SlowGuess/NeurIPS_2023_OCR huggingface dataset:
price of the items sold. Within each category, the items are sorted by mid-price. The top half is treated as high-price and the bottom half is treated as low-price. This dataset can be downloaded at https://huggingface.co/datasets/Hellisotherpeople/DebateSum with MIT license.
debate. We collect evidence compiled for American competitive policy debate, published online by debate camps from Roush & Balaji (2020). The train split is downloaded from Huggingface. For each sample, we use the abstract as the text. Arguments are categorized by type, debate camp of origin, and topic/specific argument. For topics, we use domain knowledge to list relevant keywords for each topic and include any sample with a file name that includes any keyword. A single sample can belong to multiple topics. This dataset can be downloaded at https://huggingface.co/datasets/Hellisotherpeople/DebateSum with MIT license.
dice-jobs. We collect American technology job postings on dice.com from PromptCloud (2017). Job postings are downloaded from Kaggle. Posts from the six most popular companies are categorized by company. We remove miscellaneous characters and blank descriptions. We additionally apply our splitting procedure to reduce description length. This dataset can be downloaded at https:// www.kaggle.com/datasets/PromptCloudHQ/us-technology-jobs-on-dicecom under CC BY-SA 4.0 .
diplomacy-deception. We collect dialogue from games of Diplomacy, which involves deception from Peskov et al. (2020). Diplomacy dialogues are downloaded from GitHub (all splits). The data are ASCII encoded and newlines are removed. Each message and label is treated as a sample. This dataset can be downloaded at https://huggingface.co/datasets/diplomacy_detection under unknown license.
echr-decisions. We collect facts of cases heard before the European Court of Human Rights from Chalkidis et al. (2019). Decisions are downloaded from a public archive. A random sample of 500 decisions is selected from the files. The samples with any violated articles are categorized as “violation,” while the rest are categorized as “no violation.” This dataset can be downloaded at https://paperswithcode.com/dataset/echr under unknown license.
essay-scoring. We collect essays from students from ess (2012). Essays are downloaded from a GitHub repository. Only essays from set 5 are considered. Essays with a score of at least 3 are categorized as good essays, while essays with a score less than 3 are bad essays. This dataset can be downloaded at https://www.kaggle.com/c/asap-aes under unknown license.
fake-news. We collect fake and legitimate news from Pérez-Rosas et al. (2017). Fake news articles are downloaded from the author's website. Full articles are treated as text snippets. This dataset can be downloaded at http://web.eecs.umich.edu/~mihalcea/downloads.html#FakeNews under CC-BY-4.0.
fomc-speeches. We collect Federal Open Market Committee (FOMC) speeches from 1996-2020, which describe Federal Reserve policy from Mish (2020). Fed speeches are downloaded from Kaggle. The macro indicator data are merged in on the year and month. Full speech text is split by paragraph and categorized by speaker, year, and macroeconomic indicator. This dataset can be downloaded at https://www.kaggle.com/datasets/natanm/federal-reserve-governors-speeches-1996-2020 under unknown license.
genius-lyrics. We collect lyrics collected from Genius.com before 2020 from Lim & Benson (2021). Genius lyrics are downloaded from Google Drive. The lyrics are merged with song metadata and treated as samples. We categorize lyrics by hand-selecting popular artists, common genres, time periods, and view counts (over 1M views is high, 500k-1M is medium). This dataset can be downloaded at https://www.cs.cornell.edu/\~arb/data/genius-expertise/ under unknown license.
happy-moments. We collect self-reported happy moments and demographic characteristics from Asai et al. (2018). The HappyDB dataset is downloaded from the official GitHub repository. Demographic data is cleaned and merged into happy moments. Happy moment descriptions are treated as samples and are categorized by type of happy moment, country of origin, and other demographic features. This dataset can be downloaded at https://github.com/megagonlabs/HappyDB under unknown license.
huff-post-headlines. We collect headlines from the news outlet Huffington Post from Misra & Arora (2019) and Misra & Grover (2021). Huffington Post headlines are downloaded from Kaggle. The
short description of each article is treated as a sample and tokenized at the sentence level. This dataset can be downloaded at https://rishabhmisra.github.io/publications/ under CC-BY-4.0.
