Feature Extraction
sentence-transformers
Safetensors
Model2Vec
Turkish
static-embeddings
turkish
bge-m3
Instructions to use altaidevorg/turkish-bge-m3-model2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use altaidevorg/turkish-bge-m3-model2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("altaidevorg/turkish-bge-m3-model2vec") 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] - Model2Vec
How to use altaidevorg/turkish-bge-m3-model2vec with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("altaidevorg/turkish-bge-m3-model2vec") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from altaidevorg/turkish-bge-m3-model2vec: direct link, hf CLI and curl.
- Browser
- Download file 1.55 MB
-
https://huggingface.co/altaidevorg/turkish-bge-m3-model2vec/resolve/main/tokenizer.json
- Command line
-
hf download hf://altaidevorg/turkish-bge-m3-model2vec/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/altaidevorg/turkish-bge-m3-model2vec/resolve/main/tokenizer.json
1.55 MB
File too large to display, you can check the raw version instead.