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Gopal2002
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SERVICE_LARGE_MODEL_ZEON

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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SetFit with BAAI/bge-small-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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
0
  • 'GATE ENTRY PASS\n\nae Hirakud Power - 363 12\noe pame:- (44) al: ‘bx stavelen SI.No. :- / ‘5\n\nMe sane: bey i td Bate O$ 0S) 22\npals ony poe a 7 << Fe Shift :-\n\nApproved Man Power :- feel aes Pass No. :-\n\n \n\n \n\npat\n\npetan & sé dutity\n\x0c'
  • ' \n\nLEPTH 2L09.49 Ling>@\n\nxP y\nALTAD. Catiima = —P\nDATE SEE COTUNG —RWOD_\n\n§ 26.09.17 ODM: + METAL PAD Cot ny\n\n74..9 4° o2 Wm. ‘+ -d- _ A:\n\naa 09 09. AZ OD. aw "le de. ——\e-\n\npam 29-99-19 _—Surhoy\n\nBz. 09-19 01 we d~ etre <
2
  • " \n\nSAMALESWARI CONSTRUCTION\n\nAT-BUDAKATA , PO- GADAMUNDA\nHIRAKUD, DIST: SAMBALPUR\ndetails of receipient (billed to )\nHINDALCO INDUSTRIES LTD.\nHIRAKUD POWER ,\n\n \n \n\n \n\nMOBILE NO. : 9178245293\n\n \n \n \n \n\n \n\n \n \n\n \n\nTAX INVOICE\n(ISSUEDUNDER RULE 46 OF GST/OGST RULE,2017)\n\n \n \n \n \n\nSAMBALPUR -768016\n\n \n\nINVOICE NO. SC/AP/772/2020\n\n \n \n \n \n\n \n \n \n\n21\n21AAACH1201R1ZZ\nAAACH1201R\nDETAILS OF COSIGNEE (SHIPPED }\nHINDAL CO INDUSTRIES LTD\nHIRAKUD POWER\n\n
1
  • ' \n\n \n\nGSTIN: 21AAACH1201R1ZZ\nDUSTRIES LIMITED\nHINDALCO IN eee .\nHIRAKUD POWER, HIRAKUD-768 016.DIST.SAMBALPUR (ODISHA) GST Rangeldivision: Sambelpur\nPHONE: 0663-2481365, FAX: 0663-2481342 GST Commissionerate -Cuttack\nPURCHASE ORDER\n‘AMENOMENT Z\nVendor Code: J123 P.O/No: P/PO/SRV/1920/1161 Date: 27-MAR-2020\nMis JAIDURGA CONSTRUCTION Rete ee Dater04-MAY-2020\n‘Order Type: PURCHASE ORDER\nBUDHAKATA, Effective From 01/03/2020 To 31/03/2021\nGADMUNDA Price Basis\nHIRAKUD i a ;\nMB, ISSA, 768011 ransportation arrangement\nSEA PUR OR SSN NOR roomie Ship to Location HIRAKUD - POWER\nEmail: [email protected] Carrier\nFax:() Currency 2 INR\nContact: DILIP PRADHAN () 9438452293 Hindalco Contact Person: SIDDHARTH KUNDA,\nGSTIN: 21AACFJ4294P122 —State:21- Odisha Email of Contact Person: [email protected]\nRef: ASH TRANSPORTATION TO VARIOUS BRICKS MANUFACTURING PLANT\nOrder Unit of Rate/Unit Value\nSl Stock No. & Descfiption ‘Quantity Measurement (Rs) (Rs)\n1 sera’ HSNISAC: 3600.00 MT 126.00" 4536000.00\nASH TRANSPORTATION TO VARIOUS BRICKS MANUFACTURING PLANT CCST [email protected]% 113400.\nDISTANCE TO & FRO 26KM TO 40KM Set Tego ve\nCO case Ss Gaaey SGST [email protected]% 113400.00\n36000.000 Need By: 31-MAR-2021 RCM CGST Tax@25% — -113400.00\n‘Supplier tom. DR RS.67 164. TR 27.03.20 RCM SGST [email protected]% ~113400.00\ner tem Total: —-4536000.00\n2 Sc1750 HSN/SAC: 200.000 MT _7200_¥~ 144000.00\nASH TRANSPORTATION TO VARIOUS BRICKS MANUFACTURING PLANT 1 ~ 3600.\nDISTANCE TO & FRO 11KM TO 15KM cease on\n= ees SGST [email protected]% 3600.00\n200,000 ‘Need By: 31-MAR-2021 RCM CGST [email protected]% -3600.00\nSupplier tem. D.R.RS.67.16/ TR 27 03 20 RCM SGST [email protected]% -3600.00\ntem Tota: 144000.00\n3 sciTsa HSNISAC: 2000.00 MT 96.00 192000.00\nCC Code Quantity SGST [email protected]% 4800.00\n200.000 Need By: 31-MAR-2021

