Day1Kim/bge-m3_kicon_15

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3. 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

• This model is additionally trained on the Korean dataset.
• Compound document based on text, table and markdown.

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-m3
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (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("Day1Kim/bge-m3_kicon_15")
# 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	0.6014
cosine_accuracy@3	0.7604
cosine_accuracy@5	0.8103
cosine_accuracy@10	0.8655
cosine_precision@1	0.6014
cosine_precision@3	0.2535
cosine_precision@5	0.1621
cosine_precision@10	0.0865
cosine_recall@1	0.6014
cosine_recall@3	0.7604
cosine_recall@5	0.8103
cosine_recall@10	0.8655
cosine_ndcg@10	0.7347
cosine_mrr@10	0.6927
cosine_map@100	0.6986

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
• torch_empty_cache_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}
• 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: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss	cosine_ndcg@10
0.0115	15	-	0.5942
0.0229	30	-	0.5969
0.0344	45	-	0.6025
0.0458	60	-	0.6068
0.0573	75	-	0.6108
0.0688	90	-	0.6167
0.0115	15	-	0.6170
0.0229	30	-	0.6176
0.0344	45	-	0.6189
0.0458	60	-	0.6207
0.0573	75	-	0.6225
0.0688	90	-	0.6264
0.0802	105	-	0.6297
0.0917	120	-	0.6315
0.1031	135	-	0.6334
0.1146	150	-	0.6380
0.1261	165	-	0.6426
0.1375	180	-	0.6464
0.1490	195	-	0.6507
0.1604	210	-	0.6531
0.1719	225	-	0.6575
0.1833	240	-	0.6626
0.1948	255	-	0.6646
0.2063	270	-	0.6650
0.2177	285	-	0.6667
0.2292	300	-	0.6672
0.2406	315	-	0.6697
0.2521	330	-	0.6720
0.2636	345	-	0.6745
0.2750	360	-	0.6745
0.2865	375	-	0.6780
0.2979	390	-	0.6796
0.3094	405	-	0.6779
0.3209	420	-	0.6770
0.3323	435	-	0.6803
0.3438	450	-	0.6830
0.3552	465	-	0.6824
0.3667	480	-	0.6846
0.3782	495	-	0.6815
0.3820	500	0.1899	-
0.3896	510	-	0.6831
0.4011	525	-	0.6835
0.4125	540	-	0.6852
0.4240	555	-	0.6878
0.4354	570	-	0.6889
0.4469	585	-	0.6848
0.4584	600	-	0.6852
0.4698	615	-	0.6870
0.4813	630	-	0.6925
0.4927	645	-	0.6954
0.5042	660	-	0.6881
0.5157	675	-	0.6839
0.5271	690	-	0.6849
0.5386	705	-	0.6934
0.5500	720	-	0.6937
0.5615	735	-	0.6937
0.5730	750	-	0.6870
0.5844	765	-	0.6952
0.5959	780	-	0.7008
0.6073	795	-	0.6957
0.6188	810	-	0.6927
0.6303	825	-	0.6899
0.6417	840	-	0.6874
0.6532	855	-	0.6927
0.6646	870	-	0.6991
0.6761	885	-	0.7024
0.6875	900	-	0.7010
0.6990	915	-	0.6943
0.7105	930	-	0.6987
0.7219	945	-	0.7028
0.7334	960	-	0.7004
0.7448	975	-	0.6997
0.7563	990	-	0.6993
0.7639	1000	0.0837	-
0.7678	1005	-	0.6956
0.7792	1020	-	0.6913
0.7907	1035	-	0.6930
0.8021	1050	-	0.6959
0.8136	1065	-	0.6968
0.8251	1080	-	0.7015
0.8365	1095	-	0.6982
0.8480	1110	-	0.7108
0.8594	1125	-	0.7068
0.8709	1140	-	0.7037
0.8824	1155	-	0.7031
0.8938	1170	-	0.6973
0.9053	1185	-	0.6960
0.9167	1200	-	0.6969
0.9282	1215	-	0.6936
0.9396	1230	-	0.6976
0.9511	1245	-	0.7032
0.9626	1260	-	0.7066
0.9740	1275	-	0.7051
0.9855	1290	-	0.7053
0.9969	1305	-	0.7086
1.0	1309	-	0.7075
1.0084	1320	-	0.7063
1.0199	1335	-	0.7079
1.0313	1350	-	0.7112
1.0428	1365	-	0.7075
1.0542	1380	-	0.7008
1.0657	1395	-	0.7020
1.0772	1410	-	0.7047
1.0886	1425	-	0.7051
