metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: "* 04 Hindalco Industries Ltd\nHirkaud Smelter Stores\n\n \n\n* Service Recei ot\nBUYER _ Lp / GATE ENRTY NO:\noe ADL D vA /2/0A\nRECEIPT DATE: 04-MAR-22 ATU\" ! : 1-SAMBALPUR\nUNIQUE ENTERPRISES ad ZL POL CPi pg 6 ee Q/748/2022\nASS Cer ag fe oO\nos \" -\n\n  \n \n \n\nORG CODE:\n\nBOE NO:\nBOE DATE:\ncut\n\n \n\nTT\n\nWAY BILL AIRBILL NO\n\nPo\nSoe\nDATE:\n\nTOTAL RECEIVED 21074.8 Nes REMARKS/REFERENCE: | SUPPLY FOR PAINTING\nAMOUNT INCL TAX Reverse Charge: No ~\n\nINR) : Tax Point Basis : INVOICE\n\nPO Description SUPPLY FOR PAINTER FOR 85KA EMD\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\nLOCATOR\nShelf Life\nCONTROL\n\nQUANTITY:\nCHALAN/INVOICE\nRECEIVED\n\nQUANTITY:\nACCEPTED\nREJECTED\n\n   \n\n    \n\n  \n     \n\nITEM CODE DESCRIPTION HSN / SAC\nPR NUMBER SUB INVENTORY CODE\n\nPO NO. BU/cost Center/ Account Code along with GL ACCOUNT\n\nREQUESTER CODE\n\nNote to receiver\n\n1 - 801015110326 - HIRE: MANPOWER, SKILLED;RATE TYP:STANDARD, : MANDAY\nLVL/DSGNTN:PAINTER\n\n[=] = b07-\n\nS/PO/SRV/2122/054\n2\n\n- Sekhar, Mr.\nChandra Makthala\n\n  \n   \n\n: No Control\n\n   \n \n\n  \n     \n \n\n- 3711.204.910103.50803112.9999.9999.9999.9999.9999\n- Hirakud Smelter Plant.Aluminium Smelter.Electrical.Repairs to\nMachinery- Electrical.Default.Default.Default.Default. Default\n\nP ruchasuil dG ~L— gw\n\n \n\n4atos- OF + 2622. .e, oer |\nPREPARER SECTION HEAD / INSPECTOR SECTION HEAD /\nSTORES KEEPER AREA HEAD -RECEIVING AREA HEAD — CUSTODY & ISSUE\nor\n\nals\n\f"
  - text: " \n\n \n\nDELIVERY CHALLAN ~ Phone : (0891) 2577077 |\nALUFLUORIDE LIMITED\nMULAGADA VILLAGE, MINDHI POST,\nVISAKHAPATNAM - 530 012 |\n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\nDc Nox: g22 - - : ; “Date 02-02-2016\n| HINDALCO INDUSTRIES LTD HIRAKUD\nSAMBALPUR\nODISHA\nPIN CODE: 768016\nYour Order No: ~HKDRM/1516/0001 DT: 01/04/2015\nReceived the below mentioned in good condition. Carrier No: AP 16 TC 9339\n—SI.No | ~~ PARTICULARS” | Qty. | Rate / MT\n: = | ae\n: 7\nALUMINIUM FLUORIDE . | 21.000 | ; sbatS\n|\n420 BagsX 50.120 kg. = 21.0504 MT |\nWeight of Emppty Bags:& Liners: 0.050 MT\nSoa Net Weight of Material: ~ 21.000 ~MT\nInvoice No.: 822 Date 02-02-2016\"\nAPVAT TIN : 37900106541 Dt: 02.06.2014 CST No.: 37900106541 Dt: 02.06.2014\nReceiver's Signature Signature\n\n \n\f"
