---
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\n\
ASS 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\n\
QUANTITY:\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\n\
2\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\n\
MULAGADA 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\n\
ALUMINIUM 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\
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\ 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 : cables@memindustries.com 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: 1.0
name: Accuracy
---
# SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) with contrastive learning.
2. 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](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### 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| acai denn EE Motie iho. ;\n| Weal © Gab TES 1 eensests Uasansensssssseonsenoereneorsenvenesnneasy\n\n\' 2.Cons ignce Bai:x’s Names wl ke iy at < CoO ale ysien b> € (to!\n\n“Litsakuod smalter f eat Lireatéuel Bor oa thin ~ behets ___\n\n \n\n|\n| %\n| on Sen Me te INS a sna iene tl er sues EES _KM__\nat i ag Se are ~ 7 oo 2 ne\n\'| L. US | - 1265 . - HY f Y -ataucl =\nate OF _ QESCHIPTION (SAIC TD ee wy ss _ WEIGHT at GATE PY 2 FRGH GH\neer . | we Re, ?. i\n\n| UFG Re Matta PS RO [aa =r 52 fences\nwe by “Matrtale O%, EFT Gora), ed\n\nhr\n\niia Sa ea eterna eas ean a\n\n \n\n \n\nTin Me a! pene __ aod i osem ge Wleg\n\' Lone CHARS 4 Hanne oe | 5 & ;\nt—- cee eee a = _ Ss Reece!\n| hig © pap Loading by 7S TAP ut. Crarges fon = aw\ntal | 7 “a eet ci a" or a — © =\n\nfree = w JBODs C } se ren st tet , Re 1 SURAT GOODS TRANSPORT VTALTD. * *\n\nTruck No. / Trailer No. : Capacity __\n\nscreens: eat BY SoH BUNS hs BENESME Pp\n\n \n\n \n\n \n\n. Lo\n\nAeookutd Clerk\n\n \n\x0c'
- 'J o\nALL DISPUTES SUBJECT TO KOLKATA JURISDICTION @ : 2236 6735\n\n"os Mobile : 98300 08461\n-*, _ TAXINVOICE RIG UYER\'\n\nUNITED ENTERPRISES\n\n— = Deals in : TARPAULIN, POLYTHENE, HDPE FABRIC, COTTON CLOTH,\nHESSIAN, GRANULES & GENERAL ORDER SUPPLIERS\n\n3, Amartalla Lane, Kolkata - 700 001 ~ 3 MAY 2Ui5\n\n \n \n \n\nws. HlinPAL so Taposreics Limireep | BN. bf LSS nnn\nDato......1 Sf94. LA csanscsonss\n\nSOD LSA LARS Bn Tee. Chalian No.....1.6. AS: ~(§\nDist: caumpac pon Opis HAD Date ....... LOfoy Iss sessssessseee\n\nCC OSECCLETTTECOETSSOECOHH TS ETTSSEOTHAU HE HOVER SHEUMOSECEDSOUCODESCODECE ODI SMousON RE RED\n\nBayar YATIEST No. BSS. za BIG san\n\n \n \n \n \n\n \n\n \n\nCAP TCT o Ce ERE veTe Darden vavoryDEETOeseeEDOOTEEDE\n\nRupees inwords .N.| why Fou These —\nmA YS..ntL ard cl\n\nVat No. 19511424085 dt. 20/3/08\nC.S.T. No. 19511424289 dt. 20/3/06\n\n \n \n \n \n\x0c'
|
