This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

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}) with Transformer model: 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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'dairy',
    'skin friendly pillow',
    'buffalo chicken wrap',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

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: 2
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: 2
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: 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
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0015	100	12.9406
0.0029	200	13.027
0.0044	300	12.8584
0.0058	400	12.6457
0.0073	500	12.1326
0.0087	600	11.7892
0.0102	700	11.2976
0.0116	800	10.706
0.0131	900	10.3948
0.0145	1000	10.0428
0.0160	1100	9.6988
0.0174	1200	9.4399
0.0189	1300	9.1479
0.0203	1400	8.9207
0.0218	1500	8.7778
0.0232	1600	8.6608
0.0247	1700	8.5816
0.0261	1800	8.55
0.0276	1900	8.5003
0.0290	2000	8.456
0.0305	2100	8.4379
0.0319	2200	8.4153
0.0334	2300	8.3954
0.0348	2400	8.3459
0.0363	2500	8.3365
0.0377	2600	8.3115
0.0392	2700	8.3084
0.0406	2800	8.2934
0.0421	2900	8.2832
0.0435	3000	8.2491
0.0450	3100	8.2603
0.0464	3200	8.2336
0.0479	3300	8.2284
0.0493	3400	8.2076
0.0508	3500	8.1885
0.0522	3600	8.1841
0.0537	3700	8.1657
0.0551	3800	8.1536
0.0566	3900	8.1488
0.0580	4000	8.1358
0.0595	4100	8.1406
0.0609	4200	8.1137
0.0624	4300	8.0979
0.0638	4400	8.0989
0.0653	4500	8.0916
0.0667	4600	8.0883
0.0682	4700	8.0796
0.0696	4800	8.0664
0.0711	4900	8.0758
0.0725	5000	8.0426
0.0740	5100	8.0331
0.0754	5200	8.028
0.0769	5300	8.0121
0.0783	5400	8.0181
0.0798	5500	8.003
0.0812	5600	7.9839
0.0827	5700	7.9794
0.0841	5800	7.9759
0.0856	5900	7.9925
0.0870	6000	7.9562
0.0885	6100	7.955
0.0899	6200	7.9578
0.0914	6300	7.9442
0.0928	6400	7.9483
0.0943	6500	7.9367
0.0957	6600	7.9424
0.0972	6700	7.9209
0.0987	6800	7.9181
0.1001	6900	7.9222
0.1016	7000	7.8964
0.1030	7100	7.8958
0.1045	7200	7.9098
0.1059	7300	7.897
0.1074	7400	7.8894
0.1088	7500	7.8862
0.1103	7600	7.8865
0.1117	7700	7.8917
0.1132	7800	7.8639
0.1146	7900	7.8646
0.1161	8000	7.8457
0.1175	8100	7.8574
0.1190	8200	7.8287
0.1204	8300	7.8463
0.1219	8400	7.819
0.1233	8500	7.8371
0.1248	8600	7.8367
0.1262	8700	7.8456
0.1277	8800	7.8578
0.1291	8900	7.8425
0.1306	9000	7.8096
0.1320	9100	7.8155
0.1335	9200	7.8122
0.1349	9300	7.8258
0.1364	9400	7.8128
0.1378	9500	7.7965
0.1393	9600	7.809
0.1407	9700	7.808
0.1422	9800	7.7918
0.1436	9900	7.7808
0.1451	10000	7.8129
0.1465	10100	7.7965
0.1480	10200	7.8247
0.1494	10300	7.7543
0.1509	10400	7.7307
0.1523	10500	7.7668
0.1538	10600	7.7702
0.1552	10700	7.7495
0.1567	10800	7.7934
0.1581	10900	7.778
0.1596	11000	7.7771
0.1610	11100	7.7355
0.1625	11200	7.7789
0.1639	11300	7.7446
0.1654	11400	7.748
0.1668	11500	7.7257
0.1683	11600	7.7399
0.1697	11700	7.7376
0.1712	11800	7.7711
0.1726	11900	7.7468
0.1741	12000	7.7259
0.1755	12100	7.7487
0.1770	12200	7.7229
0.1784	12300	7.729
0.1799	12400	7.7232
0.1813	12500	7.7284
0.1828	12600	7.6793
0.1842	12700	7.7303
0.1857	12800	7.7034
0.1871	12900	7.6668
0.1886	13000	7.701
0.1900	13100	7.7131
0.1915	13200	7.7077
0.1929	13300	7.7129
0.1944	13400	7.6667
0.1959	13500	7.6999
0.1973	13600	7.6799
0.1988	13700	7.6938
0.2002	13800	7.7075
0.2017	13900	7.6991
0.2031	14000	7.6632
0.2046	14100	7.7059
0.2060	14200	7.6471
0.2075	14300	7.6685
0.2089	14400	7.6488
0.2104	14500	7.6874
0.2118	14600	7.6092
0.2133	14700	7.6431
0.2147	14800	7.6521
0.2162	14900	7.6505
0.2176	15000	7.6623
0.2191	15100	7.6357
0.2205	15200	7.6516
0.2220	15300	7.6428
0.2234	15400	7.6568
0.2249	15500	7.6009
0.2263	15600	7.6283
0.2278	15700	7.6743
0.2292	15800	7.5994
0.2307	15900	7.6297
