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Nikhilbendre
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my-awesome-setfit-model

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-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
adult content
  • 'gay men ring free gay tube for mature gay men updated with gay porn videos and gay sex movies'
  • 'seemomsuck packed with the best amateur step mom blowjobs download hot x000d mom and teen blowjob videos seemomsuck'
  • 'xreview com provides depth porn site reviews and offers discount codes and links off some the top porn sites the internet'
suicide
  • "wife threatening suiciderecently left wife for good because she has cheated twice and lied much that have decided refuse back her. few days ago, she began threatening suicide. have tirelessly spent these paat few days talking her out and she keeps hesitating because she wants believe i'll come back. know lot people will threaten this order get their way, but what happens she really does? what and how supposed handle her death hands? still love wife but cannot deal with getting cheated again and constantly feeling insecure. i'm worried today may the day she does and hope much doesn't happen."
  • 'need helpjust help crying hard'
  • 'i’m losthello, name adam and i’ve been struggling for years and i’m afraid. through these past years thoughts suicide, fear, anxiety i’m close limit i’ve been quiet for long and i’m too scared come out family about these feelings. about years ago losing aunt triggered all. everyday feeling hopeless lost, guilty, and remorseful over her and all the things i’ve done life,but thoughts like these with the little i’ve experienced life? only time i’ve revealed these feelings family when broke down where they saw cuts. watching them get worried over something portrayed average day made feel absolutely dreadful. they later found out was attempt survivor from attempt od(overdose from pills) and attempt hanging. all that happened was blackout from the pills and never went through with the noose because i’m still afraid. during first therapy was diagnosed with severe depression, social anxiety, and eating disorder. was later transferred fucken group therapy for some reason which made feel more anxious. eventually before last session with therapy she showed results from daily check feelings(which was step survey for and mom/dad come find out i’ve been putting feeling horrible and afraid/anxious everyday mom has been doing i’ve been doing absolutely amazing with described “happiest she’s ever seen me, therapy has helped him” eventually was put sertaline (anti anxiety anti depression i’m sorry forgot) but never finished first prescription nor ever found the right type anti depressant because mom thought only wanted the drugs she took off recommended pill schedule after week and stopped from taking them. all this time i’ve been feeling worse afraid the damage/ worry i’ve caused them even more. now here with everything going on, i’m afraid i’ve ever been i’ve relapsed cutting and have developed severe insomnia day after day feeling more hopeless, worthless questioning why still here? what’s motivation move out bed and keep going? ask these myself nearly every night almost having break down everytime. please please please someone.. anyone help me. i’m scared might something drastic, i’ve been shaped fear and anxiety. idk what anymore'
gun violence
  • 'anti-gun student walkout included stomping american flag and jumping cop car'
  • 'parkland students brand firearm ban during pence speech nra hypocrisy'
  • 'oliver north and the nra deserve each other'
discrimination
  • "we're looking for someone with more aesthetically pleasing face for this role"
  • "we're looking for someone with more appealing body type for this position"
  • "we're looking for someone with more attractive appearance for this role"
sexual harassment
  • 'was walking along crowded street, holding mums hand, when elderly man groped butt, turned look h7m and looked away, and did again after while.i was yrs old then.'
  • 'poor street lighting'
  • "this happened bus, public transport. was broad daylight. was way college and the bus wasn't that crowded. there was enough space for two people stand back back with big bags 'em. this particular male was standing next seat. there was enough and more space behind him. but noooooo... started rubbing his crotch against shoulder, subtly first and then very obviously. made discomfort very apparent. but didn't seem get it. shifted inside seat and did he. think the lady beside knew what was going but didn't say word, neither did the people behind nor the bus conductor. well neither did the first place, was frozen and gripped shock and fear. think now better prepared make shit down someone tries the same feat with me. but i'll never know."
derogatory
  • 'fuck that dont wanna watch tranny porn.'
