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
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 128, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |