The Yahoo! Answers topic classification dataset is constructed using the 10 largest main categories. Each class contains 140,000 training samples and 6,000 testing samples. Therefore, the total number of training samples is 1,400,000, and testing samples are 60,000 in this dataset. From all the answers and other meta-information, we only used the best answer content and the main category information.

• Society & Culture
• Science & Mathematics
• Health
• Education & Reference
• Computers & Internet
• Sports
• Business & Finance
• Entertainment & Music
• Family & Relationships
• Politics & Government

The Yahoo! Answers topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)

The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. Each class contains 140,000 training samples and 6,000 testing samples. Therefore, the total number of training samples is 1,400,000 and testing samples 60,000 in this dataset. From all the answers and other meta-information, we only used the best answer content and the main category information.

The file classes.txt contains a list of classes corresponding to each label.

The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 4 columns in them, corresponding to class index (1 to 10), question title, question content and best answer. The text fields are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is " ".

Yahoo! Answers (Yahoo! Answers Comprehensive Questions and Answers version 1.0). Zhang et al. (2016) collected this set of 4,483,032 questions and used their answers across the 10 largest main categories for building the classification dataset. The used fields include question title, question content and best answer.

The files:
texts.txt: Document set (text). One per line.
score.txt: Document class whose index is associated with texts.txt
split_

The dataset used in this paper for few-shot text classification task.

The 10 largest main categories from the Yahoo! Answers Comprehensive Questions and Answers version 1.0 dataset. Each class contains 140,000 training samples and 5,000 testing samples.

The Yahoo Answers dataset contains labeled examples for topic classification used to perform error analysis on a BERT-based model.

Two large scale document classification datasets: Yahoo Answer and Yelp15 review, representing topic classification and sentiment classification data sets respectively.

YahooAnswersTopicsClassification An MTEB dataset Massive Text Embedding Benchmark

Dataset composed of questions and answers from Yahoo Answers, categorized into topics.

Task category t2c

Domains Web, Written

Referencehttps://huggingface.co/datasets/yahoo_answers_topics

Source datasets:

community-datasets/yahoo_answers_topics

  How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code: import mteb

task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/YahooAnswersTopicsClassification.