Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
text-embeddings-inference
Instructions to use danfeg/ST-ALL-MPNET_Finetuned-AR-2000 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-2000 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/ST-ALL-MPNET_Finetuned-AR-2000") 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-2000: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/danfeg/ST-ALL-MPNET_Finetuned-AR-2000/resolve/main/model.safetensors
- Command line
-
hf download hf://danfeg/ST-ALL-MPNET_Finetuned-AR-2000/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/danfeg/ST-ALL-MPNET_Finetuned-AR-2000/resolve/main/model.safetensors
438 MB
- Xet hash:
- e80a44d2dd6e720a359416920c0eb46f1c472d3b7d52aa6f2ec429d443efdadd
- Size of remote file:
- 438 MB
- SHA256:
- a055a241bd390bc8509c8d37327a6ebd83b7403fbd0b2294c7ff1ca5860a9952
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