Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:21541
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use davanstrien/iconclass-retriever-bge-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use davanstrien/iconclass-retriever-bge-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("davanstrien/iconclass-retriever-bge-ft") sentences = [ "This image features a woodcut illustration of a grove of trees enclosed within an oval frame. The trees, which appear to be a mix of deciduous and coniferous varieties, stand on a grassy bank beside a body of water. The scene is framed by architectural elements and inscribed with text in Latin, French, and German.", "Imparity, Inequality, Difference", "Contrariety; 'Contrarietà' (Ripa)", "Absoluteness, Non-relatedness", "Multiformity, Variety", "Dissimilarity, Unlikeness" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [6, 6] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa720fa37f7d0651385b834ed718e4956c16b47e74ce0cced28ef46da8d824b2
- Size of remote file:
- 5.65 kB
- SHA256:
- 18338571d5fc2fdabac851c8b804897ffd5bfb0c6b9607244a5eac82378ca215
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