0.2321	16000	7.592
0.2336	16100	7.6743
0.2350	16200	7.6259
0.2365	16300	7.6712
0.2379	16400	7.6315
0.2394	16500	7.6354
0.2408	16600	7.6264
0.2423	16700	7.6081
0.2437	16800	7.5909
0.2452	16900	7.6496
0.2466	17000	7.6705
0.2481	17100	7.6123
0.2495	17200	7.6821
0.2510	17300	7.6372
0.2524	17400	7.5777
0.2539	17500	7.638
0.2553	17600	7.5781
0.2568	17700	7.5609
0.2582	17800	7.5568
0.2597	17900	7.5951
0.2611	18000	7.6402
0.2626	18100	7.5634
0.2640	18200	7.6002
0.2655	18300	7.5669
0.2669	18400	7.574
0.2684	18500	7.6219
0.2698	18600	7.6058
0.2713	18700	7.5758
0.2727	18800	7.5767
0.2742	18900	7.5452
0.2756	19000	7.6406
0.2771	19100	7.5696
0.2785	19200	7.5664
0.2800	19300	7.6273
0.2814	19400	7.6133
0.2829	19500	7.5644
0.2843	19600	7.5973
0.2858	19700	7.5684
0.2872	19800	7.5193
0.2887	19900	7.5996
0.2901	20000	7.5662
0.2916	20100	7.5331
0.2931	20200	7.5992
0.2945	20300	7.5827
0.2960	20400	7.552
0.2974	20500	7.5793
0.2989	20600	7.5121
0.3003	20700	7.5638
0.3018	20800	7.5504
0.3032	20900	7.54
0.3047	21000	7.5766
0.3061	21100	7.5888
0.3076	21200	7.5454
0.3090	21300	7.5391
0.3105	21400	7.5666
0.3119	21500	7.4972
0.3134	21600	7.6144
0.3148	21700	7.5124
0.3163	21800	7.5458
0.3177	21900	7.5365
0.3192	22000	7.488
0.3206	22100	7.5424
0.3221	22200	7.561
0.3235	22300	7.6308
0.3250	22400	7.5842
0.3264	22500	7.5235
0.3279	22600	7.505
0.3293	22700	7.5246
0.3308	22800	7.5501
0.3322	22900	7.4736
0.3337	23000	7.5956
0.3351	23100	7.5274
0.3366	23200	7.5526
0.3380	23300	7.5847
0.3395	23400	7.5554
0.3409	23500	7.5043
0.3424	23600	7.6002
0.3438	23700	7.5239
0.3453	23800	7.5462
0.3467	23900	7.534
0.3482	24000	7.483
0.3496	24100	7.5753
0.3511	24200	7.4996
0.3525	24300	7.5646
0.3540	24400	7.5336
0.3554	24500	7.4838
0.3569	24600	7.5623
0.3583	24700	7.555
0.3598	24800	7.4792
0.3612	24900	7.5471
0.3627	25000	7.5391
0.3641	25100	7.5053
0.3656	25200	7.4548
0.3670	25300	7.5006
0.3685	25400	7.4919
0.3699	25500	7.5475
0.3714	25600	7.5257
0.3728	25700	7.475
0.3743	25800	7.5076
0.3757	25900	7.4515
0.3772	26000	7.5356
0.3786	26100	7.4857
0.3801	26200	7.5171
0.3815	26300	7.5791
0.3830	26400	7.5644
0.3844	26500	7.4906
0.3859	26600	7.5482
0.3873	26700	7.5187
0.3888	26800	7.4507
0.3903	26900	7.475
0.3917	27000	7.4759
0.3932	27100	7.4879
0.3946	27200	7.4923
0.3961	27300	7.5892
0.3975	27400	7.5171
0.3990	27500	7.5175
0.4004	27600	7.4511
0.4019	27700	7.4672
0.4033	27800	7.4772
0.4048	27900	7.5404
0.4062	28000	7.506
0.4077	28100	7.4967
0.4091	28200	7.5027
0.4106	28300	7.4403
0.4120	28400	7.4765
0.4135	28500	7.5107
0.4149	28600	7.4539
0.4164	28700	7.4968
0.4178	28800	7.5257
0.4193	28900	7.4397
0.4207	29000	7.4719
0.4222	29100	7.4504
0.4236	29200	7.6349
0.4251	29300	7.5408
0.4265	29400	7.4787
0.4280	29500	7.4982
0.4294	29600	7.5019
0.4309	29700	7.4224
0.4323	29800	7.5344
0.4338	29900	7.4064
0.4352	30000	7.4633
0.4367	30100	7.4502
0.4381	30200	7.3969
0.4396	30300	7.5308
0.4410	30400	7.4871
0.4425	30500	7.455
0.4439	30600	7.4539
0.4454	30700	7.4482
0.4468	30800	7.4583
0.4483	30900	7.4252
0.4497	31000	7.4617
0.4512	31100	7.516
0.4526	31200	7.5429
0.4541	31300	7.5021
0.4555	31400	7.4942
0.4570	31500	7.4561
0.4584	31600	7.4416
0.4599	31700	7.4393
0.4613	31800	7.4629
0.4628	31900	7.3896
0.4642	32000	7.3846
0.4657	32100	7.495
0.4671	32200	7.4906
0.4686	32300	7.4294
0.4700	32400	7.4553
0.4715	32500	7.4361
0.4729	32600	7.4681
0.4744	32700	7.4271
0.4758	32800	7.4306
0.4773	32900	7.4146
0.4787	33000	7.4556
0.4802	33100	7.4206
0.4816	33200	7.4698
0.4831	33300	7.4399
0.4845	33400	7.4652
0.4860	33500	7.4309
0.4875	33600	7.4228
0.4889	33700	7.4434
0.4904	33800	7.4183
0.4918	33900	7.4261
0.4933	34000	7.6017
0.4947	34100	7.4587
0.4962	34200	7.4396
0.4976	34300	7.4275
0.4991	34400	7.3923
0.5005	34500	7.4911
0.5020	34600	7.4429
0.5034	34700	7.5064
0.5049	34800	7.3998
0.5063	34900	7.4212
0.5078	35000	7.444
0.5092	35100	7.393
0.5107	35200	7.3896
0.5121	35300	7.4193
0.5136	35400	7.4707
0.5150	35500	7.4099
0.5165	35600	7.4312
0.5179	35700	7.4759
0.5194	35800	7.402
0.5208	35900	7.3632
0.5223	36000	7.5035
0.5237	36100	7.4089
0.5252	36200	7.4392
0.5266	36300	7.3915
0.5281	36400	7.4029
0.5295	36500	7.4066
0.5310	36600	7.4196
0.5324	36700	7.4213
0.5339	36800	7.5063
0.5353	36900	7.4064
0.5368	37000	7.4153
0.5382	37100	7.4297
0.5397	37200	7.4057
0.5411	37300	7.423
0.5426	37400	7.4402
0.5440	37500	7.3909
0.5455	37600	7.4572
0.5469	37700	7.3766
0.5484	37800	7.3829
0.5498	37900	7.4331
0.5513	38000	7.4048
0.5527	38100	7.4799
0.5542	38200	7.3978
0.5556	38300	7.3738
0.5571	38400	7.4207
0.5585	38500	7.3382
0.5600	38600	7.4345
0.5614	38700	7.4686
0.5629	38800	7.4463
0.5643	38900	7.378
0.5658	39000	7.3953
0.5672	39100	7.4133
0.5687	39200	7.4111
0.5701	39300	7.3912
0.5716	39400	7.4585
0.5730	39500	7.4443
0.5745	39600	7.3856
0.5759	39700	7.4676
0.5774	39800	7.4174
0.5788	39900	7.3785
0.5803	40000	7.5674
0.5817	40100	7.4845
0.5832	40200	7.4498
0.5847	40300	7.4074
0.5861	40400	7.4074
0.5876	40500	7.4946
0.5890	40600	7.4253
0.5905	40700	7.4462
0.5919	40800	7.3849
0.5934	40900	7.4016
0.5948	41000	7.4397
0.5963	41100	7.3705
0.5977	41200	7.4636
0.5992	41300	7.399
0.6006	41400	7.4496
0.6021	41500	7.4116
0.6035	41600	7.3557
0.6050	41700	7.3922
0.6064	41800	7.3758
0.6079	41900	7.5005
0.6093	42000	7.4038
0.6108	42100	7.3832
0.6122	42200	7.3923
0.6137	42300	7.4042
0.6151	42400	7.3656
0.6166	42500	7.3786
0.6180	42600	7.3638
0.6195	42700	7.3901
0.6209	42800	7.4051
0.6224	42900	7.3723
0.6238	43000	7.3444
0.6253	43100	7.4124
0.6267	43200	7.3888
0.6282	43300	7.4841
0.6296	43400	7.3651
0.6311	43500	7.396
0.6325	43600	7.3486
0.6340	43700	7.3666
0.6354	43800	7.3746
0.6369	43900	7.3036
0.6383	44000	7.3837
0.6398	44100	7.3918
0.6412	44200	7.3793
0.6427	44300	7.3511
0.6441	44400	7.4343
0.6456	44500	7.4548
0.6470	44600	7.3629
0.6485	44700	7.3671
0.6499	44800	7.3332
0.6514	44900	7.35
0.6528	45000	7.3927
0.6543	45100	7.329
0.6557	45200	7.3744
0.6572	45300	7.3614
0.6586	45400	7.3728
0.6601	45500	7.3306
0.6615	45600	7.4415
0.6630	45700	7.348
0.6644	45800	7.3279
0.6659	45900	7.4125
0.6673	46000	7.3706
0.6688	46100	7.4031
0.6702	46200	7.4145
0.6717	46300	7.3848
0.6731	46400	7.3573
0.6746	46500	7.3203
0.6760	46600	7.4073
0.6775	46700	7.4367
0.6789	46800	7.4251
0.6804	46900	7.3963
0.6819	47000	7.3949
0.6833	47100	7.3926
0.6848	47200	7.3081
0.6862	47300	7.4249
0.6877	47400	7.3092
0.6891	47500	7.3735
0.6906	47600	7.4299
0.6920	47700	7.2919
0.6935	47800	7.4652
0.6949	47900	7.3165
0.6964	48000	7.444
0.6978	48100	7.3852
0.6993	48200	7.3648
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0.7022	48400	7.3228
0.7036	48500	7.3889
0.7051	48600	7.3413
0.7065	48700	7.4217
0.7080	48800	7.4866
0.7094	48900	7.3115
0.7109	49000	7.35
0.7123	49100	7.4323
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0.7181	49500	7.3876
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0.7210	49700	7.2997
0.7225	49800	7.3759
0.7239	49900	7.3393
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0.7399	51000	7.3974
0.7413	51100	7.326
0.7428	51200	7.3634
0.7442	51300	7.3402
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0.7471	51500	7.3603
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0.7515	51800	7.3196
0.7529	51900	7.3399
0.7544	52000	7.3629
0.7558	52100	7.3171
0.7573	52200	7.3064
0.7587	52300	7.3864
0.7602	52400	7.3033
0.7616	52500	7.3587
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0.7645	52700	7.3275
0.7660	52800	7.3624
0.7674	52900	7.3305
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0.7718	53200	7.2483
