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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize" | |
| } | |
| ] |