Gopal2002/CASH_AND_BANK_INVOICE

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. Fine-tuning a Sentence Transformer with contrastive learning.
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

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	'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' ' \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	'. 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}
}