license: mit
dataset_info:
features:
- name: chosen
dtype: string - name: rejected
dtype: string - name: prompt
dtype: string
splits: - name: train
num_bytes: 15567399
num_examples: 9761 - name: test
num_bytes: 1761266
num_examples: 1085
download_size: 10108040
dataset_size: 17328665
configs: - config_name: default
data_files:- split: train
path: data/train-* - split: test
path: data/test-*
task_categories:
- split: train
- text-generation
language: - tr
size_categories: - 10K<n<100K
Original dataset: malhajar/orca_dpo_pairs-tr
Dataset Card for "suayptalha/Turkish-DPO-Pairs"
This Dataset is part of a series of datasets aimed at advancing Turkish LLM Developments by establishing rigid Turkish dataset collection to enhance the performance of LLM's Produced in the Turkish Language.
suayptalha/Turkish-DPO-Pairs is the processed version where only the content of messages with the role as "assistant" is extracted from both the chosen and rejected fields of malhajar/orca_dpo_pairs-tr which is a translated version of HuggingFaceH4/orca_dpo_pairs
Dataset Summary
This is a pre-processed version of the OpenOrca dataset translated to Turkish.
The original OpenOrca dataset is a collection of augmented FLAN data that aligns, as best as possible, with the distributions outlined in the Orca paper.
It has been instrumental in generating high-performing preference-tuned model checkpoints and serves as a valuable resource for all NLP researchers and developers!
Dataset Summary
The OrcaDPO Pair dataset is a subset of the OpenOrca dataset suitable for DPO preference tuning.
Usage
To load the dataset, run:
from datasets import load_dataset
ds = load_dataset("suayptalha/Turkish-DPO-Pairs")
Languages
The language of the data is primarily Turkish.
Citation
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca}},
}