| title | emoji | colorFrom | colorTo | sdk | app_file | pinned |
|---|---|---|---|---|---|---|
decontextualizer |
📤 |
green |
gray |
streamlit |
main.py |
false |
As a second step in improving our content consumption workflows, I investigated a new approach to extracting fragments from a content item before saving them for subsequent surfacing. While the lexiscore deals with content items on a holistic level -- evaluating entire books, articles, and papers -- I speculated then that going granular is a natural next step in building tools which help us locate specific valuable ideas in long-form content. The decontextualizer is a stepping stone in that direction, consisting of a pipeline for making text excerpts compact and semantically self-contained. Concretely, the decontextualizer is a web app able to take in an annotated PDF and automatically tweak the highlighted excerpts so that they make more sense on their own, even out of context.
The decontextualizer can either be deployed from source or using Docker.
To deploy the decontextualizer labeler using Docker, first make sure to have Docker installed, then simply run the following.
docker run -p 8501:8501 paulbricman/decontextualizer
The tool should be available at localhost:8501.
To set up the decontextualizer, clone the repository and run the following:
python3 -m pip install -r requirements.txt
streamlit run main.py
The tool should be available at localhost:8501.
