File size: 3,606 Bytes
3de1ba6
0a8b6b7
 
 
 
 
3de1ba6
0a8b6b7
 
 
 
 
 
d9aa495
0a8b6b7
 
 
 
 
 
 
 
 
 
 
 
4153848
0a8b6b7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f80c196
0a8b6b7
 
 
 
 
f80c196
0a8b6b7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
---
task_categories:
- text-generation
language:
- en
pretty_name: Red Pajama 1T Sample
---
# Dataset Card for Dataset Name

### Dataset Summary

RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.
This HuggingFace repo contains a 1B-token sample of the RedPajama dataset.
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      |

A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data).

### 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 downlaod 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.

<!--
### 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

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]
-->