README.md

---
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.

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