README.md

---
language:
- en
license: apache-2.0
base_model:
- google/gemma-2-2b-it
datasets:
- FinGPT/fingpt-fiqa_qa
- FinGPT/fingpt-headline
model-index:
- name: gemma-2b-def
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 26.93
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ell44ot/gemma-2b-def
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 4.59
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ell44ot/gemma-2b-def
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 1.74
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ell44ot/gemma-2b-def
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 3.13
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ell44ot/gemma-2b-def
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 5.31
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ell44ot/gemma-2b-def
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 6.36
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ell44ot/gemma-2b-def
      name: Open LLM Leaderboard
---
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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ell44ot__gemma-2b-def)

|      Metric       |Value|
|-------------------|----:|
|Avg.               | 8.01|
|IFEval (0-Shot)    |26.93|
|BBH (3-Shot)       | 4.59|
|MATH Lvl 5 (4-Shot)| 1.74|
|GPQA (0-shot)      | 3.13|
|MuSR (0-shot)      | 5.31|
|MMLU-PRO (5-shot)  | 6.36|