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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_3")
# Run inference
sentences = [
    '科目：ユニット及びその他。名称：HWC荷物棚。',
    '科目：コンクリート。名称：地上部暑中ｺﾝｸﾘｰﾄ。',
    '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=36       S18粗骨材20 高性能AE減水剤。備考：刊ｺﾝｸﾘｰﾄ   2。',
]
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: 11,961 training samples
Columns: sentence and label

Approximate statistics based on the first 1000 samples:

	sentence	label
type	string	int
details	min: 11 tokens mean: 18.2 tokens max: 54 tokens	0: ~0.30% 1: ~0.30% 2: ~0.30% 3: ~0.30% 4: ~0.30% 5: ~1.10% 6: ~0.30% 7: ~0.30% 8: ~0.30% 9: ~0.30% 10: ~0.30% 11: ~0.30% 12: ~0.30% 13: ~0.30% 14: ~0.30% 15: ~0.30% 16: ~0.40% 17: ~0.30% 18: ~0.30% 19: ~0.30% 20: ~0.90% 21: ~0.30% 22: ~0.40% 23: ~0.30% 24: ~1.10% 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: ~0.30% 41: ~0.30% 42: ~0.40% 43: ~0.30% 44: ~0.30% 45: ~0.30% 46: ~0.60% 47: ~0.70% 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: ~0.30% 58: ~0.30% 59: ~0.30% 60: ~0.30% 61: ~0.50% 62: ~0.30% 63: ~0.30% 64: ~0.30% 65: ~0.30% 66: ~0.30% 67: ~0.30% 68: ~0.30% 69: ~0.30% 70: ~0.30% 71: ~0.30% 72: ~0.30% 73: ~0.30% 74: ~0.30% 75: ~0.30% 76: ~0.30% 77: ~0.80% 78: ~0.60% 79: ~0.30% 80: ~0.30% 81: ~0.30% 82: ~0.30% 83: ~0.30% 84: ~0.30% 85: ~0.30% 86: ~0.50% 87: ~0.30% 88: ~0.30% 89: ~0.30% 90: ~0.30% 91: ~0.80% 92: ~0.60% 93: ~0.50% 94: ~0.30% 95: ~0.30% 96: ~16.50% 97: ~0.30% 98: ~0.30% 99: ~0.30% 100: ~0.30% 101: ~0.30% 102: ~0.30% 103: ~0.30% 104: ~0.30% 105: ~0.50% 106: ~0.30% 107: ~0.30% 108: ~0.30% 109: ~0.30% 110: ~0.30% 111: ~0.30% 112: ~0.30% 113: ~0.30% 114: ~0.70% 115: ~0.30% 116: ~0.30% 117: ~0.30% 118: ~0.40% 119: ~2.10% 120: ~2.10% 121: ~0.30% 122: ~0.30% 123: ~0.50% 124: ~0.50% 125: ~0.50% 126: ~0.40% 127: ~0.30% 128: ~0.30% 129: ~0.30% 130: ~0.80% 131: ~0.30% 132: ~0.30% 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: ~0.50% 144: ~0.30% 145: ~0.40% 146: ~0.30% 147: ~0.30% 148: ~0.30% 149: ~0.30% 150: ~0.30% 151: ~0.30% 152: ~0.30% 153: ~0.30% 154: ~0.30% 155: ~0.30% 156: ~0.30% 157: ~0.40% 158: ~0.30% 159: ~0.30% 160: ~0.30% 161: ~0.30% 162: ~0.30% 163: ~0.30% 164: ~0.70% 165: ~0.30% 166: ~0.30% 167: ~0.30% 168: ~1.30% 169: ~0.30% 170: ~0.40% 171: ~0.30% 172: ~0.30% 173: ~0.30% 174: ~1.50% 175: ~0.30% 176: ~0.30% 177: ~0.30% 178: ~0.30% 179: ~0.30% 180: ~0.30% 181: ~0.30% 182: ~1.60% 183: ~0.30% 184: ~0.30% 185: ~7.20% 186: ~0.30% 187: ~1.00% 188: ~0.30% 189: ~0.30% 190: ~0.30% 191: ~1.80% 192: ~0.30% 193: ~0.50% 194: ~0.70% 195: ~0.30%

