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Model: bongsoo/moco-sentencedistilbertV2.0 Source: Original Platform
2026-07-05 14:34:56 +08:00
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--- a/1_Pooling/config.json
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+{
+  "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
+}
--- a/README.md
+++ b/README.md
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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- ko
+- en
+widget:
+  source_sentence: "대한민국의 수도는?"
+  sentences:
+    - "서울특별시는 한국이 정치,경제,문화 중심 도시이다."
+    - "부산은 대한민국의 제2의 도시이자 최대의 해양 물류 도시이다."
+    - "제주도는 대한민국에서 유명한 관광지이다"
+    - "Seoul is the capital of Korea"
+    - "울산광역시는 대한민국 남동부 해안에 있는 광역시이다" 
+---
+
+# moco-sentencedistilbertV2.0
+
+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 -->
+
+- 이 모델은 [mdistilbertV1.1](https://huggingface.co/bongsoo/mdistilbertV1.1) 모델에 [moco-corpus 말뭉치](https://huggingface.co/datasets/bongsoo/moco-corpus)(MOCOMSYS 추출 3.2M 문장)로 
+<br>sentencebert로 만든 후,추가적으로 STS Tearch-student 증류 학습 시켜 만든 모델 입니다.
+- **vocab: 164,314 개**(기존 mdistilbertV1.1 vocab(146,444 개)에 17,870개 vocab 추가)
+<br> **MLM 모델 : bongsoo/mdistilbertV2.0** 
+
+## 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/moco-sentencedistilbertV2.0')
+embeddings = model.encode(sentences)
+print(embeddings)
+
+# sklearn 을 이용하여 cosine_scores를 구함
+# => 입력값 embeddings 은 (1,768) 처럼 2D 여야 함.
+from sklearn.metrics.pairwise import paired_cosine_distances, paired_euclidean_distances, paired_manhattan_distances
+cosine_scores = 1 - (paired_cosine_distances(embeddings[0].reshape(1,-1), embeddings[1].reshape(1,-1)))
+
+print(f'*cosine_score:{cosine_scores[0]}')
+
+```
+#### 출력(Outputs)
+```
+[[ 9.7172342e-02 -3.3226651e-01 -7.7130608e-05 ...  1.3900512e-02 2.1072578e-01 -1.5386048e-01]
+ [ 2.3313640e-02 -8.4675789e-02 -3.7715461e-06 ...  2.4005771e-02 -1.6602692e-01 -1.2729791e-01]]
+*cosine_score:0.3383665680885315
+```
+
+## 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.
+- 평균 폴링(mean_pooling) 방식 사용. ([cls 폴링](https://huggingface.co/sentence-transformers/bert-base-nli-cls-token), [max 폴링](https://huggingface.co/sentence-transformers/bert-base-nli-max-tokens))
+```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('bongsoo/moco-sentencedistilbertV2.0')
+model = AutoModel.from_pretrained('bongsoo/moco-sentencedistilbertV2.0')
+
+# 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)
+
+# sklearn 을 이용하여 cosine_scores를 구함
+# => 입력값 embeddings 은 (1,768) 처럼 2D 여야 함.
+from sklearn.metrics.pairwise import paired_cosine_distances, paired_euclidean_distances, paired_manhattan_distances
+cosine_scores = 1 - (paired_cosine_distances(sentence_embeddings[0].reshape(1,-1), sentence_embeddings[1].reshape(1,-1)))
+
+print(f'*cosine_score:{cosine_scores[0]}')
+```
+#### 출력(Outputs)
+```
+Sentence embeddings:
+tensor([[ 9.7172e-02, -3.3227e-01, -7.7131e-05,  ...,  1.3901e-02, 2.1073e-01, -1.5386e-01],
+        [ 2.3314e-02, -8.4676e-02, -3.7715e-06,  ...,  2.4006e-02, -1.6603e-01, -1.2730e-01]])
+*cosine_score:0.3383665680885315
+```
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+- 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)
+- 성능 지표는 **cosin.spearman** 측정하여 비교함.