1.1001	1440	-	0.7084
1.1115	1455	-	0.7112
1.1230	1470	-	0.7109
1.1345	1485	-	0.7037
1.1459	1500	0.0632	0.7033
1.1574	1515	-	0.7158
1.1688	1530	-	0.7076
1.1803	1545	-	0.7115
1.1917	1560	-	0.7109
1.2032	1575	-	0.7062
1.2147	1590	-	0.7047
1.2261	1605	-	0.7033
1.2376	1620	-	0.7055
1.2490	1635	-	0.7149
1.2605	1650	-	0.7155
1.2720	1665	-	0.7042
1.2834	1680	-	0.6972
1.2949	1695	-	0.7071
1.3063	1710	-	0.7107
1.3178	1725	-	0.7113
1.3293	1740	-	0.7115
1.3407	1755	-	0.7022
1.3522	1770	-	0.7073
1.3636	1785	-	0.6989
1.3751	1800	-	0.6995
1.3866	1815	-	0.7021
1.3980	1830	-	0.7092
1.4095	1845	-	0.7099
1.4209	1860	-	0.7040
1.4324	1875	-	0.7041
1.4439	1890	-	0.7111
1.4553	1905	-	0.7103
1.4668	1920	-	0.7030
1.4782	1935	-	0.7058
1.4897	1950	-	0.7045
1.5011	1965	-	0.7026
1.5126	1980	-	0.7142
1.5241	1995	-	0.7087
1.5279	2000	0.0573	-
1.5355	2010	-	0.7085
1.5470	2025	-	0.7108
1.5584	2040	-	0.7049
1.5699	2055	-	0.7013
1.5814	2070	-	0.6987
1.5928	2085	-	0.7116
1.6043	2100	-	0.7049
1.6157	2115	-	0.6990
1.6272	2130	-	0.6954
1.6387	2145	-	0.7007
1.6501	2160	-	0.6951
1.6616	2175	-	0.7014
1.6730	2190	-	0.7083
1.6845	2205	-	0.7091
1.6960	2220	-	0.7105
1.7074	2235	-	0.7054
1.7189	2250	-	0.6979
1.7303	2265	-	0.6966
1.7418	2280	-	0.6981
1.7532	2295	-	0.7050
1.7647	2310	-	0.7051
1.7762	2325	-	0.7086
1.7876	2340	-	0.7045
1.7991	2355	-	0.6971
1.8105	2370	-	0.7021
1.8220	2385	-	0.7037
1.8335	2400	-	0.6947
1.8449	2415	-	0.6948
1.8564	2430	-	0.6956
1.8678	2445	-	0.6959
1.8793	2460	-	0.6884
1.8908	2475	-	0.7084
1.9022	2490	-	0.7088
1.9099	2500	0.0571	-
1.9137	2505	-	0.7027
1.9251	2520	-	0.6953
1.9366	2535	-	0.7057
1.9481	2550	-	0.7064
1.9595	2565	-	0.7097
1.9710	2580	-	0.7077
1.9824	2595	-	0.6990
1.9939	2610	-	0.7011
2.0	2618	-	0.7042
2.0053	2625	-	0.7002
2.0168	2640	-	0.7037
2.0283	2655	-	0.7053
2.0397	2670	-	0.7081
2.0512	2685	-	0.6980
2.0626	2700	-	0.7129
2.0741	2715	-	0.7139
2.0856	2730	-	0.6974
2.0970	2745	-	0.7056
2.1085	2760	-	0.7145
2.1199	2775	-	0.7088
2.1314	2790	-	0.7063
2.1429	2805	-	0.7128
2.1543	2820	-	0.7086
2.1658	2835	-	0.7133
2.1772	2850	-	0.7067
2.1887	2865	-	0.7056
2.2002	2880	-	0.6942
2.2116	2895	-	0.6983
2.2231	2910	-	0.7075
2.2345	2925	-	0.7027
2.2460	2940	-	0.7078
2.2574	2955	-	0.7066
2.2689	2970	-	0.7042
2.2804	2985	-	0.7107
2.2918	3000	0.0421	0.7122
2.3033	3015	-	0.7119
2.3147	3030	-	0.7157
2.3262	3045	-	0.7152
2.3377	3060	-	0.7153
2.3491	3075	-	0.7092
2.3606	3090	-	0.7059
2.3720	3105	-	0.7070
2.3835	3120	-	0.7090
2.3950	3135	-	0.7058
2.4064	3150	-	0.7060
2.4179	3165	-	0.7161
2.4293	3180	-	0.7147
2.4408	3195	-	0.7087
2.4523	3210	-	0.7083
2.4637	3225	-	0.7085
2.4752	3240	-	0.7078
2.4866	3255	-	0.7093
2.4981	3270	-	0.7111
2.5095	3285	-	0.7138
2.5210	3300	-	0.7186
2.5325	3315	-	0.7065
2.5439	3330	-	0.7076
2.5554	3345	-	0.7044
2.5668	3360	-	0.7054
2.5783	3375	-	0.7098
2.5898	3390	-	0.7075
2.6012	3405	-	0.7051
2.6127	3420	-	0.7114
2.6241	3435	-	0.7130
2.6356	3450	-	0.7098
2.6471	3465	-	0.7138
2.6585	3480	-	0.7067
2.6700	3495	-	0.7011
2.6738	3500	0.0387	-
2.6814	3510	-	0.7088
2.6929	3525	-	0.7114
2.7044	3540	-	0.7046
2.7158	3555	-	0.7083
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2.7731	3630	-	0.7093
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2.8075	3675	-	0.7097
