Sentence Similarity
sentence-transformers
Safetensors
Transformers
Indonesian
deberta-v2
feature-extraction
text-embeddings-inference
Instructions to use muchad/embed-id-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use muchad/embed-id-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("muchad/embed-id-v2") 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-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("muchad/embed-id-v2") model = AutoModel.from_pretrained("muchad/embed-id-v2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.1.2", | |
| "transformers": "4.46.3", | |
| "pytorch": "2.5.1" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |