README.md

metadata

dataset_info:
  features:
    - name: instruction
      dtype: string
    - name: context
      dtype: string
    - name: response
      dtype: string
    - name: category
      dtype: string
    - name: instruction_en
      dtype: string
    - name: context_en
      dtype: string
    - name: response_en
      dtype: string
  splits:
    - name: es
      num_bytes: 25838910
      num_examples: 15015
    - name: fr
      num_bytes: 26641967
      num_examples: 15015
    - name: de
      num_bytes: 25921838
      num_examples: 15015
  download_size: 49985625
  dataset_size: 78402715
license: cc-by-sa-3.0
task_categories:
  - text-generation
  - text2text-generation
language:
  - es
  - de
  - fr
tags:
  - machine-translated
  - instruction-following
pretty_name: Databrick Dolly Instructions Multilingual
size_categories:
  - 10K<n<100K

Dataset Card for "databricks-dolly-15k-multilingual"

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://huggingface.co/datasets/argilla/databricks-dolly-15k-multilingual/
• Repository: https://huggingface.co/datasets/argilla/databricks-dolly-15k-multilingual/
• Paper:
• Leaderboard:
• Point of Contact: contact@argilla.io, https://github.com/argilla-io/argilla

Dataset Summary

This collection of datasets are machine-translated (and soon curated) versions of the databricks-dolly-15k dataset originally created by Databricks, Inc. in 2023.

The goal is to give practitioners a starting point for training open-source instruction-following models beyond English. However, as the translation quality will not be perfect, we highly recommend dedicating time to curate and fix translation issues. Below we explain how to load the datasets into Argilla for data curation and fixing. Additionally, we'll be improving the datasets made available here, with the help of different communities.

We highly recommend dataset curation beyond proof-of-concept experiments.

If you want to browse and curate your dataset with Argilla, you can:

1. Duplicate this Space. IMPORTANT: The Space's Visibility need to be Public, but you can setup your own password and API KEYS following this guide.
2. Setup two secrets: HF_TOKEN and LANG for indicating the language split
3. Login with admin/12345678 and start browsing and labelling.
4. Start labeling. Every 5 min the validations will be stored on a Hub dataset in your personal HF space.
5. Please get in touch to contribute fixes and improvements to the source datasets.

There's one split per language:

from datasets import load_dataset

# loads all splits
load_dataset("argilla/databricks-dolly-15k-multilingual")

# loads Spanish splits
load_dataset("argilla/databricks-dolly-15k-multilingual", split="es")

Supported Tasks and Leaderboards

As described in the README of the original dataset, this dataset can be used for:

• Training LLMs
• Synthetic Data Generation
• Data Augmentation

Languages

Currently: es, fr, de

Join Argilla Slack community if you want to help us include other languages.

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

There's one split per language:

from datasets import load_dataset

# loads all splits
load_dataset("argilla/databricks-dolly-15k-multilingual")

# loads Spanish splits
load_dataset("argilla/databricks-dolly-15k-multilingual", split="es")

Dataset Creation

These datasets have been translated using the DeepL API from the original English dataset between the 13th and 14th of April

Curation Rationale

No further curation.

Source Data

Initial Data Collection and Normalization

Refer to the original dataset for more information.

Who are the source language producers?

[More Information Needed]

Annotations

Annotations are planned but not performed yet.

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

This dataset can be used for any purpose, whether academic or commercial, under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported License.

Original dataset Owner: Databricks, Inc.

Citation Information

[More Information Needed]