
# albert-small-kor-sbert-v1.1

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.

[albert-small-kor-v1](https://huggingface.co/bongsoo/albert-small-kor-v1) 모델을 sentencebert로 만든 모델.


## 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-v1.1')
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-v1.1')
model = AutoModel.from_pretrained('bongsoo/albert-small-kor-sbert-v1.1')

# 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

- 성능 측정을 위한 말뭉치는,  아래 한국어 (kor), 영어(en)  평가 말뭉치를 이용함
<br> 한국어 : **korsts(1,379쌍문장)** 와 **klue-sts(519쌍문장)** 
<br> 영어 : [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt)(1,376쌍문장) 와 [glue:stsb](https://huggingface.co/datasets/glue/viewer/stsb/validation) (1,500쌍문장)
- 성능 지표는 **cosin.spearman**
- 평가 측정 코드는 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-test3.ipynb) 참조
- 
|모델     |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/albert-small-kor-sbert-v1      |0.8305    |0.8588    |0.8419           |0.7965|
|bongsoo/klue-sbert-v1.0                |0.8529    |0.8952    |0.8813           |0.8469|
|bongsoo/kpf-sbert-v1.1                 |0.8750    |0.8900    |0.8863           |0.8554|
|**bongsoo/albert-small-kor-sbert-v1.1**    |0.8526    |0.8833    |0.8484           |0.8286|

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
- [albert-small-kor-v1](https://huggingface.co/bongsoo/albert-small-kor-v1) 모델을 **sts(10)-distil(10) 훈련만 시킴** (**nli-sts추가 훈련시키면 정합도가 떨어짐**)
- 교사모델은 [**kpf-sbert-v1.1**](https://huggingface.co/bongsoo/kpf-sbert-v1.1) 이용함.
  
The model was trained with the parameters:

**공통**
- **do_lower_case=1, correct_bios=0, polling_mode=cls**
  
**1.STS**
- 말뭉치 : korsts(5,749) + kluestsV1.1(11,668) + stsb_multi_mt(5,749) + mteb/sickr-sts(9,927) + glue stsb(5,749) (총:38,842)
- Param : **lr: 1e-4, eps: 1e-6, warm_step=10%, epochs: 10, train_batch: 32, eval_batch: 64, max_token_len: 72**
- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sentece-bert-sts.ipynb) 참조
    
**2.distilation**
- 교사 모델 : [**kpf-sbert-v1.1**](https://huggingface.co/bongsoo/kpf-sbert-v1.1)(max_token_len:128)
- 말뭉치 : news_talk_ko_en_train.tsv (한국어-영어 대화-뉴스 병렬 말뭉치 : 1.38M)
- Param : **lr: 5e-5, epochs: 10, train_batch: 32, eval/test_batch: 64, max_token_len: 128(교사모델이 128이므로 맟춰줌)**
- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-distillaton2.ipynb) 참조



## 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
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch albert feature-extraction sentence-similarity transformers endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 

albert-small-kor-sbert-v1.1
===========================


This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.


albert-small-kor-v1 모델을 sentencebert로 만든 모델.


Usage (Sentence-Transformers)
-----------------------------


Using this model becomes easy when you have sentence-transformers installed:


Then you can use the model like this:


Usage (HuggingFace Transformers)
--------------------------------


Without sentence-transformers, 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.


Evaluation Results
------------------


* 성능 측정을 위한 말뭉치는, 아래 한국어 (kor), 영어(en) 평가 말뭉치를 이용함
  
 한국어 : korsts(1,379쌍문장) 와 klue-sts(519쌍문장)
  
 영어 : stsb\_multi\_mt(1,376쌍문장) 와 glue:stsb (1,500쌍문장)
* 성능 지표는 cosin.spearman
* 평가 측정 코드는 여기 참조
* 



For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL


Training
--------


* albert-small-kor-v1 모델을 sts(10)-distil(10) 훈련만 시킴 (nli-sts추가 훈련시키면 정합도가 떨어짐)
* 교사모델은 kpf-sbert-v1.1 이용함.


The model was trained with the parameters:


공통


* do\_lower\_case=1, correct\_bios=0, polling\_mode=cls


1.STS


* 말뭉치 : korsts(5,749) + kluestsV1.1(11,668) + stsb\_multi\_mt(5,749) + mteb/sickr-sts(9,927) + glue stsb(5,749) (총:38,842)
* Param : lr: 1e-4, eps: 1e-6, warm\_step=10%, epochs: 10, train\_batch: 32, eval\_batch: 64, max\_token\_len: 72
* 훈련코드 여기 참조


2.distilation


* 교사 모델 : kpf-sbert-v1.1(max\_token\_len:128)
* 말뭉치 : news\_talk\_ko\_en\_train.tsv (한국어-영어 대화-뉴스 병렬 말뭉치 : 1.38M)
* Param : lr: 5e-5, epochs: 10, train\_batch: 32, eval/test\_batch: 64, max\_token\_len: 128(교사모델이 128이므로 맟춰줌)
* 훈련코드 여기 참조


Full Model Architecture
-----------------------


Citing & Authors
----------------


bongsoo



TAGS
#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 

TAGS
#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 
