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") - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
#7
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Update metadata to set pipeline tag to the new
text-ranking
Changes
This is an automated pull request to update the metadata of the model card. We recently introduced the text-ranking pipeline tag for models that are used for ranking tasks, and we have a suspicion that this model is one of them.
Feel free to respond if you have questions or concerns.
- Tom Aarsen