updated readme

README.md

@@ -34,14 +34,24 @@ size_categories:
 
 The PES2O dataset is a collection of ~40M creative commmon licensed academic papers,
 cleaned, filtered, and formatted for pre-training of language models. It is derived from
-the [Semantic Scholar Open Research Corpus][2]([Lo et al, 2020][1]), or S2ORC. 
+the [Semantic Scholar Open Research Corpus][2]([Lo et al, 2020][1]), or S2ORC.
 
 We release multiple version of PES2O, each with different processing and knowledge cutoff
 date. We recommend you to use the latest version available.
 
 ## Document Format
 
-TODO
+Each document in the dataset is a dictionary with the following fields:
+
+- `added`: Date the document was added to the corpus.
+- `created`: Best-guess date for when the document was first published. Some have resolution down to the day, only down to the year.
+- `id`: Semantic Scholar Corpus ID of the document; it can be used with the [Semantic Scholar API](https://api.semanticscholar.org/) to retrieve metadata about the document (e.g., fields of study, authors).
+- `source`: Collection from which the document was sourced from. At the moment, two are supported:
+  - `s2orc`: collection of full-text papers
+  - `s2ag`: collection of title and abstracts
+- `text`: Text of the document. Paragraphs are separated by two newlines (`\n\n`).
+- `version`: version of PES2O.
+
 
 ## PES2O V1
 
@@ -49,11 +59,49 @@ TODO
 
 - *Knowledge cutoff*: 2023-01-03
 - *Number of documents*: 67.56M
-- *Number of whitespace-separated tokens**: 47,3M
+- *Number of whitespace-separated tokens*: 47.37M
 
 ### Processing
 
-TODO
+Processing differs slightly wether it was derived from the full-text corpus (`s2orc`) or the title and abstract corpus (`s2ag`).
+
+#### S2ORC-derived documents
+
+Unfiltered, S2ORC contains 11.3M papers and 46.9B whitespace-separated tokens as of 2023-01-03. To derive PES2O v1, we impose the following constraints:
+
+- The paper must have a title and abstract.
+- From each paper, we use [Grobid](https://github.com/kermitt2/grobid) to extract section headers and paragraphs; figures, tables, and references, and any other non-textual content is removed. Title and abstracts are also available, but they come from the Semantic Scholar metadata (obtained through the APIs), not Grobid.
+- The paper must be in English.
+  - To determine the language of each document, we use the [pycld3](https://github.com/bsolomon1124/pycld3) library
+  - We run pycld3 on the first 2000 characters of each paragraph in the paper.
+  - The language of the paper is the most common language of the paragraphs.
+- The paper must have at least 500 whitespace-separated words.
+- The paper was published after 1969; papers published before this date are often obtained through OCR and contain unrecoverable errors.
+- The paper must have at least 5 paragraphs.
+  - All sections that have a average log word probability of less than `-20` are removed.
+  - To calculate the average log word probability, we use word frequencies extracted from the [1T Web Ngram corpus](https://catalog.ldc.upenn.edu/LDC2006T13); specifically, we use the list available [created by Rachel Tatman](https://www.kaggle.com/datasets/rtatman/english-word-frequency). A copy is hosted [here](https://ai2-s2-research-public.s3-us-west-2.amazonaws.com/lucas/google-1T-unigram/unigram_freq.csv).
+- The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document.
+  - Words are obtained by splitting the text on whitespace.
+
+The train set contains papers published before 2022-12-01;
+the validation set includes documents published after 2022-12-01 and until 2023-01-03.
+
+#### S2AG-derived documents
+
+The S2AG corpus contains titles and abstracts of papers in Semantic Scholar.
+Unfiltered, the corpus contains 91.1M papers and 15.5B whitespace-separated tokens as of 2023-01-03. To derive PES2O v1, we impose the following constraints:
+
+- Abstract must be in English.
+  - To calculate the language, we once again use pycld3
+- Title must be in English, or have average unigram log probability greater than -20.
+- Abstract must be in English.
+- Abstract must have higher than -20 average unigram log probability.
+- Abstract must have at least 50 words.
+- Abstract must have no more than 1000 words.
+- The most frequent word in the union of text and abstract must be a 2+ character alpha word, or it can be `a` followed by a 2+ character alpha word.
+- Paper was published after 1969.
+
+#### Statistics
 
 | Dataset | Split   | # Documents | # Words        |
 |:-------:|:-------:|:-----------:|:--------------:|
@@ -85,4 +133,3 @@ TODO
 
