CASH_AND_BANK_INVOICE

Gopal2002/CASH_AND_BANK_INVOICE

CASH_AND_BANK_INVOICE 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 18, 2024
Updated: Jan 18, 2024
SHA: 5568b496

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: 2 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	<ul><li>'GHt Sa [OI uco Bank\n\nvis Free Number: 180¢-193-9125 _\n\nDICGC INSURANCE UPTO 5 LAC\n\nBRANCH\n\nUCO Bank\nP NT\n(1) Consuitants are requested to note that all moneys\n\nremitted to the Bank should either be sent by Registered\nPost or handed over to the Cash Department, as no\n\n \n\nUco BANK\n\naq\n\nName\n\n"IFSC: uceaocotms\n' dress\n\nKICK Code: 7428029504\n\nHIRAKUD\n-HIRAKED BRARCH HIRAKUS\nProae:\n\nindividual (s) outside. the Cash Department has/have JHARY EIST\nauthority to receive cash. KADAMPOLA\n(2) The account-holder should insist on delivery of Pass Book HERAKUD\n‘ made uptodate as far as possible on the same date; a 6.8%\n- otherwise he should obtain a receipt indicating when the HIRAKYD PIN .#oBlss\nPass Book will be delivered.\n(3) Deposit Rules in vogue can be obtained by account-holder TET. WaT / Asst.\nfrom the Branch on request Q28501 19027145\nPB.NG. }\n\n \n\n \n\n \n\nfe er ee me\n\n \n\x0c'</li><li>' \n\n= 2, ip\nO ~\nN 2\na\n: Y ve re ty\n) 3 x.\nNai] (F) my\n\n \n\ny Viayal chat aloala\nSH PPP ea [sys sys *\nas NB\n2\n=\n\ni x X we\na. = Xt +\n— W\nx 2
1	<ul><li>'. Ae - PR CSeathetn & 3)\n" J She ase 9 Pao\n\n‘s lad Bank Afr o Steppe\nIN

Label

Examples

<ul><li>'GHt Sa [OI uco Bank\n\nvis Free Number: 180¢-193-9125 _\n\nDICGC INSURANCE UPTO 5 LAC\n\nBRANCH\n\nUCO Bank\nP NT\n(1) Consuitants are requested to note that all moneys\n\nremitted to the Bank should either be sent by Registered\nPost or handed over to the Cash Department, as no\n\n \n\nUco BANK\n\naq\n\nName\n\n"IFSC: uceaocotms\n' dress\n\nKICK Code: 7428029504\n\nHIRAKUD\n-HIRAKED BRARCH HIRAKUS\nProae:\n\nindividual (s) outside. the Cash Department has/have JHARY EIST\nauthority to receive cash. KADAMPOLA\n(2) The account-holder should insist on delivery of Pass Book HERAKUD\n‘ made uptodate as far as possible on the same date; a 6.8%\n- otherwise he should obtain a receipt indicating when the HIRAKYD PIN .#oBlss\nPass Book will be delivered.\n(3) Deposit Rules in vogue can be obtained by account-holder TET. WaT / Asst.\nfrom the Branch on request Q28501 19027145\nPB.NG. }\n\n \n\n \n\n \n\nfe er ee me\n\n \n\x0c'</li><li>' \n\n= 2, ip\nO ~\nN 2\na\n: Y ve re ty\n) 3 x.\nNai] (F) my\n\n \n\ny Viayal chat aloala\nSH PPP ea [sys sys *\nas NB\n2\n=\n\ni x X we\na. = Xt +\n— W\nx 2

<ul><li>'. Ae - PR CSeathetn & 3)\n" J She ase 9 Pao\n\n‘s lad Bank Afr o Steppe\nIN

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/CASH_AND_BANK_INVOICE")
# Run inference
preds = model(" 
")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	201.2534	4241

Label	Training Sample Count
0	113
1	33

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
0.0023	1	0.3054	-
0.1142	50	0.1162	-
0.2283	100	0.0043	-
0.3425	150	0.0015	-
0.4566	200	0.0014	-
0.5708	250	0.0008	-
0.6849	300	0.0013	-
0.7991	350	0.001	-
0.9132	400	0.0004	-
1.0274	450	0.0008	-
1.1416	500	0.0008	-
1.2557	550	0.0011	-
1.3699	600	0.0008	-
1.4840	650	0.0007	-
1.5982	700	0.0005	-
1.7123	750	0.0005	-
1.8265	800	0.0007	-
1.9406	850	0.0005	-

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}
}

Task: Text Classification
Library: setfit
Type: bert
Created: Jan 18, 2024
Updated: Jan 18, 2024