Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2594692
loss:CachedMultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use ShiniChien/SpouseBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ShiniChien/SpouseBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ShiniChien/SpouseBERT") sentences = [ "George Lambert", "mary anne sievers 26/07/1855", "joseph-francois baudelaire 07/06/1759", "hoàng Tenmu" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "is_local": true, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "max_length": 128, | |
| "model_max_length": 128, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "tokenizer_class": "TokenizersBackend", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |