update README.md

README.md

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