Pre Trained Contextual Token Embeddings With Attention Weights

via “semantic text representation via contextual embeddings”

fill-mask model by undefined. 6,06,75,227 downloads.

Unique: Bidirectional context encoding produces embeddings that capture both left and right linguistic context, unlike unidirectional models; 768-dim vectors offer a balance between expressiveness and computational efficiency compared to larger models (1024+ dims) or smaller models (256 dims)

vs others: More semantically rich than static embeddings (Word2Vec, GloVe) due to context-awareness, and more computationally efficient than larger models (BERT-large, RoBERTa-large) while maintaining strong performance on semantic similarity benchmarks