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vipinbansal179
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SetFit_sms_Analyzer1

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
2
  • '840989 otp proceed canara bank mobile banking . valid 15 minute . share otp . - canara bank . kbl8a1ju0mt'
  • 'cheque . 000102 issue riya collection rs . 12,000.00 present / c xxxxx546157 return unpaid insufficient fund . team idfc bank'
  • 'avl bal / c xxxx0959 10 - jul-2022 06:06:24 inr 0.00 . combine avl bal inr 0.00 . use mb app track / c - kotak bank'
0
  • '/ c . xxxxxxxx7146 debit rs.11933.00 16 - 09 - 23 / c xxxxxxxx4716 credit ( imp ref 325908759095 ) . warm regard , yes bank'
  • 'send rs.290.00 kotak bank ac x4524 bharatpe90727843812@yesbankltd 13-10-23.upi ref 328684167136 . , kotak.com/fraud'
  • 'rs.295 transfer / c ... 4322 : lien_marking_fo . total bal : rs.188.8cr . avlbl amt : rs.609.97(28 - 06 - 2022 16:39:53 ) - bank baroda'
1
  • 'rs 15000credite / c xx4524via neft neofirst technology india private- utr ref hsbcn23276508097 ; avail . bal.:rs 215180.62kotak bank'
  • '/ c : xx6775 credit rs.60.00 14 - 11 - 2023 10:47:49 upi - id 8733076955@omni ( upi ref 331800008439).-canara bank'
  • 'rs.28 credit / c ... 7783 upi/323962847509 kiwicashback_ax . total bal : rs.122751.36cr . avlbl amt : rs.94671.36(27 - 08 - 2023 15:37:01 ) - bank baroda'

Evaluation

Metrics

Label Accuracy
all 0.9716

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("vipinbansal179/SetFit_sms_Analyzer1")
# Run inference
preds = model("472448 otp set hdfc bank 4 digit login pin . share otp you?call 18002586161")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 23.17 65
Label Training Sample Count
0 231
1 131
2 338

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (2, 2)
  • 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 0.2945 -
0.0026 50 0.3574 -
0.0052 100 0.2512 -
0.0079 150 0.2319 -
0.0105 200 0.2787 -
0.0131 250 0.2129 -
0.0157 300 0.2189 -
0.0183 350 0.0857 -
0.0210 400 0.0932 -
0.0236 450 0.065 -
0.0262 500 0.0553 -
0.0288 550 0.0674 -
0.0314 600 0.0239 -
0.0341 650 0.0054 -
0.0367 700 0.0025 -
0.0393 750 0.002 -
0.0419 800 0.0007 -
0.0446 850 0.001 -
0.0472 900 0.0008 -
0.0498 950 0.0008 -
0.0524 1000 0.0003 -
0.0550 1050 0.0012 -
0.0577 1100 0.002 -
0.0603 1150 0.0192 -
0.0629 1200 0.0041 -
0.0655 1250 0.0002 -
0.0681 1300 0.0001 -
0.0708 1350 0.0001 -
0.0734 1400 0.0001 -
0.0760 1450 0.0004 -
0.0786 1500 0.0003 -
0.0812 1550 0.0002 -
0.0839 1600 0.0004 -
0.0865 1650 0.0002 -
0.0891 1700 0.0002 -
0.0917 1750 0.0001 -
0.0943 1800 0.0001 -
0.0970 1850 0.0001 -
0.0996 1900 0.0001 -
0.1022 1950 0.0001 -
0.1048 2000 0.0001 -
0.1075 2050 0.0015 -
0.1101 2100 0.0001 -
0.1127 2150 0.0001 -
0.1153 2200 0.0001 -
0.1179 2250 0.0001 -
0.1206 2300 0.0 -
0.1232 2350 0.0001 -
0.1258 2400 0.0 -
0.1284 2450 0.0001 -
0.1310 2500 0.0 -
0.1337 2550 0.0001 -
0.1363 2600 0.0 -
0.1389 2650 0.0001 -
0.1415 2700 0.0 -
0.1441 2750 0.0 -
0.1468 2800 0.0 -
0.1494 2850 0.0 -
0.1520 2900 0.0 -
0.1546 2950 0.0 -
0.1572 3000 0.0 -
0.1599 3050 0.0 -
0.1625 3100 0.0 -
0.1651 3150 0.0 -
0.1677 3200 0.0 -
0.1704 3250 0.0 -
0.1730 3300 0.0 -
0.1756 3350 0.0 -
0.1782 3400 0.0 -
0.1808 3450 0.0 -
0.1835 3500 0.0 -
0.1861 3550 0.0003 -
0.1887 3600 0.0131 -
0.1913 3650 0.0004 -
0.1939 3700 0.0001 -
0.1966 3750 0.0 -
0.1992 3800 0.0001 -
0.2018 3850 0.0002 -
0.2044 3900 0.0 -
0.2070 3950 0.0 -
0.2097 4000 0.0001 -
0.2123 4050 0.0015 -
0.2149 4100 0.0002 -
0.2175 4150 0.0 -
0.2201 4200 0.0 -
0.2228 4250 0.0 -
0.2254 4300 0.0 -
0.2280 4350 0.0 -
0.2306 4400 0.0 -
0.2333 4450 0.0 -
0.2359 4500 0.0 -
0.2385 4550 0.0 -
0.2411 4600 0.0 -
0.2437 4650 0.0 -
0.2464 4700 0.0 -
0.2490 4750 0.0 -
0.2516 4800 0.0 -
0.2542 4850 0.0 -
0.2568 4900 0.0 -
0.2595 4950 0.0 -
0.2621 5000 0.0 -
0.2647 5050 0.0 -
0.2673 5100 0.0 -
0.2699 5150 0.0 -
0.2726 5200 0.0 -
0.2752 5250 0.0 -
0.2778 5300 0.0 -
0.2804 5350 0.0 -
0.2830 5400 0.0 -
0.2857 5450 0.0 -
0.2883 5500 0.0 -
0.2909 5550 0.0 -
0.2935 5600 0.0 -
0.2962 5650 0.0 -
0.2988 5700 0.0 -
0.3014 5750 0.0 -
0.3040 5800 0.0 -
0.3066 5850 0.0 -
0.3093 5900 0.0 -
0.3119 5950 0.0 -
0.3145 6000 0.0 -
0.3171 6050 0.0 -
0.3197 6100 0.0 -
0.3224 6150 0.0 -
0.3250 6200 0.0 -
0.3276 6250 0.0 -
0.3302 6300 0.0 -
0.3328 6350 0.0 -
0.3355 6400 0.0 -
0.3381 6450 0.0 -
0.3407 6500 0.0 -
0.3433 6550 0.0 -
0.3459 6600 0.0 -
0.3486 6650 0.0 -
0.3512 6700 0.0 -
0.3538 6750 0.0 -
0.3564 6800 0.0 -
0.3591 6850 0.0 -
0.3617 6900 0.0 -
0.3643 6950 0.0 -
0.3669 7000 0.0 -
0.3695 7050 0.0 -
0.3722 7100 0.0 -
0.3748 7150 0.0 -
0.3774 7200 0.0 -
0.3800 7250 0.0 -
0.3826 7300 0.0 -
0.3853 7350 0.0 -
0.3879 7400 0.0 -
0.3905 7450 0.0 -
0.3931 7500 0.0 -
0.3957 7550 0.0 -
0.3984 7600 0.0 -
0.4010 7650 0.0 -
0.4036 7700 0.0 -
0.4062 7750 0.0 -
0.4088 7800 0.0 -
0.4115 7850 0.0 -
0.4141 7900 0.0 -
0.4167 7950 0.0 -
0.4193 8000 0.0 -
0.4220 8050 0.0 -
0.4246 8100 0.0 -
0.4272 8150 0.0 -
0.4298 8200 0.0 -
0.4324 8250 0.0 -
0.4351 8300 0.0 -
0.4377 8350 0.0 -
0.4403 8400 0.0 -
0.4429 8450 0.0 -
0.4455 8500 0.0 -
0.4482 8550 0.0 -
0.4508 8600 0.0 -
0.4534 8650 0.0 -
0.4560 8700 0.0 -
0.4586 8750 0.0 -
0.4613 8800 0.0 -
0.4639 8850 0.0 -
0.4665 8900 0.0 -
0.4691 8950 0.0001 -
0.4717 9000 0.0 -
0.4744 9050 0.0 -
0.4770 9100 0.0 -
0.4796 9150 0.0 -
0.4822 9200 0.0 -
0.4849 9250 0.0 -
0.4875 9300 0.0 -
0.4901 9350 0.0 -
0.4927 9400 0.0 -
0.4953 9450 0.0 -
0.4980 9500 0.0 -
0.5006 9550 0.0 -
0.5032 9600 0.0 -
0.5058 9650 0.0 -
0.5084 9700 0.0 -
0.5111 9750 0.0 -
0.5137 9800 0.0 -
0.5163 9850 0.0 -
0.5189 9900 0.0 -
0.5215 9950 0.0 -
0.5242 10000 0.0 -
0.5268 10050 0.0 -
0.5294 10100 0.0 -
0.5320 10150 0.0 -
0.5346 10200 0.0 -
0.5373 10250 0.0 -
0.5399 10300 0.0 -
0.5425 10350 0.0 -
0.5451 10400 0.0 -
0.5478 10450 0.0 -
0.5504 10500 0.0 -
0.5530 10550 0.0 -
0.5556 10600 0.0 -
0.5582 10650 0.0 -
0.5609 10700 0.0 -
0.5635 10750 0.0 -
0.5661 10800 0.0 -
0.5687 10850 0.0 -
0.5713 10900 0.0 -
0.5740 10950 0.0 -
0.5766 11000 0.0 -
0.5792 11050 0.0 -
0.5818 11100 0.0 -
0.5844 11150 0.0 -
0.5871 11200 0.0 -
0.5897 11250 0.0 -
0.5923 11300 0.0 -
0.5949 11350 0.0 -
0.5975 11400 0.0 -
0.6002 11450 0.0 -
0.6028 11500 0.0 -
0.6054 11550 0.0 -
0.6080 11600 0.0 -
0.6107 11650 0.0 -
0.6133 11700 0.0 -
0.6159 11750 0.0 -
0.6185 11800 0.0 -
0.6211 11850 0.0 -
0.6238 11900 0.0 -
0.6264 11950 0.0 -
0.6290 12000 0.0 -
0.6316 12050 0.0 -
0.6342 12100 0.0 -
0.6369 12150 0.0 -
0.6395 12200 0.0 -
0.6421 12250 0.0 -
0.6447 12300 0.0 -
0.6473 12350 0.0 -
0.6500 12400 0.0 -
0.6526 12450 0.0 -
0.6552 12500 0.0 -
0.6578 12550 0.0 -
0.6604 12600 0.0 -
0.6631 12650 0.0 -
0.6657 12700 0.0 -
0.6683 12750 0.0 -
0.6709 12800 0.0 -
0.6736 12850 0.0 -
0.6762 12900 0.0 -
0.6788 12950 0.0 -
0.6814 13000 0.0 -
0.6840 13050 0.0 -
0.6867 13100 0.0 -
0.6893 13150 0.0 -
0.6919 13200 0.0 -
0.6945 13250 0.0 -
0.6971 13300 0.0 -
0.6998 13350 0.0 -
0.7024 13400 0.0 -
0.7050 13450 0.0 -
0.7076 13500 0.0 -
0.7102 13550 0.0 -
0.7129 13600 0.0 -
0.7155 13650 0.0 -
0.7181 13700 0.0 -
0.7207 13750 0.0 -
0.7233 13800 0.0 -
0.7260 13850 0.0 -
0.7286 13900 0.0 -
0.7312 13950 0.0 -
0.7338 14000 0.0 -
0.7365 14050 0.0 -
0.7391 14100 0.0 -
0.7417 14150 0.0 -
0.7443 14200 0.0 -
0.7469 14250 0.0 -
0.7496 14300 0.0 -
0.7522 14350 0.0 -
0.7548 14400 0.0 -
0.7574 14450 0.0 -
0.7600 14500 0.0 -
0.7627 14550 0.0 -
0.7653 14600 0.0 -
0.7679 14650 0.0 -
0.7705 14700 0.0 -
0.7731 14750 0.0 -
0.7758 14800 0.0 -
0.7784 14850 0.0 -
0.7810 14900 0.0 -
0.7836 14950 0.0 -
0.7862 15000 0.0 -
0.7889 15050 0.0 -
0.7915 15100 0.0 -
0.7941 15150 0.0 -
0.7967 15200 0.0 -
0.7994 15250 0.0 -
0.8020 15300 0.0 -
0.8046 15350 0.0 -
0.8072 15400 0.0 -
0.8098 15450 0.0 -
0.8125 15500 0.0 -
0.8151 15550 0.0 -
0.8177 15600 0.0 -
0.8203 15650 0.0 -
0.8229 15700 0.0 -
0.8256 15750 0.0 -
0.8282 15800 0.0 -
0.8308 15850 0.0 -
0.8334 15900 0.0 -
0.8360 15950 0.0 -
0.8387 16000 0.0 -
0.8413 16050 0.0 -
0.8439 16100 0.0 -
0.8465 16150 0.0 -
0.8491 16200 0.0 -
0.8518 16250 0.0 -
0.8544 16300 0.0 -
0.8570 16350 0.0 -
0.8596 16400 0.0 -
0.8622 16450 0.0 -
0.8649 16500 0.0 -
0.8675 16550 0.0 -
0.8701 16600 0.0 -
0.8727 16650 0.0 -