immigration-speeches. We collect congressional and presidential speeches that mention immigration from 1880 to the present from Card et al. (2022). Immigration speeches are downloaded from the replication package. The speech text is preprocessed to remove extraneous spaces. We engineer features corresponding to time periods, well-known speakers, other significant time periods, the racial group under discussion, and the geographic area within the United States. This dataset can be downloaded at https://github.com/dallascard/us-immigration-speeches/releases.
kickstarter. We collect names of startups on kickstarter.com from Mouillé (2017). We download a 2018 crawl from Kickstarter from Kaggle. The project name is treated as the text sample. This dataset can be downloaded at https://www.kaggle.com/datasets/chemical/ kickstarter-projects?select=ks-projects-201612.csv under CC BY-NC-SA 4.0.
microedit-humor. We collect funny sentences generated by making one-word edits to normal statements from Hossain et al. (2019). The Microedit dataset is downloaded from the author's website. We make the relevant edit to each text sample and treat the edited text sample as the data point. We bin the mean annotator grade into 4 and denote each as unfunny, neutral, funny, and very funny, respectively. This dataset can be downloaded at https://paperswithcode.com/dataset/humicroedit.
mnli. We collect a collection of sentence pairs annotated with textual entailment information from a range of genres from Williams et al. (2017). The MNLI corpus is downloaded from the official website. We treat the premise and hypothesis as text samples. This dataset can be downloaded from https://cims.nyu.edu/~sbowman/multinli/, most of which are under the OANC license.
monster-jobs. We collect American job postings on monster.com. Jobs on Monster.com are downloaded from Kaggle. Job descriptions are treated as samples and split at the paragraph and sentence level. We keep and categorize jobs from seventeen large cities. This dataset can be downloaded from https://www.kaggle.com/datasets/PromptCloudHQ/us-jobs-on-monstercom under CC BY-SA 4.0.
movie-tmdb. We collect movie plot summaries from TMDB from Kaggle (2018). TMDB movie overviews are downloaded from Kaggle. We keep only English movies and bin popularity by deciles. The top decile is considered "hits," the 70-80th percentiles are considered "average," and the 30-40th percentiles are considered "bad." This dataset can be downloaded from https://www.kaggle.com/datasets/tmdb/tmdb-movie-meta data21.
movie-wiki. We collect movie plot summaries collected from Wikipedia from Robischon (2019). Wikipedia movie summaries are downloaded from Kaggle. This dataset can be downloaded from https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots under CC BY-SA 4.0.
news-popularity. We collect news headlines posted on social media platforms from Moniz & Torgo (2018). Headlines are downloaded from a reproduction package. The headline and title text are cleaned, and the title is treated as the text sample. The 100 most positive and negative or popular and unpopular articles on each topic are used as distributions. This dataset can be downloaded from https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms.
nli-benchmarks. We collect training examples from various natural language inference (NLI) datasets from Liu et al. (2022). NLI benchmarks are downloaded from a public collection on Google Drive. We examine the premise and hypothesis separately as samples. This dataset can be downloaded from https://github.com/alisawaffles/wanli.
npt-conferences. We collect Non-Proliferation of Nuclear Weapons (NPT) conference transcripts from Barnum & Lo (2020). NPT conference notes are extracted from the accompanying replication package. Text is split by paragraph, and only paragraphs longer than 50 characters are preserved. Text is split into three time ranges: pre-2008, 2008-2012, and post-2012. This dataset can be downloaded from https://journals.sagepub.com/doi/full/10.1177/0022343320960523.
open-deception. We collect arbitrary lies and truths from any domain generated by crowdworkers from Pérez-Rosas & Mihalcea (2015). Open domain lies are downloaded from the public dataset
and lie texts are split into lies and truths. This dataset can be downloaded from https://web.eecs.umich.edu/~mihalcea/downloads.html#OpenDeception.
open-review. We collect submissions to ICLR, a machine learning conference from 2018 to 2021. Open review abstracts are accessed via the openreview API. We query for abstracts from the 2018-2021 ICLR blind submissions. Abstracts are classified based on rating:  $>=7$  ("great"), 5-6 ("good"), and  $<=4$  ("bad"). This dataset can be downloaded from https://openreview.net/.
