Instructions to use segmue/geo-distiluse-swissnames3d-config1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use segmue/geo-distiluse-swissnames3d-config1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("segmue/geo-distiluse-swissnames3d-config1") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
geo-distiluse-swissnames3d-config1
Sentence-transformer for toponym resolution against the swissNAMES3D gazetteer, for use with the Irchel Geoparser. It follows the approach of dguzh/geo-all-MiniLM-L6-v2, fine-tuned from distiluse-base-multilingual-cased-v1 on German-language Swiss news articles.
Trained on candidate descriptions enriched with spatial context from ma-geoparser-h3-resolver, discretisation config1 (H3 overlap, max. resolution 13).
Usage
from geoparser_h3_resolver import SpatialSentenceResolver
resolver = SpatialSentenceResolver(model_name="segmue/geo-distiluse-swissnames3d-config1")
Variants
| Model | Thesis | Candidate descriptions |
|---|---|---|
| geo-distiluse-swissnames3d | M3 | default |
| geo-distiluse-swissnames3d-config1 | M4 | spatial context, config1 |
| geo-distiluse-swissnames3d-config2 | M5 | spatial context, config2 |
Training
8,255 text–description pairs, contrastive loss, learning rate 1e-5, 2 epochs. Training and evaluation code: ma-experiments.
Source
Master's thesis, University of Zurich, 2026: Describing Places by Their Surroundings: Enriching Candidate Descriptions with Spatial Context for Toponym Resolution.
Gazetteer data: swissNAMES3D © swisstopo.
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