language:

• tr
  license: mit
  size_categories:
• 10M<n<100M
  task_categories:
• text-classification
• token-classification
• table-question-answering
• question-answering
• zero-shot-classification
• summarization
• feature-extraction
• text-generation
• text2text-generation
  pretty_name: OrcaDPO
  configs:
• config_name: default
  data_files:
  • split: train
    path: data/train-*
    dataset_info:
    features:
  • name: system
    dtype: string
  • name: question
    dtype: string
  • name: chosen
    dtype: string
  • name: rejected
    dtype: string
  • name: generations
    sequence: string
  • name: order
    sequence: string
  • name: labelling_model
    dtype: string
  • name: labelling_prompt
    list:
    • name: content
      dtype: string
    • name: role
      dtype: string
  • name: raw_labelling_response
    dtype: string
  • name: rating
    sequence: float64
  • name: rationale
    dtype: string
  • name: status
    dtype: string
  • name: original_chosen
    dtype: string
  • name: original_rejected
    dtype: string
  • name: chosen_score
    dtype: float64
  • name: in_gsm8k_train
    dtype: bool
    splits:
  • name: train
    num_bytes: 97012722.42875265
    num_examples: 9120
    download_size: 43511007
    dataset_size: 97012722.42875265

Dataset Card for "malhajar/orca_dpo_pairs-tr"

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.
malhajar/orca_dpo_pairs-tr is a translated version of argilla/distilabel-intel-orca-dpo-pairs

Translated by: Mohamad Alhajar

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. The dataset is stored in parquet format with each entry using the following schema:
:

{
'prompt': 'Read the following paragraph and determine if the hypothesis is true:\n\nWorld leaders expressed concern on Thursday that North Ko...'
'chosen': [
           {'content': 'You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old.',
            'role': 'system'
           },
           {'content': 'Read the following paragraph and determine if the hypothesis is true...',
            'role': 'user'
           },
           {'content': 'Okay little buddy, let\'s look at this...',
            'role': 'assistant'
           }
          ],
'rejected': [
             {'content': 'You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old.',
              'role': 'system'
             },
             {'content': 'Read the following paragraph and determine if the hypothesis is true...',
              'role': 'user'
             },
             {'content': 'Oh my gosh! Let me see if I can help you with that! ...',
              'role': 'assistant'
             }
            ],
}

Data Splits

The dataset consists of two splits, "train_prefs" and "test_prefs":

Usage

To load the dataset, run:

from datasets import load_dataset

ds = load_dataset("malhajar/distilabel-intel-orca-dpo-pairs-tr")

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}},
}