klue sroberta base continue learning by mnr

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

Released April 20221K context≈ 386 words

A Korean-language sentence embedding model that continues training from the KLUE RoBERTa base using Multiple Negatives Ranking (MNR) loss. It produces dense vector representations of text, making it suited for semantic similarity and retrieval tasks in Korean. Its small context window of 514 tokens means it works best with short to medium-length text passages.

Capabilities

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

Instruction Following

Moderate

Multilingual

klue sroberta base continue learning by mnr

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

Released April 20221K context≈ 386 words

A Korean-language sentence embedding model that continues training from the KLUE RoBERTa base using Multiple Negatives Ranking (MNR) loss. It produces dense vector representations of text, making it suited for semantic similarity and retrieval tasks in Korean. Its small context window of 514 tokens means it works best with short to medium-length text passages.

Capabilities

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

Instruction Following

Moderate

Multilingual

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Dense VectorA compact numerical representation where most values are non-zero, used to efficiently store and compare the meaning of text.EmbeddingA dense numerical vector that represents a word, sentence, or concept in a high-dimensional space.Embedding ModelA model that converts text into numerical vectors that capture semantic meaning, allowing computers to understand and compare the similarity between different pieces of text.RankingThe process of ordering search results by relevance, determining which documents best match a user's query.RetrievalThe process of finding and returning relevant documents or information from a database based on a query.Semantic SimilarityA measure of how closely related two pieces of text are in meaning, regardless of whether they use identical words.Sentence EmbeddingA technique that converts entire sentences or passages into fixed-size numerical vectors that capture their semantic meaning, enabling comparison of text similarity.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary