Update README.md

README.md

@@ -8,4 +8,58 @@ language:
 base_model:
 - google/gemma-2-2b-it
 ---
-This is a finetuned gemma2b model that is trained using FinGPT datasets
+This is a finetuned gemma2b model that is trained using FinGPT datasets
+
+Model Overview
+Model Name: Gemma 2B
+Version: 1.0
+Date: November 2023
+Task: Financial Data Analysis
+Framework: [Insert framework, e.g., TensorFlow, PyTorch]
+License: [Insert license type]
+
+Description
+Gemma 2B is a machine learning model designed to analyze and predict financial trends and behaviors using a comprehensive finance dataset. The model leverages advanced algorithms to provide insights into market movements, investment opportunities, and risk assessment.
+
+Intended Use
+Gemma 2B is intended for use by financial analysts, investors, and researchers looking to:
+
+Predict stock prices and market trends.
+Analyze financial statements and company performance.
+Assess portfolio risks and returns.
+Generate insights for strategic financial planning.
+Dataset Information
+Dataset: Finance Dataset
+Source: [Specify source, e.g., Yahoo Finance, SEC filings]
+Size: [Insert size, e.g., 100,000 records]
+Features:
+
+Historical stock prices
+Trading volumes
+Economic indicators
+Company financial metrics (e.g., revenue, earnings)
+News sentiment scores
+Performance Metrics
+The performance of Gemma 2B is evaluated using the following metrics:
+
+Mean Absolute Error (MAE)
+Root Mean Squared Error (RMSE)
+R-squared (R²)
+Benchmark Results:
+
+MAE: [Insert value]
+RMSE: [Insert value]
+R²: [Insert value]
+Limitations
+The model is trained on historical data and may not account for unprecedented market events.
+Performance can vary based on the selected features and parameters.
+Requires continuous updates with new data to maintain accuracy.
+Ethical Considerations
+Ensure compliance with financial regulations and ethical standards when using the model.
+Be aware of potential biases in the training data that may affect predictions.
+Future Work
+Future improvements may include:
+
+Incorporating additional datasets (e.g., macroeconomic data).
+Enhancing the model with deeper learning techniques or ensemble methods.
+Continuous monitoring and retraining to adapt to market changes.