Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use ukeme/sgservices-base-sentence-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ukeme/sgservices-base-sentence-transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ukeme/sgservices-base-sentence-transformer") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use ukeme/sgservices-base-sentence-transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ukeme/sgservices-base-sentence-transformer") model = AutoModel.from_pretrained("ukeme/sgservices-base-sentence-transformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 943761959281d694e7ce9e7f6ab8e3e327e7882c2d153de9e8c91e7df4de5ef5
- Size of remote file:
- 90.9 MB
- SHA256:
- 8473110565e75c50e04a86947fcfb0b2639bc7d34c9d7afd5ee6d3fb53288f80
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