{"url":"/dataset/olid","name":"OLID","full_name":"Offensive Language Identification Dataset","description_markdown":"The **OLID** is a hierarchical dataset to identify the type and the target of offensive texts in social media. The dataset is collected on Twitter and publicly available. There are 14,100 tweets in total, in which 13,240 are in the training set, and 860 are in the test set. For each tweet, there are three levels of labels: (A) Offensive/Not-Offensive, (B) Targeted-Insult/Untargeted, (C) Individual/Group/Other. The relationship between them is hierarchical. If a tweet is offensive, it can have a target or no target. If it is offensive to a specific target, the target can be an individual, a group, or some other objects. This dataset is used in the OffensEval-2019 competition in SemEval-2019.\r\n\r\nSource: [Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection](https://arxiv.org/abs/2004.13432)\r\nImage Source: [https://arxiv.org/pdf/1902.09666.pdf](https://arxiv.org/pdf/1902.09666.pdf)","description_withheld":null,"homepage":"https://scholar.harvard.edu/malmasi/olid","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/predicting-the-type-and-target-of-offensive","title":"Predicting the Type and Target of Offensive Posts in Social Media","first_author":"Marcos Zampieri","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Language Modelling","url":"/task/language-modelling","datasets_with_task":"/datasets/task/language-modelling"},{"name":"Hate Speech Detection","url":"/task/hate-speech-detection","datasets_with_task":"/datasets/task/hate-speech-detection"},{"name":"Language Identification","url":"/task/language-identification","datasets_with_task":"/datasets/task/language-identification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["OLID"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/christophsonntag/OLID","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/ahmedAlaa24494/Offensive-Language_Detection","url":"https://github.com/ahmedAlaa24494/Offensive-Language_Detection","frameworks":["pytorch"]}],"num_papers_in_archive":152,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/hate-speech-detection-on-olid","task":"Hate Speech Detection","dataset_variant":"OLID","rows":1,"metrics":["Macro F1"],"first_row_in_archive_order":{"model":"RoBERTa-large-ST","paper":"/paper/noisy-self-training-with-data-augmentations","metrics":{"Macro F1":"80.7"},"code_links":[{"title":"jaugusto97/offense-self-training","url":"https://github.com/jaugusto97/offense-self-training"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/noisy-self-training-with-data-augmentations","title":"Noisy Self-Training with Data Augmentations for Offensive and Hate Speech Detection Tasks","date":"2023-07-31","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}