Sentence Similarity
sentence-transformers
Safetensors
Transformers
bilingual
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
Instructions to use Lajavaness/bilingual-embedding-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Lajavaness/bilingual-embedding-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lajavaness/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 Lajavaness/bilingual-embedding-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lajavaness/bilingual-embedding-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from Lajavaness/bilingual-embedding-base: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/Lajavaness/bilingual-embedding-base/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://Lajavaness/bilingual-embedding-base/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/Lajavaness/bilingual-embedding-base/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |