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rafi138
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setfit-paraphrase-mpnet-base-v2-business-type

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
Landmark
  • 'Rajarbagh Shahjahanpur Moar'
  • '3 Number Mp Checkpost'
  • 'Akua Madrasha Quarter Rail Crossing'
Religious Place
  • 'Csd Masjid Jashore Cantonment'
  • 'Baytut Tayyib Jame Masjid'
  • 'Court Para Jame Masjid'
Residential
  • 'Matrichaya'
  • 'Chowdhury Mansion'
  • 'Nabila Villa'
Government
  • 'Rapid Action Battalion 3 (RAB)'
  • 'Bangladesh Muktijoddha Sangsad Tejgaon Industrial Area Unit Command'
  • 'Subhanighat Police Outpost'
Recreation
  • '5h Avenue Convention Hall'
  • 'Purobi Chinema Hall'
  • 'Shotodol Club'
Commercial
  • 'Bikers Racing Zone'
  • 'Sr Parcel Service Limited'
  • 'Vegetables Market'
Education
  • 'Dinajpur Polytechnic Institute'
  • 'Gazipur Central Public Management Institute'
  • 'Maskatadighi Government Primary School'
Bank
  • 'Commercial Bank Of Ceylon Plc Shantinagar'
  • 'Social Islami Bank Limited (SIBL) Mohammadpur'
  • 'Dhaka Bank Limited Jashore Sadar'
Fuel
  • 'Ferdaous Enterprise'
  • 'Ahmed Bd Trade'
  • 'Hasan Filling Station'
Hotel
  • 'Hotel Arafat'
  • 'World Beach Resort'
  • 'Hotel Chalontika'
Shop
  • 'Janosheba Medico'
  • 'New Mone Rekho Tailors'
  • 'Man Tailors'
Utility
  • 'Pole No 86'
  • 'Pole No 239'
  • 'Salimullah Road Water Pump'
Construction
  • 'N J Enterprise'
  • 'Bk Traders'
  • 'Sohel Glass'
Agricultural
  • 'Mango Seedlings'
  • 'Amaia Garden'
  • 'Gardey'
Office
  • 'Dalik Lekhak And Serviour'
  • 'Assistant Police Commissioner Traffic Mohammadpur Zone Office'
  • 'Challenger Elevator And Escalator Limited'
Healthcare
  • 'Afroz Homeo Hall'
  • 'Expert Dental Solution'
  • 'Bhai Bon Homeo Hall'
Transportation
  • 'Ideal Parking Jone'
  • 'Hanif Bus Counter'
  • 'Five Star Transport Agency'
Food
  • 'Madhuban Hotel'
  • 'Al Hamza Biriyani House'
  • 'Gramer Bari'
Industry
  • 'Rony Group'
  • 'Net Factory'
  • 'Kumudini Welfare Trust Of Bengal Bd Limited'

Evaluation

Metrics

Label Accuracy
all 0.59

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("rafi138/setfit-paraphrase-mpnet-base-v2-business-type")
# Run inference
preds = model("Dadon Hotel")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 3.3752 11
Label Training Sample Count
ShopCommercialGovernmentHealthcareEducationFoodOfficeReligious PlaceBankTransportationConstructionIndustryResidentialLandmarkRecreationFuelHotelUtilityAgricultural 0

Training Hyperparameters

  • batch_size: (64, 64)
  • num_epochs: (4, 4)
  • 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
0.0000 1 0.2815 -
0.0019 50 0.2543 -
0.0038 100 0.2781 -
0.0057 150 0.2328 -
0.0076 200 0.2258 -
0.0095 250 0.1884 -
0.0115 300 0.2192 -
0.0134 350 0.1935 -
0.0153 400 0.2057 -
0.0172 450 0.1949 -
0.0191 500 0.1955 -
0.0210 550 0.1744 -
0.0229 600 0.1518 -
0.0248 650 0.1804 -
0.0267 700 0.1669 -
0.0286 750 0.1565 -
0.0306 800 0.14 -
0.0325 850 0.1648 -
0.0344 900 0.1441 -
0.0363 950 0.1237 -
0.0382 1000 0.1155 -
0.0401 1050 0.1303 -
0.0420 1100 0.1115 -
0.0439 1150 0.1533 -
0.0458 1200 0.1278 -
0.0477 1250 0.0851 -
0.0496 1300 0.0982 -
0.0516 1350 0.0925 -
0.0535 1400 0.0861 -
0.0554 1450 0.1121 -
0.0573 1500 0.1226 -
0.0592 1550 0.1049 -
0.0611 1600 0.1101 -
0.0630 1650 0.113 -
0.0649 1700 0.1061 -
0.0668 1750 0.0573 -
0.0687 1800 0.0665 -
0.0707 1850 0.0657 -
0.0726 1900 0.082 -
0.0745 1950 0.0654 -
0.0764 2000 0.0783 -
0.0783 2050 0.0713 -
0.0802 2100 0.0669 -
0.0821 2150 0.0536 -
0.0840 2200 0.0556 -
0.0859 2250 0.0704 -
0.0878 2300 0.063 -
0.0897 2350 0.0472 -
0.0917 2400 0.0562 -
0.0936 2450 0.0392 -
0.0955 2500 0.0465 -
0.0974 2550 0.0424 -
0.0993 2600 0.0453 -
0.1012 2650 0.0375 -
0.1031 2700 0.0445 -
0.1050 2750 0.0231 -
0.1069 2800 0.0386 -
0.1088 2850 0.0258 -
0.1108 2900 0.0344 -
0.1127 2950 0.0143 -
0.1146 3000 0.016 -
0.1165 3050 0.0216 -
0.1184 3100 0.0365 -
0.1203 3150 0.0238 -
0.1222 3200 0.0099 -
0.1241 3250 0.0211 -
0.1260 3300 0.0149 -
0.1279 3350 0.0406 -
0.1298 3400 0.0231 -
0.1318 3450 0.0266 -
0.1337 3500 0.0225 -
0.1356 3550 0.0203 -
0.1375 3600 0.0097 -
0.1394 3650 0.0143 -
0.1413 3700 0.0052 -
0.1432 3750 0.0094 -
0.1451 3800 0.0029 -
0.1470 3850 0.0065 -
0.1489 3900 0.0078 -
0.1508 3950 0.0109 -
0.1528 4000 0.0054 -
0.1547 4050 0.0069 -
0.1566 4100 0.0248 -
0.1585 4150 0.0069 -
0.1604 4200 0.0073 -
0.1623 4250 0.0054 -
0.1642 4300 0.0027 -
0.1661 4350 0.0028 -
0.1680 4400 0.0021 -
0.1699 4450 0.0038 -
0.1719 4500 0.0034 -
0.1738 4550 0.0158 -
0.1757 4600 0.0043 -
0.1776 4650 0.0055 -
0.1795 4700 0.0064 -
0.1814 4750 0.0021 -
0.1833 4800 0.0026 -
0.1852 4850 0.0112 -
0.1871 4900 0.0029 -
0.1890 4950 0.0176 -
0.1909 5000 0.0071 -
0.1929 5050 0.0097 -
0.1948 5100 0.0032 -
0.1967 5150 0.0044 -
0.1986 5200 0.0065 -
0.2005 5250 0.0176 -
0.2024 5300 0.0026 -
0.2043 5350 0.0036 -
0.2062 5400 0.0022 -
0.2081 5450 0.0014 -
0.2100 5500 0.0073 -
0.2120 5550 0.0009 -
