Sentence Similarity
sentence-transformers
PyTorch
mpnet
feature-extraction
text-embeddings-inference
Instructions to use AISE-TUDelft/python-usage-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AISE-TUDelft/python-usage-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AISE-TUDelft/python-usage-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/python-usage-classifier: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/AISE-TUDelft/python-usage-classifier/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://AISE-TUDelft/python-usage-classifier@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/AISE-TUDelft/python-usage-classifier/resolve/refs%2Fpr%2F1/model.safetensors
438 MB
- Xet hash:
- 29778764cdc7b0f3e5bce54d84d2cdd2fb12de1dc972958d33731d1d4721a397
- Size of remote file:
- 438 MB
- SHA256:
- 531eae637b2342f69efe93eae7aae67c108aa092707bbfcc7b9beeea0e865bce
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