Statements (67)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Model
|
| gptkbp:bfsLayer |
4
|
| gptkbp:bfsParent |
gptkb:BERT
gptkb:Super_GLUE_benchmark |
| gptkbp:adapted_into |
domain-specific applications
|
| gptkbp:applies_to |
biomedical NLP tasks
|
| gptkbp:architectural_style |
gptkb:Transformers_character
|
| gptkbp:based_on |
gptkb:BERT
|
| gptkbp:coat_of_arms |
12 layers
|
| gptkbp:collaborated_with |
academic institutions
data scientists |
| gptkbp:contributed_to |
precision medicine
|
| gptkbp:developed_by |
gptkb:Job_Search_Engine
improving biomedical research |
| gptkbp:enables |
literature mining
|
| gptkbp:enhances |
clinical text analysis
|
| gptkbp:facilitates |
drug discovery
|
| gptkbp:has_achievements |
state-of-the-art performance
|
| gptkbp:has_method |
110 million
|
| gptkbp:has_website |
biomedical text mining
|
| https://www.w3.org/2000/01/rdf-schema#label |
Bio BERT
|
| gptkbp:improves |
question answering
text classification named entity recognition relation extraction |
| gptkbp:is_adopted_by |
pharmaceutical companies
health informatics |
| gptkbp:is_available_in |
multiple languages
|
| gptkbp:is_available_on |
gptkb:archive
|
| gptkbp:is_cited_in |
numerous studies
|
| gptkbp:is_compared_to |
gptkb:Bio_BERT-Base
gptkb:Bio_BERT-Large |
| gptkbp:is_documented_in |
research articles
|
| gptkbp:is_enhanced_by |
fine-tuning techniques
|
| gptkbp:is_evaluated_by |
traditional methods
benchmark datasets peer-reviewed studies |
| gptkbp:is_influenced_by |
BERT architecture
|
| gptkbp:is_integrated_with |
research workflows
other NLP tools |
| gptkbp:is_optimized_for |
biomedical data
|
| gptkbp:is_part_of |
AI research initiatives
NLP toolkit |
| gptkbp:is_promoted_by |
bioinformatics communities
|
| gptkbp:is_recognized_by |
a significant advancement in AI
leading model in biomedical NLP |
| gptkbp:is_recognized_for |
its contributions to NLP
|
| gptkbp:is_supported_by |
community contributions
funding agencies open-source community |
| gptkbp:is_tested_for |
real-world datasets
|
| gptkbp:is_used_in |
research papers
|
| gptkbp:is_utilized_in |
healthcare organizations
clinical settings medical literature analysis |
| gptkbp:performance |
biomedical NLP models
text mining tasks |
| gptkbp:released_in |
gptkb:2019
|
| gptkbp:requires |
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch |
| gptkbp:supports |
multi-task learning
|
| gptkbp:training |
PMC full-text articles
Pub Med abstracts large-scale biomedical corpora |
| gptkbp:tuning |
specific biomedical tasks
|
| gptkbp:uses |
transfer learning
|
| gptkbp:utilizes |
contextual embeddings
|