README.md

metadata

task_categories:
  - text-generation
language:
  - en
  - de
  - fr
  - es
  - it
pretty_name: Red Pajama V2 Dataset

Getting Started

RedPajama-V2 is an open dataset for training large language models. The dataset 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, and 20B documents that are deduplicated.

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 $dest)
  wget "$urls" -O "$dest"
done <"$listings_file"

# 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 $dest)
    wget "$url" -O "$dest"
  done <"$listings_file"
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 reference. This is the same fasttext model used in the RedPajama-1T dataset. Only applies to English data..	ML Heuristics	LLaMA, RedPajama-1T
rps_doc_ml_palm_score	Fasttext classifier prediction for the document being a Wikipedia article, OpenWebText sample or a RedPajama-V1 book. Only for English data.	ML Heuristics	PALM, GLaM
rps_doc_ml_wikipedia_score	Fasttext classifier prediction for the document being a Wikipedia article. This is used for non-English data	ML Heuristics	-
rps_doc_curly_bracket	The ratio between the number of occurrences of '{' or '}' and the number of characters in the raw text.	Natural Language	C4
rps_doc_frac_all_caps_words	The fraction of words in the content that only consist of uppercase letters. This is based on the raw content.	Natural Language	Pretrainer’s Guide
rps_doc_frac_lines_end_with_ellipsis	The fraction of lines that end with an ellipsis, where an ellipsis is defined as either "..." or "…".	Natural Language	RefinedWeb, Gopher
rps_doc_frac_no_alph_words	The fraction of words that contain no alphabetical character.	Natural Language	RefinedWeb, Gopher
rps_doc_lorem_ipsum	The ratio between the number of occurrences of 'lorem ipsum' and the number of characters in the content after normalisation.	Natural Language	C4
rps_doc_mean_word_length	The mean length of words in the content after normalisation.	Natural Language	RefinedWeb, Gopher
rps_doc_stop_word_fraction	The ratio between the number of stop words and the number of words in the document. Stop words are obtained from the stopwords-json repo.	Natural Language	RefinedWeb, Gopher
rps_doc_symbol_to_word_ratio	The ratio of symbols to words in the content.. Symbols are defined "#", "...", and "…".	Natural Language	RefinedWeb, Gopher
rps_doc_frac_unique_words	The fraction of unique words in the content. This is also known as the degeneracy of a text sample. Calculated based on the normalised content.	Natural Language	Pretrainer’s Guide
rps_doc_unigram_entropy	The entropy of the unigram distribution of the content. This measures the diversity of the content and is computed using sum(-x / total * log(x / total)) where the sum is taken over counts of unique words in the normalised content.	Natural Language	-
rps_doc_word_count	The number of words in the content after normalisation.	Natural Language	RefinedWeb, Gopher
rps_lines_ending_with_terminal_punctution_mark	Indicates whether a line ends with a terminal punctuation mark. A terminal punctation mark is defined as one of: ".", "!", "?", "”".	Natural Language	C4
rps_lines_javascript_counts	The number of occurrences of the word "javascript" in each line.	Natural Language	C4
rps_lines_num_words	The number of words in each line. This is computed based on the normalised text.	Natural Language	C4 , RefinedWeb
rps_lines_numerical_chars_fraction	The ratio between the number of numerical characters and total number of characters in each line. This is based on the normalised content.	Natural Language	RefinedWeb
rps_lines_start_with_bulletpoint	Whether the lines that start with a bullet point symbol. The following set of unicodes are considered a bullet point: \u2022 (bullet point), \u2023 (triangular bullet point), \u25B6 (black right pointing triangle), \u25C0 (black left pointing triangle), \u25E6 (white bullet point), \u25A0 (black square), \u25A1 (white square), \u25AA (black small square), \u25AB (white small square), \u2013 (en dash).	Natural Language	RefinedWeb, Gopher
rps_lines_uppercase_letter_fraction	The ratio between the number of uppercase letters and total number of characters in each line. This is based on the raw text.	Natural Language	RefinedWeb
rps_doc_num_sentences	The number of sentences in the content. This is calculated using the regular expression r'\b[^.!?]+[.!?]*'.	Natural Language	C4
rps_doc_frac_chars_dupe_10grams	The fraction of characters in duplicate word 10grams. This operates on the lower-cased, punctuation removed content. It is also ensured that characters in overlapping ngrams are only counted once.	Repetitiveness	RefinedWeb, Gopher
rps_doc_frac_chars_dupe_5grams	The fraction of characters in duplicate word 5grams.	Repetitiveness	RefinedWeb, Gopher
rps_doc_frac_chars_dupe_6grams	The fraction of characters in duplicate word 6grams.	Repetitiveness	RefinedWeb, Gopher
rps_doc_frac_chars_dupe_7grams	The fraction of characters in duplicate word 7grams.	Repetitiveness	RefinedWeb, Gopher
rps_doc_frac_chars_dupe_8grams	The fraction of characters in duplicate word 8grams.	Repetitiveness	RefinedWeb, Gopher
rps_doc_frac_chars_dupe_9grams	The fraction of characters in duplicate word 9grams.	Repetitiveness	RefinedWeb, Gopher
rps_doc_frac_chars_top_2gram	The fraction of characters in the top word 2gram.	Repetitiveness	RefinedWeb, Gopher
rps_doc_frac_chars_top_3gram	The fraction of characters in the top word 3gram.	Repetitiveness	RefinedWeb, Gopher
rps_doc_frac_chars_top_4gram	The fraction of characters in the top word 4gram.	Repetitiveness	RefinedWeb, Gopher
rps_doc_ldnoobw_words	The number of sequences of words that are contained in the List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words blocklist. The blocklist is obtained from the LDNOOBW repo.	toxicity	C4
rps_doc_ut1_blacklist	A categorical id corresponding to the list of categories of the domain of the document. Categories are obtained from the UT1 blacklist. The list is obtained from UT-Capitole.	toxicictiy	RefinedWeb

Document and Token Counts for the Annotated and deduplicated head_middle part of the dataset

	# Documents	Estimated Token count (deduped)
en	14.5B	20.5T
de	1.9B	3.0T
fr	1.6B	2.7T
es	1.8B	2.8T
it	0.9B	1.5T
Total	20.8B	30.4T

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:

{
  "url": "...",
  "date_download": "2014-08-20T06:48:26Z",
  "digest": "sha1:46OPKWZ7MAG5624VYYA3U3YH2MJ727B6",
  "length": 1095,
  "nlines": 8,
  "source_domain": "...",
  "title": "...",
  "raw_content": "Dear ...",
  "cc_segment": "crawl-data/CC-MAIN-2014-35/...",
  "original_nlines": 11,
  "original_length": 1174,
  "line_ids": [
    0,
    1,
    3,
    4,
    6,
    7,
    8,
    9
  ],
  "language": "en",
  "language_score": 0.92,
  "perplexity": 217.2,
  "bucket": "head"
}

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. Each snapshot was processed using the CCNet pipeline and split into head middle tail buckets, depending on the perplexity score. In a second step, the documents in the head and middle buckets were annotated with the quality signals described above. Finally, the documents were deduplicated based on the text, using a Bloomfilter. The duplicates were kept in the dataset, but are marked in the duplicates component.

Citation

To cite RedPajama-V2, please use:

@software{together2023redpajama-v2,
  author = {Together Computer},
  title = {RedPajama-Data-v2: an Open Dataset for Training Large Language Models},
  month = October,
  year = 2023,
  url = {https://github.com/togethercomputer/RedPajama-Data}
}

Acknowledgements

-- TODO --

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.