 [1]: https://aclanthology.org/2020.acl-main.447/
 [2]: https://github.com/allenai/s2orc
-

pes2o.py

@@ -0,0 +1,99 @@
+import gzip
+import json
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_URL = "https://huggingface.co/datasets/allenai/pes2o"
+
+_VARIANTS = ["v1", "v2"]
+
+_N_SHARDS_PER_SPLIT = {
+    "v1": {"train": {'s2orc': 10, 's2ag': 10}, "valid": {'s2orc': 1, 's2ag': 1}},
+    "v2": {"train": {'s2orc': 10, 's2ag': 10}, "valid": {'s2orc': 1, 's2ag': 1}},
+}
+
+_DATA_URL = "\
+https://huggingface.co/datasets/allenai/pes2o/resolve/main/\
+{name}/{subset}/{split}/{shard:05d}.json.gz\
+"
+
+_DESCRIPTION = "\
+The PES2O dataset is a collection of ~40M creative commmon licensed academic \
+papers, cleaned, filtered, and formatted for pre-training of language models. \
+It is derived from the Semantic Scholar Open Research Corpus(Lo et al, 2020), \
+or S2ORC.\
+"
+
+_CITATION = ""
+
+
+class pes2o(datasets.GeneratorBasedBuilder):
+    """Pretraining Efficiently on S2ORC!"""
+
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "added": datasets.Value("string"),
+                    "created": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                    "source": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "version": datasets.Value("string")
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_urls = {}
+        for split in ["train", "validation"]:
+            n_shards = _N_SHARDS_PER_SPLIT[self.config.name][split]
+            data_urls[split] = [
+                _DATA_URL.format(
+                    name=self.config.name,
+                    split=split,
+                    subset=subset,
+                    index=index
+                )
+                for subset, n_shards in n_shards.items()
+                for index in range(n_shards)
+            ]
+        train_downloaded_files = dl_manager.download(
+            data_urls["train"]
+        )
+        validation_downloaded_files = dl_manager.download(
+            data_urls["validation"]
+        )
+        return [
+            datasets.SplitGenerator(
+                name=str(datasets.Split.TRAIN), gen_kwargs={
+                    "filepaths": train_downloaded_files
+                }),
+            datasets.SplitGenerator(
+                name=str(datasets.Split.VALIDATION), gen_kwargs={
+                    "filepaths": validation_downloaded_files
+                }
+            ),
+        ]
+
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by
+        iterating on all the files."""
+        id_ = 0
+        for filepath in filepaths:
+            logger.info("generating examples from = %s", filepath)
+            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for line in f:
+                    if line:
+                        example = json.loads(line)
+                        yield id_, example
+                        id_ += 1

v2/s2ag.valid.2.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:880aa83b0da706316866b3706a8a1d0cdb63f282b1397acb412e3f0129850245
-size 6247186

v2/s2ag.valid.3.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:af030a42a393dd7115e0d0c3cfb84e863f38d308e388fbcea1a64643f9af39b0
-size 6208595

v2/s2ag.valid.4.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c813ac8c9f91d8276dbfc2826c42b52b0192a3555402dffdeceaa88493d1a236
-size 6196981

v2/s2ag.valid.5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fc2ee035c0d80d8c4fd485e9669c4c6091c0636e14eb01cdc725f536a2fd3b8e
-size 6190459

v2/s2ag.valid.6.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:454edae748353e4bde1f6abe89d4f7636c1a74608b139d1cdab8b05c281a090c
-size 6269943

v2/s2ag.valid.7.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3cce628a835d4116bd6d9dc521a8ece476fa0b806b7a12ee99b3c379a698e435
-size 6105991

v2/s2ag.valid.8.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ea61dceefbe1204b991e2b0042f61d6022de4256a76caeb962955f1198fb30cd
-size 6303064

v2/s2ag.valid.9.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c2558b817be309bab741a331f935471afa87ae485952387a34a0b60aaa320386
-size 6199081

v2/s2orc.valid.0.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bb0bd1a9c19b9921457bc7ba1e7ef6f5cdff5527315d451c6649568bb57fb93c
-size 49765244

v2/s2orc.valid.1.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:09c880e5b193fc05258a524e3aba5a0f7c3eea7935d11c2f75a23ec7cbc3f0d3
-size 47886027

v2/s2orc.valid.2.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5ac0ce32a1202816fe7da581396bb21fa2ccb457db3b5134065bd34b25747f0e
-size 50496634

v2/s2orc.valid.3.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a5bd3d044a9407cbc2c19ce650e36b4aac8bb350422b15962b58d31e2405bc8d
-size 48589847

v2/s2orc.valid.4.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3cbba95ad61581e55cab617f7f8b41477062397260f2de59c2d23921774035fd
-size 49718320

v2/s2orc.valid.5.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eb2390ee466be665496df4d6bba7aab859d3cc1c5405e00ab2575f3164a1f551
-size 48574182

v2/s2orc.valid.6.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a00a5b6df69130cb45bdadddb8ff242098578438da532d866424bf7a700e1e3a
-size 49826311

v2/s2orc.valid.7.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:803e6eb2fff87ad333e7dcbb9d1d1e1d9863f840aeefd0ffc16252f6e953332a
-size 49543400

v2/s2orc.valid.8.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0e82785d1649fcff70100a8e48a2c1500d95ac9d04a46ff197382b6b7227cc3e
-size 49562841

v2/s2orc.valid.9.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a56cb6a5edf51422f5e002cd1611afe1f5cffa1823e38be9994e444c52ad316a
-size 49207053