Instructions to use DunnBC22/sentence-t5-large-FT-Quora_Sentence_Similarity-400 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DunnBC22/sentence-t5-large-FT-Quora_Sentence_Similarity-400 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DunnBC22/sentence-t5-large-FT-Quora_Sentence_Similarity-400") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7724bdcde2b50d1f61e96417cbbb0e6e643fdf37d78182613c7101ce8ca9a7d
- Size of remote file:
- 3.15 MB
- SHA256:
- c335275d84ac0580106a354840a5015f780cb5f717a444a78a2859c34a2ca81d
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