Statements (82)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:adapted_into |
specific tasks
|
| gptkbp:based_on |
Transformer architecture
|
| gptkbp:developed_by |
gptkb:Google_Brain
AI applications collaborative efforts cutting-edge techniques improving AI capabilities |
| gptkbp:features |
recurrence mechanism
|
| gptkbp:has_achievements |
state-of-the-art performance
|
| https://www.w3.org/2000/01/rdf-schema#label |
Transformer-XL
|
| gptkbp:improves |
long-range dependency modeling
|
| gptkbp:introduced |
gptkb:2019
|
| gptkbp:is_adopted_by |
gptkb:Research_Institute
commercial use |
| gptkbp:is_analyzed_in |
performance metrics
research papers |
| gptkbp:is_available_on |
gptkb:archive
|
| gptkbp:is_cited_in |
numerous studies
|
| gptkbp:is_compared_to |
gptkb:GPT-2
|
| gptkbp:is_compatible_with |
gptkb:Py_Torch
|
| gptkbp:is_documented_in |
academic papers
|
| gptkbp:is_enhanced_by |
dynamic attention
|
| gptkbp:is_evaluated_by |
real-time applications
accuracy evaluation metrics language modeling tasks LSTM models perplexity metrics |
| gptkbp:is_explored_in |
workshops
case studies data scientists experiments NLP conferences |
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
|
| gptkbp:is_influenced_by |
RN Ns
|
| gptkbp:is_influential_in |
AI development
|
| gptkbp:is_integrated_with |
various platforms
other models |
| gptkbp:is_known_for |
flexible context length
|
| gptkbp:is_optimized_for |
parallel processing
|
| gptkbp:is_part_of |
gptkb:Research_Institute
deep learning frameworks AI advancements AI research community AI innovations AI toolkits transformer family |
| gptkbp:is_recognized_by |
industry leaders
AI experts a breakthrough model a leading model |
| gptkbp:is_recognized_for |
its architecture
its efficiency |
| gptkbp:is_related_to |
gptkb:BERT
|
| gptkbp:is_supported_by |
academic institutions
community contributions research funding NVIDIAGP Us |
| gptkbp:is_tested_for |
baseline models
synthetic data large-scale data real-world data benchmark datasets various datasets traditional models |
| gptkbp:is_used_for |
machine translation
|
| gptkbp:is_used_in |
dialog systems
natural language processing |
| gptkbp:is_utilized_in |
gptkb:software
question answering sentiment analysis text generation content generation text classification |
| gptkbp:performance |
gptkb:Open_AI_models
standard Transformers |
| gptkbp:supports |
unbounded context
|
| gptkbp:training |
large datasets
|
| gptkbp:uses |
self-attention mechanism
|
| gptkbp:bfsParent |
gptkb:Attention_Is_All_You_Need
|
| gptkbp:bfsLayer |
5
|