KR SBERT V40K Kluenli Augsts Ft

🚀 marigold334/KR-SBERT-V40K-klueNLI-augSTS-ft

This is a version of KR-SBERT that has been fine-tuned by the SNUNLP lab using fine-tuning. It is designed for sentence similarity tasks.

Pipeline and Tags

• Pipeline Tag: sentence-similarity
• Tags: sentence-transformers, feature-extraction, sentence-similarity, transformers

Language

• Korean (ko)

Widget Examples

• Example 1: Restaurant
  • Source Sentence: "그 식당은 파리를 날린다"
  • Comparison Sentences:
    • "그 식당에는 손님이 없다"
    • "그 식당에서는 드론을 날린다"
    • "파리가 식당에 날아다닌다"
• Example 2: Sleepy
  • Source Sentence: "잠이 옵니다"
  • Comparison Sentences:
    • "잠이 안 옵니다"
    • "졸음이 옵니다"
    • "기차가 옵니다"

🚀 Quick Start

✨ Features

This model is a fine - tuned version of KR-SBERT, which can effectively perform sentence similarity tasks.

📦 Installation

To use this model, you need to install sentence-transformers:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage (Sentence-Transformers)

If you have sentence-transformers installed, using this model is straightforward:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('snunlp/KR-SBERT-V40K-klueNLI-augSTS-ft')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model as follows: First, pass your input through the transformer model, then apply the appropriate pooling operation on top of the contextualized word embeddings.

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 = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('snunlp/KR-SBERT-V40K-klueNLI-augSTS')
model = AutoModel.from_pretrained('snunlp/KR-SBERT-V40K-klueNLI-augSTS-ft')

# 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)

🔧 Technical Details

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)