Feature Extraction
sentence-transformers
PyTorch
Transformers
English
Chinese
xlm-roberta
sentence-similarity
text-embeddings-inference
Instructions to use nitsuai/bce-embedding-base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nitsuai/bce-embedding-base_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nitsuai/bce-embedding-base_v1") 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 nitsuai/bce-embedding-base_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nitsuai/bce-embedding-base_v1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nitsuai/bce-embedding-base_v1") model = AutoModel.from_pretrained("nitsuai/bce-embedding-base_v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdfc491bf5688aac5406092735a63d64decbc5e6e7b9b614bedb73aad514d404
- Size of remote file:
- 17.1 MB
- SHA256:
- 21106b6d7dab2952c1d496fb21d5dc9db75c28ed361a05f5020bbba27810dd08
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