0.2139 5600 0.0011 -
0.2158 5650 0.0022 -
0.2177 5700 0.0011 -
0.2196 5750 0.0074 -
0.2215 5800 0.0114 -
0.2234 5850 0.0013 -
0.2253 5900 0.0009 -
0.2272 5950 0.0018 -
0.2291 6000 0.001 -
0.2310 6050 0.0013 -
0.2330 6100 0.0058 -
0.2349 6150 0.0012 -
0.2368 6200 0.0013 -
0.2387 6250 0.0009 -
0.2406 6300 0.0012 -
0.2425 6350 0.0006 -
0.2444 6400 0.0013 -
0.2463 6450 0.0005 -
0.2482 6500 0.0032 -
0.2501 6550 0.0006 -
0.2521 6600 0.0007 -
0.2540 6650 0.0029 -
0.2559 6700 0.001 -
0.2578 6750 0.0005 -
0.2597 6800 0.001 -
0.2616 6850 0.0004 -
0.2635 6900 0.0007 -
0.2654 6950 0.0006 -
0.2673 7000 0.0034 -
0.2692 7050 0.0006 -
0.2711 7100 0.0006 -
0.2731 7150 0.0013 -
0.2750 7200 0.0017 -
0.2769 7250 0.0006 -
0.2788 7300 0.0007 -
0.2807 7350 0.0005 -
0.2826 7400 0.0009 -
0.2845 7450 0.0007 -
0.2864 7500 0.0008 -
0.2883 7550 0.001 -
0.2902 7600 0.0004 -
0.2922 7650 0.0004 -
0.2941 7700 0.0007 -
0.2960 7750 0.0004 -
0.2979 7800 0.0011 -
0.2998 7850 0.0005 -
0.3017 7900 0.0004 -
0.3036 7950 0.0003 -
0.3055 8000 0.001 -
0.3074 8050 0.0005 -
0.3093 8100 0.0003 -
0.3112 8150 0.0004 -
0.3132 8200 0.0003 -
0.3151 8250 0.007 -
0.3170 8300 0.0026 -
0.3189 8350 0.0004 -
0.3208 8400 0.0005 -
0.3227 8450 0.0005 -
0.3246 8500 0.0004 -
0.3265 8550 0.0003 -
0.3284 8600 0.0003 -
0.3303 8650 0.0003 -
0.3323 8700 0.0026 -
0.3342 8750 0.0003 -
0.3361 8800 0.0002 -
0.3380 8850 0.0002 -
0.3399 8900 0.0002 -
0.3418 8950 0.0004 -
0.3437 9000 0.0003 -
0.3456 9050 0.0005 -
0.3475 9100 0.0002 -
0.3494 9150 0.0052 -
0.3513 9200 0.0138 -
0.3533 9250 0.0006 -
0.3552 9300 0.0004 -
0.3571 9350 0.0019 -
0.3590 9400 0.0003 -
0.3609 9450 0.0002 -
0.3628 9500 0.0002 -
0.3647 9550 0.0003 -
0.3666 9600 0.0003 -
0.3685 9650 0.0269 -
0.3704 9700 0.0004 -
0.3724 9750 0.0002 -
0.3743 9800 0.0005 -
0.3762 9850 0.0002 -
0.3781 9900 0.0003 -
0.3800 9950 0.0002 -
0.3819 10000 0.0002 -
0.3838 10050 0.0002 -
0.3857 10100 0.0158 -
0.3876 10150 0.0004 -
0.3895 10200 0.0094 -
0.3914 10250 0.0098 -
0.3934 10300 0.0003 -
0.3953 10350 0.0003 -
0.3972 10400 0.0004 -
0.3991 10450 0.0003 -
0.4010 10500 0.0003 -
0.4029 10550 0.0002 -
0.4048 10600 0.0006 -
0.4067 10650 0.0004 -
0.4086 10700 0.0002 -
0.4105 10750 0.0005 -
0.4124 10800 0.0002 -
0.4144 10850 0.0002 -
0.4163 10900 0.0002 -
0.4182 10950 0.0002 -
0.4201 11000 0.0002 -
0.4220 11050 0.0002 -
0.4239 11100 0.0002 -
0.4258 11150 0.0002 -
0.4277 11200 0.0002 -
0.4296 11250 0.0002 -
0.4315 11300 0.0001 -
0.4335 11350 0.0001 -
0.4354 11400 0.0001 -
0.4373 11450 0.0002 -
