deepmind
Datasets
All datasets matching “deepmind”code_contests
Dataset Card for CodeContests
Dataset Summary
CodeContests is a competitive programming dataset for machine-learning. This
dataset was used when training AlphaCode.
It consists of programming problems, from a variety of sources:
Site
URL
Source
Aizu
https://judge.u-aizu.ac.jp
CodeNet
AtCoder
https://atcoder.jp
CodeNet
CodeChef
https://www.codechef.com
description2code
Codeforces
https://codeforces.com
description2code and Codeforces
HackerEarth… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/code_contests.aqua_rat
Dataset Card for AQUA-RAT
Dataset Summary
A large-scale dataset consisting of approximately 100,000 algebraic word problems.
The solution to each question is explained step-by-step using natural language.
This data is used to train a program generation model that learns to generate the explanation,
while generating the program that solves the question.
Supported Tasks and Leaderboards
Languages
en
Dataset Structure
Data Instances… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/aqua_rat.narrativeqa
Dataset Card for Narrative QA
Dataset Summary
NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents.
Supported Tasks and Leaderboards
The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question.… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/narrativeqa.math_datasetMathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)pg19This repository contains the PG-19 language modeling benchmark.
It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919.
It also contains metadata of book titles and publication dates.
PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark.
Books are partitioned into a train, validation, and test set. Book metadata is stored in metadata.csv which contains (book_id, short_book_title, publication_date).
Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text.
To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table.
One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing.deepmind-math-large