splade cocondenser ensembledistil

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released May 20221K context≈ 384 words

A specialized retrieval model that generates sparse embeddings using the SPLADE architecture, combining the interpretability of keyword matching with the semantic understanding of transformers. It uses an ensemble distillation approach to produce efficient, high-quality sparse representations where most values are zero, making index storage compact. Unlike dense embedding models, its outputs map directly to vocabulary tokens, so you can inspect which terms it finds important.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Instruction Following

Moderate

Long Context

splade cocondenser ensembledistil

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released May 20221K context≈ 384 words

A specialized retrieval model that generates sparse embeddings using the SPLADE architecture, combining the interpretability of keyword matching with the semantic understanding of transformers. It uses an ensemble distillation approach to produce efficient, high-quality sparse representations where most values are zero, making index storage compact. Unlike dense embedding models, its outputs map directly to vocabulary tokens, so you can inspect which terms it finds important.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Instruction Following

Moderate

Long Context

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Dense EmbeddingA compact vector representation where most dimensions contain meaningful information, as opposed to sparse embeddings that are mostly zeros.DistillationA technique that compresses a large, complex model into a smaller one by training it to mimic the larger model's behavior, resulting in faster inference with minimal loss of quality.EmbeddingA dense numerical vector that represents a word, sentence, or concept in a high-dimensional space.EmbeddingsNumerical representations of text that capture semantic meaning, allowing the model to measure similarity between different words or phrases.Ensemble DistillationA training technique where knowledge from multiple models is combined and compressed into a single, smaller model for better efficiency.InterpretabilityThe ability to understand and explain how a model makes decisions and what it has learned from its training data.RetrievalThe process of finding and returning relevant documents or information from a database based on a query.Retrieval ModelA model designed to find and rank the most relevant documents or passages from a large collection based on a query.SPLADE ArchitectureA neural retrieval method that combines transformer models with sparse, interpretable outputs by mapping embeddings directly to vocabulary tokens.Semantic UnderstandingThe ability to grasp the actual meaning and context of text, rather than just matching keywords.Sparse EmbeddingsVector representations where most values are zero, allowing efficient storage and computation by only tracking non-zero elements.TokensThe basic units of text that a language model processes, typically representing words or word fragments.VocabularyThe complete set of unique words or tokens that a language model can recognize and generate.

Capabilities

Capabilities

Use Case Fit

Glossary