Add _en_nl configs that interleave english with dutch documents

README.md

@@ -125,15 +125,18 @@ For Dutch, the whole corpus of scraped text was divided in `1032` jsonl files, `
 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 208GB of disk space to download with Git LFS.
 
-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, 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      |
+For ease of use under different storage capacities, the following incremental configs are available: (note: files on disk are compressed)
+
+| subset | train size (docs, words, download + preproc disk space) | validation size |
+|:-------|--------------------------------------------------------:|----------------:|
+| 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 |
+
+For each subset as `tiny` there also exists a config `tiny_en_nl` that interleaves examples from the cleaned
+`en` variant of C4.
 
 You can load any subset like this:
 

mc4_nl_cleaned.py

@@ -19,6 +19,7 @@ import json
 import gzip
 import textwrap
 import datasets
+from itertools import zip_longest
 
 logger = datasets.logging.get_logger(__name__)
 
@@ -49,24 +50,30 @@ _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{validation}-cleaned.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
+_DATA_URL_NL = "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"
+_DATA_URL_EN = "https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/{name}/c4-{split}.{index:05d}-of-{n_shards:05d}.json.gz"
+_C4_EN_VARIANT = "en"
+
+_CONFIG_NAMES = ["micro", "tiny", "small", "medium", "large", "full"]
+_CONFIG_EN_NL_SUFFIX = "_en_nl"
 
 _CONFIGS = dict(
-    tiny={"train": 100, "validation": 1},
-    small={"train": 250, "validation": 1},
-    medium={"train": 500, "validation": 2},
-    large={"train": 750, "validation": 3},
-    full={"train": 1024, "validation": 4},
-    tiny_0={"start": 0, "train": 100, "validation": 1},
-    tiny_1={"start": 100, "train": 100, "validation": 1},
-    tiny_2={"start": 200, "train": 100, "validation": 1},
-    tiny_3={"start": 300, "train": 100, "validation": 1},
-    tiny_4={"start": 400, "train": 100, "validation": 1},
-    tiny_5={"start": 500, "train": 100, "validation": 1},
-    tiny_6={"start": 600, "train": 100, "validation": 1},
-    tiny_7={"start": 700, "train": 100, "validation": 1},
-    tiny_8={"start": 800, "train": 100, "validation": 1},
-    tiny_9={"start": 900, "train": 100, "validation": 1},
+    micro={"train": 2, "validation": 1, "estimate": "1GB"},
+    tiny={"train": 100, "validation": 1, "estimate": "10GB"},
+    small={"train": 250, "validation": 1, "estimate": "25GB"},
+    medium={"train": 500, "validation": 2, "estimate": "50GB"},
+    large={"train": 750, "validation": 3, "estimate": "75GB"},
+    full={"train": 1024, "validation": 4, "estimate": "103GB"},
+    tiny_0={"start": 0, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_1={"start": 100, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_2={"start": 200, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_3={"start": 300, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_4={"start": 400, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_5={"start": 500, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_6={"start": 600, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_7={"start": 700, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_8={"start": 800, "train": 100, "validation": 1, "estimate": "10GB"},
+    tiny_9={"start": 900, "train": 100, "validation": 1, "estimate": "10GB"},
 )
 
 
@@ -86,55 +93,16 @@ class Mc4(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         Mc4NlCleanedConfig(
-            name="tiny",
+            name=name,
             version=datasets.Version("1.0.0"),
             description=textwrap.dedent(
                 f"""\
-                A tiny cleaned version of the Dutch portion of the multilingual C4 corpus.
-                Estimated size of compressed files: 10GB
-                """
+            A {name} cleaned version of the Dutch portion of the multilingual C4 corpus.
+            Estimated size of compressed files: {_CONFIGS[name]['estimate']}
+            """
             ),
-        ),
-        Mc4NlCleanedConfig(
-            name="small",
-            version=datasets.Version("1.0.0"),
-            description=textwrap.dedent(
-                f"""\
-                A small cleaned version of the Dutch portion of the multilingual C4 corpus.
-                Estimated size of compressed files: 25GB
-                """
-            ),
-        ),
-        Mc4NlCleanedConfig(
-            name="medium",
-            version=datasets.Version("1.0.0"),
-            description=textwrap.dedent(
-                f"""\
-                A medium cleaned version of the Dutch portion of the multilingual C4 corpus.
-                Estimated size of compressed files: 50GB
-                """
-            ),
-        ),
-        Mc4NlCleanedConfig(
-            name="large",
-            version=datasets.Version("1.0.0"),
-            description=textwrap.dedent(
-                f"""\
-                A large cleaned version of the Dutch portion of the multilingual C4 corpus.
-                Estimated size of compressed files: 75GB
-                """
-            ),
-        ),
-        Mc4NlCleanedConfig(
-            name="full",
-            version=datasets.Version("1.0.0"),
-            description=textwrap.dedent(
-                f"""\
-                The full cleaned version of the Dutch portion of the multilingual C4 corpus.
-                Estimated size of compressed files: 103GB
-                """
-            ),
-        ),
+        )
+        for name in _CONFIG_NAMES
     ]
 