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0.7747	53400	7.3551
0.7761	53500	7.3497
0.7776	53600	7.3095
0.7791	53700	7.2684
0.7805	53800	7.2706
0.7820	53900	7.3338
0.7834	54000	7.4137
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0.7863	54200	7.3714
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0.7892	54400	7.372
0.7907	54500	7.3859
0.7921	54600	7.4655
0.7936	54700	7.3012
0.7950	54800	7.3107
0.7965	54900	7.3496
0.7979	55000	7.2957
0.7994	55100	7.2869
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0.8023	55300	7.3168
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0.8052	55500	7.479
0.8066	55600	7.3392
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0.8124	56000	7.3114
0.8139	56100	7.4436
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0.8168	56300	7.3473
0.8182	56400	7.2364
0.8197	56500	7.2866
0.8211	56600	7.4131
0.8226	56700	7.3731
0.8240	56800	7.32
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0.8269	57000	7.4079
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0.8342	57500	7.3611
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0.8371	57700	7.3922
0.8385	57800	7.2958
0.8400	57900	7.4085
0.8414	58000	7.4068
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0.8472	58400	7.3437
0.8487	58500	7.3121
0.8501	58600	7.3205
0.8516	58700	7.3518
0.8530	58800	7.2968
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0.8559	59000	7.3564
0.8574	59100	7.3098
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0.8617	59400	7.3978
0.8632	59500	7.3629
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0.8675	59800	7.3382
0.8690	59900	7.3219
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0.8719	60100	7.3662
0.8733	60200	7.3244
0.8748	60300	7.3408
0.8763	60400	7.2908
0.8777	60500	7.3136
0.8792	60600	7.3829
0.8806	60700	7.4448
0.8821	60800	7.3708
0.8835	60900	7.314
0.8850	61000	7.29
0.8864	61100	7.3241
0.8879	61200	7.2737
0.8893	61300	7.314
0.8908	61400	7.2747
0.8922	61500	7.2132
0.8937	61600	7.3681
0.8951	61700	7.3407
0.8966	61800	7.3741
0.8980	61900	7.2677
0.8995	62000	7.487
0.9009	62100	7.308
0.9024	62200	7.3044
0.9038	62300	7.2859
0.9053	62400	7.2811
0.9067	62500	7.3592
0.9082	62600	7.4129
0.9096	62700	7.2644
0.9111	62800	7.2298
0.9125	62900	7.4237
0.9140	63000	7.2801
0.9154	63100	7.3546
0.9169	63200	7.3754
0.9183	63300	7.3529
0.9198	63400	7.2346
0.9212	63500	7.408
0.9227	63600	7.3298
0.9241	63700	7.4733
0.9256	63800	7.3459
0.9270	63900	7.2923
0.9285	64000	7.2328
0.9299	64100	7.2707
0.9314	64200	7.3393
0.9328	64300	7.3503
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0.9357	64500	7.2707
0.9372	64600	7.3478
0.9386	64700	7.2817
0.9401	64800	7.3272
0.9415	64900	7.2375
0.9430	65000	7.3093
0.9444	65100	7.3212
0.9459	65200	7.3191
0.9473	65300	7.2883
0.9488	65400	7.2518
0.9502	65500	7.2883
0.9517	65600	7.3728
0.9531	65700	7.3553
0.9546	65800	7.3012
0.9560	65900	7.301
0.9575	66000	7.2617
0.9589	66100	7.2118
0.9604	66200	7.4651
0.9618	66300	7.3624
0.9633	66400	7.2278
0.9647	66500	7.2696
0.9662	66600	7.3305
0.9676	66700	7.3671
0.9691	66800	7.3348
0.9705	66900	7.3
0.9720	67000	7.2865
0.9735	67100	7.3312
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0.9764	67300	7.2484
0.9778	67400	7.2837
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0.9807	67600	7.2212
0.9822	67700	7.3374
0.9836	67800	7.2504
0.9851	67900	7.3122
0.9865	68000	7.2844
0.9880	68100	7.2359
0.9894	68200	7.3128
0.9909	68300	7.2761
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0.9938	68500	7.1825
0.9952	68600	7.278
0.9967	68700	7.2837
0.9981	68800	7.3169
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1.0010	69000	7.259
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1.0039	69200	7.3467
