Feature Extraction
sentence-transformers
PyTorch
Transformers
Chinese
bert
sentence-similarity
text-embeddings-inference
Instructions to use BAAI/bge-large-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-large-zh-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-large-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-large-zh-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-large-zh-v1.5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-large-zh-v1.5") model = AutoModel.from_pretrained("BAAI/bge-large-zh-v1.5", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57e0f934e2119245da805d8a6e8fb02178d5ded3f2e97ab9eaca5225f8ea47bf
- Size of remote file:
- 1.3 GB
- SHA256:
- 74541fa3bddf35c2ed35a7c21542776fb830cde7d372dbe05ca42aa70f2bf904
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