--- 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](https://github.com/facebookresearch/cc_net) pipeline. Out of these, there are 30B documents in the corpus that additionally come with quality signals. Check out our [blog post](XXXXX) for more details on the build process, dataset structure and schema. To familiarize yourself with the dataset, you can load the sample dataset using: ```python 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 ```python 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`. ```bash 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](https://github.com/togethercomputer/RedPajama-Data). ### 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 ```json { "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](https://commoncrawl.org/terms-of-use) for the data. The code used to load and process the dataset is licensed under the Apache 2.0 license.