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--- |
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language: en |
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tags: |
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- science |
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- multi-displinary |
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license: apache-2.0 |
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--- |
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# ScholarBERT-XL_100 Model |
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This is the **ScholarBERT-XL_100** variant of the ScholarBERT model family. |
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The model is pretrained on a large collection of scientific research articles (**221B tokens**). |
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This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default. |
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The model has a total of 770M parameters. |
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# Model Architecture |
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| Hyperparameter | Value | |
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|-----------------|:-------:| |
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| Layers | 36 | |
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| Hidden Size | 1280 | |
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| Attention Heads | 20 | |
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| Total Parameters | 770M | |
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# Training Dataset |
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The vocab and the model are pertrained on **100% of the PRD** scientific literature dataset. |
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The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”), |
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a nonprofit organization based in California. This dataset was constructed from a corpus |
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of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals. |
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The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences, |
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Social Sciences, and Technology. The distribution of articles is shown below. |
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# BibTeX entry and citation info |
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If using this model, please cite this paper: |
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``` |
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@inproceedings{hong2023diminishing, |
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title={The diminishing returns of masked language models to science}, |
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author={Hong, Zhi and Ajith, Aswathy and Pauloski, James and Duede, Eamon and Chard, Kyle and Foster, Ian}, |
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booktitle={Findings of the Association for Computational Linguistics: ACL 2023}, |
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pages={1270--1283}, |
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year={2023} |
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} |
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``` |
