RedPajama-Data

RedPajama-Data-v2: an Open Dataset with 30 Trillion Tokens for Training Large Language Models

This repository contains the code for the RedPajama-V2 dataset. For more information on the dataset, check out our blog post. The dataset is also available on HuggingFace. For the code used for the RedPajama-1T dataset, please refer to the rp_v1 branch in this repo.

Dataset

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.

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

The number of documents and tokens for the annotated and deduplicated head_middle part of the dataset is shown in the table below.

	# 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

Setup

Configuration

Copy the file configs/rp_v2.0.conf to e.g. configs/default.conf and configure the environment variables. These will be used throughout the pipeline.

Buid Docker image

To run with docker, build the docker image using

. configs/default.conf
cd app
docker build -t "${DOCKER_REPO}:" .

Also, make sure you have s5cmd installed and your S3 profile configured so that you can pull data from an S3 bucket.

You can run the steps of the pipeline without any containerized environment. However, the running scripts assume you have a docker and apptainer installation.

Running the Pipeline

The pipeline is composed of three steps, namely 1) preparing artifacts, 2) computing quality signals, and 3) deduplication.

Important: In case you are not running steps (1) and (2) with the provided scripts (i.e., docker containers built with the provided Dockerfile), make sure to set the PYTHONHASHSEED environment variable to a consistent value (e.g., 42) using

export PYTHONHASHSEED=42

This is to ensure consistency of hash functions used in the computation of DSIR weights.

1. Create Artifacts

This part of the pipeline creates the artifacts that are used in the subsequent steps. This includes building quality classifiers, training bag-of-ngram generative models for importance weight computation, fetching the list of bad words from the LDNOOBW repo, and fetching the most recent list of blacklisted urls from the UT1 blacklist.

As a first step, download the english wikipedia reference classifier from here and place it in ${DATA_ROOT}/wikiref-model/en/en-model.bin. This is the same fasttext classifier that was used in RedPajama-V1.

To create the remaining artifacts, make sure that the environment variables are set in the config file. Then, from the root directory of the repository, run

bash scripts/run_prep_artifacts.sh \
  --config configs/rp_v2.0.conf \
  --listings /path/to/listings/file.txt\
  --max_workers 32

where /path/to/listings/file.txt is a file that contains the keys to the ccnet data that you want to process (e.g., 2023-06/0000/en_head.json.gz).

You can set the max_workers flag to the number of parallel processes you want to use.

This step will generate an id which you can store in the environment variable ARTIFACTS_ID for the next step.

2. Compute Quality Signals

The second step of the pipeline compute the quality signals, including the minhash signatures to run fuzzy deduplication in the subsequent step. To run this step, make sure the environment variables are set in the config file. Then, from the root directory of the repository, run

bash scripts/apptainer_run_quality_signals.sh \
  --config configs/rp_v2.0.conf \
  --dump_id "2022-49" \
  --input_base_uri "file:///path/to/data/root" \
  --output_base_uri "file:///path/to/outout/data/root" \
  --max_docs -1

3. Deduplication

The third component of the pipeline consists of deduplication steps. Here we provide code to run exact and fuzzy deduplication.

Exact Deduplication using a Bloomfilter

Content based deduplication is implemented in app/src/bloomfilter.py. It can be run independently of the previous step, but the data needs to stored in an S3 bucket. For this step, from the app directory, run:

python3 app/src/bloomfilter.py \
  --listings /path/to/listings/file.txt \
  --input_base_uri "s3://path/to/ccnet/data" \
  --output_dir "/path/to/output" \
  --s3_profile "..." \
  --endpoint_url "..." \
  --parallel_readers 32 \
  --batch_size 10 \
  --capacity "..." \
  --error_rate "..."

It is important to choose the correct capacity (i.e., > #documents), since otherwise the error_rate will not be guaranteed and more false positives will appear. The implementation is based on the pybloomfiltermmap3 library.

Fuzzy Deduplication with Locality Sensitive Hashing

In the third step of the pipeline, we run locality sensitive hashing on the minhash signatures generated in the first step. To run this step, make sure that you use the same configuration as in the quality signals step. Then, from the root directory of the repository, run

bash scripts/apptainer_run_lsh.sh \
  --config configs/rp_v2.0.conf \
  --dump_id "2022-49" \
  --input_base_uri "file:///path/to/data/root" \
  --output_dir "/path/to/output" \
  --similarity "<similarity_threshold>" \
  --listings "/minhash/listings/file.txt" \
  --max_docs -1

The implementation is based on polars and was tested with 200M documents on a 64 core machine with 500G of RAM.

Summary of Quality Signals

The second step of this pipeline computes the following set of quality signals. We hope to grow this list further over time as more signals are developed.

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
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

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

Copyright 2023 Together Computer

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

For full terms, see the LICENSE file. If you have any questions, comments, or concerns about licensing please contact us.

For the dataset itself, please refer to the Common Crawl Foundation Terms of Use.

To cite RedPajama, please use:

@article{weber2024redpajama,
	title   = {RedPajama: an Open Dataset for Training Large Language Models},
	author  = {Maurice Weber and Daniel Y. 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},
	journal = {NeurIPS Datasets and Benchmarks Track},
	year    = 2024,
}