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:
- e7463c1c6b7c46ae0de79b004f3d7318bf4307ef0bef4de3be2e38ae80fab160
- Size of remote file:
- 792 kB
- SHA256:
- d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
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