https://github.com/alienkevin/l2z
https://github.com/alienkevin/l2z
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/alienkevin/l2z
- Owner: AlienKevin
- Created: 2023-01-29T00:05:08.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-01-29T16:57:14.000Z (about 3 years ago)
- Last Synced: 2025-03-17T11:50:45.505Z (11 months ago)
- Language: Python
- Size: 127 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Sources
1. `new.txt` is a list of words used in businesses: https://www.vocabulary.com/lists/234528#view=list
2. `core.txt` is a list of words required for the College English Test in China: https://github.com/mahavivo/english-wordlists/blob/master/CET4_edited.txt
* The CET-4 is mandatory for university students in China who are not English majors. It is also a prerequisite for a bachelor's degree. Many employers in China prefer applicants with CET-4 or even CET-6 certification. (https://www.wikiwand.com/en/College_English_Test)
# Prompt Algorithm
1. Prompt GPT-3 x times for sentences containing `new.txt` words.
To do this generate set of prompts following the structure:
"Generate a ___business___ conversation containing the word ___"confined"___"
where business is the topic of the conversation and confined is the word to include.
2. Filter out the sentences that contains words not in `core.txt` or `new.txt`.
```json
{
"laborious": "Person 1: \"This project is proving to be quite laborious. We've been working on it for weeks and still haven't made much progress.\"\n\nPerson 2: \"I know, it's been a challenge. But I think if we keep at it, we'll eventually get there.\"",
"inevitable": "Person 1: \"We need to start planning for the inevitable changes that will come with the new regulations.\"\n\nPerson 2: "Yes, it's inevitable that we'll have to make some adjustments. What do you think our first step should be?\""
}
```
# Dataflow
1. Start with the new words
2. Build prompts with each of the new words
3. Get responses from GPT-3 api
4. Parse the responses
```json
{
"laborious": [
{
"synonoyms": ["a", "b", "c"],
"chinese": "辛苦的",
"examples": [
"This project is proving to be quite laborious. We've been working on it for weeks and still haven't made much progress.",
"I know, it's been a challenge. But I think if we keep at it, we'll eventually get there."
]
},
[
"I can't believe how laborious this project is taking me. I thought it would be much easier!",
"Yeah, it's definitely a lot more work than we anticipated. But I'm sure if we keep at it, we'll get it done eventually."
]
]
}
```
5. Sort them into two theoretical users
# Frontend
https://github.com/AlienKevin/L2Z_app