metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: "   \n  \n    \n\nHIRAKUD POWER / SMELTER\n\n@ - Payment Order\n\n( Address )\n\nEmp.No./S.Code No. Qhle\nby Cash/Cheque/D.D./Transfer the sumof ~ 35, +S0/—\nRupees Thi ei ve therane Seven hun\nFi \\\nMail Id of Initiator: : OF)\n\n \n\n| Details of Payment\n\n;\n| AtMenclance, Cleritage ancl Otner\naa T\n\n|mise: Conveyances for ip eome No. |\n2x to 2-9, 3\\ 32 ano 34 of Qo| 2\n\nURL of payment:\n\nin . TotalRs.| 35, too /]- .\n\nPrepared by Recommended by Endorsed by Authorised By Approved by\n‘ A =\nPort Sara ee\nA ee EU (NY—\nDate 20/ 19 Plant Head Head-F &A Head - Sambalpur Cluster\n\n          \n  \n\nCharge\nAccount\n\n    \n   \n\nLeqa2\n\n~ Odisha cluoy\n\nOI) 202 Sefer (90\n\nONLINE PAYMENT\n\n \n\n \n\nCashier\nReceived Payment | Charge Account Checked by\n\nae eee _\n\n  \n\nSignature\n\f"
  - text: " \n\f"
  - text: "Expenses during visit of morning for coal logistic.\n\nSl.no. _ Date Of Visit Particulars Amount Remarks\n1 21-Feb-16 Tea,Snax ager mis. - 105.00 Along with Mr N K Kar\n\n      \n  \n\n   \n\n \n\n  \n\nft eee ‘\n~# “Lunch. ° of AVS |\nee ou SATS i\n2 22-Feb-16); | “Fea soe mis, dee aot]\n; 3 . cng! a oa hoy ‘ “e i hs ye eo 1\n3 23-Feb-1600). Téa,Snax Andithis. I Along with Mr'N K Kar .\nee _ |) Lunch... a 00° oO 2 !\n4 24-Feb-16 Tea,Snax And mis. okt? |\nLinch ~ : egthedgtt £92 ox\n5 25-Feb-16\n\nmeted FES entre? i462\n\n- Teaisnax And mis. r AS Vi on\n) ie ihe oe » Lunch eres , Mo\n\n6 26-Feb-16 Tea, Snax And nis,\n\nyeahh! ct\n\nfeo, 7\n\n  \n\n  \n  \n       \n  \n \n\n, 140.00; - Pend wih iM NK Kar\nlene . : -aaciog par rs :\n’ a7-Febalt Tea,Snax And itis” \"425.00 Along with Mr. K Kar\nLunch 280,00 es _— ,\n8 29-Feb-16 se Tea,Snax And oie Bh i af U5 5.00). \" pe. a!\n9 te Snax ‘And mis. ce n20. to & oe\n\n10\n\neee £50. Alone wit Mr N K Kar bye efort-\n\nevn, enews) :\nLaheue 325. 00 x Up £0 perenne os\neerie coer re ue\n\n \n\n,\n\n11\n\nHf figs bh.\n\n \n\n  \n \n  \n  \n\n \n\n4\n‘\na\nvt wr 4 ‘\n“ane . mae t\nwha via ‘\n‘\n5\n12 {\n_ o |\n\" nay\ni\n¥\n4\n4\n_\ni\nTew. EN at Rbiew: Caen sere 4\ntA eS : f i :\ni — Eyl 3. 4\nes, j Lax > * awe 4\nwe be oy . “ tyne eel\ni ad\n: oe\nSeog) ayM. 44\nwr\na, obo ye eect ee —\n-\n\n \n\f"
  - text: "HINDALCO INDUSTRIES LIMITED EMPLOYEES’ PROVIDENT FUND II\n\n| | B)REASON OF LEAVING SERVICE: RESIGNATION\n_ SERVICE TERMINATED ON\n\n    \n \n  \n   \n   \n \n    \n \n\n     \n\n|\n\n| | ACCOUNT OF (A) ILL\nHEALTH OF MEMBER (8)\nCONTRACTION /\n\nDISCONTINUATION — OF\nEMPLOYER'S BUSINESS OR\n\n(C) OTHER CAUSE BEYOND\n\nTHE CONTROL OF THE\n\n| EMBER\n|i PERSONAL REASON _\n\n__\n\nPAYMENT UCO BANK ,HIRAKUD SAMBALPUR ,ODISHA.\n(PLEASE ATTACH A COPY OF cmmntnnmeanisnnennenesennmaneeneisene nese\nCANCELLED  CHEQUE/ATTESTED\n| COPY OF FIRST PAGE OF BANK PASS | IFS CODE ... UCBA0000285\n| BOOK _\nTa) FULL POSTAL ADDRESS WITH E- AT. GUNDRUPADA, PO-HIRAKUD, DIST- SAMBALPUR, ODISHA-.