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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
Model card Files Community
SERVICE_LARGE_MODEL_ZEON / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
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  - text: "  \n  \n\nSLNO EQUIPMENT NAM ‘UNIT |  EQPTCODE | ‘&SLNO | LOCATION\n\n \n \n\n \n\n   \n  \n  \n\n \n\n     \n  \n \n\n \n\n \n\n \n\n  \n      \n\n \n\n \n\n \n\n    \n \n \n \n\n \n\n  \n   \n   \n\n \n\n \n\n  \n  \n \n    \n \n  \n    \n          \n   \n   \n   \n   \n\n  \n\n \n\n[ee CHP#1&2 | 1- sao i cHPreN 01 | Pc ILMS FUROR = TON UECAMIL/FHP/22/1 cna\nRCC AEC TRI puppies | 2 ad PCH- 2 Net ean? al CRUSHER ens | wecnmuremmne | 17-0020\n\neo MONnOST crema | 1-HOIST-7.5T-PCH- = Ree nooronenes | 7 UEC/HIL/FHP/22/3 \"17-01-2022 16-01-2023\n| MONO Cup#182 | 1-HOIST-6T-SCH > ee ‘| Si ecoNDaRy | 6.0 TON | UEC/HIL/FHP/22/4 | 17-01-2022 | 16-01-2023\n\n| Wowonat aca cHP#1a2 | 1-HOIST10T-SCH |\" y ae 5 | crue aOR 10 TON UEC/HIL/FHP/22/5 | 17-01-2022 | 16-01-2023\n| a | MONNOST CHP#3 , 3-SCH-HOIST-7.5T-A | ID ‘snort ooh ance . * TON | UEC/HIL/FHP/22/7 17-01-2022 16-01-2023 |\n| 8 | See om |e -SCH-HOIST-7.5T-A | = Cot oe wdc, Oia Bes a | TS TON | UEC/HIL/FHP/22/8 17-01-2022 16-01-2023\n~~ | MONORAIL ELECTRIC TIDNO:CHP/EMH-07 | SCH, CRUSHER 012022 | cananis\n\n    \n    \n  \n \n   \n   \n \n \n \n \n\nFLOOR, OVER SCAB | 5.0 TON UEC/HIL/FHP/22/9\n\nSCH, CV402A/B\n\n \n\n \n\n \n\n    \n \n \n  \n\nL 9 | HOIST CHP#4 -SCH-HOIST-5.0T-B | S|. No: H-971/ 971A\n| | MONORAIL ELECTRIC 4s To ID NO: CHP/EMH-07\n\n \n\n \n   \n  \n   \n \n  \n\n  \n\nHOIST\n\n10 ee CHP#4 | 4-SCH-HOIST-20T-B gy Ng: H-970 se eran “20 2.0TON | UEC/HIL/FHP/22/10 | 17-01-2022 | 16-01-2023\na See oO a ipODCVA0SAE | _ —-—\n02,\n| 14 | MONORAILELECTRIC | Cupieg | 4-tp2-Hoist-20T DNO-* ee HEAD PULLEY | 20TON | UEC/HIL/FHP/22/11 | 17-01-2022 16-01-2023\n\nL HOIST-2.0T FLOOR\n\nTUNNEL,OVER SUMP\nCHAIN PULLEY BLOCK | CHP #1 NA | aera 1 TON\n| aa T Vv\nCHAIN PULLEY BLOCK | CHP #1 ee Fa\n\n14 CHAIN PULLEY BLOCK | CHP #1 | 10415 i ‘ = 1 TON\n\n- L 7 ; i oe & VFA _\n\n14-01-2023\n\nUEC/HIL/FHP/22/12 | 15-01-2022\n\nUEC/HIL/FHP/22/13 | 15-01-2022\n\n \n  \n\n14-01-2023\n\n \n\n|\nUEC/HIL/FHP/22/14 | 15-01-2022 | 14-01-2023\n\n \n\n \n \n\n \n\n \n\f"
  - text: " \n\nTOTAL\n11\n\n- wl et\n\nSUPERVI\nSOR\n\n7 ce\n\n      \n\nnly\nAIN|A ale\nSale\nlale ld\nSo\n\n \n\n \n\n:\n\n9 wij im\n\n \n\n \n\n   \n\naes 3513\nsIB|e\nalg\nalg\n\nNTN\n\na 2 3 ; 3\ngle\n\no\n\nri\n\n  \n \n\n \n\n \n\f"
  - text: " \n\nBasic Value Ne arte me °\n\ngee 339980\" i\n\nO |- 4 Jo} ©: :\nRot oct DW. 159.)\n\nBS! < gum v= [AAPG\nPF - estat OE Boy S*\nWISC. DED__ssssssnens\n\nlet Payable 3 ¢) TS a\n\ntees ee\n\n  \n\f"
  - text: "Deepak Singh\n\nFrom: Swapnil Dixit <Swapnil.Dixit@thermaxglobal.com>\n\nSent: 18 August 2021 16:48\n\nTo: Deepak Singh\n\nCe: Shree Nath Mishra; Pranjal Pathak; Prashant Shripad Nagraj; Kirtiraj Jilkar; Pranjal\nPathak; Arun Kumar Singh; Ravi Kumar; Nishant Shah; Vidyanath Jha\n\nSubject: RE: Agenda for next AOH review.\n\n \n\n \n  \n\nCAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you recognize\nthe sender and know the content is safe.\n\n \n\nDear Deepak Ji,\n\n“we thankfully acknowledge the receipt of your trailing mail and would like to confirm our acceptance of 4016- &\n322-man days for a period ( Jun to Dec 20 ) and ( Jan to April 21 ) respectively.\n\nRequest to proceed further in the matter and arrange to release the order at the earliest.\nRegards\n\nSwapnil Dixit\n\nFrom: Deepak Singh <deepak.ab.singh@adityabirla.com>\nSent: 18 August 2021 12:45\nTo: Swapnil Dixit <Swapnil.Dixit@thermaxglobal.com>\nCc: shree.mishra <shree.mishra@adityabirla.com>; pranjal.pathak <pranjal.pathak@adityabirla.com>; Prashant\nShripad Nagraj <Prashant.Nagraj@Thermaxglobal.com>; Kirtiraj Jilkar <Kirtiraj.Jilkar@Thermaxglobal.com>;\npranjal.pathak <pranjal.pathak@adityabirla.com>; arun.s <arun.s@adityabirla.com>; Ravi Kumar\n\nw= <ravi.s.s@adityabirla.com>; Nishant Shah <nishant.shah@adityabirla.com>; Vidyanath Jha\n<vidyanath.jha@adityabirla.com>\nSubject: RE: Agenda for next AOH review.\n\nCAUTION: This email originated from outside the organisation. Do not click on any links or attachments\n_ unless you recognise the sender and know the content is safe. Forward suspicious mails to Information\n— Security Team.\n\nSwapnil ji;\nKeeping the discussion, we had in the meeting on 09-08-2021,our Team discussed later and following is the point-\n\n1. As per our procedure , we don’t count the day of Antigen Test as a part of Quarantine ,but at the same time\n| agree that Gate Pass processing was taking time beyond 02 days.\n“ So as a special case , for the period Jun 20 to Dec 20 ,we are considering your request of counting the\nAntigen Test day as a part of Quarantine .Hence total Quarantine Days for that period will be 4016 mandays.\n2. For the period Jan 21 to Apr 21,we have streamlined our Gate Pass Process and delivered the Gate Pass in\n02 days .So for the same period ,we are not considering the day of Antigen test as a part of Quarantine .\nVerified Man-days along with Mr. Gaurav of M/S Thermax is 322 Mandays.File is attached.\n\nKindly acknowledge so we proceed further .\n\nRegards\nDeepak\n\f"
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
  - name: SetFit with BAAI/bge-small-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9976525821596244
            name: Accuracy

