Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
xlm-roberta
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use yco/bilingual-embedding-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yco/bilingual-embedding-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yco/bilingual-embedding-base", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use yco/bilingual-embedding-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("yco/bilingual-embedding-base", trust_remote_code=True) model = AutoModel.from_pretrained("yco/bilingual-embedding-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from yco/bilingual-embedding-base: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/yco/bilingual-embedding-base/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://yco/bilingual-embedding-base@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/yco/bilingual-embedding-base/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.