Sentence Similarity
sentence-transformers
Safetensors
German
English
multilingual
xlm-roberta
feature-extraction
retrieval
semantic-search
ifc
lca
text-embeddings-inference
Instructions to use Hygroskopisch/bge-m3-ifc-kbob-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hygroskopisch/bge-m3-ifc-kbob-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hygroskopisch/bge-m3-ifc-kbob-finetuned") 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
Update training query links and add multilingual language tag
Browse files
README.md
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@@ -4,6 +4,7 @@ base_model: BAAI/bge-m3
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language:
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- de
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- en
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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The v3 run used project-internal data artifacts and generated pair files.
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- Query source:
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- Expected mapping source:
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- Pair data used for training: internal random-preselected JSONL artifact (project-internal)
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- Hard-negative strategy: fallback mode with random_preselected selection, up to 2 hard negatives per record
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Train/dev counts from run metadata:
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language:
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- de
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- en
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- multilingual
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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The v3 run used project-internal data artifacts and generated pair files.
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- Query source files: [Training/query_generation/generated_queries](https://github.com/Hygros/ifc-kbob-ai-matcher/tree/main/Training/query_generation/generated_queries)
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- Expected mapping source files: [Training/query_generation/generated_queries](https://github.com/Hygros/ifc-kbob-ai-matcher/tree/main/Training/query_generation/generated_queries)
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- Hard-negative strategy: fallback mode with random_preselected selection, up to 2 hard negatives per record
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Train/dev counts from run metadata:
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