Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:990000
loss:DenoisingAutoEncoderLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use kwondw/bert-base-uncased-tsdae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kwondw/bert-base-uncased-tsdae with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kwondw/bert-base-uncased-tsdae") sentences = [ "On home he of Asian scholar, Nikolai in the Kul", "On the trip home, he visited the grave of the Russian Asian scholar, Nikolai Przhevalsky in Karakol on the shore of Lake Issyk Kul.", "Bishop Street Methodist Chapel, also known as the Wesleyan Chapel, is church overlooking Town Hall Square in Leicester, England, U.K.", "The scholar of English literature Charles Huttar compares the combination of the Watcher in the Water and the \"clashing gate\" when the Fellowship pass through the Doors of Durin, only to have the Watcher smash the rocks behind them, to Greek mythology's Wandering Rocks near the opening of the underworld, and to Odysseus's passage between the devouring Scylla and the whirlpool Charybdis." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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