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/paraphrase-MiniLM-tokipona with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NetherQuartz/paraphrase-MiniLM-tokipona with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NetherQuartz/paraphrase-MiniLM-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
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |