Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:112
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use acradin/DK_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use acradin/DK_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("acradin/DK_embedding") sentences = [ "슈파인", "park | 장비를 파킹(대기) 위치로 이동 또는 튜브를 맨위로 | 파킹", "tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect, chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로", "tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config.json from acradin/DK_embedding: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
-
https://huggingface.co/acradin/DK_embedding/resolve/main/config.json
- Command line
-
hf download hf://acradin/DK_embedding/config.json
-
curl -L -o config.json https://huggingface.co/acradin/DK_embedding/resolve/main/config.json
1.71 kB
| { | |
| "_sliding_window_pattern": 6, | |
| "architectures": [ | |
| "Gemma3TextModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_logit_softcapping": null, | |
| "bos_token_id": 2, | |
| "dtype": "float32", | |
| "eos_token_id": 1, | |
| "final_logit_softcapping": null, | |
| "head_dim": 256, | |
| "hidden_activation": "gelu_pytorch_tanh", | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1152, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 2048, | |
| "model_type": "gemma3_text", | |
| "num_attention_heads": 3, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 1, | |
| "pad_token_id": 0, | |
| "query_pre_attn_scalar": 256, | |
| "rms_norm_eps": 1e-06, | |
| "rope_parameters": { | |
| "full_attention": { | |
| "rope_theta": 1000000.0, | |
| "rope_type": "default" | |
| }, | |
| "sliding_attention": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "default" | |
| } | |
| }, | |
| "sliding_window": 257, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "use_bidirectional_attention": true, | |
| "use_cache": true, | |
| "vocab_size": 262144 | |
| } | |