Feature Extraction
sentence-transformers
PyTorch
ONNX
Transformers
English
Chinese
xlm-roberta
sentence-similarity
text-embeddings-inference
Instructions to use Lucky94629/bce-embedding-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Lucky94629/bce-embedding-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lucky94629/bce-embedding-onnx") 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 Lucky94629/bce-embedding-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lucky94629/bce-embedding-onnx")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lucky94629/bce-embedding-onnx") model = AutoModel.from_pretrained("Lucky94629/bce-embedding-onnx") - Notebooks
- Google Colab
- Kaggle
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