0.4392 11500 0.0001 -
0.4411 11550 0.0002 -
0.4430 11600 0.0001 -
0.4449 11650 0.0001 -
0.4468 11700 0.0002 -
0.4487 11750 0.0001 -
0.4506 11800 0.0001 -
0.4525 11850 0.0002 -
0.4545 11900 0.0006 -
0.4564 11950 0.0002 -
0.4583 12000 0.0001 -
0.4602 12050 0.0001 -
0.4621 12100 0.0002 -
0.4640 12150 0.0002 -
0.4659 12200 0.0001 -
0.4678 12250 0.0001 -
0.4697 12300 0.0002 -
0.4716 12350 0.0004 -
0.4736 12400 0.0003 -
0.4755 12450 0.0279 -
0.4774 12500 0.0072 -
0.4793 12550 0.006 -
0.4812 12600 0.0325 -
0.4831 12650 0.0012 -
0.4850 12700 0.0015 -
0.4869 12750 0.0013 -
0.4888 12800 0.0002 -
0.4907 12850 0.0003 -
0.4926 12900 0.0003 -
0.4946 12950 0.001 -
0.4965 13000 0.0004 -
0.4984 13050 0.0002 -
0.5003 13100 0.0002 -
0.5022 13150 0.0002 -
0.5041 13200 0.0003 -
0.5060 13250 0.0001 -
0.5079 13300 0.0001 -
0.5098 13350 0.0002 -
0.5117 13400 0.0001 -
0.5137 13450 0.0002 -
0.5156 13500 0.0001 -
0.5175 13550 0.0002 -
0.5194 13600 0.0002 -
0.5213 13650 0.0002 -
0.5232 13700 0.0001 -
0.5251 13750 0.0002 -
0.5270 13800 0.0002 -
0.5289 13850 0.0001 -
0.5308 13900 0.0001 -
0.5327 13950 0.0002 -
0.5347 14000 0.0001 -
0.5366 14050 0.0001 -
0.5385 14100 0.0001 -
0.5404 14150 0.0001 -
0.5423 14200 0.0149 -
0.5442 14250 0.0002 -
0.5461 14300 0.0009 -
0.5480 14350 0.0057 -
0.5499 14400 0.0089 -
0.5518 14450 0.0154 -
0.5538 14500 0.0003 -
0.5557 14550 0.0016 -
0.5576 14600 0.0001 -
0.5595 14650 0.0001 -
0.5614 14700 0.0002 -
0.5633 14750 0.0002 -
0.5652 14800 0.0004 -
0.5671 14850 0.0004 -
0.5690 14900 0.0004 -
0.5709 14950 0.0002 -
0.5728 15000 0.0001 -
0.5748 15050 0.0001 -
0.5767 15100 0.0002 -
0.5786 15150 0.0002 -
0.5805 15200 0.0001 -
0.5824 15250 0.0002 -
0.5843 15300 0.0001 -
0.5862 15350 0.0002 -
0.5881 15400 0.0001 -
0.5900 15450 0.0113 -
0.5919 15500 0.0002 -
0.5939 15550 0.0002 -
0.5958 15600 0.0001 -
0.5977 15650 0.0001 -
0.5996 15700 0.0004 -
0.6015 15750 0.0009 -
0.6034 15800 0.001 -
0.6053 15850 0.0002 -
0.6072 15900 0.0002 -
0.6091 15950 0.0003 -
0.6110 16000 0.0002 -
0.6129 16050 0.0004 -
0.6149 16100 0.0001 -
0.6168 16150 0.0001 -
0.6187 16200 0.0001 -
0.6206 16250 0.0001 -
0.6225 16300 0.0001 -
0.6244 16350 0.0001 -
0.6263 16400 0.0001 -
0.6282 16450 0.0008 -
0.6301 16500 0.0001 -
0.6320 16550 0.0001 -
0.6340 16600 0.0001 -
0.6359 16650 0.0001 -
0.6378 16700 0.0001 -
0.6397 16750 0.0001 -
0.6416 16800 0.0001 -
0.6435 16850 0.0004 -
0.6454 16900 0.0001 -
0.6473 16950 0.0001 -
0.6492 17000 0.0001 -
0.6511 17050 0.0001 -
0.6530 17100 0.0001 -
0.6550 17150 0.0003 -