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1.0097	69600	7.2045
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1.0155	70000	7.2656
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1.0184	70200	7.2879
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1.0242	70600	7.3205
1.0257	70700	7.3042
1.0271	70800	7.393
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1.0300	71000	7.2721
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1.0344	71300	7.3707
1.0358	71400	7.2196
1.0373	71500	7.2274
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1.0402	71700	7.2845
1.0416	71800	7.2602
1.0431	71900	7.3173
1.0445	72000	7.248
1.0460	72100	7.2655
1.0474	72200	7.283
1.0489	72300	7.2709
1.0503	72400	7.2334
1.0518	72500	7.342
1.0532	72600	7.3004
1.0547	72700	7.3026
1.0561	72800	7.4001
1.0576	72900	7.2554
1.0590	73000	7.3312
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1.0619	73200	7.3204
1.0634	73300	7.2237
1.0648	73400	7.246
1.0663	73500	7.3053
1.0677	73600	7.2601
1.0692	73700	7.2822
1.0707	73800	7.2581
1.0721	73900	7.2571
1.0736	74000	7.2097
1.0750	74100	7.2547
1.0765	74200	7.2163
1.0779	74300	7.3213
1.0794	74400	7.3406
1.0808	74500	7.3094
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1.0837	74700	7.3479
1.0852	74800	7.2986
1.0866	74900	7.3306
1.0881	75000	7.307
1.0895	75100	7.2829
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1.0924	75300	7.2987
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1.0953	75500	7.2328
1.0968	75600	7.2212
1.0982	75700	7.3198
1.0997	75800	7.3183
1.1011	75900	7.2962
1.1026	76000	7.3928
1.1040	76100	7.2599
1.1055	76200	7.2
1.1069	76300	7.2838
1.1084	76400	7.2746
1.1098	76500	7.256
1.1113	76600	7.4477
1.1127	76700	7.3582
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1.1156	76900	7.3052
1.1171	77000	7.1684
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1.1258	77600	7.2693
1.1272	77700	7.2538
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1.1316	78000	7.2683
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1.1374	78400	7.3219
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1.1461	79000	7.2505
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1.1490	79200	7.3753
1.1504	79300	7.2738
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1.1533	79500	7.1976
1.1548	79600	7.2113
1.1562	79700	7.2996
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1.1591	79900	7.2462
1.1606	80000	7.3157
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1.1635	80200	7.2132
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1.1664	80400	7.2596
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1.1722	80800	7.3138
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1.1751	81000	7.2623
1.1766	81100	7.1986
1.1780	81200	7.4106
1.1795	81300	7.3304
1.1809	81400	7.2514
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1.1853	81700	7.2659
1.1867	81800	7.3809
1.1882	81900	7.2301
1.1896	82000	7.315
1.1911	82100	7.2374
1.1925	82200	7.189
1.1940	82300	7.4537
1.1954	82400	7.3656
1.1969	82500	7.2017
1.1983	82600	7.2854
1.1998	82700	7.2499
1.2012	82800	7.1772
1.2027	82900	7.2718
1.2041	83000	7.3552
1.2056	83100	7.2341
1.2070	83200	7.3153
1.2085	83300	7.265
1.2099	83400	7.2748
1.2114	83500	7.246
1.2128	83600	7.2111
1.2143	83700	7.1699
1.2157	83800	7.2475
1.2172	83900	7.2873
1.2186	84000	7.173
1.2201	84100	7.195
1.2215	84200	7.3038
1.2230	84300	7.2836
1.2244	84400	7.1667
1.2259	84500	7.2658
1.2273	84600	7.2664
1.2288	84700	7.2843
1.2302	84800	7.3177
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1.2331	85000	7.2224
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1.2360	85200	7.2095
1.2375	85300	7.2349
1.2389	85400	7.206
1.2404	85500	7.2005
