AI is happening. The future is updating.splade-pt-br
SPLADE (Sparse Lexical AnD Expansion) model fine-tuned for **Portuguese** text retrieval. Based on BERTimbau and trained on Portuguese question-answering datasets. SPLADE is a neural retrieval model that learns to expand queries and documents with contextually relevant terms while maintaining sparsity. Unlike dense retrievers, SPLADE produces sparse vectors (typically ~99% sparse) that are: - **Interpretable**: Each dimension corresponds to a vocabulary token - **Efficient**: Can use inverted indexes for fast retrieval - **Effective**: Combines lexical matching with semantic expansion - **Base Model**: `neuralmind/bert-base-portuguese-cased` (BERTimbau) - **Vocabulary Size**: 29,794 tokens (Portuguese-optimized) - **Training Iterations**: 150,000 - **Final Training Loss**: 0.000047 - **Sparsity**: ~99.5% (100-150 active dimensions per vector) - **Max Sequence Length**: 256 tokens
SPLADE (Sparse Lexical AnD Expansion) model fine-tuned for **Portuguese** text retrieval. Based on BERTimbau and trained on Portuguese question-answering datasets. SPLADE is a neural retrieval model that learns to expand queries and documents with contextually relevant terms while maintaining sparsity. Unlike dense retrievers, SPLADE produces sparse vectors (typically ~99% sparse) that are: - **Interpretable**: Each dimension corresponds to a vocabulary token - **Efficient**: Can use inverted indexes for fast retrieval - **Effective**: Combines lexical matching with semantic expansion - **Base Model**: `neuralmind/bert-base-portuguese-cased` (BERTimbau) - **Vocabulary Size**: 29,794 tokens (Portuguese-optimized) - **Training Iterations**: 150,000 - **Final Training Loss**: 0.000047 - **Sparsity**: ~99.5% (100-150 active dimensions per vector) - **Max Sequence Length**: 256 tokens