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README.md

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----
-license: mit
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+# 📄 Finance Document Classification API
+
+A fine-tuned [Hugging Face Transformers](https://huggingface.co/docs/transformers/index) model served via **FastAPI** for classifying finance-related documents. It uses a DistilBERT base model fine-tuned on the English subset of the Synthetic PII Finance Multilingual dataset. The API can be run locally or inside Docker and offers both `/predict` and `/health` endpoints.
+
+## 🚀 Features
+- Fine-tuned DistilBERT-based classification model
+- REST API with `/predict` and `/health` endpoints
+- Docker-ready for easy deployment
+- High accuracy with production-ready code
+
+## 📊 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%  |
+
+## 📂 Project Structure
+```
+finance_document_classification/
+├─ app/
+│  └─ main.py               # FastAPI app
+├─ final_model/              # Saved model & tokenizer
+├─ requirements.txt
+├─ Dockerfile
+├─ .dockerignore
+└─ README.md
+```
+
+## 🛠 Installation
+Clone the repository:
+```bash
+git clone https://github.com/Ar86Bat/Finance-Document-Text-Classification.git
+cd Finance-Document-Text-Classification
+```
+
+Create a virtual environment:
+```bash
+python3 -m venv .venv
+source .venv/bin/activate   # Windows: .venv\Scripts\activate
+```
+
+Install dependencies:
+```bash
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+## ▶️ Run Locally
+```bash
+uvicorn app.main:app --reload
+```
+The API will be available at:
+```
+http://127.0.0.1:8000/docs
+```
+
+## 🐳 Run with Docker
+```bash
+docker build -t finance-doc-classifier .
+docker run -p 8000:8000 finance-doc-classifier
+```
+
+## 📡 API Endpoints
+### `POST /predict`
+**Request:**
+```json
+{
+  "text": "Client requested details about investment restrictions."
+}
+```
+**Response:**
+```json
+{
+  "label": "Investment Restrictions",
+  "confidence": 0.987
+}
+```
+
+### `GET /health`
+Returns API health status.
+
+## 📦 Use the Model in Python
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_path = "Ar86Bat/Finance-Document-Text-Classification"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+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)
+```
+
+## 📜 License
+MIT License.