jerryyun/kicon_e5large_15_v1

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 Type: Sentence Transformer
• Base model: embaas/sentence-transformers-multilingual-e5-large
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 tokens
• 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: 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

• Evaluated with InformationRetrievalEvaluator

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
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2.7846	3645	-	0.6603
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2.8304	3705	-	0.6673
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2.8533	3735	-	0.6766
2.8648	3750	-	0.6654
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2.8877	3780	-	0.6606
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3.0	3927	-	0.6654
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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
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3.1742	4155	-	0.6731
3.1856	4170	-	0.6700
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3.2086	4200	-	0.6746
3.2200	4215	-	0.6716
3.2315	4230	-	0.6755
3.2429	4245	-	0.6700
3.2544	4260	-	0.6652
3.2659	4275	-	0.6700
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3.2888	4305	-	0.6643
3.3002	4320	-	0.6737
3.3117	4335	-	0.6756
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3.3346	4365	-	0.6682
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3.3690	4410	-	0.6753
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3.4377	4500	0.0185	0.6700
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3.4607	4530	-	0.6696
3.4721	4545	-	0.6822
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3.4950	4575	-	0.6717
3.5065	4590	-	0.6590
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3.5294	4620	-	0.6660
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3.5752	4680	-	0.6696
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3.5982	4710	-	0.6666
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3.6211	4740	-	0.6691
3.6325	4755	-	0.6648
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3.6669	4800	-	0.6667
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3.6898	4830	-	0.6704
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3.7815	4950	-	0.6668
3.7930	4965	-	0.6587
3.8044	4980	-	0.6653
3.8159	4995	-	0.6683
3.8197	5000	0.0165	-
3.8273	5010	-	0.6671
3.8388	5025	-	0.6671
3.8503	5040	-	0.6709
3.8617	5055	-	0.6708
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3.8961	5100	-	0.6590
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3.9190	5130	-	0.6607
3.9305	5145	-	0.6674
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3.9649	5190	-	0.6657
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4.0	5236	-	0.6611
4.0107	5250	-	0.6639
4.0222	5265	-	0.6735
4.0336	5280	-	0.6779
4.0451	5295	-	0.6802
4.0565	5310	-	0.6761
4.0680	5325	-	0.6718
4.0794	5340	-	0.6678
4.0909	5355	-	0.6726
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4.1138	5385	-	0.6756
4.1253	5400	-	0.6787
4.1367	5415	-	0.6676
4.1482	5430	-	0.6689
4.1597	5445	-	0.6674
4.1711	5460	-	0.6667
4.1826	5475	-	0.6773
4.1940	5490	-	0.6780
4.2017	5500	0.013	-
4.2055	5505	-	0.6732
4.2170	5520	-	0.6697
4.2284	5535	-	0.6683
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4.2513	5565	-	0.6692
4.2628	5580	-	0.6606
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4.2972	5625	-	0.6691
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4.3201	5655	-	0.6765
4.3316	5670	-	0.6658
4.3430	5685	-	0.6756
4.3545	5700	-	0.6713
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4.4003	5760	-	0.6639
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4.5493	5955	-	0.6687
4.5607	5970	-	0.6656
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4.5837	6000	0.015	0.6657
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4.6066	6030	-	0.6608
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4.8358	6330	-	0.6606
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4.8816	6390	-	0.6654
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4.9618	6495	-	0.6576
4.9656	6500	0.0115	-
4.9733	6510	-	0.6542
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4.9962	6540	-	0.6605
5.0	6545	-	0.6599
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5.0191	6570	-	0.6657
5.0306	6585	-	0.6644
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5.0764	6645	-	0.6703
5.0879	6660	-	0.6693
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5.1108	6690	-	0.6630
5.1222	6705	-	0.6652
5.1337	6720	-	0.6672
5.1451	6735	-	0.6647
5.1566	6750	-	0.6707
5.1681	6765	-	0.6672
5.1795	6780	-	0.6588
5.1910	6795	-	0.6606
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5.2139	6825	-	0.6695
5.2254	6840	-	0.6693
5.2368	6855	-	0.6729
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5.2597	6885	-	0.6656
5.2712	6900	-	0.6556
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5.2941	6930	-	0.6702
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5.3170	6960	-	0.6704
5.3285	6975	-	0.6671
5.3400	6990	-	0.6645
5.3476	7000	0.0119	-
5.3514	7005	-	0.6719
5.3629	7020	-	0.6710
5.3743	7035	-	0.6666
5.3858	7050	-	0.6699
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5.4316	7110	-	0.6666
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5.4545	7140	-	0.6603
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5.4775	7170	-	0.6656
5.4889	7185	-	0.6672
5.5004	7200	-	0.6606
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5.5233	7230	-	0.6628
5.5348	7245	-	0.6602
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5.5577	7275	-	0.6642
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5.6035	7335	-	0.6603
5.6150	7350	-	0.6657
5.6264	7365	-	0.6709
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5.6952	7455	-	0.6651
5.7066	7470	-	0.6692
5.7181	7485	-	0.6687
5.7296	7500	0.0114	0.6551
5.7410	7515	-	0.6559
5.7525	7530	-	0.6590
5.7639	7545	-	0.6576
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5.7983	7590	-	0.6600
5.8098	7605	-	0.6611
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5.8556	7665	-	0.6711
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5.8900	7710	-	0.6527
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5.9244	7755	-	0.6663
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5.9473	7785	-	0.6634
5.9587	7800	-	0.6641
5.9702	7815	-	0.6565
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5.9931	7845	-	0.6540
6.0	7854	-	0.6546
6.0046	7860	-	0.6577
6.0160	7875	-	0.6640
6.0275	7890	-	0.6682
6.0390	7905	-	0.6678
6.0504	7920	-	0.6692
6.0619	7935	-	0.6641
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6.0848	7965	-	0.6571
6.0963	7980	-	0.6618
6.1077	7995	-	0.6672
6.1115	8000	0.009	-
6.1192	8010	-	0.6695
6.1306	8025	-	0.6673
6.1421	8040	-	0.6651
6.1536	8055	-	0.6671
6.1650	8070	-	0.6693
6.1765	8085	-	0.6629
6.1879	8100	-	0.6642
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6.2108	8130	-	0.6709
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6.2338	8160	-	0.6670
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6.2567	8190	-	0.6603
6.2681	8205	-	0.6596
6.2796	8220	-	0.6623
6.2911	8235	-	0.6643
6.3025	8250	-	0.6669
6.3140	8265	-	0.6750
6.3254	8280	-	0.6714
6.3369	8295	-	0.6529
6.3484	8310	-	0.6559
6.3598	8325	-	0.6644
6.3713	8340	-	0.6665
6.3827	8355	-	0.6677
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6.4057	8385	-	0.6766
6.4171	8400	-	0.6740
6.4286	8415	-	0.6690
6.4400	8430	-	0.6586
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6.4629	8460	-	0.6566
6.4744	8475	-	0.6576
6.4859	8490	-	0.6555
6.4935	8500	0.0103	-
6.4973	8505	-	0.6641
6.5088	8520	-	0.6660
6.5202	8535	-	0.6628
6.5317	8550	-	0.6612
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6.5546	8580	-	0.6609
6.5661	8595	-	0.6665
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6.6119	8655	-	0.6566
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6.6348	8685	-	0.6696
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6.6578	8715	-	0.6625
6.6692	8730	-	0.6607
6.6807	8745	-	0.6611
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6.7036	8775	-	0.6688
6.7150	8790	-	0.6689
6.7265	8805	-	0.6655
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6.7838	8880	-	0.6646
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6.8296	8940	-	0.6657
6.8411	8955	-	0.6665
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6.8640	8985	-	0.6692
6.8755	9000	0.0108	0.6666
6.8869	9015	-	0.6601
6.8984	9030	-	0.6598
6.9099	9045	-	0.6644
6.9213	9060	-	0.6608
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7.0244	9195	-	0.6608
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7.0703	9255	-	0.6640
7.0817	9270	-	0.6603
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7.1047	9300	-	0.6602
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7.1276	9330	-	0.6564
7.1390	9345	-	0.6568
7.1505	9360	-	0.6620
7.1620	9375	-	0.6683
7.1734	9390	-	0.6624
7.1849	9405	-	0.6546
7.1963	9420	-	0.6565
7.2078	9435	-	0.6594
7.2193	9450	-	0.6620
7.2307	9465	-	0.6624
7.2422	9480	-	0.6649
