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 modules.json from Gameselo/STS-multilingual-mpnet-base-v2: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/Gameselo/STS-multilingual-mpnet-base-v2/resolve/main/modules.json
- Command line
-
hf download hf://Gameselo/STS-multilingual-mpnet-base-v2/modules.json
-
curl -L -o modules.json https://huggingface.co/Gameselo/STS-multilingual-mpnet-base-v2/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |