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 = [
    'ramdan tagine',
    'glass',
    'samsung',
]
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
0.0016	100	13.8388
0.0033	200	13.6758
0.0049	300	13.2027
0.0065	400	12.9865
0.0082	500	12.4647
0.0098	600	11.9259
0.0114	700	11.514
0.0131	800	10.7788
0.0147	900	10.2176
0.0164	1000	9.8098
0.0180	1100	9.4029
0.0196	1200	9.0311
0.0213	1300	8.8268
0.0229	1400	8.6939
0.0245	1500	8.5978
0.0262	1600	8.5436
0.0278	1700	8.4912
0.0294	1800	8.4454
0.0311	1900	8.4029
0.0327	2000	8.3616
0.0343	2100	8.3393
0.0360	2200	8.3268
0.0376	2300	8.2938
0.0392	2400	8.2529
0.0409	2500	8.2363
0.0425	2600	8.2313
0.0442	2700	8.2013
0.0458	2800	8.1815
0.0474	2900	8.1639
0.0491	3000	8.1823
0.0507	3100	8.1366
0.0523	3200	8.1295
0.0540	3300	8.1365
0.0556	3400	8.1125
0.0572	3500	8.1136
0.0589	3600	8.1254
0.0605	3700	8.0799
0.0621	3800	8.0873
0.0638	3900	8.0774
0.0654	4000	8.0598
0.0670	4100	8.0577
0.0687	4200	8.0371
0.0703	4300	8.0379
0.0720	4400	8.0426
0.0736	4500	8.0091
0.0752	4600	7.9718
0.0769	4700	7.9905
0.0785	4800	8.009
0.0801	4900	7.9921
0.0818	5000	7.995
0.0834	5100	7.977
0.0850	5200	7.9612
0.0867	5300	7.9783
0.0883	5400	7.9462
0.0899	5500	7.9508
0.0916	5600	7.9445
0.0932	5700	7.9308
0.0949	5800	7.9476
0.0965	5900	7.9107
0.0981	6000	7.8842
0.0998	6100	7.9056
0.1014	6200	7.91
0.1030	6300	7.8844
0.1047	6400	7.8953
0.1063	6500	7.8874
0.1079	6600	7.9254
0.1096	6700	7.9131
0.1112	6800	7.8585
0.1128	6900	7.9062
0.1145	7000	7.8495
0.1161	7100	7.8944
0.1177	7200	7.8892
0.1194	7300	7.838
0.1210	7400	7.8807
0.1227	7500	7.8692
0.1243	7600	7.8472
0.1259	7700	7.8463
0.1276	7800	7.864
0.1292	7900	7.8081
0.1308	8000	7.8333
0.1325	8100	7.8473
0.1341	8200	7.8218
0.1357	8300	7.8336
0.1374	8400	7.8246
0.1390	8500	7.8244
0.1406	8600	7.832
0.1423	8700	7.7924
0.1439	8800	7.7813
0.1455	8900	7.8092
0.1472	9000	7.8171
0.1488	9100	7.7756
0.1505	9200	7.7841
0.1521	9300	7.7821
0.1537	9400	7.8078
0.1554	9500	7.7754
0.1570	9600	7.7715
0.1586	9700	7.739
0.1603	9800	7.772
0.1619	9900	7.7508
0.1635	10000	7.8111
0.1652	10100	7.7507
0.1668	10200	7.7508
0.1684	10300	7.7617
0.1701	10400	7.7572
0.1717	10500	7.7416
0.1733	10600	7.7713
0.1750	10700	7.741
0.1766	10800	7.7305
0.1783	10900	7.7337
0.1799	11000	7.7444
0.1815	11100	7.73
0.1832	11200	7.7371
0.1848	11300	7.7606
0.1864	11400	7.7075
0.1881	11500	7.6909
0.1897	11600	7.7074
0.1913	11700	7.7021
0.1930	11800	7.7066
0.1946	11900	7.7203
0.1962	12000	7.7375
0.1979	12100	7.6847
0.1995	12200	7.7313
0.2011	12300	7.7125
0.2028	12400	7.652
0.2044	12500	7.6687
0.2061	12600	7.6816
0.2077	12700	7.6722
0.2093	12800	7.6984