parenting-subreddits. We collect posts from various parenting-related subreddits, which are text-based forums on the site Reddit from Gao et al. (2021). Posts from various subreddits are downloaded from the paper's GitHub repository. We clean the text and split the posts according to the topic(s) each post is tagged with. This dataset can be downloaded from https://github.com/SALT-NLP/Parenting_OnlineUsage.
poetry. We collect poems from PoetryFoundation.com from Bramhecha (2019). Poems are downloaded from a 2019 scrape of the PoetryFoundation website from Kaggle. The text is cleaned and split according to subject tags and authorship. This dataset can be downloaded from https://www.kaggle.com/datasets/tgdivy/poetry-foundation-poems under GNU Affero General Public License.
political-ads. We collect political ads observed by Facebook users from pol (2021). Ads are downloaded from the Ad Observer website, which maintains an aggregate of all collected ads. We extract targeting metadata from the targeting field and define splits according to age, gender, location, interests, time, and political lean. This dataset can be downloaded from https://adobserver.org/ad-database/.
qqp. We collect questions from Quora.com from Quora (2017).
rate-my-prof. We collect reviews of lecturers from RateMyProfessor.com from He (2020). We download a sample of RateMyProfessor.com reviews from an online repo. We clean the text and guess the gender of the reviewed lecturer from the first name using the gender-guesser package. Due to data availability, we consider only male and female names. To improve the quality of the classification, we remove any posts which use pronouns from the opposing sex (e.g. "him"). This dataset can be downloaded from https://data.mendeley.com/datasets/fvtfjyvw7d/2 under CC BY 4.0.
radiology-diagnosis. We collect impressions and medical histories of radiology patients from Pestian et al. (2007). Radiology diagnoses are downloaded from a GitHub copy of the original task dataset. We parse the metadata to retrieve the diagnostic code, decision type, impression, and patient history. Referencing the associated ICD codes, we convert codes to colloquial diagnoses (e.g. 786.2 denotes cough). We treat the histories and impressions as samples and split them according to diagnosis and level of consensus.
reddit-humor. We collect jokes posted on the Reddit forum r/Jokes, a message board for sharing jokes from Weller & Seppi (2020). Jokes are downloaded from the dev and test splits of the dataset. We clean the text and split the dataset according to whether they are labeled as funny. This dataset can be downloaded from https://github.com/orionw/rJokesData under Reddit License and Terms of Service, and users must follow the Reddit User Agreement and Privacy Policy, as well as remove any posts if asked to by the original user.
reddit-stress. We collect stress-related posts on Reddit from Turcan & McKeown (2019). We split the post text based on which subreddit they are posted on (related to PTSD, anxiety, or stress generally). Reddit posts are downloaded from https://github.com/gillian850413/Insight_Stress_Analysis, and we recommend following the Reddit User Agreement and Privacy Policy, as well as remove any posts if asked to by the original user.
reuters-authorship. We collect articles from various Reuters authors from Liu (2011). The articles are split according to the author. Reuters articles are downloaded from the UCI repository https://archive.ics.uci.edu/ml/datasets/Reuter_50_50.
riddles. We generated several riddles. The 3000 most common English words are manually copied from a website. Words with between 5 and 8 characters are kept. We create two popular riddles. First, we split words based on whether they have a duplicate character. We exclude any words with multiple "doubles" or more than 2 of any character. Second, we split words based on whether they have the letter T.
scotus-cases. We collect facts from cases heard by the Supreme Court of the United States (SCOTUS) from Alali et al. (2021). Supreme Court cases are downloaded from a GitHub repository. We identify state/federal parties by manually defining keywords. We split based on the winning party, the identity of each party, and the type of decision. We then define several time periods and relevant political eras and split decisions accordingly. Finally, we split according to the ruling's policy area and how it changes over time. The dataset can be downloaded from https://paperswithcode.com/paper/justice-a-benchmark-dataset-for-supreme-court under CC-BY-SA.
short-answer-scoring. We collect short answers from students from sho (2013). Short answers are downloaded from a GitHub mirror of the dataset. We consider only responses to essay set 1. The two scores are averaged and binned into good  $(>= 2.5)$ , medium (1.5-2.5), and bad  $(< 1.5)$ . The dataset can be downloaded from https://www.kaggle.com/c/asap-sas.
