---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language: ko
---
# kf-deberta-multitask
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. You can check the training recipes on [GitHub](https://github.com/upskyy/kf-deberta-multitask).
## 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 = ["안녕하세요?", "한국어 문장 임베딩을 위한 버트 모델입니다."]
model = SentenceTransformer("upskyy/kf-deberta-multitask")
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 = ["안녕하세요?", "한국어 문장 임베딩을 위한 버트 모델입니다."]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("upskyy/kf-deberta-multitask")
model = AutoModel.from_pretrained("upskyy/kf-deberta-multitask")
# 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)
```
## Evaluation Results
KorSTS, KorNLI 학습 데이터셋으로 멀티 태스크 학습을 진행한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다.
- Cosine Pearson: 85.75
- Cosine Spearman: 86.25
- Manhattan Pearson: 84.80
- Manhattan Spearman: 85.27
- Euclidean Pearson: 84.79
- Euclidean Spearman: 85.25
- Dot Pearson: 82.93
- Dot Spearman: 82.86
|model|cosine_pearson|cosine_spearman|euclidean_pearson|euclidean_spearman|manhattan_pearson|manhattan_spearman|dot_pearson|dot_spearman|
|:-------------------------|-----------------:|------------------:|--------------------:|---------------------:|--------------------:|---------------------:|--------------:|---------------:|
|[kf-deberta-multitask](https://huggingface.co/upskyy/kf-deberta-multitask)|**85.75**|**86.25**|**84.79**|**85.25**|**84.80**|**85.27**|**82.93**|**82.86**|
|[ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask)|84.77|85.6|83.71|84.40|83.70|84.38|82.42|82.33|
|[ko-sbert-multitask](https://huggingface.co/jhgan/ko-sbert-multitask)|84.13|84.71|82.42|82.66|82.41|82.69|80.05|79.69|
|[ko-sroberta-base-nli](https://huggingface.co/jhgan/ko-sroberta-nli)|82.83|83.85|82.87|83.29|82.88|83.28|80.34|79.69|
|[ko-sbert-nli](https://huggingface.co/jhgan/ko-sbert-multitask)|82.24|83.16|82.19|82.31|82.18|82.3|79.3|78.78|
|[ko-sroberta-sts](https://huggingface.co/jhgan/ko-sroberta-sts)|81.84|81.82|81.15|81.25|81.14|81.25|79.09|78.54|
|[ko-sbert-sts](https://huggingface.co/jhgan/ko-sbert-sts)|81.55|81.23|79.94|79.79|79.9|79.75|76.02|75.31|
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 4442 with parameters:
```
{'batch_size': 128}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 719 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 719,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@proceedings{jeon-etal-2023-kfdeberta,
title = {KF-DeBERTa: Financial Domain-specific Pre-trained Language Model},
author = {Eunkwang Jeon, Jungdae Kim, Minsang Song, and Joohyun Ryu},
booktitle = {Proceedings of the 35th Annual Conference on Human and Cognitive Language Technology},
moth = {oct},
year = {2023},
publisher = {Korean Institute of Information Scientists and Engineers},
url = {http://www.hclt.kr/symp/?lnb=conference},
pages = {143--148},
}
```
```bibtex
@article{ham2020kornli,
title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
journal={arXiv preprint arXiv:2004.03289},
year={2020}
}
```