Duplicate from ucsbnlp/liar

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids: []
+paperswithcode_id: liar
+pretty_name: LIAR
+tags:
+- fake-news-detection
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': 'false'
+          '1': half-true
+          '2': mostly-true
+          '3': 'true'
+          '4': barely-true
+          '5': pants-fire
+  - name: statement
+    dtype: string
+  - name: subject
+    dtype: string
+  - name: speaker
+    dtype: string
+  - name: job_title
+    dtype: string
+  - name: state_info
+    dtype: string
+  - name: party_affiliation
+    dtype: string
+  - name: barely_true_counts
+    dtype: float32
+  - name: false_counts
+    dtype: float32
+  - name: half_true_counts
+    dtype: float32
+  - name: mostly_true_counts
+    dtype: float32
+  - name: pants_on_fire_counts
+    dtype: float32
+  - name: context
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2730651
+    num_examples: 10269
+  - name: test
+    num_bytes: 341414
+    num_examples: 1283
+  - name: validation
+    num_bytes: 341592
+    num_examples: 1284
+  download_size: 1013571
+  dataset_size: 3413657
+train-eval-index:
+- config: default
+  task: text-classification
+  task_id: multi_class_classification
+  splits:
+    train_split: train
+    eval_split: test
+  col_mapping:
+    statement: text
+    label: target
+  metrics:
+  - type: accuracy
+    name: Accuracy
+  - type: f1
+    name: F1 macro
+    args:
+      average: macro
+  - type: f1
+    name: F1 micro
+    args:
+      average: micro
+  - type: f1
+    name: F1 weighted
+    args:
+      average: weighted
+  - type: precision
+    name: Precision macro
+    args:
+      average: macro
+  - type: precision
+    name: Precision micro
+    args:
+      average: micro
+  - type: precision
+    name: Precision weighted
+    args:
+      average: weighted
+  - type: recall
+    name: Recall macro
+    args:
+      average: macro
+  - type: recall
+    name: Recall micro
+    args:
+      average: micro
+  - type: recall
+    name: Recall weighted
+    args:
+      average: weighted
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://sites.cs.ucsb.edu/~william/
+- **Repository:**
+- **Paper:** https://arxiv.org/abs/1705.00648
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for all other labels range from 2,063 to 2,638. In each case, the labeler provides a lengthy analysis report to ground each judgment.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English.
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]
+
+## 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
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.

liar.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.
+"""LIAR is a dataset for fake news detection with annotated claims."""
+
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{wang-2017-liar,
+title = "{``}Liar, Liar Pants on Fire{''}: A New Benchmark Dataset for Fake News Detection",
+author = "Wang, William Yang",
+booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
+month = jul,
+year = "2017",
+address = "Vancouver, Canada",
+publisher = "Association for Computational Linguistics",
+url = "https://www.aclweb.org/anthology/P17-2067",
+doi = "10.18653/v1/P17-2067",
+pages = "422--426",
+abstract = "Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.",
+}
+"""
+
+_DESCRIPTION = """\
+LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for all other labels range from 2,063 to 2,638. In each case, the labeler provides a lengthy analysis report to ground each judgment.
+"""
+
+_HOMEPAGE = "https://www.aclweb.org/anthology/P17-2067"
+
+_LICENSE = "Unknown"
+
+_URL = "https://www.cs.ucsb.edu/~william/data/liar_dataset.zip"
+
+
+class Liar(datasets.GeneratorBasedBuilder):
+    """LIAR is a dataset for fake news detection with annotated claims."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "label": datasets.ClassLabel(
+                        names=[
+                            "false",
+                            "half-true",
+                            "mostly-true",
+                            "true",
+                            "barely-true",
+                            "pants-fire",
+                        ]
+                    ),
+                    "statement": datasets.Value("string"),
+                    "subject": datasets.Value("string"),
+                    "speaker": datasets.Value("string"),
+                    "job_title": datasets.Value("string"),
+                    "state_info": datasets.Value("string"),
+                    "party_affiliation": datasets.Value("string"),
+                    "barely_true_counts": datasets.Value("float"),
+                    "false_counts": datasets.Value("float"),
+                    "half_true_counts": datasets.Value("float"),
+                    "mostly_true_counts": datasets.Value("float"),
+                    "pants_on_fire_counts": datasets.Value("float"),
+                    "context": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("statement", "label"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.tsv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv"), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "valid.tsv"),
+                    "split": "valid",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+
+        with open(filepath, encoding="utf-8") as tsv_file:
+            reader = csv.reader(tsv_file, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for id_, row in enumerate(reader):
+                yield id_, {
+                    "id": row[0],
+                    "label": row[1],
+                    "statement": row[2],
+                    "subject": row[3],
+                    "speaker": row[4],
+                    "job_title": row[5],
+                    "state_info": row[6],
+                    "party_affiliation": row[7],
+                    "barely_true_counts": row[8],
+                    "false_counts": row[9],
+                    "half_true_counts": row[10],
+                    "mostly_true_counts": row[11],
+                    "pants_on_fire_counts": row[12],
+                    "context": row[13],
+                }