Modelpublic

wanadzhar913/debertav3-finetuned-banking-transaction-classification-text-only

sourceHugging Faceupdated 2y agoView on Hugging Face

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Model Card

Model Performance

Model	Epoch	Learning Rate	Grad Norm (Mean)	Training Loss	Validation Loss	Accuracy	F1 Score (Weighted)	F1 Score (Macro)	Precision (Weighted)	Precision (Macro)	Recall (Weighted)	Recall (Macro)
DeBERTaV3 (Text Only)	10	0.0000050	3.755	0.102	0.309	0.913	0.914	0.858	0.918	0.855	0.913	0.868

How to Use

python

from transformers import AutoTokenizer, AutoConfig, pipeline, \
                         DebertaV2ForSequenceClassification

config = AutoConfig.from_pretrained('wanadzhar913/debertav3-finetuned-banking-transaction-classification-text-only')
model = DebertaV2ForSequenceClassification.from_pretrained('wanadzhar913/debertav3-finetuned-banking-transaction-classification-text-only', config = config)
tokenizer = AutoTokenizer.from_pretrained('wanadzhar913/debertav3-finetuned-banking-transaction-classification-text-only')

pipe = pipeline(
    "text-classification",
    tokenizer = tokenizer,
    model=model,
    padding=True,
    device=0,
)

pipe([
    "Online Banking transfer from CHK 6479 Confirmation# 1425	",
    "DEPOSIT", # Supposed to be 'Payroll'
    "SELF LENDER AUSTIN TX 23267 Debit Card Purchase 09/23 10:20a #6410",
    "SECU Foundation",
    "RECURRING PAYMENT AUTHORIZED ON 06/02 GEICO *AUTO 1036 DC S583153489705993 111",
])
>>>[{'label': 'Internal Account Transfer', 'score': 0.9998998641967773},
>>> {'label': 'Transfer Deposit', 'score': 0.35954612493515015},
>>> {'label': 'Uncategorized', 'score': 0.9998960494995117},
>>> {'label': 'Restaurants', 'score': 0.6260305047035217},
>>> {'label': 'Insurance', 'score': 0.9998502731323242}]