Text Classification
sentence-transformers
OpenVINO
Transformers
multilingual
xlm-roberta
text-embeddings-inference
openvino-export
Instructions to use sridhariyer/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 sridhariyer/bge-reranker-v2-m3-openvino with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sridhariyer/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 sridhariyer/bge-reranker-v2-m3-openvino with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sridhariyer/bge-reranker-v2-m3-openvino")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sridhariyer/bge-reranker-v2-m3-openvino") model = AutoModelForSequenceClassification.from_pretrained("sridhariyer/bge-reranker-v2-m3-openvino", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload openvino_model.xml with huggingface_hub
Browse files- openvino_model.xml +0 -0
openvino_model.xml
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|