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

Click to expand

Epoch	Step	Training Loss
2.3333	50	0.0589
4.6667	100	0.0668
7.125	150	0.0677
9.4583	200	0.0655
11.7917	250	0.062
14.25	300	0.0601
16.5833	350	0.0604
19.0417	400	0.0602
21.375	450	0.0546
23.7083	500	0.0575
26.1667	550	0.0569
28.5	600	0.0533
30.8333	650	0.0527
33.2917	700	0.0518
35.625	750	0.0487
38.0833	800	0.0514
40.4167	850	0.0469
42.75	900	0.0464
45.2083	950	0.0481
47.5417	1000	0.0502
49.875	1050	0.0511
52.3333	1100	0.0449
54.6667	1150	0.0439
57.125	1200	0.0443
59.4583	1250	0.0445
61.7917	1300	0.0455
64.25	1350	0.0417
66.5833	1400	0.0397
69.0417	1450	0.0392
71.375	1500	0.0411
73.7083	1550	0.0375
76.1667	1600	0.0444
78.5	1650	0.0353
80.8333	1700	0.0402
83.2917	1750	0.0353
85.625	1800	0.0354
88.0833	1850	0.0347
90.4167	1900	0.0368
92.75	1950	0.0353
95.2083	2000	0.0374
97.5417	2050	0.0375
99.875	2100	0.0324
1.7576	50	0.0365
3.7576	100	0.0372
5.7576	150	0.0392
7.7576	200	0.0392
9.7576	250	0.0386
11.7576	300	0.0402
13.7576	350	0.0342
15.7576	400	0.037
17.7576	450	0.0355
19.7576	500	0.0341
21.7576	550	0.0354
23.7576	600	0.0322
25.7576	650	0.0361
27.7576	700	0.0316
29.7576	750	0.0338
31.7576	800	0.0311
33.7576	850	0.0288
35.7576	900	0.0311
37.7576	950	0.0307
39.7576	1000	0.0288
41.7576	1050	0.0324
43.7576	1100	0.0276
45.7576	1150	0.0304
47.7576	1200	0.0267
49.7576	1250	0.0272
51.7576	1300	0.0269
53.7576	1350	0.0264
55.7576	1400	0.0324
57.7576	1450	0.0278
59.7576	1500	0.0315
61.7576	1550	0.0285
63.7576	1600	0.0241
65.7576	1650	0.0288
67.7576	1700	0.0263
69.7576	1750	0.0295
71.7576	1800	0.0238
73.7576	1850	0.0214
75.7576	1900	0.0281
77.7576	1950	0.0269
79.7576	2000	0.0268
81.7576	2050	0.0242
83.7576	2100	0.0226
85.7576	2150	0.0249
87.7576	2200	0.0254
89.7576	2250	0.0226
91.7576	2300	0.0181
93.7576	2350	0.019
95.7576	2400	0.0207
97.7576	2450	0.0205
99.7576	2500	0.0241
101.7576	2550	0.0219
103.7576	2600	0.0237
105.7576	2650	0.0194
107.7576	2700	0.0184
109.7576	2750	0.0206
111.7576	2800	0.0189
113.7576	2850	0.0216
115.7576	2900	0.0234
117.7576	2950	0.0192
119.7576	3000	0.0193
121.7576	3050	0.0211
123.7576	3100	0.0161
125.7576	3150	0.022
127.7576	3200	0.0176
129.7576	3250	0.0227
131.7576	3300	0.0224
133.7576	3350	0.0172
135.7576	3400	0.0168
137.7576	3450	0.0165
139.7576	3500	0.016
141.7576	3550	0.0143
143.7576	3600	0.0165
145.7576	3650	0.0202
147.7576	3700	0.0118
149.7576	3750	0.0163
151.7576	3800	0.0188
153.7576	3850	0.0137
155.7576	3900	0.0172
157.7576	3950	0.0175
159.7576	4000	0.0204
161.7576	4050	0.0175
163.7576	4100	0.0169
165.7576	4150	0.0184
167.7576	4200	0.0176
169.7576	4250	0.0102
171.7576	4300	0.014
173.7576	4350	0.0164
175.7576	4400	0.0203
177.7576	4450	0.0099
179.7576	4500	0.0143
181.7576	4550	0.0182
183.7576	4600	0.009
185.7576	4650	0.0157
187.7576	4700	0.015
189.7576	4750	0.0168
191.7576	4800	0.0172
193.7576	4850	0.0154
195.7576	4900	0.0162
197.7576	4950	0.0143
199.7576	5000	0.0156

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

bibtex

@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

bibtex

@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}
}