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SentenceTransformer based on embaas/sentence-transformers-multilingual-e5-large

This is a sentence-transformers model finetuned from embaas/sentence-transformers-multilingual-e5-large. It maps sentences & paragraphs to a 1024-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 Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("jerryyun/kicon_e5large_15_v1")
# Run inference
sentences = [
    '억지말뚝으로 보강된 비탈면의 내진설계 결정 기준은 무엇인가요?',
    '비탈면보강공법 KDS117015:2020KDS110000지반설계기준 9입시켜야한다 .4.3.3내진설계여부(1)억지말뚝으로보강된비탈면의내진설계는보강되지않은비탈면의내진설계여부에따라결정하며 ,KDS 119000의비탈면내진등급을참고한다 .(2)억지말뚝으로보강된비탈면의지진시안정해석은 4.4및KDS 119000을참조한다 .4.4지진시안정해석4.4.1네일(1)지진시네일로보강된비탈면의안정해석에서는내적안정과외적안정성을검토한다 .(2)네일로보강된비탈면의지진시안정해석에서고려하는지진하중은파괴토체의자중과지진계수 (Am)를곱한등가지진력으로 고려하며 ,파괴토체의중심에횡방향으로작용시킨다 .(3)지진에의한지진계수는 KDS 119000(1.6.5)에서제시하는유효수평지반가 속도(S)를이용하여산정한다 .4.4.2록볼트(1)지진시록볼트로보강된비탈면의안정해석에서는외적안정성을검토한다 .(2)록볼트로보강된비탈면의지진시안정해석에서고려하는지진하중은파괴토체의자중과지진계수 (Am)를곱한등가지진력으로 고려하며 ,파괴토체의중심에횡방향으로작용시킨다 .',
    '지반계측 KDS111015:2021KDS110000지반설계기준 344.2.2.2 계측기기운용기법(1) 인력에의한계측기기운용과자동화장비에의한운용기법으로크게구분할수있으며, 붕괴및활동의진행특성, 계측대상시설물의중요도 , 피해발생시영향, 경제성, 계측빈도등을고려하여운용기법을선택하여야한다.4.2.2.3 일반적인계측관리의자동화(1) 기록지또는저장장치에계측자료를기록할때까지를자동화하고 , 그후의처리는별도로컴퓨터로실시하는반자동계측관리기법과 , 자료수집ㆍ해석ㆍ그래프화까지를유선ㆍ무선으로온라인화된시스템으로일관하여실시하는전자동계측관리기법및상기의두가지방법을병용하는기법으로구분하며 , 계측대상조건을고려하여운용기법을선정하여야한다.4.2.2.4 피해방지및최소화방법(1) 조기에징후를감지하는것이중요하고 , 모니터링과동시에신속하게그정보를전달ㆍ처리하는것이필요하며계측자료의수집ㆍ처리ㆍ해석까지를일괄하여처리하는자동화기술을사용하는것을고려하여야한다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.5449
cosine_accuracy@3 0.7218
cosine_accuracy@5 0.778
cosine_accuracy@10 0.8453
cosine_precision@1 0.5449
cosine_precision@3 0.2406
cosine_precision@5 0.1556
cosine_precision@10 0.0845
cosine_recall@1 0.5449
cosine_recall@3 0.7218
cosine_recall@5 0.778
cosine_recall@10 0.8453
cosine_ndcg@10 0.694
cosine_mrr@10 0.6457
cosine_map@100 0.652
dot_accuracy@1 0.5449
dot_accuracy@3 0.7218
dot_accuracy@5 0.778
dot_accuracy@10 0.8453
dot_precision@1 0.5449
dot_precision@3 0.2406
dot_precision@5 0.1556
dot_precision@10 0.0845
dot_recall@1 0.5449
dot_recall@3 0.7218
dot_recall@5 0.778
dot_recall@10 0.8453
dot_ndcg@10 0.694
dot_mrr@10 0.6457
dot_map@100 0.652

Training Details

Training Dataset

Unnamed Dataset

  • Size: 41,881 training samples
  • Columns: sentence_0 and sentence_1
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1
    type string string
    details
    • min: 10 tokens
    • mean: 24.26 tokens
    • max: 50 tokens
    • min: 15 tokens
    • mean: 237.12 tokens
    • max: 424 tokens
  • Samples:
    sentence_0 sentence_1
    KDS 10 00 00 설계기준은 어느 나라의 표준인가요? KDS 10 00 00설계기준 Korean Design StandardKDS 10 00 00 : 2021공통설계기준.2021년5월12일개정http://www.kcsc.re.kr
    KDS 10 00 00 설계기준은 최근에 언제 개정되었나요? KDS 10 00 00설계기준 Korean Design StandardKDS 10 00 00 : 2021공통설계기준.2021년5월12일개정http://www.kcsc.re.kr
    KDS 10 10 00 설계총칙 문서는 어떤 분야의 설계 기준을 다루고 있나요? 공통설계기준체계KDS 10 10 00 설계총칙 `21.05
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • num_train_epochs: 15
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • learning_rate: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 15
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • 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: False
  • 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}
  • 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: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • 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
  • dispatch_batches: None
  • split_batches: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand
Epoch Step Training Loss cosine_map@100
0.0115 15 - 0.4930
0.0229 30 - 0.5107
0.0344 45 - 0.5290
0.0458 60 - 0.5474
0.0573 75 - 0.5568
0.0688 90 - 0.5569
0.0802 105 - 0.5483
0.0917 120 - 0.5353
0.1031 135 - 0.5278
0.1146 150 - 0.5300
0.1261 165 - 0.5439
0.1375 180 - 0.5566
0.1490 195 - 0.5671
0.1604 210 - 0.5747
0.1719 225 - 0.5794
0.1833 240 - 0.5870
0.1948 255 - 0.5919
0.2063 270 - 0.5956
0.2177 285 - 0.6004
0.2292 300 - 0.6009
0.2406 315 - 0.6017
0.2521 330 - 0.6084
0.2636 345 - 0.6156
0.2750 360 - 0.6073
0.2865 375 - 0.6096
0.2979 390 - 0.6168
0.3094 405 - 0.6219
0.3209 420 - 0.6208
0.3323 435 - 0.6180
0.3438 450 - 0.6253
0.3552 465 - 0.6225
0.3667 480 - 0.6297
0.3782 495 - 0.6315
0.3820 500 0.5905 -
0.3896 510 - 0.6347
0.4011 525 - 0.6398
0.4125 540 - 0.6462
0.4240 555 - 0.6459
0.4354 570 - 0.6441
0.4469 585 - 0.6359
0.4584 600 - 0.6424
0.4698 615 - 0.6405
0.4813 630 - 0.6340
0.4927 645 - 0.6416
0.5042 660 - 0.6444
0.5157 675 - 0.6381
0.5271 690 - 0.6363
0.5386 705 - 0.6408
0.5500 720 - 0.6414
0.5615 735 - 0.6509
0.5730 750 - 0.6509
0.5844 765 - 0.6516
0.5959 780 - 0.6505
0.6073 795 - 0.6493
0.6188 810 - 0.6476
0.6303 825 - 0.6458
0.6417 840 - 0.6461
0.6532 855 - 0.6538
0.6646 870 - 0.6439
0.6761 885 - 0.6530
0.6875 900 - 0.6432
0.6990 915 - 0.6460