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3.0	3927	-	0.7142
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3.0138	3945	-	0.7055
3.0252	3960	-	0.7021
3.0367	3975	-	0.7030
3.0481	3990	-	0.7064
3.0558	4000	0.0402	-
3.0596	4005	-	0.7115
3.0710	4020	-	0.7108
3.0825	4035	-	0.7114
3.0940	4050	-	0.7112
3.1054	4065	-	0.7050
3.1169	4080	-	0.7099
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3.1513	4125	-	0.7063
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3.1742	4155	-	0.7085
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3.2315	4230	-	0.7062
3.2429	4245	-	0.7166
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3.3002	4320	-	0.7089
3.3117	4335	-	0.7097
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3.4377	4500	0.0312	0.7155
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3.5065	4590	-	0.7086
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3.5638	4665	-	0.7111
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3.5867	4695	-	0.7188
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3.6096	4725	-	0.7117
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3.6325	4755	-	0.7083
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3.6669	4800	-	0.7076
3.6784	4815	-	0.7099
3.6898	4830	-	0.7081
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3.7242	4875	-	0.7111
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3.7930	4965	-	0.7115
3.8044	4980	-	0.7098
3.8159	4995	-	0.7099
3.8197	5000	0.0296	-
3.8273	5010	-	0.7140
3.8388	5025	-	0.7104
3.8503	5040	-	0.7102
3.8617	5055	-	0.7136
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3.9992	5235	-	0.7216
4.0	5236	-	0.7211
4.0107	5250	-	0.7189
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4.0451	5295	-	0.7145
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4.0680	5325	-	0.7167
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4.0909	5355	-	0.7161
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4.1138	5385	-	0.7080
4.1253	5400	-	0.6994
4.1367	5415	-	0.7065
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4.1597	5445	-	0.7142
4.1711	5460	-	0.7203
4.1826	5475	-	0.7210
4.1940	5490	-	0.7127
4.2017	5500	0.0258	-
4.2055	5505	-	0.7059
4.2170	5520	-	0.7090
4.2284	5535	-	0.7094
4.2399	5550	-	0.7126
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4.3316	5670	-	0.7167
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4.4232	5790	-	0.7159
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4.5722	5985	-	0.7203
4.5837	6000	0.0274	0.7194
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4.6295	6060	-	0.7168
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4.9656	6500	0.0264	-
4.9733	6510	-	0.7213
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4.9962	6540	-	0.7166
5.0	6545	-	0.7187
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5.0420	6600	-	0.7189
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5.1337	6720	-	0.7187
5.1451	6735	-	0.7170
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5.1795	6780	-	0.7111
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5.2254	6840	-	0.7152
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5.2483	6870	-	0.7166
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5.3285	6975	-	0.7216
