README.md · togethercomputer/RedPajama-Data-V2 at 34b075217b9d7bc26a5f8e5fcdc3ba14bfd19602

metadata

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 pipeline. Out of these, there are 30B documents in the corpus that additionally come with quality signals.

Check out our blog post for more details on the build process, dataset structure and schema.

To familiarize yourself with the dataset, you can load the sample dataset using:

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

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.

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.

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

{
  "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 for the data. The code used to load and process the dataset is licensed under the Apache 2.0 license.