Update parquet files

.gitignore

@@ -1 +0,0 @@
-*.lock

README.md

@@ -1,100 +0,0 @@
----
-annotations_creators:
-- no-annotation
-language:
-- en
-language_creators:
-- found
-license: []
-multilinguality:
-- monolingual
-pretty_name: proof-pile
-size_categories: []
-source_datasets: []
-tags:
-- math
-- mathematics
-- formal-mathematics
-task_categories:
-- text-generation
-task_ids:
-- language-modeling
----
-
-# Dataset Description
-The `proof-pile` is a 36GB pre-training dataset of mathematical text that comprises roughly 15 billion tokens. Models trained on this dataset are coming soon :) The dataset is composed of diverse sources of both informal and formal mathematics, namely
-- ArXiv.math (35GB)
-- Open-source math textbooks (50MB)
-- Formal mathematics libraries (500MB)
-    - Lean mathlib and other Lean repositories 
-    - Isabelle AFP
-    - Coq mathematical components and other Coq repositories 
-    - HOL Light
-    - set.mm
-    - Mizar Mathematical Library
-- Math Overflow and Math Stack Exchange (500MB)
-- Wiki-style sources (50MB)
-  - ProofWiki
-  - Wikipedia math articles
-- MATH dataset (6MB)
-
-The construction of the dataset is reproducible using the code and instructions in the [proof-pile Github
-repo](https://github.com/zhangir-azerbayev/proof-pile). 
-
-# Supported Tasks
-This dataset is intended to be used for pre-training and fine-tuning language models. We envision models trained on the `proof-pile` will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization. 
-
-# Languages
-All informal mathematics in the `proof-pile` is written in English and LaTeX (arXiv articles in other languages are filtered out using [languagedetect](https://github.com/shuyo/language-detection/blob/wiki/ProjectHome.md)). Formal theorem proving languages represented in this dataset are Lean 3, Isabelle, Coq, HOL Light, Metamath, and Mizar.
- 
-# Evaluation
-The version of `set.mm` in this dataset has 10% of proofs replaced with the `?` character in order to preserve a validation and test set for Metamath provers pre-trained on the `proof-pile`. The precise split can be found here: [validation](https://github.com/zhangir-azerbayev/mm-extract/blob/main/valid_decls.json) and [test](https://github.com/zhangir-azerbayev/mm-extract/blob/main/test_decls.json). 
-The Lean mathlib commit used in this dataset is `6313863`. Theorems created in subsequent commits can be used for evaluating Lean theorem provers. 
-
-This dataset contains only the training set of the [MATH dataset](https://github.com/hendrycks/math). However, because this dataset contains ProofWiki, the Stacks Project, Trench's Analysis, and Stein's Number Theory, models trained on it cannot be evaluated on the [NaturalProofs dataset](https://github.com/wellecks/naturalproofs). 
-
-# Data Preprocessing
-This section describes any significant filtering and transformations made to various subsets of the data. 
-
-**arXiv.math.**
-The arXiv.math dataset is large, heterogeneous, and contains a great deal of noise. We used the following heuristics
-when choosing which files from arXiv.math source folders to include in the dataset:
-- Keep only files with a `.tex` extension.
-- Only include files that use either a `utf-8/16/32` or `latin-1` text encoding. 
-- Discard files that do not contain a part, chapter, section, sub...section, paragraph, or subparagraph heading. 
-- Delete files that contain the keyword `gnuplot`. Gnuplot-latex is an old command line utility that generates blocks
-  of entirely unintelligible source. 
-- Include only articles in English, as determined by the [langdetect library](https://pypi.org/project/langdetect/). \n",
-    "\n",
-- Exclude files shorter than 280 characters (characters counted after substring removal described below).
-
-In addition, we apply the following transformations to arXiv.math texts: 
-
-- Delete everything outside of `\begin{document}` and `\end{document}`. 
-- Delete everything including or after `\Refs`, `\begin{thebibliography}`, or `\begin{bibdiv}`
-- Delete comments. 
-- Any more than three consecutive newlines are replaced by three consecutive newlines. 
-In [this notebook](https://github.com/zhangir-azerbayev/proof-pile/blob/main/analysis/arxiv_noisedetection.ipynb), we provide an analysis of the prevalence of noisy documents in the arXiv.math subset of the
-proof-pile. 
-
-**Stack Exchange.**
-We only include questions that have at least 5 upvotes and an answer. We format Stack Exchange posts as follows
-```
-QUESTION [{num_upvotes} upvotes]: {text of question}
-
-REPLY [{num_upvotes} votes]: {text of reply}
-
-REPLY [{num_upvotes} votes]: {text of reply}
-
-.
-.
-.
-```
-
-**set.mm.**
-We converted `set.mm` into human-readable form by following the instructions in the [mm-extract repo](https://github.com/zhangir-azerbayev/mm-extract)
-
-## Contributions
-Authors: Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski. 
-
-We would like to thank Jeremy Avigad, Albert Jiang, and Wenda Li for their invaluable guidance, and the Hoskinson Center for Formal Mathematics for its support. 

