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).


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<your_Github_handle>/datasets
cd datasets
git remote add upstream

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

git checkout -b my-new-dataset


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]"


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

Make a data directory, for example called my_local_dataset, containing, at a minimum, my_local_dataset/, but also whatever other files your dataset needs.

Then, simply upload with datasets-cli from the command line:

datasets-cli login  # use your credentials, only needs to be run once.
datasets-cli upload_dataset my_local_dataset

This uploads the dataset to your personal account. If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:

--organization organization_name

After upload_dataset, the following python code should work:

import datasets

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.


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": "",
        "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": {
            "": {
                "num_bytes": 335858,
                "checksum": "9e4c8bdcaffb96ed61041bd64b564183d52793a8e91d84fc3a8646885f466ec3"
            "": {
                "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 '' with the command `zip -r 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 with the command `unzip`

- Make sure you have created the file '' 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


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]
                                                  [--cache_dir CACHE_DIR]
                                                  [--encoding ENCODING]

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/<your-dataset-name>

And for the dummy data:

RUN_SLOW=1 pytest tests/<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/ 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.