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:
# 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
| base_model: muchad/mdeberta-hybrid-30k | |
| library_name: sentence-transformers | |
| pipeline_tag: sentence-similarity | |
| language: | |
| - id | |
| tags: | |
| - sentence-transformers | |
| - feature-extraction | |
| - sentence-similarity | |
| - transformers | |
| license: apache-2.0 | |
| # Embed-ID | |
| _Note: This model is part of an ongoing research project._ | |