setfit_zeon / README.md

Gopal2002

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metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
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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
            name: Accuracy

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
2	' M/S. JOSEPH SUNA DATABI Tankipaa.MIRAKU SAMBALPUR.768Q16 DMB Nb N.9861345883 Deals Which :Altypes of ChickenGNALOR REGUMING) DISALI WINNALIZED CHOCO SUSPECIALIZE TWICENCHE SHRANGKANG POWER LATHOCO TWICENKO: JERYUNG CHOCO TWICENKO: JERYUNG HZYGANGKAN DIFF-SAWALAPUKU SAMBALPUR.76801GHOLIZEG DATE DATE DATE: 01/01/01/01/01/01/01/01/01/01/01/01/01/01/01/01/01 PAN No.: PPODATE 01/01/01/01/01/01/01/01/01/01/01 DATE OPSE HANDUPPOWER 30.12221 1,945.00 SUSPENGGANGURG.GUSTAGUR GUSTAGANGKANGURGUSTAGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTG' ' GST INVOLICE ORIGINAL FOR KEGINGLI 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' ' TAX INVOICE ORIGINAL FOR AQUALIZE 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 '
1	' UNDALCO INDUSTRIES LTB. HIRAKUD POWER ASH WPTCH BRIOGE TIMOL CATE BRIOUS DATE SUSCEE SUSCE 1 SUSCE MSCED SUSCEE SUSCE 1 SUSCE MICHI CHOCO KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KE' ' UNDALCO INDUSTRIES LTB. 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0	' HANDALCO 이미지ES LIMITED SUNDAYGHOCO SUSHIZEH CINCEHANGKAGHOCO SUSHIZEHANGKAGHOUSHIZEHANGKAGHOUSHIZEHANGKAGHOUSHIZEHANGKAGHOUSHIZEHANGKAGHOUSHIZEHANGKANG PURCHASE ORDER WANTE CHOCO CAKE CONSULATANCE PYI LOTHO NUMPIC UPICK CHOCO CHOCO CHOCOCO SUSHIZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHERNt.Minitie HGHOCEHINEN.Minitie HGUMAGHONN.Minitie HUMAGHONN 436.0 OxMini WHEN HUMAGHUNG SUSHIZEHITEGHOUSHILIZEHENCE COTTING THOGEHGHOCO SUSHIZEHITEGHTGHOLIZEHGHOLIZEHGHOLIZEHGHOLIZEHGPICYGLIZEHGHTG SOUTING SUSHIZEHITEGHTGHOLIZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEH' ' WINGllaco Industries Limited LIKING PICCE CHOCOLOGY VICE LIKING 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' HINDALCO INDUSTRIES LIMITED GSTING&NAACHI201 WBABUPOWER HEROGUSTAMPURGANGKANCE 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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.

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}
}