dev/proofpile_dev.jsonl.gz → default/partial-test/0000.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2129d758ca520d8a4ad4eff22a77ab880c603650a796e047e981419a7d0d8f04
-size 144101628
+oid sha256:5de687a23ad7273f9f93c7dbdc7c6e77744f130527092efbbdd1aa5f44f7e7f4
+size 230154170

formal/afp_train.jsonl.gz → default/partial-test/0001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e7e055c57b5cadd539a70340e33ea7c4d66aaf2ba286a6ffaa3d9f59498ee7d4
-size 30850711
+oid sha256:2121be666a6c3a6825459555523090c3ffb08892155356154cd509225f7f0700
+size 30168961

formal/coq_train.jsonl.gz → default/partial-train/0000.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:500585c5cde16d4dd027e91ce07c9939d530223359c17b2275530bd27f2195de
-size 2347527
+oid sha256:5db4c29e0ff94e5812aa64057b4580d8d6d9e6c7a6db5a32afa40d4c8fe8d453
+size 227605020

default/partial-train/0001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef6b8b4d35151ab883d4035036d8080745d4b6b2a36c15e9cc7f008d74c26163
+size 227549868

default/partial-train/0002.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8b5aaf536f9620638e7e22a318d8e5656f4e0c6db78bf53f63b62f8e5d94aef
+size 227381629

default/partial-train/0003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aec4b2d47d0db2cb4900e655f753fdd8f8385b33182dae7f66fefaeb614c091c
+size 228255548

default/partial-train/0004.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b7a0c72a98c40a078c5eacf3a106828f68395135bdfaba2ec4f9cc6673032b1
+size 229845032

default/partial-train/0005.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b197ae2131e5ac240228748574a5596a74322a0faf240fcb0e0d428726e232b
+size 230760904

default/partial-train/0006.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0dfd7e08c9c403853a0809a9b6dff3ff8fa59572a9f1a7de61b3bae490c478c7
+size 231439920

default/partial-train/0007.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:781809b1f7e27a2333e07a122392f81eb6f45cf54fa0124945d49d21a29166e5
+size 228395703

default/partial-train/0008.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3139bfb99953a4d7c294b6b42c6d86f657c6d08258ca26966c4052422aa72792
+size 228682445

default/partial-train/0009.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d463130819c65740cb09dcf51c73eb3832ee47ba32364af33abd1932e5c9e5a4
+size 193406030

default/partial-validation/0000.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c913e24ec77a0497fc118721234ba2d367fcf2822696e777e954356de7181506
+size 230496142

formal/afp_val.jsonl.gz → default/partial-validation/0001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7476f958b6ccc7e7d75c0cd9ebfca87d25b67cc550ea3cb3ef09b15a766572c2
-size 1666321
+oid sha256:864b5b6c04865d2275efc806382db4bbcc1245050b77850540df8a8aa01d39fd
+size 28620687

example.py

@@ -1,26 +0,0 @@
-from datasets import load_dataset
-from itertools import islice
-import sys
-import time
-from tqdm import tqdm
-
-dataset = load_dataset("./proof-pile.py", "default")
-
-size = dataset["train"].dataset_size / 2**30
-print(f"{size} GB TRAIN TOTAL")
-print(dataset)
-for x in tqdm(dataset["train"]): 
-    print("EXAMPLE INSTANCE (trimmed):")
-    print(x["text"][:100])
-    break
-
-then = time.time()
-for x in tqdm(dataset["train"]):
-    pass
-now = time.time()
-print(f"{size} GB TRAIN TOTAL")
-print(f"TRAVERSED IN {now-then} SECONDS")
-
-size += dataset["validation"].dataset_size/2**30 + dataset["test"].dataset_size/2**30
-print(f"{size} GB TOTAL (TRAIN, VAL, TEST)")
-