0.6569 17200 0.0003 -
0.6588 17250 0.0002 -
0.6607 17300 0.0002 -
0.6626 17350 0.0011 -
0.6645 17400 0.0003 -
0.6664 17450 0.0001 -
0.6683 17500 0.0003 -
0.6702 17550 0.0001 -
0.6721 17600 0.0002 -
0.6741 17650 0.0001 -
0.6760 17700 0.0001 -
0.6779 17750 0.0001 -
0.6798 17800 0.0001 -
0.6817 17850 0.0001 -
0.6836 17900 0.0001 -
0.6855 17950 0.0001 -
0.6874 18000 0.0001 -
0.6893 18050 0.0001 -
0.6912 18100 0.0001 -
0.6931 18150 0.0023 -
0.6951 18200 0.0001 -
0.6970 18250 0.0001 -
0.6989 18300 0.0001 -
0.7008 18350 0.0001 -
0.7027 18400 0.0001 -
0.7046 18450 0.0001 -
0.7065 18500 0.0001 -
0.7084 18550 0.0001 -
0.7103 18600 0.0001 -
0.7122 18650 0.0001 -
0.7141 18700 0.0001 -
0.7161 18750 0.0001 -
0.7180 18800 0.0001 -
0.7199 18850 0.0001 -
0.7218 18900 0.0001 -
0.7237 18950 0.0001 -
0.7256 19000 0.0001 -
0.7275 19050 0.0001 -
0.7294 19100 0.0104 -
0.7313 19150 0.0041 -
0.7332 19200 0.0045 -
0.7352 19250 0.0002 -
0.7371 19300 0.0007 -
0.7390 19350 0.0021 -
0.7409 19400 0.0001 -
0.7428 19450 0.0001 -
0.7447 19500 0.0001 -
0.7466 19550 0.0001 -
0.7485 19600 0.0001 -
0.7504 19650 0.0001 -
0.7523 19700 0.0001 -
0.7542 19750 0.0001 -
0.7562 19800 0.0001 -
0.7581 19850 0.0001 -
0.7600 19900 0.0001 -
0.7619 19950 0.0001 -
0.7638 20000 0.0001 -
0.7657 20050 0.0001 -
0.7676 20100 0.0001 -
0.7695 20150 0.0001 -
0.7714 20200 0.0001 -
0.7733 20250 0.0001 -
0.7753 20300 0.0001 -
0.7772 20350 0.0001 -
0.7791 20400 0.0001 -
0.7810 20450 0.0001 -
0.7829 20500 0.0001 -
0.7848 20550 0.0001 -
0.7867 20600 0.0072 -
0.7886 20650 0.0034 -
0.7905 20700 0.0001 -
0.7924 20750 0.0002 -
0.7943 20800 0.0001 -
0.7963 20850 0.0001 -
0.7982 20900 0.0004 -
0.8001 20950 0.0001 -
0.8020 21000 0.0001 -
0.8039 21050 0.0001 -
0.8058 21100 0.0001 -
0.8077 21150 0.0001 -
0.8096 21200 0.0001 -
0.8115 21250 0.0001 -
0.8134 21300 0.0001 -
0.8154 21350 0.0001 -
0.8173 21400 0.0001 -
0.8192 21450 0.0001 -
0.8211 21500 0.0002 -
0.8230 21550 0.0002 -
0.8249 21600 0.0001 -
0.8268 21650 0.0001 -
0.8287 21700 0.0001 -
0.8306 21750 0.0001 -
0.8325 21800 0.0001 -
0.8344 21850 0.0019 -
0.8364 21900 0.0001 -
0.8383 21950 0.0001 -
0.8402 22000 0.0001 -
0.8421 22050 0.0001 -
0.8440 22100 0.0001 -
0.8459 22150 0.0001 -
0.8478 22200 0.0001 -
0.8497 22250 0.0001 -
0.8516 22300 0.0001 -
0.8535 22350 0.0001 -
0.8555 22400 0.0001 -
0.8574 22450 0.0001 -
0.8593 22500 0.002 -
0.8612 22550 0.0001 -
0.8631 22600 0.0001 -
0.8650 22650 0.0001 -
0.8669 22700 0.0001 -
0.8688 22750 0.0001 -
0.8707 22800 0.0001 -
0.8726 22850 0.0001 -
