iambestfeed/phobert-base-v2-wiki-data-filter_fasttext_0.6_wseg-lr2e-05-1-epochs-bs-48

SentenceTransformer based on vinai/phobert-base-v2

This is a sentence-transformers model finetuned from vinai/phobert-base-v2 on the wiki-data dataset. 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
• Base model: vinai/phobert-base-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • wiki-data

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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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("iambestfeed/phobert-base-v2-wiki-data-filter_fasttext_0.6_wseg-lr2e-05-1-epochs-bs-48")
# Run inference
sentences = [
    'Giao_hữu trước mùa giải của Manchester_United F.C. mùa giải 2018 – 19',
    'United chuẩn_bị cho mùa giải 2018 – 19 với một tour thi_đấu tại Hoa_Kỳ . Ba trận đấu đầu_tiên được công_bố vào ngày 3 tháng 4 năm 2018 , với các đối_thủ Club_América , San_Jose_Earthquakes and Liverpool . ( ) Câu_lạc_bộ sau đó thông_báo tham_dự International_Champions_Cup 2018 . ( ) Ở giải đấu năm 2018 , United đấu với Milan tại StubHub_Center ở Carson , California , Liverpool tại Sân_vận_động Michigan ở Ann_Arbor , Michigan , và Real_Madrid tại Sân_vận_động Hard_Rock ở Miami_Gardens , Florida . ( ) Alexis_Sánchez đến Hoa_Kỳ bị trì_hoãn vì anh không được cung_cấp visa vì bản_án treo 16 tháng mà anh đã chấp_nhận vào tháng 2 do trốn lậu thuế trong thời_gian anh ở Tây_Ban_Nha . ( ) Trận giao_hữu cuối_cùng trước mùa giải của Manchester_United là trận đấu trên sân_khách trước Bayern_Munich tại Allianz_Arena vào ngày 5 tháng 8 . ( )',
    'USS Young ( DD-580 ) là một tàu_khu_trục lớp Fletcher được Hải_quân Hoa_Kỳ chế_tạo trong Chiến_tranh Thế_giới thứ hai . Nó là chiếc tàu_chiến thứ hai của Hải_quân Mỹ mang cái tên này , và là chiếc duy_nhất được đặt theo tên Chuẩn đô_đốc Lucien_Young ( 1852-1912 ) , người tham_gia cuộc Chiến_tranh Tây_Ban Nha-Hoa Kỳ . Nó hoạt_động cho đến hết Thế_Chiến II , được cho xuất_biên chế năm 1947 , xuất đăng_bạ năm 1968 và cuối_cùng bị đánh chìm như một mục_tiêu năm 1970 . Nó được tặng_thưởng năm Ngôi_sao Chiến_trận do thành_tích phục_vụ trong Thế_Chiến II .',
]
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

wiki-data

• Dataset: wiki-data at 4567021
• Size: 1,659,403 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 10.08 tokens max: 25 tokens	min: 15 tokens mean: 99.66 tokens max: 256 tokens

• Samples:

  anchor	positive
  Tiểu_sử của Hàm_Phong	Hàm_Phong_Đế tên thật là Ái_Tân_Giác_La Dịch_Trữ ( 爱新觉罗 · 奕詝 ) , sinh ngày 9 tháng 6 năm Đạo_Quang thứ 11 ( tức ngày 17 tháng 7 , năm 1831 ) tại Viên Minh_Viên ở Bắc_Kinh . Ông là con trai thứ tư của Thanh_Tuyên_Tông_Đạo Quang_Đế , mẹ là Hiếu_Toàn_Thành_Hoàng hậu Nữu_Hỗ_Lộc thị , Hoàng_hậu thứ hai của Đạo_Quang , con gái Nhị đẳng Thị_vệ Di_Linh , gia_thế không hiển_hách nhưng nhan_sắc mĩ mạo , được Đạo_Quang_Đế sủng_ái nên nhanh_chóng được tấn_phong . Khi Dịch_Trữ được ra_đời thì bà còn là Toàn Quý_phi . Theo thứ_tự thì Dịch_Trữ là Hoàng tứ tử , nhưng lại là con trai lớn nhất của Đạo_Quang_Đế vì cùng năm đó Hoàng trưởng tử Dịch_Vĩ mắc bệnh qua_đời ở tuổi 23 , Hoàng nhị tử Dịch_Cương và Hoàng tam tử Dịch_Kế lại mất sớm . Toàn Quý_phi sinh con được 2 năm thì Hiếu_Thận_Thành_Hoàng hậu Đông_Giai thị qua_đời nên bà được sách phong Hoàng quý_phi . Năm Đạo_Quang thứ 14 ( 1834 ) , Hoàng quý_phi được lập làm Kế hậu . Vì mẹ làm Hoàng_hậu , Hoàng tứ tử Dịch_Trữ nghiễm_nhiên trở_thành [ Đích tử ] ...
  Giới_thiệu của Eristalis bastardii	Eristalis bastardii là một loài ruồi trong họ Ruồi giả ong ( Syrphidae ) . Loài này được Macquart mô_tả khoa_học đầu_tiên năm 1842 . Eristalis bastardii phân_bố ở miền Tân_bắc ( ) ( )
  Lịch_sử thuật_ngữ tẩy_chay của Tẩy_chay	Trong tiếng Anh , tẩy_chay được biết đến với từ " boycott " , từ này cũng như toàn_bộ ý_nghĩa của nó ra_đời trong thời_kỳ " chiến_tranh đất_đai " tại Anh vào nữa sau thế_kỷ XIX . Boycott vốn là tên của một chủ đất - Charles_Cunning_Boycott - người Anh tại thị_trấn Mayo , hạt Mayo , Ireland . Những tá_điền Ireland bất_bình vì số tiền thuê đất quá cao mà điền_chủ đặt ra . Vì_thế năm 1880 , họ thành_lập một tổ_chức gọi là Liên_đoàn Đất_đai , và phong_trào nhanh_chóng đã lan nhanh trong toàn_quốc . Quá_trình đó đã sản_sinh ra chiến_thuật mới và trở_thành yếu_tố chính cho những tổ_chức bất_bạo_động qua nhiều thế_kỷ . Một trong những mục_tiêu đầu_tiên và tai_tiếng nhất là_viên quản_lý điền_trang người Anh ở thị_trấn Mayo . Liên_đoàn Đất_đai yêu_cầu ông ta giảm_giá thuê đất vì mùa_vụ thất_bát . Không_những không hoà_giải , ông ta còn đưa cảnh_sát đến đuổi những người tá_điền . Liên_đoàn Đất_đai phản_ứng theo cách mà sau đó trở_thành một_cách cư_xử trên thế_giới . Những cư_dân địa_phương từ_ch...

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 48
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• save_safetensors: False
• fp16: True
• push_to_hub: True
• hub_model_id: iambestfeed/phobert-base-v2-wiki-data-filter_fasttext_0.6_wseg-lr2e-05-1-epochs-bs-48
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 48
• per_device_eval_batch_size: 8
• 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: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: False
• 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: True
• 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: True
• resume_from_checkpoint: None
• hub_model_id: iambestfeed/phobert-base-v2-wiki-data-filter_fasttext_0.6_wseg-lr2e-05-1-epochs-bs-48
• 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
• 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
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0006	10	3.232
0.0012	20	3.296
0.0017	30	3.1884
0.0023	40	2.9286
0.0029	50	2.7017
0.0035	60	2.4331
0.0040	70	2.2524
0.0046	80	2.025
0.0052	90	1.857
0.0058	100	1.7118
0.0064	110	1.5546
0.0069	120	1.3802
0.0075	130	1.1712
0.0081	140	1.0374
0.0087	150	0.8431
0.0093	160	0.6916
0.0098	170	0.5552
0.0104	180	0.4548
0.0110	190	0.3868
0.0116	200	0.3341
0.0121	210	0.3351
0.0127	220	0.2555
0.0133	230	0.2493
0.0139	240	0.2314
0.0145	250	0.2261
0.0150	260	0.1939
0.0156	270	0.1949
0.0162	280	0.177
0.0168	290	0.1625
0.0174	300	0.1407
0.0179	310	0.1451
0.0185	320	0.1206
0.0191	330	0.1439
0.0197	340	0.1376
0.0202	350	0.1066
0.0208	360	0.1094
0.0214	370	0.1275
0.0220	380	0.125
0.0226	390	0.1138
0.0231	400	0.1185
0.0237	410	0.093
0.0243	420	0.1091
0.0249	430	0.1053
0.0255	440	0.0853
0.0260	450	0.1058
0.0266	460	0.0911
0.0272	470	0.0686
0.0278	480	0.0725
0.0283	490	0.0889
0.0289	500	0.0747
0.0295	510	0.0929
0.0301	520	0.0886
0.0307	530	0.067
0.0312	540	0.0733
0.0318	550	0.0746
0.0324	560	0.0688
0.0330	570	0.0576
0.0336	580	0.0678
0.0341	590	0.0662
0.0347	600	0.0779
0.0353	610	0.06
0.0359	620	0.0793
0.0364	630	0.0696
0.0370	640	0.0573
0.0376	650	0.0459
0.0382	660	0.0584
0.0388	670	0.0657
0.0393	680	0.0678
0.0399	690	0.0771
0.0405	700	0.0522
0.0411	710	0.0786
0.0417	720	0.0757
0.0422	730	0.0742
0.0428	740	0.0616
0.0434	750	0.0548
0.0440	760	0.0417
0.0445	770	0.071
0.0451	780	0.0724
0.0457	790	0.0548
0.0463	800	0.0586
0.0469	810	0.0447
0.0474	820	0.0612
0.0480	830	0.0539
0.0486	840	0.064
0.0492	850	0.0636
0.0498	860	0.0478
0.0503	870	0.0584
0.0509	880	0.0592
0.0515	890	0.0437
0.0521	900	0.0604
0.0526	910	0.0595
0.0532	920	0.0439
0.0538	930	0.0626
0.0544	940	0.0457
0.0550	950	0.0316
0.0555	960	0.03
0.0561	970	0.0401
0.0567	980	0.0632
0.0573	990	0.049
0.0579	1000	0.0459
0.0584	1010	0.0619
0.0590	1020	0.0566
0.0596	1030	0.0387
0.0602	1040	0.049
0.0607	1050	0.0439
0.0613	1060	0.0362
0.0619	1070	0.0344
0.0625	1080	0.0378
0.0631	1090	0.0474
0.0636	1100	0.0452
0.0642	1110	0.0383
0.0648	1120	0.0259
0.0654	1130	0.0324
0.0660	1140	0.0561
0.0665	1150	0.0365
0.0671	1160	0.0356
0.0677	1170	0.0559
0.0683	1180	0.0404
0.0688	1190	0.0343
0.0694	1200	0.0514
0.0700	1210	0.0518
0.0706	1220	0.0441
0.0712	1230	0.0312
0.0717	1240	0.0356
0.0723	1250	0.0378
0.0729	1260	0.0426
0.0735	1270	0.0611
0.0741	1280	0.0477
0.0746	1290	0.0413
0.0752	1300	0.0449
0.0758	1310	0.0348
0.0764	1320	0.0515
0.0769	1330	0.0387
0.0775	1340	0.0359
0.0781	1350	0.0425
0.0787	1360	0.0285
0.0793	1370	0.0444
0.0798	1380	0.0239
0.0804	1390	0.0285
0.0810	1400	0.0254
0.0816	1410	0.0455
0.0822	1420	0.0346
0.0827	1430	0.0251
0.0833	1440	0.0308
0.0839	1450	0.0291
0.0845	1460	0.037
0.0850	1470	0.0458
0.0856	1480	0.0328
0.0862	1490	0.0403
0.0868	1500	0.0314
0.0874	1510	0.0261
0.0879	1520	0.0498
0.0885	1530	0.047
0.0891	1540	0.04
0.0897	1550	0.0366
0.0903	1560	0.0358
0.0908	1570	0.0344
0.0914	1580	0.0456
0.0920	1590	0.0395
0.0926	1600	0.0352
0.0931	1610	0.03
0.0937	1620	0.02
0.0943	1630	0.0256
0.0949	1640	0.035
0.0955	1650	0.0318
0.0960	1660	0.0322
0.0966	1670	0.0319
0.0972	1680	0.0317
0.0978	1690	0.0409
0.0984	1700	0.0487
0.0989	1710	0.0306
0.0995	1720	0.0319
0.1001	1730	0.0392
0.1007	1740	0.0427
0.1012	1750	0.0393
0.1018	1760	0.0183
0.1024	1770	0.0337
0.1030	1780	0.0269
0.1036	1790	0.0305
0.1041	1800	0.0379
0.1047	1810	0.0411
0.1053	1820	0.0351
0.1059	1830	0.039
0.1065	1840	0.0196
0.1070	1850	0.0427
0.1076	1860	0.0358
0.1082	1870	0.0328
0.1088	1880	0.0316
0.1093	1890	0.0268
0.1099	1900	0.0254
0.1105	1910	0.0262
0.1111	1920	0.0317
0.1117	1930	0.0284
0.1122	1940	0.0273
0.1128	1950	0.0382
0.1134	1960	0.0285
0.1140	1970	0.028
0.1146	1980	0.0298
0.1151	1990	0.0336
0.1157	2000	0.029
0.1163	2010	0.0332
0.1169	2020	0.0338
0.1174	2030	0.043
0.1180	2040	0.0341
0.1186	2050	0.0376
0.1192	2060	0.0326
0.1198	2070	0.0452
0.1203	2080	0.0262
0.1209	2090	0.017
0.1215	2100	0.0275
0.1221	2110	0.0275
0.1226	2120	0.0341
0.1232	2130	0.0292
0.1238	2140	0.0445
0.1244	2150	0.0241
0.1250	2160	0.0296
0.1255	2170	0.021
0.1261	2180	0.0189
0.1267	2190	0.035
0.1273	2200	0.0341
0.1279	2210	0.0346
0.1284	2220	0.0331
0.1290	2230	0.0473
0.1296	2240	0.0277
0.1302	2250	0.0374
0.1307	2260	0.026
0.1313	2270	0.0257
0.1319	2280	0.0389
0.1325	2290	0.0307
0.1331	2300	0.0268
0.1336	2310	0.0245
0.1342	2320	0.0235
0.1348	2330	0.034
0.1354	2340	0.028
0.1360	2350	0.0433
0.1365	2360	0.0302
0.1371	2370	0.0244
0.1377	2380	0.0304
0.1383	2390	0.0229
0.1388	2400	0.0239
0.1394	2410	0.033
0.1400	2420	0.0215
0.1406	2430	0.0201
0.1412	2440	0.02
0.1417	2450	0.024
0.1423	2460	0.0316
0.1429	2470	0.0326
0.1435	2480	0.0196
0.1441	2490	0.0184
0.1446	2500	0.0233
0.1452	2510	0.0227
0.1458	2520	0.0181
0.1464	2530	0.0349
0.1469	2540	0.0221
0.1475	2550	0.025
0.1481	2560	0.0293
0.1487	2570	0.0211
0.1493	2580	0.0204
0.1498	2590	0.0246
0.1504	2600	0.0266
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0.1984	3430	0.0282
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0.2002	3460	0.0333
