sumink/ftgpt

Model Card for Fine-Tuned GPT (ftgpt)

This model was created for use in a university assignment.

Model Name: ftgpt

Model URL: sumink/ftgpt

Overview

This model, ftgpt, is a fine-tuned version of GPT-2. It was specifically created for an academic project as part of a university experiment. The purpose of this experiment was to explore the capabilities of language models in various natural language processing (NLP) tasks, such as text generation, summarization, and dialogue systems. The model was trained with a focus on improving text coherence, factual consistency, and contextual understanding.

Model Details

• Model Type: Transformer-based Language Model
• Base Model: GPT-2
• Parameter Count: Approximately 1.5 billion parameters
• Fine-Tuning Approach: The model was fine-tuned with task-specific datasets to improve its performance on particular NLP tasks, including dialogue generation and summarization.

Intended Use

This model is intended for educational and experimental purposes, particularly for understanding how fine-tuning affects the performance of language models on different tasks. It can be used for:

• Text Generation: Generating coherent and contextually relevant text based on user prompts.
• Summarization: Creating concise summaries of longer texts.
• Dialogue Systems: Assisting in dialogue generation experiments.

It is not recommended for production-level applications or sensitive use cases.

Performance

• Evaluation Metrics: The model was evaluated using perplexity and qualitative assessments of text coherence, fluency, and relevance. While it performs well in generating fluent text, it may struggle with factual consistency and out-of-domain prompts.
• Known Limitations: As this model was created for a university experiment, it has limited robustness, especially with out-of-distribution data or highly specialized domains. Ethical considerations such as bias and hallucination have not been fully addressed.

Limitations

• Biases: The model inherits biases present in the training data and may produce biased or harmful content.
• Factual Inaccuracies: The model may generate factually incorrect information. It should not be relied upon for critical decision-making.
• Generalization: Limited ability to generalize beyond the domains seen during training.

Ethical Considerations

Users should be cautious of the potential biases and ethical concerns associated with language models. The model was not rigorously tested for ethical implications, and its responses may reflect societal biases present in the training data. It is essential to use this model with appropriate caution, especially in applications that involve sensitive or impactful content.

Acknowledgements

Special thanks to the university, academic advisors, and fellow students who provided guidance and resources for this project. The fine-tuning and experimentation were conducted using resources available to students, including computational resources provided by the university.

Disclaimer

This model was produced for educational purposes as part of a university assignment and should not be used for real-world decision-making or in production environments without further validation and safety testing. The creators do not take responsibility for any misuse of this model.