Model Card for cjerzak/trump-speeches-gpt2-finetune
This is a fine-tuned version of the GPT-2 language model on a collection of 10 Donald Trump speeches. The original speeches are available at ryanmcdermott/trump-speeches. This model is intended for experimentation with text generation and for demonstration/educational purposes.
Model Details
Model Description
- Developed by: Fine-tuning OpenAI's original GPT-2.
- Model type: Causal Language Model (GPT-2).
- Language(s) (NLP): English.
- License: GPT-2’s license (MIT-based). The fine-tuning code is also available and may be under a different open-source license; consult the repository for details.
- Finetuned from model:
gpt2. - Shared by: cjerzak on Hugging Face.
Because it was fine-tuned on a small set of speeches, the model has learned stylistic patterns, phrases, and vocabulary frequently used in Donald Trump’s rhetoric. Note: This model is primarily for demonstration and to illustrate the use of GPT-2 fine-tuning. It should not be considered a comprehensive or robust representation of all of Donald Trump’s speeches.
Model Sources
- Repository: cjerzak/trump-speeches-gpt2-finetune on Hugging Face
- Training Data Source: ryanmcdermott/trump-speeches
Uses
Direct Use
- Text Generation / Experimentation: You can use this model to generate text in a style that somewhat mimics Donald Trump's speeches. It is well-suited as a teaching or demonstration model for fine-tuning GPT-2.
Downstream Use
- Creative Projects / Educational Examples: If you want to incorporate a “Trump-like” text generation style in creative applications or show how GPT-2 behaves when fine-tuned on a small, domain-specific dataset.
Bias, Risks, and Limitations
Because the training data consists of Donald Trump's speeches, the model may exhibit:
- Stylistic Bias: The model might produce text with repetitive rhetorical patterns or phrases.
- Political Bias / Offensiveness: The original speeches may contain language or statements that some users find offensive or controversial.
- Limited Generalization: With only 10 speeches, the model’s language patterns are narrowly focused. It may produce text with limited variety or slightly nonsensical completions outside of the style/subject matter present in the training data.
Recommendations
- Content Filtering: If deploying publicly, consider adding filters or moderation layers to avoid offensive content.
- Awareness: Users should be aware of potential biases in the generated text. Given the small dataset, it can amplify certain topics or phrasings.
- Educational Use: Primarily recommended for demonstration or educational projects, not as a production-level model.
How to Get Started with the Model
Install the necessary packages:
pip install transformers accelerate torch
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