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
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 8, | |
| "pad_token_id": 0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.9.0", | |
| "type_vocab_size": 1, | |
| "use_cache": false, | |
| "vocab_size": 6426 | |
| } | |