Text Classification
sentence-transformers
Safetensors
Transformers
multilingual
xlm-roberta
feature-extraction
bnb-my-repo
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use colabbear/bge-reranker-v2-m3-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use colabbear/bge-reranker-v2-m3-bnb-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("colabbear/bge-reranker-v2-m3-bnb-4bit") 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 colabbear/bge-reranker-v2-m3-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="colabbear/bge-reranker-v2-m3-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("colabbear/bge-reranker-v2-m3-bnb-4bit") model = AutoModelForMultimodalLM.from_pretrained("colabbear/bge-reranker-v2-m3-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23c6b10cba85272d8ee4338b16f5f742ed70aaae64f29c7242041c6fe4aa4fe6
- Size of remote file:
- 1.22 GB
- SHA256:
- 1f98bd4031af52b6b36a882550c5a7e47f486ecc53e5ef76e5028dd4c40e0d57
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