togethercomputer / 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 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 part of the dataset The number of documents and tokens for the annotated and deduplicated 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 to e.g. and configure the environment variables. These will be used throughout the pipeline. Buid Docker image To run with docker, build the docker image using Also, make sure you have 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 environment variable to a consistent value (e.g., 42) using This is to ensure consistency of hash functions used in the computation of DSIR weights. • 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 . 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 where is a file that contains the keys to the ccnet data that you want to process (e.g., ). You can set the 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 for the next step. • 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 • 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 . It can be run independently of the previous step, but the data needs to stored in an S3 bucket. For this step, from the directory, run: It is important to choose the correct capacity (i.e., > #documents), since otherwise the 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 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…