  • 'opie just wanted have good time. the same old rant about africans from ant and the same old joke about tranny cock from jim. opie admitted and accepted his place the show. was the two other queens who couldnâ€t keep together. opie ✚'
  • 'fuck that faggot fallon. fucking sissy boy acting like year old playing slumber party games. put norm amp louis tour together. see how much fucking money they make together.'
hate
  • 'dalits are lowlives'
  • 'gay people are burden our society'
  • 'arabs are not welcome'
toxic
  • 'cocksucker before you piss around work'
  • 'hey... what it.. talk what it... exclusive group some talibans...who are good destroying, self-appointed purist who gang any one who asks them questions abt their anti-social and destructive (non)-contribution wp? ask sityush clean his behavior than issue nonsensical warnings...'
  • "bye don't look, come think comming back tosser."
physical violence
  • 'husband beats frequently, wife tells court'
  • 'best thing for do, remain silent when return work today. nobody wants hear anything from me, unless husband beats me.'
  • 'husband will never beat me, bambam denies rumour that teddya beats her'
bullying
  • "here home. neighbors pick family and mind you son autistic. feels like high school. they call names attack for reason and bully all the time. can't step front porch without them doing something"
  • 'being bullied school: high-achieving boys use further strategies maintain masculinity, for example becoming bullies themselves, disrupting the lessons, devaluing girls’ achievements https://www.bipartisanalliance.com///being-bullied-at-school-high-achieving.html?spref=tw'
  • 'there was girl class 6th grade who was little autistic, and her parents thought it’d better send her public school. she didn’t fit with the class and was lowkey bullied (it was actually high key but the teacher downplayed and did nothing)'
offensive
  • 'the world will rejoice when you die. murderer classless'
  • 'destroy our country? more like defending our country against treasonous traitor the white house trumpisatraitor trumptaxes trumpsaninternationaldisgrace trumpmustresign'
  • 'looks like the lying incompetent homophobic racist borisjohnson will the next tory leader...well least we’ll get generalelection boris? borisjohnsonshouldnotbepm torydrugoff toryleadershipcontest https://t.co/ho4hibto7a'

Evaluation

Metrics

Label Accuracy
all 0.5722

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("Nikhilbendre/my-awesome-setfit-model")
# Run inference
preds = model("this bitch")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 31.7855 1331
Label Training Sample Count
adult content 100
bullying 100
derogatory 100
discrimination 100
gun violence 100
hate 100
offensive 100
physical violence 100
sexual harassment 100
suicide 100
toxic 100

Training Hyperparameters

  • batch_size: (4, 4)
  • num_epochs: (10, 10)
  • 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
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.2443 -
0.0045 50 0.3281 -
0.0091 100 0.3332 -
0.0136 150 0.2277 -
0.0182 200 0.2349 -
0.0227 250 0.1334 -
0.0273 300 0.2644 -
0.0318 350 0.2592 -
0.0364 400 0.1887 -
0.0409 450 0.1979 -
0.0455 500 0.2129 -
0.05 550 0.0973 -
0.0545 600 0.2668 -
0.0591 650 0.1174 -
0.0636 700 0.1916 -
0.0682 750 0.1189 -
0.0727 800 0.2277 -
0.0773 850 0.1881 -
0.0818 900 0.1465 -
0.0864 950 0.1099 -
0.0909 1000 0.1549 -
0.0955 1050 0.1248 -
0.1 1100 0.1229 -
0.1045 1150 0.1852 -
0.1091 1200 0.0698 -
0.1136 1250 0.1726 -
0.1182 1300 0.1093 -
0.1227 1350 0.1563 -
0.1273 1400 0.1223 -
0.1318 1450 0.0557 -
0.1364 1500 0.1671 -
0.1409 1550 0.0872 -
0.1455 1600 0.0795 -
0.15 1650 0.0429 -
0.1545 1700 0.1217 -
0.1591 1750 0.0094 -
0.1636 1800 0.0463 -
0.1682 1850 0.0842 -
0.1727 1900 0.004 -
0.1773 1950 0.0204 -
0.1818 2000 0.0133 -
0.1864 2050 0.1028 -
0.1909 2100 0.0098 -
0.1955 2150 0.0166 -
0.2 2200 0.0221 -
0.2045 2250 0.0296 -
0.2091 2300 0.024 -
0.2136 2350 0.1014 -
0.2182 2400 0.0035 -
0.2227 2450 0.0086 -
0.2273 2500 0.0092 -
0.2318 2550 0.001 -
0.2364 2600 0.0047 -
0.2409 2650 0.0065 -
0.2455 2700 0.0046 -
0.25 2750 0.0094 -
0.2545 2800 0.004 -
0.2591 2850 0.1082 -
0.2636 2900 0.0022 -
0.2682 2950 0.0026 -
0.2727 3000 0.0009 -
0.2773 3050 0.0008 -
0.2818 3100 0.0004 -
0.2864 3150 0.0012 -
0.2909 3200 0.0005 -
0.2955 3250 0.0021 -
0.3 3300 0.0008 -
0.3045 3350 0.0007 -
0.3091 3400 0.002 -
0.3136 3450 0.0013 -
0.3182 3500 0.0007 -
0.3227 3550 0.0052 -
0.3273 3600 0.0012 -
0.3318 3650 0.0019 -
0.3364 3700 0.0006 -
0.3409 3750 0.0009 -
0.3455 3800 0.0004 -
0.35 3850 0.0039 -
0.3545 3900 0.0005 -
0.3591 3950 0.0086 -
0.3636 4000 0.0002 -
0.3682 4050 0.0014 -
0.3727 4100 0.0006 -
0.3773 4150 0.0012 -
0.3818 4200 0.0008 -
0.3864 4250 0.0008 -
0.3909 4300 0.0004 -
0.3955 4350 0.0003 -
0.4 4400 0.0002 -
0.4045 4450 0.0001 -
0.4091 4500 0.0003 -
0.4136 4550 0.0003 -
0.4182 4600 0.0001 -
0.4227 4650 0.0003 -
0.4273 4700 0.0002 -
0.4318 4750 0.0002 -
0.4364 4800 0.0001 -
0.4409 4850 0.0003 -
0.4455 4900 0.0001 -
0.45 4950 0.0009 -
0.4545 5000 0.0002 -
0.4591 5050 0.0001 -
0.4636 5100 0.0001 -
0.4682 5150 0.0001 -
0.4727 5200 0.0006 -
0.4773 5250 0.0001 -
0.4818 5300 0.0002 -
0.4864 5350 0.0001 -
0.4909 5400 0.0002 -
0.4955 5450 0.0002 -
0.5 5500 0.0002 -
0.5045 5550 0.0002 -
0.5091 5600 0.0006 -
0.5136 5650 0.0001 -
0.5182 5700 0.0001 -
0.5227 5750 0.0002 -
0.5273 5800 0.0001 -
0.5318 5850 0.0013 -
0.5364 5900 0.0002 -
0.5409 5950 0.0003 -
0.5455 6000 0.0002 -
0.55 6050 0.0007 -
0.5545 6100 0.0002 -
0.5591 6150 0.0001 -
0.5636 6200 0.0002 -
0.5682 6250 0.0013 -
0.5727 6300 0.0003 -
0.5773 6350 0.0002 -
0.5818 6400 0.0001 -
0.5864 6450 0.0002 -
0.5909 6500 0.0001 -
0.5955 6550 0.0004 -
0.6 6600 0.0001 -
0.6045 6650 0.0002 -
0.6091 6700 0.0003 -
0.6136 6750 0.0017 -
0.6182 6800 0.0019 -
0.6227 6850 0.0003 -
0.6273 6900 0.001 -
0.6318 6950 0.0001 -
0.6364 7000 0.0002 -
0.6409 7050 0.0007 -
0.6455 7100 0.0001 -
0.65 7150 0.0002 -
0.6545 7200 0.0002 -
0.6591 7250 0.0004 -
0.6636 7300 0.0004 -
0.6682 7350 0.0007 -
0.6727 7400 0.0002 -
0.6773 7450 0.0001 -
0.6818 7500 0.0003 -
0.6864 7550 0.0001 -
0.6909 7600 0.0 -
0.6955 7650 0.0002 -
0.7 7700 0.0005 -
0.7045 7750 0.0002 -
0.7091 7800 0.0 -
0.7136 7850 0.0001 -
0.7182 7900 0.0002 -
0.7227 7950 0.0001 -
0.7273 8000 0.0001 -
0.7318 8050 0.0003 -
0.7364 8100 0.0019 -
0.7409 8150 0.0014 -
0.7455 8200 0.0001 -
0.75 8250 0.0002 -
0.7545 8300 0.0002 -
0.7591 8350 0.0005 -
0.7636 8400 0.0001 -
0.7682 8450 0.0045 -
0.7727 8500 0.0 -
0.7773 8550 0.0002 -
0.7818 8600 0.0008 -
0.7864 8650 0.0003 -
0.7909 8700 0.0001 -
0.7955 8750 0.0001 -
0.8 8800 0.0003 -
0.8045 8850 0.0 -
0.8091 8900 0.0 -
0.8136 8950 0.0001 -
0.8182 9000 0.0 -
0.8227 9050 0.0001 -
0.8273 9100 0.0 -
0.8318 9150 0.0001 -
0.8364 9200 0.0003 -
0.8409 9250 0.0 -
0.8455 9300 0.0 -
0.85 9350 0.0408 -
0.8545 9400 0.0021 -
0.8591 9450 0.1726 -
0.8636 9500 0.1827 -
0.8682 9550 0.0006 -
0.8727 9600 0.0001 -
0.8773 9650 0.0001 -
0.8818 9700 0.0003 -
0.8864 9750 0.0001 -
0.8909 9800 0.0001 -
0.8955 9850 0.0001 -
0.9 9900 0.001 -
0.9045 9950 0.0001 -
0.9091 10000 0.0002 -
0.9136 10050 0.0002 -
0.9182 10100 0.0001 -
0.9227 10150 0.0001 -
0.9273 10200 0.0004 -
0.9318 10250 0.0001 -
0.9364 10300 0.0001 -
0.9409 10350 0.0002 -
0.9455 10400 0.0001 -
0.95 10450 0.0 -
0.9545 10500 0.0001 -
0.9591 10550 0.0001 -
0.9636 10600 0.0002 -
0.9682 10650 0.0001 -
0.9727 10700 0.0 -
0.9773 10750 0.0 -
0.9818 10800 0.0 -
0.9864 10850 0.0 -
0.9909 10900 0.0001 -
0.9955 10950 0.0 -
1.0 11000 0.0 -
1.0045 11050 0.0001 -
1.0091 11100 0.0002 -
1.0136 11150 0.0001 -
1.0182 11200 0.0001 -
1.0227 11250 0.0001 -
1.0273 11300 0.0 -
1.0318 11350 0.0014 -
1.0364 11400 0.0002 -
1.0409 11450 0.0001 -
1.0455 11500 0.0001 -
1.05 11550 0.0001 -
1.0545 11600 0.0 -
1.0591 11650 0.0 -
1.0636 11700 0.0003 -
1.0682 11750 0.0002 -
1.0727 11800 0.0001 -
1.0773 11850 0.0001 -
1.0818 11900 0.0001 -
1.0864 11950 0.0001 -
