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Korea-MES
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Length_ModernBERT

Safetensors
modernbert
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Upload Length ModernBERT model artifacts

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Files changed (8) hide show
  1. BERT_inference.py +216 -0
  2. config.json +70 -0
  3. config.yaml +57 -0
  4. model.safetensors +3 -0
  5. special_tokens_map.json +37 -0
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +945 -0
  8. training_args.bin +3 -0
BERT_inference.py ADDED
@@ -0,0 +1,216 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Simple Question Type Classification Inference
3
+ 텍스트 입력 → 질문 타입 라벨 출력 (배치 처리 지원)
4
+ """
5
+
6
+ import torch
7
+ import argparse
8
+ from typing import List, Tuple
9
+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
10
+
11
+
12
+
13
+ def get_question_type_label_info():
14
+
15
+
16
+ mlt_labels = ["example", "judgmental", "cause", "procedural", "verification", "concept", "extent", "comparison", "disjunction", "consequence"]
17
+
18
+
19
+ return {
20
+ 'labels': mlt_labels,
21
+ 'num_labels': len(mlt_labels),
22
+ 'label_to_id': {label: idx for idx, label in enumerate(mlt_labels)},
23
+ 'id_to_label': {idx: label for idx, label in enumerate(mlt_labels)}
24
+ }
25
+
26
+
27
+
28
+ def load_model_and_tokenizer(model_path: str, device: str = 'cuda:0'):
29
+ """
30
+ 모델과 토크나이저를 로드 (한 번만 실행)
31
+
32
+ Args:
33
+ model_path (str): 학습된 모델 경로
34
+ device (str): 디바이스 ('cuda:0' or 'cpu')
35
+
36
+ Returns:
37
+ tuple: (model, tokenizer, id_to_label)
38
+ """
39
+ device = torch.device(device if torch.cuda.is_available() else 'cpu')
40
+
41
+ # 라벨 정보
42
+ label_info = get_question_type_label_info()
43
+ id_to_label = label_info['id_to_label']
44
+
45
+ # 토크나이저 로드
46
+ tokenizer = AutoTokenizer.from_pretrained(model_path)
47
+
48
+ # 모델 로드
49
+ model = AutoModelForSequenceClassification.from_pretrained(
50
+ model_path,
51
+ num_labels=10
52
+ )
53
+ model.to(device)
54
+ model.eval()
55
+
56
+ print(f"✅ Model and tokenizer loaded on {device}")
57
+
58
+ return model, tokenizer, id_to_label
59
+
60
+
61
+ def batch_predict(
62
+ texts: List[str],
63
+ model,
64
+ tokenizer,
65
+ id_to_label: dict,
66
+ max_length: int = 512
67
+ ) -> List[Tuple[str, float]]:
68
+ """
69
+ 배치 단위로 질문 타입을 예측
70
+
71
+ Args:
72
+ texts (List[str]): 입력 텍스트 리스트
73
+ model: 로드된 모델
74
+ tokenizer: 로드된 토크나이저
75
+ id_to_label (dict): ID → 라벨 매핑
76
+ max_length (int): 최대 토큰 길이
77
+
78
+ Returns:
79
+ List[Tuple[str, float]]: [(예측 라벨, 확신도), ...] 리스트
80
+ """
81
+ device = next(model.parameters()).device
82
+
83
+ # 배치 토크나이징 (효율적인 동적 패딩)
84
+ inputs = tokenizer(
85
+ texts,
86
+ truncation=True,
87
+ padding='longest', # 배치 내 가장 긴 시퀀스에만 맞춤 (메모리/속도 최적화)
88
+ max_length=max_length,
89
+ return_tensors='pt'
90
+ )
91
+
92
+ # GPU로 이동
93
+ inputs = {k: v.to(device) for k, v in inputs.items()}
94
+
95
+ # 예측
96
+ with torch.no_grad():
97
+ outputs = model(**inputs)
98
+ logits = outputs.logits
99
+ probabilities = torch.softmax(logits, dim=-1)
100
+ predicted_ids = torch.argmax(logits, dim=-1)
101
+ confidences = probabilities[range(len(texts)), predicted_ids]
102
+
103
+ # 결과 생성
104
+ results = []
105
+ for i, (pred_id, conf) in enumerate(zip(predicted_ids, confidences)):
106
+ label = id_to_label[pred_id.item()]
107
+ confidence = conf.item()
108
+ results.append((label, confidence))
109
+
110
+ return results
111
+
112
+
113
+ def predict_single(
114
+ text: str,
115
+ model,
116
+ tokenizer,
117
+ id_to_label: dict,
118
+ max_length: int = 512
119
+ ) -> Tuple[str, float]:
120
+ """
121
+ 단일 텍스트 예측
122
+
123
+ Args:
124
+ text (str): 입력 텍스트
125
+ model: 로드된 모델
126
+ tokenizer: 로드된 토크나이저
127
+ id_to_label (dict): ID → 라벨 매핑
128
+ max_length (int): 최대 토큰 길이
129
+
130
+ Returns:
131
+ Tuple[str, float]: (예측 라벨, 확신도)
132
+ """
133
+ results = batch_predict([text], model, tokenizer, id_to_label, max_length)
134
+ return results[0]
135
+
136
+
137
+ def main():
138
+ parser = argparse.ArgumentParser(
139
+ description="Question Type Classification with Batch Processing",
140
+ formatter_class=argparse.RawDescriptionHelpFormatter,
141
+ epilog="""
142
+ Examples:
143
+ # Single question
144
+ python inference_question_type.py \\
145
+ --texts "Where is the capital of Korea?"
