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prooflang.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""The ProofLang Corpus of arXiv Proofs"""
+
+
+import csv
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@unpublished{prooflang:dataset,
+title = {ProofLang: the Language of arXiv Proofs},
+author = {Henry Hammer and Nanako Noda and Christopher A. Stone},
+year = {2023}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\ The ProofLang Corpus includes over three million
+English-language proofs—558 million words—mechanically extracted from the papers
+(Math, CS, Physics, etc.) posted on arXiv.org between 1992 and 2020. The focus
+of this corpus is written proofs, not the explanatory text that surrounds them,
+and more specifically on the language used in such proofs; mathematical
+content is filtered out, resulting in sentences such as ``Let MATH be
+the restriction of MATH to MATH.'' This dataset reflects how people prefer to
+write informal proofs. It is also amenable to statistical analyses and to
+experiments with  Natural Language Processing (NLP) techniques.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "CC-BY 4.0"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "proofs": "https://huggingface.co/proofcheck/prooflang/proofs.zip",
+    "sentences": "https://huggingface.co/proofcheck/prooflang/sentences.zip",
+    # "proofs": "proofs.zip",
+    # "sentences": "sentences.zip",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class ArxivProofs(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.5.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'proofs')
+    # data = datasets.load_dataset('my_dataset', 'sentences')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="proofs", version=VERSION, description="One proof per line"),
+        datasets.BuilderConfig(name="sentences", version=VERSION, description="One sentence per line"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "proofs"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "proofs":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "fileID": datasets.Value("string"),
+                    "proof": datasets.Value("string"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        else:  # This is an example to show how to have different features for "proofs" and "sentences"
+            features = datasets.Features(
+                {
+                    "fileID": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        # data_file = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir, # os.path.join(data_dir, "train.jsonl"), # data_file,
+                    "split": "train",
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "test.jsonl"),
+            #         "split": "test"
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
+            #         "split": "dev",
+            #     },
+            # ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(os.path.join(filepath, self.config.name + ".tsv"), encoding="utf-8") as f:
+            reader = csv.reader(f, delimiter='\t')
+            for key, data in enumerate(reader):
+                if self.config.name == "proofs":
+                    # Yields examples as (key, example) tuples
+                    # print(key, repr(data))
+                    yield key, {
+                        "fileID" : data[0],
+                        "proof": data[1],
+                    }
+                else:
+                    yield key, {
+                        "fileID" : data[0],
+                        "sentence": data[1],
+                    }

proofs.zip

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+oid sha256:24e9990fe719b420be5e926e384f1d6d445208c5409e09ddcd394823b2c5e042
+size 720642714

sentences.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f1237d489c67ab3296f5509ea2b25a45a12918a1188ce2708dfe0c6cb63e9aaf
+size 753492504