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dataset_infos.json

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+{"wikitext-103-v1": {"description": " 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)", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-103-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1295579, "num_examples": 4358, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 545142639, "num_examples": 1801350, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1154755, "num_examples": 3760, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip": {"num_bytes": 190229076, "checksum": "242ba0f20b329cfdf1ccc61e9e9e5b59becf189db7f7a81cd2a0e2fc31539590"}}, "download_size": 190229076, "post_processing_size": null, "dataset_size": 547592973, "size_in_bytes": 737822049}, "wikitext-2-v1": {"description": " 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)", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-2-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1270951, "num_examples": 4358, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 10918134, "num_examples": 36718, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1134127, "num_examples": 3760, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip": {"num_bytes": 4475746, "checksum": "92675f1d63015c1c8b51f1656a52d5bdbc33aafa60cc47a218a66e7ee817488c"}}, "download_size": 4475746, "post_processing_size": null, "dataset_size": 13323212, "size_in_bytes": 17798958}, "wikitext-103-raw-v1": {"description": " 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)", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-103-raw-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1305092, "num_examples": 4358, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 546501673, "num_examples": 1801350, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1159292, "num_examples": 3760, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip": {"num_bytes": 191984949, "checksum": "91c00ae287f0d699e18605c84afc9e45c192bc6b7797ff8837e5474655a33794"}}, "download_size": 191984949, "post_processing_size": null, "dataset_size": 548966057, "size_in_bytes": 740951006}, "wikitext-2-raw-v1": {"description": " 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)", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-2-raw-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1305092, "num_examples": 4358, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 11061733, "num_examples": 36718, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1159292, "num_examples": 3760, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip": {"num_bytes": 4721645, "checksum": "ef7edb566e3e2b2d31b29c1fdb0c89a4cc683597484c3dc2517919c615435a11"}}, "download_size": 4721645, "post_processing_size": null, "dataset_size": 13526117, "size_in_bytes": 18247762}}

gitattributes.txt

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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wikitext.py