0.7105 930 - 0.6594
0.7219 945 - 0.6650
0.7334 960 - 0.6536
0.7448 975 - 0.6484
0.7563 990 - 0.6426
0.7639 1000 0.086 -
0.7678 1005 - 0.6509
0.7792 1020 - 0.6485
0.7907 1035 - 0.6464
0.8021 1050 - 0.6595
0.8136 1065 - 0.6538
0.8251 1080 - 0.6517
0.8365 1095 - 0.6638
0.8480 1110 - 0.6624
0.8594 1125 - 0.6582
0.8709 1140 - 0.6517
0.8824 1155 - 0.6504
0.8938 1170 - 0.6545
0.9053 1185 - 0.6588
0.9167 1200 - 0.6558
0.9282 1215 - 0.6615
0.9396 1230 - 0.6640
0.9511 1245 - 0.6612
0.9626 1260 - 0.6626
0.9740 1275 - 0.6519
0.9855 1290 - 0.6489
0.9969 1305 - 0.6542
1.0 1309 - 0.6542
1.0084 1320 - 0.6554
1.0199 1335 - 0.6596
1.0313 1350 - 0.6671
1.0428 1365 - 0.6636
1.0542 1380 - 0.6729
1.0657 1395 - 0.6608
1.0772 1410 - 0.6718
1.0886 1425 - 0.6614
1.1001 1440 - 0.6639
1.1115 1455 - 0.6649
1.1230 1470 - 0.6680
1.1345 1485 - 0.6516
1.1459 1500 0.0704 0.6764
1.1574 1515 - 0.6713
1.1688 1530 - 0.6690
1.1803 1545 - 0.6750
1.1917 1560 - 0.6730
1.2032 1575 - 0.6690
1.2147 1590 - 0.6590
1.2261 1605 - 0.6699
1.2376 1620 - 0.6627
1.2490 1635 - 0.6747
1.2605 1650 - 0.6726
1.2720 1665 - 0.6548
1.2834 1680 - 0.6607
1.2949 1695 - 0.6678
1.3063 1710 - 0.6632
1.3178 1725 - 0.6505
1.3293 1740 - 0.6583
1.3407 1755 - 0.6713
1.3522 1770 - 0.6434
1.3636 1785 - 0.6715
1.3751 1800 - 0.6699
1.3866 1815 - 0.6663
1.3980 1830 - 0.6646
1.4095 1845 - 0.6547
1.4209 1860 - 0.6650
1.4324 1875 - 0.6569
1.4439 1890 - 0.6496
1.4553 1905 - 0.6638
1.4668 1920 - 0.6714
1.4782 1935 - 0.6474
1.4897 1950 - 0.6741
1.5011 1965 - 0.6626
1.5126 1980 - 0.6562
1.5241 1995 - 0.6525
1.5279 2000 0.0563 -
1.5355 2010 - 0.6672
1.5470 2025 - 0.6577
1.5584 2040 - 0.6609
1.5699 2055 - 0.6583
1.5814 2070 - 0.6734
1.5928 2085 - 0.6592
1.6043 2100 - 0.6553
1.6157 2115 - 0.6584
1.6272 2130 - 0.6515
1.6387 2145 - 0.6541
1.6501 2160 - 0.6601
1.6616 2175 - 0.6641
1.6730 2190 - 0.6605
1.6845 2205 - 0.6618
1.6960 2220 - 0.6637
1.7074 2235 - 0.6614
1.7189 2250 - 0.6577
1.7303 2265 - 0.6587
1.7418 2280 - 0.6556
1.7532 2295 - 0.6464
1.7647 2310 - 0.6655
1.7762 2325 - 0.6641
1.7876 2340 - 0.6518
1.7991 2355 - 0.6767
1.8105 2370 - 0.6693
1.8220 2385 - 0.6599
1.8335 2400 - 0.6651
1.8449 2415 - 0.6736
1.8564 2430 - 0.6568
1.8678 2445 - 0.6551
1.8793 2460 - 0.6615
1.8908 2475 - 0.6634
1.9022 2490 - 0.6547
1.9099 2500 0.0407 -
1.9137 2505 - 0.6636
1.9251 2520 - 0.6721
1.9366 2535 - 0.6656
1.9481 2550 - 0.6667
1.9595 2565 - 0.6624
1.9710 2580 - 0.6605
1.9824 2595 - 0.6463
1.9939 2610 - 0.6723
2.0 2618 - 0.6774
2.0053 2625 - 0.6737
2.0168 2640 - 0.6717
2.0283 2655 - 0.6728
2.0397 2670 - 0.6665
2.0512 2685 - 0.6725
2.0626 2700 - 0.6606
2.0741 2715 - 0.6644
2.0856 2730 - 0.6708
2.0970 2745 - 0.6606
