Text Classification
sentence-transformers
Safetensors
Transformers
multilingual
gemma
text-generation
Instructions to use BAAI/bge-reranker-v2-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-reranker-v2-gemma with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-reranker-v2-gemma") 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-gemma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BAAI/bge-reranker-v2-gemma")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-v2-gemma") model = AutoModelForCausalLM.from_pretrained("BAAI/bge-reranker-v2-gemma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d801d5d51a7154272c47652aa97b6071e7bef26cbf36dac7ca85496304dc30d6
- Size of remote file:
- 17.5 MB
- SHA256:
- d0d908b4f9326e0998815690e325b6abbd378978553e10627924dd825db7e243
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