readme: ---
  language: en
  tags:
  - science
  - multi-displinary
  license: apache-2.0
  ---
  
  # ScholarBERT_100 Model
  
  This is the **ScholarBERT_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 is based on the same architecture as [BERT-large](https://huggingface.co/bert-large-cased) and has a total of 340M parameters.
  
  
  # Model Architecture
  
  | Hyperparameter  | Value |
  |-----------------|:-------:|
  | Layers     | 24    |
  | Hidden Size     | 1024  |
  | Attention Heads | 16    |
  | Total Parameters | 340M |
  
  
  # 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.
  
  ![corpus pie chart](corpus_pie_chart.png)
  
  
  # BibTeX entry and citation info
  If using this model, please cite this paper:
  ```
  @misc{hong2022scholarbert,
    doi = {10.48550/ARXIV.2205.11342},  
    url = {https://arxiv.org/abs/2205.11342},  
    author = {Hong, Zhi and Ajith, Aswathy and Pauloski, Gregory and Duede, Eamon and Malamud, Carl and Magoulas, Roger and Chard, Kyle and Foster, Ian},  
    title = {ScholarBERT: Bigger is Not Always Better},  
    publisher = {arXiv},  
    year = {2022}
  }
  ```
  