init

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text

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dataset.py

@@ -0,0 +1,59 @@
+# HuggingFace loading script for generic sprucfluo datasets
+# This script was automatically generated by convert_hf_to_sprucfluo
+import json
+import pathlib
+
+import datasets
+import fsspec
+from datasets import DatasetInfo, Value, Features
+
+logger = datasets.logging.get_logger(__name__)
+
+_INFO = DatasetInfo(
+            description='Automatically generated for wikitext (wikitext-103-raw-v1), split into 8 shards, detokenized.\n\nOriginal Description:\n The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n',
+            citation='@misc{merity2016pointer,\n      title={Pointer Sentinel Mixture Models},\n      author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n      year={2016},\n      eprint={1609.07843},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n',
+            homepage='https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/',
+            license='Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)',
+            version="1.0.0",
+            features=Features.from_dict({'text': {'dtype': 'string', 'id': None, '_type': 'Value'}}),
+            supervised_keys=None)
+
+
+class AutoDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [datasets.BuilderConfig()]
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def _info(self):
+        return _INFO
+
+    @property
+    def dataset_dir(self):
+        return pathlib.Path(__file__).parent
+
+    def _split_generators(self, dl_manager):
+        metadata = json.load(open(dl_manager.download("metadata.json"), 'rt'))
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={"filepaths": dl_manager.download(split_metadata["files"])},
+            )
+            for split, split_metadata in metadata["splits"].items()
+        ]
+
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_: int = 0
+        for filepath in filepaths:
+            logger.info(f"Generating examples from {filepath}")
+            with fsspec.open(filepath, mode="rt", compression="infer", encoding="utf-8") as f:
+                for line in f:
+                    if line:
+                        example = json.loads(line)
+                        yield id_, example
+                        id_ += 1
+
+
+if __name__ == "__main__":
+    AutoDataset().download_and_prepare()

metadata.json

@@ -0,0 +1,27 @@
+{
+  "name": "wikitext-wikitext-103-raw-v1",
+  "splits": {
+    "test": {
+      "files": [
+        "data/test/test.jsonl.zst"
+      ]
+    },
+    "train": {
+      "files": [
+        "data/train/train_0_of_8.jsonl.zst",
+        "data/train/train_1_of_8.jsonl.zst",
+        "data/train/train_2_of_8.jsonl.zst",
+        "data/train/train_3_of_8.jsonl.zst",
+        "data/train/train_4_of_8.jsonl.zst",
+        "data/train/train_5_of_8.jsonl.zst",
+        "data/train/train_6_of_8.jsonl.zst",
+        "data/train/train_7_of_8.jsonl.zst"
+      ]
+    },
+    "validation": {
+      "files": [
+        "data/validation/validation.jsonl.zst"
+      ]
+    }
+  }
+}