Feature Extraction
Transformers
Safetensors
sentence-transformers
Vietnamese
bgem3_projection
image-feature-extraction
sentence-similarity
embeddings
vietnamese
rental
real-estate
custom_code
Instructions to use lamdx4/bge-m3-vietnamese-rental-projection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lamdx4/bge-m3-vietnamese-rental-projection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lamdx4/bge-m3-vietnamese-rental-projection", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lamdx4/bge-m3-vietnamese-rental-projection", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use lamdx4/bge-m3-vietnamese-rental-projection with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lamdx4/bge-m3-vietnamese-rental-projection", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28e7f1aad72bcd5240bda837b30b464c61bf1b1cd6347bbf65931c7baa99183d
- Size of remote file:
- 524 kB
- SHA256:
- 3a3ebb9ed58b96e1d14a69857eac56ce072e416f8bc79a68f641b1582e0f24b3
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