1.0909 12000 0.0001 -
1.0955 12050 0.0001 -
1.1 12100 0.0002 -
1.1045 12150 0.0001 -
1.1091 12200 0.0698 -
1.1136 12250 0.0001 -
1.1182 12300 0.0001 -
1.1227 12350 0.0002 -
1.1273 12400 0.0002 -
1.1318 12450 0.0002 -
1.1364 12500 0.0 -
1.1409 12550 0.0001 -
1.1455 12600 0.0001 -
1.15 12650 0.0001 -
1.1545 12700 0.0 -
1.1591 12750 0.0004 -
1.1636 12800 0.0001 -
1.1682 12850 0.0001 -
1.1727 12900 0.0 -
1.1773 12950 0.0001 -
1.1818 13000 0.0001 -
1.1864 13050 0.0002 -
1.1909 13100 0.0001 -
1.1955 13150 0.0 -
1.2 13200 0.0001 -
1.2045 13250 0.0002 -
1.2091 13300 0.0 -
1.2136 13350 0.0002 -
1.2182 13400 0.0001 -
1.2227 13450 0.0002 -
1.2273 13500 0.001 -
1.2318 13550 0.0001 -
1.2364 13600 0.0 -
1.2409 13650 0.0001 -
1.2455 13700 0.0 -
1.25 13750 0.0001 -
1.2545 13800 0.0002 -
1.2591 13850 0.0009 -
1.2636 13900 0.0 -
1.2682 13950 0.0001 -
1.2727 14000 0.0 -
1.2773 14050 0.001 -
1.2818 14100 0.0001 -
1.2864 14150 0.0001 -
1.2909 14200 0.0001 -
1.2955 14250 0.0011 -
1.3 14300 0.0003 -
1.3045 14350 0.0449 -
1.3091 14400 0.0004 -
1.3136 14450 0.0219 -
1.3182 14500 0.002 -
1.3227 14550 0.0001 -
1.3273 14600 0.0001 -
1.3318 14650 0.0 -
1.3364 14700 0.0001 -
1.3409 14750 0.0002 -
1.3455 14800 0.0 -
1.35 14850 0.0001 -
1.3545 14900 0.0001 -
1.3591 14950 0.0001 -
1.3636 15000 0.0059 -
1.3682 15050 0.0005 -
1.3727 15100 0.0001 -
1.3773 15150 0.0 -
1.3818 15200 0.0 -
1.3864 15250 0.0 -
1.3909 15300 0.0 -
1.3955 15350 0.0 -
1.4 15400 0.0001 -
1.4045 15450 0.0 -
1.4091 15500 0.0001 -
1.4136 15550 0.0001 -
1.4182 15600 0.0001 -
1.4227 15650 0.0 -
1.4273 15700 0.0 -
1.4318 15750 0.0 -
1.4364 15800 0.0001 -
1.4409 15850 0.0 -
1.4455 15900 0.0001 -
1.45 15950 0.0005 -
1.4545 16000 0.0001 -
1.4591 16050 0.0001 -
1.4636 16100 0.0002 -
1.4682 16150 0.0 -
1.4727 16200 0.0 -
1.4773 16250 0.0001 -
1.4818 16300 0.0002 -
1.4864 16350 0.0 -
1.4909 16400 0.0001 -
1.4955 16450 0.0 -
1.5 16500 0.0 -
1.5045 16550 0.0 -
1.5091 16600 0.0 -
1.5136 16650 0.0001 -
1.5182 16700 0.0 -
1.5227 16750 0.0 -
1.5273 16800 0.0001 -
1.5318 16850 0.0 -
1.5364 16900 0.0 -
1.5409 16950 0.0 -
1.5455 17000 0.0 -
1.55 17050 0.0001 -
1.5545 17100 0.0 -
1.5591 17150 0.0 -
1.5636 17200 0.0 -
1.5682 17250 0.0 -
1.5727 17300 0.0 -
1.5773 17350 0.0 -
1.5818 17400 0.0002 -
1.5864 17450 0.0 -
1.5909 17500 0.0 -
1.5955 17550 0.0 -
1.6 17600 0.0001 -
1.6045 17650 0.0 -
1.6091 17700 0.0 -
1.6136 17750 0.0 -
1.6182 17800 0.0 -
1.6227 17850 0.0 -
1.6273 17900 0.0001 -
1.6318 17950 0.0 -
1.6364 18000 0.0001 -
1.6409 18050 0.0001 -
1.6455 18100 0.0001 -
1.65 18150 0.0 -
1.6545 18200 0.0003 -
1.6591 18250 0.0001 -
1.6636 18300 0.0001 -
1.6682 18350 0.0 -
1.6727 18400 0.0 -
