README.md

---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- ko
widget:
- source_sentence: "그 식당은 파리를 날린다"
  sentences:
    - "그 식당에는 손님이 없다"
    - "그 식당에서는 드론을 날린다"
    - "파리가 식당에 날아다닌다"
  example_title: "Restaurant"
- source_sentence: "잠이 옵니다"
  sentences:
    - "잠이 안 옵니다"
    - "졸음이 옵니다"
    - "기차가 옵니다"
  example_title: "Sleepy"
---

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

SNUNLP lab에서 tuning한 [KR-SBERT](snunlp/KR-SBERT-V40K-klueNLI-augSTS)를 다시 [fine-tuning](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss)한 버전이다.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
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)
```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
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)
```

## 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})
)
```