KoBERT 텍스트 분류 모델 업로드
Browse files- README.md +26 -0
- config.json +59 -0
- model.safetensors +3 -0
- special_tokens_map.json +7 -0
- tokenizer_config.json +58 -0
- vocab.txt +0 -0
README.md
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# KoBERT 분류 모델
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이 모델은 KoBERT를 기반으로 텍스트 분류를 위해 파인튜닝된 모델입니다.
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## 모델 정보
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- 기본 모델: beomi/kcbert-base
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- 클래스 수: 12
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- 사용 방법: 아래 코드를 참조하세요
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## 사용 예시
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```python
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from transformers import BertForSequenceClassification, BertTokenizer
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# 모델과 토크나이저 로드
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model_name = "rmsdud/kobert-classifier"
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tokenizer = BertTokenizer.from_pretrained(model_name)
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model = BertForSequenceClassification.from_pretrained(model_name)
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# 추론
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text = "분류할 텍스트를 입력하세요."
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
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outputs = model(**inputs)
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logits = outputs.logits
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predicted_class = logits.argmax(-1).item()
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print(f"예측 클래스: predicted_class")
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```
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config.json
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{
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"_name_or_path": "beomi/kcbert-base",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"directionality": "bidi",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2",
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"3": "LABEL_3",
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"4": "LABEL_4",
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"5": "LABEL_5",
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"6": "LABEL_6",
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"7": "LABEL_7",
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"8": "LABEL_8",
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"9": "LABEL_9",
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"10": "LABEL_10",
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"11": "LABEL_11"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_10": 10,
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"LABEL_11": 11,
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"LABEL_2": 2,
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"LABEL_3": 3,
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"LABEL_4": 4,
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"LABEL_5": 5,
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"LABEL_6": 6,
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"LABEL_7": 7,
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"LABEL_8": 8,
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"LABEL_9": 9
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 300,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.48.3",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30000
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f475dcaf89475be4cf5d1dcfb5bd3d2e32683b07271282a81aa40ae743e222d4
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size 435734552
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": false,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 300,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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