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0.4900	8470	0.0178
0.4906	8480	0.0235
0.4912	8490	0.0126
0.4918	8500	0.0186
0.4923	8510	0.0181
0.4929	8520	0.0173
0.4935	8530	0.0165
0.4941	8540	0.0148
0.4946	8550	0.0215
0.4952	8560	0.0107
0.4958	8570	0.012
0.4964	8580	0.0194
0.4970	8590	0.01
0.4975	8600	0.0107
0.4981	8610	0.0181
0.4987	8620	0.0153
0.4993	8630	0.0128
0.4999	8640	0.0272
0.5004	8650	0.0106
0.5010	8660	0.0167
0.5016	8670	0.0067
0.5022	8680	0.0192
0.5027	8690	0.0105
0.5033	8700	0.0162
0.5039	8710	0.0245
0.5045	8720	0.0168
0.5051	8730	0.0193
0.5056	8740	0.0187
0.5062	8750	0.0403
0.5068	8760	0.0192
0.5074	8770	0.0131
0.5080	8780	0.0158
0.5085	8790	0.0142
0.5091	8800	0.0128
0.5097	8810	0.0119
0.5103	8820	0.0211
0.5108	8830	0.0085
0.5114	8840	0.0187
0.5120	8850	0.0078
0.5126	8860	0.0124
0.5132	8870	0.0067
0.5137	8880	0.0082
0.5143	8890	0.0118
0.5149	8900	0.011
0.5155	8910	0.011
0.5161	8920	0.0166
0.5166	8930	0.0123
0.5172	8940	0.0099
0.5178	8950	0.0101
0.5184	8960	0.0092
0.5189	8970	0.0098
0.5195	8980	0.0261
0.5201	8990	0.0211
0.5207	9000	0.0211
0.5213	9010	0.0161
0.5218	9020	0.0244
0.5224	9030	0.0128
0.5230	9040	0.0135
0.5236	9050	0.0117
0.5242	9060	0.0168
0.5247	9070	0.0084
0.5253	9080	0.0141
0.5259	9090	0.013
0.5265	9100	0.0256
0.5270	9110	0.0205
0.5276	9120	0.0176
0.5282	9130	0.01
0.5288	9140	0.0131
0.5294	9150	0.0157
0.5299	9160	0.0116
0.5305	9170	0.0098
0.5311	9180	0.0282
0.5317	9190	0.0188
0.5323	9200	0.0148
0.5328	9210	0.0179
0.5334	9220	0.0273
0.5340	9230	0.0115
0.5346	9240	0.0162
0.5351	9250	0.0104
0.5357	9260	0.015
0.5363	9270	0.0103
0.5369	9280	0.0146
0.5375	9290	0.0161
0.5380	9300	0.0093
0.5386	9310	0.0127
0.5392	9320	0.0152
0.5398	9330	0.0149
0.5404	9340	0.0069
0.5409	9350	0.0125
0.5415	9360	0.0102
0.5421	9370	0.0131
0.5427	9380	0.0064
0.5432	9390	0.0145
0.5438	9400	0.0099
0.5444	9410	0.0067
0.5450	9420	0.0095
0.5456	9430	0.0115
0.5461	9440	0.0103
0.5467	9450	0.0115
0.5473	9460	0.025
0.5479	9470	0.0096
0.5485	9480	0.0066
0.5490	9490	0.0157
0.5496	9500	0.0149
0.5502	9510	0.0142
0.5508	9520	0.0146
0.5513	9530	0.0143
0.5519	9540	0.0084
0.5525	9550	0.0198
0.5531	9560	0.0116
0.5537	9570	0.0082
0.5542	9580	0.0201
0.5548	9590	0.0147
0.5554	9600	0.0235
0.5560	9610	0.0137
0.5566	9620	0.0182
0.5571	9630	0.0195
0.5577	9640	0.0119
0.5583	9650	0.0072
0.5589	9660	0.0303
0.5594	9670	0.0083
0.5600	9680	0.0099
0.5606	9690	0.0256
0.5612	9700	0.0112
0.5618	9710	0.0113
0.5623	9720	0.0116
0.5629	9730	0.0169
0.5635	9740	0.0083
0.5641	9750	0.0116
0.5647	9760	0.0148
0.5652	9770	0.0118
0.5658	9780	0.0136
0.5664	9790	0.0189
0.5670	9800	0.0132
0.5675	9810	0.0078
0.5681	9820	0.0045
0.5687	9830	0.0149
0.5693	9840	0.0148
0.5699	9850	0.0126
0.5704	9860	0.0141
0.5710	9870	0.0108
0.5716	9880	0.0157
0.5722	9890	0.021
0.5728	9900	0.008
0.5733	9910	0.0083
0.5739	9920	0.0123
0.5745	9930	0.0202
0.5751	9940	0.0109
0.5756	9950	0.0159
0.5762	9960	0.0131
0.5768	9970	0.0155
0.5774	9980	0.0124
0.5780	9990	0.0215
0.5785	10000	0.0098
0.5791	10010	0.0161
0.5797	10020	0.0078
0.5803	10030	0.0082
0.5809	10040	0.0211
0.5814	10050	0.0153
0.5820	10060	0.01
0.5826	10070	0.0102
0.5832	10080	0.0101
0.5837	10090	0.022
0.5843	10100	0.0244
0.5849	10110	0.0146
0.5855	10120	0.0138
0.5861	10130	0.0248
0.5866	10140	0.0107
0.5872	10150	0.0148
0.5878	10160	0.0197
0.5884	10170	0.0102
0.5889	10180	0.0128
0.5895	10190	0.0193
0.5901	10200	0.0193
0.5907	10210	0.0104
0.5913	10220	0.0134
0.5918	10230	0.0137
0.5924	10240	0.0093
0.5930	10250	0.0128
0.5936	10260	0.014
0.5942	10270	0.007
