metadata
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 17427403
num_examples: 7854
- name: test
num_bytes: 3772766
num_examples: 1683
- name: validation
num_bytes: 3687534
num_examples: 1683
download_size: 13067873
dataset_size: 24887703
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
Dataset Card for Thesis-Abstract-Classification-11K
Table of Contents
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
subjectfield 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