datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mbpp
Dataset Card for Mostly Basic Python Problems (mbpp)
Dataset Summary
The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us.
Released here as part of… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/mbpp.paws
Dataset Card for PAWS: Paraphrase Adversaries from Word Scrambling
Dataset Summary
PAWS: Paraphrase Adversaries from Word Scrambling
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset.
For further… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/paws.boolq
Dataset Card for Boolq
Dataset Summary
BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair classification setup is similar to existing natural language inference tasks.
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google/boolq.fleurs
FLEURS
Fleurs is the speech version of the FLoRes machine translation benchmark.
We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages.
Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is
used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven… See the full description on the dataset page: https://huggingface.co/datasets/google/fleurs.natural_questions
Dataset Card for Natural Questions
Dataset Summary
The NQ corpus contains questions from real users, and it requires QA systems to
read and comprehend an entire Wikipedia article that may or may not contain the
answer to the question. The inclusion of real user questions, and the
requirement that solutions should read an entire page to find the answer, cause
NQ to be a more realistic and challenging task than prior QA datasets.
Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/natural_questions.nq_open
Dataset Card for nq_open
Dataset Summary
The NQ-Open task, introduced by Lee et.al. 2019,
is an open domain question answering benchmark that is derived from Natural Questions.
The goal is to predict an English answer string for an input English question.
All questions can be answered using the contents of English Wikipedia.
Supported Tasks and Leaderboards
Open Domain Question-Answering,
EfficientQA Leaderboard:… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/nq_open.xtreme
Dataset Card for "xtreme"
Dataset Summary
The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and
2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into
14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese,
Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the… See the full description on the dataset page: https://huggingface.co/datasets/google/xtreme.tydiqa
Dataset Card for "tydiqa"
Dataset Summary
TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/tydiqa.go_emotions
Dataset Card for GoEmotions
Dataset Summary
The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test
splits.
Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
Languages
The data is in English.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/go_emotions.WaxalNLP
Waxal Datasets
The WAXAL dataset is a large-scale multilingual speech corpus for African languages, introduced in the paper WAXAL: A Large-Scale Multilingual African Language Speech Corpus.
Dataset Description
The Waxal project provides datasets for both Automated Speech Recognition (ASR)
and Text-to-Speech (TTS) for African languages. The goal of this dataset's
creation and release is to facilitate research that improves the accuracy and
fluency of speech and… See the full description on the dataset page: https://huggingface.co/datasets/google/WaxalNLP.conceptual_captions
Dataset Card for Conceptual Captions
Dataset Summary
Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/conceptual_captions.wiki40b
Dataset Card for "wiki40b"
Dataset Summary
Clean-up text for 40+ Wikipedia languages editions of pages
correspond to entities. The datasets have train/dev/test splits per language.
The dataset is cleaned up by page filtering to remove disambiguation pages,
redirect pages, deleted pages, and non-entity pages. Each example contains the
wikidata id of the entity, and the full Wikipedia article after page processing
that removes non-content sections and structured objects.… See the full description on the dataset page: https://huggingface.co/datasets/google/wiki40b.xquad
Dataset Card for "xquad"
Dataset Summary
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi.… See the full description on the dataset page: https://huggingface.co/datasets/google/xquad.civil_comments
Dataset Card for "civil_comments"
Dataset Summary
The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make the public comments available in a lasting open archive to enable future
research. The original data… See the full description on the dataset page: https://huggingface.co/datasets/google/civil_comments.paws-x
Dataset Card for PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
Dataset Summary
This dataset 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.
For further details, see the accompanying paper:
PAWS-X: A Cross-lingual Adversarial Dataset for… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/paws-x.code_x_glue_ct_code_to_text
Dataset Card for "code_x_glue_ct_code_to_text"
Dataset Summary
CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
Remove examples that codes cannot be parsed into an abstract syntax tree.
Remove examples that #tokens of documents is < 3 or >256
Remove examples that documents contain special tokens (e.g. <img ...> or… See the full description on the dataset page: https://huggingface.co/datasets/google/code_x_glue_ct_code_to_text.MapTrace
MapTrace: A 2M-Sample Synthetic Dataset for Path Tracing on Maps
Welcome to the MapTrace dataset! If you use this dataset in your work, please cite our paper below.
