Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
text-embeddings-inference
Instructions to use GebeyaTalent/mpnetwithoutchunking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GebeyaTalent/mpnetwithoutchunking with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GebeyaTalent/mpnetwithoutchunking") 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 GebeyaTalent/mpnetwithoutchunking: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/GebeyaTalent/mpnetwithoutchunking/resolve/main/model.safetensors
- Command line
-
hf download hf://GebeyaTalent/mpnetwithoutchunking/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/GebeyaTalent/mpnetwithoutchunking/resolve/main/model.safetensors
438 MB
- Xet hash:
- 979fa40c7782a9127896b107939b86e08454f7554e6624f495a5560668922f3f
- Size of remote file:
- 438 MB
- SHA256:
- 0b3c8c717335c801abb15983036a6f1df4b6943fd6b93717969efd96d22eeec6
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