Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use BAAI/bge-large-en-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-large-en-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-large-en-v1.5") 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] - Transformers
How to use BAAI/bge-large-en-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-large-en-v1.5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-large-en-v1.5") model = AutoModel.from_pretrained("BAAI/bge-large-en-v1.5", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Multilingual powerhouse — testing for mobile deployment
#39
by 3morixd - opened
This model covers Italian, Turkish, Polish, Arabic, German — exactly the kind of multilingual capability we need for global mobile AI.
At Dispatch AI (FZE, UAE), we're building mobile AI that works for everyone, not just English speakers. Models like this are the foundation.
We benchmark multilingual models on our 40-phone farm (Snapdragon 865) to see which ones maintain quality across languages when quantized to 4-bit. The results vary wildly — some models lose 30% quality in non-English languages after quantization.
Would love to see multilingual evaluation results at different quantization levels.
- Dispatch AI (FZE), Sharjah UAE