -
0.6803 19600 0.0001 -
0.6821 19650 0.0001 -
0.6838 19700 0.0001 -
0.6855 19750 0.0002 -
0.6873 19800 0.0001 -
0.6890 19850 0.0001 -
0.6907 19900 0.0001 -
0.6925 19950 0.0001 -
0.6942 20000 0.0002 -
0.6959 20050 0.0015 -
0.6977 20100 0.0002 -
0.6994 20150 0.0001 -
0.7011 20200 0.0001 -
0.7029 20250 0.0001 -
0.7046 20300 0.0011 -
0.7064 20350 0.0001 -
0.7081 20400 0.0001 -
0.7098 20450 0.0001 -
0.7116 20500 0.0057 -
0.7133 20550 0.0 -
0.7150 20600 0.0001 -
0.7168 20650 0.0001 -
0.7185 20700 0.0001 -
0.7202 20750 0.0001 -
0.7220 20800 0.0001 -
0.7237 20850 0.0001 -
0.7254 20900 0.0002 -
0.7272 20950 0.0001 -
0.7289 21000 0.0001 -
0.7306 21050 0.0 -
0.7324 21100 0.0002 -
0.7341 21150 0.0001 -
0.7359 21200 0.0001 -
0.7376 21250 0.0001 -
0.7393 21300 0.0001 -
0.7411 21350 0.0001 -
0.7428 21400 0.0001 -
0.7445 21450 0.0001 -
0.7463 21500 0.0001 -
0.7480 21550 0.005 -
0.7497 21600 0.0001 -
0.7515 21650 0.0001 -
0.7532 21700 0.0001 -
0.7549 21750 0.0002 -
0.7567 21800 0.0001 -
0.7584 21850 0.0013 -
0.7602 21900 0.0001 -
0.7619 21950 0.0002 -
0.7636 22000 0.0 -
0.7654 22050 0.0001 -
0.7671 22100 0.0002 -
0.7688 22150 0.0001 -
0.7706 22200 0.0002 -
0.7723 22250 0.0001 -
0.7740 22300 0.0001 -
0.7758 22350 0.0002 -
0.7775 22400 0.0001 -
0.7792 22450 0.0013 -
0.7810 22500 0.0001 -
0.7827 22550 0.0002 -
0.7844 22600 0.0002 -
0.7862 22650 0.0069 -
0.7879 22700 0.0001 -
0.7897 22750 0.0001 -
0.7914 22800 0.0001 -
0.7931 22850 0.0001 -
0.7949 22900 0.0001 -
0.7966 22950 0.0001 -
0.7983 23000 0.0001 -
0.8001 23050 0.0002 -
0.8018 23100 0.0001 -
0.8035 23150 0.0001 -
0.8053 23200 0.0001 -
0.8070 23250 0.0001 -
0.8087 23300 0.0001 -
0.8105 23350 0.0001 -
0.8122 23400 0.0027 -
0.8140 23450 0.0001 -
0.8157 23500 0.0001 -
0.8174 23550 0.0027 -
0.8192 23600 0.0002 -
0.8209 23650 0.0002 -
0.8226 23700 0.0001 -
0.8244 23750 0.0003 -
0.8261 23800 0.0001 -
0.8278 23850 0.0001 -
0.8296 23900 0.0001 -
0.8313 23950 0.0001 -
0.8330 24000 0.0014 -
0.8348 24050 0.0083 -
0.8365 24100 0.0001 -
0.8383 24150 0.0001 -
0.8400 24200 0.0001 -
0.8417 24250 0.0001 -
0.8435 24300 0.0001 -
0.8452 24350 0.0001 -
0.8469 24400 0.0 -
0.8487 24450 0.0001 -
0.8504 24500 0.0001 -
0.8521 24550 0.022 -
0.8539 24600 0.0001 -
0.8556 24650 0.0001 -
0.8573 24700 0.0003 -
0.8591 24750 0.0001 -
0.8608 24800 0.0002 -
0.8625 24850 0.0001 -
0.8643 24900 0.0001 -
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Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.0
  • PyTorch: 2.3.0+cu121
  • Datasets: 2.19.2
  • 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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