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Template files for dataset scrip and config

datasetcard.md

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+---
+# Example metadata to be added to a dataset card.  
+# Full dataset card template at https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
+language:
+- {lang_0}  # Example: fr
+- {lang_1}  # Example: en
+license: {license}  # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
+license_name: {license_name}  # If license = other (license not in https://hf.co/docs/hub/repositories-licenses), specify an id for it here, like `my-license-1.0`.
+license_link: {license_link}  # If license = other, specify "LICENSE" or "LICENSE.md" to link to a file of that name inside the repo, or a URL to a remote file.
+license_details: {license_details}  # Legacy, textual description of a custom license.
+tags:
+- {tag_0}  # Example: audio
+- {tag_1}  # Example: bio
+- {tag_2}  # Example: natural-language-understanding
+- {tag_3}  # Example: birds-classification
+annotations_creators:
+- {creator}  # Example: crowdsourced, found, expert-generated, machine-generated
+language_creators:
+- {creator}  # Example: crowdsourced, ...
+language_details:
+- {bcp47_lang_0}  # Example: fr-FR
+- {bcp47_lang_1}  # Example: en-US
+pretty_name: {pretty_name}  # Example: SQuAD
+size_categories:
+- {number_of_elements_in_dataset}  # Example: n<1K, 100K<n<1M, …
+source_datasets:
+- {source_dataset_0}  # Example: wikipedia
+- {source_dataset_1}  # Example: laion/laion-2b
+task_categories:  # Full list at https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/pipelines.ts
+- {task_0}  # Example: question-answering
+- {task_1}  # Example: image-classification
+task_ids:
+- {subtask_0}  # Example: extractive-qa
+- {subtask_1}  # Example: multi-class-image-classification
+paperswithcode_id: {paperswithcode_id}  # Dataset id on PapersWithCode (from the URL). Example for SQuAD: squad
+configs:  # Optional for datasets with multiple configurations like glue.
+- {config_0}  # Example for glue: sst2
+- {config_1}  # Example for glue: cola
+
+# Optional. This part can be used to store the feature types and size of the dataset to be used in python. This can be automatically generated using the datasets-cli.
+dataset_info:
+  features:
+    - name: {feature_name_0}    # Example: id
+      dtype: {feature_dtype_0}  # Example: int32
+    - name: {feature_name_1}    # Example: text
+      dtype: {feature_dtype_1}  # Example: string
+    - name: {feature_name_2}    # Example: image
+      dtype: {feature_dtype_2}  # Example: image
+    # Example for SQuAD:
+    # - name: id
+    #   dtype: string
+    # - name: title
+    #   dtype: string
+    # - name: context
+    #   dtype: string
+    # - name: question
+    #   dtype: string
+    # - name: answers
+    #   sequence:
+    #     - name: text
+    #       dtype: string
+    #     - name: answer_start
+    #       dtype: int32
+  config_name: {config_name}  # Example for glue: sst2
+  splits:
+    - name: {split_name_0}                  # Example: train
+      num_bytes: {split_num_bytes_0}        # Example for SQuAD: 79317110
+      num_examples: {split_num_examples_0}  # Example for SQuAD: 87599
+  download_size: {dataset_download_size}   # Example for SQuAD: 35142551
+  dataset_size: {dataset_size}             # Example for SQuAD: 89789763
+
+# It can also be a list of multiple configurations:
+# ```yaml
+# dataset_info:
+#   - config_name: {config0}
+#     features:
+#       ...
+#   - config_name: {config1}
+#     features:
+#       ...
+# ```
+
+# Optional. If you want your dataset to be protected behind a gate that users have to accept to access the dataset. More info at https://huggingface.co/docs/hub/datasets-gated
+extra_gated_fields:
+- {field_name_0}: {field_type_0}  # Example: Name: text
+- {field_name_1}: {field_type_1}  # Example: Affiliation: text
+- {field_name_2}: {field_type_2}  # Example: Email: text
+- {field_name_3}: {field_type_3}  # Example for speech datasets: I agree to not attempt to determine the identity of speakers in this dataset: checkbox
+extra_gated_prompt: {extra_gated_prompt}  # Example for speech datasets: By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the dataset.
+
+# Optional. Add this if you want to encode a train and evaluation info in a structured way for AutoTrain or Evaluation on the Hub
+train-eval-index:
+  - config: {config_name}           # The dataset config name to use. Example for datasets without configs: default. Example for glue: sst2
+    task: {task_name}               # The task category name (same as task_category). Example: question-answering
+    task_id: {task_type}            # The AutoTrain task id. Example: extractive_question_answering
+    splits:
+      train_split: train            # The split to use for training. Example: train
+      eval_split: validation        # The split to use for evaluation. Example: test
+    col_mapping:                    # The columns mapping needed to configure the task_id.
+    # Example for extractive_question_answering:
+      # question: question
+      # context: context
+      # answers:
+      #   text: text
+      #   answer_start: answer_start
+    metrics:
+      - type: {metric_type}         # The metric id. Example: wer. Use metric id from https://hf.co/metrics
+        name: {metric_name}         # Tne metric name to be displayed. Example: Test WER
+---
+
+Valid license identifiers can be found in [our docs](https://huggingface.co/docs/hub/repositories-licenses).
+
+For the full dataset card template, see: [datasetcard_template.md file](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md).

new_dataset_script.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "option1": datasets.Value("string"),
+                    "answer": datasets.Value("string")
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "option2": datasets.Value("string"),
+                    "second_domain_answer": datasets.Value("string")
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "dev.jsonl"),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.jsonl"),
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "first_domain":
+                    # Yields examples as (key, example) tuples
+                    yield key, {
+                        "sentence": data["sentence"],
+                        "option1": data["option1"],
+                        "answer": "" if split == "test" else data["answer"],
+                    }
+                else:
+                    yield key, {
+                        "sentence": data["sentence"],
+                        "option2": data["option2"],
+                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
+                    }