7.2536	9495	-	0.6649
7.2574	9500	0.0091	-
7.2651	9510	-	0.6605
7.2765	9525	-	0.6595
7.2880	9540	-	0.6559
7.2995	9555	-	0.6618
7.3109	9570	-	0.6637
7.3224	9585	-	0.6711
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7.3797	9660	-	0.6641
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7.4026	9690	-	0.6660
7.4141	9705	-	0.6673
7.4255	9720	-	0.6669
7.4370	9735	-	0.6611
7.4484	9750	-	0.6574
7.4599	9765	-	0.6543
7.4714	9780	-	0.6572
7.4828	9795	-	0.6594
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7.5057	9825	-	0.6530
7.5172	9840	-	0.6547
7.5286	9855	-	0.6598
7.5401	9870	-	0.6629
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7.5630	9900	-	0.6611
7.5745	9915	-	0.6568
7.5859	9930	-	0.6580
7.5974	9945	-	0.6640
7.6089	9960	-	0.6594
7.6203	9975	-	0.6601
7.6318	9990	-	0.6630
7.6394	10000	0.0086	-
7.6432	10005	-	0.6676
7.6547	10020	-	0.6668
7.6662	10035	-	0.6689
7.6776	10050	-	0.6674
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7.7005	10080	-	0.6633
7.7120	10095	-	0.6588
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7.7349	10125	-	0.6568
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7.7578	10155	-	0.6595
7.7693	10170	-	0.6606
7.7807	10185	-	0.6590
7.7922	10200	-	0.6505
7.8037	10215	-	0.6562
7.8151	10230	-	0.6666
7.8266	10245	-	0.6685
7.8380	10260	-	0.6633
7.8495	10275	-	0.6645
7.8610	10290	-	0.6757
7.8724	10305	-	0.6737
7.8839	10320	-	0.6679
7.8953	10335	-	0.6594
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7.9297	10380	-	0.6599
7.9412	10395	-	0.6595
7.9526	10410	-	0.6575
7.9641	10425	-	0.6568
7.9756	10440	-	0.6541
7.9870	10455	-	0.6527
7.9985	10470	-	0.6554
8.0	10472	-	0.6562
8.0099	10485	-	0.6500
8.0214	10500	0.0068	0.6567
8.0328	10515	-	0.6658
8.0443	10530	-	0.6695
8.0558	10545	-	0.6662
8.0672	10560	-	0.6643
8.0787	10575	-	0.6622
8.0901	10590	-	0.6582
8.1016	10605	-	0.6594
8.1131	10620	-	0.6567
8.1245	10635	-	0.6588
8.1360	10650	-	0.6587
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8.1589	10680	-	0.6604
8.1704	10695	-	0.6600
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8.1933	10725	-	0.6609
8.2047	10740	-	0.6605
8.2162	10755	-	0.6587
8.2277	10770	-	0.6593
8.2391	10785	-	0.6586
8.2506	10800	-	0.6600
8.2620	10815	-	0.6581
8.2735	10830	-	0.6561
8.2850	10845	-	0.6565
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13.9572	18270	-	0.6503
13.9687	18285	-	0.6492
13.9801	18300	-	0.6495
13.9916	18315	-	0.6504
14.0	18326	-	0.6503
14.0031	18330	-	0.6498
14.0145	18345	-	0.6499
14.0260	18360	-	0.6500
14.0374	18375	-	0.6507
14.0489	18390	-	0.6513
14.0604	18405	-	0.6511
14.0718	18420	-	0.6514
14.0833	18435	-	0.6519
14.0947	18450	-	0.6516
14.1062	18465	-	0.6522
14.1176	18480	-	0.6527
14.1291	18495	-	0.6524
14.1329	18500	0.0045	-
14.1406	18510	-	0.6527
14.1520	18525	-	0.6532
14.1635	18540	-	0.6530
14.1749	18555	-	0.6531
14.1864	18570	-	0.6531
14.1979	18585	-	0.6527
14.2093	18600	-	0.6535
14.2208	18615	-	0.6536
14.2322	18630	-	0.6531
14.2437	18645	-	0.6527
14.2552	18660	-	0.6532
14.2666	18675	-	0.6535
14.2781	18690	-	0.6535
14.2895	18705	-	0.6535
14.3010	18720	-	0.6535
14.3125	18735	-	0.6537
14.3239	18750	-	0.6536
14.3354	18765	-	0.6534
14.3468	18780	-	0.6547
14.3583	18795	-	0.6541
14.3697	18810	-	0.6539
14.3812	18825	-	0.6548
14.3927	18840	-	0.6547
14.4041	18855	-	0.6548
14.4156	18870	-	0.6546
14.4270	18885	-	0.6544
14.4385	18900	-	0.6543
14.4500	18915	-	0.6541
14.4614	18930	-	0.6537
14.4729	18945	-	0.6536
14.4843	18960	-	0.6533
14.4958	18975	-	0.6531
14.5073	18990	-	0.6517
14.5149	19000	0.0055	-
14.5187	19005	-	0.6517
14.5302	19020	-	0.6521
14.5416	19035	-	0.6522
14.5531	19050	-	0.6528
14.5646	19065	-	0.6524
14.5760	19080	-	0.6529
14.5875	19095	-	0.6529
14.5989	19110	-	0.6525
14.6104	19125	-	0.6523
14.6218	19140	-	0.6524
14.6333	19155	-	0.6528
14.6448	19170	-	0.6526
14.6562	19185	-	0.6530
14.6677	19200	-	0.6532
14.6791	19215	-	0.6532
14.6906	19230	-	0.6532
14.7021	19245	-	0.6533
14.7135	19260	-	0.6526
14.7250	19275	-	0.6522
14.7364	19290	-	0.6520
14.7479	19305	-	0.6522
14.7594	19320	-	0.6522
14.7708	19335	-	0.6522
14.7823	19350	-	0.6521
14.7937	19365	-	0.6521
14.8052	19380	-	0.6521
14.8167	19395	-	0.6521
14.8281	19410	-	0.6522
14.8396	19425	-	0.6523
14.8510	19440	-	0.6523
14.8625	19455	-	0.6523
14.8739	19470	-	0.6521
14.8854	19485	-	0.6522
14.8969	19500	0.0039	0.6522
14.9083	19515	-	0.6521
14.9198	19530	-	0.6523
14.9312	19545	-	0.6522
14.9427	19560	-	0.6521
14.9542	19575	-	0.6520
14.9656	19590	-	0.6521
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}
}