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 tokenizer.json from GebeyaTalent/mpnetwithoutchunking: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/GebeyaTalent/mpnetwithoutchunking/resolve/main/tokenizer.json
- Command line
-
hf download hf://GebeyaTalent/mpnetwithoutchunking/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/GebeyaTalent/mpnetwithoutchunking/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.