5.3400	6990	-	0.7220
5.3476	7000	0.0206	-
5.3514	7005	-	0.7216
5.3629	7020	-	0.7167
5.3743	7035	-	0.7161
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5.5118	7215	-	0.7140
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5.6035	7335	-	0.7166
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5.6952	7455	-	0.7215
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5.7296	7500	0.0218	0.7204
5.7410	7515	-	0.7270
5.7525	7530	-	0.7260
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5.8785	7695	-	0.7244
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5.9931	7845	-	0.7212
6.0	7854	-	0.7168
6.0046	7860	-	0.7166
6.0160	7875	-	0.7193
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6.0390	7905	-	0.7153
6.0504	7920	-	0.7223
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6.0733	7950	-	0.7319
6.0848	7965	-	0.7268
6.0963	7980	-	0.7233
6.1077	7995	-	0.7232
6.1115	8000	0.022	-
6.1192	8010	-	0.7223
6.1306	8025	-	0.7201
6.1421	8040	-	0.7238
6.1536	8055	-	0.7221
6.1650	8070	-	0.7215
6.1765	8085	-	0.7196
6.1879	8100	-	0.7271
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6.2108	8130	-	0.7287
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6.2338	8160	-	0.7253
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6.2681	8205	-	0.7304
6.2796	8220	-	0.7271
6.2911	8235	-	0.7245
6.3025	8250	-	0.7269
6.3140	8265	-	0.7266
6.3254	8280	-	0.7248
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6.3484	8310	-	0.7252
6.3598	8325	-	0.7264
6.3713	8340	-	0.7253
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6.3942	8370	-	0.7252
6.4057	8385	-	0.7200
6.4171	8400	-	0.7228
6.4286	8415	-	0.7221
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6.4629	8460	-	0.7204
6.4744	8475	-	0.7235
6.4859	8490	-	0.7268
6.4935	8500	0.0201	-
6.4973	8505	-	0.7282
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6.5202	8535	-	0.7192
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6.6692	8730	-	0.7300
6.6807	8745	-	0.7242
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6.7036	8775	-	0.7286
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6.7380	8820	-	0.7171
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6.8296	8940	-	0.7287
6.8411	8955	-	0.7300
6.8526	8970	-	0.7276
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6.8755	9000	0.0217	0.7256
6.8869	9015	-	0.7236
6.8984	9030	-	0.7222
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6.9442	9090	-	0.7286
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7.0	9163	-	0.7269
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7.0130	9180	-	0.7287
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7.0359	9210	-	0.7252
7.0474	9225	-	0.7243
7.0588	9240	-	0.7246
7.0703	9255	-	0.7245
7.0817	9270	-	0.7223
7.0932	9285	-	0.7198
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7.1161	9315	-	0.7243
7.1276	9330	-	0.7230
7.1390	9345	-	0.7197
7.1505	9360	-	0.7201
7.1620	9375	-	0.7197
7.1734	9390	-	0.7212
7.1849	9405	-	0.7213
7.1963	9420	-	0.7239
7.2078	9435	-	0.7220
7.2193	9450	-	0.7206
7.2307	9465	-	0.7213
7.2422	9480	-	0.7207
7.2536	9495	-	0.7268
7.2574	9500	0.0186	-
7.2651	9510	-	0.7265
7.2765	9525	-	0.7203
7.2880	9540	-	0.7193
7.2995	9555	-	0.7185
