Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"byarticle": {"description": "Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.\nGiven a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.\n\nThere are 2 parts:\n- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.\n- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.\n", "citation": "@article{kiesel2019data,\n  title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection},\n  author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin},\n  year={2019}\n}\n", "homepage": "https://pan.webis.de/semeval19/semeval19-web/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "hyperpartisan": {"dtype": "bool", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "published_at": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}}}, "supervised_keys": {"input": "text", "output": "label"}, "builder_name": "hyperpartisan_news_detection", "config_name": "byarticle", "version": {"version_str": "1.0.0", "description": "Version Training and validation v1", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2803943, "num_examples": 645, "dataset_name": "hyperpartisan_news_detection"}}, "download_checksums": {"https://zenodo.org/record/1489920/files/articles-training-byarticle-20181122.zip?download=1": {"num_bytes": 971841, "checksum": "62b4a71275ef2724faddc74d6ff3d782ee9898c732cd66119c7976f6f5168990"}, "https://zenodo.org/record/1489920/files/ground-truth-training-byarticle-20181122.zip?download=1": {"num_bytes": 28511, "checksum": "0c02f4c33317287758e6fbbc976cbfd7a0978923899ddf30cb9dd2cd740af43c"}}, "download_size": 1000352, "post_processing_size": 0, "dataset_size": 2803943, "size_in_bytes": 3804295}, "bypublisher": {"description": "Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.\nGiven a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.\n\nThere are 2 parts:\n- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.\n- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.\n", "citation": "@article{kiesel2019data,\n  title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection},\n  author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin},\n  year={2019}\n}\n", "homepage": "https://pan.webis.de/semeval19/semeval19-web/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "hyperpartisan": {"dtype": "bool", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "published_at": {"dtype": "string", "id": null, "_type": "Value"}, "bias": {"num_classes": 5, "names": ["right", "right-center", "least", "left-center", "left"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "validation": {}}}, "supervised_keys": {"input": "text", "output": "label"}, "builder_name": "hyperpartisan_news_detection", "config_name": "bypublisher", "version": {"version_str": "1.0.0", "description": "Version Training and validation v1", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2805711609, "num_examples": 600000, "dataset_name": "hyperpartisan_news_detection"}, "validation": {"name": "validation", "num_bytes": 2805711609, "num_examples": 600000, "dataset_name": "hyperpartisan_news_detection"}}, "download_checksums": {"https://zenodo.org/record/1489920/files/articles-training-bypublisher-20181122.zip?download=1": {"num_bytes": 980769009, "checksum": "e5816b0c9fecd1a38f6cba8eb4f6f77d04637b5c6209e714b7ab32dc3bc24e28"}, "https://zenodo.org/record/1489920/files/ground-truth-training-bypublisher-20181122.zip?download=1": {"num_bytes": 22426411, "checksum": "f1c0494af86ff1e961479a63d432d649ccda875d302888f4d080dbec0382b1ef"}}, "download_size": 1003195420, "post_processing_size": 0, "dataset_size": 5611423218, "size_in_bytes": 6614618638}}

dummy/byarticle/1.0.0/dummy_data.zip

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dummy/bypublisher/1.0.0/dummy_data.zip

