attempt to format according to datasets library

dataset_infos.json

@@ -0,0 +1 @@
+{"all": {"description": "The `tldr_news` dataset was constructed by collecting a daily tech newsletter (available at \nhttps://tldr.tech/newsletter). Then for every piece of news, the \"headline\" and its corresponding \"content\" were \ncollected. Such a dataset can be used to train a model to generate a headline from a input piece of text.\n", "citation": "", "homepage": "", "license": "", "features": {"headline": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "tldr_news", "config_name": "all", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3880430, "num_examples": 7055, "dataset_name": "tldr_news"}, "test": {"name": "test", "num_bytes": 429049, "num_examples": 784, "dataset_name": "tldr_news"}}, "download_checksums": {"https://github.com/JulesBelveze/tldr_news/blob/main/1.1.0.tar.gz?raw=true": {"num_bytes": 1663243, "checksum": "2cdb7b21a2b06af5e1318c0155a20ae652aa418e0d599ff03f73e633b5ada052"}}, "download_size": 1663243, "post_processing_size": null, "dataset_size": 4309479, "size_in_bytes": 5972722}}

tldr_news.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.
+
+import json
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+The `tldr_news` dataset was constructed by collecting a daily tech newsletter (available at 
+https://tldr.tech/newsletter). Then for every piece of news, the "headline" and its corresponding "content" were 
+collected. Such a dataset can be used to train a model to generate a headline from a input piece of text.
+"""
+
+# 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 = {"all": "https://github.com/JulesBelveze/tldr_news/blob/main/1.1.0.tar.gz?raw=true"}
+
+
+class TLDRNewsConfig(datasets.BuilderConfig):
+    """BuilderConfig for TLDRNews."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for TLDRNews.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TLDRNewsConfig, self).__init__(**kwargs)
+
+
+class TLDRNewsDataset(datasets.GeneratorBasedBuilder):
+    """Dataset containing headline & content of pieces of news from the tldr tech newsletter."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        TLDRNewsConfig(name="all", version=VERSION, description="This contains all the existing newsletter"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "headline": datasets.Value("string"),
+                "content": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(description=_DESCRIPTION, features=features)
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        data_dir = os.path.join(data_dir, str(self.config.version))
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.json"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.json"), "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+        for key, row in enumerate(data):
+            yield key, {"headline": row["headline"], "content": row["content"]}