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Gopal2002/
SERVICE_LARGE_MODEL_ZEON

Name: SERVICE_LARGE_MODEL_ZEON
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

BAAI/bge-small-en-v1.5

finetune

model-index

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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: 3 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	'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.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Gopal2002/SERVICE_LARGE_MODEL_ZEON")
# Run inference
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

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}
}