README.md

---
license: odc-by
task_categories:
- text-generation
language:
- en
tags:
- biology
- chemistry
- engineering
- computer science
- physics
- material science
- math
- psychology
- economics
- political science
- business
- geology
- sociology
- geography
- environmental science
- art
- history
- philosophy
pretty_name: PES2O
size_categories:
- 10B<n<100B
---

# PES2O 🌿🎓

*Pretraining Efficiently on [S2ORC][2]!*

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.

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

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

### Key Facts

- *Knowledge cutoff*: 2023-01-03
- *Number of documents*: 67.56M
- *Number of whitespace-separated tokens*: 47.37M

### Processing

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        |
|:-------:|:-------:|:-----------:|:--------------:|
|s2orc    | train   | 8,242,162   | 36,088,195,908 |
|s2orc    | valid   | 51,323      | 255,139,074    |
|s2ag     | train   | 59,382,301  | 11,009,123,378 |
|s2ag     | valid   | 111,228     | 24,398,512     |


## PES2O V2


### Key Facts

- *Knowledge cutoff*: 2023-01-03
- *Number of documents*: 38.97M
- *Number of whitespace-separated tokens**: 42,28

### Processing

TODO

| Dataset | Split | # Documents | # Words        |
|:-------:|:-----:|------------:|---------------:|
| s2orc   | train |  8,242,162  | 36,088,195,908 |
| s2orc   | valid |     51,323  |    255,139,074 |
| s2ag    | train | 30,569,017  |  5,920,099,207 |
| s2ag    | valid |    109,709  |     24,029,459 |

[1]: https://aclanthology.org/2020.acl-main.447/
[2]: https://github.com/allenai/s2orc