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- '| ie\n\n \n \n \n \n\n \n\ntn eee i he _#\nTrivveiece Dae oo og OF\n1 Cxors d arimeant hoo &\n\nLearner: £ DA ted\n\n \n \n \n \n \n\n \n\nae ‘Beam teas” 8 GIR-sae? DY .mada 18 & GTR BBse “DT.13.1.38 GENO, S388\n4, Mandar Meum 2 DTV2.2.18 & G.E.NMO.S164 DT. LSeud. Le INV.NO.G5¥=1 71.8-EM-O1BS\nExcess a » DT.?.L.18 :\nSUMAM IND-AGRO SALES PYT. LTD.\n7 i\n(Te Quantity-—----—----— Value\nCAL) sence me i ee et “Received Reject Delivered: en ag ne tec enw\n\nLOCATION\n\nat\nSat OD\n\nROLFES7 5.\n\n \n\nAES FORCE | EXTRACT I,\n\non ie.\n\nDs so17Eave. au\n“6 OMELETTE MOTORISED\n\nhs norzasra 2.000\nCOMPLETE MOTORISED\n\n| GLOBE VALVE\n\nOO PATERIAL~OAS1. SIZE\n\n \n\nest AF 18 BO LEXS\nreli\n\n» COMPLETE MOM PETUBM VaLVr\na VTE TAL -- CAS d. a SIZE SOME,\n\nVALME\ney hai: Pu. WABI SIZE .\n\nALE\nTAMOHIMG TYRE.\n—LOONE ,\n\n \n\nMRR SUBMITTED -\n\n‘MATERIAL RECEIPT + REPORT -_ WITH PARTY S. INVIOCE |\n\neneeiae me\neden\n\n: “RRR Reece i at Pig\n, MARA Re ced pt Dahes\n\nPTY SPRY i Fibs\nOF -FER-LS\n\nv9\nore\n\nPO phos\n\nPEC SFRY v8 Ore\nFO Thahes\n\nOL AU?\n\n-#\n\n9.000 3.600 EET a OK 1SE460. ‘ OO a\n\nNMOS LST Tae oe PEGE IO pS\n860\nON-4as RELIVERY DATE es\nLaF EE 8 Srctual Tax Vailue 4922.20. ;\nOILST. ATTY “CO ; Stabs Torhals LoS\n\n2900\n\nSn encewn es bovese es an be neeven os ones ntES Oe pts wt 90H On eden ov ET Om aUReeR ones Mt eretereneneesa stoner mint o>\n\nOu OK)\nGTs--\n““LEOME,, 800\nDELIVERY DATE\n© Date FETE 1\n\nLE OOOD 00\nIGST Taxaiex\n\n -3ROOGO JOD\n\n- 6BA00.00 |\n\nPEI:\n\na\na\n\nfaz tua dL. Tax MaiLure E8400. 00.\n\nDIST. OTYVEC\n2.000\n\n2,000 oO\npene\n\nste os evenen enan en enetan ue saareberernestenereens eueaan ane ed ateras ony wReniboens mnotvnes cesumewtneey\n\n0.000\nTYRE\n\nnw CHD. BL OOO o OD\n\nABO . OO\nIGST Tax@iex\n\n75600. oe\n\nLOOODELUVERY DATE\n\noo Pe LASS. END CCOMMEDTION - BUTT 2a-FEERIS Actual Tax Value s FBO « 00\nPS. WELD. | . senpoeapcinatimane licleshicisunanpatal fe sini\nkG 2 BIST. OTY-/CC Suh Terkale 4PEGOO 00\nSat ya 8h Be KOCH. aetctemnneeetenectnimetnnnngeeeren tienen manent eneeremencinessirnatibioe\n\nmy\n(TW beeeninenminnien casein annnsnene sae wonenaennnnntnnneennneenunedenennineneneniannnecnenucntannennnniennacuccnannpaansuneaancinnnnnennnn nn aeeseanininc\nTN | Grand Total — LAO7F S82. 80\n-~ | SUPLICATE FOR TRANSPORTER : eo\n\n \n\n7 senvecauvenenen eqs quanvernsemn seesmaneseseneasnen amen etetenanenacesense eves anne ne on enemies ests\n\n \n \n\n“Pa ane a: of 2\n\n \n\n \n\noi eoeneens ater et et ote neat eegtas ent cege antes enewen ten mes eeenme webeei anemone anetes eran en seeeaterarts dat aneneree spans cums ct maretenen et seeterieen ment te et arereratet srereveneias cosesnesescipsenaceeie sncbntensuseeeth pesasemmeccnsnsaunsier sees lenses\n\nA ym\n\naren ra nit i\n\nee\n\ni\n\nnoe en Sep eet St ee\n\nagai e teoncrescs 7 aS\n\naaa Se Ss:\n\ncote\n\nco hegiecssoscse\n\nsenalt\n\naa\n\nJI J FF JF JF DF JD\n\nee\n\nee\n\n \n\nKoy\n\nwy \\\nae “ r\n\\\n\nZ\n\n \n\x0c'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
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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5)
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Bea
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Pe eS
se aE a
4 |] | tat [ety
tt pe Ta
&
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OK
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SRLS ia Leh coe
")
```
## 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
```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}
}
```