1.2418	85600	7.2309
1.2433	85700	7.3086
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1.2476	86000	7.4116
1.2491	86100	7.1708
1.2505	86200	7.2307
1.2520	86300	7.2017
1.2534	86400	7.3533
1.2549	86500	7.1598
1.2563	86600	7.3376
1.2578	86700	7.2368
1.2592	86800	7.1713
1.2607	86900	7.2531
1.2622	87000	7.222
1.2636	87100	7.1982
1.2651	87200	7.2025
1.2665	87300	7.4178
1.2680	87400	7.2592
1.2694	87500	7.2311
1.2709	87600	7.1747
1.2723	87700	7.2667
1.2738	87800	7.258
1.2752	87900	7.264
1.2767	88000	7.2469
1.2781	88100	7.1759
1.2796	88200	7.2578
1.2810	88300	7.2246
1.2825	88400	7.4164
1.2839	88500	7.1923
1.2854	88600	7.2276
1.2868	88700	7.2126
1.2883	88800	7.2507
1.2897	88900	7.2142
1.2912	89000	7.2991
1.2926	89100	7.166
1.2941	89200	7.2347
1.2955	89300	7.2496
1.2970	89400	7.2074
1.2984	89500	7.1882
1.2999	89600	7.2151
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1.3042	89900	7.2504
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1.3086	90200	7.2443
1.3100	90300	7.2404
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1.3144	90600	7.2468
1.3158	90700	7.1899
1.3173	90800	7.2764
1.3187	90900	7.3764
1.3202	91000	7.2008
1.3216	91100	7.223
1.3231	91200	7.1656
1.3245	91300	7.2959
1.3260	91400	7.2345
1.3274	91500	7.3
1.3289	91600	7.2364
1.3303	91700	7.2499
1.3318	91800	7.3536
1.3332	91900	7.1878
1.3347	92000	7.2002
1.3361	92100	7.2736
1.3376	92200	7.2102
1.3390	92300	7.2082
1.3405	92400	7.255
1.3419	92500	7.2984
1.3434	92600	7.2955
1.3448	92700	7.2105
1.3463	92800	7.1842
1.3477	92900	7.1726
1.3492	93000	7.1891
1.3506	93100	7.2408
1.3521	93200	7.2349
1.3535	93300	7.2777
1.3550	93400	7.2967
1.3564	93500	7.1823
1.3579	93600	7.2017
1.3594	93700	7.2102
1.3608	93800	7.2688
1.3623	93900	7.3601
1.3637	94000	7.2129
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1.3666	94200	7.2365
1.3681	94300	7.2332
1.3695	94400	7.2264
1.3710	94500	7.3086
1.3724	94600	7.2924
1.3739	94700	7.1663
1.3753	94800	7.2429
1.3768	94900	7.3012
1.3782	95000	7.2854
1.3797	95100	7.2242
1.3811	95200	7.3575
1.3826	95300	7.2296
1.3840	95400	7.2191
1.3855	95500	7.2395
1.3869	95600	7.2786
1.3884	95700	7.4124
1.3898	95800	7.3207
1.3913	95900	7.2167
1.3927	96000	7.2799
1.3942	96100	7.2316
1.3956	96200	7.2393
1.3971	96300	7.2299
1.3985	96400	7.1464
1.4000	96500	7.2781
1.4014	96600	7.2986
1.4029	96700	7.1583
1.4043	96800	7.2043
1.4058	96900	7.2922
1.4072	97000	7.2951
1.4087	97100	7.2647
1.4101	97200	7.3304
1.4116	97300	7.1791
1.4130	97400	7.2872
1.4145	97500	7.1827
1.4159	97600	7.1795
1.4174	97700	7.1818
1.4188	97800	7.253
1.4203	97900	7.3293
1.4217	98000	7.2292
1.4232	98100	7.2255
1.4246	98200	7.3715
1.4261	98300	7.1778
1.4275	98400	7.2625
1.4290	98500	7.2401
1.4304	98600	7.2375
1.4319	98700	7.309
1.4333	98800	7.1035
1.4348	98900	7.2621
1.4362	99000	7.1957
1.4377	99100	7.24
1.4391	99200	7.2674
1.4406	99300	7.2338
1.4420	99400	7.2034
1.4435	99500	7.2177
1.4449	99600	7.2464
1.4464	99700	7.3464
1.4478	99800	7.2776
1.4493	99900	7.2709
1.4507	100000	7.2661
1.4522	100100	7.1875
1.4536	100200	7.2424
1.4551	100300	7.1838
1.4566	100400	7.2626
1.4580	100500	7.2717
1.4595	100600	7.1745
1.4609	100700	7.1964
1.4624	100800	7.223
1.4638	100900	7.2205
1.4653	101000	7.3256
1.4667	101100	7.2498
1.4682	101200	7.2029
1.4696	101300	7.4786
1.4711	101400	7.1836
1.4725	101500	7.2453
1.4740	101600	7.1911
1.4754	101700	7.1539
1.4769	101800	7.2576
1.4783	101900	7.3469
1.4798	102000	7.1994
1.4812	102100	7.2129
1.4827	102200	7.1083
1.4841	102300	7.1974
1.4856	102400	7.2625
1.4870	102500	7.2098
1.4885	102600	7.2135
1.4899	102700	7.1852
1.4914	102800	7.1693
1.4928	102900	7.1697
1.4943	103000	7.1868
1.4957	103100	7.21
1.4972	103200	7.2178
1.4986	103300	7.2174
1.5001	103400	7.2828
1.5015	103500	7.2731
