ai4data/devdata-search-granite-97m-multilingual-nopermute-cmnrl

devdata-search-granite-97m-multilingual-nopermute-cmnrl

A bi-encoder embedding model for search over structured statistical metadata, part of the DevData Search family. It is a fine-tune of ibm-granite/granite-embedding-97m-multilingual-r2 produced with schema-invariant fine-tuning on DevDataBench: full-schema serialization with per-example field-order permutation and field dropout, so the encoder binds meaning to field labels rather than to serialization order. This is an embedding model that powers retrieval; it is not a hosted search service.

See the paper Field Order Should Not Matter: Permutation-Invariant Fine-Tuning for Structured Metadata Retrieval.

Training

• —Base model: ibm-granite/granite-embedding-97m-multilingual-r2
• —Loss: cmnrl
• —Field permutation: False; field dropout: 0.0
• —Max sequence length: 512
• —No query/document prefixes

Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("ai4data/devdata-search-granite-97m-multilingual-nopermute-cmnrl")
queries = ["mobile-broadband subscriptions per 100 people, reported annually"]
docs = ["name: Active mobile-broadband subscriptions | ..."]
q = model.encode(queries)
d = model.encode(docs)

Cosine similarity of q and d ranks documents for each query.

License

Apache-2.0. Derived from ibm-granite/granite-embedding-97m-multilingual-r2; trained on public World Bank Data360 metadata.