For more details about our methodology and findings, please visit our project page or read the official white paper.
This work was also recently featured on the Google Research Blog.
Code & Scripts
Official training and data loading scripts are available in our GitHub repository:… See the full description on the dataset page: https://huggingface.co/datasets/google/MapTrace.0-9up_google_speech_commands_augmented_raw
Dataset Card for "google_speech_commands_augmented_raw_fixed"
More Information needed
ioi-eval-openrouter_google_gemini-2_0-flash-thinking-exp-prompt-mem-limitcode_x_glue_cc_defect_detection
Dataset Card for "code_x_glue_cc_defect_detection"
Dataset Summary
CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection
Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure… See the full description on the dataset page: https://huggingface.co/datasets/google/code_x_glue_cc_defect_detection.google-code-archive
Google Code Archive Dataset
Dataset Description
This dataset was compiled from the Google Code Archive, a preserved snapshot of projects hosted on Google Code, Google's open-source project hosting service that operated from 2006 to 2016. Google Code was one of the major code hosting platforms of its era, hosting hundreds of thousands of open-source projects before its shutdown. The archive provides a unique historical record of open-source development during a formative… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/google-code-archive.poem_sentiment
Dataset Card for Gutenberg Poem Dataset
Dataset Summary
Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg.
This dataset can be used for tasks such as sentiment classification or style transfer for poems.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
The text in the dataset is in English (en).
Dataset Structure
Data Instances
Example of one instance in the dataset.
{'id': 0… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/poem_sentiment.discofuse
Dataset Card for "discofuse"
Dataset Summary
DiscoFuse is a large scale dataset for discourse-based sentence fusion.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
discofuse-sport
Size of downloaded dataset files: 4.33 GB
Size of the generated dataset: 15.04 GB
Total amount of disk used: 19.36 GB
An example of 'train' looks as follows.
{… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/discofuse.scin
SCIN Dataset
The SCIN (Skin Condition Image Network) open access dataset aims to supplement publicly available dermatology datasets from health system sources with representative images from internet users. To this end, the SCIN dataset was collected from Google Search users in the United States through a voluntary, consented image donation application. The SCIN dataset is intended for health education and research, and to increase the diversity of dermatology images available for… See the full description on the dataset page: https://huggingface.co/datasets/google/scin.circa
Dataset Card for CIRCA
Dataset Summary
The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions.
The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend).
The following are the situational… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/circa.code_x_glue_cc_code_completion_token
Dataset Card for "code_x_glue_cc_code_completion_token"
Dataset Summary
CodeXGLUE CodeCompletion-token dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token
Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software… See the full description on the dataset page: https://huggingface.co/datasets/google/code_x_glue_cc_code_completion_token.code_x_glue_cc_code_refinement
Dataset Card for "code_x_glue_cc_code_refinement"
Dataset Summary
CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement
We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on… See the full description on the dataset page: https://huggingface.co/datasets/google/code_x_glue_cc_code_refinement.code_x_glue_cc_clone_detection_big_clone_bench
Dataset Card for "code_x_glue_cc_clone_detection_big_clone_bench"
Dataset Summary
CodeXGLUE Clone-detection-BigCloneBench dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench
Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
The dataset we use is BigCloneBench and filtered following the paper… See the full description on the dataset page: https://huggingface.co/datasets/google/code_x_glue_cc_clone_detection_big_clone_bench.polaris-bench
The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space
Polaris-Bench: Official Evaluation Dataset
Xia Hu1,
Zhenrui Yue1,
Brian Potetz1,
Howard Zhou1,
Leonidas Guibas1,2,
Chun-Ta Lu3,
Zhicheng Wang1
1Google DeepMind 2Stanford University 3Google Research
Overview
As current Multimodal Large Language Models (MLLMs) rapidly saturate canonical visual reasoning benchmarks, a key… See the full description on the dataset page: https://huggingface.co/datasets/google/polaris-bench.google-patents-data-preview