The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews. Only highly polarizing reviews are considered. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. No more than 30 reviews are included per movie. The dataset contains additional unlabeled data.
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The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
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The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Big-bench include more than 200 tasks.
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The LAMBADA (LAnguage Modeling Broadened to Account for Discourse Aspects) benchmark is an open-ended cloze task which consists of about 10,000 passages from BooksCorpus where a missing target word is predicted in the last sentence of each passage. The missing word is constrained to always be the last word of the last sentence and there are no candidate words to choose from. Examples were filtered by humans to ensure they were possible to guess given the context, i.e., the sentences in the passage leading up to the last sentence. Examples were further filtered to ensure that missing words could not be guessed without the context, ensuring that models attempting the dataset would need to reason over the entire paragraph to answer questions.
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WiC is a benchmark for the evaluation of context-sensitive word embeddings. WiC is framed as a binary classification task. Each instance in WiC has a target word w, either a verb or a noun, for which two contexts are provided. Each of these contexts triggers a specific meaning of w. The task is to identify if the occurrences of w in the two contexts correspond to the same meaning or not. In fact, the dataset can also be viewed as an application of Word Sense Disambiguation in practise.
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Dataset of hate speech annotated on Internet forum posts in English at sentence-level. The source forum in Stormfront, a large online community of white nacionalists. A total of 10,568 sentence have been been extracted from Stormfront and classified as conveying hate speech or not.
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ATOMIC is an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment").
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PAWS-X contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in PAWS-Wiki.
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Long-range arena (LRA) is an effort toward systematic evaluation of efficient transformer models. The project aims at establishing benchmark tasks/datasets using which we can evaluate transformer-based models in a systematic way, by assessing their generalization power, computational efficiency, memory foot-print, etc. Long-Range Arena is specifically focused on evaluating model quality under long-context scenarios. The benchmark is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning.
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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.
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Clotho is an audio captioning dataset, consisting of 4981 audio samples, and each audio sample has five captions (a total of 24 905 captions). Audio samples are of 15 to 30 s duration and captions are eight to 20 words long.
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The One Billion Word dataset is a dataset for language modeling. The training/held-out data was produced from the WMT 2011 News Crawl data using a combination of Bash shell and Perl scripts.
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The BLUE benchmark consists of five different biomedicine text-mining tasks with ten corpora. These tasks cover a diverse range of text genres (biomedical literature and clinical notes), dataset sizes, and degrees of difficulty and, more importantly, highlight common biomedicine text-mining challenges.
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This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository.
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CLUE is a Chinese Language Understanding Evaluation benchmark. It consists of different NLU datasets. It is a community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text.
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KP20k is a large-scale scholarly articles dataset with 528K articles for training, 20K articles for validation and 20K articles for testing.
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TweetEval introduces an evaluation framework consisting of seven heterogeneous Twitter-specific classification tasks.
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End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena.
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CMRC is a dataset is annotated by human experts with near 20,000 questions as well as a challenging set which is composed of the questions that need reasoning over multiple clues.
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OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. The dataset used for training multilingual models such as BART incorporates 138 GB of text.
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CMRC 2018 is a dataset for Chinese Machine Reading Comprehension. Specifically, it is a span-extraction reading comprehension dataset that is similar to SQuAD.
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EmotionLines contains a total of 29245 labeled utterances from 2000 dialogues. Each utterance in dialogues is labeled with one of seven emotions, six Ekman’s basic emotions plus the neutral emotion. Each labeling was accomplished by 5 workers, and for each utterance in a label, the emotion category with the highest votes was set as the label of the utterance. Those utterances voted as more than two different emotions were put into the non-neutral category. Therefore the dataset has a total of 8 types of emotion labels, anger, disgust, fear, happiness, sadness, surprise, neutral, and non-neutral.
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A large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.
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Tatoeba is a free collection of example sentences with translations geared towards foreign language learners. It is available in more than 400 languages. Its name comes from the Japanese phrase “tatoeba” (例えば), meaning “for example”. It is written and maintained by a community of volunteers through a model of open collaboration. Individual contributors are known as Tatoebans.
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Worldtree is a corpus of explanation graphs, explanatory role ratings, and associated tablestore. It contains explanation graphs for 1,680 questions, and 4,950 tablestore rows across 62 semi-structured tables are provided. This data is intended to be paired with the AI2 Mercury Licensed questions.
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PearRead is a dataset of scientific peer reviews. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.
