Sentence Similarity
sentence-transformers
Safetensors
Transformers
Indonesian
deberta-v2
feature-extraction
text-embeddings-inference
Instructions to use muchad/embed-id with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use muchad/embed-id with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("muchad/embed-id") 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] - Transformers
How to use muchad/embed-id with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("muchad/embed-id") model = AutoModel.from_pretrained("muchad/embed-id", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Clean up: remove jupyter checkpoints
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Clean up: remove jupyter checkpoints
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Add ONNX version of the model
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Initial upload
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initial commit
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