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--- |
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license: odc-by |
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task_categories: |
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- text-generation |
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language: |
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- en |
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tags: |
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- biology |
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- chemistry |
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- engineering |
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- computer science |
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- physics |
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- material science |
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- math |
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- psychology |
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- economics |
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- political science |
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- business |
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- geology |
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- sociology |
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- geography |
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- environmental science |
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- art |
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- history |
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- philosophy |
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pretty_name: PES2O |
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size_categories: |
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- 10B<n<100B |
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--- |
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# PES2O ๐ฟ๐ |
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*Pretraining Efficiently on [S2ORC][2]!* |
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The PES2O dataset is a collection of ~40M creative commmon licensed academic papers, |
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cleaned, filtered, and formatted for pre-training of language models. It is derived from |
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the [Semantic Scholar Open Research Corpus][2]([Lo et al, 2020][1]), or S2ORC. |
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We release multiple version of PES2O, each with different processing and knowledge cutoff |
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date. We recommend you to use the latest version available. |
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## Document Format |
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Each document in the dataset is a dictionary with the following fields: |
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- `added`: Date the document was added to the corpus. |
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- `created`: Best-guess date for when the document was first published. Some have resolution down to the day, only down to the year. |
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- `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). |
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- `source`: Collection from which the document was sourced from. At the moment, two are supported: |
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- `s2orc`: collection of full-text papers |
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- `s2ag`: collection of title and abstracts |
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- `text`: Text of the document. Paragraphs are separated by two newlines (`\n\n`). |
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- `version`: version of PES2O. |
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## PES2O V1 |
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### Key Facts |
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- *Knowledge cutoff*: 2023-01-03 |
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- *Number of documents*: 67.56M |
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- *Number of whitespace-separated tokens*: 47.37M |
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### Processing |
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Processing differs slightly wether it was derived from the full-text corpus (`s2orc`) or the title and abstract corpus (`s2ag`). |
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#### S2ORC-derived documents |
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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: |
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- The paper must have a title and abstract. |
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- 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. |
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- The paper must be in English. |
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- To determine the language of each document, we use the [pycld3](https://github.com/bsolomon1124/pycld3) library |
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- We run pycld3 on the first 2000 characters of each paragraph in the paper. |
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- The language of the paper is the most common language of the paragraphs. |
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- The paper must have at least 500 whitespace-separated words. |
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- The paper was published after 1969; papers published before this date are often obtained through OCR and contain unrecoverable errors. |
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- The paper must have at least 5 paragraphs. |
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- All sections that have a average log word probability of less than `-20` are removed. |
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- 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). |
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- The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document. |
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- Words are obtained by splitting the text on whitespace. |
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The train set contains papers published before 2022-12-01; |
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the validation set includes documents published after 2022-12-01 and until 2023-01-03. |
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#### S2AG-derived documents |
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The S2AG corpus contains titles and abstracts of papers in Semantic Scholar. |
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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: |
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- Abstract must be in English. |
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- To calculate the language, we once again use pycld3 |
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- Title must be in English, or have average unigram log probability greater than -20. |
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- Abstract must be in English. |
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- Abstract must have higher than -20 average unigram log probability. |
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- Abstract must have at least 50 words. |
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- Abstract must have no more than 1000 words. |
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- 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. |
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- Paper was published after 1969. |
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#### Statistics |
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| Dataset | Split | # Documents | # Words | |
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|:-------:|:-------:|:-----------:|:--------------:| |
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|s2orc | train | 8,242,162 | 36,088,195,908 | |
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|s2orc | valid | 51,323 | 255,139,074 | |
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|s2ag | train | 59,382,301 | 11,009,123,378 | |
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|s2ag | valid | 111,228 | 24,398,512 | |
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## PES2O V2 |
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### Key Facts |
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- *Knowledge cutoff*: 2023-01-03 |
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- *Number of documents*: 38.97M |
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- *Number of whitespace-separated tokens**: 42,28 |
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### Processing |
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TODO |
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| Dataset | Split | # Documents | # Words | |
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|:-------:|:-----:|------------:|---------------:| |
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| s2orc | train | 8,242,162 | 36,088,195,908 | |
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| s2orc | valid | 51,323 | 255,139,074 | |
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| s2ag | train | 30,569,017 | 5,920,099,207 | |
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| s2ag | valid | 109,709 | 24,029,459 | |
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[1]: https://aclanthology.org/2020.acl-main.447/ |
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[2]: https://github.com/allenai/s2orc |
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