7.3109	9570	-	0.7228
7.3224	9585	-	0.7270
7.3338	9600	-	0.7247
7.3453	9615	-	0.7204
7.3568	9630	-	0.7202
7.3682	9645	-	0.7211
7.3797	9660	-	0.7243
7.3911	9675	-	0.7270
7.4026	9690	-	0.7298
7.4141	9705	-	0.7315
7.4255	9720	-	0.7339
7.4370	9735	-	0.7325
7.4484	9750	-	0.7289
7.4599	9765	-	0.7264
7.4714	9780	-	0.7257
7.4828	9795	-	0.7242
7.4943	9810	-	0.7265
7.5057	9825	-	0.7276
7.5172	9840	-	0.7311
7.5286	9855	-	0.7296
7.5401	9870	-	0.7259
7.5516	9885	-	0.7291
7.5630	9900	-	0.7298
7.5745	9915	-	0.7252
7.5859	9930	-	0.7302
7.5974	9945	-	0.7289
7.6089	9960	-	0.7288
7.6203	9975	-	0.7282
7.6318	9990	-	0.7230
7.6394	10000	0.0201	-
7.6432	10005	-	0.7212
7.6547	10020	-	0.7224
7.6662	10035	-	0.7221
7.6776	10050	-	0.7217
7.6891	10065	-	0.7245
7.7005	10080	-	0.7268
7.7120	10095	-	0.7280
7.7235	10110	-	0.7247
7.7349	10125	-	0.7250
7.7464	10140	-	0.7250
7.7578	10155	-	0.7218
7.7693	10170	-	0.7202
7.7807	10185	-	0.7226
7.7922	10200	-	0.7244
7.8037	10215	-	0.7267
7.8151	10230	-	0.7221
7.8266	10245	-	0.7225
7.8380	10260	-	0.7223
7.8495	10275	-	0.7218
7.8610	10290	-	0.7228
7.8724	10305	-	0.7229
7.8839	10320	-	0.7198
7.8953	10335	-	0.7226
7.9068	10350	-	0.7235
7.9183	10365	-	0.7197
7.9297	10380	-	0.7136
7.9412	10395	-	0.7095
7.9526	10410	-	0.7203
7.9641	10425	-	0.7255
7.9756	10440	-	0.7244
7.9870	10455	-	0.7220
7.9985	10470	-	0.7232
8.0	10472	-	0.7232
8.0099	10485	-	0.7217
8.0214	10500	0.0184	0.7204
8.0328	10515	-	0.7234
8.0443	10530	-	0.7291
8.0558	10545	-	0.7322
8.0672	10560	-	0.7327
8.0787	10575	-	0.7316
8.0901	10590	-	0.7286
8.1016	10605	-	0.7257
8.1131	10620	-	0.7206
8.1245	10635	-	0.7199
8.1360	10650	-	0.7228
8.1474	10665	-	0.7254
8.1589	10680	-	0.7211
8.1704	10695	-	0.7202
8.1818	10710	-	0.7197
8.1933	10725	-	0.7206
8.2047	10740	-	0.7220
8.2162	10755	-	0.7244
8.2277	10770	-	0.7241
8.2391	10785	-	0.7248
8.2506	10800	-	0.7231
8.2620	10815	-	0.7220
8.2735	10830	-	0.7198
8.2850	10845	-	0.7191
8.2964	10860	-	0.7170
8.3079	10875	-	0.7228
8.3193	10890	-	0.7273
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8.4034	11000	0.0178	-
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9.5493	12500	0.0147	-
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10.6952	14000	0.0161	-
10.7028	14010	-	0.7242
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11.0772	14500	0.0152	-
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11.8487	15510	-	0.7273
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12.9717	16980	-	0.7331
12.9832	16995	-	0.7324
12.9870	17000	0.0133	-
12.9947	17010	-	0.7333
13.0	17017	-	0.7323
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13.2238	17310	-	0.7331
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13.2926	17400	-	0.7331
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13.3155	17430	-	0.7334
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13.3499	17475	-	0.7335
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13.3690	17500	0.014	-
13.3728	17505	-	0.7334
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13.5561	17745	-	0.7353
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13.5791	17775	-	0.7354
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13.6020	17805	-	0.7345
