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@@ -21,6 +21,11 @@ used to create a dataset with 20B deduplicated documents.
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  Check out our [blog post](https://together.ai/blog/redpajama-data-v2) for more details on the build process, dataset
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  structure and schema.
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  To familiarize yourself with the dataset, you can load the sample dataset using:
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  ```python
@@ -29,10 +34,13 @@ from datasets import load_dataset
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  ds = load_dataset("togethercomputer/RedPajama-Data-V2", name="sample")
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  ```
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- To download a the dataset for a specific combination of `{partition} x {snapshot_id} x {language}` (e.g., English and
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- German data from the `head_middle` partition of the 2023-06 and the 2022-49 dumps), you can run the following command
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- which downloads the raw (i.e., not deduplicated) part of the dataset.
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- _Note that this will download the entire dumps and requires ~1TB disk space per dump_.
 
 
 
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  ```python
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  from datasets import load_dataset
@@ -44,10 +52,29 @@ ds = load_dataset("togethercomputer/RedPajama-Data-V2",
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  languages=["en", "de"])
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  ```
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- Alternatively, you can also directly download the files using the following instructions, using English data from the
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- `2023-06` snapshot and the `head_middle` partition as an example. The full set of CC snapshots included in the dataset
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- is given in `_CC_SNAPSHOT_IDS`, and the available partitions are `tail` and `head_middle`. The available language tags
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- are `en`, `de`, `fr`, `es`, `it`.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  To download the plain text data, available for both the `head_middle` and `tail` partitions, you can run
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@@ -106,9 +133,6 @@ for comp in "${COMPS[@]}"; do
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  done
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  ```
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- A full set of scripts to recreate the dataset, including the quality signals, can be
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- found [here](https://github.com/togethercomputer/RedPajama-Data).
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-
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  ### Applying Filtering Rules
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  You can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider
 
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  Check out our [blog post](https://together.ai/blog/redpajama-data-v2) for more details on the build process, dataset
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  structure and schema.
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+ A full set of scripts to recreate the dataset, including the quality signals, can be
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+ found [here](https://github.com/togethercomputer/RedPajama-Data).
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+
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+ #### Downloading the raw Dataset with Quality Annotations
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+
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  To familiarize yourself with the dataset, you can load the sample dataset using:
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  ```python
 
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  ds = load_dataset("togethercomputer/RedPajama-Data-V2", name="sample")
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  ```
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+ To download a the dataset for a specific combination of `{partition} x {snapshot_id} x {language}`, you can use the
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+ following command which downloads the raw (i.e., *not* deduplicated) part of the dataset and the corresponding quality
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+ signals. In the example below, we use English and German data from the `head_middle` partition of the 2023-06 and the
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+ 2022-49 snapshots. The full set of available snapshots is specified in `_CC_SNAPSHOT_IDS`. The available partitions
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+ are `tail` and `head_middle`. The available language tags are `en`, `de`, `fr`, `es`, `it`.
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+ _Note that this will download the entire snapshots specified in the `snapshots` argument and requires ~1TB of disk space
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+ per snapshot_.
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  ```python
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  from datasets import load_dataset
 
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  languages=["en", "de"])
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  ```
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+ #### Downloading the dataset via wget
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+
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+ If you prefer to download the full dataset via wget, you can download the following lists of urls and use them to
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+ download the dataset:
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+
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+ ```bash
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+ # get list of urls pointing to the text documents
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+ wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/document-urls.txt" -O "document-urls.txt"
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+
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+ # get list of urls pointing to the quality signals
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+ wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/quality_signals-urls.txt" -O "quality_signals-urls.txt"
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+
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+ # get list of urls pointing to the ids of duplicate documents
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+ wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/duplicates-urls.txt" -O "duplicates-urls.txt"
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+
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+ # get list of urls pointing to the minhash signatures
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+ wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/minhash-urls.txt" -O "minhash-urls.txt"
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+ ```
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+
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+ You can also directly download subsets of the dataset using the following instructions. Here we use English
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+ data from the `2023-06` snapshot and the `head_middle` partition as an example. The full set of CC snapshots included in
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+ the dataset is given in `_CC_SNAPSHOT_IDS`. The available partitions are `tail` and `head_middle`. The available
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+ language tags are `en`, `de`, `fr`, `es`, `it`.
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  To download the plain text data, available for both the `head_middle` and `tail` partitions, you can run
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  done
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  ```
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  ### Applying Filtering Rules
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  You can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider