metadata
language:
- en
- multilingual
license: apache-2.0
tags:
- cross-encoder
- reranker
- sentence-transformers
- ror
- affiliation-matching
base_model: cross-encoder/ms-marco-MiniLM-L-12-v2
datasets:
- cometadata/ror-pipeline-traces
pipeline_tag: text-classification
ms-marco-ror-reranker
A cross-encoder reranker fine-tuned for Research Organization Registry (ROR) affiliation matching.
Model Description
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
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