no message

.gitignore

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+.idea/

README.md

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 ---
-TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
+annotations_creators: []
+language: []
+language_creators: []
+license: []
+multilinguality: []
+pretty_name: The DJ Mix Dataset
+size_categories: []
+source_datasets: []
+tags: []
+task_categories: []
+task_ids: []
 ---
 
 # Dataset Card for [Dataset Name]

djmix.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 DJMixDataset(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"],
+          }