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
File size: 133 Bytes
09a9ece | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:69564b696052886ed0ac63fa393e928384e0f8caada38c1f4864a9bfbf379c15
size 17098273
|