
# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

### Languages

Langugage of the dataset is mostly English.

## Dataset Structure

### Data Fields

The fields are:

- 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
- 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
- 'question', representing a question entry as provided by the FLAN Collection
- 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.


### Data Splits

[More Information Needed]



### Source Data

#### Initial Data Collection and Normalization

Dataset is collected from huggingface's Open-Orca/OpenOrca.


## Additional Information

### Dataset Curators

This dataset is taken from `Open-Orca/OpenOrca` and then modified it's prompt. Made it's overall length of `prompt` + `question` 
less than 512 to make it possible to give it input to mostly models whose Maximum input length is 512. 


# Citation
```bibtex
@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},
}
```
```bibtex
@misc{mukherjee2023orca,
      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
      year={2023},
      eprint={2306.02707},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
      title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, 
      author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
      year={2023},
      eprint={2301.13688},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
```
```bibtex
@software{touvron2023llama,
  title={LLaMA: Open and Efficient Foundation Language Models},
  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}
```
task_categories:text-classification task_categories:conversational language:en license:mit arxiv:2306.02707 arxiv:2301.13688 region:us
{"language": ["en"], "license": "mit", "task_categories": ["text-classification", "conversational"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "length_before_preprocessing", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3671168412.416216, "num_examples": 2872771}, {"name": "test", "num_bytes": 458896850.2513517, "num_examples": 359097}, {"name": "validation", "num_bytes": 458895572.3324322, "num_examples": 359096}], "download_size": 2553683923, "dataset_size": 4588960835.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
TAGS
#task_categories-text-classification #task_categories-conversational #language-English #license-mit #arxiv-2306.02707 #arxiv-2301.13688 #region-us 


# Dataset Card for Dataset Name

## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

### Languages

Langugage of the dataset is mostly English.

## Dataset Structure

### Data Fields

The fields are:

- 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
- 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
- 'question', representing a question entry as provided by the FLAN Collection
- 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.


### Data Splits





### Source Data

#### Initial Data Collection and Normalization

Dataset is collected from huggingface's Open-Orca/OpenOrca.


## Additional Information

### Dataset Curators

This dataset is taken from 'Open-Orca/OpenOrca' and then modified it's prompt. Made it's overall length of 'prompt' + 'question' 
less than 512 to make it possible to give it input to mostly models whose Maximum input length is 512. 



# Dataset Card for Dataset Name ## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Languages

Langugage of the dataset is mostly English. ## Dataset Structure ### Data Fields

The fields are:

- 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
- 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
- 'question', representing a question entry as provided by the FLAN Collection
- 'response', a response to that question received from a query to either GPT-3.5 or GPT-4. ### Data Splits ### Source Data #### Initial Data Collection and Normalization

Dataset is collected from huggingface's Open-Orca/OpenOrca. ## Additional Information ### Dataset Curators

This dataset is taken from 'Open-Orca/OpenOrca' and then modified it's prompt. Made it's overall length of 'prompt' + 'question' 
less than 512 to make it possible to give it input to mostly models whose Maximum input length is 512.
TAGS
#task_categories-text-classification #task_categories-conversational #language-English #license-mit #arxiv-2306.02707 #arxiv-2301.13688 #region-us 
 # Dataset Card for Dataset Name ## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Languages

Langugage of the dataset is mostly English. ## Dataset Structure ### Data Fields

The fields are:

- 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
- 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
- 'question', representing a question entry as provided by the FLAN Collection
- 'response', a response to that question received from a query to either GPT-3.5 or GPT-4. ### Data Splits ### Source Data #### Initial Data Collection and Normalization

Dataset is collected from huggingface's Open-Orca/OpenOrca. ## Additional Information ### Dataset Curators

This dataset is taken from 'Open-Orca/OpenOrca' and then modified it's prompt. Made it's overall length of 'prompt' + 'question' 
less than 512 to make it possible to give it input to mostly models whose Maximum input length is 512.
passage: TAGS
#task_categories-text-classification #task_categories-conversational #language-English #license-mit #arxiv-2306.02707 #arxiv-2301.13688 #region-us 
# Dataset Card for Dataset Name## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact:### Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Languages

Langugage of the dataset is mostly English.## Dataset Structure### Data Fields

The fields are:

- 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
- 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
- 'question', representing a question entry as provided by the FLAN Collection
- 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.### Data Splits### Source Data#### Initial Data Collection and Normalization

Dataset is collected from huggingface's Open-Orca/OpenOrca.## Additional Information### Dataset Curators

This dataset is taken from 'Open-Orca/OpenOrca' and then modified it's prompt. Made it's overall length of 'prompt' + 'question' 
less than 512 to make it possible to give it input to mostly models whose Maximum input length is 512.