2.1085 2760 - 0.6699
2.1199 2775 - 0.6730
2.1314 2790 - 0.6671
2.1429 2805 - 0.6669
2.1543 2820 - 0.6760
2.1658 2835 - 0.6710
2.1772 2850 - 0.6584
2.1887 2865 - 0.6749
2.2002 2880 - 0.6680
2.2116 2895 - 0.6741
2.2231 2910 - 0.6693
2.2345 2925 - 0.6634
2.2460 2940 - 0.6585
2.2574 2955 - 0.6669
2.2689 2970 - 0.6737
2.2804 2985 - 0.6592
2.2918 3000 0.0345 0.6662
2.3033 3015 - 0.6731
2.3147 3030 - 0.6670
2.3262 3045 - 0.6604
2.3377 3060 - 0.6682
2.3491 3075 - 0.6610
2.3606 3090 - 0.6683
2.3720 3105 - 0.6785
2.3835 3120 - 0.6662
2.3950 3135 - 0.6603
2.4064 3150 - 0.6652
2.4179 3165 - 0.6604
2.4293 3180 - 0.6665
2.4408 3195 - 0.6630
2.4523 3210 - 0.6512
2.4637 3225 - 0.6625
2.4752 3240 - 0.6559
2.4866 3255 - 0.6612
2.4981 3270 - 0.6696
2.5095 3285 - 0.6558
2.5210 3300 - 0.6530
2.5325 3315 - 0.6571
2.5439 3330 - 0.6609
2.5554 3345 - 0.6587
2.5668 3360 - 0.6687
2.5783 3375 - 0.6630
2.5898 3390 - 0.6706
2.6012 3405 - 0.6504
2.6127 3420 - 0.6652
2.6241 3435 - 0.6718
2.6356 3450 - 0.6717
2.6471 3465 - 0.6604
2.6585 3480 - 0.6554
2.6700 3495 - 0.6566
2.6738 3500 0.0254 -
2.6814 3510 - 0.6603
2.6929 3525 - 0.6765
2.7044 3540 - 0.6709
2.7158 3555 - 0.6663
2.7273 3570 - 0.6617
2.7387 3585 - 0.6595
2.7502 3600 - 0.6613
2.7617 3615 - 0.6581
2.7731 3630 - 0.6727
2.7846 3645 - 0.6603
2.7960 3660 - 0.6587
2.8075 3675 - 0.6703
2.8189 3690 - 0.6708
2.8304 3705 - 0.6673
2.8419 3720 - 0.6673
2.8533 3735 - 0.6766
2.8648 3750 - 0.6654
2.8762 3765 - 0.6568
2.8877 3780 - 0.6606
2.8992 3795 - 0.6522
2.9106 3810 - 0.6550
2.9221 3825 - 0.6773
2.9335 3840 - 0.6714
2.9450 3855 - 0.6721
2.9565 3870 - 0.6667
2.9679 3885 - 0.6639
2.9794 3900 - 0.6674
2.9908 3915 - 0.6626
3.0 3927 - 0.6654
3.0023 3930 - 0.6667
3.0138 3945 - 0.6707
3.0252 3960 - 0.6687
3.0367 3975 - 0.6731
3.0481 3990 - 0.6771
3.0558 4000 0.0198 -
3.0596 4005 - 0.6792
3.0710 4020 - 0.6672
3.0825 4035 - 0.6714
3.0940 4050 - 0.6707
3.1054 4065 - 0.6688
3.1169 4080 - 0.6838
3.1283 4095 - 0.6718
3.1398 4110 - 0.6595
3.1513 4125 - 0.6741
3.1627 4140 - 0.6737
3.1742 4155 - 0.6731
3.1856 4170 - 0.6700
3.1971 4185 - 0.6748
3.2086 4200 - 0.6746
3.2200 4215 - 0.6716
3.2315 4230 - 0.6755
3.2429 4245 - 0.6700
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14.8969 19500 0.0039 0.6522
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14.9771 19605 - 0.6520
14.9885 19620 - 0.6520
15.0 19635 - 0.6520

Framework Versions

  • Python: 3.11.0rc1
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.1
  • PyTorch: 2.2.2+cu121
  • Accelerate: 0.30.1
  • Datasets: 2.20.0
  • Tokenizers: 0.19.1

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
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