Embedding Layers With Weight Sharing And Vocabulary Management

via “cross-lingual transfer learning via shared multilingual vocabulary”

fill-mask model by undefined. 37,80,561 downloads.

Unique: Single shared 119K vocabulary across 104 languages enables parameter-efficient cross-lingual transfer without language-specific adapters or separate models, using bidirectional transformer pretraining to learn language-agnostic representations that generalize across typologically diverse languages

vs others: Simpler deployment than language-specific model ensembles and supports more languages (104) than most alternatives, but shows larger performance gaps between high and low-resource languages compared to language-specific fine-tuned models or more recent multilingual models with larger vocabularies

Flax: A neural network library for JAX designed for flexibility

Unique: Provides explicit weight-sharing utilities for input/output embedding layers, enabling parameter reduction in language models while maintaining functional purity through pytree parameter passing

vs others: More flexible than PyTorch embeddings because weight sharing is explicit and composable, and more efficient than naive implementations because it uses JAX's optimized indexing operations