Instructions to use acayir64/turkish-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use acayir64/turkish-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("acayir64/turkish-embedding-model") 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:
- e523a9801024aeff383ec27d8a147aadd9fcc9b410e6452806c9c40d765aa8e7
- Size of remote file:
- 5.37 kB
- SHA256:
- 25f2c16c1654f713df68c72180bda7d273f3cdf401edb9ec64dfe4946a012590
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.