metadata

dataset_info:
  features:
    - name: claim_id
      dtype: int64
    - name: claim
      dtype: string
    - name: evidence
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': SUPPORTS
            '1': REFUTES
            '2': NOT_ENOUGH_INFO
    - name: category
      dtype: string
  splits:
    - name: train
      num_bytes: 1467456
      num_examples: 4298
    - name: test
      num_bytes: 526276
      num_examples: 1535
    - name: valid
      num_bytes: 635174
      num_examples: 1842
  download_size: 1372892
  dataset_size: 2628906
license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - climate
pretty_name: climate_fever dataset with one-to-one claim-evidence pair
size_categories:
  - 1K<n<10K

Dataset Card for "climate_fever_fixed"

Dataset Summary

This dataset was created to aid our team in developing a model to more accurately perform climate change-related fact checking. We approach this task from a perspective heavily impacted by the work of the ClimateBERT team. With that in mind, our team likewise leveraged a BERT Language model to solve this task. This dataset presents an edited version of the Climate_Fever dataset, hosted by HuggingFace. Climate_Fever is composed of climate-related documents that have been annotated with labels related to fact-checking and misinformation. However, in the climate-plus project, we decided to modify the dataset to remove redundancy and keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis.

Data Fields

This dataset contains 7675 records, each of which is composed of several attributes:

claim_id: a integer feature, which serves as a unique identifier for each record/row.
claim: a string feature, containes the raw text of a given climate-related claim.
evidence: a string feature, which provides free text evidence that relates to the previously established claim.
label: a class label feature representing an assigned class, where values can either be 0: "supports", 1: "refutes" and 2: "not enough info".
category: a string feature, which provides additional detail about the particular focus of a given claim.

This dataset was then broken into train, test and validation sets to enable proper evaluation of our model. These splits contain the following amount of data:

Train: 4300 Records
Test: 1540 Records
Val: 1840 Records

Source Data

This dataset represents an evolved version of the original Climate_Fever dataset, hosted by HuggingFace. It was adapted to meet the needs of our team, as we attempted to solve a specific climate change-related task. The original dataset adopted the FEVER methodology, discussed in more detail here. Their original dataset consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs.

Methodology

This dataset was curated by our team to reduce redundancy and keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis. For each given claim, there are multiple sentences of evidence. We decided to expand the one-to-many relation to one-to-one. This resulted in a modified version of the climate_fever dataset that includes only one evidence sentence per claim.

Languages

The text contained in the dataset is entirely in English, as found in the real-world financial disclosures identified by the TCFD. The associated BCP-47 code is en, to ensure clear labeling of language usage for downstream tasks and other future applications.

More Information needed