Instructions to use nampham1106/bkcare-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nampham1106/bkcare-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nampham1106/bkcare-embedding") 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 nampham1106/bkcare-embedding with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nampham1106/bkcare-embedding") model = AutoModel.from_pretrained("nampham1106/bkcare-embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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