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