Update README.md

README.md

@@ -61,7 +61,7 @@ paperswithcode_id: mc4
 
 ### Dataset Summary
 
-A thoroughly cleaned version of the Dutch split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4).
+A cleaned version (151GB) of the Dutch split (277GB) of the C4 multilingual dataset (mC4).
 Based on the [Common Crawl dataset](https://commoncrawl.org).
 The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4).
 
@@ -78,7 +78,7 @@ In summary, the preprocessing procedure includes:
  
    - Less than 3 words.
    
-   - A word longer than 1000 characters.
+   - A word longer than 250 characters.
    
    - An end symbol not matching end-of-sentence punctuation.
    
@@ -123,17 +123,17 @@ The data contains the following fields:
 To build mC4, the original authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages.
 For Dutch, the whole corpus of scraped text was divided in `1032` jsonl files, `1024` for training following
 the naming style `c4-nl-cleaned.tfrecord-0XXXX-of-01024.json.gz` and 4 for validation following the
-naming style `c4-nl-cleaned.tfrecord-0000X-of-00004.json.gz`. The full set of preprocessed files takes roughly 215GB of disk space to download with Git LFS.
+naming style `c4-nl-cleaned.tfrecord-0000X-of-00004.json.gz`. The full set of preprocessed files takes roughly 208GB of disk space to download with Git LFS.
 
-For ease of use under different storage capacities, the following incremental splits are available (sizes are estimates). **Important**: The sizes in GB represent the estimated weight for :
+For ease of use under different storage capacities, the following incremental splits are available: (note: files on disk are compressed)
 
 |split |train size (docs, words, download + preproc disk space)|validation size|
 |:-----|------------------------------------------------------:|--------------:|
-|tiny  | 6M docs, 4B words (9 GB + 27 GB)                      | 16k docs      |
-|small | 20M docs, 8B words (18 GB + 54 GB)                    | 24k docs      |
-|medium| 50M docs, 20B words (47 GB + 135 GB)                  | 48k docs      |
-|large | 75M docs, 30B words (71 GB + 203 GB)                  | 72k docs      |
-|full  | 103M docs, 41B words (109 GB + 279 GB)                | 96k docs      |
+|tiny  | 6M docs, 2B words (6 GB + 15 GB)                      | 16k docs      |
+|small | 15M docs, 6B words (14 GB + 36 GB)                    | 16k docs      |
+|medium| 31M docs, 12B words (28 GB + 72 GB)                   | 32k docs      |
+|large | 47M docs, 19B words (42 GB + 108 GB)                  | 48k docs      |
+|full  | 64M docs, 25B words (58 GB + 148 GB)                  | 64k docs      |
 
 You can load any subset like this:
 
@@ -176,14 +176,14 @@ Refer to the original paper for more considerations regarding the choice of sour
 
 ### Social Impact of Dataset
 
-With more than 200GB of cleaned Dutch text and more than 41B estimated words, this is by far the largest available corpus for the Dutch language.
-The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 39GB in size for its deduplicated variant.
+With more than 151GB (58GB compressed) of cleaned Dutch text and more than 23B estimated words, this is by far the largest available cleaned corpus for the Dutch language.
+The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 39GB in size for its deduplicated variant, and contains vulgarity.
 Using this corpus for training language models with adequate computational resources will allow researchers to reach parity with the performances observed for the English language.
 This can in turn have important repercussions for the development of commercial language technology applications for the Dutch language.
 
 ### Discussion of Biases
 
-Despit the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will
+Despite the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will
 inevitably reflect biases present in blog articles and comments on the Internet.
 This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.