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+# OLID-BR
+
+Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR) is a dataset with multi-task annotations for the detection of offensive language.
+
+The current version (v1.0) contains **7,943** (extendable to 13,538) comments from different sources, including social media (YouTube and Twitter) and related datasets.
+
+OLID-BR contains a collection of annotated sentences in Brazilian Portuguese using an annotation model that encompasses the following levels:
+
+- [[Offensive content detection](#offensive-content-detection)]{Detect offensive content in sentences and categorize it.|top-right}
+- [[Offense target identification](#offense-target-identification)]{Detect if an offensive sentence is targeted to a person or group of people.|top-right}
+- [[Offensive spans identification](#offensive-spans-identification)]{Detect curse words in sentences.|top-right}
+
+![](https://dougtrajano.github.io/olid-br/images/olid-br-taxonomy.png)
+
+## Categorization
+
+### Offensive Content Detection
+
+This level is used to detect offensive content in the sentence.
+
+**Is this text offensive?**
+
+We use the [[Perspective API](https://www.perspectiveapi.com/)]{Perspective API is the product of a collaborative research effort by Jigsaw and Google's Counter Abuse Technology team.|top-right} to detect if the sentence contains offensive content with double-checking by our [qualified annotators](annotation/index.en.md#who-are-qualified-annotators).
+
+- `OFF` Offensive: Inappropriate language, insults, or threats.
+- `NOT` Not offensive: No offense or profanity.
+
+**Which kind of offense does it contain?**
+
+The following labels were tagged by our annotators:
+
+`Health`, `Ideology`, `Insult`, `LGBTQphobia`, `Other-Lifestyle`, `Physical Aspects`, `Profanity/Obscene`, `Racism`, `Religious Intolerance`, `Sexism`, and `Xenophobia`.
+
+See the [Glossary](glossary.en.md) for further information.
+
+### Offense Target Identification
+
+This level is used to detect if an offensive sentence is targeted to a person or group of people.
+
+**Is the offensive text targeted?**
+
+- `TIN` Targeted Insult: Targeted insult or threat towards an individual, a group or other.
+- `UNT` Untargeted: Non-targeted profanity and swearing.
+
+**What is the target of the offense?**
+
+- `IND` The offense targets an individual, often defined as “cyberbullying”.
+- `GRP` The offense targets a group of people based on ethnicity, gender, sexual
+- `OTH` The target can belong to other categories, such as an organization, an event, an issue, etc.
+
+### Offensive Spans Identification
+
+As toxic spans, we define a sequence of words that attribute to the text's toxicity.
+
+For example, let's consider the following text:
+
+> "USER `Canalha` URL"
+
+The toxic spans are:
+
+```python
+[5, 6, 7, 8, 9, 10, 11, 12, 13]
+```
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance is a social media comment with a corresponding ID and annotations for all the tasks described below.
+
+### Data Fields
+
+The simplified configuration includes:
+
+- `id` (string): Unique identifier of the instance.
+- `text` (string): The text of the instance.
+- `is_offensive` (string): Whether the text is offensive (`OFF`) or not (`NOT`).
+- `is_targeted` (string): Whether the text is targeted (`TIN`) or untargeted (`UNT`).
+- `targeted_type` (string): Type of the target (individual `IND`, group `GRP`, or other `OTH`). Only available if `is_targeted` is `True`.
+- `toxic_spans` (string): List of toxic spans.
+- `health` (boolean): Whether the text contains hate speech based on health conditions such as disability, disease, etc.
+- `ideology` (boolean): Indicates if the text contains hate speech based on a person's ideas or beliefs.
+- `insult` (boolean): Whether the text contains insult, inflammatory, or provocative content.
+- `lgbtqphobia` (boolean): Whether the text contains harmful content related to gender identity or sexual orientation.
+- `other_lifestyle` (boolean): Whether the text contains hate speech related to life habits (e.g. veganism, vegetarianism, etc.).
+- `physical_aspects` (boolean): Whether the text contains hate speech related to physical appearance.
+- `profanity_obscene` (boolean): Whether the text contains profanity or obscene content.
+- `racism` (boolean): Whether the text contains prejudiced thoughts or discriminatory actions based on differences in race/ethnicity.
+- `religious_intolerance` (boolean): Whether the text contains religious intolerance.
+- `sexism` (boolean): Whether the text contains discriminatory content based on differences in sex/gender (e.g. sexism, misogyny, etc.).
+- `xenophobia` (boolean): Whether the text contains hate speech against foreigners.
+
+See the [**Get Started**](get-started.en.md) page for more information.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+Toxicity detection is a worthwhile problem that can ensure a safer online environment for everyone.
+
+However, toxicity detection algorithms have focused on English and do not consider the specificities of other languages.
+
+This is a problem because the toxicity of a comment can be different in different languages.
+
+Additionally, the toxicity detection algorithms focus on the binary classification of a comment as toxic or not toxic.
+
+Therefore, we believe that the OLID-BR dataset can help to improve the performance of toxicity detection algorithms in Brazilian Portuguese.
+
+### Discussion of Biases
+
+We are aware that the dataset contains biases and is not representative of global diversity.
+
+We are aware that the language used in the dataset could not represent the language used in different contexts.
+
+Potential biases in the data include: Inherent biases in the social media and user base biases, the offensive/vulgar word lists used for data filtering, and inherent or unconscious bias in the assessment of offensive identity labels.
+
+All these likely affect labeling, precision, and recall for a trained model.
+
+## Citation
+
+Pending