Datasets:

cathw
/

reddit_climate_comment

@@ -1,182 +0,0 @@
-"""TODO: Add a description here."""
-import csv
-import json
-import os
-import logging
-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={2024}
-}
-"""
-# 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 = {
-    "reddit_climate": "cathw/reddit_climate_comment"
-}
-# 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="reddit_climate", version=VERSION, description="This part of my dataset covers a first domain")
-    ]
-    DEFAULT_CONFIG_NAME = "reddit_climate"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-    def _info(self):
-        features = datasets.Features({
-            "Subreddit": datasets.Value("string"),
-            "Posts": datasets.Sequence({
-                "PostID": datasets.Value("int32"),
-                "PostTitle": datasets.Value("string"),
-                "Comments": datasets.Sequence({
-                    "CommentID": datasets.Value("string"),
-                    "Author": datasets.Value("string"),
-                    "CommentBody": datasets.Value("string"),
-                    "Timestamp": datasets.Value("string"),
-                    "Upvotes": datasets.Value("int32"),
-                    "NumberofReplies": datasets.Value("int32"),
-                }),
-            }),
-        })
-        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
-        config_name = getattr(self.config, 'name', self.DEFAULT_CONFIG_NAME)
-        urls = _URLS.get(config_name, {})  # Get the URLs for the configuration name, if not found, return an empty dictionary
-        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):
-        # 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:
-            data = json.load(f)
-            for idx, row in enumerate(data):
-                subreddit = row["Subreddit"]
-                posts = []
-                # Check if the "Posts" key is present in the current row
-                if "Posts" in row:
-                    for post in row["Posts"]:
-                        post_id = post["PostID"]
-                        post_title = post["PostTitle"]
-                        comments = []
-                        for comment in post["Comments"]:
-                            comment_id = comment["CommentID"]
-                            author = comment["Author"]
-                            comment_body = comment["CommentBody"]
-                            timestamp = comment["Timestamp"]
-                            upvotes = comment["Upvotes"]
-                            number_of_replies = comment["NumberofReplies"]
-                            logging.debug(f"Processing comment: {comment_id}, Upvotes: {upvotes}")
-                            comments.append({
-                                "CommentID": comment_id,
-                                "Author": author,
-                                "CommentBody": comment_body,
-                                "Timestamp": timestamp,
-                                "Upvotes": upvotes,
-                                "NumberofReplies": number_of_replies
-                            })
-                        posts.append({
-                            "PostID": post_id,
-                            "PostTitle": post_title,
-                            "Comments": comments
-                        })
-                else:
-                    # Handle cases where the "Posts" key is missing
-                    posts = None
-                yield idx, {
-                    "Subreddit": subreddit,
-                    "Posts": posts
-                }