2nd/10_Wiki/Topics/AI_and_ML/Bert-Language-Model.md

---
id: wiki-2026-0508-bert-language-model
title: BERT (Bidirectional Encoder Representations from Transformers)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [BERT, RoBERTa, DeBERTa, ModernBERT, encoder model, MLM, sentence embedding]
duplicate_of: none
source_trust_level: A
confidence_score: 0.95
verification_status: applied
tags: [bert, transformer, encoder, mlm, pretraining, fine-tuning, embedding, classification, nlp]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: Transformers (HuggingFace) / PyTorch
---

# BERT

## 📌 한 줄 통찰
> **"매 양방향 의 천재"**. 매 left-only 의 LM 의 break — 매 bidirectional context. 매 NLU 의 dominator. 매 GPT 시대 의 generation 의 lose 가, 매 classification / retrieval / embedding 의 still gold standard. 매 ModernBERT (2024) 의 revival.

## 📖 핵심

### 매 architecture
- 매 Transformer encoder 만 (decoder X).
- 매 12 layer (base) / 24 (large).
- 매 hidden 768 (base) / 1024 (large).
- 매 110M (base) / 340M (large) param.

### 매 training objective

#### MLM (Masked Language Model)
- 매 15% token 의 mask.
- 매 80% [MASK], 매 10% random, 매 10% unchanged.
- 매 bidirectional context 의 predict.

#### NSP (Next Sentence Prediction)
- 매 두 sentence 가 이어지는가.
- → 매 RoBERTa 가 drop (매 useless).

### 매 input format
- `[CLS] sentence A [SEP] sentence B [SEP]`
- 매 [CLS] 의 final representation = 매 classification.
- 매 segment embedding (A vs B).
- 매 position embedding (learned).

### 매 fine-tuning task
1. **Classification**: 매 [CLS] → 매 linear → 매 label.
2. **NER** (token classification): 매 token 별 label.
3. **QA** (extractive): 매 start + end token.
4. **Sentence pair**: 매 NLI, 매 STS.
5. **Embedding**: 매 [CLS] or 매 mean pool.

### 매 variant
| Model | 변경 |
|---|---|
| RoBERTa | 매 NSP X, 매 더 많은 data, 매 dynamic mask |
| ALBERT | 매 param share, 매 small |
| DistilBERT | 매 distill — 매 60% size |
| DeBERTa (v3) | 매 disentangled attention |
| ELECTRA | 매 replaced token detection |
| ModernBERT (2024) | 매 8K context, 매 GeGLU, 매 fast |

### 매 modern relevance
- **Embedding**: sentence-transformers 의 base.
- **Classification**: 매 fast + cheap.
- **Retrieval**: 매 dense retriever.
- **Cross-encoder reranker**: 매 bi-encoder candidate 의 rerank.
- **Token-level task**: 매 NER, 매 POS.

→ 매 GPT 의 substitute X — 매 different niche.

### 매 BERT vs GPT
| 측면 | BERT | GPT |
|---|---|---|
| Architecture | Encoder | Decoder |
| Direction | Bidirectional | Causal |
| Task | NLU, embed | NLG |
| Cost | Low | High |
| Latency | Low | High |
| Size | 100M-1B | 1B-1T |

## 💻 패턴

### Classification (HuggingFace)
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained(
    'bert-base-uncased', num_labels=2,
)

dataset = load_dataset('imdb')
def tokenize(examples):
    return tokenizer(examples['text'], truncation=True, padding='max_length')
tokenized = dataset.map(tokenize, batched=True)

args = TrainingArguments(output_dir='./out', num_train_epochs=3, per_device_train_batch_size=16)
trainer = Trainer(model=model, args=args, train_dataset=tokenized['train'], eval_dataset=tokenized['test'])
trainer.train()
```

### NER (token classification)
```python
from transformers import AutoModelForTokenClassification

model = AutoModelForTokenClassification.from_pretrained(
    'bert-base-cased', num_labels=len(label_list),
)

# 매 BIO tagging: B-PER, I-PER, B-LOC, ...
def tokenize_align_labels(examples):
    tokenized = tokenizer(examples['tokens'], is_split_into_words=True, truncation=True)
    labels = []
    for i, label in enumerate(examples['ner_tags']):
        word_ids = tokenized.word_ids(i)
        aligned = [-100 if w is None else label[w] for w in word_ids]
        labels.append(aligned)
    tokenized['labels'] = labels
    return tokenized
```

### QA (extractive)
```python
from transformers import AutoModelForQuestionAnswering

model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased')

inputs = tokenizer(question, context, return_tensors='pt')
outputs = model(**inputs)
start = outputs.start_logits.argmax()
end = outputs.end_logits.argmax()
answer = tokenizer.decode(inputs['input_ids'][0][start:end+1])
```

### Sentence embedding (sentence-transformers)
```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')  # 매 BERT 변형
embeddings = model.encode(['hello world', 'foo bar'])
# (2, 384)

# 매 similarity
from sklearn.metrics.pairwise import cosine_similarity
sim = cosine_similarity([embeddings[0]], [embeddings[1]])
```

### Cross-encoder reranker
```python
from sentence_transformers import CrossEncoder

reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

query = 'What is BERT?'
candidates = retriever.search(query, top_k=100)  # 매 bi-encoder
pairs = [[query, c.text] for c in candidates]
scores = reranker.predict(pairs)
top10 = sorted(zip(candidates, scores), key=lambda x: -x[1])[:10]
```

### ModernBERT (2024)
```python
from transformers import AutoModel

# 매 8K context, 매 GeGLU, 매 RoPE
model = AutoModel.from_pretrained('answerdotai/ModernBERT-base')
inputs = tokenizer(long_doc, return_tensors='pt', max_length=8192)
outputs = model(**inputs)
```

→ 매 BERT 의 modern revival — 매 long context.

### LoRA fine-tune (efficient)
```python
from peft import LoraConfig, get_peft_model

lora = LoraConfig(
    r=8, lora_alpha=16, target_modules=['query', 'value'],
    lora_dropout=0.1, bias='none', task_type='SEQ_CLS',
)
model = get_peft_model(base_model, lora)
# 매 0.1% param 만 학습
```

## 🤔 결정 기준
| Task | Model |
|---|---|
| Classification | BERT / RoBERTa / DeBERTa |
| NER / token | BERT / DeBERTa |
| QA (extractive) | BERT / RoBERTa |
| Sentence similarity | Sentence-BERT (MiniLM, MPNet) |
| Retrieval (dense) | DPR / E5 / BGE |
| Reranker | Cross-encoder |
| Long doc | ModernBERT / Longformer |
| Edge / fast | DistilBERT / ALBERT |
| Generation | GPT (NOT BERT) |

**기본값**: BERT-base 의 classification baseline. 매 retrieval = sentence-transformers.

## 🔗 Graph
- 부모: [[Transformer]]
- 변형: [[RoBERTa]] · [[DeBERTa]] · [[ModernBERT]]
- 응용: [[NER]] · [[Sentence-Embedding]]
- Adjacent: [[Sentence-Transformers]] · [[LoRA]] · [[BPE]]

## 🤖 LLM 활용
**언제**: 매 classification. 매 NER. 매 dense retrieval. 매 sentence similarity. 매 fast NLU.
**언제 X**: 매 generation. 매 chat. 매 long-context creative.

## ❌ 안티패턴
- **모든 task 의 GPT**: 매 BERT 의 fast / cheap 의 lose.
- **No truncation handling**: 매 max_length overflow.
- **Mean pool 없 + [CLS] 의 untrained**: 매 weak embedding.
- **NSP 의 keep**: 매 useless (RoBERTa lesson).
- **No padding mask**: 매 attention pollution.
- **Cross-encoder 의 retrieval (large corpus)**: 매 N² cost.

## 🧪 검증 / 중복
- Verified (Devlin et al. 2018, RoBERTa, ModernBERT 2024).
- 신뢰도 A.
- Related: [[Transformer]] · [[Sentence-Transformers]] · [[ModernBERT]] · [[Fine-Tuning]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — variant + ModernBERT + 매 HF code (classification, NER, QA, embedding, reranker) |