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13.6478	17865	-	0.7348
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13.6707	17895	-	0.7346
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13.6937	17925	-	0.7347
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13.7166	17955	-	0.7346
13.7280	17970	-	0.7337
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13.7510	18000	0.0128	0.7345
13.7624	18015	-	0.7341
13.7739	18030	-	0.7341
13.7853	18045	-	0.7334
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13.8197	18090	-	0.7333
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13.8885	18180	-	0.7333
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13.9228	18225	-	0.7342
13.9343	18240	-	0.7349
13.9458	18255	-	0.7349
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13.9687	18285	-	0.7366
13.9801	18300	-	0.7365
13.9916	18315	-	0.7372
14.0	18326	-	0.7370
14.0031	18330	-	0.7372
14.0145	18345	-	0.7372
14.0260	18360	-	0.7380
14.0374	18375	-	0.7379
14.0489	18390	-	0.7366
14.0604	18405	-	0.7362
14.0718	18420	-	0.7363
14.0833	18435	-	0.7361
14.0947	18450	-	0.7360
14.1062	18465	-	0.7360
14.1176	18480	-	0.7365
14.1291	18495	-	0.7361
14.1329	18500	0.0132	-
14.1406	18510	-	0.7360
14.1520	18525	-	0.7360
14.1635	18540	-	0.7360
14.1749	18555	-	0.7352
14.1864	18570	-	0.7347
14.1979	18585	-	0.7349
14.2093	18600	-	0.7345
14.2208	18615	-	0.7347
14.2322	18630	-	0.7345
14.2437	18645	-	0.7348
14.2552	18660	-	0.7345
14.2666	18675	-	0.7342
14.2781	18690	-	0.7342
14.2895	18705	-	0.7339
14.3010	18720	-	0.7342
14.3125	18735	-	0.7350
14.3239	18750	-	0.7351
14.3354	18765	-	0.7348
14.3468	18780	-	0.7349
14.3583	18795	-	0.7347
14.3697	18810	-	0.7343
14.3812	18825	-	0.7344
14.3927	18840	-	0.7348
14.4041	18855	-	0.7348
14.4156	18870	-	0.7342
14.4270	18885	-	0.7343
14.4385	18900	-	0.7345
14.4500	18915	-	0.7347
14.4614	18930	-	0.7348
14.4729	18945	-	0.7351
14.4843	18960	-	0.7348
14.4958	18975	-	0.7350
14.5073	18990	-	0.7350
14.5149	19000	0.0127	-
14.5187	19005	-	0.7351
14.5302	19020	-	0.7351
14.5416	19035	-	0.7351
14.5531	19050	-	0.7348
14.5646	19065	-	0.7346
14.5760	19080	-	0.7344
14.5875	19095	-	0.7341
14.5989	19110	-	0.7338
14.6104	19125	-	0.7338
14.6218	19140	-	0.7335
14.6333	19155	-	0.7339
14.6448	19170	-	0.7341
14.6562	19185	-	0.7342
14.6677	19200	-	0.7342
14.6791	19215	-	0.7344
14.6906	19230	-	0.7345
14.7021	19245	-	0.7345
14.7135	19260	-	0.7343
14.7250	19275	-	0.7346
14.7364	19290	-	0.7345
14.7479	19305	-	0.7347
14.7594	19320	-	0.7349
14.7708	19335	-	0.7349
14.7823	19350	-	0.7352
14.7937	19365	-	0.7351
14.8052	19380	-	0.7349
14.8167	19395	-	0.7351
14.8281	19410	-	0.7349
14.8396	19425	-	0.7347
14.8510	19440	-	0.7347
14.8625	19455	-	0.7346
14.8739	19470	-	0.7345
14.8854	19485	-	0.7346
14.8969	19500	0.0124	0.7345
14.9083	19515	-	0.7346
14.9198	19530	-	0.7346
14.9312	19545	-	0.7347
14.9427	19560	-	0.7347
14.9542	19575	-	0.7347
14.9656	19590	-	0.7346
14.9771	19605	-	0.7347
14.9885	19620	-	0.7347
15.0	19635	-	0.7347

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 4.1.0
• Transformers: 4.51.0
• PyTorch: 2.6.0+cu126
• Accelerate: 1.6.0
• Datasets: 3.6.0
• 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",
}

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}
}