Sharing your dataset

Once you’ve written a new dataset loading script as detailed on the Writing a dataset loading script page, you may want to share it with the community for instance on the HuggingFace Hub. There are two options to do that:

Here are the main differences between these two options.

  • Community provided datasets:

    • are faster to share (no reviewing process)

    • can contain the data files themselves on the Hub

    • are identified under the namespace of a user or organization: thomwolf/my_dataset or huggingface/our_dataset

    • are flagged as unsafe by default because a dataset contains executable code so the users need to inspect and opt-in to use the datasets

  • Canonical datasets:

    • are slower to add (need to go through the reviewing process on the githup repo)

    • are identified under the root namespace (my_dataset) so they need to select a shortname which is still free

    • usually don’t contain the data files which are retrieved from the original URLs (but this can be changed under specific request to add the files to the Hub)

    • are flagged as safe by default since they went through the reviewing process (no need to opt-in).

Note

The distinctions between “canonical” and “community provided” datasets is made purely based on the selected sharing workflow and don’t involve any ranking, decision or opinion regarding the content of the dataset it-self.

Sharing a “canonical” dataset

To add a “canonical” dataset to the library, you need to go through the following steps:

1. Fork the 🤗datasets repository by clicking on the ‘Fork’ button on the repository’s home page. This creates a copy of the code under your GitHub user account.

2. Clone your fork to your local disk, and add the base repository as a remote:

git clone https://github.com/<your_Github_handle>/datasets
cd datasets
git remote add upstream https://github.com/huggingface/datasets.git

3. Create a new branch to hold your development changes:

git checkout -b my-new-dataset

Note

do not work on the master branch.

4. Set up a development environment by running the following command in a virtual environment:

pip install -e ".[dev]"

Note

If 🤗datasets was already installed in the virtual environment, remove it with pip uninstall datasets before reinstalling it in editable mode with the -e flag.

5. Create a new folder with your dataset name inside the datasets folder of the repository and add the dataset script you wrote and tested while following the instructions on the Writing a dataset loading script page.

6. Format your code. Run black and isort so that your newly added files look nice with the following command:

make style
make quality

7. Once you’re happy with your dataset script file, add your changes and make a commit to record your changes locally:

git add datasets/<my-new-dataset>
git commit

It is a good idea to sync your copy of the code with the original repository regularly. This way you can quickly account for changes:

git fetch upstream
git rebase upstream/master

Push the changes to your account using:

git push -u origin my-new-dataset

8. We also recommend adding tests and metadata to the dataset script if possible. Go through the Adding tests and metadata to the dataset section to do so.

9. Once you are satisfied with the dataset, go the webpage of your fork on GitHub and click on “Pull request” to open a pull-request on the main github repository for review.

Sharing a “community provided” dataset

In this page, we will show you how to share a dataset with the community on the datasets hub.

Note

You will need to create an account on huggingface.co for this.

Optionally, you can join an existing organization or create a new one.

Prepare your dataset for uploading

We have seen in the dataset script tutorial: how to write a dataset loading script. Let’s see how you can share it on the datasets hub.

Dataset versioning

Since version 2.0, the datasets hub has built-in dataset versioning based on git and git-lfs. It is based on the paradigm that one dataset is one repo.

This allows:

  • built-in versioning

  • access control

  • scalability

This is built around revisions, which is a way to pin a specific version of a dataset, using a commit hash, tag or branch.

For instance:

>>> dataset = load_dataset(
>>>   "lhoestq/custom_squad",
>>>   script_version="main"  # tag name, or branch name, or commit hash
>>> )

Basic steps

In order to upload a dataset, you’ll need to first create a git repo. This repo will live on the datasets hub, allowing users to clone it and you (and your organization members) to push to it.

You can create a dataset repo directly from the /new page on the website.

Alternatively, you can use the huggingface-cli. The next steps describe that process:

Go to a terminal and run the following command. It should be in the virtual environment where you installed 🤗 Datasets, since that command huggingface-cli comes from the library.

huggingface-cli login

Once you are logged in with your datasets hub credentials, you can start building your repositories. To create a repo:

huggingface-cli repo create your_dataset_name --type dataset

If you want to create a repo under a specific organization, you should add a –organization flag:

huggingface-cli repo create your_dataset_name --type dataset --organization your-org-name

This creates a repo on the datasets hub, which can be cloned.

# Make sure you have git-lfs installed
# (https://git-lfs.github.com/)
git lfs install

git clone https://huggingface.co/datasets/username/your_dataset_name

When you have your local clone of your repo and lfs installed, you can then add/remove from that clone as you would with any other git repo.

