--- 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/—\n\ Rupees 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 :\n\ i — 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\n\ PAYMENT 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: 1.0 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 | | | 1 | | ## 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/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 ```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} } ```