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
File size: 622 Bytes
487c0ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"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
}
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