Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+- multi-class-classification
+---
+
+# Dataset Card for PUBHEALTH
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [PUBHEALTH homepage](https://github.com/neemakot/Health-Fact-Checking)
+- **Repository:** [PUBHEALTH repository](https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md)
+- **Paper:** [Explainable Automated Fact-Checking for Public Health Claims"](https://arxiv.org/abs/2010.09926)
+- **Point of Contact:**[Neema Kotonya](mailto:nk2418@ic.ac.uk)
+
+### Dataset Summary
+
+PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The text in the dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+The following is an example instance of the PUBHEALTH dataset:
+|  Field              |  Example                                                     |
+| -----------------   | -------------------------------------------------------------|
+| __claim__  	      | Expired boxes of cake and pancake mix are dangerously toxic. |
+| __explanation__     | What's True:  Pancake and cake mixes that contain mold can cause life-threatening allergic reactions. What's False: Pancake and cake mixes that have passed their expiration dates are not inherently dangerous to ordinarily healthy people, and the yeast in packaged baking products does not "over time develops spores." |
+| __label__           |  mixture                                                     |
+| __author(s)__       | David Mikkelson                                              |
+| __date published__  | April 19, 2006                                               |
+| __tags__            | food, allergies, baking, cake                                |
+| __main_text__        |   In April 2006, the experience of a 14-year-old who had eaten pancakes made from a mix that had gone moldy was described in the popular newspaper column Dear Abby. The account has since been circulated widely on the Internet as scores of concerned homemakers ponder the safety of the pancake and other baking mixes lurking in their larders [...]       |
+| __evidence sources__    | [1] Bennett, Allan and Kim Collins.  “An Unusual Case of Anaphylaxis: Mold in Pancake Mix.” American Journal of Forensic Medicine & Pathology.   September 2001   (pp. 292-295). [2] Phillips, Jeanne. “Dear Abby.” 14 April 2006   [syndicated column]. |
+
+### Data Fields
+
+Mentioned above in data instances.
+
+### Data Splits
+
+|           | # Instances |
+|-----------|-------------|
+| train.tsv | 9832        |
+| dev.tsv   | 1221        |
+| test.tsv  | 1235        |
+| total     | 12288       |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise.
+
+It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset was retrieved from the following fact-checking, news reviews and news websites:
+
+| URL                               | Type               |
+|-----------------------------------|--------------------|
+| http://snopes.com/                | fact-checking      |
+| http://politifact.com/            | fact-checking      |
+| http://truthorfiction.com/        | fact-checking      |
+| https://www.factcheck.org/        | fact-checking      |
+| https://fullfact.org/             | fact-checking      |
+| https://apnews.com/               | news               |
+| https://uk.reuters.com/           | news               |
+| https://www.healthnewsreview.org/ | health news review |
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+Not to our knowledge, but if it is brought to our attention that we are mistaken we will make the appropriate corrections to the dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was created by Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020.
+
+### Licensing Information
+
+MIT License
+
+### Citation Information
+```
+@inproceedings{kotonya-toni-2020-explainable,
+    title = "Explainable Automated Fact-Checking for Public Health Claims",
+    author = "Kotonya, Neema  and
+      Toni, Francesca",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
+    pages = "7740--7754",
+}
+```

dataset_infos.json

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+{"default": {"description": "PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of\npublic health claims. Each instance in the PUBHEALTH dataset has an associated\nveracity label (true, false, unproven, mixture). Furthermore each instance in the\ndataset has an explanation text field. The explanation is a justification for which\nthe claim has been assigned a particular veracity label.\n\nThe dataset was created to explore fact-checking of difficult to verify claims i.e.,\nthose which require expertise from outside of the journalistics domain, in this case\nbiomedical and public health expertise.\n\nIt was also created in response to the lack of fact-checking datasets which provide\ngold standard natural language explanations for verdicts/labels.\n\nNOTE: There are missing labels in the dataset and we have replaced them with -1.\n", "citation": "@inproceedings{kotonya-toni-2020-explainable,\n    title = \"Explainable Automated Fact-Checking for Public Health Claims\",\n    author = \"Kotonya, Neema and Toni, Francesca\",\n    booktitle = \"Proceedings of the 2020 Conference on Empirical Methods\n    in Natural Language Processing (EMNLP)\",\n    month = nov,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.emnlp-main.623\",\n    pages = \"7740--7754\",\n}\n", "homepage": "https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md", "license": "", "features": {"claim_id": {"dtype": "string", "id": null, "_type": "Value"}, "claim": {"dtype": "string", "id": null, "_type": "Value"}, "date_published": {"dtype": "string", "id": null, "_type": "Value"}, "explanation": {"dtype": "string", "id": null, "_type": "Value"}, "fact_checkers": {"dtype": "string", "id": null, "_type": "Value"}, "main_text": {"dtype": "string", "id": null, "_type": "Value"}, "sources": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 4, "names": ["false", "mixture", "true", "unproven"], "names_file": null, "id": null, "_type": "ClassLabel"}, "subjects": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "health_fact", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 53985377, "num_examples": 9832, "dataset_name": "health_fact"}, "test": {"name": "test", "num_bytes": 6825221, "num_examples": 1235, "dataset_name": "health_fact"}, "validation": {"name": "validation", "num_bytes": 6653044, "num_examples": 1225, "dataset_name": "health_fact"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1eTtRs5cUlBP5dXsx-FTAlmXuB6JQi2qj": {"num_bytes": 24892660, "checksum": "3f0a5541f4a60c09a138a896621402893ce4b3a37060363d9257010c2c27fc3a"}}, "download_size": 24892660, "post_processing_size": null, "dataset_size": 67463642, "size_in_bytes": 92356302}}

