# 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](https://climatebert.ai/about) 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](https://huggingface.co/datasets/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.

<br>
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](https://huggingface.co/datasets/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](https://www.amazon.science/blog/the-fever-data-set-what-doesnt-kill-it-will-make-it-stronger).
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`](https://www.techonthenet.com/js/language_tags.php), to ensure clear labeling of language usage for downstream tasks and other future applications.

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
task_categories:text-classification size_categories:1K<n<10K language:en license:mit climate region:us
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "climate_fever dataset with one-to-one claim-evidence pair", "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}, "tags": ["climate"]}
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #climate #region-us 

# 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.

<br>
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
# 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.

<br>
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
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #climate #region-us 
 # 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.

<br>
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
passage: TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #climate #region-us 
# 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.

<br>
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