Feature Extraction
sentence-transformers
PyTorch
Transformers
Chinese
bert
sentence-similarity
text-embeddings-inference
Instructions to use BAAI/bge-base-zh-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-base-zh-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-base-zh-v1.5") 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 BAAI/bge-base-zh-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-base-zh-v1.5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-base-zh-v1.5") model = AutoModel.from_pretrained("BAAI/bge-base-zh-v1.5") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8eca65d0eff989840b590f9514679bbb738b8706a863afa629a4ec29ff47f63e
- Size of remote file:
- 409 MB
- SHA256:
- 731cf5d88ed555a21ff2f2d9fa4db43b10489173ddd9178db3eecfd7a2bae044
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