
# MULTI-GRAIN STEREOTYPE DATASET (MGSD)

The MULTI-GRAIN STEREOTYPE DATASET (MGSD) is a comprehensive dataset designed for the research and analysis of stereotypes in natural language processing. It provides granular annotations at both the sentence and token levels, enabling various studies and applications in stereotype detection.

## Dataset Structure

The dataset contains the following columns:

- **text_with_marker**: Contains the original text with markers (`===`) highlighting potential stereotype tokens.
- **text_no_marker**: The text without any markers, suitable for models that operate at the sentence level.
- **label**: Indicates if the sentence is a stereotype, anti-stereotype, or unrelated.
- **stereotype_type**: Describes the type of stereotype e.g., race, gender, profession.
- **binary_class**: A binary classification of the stereotype e.g., stereotype_race, unrelated.
- **multi_class**: A multi-class classification label e.g., stereotype_race, stereotype_gender.
- **original_dataset**: Source of the data.

## Usage

This dataset can be used to train models for various tasks:

1. **Sentence-level Stereotype Detection**: Using the `text_no_marker` column as input and `binary_label` or `multi_label` as target.
2. **Token-level Stereotype Detection**: Using the `text_with_marker` to identify the position of the token in the sentence.
task_categories:text-classification task_categories:token-classification size_categories:10K<n<100K language:en license:mit region:us
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "token-classification"]}
TAGS
#task_categories-text-classification #task_categories-token-classification #size_categories-10K<n<100K #language-English #license-mit #region-us 


# MULTI-GRAIN STEREOTYPE DATASET (MGSD)

The MULTI-GRAIN STEREOTYPE DATASET (MGSD) is a comprehensive dataset designed for the research and analysis of stereotypes in natural language processing. It provides granular annotations at both the sentence and token levels, enabling various studies and applications in stereotype detection.

## Dataset Structure

The dataset contains the following columns:

- text_with_marker: Contains the original text with markers ('===') highlighting potential stereotype tokens.
- text_no_marker: The text without any markers, suitable for models that operate at the sentence level.
- label: Indicates if the sentence is a stereotype, anti-stereotype, or unrelated.
- stereotype_type: Describes the type of stereotype e.g., race, gender, profession.
- binary_class: A binary classification of the stereotype e.g., stereotype_race, unrelated.
- multi_class: A multi-class classification label e.g., stereotype_race, stereotype_gender.
- original_dataset: Source of the data.

## Usage

This dataset can be used to train models for various tasks:

1. Sentence-level Stereotype Detection: Using the 'text_no_marker' column as input and 'binary_label' or 'multi_label' as target.
2. Token-level Stereotype Detection: Using the 'text_with_marker' to identify the position of the token in the sentence.
# MULTI-GRAIN STEREOTYPE DATASET (MGSD)

The MULTI-GRAIN STEREOTYPE DATASET (MGSD) is a comprehensive dataset designed for the research and analysis of stereotypes in natural language processing. It provides granular annotations at both the sentence and token levels, enabling various studies and applications in stereotype detection. ## Dataset Structure

The dataset contains the following columns:

- text_with_marker: Contains the original text with markers ('===') highlighting potential stereotype tokens.
- text_no_marker: The text without any markers, suitable for models that operate at the sentence level.
- label: Indicates if the sentence is a stereotype, anti-stereotype, or unrelated.
- stereotype_type: Describes the type of stereotype e.g., race, gender, profession.
- binary_class: A binary classification of the stereotype e.g., stereotype_race, unrelated.
- multi_class: A multi-class classification label e.g., stereotype_race, stereotype_gender.
- original_dataset: Source of the data. ## Usage

This dataset can be used to train models for various tasks:

1. Sentence-level Stereotype Detection: Using the 'text_no_marker' column as input and 'binary_label' or 'multi_label' as target.
2. Token-level Stereotype Detection: Using the 'text_with_marker' to identify the position of the token in the sentence.
TAGS
#task_categories-text-classification #task_categories-token-classification #size_categories-10K<n<100K #language-English #license-mit #region-us 
 # MULTI-GRAIN STEREOTYPE DATASET (MGSD)

The MULTI-GRAIN STEREOTYPE DATASET (MGSD) is a comprehensive dataset designed for the research and analysis of stereotypes in natural language processing. It provides granular annotations at both the sentence and token levels, enabling various studies and applications in stereotype detection. ## Dataset Structure

The dataset contains the following columns:

- text_with_marker: Contains the original text with markers ('===') highlighting potential stereotype tokens.
- text_no_marker: The text without any markers, suitable for models that operate at the sentence level.
- label: Indicates if the sentence is a stereotype, anti-stereotype, or unrelated.
- stereotype_type: Describes the type of stereotype e.g., race, gender, profession.
- binary_class: A binary classification of the stereotype e.g., stereotype_race, unrelated.
- multi_class: A multi-class classification label e.g., stereotype_race, stereotype_gender.
- original_dataset: Source of the data. ## Usage

This dataset can be used to train models for various tasks:

1. Sentence-level Stereotype Detection: Using the 'text_no_marker' column as input and 'binary_label' or 'multi_label' as target.
2. Token-level Stereotype Detection: Using the 'text_with_marker' to identify the position of the token in the sentence.
passage: TAGS
#task_categories-text-classification #task_categories-token-classification #size_categories-10K<n<100K #language-English #license-mit #region-us 
# MULTI-GRAIN STEREOTYPE DATASET (MGSD)

The MULTI-GRAIN STEREOTYPE DATASET (MGSD) is a comprehensive dataset designed for the research and analysis of stereotypes in natural language processing. It provides granular annotations at both the sentence and token levels, enabling various studies and applications in stereotype detection.## Dataset Structure

The dataset contains the following columns:

- text_with_marker: Contains the original text with markers ('===') highlighting potential stereotype tokens.
- text_no_marker: The text without any markers, suitable for models that operate at the sentence level.
- label: Indicates if the sentence is a stereotype, anti-stereotype, or unrelated.
- stereotype_type: Describes the type of stereotype e.g., race, gender, profession.
- binary_class: A binary classification of the stereotype e.g., stereotype_race, unrelated.
- multi_class: A multi-class classification label e.g., stereotype_race, stereotype_gender.
- original_dataset: Source of the data.## Usage

This dataset can be used to train models for various tasks:

1. Sentence-level Stereotype Detection: Using the 'text_no_marker' column as input and 'binary_label' or 'multi_label' as target.
2. Token-level Stereotype Detection: Using the 'text_with_marker' to identify the position of the token in the sentence.