README.md

---
dataset_info:
  features:
  - name: query
    dtype: string
  - name: doc
    dtype: string
  splits:
  - name: train
    num_bytes: 38429966
    num_examples: 13951
  - name: test
    num_bytes: 5378012
    num_examples: 1994
  - name: validation
    num_bytes: 10639697
    num_examples: 3986
  download_size: 28977376
  dataset_size: 54447675
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
---
# Dataset Card for Stackoverflow-QA-TR

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

## Dataset Description

Stackoverflow-QA-TR dataset is obtained by translating both the queries and corpora of [StackoverflowQA](https://huggingface.co/datasets/CoIR-Retrieval/stackoverflow-qa) into Turkish using GPT4.1.


### Dataset Structure

The original dataset only had `train` and `test` splits. We applied the following splitting methodology to obtain `validation` and `test` splits:

* 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

- **query**(string) : Stackoverflow question
- **doc**(string) : Stackoverflow answer

## Source Dataset

[StackoverflowQA](https://huggingface.co/datasets/CoIR-Retrieval/stackoverflow-qa)