README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:16547
  - loss:CustomBatchAllTripletLoss
widget:
  - source_sentence: 科目：ユニット及びその他。名称：突出ｻｲﾝ。
    sentences:
      - 科目：ユニット及びその他。名称：ｶｳﾝﾀｰ上ｶﾞﾗｽ台。
      - 科目：ユニット及びその他。名称：ｼｬﾜｰﾕﾆｯﾄ枠。
      - 科目：ユニット及びその他。名称：ｴﾝﾄﾗﾝｽｻｲﾝ。
  - source_sentence: 科目：タイル。名称：階段踏面ﾀｲﾙ。
    sentences:
      - 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC42N/mm2 ｽﾗﾝﾌﾟ21高性能AE減水剤。備考：代価表    0056。
      - 科目：ユニット及びその他。名称：配膳棚。
      - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設。
  - source_sentence: 科目：ユニット及びその他。名称：ｵｰﾊﾞｰﾌﾛｰ管。
    sentences:
      - 科目：ユニット及びその他。名称：自動支払機ｻｲﾝ。
      - 科目：ユニット及びその他。名称：SPﾎﾞｯｸｽ。
      - 科目：コンクリート。名称：捨てｺﾝｸﾘｰﾄ。
  - source_sentence: 科目：ユニット及びその他。名称：執務室#-#規格品ｶｳﾝﾀｰ。
    sentences:
      - 科目：ユニット及びその他。名称：床ｺﾝｸﾘｰﾄ平板ﾃﾞｯｷ。
      - 科目：ユニット及びその他。名称：#階守衛室ｶｳﾝﾀｰ。
      - 科目：ユニット及びその他。名称：ｺﾝｸﾘｰﾄ舗装。
  - source_sentence: 科目：ユニット及びその他。名称：#FHCU#床室ｶｳﾝﾀｰ。
    sentences:
      - 科目：ユニット及びその他。名称：室名ｻｲﾝ。
      - 科目：ユニット及びその他。名称：#階数表示（階段室内･踊り場）。
      - 科目：ユニット及びその他。名称：Co-#ﾋﾟｸﾄｻｲﾝ。
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• 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)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_2")
# Run inference
sentences = [
    '科目：ユニット及びその他。名称：#FHCU#床室ｶｳﾝﾀｰ。',
    '科目：ユニット及びその他。名称：#階数表示（階段室内･踊り場）。',
    '科目：ユニット及びその他。名称：Co-#ﾋﾟｸﾄｻｲﾝ。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

• Size: 16,547 training samples
• Columns: sentence and label
• Approximate statistics based on the first 1000 samples:

  	sentence	label
  type	string	int
  details	min: 11 tokens mean: 18.77 tokens max: 72 tokens	0: ~0.30% 1: ~0.30% 2: ~0.30% 3: ~0.30% 4: ~0.30% 5: ~3.40% 6: ~0.30% 7: ~0.30% 8: ~0.30% 9: ~0.30% 10: ~0.30% 11: ~0.30% 12: ~0.40% 13: ~0.30% 14: ~0.30% 15: ~0.40% 16: ~0.30% 17: ~0.30% 18: ~0.30% 19: ~0.90% 20: ~0.30% 21: ~0.30% 22: ~1.10% 23: ~0.30% 24: ~0.30% 25: ~0.30% 26: ~0.30% 27: ~0.30% 28: ~0.30% 29: ~0.30% 30: ~0.30% 31: ~0.30% 32: ~0.30% 33: ~0.30% 34: ~0.30% 35: ~0.30% 36: ~0.30% 37: ~0.30% 38: ~0.30% 39: ~0.30% 40: ~0.40% 41: ~0.30% 42: ~0.30% 43: ~0.30% 44: ~0.60% 45: ~0.70% 46: ~0.30% 47: ~0.30% 48: ~0.30% 49: ~0.30% 50: ~0.30% 51: ~0.30% 52: ~0.30% 53: ~0.30% 54: ~0.30% 55: ~0.30% 56: ~0.30% 57: ~0.80% 58: ~0.30% 59: ~0.30% 60: ~0.60% 61: ~0.30% 62: ~0.30% 63: ~0.30% 64: ~0.50% 65: ~0.30% 66: ~0.30% 67: ~0.30% 68: ~0.30% 69: ~0.50% 70: ~0.60% 71: ~0.30% 72: ~0.30% 73: ~0.30% 74: ~0.30% 75: ~0.30% 76: ~0.30% 77: ~0.30% 78: ~0.50% 79: ~0.30% 80: ~0.30% 81: ~0.30% 82: ~0.30% 83: ~0.80% 84: ~0.60% 85: ~0.50% 86: ~0.30% 87: ~0.30% 88: ~16.50% 89: ~0.30% 90: ~0.30% 91: ~0.30% 92: ~0.30% 93: ~0.30% 94: ~0.30% 95: ~0.30% 96: ~0.30% 97: ~0.50% 98: ~0.30% 99: ~0.30% 100: ~0.30% 101: ~0.30% 102: ~0.30% 103: ~0.30% 104: ~0.30% 105: ~0.70% 106: ~0.70% 107: ~0.30% 108: ~3.20% 109: ~0.30% 110: ~0.40% 111: ~2.30% 112: ~0.30% 113: ~0.30% 114: ~0.50% 115: ~0.50% 116: ~0.50% 117: ~0.40% 118: ~0.30% 119: ~0.30% 120: ~0.30% 121: ~0.80% 122: ~0.30% 123: ~0.30% 124: ~0.30% 125: ~0.30% 126: ~0.30% 127: ~0.30% 128: ~0.30% 129: ~0.30% 130: ~0.50% 131: ~0.30% 132: ~0.40% 133: ~0.30% 134: ~0.30% 135: ~0.30% 136: ~0.30% 137: ~0.30% 138: ~0.30% 139: ~0.30% 140: ~0.30% 141: ~0.30% 142: ~0.30% 143: ~0.40% 144: ~0.30% 145: ~0.30% 146: ~0.30% 147: ~0.30% 148: ~0.30% 149: ~0.30% 150: ~0.70% 151: ~0.30% 152: ~0.30% 153: ~0.30% 154: ~1.30% 155: ~0.30% 156: ~0.40% 157: ~0.30% 158: ~0.30% 159: ~0.30% 160: ~1.50% 161: ~0.30% 162: ~0.30% 163: ~0.30% 164: ~0.30% 165: ~0.30% 166: ~0.30% 167: ~0.30% 168: ~1.50% 169: ~0.30% 170: ~0.30% 171: ~7.20% 172: ~0.30% 173: ~1.00% 174: ~0.30% 175: ~0.30% 176: ~0.30% 177: ~1.80% 178: ~0.30% 179: ~0.50% 180: ~0.70% 181: ~0.10%

• Samples:

  sentence	label
  科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。	0
  科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。	0
  科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。	0

• Loss: sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• learning_rate: 1e-05
• weight_decay: 0.01
• num_train_epochs: 200
• warmup_ratio: 0.15
• fp16: True
• batch_sampler: group_by_label

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 1e-05
• weight_decay: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 200
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.15
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• tp_size: 0
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: group_by_label
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
1.7576	50	0.0447
3.7576	100	0.048
5.7576	150	0.0472
7.7576	200	0.0505
9.7576	250	0.0547
11.7576	300	0.0548
13.7576	350	0.0527
15.7576	400	0.0522
17.7576	450	0.0496
19.7576	500	0.0506
21.7576	550	0.048
23.7576	600	0.0508
25.7576	650	0.0499
27.7576	700	0.0474
29.7576	750	0.0467
31.7576	800	0.0483
33.7576	850	0.0438
35.7576	900	0.0457
37.7576	950	0.0445
39.7576	1000	0.0452
41.7576	1050	0.046
43.7576	1100	0.0433
45.7576	1150	0.0419
47.7576	1200	0.0407
49.7576	1250	0.0397
51.7576	1300	0.043
53.7576	1350	0.0393
55.7576	1400	0.0411
57.7576	1450	0.0434
59.7576	1500	0.0446
61.7576	1550	0.0396
63.7576	1600	0.0375
65.7576	1650	0.0413
67.7576	1700	0.0398
69.7576	1750	0.0382
71.7576	1800	0.0346
73.7576	1850	0.0388
75.7576	1900	0.0347
77.7576	1950	0.0349
79.7576	2000	0.0402
81.7576	2050	0.039
83.7576	2100	0.0343
85.7576	2150	0.0465
87.7576	2200	0.033
89.7576	2250	0.0385
91.7576	2300	0.0305
93.7576	2350	0.0367
95.7576	2400	0.0377
97.7576	2450	0.0322
99.7576	2500	0.0354
101.7576	2550	0.0332
103.7576	2600	0.0365
105.7576	2650	0.0357
107.7576	2700	0.0301
109.7576	2750	0.0323
111.7576	2800	0.0328
113.7576	2850	0.0339
115.7576	2900	0.0379
117.7576	2950	0.0334
119.7576	3000	0.0338
121.7576	3050	0.0328
123.7576	3100	0.0281
125.7576	3150	0.0316
127.7576	3200	0.0387
129.7576	3250	0.0327
131.7576	3300	0.026
133.7576	3350	0.0247
135.7576	3400	0.0319
137.7576	3450	0.0299
139.7576	3500	0.0252
141.7576	3550	0.0265
143.7576	3600	0.0244
145.7576	3650	0.0317
147.7576	3700	0.0291
149.7576	3750	0.03
151.7576	3800	0.0299
153.7576	3850	0.0303
155.7576	3900	0.0296
157.7576	3950	0.0303
159.7576	4000	0.0282
161.7576	4050	0.0301
163.7576	4100	0.027
165.7576	4150	0.0259
167.7576	4200	0.0294
169.7576	4250	0.0267
171.7576	4300	0.0303
173.7576	4350	0.0199
175.7576	4400	0.0253
177.7576	4450	0.0254
179.7576	4500	0.0202
181.7576	4550	0.0263
183.7576	4600	0.0302
185.7576	4650	0.0292
187.7576	4700	0.0264
189.7576	4750	0.0289
191.7576	4800	0.026
193.7576	4850	0.0285
195.7576	4900	0.0234
197.7576	4950	0.0297
199.7576	5000	0.0238

Framework Versions

• Python: 3.11.12
• Sentence Transformers: 3.4.1
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.2
• Datasets: 3.5.0
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CustomBatchAllTripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}