1.5030	103600	7.1215
1.5044	103700	7.2617
1.5059	103800	7.1843
1.5073	103900	7.2279
1.5088	104000	7.2751
1.5102	104100	7.3623
1.5117	104200	7.1917
1.5131	104300	7.2277
1.5146	104400	7.2894
1.5160	104500	7.3754
1.5175	104600	7.1816
1.5189	104700	7.2402
1.5204	104800	7.261
1.5218	104900	7.1941
1.5233	105000	7.3078
1.5247	105100	7.3108
1.5262	105200	7.1566
1.5276	105300	7.2064
1.5291	105400	7.2603
1.5305	105500	7.2434
1.5320	105600	7.1932
1.5334	105700	7.1801
1.5349	105800	7.2148
1.5363	105900	7.3564
1.5378	106000	7.1438
1.5392	106100	7.1641
1.5407	106200	7.2043
1.5421	106300	7.1688
1.5436	106400	7.135
1.5450	106500	7.2125
1.5465	106600	7.1826
1.5479	106700	7.2009
1.5494	106800	7.1943
1.5508	106900	7.1598
1.5523	107000	7.1596
1.5538	107100	7.2316
1.5552	107200	7.1596
1.5567	107300	7.2746
1.5581	107400	7.3602
1.5596	107500	7.2657
1.5610	107600	7.1554
1.5625	107700	7.2113
1.5639	107800	7.137
1.5654	107900	7.2269
1.5668	108000	7.3221
1.5683	108100	7.1854
1.5697	108200	7.25
1.5712	108300	7.3238
1.5726	108400	7.1586
1.5741	108500	7.2338
1.5755	108600	7.1514
1.5770	108700	7.2144
1.5784	108800	7.3535
1.5799	108900	7.1395
1.5813	109000	7.2299
1.5828	109100	7.205
1.5842	109200	7.1154
1.5857	109300	7.3584
1.5871	109400	7.2308
1.5886	109500	7.1952
1.5900	109600	7.1963
1.5915	109700	7.2026
1.5929	109800	7.1545
1.5944	109900	7.153
1.5958	110000	7.156
1.5973	110100	7.2812
1.5987	110200	7.2105
1.6002	110300	7.268
1.6016	110400	7.2062
1.6031	110500	7.2422
1.6045	110600	7.1598
1.6060	110700	7.1343
1.6074	110800	7.381
1.6089	110900	7.2098
1.6103	111000	7.2246
1.6118	111100	7.1944
1.6132	111200	7.1991
1.6147	111300	7.2289
1.6161	111400	7.2578
1.6176	111500	7.2953
1.6190	111600	7.3101
1.6205	111700	7.1879
1.6219	111800	7.2002
1.6234	111900	7.1671
1.6248	112000	7.3259
1.6263	112100	7.2716
1.6277	112200	7.309
1.6292	112300	7.1647
1.6306	112400	7.1964
1.6321	112500	7.2476
1.6335	112600	7.1541
1.6350	112700	7.3043
1.6364	112800	7.2093
1.6379	112900	7.1503
1.6393	113000	7.3591
1.6408	113100	7.1934
1.6422	113200	7.2943
1.6437	113300	7.272
1.6451	113400	7.2545
1.6466	113500	7.1619
1.6480	113600	7.151
1.6495	113700	7.2713
1.6510	113800	7.1488
1.6524	113900	7.3434
1.6539	114000	7.2193
1.6553	114100	7.1353
1.6568	114200	7.3057
1.6582	114300	7.2861
1.6597	114400	7.2459
1.6611	114500	7.2736
1.6626	114600	7.2227
1.6640	114700	7.2684
1.6655	114800	7.1461
1.6669	114900	7.2167
1.6684	115000	7.2059
1.6698	115100	7.1736
1.6713	115200	7.1542
1.6727	115300	7.1769
1.6742	115400	7.2679
1.6756	115500	7.2218
1.6771	115600	7.1985
1.6785	115700	7.129
1.6800	115800	7.4028
1.6814	115900	7.2482
1.6829	116000	7.1872
1.6843	116100	7.2251
1.6858	116200	7.2076
1.6872	116300	7.186
1.6887	116400	7.2355
1.6901	116500	7.1839
1.6916	116600	7.1738
1.6930	116700	7.1798
1.6945	116800	7.1351
1.6959	116900	7.2726
1.6974	117000	7.1277
1.6988	117100	7.2429
1.7003	117200	7.1544
1.7017	117300	7.2503
1.7032	117400	7.3181
1.7046	117500	7.1616
1.7061	117600	7.1289
1.7075	117700	7.1757
1.7090	117800	7.2318
1.7104	117900	7.3686
1.7119	118000	7.2097
1.7133	118100	7.1598
1.7148	118200	7.0969
1.7162	118300	7.1132
1.7177	118400	7.2823
1.7191	118500	7.1536
1.7206	118600	7.2077
1.7220	118700	7.1452
1.7235	118800	7.2714
1.7249	118900	7.1392
1.7264	119000	7.1296
1.7278	119100	7.1569
1.7293	119200	7.121
1.7307	119300	7.24
1.7322	119400	7.1903
1.7336	119500	7.147
1.7351	119600	7.2807
1.7365	119700	7.3659
1.7380	119800	7.2397
1.7394	119900	7.1596
1.7409	120000	7.1245
1.7423	120100	7.1362
1.7438	120200	7.2544
1.7452	120300	7.1631
1.7467	120400	7.2098
1.7482	120500	7.1435
1.7496	120600	7.3286
1.7511	120700	7.2086
1.7525	120800	7.2418
1.7540	120900	7.1677
1.7554	121000	7.1639
1.7569	121100	7.2157
1.7583	121200	7.2562
1.7598	121300	7.2396
