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harvardnlp / repository
An annotated implementation of the Transformer paper.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
Code for The Annotated Transformer blog post:
http://nlp.seas.harvard.edu/annotated-transformer/
Use requirements.txt to install library dependencies with pip:
pip install -r requirements.txt
The Annotated Transformer is created using jupytext.
Regular notebooks pose problems for source control - cell outputs end up in the repo history and diffs between commits are difficult to examine. Using jupytext, there is a python script (.py file) that is automatically kept in sync with the notebook file by the jupytext plugin.
The python script is committed contains all the cell content and can be used to generate the notebook file. The python script is a regular python source file, markdown sections are included using a standard comment convention, and outputs are not saved. The notebook itself is treated as a build artifact and is not commited to the git repository.
Prior to using this repo, make sure jupytext is installed by following the installation instructions here.
To produce the .ipynb notebook file using the markdown source, run (under the hood, the notebook build target simply runs jupytext --to ipynb the_annotated_transformer.py):
make notebook
To produce the html version of the notebook, run:
make html
make html is just a shortcut for for generating the notebook with jupytext --to ipynb the_annotated_transformer.py followed by using the jupyter nbconvert command to produce html using jupyter nbconvert --to html the_annotated_transformer.ipynb
To keep the code formatting clean, the annotated transformer git repo has a git action to check that the code conforms to PEP8 coding standards.
To make this easier, there are two Makefile build targets to run automatic code formatting with black and flake8.
Be sure to install black and flake8.
You can then run:
make black
(or alternatively manually call black black --line-length 79 the_annotated_transformer.py) to format code automatically using black and:
make flake
(or manually call flake8 `flake8 --show-source the_annotated_transformer.py) to check for PEP8 violations.
It's recommended to run these two commands and fix any flake8 errors that arise, when submitting a PR, otherwise the github actions CI will report an error.
Selected from shared topics, language and repository description—not editorial ratings.
shaadclt /
This Jupyter notebook project uses YOLOv8 for vehicle tracking and implements a line crossing detection algorithm. The system counts vehicles that cross a specified line in a video, annotates the frames, and generates an output video with visualizations.
29/100 health