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language:
- en
- multilingual
license: apache-2.0
tags:
- sentence-transformers
- embedding
- ror
- affiliation-matching
- contrastive-learning
base_model: Snowflake/snowflake-arctic-embed-l-v2.0
datasets:
- SIRIS-Lab/affilgood-contrastive-dataset
pipeline_tag: sentence-similarity
---
# Snowflake Arctic Embeddings for ROR Affiliation Matching
A sentence embedding model fine-tuned for Research Organization Registry (ROR) affiliation matching.
## Model Description
This model is fine-tuned from `Snowflake/snowflake-arctic-embed-l-v2.0` using contrastive learning
on the AffilGood contrastive dataset. It produces embeddings optimized for matching affiliation
strings to ROR organization records.
## Training
- **Base model**: Snowflake/snowflake-arctic-embed-l-v2.0
- **Training dataset**: SIRIS-Lab/affilgood-contrastive-dataset
- **Training examples**: 50,255
- **Validation examples**: 2,645
- **Loss**: MultipleNegativesRankingLoss (with hard negatives)
- **Epochs**: 3
- **Batch size**: 32
- **Learning rate**: 2e-05
- **Max sequence length**: 256
## Usage
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("cometadata/snowflake-arctic-ror-affiliations")
# Encode affiliations
affiliations = [
"Department of Physics, MIT, Cambridge, MA",
"Harvard Medical School, Boston",
]
embeddings = model.encode(affiliations, normalize_embeddings=True)
# Encode ROR organization names for matching
organizations = [
"Massachusetts Institute of Technology",
"Harvard University",
]
org_embeddings = model.encode(organizations, normalize_embeddings=True)
# Compute similarity
import numpy as np
similarities = np.dot(embeddings, org_embeddings.T)
```
## Intended Use
This model is designed for dense retrieval in affiliation matching pipelines.
It should be used as the first-stage retriever to find candidate ROR organizations
for a given affiliation string.
## Training Data
Fine-tuned on [SIRIS-Lab/affilgood-contrastive-dataset](https://huggingface.co/datasets/SIRIS-Lab/affilgood-contrastive-dataset),
which contains 52,900 affiliation-organization pairs with curated hard negatives across 105 languages.
## Timestamp
2026-01-07T08:08:33.561241+00:00
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