数据集:
flax-sentence-embeddings/stackexchange_math_jsonl
任务:
问答子任务:
closed-domain-qa语言:
en计算机处理:
multilingual语言创建人:
found批注创建人:
found源数据集:
original许可:
cc-by-nc-sa-4.0We automatically extracted question and answer (Q&A) pairs from Stack Exchange network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available here .
Stack Exchange mainly consist of english language (en).
Each data samples is presented as follow:
{'title_body': 'How to determine if 3 points on a 3-D graph are collinear? Let the points $A, B$ and $C$ be $(x_1, y_1, z_1), (x_2, y_2, z_2)$ and $(x_3, y_3, z_3)$ respectively. How do I prove that the 3 points are collinear? What is the formula?', 'upvoted_answer': 'From $A(x_1,y_1,z_1),B(x_2,y_2,z_2),C(x_3,y_3,z_3)$ we can get their position vectors.\n\n$\\vec{AB}=(x_2-x_1,y_2-y_1,z_2-z_1)$ and $\\vec{AC}=(x_3-x_1,y_3-y_1,z_3-z_1)$.\n\nThen $||\\vec{AB}\\times\\vec{AC}||=0\\implies A,B,C$ collinear.', 'downvoted_answer': 'If the distance between |AB|+|BC|=|AC| then A,B,C are collinear.'}
This particular exampe corresponds to the following page
The fields present in the dataset contain the following informations:
We provide three splits for this dataset, which only differs by the structure of the fieds which are retrieved:
Number of pairs | |
---|---|
titlebody_upvoted_downvoted_answer | 17,083 |
title_answer | 1,100,953 |
titlebody_answer | 1,100,953 |
We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically.
The source data are dumps from Stack Exchange
Initial Data Collection and NormalizationWe collected the data from the math community.
We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters. When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers.
Who are the source language producers?Questions and answers are written by the community developpers of Stack Exchange.
Please see the license information at: https://archive.org/details/stackexchange
@misc{StackExchangeDataset, author = {Flax Sentence Embeddings Team}, title = {Stack Exchange question pairs}, year = {2021}, howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/}, }
Thanks to the Flax Sentence Embeddings team for adding this dataset.