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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# albert-small-kor-sbert-v1
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('bongsoo/albert-small-kor-sbert-v1')
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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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('bongsoo/albert-small-kor-sbert-v1')
model = AutoModel.from_pretrained('bongsoo/albert-small-kor-sbert-v1')
# 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, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
- ์ฑ๋ฅ ์ธก์ ์ ์ํ ๋ง๋ญ์น๋, ์๋ ํ๊ตญ์ด (kor), ์์ด(en) ํ๊ฐ ๋ง๋ญ์น๋ฅผ ์ด์ฉํจ
<br> ํ๊ตญ์ด : **korsts(1,379์๋ฌธ์ฅ)** ์ **klue-sts(519์๋ฌธ์ฅ)**
<br> ์์ด : [stsb_multi_mt](https://huggingface.co./datasets/stsb_multi_mt)(1,376์๋ฌธ์ฅ) ์ [glue:stsb](https://huggingface.co./datasets/glue/viewer/stsb/validation) (1,500์๋ฌธ์ฅ)
- ์ฑ๋ฅ ์งํ๋ **cosin.spearman**
- ํ๊ฐ ์ธก์ ์ฝ๋๋ [์ฌ๊ธฐ](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-test3.ipynb) ์ฐธ์กฐ
-
|๋ชจ๋ธ |korsts|klue-sts|glue(stsb)|stsb_multi_mt(en)|
|:--------|------:|--------:|--------------:|------------:|
|distiluse-base-multilingual-cased-v2 |0.7475 |0.7855 |0.8193 |0.8075|
|paraphrase-multilingual-mpnet-base-v2 |0.8201 |0.7993 |0.8907 |0.8682|
|bongsoo/moco-sentencedistilbertV2.1 |0.8390 |0.8767 |0.8805 |0.8548|
|bongsoo/albert-small-kor-sbert-v1 |0.8305 |0.8588 |0.8419 |0.7965|
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
- sts(10)-distil(10)-nli(3)-sts(10)
The model was trained with the parameters:
**๊ณตํต**
- **do_lower_case=1, correct_bios=0, polling_mode=cls**
**1.STS**
- ๋ง๋ญ์น : korsts(5,749) + kluestsV1.1(11,668) + stsb_multi_mt(5,749) + mteb/sickr-sts(9,927) + glue stsb(5,749) (์ด:38,842)
- Param : **lr: 1e-4, eps: 1e-6, warm_step=10%, epochs: 10, train_batch: 32, eval_batch: 64, max_token_len: 72**
- ํ๋ จ์ฝ๋ [์ฌ๊ธฐ](https://github.com/kobongsoo/BERT/blob/master/sbert/sentece-bert-sts.ipynb) ์ฐธ์กฐ
**2.distilation**
- ๊ต์ฌ ๋ชจ๋ธ : paraphrase-multilingual-mpnet-base-v2(max_token_len:128)
- ๋ง๋ญ์น : news_talk_en_ko_train.tsv (์์ด-ํ๊ตญ์ด ๋ํ-๋ด์ค ๋ณ๋ ฌ ๋ง๋ญ์น : 1.38M)
- Param : **lr: 5e-5, epochs: 10, train_batch: 128, eval/test_batch: 64, max_token_len: 128(๊ต์ฌ๋ชจ๋ธ์ด 128์ด๋ฏ๋ก ๋ง์ถฐ์ค)**
- ํ๋ จ์ฝ๋ [์ฌ๊ธฐ](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-distillaton.ipynb) ์ฐธ์กฐ
**3.NLI**
- ๋ง๋ญ์น : ํ๋ จ(967,852) : kornli(550,152), kluenli(24,998), glue-mnli(392,702) / ํ๊ฐ(3,519) : korsts(1,500), kluests(519), gluests(1,500) ()
- HyperParameter : **lr: 3e-5, eps: 1e-8, warm_step=10%, epochs: 3, train/eval_batch: 64, max_token_len: 128**
- ํ๋ จ์ฝ๋ [์ฌ๊ธฐ](https://github.com/kobongsoo/BERT/blob/master/sbert/sentence-bert-nli.ipynb) ์ฐธ์กฐ
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': True}) with Transformer model: AlbertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
bongsoo |