-
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 32 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 2 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
Collections
Discover the best community collections!
Collections including paper arxiv:1706.03762
-
Attention Is All You Need
Paper • 1706.03762 • Published • 133 -
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paper • 1912.01703 • Published • 2 -
google-bert/bert-base-uncased
Fill-Mask • 0.1B • Updated • 95.2M • • 2.71k -
openai-community/gpt2
Text Generation • 0.1B • Updated • 14M • 3.37k
-
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Paper • 2006.03654 • Published • 3 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 32 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
Attention Is All You Need
Paper • 1706.03762 • Published • 133
-
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Paper • 1406.1078 • Published • 1 -
Distributed Representations of Sentences and Documents
Paper • 1405.4053 • Published -
Sequence to Sequence Learning with Neural Networks
Paper • 1409.3215 • Published • 4 -
Neural Machine Translation by Jointly Learning to Align and Translate
Paper • 1409.0473 • Published • 7
-
Attention Is All You Need
Paper • 1706.03762 • Published • 133 -
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Paper • 2205.14135 • Published • 15 -
Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper • 2309.06180 • Published • 63 -
FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Paper • 2307.08691 • Published • 9
-
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 32 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 2 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
-
Attention Is All You Need
Paper • 1706.03762 • Published • 133 -
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Paper • 2205.14135 • Published • 15 -
Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper • 2309.06180 • Published • 63 -
FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Paper • 2307.08691 • Published • 9
-
Attention Is All You Need
Paper • 1706.03762 • Published • 133 -
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paper • 1912.01703 • Published • 2 -
google-bert/bert-base-uncased
Fill-Mask • 0.1B • Updated • 95.2M • • 2.71k -
openai-community/gpt2
Text Generation • 0.1B • Updated • 14M • 3.37k
-
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Paper • 2006.03654 • Published • 3 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 32 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
Attention Is All You Need
Paper • 1706.03762 • Published • 133
-
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Paper • 1406.1078 • Published • 1 -
Distributed Representations of Sentences and Documents
Paper • 1405.4053 • Published -
Sequence to Sequence Learning with Neural Networks
Paper • 1409.3215 • Published • 4 -
Neural Machine Translation by Jointly Learning to Align and Translate
Paper • 1409.0473 • Published • 7