Text Classification
sentence-transformers
Safetensors
Transformers
multilingual
xlm-roberta
text-embeddings-inference
Instructions to use Oracle/bge-reranker-v2-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Oracle/bge-reranker-v2-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Oracle/bge-reranker-v2-m3") 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 Oracle/bge-reranker-v2-m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Oracle/bge-reranker-v2-m3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Oracle/bge-reranker-v2-m3") model = AutoModelForSequenceClassification.from_pretrained("Oracle/bge-reranker-v2-m3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6f245d5141ef77b3b89d7b7b9abc5ffcc095aad2908a46268b077c87d7319b6
- Size of remote file:
- 17.1 MB
- SHA256:
- 69564b696052886ed0ac63fa393e928384e0f8caada38c1f4864a9bfbf379c15
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