Ar86Bat/Finance-Document-Text-Classification

📄 Finance Document Classification

A fine-tuned DistilBERT model for classifying finance-related documents. This model is based on distilbert-base-uncased and fine-tuned on the English subset of the Synthetic PII Finance Multilingual dataset. It is suitable for multi-class document classification tasks in the finance domain.

Model Details

• —Base Model: distilbert-base-uncased
• —Task: Multi-class finance document classification
• —Language: English
• —Dataset: Synthetic PII Finance Multilingual (English subset)
• —Framework: Hugging Face Transformers

Metrics

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "Ar86Bat/Finance-Document-Text-Classification"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "Client requested details about investment restrictions."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    pred_id = torch.argmax(probs, dim=1).item()

print("Predicted class ID:", pred_id)

Intended Uses & Limitations

• —Intended use: Automated classification of finance-related documents for compliance, organization, or workflow automation.
• —Not suitable for: Non-financial or out-of-domain documents without further fine-tuning.

Example API Usage

This model can be served via FastAPI or other REST frameworks. Example request/response:

Request:

{
  "text": "Client requested details about investment restrictions."
}

Response:

{
  "label": "Investment Restrictions",
  "confidence": 0.987
}

Citation

If you use this model, please cite the repository:

@misc{ar86bat_finance_doc_classification_2025,
  author = {Arif Hizlan},
  title = {Finance Document Text Classification},
  year = {2025},
  howpublished = {\\url{https://huggingface.co/Ar86Bat/Finance-Document-Text-Classification}}
}

License

MIT License