Overview
Open AI GPT (Generative Pre-trained Transformer) is a natural language processing model developed by OpenAI. It is based on the transformer architecture, which provides a way to process sequential data.
Features
Open AI GPT has several features that make it stand out as a prominent natural language processing model:
- It has been pre-trained on a massive corpus of data, allowing it to understand patterns and relationships in language.
- It is capable of generating natural language text that is coherent and contextually appropriate.
- It can be fine-tuned for a wide range of natural language processing tasks, such as language translation, text classification and summarization.
Applications
Open AI GPT has numerous applications in the field of natural language processing. Some of its applications include:
- Language translation
- Text summarization
- Chatbots and conversational AI
- Sentiment analysis
- Language modeling
Conclusion
Open AI GPT is a powerful tool for natural language processing tasks, thanks to its pre-training on a massive corpus of data and its ability to generate contextually appropriate text. Its flexibility and range of applications make it a valuable addition to any NLP toolkit.
An Example
To call the GPT API, you can follow these steps:
- Obtain your API key from OpenAI
- Install the
openaiPython library using pip -
In your Python script, import the OpenAI library and set up your API key:
import openai openai.api_key = "YOUR_API_KEY" -
Call the
openai.Completion.create()method and pass in your prompt as a string:response = openai.Completion.create( engine="text-davinci-002", prompt="Write a short story about a robot who learns how to love", max_tokens=60 ) -
The
responseobject will contain the generated text from the GPT API. -
Print out the generated text in your Python script:
print(response.choices[0].text)
This will print out the generated text to your console.
Comparison table for the Models
| Model | Parameters | Description | Capabilities | Price |
|---|---|---|---|---|
| GPT-4 | 8.192B - 32.768B | A large multimodal model that can solve difficult problems with greater accuracy than any of the previous models, thanks to its broader general knowledge and advanced reasoning capabilities. Optimized for chat but works well for traditional completions tasks. | Natural language understanding and generation, code understanding and generation, image generation (future) | Limited beta, join waitlist |
| GPT-3.5 | 6.7B - 175B | A set of models that can understand and generate natural language or code. Improved on GPT-3 with more data and better fine-tuning. gpt-3.5-turbo is the most capable and cost effective model in the GPT-3.5 family, optimized for chat but works well for traditional completions tasks as well. | Natural language understanding and generation, code understanding and generation | $0.06 - $0.12 per token |
| Codex | 12B - 175B | A set of models that can understand and generate code, including translating natural language to code. Powered by the same technology as GPT-3.5 but fine-tuned on a large corpus of code from GitHub. | Code understanding and generation, natural language to code translation | $0.06 - $0.12 per token |
| DALL-E | 12B | A model that can generate and edit images given a natural language prompt. Powered by the same technology as GPT-3 but fine-tuned on a large dataset of text-image pairs. | Image generation and editing from natural language prompts | Beta, join waitlist |
| GPT-3 | 125M - 175B | A set of models that can understand and generate natural language. The first large-scale language model that achieved state-of-the-art results on many natural language tasks. Powered by deep learning and a massive amount of text data from the web. | Natural language understanding and generation | $0.06 - $0.12 per token |