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.IFEval
Dataset Card for IFEval
Dataset Summary
This dataset contains the prompts used in the Instruction-Following Eval (IFEval) benchmark for large language models. It contains around 500 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times" which can be verified by heuristics. To load the dataset, run:
from datasets import load_dataset
ifeval = load_dataset("google/IFEval")
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google/IFEval.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.svqnq_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.deepsearchqa
DeepSearchQA
A 900-prompt factuality benchmark from Google DeepMind, designed to evaluate agents on difficult multi-step information-seeking tasks across 17 different fields.
▶ Google DeepMind Release Blog Post▶ DeepSearchQA Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code
Benchmark
DeepSearchQA is a 900-prompt benchmark for evaluating agents on difficult multi-step information-seeking tasks across 17 different fields. Unlike traditional… See the full description on the dataset page: https://huggingface.co/datasets/google/deepsearchqa.wmt24pp
WMT24++
This repository contains the human translation and post-edit data for the 55 en->xx language pairs released in
the publication
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects.
If you are interested in the MT/LLM system outputs and automatic metric scores, please see MTME.
If you are interested in the images of the source URLs for each document, please see here.
Schema
Each language pair is stored in its own jsonl file.
Each row… See the full description on the dataset page: https://huggingface.co/datasets/google/wmt24pp.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.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.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.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.frames-benchmark
FRAMES: Factuality, Retrieval, And reasoning MEasurement Set
FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning.
Our paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941.
Dataset Overview
824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles
Questions span diverse topics… See the full description on the dataset page: https://huggingface.co/datasets/google/frames-benchmark.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.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.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.google-landmark-v2-chinese-filtered
Google Landmark V2 Chinese Filtered Dataset
This dataset contains landmark images and metadata for training landmark retrieval models, with Chinese translations of landmark names to facilitate Chinese multimodal retrieval tasks.
Dataset Source
This dataset is based on the Google Landmarks V2 dataset from Kaggle. The original data has been filtered and processed to create a high-quality training dataset for landmark retrieval.
Key Features
Filtered… See the full description on the dataset page: https://huggingface.co/datasets/86Cao/google-landmark-v2-chinese-filtered.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.fleurs-rSynthetic-Persona-Chat
Dataset Card for SPC: Synthetic-Persona-Chat Dataset
Abstract from the paper introducing this dataset:
High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and… See the full description on the dataset page: https://huggingface.co/datasets/google/Synthetic-Persona-Chat.simpleqa-verified
SimpleQA Verified
A 1,000-prompt factuality benchmark from Google DeepMind and Google Research, designed to reliably evaluate LLM parametric knowledge.
▶ SimpleQA Verified Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code
Benchmark
SimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality
and parametric knowledge. The authors from Google DeepMind and Google Research… See the full description on the dataset page: https://huggingface.co/datasets/google/simpleqa-verified.0-9up_google_speech_commands_augmented_raw
Dataset Card for "google_speech_commands_augmented_raw_fixed"
More Information needed
bigbenchThe Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to
probe large language models, and extrapolate their future capabilities.speech_commandsThis is a set of one-second .wav audio files, each containing a single spoken
English word or background noise. These words are from a small set of commands, and are spoken by a
variety of different speakers. This data set is designed to help train simple
machine learning models. This dataset is covered in more detail at
[https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
64,727 audio files.
In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".
In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".
In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation
it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words
from unrecognized ones.
The `_silence_` class contains a set of longer audio clips that are either recordings or
a mathematical simulation of noise.code_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.