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 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
model = SetFitModel.from_pretrained("Gopal2002/SERVICE_LARGE_MODEL_ZEON")
preds = model("
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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}
}