Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
mteb
feature-extraction
dataset_size:100K<n<1M
loss:AnglELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Gameselo/STS-multilingual-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Gameselo/STS-multilingual-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Gameselo/STS-multilingual-mpnet-base-v2") sentences = [ "有些人在路上溜达。", "Folk går", "Otururken gitar çalan adam.", "ארה\"ב קבעה שסוריה השתמשה בנשק כימי" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Gameselo/STS-multilingual-mpnet-base-v2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Gameselo/STS-multilingual-mpnet-base-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Gameselo/STS-multilingual-mpnet-base-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Gameselo/STS-multilingual-mpnet-base-v2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5b1eebf28d51b425919503bde32f76a54f1055eb4b3e3f6a03946174a9a2060d
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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