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+"""TODO(wikitext): Add a description here."""
+
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{merity2016pointer,
+      title={Pointer Sentinel Mixture Models},
+      author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
+      year={2016},
+      eprint={1609.07843},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+ The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
+ Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike
+ License.
+"""
+_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)"
+_DATA_URL = "https://s3.amazonaws.com/research.metamind.io/wikitext"
+
+
+class WikitextConfig(datasets.BuilderConfig):
+    """BuilderConfig for GLUE."""
+
+    def __init__(self, data_url, **kwargs):
+        """BuilderConfig for Wikitext
+
+        Args:
+          data_url: `string`, url to the dataset (word or raw level)
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(WikitextConfig, self).__init__(
+            version=datasets.Version(
+                "1.0.0",
+            ),
+            **kwargs,
+        )
+        self.data_url = data_url
+
+
+class Wikitext(datasets.GeneratorBasedBuilder):
+    """TODO(wikitext_103): Short description of my dataset."""
+
+    # TODO(wikitext_103): Set up version.
+    VERSION = datasets.Version("0.1.0")
+    BUILDER_CONFIGS = [
+        WikitextConfig(
+            name="wikitext-103-v1",
+            data_url=_DATA_URL + "/" + "wikitext-103-v1.zip",
+            description="Word level dataset. No processing is needed other than replacing newlines with <eos> tokens.",
+        ),
+        WikitextConfig(
+            name="wikitext-2-v1",
+            data_url=_DATA_URL + "/" + "wikitext-2-v1.zip",
+            description="Word level dataset. No processing is needed other than replacing newlines with <eos> tokens.",
+        ),
+        WikitextConfig(
+            name="wikitext-103-raw-v1",
+            data_url=_DATA_URL + "/" + "wikitext-103-raw-v1.zip",
+            description="Raw level dataset: the raw tokens before the addition of <unk> tokens. "
+            "They should only be used for character level work or for creating newly derived datasets.",
+        ),
+        WikitextConfig(
+            name="wikitext-2-raw-v1",
+            data_url=_DATA_URL + "/" + "wikitext-2-raw-v1.zip",
+            description="Raw level dataset: the raw tokens before the addition of <unk> tokens. "
+            "They should only be used for character level work or for creating newly derived datasets.",
+        ),
+    ]
+
+    def _info(self):
+        # TODO(wikitext): Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string")
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(wikitext): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        if self.config.name == "wikitext-103-v1":
+            data_file = dl_manager.download_and_extract(self.config.data_url)
+            data_dir = os.path.join(data_file, "wikitext-103")
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"data_file": os.path.join(data_dir, "wiki.test.tokens"), "split": "test"},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"data_file": os.path.join(data_dir, "wiki.train.tokens"), "split": "train"},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"data_file": os.path.join(data_dir, "wiki.valid.tokens"), "split": "valid"},
+                ),
+            ]
+        else:
+            if self.config.name == "wikitext-103-raw-v1":
+                data_file = dl_manager.download_and_extract(self.config.data_url)
+                data_dir = os.path.join(data_file, "wikitext-103-raw")
+                return [
+                    datasets.SplitGenerator(
+                        name=datasets.Split.TEST,
+                        gen_kwargs={"data_file": os.path.join(data_dir, "wiki.test.raw"), "split": "test"},
+                    ),
+                    datasets.SplitGenerator(
+                        name=datasets.Split.TRAIN,
+                        gen_kwargs={"data_file": os.path.join(data_dir, "wiki.train.raw"), "split": "train"},
+                    ),
+                    datasets.SplitGenerator(
+                        name=datasets.Split.VALIDATION,
+                        gen_kwargs={"data_file": os.path.join(data_dir, "wiki.valid.raw"), "split": "valid"},
+                    ),
+                ]
+            else:
+                if self.config.name == "wikitext-2-raw-v1":
+                    data_file = dl_manager.download_and_extract(self.config.data_url)
+                    data_dir = os.path.join(data_file, "wikitext-2-raw")
+                    return [
+                        datasets.SplitGenerator(
+                            name=datasets.Split.TEST,
+                            gen_kwargs={"data_file": os.path.join(data_dir, "wiki.test.raw"), "split": "test"},
+                        ),
+                        datasets.SplitGenerator(
+                            name=datasets.Split.TRAIN,
+                            gen_kwargs={"data_file": os.path.join(data_dir, "wiki.train.raw"), "split": "train"},
+                        ),
+                        datasets.SplitGenerator(
+                            name=datasets.Split.VALIDATION,
+                            gen_kwargs={"data_file": os.path.join(data_dir, "wiki.valid.raw"), "split": "valid"},
+                        ),
+                    ]
+                else:
+                    if self.config.name == "wikitext-2-v1":
+                        data_file = dl_manager.download_and_extract(self.config.data_url)
+                        data_dir = os.path.join(data_file, "wikitext-2")
+                        return [
+                            datasets.SplitGenerator(
+                                name=datasets.Split.TEST,
+                                gen_kwargs={"data_file": os.path.join(data_dir, "wiki.test.tokens"), "split": "test"},
+                            ),
+                            datasets.SplitGenerator(
+                                name=datasets.Split.TRAIN,
+                                gen_kwargs={
+                                    "data_file": os.path.join(data_dir, "wiki.train.tokens"),
+                                    "split": "train",
+                                },
+                            ),
+                            datasets.SplitGenerator(
+                                name=datasets.Split.VALIDATION,
+                                gen_kwargs={
+                                    "data_file": os.path.join(data_dir, "wiki.valid.tokens"),
+                                    "split": "valid",
+                                },
+                            ),
+                        ]
+
+    def _generate_examples(self, data_file, split):
+
+        """Yields examples."""
+        # TODO(wikitext): Yields (key, example) tuples from the dataset
+        with open(data_file, encoding="utf-8") as f:
+            for idx, row in enumerate(f):
+                if row.strip():
+                    yield idx, {"text": row}
+                else:
+                    yield idx, {"text": ""}