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Project description
I "Hate" Papers
Create easily readable versions of papers via OpenAI
I often need to read a paper to provide background on a related topic. In these cases the technical depth of a paper can be a major obstacle. So I created I Hate Papers to create easily digestible versions of academic research.
Installation
pip install i-hate-papers
Example use
# First set your OpenAI API key
❱ export OPENAI_API_KEY=...
# Summarise a arXiv paper ID
❱ i_hate_papers 2106.09685
# Summarise a latex file
❱ i_hate_papers path/to/some-paper.tex
Example output
- Example Markdown
- Example HTML (includes rendered math using MathJax)
Reference
❱ i_hate_papers --help
usage: i_hate_papers [-h] [--verbosity {0,1,2}] [--no-input] [--no-html] [--no-open]
[--detail-level {0,1,2}] [--model MODEL] INPUT
Summarise an arXiv paper
You must set the OPENAI_API_KEY environment variable using your OpenAi.com API key
positional arguments:
INPUT arXiv paper ID (example: 1234.56789) or path to a .tex file
options:
-h, --help show this help message and exit
--verbosity {0,1,2} Set the logging verbosity (0 = quiet, 1 = info logging, 2 = debug logging). Default is 1
--no-input Don't prompt for file selection, just use the largest tex file
--no-html Skip HTML file generation
--no-open Don't open the HTML file when complete (macOS only)
--detail-level {0,1,2}
How detailed should the summary be? (0 = minimal detail, 1 = normal, 2 = more detail)
--model MODEL What model to use to generate the summaries
Release process
For internal use:
export VERSION=0.1.0
git tag "v$VERSION"
git push origin refs/tags/v$VERSION
poetry publish --build
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