146
+
147
+ # Multiple questions (batch)
148
+ python inference_question_type.py \\
149
+ --texts "Where is the capital of Korea?" "How does photosynthesis work?" "What is democracy?"
150
+
151
+ # Custom model path
152
+ python inference_question_type.py \\
153
+ --model_path ./models/custom_model/best_model \\
154
+ --texts "Question 1" "Question 2"
155
+ """
156
+ )
157
+
158
+ parser.add_argument(
159
+ "--model_path",
160
+ type=str,
161
+ required=False,
162
+ default='./distilbert/best_model',
163
+ help="Path to trained model"
164
+ )
165
+ parser.add_argument(
166
+ "--texts",
167
+ type=str,
168
+ nargs='+',
169
+ required=False,
170
+ default=["Where is the capital of Korea?", "How does quantum computing work?", "What is democracy?"],
171
+ help="Text(s) to predict. Provide multiple texts separated by spaces for batch processing."
172
+ )
173
+ parser.add_argument(
174
+ "--max_length",
175
+ type=int,
176
+ default=512,
177
+ help="Maximum token length (default: 512)"
178
+ )
179
+ parser.add_argument(
180
+ "--device",
181
+ type=str,
182
+ default='cuda:0',
183
+ help="Device to use (default: cuda:0)"
184
+ )
185
+
186
+ args = parser.parse_args()
187
+
188
+ print("=" * 80)
189
+ print("🚀 Question Type Classification")
190
+ print("=" * 80)
191
+
192
+ # 모델과 토크나이저 로드 (한 번만)
193
+ model, tokenizer, id_to_label = load_model_and_tokenizer(args.model_path, args.device)
194
+
195
+ print(f"\n📊 Processing {len(args.texts)} question(s)")
196
+ print("=" * 80)
197
+
198
+ # 배치 예측
199
+ results = batch_predict(args.texts, model, tokenizer, id_to_label, args.max_length)
200
+
201
+ # 결과 출력
202
+ for i, (text, (label, confidence)) in enumerate(zip(args.texts, results), 1):
203
+ print(f"\n{'=' * 80}")
204
+ print(f"📝 Question {i}/{len(args.texts)}")
205
+ print(f"{'=' * 80}")
206
+ print(f"Input: {text}")
207
+ print(f"Predicted Type: {label}")
208
+ print(f"Confidence: {confidence:.4f} ({confidence*100:.2f}%)")
209
+
210
+ print(f"\n{'=' * 80}")
211
+ print("✅ All predictions completed!")
