Collections
Discover the best community collections!
Collections including paper arxiv:1907.11692
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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 • 5 -
Neural Machine Translation by Jointly Learning to Align and Translate
Paper • 1409.0473 • Published • 7
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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 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 21
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Attention Is All You Need
Paper • 1706.03762 • Published • 139 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 25 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 21 -
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Paper • 2407.21770 • Published • 22
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The Curious Case of Neural Text Degeneration
Paper • 1904.09751 • Published • 3 -
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Paper • 2404.01197 • Published • 31 -
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Paper • 1905.10044 • Published • 2 -
PIQA: Reasoning about Physical Commonsense in Natural Language
Paper • 1911.11641 • Published • 5
-
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 • 139
-
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
FacebookAI/xlm-roberta-large
Fill-Mask • 0.6B • Updated • 5.97M • • 525 -
google-bert/bert-base-uncased
Fill-Mask • 0.1B • Updated • 114M • • 2.73k -
FacebookAI/xlm-roberta-base
Fill-Mask • 0.3B • Updated • 17.8M • • 882
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Paper • 1907.12461 • Published • 1 -
Transformer Language Models without Positional Encodings Still Learn Positional Information
Paper • 2203.16634 • Published • 6 -
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Paper • 2102.04664 • Published • 2
-
Distributed Representations of Sentences and Documents
Paper • 1405.4053 • Published -
Sequence to Sequence Learning with Neural Networks
Paper • 1409.3215 • Published • 5 -
PaLM: Scaling Language Modeling with Pathways
Paper • 2204.02311 • Published • 3 -
Recent Trends in Deep Learning Based Natural Language Processing
Paper • 1708.02709 • Published
-
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 • 139
-
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 • 5 -
Neural Machine Translation by Jointly Learning to Align and Translate
Paper • 1409.0473 • Published • 7
-
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
FacebookAI/xlm-roberta-large
Fill-Mask • 0.6B • Updated • 5.97M • • 525 -
google-bert/bert-base-uncased
Fill-Mask • 0.1B • Updated • 114M • • 2.73k -
FacebookAI/xlm-roberta-base
Fill-Mask • 0.3B • Updated • 17.8M • • 882
-
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 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 21
-
Attention Is All You Need
Paper • 1706.03762 • Published • 139 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 25 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 21 -
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Paper • 2407.21770 • Published • 22
-
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Paper • 1907.12461 • Published • 1 -
Transformer Language Models without Positional Encodings Still Learn Positional Information
Paper • 2203.16634 • Published • 6 -
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Paper • 2102.04664 • Published • 2
-
The Curious Case of Neural Text Degeneration
Paper • 1904.09751 • Published • 3 -
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Paper • 2404.01197 • Published • 31 -
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Paper • 1905.10044 • Published • 2 -
PIQA: Reasoning about Physical Commonsense in Natural Language
Paper • 1911.11641 • Published • 5
-
Distributed Representations of Sentences and Documents
Paper • 1405.4053 • Published -
Sequence to Sequence Learning with Neural Networks
Paper • 1409.3215 • Published • 5 -
PaLM: Scaling Language Modeling with Pathways
Paper • 2204.02311 • Published • 3 -
Recent Trends in Deep Learning Based Natural Language Processing
Paper • 1708.02709 • Published