| --- |
| license: mit |
| language: |
| - en |
| base_model: |
| - distilbert/distilbert-base-uncased |
| tags: |
| - finance |
| - document-classification |
| datasets: |
| - gretelai/synthetic_pii_finance_multilingual |
| metrics: |
| - accuracy |
| pipeline_tag: 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 |
| | Metric | Score | |
| |-------------|---------| |
| | Accuracy | 98.65% | |
| | Precision | 98.70% | |
| | Recall | 98.65% | |
| | F1 | 98.65% | |
|
|
| ## How to Use |
|
|
| ```python |
| 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:** |
| ```json |
| { |
| "text": "Client requested details about investment restrictions." |
| } |
| ``` |
| **Response:** |
| ```json |
| { |
| "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 |
