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:
- 34119046bff49966cb8dfc5b834cee4162eff178d3b4505b97aff4601d3fcd9e
- Size of remote file:
- 1.34 GB
- SHA256:
- 69688c973863e13c78147d878ca5686d613286f751b473038458c63f92e2614d
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