  - text: " \n\n \n\n \n\n \n\n   \n\n        \n\n| rad nas Bi Tiapz Ke en\nap | pa ape EE By EY ED ITT? ON matte / ON moray |\nP| airing swodanraa boc pia oe ne ed ee v , 4\n! e i ma | VeACLA Baus §uOQ souBisua¢ of\n| “P io | . [ | seBieUo IS | wal VY | Loo abi +A Buipe spun |\n| | fe) De [ nl oman «| OE U :\nmS, (Spe fb) to ae\n| eo Ss | | Pepe (GEOUVHO | GE SOF ae\nE 4 ’ : E  sapesecascnsctute saps Ln + ad et an\nme | | a | es ' | xR Uag ob iw aa ae 32\n' a a] i as aN Ne paneer\nRe is pad on\n| ee | Sel Nmd Oe oy ld,\n| ix | ; | ‘lwnov L PP. ‘dg py\n| . Pe eh\n\n \n\nmo sory oR! wor,\n\nou d&- ane eer\n\n: \"ORL\n\n   \n \n\n \n\n‘PO 0Es - “ay Sink /BUSIA,\n‘eyemfes eipug weayediueaewueyepsd JeaK\n\"UINYD BPISGG SE-’-S7Z ON 100G\n\nBu. NOUMIS BNDIOOS\n\ney\nWeve! se\n\n \n\n    \n\nhceaitbaaor re\n\n! AMoaAM\n\n \n\n \n\n> tewe-3™\n\noy eee\n\nY3WOISH) Ad GAUNSNI SI ODUYO\n— MSIH S.HSNMO LY\n\nAdOD HONDIS. NOD\n\nene os roarans\n\n \n\nKINO NOMIC unr\n\nWaalarad Ta soz - ‘Sn\n\n \n\n- “eu = 3 re\n\neagaee\n\nGY oe Ae\n\nBA OFT OVI\nfoe, 17 :\n\n“OL\n\n       \n\nivan OL.Givs) NOiAIOSaa\n\n \n\neT ea ‘ON aGOW\n\n       \n\n \n\n(sour g) 9292 94924 920P : 181 600 OOF - IVAW angus Wi0l <\n‘OVOY OTIS .G 'Zy “.BSNOH X3dINI PVHIA. ¢°O\"H\n\n? tAd LHOdSNU 4! 88909 LVENS\n\n-_ wd\nfe\n\n»\n\f"
  - text: "SOT Ue\n\n \n\n         \n\noH\n\n| ia\n\nI\nod\n\nHi\n\na\n\n|\nTo) Sig Pere\na\n\nal |g\n&%\n5)\n\nwS\\\neB\nSB\n“5\n“O\nS\n€X\n\nBea\n\nem\n\nPe eS\n\nse aE a\n\n4 |] | tat [ety\n\ntt pe Ta\n&\na\n\nOK\n\n¢\n\nSRLS ia Leh coe\n\n \n \n\f"
  - text: "  \n \n  \n   \n\nAUSEOOUSRGSEEENSSRCESRORROGS\n\nMise oaeta\nMis tnaes Lo Q) duty at col ane\n\nDate 12.8820\n‘Stra Bort as Corry Ub 2.\n\nexeauscscotecne: aneasese\n\nMm. €.M. NBUSTRIES\n\nAn ISO 9001 : 2008 COMPANY\n\n“PODDAR COURT\", Phones : 2235 2096 / 3985 2494 Lo Wi. TEE OLL, a¥ahe Package Ae 2\natadiee Fax 033-2235 1868\n\nE-mail : [email protected] Tame Ahr SLM, Freight eng\n\n   \n\nRaut WAR OKA O Van weg 9 at ai sl age Reve\nCorny u. )\n\nGABLES ARE IN GUR CONTROL\n\nFrease sign & return VAT No. : 19570720098 e TIN/ CST No. : 19570720292\n—~ = Office : 55, Ezra Street, 2nd Floor, Kolkata - 700 001\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: 1
            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:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: BAAI/bge-small-en-v1.5
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
0	'_\ni.\nSe\nNew\n~~\ned\nTy\nSw\nNe\nNw\ned\n2:\n\n \n\x0c' 'ne.\n\n \n \n\n \n \n\n \n\nbBo fy20 5 ‘ )\n- wi Pas BOOKING STATION\nstat” SURAT GEIS TRA: BSPORT HOT. LTE, DIMPLE COURT, 2ND FLOOR,\n H.O.: “VIRAJ IMPEX HOUSE”, 47, D' M= -toRoaD, + AT_OW. ER’S RISK oer\n' , a” MUMBAI - 400 009 Tel. : 4076 7676 sianan Gece i al CARGO iS INSUR BY CUSTOMER — PH, : 033-30821697, 22\n{ 1. Consignor’s Name & Address As. ExOme peas Br. Code\ndT ncuer\n
1	"Posatis ils. H\n\n \n\niS\nvs\na (uf\n\noe\n\n \n\n-\n\n \n\nSarichor Pls: q\n\nPea :\n\nITEM /\n\n1. Description/ Received Reject Delivered Retur\n\n \n \n\nSPARE TX. Phat\n\n(MARKETED BY MESAPD\n\nPact eta\n\n \n\nMATERIAL RECEIPT REPORT\n\n \n\n \n \n \n\n \n\nCUM nea\n\n00 LeTlooo 0.000\n\nPAS\n\n \n \n\nELT\n\nJUPLICATE FOR TRANSPORTE?-\nOGPY (EMGISE INVOICE) RECEIVED\n\nMite ariant Eee\n\nPRAM MUIMAFE RCL RE\n\n \n\n \n\nFrys\n\n \n\not\n\nSuds oT\n\n \n \n\npeas\n\nee ase\n\n. Tax Gelig\n\nGrand Tooke\n\ni\n\nRM\n\nRate/Unit\n\nMRR SUBMITTED\nwv\n\nITH PARTY'S INVIGCE\n\nEET RY MO SSO OT Soe ELS\n\nLS.\n\n \n\n \n\n \n\nWee\n\n7; Ae 18\n\nTrcic\n\ni\nSu\n\n~s\n\n“en\n\nnny\n\x0c" "«= ITER /\ncit BDescription/ Received\n\nms\n\n \n \n\n \n\nIces\n\ne to\n\ntea tae\n\nhoimeryh bea\n\nPorccheninernyh Qerkees\n\nRican dec\n\nrarer:\n\nPAD RP eAR eR\n\nMeare\n\n \n\nMATERIAL RECEIPT\n\n \n\nREPORT\n\n \n\nwe ie 7\nhe\n\nSeba.\nbh ETS\n\n \n\nReject Delivered Retur\n\nTESLA y’\n\n \n\n \n\n \n\nLF PIE\n\nTAIT a\n\nSUPLICATE FOR TRANSPORTER\nOGPY (EXGISE INVOICE) RECEIVED\n\noy\n\nf\n\n“soarewe Pk Beak\nree\n\nRAF

