Feature Extraction
Transformers
Safetensors
sentence-transformers
Vietnamese
bgem3_projection
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
File size: 447 Bytes
62e0350 3fac875 62e0350 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"model_type": "bgem3_projection",
"base_model": "BAAI/bge-m3",
"d_in": 1024,
"d_out": 128,
"use_layernorm": false,
"freeze_encoder": true,
"max_length": 512,
"architectures": [
"BGEM3ProjectionModel"
],
"auto_map": {
"AutoConfig": "modeling_bgem3_projection.BGEM3ProjectionConfig",
"AutoModel": "modeling_bgem3_projection.BGEM3ProjectionModel"
},
"torch_dtype": "float32",
"transformers_version": "4.36.0"
} |