0.8754 16700 0.0 -
0.8780 16750 0.0 -
0.8806 16800 0.0 -
0.8832 16850 0.0 -
0.8858 16900 0.0 -
0.8885 16950 0.0 -
0.8911 17000 0.0 -
0.8937 17050 0.0 -
0.8963 17100 0.0 -
0.8989 17150 0.0 -
0.9016 17200 0.0 -
0.9042 17250 0.0 -
0.9068 17300 0.0 -
0.9094 17350 0.0 -
0.9120 17400 0.0 -
0.9147 17450 0.0 -
0.9173 17500 0.0 -
0.9199 17550 0.0 -
0.9225 17600 0.0 -
0.9251 17650 0.0 -
0.9278 17700 0.0 -
0.9304 17750 0.0 -
0.9330 17800 0.0 -
0.9356 17850 0.0 -
0.9383 17900 0.0 -
0.9409 17950 0.0 -
0.9435 18000 0.0 -
0.9461 18050 0.0 -
0.9487 18100 0.0 -
0.9514 18150 0.0 -
0.9540 18200 0.0 -
0.9566 18250 0.0 -
0.9592 18300 0.0 -
0.9618 18350 0.0 -
0.9645 18400 0.0 -
0.9671 18450 0.0 -
0.9697 18500 0.0 -
0.9723 18550 0.0 -
0.9749 18600 0.0 -
0.9776 18650 0.0 -
0.9802 18700 0.0 -
0.9828 18750 0.0 -
0.9854 18800 0.0 -
0.9880 18850 0.0 -
0.9907 18900 0.0 -
0.9933 18950 0.0 -
0.9959 19000 0.0 -
0.9985 19050 0.0 -
1.0 19078 - 0.0437
1.0012 19100 0.0 -
1.0038 19150 0.0 -
1.0064 19200 0.0 -
1.0090 19250 0.0 -
1.0116 19300 0.0 -
1.0143 19350 0.0 -
1.0169 19400 0.0 -
1.0195 19450 0.3698 -
1.0221 19500 0.1546 -
1.0247 19550 0.0179 -
1.0274 19600 0.0004 -
1.0300 19650 0.0005 -
1.0326 19700 0.0 -
1.0352 19750 0.0002 -
1.0378 19800 0.0 -
1.0405 19850 0.0 -
1.0431 19900 0.0 -
1.0457 19950 0.0002 -
1.0483 20000 0.0011 -
1.0509 20050 0.0 -
1.0536 20100 0.0 -
1.0562 20150 0.0 -
1.0588 20200 0.0003 -
1.0614 20250 0.0 -
1.0641 20300 0.0003 -
1.0667 20350 0.0003 -
1.0693 20400 0.0 -
1.0719 20450 0.0 -
1.0745 20500 0.0 -
1.0772 20550 0.0 -
1.0798 20600 0.0 -
1.0824 20650 0.0 -
1.0850 20700 0.0 -
1.0876 20750 0.0 -
1.0903 20800 0.0 -
1.0929 20850 0.0 -
1.0955 20900 0.0 -
1.0981 20950 0.0 -
1.1007 21000 0.0 -
1.1034 21050 0.0 -
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1.4283 27250 0.0 -
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1.5437 29450 0.0 -
1.5463 29500 0.0 -
1.5489 29550 0.0 -
1.5515 29600 0.0 -
1.5541 29650 0.0 -
1.5568 29700 0.0 -
1.5594 29750 0.0 -
1.5620 29800 0.0 -
1.5646 29850 0.0 -
1.5673 29900 0.0 -
1.5699 29950 0.0 -
1.5725 30000 0.0 -
1.5751 30050 0.0 -
1.5777 30100 0.0 -
1.5804 30150 0.0 -
1.5830 30200 0.0 -
1.5856 30250 0.0 -
1.5882 30300 0.0 -
1.5908 30350 0.0 -
1.5935 30400 0.0 -
1.5961 30450 0.0 -
1.5987 30500 0.0 -
1.6013 30550 0.0 -
1.6039 30600 0.0 -
1.6066 30650 0.0 -
1.6092 30700 0.0 -
1.6118 30750 0.0 -
1.6144 30800 0.0 -
1.6170 30850 0.0 -
1.6197 30900 0.0 -
1.6223 30950 0.0 -
1.6249 31000 0.0 -
1.6275 31050 0.0 -
1.6301 31100 0.0 -
1.6328 31150 0.0 -
1.6354 31200 0.0 -
1.6380 31250 0.0 -
1.6406 31300 0.0 -
1.6433 31350 0.0 -
1.6459 31400 0.0 -
1.6485 31450 0.0 -
1.6511 31500 0.0 -
1.6537 31550 0.0 -
1.6564 31600 0.0 -
1.6590 31650 0.0 -
