
# Dataset Card for [Dataset Name]

## 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)

## Dataset Description

- **Homepage:** https://huggingface.co/datasets/RayhanADev/replit-comments-categorized
- **Repository:** https://huggingface.co/datasets/RayhanADev/replit-comments-categorized
- **Point of Contact:** ray@furret.dev

### Dataset Summary

Comments from [Replit](https://replit.com/)'s Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon.

### Supported Tasks and Leaderboards

Text Classification

### Languages

English

## Dataset Structure

### Data Instances

```json
{"label":3,"text":"@KENDALPETERSON\nShut up you dont have a permit to brag."}
```

Labels
- 0: General
- 1: Spam
- 2: NSFW
- 3: Harassment

### Data Fields

Label, Text

### Data Splits

Train, Validation, Test

## Dataset Creation

### Curation Rationale

Fine-tuning data for the Replit + Weights and Biases Hackathon.

### Source Data

#### Initial Data Collection and Normalization

This data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a
pattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023.

#### Who are the source language producers?

Replit Community users

### Annotations

#### Annotation process

I tagged the data using a small website that I made to assign labels to individual comments.

![](https://media.furret.dev/JKnDLTKMqF)

#### Who are the annotators?

Me ([@rayhanadev](https://www.furret.dev))

### Personal and Sensitive Information

This dataset is purely unfiltered and may contain personal and sensitive information.

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

The data is categorized according to three categories of content that are not allowed on Replit (and would fall into a fourth "general" category if it is acceptable
content).

### Other Known Limitations

The dataset is small (n<1k) but I am working on increasing the amount of available data. There is also significantly more unacceptable content labelled than there is
acceptable ("general") content.

## Additional Information

### Dataset Curators

Me ([@rayhanadev](https://www.furret.dev))

### Licensing Information

This data is licensed under MIT but Replit or any concerned parties is free to issue a takedown of this dataset. Any person who's data is available in this dataset is free
to request that their data is deleted, however it is not a breach of Replit's data use policy for this data to be available.

task_categories:text-classification task_ids:intent-classification annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:n<1K source_datasets:original language:en license:mit replit comments forum chat intent classification doi:10.57967/hf/0351 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["intent-classification"], "pretty_name": "Replit Comments Categorized", "tags": ["replit", "comments", "forum", "chat", "intent", "classification"]}
TAGS
#task_categories-text-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-mit #replit #comments #forum #chat #intent #classification #doi-10.57967/hf/0351 #region-us 


# Dataset Card for [Dataset Name]

## 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

## Dataset Description

- Homepage: URL
- Repository: URL
- Point of Contact: ray@URL

### Dataset Summary

Comments from Replit's Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon.

### Supported Tasks and Leaderboards

Text Classification

### Languages

English

## Dataset Structure

### Data Instances



Labels
- 0: General
- 1: Spam
- 2: NSFW
- 3: Harassment

### Data Fields

Label, Text

### Data Splits

Train, Validation, Test

## Dataset Creation

### Curation Rationale

Fine-tuning data for the Replit + Weights and Biases Hackathon.

### Source Data

#### Initial Data Collection and Normalization

This data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a
pattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023.

#### Who are the source language producers?

Replit Community users

### Annotations

#### Annotation process

I tagged the data using a small website that I made to assign labels to individual comments.

![](URL

#### Who are the annotators?

Me (@rayhanadev)

### Personal and Sensitive Information

This dataset is purely unfiltered and may contain personal and sensitive information.

## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases

The data is categorized according to three categories of content that are not allowed on Replit (and would fall into a fourth "general" category if it is acceptable
content).

### Other Known Limitations

The dataset is small (n<1k) but I am working on increasing the amount of available data. There is also significantly more unacceptable content labelled than there is
acceptable ("general") content.

## Additional Information

### Dataset Curators

Me (@rayhanadev)

### Licensing Information

This data is licensed under MIT but Replit or any concerned parties is free to issue a takedown of this dataset. Any person who's data is available in this dataset is free
to request that their data is deleted, however it is not a breach of Replit's data use policy for this data to be available.

# Dataset Card for [Dataset Name] ## 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 ## Dataset Description

- Homepage: URL
- Repository: URL
- Point of Contact: ray@URL ### Dataset Summary

Comments from Replit's Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon. ### Supported Tasks and Leaderboards

Text Classification ### Languages

English ## Dataset Structure ### Data Instances



Labels
- 0: General
- 1: Spam
- 2: NSFW
- 3: Harassment ### Data Fields

Label, Text ### Data Splits

Train, Validation, Test ## Dataset Creation ### Curation Rationale

Fine-tuning data for the Replit + Weights and Biases Hackathon. ### Source Data #### Initial Data Collection and Normalization

This data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a
pattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023. #### Who are the source language producers?

Replit Community users ### Annotations #### Annotation process

I tagged the data using a small website that I made to assign labels to individual comments.

![](URL #### Who are the annotators?

Me (@rayhanadev) ### Personal and Sensitive Information

This dataset is purely unfiltered and may contain personal and sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases

The data is categorized according to three categories of content that are not allowed on Replit (and would fall into a fourth "general" category if it is acceptable
content). ### Other Known Limitations

The dataset is small (n<1k) but I am working on increasing the amount of available data. There is also significantly more unacceptable content labelled than there is
acceptable ("general") content. ## Additional Information ### Dataset Curators

Me (@rayhanadev) ### Licensing Information

This data is licensed under MIT but Replit or any concerned parties is free to issue a takedown of this dataset. Any person who's data is available in this dataset is free
to request that their data is deleted, however it is not a breach of Replit's data use policy for this data to be available.
TAGS
#task_categories-text-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-mit #replit #comments #forum #chat #intent #classification #doi-10.57967/hf/0351 #region-us 
 # Dataset Card for [Dataset Name] ## 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 ## Dataset Description

- Homepage: URL
- Repository: URL
- Point of Contact: ray@URL ### Dataset Summary

Comments from Replit's Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon. ### Supported Tasks and Leaderboards

Text Classification ### Languages

English ## Dataset Structure ### Data Instances



Labels
- 0: General
- 1: Spam
- 2: NSFW
- 3: Harassment ### Data Fields

Label, Text ### Data Splits

Train, Validation, Test ## Dataset Creation ### Curation Rationale

Fine-tuning data for the Replit + Weights and Biases Hackathon. ### Source Data #### Initial Data Collection and Normalization

This data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a
pattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023. #### Who are the source language producers?

Replit Community users ### Annotations #### Annotation process

I tagged the data using a small website that I made to assign labels to individual comments.

![](URL #### Who are the annotators?

Me (@rayhanadev) ### Personal and Sensitive Information

This dataset is purely unfiltered and may contain personal and sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases

The data is categorized according to three categories of content that are not allowed on Replit (and would fall into a fourth "general" category if it is acceptable
content). ### Other Known Limitations

The dataset is small (n<1k) but I am working on increasing the amount of available data. There is also significantly more unacceptable content labelled than there is
acceptable ("general") content. ## Additional Information ### Dataset Curators

Me (@rayhanadev) ### Licensing Information

This data is licensed under MIT but Replit or any concerned parties is free to issue a takedown of this dataset. Any person who's data is available in this dataset is free
to request that their data is deleted, however it is not a breach of Replit's data use policy for this data to be available.