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Guide to Implementing a dataset

All dataset loading scripts will be hosted on the Official BigBIO Hub. We use this github repository to accept new submissions, and standardize quality control.

Pre-Requisites

Please make a github account prior to implementing a dataset; you can follow instructions to install git here.

You will also need at least Python 3.6+. If you are installing python, we recommend downloading anaconda to curate a python environment with necessary packages. We strongly recommend Python 3.8+ for stability.

Optional Setup your GitHub account with SSH (instructions here.)

1. Fork the BigBIO repository

Fork the BigBIOrepository. To do this, click the link to the repository and click "fork" in the upper-right corner. You should get an option to fork to your account, provided you are signed into Github.

After you fork, clone the repository locally. You can do so as follows:

git clone [email protected]:<your_github_username>/biomedical.git
cd biomedical  # enter the directory

Next, you want to set your upstream location to enable you to push/pull (add or receive updates). You can do so as follows:

git remote add upstream [email protected]:bigscience-workshop/biomedical.git

You can optionally check that this was set properly by running the following command:

git remote -v

The output of this command should look as follows:

origin  [email protected]:<your_github_username>/biomedical.git(fetch)
origin  [email protected]:<your_github_username>/biomedical.git (push)
upstream    [email protected]:bigscience-workshop/biomedical.git (fetch)
upstream    [email protected]:bigscience-workshop/biomedical.git (push)

If you do NOT have an origin for whatever reason, then run:

git remote add origin [email protected]:<your_github_username>/biomedical.git

The goal of upstream is to keep your repository up-to-date to any changes that are made officially to the datasets library. You can do this as follows by running the following commands:

git fetch upstream
git pull

Provided you have no merge conflicts, this will ensure the library stays up-to-date as you make changes. However, before you make changes, you should make a custom branch to implement your changes.

You can make a new branch as such:

git checkout -b <dataset_name>

Please do not make changes on the master branch!

Always make sure you're on the right branch with the following command:

git branch

The correct branch will have a asterisk * in front of it.

2. Create a development environment

You can make an environment in any way you choose to. We highlight two possible options:

2a) Create a conda environment

The following instructions will create an Anaconda BigBIO environment.

  • Install anaconda for your appropriate operating system.
  • Run the following command while in the biomedical folder (you can pick your python version):
conda env create -f conda.yaml  # Creates a conda env
conda activate bigbio  # Activate your conda environment

You can deactivate your environment at any time by either exiting your terminal or using conda deactivate.

2b) Create a venv environment

Python 3.3+ has venv automatically installed; official information is found here.

python3 -m venv <your_env_name_here>
source <your_env_name_here>/bin/activate  # activate environment
pip install -r dev-requirements.txt # Install this while in the datasets folder

Make sure your pip package points to your environment's source.

3. Prepare the folder in hub_repos for your dataloader

Make a new directory within the biomedical/bigbio/hub/hub_repos/ directory:

mkdir bigbio/hub/hub_repos/<dataset_name>

NOTE: Please use lowercase letters and underscores when choosing a <dataset_name>.

Add an __init__.py file to this directory:

touch bigbio/hub/hub_repos/<dataset_name>/__init__.py

Next, copy the contents of template into your dataset folder. This contains a README.md file and 2 scripts: bigbiohub.py that contains all data structures/classes for your dataloader, and template.py which has "TODOs" to fill in for your dataloading script. The README.md is from the scitail dataset and you will need to edit it for your dataset. Remove the text between square brackets before you make your PR. This file will determine what the landing page for the dataset on the hub looks like.

cp templates/template.py bigbio/hub/hub_repos/<dataset_name>/<dataset_name>.py
cp templates/bigbiohub.py bigbio/hub/hub_repos/<dataset_name>/
cp templates/README.md bigbio/hub/hub_repos/<dataset_name>/

4. Implement your dataset

To implement your dataloader, you will need to follow template.py and fill in all necessary TODOs. There are three key methods that are important:

  • _info: Specifies the schema of the expected dataloader
  • _split_generators: Downloads and extracts data for each split (e.g. train/val/test) or associate local data with each split.
  • _generate_examples: Create examples from data that conform to each schema defined in _info.

For the _info_ function, you will need to define features for your DatasetInfo object. For the bigbio config, choose the right schema from our list of examples. You can find a description of these in the Task Schemas Document. You can find the actual schemas in the schemas directory.

You will use this schema in the _generate_examples return value.

Populate the information in the dataset according to this schema; some fields may be empty.

To enable quality control, please add the following line in your file before the class definition:

from .bigbiohub import Tasks
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]

If your dataset is in a standard format, please use a recommended parser if available:

If the recommended parser does not work for you dataset, please alert us in Discord, Slack, or a github issue (please make it a thread in your official project issue).

Example scripts:

To help you implement a dataset, we offer example scripts. Checkout which task, and schema best suit your dataset!

Running & Debugging:

You can run your data loader script during development by appending the following statement to your code (templates/template.py already includes this):

if __name__ == "__main__":
    datasets.load_dataset(__file__)

If you want to use an interactive debugger during development, you will have to use breakpoint() instead of setting breakpoints directly in your IDE. Most IDEs will recognize the breakpoint() statement and pause there during debugging. If your prefered IDE doesn't support this, you can always run the script in your terminal and debug with pdb.

4. Check if your dataloader works

Make sure your dataset is implemented correctly by checking in python the following commands:

from datasets import load_dataset

data = load_dataset("bigbio/hub/hub_repos/<dataset_name>/<dataset_name>.py", name="<dataset_name>_bigbio_<schema>")

Run these commands from the top level of the biomedical repo (i.e. the same directory that contains the requirements.txt file).

Once this is done, please also check if your dataloader satisfies our unit tests as follows by using this command in the terminal:

python -m tests.test_bigbio_hub <dataset_name> [--data_dir /path/to/local/data] --test_local

You MUST include the --test_local flag to specifically test the script for your PR, otherwise the script will default to downloading a dataloader script from the Hub. Your particular dataset may require use of some of the other command line args in the test script (ex: --data_dir for dataloaders that read local files).
To view full usage instructions you can use the --help command:

python -m tests.test_bigbio --help

This will explain the types of arguments you may need to test for. A brief annotation is as such:

  • dataset_name: Name of the dataset you want to test
  • data_dir: The location of the data for datasets where LOCAL_ = True
  • config_name: Name of the configuration you want to test. By default, the script will test all configs, but if you can use this to debug a specific split, or if your data is prohibitively large.
  • ishub: Use this when unit testing scripts that are not yet uploaded to the hub (this is True for most cases)

If you need advanced arguments (i.e. skipping a key from a specific data split), please contact admins. You are welcome to make a PR and ask admin for help if your code does not pass the unit tests.

5. Format your code

From the main directory, run the Makefile via the following command:

make check_file=bigbio/hub/hub_repos/<dataset_name>/<dataset_name>.py

This runs the black formatter, isort, and lints to ensure that the code is readable and looks nice. Flake8 linting errors may require manual changes.

6. Commit your changes

First, commit your changes to the branch to "add" the work:

git add bigbio/hub/hub_repos/<dataset_name>/<dataset_name>.py
git add bigbio/hub/hub_repos/<dataset_name>/bigbiohub.py
git add bigbio/hub/hub_repos/<dataset_name>/README.md
git commit -m "A message describing your commits"

Then, run the following commands to incorporate any new changes in the master branch of datasets as follows:

git fetch upstream
git rebase upstream/master

Run these commands in your custom branch.

Push these changes to your fork with the following command:

git push -u origin <dataset_name>

7. Make a pull request

Make a Pull Request to implement your changes on the main repository here. To do so, click "New Pull Request". Then, choose your branch from your fork to push into "base:master".

When opening a PR, please link the issue corresponding to your dataset using closing keywords in the PR's description (not the PR title), e.g. resolves #17.