metadata
license: apache-2.0
language:
- en
pretty_name: NumGLUE
configs:
- config_name: all
default: true
data_files:
- split: train
path: data/all/train.parquet
- split: validation
path: data/all/validation.parquet
- split: test
path: data/all/test.parquet
- config_name: type_1
data_files:
- split: train
path: data/type_1/train.parquet
- split: validation
path: data/type_1/validation.parquet
- split: test
path: data/type_1/test.parquet
- config_name: type_2
data_files:
- split: train
path: data/type_2/train.parquet
- split: validation
path: data/type_2/validation.parquet
- split: test
path: data/type_2/test.parquet
- config_name: type_3
data_files:
- split: train
path: data/type_3/train.parquet
- split: validation
path: data/type_3/validation.parquet
- split: test
path: data/type_3/test.parquet
- config_name: type_4
data_files:
- split: train
path: data/type_4/train.parquet
- split: validation
path: data/type_4/validation.parquet
- split: test
path: data/type_4/test.parquet
- config_name: type_5
data_files:
- split: train
path: data/type_5/train.parquet
- split: validation
path: data/type_5/validation.parquet
- split: test
path: data/type_5/test.parquet
- config_name: type_6
data_files:
- split: train
path: data/type_6/train.parquet
- split: validation
path: data/type_6/validation.parquet
- split: test
path: data/type_6/test.parquet
- config_name: type_7
data_files:
- split: train
path: data/type_7/train.parquet
- split: validation
path: data/type_7/validation.parquet
- split: test
path: data/type_7/test.parquet
- config_name: type_8
data_files:
- split: train
path: data/type_8/train.parquet
- split: validation
path: data/type_8/validation.parquet
- split: test
path: data/type_8/test.parquet
NumGLUE
This repository provides NumGLUE in a normalized Parquet layout for convenient loading from Hugging Face Datasets.
The default all config is balanced for unified training and evaluation: each NumGLUE type contributes at most 1,000 examples per split. It is ordered with prettyorder on type, using a 3-example interleaved prefix followed by a seeded shuffle (seed=42) of the remaining rows. The type_1 through type_8 configs expose each original NumGLUE type separately at full size with the same column schema as all.
Usage
from datasets import load_dataset
dataset = load_dataset("tasksource/num-glue")
type_5 = load_dataset("tasksource/num-glue", "type_5")
Columns
id: deterministic row id built from split and source row index.type: original NumGLUE type label, fromType_1toType_8.question,passage,statement1,statement2,options,option1,option2: normalized input fields. Missing source fields are null.answer: simple string answer for direct use.answer_json: exact JSON serialization of the originalanswervalue.answer_type: original JSON answer type (int,str, ordict).
Original NumGLUE_dev.json is exposed as the Hugging Face validation split.
Split Sizes
| config | train | validation | test |
|---|---|---|---|
| all | 6,502 | 3,492 | 4,042 |
| type_1 | 282 | 41 | 81 |
| type_2 | 1,131 | 164 | 325 |
| type_3 | 564 | 81 | 162 |
| type_4 | 770 | 110 | 220 |
| type_5 | 37,949 | 5,421 | 4,835 |
| type_6 | 22,908 | 3,272 | 3,015 |
| type_7 | 6,791 | 970 | 1,691 |
| type_8 | 886 | 126 | 254 |
Citation
Please cite the NumGLUE paper:
@article{mishra2022numglue,
title = {NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks},
author = {Mishra, Swaroop and Mitra, Arindam and Varshney, Neeraj and Sachdeva, Bhavdeep and Clark, Peter and Baral, Chitta and Kalyan, Ashwin},
journal = {arXiv preprint arXiv:2204.05660},
year = {2022},
url = {https://arxiv.org/abs/2204.05660}
}