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MassiveText is a collection of large English-language text datasets from multiple sources: web pages, books, news articles, and code. The data pipeline includes text quality filtering, removal of repetitious text, deduplication of similar documents, and removal of documents with significant test-set overlap. MassiveText contains 2.35 billion documents or about 10.5 TB of text.
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The Natural Stories dataset consists of English texts edited to contain many low-frequency syntactic constructions while still sounding fluent to native speakers. The corpus is annotated with hand-corrected parse trees and includes self-paced reading time data.
Adversarial GLUE (AdvGLUE) is a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks. In particular, we systematically apply 14 textual adversarial attack methods to GLUE tasks to construct AdvGLUE, which is further validated by humans for reliable annotations.
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There are now many computer programs for automatically determining the sense of a word in context (Word Sense Disambiguation or WSD). The purpose of SENSEVAL is to evaluate the strengths and weaknesses of such programs with respect to different words, different varieties of language, and different languages.
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Taskmaster-1 is a dialog dataset consisting of 13,215 task-based dialogs in English, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations.
The Cloze Test by Teachers (CLOTH) benchmark is a collection of nearly 100,000 4-way multiple-choice cloze-style questions from middle- and high school-level English language exams, where the answer fills a blank in a given text. Each question is labeled with a type of deep reasoning it involves, where the four possible types are grammar, short-term reasoning, matching/paraphrasing, and long-term reasoning, i.e., reasoning over multiple sentences
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Humicroedit is a humorous headline dataset. The data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. The authors carefully curated crowdsourced editors to create funny headlines and judges to score a to a total of 15,095 edited headlines, with five judges per headline.
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Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attribute Detection (OVAD) task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. To this end, we created a clean and densely annotated test set covering 117 attribute classes on the 80 object classes of MS COCO. It includes positive and negative annotations, which enables open-vocabulary evaluation. Overall, the benchmark consists of 1.4 million annotations. For reference, we provide a first baseline method for open-vocabulary attribute detection. Moreover, we demonstrate the benchmark's value by studying the attribute dete
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The Hutter Prize Wikipedia dataset, also known as enwiki8, is a byte-level dataset consisting of the first 100 million bytes of a Wikipedia XML dump. For simplicity we shall refer to it as a character-level dataset. Within these 100 million bytes are 205 unique tokens.
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ART consists of over 20k commonsense narrative contexts and 200k explanations.
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BabyLM is a dataset for small scale language modeling, human language acquisition, low-resource NLP, and cognitive modeling. In partnership with CoNLL and CMCL, it provides a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children. The task has three tracks, two of which restrict the training data to pre-released datasets of 10M and 100M words and are dedicated to explorations of approaches such as architectural variations, self-supervised objectives, or curriculum learning. The final track only restricts the amount of text used, allowing innovation in the choice of the data, its domain, and even its modality (i.e., data from sources other than text is welcome).
News translation is a recurring WMT task. The test set is a collection of parallel corpora consisting of about 1500 English sentences translated into 5 languages (Chinese, Czech, Estonian, German, Finnish, Russian, Turkish) and additional 1500 sentences from each of the 7 languages translated to English. The sentences were selected from dozens of news websites and translated by professional translators.
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Coached Conversational Preference Elicitation is a dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user'.
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A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language.
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Databricks Dolly 15k is a dataset containing 15,000 high-quality human-generated prompt / response pairs specifically designed for instruction tuning large language models. It is authored by more than 5,000 Databricks employees during March and April of 2023. The training records are natural, expressive and designed to represent a wide range of the behaviors, from brainstorming and content generation to information extraction and summarization.
Benchmark dataset for abstracts and titles of 100,000 ArXiv scientific papers. This dataset contains 10 classes and is balanced (exactly 10,000 per class). The classes include subcategories of computer science, physics, and math.
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The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions.
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Comparative Question Completion is a dataset to evaluate what do large Language Models learn.
A collection of 385,705 scientific abstracts about Cognitive Control and their GPT-3 embeddings.
RTC is a benchmark corpus of social media comments sampled over three years. The corpus consists of 36.36m unlabelled comments for adaptation and evaluation on an upstream masked language modelling task as well as 0.9m labelled comments for finetuning and evaluation on a downstream document classification task. The Reddit Time Corpus (RTC) covers three years between March 2017 and February 2020 and is split into 36 evenly-sized monthly subsets based on comment timestamps. RTC is sampled from the Pushshift Reddit dataset.