Remonatef/all-MiniLM-L6-v37-pair_score

all-MiniLM-L6-v36-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v36-pair_score on the pairs_three_ways_noise_reduction_v1 dataset. It maps sentences & paragraphs to a 384-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: KhaledReda/all-MiniLM-L6-v36-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_three_ways_noise_reduction_v1
• Language: en
• License: apache-2.0

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, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'spr 2 ex.new',
    'midi length dress - navy blue color navy blue size midi 65 cotton 35 polyester.',
    'cupcakes christmas per piece.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.2703, -0.1048],
#         [-0.2703,  1.0000, -0.0432],
#         [-0.1048, -0.0432,  1.0000]])

Training Details

Training Dataset

pairs_three_ways_noise_reduction_v1

• Dataset: pairs_three_ways_noise_reduction_v1 at 363acad
• Size: 19,764,079 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 5.32 tokens max: 17 tokens	min: 3 tokens mean: 64.26 tokens max: 225 tokens	min: 0.0 mean: 0.05 max: 1.0

• Samples:

  sentence1	sentence2	score
  qahwa	dahlia ruffle 2 piece swimsuit gender women brand rafeya generic name swimsuit product name dahlia size xs types of fashion styles beachwear cut two pieces ruffled units 2 piece	0.0
  20/tab ex.new	woof fetch dog food turkey - small size small spoil your dog with the delectable taste of woof fetch turkey. packed with quality ingredients it s a savory and nutritious meal for your cherished companion.	0.0
  bath linen	run support women s running shoes green gender women brand decathlon generic name shoes size 3 36 features cushioning support color green activity running target group women sport running our design teams developed these supportive women s shoes with cushioning for running up to 10 km per week. with cushioning and special laces for support these women s running shoes are safe and comfortable on the road and trails.	0.0

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_three_ways_noise_reduction_v1

• Dataset: pairs_three_ways_noise_reduction_v1 at 363acad
• Size: 99,317 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 5.45 tokens max: 16 tokens	min: 3 tokens mean: 66.96 tokens max: 239 tokens	min: 0.0 mean: 0.04 max: 1.0

• Samples:

  sentence1	sentence2	score
  lines pot	wild legging extension gender women age we didn t want you to have to purchase multiple leggings to go with your different rashguards when you don t need that many. so we designed these legging-extensions that you can wear over your leggings to add coherence in your outfit and color into your basic black leggings it s a simple and creative way to create so many outfits easily. you must size down the legging extensions not like the size of the leggings.	0.0
  blended nan matcha	pink and baby blue bracelet color pink blue target group baby bracelet inside material copper silver plated.	0.0
  bunnzy outfit	nourish hair therapy energizing hydrating hair treatment with peppermint rosemary and lavender essential oils. the botanical plant oil blend nourishes and conditions hair and scalp while helping to maintain the proper oil balance to promote softer more manageable hair. the essential oil blend has many benefits for the hair and scalp peppermint oil gently stimulates the scalp and helps hair receive proper nourishment to grow. lavender oil is soothing and calming for hair and helps to balance oil production to replenish hair and scalp. rosemary oil works well to remedy dry hair and itchy scalp. for weekly intense hydrating treatment massage into scalp and hair from roots to ends. wrap hair in a thin tow	0.0

• Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• 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: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• 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
• hub_revision: None
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0006	100	0.259
0.0013	200	0.279
0.0019	300	0.2344
0.0026	400	0.3457
0.0032	500	0.2702
0.0039	600	0.3346
0.0045	700	0.223
0.0052	800	0.1661
0.0058	900	0.296
0.0065	1000	0.344
0.0071	1100	0.2829
0.0078	1200	0.4639
0.0084	1300	0.3604
0.0091	1400	0.1457
0.0097	1500	0.0742
0.0104	1600	0.3248
0.0110	1700	0.097
0.0117	1800	0.1162
0.0123	1900	0.1608
0.0130	2000	0.1002
0.0136	2100	0.1361
0.0142	2200	0.1319
0.0149	2300	0.1192
0.0155	2400	0.1991
0.0162	2500	0.1832
0.0168	2600	0.0877
0.0175	2700	0.1192
0.0181	2800	0.1245
0.0188	2900	0.0773
0.0194	3000	0.2334
0.0201	3100	0.1719
0.0207	3200	0.1367
0.0214	3300	0.0215
0.0220	3400	0.053
0.0227	3500	0.0998
0.0233	3600	0.0643
0.0240	3700	0.2129
0.0246	3800	0.0867
0.0253	3900	0.0877
0.0259	4000	0.0463
0.0266	4100	0.1647
0.0272	4200	0.0719
0.0278	4300	0.1512
0.0285	4400	0.1003
0.0291	4500	0.123
0.0298	4600	0.1753
0.0304	4700	0.0695
0.0311	4800	0.1102
0.0317	4900	0.1685
0.0324	5000	0.0635
0.0330	5100	0.1746
0.0337	5200	0.2026
0.0343	5300	0.11
0.0350	5400	0.0866
0.0356	5500	0.0698
0.0363	5600	0.1037
0.0369	5700	0.1343
0.0376	5800	0.0314
0.0382	5900	0.0822
0.0389	6000	0.0986
0.0395	6100	0.0795
0.0402	6200	0.1384
0.0408	6300	0.0839
0.0414	6400	0.1448
0.0421	6500	0.0511
0.0427	6600	0.0399
0.0434	6700	0.0311
0.0440	6800	0.0832
0.0447	6900	0.0896
0.0453	7000	0.1081
0.0460	7100	0.1334
0.0466	7200	0.0465
0.0473	7300	0.1098
0.0479	7400	0.0598
0.0486	7500	0.0837
0.0492	7600	0.1034
0.0499	7700	0.029
0.0505	7800	0.0595
0.0512	7900	0.066
0.0518	8000	0.0667
0.0525	8100	0.1442
0.0531	8200	0.1957
0.0538	8300	0.0941
0.0544	8400	0.0958
0.0550	8500	0.1317
0.0557	8600	0.132
0.0563	8700	0.0534
0.0570	8800	0.0724
0.0576	8900	0.1029
0.0583	9000	0.105
0.0589	9100	0.0802
0.0596	9200	0.0364
0.0602	9300	0.1144
0.0609	9400	0.1106
0.0615	9500	0.0947
0.0622	9600	0.1798
0.0628	9700	0.0487
0.0635	9800	0.1271
0.0641	9900	0.1112
0.0648	10000	0.0567
0.0654	10100	0.0344
0.0661	10200	0.0921
0.0667	10300	0.0216
0.0674	10400	0.0702
0.0680	10500	0.142
0.0686	10600	0.1187
0.0693	10700	0.1104
0.0699	10800	0.0881
0.0706	10900	0.1065
0.0712	11000	0.0243
0.0719	11100	0.046
0.0725	11200	0.0479
0.0732	11300	0.0516
0.0738	11400	0.0718
0.0745	11500	0.0481
0.0751	11600	0.029
0.0758	11700	0.0752
0.0764	11800	0.0844
0.0771	11900	0.0382
0.0777	12000	0.0878
0.0784	12100	0.1511
0.0790	12200	0.0596
0.0797	12300	0.0922
0.0803	12400	0.0133
0.0810	12500	0.0775
0.0816	12600	0.0999
0.0823	12700	0.0512
0.0829	12800	0.1226
0.0835	12900	0.1409
0.0842	13000	0.1595
0.0848	13100	0.0787
0.0855	13200	0.1223
0.0861	13300	0.2356
0.0868	13400	0.0216
0.0874	13500	0.0535
0.0881	13600	0.0961