0.2110	12900	7.6941
0.2126	13000	7.6875
0.2142	13100	7.6958
0.2159	13200	7.7064
0.2175	13300	7.6761
0.2191	13400	7.68
0.2208	13500	7.6614
0.2224	13600	7.673
0.2240	13700	7.6679
0.2257	13800	7.641
0.2273	13900	7.6509
0.2289	14000	7.6539
0.2306	14100	7.6788
0.2322	14200	7.6631
0.2339	14300	7.6815
0.2355	14400	7.6796
0.2371	14500	7.6432
0.2388	14600	7.6244
0.2404	14700	7.7122
0.2420	14800	7.6317
0.2437	14900	7.66
0.2453	15000	7.6164
0.2469	15100	7.6566
0.2486	15200	7.6487
0.2502	15300	7.6119
0.2518	15400	7.606
0.2535	15500	7.6114
0.2551	15600	7.6255
0.2567	15700	7.6003
0.2584	15800	7.5847
0.2600	15900	7.6303
0.2617	16000	7.661
0.2633	16100	7.6191
0.2649	16200	7.6152
0.2666	16300	7.6118
0.2682	16400	7.6169
0.2698	16500	7.6043
0.2715	16600	7.6195
0.2731	16700	7.5836
0.2747	16800	7.6048
0.2764	16900	7.6036
0.2780	17000	7.608
0.2796	17100	7.5983
0.2813	17200	7.644
0.2829	17300	7.6115
0.2846	17400	7.6256
0.2862	17500	7.5924
0.2878	17600	7.6019
0.2895	17700	7.6021
0.2911	17800	7.6095
0.2927	17900	7.6554
0.2944	18000	7.6036
0.2960	18100	7.577
0.2976	18200	7.5874
0.2993	18300	7.5839
0.3009	18400	7.5742
0.3025	18500	7.6244
0.3042	18600	7.5502
0.3058	18700	7.5805
0.3074	18800	7.5563
0.3091	18900	7.595
0.3107	19000	7.5715
0.3124	19100	7.6142
0.3140	19200	7.541
0.3156	19300	7.5641
0.3173	19400	7.6212
0.3189	19500	7.61
0.3205	19600	7.5853
0.3222	19700	7.5599
0.3238	19800	7.5795
0.3254	19900	7.6039
0.3271	20000	7.541
0.3287	20100	7.5619
0.3303	20200	7.5246
0.3320	20300	7.6084
0.3336	20400	7.5701
0.3352	20500	7.5451
0.3369	20600	7.537
0.3385	20700	7.5828
0.3402	20800	7.549
0.3418	20900	7.5503
0.3434	21000	7.5291
0.3451	21100	7.5628
0.3467	21200	7.597
0.3483	21300	7.5617
0.3500	21400	7.5053
0.3516	21500	7.5785
0.3532	21600	7.5539
0.3549	21700	7.5828
0.3565	21800	7.5594
0.3581	21900	7.5109
0.3598	22000	7.5413
0.3614	22100	7.503
0.3630	22200	7.5658
0.3647	22300	7.5586
0.3663	22400	7.6085
0.3680	22500	7.5849
0.3696	22600	7.5378
0.3712	22700	7.5343
0.3729	22800	7.5606
0.3745	22900	7.5982
0.3761	23000	7.5509
0.3778	23100	7.532
0.3794	23200	7.5014
0.3810	23300	7.5394
0.3827	23400	7.5351
0.3843	23500	7.5135
0.3859	23600	7.5168
0.3876	23700	7.5525
0.3892	23800	7.4786
0.3908	23900	7.5134
0.3925	24000	7.5052
0.3941	24100	7.5304
0.3958	24200	7.4864
0.3974	24300	7.5435
0.3990	24400	7.5637
0.4007	24500	7.528
0.4023	24600	7.5458
0.4039	24700	7.5656
0.4056	24800	7.4848
0.4072	24900	7.4949
0.4088	25000	7.5474
0.4105	25100	7.4909
0.4121	25200	7.5029
0.4137	25300	7.5202
0.4154	25400	7.5393
0.4170	25500	7.5203
0.4186	25600	7.455
0.4203	25700	7.526
0.4219	25800	7.4839
0.4236	25900	7.4724
0.4252	26000	7.4277
0.4268	26100	7.5372
0.4285	26200	7.4817
0.4301	26300	7.4961
0.4317	26400	7.4749
0.4334	26500	7.6095
0.4350	26600	7.4773
0.4366	26700	7.4994
0.4383	26800	7.4959
0.4399	26900	7.4702
0.4415	27000	7.4914
0.4432	27100	7.5124
0.4448	27200	7.5087
0.4465	27300	7.4701
0.4481	27400	7.4348
0.4497	27500	7.4994
0.4514	27600	7.4949
0.4530	27700	7.4409
0.4546	27800	7.4319
0.4563	27900	7.4545
0.4579	28000	7.5283
0.4595	28100	7.4309
0.4612	28200	7.4703
0.4628	28300	7.4963
0.4644	28400	7.5041
0.4661	28500	7.4346
0.4677	28600	7.4382
0.4693	28700	7.4151
0.4710	28800	7.5072
0.4726	28900	7.4357
0.4743	29000	7.4584
0.4759	29100	7.4853
0.4775	29200	7.486
0.4792	29300	7.4955
0.4808	29400	7.4086
0.4824	29500	7.4797
0.4841	29600	7.4295
0.4857	29700	7.5126
0.4873	29800	7.4834
0.4890	29900	7.4265
0.4906	30000	7.4643
0.4922	30100	7.4576
0.4939	30200	7.4444
0.4955	30300	7.4734
0.4971	30400	7.4238
0.4988	30500	7.4368
0.5004	30600	7.4485
0.5021	30700	7.3991
0.5037	30800	7.5196
0.5053	30900	7.4278
0.5070	31000	7.3866
0.5086	31100	7.524
0.5102	31200	7.4102
0.5119	31300	7.4421
0.5135	31400	7.4615
0.5151	31500	7.4866
0.5168	31600	7.4128
0.5184	31700	7.3935
0.5200	31800	7.4738
0.5217	31900	7.4116
0.5233	32000	7.4334
0.5249	32100	7.4907
0.5266	32200	7.4142
0.5282	32300	7.4495
0.5299	32400	7.4323
0.5315	32500	7.424
0.5331	32600	7.3877
0.5348	32700	7.449
0.5364	32800	7.468
0.5380	32900	7.4253
0.5397	33000	7.475
0.5413	33100	7.3939
0.5429	33200	7.4668
0.5446	33300	7.5031
0.5462	33400	7.4263
0.5478	33500	7.5039
0.5495	33600	7.392
0.5511	33700	7.4652
0.5527	33800	7.4136
0.5544	33900	7.4503
0.5560	34000	7.3902
0.5577	34100	7.4782
0.5593	34200	7.4757
0.5609	34300	7.3712
0.5626	34400	7.4433
0.5642	34500	7.4549
0.5658	34600	7.4112
0.5675	34700	7.3975
0.5691	34800	7.4119
0.5707	34900	7.3947
0.5724	35000	7.4854
0.5740	35100	7.5613
0.5756	35200	7.4618
0.5773	35300	7.515
0.5789	35400	7.3594
0.5805	35500	7.4234
0.5822	35600	7.4534
0.5838	35700	7.4053
0.5855	35800	7.3663
0.5871	35900	7.3857
0.5887	36000	7.3689
0.5904	36100	7.4165
0.5920	36200	7.3795
0.5936	36300	7.3836
0.5953	36400	7.4077
0.5969	36500	7.3843
0.5985	36600	7.3774
0.6002	36700	7.4366
0.6018	36800	7.4714
0.6034	36900	7.4584
0.6051	37000	7.3623
0.6067	37100	7.4035
0.6084	37200	7.365
0.6100	37300	7.4943
0.6116	37400	7.4059
0.6133	37500	7.3909
0.6149	37600	7.3231
0.6165	37700	7.412
0.6182	37800	7.3996
0.6198	37900	7.3389
0.6214	38000	7.402
0.6231	38100	7.465
0.6247	38200	7.3514
0.6263	38300	7.3865
0.6280	38400	7.4873
0.6296	38500	7.4416
0.6312	38600	7.3992
0.6329	38700	7.3743
0.6345	38800	7.4035
0.6362	38900	7.3766
0.6378	39000	7.3851
0.6394	39100	7.3633
