Text Classification
sentence-transformers
OpenVINO
Transformers
multilingual
xlm-roberta
text-embeddings-inference
openvino-export
Instructions to use NumberEight/bge-reranker-v2-m3-openvino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NumberEight/bge-reranker-v2-m3-openvino with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NumberEight/bge-reranker-v2-m3-openvino") 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 NumberEight/bge-reranker-v2-m3-openvino with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NumberEight/bge-reranker-v2-m3-openvino")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NumberEight/bge-reranker-v2-m3-openvino") model = AutoModelForSequenceClassification.from_pretrained("NumberEight/bge-reranker-v2-m3-openvino") - Notebooks
- Google Colab
- Kaggle
This model was converted to OpenVINO from BAAI/bge-reranker-v2-m3 using optimum-intel
via the export space.
First make sure you have optimum-intel installed:
pip install optimum[openvino]
To load your model you can do as follows:
from optimum.intel import OVModelForSequenceClassification
model_id = "NumberEight/bge-reranker-v2-m3-openvino"
model = OVModelForSequenceClassification.from_pretrained(model_id)
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Model tree for NumberEight/bge-reranker-v2-m3-openvino
Base model
BAAI/bge-reranker-v2-m3