0.0887	13700	0.0831
0.0894	13800	0.0357
0.0900	13900	0.0839
0.0907	14000	0.1621
0.0913	14100	0.1452
0.0920	14200	0.0597
0.0926	14300	0.068
0.0933	14400	0.0291
0.0939	14500	0.1022
0.0946	14600	0.0928
0.0952	14700	0.1007
0.0959	14800	0.1816
0.0965	14900	0.1176
0.0971	15000	0.1454
0.0978	15100	0.0783
0.0984	15200	0.0951
0.0991	15300	0.0217
0.0997	15400	0.0345
0.1004	15500	0.1047
0.1010	15600	0.0966
0.1017	15700	0.1663
0.1023	15800	0.1359
0.1030	15900	0.0866
0.1036	16000	0.0914
0.1043	16100	0.0869
0.1049	16200	0.0288
0.1056	16300	0.0512
0.1062	16400	0.0459
0.1069	16500	0.0844
0.1075	16600	0.0574
0.1082	16700	0.09
0.1088	16800	0.0717
0.1095	16900	0.0587
0.1101	17000	0.0255
0.1107	17100	0.048
0.1114	17200	0.1286
0.1120	17300	0.1196
0.1127	17400	0.1476
0.1133	17500	0.0417
0.1140	17600	0.0925
0.1146	17700	0.1106
0.1153	17800	0.0487
0.1159	17900	0.0595
0.1166	18000	0.1187
0.1172	18100	0.045
0.1179	18200	0.1279
0.1185	18300	0.122
0.1192	18400	0.0638
0.1198	18500	0.0798
0.1205	18600	0.1502
0.1211	18700	0.0489
0.1218	18800	0.0747
0.1224	18900	0.1417
0.1231	19000	0.0671
0.1237	19100	0.04
0.1243	19200	0.031
0.1250	19300	0.0868
0.1256	19400	0.076
0.1263	19500	0.058
0.1269	19600	0.0709
0.1276	19700	0.1214
0.1282	19800	0.0211
0.1289	19900	0.0663
0.1295	20000	0.0677
0.1302	20100	0.1445
0.1308	20200	0.0949
0.1315	20300	0.0315
0.1321	20400	0.1157
0.1328	20500	0.1015
0.1334	20600	0.0795
0.1341	20700	0.1039
0.1347	20800	0.0573
0.1354	20900	0.1311
0.1360	21000	0.0647
0.1367	21100	0.0591
0.1373	21200	0.0775
0.1379	21300	0.121
0.1386	21400	0.086
0.1392	21500	0.0775
0.1399	21600	0.0746
0.1405	21700	0.0715
0.1412	21800	0.1398
0.1418	21900	0.0901
0.1425	22000	0.1747
0.1431	22100	0.0627
0.1438	22200	0.0415
0.1444	22300	0.0249
0.1451	22400	0.0863
0.1457	22500	0.0861
0.1464	22600	0.0496
0.1470	22700	0.0513
0.1477	22800	0.21
0.1483	22900	0.0543
0.1490	23000	0.0907
0.1496	23100	0.0455
0.1503	23200	0.1405
0.1509	23300	0.0384
0.1515	23400	0.1444
0.1522	23500	0.1216
0.1528	23600	0.0123
0.1535	23700	0.1084
0.1541	23800	0.0385
0.1548	23900	0.0856
0.1554	24000	0.026
0.1561	24100	0.069
0.1567	24200	0.0309
0.1574	24300	0.1088
0.1580	24400	0.0187
0.1587	24500	0.0809
0.1593	24600	0.068
0.1600	24700	0.0628
0.1606	24800	0.1488
0.1613	24900	0.0617
0.1619	25000	0.1207
0.1626	25100	0.0311
0.1632	25200	0.0964
0.1639	25300	0.0561
0.1645	25400	0.0735
0.1651	25500	0.0182
0.1658	25600	0.1115
0.1664	25700	0.0628
0.1671	25800	0.144
0.1677	25900	0.1596
0.1684	26000	0.0612
0.1690	26100	0.1275
0.1697	26200	0.1007
0.1703	26300	0.1534
0.1710	26400	0.0757
0.1716	26500	0.1128
0.1723	26600	0.0503
0.1729	26700	0.1042
0.1736	26800	0.015
0.1742	26900	0.0747
0.1749	27000	0.0365
0.1755	27100	0.0794
0.1762	27200	0.0535
0.1768	27300	0.0256
0.1775	27400	0.0758
0.1781	27500	0.0444
0.1787	27600	0.039
0.1794	27700	0.0864
0.1800	27800	0.0653
0.1807	27900	0.1293
0.1813	28000	0.0382
0.1820	28100	0.1008
0.1826	28200	0.0748
0.1833	28300	0.0499
0.1839	28400	0.0839
0.1846	28500	0.0435
0.1852	28600	0.0616
0.1859	28700	0.0678
0.1865	28800	0.0274
0.1872	28900	0.0404
0.1878	29000	0.1013
0.1885	29100	0.1028
0.1891	29200	0.0855
0.1898	29300	0.0437
0.1904	29400	0.1489
0.1911	29500	0.0632
0.1917	29600	0.0125
0.1923	29700	0.0489
0.1930	29800	0.022
0.1936	29900	0.128
0.1943	30000	0.0465
0.1949	30100	0.0726
0.1956	30200	0.0417
0.1962	30300	0.0488
0.1969	30400	0.0175
0.1975	30500	0.0609
0.1982	30600	0.0353
0.1988	30700	0.0239
0.1995	30800	0.0255
0.2001	30900	0.0591
0.2008	31000	0.0369
0.2014	31100	0.0964
0.2021	31200	0.0823
0.2027	31300	0.0117
0.2034	31400	0.0172
0.2040	31500	0.0396
0.2047	31600	0.043
0.2053	31700	0.05
0.2059	31800	0.0408
0.2066	31900	0.0568
0.2072	32000	0.0435
0.2079	32100	0.0644
0.2085	32200	0.0541
0.2092	32300	0.0922
0.2098	32400	0.0269
0.2105	32500	0.0481
0.2111	32600	0.09
0.2118	32700	0.0448
0.2124	32800	0.1076
0.2131	32900	0.0924
0.2137	33000	0.0138
0.2144	33100	0.1283
0.2150	33200	0.0489
0.2157	33300	0.0318
0.2163	33400	0.0577
0.2170	33500	0.0733
0.2176	33600	0.0521
0.2183	33700	0.0091
0.2189	33800	0.0492
0.2195	33900	0.1607
0.2202	34000	0.1402
0.2208	34100	0.0162
0.2215	34200	0.0367
0.2221	34300	0.0719
0.2228	34400	0.1095
0.2234	34500	0.0305
0.2241	34600	0.0403
0.2247	34700	0.0615
0.2254	34800	0.0616
0.2260	34900	0.0195
0.2267	35000	0.1135
0.2273	35100	0.0832
0.2280	35200	0.0162
0.2286	35300	0.072
0.2293	35400	0.0632
0.2299	35500	0.047
0.2306	35600	0.0198
0.2312	35700	0.0898
0.2319	35800	0.0951
0.2325	35900	0.0779
0.2332	36000	0.1065
0.2338	36100	0.027
0.2344	36200	0.0812
0.2351	36300	0.005
0.2357	36400	0.0334
0.2364	36500	0.0706
0.2370	36600	0.0285
0.2377	36700	0.0731
0.2383	36800	0.094
0.2390	36900	0.1747
0.2396	37000	0.1123
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0.6159	95100	0.021
0.6166	95200	0.0337
0.6172	95300	0.0293
0.6178	95400	0.0183
0.6185	95500	0.004
0.6191	95600	0.0475
0.6198	95700	0.0293
0.6204	95800	0.0144
0.6211	95900	0.0795
0.6217	96000	0.1282
0.6224	96100	0.0188
0.6230	96200	0.0616
0.6237	96300	0.0058
0.6243	96400	0.0553
0.6250	96500	0.0097
0.6256	96600	0.0965
0.6263	96700	0.0107
0.6269	96800	0.0586
0.6276	96900	0.0512
0.6282	97000	0.1051
0.6289	97100	0.0551
0.6295	97200	0.0184
0.6302	97300	0.0161
0.6308	97400	0.0538
0.6314	97500	0.0093
0.6321	97600	0.017
0.6327	97700	0.0197
0.6334	97800	0.0204
0.6340	97900	0.0885
0.6347	98000	0.0287
0.6353	98100	0.0372
0.6360	98200	0.0206
0.6366	98300	0.07
0.6373	98400	0.0168
0.6379	98500	0.0276
0.6386	98600	0.0205
0.6392	98700	0.0082
0.6399	98800	0.0866
0.6405	98900	0.0861
0.6412	99000	0.0242
0.6418	99100	0.0634
0.6425	99200	0.0062
0.6431	99300	0.0528
0.6438	99400	0.0212
0.6444	99500	0.0256
0.6450	99600	0.0238
0.6457	99700	0.1062
0.6463	99800	0.0486
0.6470	99900	0.0733
0.6476	100000	0.0679
0.6483	100100	0.0296
0.6489	100200	0.0652
0.6496	100300	0.1469
