togethercomputer/RedPajama-Data-V2

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. In addition, we also provide the ids of duplicated documents which can be used to create a dataset with 20B deduplicated documents.

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

A full set of scripts to recreate the dataset, including the quality signals, can be found here.

Downloading the raw Dataset with Quality Annotations

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 use the following command which downloads the raw (i.e., not deduplicated) part of the dataset and the corresponding quality signals. In the example below, we use English and German data from the head_middle partition of the 2023-06 and the 2022-49 snapshots. The full set of available snapshots is specified in _CC_SNAPSHOT_IDS. The available partitions are tail and head_middle. The available language tags are en, de, fr, es, it. Note that this will download the entire snapshots specified in the snapshots argument and requires ~1TB of disk space per snapshot.

from datasets import load_dataset

ds = load_dataset("togethercomputer/RedPajama-Data-V2",
                  name="default",
                  partition="head_middle",
                  snapshots=["2023-06", "2022-49"],
                  languages=["en", "de"]) 

Downloading the dataset via wget

If you prefer to download the full dataset via wget, you can download the following lists of urls and use them to download the dataset:

# get list of urls pointing to the text documents
wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/document-urls.txt" -O "document-urls.txt"

# get list of urls pointing to the quality signals
wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/quality_signals-urls.txt" -O "quality_signals-urls.txt"

# get list of urls pointing to the ids of duplicate documents
wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/duplicates-urls.txt" -O "duplicates-urls.txt"

# get list of urls pointing to the minhash signatures
wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/minhash-urls.txt" -O "minhash-urls.txt"

You can also directly download subsets of the dataset using the following instructions. Here we use 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. The available partitions are tail and head_middle. The available language tags are en, de, fr, es, it.

To download the plain text data, available for both the head_middle and tail partitions, you can run

CC_SNAPSHOT="2023-06"
LANG="en"
PARTITION="head_middle"
BASE_URL="https://data.together.xyz/redpajama-data-v2/v1.0.0"

listings_tag="${LANG}-${CC_SNAPSHOT}-${PARTITION}"
mkdir listings
wget "${BASE_URL}/listings/${listings_tag}.txt" -O "listings/${listings_tag}.txt"
listings_file="listings/${listings_tag}.txt"

# download documents
while read line; do
  url="${BASE_URL}/documents/${line}.json.gz"
  dest="documents/${line}.json.gz"
  mkdir -p $(dirname $dest)
  wget "$url" -O "$dest"
done <"$listings_file"

In addition, for the head_middle partition, you can also download the quality signals, minhash signatures and duplicate ids using the following commands:

CC_SNAPSHOT="2023-06"
LANG="en"
BASE_URL="https://data.together.xyz/redpajama-data-v2/v1.0.0"

listings_tag="${LANG}-${CC_SNAPSHOT}-head_middle"
mkdir listings
wget "${BASE_URL}/listings/${listings_tag}.txt" -O "listings/${listings_tag}.txt"
listings_file="listings/${listings_tag}.txt"

# download quality signals
while read line; do
  url="${BASE_URL}/quality_signals/${line}.signals.json.gz"
  dest="quality_signals/${line}.signals.json.gz"
  mkdir -p $(dirname $dest)
  wget "$url" -O "$dest"
done <"$listings_file"


# download other components
COMPS=("minhash" "duplicates")
for comp in "${COMPS[@]}"; do
  while read line; do
    url="${BASE_URL}/${comp}/${line}.${comp}.parquet"
    dest="${comp}/${line}.${comp}.parquet"
    mkdir -p $(dirname $dest)
    wget "$url" -O "$dest"
  done <"$listings_file"
done

Applying Filtering Rules

You can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider the following set of rules used in Gopher:

def gopher_rules_pass(sample) -> bool:
    """ function returns True if the sample complies with Gopher rules """
    signals = json.loads(sample["quality_signals"])

    # rule 1: number of words between 50 and 10'000
    word_count = signals["rps_doc_word_count"][0][2]
    if word_count < 50 or word_count > 100_000:
        return False

    # rule 2: mean word length between 3 and 10
    mean_word_length = signals["rps_doc_mean_word_length"][0][2]
    if mean_word_length < 3 or mean_word_length > 10:
        return False

    # rule 2: symbol to word ratio below 0.1
    symbol_word_ratio = signals["rps_doc_symbol_to_word_ratio"][0][2]
    if symbol_word_ratio > 0.1:
        return False

    # rule 3: 90% of lines need to start without a bullet point
    n_lines = signals["ccnet_nlines"][0][2]
    n_lines_bulletpoint_start = sum(map(lambda ln: ln[2], signals["rps_lines_start_with_bulletpoint"]))
    if n_lines_bulletpoint_start / n_lines > 0.9:
        return False

    # rule 4: the ratio between characters in the most frequent 2-gram and the total number 
    # of characters must be below 0.2
    top_2_gram_frac = signals["rps_doc_frac_chars_top_2gram"][0][2]
    if top_2_gram_frac > 0.2:
        return False

    # rule 5: ...

    return True

Filtering the RedPajama-V2 dataset with this set of rules is then as easy as:

ds_iterator = load_dataset(
    "togethercomputer/RedPajama-Data-V2",
    snapshots=["2023-14"],
    languages=["en"],
    name="default",
    streaming=True
)

filtered_dataset = []

for sample in ds_iterator["train"]:

    if not gopher_rules_pass(sample):
        continue

    filtered_dataset.append(sample)

Dataset Summary

RedPajama-V2 is an open dataset for training large language models and includes over 100B text documents. Out of these, 30B documents come with quality annotations. Out of these, there are 20B unique documents.

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 line-level deduplication	CCNet	CCNet
ccnet_original_nlines	number of lines before line-level 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
minhash_signature_0.7	Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.7. The signature is based on 128 hash functions and grouped into 14 bands and 9 rows for LSH.	Deduplication
minhash_signature_0.8	Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.8. The signature is based on 128 hash functions and grouped into 9 bands and 13 rows for LSH.	Deduplication
minhash_signature_0.9	Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.9. The signature is based on 128 hash functions and grouped into 5 bands and 25 rows for LSH..	Deduplication
minhash_signature_1.0	Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 1.0. The signature is based on 128 hash functions and grouped into 1 band and 128 rows for LSH.	Deduplication

The quality signal rps_doc_ut1_blacklist is given by a categorical id indicating the UT1 blacklisted domain categories to which the domain of the document belongs. The mapping id -> [category_1, ..., category_k] is given in ut1_domain_categories.json. It can also be downloaded from this link.

Raw Document and Token Counts (head_middle)

	# Documents (deduped)	Estimated Token count (deduped)
en	24.5B	37.0T
de	2.7B	4.1T
fr	2.2B	3.7T
es	2.3B	3.9T
it	1.2B	1.9T
Total	32.9B	50.6T

Deduplicated Document and Token Counts (head_middle)

	# Documents (total)	Estimated Token count (total)
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, please use:

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

You can also cite the paper describing this dataset

@misc{weber2024redpajamaopendatasettraining,
      title={RedPajama: an Open Dataset for Training Large Language Models}, 
      author={Maurice Weber and Daniel Fu and Quentin Anthony and Yonatan Oren and Shane Adams and Anton Alexandrov and Xiaozhong Lyu and Huu Nguyen and Xiaozhe Yao and Virginia Adams and Ben Athiwaratkun and Rahul Chalamala and Kezhen Chen and Max Ryabinin and Tri Dao and Percy Liang and Christopher Ré and Irina Rish and Ce Zhang},
      year={2024},
      eprint={2411.12372},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.12372}, 
}

Acknowledgements

We are appreciative to so many partners and collaborators that together are pushing forward the frontier of open LLM models.

• Thank you to the OLMo team at AI2 and friends at OpenGPT-X for the insightful discussions about datasets and data quality! Also for everyone who builds on the RedPajama dataset, including Cerebras for their SlimPajama efforts, and the over 500 models built on RedPajam to date by the open-source AI community.
• We are grateful to the great team at EleutherAI for paving the path on open training datasets with The Pile and for open-sourcing code we use in training some of the RedPajama models.
• Thank you to our partners of RedPajama-v1, including Ontocord.ai, MILA Québec AI Institute, ETH DS3Lab, Université de Montréal, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.

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.