Text Classification
sentence-transformers
Safetensors
Transformers
multilingual
xlm-roberta
text-embeddings-inference
Instructions to use BAAI/bge-reranker-v2-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-reranker-v2-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/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 BAAI/bge-reranker-v2-m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BAAI/bge-reranker-v2-m3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-v2-m3") model = AutoModelForSequenceClassification.from_pretrained("BAAI/bge-reranker-v2-m3", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ed473ea2f42f1bab57bc585c3a90e9eb52ad04c8c15607e59006635dd305847
- Size of remote file:
- 2.27 GB
- SHA256:
- d9e3e081faff1eefb84019509b2f5558fd74c1a05a2c7db22f74174fcedb5286
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.