snli. We collect a collection of sentence pairs annotated with textual entailment information from images from Bowman et al. (2015). The dataset can be downloaded from https://nlp.stanford.edu/projects/snli/ under CC BY-SA 4.0.
squad-v2. We collect reading comprehension questions crowdsourced from Wikipedia articles from Rajpurkar et al. (2018). The dataset can be downloaded from https://rajpurkar.github.io/ SQuAD-explorer/ under CC BY-SA 4.0.
stock-news. We collect top news headlines on Reddit, an online message board from Sun (2017). Headlines are downloaded from a GitHub mirror. We clean the text and divide the samples based on whether the DOW rose or fell that day. The dataset can be downloaded from https://github.com/ShravanChintha/Stock-Market-prediction-using-daily-news-headlines under Reddit License and Terms of Service, and users must follow the Reddit User Agreement and Privacy Policy, as well as remove any posts if asked to by the original user.
suicide-notes. We collect posts from r/SuicideWatch and r/depression, two forums on Reddit fromHe (2021). The post title and body are combined to form the text samples. Samples are split based on whether they were posted in a suicide-related Subreddit. The dataset can be downloaded from a github: https://github.com/hesamuel/goodbye_world, under Reddit License and Terms of Service, and users must follow the Reddit User Agreement and Privacy Policy, as well as remove any posts if asked to by the original user.
times-india-headlines. We collect headlines from Times of India news from Kulkarni (2022). Headlines are downloaded from a Dataverse mirror. We use the first 1000 headlines in each year as samples. The dataset can be downloaded from https://www.kaggle.com/datasets/therohk/ india-headlines-news-dataset under CC0 Public Domain.
trial-deception. We collect testimonies from witnesses in real trials from Pérez-Rosas et al. (2015). Trial testimonies are downloaded from the author's website. The testimonies are divided based on whether they are considered truthful. The dataset can be downloaded from https://web.eecs.umich.edu/~mihalcea/downloads.html#RealLifeDeception.
un-debates. We collect speeches from debates at the United Nations from Baturo et al. (2017). Debate transcripts are downloaded from the Dataverse reproduction package. Samples are divided based on the country and year of the snippet. First, we isolate samples from Russia, China, and the United States and specify 3 time periods of interest. Next, we divide all samples by the decade. Finally, we create distributions for 19 countries of interest. The dataset can be downloaded from https://doi.org/10.7910/DVN/0TJX8Y under CC0 1.0.
unhealthy-conversations. We collect expert-annotated unhealthy conversations from Price et al. (2020). Conversation transcripts are downloaded from the official GitHub repository. For each annotated attribute, we split the dataset based on whether that form of unhealthy conversation is present in the sample. The dataset can be downloaded from https://github.com/conversationai/unhealthy-conversations under CC BY-NC-SA 4.0.
urban-dictionary. We collect definitions from UrbanDictionary.com, a crowdsourced English dictionary from Kulkarni (2020b). Urban Dictionary entries are downloaded from Kaggle. Definitions are split into groups representing the top 1, 5, and 10 percent of definitions ranked by both upvotes and downvotes; we sample 10,000 from each and create a control distribution by randomly sampling 10,000 definitions from all entries. The dataset can be downloaded from https://www.kaggle.com/therohk/urban-dictionary-words-dataset under CC0 Public Domain.
wikitext. We collect text snippets from Wikipedia from Merity et al. (2016). The Wikipedia snippets are loaded from HuggingFace. We remove any samples that are empty or start with '=' (which represent headings); samples are tokenized at the sentence level and used for clustering. The dataset can be downloaded from https://huggingface.co/datasets/wikitext under CC BY-SA 3.0.
yc—startups. We collect descriptions of companies that were part of the Y Combinator startup incubator from Bhalotia (2022). YCombinator company descriptions are downloaded from a 2022 scrape on GitHub. Only companies with long descriptions are preserved. Companies are split according to founder characteristics, year, "top company" designation, operating status, and location. The dataset can be downloaded from https://www.kaggle.com/datasets/benhammer/y-combinator-companies.