0.5947	10280	0.0129
0.5953	10290	0.014
0.5959	10300	0.0139
0.5965	10310	0.0163
0.5970	10320	0.0151
0.5976	10330	0.0114
0.5982	10340	0.0127
0.5988	10350	0.0164
0.5994	10360	0.0189
0.5999	10370	0.0071
0.6005	10380	0.0157
0.6011	10390	0.0146
0.6017	10400	0.0058
0.6023	10410	0.0165
0.6028	10420	0.0197
0.6034	10430	0.0115
0.6040	10440	0.0107
0.6046	10450	0.012
0.6051	10460	0.0212
0.6057	10470	0.0096
0.6063	10480	0.0121
0.6069	10490	0.0224
0.6075	10500	0.0094
0.6080	10510	0.0222
0.6086	10520	0.0138
0.6092	10530	0.0163
0.6098	10540	0.0109
0.6104	10550	0.0184
0.6109	10560	0.0082
0.6115	10570	0.0186
0.6121	10580	0.0052
0.6127	10590	0.016
0.6132	10600	0.0105
0.6138	10610	0.0084
0.6144	10620	0.0087
0.6150	10630	0.0121
0.6156	10640	0.0125
0.6161	10650	0.019
0.6167	10660	0.007
0.6173	10670	0.0094
0.6179	10680	0.009
0.6185	10690	0.0102
0.6190	10700	0.0106
0.6196	10710	0.0138
0.6202	10720	0.0271
0.6208	10730	0.0166
0.6213	10740	0.0131
0.6219	10750	0.0107
0.6225	10760	0.0224
0.6231	10770	0.0136
0.6237	10780	0.0117
0.6242	10790	0.0195
0.6248	10800	0.0145
0.6254	10810	0.0131
0.6260	10820	0.0115
0.6266	10830	0.0102
0.6271	10840	0.0129
0.6277	10850	0.0125
0.6283	10860	0.0173
0.6289	10870	0.0198
0.6294	10880	0.016
0.6300	10890	0.0066
0.6306	10900	0.0094
0.6312	10910	0.0046
0.6318	10920	0.0125
0.6323	10930	0.0154
0.6329	10940	0.0064
0.6335	10950	0.0125
0.6341	10960	0.0112
0.6347	10970	0.0174
0.6352	10980	0.0253
0.6358	10990	0.0126
0.6364	11000	0.0113
0.6370	11010	0.0169
0.6375	11020	0.0143
0.6381	11030	0.0166
0.6387	11040	0.0099
0.6393	11050	0.0061
0.6399	11060	0.016
0.6404	11070	0.0116
0.6410	11080	0.0099
0.6416	11090	0.0225
0.6422	11100	0.0075
0.6428	11110	0.0068
0.6433	11120	0.0141
0.6439	11130	0.015
0.6445	11140	0.011
0.6451	11150	0.0125
0.6456	11160	0.0157
0.6462	11170	0.0039
0.6468	11180	0.0056
0.6474	11190	0.0097
0.6480	11200	0.0142
0.6485	11210	0.0147
0.6491	11220	0.0099
0.6497	11230	0.006
0.6503	11240	0.0121
0.6509	11250	0.0174
0.6514	11260	0.0089
0.6520	11270	0.0087
0.6526	11280	0.0101
0.6532	11290	0.0113
0.6537	11300	0.0102
0.6543	11310	0.0124
0.6549	11320	0.0155
0.6555	11330	0.0122
0.6561	11340	0.0111
0.6566	11350	0.0153
0.6572	11360	0.0117
0.6578	11370	0.0096
0.6584	11380	0.0162
0.6590	11390	0.0152
0.6595	11400	0.022
0.6601	11410	0.0107
0.6607	11420	0.008
0.6613	11430	0.0184
0.6618	11440	0.0105
0.6624	11450	0.01
0.6630	11460	0.0136
0.6636	11470	0.0073
0.6642	11480	0.0192
0.6647	11490	0.0177
0.6653	11500	0.0118
0.6659	11510	0.0264
0.6665	11520	0.0152
0.6671	11530	0.0092
0.6676	11540	0.0155
0.6682	11550	0.0064
0.6688	11560	0.0232
0.6694	11570	0.0076
0.6699	11580	0.0049
0.6705	11590	0.0104
0.6711	11600	0.0225
0.6717	11610	0.0107
0.6723	11620	0.0143
0.6728	11630	0.0113
0.6734	11640	0.0122
0.6740	11650	0.0125
0.6746	11660	0.0096
0.6752	11670	0.0167
0.6757	11680	0.019
0.6763	11690	0.0149
0.6769	11700	0.0099
0.6775	11710	0.0187
0.6780	11720	0.008
0.6786	11730	0.0129
0.6792	11740	0.0102
0.6798	11750	0.0118
0.6804	11760	0.0193
0.6809	11770	0.0092
0.6815	11780	0.0065
0.6821	11790	0.0319
0.6827	11800	0.0105
0.6833	11810	0.0155
0.6838	11820	0.0087
0.6844	11830	0.0092
0.6850	11840	0.0239
0.6856	11850	0.0207
0.6861	11860	0.0084
0.6867	11870	0.0249
0.6873	11880	0.009
0.6879	11890	0.007
0.6885	11900	0.0156
0.6890	11910	0.0125
0.6896	11920	0.0115
0.6902	11930	0.0101
0.6908	11940	0.0121
0.6914	11950	0.0209
0.6919	11960	0.0103
0.6925	11970	0.0174
0.6931	11980	0.0105
0.6937	11990	0.0173
0.6942	12000	0.0127
0.6948	12010	0.0139
0.6954	12020	0.0148
0.6960	12030	0.0125
0.6966	12040	0.01
0.6971	12050	0.0142
