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") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "muchad/mdeberta-hybrid-30k", | |
| "architectures": [ | |
| "DebertaV2Model" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-07, | |
| "legacy": true, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta-v2", | |
| "norm_rel_ebd": "layer_norm", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 768, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.3", | |
| "type_vocab_size": 0, | |
| "vocab_size": 30000 | |
| } | |