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: ckppni@rediffmail.com Carrier\nFax:() Currency 2 INR\nContact: DILIP PRADHAN () 9438452293 Hindalco Contact Person: SIDDHARTH KUNDA,\nGSTIN: 21AACFJ4294P122 —State:21- Odisha Email of Contact Person: sidharth.kunda@adityabirta.com\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 Tax@2.5% 113400.\nDISTANCE TO & FRO 26KM TO 40KM Set Tego ve\nCO case Ss Gaaey SGST Tax@2.5% 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 Tax@2.5% ~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 Tax@2.5% 3600.00\n200,000 ‘Need By: 31-MAR-2021 RCM CGST Tax@2.5% -3600.00\nSupplier tem. D.R.RS.67.16/ TR 27 03 20 RCM SGST Tax@2.5% -3600.00\ntem Tota: 144000.00\n3 sciTsa HSNISAC: 2000.00 MT 96.00 192000.00\nCC Code Quantity SGST Tax@2.5% 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(" 

TOTAL
11

- wl et

SUPERVI
SOR

7 ce

      

nly
AIN|A ale
Sale
lale ld
So

 

 

:

9 wij im

 

 

   

aes 3513
sIB|e
alg
alg

NTN

a 2 3 ; 3
gle

o

ri

  
 

 

 
")

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}
}