Instructions to use newmindai/TurkEmbed4Retrieval-Static with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use newmindai/TurkEmbed4Retrieval-Static with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("newmindai/TurkEmbed4Retrieval-Static") - sentence-transformers
How to use newmindai/TurkEmbed4Retrieval-Static with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("newmindai/TurkEmbed4Retrieval-Static") 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
- Xet hash:
- fd8b244b613f7189b1547758d849817b284f0f3ae52113a915789f927ee4b164
- Size of remote file:
- 17.1 MB
- SHA256:
- 3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.