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:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nampham1106/bkcare-embedding") model = AutoModel.from_pretrained("nampham1106/bkcare-embedding") - Notebooks
- Google Colab
- Kaggle
Add exported onnx model 'model_qint8_avx512.onnx'
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by nampham1106 - opened
onnx/model_qint8_avx512.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:fc5458c39e27e43b0570564959ac32631ce498adba045e9f384521d4071cc146
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size 135704960
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