0.6502	100400	0.018
0.6509	100500	0.0151
0.6515	100600	0.035
0.6522	100700	0.0209
0.6528	100800	0.0152
0.6535	100900	0.0184
0.6541	101000	0.0369
0.6548	101100	0.0044
0.6554	101200	0.0282
0.6561	101300	0.0142
0.6567	101400	0.0299
0.6574	101500	0.0227
0.6580	101600	0.0135
0.6586	101700	0.0105
0.6593	101800	0.0127
0.6599	101900	0.0217
0.6606	102000	0.0212
0.6612	102100	0.0192
0.6619	102200	0.01
0.6625	102300	0.063
0.6632	102400	0.0906
0.6638	102500	0.0269
0.6645	102600	0.0226
0.6651	102700	0.0118
0.6658	102800	0.0427
0.6664	102900	0.0197
0.6671	103000	0.0171
0.6677	103100	0.0085
0.6684	103200	0.0231
0.6690	103300	0.019
0.6697	103400	0.1104
0.6703	103500	0.0625
0.6710	103600	0.022
0.6716	103700	0.0112
0.6722	103800	0.0076
0.6729	103900	0.0037
0.6735	104000	0.0059
0.6742	104100	0.0594
0.6748	104200	0.0179
0.6755	104300	0.0302
0.6761	104400	0.0266
0.6768	104500	0.0716
0.6774	104600	0.0276
0.6781	104700	0.0283
0.6787	104800	0.0252
0.6794	104900	0.0721
0.6800	105000	0.0616
0.6807	105100	0.0327
0.6813	105200	0.0304
0.6820	105300	0.0725
0.6826	105400	0.0057
0.6833	105500	0.0185
0.6839	105600	0.0116
0.6846	105700	0.0208
0.6852	105800	0.0544
0.6858	105900	0.0084
0.6865	106000	0.0902
0.6871	106100	0.0278
0.6878	106200	0.0077
0.6884	106300	0.0313
0.6891	106400	0.0103
0.6897	106500	0.0063
0.6904	106600	0.0516
0.6910	106700	0.1234
0.6917	106800	0.0079
0.6923	106900	0.0071
0.6930	107000	0.0214
0.6936	107100	0.0374
0.6943	107200	0.0042
0.6949	107300	0.0104
0.6956	107400	0.0488
0.6962	107500	0.0044
0.6969	107600	0.057
0.6975	107700	0.0086
0.6982	107800	0.0201
0.6988	107900	0.0184
0.6995	108000	0.0124
0.7001	108100	0.0047
0.7007	108200	0.007
0.7014	108300	0.0308
0.7020	108400	0.0104
0.7027	108500	0.0177
0.7033	108600	0.0774
0.7040	108700	0.0487
0.7046	108800	0.0055
0.7053	108900	0.0333
0.7059	109000	0.0122
0.7066	109100	0.0257
0.7072	109200	0.0283
0.7079	109300	0.0887
0.7085	109400	0.0383
0.7092	109500	0.0408
0.7098	109600	0.0091
0.7105	109700	0.0063
0.7111	109800	0.0067
0.7118	109900	0.0218
0.7124	110000	0.0172
0.7131	110100	0.0321
0.7137	110200	0.0133
0.7143	110300	0.0171
0.7150	110400	0.022
0.7156	110500	0.0503
0.7163	110600	0.0213
0.7169	110700	0.0789
0.7176	110800	0.0452
0.7182	110900	0.0147
0.7189	111000	0.0079
0.7195	111100	0.0062
0.7202	111200	0.0522
0.7208	111300	0.07
0.7215	111400	0.094
0.7221	111500	0.0278
0.7228	111600	0.0212
0.7234	111700	0.0345
0.7241	111800	0.0297
0.7247	111900	0.0136
0.7254	112000	0.055
0.7260	112100	0.013
0.7267	112200	0.0392
0.7273	112300	0.044
0.7279	112400	0.0175
0.7286	112500	0.0544
0.7292	112600	0.0102
0.7299	112700	0.0198
0.7305	112800	0.0232
0.7312	112900	0.0247
0.7318	113000	0.019
0.7325	113100	0.0366
0.7331	113200	0.0417
0.7338	113300	0.0238
0.7344	113400	0.0377
0.7351	113500	0.0383
0.7357	113600	0.0247
0.7364	113700	0.0276
0.7370	113800	0.0309
0.7377	113900	0.0165
0.7383	114000	0.0075
0.7390	114100	0.0064
0.7396	114200	0.0081
0.7403	114300	0.0449
0.7409	114400	0.0073
0.7415	114500	0.0352
0.7422	114600	0.0099
0.7428	114700	0.0674
0.7435	114800	0.0089
0.7441	114900	0.095
0.7448	115000	0.0356
0.7454	115100	0.011
0.7461	115200	0.0081
0.7467	115300	0.0138
0.7474	115400	0.0414
0.7480	115500	0.0416
0.7487	115600	0.075
0.7493	115700	0.0085
0.7500	115800	0.0127
0.7506	115900	0.0246
0.7513	116000	0.0089
0.7519	116100	0.0108
0.7526	116200	0.0475
0.7532	116300	0.0419
0.7539	116400	0.0056
0.7545	116500	0.0213
0.7551	116600	0.0298
0.7558	116700	0.0255
0.7564	116800	0.0363
0.7571	116900	0.0217
0.7577	117000	0.0474
0.7584	117100	0.0743
0.7590	117200	0.0549
0.7597	117300	0.0423
0.7603	117400	0.0036
0.7610	117500	0.0425
0.7616	117600	0.0087
0.7623	117700	0.0106
0.7629	117800	0.0274
0.7636	117900	0.0289
0.7642	118000	0.0171
0.7649	118100	0.0183
0.7655	118200	0.0228
0.7662	118300	0.0155
0.7668	118400	0.0347
0.7675	118500	0.0238
0.7681	118600	0.0032
0.7687	118700	0.0452
0.7694	118800	0.0268
0.7700	118900	0.0325
0.7707	119000	0.0262
0.7713	119100	0.0687
0.7720	119200	0.0872
0.7726	119300	0.0244
0.7733	119400	0.0439
0.7739	119500	0.012
0.7746	119600	0.0143
0.7752	119700	0.0584
0.7759	119800	0.0092
0.7765	119900	0.04
0.7772	120000	0.0768
0.7778	120100	0.0539
0.7785	120200	0.0406
0.7791	120300	0.0543
0.7798	120400	0.0144
0.7804	120500	0.002
0.7811	120600	0.023
0.7817	120700	0.0539
0.7823	120800	0.0592
0.7830	120900	0.0153
0.7836	121000	0.0042
0.7843	121100	0.0678
0.7849	121200	0.0107
0.7856	121300	0.0255
0.7862	121400	0.0474
0.7869	121500	0.045
0.7875	121600	0.0672
0.7882	121700	0.0798
0.7888	121800	0.02
0.7895	121900	0.0736
0.7901	122000	0.0573
0.7908	122100	0.0039
0.7914	122200	0.0182
0.7921	122300	0.013
0.7927	122400	0.0343
0.7934	122500	0.0147
0.7940	122600	0.0121
0.7947	122700	0.0488
0.7953	122800	0.0232
0.7959	122900	0.002
0.7966	123000	0.0626
0.7972	123100	0.0188
0.7979	123200	0.0163
0.7985	123300	0.0451
0.7992	123400	0.029
0.7998	123500	0.0918
0.8005	123600	0.0253
0.8011	123700	0.0138
0.8018	123800	0.0703
0.8024	123900	0.0813
0.8031	124000	0.0487
0.8037	124100	0.0844
0.8044	124200	0.0041
0.8050	124300	0.0068
0.8057	124400	0.0332
0.8063	124500	0.0233
0.8070	124600	0.0302
0.8076	124700	0.0108
0.8083	124800	0.0123
0.8089	124900	0.0295
0.8095	125000	0.0391
0.8102	125100	0.0153
0.8108	125200	0.0435
0.8115	125300	0.0534
0.8121	125400	0.0545
0.8128	125500	0.0263
0.8134	125600	0.016
0.8141	125700	0.018
0.8147	125800	0.0344
0.8154	125900	0.02
0.8160	126000	0.0124
0.8167	126100	0.0467
0.8173	126200	0.0831
0.8180	126300	0.0177
0.8186	126400	0.0376
0.8193	126500	0.0074
0.8199	126600	0.015
0.8206	126700	0.0401
0.8212	126800	0.0168
0.8219	126900	0.0575
0.8225	127000	0.0009
0.8231	127100	0.0061
0.8238	127200	0.0142
0.8244	127300	0.0844
0.8251	127400	0.0388
0.8257	127500	0.0688
0.8264	127600	0.0063
0.8270	127700	0.0043
0.8277	127800	0.0178
0.8283	127900	0.0137
