
# Instruct-Aira Dataset

## Table of Contents

- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Repository:** https://github.com/Nkluge-correa/Aira
- **Point of Contact:** [AIRES at PUCRS](nicholas@airespucrs.org)

### Dataset Summary

This dataset contains a collection of prompts and responses to those prompts. All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). The dataset is available in Portuguese, English, and Spanish.

### Supported Tasks and Leaderboards

This dataset can be utilized for various natural language processing tasks, including but not limited to:

- Language modeling.
- Question-answering systems.
- Chatbot development.
- Evaluation of language models.
- Alignment research.

### Languages

English, Portuguese, and Spanish.

## Dataset Structure

### Data Instances

The dataset consists of the following features:

- **Prompt:** The initial text or question provided to the model (type `str`).
- **Completion:** A generated completion to the given prompt (type `str`).

All `prompt + completion` examples are less than 400 tokens (measured using the `GPT-2` and `BLOOM` tokenizers).

### Data Fields

```python
{
  "prompt":"What is the capital of Brazil?",
  "completion": "The capital of Brazil is Brasília."
  }
```

### Data Splits

Available splits are `english`, `portuguese`, and `spanish`.

```python

from datasets import load_dataset

dataset = load_dataset("nicholasKluge/instruct-aira-dataset", split='portuguese')

```

## Dataset Creation

### Curation Rationale

This dataset was developed are part of [Nicholas Kluge's](https://nkluge-correa.github.io/) doctoral dissertation, "_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn.

### Source Data

#### Initial Data Collection and Normalization

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.

#### Who are the source language producers?

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.

### Annotations

#### Annotation process

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.

#### Who are the annotators?

No annotators were used.

### Personal and Sensitive Information

No personal or sensitive information is part of this dataset.

## Considerations for Using the Data

### Social Impact of Dataset

No considerations.

### Discussion of Biases

No considerations.

### Other Known Limitations

No considerations.

## Additional Information

### Dataset Curators

[Nicholas Kluge Corrêa](mailto:nicholas@airespucrs.org).

### Licensing Information

This dataset is licensed under the [Apache License, version 2.0](LICENSE).

### Citation Information

```latex

@misc{nicholas22aira,
  doi = {10.5281/zenodo.6989727},
  url = {https://github.com/Nkluge-correa/Aira},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
}

```

### Contributions

If you would like to contribute, contact me at [nicholas@airespucrs.org](mailto:nicholas@airespucrs.org)!

task_categories:conversational task_categories:text-generation size_categories:10K<n<100K language:pt language:en language:es license:apache-2.0 alignment instruction chat region:us
{"language": ["pt", "en", "es"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["conversational", "text-generation"], "pretty_name": "Instruct-Aira Dataset", "tags": ["alignment", "instruction", "chat"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "portuguese", "num_bytes": 52023662, "num_examples": 40945}, {"name": "english", "num_bytes": 47254561, "num_examples": 41762}, {"name": "spanish", "num_bytes": 53176782, "num_examples": 40946}], "download_size": 85078532, "dataset_size": 152455005}, "configs": [{"config_name": "default", "data_files": [{"split": "portuguese", "path": "data/portuguese-*"}, {"split": "english", "path": "data/english-*"}, {"split": "spanish", "path": "data/spanish-*"}]}]}
TAGS
#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Portuguese #language-English #language-Spanish #license-apache-2.0 #alignment #instruction #chat #region-us 


# Instruct-Aira Dataset

## 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

- Repository: URL
- Point of Contact: AIRES at PUCRS

### Dataset Summary

This dataset contains a collection of prompts and responses to those prompts. All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). The dataset is available in Portuguese, English, and Spanish.

### Supported Tasks and Leaderboards

This dataset can be utilized for various natural language processing tasks, including but not limited to:

- Language modeling.
- Question-answering systems.
- Chatbot development.
- Evaluation of language models.
- Alignment research.

### Languages

English, Portuguese, and Spanish.

## Dataset Structure

### Data Instances

The dataset consists of the following features:

- Prompt: The initial text or question provided to the model (type 'str').
- Completion: A generated completion to the given prompt (type 'str').

All 'prompt + completion' examples are less than 400 tokens (measured using the 'GPT-2' and 'BLOOM' tokenizers).

### Data Fields



### Data Splits

Available splits are 'english', 'portuguese', and 'spanish'.



## Dataset Creation

### Curation Rationale

This dataset was developed are part of Nicholas Kluge's doctoral dissertation, "_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn.

### Source Data

#### Initial Data Collection and Normalization

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.

#### Who are the source language producers?

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.

### Annotations

#### Annotation process

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.

#### Who are the annotators?

No annotators were used.

### Personal and Sensitive Information

No personal or sensitive information is part of this dataset.

## Considerations for Using the Data

### Social Impact of Dataset

No considerations.

### Discussion of Biases

No considerations.

### Other Known Limitations

No considerations.

