
# ScholarBERT_10_WB Model

This is the **ScholarBERT_10_WB** variant of the ScholarBERT model family.

The model is pretrained on a large collection of scientific research articles (**22.1B tokens**).
Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pretrain the [BERT-base](https://huggingface.co/bert-base-cased) and [BERT-large](https://huggingface.co/bert-large-cased) models.

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 **10% of the PRD** scientific literature dataset and Wikipedia+BookCorpus.
 
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](https://huggingface.co/globuslabs/ScholarBERT/resolve/main/corpus_pie_chart.png)


# BibTeX entry and citation info
If using this model, please cite this paper:
```
@misc{hong2023diminishing,
      title={The Diminishing Returns of Masked Language Models to Science}, 
      author={Zhi Hong and Aswathy Ajith and Gregory Pauloski and Eamon Duede and Kyle Chard and Ian Foster},
      year={2023},
      eprint={2205.11342},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": "en", "license": "apache-2.0", "tags": ["science", "multi-displinary"]}
transformers pytorch bert fill-mask science multi-displinary en arxiv:2205.11342 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

ScholarBERT\_10\_WB Model
=========================


This is the ScholarBERT\_10\_WB variant of the ScholarBERT model family.


The model is pretrained on a large collection of scientific research articles (22.1B tokens).
Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pretrain the BERT-base and BERT-large models.


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 and has a total of 340M parameters.


Model Architecture
==================



Training Dataset
================


The vocab and the model are pertrained on 10% of the PRD scientific literature dataset and Wikipedia+BookCorpus.


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


BibTeX entry and citation info
==============================


If using this model, please cite this paper:



TAGS
#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