# Commit as usual
cd your_dataset_name
echo "hello" >> README.md
git add . && git commit -m "Update from $USER"

We are intentionally not wrapping git too much, so that you can go on with the workflow you’re used to and the tools you already know.

The only learning curve you might have compared to regular git is the one for git-lfs. The documentation at git-lfs.github.com is decent, but we’ll work on a tutorial with some tips and tricks in the coming weeks!

Additionally, if you want to change multiple repos at once, the change_config.py script can probably save you some time.

Check the directory before pushing to the datasets hub.

Make sure there are no garbage files in the directory you’ll upload. It should only have:

  • a your_dataset_name.py file, which is the dataset script;

  • an optional dataset_infos.json file, which contains metadata about your dataset like the split sizes;

  • optional dummy data files, which contains only a small subset from the dataset for tests and preview;

  • your raw data files (json, csv, txt, etc.) that you need for your dataset

Other files can safely be deleted.

Uploading your files

Once the repo is cloned, you can add the dataset script and dataset infos.

You can add these files to the staging environment and verify that they have been correctly staged with the git status command:

git add --all
git status

Finally, the files should be committed:

git commit -m "First version of the your_dataset_name dataset."

And pushed to the remote:

git push

This will upload the folder containing the dataset script and dataset infos that we have just prepared.

Using your dataset

Your dataset now has a page on huggingface.co/datasets 🔥

Anyone can load it from code:

>>> dataset = load_dataset("namespace/your_dataset_name")

You may specify a version by using the script-version flag in the load_dataset function:

>>> dataset = load_dataset(
>>>   "lhoestq/custom_squad",
>>>   script_version="main"  # tag name, or branch name, or commit hash
>>> )

Adding tests and metadata to the dataset

We recommend adding testing data and checksum metadata to your dataset so its behavior can be tested and verified, and the generated dataset can be certified. In this section we’ll explain how you can add two objects to the repository to do just that:

  • dummy data which are used for testing the behavior of the script (without having to download the full data files), and

  • dataset_infos.json which are metadata used to store the metadata of the dataset including the data files checksums and the number of examples required to confirm that the dataset generation procedure went well.

Note

In the rest of this section, you should make sure that you run all of the commands from the root of your local datasets repository.

1. Adding metadata

You can check that the new dataset loading script works correctly and create the dataset_infos.json file at the same time by running the command:

python datasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs

If the command was succesful, you should now have a dataset_infos.json file created in the folder of your dataset loading script. Here is a dummy example of the content for a dataset with a single configuration:

{
    "default": {
        "description": "The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 ...\n",
        "citation": "@inproceedings{li-roth-2002-learning,\n    title = \"Learning Question Classifiers\",..\",\n}\n",
        "homepage": "https://cogcomp.seas.upenn.edu/Data/QA/QC/",
        "license": "",
        "features": {
            "label-coarse": {
                "num_classes": 6,
                "names": ["DESC", "ENTY", "ABBR", "HUM", "NUM", "LOC"],
                "names_file": null,
                "id": null,
                "_type": "ClassLabel"
            },
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "supervised_keys": null,
        "builder_name": "trec",
        "config_name": "default",
        "version": {
            "version_str": "1.1.0", "description": null,
            "datasets_version_to_prepare": null,
            "major": 1, "minor": 1, "patch": 0
        },
        "splits": {
            "train": {
                "name": "train",
                "num_bytes": 385090,
                "num_examples": 5452,
                "dataset_name": "trec"
            },
            "test": {
                "name": "test",
                "num_bytes": 27983,
                "num_examples": 500,
                "dataset_name": "trec"
            }
        },
        "download_checksums": {
            "http://cogcomp.org/Data/QA/QC/train_5500.label": {
                "num_bytes": 335858,
                "checksum": "9e4c8bdcaffb96ed61041bd64b564183d52793a8e91d84fc3a8646885f466ec3"
            },
            "http://cogcomp.org/Data/QA/QC/TREC_10.label": {
                "num_bytes": 23354,
                "checksum": "033f22c028c2bbba9ca682f68ffe204dc1aa6e1cf35dd6207f2d4ca67f0d0e8e"
            }
        },
        "download_size": 359212,
        "dataset_size": 413073,
        "size_in_bytes": 772285
    }
}

2. Adding dummy data

Now that we have the metadata prepared we can also create some dummy data for automated testing. You can use the following command to get in-detail instructions on how to create the dummy data:

python datasets-cli dummy_data datasets/<your-dataset-folder>

This command will output instructions specifically tailored to your dataset and will look like:

==============================DUMMY DATA INSTRUCTIONS==============================
- In order to create the dummy data for my-dataset, please go into the folder './datasets/my-dataset/dummy/1.1.0' with `cd ./datasets/my-dataset/dummy/1.1.0` .

