# Dataset Card for Dataset Name

## Dataset Description

Old ChatGPT scrapes, the RAW version.

### Dataset Summary

This is a result of a colab in a virtual shed. Really old stuff, before Plus even. Everything was generated by the model itself.

I think this is from what we call "alpha" now? Might even be before alpha idfk.

### Supported Tasks and Leaderboards

See dataset for more info.

### Languages

English only iirc, might be some translations thrown in there.

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

Not much data was actually curated, it is recommended to go over the data yourself and fix some answers.

### Source Data

#### Initial Data Collection and Normalization

First, user queries were generated, then Assistant's answers.

#### Who are the source language producers?

OpenAI?

### Annotations

#### 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

None. Z E R O.

### Discussion of Biases

Has some biases towards talking about OpenAI stuff and some weird-ish stuff. "NDA" stuff is missing.

### Other Known Limitations

Some of the quries contain answers, hence models trained on data as is will be fucked up. Raw data contains "today's date" and other stuff I didn't include in my Neo(X) finetune.

## Additional Information

### Dataset Curators

MrSteyk and old ChatGPT. RIP in pepperoni, you will be missed.

### Licensing Information

[More Information Needed]

### Citation Information

Don't

### Contributions

They know themselves, apart from OAI.
task_categories:conversational language:en license:apache-2.0 chatgpt openai gpt35-alpha region:us
{"language": ["en"], "license": "apache-2.0", "task_categories": ["conversational"], "tags": ["chatgpt", "openai", "gpt35-alpha"]}
TAGS
#task_categories-conversational #language-English #license-apache-2.0 #chatgpt #openai #gpt35-alpha #region-us 

# Dataset Card for Dataset Name

## Dataset Description

Old ChatGPT scrapes, the RAW version.

### Dataset Summary

This is a result of a colab in a virtual shed. Really old stuff, before Plus even. Everything was generated by the model itself.

I think this is from what we call "alpha" now? Might even be before alpha idfk.

### Supported Tasks and Leaderboards

See dataset for more info.

### Languages

English only iirc, might be some translations thrown in there.

## Dataset Structure

### Data Instances



### Data Fields



### Data Splits



## Dataset Creation

### Curation Rationale

Not much data was actually curated, it is recommended to go over the data yourself and fix some answers.

### Source Data

#### Initial Data Collection and Normalization

First, user queries were generated, then Assistant's answers.

#### Who are the source language producers?

OpenAI?

### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset

None. Z E R O.

### Discussion of Biases

Has some biases towards talking about OpenAI stuff and some weird-ish stuff. "NDA" stuff is missing.

### Other Known Limitations

Some of the quries contain answers, hence models trained on data as is will be fucked up. Raw data contains "today's date" and other stuff I didn't include in my Neo(X) finetune.

## Additional Information

### Dataset Curators

MrSteyk and old ChatGPT. RIP in pepperoni, you will be missed.

### Licensing Information





Don't

### Contributions

They know themselves, apart from OAI.
# Dataset Card for Dataset Name ## Dataset Description

Old ChatGPT scrapes, the RAW version. ### Dataset Summary

This is a result of a colab in a virtual shed. Really old stuff, before Plus even. Everything was generated by the model itself.

I think this is from what we call "alpha" now? Might even be before alpha idfk. ### Supported Tasks and Leaderboards

See dataset for more info. ### Languages

English only iirc, might be some translations thrown in there. ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale

Not much data was actually curated, it is recommended to go over the data yourself and fix some answers. ### Source Data #### Initial Data Collection and Normalization

First, user queries were generated, then Assistant's answers. #### Who are the source language producers?

OpenAI? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset

None. Z E R O. ### Discussion of Biases

Has some biases towards talking about OpenAI stuff and some weird-ish stuff. "NDA" stuff is missing. ### Other Known Limitations

Some of the quries contain answers, hence models trained on data as is will be fucked up. Raw data contains "today's date" and other stuff I didn't include in my Neo(X) finetune. ## Additional Information ### Dataset Curators

MrSteyk and old ChatGPT. RIP in pepperoni, you will be missed. ### Licensing Information





Don't ### Contributions

They know themselves, apart from OAI.
TAGS
#task_categories-conversational #language-English #license-apache-2.0 #chatgpt #openai #gpt35-alpha #region-us 
 # Dataset Card for Dataset Name ## Dataset Description

Old ChatGPT scrapes, the RAW version. ### Dataset Summary

This is a result of a colab in a virtual shed. Really old stuff, before Plus even. Everything was generated by the model itself.

I think this is from what we call "alpha" now? Might even be before alpha idfk. ### Supported Tasks and Leaderboards

See dataset for more info. ### Languages

English only iirc, might be some translations thrown in there. ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale

Not much data was actually curated, it is recommended to go over the data yourself and fix some answers. ### Source Data #### Initial Data Collection and Normalization

First, user queries were generated, then Assistant's answers. #### Who are the source language producers?

OpenAI? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset

None. Z E R O. ### Discussion of Biases

Has some biases towards talking about OpenAI stuff and some weird-ish stuff. "NDA" stuff is missing. ### Other Known Limitations

Some of the quries contain answers, hence models trained on data as is will be fucked up. Raw data contains "today's date" and other stuff I didn't include in my Neo(X) finetune. ## Additional Information ### Dataset Curators

MrSteyk and old ChatGPT. RIP in pepperoni, you will be missed. ### Licensing Information





Don't ### Contributions

They know themselves, apart from OAI.