     for i in range(10):
@@ -151,6 +119,19 @@ class Mc4(datasets.GeneratorBasedBuilder):
             ),
         )
 
+    BUILDER_CONFIGS += [
+        Mc4NlCleanedConfig(
+            name=f"{name}{_CONFIG_EN_NL_SUFFIX}",
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                f"""\
+            A {name} cleaned version of the Dutch and English portion of the multilingual C4 corpus.
+            """
+            ),
+        )
+        for name in _CONFIG_NAMES
+    ]
+
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -169,20 +150,31 @@ class Mc4(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         data_urls = {}
+        config = _CONFIGS[self.config.name.replace(_CONFIG_EN_NL_SUFFIX, "")]
         for split in ["train", "validation"]:
-            start_file = (
-                _CONFIGS[self.config.name].get("start", 0) if split == "train" else 0
-            )
-            num_files = _CONFIGS[self.config.name].get(split)
-            data_urls[split] = [
-                _BASE_URL.format(
-                    split=split,
-                    index=index,
-                    validation="-validation" if split == "validation" else "",
-                    n_shards=4 if split == "validation" else 1024,
+            start_file = config.get("start", 0) if split == "train" else 0
+            num_files = config.get(split)
+
+            data_urls[split] = []
+            for index in range(start_file, start_file + num_files):
+                data_urls[split].append(
+                    _DATA_URL_NL.format(
+                        split=split,
+                        index=index,
+                        validation="-validation" if split == "validation" else "",
+                        n_shards=4 if split == "validation" else 1024,
+                    )
                 )
-                for index in range(start_file, start_file + num_files)
-            ]
+                if self.config.name.endswith(_CONFIG_EN_NL_SUFFIX):
+                    data_urls[split].append(
+                        _DATA_URL_EN.format(
+                            name=_C4_EN_VARIANT,
+                            split=split,
+                            index=index,
+                            validation="-validation" if split == "validation" else "",
+                            n_shards=8 if split == "validation" else 1024,
+                        )
+                    )
         train_downloaded_files = dl_manager.download(data_urls["train"])
         validation_downloaded_files = dl_manager.download(data_urls["validation"])
         return [
@@ -196,14 +188,27 @@ class Mc4(datasets.GeneratorBasedBuilder):
             ),
         ]
 
+    @staticmethod
+    def grouper(iterable, n, fillvalue=None):
+        """Collect data into fixed-length chunks or blocks"""
+        # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
+        args = [iter(iterable)] * n
+        return zip_longest(*args, fillvalue=fillvalue)
+
     def _generate_examples(self, filepaths):
         """This function returns the examples in the raw (text) form by iterating on all the files."""
         id_ = 0
-        for filepath in filepaths:
-            logger.info(f"Generating examples from {filepath}")
-            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
-                for line in f:
-                    if line:
-                        example = json.loads(line)
+        for filepath1, filepath2 in self.grouper(filepaths, 2, None):
+            logger.info(f"Generating examples from {filepath1} and {filepath2}")
+            with gzip.open(
+                open(filepath1, "rb"), "rt", encoding="utf-8"
+            ) as f1, gzip.open(open(filepath2, "rb"), "rt", encoding="utf-8") as f2:
+                for line1, line2 in zip(f1, f2):
+                    if line1:
+                        example = json.loads(line1)
+                        yield id_, example
+                        id_ += 1
+                    if line2:
+                        example = json.loads(line2)
                         yield id_, example
                         id_ += 1