\n\n| 12 I BANK ACCOUNT DETAILS “FOR SAVING BANK ACCOUNT NO — 02850110044179\n!\n\nMAIL ID (IF ANY)\n\n \n\nPIN ...768016\n| E-MAIL ID :-\n\n- INCASE THE AMOUNT IS USED FOR ANY PURPOSE OTHER THAN STATED IN COLUMN (9) ABOVE, | AM\nLIABLE TO RETURN THE ENTIRE AMOUNT WITH PENAL INTEREST.\nTHE MEMBER HEREBY DECLARES THAT HE HAD NOT BEEN EMPLOYED FOR 2 MONTH (YES/NO)\n\n(APPLICABLE FOR PF SETTLEMENT ONLY)\nve SIG N41\n\nMEMBER SIGNATURE AND DATE\n\nCERTIFIED THAT THE APPLICATION HAS BEEN SIGNED BY THE MEMBER IN MY PRESENCE AFTER HE/SHE HAD\nREAD THE CONTENT / THE CONTENT HAD BEEN EXPLAINED TO HIM / HER BY ME AND THAT THE\nINFORMATION GIVEN IN THE APPLICATION FORM |S CORRECT\n\nDATE:- : yA\nye\"\nEMPLOYER'S SIGNATURE\n\nDESIGNATION & SEAL OF EMPLOYER\n(OPTIANAL FOR FINAL PF SETTLEMENT)\n\nENCLOSURES: WV SELF ATTESTED AADHAR CARD & PANCARD\n2 cory OF CANCELLED CHEQUE / SELF ATTESTED COPY OF 15° PAGE OF PASS BOOK.\n\f"
  - text: " \n\nHINDALCO INDUSTRIES LIMITED\nHIRAKUD\n\nPAYMENT ORDER\n\nPayto Simanchal Khatai\nCash Vr.No.\n\n \n\nEmp.No/S.Code No. _ ~\nby Cash/Cheque/D.D./Transfer the sum of Rs.2,00,000.00 apvrno,lOlY% 3s\nRupees Two Lakh only\n\n \n\nDetails of Payment Amount (Rs)\n\n \n\nns . 2,00,000\n\n \n\n \n\n \n\n \n\n \n\n \n\n2,00,000.\nPrepared by Recommeded by Endorsed by Authorised By Approved by\n\n9 ’\nner (wy\nDate Dept. Head Plant Head -F&A Head - Sambalpur Cluster\nPayment made on Charge\noem ra\n\n(b) By Cheque ner 2] 2G ~ 2> 7 + DA SHO-KLB (321)\n\nState Bank of india, Burla\nState Bank of India, Hirakud\nPunjab National Bank, Sambalpur [PNB-1]\n\n \n\n \n\n \n\n \n\n \n\nUuCcO Hirakud\n\n \n\nUCO’Bank, Sambalpur\n{DBI , Sambalpur (IDBI -1)\nIDB! , Sambalpur (IDBI -2)\n\nReceived Payment Charge Account Checked by\nSignature\n\n \n\n \n\n \n\n \n\n \n\n \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	'GHt Sa [OI uco Bank\n\nvis Free Number: 180¢-193-9125 _\n\nDICGC INSURANCE UPTO 5 LAC\n\nBRANCH\n\nUCO Bank\nP NT\n(1) Consuitants are requested to note that all moneys\n\nremitted to the Bank should either be sent by Registered\nPost or handed over to the Cash Department, as no\n\n \n\nUco BANK\n\naq\n\nName\n\n"IFSC: uceaocotms\n' dress\n\nKICK Code: 7428029504\n\nHIRAKUD\n-HIRAKED BRARCH HIRAKUS\nProae:\n\nindividual (s) outside. the Cash Department has/have JHARY EIST\nauthority to receive cash. KADAMPOLA\n(2) The account-holder should insist on delivery of Pass Book HERAKUD\n‘ made uptodate as far as possible on the same date; a 6.8%\n- otherwise he should obtain a receipt indicating when the HIRAKYD PIN .#oBlss\nPass Book will be delivered.\n(3) Deposit Rules in vogue can be obtained by account-holder TET. WaT / Asst.\nfrom the Branch on request Q28501 19027145\nPB.NG. }\n\n \n\n \n\n \n\nfe er ee me\n\n \n\x0c' ' \n\n= 2, ip\nO ~\nN 2\na\n: Y ve re ty\n) 3 x.\nNai] (F) my\n\n \n\ny Viayal chat aloala\nSH PPP ea [sys sys *\nas NB\n2\n=\n\ni x X we\na. = Xt +\n— W\nx 2
1	'. Ae - PR CSeathetn & 3)\n" J She ase 9 Pao\n\n‘s lad Bank Afr o Steppe\nIN

