Dataset Card for Thesis-Abstract-Classification-11K

Dataset Description

Thesis-Abstract-Classification-11K dataset is obtained by processing a subset of Turkish Academic Theses dataset.

Dataset Structure

The original dataset was large and examples had several subject fields, representing the field of the thesis.

In order to construct a single-class classification problem with a reasonable data size, the following steps are carried out:

• For each example, only the first value of subject field was kept as the main field of the thesis to act as a label.
• Data points for a label with less than 60 examples were dropped, which resulted in 187 unique labels.
• Random 60 examples for each label is selected to construct a dataset of 11,220 examples.

Split Methodology

• If a train-val-test split is available, we use the existing divisions as provided.
• For datasets with a train-test split only, we create a val split from the training set, sized to match the test set, and apply this across all models.
• In cases with a train-val split, we reassign the val set as the test split, then generate a new val split from the training data following the approach above.
• In cases with a val-test split, we split validation into train and vad sets in 80% and 20% proportions, respectively.
• When only a single combined split is present, we partition the data into train, val, and test sets in 70%, 15%, and 15% proportions, respectively.

Data Fields

• text(string) : Thesis abstract
• label(string) : Field of the thesis

Source Dataset

HuggingFace