Label

Examples

'_\ni.\nSe\nNew\n~~\ned\nTy\nSw\nNe\nNw\ned\n2:\n\n \n\x0c'
'ne.\n\n \n \n\n \n \n\n \n\nbBo fy20 5 ‘ )\n- wi Pas BOOKING STATION\nstat” SURAT GEIS TRA: BSPORT HOT. LTE, DIMPLE COURT, 2ND FLOOR,\n H.O.: “VIRAJ IMPEX HOUSE”, 47, D' M= -toRoaD, + AT_OW. ER’S RISK oer\n' , a” MUMBAI - 400 009 Tel. : 4076 7676 sianan Gece i al CARGO iS INSUR BY CUSTOMER — PH, : 033-30821697, 22\n{ 1. Consignor’s Name & Address As. ExOme peas Br. Code\ndT ncuer\n

"Posatis ils. H\n\n \n\niS\nvs\na (uf\n\noe\n\n \n\n-\n\n \n\nSarichor Pls: q\n\nPea :\n\nITEM /\n\n1. Description/ Received Reject Delivered Retur\n\n \n \n\nSPARE TX. Phat\n\n(MARKETED BY MESAPD\n\nPact eta\n\n \n\nMATERIAL RECEIPT REPORT\n\n \n\n \n \n \n\n \n\nCUM nea\n\n00 LeTlooo 0.000\n\nPAS\n\n \n \n\nELT\n\nJUPLICATE FOR TRANSPORTE?-\nOGPY (EMGISE INVOICE) RECEIVED\n\nMite ariant Eee\n\nPRAM MUIMAFE RCL RE\n\n \n\n \n\nFrys\n\n \n\not\n\nSuds oT\n\n \n \n\npeas\n\nee ase\n\n. Tax Gelig\n\nGrand Tooke\n\ni\n\nRM\n\nRate/Unit\n\nMRR SUBMITTED\nwv\n\nITH PARTY'S INVIGCE\n\nEET RY MO SSO OT Soe ELS\n\nLS.\n\n \n\n \n\n \n\nWee\n\n7; Ae 18\n\nTrcic\n\ni\nSu\n\n~s\n\n“en\n\nnny\n\x0c"
"«= ITER /\ncit BDescription/ Received\n\nms\n\n \n \n\n \n\nIces\n\ne to\n\ntea tae\n\nhoimeryh bea\n\nPorccheninernyh Qerkees\n\nRican dec\n\nrarer:\n\nPAD RP eAR eR\n\nMeare\n\n \n\nMATERIAL RECEIPT\n\n \n\nREPORT\n\n \n\nwe ie 7\nhe\n\nSeba.\nbh ETS\n\n \n\nReject Delivered Retur\n\nTESLA y’\n\n \n\n \n\n \n\nLF PIE\n\nTAIT a\n\nSUPLICATE FOR TRANSPORTER\nOGPY (EXGISE INVOICE) RECEIVED\n\noy\n\nf\n\n“soarewe Pk Beak\nree\n\nRAF

Evaluation

Metrics

Label	Accuracy
all	1.0

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/Material_Receipt_Report_ZEON")
# Run inference
preds = model("SOT Ue

 

         

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")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	182.1336	1108

Label	Training Sample Count
0	202
1	45

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
0.0007	1	0.2952	-
0.0371	50	0.2253	-
0.0742	100	0.1234	-
0.1114	150	0.0115	-
0.1485	200	0.0036	-
0.1856	250	0.0024	-
0.2227	300	0.0015	-
0.2598	350	0.0011	-
0.2970	400	0.0009	-
0.3341	450	0.0007	-
0.3712	500	0.0011	-
0.4083	550	0.0008	-
0.4454	600	0.0008	-
0.4826	650	0.0007	-
0.5197	700	0.0005	-
0.5568	750	0.0006	-
0.5939	800	0.0005	-
0.6310	850	0.0005	-
0.6682	900	0.0004	-
0.7053	950	0.0003	-
0.7424	1000	0.0004	-
0.7795	1050	0.0005	-
0.8166	1100	0.0004	-
0.8537	1150	0.0004	-
0.8909	1200	0.0005	-
0.9280	1250	0.0004	-
0.9651	1300	0.0003	-
1.0022	1350	0.0003	-
1.0393	1400	0.0003	-
1.0765	1450	0.0004	-
1.1136	1500	0.0003	-
1.1507	1550	0.0004	-
1.1878	1600	0.0004	-
1.2249	1650	0.0004	-
1.2621	1700	0.0003	-
1.2992	1750	0.0003	-
1.3363	1800	0.0003	-
1.3734	1850	0.0003	-
1.4105	1900	0.0003	-
1.4477	1950	0.0002	-
1.4848	2000	0.0003	-
1.5219	2050	0.0003	-
1.5590	2100	0.0003	-
1.5961	2150	0.0002	-
1.6333	2200	0.0003	-
1.6704	2250	0.0004	-
1.7075	2300	0.0004	-
1.7446	2350	0.0003	-
1.7817	2400	0.0002	-
1.8189	2450	0.0002	-
1.8560	2500	0.0003	-
1.8931	2550	0.0002	-
1.9302	2600	0.0003	-
1.9673	2650	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}
}