Label

Examples

'GHt Sa [OI uco Bank\n\nvis Free Number: 180¢-193-9125 _\n\nDICGC INSURANCE UPTO 5 LAC\n\nBRANCH\n\nUCO Bank\nP NT\n(1) Consuitants are requested to note that all moneys\n\nremitted to the Bank should either be sent by Registered\nPost or handed over to the Cash Department, as no\n\n \n\nUco BANK\n\naq\n\nName\n\n"IFSC: uceaocotms\n' dress\n\nKICK Code: 7428029504\n\nHIRAKUD\n-HIRAKED BRARCH HIRAKUS\nProae:\n\nindividual (s) outside. the Cash Department has/have JHARY EIST\nauthority to receive cash. KADAMPOLA\n(2) The account-holder should insist on delivery of Pass Book HERAKUD\n‘ made uptodate as far as possible on the same date; a 6.8%\n- otherwise he should obtain a receipt indicating when the HIRAKYD PIN .#oBlss\nPass Book will be delivered.\n(3) Deposit Rules in vogue can be obtained by account-holder TET. WaT / Asst.\nfrom the Branch on request Q28501 19027145\nPB.NG. }\n\n \n\n \n\n \n\nfe er ee me\n\n \n\x0c'
' \n\n= 2, ip\nO ~\nN 2\na\n: Y ve re ty\n) 3 x.\nNai] (F) my\n\n \n\ny Viayal chat aloala\nSH PPP ea [sys sys *\nas NB\n2\n=\n\ni x X we\na. = Xt +\n— W\nx 2

'. Ae - PR CSeathetn & 3)\n" J She ase 9 Pao\n\n‘s lad Bank Afr o Steppe\nIN

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/CASH_AND_BANK_INVOICE")
# Run inference
preds = model(" 
")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	201.2534	4241

Label	Training Sample Count
0	113
1	33

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
0.0023	1	0.3054	-
0.1142	50	0.1162	-
0.2283	100	0.0043	-
0.3425	150	0.0015	-
0.4566	200	0.0014	-
0.5708	250	0.0008	-
0.6849	300	0.0013	-
0.7991	350	0.001	-
0.9132	400	0.0004	-
1.0274	450	0.0008	-
1.1416	500	0.0008	-
1.2557	550	0.0011	-
1.3699	600	0.0008	-
1.4840	650	0.0007	-
1.5982	700	0.0005	-
1.7123	750	0.0005	-
1.8265	800	0.0007	-
1.9406	850	0.0005	-

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}
}