Sentence Similarity
Model2Vec
Safetensors
sentence-transformers
Ukrainian
static-embeddings
embeddings
retrieval
ukrainian
Instructions to use SyntaxSpirits/ukr-static-m3-64k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use SyntaxSpirits/ukr-static-m3-64k with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("SyntaxSpirits/ukr-static-m3-64k") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use SyntaxSpirits/ukr-static-m3-64k with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SyntaxSpirits/ukr-static-m3-64k") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SyntaxSpirits/ukr-static-m3-64k: direct link, hf CLI and curl.
- Browser
- Download file 2.27 MB
-
https://huggingface.co/SyntaxSpirits/ukr-static-m3-64k/resolve/main/tokenizer.json
- Command line
-
hf download hf://SyntaxSpirits/ukr-static-m3-64k/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SyntaxSpirits/ukr-static-m3-64k/resolve/main/tokenizer.json
2.27 MB
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