Feature Extraction
sentence-transformers
Safetensors
Transformers
mistral
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-en-icl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-en-icl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-en-icl") 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-en-icl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-en-icl")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-en-icl") model = AutoModel.from_pretrained("BAAI/bge-en-icl") - Notebooks
- Google Colab
- Kaggle
Distill loss in retrieval task?
#6
by YWang17 - opened
Hi, it is mentioned in the paper "To distill the score from reranker in retrieval tasks, we use the bge-reranker model as the teacher." Does it mean that you involve the distillation in this work? Could you please explain more about this reranker and distill loss? What's the metric if there is no distill?
Yes, during training, we used distillation with the teacher model being bge-reranker-v2.5-gemma2-lightweight. We utilized KL divergence loss and combined it with InfoNCE loss as the final loss.
Since distillation is definitely effective, we don't have metrics for the case without distillation.