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hyperpartisan_news_detection.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Hyperpartisan News Detection"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+import textwrap
+import xml.etree.ElementTree as ET
+
+import datasets
+
+
+_CITATION = """\
+@article{kiesel2019data,
+  title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection},
+  author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
+Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.
+
+There are 2 parts:
+- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.
+- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.
+"""
+_URL_BASE = "https://zenodo.org/record/1489920/files/"
+
+
+class HyperpartisanNewsDetection(datasets.GeneratorBasedBuilder):
+    """Hyperpartisan News Detection Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="byarticle",
+            version=datasets.Version("1.0.0", "Version Training and validation v1"),
+            description=textwrap.dedent(
+                """
+                    This part of the data (filename contains "byarticle") is labeled through crowdsourcing on an article basis.
+                    The data contains only articles for which a consensus among the crowdsourcing workers existed. It contains
+                    a total of 645 articles. Of these, 238 (37%) are hyperpartisan and 407 (63%) are not, We will use a similar
+                    (but balanced!) test set. Again, none of the publishers in this set will occur in the test set.
+                """
+            ),
+        ),
+        datasets.BuilderConfig(
+            name="bypublisher",
+            version=datasets.Version("1.0.0", "Version Training and validation v1"),
+            description=textwrap.dedent(
+                """
+                    This part of the data (filename contains "bypublisher") is labeled by the overall bias of the publisher as provided
+                    by BuzzFeed journalists or MediaBiasFactCheck.com. It contains a total of 750,000 articles, half of which (375,000)
+                    are hyperpartisan and half of which are not. Half of the articles that are hyperpartisan (187,500) are on the left side
+                    of the political spectrum, half are on the right side. This data is split into a training set (80%, 600,000 articles) and
+                    a validation set (20%, 150,000 articles), where no publisher that occurs in the training set also occurs in the validation
+                    set. Similarly, none of the publishers in those sets will occur in the test set.
+                """
+            ),
+        ),
+    ]
+
+    def _info(self):
+        features = {
+            "text": datasets.Value("string"),
+            "title": datasets.Value("string"),
+            "hyperpartisan": datasets.Value("bool"),
+            "url": datasets.Value("string"),
+            "published_at": datasets.Value("string"),
+        }
+
+        if self.config.name == "bypublisher":
+            # Bias is only included in the bypublisher config
+            features["bias"] = datasets.ClassLabel(names=["right", "right-center", "least", "left-center", "left"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=("text", "label"),
+            homepage="https://pan.webis.de/semeval19/semeval19-web/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls = {
+            datasets.Split.TRAIN: {
+                "articles_file": _URL_BASE + "articles-training-" + self.config.name + "-20181122.zip?download=1",
+                "labels_file": _URL_BASE + "ground-truth-training-" + self.config.name + "-20181122.zip?download=1",
+            },
+        }
+        if self.config.name == "bypublisher":
+            urls[datasets.Split.VALIDATION] = {
+                "articles_file": _URL_BASE + "articles-training-" + self.config.name + "-20181122.zip?download=1",
+                "labels_file": _URL_BASE + "ground-truth-training-" + self.config.name + "-20181122.zip?download=1",
+            }
+
+        data_dir = {}
+        for key in urls:
+            data_dir[key] = dl_manager.download_and_extract(urls[key])
+
+        splits = []
+        for split in data_dir:
+            for key in data_dir[split]:
+                data_dir[split][key] = os.path.join(data_dir[split][key], os.listdir(data_dir[split][key])[0])
+            splits.append(datasets.SplitGenerator(name=split, gen_kwargs=data_dir[split]))
+        return splits
+
+    def _generate_examples(self, articles_file=None, labels_file=None):
+        """Yields examples."""
+        labels = {}
+        with open(labels_file, "rb") as f_labels:
+            tree = ET.parse(f_labels)
+            root = tree.getroot()
+            for label in root:
+                article_id = label.attrib["id"]
+                del label.attrib["labeled-by"]
+                labels[article_id] = label.attrib
+
+        with open(articles_file, "rb") as f_articles:
+            tree = ET.parse(f_articles)
+            root = tree.getroot()
+            for idx, article in enumerate(root):
+                example = {}
+                example["title"] = article.attrib["title"]
+                example["published_at"] = article.attrib.get("published-at", "")
+                example["id"] = article.attrib["id"]
+                example = {**example, **labels[example["id"]]}
+                example["hyperpartisan"] = example["hyperpartisan"] == "true"
+
+                example["text"] = ""
+                for child in article.getchildren():
+                    example["text"] += ET.tostring(child).decode() + "\n"
+                example["text"] = example["text"].strip()
+                del example["id"]
+                yield idx, example