0.8290	128000	0.0048
0.8296	128100	0.0129
0.8303	128200	0.0304
0.8309	128300	0.0154
0.8316	128400	0.0202
0.8322	128500	0.0489
0.8329	128600	0.0103
0.8335	128700	0.0394
0.8342	128800	0.0635
0.8348	128900	0.0649
0.8355	129000	0.0164
0.8361	129100	0.03
0.8367	129200	0.0031
0.8374	129300	0.0056
0.8380	129400	0.0025
0.8387	129500	0.0069
0.8393	129600	0.0376
0.8400	129700	0.0506
0.8406	129800	0.0138
0.8413	129900	0.03
0.8419	130000	0.0089
0.8426	130100	0.038
0.8432	130200	0.0299
0.8439	130300	0.0369
0.8445	130400	0.0325
0.8452	130500	0.0115
0.8458	130600	0.0305
0.8465	130700	0.0061
0.8471	130800	0.0075
0.8478	130900	0.0206
0.8484	131000	0.0074
0.8491	131100	0.0333
0.8497	131200	0.0323
0.8504	131300	0.0095
0.8510	131400	0.0099
0.8516	131500	0.0194
0.8523	131600	0.0238
0.8529	131700	0.0131
0.8536	131800	0.0031
0.8542	131900	0.0025
0.8549	132000	0.0147
0.8555	132100	0.0377
0.8562	132200	0.04
0.8568	132300	0.0095
0.8575	132400	0.057
0.8581	132500	0.0138
0.8588	132600	0.0048
0.8594	132700	0.0061
0.8601	132800	0.0213
0.8607	132900	0.02
0.8614	133000	0.1095
0.8620	133100	0.0135
0.8627	133200	0.0074
0.8633	133300	0.0087
0.8640	133400	0.0136
0.8646	133500	0.0089
0.8652	133600	0.0081
0.8659	133700	0.0144
0.8665	133800	0.0133
0.8672	133900	0.028
0.8678	134000	0.0254
0.8685	134100	0.0094
0.8691	134200	0.0045
0.8698	134300	0.0141
0.8704	134400	0.0465
0.8711	134500	0.0103
0.8717	134600	0.049
0.8724	134700	0.0058
0.8730	134800	0.0464
0.8737	134900	0.0076
0.8743	135000	0.0271
0.8750	135100	0.0129
0.8756	135200	0.0138
0.8763	135300	0.0962
0.8769	135400	0.0637
0.8776	135500	0.0492
0.8782	135600	0.0232
0.8788	135700	0.0544
0.8795	135800	0.0098
0.8801	135900	0.0109
0.8808	136000	0.0514
0.8814	136100	0.0333
0.8821	136200	0.0676
0.8827	136300	0.0219
0.8834	136400	0.0484
0.8840	136500	0.0138
0.8847	136600	0.0285
0.8853	136700	0.0283
0.8860	136800	0.0444
0.8866	136900	0.0458
0.8873	137000	0.0859
0.8879	137100	0.0369
0.8886	137200	0.0017
0.8892	137300	0.0382
0.8899	137400	0.024
0.8905	137500	0.0391
0.8912	137600	0.0357
0.8918	137700	0.0299
0.8924	137800	0.0236
0.8931	137900	0.0252
0.8937	138000	0.0036
0.8944	138100	0.0605
0.8950	138200	0.027
0.8957	138300	0.0587
0.8963	138400	0.0333
0.8970	138500	0.0127
0.8976	138600	0.0128
0.8983	138700	0.018
0.8989	138800	0.0153
0.8996	138900	0.052
0.9002	139000	0.0761
0.9009	139100	0.0164
0.9015	139200	0.016
0.9022	139300	0.0114
0.9028	139400	0.0104
0.9035	139500	0.0029
0.9041	139600	0.0255
0.9048	139700	0.0191
0.9054	139800	0.0082
0.9060	139900	0.0069
0.9067	140000	0.0245
0.9073	140100	0.019
0.9080	140200	0.0329
0.9086	140300	0.0025
0.9093	140400	0.0032
0.9099	140500	0.0314
0.9106	140600	0.0419
0.9112	140700	0.002
0.9119	140800	0.0084
0.9125	140900	0.0049
0.9132	141000	0.01
0.9138	141100	0.0058
0.9145	141200	0.0055
0.9151	141300	0.0072
0.9158	141400	0.0091
0.9164	141500	0.0083
0.9171	141600	0.0158
0.9177	141700	0.0858
