# A large instruct dataset

This dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.

## Description

The Swype.com dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:

- [GPT4All](https://github.com/nomic-ai/gpt4all): A dataset containing a wide range of tasks for training and evaluating general-purpose language models.
- [Alpaca dataset from Stanford](https://github.com/tatsu-lab/stanford_alpaca): A dataset containing prompts, completions, and annotations for controllable text generation.
- Custom generation using [AllenAI augmentation](https://allenai.org): Augmentation performed using the advanced NLP tools provided by AllenAI.
- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.

The dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.

## Dataset Structure

The dataset contains the following columns:

- `prompt`: The input prompt string, representing a task or question.
- `completion`: The output completion string, representing the answer or generated text based on the prompt.

## Citation

If you use this dataset in your research or work, please cite it as follows:

@misc{srikanth2023swypedataset,
author = {Srikanth Srinivas},
title = {Swype.com Dataset},
year = {2023},
publisher = {Swype.com},
howpublished = {\url{https://swype.com}},
email = {s@swype.com}
}
license:mit region:us
{"license": "mit"}
TAGS
#license-mit #region-us 

# A large instruct dataset

This dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.

## Description

The URL dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:

- GPT4All: A dataset containing a wide range of tasks for training and evaluating general-purpose language models.
- Alpaca dataset from Stanford: A dataset containing prompts, completions, and annotations for controllable text generation.
- Custom generation using AllenAI augmentation: Augmentation performed using the advanced NLP tools provided by AllenAI.
- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.

The dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.

## Dataset Structure

The dataset contains the following columns:

- 'prompt': The input prompt string, representing a task or question.
- 'completion': The output completion string, representing the answer or generated text based on the prompt.

If you use this dataset in your research or work, please cite it as follows:

@misc{srikanth2023swypedataset,
author = {Srikanth Srinivas},
title = {URL Dataset},
year = {2023},
publisher = {URL},
howpublished = {\url{URL}},
email = {s@URL}
}
# A large instruct dataset

This dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing. ## Description

The URL dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:

- GPT4All: A dataset containing a wide range of tasks for training and evaluating general-purpose language models.
- Alpaca dataset from Stanford: A dataset containing prompts, completions, and annotations for controllable text generation.
- Custom generation using AllenAI augmentation: Augmentation performed using the advanced NLP tools provided by AllenAI.
- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.

The dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation. ## Dataset Structure

The dataset contains the following columns:

- 'prompt': The input prompt string, representing a task or question.
- 'completion': The output completion string, representing the answer or generated text based on the prompt.

If you use this dataset in your research or work, please cite it as follows:

@misc{srikanth2023swypedataset,
author = {Srikanth Srinivas},
title = {URL Dataset},
year = {2023},
publisher = {URL},
howpublished = {\url{URL}},
email = {s@URL}
}
TAGS
#license-mit #region-us 
 # A large instruct dataset

This dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing. ## Description

The URL dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:

- GPT4All: A dataset containing a wide range of tasks for training and evaluating general-purpose language models.
- Alpaca dataset from Stanford: A dataset containing prompts, completions, and annotations for controllable text generation.
- Custom generation using AllenAI augmentation: Augmentation performed using the advanced NLP tools provided by AllenAI.
- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.

The dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation. ## Dataset Structure

The dataset contains the following columns:

- 'prompt': The input prompt string, representing a task or question.
- 'completion': The output completion string, representing the answer or generated text based on the prompt.

If you use this dataset in your research or work, please cite it as follows:

@misc{srikanth2023swypedataset,
author = {Srikanth Srinivas},
title = {URL Dataset},
year = {2023},
publisher = {URL},
howpublished = {\url{URL}},
email = {s@URL}
}
passage: TAGS
#license-mit #region-us 
# A large instruct dataset

This dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.## Description

The URL dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:

- GPT4All: A dataset containing a wide range of tasks for training and evaluating general-purpose language models.
- Alpaca dataset from Stanford: A dataset containing prompts, completions, and annotations for controllable text generation.
- Custom generation using AllenAI augmentation: Augmentation performed using the advanced NLP tools provided by AllenAI.
- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.

The dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.## Dataset Structure

The dataset contains the following columns:

- 'prompt': The input prompt string, representing a task or question.
- 'completion': The output completion string, representing the answer or generated text based on the prompt.

If you use this dataset in your research or work, please cite it as follows:

@misc{srikanth2023swypedataset,
author = {Srikanth Srinivas},
title = {URL Dataset},
year = {2023},
publisher = {URL},
howpublished = {\url{URL}},
email = {s@URL}
}