setfit_zeon

Gopal2002/setfit_zeon

setfit_zeon is a text classification model from Gopal2002. Use it when you need a label for a piece of text. It is set up for setfit.

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.

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At a glance

Task: Text Classification
Library: setfit
Model type: bert
Access: Public
Created: Jan 2, 2024
Updated: Jan 16, 2024
SHA: 8523935c

Try a prompt

Base models

BAAI/bge-small-en-v1.5

Model card

From the Hugging Face model README

SetFit with BAAI/bge-small-en-v1.5

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
2	<ul><li>'<s_cord-v2><s_menu><s_nm> M/S. JOSEPH SUNA</s_nm><s_num> DATABI<sep/><s_nm> Tankipaa.MIRAKU SAMBALPUR.768Q16</s_nm><s_num> DMB Nb N.9861345883<sep/><s_nm> Deals Which :Altypes of ChickenGNALOR REGUMING)</s_nm><s_num> DISALI<sep/><s_nm> WINNALIZED</s_nm><s_num> CHOCO SUSPECIALIZE</s_nm><s_num> TWICENCHE<sep/><s_nm> SHRANGKANG POWER</s_nm><s_num> LATHOCO TWICENKO:</s_nm><s_num> JERYUNG CHOCO TWICENKO:</s_nm><s_num> JERYUNG HZYGANGKAN<sep/><s_nm> DIFF-SAWALAPUKU SAMBALPUR.76801GHOLIZEG DATE</s_nm><s_num> DATE</s_nm><s_num> DATE:</s_nm><s_num> 01/01/01/01/01/01/01/01/01/01/01/01/01/01/01/01/01<sep/><s_nm> PAN No.:</s_nm><s_num> PPODATE</s_nm><s_num> 01/01/01/01/01/01/01/01/01/01/01<sep/><s_nm> DATE OPSE<sep/><s_nm> HANDUPPOWER</s_nm><s_num> 30.12221</s_num><s_price> 1,945.00</s_price><sep/><s_nm> SUSPENGGANGURG.GUSTAGUR GUSTAGANGKANGURGUSTAGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTG'</li><li>'<s_cord-v2><s_menu><s_nm> GST INVOLICE</s_nm><s_price> ORIGINAL FOR KEGINGLI</s_nm><s_price> WOUCE BREGRAMING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHI'</li><li>'<s_cord-v2><s_menu><s_nm> TAX INVOICE</s_nm><s_price> ORIGINAL FOR AQUALIZE</s_nm><s_price> SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO '</li></ul>
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SUSHIBILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILI'</li><li>'<s_cord-v2><s_menu><s_nm> HINDALCO INDUSTRIES LIMITED</s_nm><s_price> GSTING&NAACHI201</s_price><sep/><s_nm> WBABUPOWER HEROGUSTAMPURGANGKANCE CHOCOLOGALINGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGA'</li></ul>

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/setfit_zeon")
# Run inference
preds = model("<s_cord-v2><s_menu><s_nm> HINALCO INDUSTRIES LTB. HIRAKUR</s_nm><s_unitprice> 1344</s_unitprice><s_cnt> 1</s_cnt><s_price> 4,436</s_price><sep/><s_nm> ASTRICA BRIOC</s_nm><s_unitprice> 12.082</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> 12.027</s_discountprice><s_price> SUSPICY TEMPURA HIRAKUR</s_nm><s_unitprice> 12.027.00.0020</s_discountprice><s_price> PAK SUSHI HIRAKURURUR</s_nm><s_unitprice> 12.027.00.0020</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> 12.027</s_discountprice><s_price> 4,436</s_price><sep/><s_nm> SUSHI SALT CALLOCALI</s_nm><s_unitprice> 12.027.0020</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> 1,003</s_discountprice><s_price> 1,00</s_price></s_menu><s_sub_total><s_subtotal_price> 3,003</s_subtotal_price><s_discount_price> 3,003<sep/> 0.00</s_discount_price></s_sub_total><s_total><s_total_price> 3,00</s_total_price><s_cashprice> 3,00</s_cashprice><s_changeprice> 1,00</s_changeprice></s_total>")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	107.8041	763

Label	Training Sample Count
0	47
1	51
2	50

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
0.0022	1	0.3004	-
0.1094	50	0.2457	-
0.2188	100	0.1464	-
0.3282	150	0.0079	-
0.4376	200	0.0028	-
0.5470	250	0.0027	-
0.6565	300	0.0017	-
0.7659	350	0.0014	-
0.8753	400	0.0015	-
0.9847	450	0.0011	-
1.0941	500	0.001	-
1.2035	550	0.0011	-
1.3129	600	0.001	-
1.4223	650	0.0011	-
1.5317	700	0.0011	-
1.6411	750	0.0009	-
1.7505	800	0.0008	-
1.8600	850	0.001	-
1.9694	900	0.0009	-

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

@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}
}

Task: Text Classification
Library: setfit
Type: bert
Created: Jan 2, 2024
Updated: Jan 16, 2024