Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use iris-ting/graduate_design_model02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use iris-ting/graduate_design_model02 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("iris-ting/graduate_design_model02") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use iris-ting/graduate_design_model02 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("iris-ting/graduate_design_model02") model = AutoModel.from_pretrained("iris-ting/graduate_design_model02") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9abfbcdb4ffc00d355a5aceeb3df3cad5ed9f079223b9434115e96b6423ffb1
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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