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license: apache-2.0 |
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datasets: |
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- FinGPT/fingpt-fiqa_qa |
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- FinGPT/fingpt-headline |
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language: |
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- en |
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base_model: |
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- google/gemma-2-2b-it |
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--- |
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This is a finetuned gemma2b model that is trained using FinGPT datasets |
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Model Overview |
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Model Name: Gemma 2B |
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Version: 1.0 |
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Date: November 2023 |
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Task: Financial Data Analysis |
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Framework: [Insert framework, e.g., TensorFlow, PyTorch] |
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License: [Insert license type] |
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Description |
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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. |
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Intended Use |
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Gemma 2B is intended for use by financial analysts, investors, and researchers looking to: |
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Predict stock prices and market trends. |
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Analyze financial statements and company performance. |
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Assess portfolio risks and returns. |
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Generate insights for strategic financial planning. |
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Dataset Information |
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Dataset: Finance Dataset |
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Source: [Specify source, e.g., Yahoo Finance, SEC filings] |
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Size: [Insert size, e.g., 100,000 records] |
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Features: |
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Historical stock prices |
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Trading volumes |
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Economic indicators |
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Company financial metrics (e.g., revenue, earnings) |
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News sentiment scores |
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Performance Metrics |
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The performance of Gemma 2B is evaluated using the following metrics: |
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Mean Absolute Error (MAE) |
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Root Mean Squared Error (RMSE) |
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R-squared (R²) |
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Benchmark Results: |
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MAE: [Insert value] |
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RMSE: [Insert value] |
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R²: [Insert value] |
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Limitations |
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The model is trained on historical data and may not account for unprecedented market events. |
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Performance can vary based on the selected features and parameters. |
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Requires continuous updates with new data to maintain accuracy. |
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Ethical Considerations |
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Ensure compliance with financial regulations and ethical standards when using the model. |
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Be aware of potential biases in the training data that may affect predictions. |
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Future Work |
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Future improvements may include: |
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Incorporating additional datasets (e.g., macroeconomic data). |
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Enhancing the model with deeper learning techniques or ensemble methods. |
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Continuous monitoring and retraining to adapt to market changes. |
