Gopal2002/COAL_INVOICE_ZEON

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:

1. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 2.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: 2 classes <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Repository: SetFit on GitHub
• —Paper: Efficient Few-Shot Learning Without Prompts
• —Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Evaluation

Metrics

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/COAL_INVOICE_ZEON")
# Run inference
preds = model("UNITED MEDICAL STORE Patient Name: KASTURI uENA
‘EW MARKET, C/O PRAFULLA KUMAR JENA
HIRAKUD. SAMBALPUR. Dr. Name :

Medicine Advice Slip: MA/2223/0668 “
Phone :0663-2431670 Prescription Indent:M/2223/06299

DL No. :SAWZ 486 R/487 RC Invoice No. ; 0002785 Date : 21/11/2022

Se|__Qiy. [Pack [Product “Batch [Exp] HSN [ MRP | Table | Dis [5051] CO3i] Amount |

1. 30 TAB] 30'S TELMA H TAB 11/24 | 30049099; 484.00! 432.14 0.001 6.00
NEOPRIDE TOTAL CAP 7/24 30049099) 445.00) 0,00; 6.00

 

 

 

SUB TOTAL :

SGST
er rH 2 ROFF :
— ha GRAND TOTAL

Te & Con itions For UNITED MEDICAL STORE R a ah
BILL GRAND TOTAL IS CALCULATED ACCORDING TO 1D- 3306 Im- 1220
MRP PRICE ( INCLUDING ALL GST TAXES ) Q _ 06 (ped)

 
")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (32, 32)
• —num_epochs: (2, 2)
• —max_steps: -1
• —sampling_strategy: oversampling
• —bodylearningrate: (2e-05, 1e-05)
• —headlearningrate: 0.01
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: False

Training Results

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}
}

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