0.8745 22900 0.0001 -
0.8765 22950 0.0001 -
0.8784 23000 0.0001 -
0.8803 23050 0.0001 -
0.8822 23100 0.0001 -
0.8841 23150 0.0001 -
0.8860 23200 0.0001 -
0.8879 23250 0.0001 -
0.8898 23300 0.0001 -
0.8917 23350 0.0001 -
0.8936 23400 0.0001 -
0.8956 23450 0.0001 -
0.8975 23500 0.0001 -
0.8994 23550 0.0001 -
0.9013 23600 0.0001 -
0.9032 23650 0.0001 -
0.9051 23700 0.0001 -
0.9070 23750 0.0001 -
0.9089 23800 0.0001 -
0.9108 23850 0.0001 -
0.9127 23900 0.0001 -
0.9146 23950 0.0001 -
0.9166 24000 0.0001 -
0.9185 24050 0.0001 -
0.9204 24100 0.0015 -
0.9223 24150 0.0001 -
0.9242 24200 0.0009 -
0.9261 24250 0.0002 -
0.9280 24300 0.0001 -
0.9299 24350 0.0001 -
0.9318 24400 0.0001 -
0.9337 24450 0.0001 -
0.9357 24500 0.0001 -
0.9376 24550 0.0001 -
0.9395 24600 0.0001 -
0.9414 24650 0.0004 -
0.9433 24700 0.0001 -
0.9452 24750 0.0001 -
0.9471 24800 0.0001 -
0.9490 24850 0.0001 -
0.9509 24900 0.0001 -
0.9528 24950 0.0001 -
0.9547 25000 0.0001 -
0.9567 25050 0.0001 -
0.9586 25100 0.0001 -
0.9605 25150 0.0001 -
0.9624 25200 0.0001 -
0.9643 25250 0.0001 -
0.9662 25300 0.0001 -
0.9681 25350 0.0001 -
0.9700 25400 0.0001 -
0.9719 25450 0.0001 -
0.9738 25500 0.0001 -
0.9757 25550 0.0001 -
0.9777 25600 0.0277 -
0.9796 25650 0.0008 -
0.9815 25700 0.0001 -
0.9834 25750 0.0001 -
0.9853 25800 0.0001 -
0.9872 25850 0.0001 -
0.9891 25900 0.0001 -
0.9910 25950 0.0001 -
0.9929 26000 0.0001 -
0.9948 26050 0.0001 -
0.9968 26100 0.0001 -
0.9987 26150 0.0001 -
1.0 26185 - 0.3529
1.0006 26200 0.0001 -
1.0025 26250 0.0001 -
1.0044 26300 0.0001 -
1.0063 26350 0.0001 -
1.0082 26400 0.0001 -
1.0101 26450 0.0001 -
1.0120 26500 0.0001 -
1.0139 26550 0.0001 -
1.0158 26600 0.0001 -
1.0178 26650 0.0001 -
1.0197 26700 0.0001 -
1.0216 26750 0.0001 -
1.0235 26800 0.0001 -
1.0254 26850 0.0001 -
1.0273 26900 0.0001 -
1.0292 26950 0.0001 -
1.0311 27000 0.0001 -
1.0330 27050 0.0001 -
1.0349 27100 0.0002 -
1.0369 27150 0.0001 -
1.0388 27200 0.0001 -
1.0407 27250 0.0001 -
1.0426 27300 0.0001 -
1.0445 27350 0.0001 -
1.0464 27400 0.0001 -
1.0483 27450 0.0001 -
1.0502 27500 0.0001 -
1.0521 27550 0.0001 -
1.0540 27600 0.0001 -
1.0559 27650 0.0001 -
1.0579 27700 0.0001 -
1.0598 27750 0.0001 -
1.0617 27800 0.0001 -
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4.0 104740 - 0.3749
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.2.2
  • Transformers: 4.36.2
  • PyTorch: 2.1.2+cu121
  • Datasets: 2.16.1
  • 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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