| Quantitative 101 dataset, which is the combination of one generated dataset (CND) and three benchmark datasets: Numeracy-600K [1], EQUATE [2], and NumGLUE Task 3 [3]. The tasks in Quantitative 101 include Comparing Numbers (ComNum), Quantitative Prediction (QP), Quantitative Natural Language Inference (QNLI), and Quantitative Question Answering (QQA). | |
| The details of how to separate the dataset are shown in this document. | |
| (1) Task: ComNum | |
| There are two JSON files in the ComNum folder. "CND-OOR.json" is used for testing the phenomenon. "CND-IR.json" can be separated into training and test sets with the following code: | |
| import pandas as pd | |
| train_size = 0.8 | |
| train_dataset=new_df.sample(frac=train_size,random_state=200) | |
| test_dataset=new_df.drop(train_dataset.index).reset_index(drop=True) | |
| train_dataset = train_dataset.reset_index(drop=True) | |
| (2) Task: QP | |
| In the QP folder, we already separated Numeracy-600K into training, development, and test sets. Note that, the original Numeracy-600K [1] did not provide such information. | |
| (3) Task: QNLI | |
| EQUATE has five subsets collected from different sources, including RTE-QUANT, AWP-NLI, NEWSNLI, REDDITNLI, and Stress Test. | |
| For Stress Test, which contains 7,500 instances, we follow the splitting method in NumGLUE Task 7 to separate it into training, development, and test sets. All sets are in the "QNLI-Stress Test" folder. | |
| Because other subsets are less than 1,000 instances, we perform the 10-fold cross-validation in the experiments. Please use the following code to separate folds: | |
| from sklearn.model_selection import KFold | |
| kf = KFold(n_splits=10,random_state=200) | |
| (4) Task: QQA | |
| We follow [3] to separate the dataset into training, development, and test sets. | |
| Reference: | |
| [1] Chen, Chung-Chi, et al. "Numeracy-600K: Learning numeracy for detecting exaggerated information in market comments." Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. | |
| [2] Ravichander, Abhilasha, et al. "EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference." Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). 2019. | |
| [3] Mishra, Swaroop, et al. "NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks." Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 2022 | |