Duplicate from proofcheck/prooflang

Co-authored-by: Christopher A. Stone <christopherastone@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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+# Image files - uncompressed
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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-generation
+language:
+- en
+size_categories:
+- 1B<n<10B
+pretty_name: ProofLang Corpus
+dataset_info:
+  - config_name: proofs
+    num_bytes: 3197091800
+    num_examples: 3681901
+    features:
+      - name: fileID
+        dtype: string
+      - name: proof
+        dtype: string
+    
+  - config_name: sentences
+    num_bytes: 3736579062
+    num_examples: 38899130
+    features:
+    - name: fileID
+      dtype: string
+    - name: sentence
+      dtype: string
+    
+download_size: 6933683563
+dataset_size: 6933670862
+---
+# Dataset Card for the ProofLang Corpus
+
+## Dataset Summary
+
+The ProofLang Corpus includes 3.7M proofs (558 million words) mechanically extracted from papers that were posted on [arXiv.org](https://arXiv.org) between 1992 and 2020. 
+The focus of this corpus is proofs, rather than the explanatory text that surrounds them, and more specifically on the *language* used in such proofs. 
+Specific 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 (non-formalized) proofs, and is also amenable to statistical analyses and experiments with Natural Language Processing (NLP) techniques. 
+We hope it can serve as an aid in the development of language-based proof assistants and proof checkers for professional and educational purposes.
+
+## Dataset Structure
+
+There are multiple TSV versions of the data. Primarily, `proofs` divides up the data proof-by-proof, and `sentences` further divides up the same data sentence-by-sentence.
+The `raw` dataset is a less-cleaned-up version of `proofs`. More usefully, the `tags` dataset gives arXiv subject tags for each paper ID found in the other data files.
+
+* The data in `proofs` (and `raw`) consists of a `paper` ID (identifying where the proof was extracted from), and the `proof` as a string.
+
+* The data in `sentences` consists of a `paper` ID, and the `sentence` as a string.
+
+* The data in `tags` consists of a `paper` ID, and the arXiv subject tags for that paper as a single comma-separated string.
+
+Further metadata about papers can be queried from arXiv.org using the paper ID.
+
+In particular, each paper `<id>` in the dataset can be accessed online at the url `https://arxiv.org/abs/<id>`
+
+## Dataset Size
+
+* `proofs` is 3,094,779,182 bytes (unzipped) and has 3,681,893 examples.
+* `sentences` is 3,545,309,822 bytes (unzipped) and has 38,899,132 examples.
+* `tags` is 7,967,839 bytes (unzipped) and has 328,642 rows.
+* `raw` is 3,178,997,379 bytes (unzipped) and has 3,681,903 examples.
+
+## Dataset Statistics
+
+* The average length of `sentences` is 14.1 words.
+
+* The average length of `proofs` is 10.5 sentences.
+
+## Dataset Usage
+
+Data can be downloaded as (zipped) TSV files. 
+
+Accessing the data programmatically from Python is also possible using the `Datasets` library. 
+For example, to print the first 10 proofs:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('proofcheck/prooflang', 'proofs', split='train', streaming='True')
+for d in dataset.take(10):
+   print(d['paper'], d['proof'])
+```
+
+To look at individual sentences from the proofs, 
+
+```python
+dataset = load_dataset('proofcheck/prooflang', 'proofs', split='train', streaming='True')
+for d in dataset.take(10):
+   print(d['paper'], d['sentence'])
+```
+
+To get a comma-separated list of arXiv subject tags for each paper,
+```python
+from datasets import load_dataset
+dataset = load_dataset('proofcheck/prooflang', 'tags', split='train', streaming='True')
+for d in dataset.take(10):
+   print(d['paper'], d['tags'])
+```
+
+Finally, to look at a version of the proofs with less aggressive cleanup (straight from the LaTeX extraction),
+
+```python
+dataset = load_dataset('proofcheck/prooflang', 'raw', split='train', streaming='True')
+for d in dataset.take(10):
+   print(d['paper'], d['proof'])
+```
+
+
+### Data Splits
+
+There is currently no train/test split; all the data is in `train`.
+
+
+## Dataset Creation
+
+We started with the LaTeX source of 1.6M papers that were submitted to [arXiv.org](https://arXiv.org) between 1992 and April 2022.
+
+The proofs were extracted using a Python script simulating parts of LaTeX (including defining and expanding macros). 
+It does no actual typesetting, throws away output not between `\begin{proof}...\end{proof}`, and skips math content.  During extraction,
+
+* Math-mode formulas (signalled by `$`, `\begin{equation}`, etc.) become `MATH`
+* `\ref{...}` and variants (`autoref`, `\subref`, etc.) become `REF`
+* `\cite{...}` and variants (`\Citet`, `\shortciteNP`, etc.) become `CITE`
+* Words that appear to be proper names become `NAME`
+* `\item` becomes `CASE:`
+
+We then run a cleanup pass on the extracted proofs that includes
+
+* Cleaning up common extraction errors (e.g., due to uninterpreted macros)
+* Replacing more references by `REF`, e.g., `Theorem 2(a)` or `Postulate (*)`
+* Replacing more citations with `CITE`, e.g., `Page 47 of CITE`
+* Replacing more proof-case markers with `CASE:`, e.g., `Case (a).`
+* Fixing a few common misspellings
+
+
+## Additional Information
+
+This dataset is released under the Creative Commons Attribution 4.0 licence.
+
+Copyright for the actual proofs remains with the authors of the papers on [arXiv.org](https://arXiv.org), but these simplified snippets are fair use under US copyright law.
+

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.
+"""The ProofLang Corpus of arXiv Proofs"""
+
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{prooflang:dataset,
+title = "{ProofLang: the Language of arXiv Proofs}",
+booktitle = "{Intelligent Computer Mathematics (CICM 2023)}",
+author = "{Henry Hammer and Nanako Noda and Christopher A. Stone}",
+year = {2023},
+note = {To appear}
+}
+"""
+
+_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.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/proofcheck/prooflang"
+
+_LICENSE = "CC-BY 4.0"
+
+_URLS = {
+    "proofs": "proofs.zip",
+    "sentences": "sentences.zip",
+    "raw": "raw.zip",
+    "tags": "tags.zip",
+}
+
+class ArxivProofs(datasets.GeneratorBasedBuilder):
+    """English text from proofs found in arXiv preprints."""
+
+    VERSION = datasets.Version("0.6.0")
+
+    # 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"),
+        datasets.BuilderConfig(name="raw", version=VERSION, description="One (less agressively cleaned) proof per line"),
+        datasets.BuilderConfig(name="tags", version=VERSION, description="arXiv subject tags for each paper"),
+    ]
+
+    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 in {"proofs", "raw"}:  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "paper": datasets.Value("string"),
+                    "proof": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "tags":  # This is an example to show how to have different features for "proofs" and "sentences"
+            features = datasets.Features(
+                {
+                    "paper": datasets.Value("string"),
+                    "tags": datasets.Value("string"),
+                }
+            )
+        else:  # This is an example to show how to have different features for "proofs" and "sentences"
+            features = datasets.Features(
+                {
+                    "paper": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                }
+            )
+        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,
+                    "split": "train",   # Prooflang doesn't have a train/test split.
+                },
+            ),
+            # 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.
+        csv.field_size_limit(256000)  # Some of the raw proofs are slightly longer than 131072 characters
+        with open(os.path.join(filepath, self.config.name + ".tsv"), encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
+            for key, data in enumerate(reader):
+                yield key, data
+                # 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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raw.zip

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sentences.zip

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tags.zip

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