Sentence Similarity
sentence-transformers
PyTorch
mpnet
feature-extraction
text-embeddings-inference
Instructions to use AISE-TUDelft/java-pointer-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AISE-TUDelft/java-pointer-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AISE-TUDelft/java-pointer-classifier") 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
Download model.safetensors from AISE-TUDelft/java-pointer-classifier: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/AISE-TUDelft/java-pointer-classifier/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://AISE-TUDelft/java-pointer-classifier@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/AISE-TUDelft/java-pointer-classifier/resolve/refs%2Fpr%2F1/model.safetensors
438 MB
- Xet hash:
- bbe6636e93804fd5a6874d91463a834fd1aca83ad670a9c5ae717471371aef22
- Size of remote file:
- 438 MB
- SHA256:
- 13974db36fe400449ec28c8f5bbb2b9af298157739b5e79866fc79e5ae0a13b4
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