---
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](#dataset-description)
  - [Dataset Structure](#dataset-structure)
  - [Data Fields](#data-fields)
- [Source Data](#source-data)

## Dataset Description

Thesis-Abstract-Classification-11K dataset is obtained by processing a subset of [Turkish Academic Theses](https://huggingface.co/datasets/umutertugrul/turkish-academic-theses-dataset) 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](umutertugrul/turkish-academic-theses-dataset)