formal/coq_val.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7e34411db12a3c4dc95c1dce3bfd198771b05916aceb821d565341870458ea6e
-size 139624

formal/hol_train.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:23bef79158a370cac2ded01004869e3c7a64d345993deeb7ae9ee2df500bb0bf
-size 5326465

formal/hol_val.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1bf3593dcd77b5ceee44bb1cc702a754f1fa9330daa145f38d5d2d048b31332b
-size 324645

formal/lean_train.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:14d3b07aababcda09a5480bf6f2f3011c49ec73be2ead91cb0772dbfbef3b7ff
-size 9306443

formal/lean_val.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8c101902aa857285660bccbf7cd56f0c4ef54c825aaf31d4da7fc69c7559bfbe
-size 567956

formal/mizar_train.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e1a8b52f7a338b43defd173c42e1fc67c3acd8b3113e1230a21482234bff9566
-size 18618814

formal/mizar_val.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4fc71207e51e673e4f58fb18e460c5aad54f793010fa273e63c747462916388d
-size 626988

formal/setmm_train.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d6540c3eb70e23597429ff330b00b024512f05a457743415d9ed132886068be8
-size 22411600

formal/setmm_val.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:236fc9d544f2c96adfc9b559910022fd354cef34f08956e91ee97609b027511f
-size 36

proof-pile.py

@@ -1,149 +0,0 @@
-# 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.
-"""A dataset of high quality mathematical text."""
-
-
-import csv
-import json
-import ndjson
-import os
-import sys # just for debugging, delete
-
-import itertools
-from itertools import islice
-
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {proof-pile},
-author={Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski
-},
-year={2022}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-A dataset of high quality mathematical text. """
-_HOMEPAGE = "https://huggingface.co/datasets/hoskinson-center/proof-pile"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "MIT"
-
-# 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 = {
-    "first_domain": "https://huggingface.co/datasets/hoskinson-center/proof-pile",
-}
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class ProofPile(datasets.GeneratorBasedBuilder):
-    """A dataset of high quality mathematical text"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    # 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', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-    datasets.BuilderConfig(name="default", version=VERSION, description=""),
-    ]
-
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        features = datasets.Features(
-            {
-                "text": datasets.Value("string"),
-                "meta": 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
-
-            train_files = [dl_manager.download_and_extract(f"train/proofpile_train_{i}.jsonl.gz") for i in range(8)]
-            val_files = [dl_manager.download_and_extract("dev/proofpile_dev.jsonl.gz")]
-            test_files = [dl_manager.download_and_extract("test/proofpile_test.jsonl.gz")]
- 
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "data_files": train_files,
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION, 
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "data_files": val_files,
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST, 
-                    gen_kwargs={
-                        "data_files": test_files, 
-                    }, 
-                ), 
-            ]
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, data_files):
-        # 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.
-        key = 0 
-        for name in data_files: 
-            with open(name) as f: 
-                instances = ndjson.load(f)
-            for instance in instances: 
-                yield key, {"text": instance["text"], 
-                        "meta": json.dumps(instance["meta"])}
-                key += 1 

test/proofpile_test.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3de76bef2067cd06de681bf7bc8391cfd46cff7c964aea4b8be22002881a2cff
-size 138257828

train/proofpile_train_0.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ebd551043986f06477bee3af11d0d57180e9e440c3c9d8be0a378ace984ca525
-size 1245144003

train/proofpile_train_1.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1b90e03d1dc1df325985af452aa5abc69b5e211adf855914a375c72f1e691153
-size 1248678238

train/proofpile_train_2.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4ce123e250490a8b86b1df06a871d3f12513d8731d468fcd6443d8b25f358f0c
-size 1248942967

train/proofpile_train_3.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:535055dbdc6a0e629d7de6a2a8d4b61d86410f1d939fdba773621fc01c5e8f66
-size 1254135740

train/proofpile_train_4.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4ab7d1b0c2c158622da7b2c4f24233b9d225d4fc8113099eefa1abc0fbcf68f0
-size 1243365834

train/proofpile_train_5.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e58deabc3bf0e47c5e49b5e6a8f42d4f3ab467a3f4cb5d05114004e78324c67b
-size 1271854185

train/proofpile_train_6.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d0b301a389f8f050f05485d28d45e62a3cfd838dbc2f044f8cf0eb3da4da2813
-size 1233941494

train/proofpile_train_7.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:110db34dfd1878ab9c7f67c14532fb90cf41c72182c2563e39c9ff42384f31da
-size 1005422313