0.6977	12060	0.0084
0.6983	12070	0.0089
0.6989	12080	0.0104
0.6995	12090	0.0117
0.7000	12100	0.0123
0.7006	12110	0.0187
0.7012	12120	0.0088
0.7018	12130	0.0154
0.7023	12140	0.0153
0.7029	12150	0.0109
0.7035	12160	0.0116
0.7041	12170	0.0106
0.7047	12180	0.011
0.7052	12190	0.007
0.7058	12200	0.0127
0.7064	12210	0.0134
0.7070	12220	0.0153
0.7075	12230	0.0154
0.7081	12240	0.0099
0.7087	12250	0.0209
0.7093	12260	0.0116
0.7099	12270	0.0127
0.7104	12280	0.0089
0.7110	12290	0.0056
0.7116	12300	0.0146
0.7122	12310	0.0188
0.7128	12320	0.0309
0.7133	12330	0.0107
0.7139	12340	0.0116
0.7145	12350	0.0118
0.7151	12360	0.0053
0.7156	12370	0.0118
0.7162	12380	0.0125
0.7168	12390	0.0103
0.7174	12400	0.0144
0.7180	12410	0.0079
0.7185	12420	0.0084
0.7191	12430	0.0177
0.7197	12440	0.0066
0.7203	12450	0.0085
0.7209	12460	0.0125
0.7214	12470	0.0092
0.7220	12480	0.0086
0.7226	12490	0.0087
0.7232	12500	0.0104
0.7237	12510	0.0089
0.7243	12520	0.0173
0.7249	12530	0.009
0.7255	12540	0.0127
0.7261	12550	0.0189
0.7266	12560	0.0121
0.7272	12570	0.0075
0.7278	12580	0.0159
0.7284	12590	0.0053
0.7290	12600	0.0257
0.7295	12610	0.018
0.7301	12620	0.0139
0.7307	12630	0.0098
0.7313	12640	0.0154
0.7318	12650	0.0228
0.7324	12660	0.0145
0.7330	12670	0.0137
0.7336	12680	0.009
0.7342	12690	0.0123
0.7347	12700	0.0138
0.7353	12710	0.0087
0.7359	12720	0.0163
0.7365	12730	0.0067
0.7371	12740	0.0133
0.7376	12750	0.0037
0.7382	12760	0.007
0.7388	12770	0.0134
0.7394	12780	0.0174
0.7399	12790	0.0098
0.7405	12800	0.0076
0.7411	12810	0.0077
0.7417	12820	0.008
0.7423	12830	0.0152
0.7428	12840	0.015
0.7434	12850	0.0072
0.7440	12860	0.0179
0.7446	12870	0.0138
0.7452	12880	0.013
0.7457	12890	0.0175
0.7463	12900	0.0161
0.7469	12910	0.0111
0.7475	12920	0.0115
0.7480	12930	0.0162
0.7486	12940	0.0111
0.7492	12950	0.0216
0.7498	12960	0.0078
0.7504	12970	0.0097
0.7509	12980	0.0092
0.7515	12990	0.0061
0.7521	13000	0.0119
0.7527	13010	0.016
0.7533	13020	0.0188
0.7538	13030	0.0086
0.7544	13040	0.0162
0.7550	13050	0.012
0.7556	13060	0.01
0.7561	13070	0.0065
0.7567	13080	0.0088
0.7573	13090	0.0089
0.7579	13100	0.0143
0.7585	13110	0.0074
0.7590	13120	0.0116
0.7596	13130	0.0075
0.7602	13140	0.008
0.7608	13150	0.012
0.7614	13160	0.0076
0.7619	13170	0.0131
0.7625	13180	0.0059
0.7631	13190	0.0108
0.7637	13200	0.01
0.7642	13210	0.0171
0.7648	13220	0.016
0.7654	13230	0.0149
0.7660	13240	0.0083
0.7666	13250	0.0114
0.7671	13260	0.0134
0.7677	13270	0.0135
0.7683	13280	0.0075
0.7689	13290	0.0143
0.7695	13300	0.0149
0.7700	13310	0.0131
0.7706	13320	0.0056
0.7712	13330	0.0133
0.7718	13340	0.0118
0.7723	13350	0.0108
0.7729	13360	0.0122
0.7735	13370	0.0057
0.7741	13380	0.0072
0.7747	13390	0.0117
0.7752	13400	0.0098
0.7758	13410	0.0114
0.7764	13420	0.0118
0.7770	13430	0.0105
0.7776	13440	0.0089
0.7781	13450	0.016
0.7787	13460	0.0185
0.7793	13470	0.0108
0.7799	13480	0.0124
0.7804	13490	0.0095
0.7810	13500	0.0124
0.7816	13510	0.0053
0.7822	13520	0.0204
0.7828	13530	0.0072
0.7833	13540	0.0132
0.7839	13550	0.0133
0.7845	13560	0.0092
0.7851	13570	0.0088
0.7857	13580	0.0077
0.7862	13590	0.0253
0.7868	13600	0.0076
0.7874	13610	0.0086
0.7880	13620	0.0157
0.7885	13630	0.0138
0.7891	13640	0.0177
0.7897	13650	0.0126
0.7903	13660	0.0047
0.7909	13670	0.0087
0.7914	13680	0.0167
0.7920	13690	0.0071
0.7926	13700	0.0068
0.7932	13710	0.0126
0.7938	13720	0.0185
0.7943	13730	0.0098
0.7949	13740	0.0065
0.7955	13750	0.0134
0.7961	13760	0.0095
0.7966	13770	0.0166
0.7972	13780	0.0204
0.7978	13790	0.0095
0.7984	13800	0.0186
0.7990	13810	0.017
0.7995	13820	0.0063
0.8001	13830	0.0153
0.8007	13840	0.0074
0.8013	13850	0.0113
0.8019	13860	0.0063
0.8024	13870	0.0103
0.8030	13880	0.019
0.8036	13890	0.0208
0.8042	13900	0.01
0.8047	13910	0.0238
0.8053	13920	0.0075
0.8059	13930	0.009
0.8065	13940	0.0117
0.8071	13950	0.0086
0.8076	13960	0.0061
0.8082	13970	0.0129
0.8088	13980	0.013
0.8094	13990	0.0101
0.8100	14000	0.0154
0.8105	14010	0.0234
0.8111	14020	0.0095
0.8117	14030	0.0148
0.8123	14040	0.0069
0.8128	14050	0.015
0.8134	14060	0.012
0.8140	14070	0.0064
0.8146	14080	0.0164
0.8152	14090	0.0123
0.8157	14100	0.0137
0.8163	14110	0.0131
0.8169	14120	0.0067
0.8175	14130	0.0103
0.8181	14140	0.0136
0.8186	14150	0.01
0.8192	14160	0.0125
0.8198	14170	0.0037
0.8204	14180	0.0101
0.8209	14190	0.0154
0.8215	14200	0.0076
0.8221	14210	0.0061
0.8227	14220	0.0076
0.8233	14230	0.019
0.8238	14240	0.0104
0.8244	14250	0.009
0.8250	14260	0.0101
0.8256	14270	0.0141
0.8261	14280	0.0081
0.8267	14290	0.0144
0.8273	14300	0.0154
0.8279	14310	0.0103
0.8285	14320	0.0106
0.8290	14330	0.0106
0.8296	14340	0.0134
0.8302	14350	0.0127
0.8308	14360	0.0119
0.8314	14370	0.0064
0.8319	14380	0.0137
0.8325	14390	0.0104
0.8331	14400	0.0045
0.8337	14410	0.0061
0.8342	14420	0.0162
0.8348	14430	0.0109
0.8354	14440	0.0053
0.8360	14450	0.0048
0.8366	14460	0.0118
0.8371	14470	0.0206
0.8377	14480	0.0089
0.8383	14490	0.0103
0.8389	14500	0.0115
0.8395	14510	0.0093
0.8400	14520	0.0112
0.8406	14530	0.0052
0.8412	14540	0.0149
0.8418	14550	0.0146
0.8423	14560	0.0092
0.8429	14570	0.0152
0.8435	14580	0.0048
0.8441	14590	0.0119
0.8447	14600	0.0116
0.8452	14610	0.0126
0.8458	14620	0.0104
0.8464	14630	0.0085
0.8470	14640	0.0157
0.8476	14650	0.01
0.8481	14660	0.0083
0.8487	14670	0.012
0.8493	14680	0.0165
0.8499	14690	0.0092
0.8504	14700	0.013
0.8510	14710	0.0092
0.8516	14720	0.0149
0.8522	14730	0.025
0.8528	14740	0.0095
0.8533	14750	0.0142
0.8539	14760	0.0092
0.8545	14770	0.0134
0.8551	14780	0.0117
0.8557	14790	0.0131
0.8562	14800	0.0098
0.8568	14810	0.0162
0.8574	14820	0.0123
0.8580	14830	0.0122
0.8585	14840	0.0105
0.8591	14850	0.0051
0.8597	14860	0.0226
0.8603	14870	0.0089
0.8609	14880	0.0082
0.8614	14890	0.0125
0.8620	14900	0.0197
0.8626	14910	0.0047
0.8632	14920	0.0101
0.8638	14930	0.0136
0.8643	14940	0.0052
0.8649	14950	0.0125
0.8655	14960	0.0061
0.8661	14970	0.0142
0.8666	14980	0.0064
0.8672	14990	0.0032
0.8678	15000	0.0069
0.8684	15010	0.0056
0.8690	15020	0.0075
0.8695	15030	0.013
0.8701	15040	0.0087
0.8707	15050	0.0111
0.8713	15060	0.014
0.8719	15070	0.013
0.8724	15080	0.0188
0.8730	15090	0.0063
0.8736	15100	0.0107
0.8742	15110	0.014
0.8747	15120	0.0103
0.8753	15130	0.0097
0.8759	15140	0.0089
0.8765	15150	0.0116
0.8771	15160	0.011
0.8776	15170	0.0111
0.8782	15180	0.0076
0.8788	15190	0.0107
0.8794	15200	0.01
0.8800	15210	0.0055
0.8805	15220	0.0189
0.8811	15230	0.0066
0.8817	15240	0.0057
0.8823	15250	0.0115
0.8828	15260	0.0072
0.8834	15270	0.0096
0.8840	15280	0.014
0.8846	15290	0.0133
0.8852	15300	0.0089
0.8857	15310	0.0122
0.8863	15320	0.0054
0.8869	15330	0.0209
0.8875	15340	0.013
0.8881	15350	0.0153
0.8886	15360	0.0098
0.8892	15370	0.0127
0.8898	15380	0.0081
0.8904	15390	0.0113
0.8909	15400	0.0141
0.8915	15410	0.0092
0.8921	15420	0.0112
0.8927	15430	0.0059
0.8933	15440	0.0123
0.8938	15450	0.0054
0.8944	15460	0.0071
0.8950	15470	0.0139
0.8956	15480	0.0112
0.8962	15490	0.0101
0.8967	15500	0.0074
0.8973	15510	0.0234
0.8979	15520	0.0233
0.8985	15530	0.0161
0.8990	15540	0.0077
0.8996	15550	0.0123
0.9002	15560	0.0124
0.9008	15570	0.0107
0.9014	15580	0.007
0.9019	15590	0.0077
0.9025	15600	0.0113
0.9031	15610	0.0075
0.9037	15620	0.0073
0.9043	15630	0.0067
0.9048	15640	0.0201
0.9054	15650	0.0177
0.9060	15660	0.0036
0.9066	15670	0.0085
0.9071	15680	0.0113
0.9077	15690	0.0097
0.9083	15700	0.0128
0.9089	15710	0.0106
0.9095	15720	0.0118
0.9100	15730	0.0105
0.9106	15740	0.0183
0.9112	15750	0.0149
0.9118	15760	0.0146
0.9124	15770	0.0101
0.9129	15780	0.0092
0.9135	15790	0.0127
0.9141	15800	0.0105
0.9147	15810	0.0095
0.9152	15820	0.0099
0.9158	15830	0.021
0.9164	15840	0.0166
0.9170	15850	0.0036
0.9176	15860	0.0142
0.9181	15870	0.014
0.9187	15880	0.006
0.9193	15890	0.01
0.9199	15900	0.0165
0.9205	15910	0.0098
0.9210	15920	0.0089
0.9216	15930	0.0133
0.9222	15940	0.0106
0.9228	15950	0.008
0.9233	15960	0.0062
0.9239	15970	0.0055
0.9245	15980	0.0164
0.9251	15990	0.0115
0.9257	16000	0.0162
0.9262	16010	0.0094
0.9268	16020	0.0158
0.9274	16030	0.0081
0.9280	16040	0.0065
0.9286	16050	0.0088
0.9291	16060	0.0033
0.9297	16070	0.0077
0.9303	16080	0.009
0.9309	16090	0.0165
0.9314	16100	0.0076
0.9320	16110	0.0076
0.9326	16120	0.006
0.9332	16130	0.0095
0.9338	16140	0.0158
0.9343	16150	0.0094
0.9349	16160	0.0093
0.9355	16170	0.0154
0.9361	16180	0.0101
0.9367	16190	0.0134
0.9372	16200	0.0087
0.9378	16210	0.0171
0.9384	16220	0.0161
0.9390	16230	0.0154
0.9395	16240	0.0161
0.9401	16250	0.0102
0.9407	16260	0.012
0.9413	16270	0.0139
0.9419	16280	0.0107
0.9424	16290	0.0056
0.9430	16300	0.0117
0.9436	16310	0.0193
0.9442	16320	0.0206
0.9447	16330	0.016
0.9453	16340	0.0071
0.9459	16350	0.0162
0.9465	16360	0.006
0.9471	16370	0.0149
0.9476	16380	0.0064
0.9482	16390	0.0157
0.9488	16400	0.0074
0.9494	16410	0.0134
0.9500	16420	0.016
0.9505	16430	0.0077
0.9511	16440	0.0085
0.9517	16450	0.0047
0.9523	16460	0.0111
0.9528	16470	0.0063
0.9534	16480	0.0136
0.9540	16490	0.0094
0.9546	16500	0.0106
0.9552	16510	0.0066
0.9557	16520	0.0091
0.9563	16530	0.0048
0.9569	16540	0.0195
0.9575	16550	0.0118
0.9581	16560	0.0155
0.9586	16570	0.03
0.9592	16580	0.0127
0.9598	16590	0.0102
0.9604	16600	0.0132
0.9609	16610	0.0031
0.9615	16620	0.0051
0.9621	16630	0.0099
0.9627	16640	0.013
0.9633	16650	0.0067
0.9638	16660	0.0082
0.9644	16670	0.015
0.9650	16680	0.0131
0.9656	16690	0.0111
0.9662	16700	0.015
0.9667	16710	0.009
0.9673	16720	0.0097
0.9679	16730	0.0127
0.9685	16740	0.0195
0.9690	16750	0.0041
0.9696	16760	0.0029
0.9702	16770	0.0112
0.9708	16780	0.0112
0.9714	16790	0.0184
0.9719	16800	0.0119
0.9725	16810	0.0079
0.9731	16820	0.0071
0.9737	16830	0.0077
0.9743	16840	0.0108
0.9748	16850	0.0067
0.9754	16860	0.0077
0.9760	16870	0.0101
0.9766	16880	0.0106
0.9771	16890	0.0067
0.9777	16900	0.0043
0.9783	16910	0.0075
0.9789	16920	0.0126
0.9795	16930	0.0074
0.9800	16940	0.0114
0.9806	16950	0.0147
0.9812	16960	0.009
0.9818	16970	0.0093
0.9824	16980	0.0148
0.9829	16990	0.0076
0.9835	17000	0.0042
0.9841	17010	0.008
0.9847	17020	0.0146
0.9852	17030	0.0102
0.9858	17040	0.0043
0.9864	17050	0.0113
0.9870	17060	0.0197
0.9876	17070	0.0054
0.9881	17080	0.0038
0.9887	17090	0.006
0.9893	17100	0.0174
0.9899	17110	0.0156
0.9905	17120	0.0182
0.9910	17130	0.0137
0.9916	17140	0.0068
0.9922	17150	0.0153
0.9928	17160	0.0051
0.9933	17170	0.0085
0.9939	17180	0.0111
0.9945	17190	0.0097
0.9951	17200	0.0064
0.9957	17210	0.0094
0.9962	17220	0.0071
0.9968	17230	0.0085
0.9974	17240	0.0115
0.9980	17250	0.0175
0.9986	17260	0.0108
0.9991	17270	0.0201
0.9997	17280	0.0105

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.47.0
• PyTorch: 2.5.1+cu121
• Accelerate: 1.2.1
• Datasets: 3.3.1
• Tokenizers: 0.21.0

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}
}