financial-advice-gpt2

Model Card for Financial Advice GPT-2

Model Summary

This model is a fine-tuned version of the GPT-2 model specifically designed to provide financial advice. Leveraging the GPT-2 architecture, this model has been fine-tuned on financial datasets to assist users with queries related to budgeting, investment strategies, financial planning, and other financial advice topics. It is intended to be integrated into applications requiring automated financial guidance.

Model Details

Model Description

This model, developed by Nonim Samarakoon and shared under the RoamifyMML account, is a fine-tuned variant of the GPT-2 model. The model has been trained on custom financial datasets to enhance its ability to generate relevant and practical financial advice. It retains the transformer-based architecture of GPT-2 while focusing on financial contexts.

• Developed by: Nonim Samarakoon
• Model type: Transformer Model
• Base model: OpenAI Community's GPT-2
• Finetuned from model: Custom Financial Dataset

Model Sources

• Repository: RoamifyMML/financial-advice-gpt2

Uses

Direct Use

This model can be used directly to generate financial advice and recommendations. It can answer questions related to budgeting, investment strategies, saving plans, and other financial queries.

Downstream Use

This model can be further fine-tuned or integrated into larger financial advisory systems, chatbots, or automated customer service platforms to enhance the financial guidance offered.

Out-of-Scope Use

This model is not intended for use in non-financial contexts or for providing legal or tax advice. It should not be used for making critical financial decisions without human oversight.

Bias, Risks, and Limitations

The model may reflect biases present in the training data, particularly if the data sources were not fully representative. Users should be cautious about potential inaccuracies and should not rely solely on the model for financial decisions.

Recommendations

• Awareness: Users should be aware of the model's limitations, especially regarding the reliability of the financial advice provided.
• Human Oversight: Financial decisions should be reviewed by a qualified human advisor to ensure accuracy and appropriateness.

How to Get Started with the Model

To use the model, install the transformers library and load the model as follows:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained("RoamifyMML/financial-advice-gpt2")
model = GPT2LMHeadModel.from_pretrained("RoamifyMML/financial-advice-gpt2")

input_text = "How should I plan my budget for the next year?"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

outputs = model.generate(input_ids, max_length=150, num_return_sequences=1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned on a custom dataset comprising financial documents, articles, and Q&A datasets. The dataset was curated to cover a wide range of financial topics to ensure the model's responses are relevant and helpful.

Training Procedure

• Training regime: The model was trained using mixed precision (fp16) to optimize performance and training time.

Evaluation

Testing Data, Factors & Metrics

The model was evaluated on a separate set of financial questions and scenarios to test its accuracy and relevance. Metrics such as BLEU score and human evaluation were used to assess the model's performance.

Results

The model demonstrates strong performance in generating coherent and contextually relevant financial advice. However, it may occasionally provide generic or incomplete answers, particularly on highly specific or niche topics.

Summary

While the model provides valuable financial insights, it is recommended to consult a human expert for critical decisions.

Environmental Impact

The environmental impact of training the model was calculated using the Machine Learning Impact calculator.

• Hardware Type: NVIDIA V100 GPUs
• Hours used: 48 hours
• Cloud Provider: AWS
• Compute Region: US East (N. Virginia)
• Carbon Emitted: Estimated 25 kg of CO2

Technical Specifications

Model Architecture and Objective

The model uses the GPT-2 architecture, a transformer-based model pre-trained on a large corpus of text and then fine-tuned on financial data to enhance its ability to generate financial advice.

Compute Infrastructure

Hardware

• GPUs: NVIDIA V100
• RAM: 64 GB

Software

• Framework: PyTorch
• Libraries: Transformers by Hugging Face

Citation

If you use this model in your work, please cite it as follows:

BibTeX:

@misc{financial-advice-gpt2,
  author = {Nonim Samarakoon},
  title = {Financial Advice GPT-2},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/RoamifyMML/financial-advice-gpt2}},
}

APA: Samarakoon, N. (2024). Financial Advice GPT-2. Hugging Face. Retrieved from https://huggingface.co/RoamifyMML/financial-advice-gpt2

Contact

For more information, please contact the model developer at [[email protected]].