---
library_name: transformers
language:
- en
base_model:
- microsoft/deberta-v3-base
pipeline_tag: text-classification
---
### 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}]
```