ByT5: Towards a token-free future with pre-trained byte-to-byte models
Paper
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2105.13626
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Published
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2
Byte-level
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Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 49 -
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers
Paper • 2305.07185 • Published • 9 -
Byte-Level Recursive Convolutional Auto-Encoder for Text
Paper • 1802.01817 • Published -
Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering
Paper • 2403.09622 • Published • 16 -
Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes
Paper • 1811.09021 • Published • 1 -
Neural Machine Translation with Byte-Level Subwords
Paper • 1909.03341 • Published -
Neural Machine Translation without Embeddings
Paper • 2008.09396 • Published -
ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5
Paper • 2110.15248 • Published -
MonoByte: A Pool of Monolingual Byte-level Language Models
Paper • 2209.11035 • Published -
Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation
Paper • 2302.14220 • Published -
Bilingual End-to-End ASR with Byte-Level Subwords
Paper • 2205.00485 • Published -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 49 -
CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
Paper • 2103.06874 • Published -
SpaceByte: Towards Deleting Tokenization from Large Language Modeling
Paper • 2404.14408 • Published • 6 -
Integrating Multi-scale Contextualized Information for Byte-based Neural Machine Translation
Paper • 2405.19290 • Published -
Word-Level Representation From Bytes For Language Modeling
Paper • 2211.12677 • Published -
byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings
Paper • 2106.13302 • Published