Renamed validation files back to '-validation'

README.md

@@ -94,7 +94,7 @@ In summary, the preprocessing procedure includes:
    - Not identified as prevalently Dutch by the `LangDetect` package.
 
 Using parallel processing with 96 CPU cores on a TPUv3 via Google Cloud to perform the complete clean of all the original Dutch
-shards of mC4 (1024 of ~220Mb train, 8 of ~24Mb validation) required roughly 10 hours due to the demanding steps of sentence
+shards of mC4 (1024 of ~220Mb train, 4 of ~24Mb validation) required roughly 10 hours due to the demanding steps of sentence
 tokenization and language detection. The total size of compressed `.json.gz` files is roughly halved after the procedure.
 
 ## Dataset Structure
@@ -121,13 +121,16 @@ The data contains the following fields:
 
 ### Data Splits
 
-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-it.tfrecord-0XXXX-of-01024.json.gz` and 8 for validation following the naming style `c4-it-validation.tfrecord-0000X-of-00008.json.gz`. The full set of preprocessed files takes roughly 215GB of disk space to download with Git LFS.
+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.
 
 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 :
 
 |split |train size (docs, words, download + preproc disk space)|validation size|
 |:-----|------------------------------------------------------:|--------------:|
-|tiny  | 10M docs, 4B words (9 GB + 27 GB)                     | 12k docs      |
+|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      |
@@ -139,6 +142,22 @@ You can load any subset like this:
 from datasets import load_dataset
 
 datasets = load_dataset('yhavinga/mc4_nl_cleaned', 'tiny', streaming=True)
+print(datasets)
+```
+
+Yields output
+
+```
+DatasetDict({
+    train: Dataset({
+        features: ['text', 'timestamp', 'url'],
+        num_rows: 6303893
+    })
+    validation: Dataset({
+        features: ['text', 'timestamp', 'url'],
+        num_rows: 16189
+    })
+})
 ```
 
 Since splits are quite large, you may want to traverse them using the streaming mode available starting from — Datasets v1.9.0:

mc4_nl_cleaned.py

@@ -49,11 +49,11 @@ _HOMEPAGE = "https://github.com/allenai/allennlp/discussions/5056"
 
 _LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0"
 
-_BASE_URL = "https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/resolve/main/mc4_nl_cleaned/{split}/c4-nl-cleaned.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
+_BASE_URL = "https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/resolve/main/mc4_nl_cleaned/{split}/c4-nl{validation}-cleaned.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
 
 _CONFIGS = dict(
     tiny={"train": 100, "validation": 1},
-    small={"train": 250, "validation": 2},
+    small={"train": 250, "validation": 1},
     medium={"train": 500, "validation": 2},
     large={"train": 750, "validation": 3},
     full={"train": 1024, "validation": 4},
@@ -150,6 +150,7 @@ class Mc4(datasets.GeneratorBasedBuilder):
                 _BASE_URL.format(
                     split=split,
                     index=index,
+                    validation="-validation" if split=="validation" else "",
                     n_shards=4 if split == "validation" else 1024,
                 )
                 for index in range(_CONFIGS[self.config.name][split])