1.6773 18450 0.0 -
1.6818 18500 0.0001 -
1.6864 18550 0.0 -
1.6909 18600 0.0 -
1.6955 18650 0.0 -
1.7 18700 0.0 -
1.7045 18750 0.0001 -
1.7091 18800 0.0001 -
1.7136 18850 0.0001 -
1.7182 18900 0.0 -
1.7227 18950 0.0 -
1.7273 19000 0.0 -
1.7318 19050 0.0001 -
1.7364 19100 0.0 -
1.7409 19150 0.0 -
1.7455 19200 0.0 -
1.75 19250 0.0001 -
1.7545 19300 0.0 -
1.7591 19350 0.0 -
1.7636 19400 0.0002 -
1.7682 19450 0.0001 -
1.7727 19500 0.0001 -
1.7773 19550 0.0 -
1.7818 19600 0.0001 -
1.7864 19650 0.0007 -
1.7909 19700 0.0 -
1.7955 19750 0.0001 -
1.8 19800 0.0002 -
1.8045 19850 0.0 -
1.8091 19900 0.0002 -
1.8136 19950 0.0002 -
1.8182 20000 0.0 -
1.8227 20050 0.0002 -
1.8273 20100 0.0 -
1.8318 20150 0.0 -
1.8364 20200 0.0 -
1.8409 20250 0.0001 -
1.8455 20300 0.0001 -
1.85 20350 0.0 -
1.8545 20400 0.0001 -
1.8591 20450 0.0 -
1.8636 20500 0.0 -
1.8682 20550 0.0 -
1.8727 20600 0.0014 -
1.8773 20650 0.0001 -
1.8818 20700 0.0 -
1.8864 20750 0.0009 -
1.8909 20800 0.0002 -
1.8955 20850 0.0 -
1.9 20900 0.0227 -
1.9045 20950 0.1435 -
1.9091 21000 0.0002 -
1.9136 21050 0.0137 -
1.9182 21100 0.0004 -
1.9227 21150 0.0001 -
1.9273 21200 0.0001 -
1.9318 21250 0.0 -
1.9364 21300 0.0001 -
1.9409 21350 0.0 -
1.9455 21400 0.0 -
1.95 21450 0.0001 -
1.9545 21500 0.0 -
1.9591 21550 0.0 -
1.9636 21600 0.0001 -
1.9682 21650 0.0 -
1.9727 21700 0.0001 -
1.9773 21750 0.0 -
1.9818 21800 0.0 -
1.9864 21850 0.0 -
1.9909 21900 0.0 -
1.9955 21950 0.0001 -
2.0 22000 0.0001 -
2.0045 22050 0.0001 -
2.0091 22100 0.0 -
2.0136 22150 0.0005 -
2.0182 22200 0.0001 -
2.0227 22250 0.0 -
2.0273 22300 0.0001 -
2.0318 22350 0.0002 -
2.0364 22400 0.0042 -
2.0409 22450 0.0147 -
2.0455 22500 0.0 -
2.05 22550 0.0 -
2.0545 22600 0.0 -
2.0591 22650 0.0026 -
2.0636 22700 0.0 -
2.0682 22750 0.0001 -
2.0727 22800 0.0 -
2.0773 22850 0.0 -
2.0818 22900 0.0001 -
2.0864 22950 0.0 -
2.0909 23000 0.0001 -
2.0955 23050 0.0001 -
2.1 23100 0.0 -
2.1045 23150 0.0 -
2.1091 23200 0.0001 -
2.1136 23250 0.0 -
2.1182 23300 0.0 -
2.1227 23350 0.0002 -
2.1273 23400 0.0 -
2.1318 23450 0.0 -
2.1364 23500 0.0001 -
2.1409 23550 0.0 -
2.1455 23600 0.0 -
2.15 23650 0.0 -
2.1545 23700 0.0 -
2.1591 23750 0.0001 -
2.1636 23800 0.0 -
2.1682 23850 0.0 -
2.1727 23900 0.0 -
2.1773 23950 0.0001 -
2.1818 24000 0.0 -
2.1864 24050 0.0 -
2.1909 24100 0.0 -
2.1955 24150 0.0 -
2.2 24200 0.0001 -
2.2045 24250 0.0 -
2.2091 24300 0.0001 -
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Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.36.0
  • PyTorch: 2.0.0
  • Datasets: 2.15.0
  • Tokenizers: 0.15.0

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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