GTE ModernColBERT v1

gte

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

Released April 20258K context≈ 6,144 words

GTE ModernColBERT v1 operates as a late-interaction retrieval specialist — instead of collapsing a document into a single vector, it retains per-token embeddings and scores matches at the token level. This makes it more precise at finding relevant passages, especially when exact phrasing or rare terms matter. The trade-off is higher storage and compute cost compared to single-vector models.

Capabilities

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

Long Context

Moderate

Coding

GTE ModernColBERT v1

gte

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

Released April 20258K context≈ 6,144 words

GTE ModernColBERT v1 operates as a late-interaction retrieval specialist — instead of collapsing a document into a single vector, it retains per-token embeddings and scores matches at the token level. This makes it more precise at finding relevant passages, especially when exact phrasing or rare terms matter. The trade-off is higher storage and compute cost compared to single-vector models.

Capabilities

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

Long Context

Moderate

Coding

Glossary

EmbeddingsNumerical representations of text that capture semantic meaning, allowing the model to measure similarity between different words or phrases.Late-Interaction RetrievalA retrieval approach that compares individual token embeddings between query and document at search time, rather than comparing pre-computed single vectors, allowing more precise matching of specific phrases and rare terms.Per-Token EmbeddingsA representation where each word or subword in a text gets its own embedding vector, rather than combining all tokens into a single vector for the entire text.RetrievalThe process of finding and returning relevant documents or information from a database based on a query.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.Token EmbeddingsNumerical representations of individual words or subwords that capture their meaning and relationships in a way machines can process.

Capabilities

Capabilities

Use Case Fit

Glossary