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Step 1: Ask Questions. In this step, you’ll learn how to create a program that can ask questions and check answers. This will be the foundation of your quiz application, which you’ll improve upon in the rest of the tutorial. At the end of this step, your program will look like this:
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- Overview
- Installation
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- Inner-workings
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- Footnotes
Large language models can be instruction tuned with a set of questions and answers. However, to further fine-tune a model on your own data, you need a large number of questions and answers about your data. Producing those questions and answers can be a lot of manual work.
This repository lets you use a non-fine-tuned language model (ChatGPT) to extract question/answer pairs automatically from existing textual data, eliminating all manual work.
To run this code, you will need to clone this repository then install the following Python packages:
•tiktoken, the OpenAI tokeniser,
•openai, the official OpenAI API client,
•langchain, glue code used to combine models and utilities.
This script is designed to turn a folder of markdown (.md) documents into a .json file containing a list of questions, answers and paths to the source documents that were used to produce them.
To run the code, set the relevant file paths in the question_extractor.py file (both the input folder and the output path) and insure that your OpenAI API key is in the environment. Then run the script with Python:
The code loops on all files, for each file it extracts a list of questions using the following prompt followed by a chunk of text:
It then loops on the questions, producing an answer by passing the following prompt followed by a chunk of text and a question:
Most of the actual logic of the code is dedicated to processing the files concurrently (for speed) and insuring that text chunks passed to the model are small enough to leave enough tokens for answering.
If a text is too long to be sent to the model, it is split along its highest markdown heading level (the process can be repeated recursively if needed until we get down to single paragraphs).
•make it possible to use GPT4 for the question answering, improving the quality of the answers at the cost of a slower runtime and significantly increased costs
•save intermediate results to be able to restart an interrupted job
1.Running at about 93% of the model's rate limit. ↩
Nov 7, 2014 · First of all your code is (no offence) all whack, one your completely ignoring the :s, don't use the normal input unless necessary or your in python 3. Also, please explain how your going to make the the quiz, string formatting, a list of questions and answers, a dictionary, server, etc.
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Jul 5, 2016 · What I'd do is: Create three lists: one containing questions, one answers and another one which contains the correct answers. Then create a simple method, and basically do what you've done, but in a shorter / nicer way. Let's see what we have:
