8000 correct quickstart by hamelsmu · Pull Request #4 · github/CodeSearchNet · GitHub
[go: up one dir, main page]
Skip to content
This repository was archived by the owner on Apr 11, 2023. It is now read-only.

correct quickstart #4

Merged
merged 1 commit into from
Sep 19, 2019
Merged

correct quickstart #4

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 5 additions & 3 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@ More context regarding the motivation for this problem is in [this paper][paper]
```
This will build Docker containers and download the datasets. By default, the data is downloaded into the `resources/data/` folder inside this repository, with the directory structure described [here](resources/README.md).

**The datasets you will download (most of them compressed) have a combined size of only ~ 3.5 GB.**
**The datasets you will download (most of them compressed) have a combined size of only ~ 3.5 GB.**

For more about the data, see [Data Details](#data-details) below as well as [this notebook](notebooks/ExploreData.ipynb).

Expand Down Expand Up @@ -229,6 +229,8 @@ Make sure you have [Docker](https://docs.docker.com/get-started/) and [Nvidia-Do
# (this will land you inside the Docker container, starting in the /src directory--you can detach from/attach to this container to pause/continue your work)
cd CodeSearchNet/
script/setup
# this will drop you into the shell inside a docker container.
script/console
Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@staceysv just added this line

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

great thanks!

# optional: log in to W&B to see your training metrics, track your experiments, and submit your models to the community benchmark
wandb login
# verify your setup by training a tiny model
Expand All @@ -253,7 +255,7 @@ Once you're satisfied with a new model, test it against the CodeSearchNet Challe
The query has a single encoder, whereas each programming language has its own encoder. The available encoders are Neural-Bag-Of-Words, RNN, 1D-CNN, Self-Attention (BERT), and a 1D-CNN+Self-Attention Hybrid.

The diagram below illustrates the general architecture of our baseline models:

![alt text](images/architecture.png "Architecture")

## Training
Expand Down Expand Up @@ -321,7 +323,7 @@ Additional notes:
We invite the community to submit their runs to this benchmark to facilitate transperency by following [these instructions](src/docs/BENCHMARK.md).

## How to Contribute

We anticipate that the community will design custom architectures and use frameworks other than Tensorflow. Furthermore, we anticipate that additional datasets will be useful. It is not our intention to integrate these models, approaches, and datasets into this repository as a superset of all available ideas. Rather, we intend to maintain the baseline models and links to the data in this repository as a central place of reference. We are accepting PRs that update the documentation, link to your project(s) with improved benchmarks, fix bugs, or make minor improvements to the code. Here are [more specific guidelines for contributing to this repository](CONTRIBUTING.md); note particularly our [Code of Conduct](CODE_OF_CONDUCT.md). Please open an issue if you are unsure of the best course of action.

## Other READMEs
Expand Down
0