
# 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})
)
```

{"language": ["ko"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "\uadf8 \uc2dd\ub2f9\uc740 \ud30c\ub9ac\ub97c \ub0a0\ub9b0\ub2e4", "sentences": ["\uadf8 \uc2dd\ub2f9\uc5d0\ub294 \uc190\ub2d8\uc774 \uc5c6\ub2e4", "\uadf8 \uc2dd\ub2f9\uc5d0\uc11c\ub294 \ub4dc\ub860\uc744 \ub0a0\ub9b0\ub2e4", "\ud30c\ub9ac\uac00 \uc2dd\ub2f9\uc5d0 \ub0a0\uc544\ub2e4\ub2cc\ub2e4"], "example_title": "Restaurant"}, {"source_sentence": "\uc7a0\uc774 \uc635\ub2c8\ub2e4", "sentences": ["\uc7a0\uc774 \uc548 \uc635\ub2c8\ub2e4", "\uc878\uc74c\uc774 \uc635\ub2c8\ub2e4", "\uae30\ucc28\uac00 \uc635\ub2c8\ub2e4"], "example_title": "Sleepy"}]}
sentence-transformers pytorch bert feature-extraction sentence-similarity transformers ko endpoints_compatible region:us