Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:574389
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use josangho99/ko-multilingual-e5-small-multiTask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use josangho99/ko-multilingual-e5-small-multiTask with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("josangho99/ko-multilingual-e5-small-multiTask") sentences = [ "전 나치 죽음의 수용소 경비원 뎀잔주크 91세 사망", "나치 사형수 수용소 경비원으로 유죄 판결을 받은 존 뎀잔죽 은 91세의 나이로 사망한다", "2040년까지 은퇴자들은 인구의 3분의 1을 차지할 것이며, 이는 오늘의 5분의 1에서 증가할 것이다.", "이집트에서 살해된 스카이 뉴스 카메라맨" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "transformers_version": "4.56.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 250037 | |
| } | |