Here’s 5 of the most valuable ways to convert unstructured text to structured data with natural language processing
Source: 5 Most Valuable Ways To Convert Unstructured Text To Structured Data | Width.ai
From Medium :: Run Very Large Language Models on Your Computer | by Benjamin Marie | Towards AI
New large language models are publicly released almost every month. They are getting better and larger.
You may assume that these models can only be run on big clusters or in the cloud.
Fortunately, this is not the case. Recent versions of PyTorch propose several mechanisms that make the use of large language models relatively easy on a standard computer and without much engineering, thanks to the Hugging Face Accelerate package.
Source: Run Very Large Language Models on Your Computer | by Benjamin Marie | Towards AI
From Medium :: Mastering AI Summarization: Your Ultimate Productivity Hack
Unlock Your Second Brain with Streamlit and Hugging Face’s Free LLM Summarization: build a Python Webapp running on your PC.
Source: Mastering AI Summarization: Your Ultimate Productivity Hack
This uses a smaller language model tailored to text summarization. Maybe a good path for assessing student short answers and essays.
Customizing GPT-3 for Your Application :: OpenAI
Developers can now fine-tune GPT-3 on their own data, creating a custom version tailored to their application. Customizing makes GPT-3 reliable for a wider variety of use cases and makes running the model cheaper and faster.
You can use an existing dataset of virtually any shape and size, or incrementally add data based on user feedback. With fine-tuning, one API customer was able to increase correct outputs from 83% to 95%. By adding new data from their product each week, another reduced error rates by 50%.