dummy/1.1.0/dummy_data.zip

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+size 27884

health_fact.py

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+# coding=utf-8
+# 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.
+"""Dataset for explainable fake news detection of public health claims."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{kotonya-toni-2020-explainable,
+    title = "Explainable Automated Fact-Checking for Public Health Claims",
+    author = "Kotonya, Neema and Toni, Francesca",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods
+    in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
+    pages = "7740--7754",
+}
+"""
+
+_DESCRIPTION = """\
+PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of
+public health claims. Each instance in the PUBHEALTH dataset has an associated
+veracity label (true, false, unproven, mixture). Furthermore each instance in the
+dataset has an explanation text field. The explanation is a justification for which
+the claim has been assigned a particular veracity label.
+
+The dataset was created to explore fact-checking of difficult to verify claims i.e.,
+those which require expertise from outside of the journalistics domain, in this case
+biomedical and public health expertise.
+
+It was also created in response to the lack of fact-checking datasets which provide
+gold standard natural language explanations for verdicts/labels.
+
+NOTE: There are missing labels in the dataset and we have replaced them with -1.
+"""
+
+_DATA_URL = "https://drive.google.com/uc?export=download&id=1eTtRs5cUlBP5dXsx-FTAlmXuB6JQi2qj"
+_TEST_FILE_NAME = "PUBHEALTH/test.tsv"
+_TRAIN_FILE_NAME = "PUBHEALTH/train.tsv"
+_VAL_FILE_NAME = "PUBHEALTH/dev.tsv"
+
+
+class HealthFact(datasets.GeneratorBasedBuilder):
+    """Dataset for explainable fake news detection of public health claims."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        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=datasets.Features(
+                {
+                    "claim_id": datasets.Value("string"),
+                    "claim": datasets.Value("string"),
+                    "date_published": datasets.Value("string"),
+                    "explanation": datasets.Value("string"),
+                    "fact_checkers": datasets.Value("string"),
+                    "main_text": datasets.Value("string"),
+                    "sources": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["false", "mixture", "true", "unproven"]),
+                    "subjects": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, _TRAIN_FILE_NAME),
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, _TEST_FILE_NAME),
+                    "split": datasets.Split.TEST,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, _VAL_FILE_NAME),
+                    "split": datasets.Split.VALIDATION,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            label_list = ["false", "mixture", "true", "unproven"]
+            data = csv.reader(f, delimiter="\t")
+            next(data, None)  # skip the headers
+            for row_id, row in enumerate(data):
+                row = [x if x != "nan" else "" for x in row]  # nan values changed to empty string
+                if split != "test":
+                    if len(row) <= 9:
+                        elements = ["" for x in range(9 - len(row))]
+                        row = row + elements
+                    (
+                        claim_id,
+                        claim,
+                        date_published,
+                        explanation,
+                        fact_checkers,
+                        main_text,
+                        sources,
+                        label,
+                        subjects,
+                    ) = row
+                    if label not in label_list:  # remove stray labels in dev.tsv, train.tsv
+                        label = -1
+                else:
+                    if len(row) <= 10:
+                        elements = ["" for x in range(10 - len(row))]
+                        row = row + elements
+                    (
+                        _,
+                        claim_id,
+                        claim,
+                        date_published,
+                        explanation,
+                        fact_checkers,
+                        main_text,
+                        sources,
+                        label,
+                        subjects,
+                    ) = row
+                    if label not in label_list:  # remove stray labels in test.tsv
+                        label = -1
+                if label == "":
+                    label = -1
+                yield row_id, {
+                    "claim_id": claim_id,
+                    "claim": claim,
+                    "date_published": date_published,
+                    "explanation": explanation,
+                    "fact_checkers": fact_checkers,
+                    "main_text": main_text,
+                    "sources": sources,
+                    "label": label,
+                    "subjects": subjects,
+                }