0.9184	141800	0.0121
0.9190	141900	0.0047
0.9196	142000	0.0066
0.9203	142100	0.0304
0.9209	142200	0.0332
0.9216	142300	0.0113
0.9222	142400	0.0221
0.9229	142500	0.0087
0.9235	142600	0.0419
0.9242	142700	0.006
0.9248	142800	0.0535
0.9255	142900	0.0148
0.9261	143000	0.0417
0.9268	143100	0.0076
0.9274	143200	0.0092
0.9281	143300	0.0059
0.9287	143400	0.0342
0.9294	143500	0.0093
0.9300	143600	0.0052
0.9307	143700	0.0399
0.9313	143800	0.0719
0.9320	143900	0.0238
0.9326	144000	0.0323
0.9332	144100	0.0365
0.9339	144200	0.0148
0.9345	144300	0.0037
0.9352	144400	0.0108
0.9358	144500	0.0926
0.9365	144600	0.0294
0.9371	144700	0.0134
0.9378	144800	0.0162
0.9384	144900	0.0264
0.9391	145000	0.0613
0.9397	145100	0.0688
0.9404	145200	0.0074
0.9410	145300	0.0043
0.9417	145400	0.033
0.9423	145500	0.0037
0.9430	145600	0.0701
0.9436	145700	0.03
0.9443	145800	0.0117
0.9449	145900	0.0134
0.9456	146000	0.0066
0.9462	146100	0.0264
0.9468	146200	0.0099
0.9475	146300	0.0151
0.9481	146400	0.0625
0.9488	146500	0.0339
0.9494	146600	0.0135
0.9501	146700	0.008
0.9507	146800	0.0153
0.9514	146900	0.0097
0.9520	147000	0.0341
0.9527	147100	0.0506
0.9533	147200	0.0162
0.9540	147300	0.0021
0.9546	147400	0.0064
0.9553	147500	0.0258
0.9559	147600	0.071
0.9566	147700	0.0175
0.9572	147800	0.0338
0.9579	147900	0.0129
0.9585	148000	0.0022
0.9592	148100	0.087
0.9598	148200	0.0358
0.9604	148300	0.0083
0.9611	148400	0.0323
0.9617	148500	0.0416
0.9624	148600	0.0589
0.9630	148700	0.0175
0.9637	148800	0.0219
0.9643	148900	0.0249
0.9650	149000	0.0092
0.9656	149100	0.0646
0.9663	149200	0.0184
0.9669	149300	0.0307
0.9676	149400	0.0616
0.9682	149500	0.0108
0.9689	149600	0.0482
0.9695	149700	0.0239
0.9702	149800	0.0024
0.9708	149900	0.0043
0.9715	150000	0.0049
0.9721	150100	0.0136
0.9728	150200	0.0363
0.9734	150300	0.0695
0.9740	150400	0.0527
0.9747	150500	0.028
0.9753	150600	0.0184
0.9760	150700	0.0257
0.9766	150800	0.0074
0.9773	150900	0.023
0.9779	151000	0.0862
0.9786	151100	0.0469
0.9792	151200	0.0314
0.9799	151300	0.0153
0.9805	151400	0.1128
0.9812	151500	0.0119
0.9818	151600	0.0221
0.9825	151700	0.0094
0.9831	151800	0.0144
0.9838	151900	0.0209
0.9844	152000	0.0267
0.9851	152100	0.0339
0.9857	152200	0.0133
0.9864	152300	0.0515
0.9870	152400	0.0223
0.9876	152500	0.0032
0.9883	152600	0.0116
0.9889	152700	0.0292
0.9896	152800	0.0118
0.9902	152900	0.0709
0.9909	153000	0.0305
0.9915	153100	0.0298
0.9922	153200	0.0297
0.9928	153300	0.0049
0.9935	153400	0.0045
0.9941	153500	0.0211
0.9948	153600	0.024
0.9954	153700	0.0268
0.9961	153800	0.0271
0.9967	153900	0.0425
0.9974	154000	0.0395
0.9980	154100	0.067
0.9987	154200	0.0613
0.9993	154300	0.0051
1.0000	154400	0.0136

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 5.1.0
• Transformers: 4.55.0
• PyTorch: 2.7.1+cu118
• Accelerate: 1.10.0
• Datasets: 4.0.0
• Tokenizers: 0.21.4

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}