| --- |
| license: mit |
| task_categories: |
| - text-classification |
| language: |
| - en |
| pretty_name: MPQA |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| 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\>: |
| - train_fold_\<k\>.parquet: pandas DataFrame with texts and associated encoded labels for each document on the training split k (according to split_\<k\>.pkl) |
| - train_fold_\<k\>.parquet: pandas DataFrame with texts and associated encoded labels for each document on the testing split k (according to split_\<k\>.pkl) |
| |
