Update README.md

README.md

@@ -21,8 +21,29 @@ more filtered, it may lack the recall needed for some applications.
 There are two versions released: the **noisy** dataset, which has no filtering
 except document-level LangID, and the **clean** dataset, which has a variety of
 filters applied, though it naturally has a fair amount of noise itself. Each
-dataset is released in both a document-level form and a sentence-level form;
-both are deduplicated.
+dataset is released in a document-level form that has been deduplicated.
+
+## Loading
+
+You can load both the clean and noisy versions of any language by specifing its LangID:
+
+~~~
+madlad_ape = load_dataset("allenai/madlad-400", "ape")
+~~~
+
+A list of langagues can also be supplied with a keyword argument:
+
+~~~
+madlad_multilang = load_dataset("allenai/madlad-400", languages=["ape", "abt", "ace"])
+~~~
+
+Additionally, you can load the noisy and clean subsets seperately with the split keyword argument:
+
+~~~
+madlad_multilang_clean = load_dataset("allenai/madlad-400", languages=["ape", "abt", "ace"], split="clean")
+~~~
+
+
 
 ## LangID model and Crawl
 
@@ -930,12 +951,14 @@ A few comments too long to fit in the table above:
 The number of documents, sentences, tokens, characters, and bytes for the noisy
 and clean splits of the data. Note that the "toks" field below uses whitespace
 for tokenization, so is not appropriate for non-whitespace-separating languages
-like Chinese (see section above).
+like Chinese (see section above). Note that the english subset in this version of
+is missing 18% of documents that were included in the published analysis of the dataset.
+These documents will be added in an update coming soon.
 
 BCP-47          | docs (noisy)   | docs (clean)   | sents (noisy)   | sents (clean)   | toks (noisy)   | toks (clean)   | chars (noisy)   | chars (clean)   | clean    | noisy    |
 ----------------|:---------------|:---------------|:----------------|:----------------|:---------------|:---------------|:----------------|:----------------|:---------|:---------|
-total           | 7.2B           | 3.7B           | 133.1B          | 97.5B           | 4.6T           | 2.6T           | 30.6T           | 16.0T           | 11.4 T   | 6.3 T
-en              | 3.0B           | 1.5B           | 71.1B           | 45.4B           | 2.0T           | 1.3T           | 12.3T           | 7.6T            | 2.6 T    | 4.3 T    |
+total*          | 7.2B           | 3.7B           | 133.1B          | 97.5B           | 4.6T           | 2.6T           | 30.6T           | 16.0T           | 11.4 T   | 6.3 T
+en*             | 3.0B           | 1.5B           | 71.1B           | 45.4B           | 2.0T           | 1.3T           | 12.3T           | 7.6T            | 2.6 T    | 4.3 T    |
 ru              | 823M           | 402.5M         | 823M            | 12.4B           | 416.5B         | 240.9B         | 3.1T            | 1.8T            | 832.9 G  | 1.4 T    |
 es              | 476.4M         | 250.9M         | 8.3B            | 4.5B            | 325.7B         | 170.4B         | 2.1T            | 1.1T            | 380.9 G  | 747.5 G  |
 de              | 478.6M         | 225.1M         | 11.5B           | 6B              | 299.5B         | 139.6B         | 2.2T            | 1T              | 370.6 G  | 815.5 G  |