Datasets:

Sub-tasks:
fact-checking
Languages:
Tagalog
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
system HF staff commited on
Commit
ca087a4
1 Parent(s): 6d26fcd

Update files from the datasets library (from 1.8.0)

Browse files

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

Files changed (2) hide show
  1. dataset_infos.json +1 -1
  2. fake_news_filipino.py +2 -0
dataset_infos.json CHANGED
@@ -1 +1 @@
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- {"default": {"description": " Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.\n", "citation": " @inproceedings{cruz2020localization,\n title={Localization of Fake News Detection via Multitask Transfer Learning},\n author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},\n booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},\n pages={2596--2604},\n year={2020}\n }\n", "homepage": "https://github.com/jcblaisecruz02/Tagalog-fake-news", "license": "", "features": {"label": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "fake_news_filipino", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3623685, "num_examples": 3206, "dataset_name": "fake_news_filipino"}}, "download_checksums": {"https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/fakenews/fakenews.zip": {"num_bytes": 1313458, "checksum": "d3b823099f819e18594294487939c5534dc995677162fe744f103c01ff4c74f3"}}, "download_size": 1313458, "post_processing_size": null, "dataset_size": 3623685, "size_in_bytes": 4937143}}
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+ {"default": {"description": " Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.\n", "citation": " @inproceedings{cruz2020localization,\n title={Localization of Fake News Detection via Multitask Transfer Learning},\n author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},\n booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},\n pages={2596--2604},\n year={2020}\n }\n", "homepage": "https://github.com/jcblaisecruz02/Tagalog-fake-news", "license": "", "features": {"label": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "text-classification", "text_column": "article", "label_column": "label", "labels": ["0", "1"]}], "builder_name": "fake_news_filipino", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3623685, "num_examples": 3206, "dataset_name": "fake_news_filipino"}}, "download_checksums": {"https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/fakenews/fakenews.zip": {"num_bytes": 1313458, "checksum": "d3b823099f819e18594294487939c5534dc995677162fe744f103c01ff4c74f3"}}, "download_size": 1313458, "post_processing_size": null, "dataset_size": 3623685, "size_in_bytes": 4937143}}
fake_news_filipino.py CHANGED
@@ -18,6 +18,7 @@ import csv
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  import os
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  import datasets
 
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  _DESCRIPTION = """\
@@ -58,6 +59,7 @@ class FakeNewsFilipino(datasets.GeneratorBasedBuilder):
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  homepage=_HOMEPAGE,
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  license=_LICENSE,
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  citation=_CITATION,
 
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  )
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  def _split_generators(self, dl_manager):
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  import os
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  import datasets
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+ from datasets.tasks import TextClassification
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  _DESCRIPTION = """\
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  homepage=_HOMEPAGE,
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  license=_LICENSE,
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  citation=_CITATION,
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+ task_templates=[TextClassification(text_column="article", label_column="label")],
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  )
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  def _split_generators(self, dl_manager):