1.7612	121400	7.2514
1.7627	121500	7.2454
1.7641	121600	7.1899
1.7656	121700	7.1415
1.7670	121800	7.2243
1.7685	121900	7.1661
1.7699	122000	7.1955
1.7714	122100	7.2374
1.7728	122200	7.1602
1.7743	122300	7.3033
1.7757	122400	7.2356
1.7772	122500	7.1476
1.7786	122600	7.1767
1.7801	122700	7.1763
1.7815	122800	7.3496
1.7830	122900	7.0778
1.7844	123000	7.2753
1.7859	123100	7.2039
1.7873	123200	7.1035
1.7888	123300	7.1969
1.7902	123400	7.1817
1.7917	123500	7.2926
1.7931	123600	7.1553
1.7946	123700	7.2357
1.7960	123800	7.3222
1.7975	123900	7.216
1.7989	124000	7.1655
1.8004	124100	7.1662
1.8018	124200	7.2033
1.8033	124300	7.1864
1.8047	124400	7.2686
1.8062	124500	7.2556
1.8076	124600	7.1678
1.8091	124700	7.1769
1.8105	124800	7.2453
1.8120	124900	7.2099
1.8134	125000	7.2256
1.8149	125100	7.2116
1.8163	125200	7.3909
1.8178	125300	7.11
1.8192	125400	7.2632
1.8207	125500	7.2604
1.8221	125600	7.3064
1.8236	125700	7.2577
1.8250	125800	7.2957
1.8265	125900	7.1698
1.8279	126000	7.0963
1.8294	126100	7.1681
1.8308	126200	7.2173
1.8323	126300	7.269
1.8337	126400	7.1635
1.8352	126500	7.2284
1.8366	126600	7.1664
1.8381	126700	7.2332
1.8395	126800	7.2108
1.8410	126900	7.2006
1.8424	127000	7.1449
1.8439	127100	7.197
1.8454	127200	7.2331
1.8468	127300	7.1949
1.8483	127400	7.3798
1.8497	127500	7.266
1.8512	127600	7.1908
1.8526	127700	7.1272
1.8541	127800	7.2454
1.8555	127900	7.2053
1.8570	128000	7.1379
1.8584	128100	7.4103
1.8599	128200	7.1312
1.8613	128300	7.1742
1.8628	128400	7.1966
1.8642	128500	7.1605
1.8657	128600	7.2
1.8671	128700	7.2278
1.8686	128800	7.1493
1.8700	128900	7.224
1.8715	129000	7.28
1.8729	129100	7.2298
1.8744	129200	7.1972
1.8758	129300	7.2552
1.8773	129400	7.0971
1.8787	129500	7.1208
1.8802	129600	7.2428
1.8816	129700	7.1744
1.8831	129800	7.1957
1.8845	129900	7.2358
1.8860	130000	7.2524
1.8874	130100	7.3145
1.8889	130200	7.17
1.8903	130300	7.1912
1.8918	130400	7.1248
1.8932	130500	7.1168
1.8947	130600	7.18
1.8961	130700	7.2399
1.8976	130800	7.1988
1.8990	130900	7.1501
1.9005	131000	7.342
1.9019	131100	7.2703
1.9034	131200	7.2915
1.9048	131300	7.2106
1.9063	131400	7.1687
1.9077	131500	7.2873
1.9092	131600	7.2178
1.9106	131700	7.1273
1.9121	131800	7.363
1.9135	131900	7.1924
1.9150	132000	7.3224
1.9164	132100	7.1551
1.9179	132200	7.2864
1.9193	132300	7.1798
1.9208	132400	7.2443
1.9222	132500	7.3269
1.9237	132600	7.3145
1.9251	132700	7.212
1.9266	132800	7.1377
1.9280	132900	7.1437
1.9295	133000	7.2541
1.9309	133100	7.1828
1.9324	133200	7.179
1.9338	133300	7.1152
1.9353	133400	7.19
1.9367	133500	7.3336
1.9382	133600	7.166
1.9396	133700	7.1101
1.9411	133800	7.3584
1.9426	133900	7.2676
1.9440	134000	7.2241
1.9455	134100	7.2064
1.9469	134200	7.2955
1.9484	134300	7.1775
1.9498	134400	7.3157
1.9513	134500	7.1877
1.9527	134600	7.2107
1.9542	134700	7.1077
1.9556	134800	7.1995
1.9571	134900	7.3072
1.9585	135000	7.1454
1.9600	135100	7.195
1.9614	135200	7.1234
1.9629	135300	7.1613
1.9643	135400	7.2272
1.9658	135500	7.283
1.9672	135600	7.2511
1.9687	135700	7.2221
1.9701	135800	7.2522
1.9716	135900	7.1319
1.9730	136000	7.3165
1.9745	136100	7.3674
1.9759	136200	7.0887
1.9774	136300	7.1835
1.9788	136400	7.1564
1.9803	136500	7.1821
1.9817	136600	7.1491
1.9832	136700	7.2409
1.9846	136800	7.1122
1.9861	136900	7.2579
1.9875	137000	7.2762
1.9890	137100	7.3117
1.9904	137200	7.1045
1.9919	137300	7.2866
1.9933	137400	7.3154
1.9948	137500	7.2134
1.9962	137600	7.1569
1.9977	137700	7.2418
1.9991	137800	7.2169

Framework Versions

Python: 3.8.10
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.4.1+cu118
Accelerate: 1.0.1
Datasets: 3.0.1
Tokenizers: 0.20.3

Citation

BibTeX

Sentence Transformers

bibtex

@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