This model is fine-tuned from cross-encoder/ms-marco-MiniLM-L-12-v2 on ROR affiliation matching data. It reranks candidate ROR organizations given an affiliation string query.

Training

Base model: cross-encoder/ms-marco-MiniLM-L-12-v2
Training examples: 45,061
Training traces: 2,004
Negative sampling: Hard negatives from retrieval candidates
Epochs: 3
Batch size: 16
Learning rate: 2e-05
Max sequence length: 256

Usage

python

from sentence_transformers import CrossEncoder

model = CrossEncoder("cometadata/ms-marco-ror-reranker")

# Score affiliation-candidate pairs
pairs = [
    ["University of California, Berkeley", "University of California, Berkeley"],
    ["University of California, Berkeley", "University of California, Los Angeles"],
]
scores = model.predict(pairs)
print(scores)  # Higher score = better match

Intended Use

This model is designed for reranking ROR organization candidates in affiliation matching pipelines. It should be used after an initial retrieval step (e.g., dense retrieval with Snowflake Arctic).

Training Data

Trained on traces from cometadata/ror-pipeline-traces (affrodb_s2aff_traces config).

Timestamp

2026-01-07T21:35:26.376404+00:00