212
+ print(f"{'=' * 80}")
213
+
214
+
215
+ if __name__ == "__main__":
216
+ main()
config.json ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "ModernBertForSequenceClassification"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "bos_token_id": 50281,
8
+ "classifier_activation": "gelu",
9
+ "classifier_bias": false,
10
+ "classifier_dropout": 0.0,
11
+ "classifier_pooling": "mean",
12
+ "cls_token_id": 50281,
13
+ "decoder_bias": true,
14
+ "deterministic_flash_attn": false,
15
+ "embedding_dropout": 0.0,
16
+ "eos_token_id": 50282,
17
+ "global_attn_every_n_layers": 3,
18
+ "global_rope_theta": 160000.0,
19
+ "gradient_checkpointing": false,
20
+ "hidden_activation": "gelu",
21
+ "hidden_size": 1024,
22
+ "id2label": {
23
+ "0": "LABEL_0",
24
+ "1": "LABEL_1",
25
+ "2": "LABEL_2",
26
+ "3": "LABEL_3",
27
+ "4": "LABEL_4",
28
+ "5": "LABEL_5",
29
+ "6": "LABEL_6",
30
+ "7": "LABEL_7",
31
+ "8": "LABEL_8",
32
+ "9": "LABEL_9"
33
+ },
34
+ "initializer_cutoff_factor": 2.0,
35
+ "initializer_range": 0.02,
36
+ "intermediate_size": 2624,
37
+ "label2id": {
38
+ "LABEL_0": 0,
39
+ "LABEL_1": 1,
40
+ "LABEL_2": 2,
41
+ "LABEL_3": 3,
42
+ "LABEL_4": 4,
43
+ "LABEL_5": 5,
44
+ "LABEL_6": 6,
45
+ "LABEL_7": 7,
46
+ "LABEL_8": 8,
47
+ "LABEL_9": 9
48
+ },
49
+ "layer_norm_eps": 1e-05,
50
+ "local_attention": 128,
51
+ "local_rope_theta": 10000.0,
52
+ "max_position_embeddings": 8192,
53
+ "mlp_bias": false,
54
+ "mlp_dropout": 0.0,
55
+ "model_type": "modernbert",
56
+ "norm_bias": false,
57
+ "norm_eps": 1e-05,
58
+ "num_attention_heads": 16,
59
+ "num_hidden_layers": 28,
60
+ "pad_token_id": 50283,
61
+ "position_embedding_type": "absolute",
62
+ "problem_type": "single_label_classification",
63
+ "repad_logits_with_grad": false,
64
+ "sep_token_id": 50282,
65
+ "sparse_pred_ignore_index": -100,
66
+ "sparse_prediction": false,
67
+ "torch_dtype": "float32",
68
+ "transformers_version": "4.53.3",
69
+ "vocab_size": 50368
70
+ }
config.yaml ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # AI Text Detection Configuration
2
+
3
+ # Project settings
4
+ project_name: "Token_Length_Prediction"
5
+ task_type: "multi_classification"
6
+
7
+ # Data settings
8
+ data:
9
+ dataset: "Korea-MES/open_question_type"
10
+ text_columns: ['question']
11
+ #target_column: ["r_scores"]
12
+ #target_column: "mlt"
13
+ target_column: "resolve_type"
14
+ #target_column : ['normalized_informativeness_score', 'token_count']
15
+ max_length: 512
16
+ #validation_split: 100 # 각 MLT 라벨별로 500개씩 validation으로 사용
17
+ validation_split: 0.1 # 각 MLT 라벨별로 500개씩 validation으로 사용
18
+
19
+ random_state: 42
20
+
21
+ # Model settings
22
+ model:
23
+ model_names: # 사용할 모델 리스트 (우선순위)
24
+ - "answerdotai/ModernBERT-large"
25
+ #type: "grice"
26
+ type: "classification"
27
+ #type : "regression"
28
+ num_labels: 10 # 이진 분류를 위한 설정 (BCEWithLogitsLoss 사용)
29
+ dropout_rate: 0.1
30
+ hidden_size: 768
31
+
32
+ # Training settings
33
+ training:
34
+ num_epochs: 5
35
+ batch_size: 64
36
+ learning_rate: 5.0e-5
37
+ weight_decay: 0.01
38
+ warmup_steps: 15
39
+ max_grad_norm: 1.0
40
+
41
+ # Evaluation settings
42
+ evaluation:
43
+ strategy: "epoch" # "epoch" 또는 "steps"
44
+ metric: "f1" # "accuracy", "f1", "auc" 등
45
+ #metric: "eval_mse"
46
+ # Hardware settings
47
+ hardware:
48
+ device: "cuda:0"
49
+ dataloader_num_workers: 4
50
+ fp16: true
51
+
52
+ # Output settings
53
+ output:
54
+ model_save_dir: "./models"
55
+ logs_dir: "./logs"
56
+ results_dir: "./results"
57
+ submission_file: "submission.csv"
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+ oid sha256:42345ac1c93ba1f5958db1e32c59ef15731b6c27d0f405a69c3c852763260264
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+ size 1583384440
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+ {
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+ "lstrip": false,
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+ "rstrip": false,
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+ "single_word": false
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+ "mask_token": {
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+ "content": "[MASK]",
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+ "lstrip": true,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "pad_token": {
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+ "lstrip": false,
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+ "rstrip": false,
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+ "single_word": false
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+ "rstrip": false,
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+ "single_word": false
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+ }
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+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
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