Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use bingcheng9/bert-base-uncased-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bingcheng9/bert-base-uncased-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bingcheng9/bert-base-uncased-sts") sentences = [ "The man talked to a girl over the internet camera.", "A group of elderly people pose around a dining table.", "A teenager talks to a girl over a webcam.", "There is no 'still' that is not relative to some other object." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85991bf3f689ba027f4ac9b83e66b49f40a073f2856ae21dfc6dc1bc99eb1247
- Size of remote file:
- 438 MB
- SHA256:
- 75cec214a218d408a1770d3e7bebd4e92d4b2aec578332b856f7816e6c14121b
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