update README.md

README.md

@@ -18,7 +18,7 @@ snapshots and processed using the [CCNet](https://github.com/facebookresearch/cc
 
 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 with the following command:
+To familiarize yourself with the dataset, you can load the sample dataset using:
 
 ```python
 from datasets import load_dataset
@@ -26,10 +26,22 @@ 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`.
+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"
@@ -66,8 +78,37 @@ found [here](https://github.com/togethercomputer/RedPajama-Data).
 
 ### Dataset Summary
 
-RedPajama-V2 is a data foundation that includes over 100B text documents, out of which 30B documents come with
-quality annotations.
+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
 
@@ -151,7 +192,8 @@ Documents files, which contain the text, folow the schema defined by CCNet, and
 }
 ```
 
-where signal scores are encoded as list of tuple `(start, end, score)`, where `start` and `end` are the locations in the
+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