# Dataset Card for Dataset Name

### Dataset Summary

This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.

This dataset contains 64 samples from each of the 7 sources.

The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T):

| Dataset       | Token Count |
|---------------|-------------|
| Commoncrawl   | 878 Billion        |
| C4            | 175 Billion        |
| GitHub        | 59 Billion         |
| Books         | 26 Billion         |
| ArXiv         | 28 Billion         |
| Wikipedia     | 24 Billion         |
| StackExchange | 20 Billion         |
| Total         | 1.2 Trillion      |

### Languages

Primarily English, though the Wikipedia slice contains multiple languages.

## Dataset Structure

The dataset structure is as follows:

```
{
    "text": ...,
    "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}
}
```

## Dataset Creation

This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.

### Source Data

#### Commoncrawl

We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples.

#### C4

C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.

#### GitHub

The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license.

#### Wikipedia
We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed.

#### Gutenberg and Books3
The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates.

#### ArXiv
ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies.

#### Stackexchange
The Stack Exchange split of the dataset is download from the
[Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites,
remove html tags, group the posts into question-answer pairs, and order answers by their score.

task_categories:text-generation language:en region:us
{"language": ["en"], "task_categories": ["text-generation"], "pretty_name": "RedPajama Tiny"}
TAGS
#task_categories-text-generation #language-English #region-us 

Dataset Card for Dataset Name
=============================


### Dataset Summary


This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.


This dataset contains 64 samples from each of the 7 sources.


The full dataset has the following token counts and is available for download:



### Languages


Primarily English, though the Wikipedia slice contains multiple languages.


Dataset Structure
-----------------


The dataset structure is as follows:


Dataset Creation
----------------


This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.


### Source Data


#### Commoncrawl


We download five dumps from Commoncrawl, and run the dumps through the official 'cc\_net' pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples.


#### C4


C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.


#### GitHub


The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license.


#### Wikipedia


We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed.


#### Gutenberg and Books3


The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates.


#### ArXiv


ArXiv data is downloaded from Amazon S3 in the 'arxiv' requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies.


#### Stackexchange


The Stack Exchange split of the dataset is download from the
Internet Archive. Here we only keep the posts from the 28 largest sites,
remove html tags, group the posts into question-answer pairs, and order answers by their score.



### Dataset Summary


This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.


This dataset contains 64 samples from each of the 7 sources.


The full dataset has the following token counts and is available for download: ### Languages


Primarily English, though the Wikipedia slice contains multiple languages.


Dataset Structure
-----------------


The dataset structure is as follows:


Dataset Creation
----------------


This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl


We download five dumps from Commoncrawl, and run the dumps through the official 'cc\_net' pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4


C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub


The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia


We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed. #### Gutenberg and Books3


The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates. #### ArXiv


ArXiv data is downloaded from Amazon S3 in the 'arxiv' requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies. #### Stackexchange


The Stack Exchange split of the dataset is download from the
Internet Archive. Here we only keep the posts from the 28 largest sites,
remove html tags, group the posts into question-answer pairs, and order answers by their score.
TAGS
#task_categories-text-generation #language-English #region-us 
 ### Dataset Summary


This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.


This dataset contains 64 samples from each of the 7 sources.


The full dataset has the following token counts and is available for download: ### Languages


Primarily English, though the Wikipedia slice contains multiple languages.


Dataset Structure
-----------------


The dataset structure is as follows:


Dataset Creation
----------------


This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl


We download five dumps from Commoncrawl, and run the dumps through the official 'cc\_net' pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4


C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub


The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia


We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed. #### Gutenberg and Books3


The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates. #### ArXiv


ArXiv data is downloaded from Amazon S3 in the 'arxiv' requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies. #### Stackexchange


The Stack Exchange split of the dataset is download from the
Internet Archive. Here we only keep the posts from the 28 largest sites,
remove html tags, group the posts into question-answer pairs, and order answers by their score.
passage: TAGS
#task_categories-text-generation #language-English #region-us 
### Dataset Summary


This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.


This dataset contains 64 samples from each of the 7 sources.


The full dataset has the following token counts and is available for download:### Languages


Primarily English, though the Wikipedia slice contains multiple languages.


Dataset Structure
-----------------


The dataset structure is as follows:


Dataset Creation
----------------


This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.### Source Data#### Commoncrawl


We download five dumps from Commoncrawl, and run the dumps through the official 'cc\_net' pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples.#### C4


C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.#### GitHub


The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license.#### Wikipedia


We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed.#### Gutenberg and Books3


The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates.#### ArXiv


ArXiv data is downloaded from Amazon S3 in the 'arxiv' requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies.