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	---
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	datasets:
	- klue
	language:
	- ko
	license: cc-by-4.0
	---

	# bespin-global/klue-sroberta-base-continue-learning-by-mnr

	해당 모델은 KLUE/NLI, KLUE/STS 데이터셋을 활용하였으며, sentence-transformers의 공식 문서 내 소개된 [continue-learning](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) 방법을 통해 아래와 같이 학습되었습니다.
	1. NLI 데이터셋을 통해 nagative sampling 후, MultipleNegativeRankingLoss를 활용하여 1차 NLI training 수행
	2. 1에서 학습완료 된 모델에 STS 데이터셋을 통해, CosineSimilarityLoss를 활용하여 2차 STS training 수행

	학습에 관한 자세한 내용은 [Blog](https://velog.io/@jaehyeong/Basic-NLP-sentence-transformers-%EB%9D%BC%EC%9D%B4%EB%B8%8C%EB%9F%AC%EB%A6%AC%EB%A5%BC-%ED%99%9C%EC%9A%A9%ED%95%9C-SBERT-%ED%95%99%EC%8A%B5-%EB%B0%A9%EB%B2%95#225-continue-learning-by-sts)와 [Colab 실습 코드](https://colab.research.google.com/drive/1uDt3o_Nv2cTiVbIAIUkst_eOSD37Wkmf)를 참고해주세요.

	---
	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.

	<!--- 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("bespin-global/klue-sroberta-base-continue-learning-by-mnr")
	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("bespin-global/klue-sroberta-base-continue-learning-by-mnr")
	model = AutoModel.from_pretrained("bespin-global/klue-sroberta-base-continue-learning-by-mnr")

	# 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

	<!--- Describe how your model was evaluated -->

	EmbeddingSimilarityEvaluator: Evaluating the model on sts-test dataset:
	- Cosine-Similarity :
	- Pearson: 0.8901 Spearman: 0.8893
	- Manhattan-Distance:
	- Pearson: 0.8867 Spearman: 0.8818
	- Euclidean-Distance:
	- Pearson: 0.8875 Spearman: 0.8827
	- Dot-Product-Similarity:
	- Pearson: 0.8786 Spearman: 0.8735
	- Average : 0.8892573547643868


	## Training
	The model was trained with the parameters:

	DataLoader:

	`torch.utils.data.dataloader.DataLoader` of length 329 with parameters:
	```
	{'batch_size': 32, '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": 4,
	"evaluation_steps": 32,
	"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
	"max_grad_norm": 1,
	"optimizer_class": "<class 'transformers.optimization.AdamW'>",
	"optimizer_params": {
	"lr": 2e-05
	},
	"scheduler": "WarmupLinear",
	"steps_per_epoch": null,
	"warmup_steps": 132,
	"weight_decay": 0.01
	}
	```


	## Full Model Architecture
	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) 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})
	)
	```

	## Citing & Authors

	<!--- Describe where people can find more information -->
	[JaeHyeong AN](https://huggingface.co/Copycats) at [Bespin Global](https://www.bespinglobal.com/)