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Gopal2002/
setfit_zeon_3456

Name: setfit_zeon_3456
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: 4 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
5	'<s_cord-v2><s_menu><s_nm> Original for Buyer/ Duplicate for Temponte/ Teplicate for Assesce<s_nm> OMCL</s_nm><s_num> 93103986</s_num><s_unitprice> 76000</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> SUSHIBILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILI' '<s_cord-v2><s_menu><s_nm> Original Box buyer/ Duplicate for Temponte/ Teisplicate Box Assesce<s_nm> MCL</s_nm><s_num> 9310301</s_num><s_unitprice> @099</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> SUSHIBILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILI' '<s_cord-v2><s_menu><s_nm> Original Box Buyer/ Duplicate for Taquequeque/ Tcquil<s_nm> OMCL</s_nm><s_unitprice> @MCL</s_nm><s_unitprice> @MCL</s_unitprice><s_cnt> 1</s_cnt><s_price> @020</s_price><s_nm> SUPPOLIZE Vitter. Musc</s_nm><s_unitprice> @MACHIZED WHITED</s_nm><s_unitprice> @MICEDICED WHITED Claysucci Cryccucee</s_nm><s_unitprice> @MADEMI SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDENTERTAINCE SUSHIZEDICEDGE CLIZEDGE SUSHIZEDGEECECECECECECECE SUSHIZEDMIZEDENTERTAINCE SUSHIZEDICEDICEDGEECECECECECE SUSHIZEDENTERTAINCEE Cry SUSHIZEDGEECECECECECECECECE SUSHIZEDSUMIZEDENTERTAINCE SUSHIZEGGYECECECECECECECECEE SUSHIZEDSUMIZED SUSHIZEGGYECECECECECECECECECEECECE SUSHIZEDSUMIZED SUSHIZEDSUMIZED SUSHIZEGGYECECECECECECECECECEECECE SUSHIZEDSUMIZED SUSHIZEDSUKECECECECECECEECE SUSHIZEDSUKECECECECECEECECE SUSHIZEDSUKECECECECECEECECE SUSHIZEDSUKECECECECEECECECECEECECECECECECECEECECECECECECECECECEECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECE'
6	'<s_cord-v2><s_menu><s_nm> GARDANG CHOCO POWE IHADOUS</s_nm><s_nm> GENERAL TERM & CHOCOLIONSE</s_nm><s_nm> LIGHTEGAL TERS A CHOCOLIONSE</s_nm><s_nm> LIGHTICE</s_nm></s_sub><s_nm> ICE LIGHTING CHOCOLIZEDAYOSE</s_nm><s_cnt> 3</s_cnt><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm></s_sub><s_nm> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_cnt> 3</s_cnt><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_price> SUSHIKAIDEE' "<s_cord-v2><s_menu><s_nm> SUSHI SALMONG</s_nm><s_cnt> 1</s_cnt><s_price> SUSHI SALMONG</s_nm><s_cnt> 1</s_cnt><s_price> SUSHI SALMONG</s_nm></s_sub></s_menu><s_sub_total><s_subtotal_price> SUSHI SALMONG</s_subtotal_price><s_tax_price> 11+727</s_tax_price></s_sub_total><s_total><s_total_price> Dolla's</s_total_price></s_total>" '<s_cord-v2><s_menu><s_nm> WESEC</s_nm><s_price> 63644,836</s_price></s_menu><s_sub_total><s_subtotal_price> 636</s_subtotal_price></s_sub_total><s_total><s_total_price> 636</s_total_price><s_cashprice> 636-modern</s_cashprice></s_total>'
4	'<s_cord-v2><s_menu><s_nm> MATERIAL REPORT</s_nm><s_unitprice> 06/28世纪 -85-18 -To</s_nm><s_unitprice> @0.9세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기세기' "<s_cord-v2><s_menu><s_nm> MATERIAL REGIRI REPORT</s_nm><s_cnt> MRR SUBMITTED</s_nm><s_num> WHTPART'S INVICE</s_nm><s_nm> MRR/A/Rucciniot Box PHARK/CHECKGRAM/CHE<s_nm> Nc<s_nm> N&Eceivet N&Eceivet N&Eceivet N&Eceivet<s_nm> PO N&E P/PO/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G/G" '<s_cord-v2><s_menu><s_nm> MATERAL REGUT REPORT</s_nm><s_unitprice> PMVRRMM1810/0225</s_unitprice><s_cnt> x</s_cnt><s_price> 416</s_price><s_nm> WBRR&Gapp Cate &HG+8</s_nm><s_unitprice> Rp. PO PO POWERMMISB19002</s_unitprice><s_cnt> x</s_cnt><s_discountprice> : 1</s_discountprice><s_price> Rp. 0</s_price><s_nm> Pollow & JUN-16</s_nm><s_unitprice> Rp.Net</s_nm><s_unitprice> Rp. Pollow &Ham</s_nm><s_unitprice> Rp. 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3	'<s_cord-v2><s_menu><s_nm> HNDALCO INDUSTRIES LIMITED</s_nm><s_unitprice> HIRAKUD</s_nm></s_sub><s_nm> PAYMENT ORDER</s_nm><s_cnt> 1</s_cnt><s_price> 395-48</s_price><s_nm> Pauto Bresanta Kumar Mohany</s_nm><s_unitprice> 636</s_unitprice><s_cnt> 1</s_cnt><s_price> 636</s_price><s_nm> Emp. 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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.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Gopal2002/setfit_zeon_3456")
# Run inference
preds = model("<s_cord-v2><s_menu><s_nm> RT @kanaka<s_nm> RT @kanaka</s_total>")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	193.4045	1530

Label	Training Sample Count
3	38
4	45
5	54
6	41

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
0.0014	1	0.2908	-
0.0677	50	0.1735	-
0.1353	100	0.0824	-
0.2030	150	0.0505	-
0.2706	200	0.014	-
0.3383	250	0.0066	-
0.4060	300	0.0035	-
0.4736	350	0.0018	-
0.5413	400	0.0022	-
0.6089	450	0.0019	-
0.6766	500	0.0015	-
0.7442	550	0.0014	-
0.8119	600	0.0012	-
0.8796	650	0.0013	-
0.9472	700	0.0014	-
1.0149	750	0.0012	-
1.0825	800	0.001	-
1.1502	850	0.0012	-
1.2179	900	0.001	-
1.2855	950	0.0009	-
1.3532	1000	0.0009	-
1.4208	1050	0.0008	-
1.4885	1100	0.001	-
1.5562	1150	0.0009	-
1.6238	1200	0.001	-
1.6915	1250	0.0009	-
1.7591	1300	0.0009	-
1.8268	1350	0.0008	-
1.8945	1400	0.0007	-
1.9621	1450	0.0008	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.2
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}
}