Text Classification
sentence-transformers
Safetensors
Transformers
multilingual
minicpm
text-generation
custom_code
Instructions to use BAAI/bge-reranker-v2-minicpm-layerwise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-reranker-v2-minicpm-layerwise with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-reranker-v2-minicpm-layerwise", trust_remote_code=True) 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-reranker-v2-minicpm-layerwise with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BAAI/bge-reranker-v2-minicpm-layerwise", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("BAAI/bge-reranker-v2-minicpm-layerwise", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
yes_loc is not used?
#10
by ymotter - opened
Just trying to understand the logic. The yes_loc is used in FlagLLMReranker, but not in LayerWiseFlagLLMReranker. How is the probability computed?
The FlagLLMReranker retains the original structure of the model such that the final head layer maps to multiple tokens, necessitating the extraction of logits at the position where the 'Yes' token is located. In contrast, the LayerWiseFlagLLMReranker modifies the structure of the original model by keeping only the linear layer in the head that maps to the 'Yes' token, thus the final output consists solely of the logits for 'Yes'.