- Please create the following dummy data files 'dummy_data/TREC_10.label, dummy_data/train_5500.label' from the folder './datasets/my-dataset/dummy/1.1.0'

- For each of the splits 'train, test', make sure that one or more of the dummy data files provide at least one example

- If the method `_generate_examples(...)` includes multiple `open()` statements, you might have to create other files in addition to 'dummy_data/TREC_10.label, dummy_data/train_5500.label'. In this case please refer to the `_generate_examples(...)` method

-After all dummy data files are created, they should be zipped recursively to 'dummy_data.zip' with the command `zip -r dummy_data.zip dummy_data/`

-You can now delete the folder 'dummy_data' with the command `rm -r dummy_data`

- To get the folder 'dummy_data' back for further changes to the dummy data, simply unzip dummy_data.zip with the command `unzip dummy_data.zip`

- Make sure you have created the file 'dummy_data.zip' in './datasets/my-dataset/dummy/1.1.0'
===================================================================================

There is a tool that automatically generates dummy data for you. At the moment it supports data files in the following format: txt, csv, tsv, jsonl, json, xml. If the extensions of the raw data files of your dataset are in this list, then you can automatically generate your dummy data with:

python datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate

Examples:

python datasets-cli dummy_data ./datasets/snli --auto_generate
python datasets-cli dummy_data ./datasets/squad --auto_generate --json_field data
python datasets-cli dummy_data ./datasets/iwslt2017 --auto_generate --xml_tag seg --match_text_files "train*" --n_lines 15
# --xml_tag seg => each sample corresponds to a "seg" tag in the xml tree
# --match_text_files "train*" =>  also match text files that don't have a proper text file extension (no suffix like ".txt" for example)
# --n_lines 15 => some text files have headers so we have to use at least 15 lines

Usage of the command:

usage: datasets-cli <command> [<args>] dummy_data [-h] [--auto_generate]
                                                  [--n_lines N_LINES]
                                                  [--json_field JSON_FIELD]
                                                  [--xml_tag XML_TAG]
                                                  [--match_text_files MATCH_TEXT_FILES]
                                                  [--keep_uncompressed]
                                                  [--cache_dir CACHE_DIR]
                                                  [--encoding ENCODING]
                                                  path_to_dataset

positional arguments:
path_to_dataset       Path to the dataset (example: ./datasets/squad)

optional arguments:
-h, --help            show this help message and exit
--auto_generate       Automatically generate dummy data
--n_lines N_LINES     Number of lines or samples to keep when auto-
                        generating dummy data
--json_field JSON_FIELD
                        Optional, json field to read the data from when auto-
                        generating dummy data. In the json data files, this
                        field must point to a list of samples as json objects
                        (ex: the 'data' field for squad-like files)
--xml_tag XML_TAG     Optional, xml tag name of the samples inside the xml
                        files when auto-generating dummy data.
--match_text_files MATCH_TEXT_FILES
                        Optional, a comma separated list of file patterns that
                        looks for line-by-line text files other than *.txt or
                        *.csv. Example: --match_text_files *.label
--keep_uncompressed   Whether to leave the dummy data folders uncompressed
                        when auto-generating dummy data. Useful for debugging
                        for to do manual adjustements before compressing.
--cache_dir CACHE_DIR
                        Cache directory to download and cache files when auto-
                        generating dummy data
--encoding ENCODING   Encoding to use when auto-generating dummy data.
                        Defaults to utf-8

3. Testing

Now test that both the real data and the dummy data work correctly. Go back to the root of your datasets folder and use the following command:

For the real data:

RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_<your_dataset_name>

And for the dummy data:

RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your_dataset_name>

If all tests pass, your dataset works correctly. Awesome! You can now follow the last steps of the Sharing a “canonical” dataset or Sharing a “community provided” dataset sections to share the dataset with the community. If you experienced problems with the dummy data tests, here are some additional tips:

  • Verify that all filenames are spelled correctly. Rerun the command

python datasets-cli dummy_data datasets/<your-dataset-folder>

and make sure you follow the exact instructions provided by the command.

  • Your datascript might require a difficult dummy data structure. In this case make sure you fully understand the data folder logit created by the function _split_generations(...) and expected by the function _generate_examples(...) of your dataset script. Also take a look at tests/README.md which lists different possible cases of how the dummy data should be created.

  • If the dummy data tests still fail, open a PR in the main repository on github and make a remark in the description that you need help creating the dummy data and we will be happy to help you.