Klue Roberta Small Nli Sts

🚀 ddobokki/klue-roberta-small-nli-sts

這是一個韓語Sentence Transformer模型，可用於句子相似度計算等任務，為韓語相關的語義處理提供支持。

🚀 快速開始

本模型可以通過sentence-transformers庫或transformers庫來使用。下面分別介紹具體的使用方法。

📦 安裝指南

使用sentence-transformers庫前，需要先安裝它：

pip install -U sentence-transformers

💻 使用示例

基礎用法（Sentence-Transformers）

使用sentence-transformers庫的示例代碼如下：

from sentence_transformers import SentenceTransformer
sentences = ["흐르는 강물을 거꾸로 거슬러 오르는", "세월이 가면 가슴이 터질 듯한"]

model = SentenceTransformer('ddobokki/klue-roberta-small-nli-sts')
embeddings = model.encode(sentences)
print(embeddings)

高級用法（HuggingFace Transformers）

若僅使用transformers庫，可參考以下代碼：

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ["흐르는 강물을 거꾸로 거슬러 오르는", "세월이 가면 가슴이 터질 듯한"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ddobokki/klue-roberta-small-nli-sts')
model = AutoModel.from_pretrained('ddobokki/klue-roberta-small-nli-sts')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

📚 詳細文檔

性能表現

• 語義文本相似度測試集結果
  

完整模型架構

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

引用與作者

如需瞭解更多信息，請參考相關文檔。