task_categories:question-answering task_categories:text-generation size_categories:10K<n<100K language:nl license:cc-by-nc-sa-3.0 dolly instruct instruction doi:10.57967/hf/0785 region:us
---
license: cc-by-nc-sa-3.0
task_categories:
- question-answering
- text-generation
language:
- nl
tags:
- dolly
- instruct
- instruction
pretty_name: Dolly 15k Dutch
size_categories:
- 10K<n<100K
---

# Dataset Card for Dolly 15k Dutch

## Dataset Description

- **Homepage:** N/A
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** Bram Vanroy

### Dataset Summary

This dataset contains 14,934 instructions, contexts and responses, in several natural language categories such as classification, closed QA, generation, etc. The English [original dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k) was created by @databricks, who crowd-sourced the data creation via its employees. The current dataset is a translation of that dataset through ChatGPT (`gpt-3.5-turbo`).

☕ [**Want to help me out?**](https://www.buymeacoffee.com/bramvanroy) Translating the data with the OpenAI API, and prompt testing, cost me 💸$19.38💸. If you like this dataset, please consider [buying me a coffee](https://www.buymeacoffee.com/bramvanroy) to offset a portion of this cost, I appreciate it a lot! ☕

### Languages

- Dutch

## Dataset Structure

### Data Instances

```python
{
  "id": 14963,
  "instruction": "Wat zijn de duurste steden ter wereld?",
  "context": "",
  "response": "Dit is een uitgebreide lijst van de duurste steden: Singapore, Tel Aviv, New York, Hong Kong, Los Angeles, Zurich, Genève, San Francisco, Parijs en Sydney.",
  "category": "brainstorming"
}
```

### Data Fields

- **id**: the ID of the item. The following 77 IDs are not included because they could not be translated (or were too long): `[1502, 1812, 1868, 4179, 4541, 6347, 8851, 9321, 10588, 10835, 11257, 12082, 12319, 12471, 12701, 12988, 13066, 13074, 13076, 13181, 13253, 13279, 13313, 13346, 13369, 13446, 13475, 13528, 13546, 13548, 13549, 13558, 13566, 13600, 13603, 13657, 13668, 13733, 13765, 13775, 13801, 13831, 13906, 13922, 13923, 13957, 13967, 13976, 14028, 14031, 14045, 14050, 14082, 14083, 14089, 14110, 14155, 14162, 14181, 14187, 14200, 14221, 14222, 14281, 14473, 14475, 14476, 14587, 14590, 14667, 14685, 14764, 14780, 14808, 14836, 14891, 1
4966]`
- **instruction**: the instruction (question)
- **context**: additional context that the AI can use to answer the question
- **response**: the AI's expected response
- **category**: the category of this type of question (see [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k#annotator-guidelines) for more info)


## Dataset Creation

Both the translations and the topics were translated with OpenAI's API for `gpt-3.5-turbo`. `max_tokens=1024, temperature=0` as parameters.

The prompt template to translate the input is (where `src_lang` was English and `tgt_lang` Dutch):

```python
CONVERSATION_TRANSLATION_PROMPT = """You are asked to translate a task's instruction, optional context to the task, and the response to the task, from {src_lang} to {tgt_lang}.

Here are the requirements that you should adhere to:
1. maintain the format: the task consists of a task instruction (marked `instruction: `), optional context to the task (marked `context: `) and response for the task marked with `response: `;
2. do not translate the identifiers `instruction: `, `context: `, and `response: ` but instead copy them to your output;
3. make sure that text is fluent to read and does not contain grammatical errors. Use standard {tgt_lang} without regional bias;
4. translate the instruction and context text using informal, but standard, language;
5. make sure to avoid biases (such as gender bias, grammatical bias, social bias);
6. if the instruction is to correct grammar mistakes or spelling mistakes then you have to generate a similar mistake in the context in {tgt_lang}, and then also generate a corrected output version in the output in {tgt_lang};
7. if the instruction is to translate text from one language to another, then you do not translate the text that needs to be translated in the instruction or the context, nor the translation in the response (just copy them as-is);
8. do not translate code fragments but copy them to your output. If there are English examples, variable names or definitions in code fragments, keep them in English.

Now translate the following task with the requirements set out above. Do not provide an explanation and do not add anything else.\n\n"""
```


The system message was:

```
You are a helpful assistant that translates English to Dutch according to the requirements that are given to you.
```

Note that 77 items (0.5%) were not successfully translated. This can either mean that the prompt was too long for the given limit (`max_tokens=1024`) or that the generated translation could not be parsed into `instruction`, `context` and `response` fields. The missing IDs are `[1502, 1812, 1868, 4179, 4541, 6347, 8851, 9321, 10588, 10835, 11257, 12082, 12319, 12471, 12701, 12988, 13066, 13074, 13076, 13181, 13253, 13279, 13313, 13346, 13369, 13446, 13475, 13528, 13546, 13548, 13549, 13558, 13566, 13600, 13603, 13657, 13668, 13733, 13765, 13775, 13801, 13831, 13906, 13922, 13923, 13957, 13967, 13976, 14028, 14031, 14045, 14050, 14082, 14083, 14089, 14110, 14155, 14162, 14181, 14187, 14200, 14221, 14222, 14281, 14473, 14475, 14476, 14587, 14590, 14667, 14685, 14764, 14780, 14808, 14836, 14891, 1
4966]`. 

### Source Data

#### Initial Data Collection and Normalization

Initial data collection by [databricks](https://huggingface.co/datasets/databricks/databricks-dolly-15k). See their repository for more information about this dataset.

## Considerations for Using the Data

Note that the translations in this new dataset have not been verified by humans! Use at your own risk, both in terms of quality and biases.

### Discussion of Biases

As with any machine-generated texts, users should be aware of potential biases that are included in this dataset. Although the prompt specifically includes `make sure to avoid biases (such as gender bias, grammatical bias, social bias)`, of course the impact of such command is not known. It is likely that biases remain in the dataset so use with caution.

### Other Known Limitations

The translation quality has not been verified. Use at your own risk!

### Licensing Information

This repository follows the original databricks license, which is CC BY-SA 3.0 but see below for a specific restriction.

This text was generated (either in part or in full) with GPT-3 (`gpt-3.5-turbo`), OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

If you use this dataset, you must also follow the [Sharing](https://openai.com/policies/sharing-publication-policy) and [Usage](https://openai.com/policies/usage-policies) policies. 

As clearly stated in their [Terms of Use](https://openai.com/policies/terms-of-use), specifically 2c.iii, "[you may not] use output from the Services to develop models that compete with OpenAI". That means that you cannot use this dataset to build models that are intended to commercially compete with OpenAI. [As far as I am aware](https://law.stackexchange.com/questions/93308/licensing-material-generated-with-chatgpt), that is a specific restriction that should serve as an addendum to the current license.


### Citation Information

If you use this data set, please cite :


Vanroy, B. (2023). Dolly 15k Dutch [Data set]. Hugging Face. https://doi.org/10.57967/hf/0785

```bibtex
@misc {https://doi.org/10.57967/hf/0785,
	author       = {Vanroy, Bram },
	title        = { {D}olly 15k {D}utch },
	year         = 2023,
	url          = { https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch },
	doi          = { 10.57967/hf/0785 },
	publisher    = { Hugging Face }
}
```


### Contributions

Thanks to [databricks](https://huggingface.co/datasets/databricks/databricks-dolly-15k) for the initial, high-quality dataset.