waashk/yelp_2013
Dataset used in the paper: A thorough benchmark of automatic text classification From traditional approaches to large language models https://github.com/waashk/atcBench To guarantee the reproducibility of the obtained results, the dataset and its respective CV train-test partitions is available here. Each dataset contains the following files: data.parquet: pandas DataFrame with texts and associated encoded labels for each document. split_<k>.pkl: pandas DataFrame with k-cross validation… See the full description on the dataset page: https://huggingface.co/datasets/waashk/yelp_2013.
Dataset used in the paper:
A thorough benchmark of automatic text classification From traditional approaches to large language models
https://github.com/waashk/atcBench
To guarantee the reproducibility of the obtained results, the dataset and its respective CV train-test partitions is available here.
Each dataset contains the following files:
- data.parquet: pandas DataFrame with texts and associated encoded labels for each document.
- split_\<k\>.pkl: pandas DataFrame with k-cross validation partition.
- For each fold \<k\>:
- trainfold\<k\>.parquet: pandas DataFrame with texts and associated encoded labels for each document on the training split k (according to split_\<k\>.pkl)
- trainfold\<k\>.parquet: pandas DataFrame with texts and associated encoded labels for each document on the testing split k (according to split_\<k\>.pkl)