Evaluation

Metrics

Label Accuracy
all 0.9977

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("Gopal2002/SERVICE_LARGE_MODEL_ZEON")
# Run inference
preds = model(" 

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Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 225.8451 1106
Label Training Sample Count
0 267
1 74
2 85

Training Hyperparameters

  • batch_size: (32, 32)
  • 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: False

Training Results

Epoch Step Training Loss Validation Loss
0.0003 1 0.3001 -
0.0164 50 0.2586 -
0.0328 100 0.1809 -
0.0492 150 0.0534 -
0.0656 200 0.0285 -
0.0820 250 0.0144 -
0.0985 300 0.0045 -
0.1149 350 0.0281 -
0.1313 400 0.0432 -
0.1477 450 0.0045 -
0.1641 500 0.0023 -
0.1805 550 0.0022 -
0.1969 600 0.0011 -
0.2133 650 0.0008 -
0.2297 700 0.0226 -
0.2461 750 0.0009 -
0.2626 800 0.0008 -
0.2790 850 0.001 -
0.2954 900 0.001 -
0.3118 950 0.001 -
0.3282 1000 0.0007 -
0.3446 1050 0.0012 -
0.3610 1100 0.0008 -
0.3774 1150 0.0008 -
0.3938 1200 0.0008 -
0.4102 1250 0.0034 -
0.4266 1300 0.0007 -
0.4431 1350 0.0007 -
0.4595 1400 0.0008 -
0.4759 1450 0.0007 -
0.4923 1500 0.0004 -
0.5087 1550 0.0005 -
0.5251 1600 0.0007 -
0.5415 1650 0.0005 -
0.5579 1700 0.0005 -
0.5743 1750 0.0004 -
0.5907 1800 0.0009 -
0.6072 1850 0.0025 -
0.6236 1900 0.0003 -
0.6400 1950 0.0023 -
0.6564 2000 0.0004 -
0.6728 2050 0.0045 -
0.6892 2100 0.0005 -
0.7056 2150 0.0109 -
0.7220 2200 0.0003 -
0.7384 2250 0.0021 -
0.7548 2300 0.0005 -
0.7713 2350 0.0004 -
0.7877 2400 0.0118 -
0.8041 2450 0.0003 -
0.8205 2500 0.0003 -
0.8369 2550 0.0126 -
0.8533 2600 0.0004 -
0.8697 2650 0.0162 -
0.8861 2700 0.0003 -
0.9025 2750 0.0004 -
0.9189 2800 0.0005 -
0.9353 2850 0.0004 -
0.9518 2900 0.0032 -
0.9682 2950 0.0003 -
0.9846 3000 0.0004 -
1.0010 3050 0.0003 -
1.0174 3100 0.0003 -
1.0338 3150 0.0019 -
1.0502 3200 0.0194 -
1.0666 3250 0.0003 -
1.0830 3300 0.0004 -
1.0994 3350 0.01 -
1.1159 3400 0.0002 -
1.1323 3450 0.0003 -
1.1487 3500 0.0004 -
1.1651 3550 0.0004 -
1.1815 3600 0.0002 -
1.1979 3650 0.0005 -
1.2143 3700 0.0002 -
1.2307 3750 0.0019 -
1.2471 3800 0.0003 -
1.2635 3850 0.0048 -
1.2799 3900 0.013 -
1.2964 3950 0.0031 -
1.3128 4000 0.0002 -
1.3292 4050 0.0024 -
1.3456 4100 0.0002 -
1.3620 4150 0.0003 -
1.3784 4200 0.0003 -
1.3948 4250 0.0002 -
1.4112 4300 0.003 -
1.4276 4350 0.0002 -
1.4440 4400 0.0002 -
1.4605 4450 0.0022 -
1.4769 4500 0.0002 -
1.4933 4550 0.0078 -
1.5097 4600 0.0027 -
1.5261 4650 0.0002 -
1.5425 4700 0.0002 -
1.5589 4750 0.0002 -
1.5753 4800 0.0002 -
1.5917 4850 0.0002 -
1.6081 4900 0.0118 -
1.6245 4950 0.0002 -
1.6410 5000 0.0002 -
1.6574 5050 0.0003 -
1.6738 5100 0.0003 -
1.6902 5150 0.0068 -
1.7066 5200 0.0003 -
1.7230 5250 0.0112 -
1.7394 5300 0.0002 -
1.7558 5350 0.0002 -
1.7722 5400 0.0003 -
1.7886 5450 0.0002 -
1.8051 5500 0.0002 -
1.8215 5550 0.0002 -
1.8379 5600 0.0002 -
1.8543 5650 0.0003 -
1.8707 5700 0.0047 -
1.8871 5750 0.0121 -
1.9035 5800 0.0003 -
1.9199 5850 0.013 -
1.9363 5900 0.005 -
1.9527 5950 0.0001 -
1.9691 6000 0.0002 -
1.9856 6050 0.0003 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.0+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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