Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:82069
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use NetherQuartz/LaBSE-tokipona with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NetherQuartz/LaBSE-tokipona with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NetherQuartz/LaBSE-tokipona") sentences = [ "Kendi kendine yardım etsen Tanrı da sana yardımcı olur.", "nasin sina li pona seme?", "ona li jan sona.", "o pana e pona tawa sama sina la mama sewi li pana e pona tawa sina." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44f06bab9d7b4c29e371d398b65e273a4134e4557b8843c0692b53eaa7677ece
- Size of remote file:
- 6.03 kB
- SHA256:
- b9fa7c385aea081f9f6a42e4986751eb93a007e448b01a92f52599b96a0a740b
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