Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+  - tax-compliance
+  - financial-compliance
+  - machine-learning
+  - tax-regulations
+model-index:
+  - name: Finlytic-Compliance
+    results:
+      - task:
+          type: compliance-check
+        dataset:
+          name: finlytic-compliance-data
+          type: financial-transactions
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 92.00
+          - name: Precision
+            type: precision
+            value: 90.00
+          - name: Recall
+            type: recall
+            value: 88.00
+          - name: F1-Score
+            type: f1
+            value: 89.00
+        source:
+          name: Internal Evaluation
+          url: https://huggingface.co/comethrusws/finlytic-compliance
+---
+
+# Finlytic-Compliance
+
+**Finlytic-Compliance** is an AI-driven model built to automate the task of ensuring financial transactions meet regulatory tax requirements. It helps SMEs remain compliant with tax laws in Nepal by constantly monitoring financial records.
+
+## Model Details
+
+- **Model Name**: Finlytic-Compliance
+- **Model Type**: Compliance Check
+- **Framework**: TensorFlow, Scikit-learn, Keras
+- **Dataset**: The model is trained on financial transactions labeled for tax compliance.
+- **Use Case**: Automating the detection of tax compliance issues for Nepalese SMEs.
+- **Hosting**: Huggingface model repository (locally used)
+
+## Objective
+
+The model reduces the need for manual checking and reliance on tax consultants by automatically flagging transactions that do not comply with Nepalese tax laws.
+
+## Model Architecture
+
+The model is built on a transformer architecture, fine-tuned specifically for identifying compliance issues in financial transactions. It has been trained on a dataset of transactions with known compliance statuses.
+
+## How to Use
+
+1. **Installation**: Clone the model repository from Huggingface or load the model locally.
+   
+   ```bash
+   git clone https://huggingface.co/comethrusws/finlytic-compliance
+   ```
+   
+2. **Load the Model**:
+
+   ```python
+   from transformers import AutoTokenizer, AutoModel
+
+   tokenizer = AutoTokenizer.from_pretrained("path_to/finlytic-compliance")
+   model = AutoModel.from_pretrained("path_to/finlytic-compliance")
+   ```
+
+3. **Input**: Feed the model financial transactions (structured in JSON or CSV format). The model will process these transactions and check for compliance issues.
+
+4. **Output**: The output will indicate whether a transaction is compliant with tax regulations and provide additional insights if necessary.
+
+## Dataset
+
+The model was trained using annotated financial records, with transactions labeled as either compliant or non-compliant with Nepalese tax laws.
+
+## Evaluation
+
+The model was evaluated using a hold-out test dataset. The performance metrics are as follows:
+
+- **Accuracy**: 92%
+- **Precision**: 90%
+- **Recall**: 88%
+- **F1-Score**: 89%
+
+These results indicate that the model is highly effective in flagging non-compliant transactions and ensuring financial records are accurate.
+
+## Limitations
+
+- The model is designed for Nepalese tax laws, so it may need adjustments for different regulatory frameworks.
+- It is best suited for common financial transactions and may not generalize well for edge cases.
+
+## Future Improvements
+
+- Expanding the dataset to cover more complex financial scenarios.
+- Adapting the model to work with tax regulations from other countries.
+
+## Contact
+
+For queries or contributions, reach out to the Finlytic development team at [[email protected]](mailto:[email protected]).