SentenceTransformer based on cl-nagoya/sup-simcse-ja-base

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: cl-nagoya/sup-simcse-ja-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

• Size: 8,788 training samples
• Columns: sentence and label
• Approximate statistics based on the first 1000 samples:

  	sentence	label
  type	string	int
  details	min: 13 tokens mean: 23.19 tokens max: 41 tokens	0: ~0.20% 1: ~0.30% 2: ~0.30% 3: ~0.30% 4: ~0.20% 5: ~0.20% 6: ~0.20% 7: ~0.20% 8: ~0.30% 9: ~0.30% 10: ~0.20% 11: ~0.20% 12: ~0.20% 13: ~0.20% 14: ~0.20% 15: ~0.20% 16: ~0.40% 17: ~0.20% 18: ~0.20% 19: ~0.20% 20: ~0.20% 21: ~0.20% 22: ~0.20% 23: ~0.20% 24: ~0.20% 25: ~0.20% 26: ~0.20% 27: ~0.20% 28: ~0.20% 29: ~0.20% 30: ~0.20% 31: ~0.20% 32: ~0.20% 33: ~0.20% 34: ~0.20% 35: ~0.20% 36: ~0.20% 37: ~0.20% 38: ~0.20% 39: ~0.20% 40: ~0.20% 41: ~0.20% 42: ~0.60% 43: ~0.70% 44: ~0.20% 45: ~0.30% 46: ~0.20% 47: ~0.20% 48: ~0.30% 49: ~0.20% 50: ~0.20% 51: ~0.20% 52: ~0.20% 53: ~0.30% 54: ~0.40% 55: ~0.30% 56: ~0.20% 57: ~0.20% 58: ~0.20% 59: ~0.20% 60: ~0.20% 61: ~0.30% 62: ~0.20% 63: ~0.20% 64: ~0.20% 65: ~0.20% 66: ~0.40% 67: ~0.40% 68: ~0.20% 69: ~0.60% 70: ~0.20% 71: ~0.20% 72: ~0.20% 73: ~0.20% 74: ~0.20% 75: ~0.30% 76: ~0.20% 77: ~0.40% 78: ~0.20% 79: ~0.20% 80: ~0.50% 81: ~0.30% 82: ~0.60% 83: ~0.20% 84: ~0.30% 85: ~0.20% 86: ~0.20% 87: ~0.20% 88: ~0.20% 89: ~1.10% 90: ~1.70% 91: ~2.20% 92: ~0.50% 93: ~0.20% 94: ~0.20% 95: ~1.60% 96: ~0.20% 97: ~0.20% 98: ~0.20% 99: ~0.20% 100: ~0.30% 101: ~1.70% 102: ~0.20% 103: ~0.20% 104: ~0.40% 105: ~0.40% 106: ~0.20% 107: ~0.20% 108: ~0.20% 109: ~1.10% 110: ~0.20% 111: ~0.40% 112: ~0.50% 113: ~0.20% 114: ~0.20% 115: ~0.20% 116: ~0.20% 117: ~0.50% 118: ~0.20% 119: ~0.20% 120: ~0.20% 121: ~0.20% 122: ~0.20% 123: ~0.20% 124: ~0.30% 125: ~0.20% 126: ~0.20% 127: ~0.20% 128: ~0.40% 129: ~0.20% 130: ~0.20% 131: ~0.20% 132: ~0.20% 133: ~0.20% 134: ~0.20% 135: ~0.20% 136: ~0.20% 137: ~0.20% 138: ~0.30% 139: ~0.20% 140: ~0.30% 141: ~0.30% 142: ~0.20% 143: ~0.20% 144: ~0.20% 145: ~0.20% 146: ~0.20% 147: ~0.20% 148: ~0.20% 149: ~0.20% 150: ~0.20% 151: ~0.20% 152: ~0.20% 153: ~0.20% 154: ~0.20% 155: ~0.20% 156: ~0.60% 157: ~0.20% 158: ~1.50% 159: ~0.20% 160: ~0.20% 161: ~0.20% 162: ~0.20% 163: ~0.50% 164: ~0.20% 165: ~0.20% 166: ~0.20% 167: ~0.20% 168: ~0.20% 169: ~0.30% 170: ~0.30% 171: ~0.20% 172: ~0.20% 173: ~1.30% 174: ~0.20% 175: ~0.20% 176: ~0.20% 177: ~0.20% 178: ~0.60% 179: ~0.20% 180: ~0.20% 181: ~0.20% 182: ~0.20% 183: ~0.20% 184: ~0.20% 185: ~0.30% 186: ~0.20% 187: ~0.20% 188: ~0.30% 189: ~0.20% 190: ~0.90% 191: ~0.30% 192: ~0.30% 193: ~0.20% 194: ~0.30% 195: ~0.20% 196: ~0.80% 197: ~0.20% 198: ~0.20% 199: ~0.30% 200: ~0.20% 201: ~0.20% 202: ~0.20% 203: ~0.20% 204: ~0.20% 205: ~1.20% 206: ~0.40% 207: ~0.20% 208: ~0.20% 209: ~0.20% 210: ~0.20% 211: ~0.30% 212: ~0.20% 213: ~0.80% 214: ~0.30% 215: ~0.20% 216: ~1.10% 217: ~0.30% 218: ~0.20% 219: ~0.20% 220: ~0.20% 221: ~0.20% 222: ~0.20% 223: ~0.20% 224: ~0.20% 225: ~0.30% 226: ~0.20% 227: ~0.90% 228: ~4.70% 229: ~0.20% 230: ~0.20% 231: ~0.20% 232: ~0.70% 233: ~0.20% 234: ~0.80% 235: ~0.20% 236: ~0.40% 237: ~0.30% 238: ~0.40% 239: ~0.20% 240: ~0.30% 241: ~0.50% 242: ~0.30% 243: ~0.20% 244: ~0.20% 245: ~0.30% 246: ~0.30% 247: ~0.30% 248: ~0.60% 249: ~0.20% 250: ~0.20% 251: ~0.20% 252: ~0.30% 253: ~0.30% 254: ~1.90% 255: ~0.20% 256: ~0.20% 257: ~0.20% 258: ~0.20% 259: ~0.20% 260: ~0.50% 261: ~0.20% 262: ~0.30% 263: ~0.20% 264: ~0.20% 265: ~1.00% 266: ~0.20% 267: ~0.20% 268: ~0.20% 269: ~0.40% 270: ~0.20% 271: ~0.20% 272: ~0.20% 273: ~0.20% 274: ~0.20% 275: ~0.20% 276: ~0.20% 277: ~3.70% 278: ~0.20% 279: ~0.40% 280: ~0.20% 281: ~0.20% 282: ~0.90% 283: ~0.40% 284: ~0.20% 285: ~2.30% 286: ~0.30% 287: ~0.20% 288: ~0.30% 289: ~0.60%

• Samples:

  sentence	label
  科目：コンクリート。名称：免震基礎天端グラウト注入。	0
  科目：コンクリート。名称：免震基礎天端グラウト注入。	0
  科目：コンクリート。名称：コンクリートポンプ圧送。	1

• Loss: BatchAllTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• learning_rate: 1e-05
• weight_decay: 0.01
• num_train_epochs: 250
• warmup_ratio: 0.1
• fp16: True
• batch_sampler: group_by_label

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.3.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

BatchAllTripletLoss