https://github.com/nikk0001/text-generation-by-using-gpt-2
Text Generation By Using GPT-2 Model
https://github.com/nikk0001/text-generation-by-using-gpt-2
generative-adversarial-network generative-ai gpt gpt-2 textgeneration transformer-models transformers
Last synced: 3 months ago
JSON representation
Text Generation By Using GPT-2 Model
- Host: GitHub
- URL: https://github.com/nikk0001/text-generation-by-using-gpt-2
- Owner: NIKK0001
- Created: 2024-08-20T14:47:47.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-08-20T14:49:34.000Z (9 months ago)
- Last Synced: 2024-12-17T13:11:59.983Z (5 months ago)
- Topics: generative-adversarial-network, generative-ai, gpt, gpt-2, textgeneration, transformer-models, transformers
- Language: Jupyter Notebook
- Homepage:
- Size: 9.77 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## GPT-1 and GPT-2 Models
### GPT-1
GPT-1 (Generative Pre-trained Transformer 1) was the first in the series of Generative Pre-trained Transformer models developed by OpenAI. Released in 2018, GPT-1 utilized the transformer architecture and was trained on a large corpus of text data. The key innovation of GPT-1 was the concept of pre-training a language model on a large dataset and then fine-tuning it for specific tasks. GPT-1 demonstrated significant improvements in various NLP tasks, laying the foundation for subsequent models.
- *Architecture:* Transformer
- *Training Data:* BooksCorpus (over 7,000 unpublished books)
- *Parameters:* 110 million
### GPT-2
GPT-2 (Generative Pre-trained Transformer 2) is the successor to GPT-1 and was released in 2019. GPT-2 is much larger than GPT-1, with 1.5 billion parameters, and was trained on a more diverse and larger dataset. The model demonstrated the ability to generate coherent and contextually relevant text, even with minimal input. GPT-2's release sparked discussions around the ethical implications of advanced AI models due to its ability to generate realistic and human-like text.
- *Architecture:* Transformer
- *Training Data:* WebText dataset (8 million web pages)
- *Parameters:* 1.5 billion
- *Notable Features:* Zero-shot learning, text generation
GPT-2 remains a significant milestone in the development of NLP models and has inspired further advancements in the field, leading to even more powerful models like GPT-3 and GPT-4.
For more detailed information, you can explore the [OpenAI GPT-2 paper](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).