1.6616 31700 0.0 -
1.6642 31750 0.0 -
1.6668 31800 0.0 -
1.6695 31850 0.0 -
1.6721 31900 0.0 -
1.6747 31950 0.0 -
1.6773 32000 0.0 -
1.6799 32050 0.0 -
1.6826 32100 0.0 -
1.6852 32150 0.0 -
1.6878 32200 0.0 -
1.6904 32250 0.0 -
1.6930 32300 0.0 -
1.6957 32350 0.0 -
1.6983 32400 0.0 -
1.7009 32450 0.0 -
1.7035 32500 0.0 -
1.7062 32550 0.0 -
1.7088 32600 0.0 -
1.7114 32650 0.0 -
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1.7219 32850 0.0 -
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1.7297 33000 0.0 -
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1.7350 33100 0.0 -
1.7376 33150 0.0 -
1.7402 33200 0.0 -
1.7428 33250 0.0 -
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1.7612 33600 0.0 -
1.7638 33650 0.0 -
1.7664 33700 0.0 -
1.7691 33750 0.0 -
1.7717 33800 0.0 -
1.7743 33850 0.0 -
1.7769 33900 0.0 -
1.7795 33950 0.0 -
1.7822 34000 0.0 -
1.7848 34050 0.0 -
1.7874 34100 0.0 -
1.7900 34150 0.0 -
1.7926 34200 0.0 -
1.7953 34250 0.0 -
1.7979 34300 0.0 -
1.8005 34350 0.0 -
1.8031 34400 0.0 -
1.8057 34450 0.0 -
1.8084 34500 0.0 -
1.8110 34550 0.0 -
1.8136 34600 0.0 -
1.8162 34650 0.0 -
1.8188 34700 0.0 -
1.8215 34750 0.0 -
1.8241 34800 0.0 -
1.8267 34850 0.0 -
1.8293 34900 0.0 -
1.8320 34950 0.0 -
1.8346 35000 0.0 -
1.8372 35050 0.0 -
1.8398 35100 0.0 -
1.8424 35150 0.0 -
1.8451 35200 0.0 -
1.8477 35250 0.0 -
1.8503 35300 0.0 -
1.8529 35350 0.0 -
1.8555 35400 0.0 -
1.8582 35450 0.0 -
1.8608 35500 0.0 -
1.8634 35550 0.0 -
1.8660 35600 0.0 -
1.8686 35650 0.0 -
1.8713 35700 0.0 -
1.8739 35750 0.0 -
1.8765 35800 0.0 -
1.8791 35850 0.0 -
1.8817 35900 0.0 -
1.8844 35950 0.0 -
1.8870 36000 0.0 -
1.8896 36050 0.0 -
1.8922 36100 0.0 -
1.8949 36150 0.0 -
1.8975 36200 0.0 -
1.9001 36250 0.0 -
1.9027 36300 0.0 -
1.9053 36350 0.0 -
1.9080 36400 0.0 -
1.9106 36450 0.0 -
1.9132 36500 0.0 -
1.9158 36550 0.0 -
1.9184 36600 0.0 -
1.9211 36650 0.0 -
1.9237 36700 0.0 -
1.9263 36750 0.0 -
1.9289 36800 0.0 -
1.9315 36850 0.0 -
1.9342 36900 0.0 -
1.9368 36950 0.0 -
1.9394 37000 0.0 -
1.9420 37050 0.0 -
1.9446 37100 0.0 -
1.9473 37150 0.0 -
1.9499 37200 0.0 -
1.9525 37250 0.0 -
1.9551 37300 0.0 -
1.9578 37350 0.0 -
1.9604 37400 0.0 -
1.9630 37450 0.0 -
1.9656 37500 0.0 -
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1.9709 37600 0.0 -
1.9735 37650 0.0 -
1.9761 37700 0.0 -
1.9787 37750 0.0 -
1.9813 37800 0.0 -
1.9840 37850 0.0 -
1.9866 37900 0.0 -
1.9892 37950 0.0 -
1.9918 38000 0.0 -
1.9944 38050 0.0 -
1.9971 38100 0.0 -
1.9997 38150 0.0 -
2.0 38156 - 0.0438
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.36.0
  • PyTorch: 2.0.0
  • Datasets: 2.16.1
  • 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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