Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:5483754
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use greyplan/loinc-multilingual-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use greyplan/loinc-multilingual-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("greyplan/loinc-multilingual-embeddings") sentences = [ "Flurazepam [Presence] in Serum or Plasma", "Cuantitativo", "Flurazepam: Suero o Plasma : Punto temporal: Presencia o umbral: Ordinal:", "连续数值型标尺 时刻" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from greyplan/loinc-multilingual-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/greyplan/loinc-multilingual-embeddings/resolve/main/tokenizer.json
- Command line
-
hf download hf://greyplan/loinc-multilingual-embeddings/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/greyplan/loinc-multilingual-embeddings/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- b467375d0802c8cddba8c8dad7a6883eb3e5a63f2c69f0022cb30f15a6f556e4
- Size of remote file:
- 33.4 MB
- SHA256:
- e2039575615d76b80e31a8358daccc19a0052a448e08259bd7e039cb2232e33d
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