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
In reranker, does the order of query and passage matter?
#23
by samwu66 - opened
I'm testing if a video description matches a short topic. Usually the video description(~10 words) is longer than the short topic(3-4 words). Switching the order of them result in totally different score.
For example:
Query: "Space exploration"
Passage: "This video covers the history and future of space exploration, including NASA’s Apollo missions, the Mars Rover, and recent advancements in private spaceflight."
I wonder if there is any guideline on how to set the order?