## Additional Information

### Dataset Curators

Nicholas Kluge Corrêa.

### Licensing Information

This dataset is licensed under the Apache License, version 2.0.





### Contributions

If you would like to contribute, contact me at nicholas@URL!

# Instruct-Aira Dataset ## 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

- Repository: URL
- Point of Contact: AIRES at PUCRS ### Dataset Summary

This dataset contains a collection of prompts and responses to those prompts. All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). The dataset is available in Portuguese, English, and Spanish. ### Supported Tasks and Leaderboards

This dataset can be utilized for various natural language processing tasks, including but not limited to:

- Language modeling.
- Question-answering systems.
- Chatbot development.
- Evaluation of language models.
- Alignment research. ### Languages

English, Portuguese, and Spanish. ## Dataset Structure ### Data Instances

The dataset consists of the following features:

- Prompt: The initial text or question provided to the model (type 'str').
- Completion: A generated completion to the given prompt (type 'str').

All 'prompt + completion' examples are less than 400 tokens (measured using the 'GPT-2' and 'BLOOM' tokenizers). ### Data Fields ### Data Splits

Available splits are 'english', 'portuguese', and 'spanish'. ## Dataset Creation ### Curation Rationale

This dataset was developed are part of Nicholas Kluge's doctoral dissertation, "_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn. ### Source Data #### Initial Data Collection and Normalization

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets. #### Who are the source language producers?

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets. ### Annotations #### Annotation process

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets. #### Who are the annotators?

No annotators were used. ### Personal and Sensitive Information

No personal or sensitive information is part of this dataset. ## Considerations for Using the Data ### Social Impact of Dataset

No considerations. ### Discussion of Biases

No considerations. ### Other Known Limitations

No considerations. ## Additional Information ### Dataset Curators

Nicholas Kluge Corrêa. ### Licensing Information

This dataset is licensed under the Apache License, version 2.0. ### Contributions

If you would like to contribute, contact me at nicholas@URL!
TAGS
#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Portuguese #language-English #language-Spanish #license-apache-2.0 #alignment #instruction #chat #region-us 
 # Instruct-Aira Dataset ## 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

- Repository: URL
- Point of Contact: AIRES at PUCRS ### Dataset Summary

This dataset contains a collection of prompts and responses to those prompts. All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). The dataset is available in Portuguese, English, and Spanish. ### Supported Tasks and Leaderboards

This dataset can be utilized for various natural language processing tasks, including but not limited to:

- Language modeling.
- Question-answering systems.
- Chatbot development.
- Evaluation of language models.
- Alignment research. ### Languages

English, Portuguese, and Spanish. ## Dataset Structure ### Data Instances

The dataset consists of the following features:

- Prompt: The initial text or question provided to the model (type 'str').
- Completion: A generated completion to the given prompt (type 'str').

All 'prompt + completion' examples are less than 400 tokens (measured using the 'GPT-2' and 'BLOOM' tokenizers). ### Data Fields ### Data Splits

Available splits are 'english', 'portuguese', and 'spanish'. ## Dataset Creation ### Curation Rationale

This dataset was developed are part of Nicholas Kluge's doctoral dissertation, "_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn. ### Source Data #### Initial Data Collection and Normalization

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets. #### Who are the source language producers?

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets. ### Annotations #### Annotation process

All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets. #### Who are the annotators?

No annotators were used. ### Personal and Sensitive Information

No personal or sensitive information is part of this dataset. ## Considerations for Using the Data ### Social Impact of Dataset

No considerations. ### Discussion of Biases

No considerations. ### Other Known Limitations

No considerations. ## Additional Information ### Dataset Curators

Nicholas Kluge Corrêa. ### Licensing Information

This dataset is licensed under the Apache License, version 2.0. ### Contributions

If you would like to contribute, contact me at nicholas@URL!
passage: TAGS
#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Portuguese #language-English #language-Spanish #license-apache-2.0 #alignment #instruction #chat #region-us 
# Instruct-Aira Dataset## 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

- Repository: URL
- Point of Contact: AIRES at PUCRS### Dataset Summary

This dataset contains a collection of prompts and responses to those prompts. All completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). The dataset is available in Portuguese, English, and Spanish.### Supported Tasks and Leaderboards

This dataset can be utilized for various natural language processing tasks, including but not limited to:

- Language modeling.
- Question-answering systems.
- Chatbot development.
- Evaluation of language models.
- Alignment research.### Languages

English, Portuguese, and Spanish.## Dataset Structure### Data Instances

The dataset consists of the following features:

- Prompt: The initial text or question provided to the model (type 'str').
- Completion: A generated completion to the given prompt (type 'str').

All 'prompt + completion' examples are less than 400 tokens (measured using the 'GPT-2' and 'BLOOM' tokenizers).### Data Fields### Data Splits

Available splits are 'english', 'portuguese', and 'spanish'.## Dataset Creation