Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
text-embeddings-inference
Instructions to use danfeg/ST-ALL-MPNET_Finetuned-AR-1500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/ST-ALL-MPNET_Finetuned-AR-1500 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/ST-ALL-MPNET_Finetuned-AR-1500") 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 danfeg/ST-ALL-MPNET_Finetuned-AR-1500: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/danfeg/ST-ALL-MPNET_Finetuned-AR-1500/resolve/663ee3e4ea877d4384f761b95ae3c0461dd202b0/model.safetensors
- Command line
-
hf download hf://danfeg/ST-ALL-MPNET_Finetuned-AR-1500@663ee3e4ea877d4384f761b95ae3c0461dd202b0/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/danfeg/ST-ALL-MPNET_Finetuned-AR-1500/resolve/663ee3e4ea877d4384f761b95ae3c0461dd202b0/model.safetensors
438 MB
- Xet hash:
- 358acfc2ff24eaa701ce890854a55725867df67c5ccd826763c649797b7a3966
- Size of remote file:
- 438 MB
- SHA256:
- 25ef7d72cf9ac9f62a633b0d541e9e1b7e32b138b5a125178e872246715f29d9
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