Sentence Similarity
sentence-transformers
PyTorch
ONNX
Transformers
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use SmartComponents/bge-micro-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SmartComponents/bge-micro-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SmartComponents/bge-micro-v2") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use SmartComponents/bge-micro-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SmartComponents/bge-micro-v2") model = AutoModel.from_pretrained("SmartComponents/bge-micro-v2") - Notebooks
- Google Colab
- Kaggle
File size: 123 Bytes
b736012 | 1 2 3 4 5 6 7 | {
"__version__": {
"sentence_transformers": "2.2.2",
"transformers": "4.34.0",
"pytorch": "2.0.1+cu118"
}
} |