+- 평가 측정 코드는 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-test.ipynb) 참조
+
+|모델     |korsts|klue-sts|korsts+klue-sts|stsb_multi_mt
+|:--------|------:|--------:|--------------:|------------:|
+|bongsoo/sentencedistilbertV1.2|0.819|0.858|0.630|0.837|
+|distiluse-base-multilingual-cased-v2|0.747|0.785|0.577|0.807|
+|paraphrase-multilingual-mpnet-base-v2|0.820|0.799|0.711|0.868|
+|bongsoo/moco-sentencedistilbertV2.0|0.812|0.847|0.627|0.837|
+
+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(훈련 과정)
+The model was trained with the parameters:
+
+**1. MLM 훈련**
+- 입력 모델 : bongsoo/mdistilbertV1.1(*kowiki20220620(4.4M) 말뭉치 훈련된 distilbert-base-multilingual-cased)
+- 말뭉치 : nlp_corpus(3.2M) : MOCOMSYS 파일들 정제한 말뭉치
+- HyperParameter : LearningRate : 5e-5, epochs: 8, batchsize: 32, max_token_len : 128
+- 출력 모델 : mdistilbertV2.0
+- 훈련시간 : 27h
+- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/distilbert/distilbert-MLM-Trainer-V1.2.ipynb) 참조
+
+**2. STS 훈련**
+- distilbert를 sentencebert로 만듬. 
+- 입력 모델 : mdistilbertV2.0
+- 말뭉치 : korsts + kluestsV1.1 + stsb_multi_mt + mteb/sickr-sts (총:33,093)
+- HyperParameter : LearningRate : 2e-5, epochs: 200, batchsize: 32, max_token_len : 128
+- 출력 모델 : sbert-mdistilbertV2.0
+- 훈련시간 : 5h
+- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sentece-bert-sts.ipynb) 참조
+    
+**3.증류(distilation) 훈련**
+- 학생 모델 : sbert-mdistilbertV2.0
+- 교사 모델 : paraphrase-multilingual-mpnet-base-v2
+- 말뭉치 : en_ko_train.tsv(한국어-영어 사회과학분야 병렬 말뭉치 : 1.1M)
+- HyperParameter : LearningRate : 5e-5, epochs: 40, batchsize: 32, max_token_len : 128
+- 출력 모델 : sbert-mdistilbertV2.0.2-distil
+- 훈련시간 : 11h
+- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-distillaton.ipynb) 참조
+
+**4.STS 훈련**
+-sentencebert 모델을 sts 훈련시킴
+- 입력 모델 : sbert-mdistilbertV2.0.2-distil
+- 말뭉치 : korsts + kluestsV1.1 + stsb_multi_mt + mteb/sickr-sts (총:33,093)
+- HyperParameter : LearningRate : 3e-5, epochs: 800, batchsize: 32, max_token_len : 128
+- 출력 모델 : moco-sentencedistilbertV2.0
+- 훈련시간 : 15h
+- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sentece-bert-sts.ipynb) 참조
+
+<br>모델 제작 과정에 대한 자세한 내용은 [여기](https://github.com/kobongsoo/BERT/tree/master)를 참조 하세요.
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 1035 with parameters:
+```
+{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Config**:
+
+```
+{
+  "_name_or_path": "../../data11/model/sbert/sbert-mdistilbertV2.0.2-distil",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.21.2",
+  "vocab_size": 164314
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
+  (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 -->
+bongsoo
--- a/config.json
+++ b/config.json
@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "../../data11/model/sbert/sbert-mdistilbertV2.0.2-distil",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.21.2",
+  "vocab_size": 164314
+}
--- a/config_sentence_transformers.json
+++ b/config_sentence_transformers.json
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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.21.2",
+    "pytorch": "1.10.1"
+  }
+}
--- a/modules.json
+++ b/modules.json
@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]
--- a/pytorch_model.bin
+++ b/pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a9f5905efbcfc610dcc42b277aa5b86a4f35a28e6c0b3c76e9673b9ad568bc6
+size 676492537
--- a/sentence_bert_config.json
+++ b/sentence_bert_config.json
@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}
--- a/similarity_evaluation_sts-test_results.csv
+++ b/similarity_evaluation_sts-test_results.csv
@@ -0,0 +1,2 @@
+epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
+-1,-1,0.8128835515645858,0.8124551079585406,0.8071323834704772,0.8137510872306414,0.8072607724138101,0.8128531495884711,0.7697509491944916,0.7648132782295333
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,16 @@
+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 128,
+  "name_or_path": "../../data11/model/sbert/sbert-mdistilbertV2.0.2-distil",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": "../tokenizer_sample/moco-vocab/mdistilbertV1.2/special_tokens_map.json",
+  "strip_accents": false,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}
--- a/vocab.txt
+++ b/vocab.txt