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RedPajama-Data-V2

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RedPajama-Data-V2 / README.md
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---
task_categories:
- text-generation
language:
- en
- de
- fr
- es
- it
pretty_name: Red Pajama V2 Data Foundation
---
### Getting Started
The full RedPajama-V2 dataset is a data foundation that includes over 100B text documents coming from 84 CommonCrawl
snapshots and processed using the [CCNet](https://github.com/facebookresearch/cc_net) pipeline. Out of these, there are
30B documents in the corpus that additionally come with quality signals.
Check out our [blog post](XXXXX) for more details on the build process, dataset structure and schema.
To familiarize yourself with the dataset, you can load the sample dataset using:
```python
from datasets import load_dataset
ds = load_dataset("togethercomputer/RedPajama-Data-V2", name="sample")
```
To download a the dataset for a specific combination of `{partition} x {snapshot_id} x {language}`, you can run
```python
from datasets import load_dataset
ds = load_dataset("togethercomputer/RedPajama-Data-V2",
name="sample",
partition="head_middle",
snapshots=["2023-06", "2022-49"],
languages=["en", "de"])
```
Alternatively, you can also directly download the files using the following instructions, using English data from the
`2023-06` snapshot and the `head_middle` partition as an example. The full set of CC snapshots included in the dataset
is given in `_CC_SNAPSHOT_IDS`, and the available partitions are `tail` and `head_middle`. The available language tags
are `en`, `de`, `fr`, `es`, `it`.
```bash
CC_SNAPSHOT="2023-06"
LANG="en"
PARTITION="head_middle"
BASE_URL="https://data.together.xyz/redpajama-data-v2/v1.0.0/"
listings_file="${LANG}-${CC_SNAPSHOT}-${PARTITION}.txt"
wget "${BASE_URL}/listings/${listings_file}"
# download documents
while read line; do
url="${BASE_URL}/documents/${line}.json.gz"
dest="documents/${line}.json.gz"
mkdir -p $(dirname $dest)
wget "$line" -O "$dest"
done <"${LANG}-${CC_SNAPSHOT}-${PARTITION}.txt"
# download other components
COMPS=("quality_signals" "minhash" "duplicates")
for comp in "${COMPS[@]}"; do
while read line; do
url="${BASE_URL}/${comp}/${line}.${comp}.json.gz"
dest="${comp}/${line}.${comp}.json.gz"
mkdir -p $(dirname $dest)
wget "$line" -O "$dest"
done <"${LANG}-${CC_SNAPSHOT}-${PARTITION}.txt"
done
```
A full set of scripts to recreate the dataset, including the quality signals, can be
found [here](https://github.com/togethercomputer/RedPajama-Data).
### Dataset Summary
RedPajama-V2 is an open dataset for training large laguage models and includes over 100B text documents. Out of these,
30B documents come with quality annotations.
#### Quality Annotations
| Annotation Tag | Description | Category | Reference |
|--------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|------------------------------------|
| ccnet_bucket | head, middle or tail bucket of the perplexity score | ccnet | ccnet |
| ccnet_language_score | score of the language identification model | ccnet | ccnet |
| ccnet_length | number of characters | ccnet | ccnet |
| ccnet_nlines | number of lines | ccnet | ccnet |
| ccnet_original_length | number of characters before in-document line deduplication | ccnet | ccnet |
| ccnet_original_nlines | number of lines before in-document line deduplication | ccnet | ccnet |
| ccnet_perplexity | perplexity of an LM trained on Wikipedia | ccnet | ccnet |
| rps_doc_books_importance | Given a bag of {1,2}-wordgram model trained on Books p, and a model trained on the source domain q, This is the logarithm of the ratio p(doc)/q(doc) | ML Heuristics | Importance Resampling (Xie et al.) |
| rps_doc_openwebtext_importance | Given a bag of {1,2}-wordgram model trained on OpenWebText p, and a model trained on the source domain q, this is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | Importance Resampling (Xie et al.) |
| rps_doc_wikipedia_importance | Given a bag of {1,2}-wordgram model trained on Wikipedia articles p, and a model trained on the source domain q, this is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | Importance Resampling (Xie et al.) |
| rps_doc_ml_wikiref_score | Fasttext classifier prediction for the document being a Wikipedia | ML Heuristics | LLaMA, RedPajama-1T |
| | reference. This is the same fasttext model used in the RedPajama-1T | | |
| | dataset. Only applies to English data. | | |
| rps_doc_ml_palm_score | Fasttext classifier prediction for the document being a Wikipedia | ML Heuristics | PaLM, GLaM |
| | article, OpenWebText sample or a RedPajama-V1 book. Only for English | | |
| | data. | | |
| rps_doc_ml_wikipedia_score | Fasttext classifier prediction for the document being a Wikipedia | ML Heuristics | - |
| | article. This is used for non-English data | | |
#### Document Counts for the Annotated part of the dataset
| | en | de | fr | es | it | Total |
|-------------|-------|------|------|------|------|-------|
| # Documents | 24.5B | 2.7B | 2.2B | 2.3B | 1.2B | 32.9B |
### Languages
English, German, French, Italian, Spanish
## Dataset Structure
The dataset is structured into four components, each following the same key structure:
```
├── documents
├── 2018-43
├── 0000
├── en_head.json.gz
├── ...
├── it_middle.json.gz
├── quality_signals
├── 2018-43
├── 0000
├── en_head.signals.json.gz
├── ...
├── it_middle.json.gz
├── duplicates
├── 2018-43
├── 0000
├── en_head.duplicates.parquet
├── ...
├── it_middle.duplicates.parquet
├── minhash
├── 2018-43
├── 0000
├── en_head.minhash.parquet
├── ...
├── it_middle.minhash.parquet
```
Documents files, which contain the text, folow the schema defined by CCNet, and the quality signals follow the schema
```json
{
"id": "2018-43/0000/en_head.json.gz/0",
"id_int": 7972430436813205988,
"metadata": {
"cc_segment": "crawl-data/...",
"cc_net_source": "2018-43/0000/en_head.json.gz",
"url": "...",
"source_domain": "...",
"language": "en",
"snapshot_id": "2018-43"
},
"quality_signals": {
"ccnet_original_length": [
[
0,
7033,
8711.0
]
],
...,
"rps_doc_stop_word_fraction": [
[
0,
7033,
0.45121107
]
],
"rps_lines_num_words": [
[
0,
25,
2
],
...,
[
6980,
7033,
10
]
]
}
}
```
where signal scores are encoded as a list of tuples `(start, end, score)`, where `start` and `end` are the locations in
the
`raw_content` string where the `score` applies.
## Dataset Creation
The dataset is based on 84 snapshots provided by Common Crawl.
## Citation
To cite RedPajama-V2, please use:
```
@software{together2023redpajama-v2,
author = {Together Computer},
title = {RedPajama-Data-v2: a living data foundation for training open LLM models},
month = October,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
## License
Please refer to the [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use) for the data.
The code used to load and process the dataset is licensed under the Apache 2.0 license.
<!--
### Annotations
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#### Who are the annotators?
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## Considerations for Using the Data
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### Discussion of Biases
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### Other Known Limitations
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## Additional Information
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-->