0.6411	39200	7.3937
0.6427	39300	7.4384
0.6443	39400	7.4224
0.6460	39500	7.4045
0.6476	39600	7.3564
0.6492	39700	7.3494
0.6509	39800	7.3939
0.6525	39900	7.3973
0.6541	40000	7.3799
0.6558	40100	7.3509
0.6574	40200	7.3799
0.6590	40300	7.4378
0.6607	40400	7.3407
0.6623	40500	7.3713
0.6640	40600	7.3913
0.6656	40700	7.3822
0.6672	40800	7.3421
0.6689	40900	7.4415
0.6705	41000	7.3794
0.6721	41100	7.3486
0.6738	41200	7.3653
0.6754	41300	7.3587
0.6770	41400	7.4195
0.6787	41500	7.4282
0.6803	41600	7.385
0.6819	41700	7.3735
0.6836	41800	7.4122
0.6852	41900	7.4305
0.6868	42000	7.4394
0.6885	42100	7.4004
0.6901	42200	7.3411
0.6918	42300	7.384
0.6934	42400	7.3436
0.6950	42500	7.4166
0.6967	42600	7.3831
0.6983	42700	7.3274
0.6999	42800	7.3397
0.7016	42900	7.4724
0.7032	43000	7.3562
0.7048	43100	7.3559
0.7065	43200	7.4245
0.7081	43300	7.4727
0.7097	43400	7.3575
0.7114	43500	7.296
0.7130	43600	7.4019
0.7146	43700	7.3603
0.7163	43800	7.3705
0.7179	43900	7.3858
0.7196	44000	7.3836
0.7212	44100	7.3931
0.7228	44200	7.3568
0.7245	44300	7.3929
0.7261	44400	7.3522
0.7277	44500	7.3117
0.7294	44600	7.4005
0.7310	44700	7.3491
0.7326	44800	7.3385
0.7343	44900	7.3477
0.7359	45000	7.3107
0.7375	45100	7.3781
0.7392	45200	7.3259
0.7408	45300	7.3322
0.7424	45400	7.4162
0.7441	45500	7.3467
0.7457	45600	7.3858
0.7474	45700	7.3533
0.7490	45800	7.4116
0.7506	45900	7.3483
0.7523	46000	7.2907
0.7539	46100	7.3332
0.7555	46200	7.2816
0.7572	46300	7.4065
0.7588	46400	7.3926
0.7604	46500	7.4213
0.7621	46600	7.5124
0.7637	46700	7.3141
0.7653	46800	7.2922
0.7670	46900	7.4721
0.7686	47000	7.2959
0.7702	47100	7.3431
0.7719	47200	7.4238
0.7735	47300	7.3423
0.7752	47400	7.3408
0.7768	47500	7.2973
0.7784	47600	7.3127
0.7801	47700	7.391
0.7817	47800	7.2829
0.7833	47900	7.2725
0.7850	48000	7.3021
0.7866	48100	7.522
0.7882	48200	7.2864
0.7899	48300	7.3879
0.7915	48400	7.3804
0.7931	48500	7.3101
0.7948	48600	7.3957
0.7964	48700	7.2516
0.7981	48800	7.2707
0.7997	48900	7.4598
0.8013	49000	7.3174
0.8030	49100	7.3302
0.8046	49200	7.3706
0.8062	49300	7.4527
0.8079	49400	7.3
0.8095	49500	7.3419
0.8111	49600	7.2703
0.8128	49700	7.2812
0.8144	49800	7.4201
0.8160	49900	7.3289
0.8177	50000	7.3127
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0.8209	50200	7.3472
0.8226	50300	7.3606
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0.8259	50500	7.3875
0.8275	50600	7.2701
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0.8308	50800	7.2969
0.8324	50900	7.4394
0.8340	51000	7.3581
0.8357	51100	7.39
0.8373	51200	7.3643
0.8389	51300	7.2921
0.8406	51400	7.3143
0.8422	51500	7.3363
0.8438	51600	7.3063
0.8455	51700	7.3726
0.8471	51800	7.2524
0.8487	51900	7.4085
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0.8520	52100	7.4392
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0.8553	52300	7.3257
0.8569	52400	7.2866
0.8586	52500	7.4021
0.8602	52600	7.2689
0.8618	52700	7.3521
0.8635	52800	7.3034
0.8651	52900	7.3527
0.8667	53000	7.335
0.8684	53100	7.3696
0.8700	53200	7.4368
0.8716	53300	7.2861
0.8733	53400	7.4529
0.8749	53500	7.3502
0.8765	53600	7.3355
0.8782	53700	7.3174
0.8798	53800	7.3378
0.8815	53900	7.2709
0.8831	54000	7.2881
0.8847	54100	7.2624
0.8864	54200	7.3725
0.8880	54300	7.2693
0.8896	54400	7.2983
0.8913	54500	7.2391
0.8929	54600	7.433
0.8945	54700	7.3061
0.8962	54800	7.2693
0.8978	54900	7.2513
0.8994	55000	7.3248
0.9011	55100	7.3206
0.9027	55200	7.4116
0.9043	55300	7.2521
0.9060	55400	7.3042
0.9076	55500	7.314
0.9093	55600	7.3024
0.9109	55700	7.2841
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0.9142	55900	7.3607
0.9158	56000	7.2728
0.9174	56100	7.3099
0.9191	56200	7.3691
0.9207	56300	7.2881
0.9223	56400	7.3155
0.9240	56500	7.455
0.9256	56600	7.3725
0.9272	56700	7.2735
0.9289	56800	7.3249
0.9305	56900	7.3662
0.9321	57000	7.419
0.9338	57100	7.364
0.9354	57200	7.289
0.9371	57300	7.2735
0.9387	57400	7.3368
0.9403	57500	7.2925
0.9420	57600	7.2443
0.9436	57700	7.3478
0.9452	57800	7.2528
0.9469	57900	7.2519
0.9485	58000	7.3557
0.9501	58100	7.2487
0.9518	58200	7.2763
0.9534	58300	7.2408
0.9550	58400	7.2685
0.9567	58500	7.3501
0.9583	58600	7.2317
0.9600	58700	7.2881
0.9616	58800	7.3157
0.9632	58900	7.2229
0.9649	59000	7.3597
0.9665	59100	7.3186
0.9681	59200	7.3675
0.9698	59300	7.3413
0.9714	59400	7.2692
0.9730	59500	7.3209
0.9747	59600	7.3498
0.9763	59700	7.3146
0.9779	59800	7.2832
0.9796	59900	7.3765
0.9812	60000	7.3916
0.9828	60100	7.2931
0.9845	60200	7.2765
0.9861	60300	7.3638
0.9878	60400	7.3035
0.9894	60500	7.3215
0.9910	60600	7.2872
0.9927	60700	7.2237
0.9943	60800	7.3137
0.9959	60900	7.3152
0.9976	61000	7.3608
0.9992	61100	7.2921
1.0008	61200	7.2274
1.0025	61300	7.2672
1.0041	61400	7.3674
1.0057	61500	7.3094
1.0074	61600	7.2118
1.0090	61700	7.3063
1.0106	61800	7.2649
1.0123	61900	7.2727
1.0139	62000	7.2473
1.0156	62100	7.2649
1.0172	62200	7.35
1.0188	62300	7.2631
1.0205	62400	7.2715
1.0221	62500	7.3328
1.0237	62600	7.2523
1.0254	62700	7.2752
1.0270	62800	7.3611
1.0286	62900	7.3405
1.0303	63000	7.2925
1.0319	63100	7.2284
1.0335	63200	7.3216
1.0352	63300	7.2805
1.0368	63400	7.4077
1.0384	63500	7.2059
1.0401	63600	7.3006
1.0417	63700	7.2715
1.0434	63800	7.2879
1.0450	63900	7.3043
1.0466	64000	7.3379
1.0483	64100	7.2326
1.0499	64200	7.2336
1.0515	64300	7.3712
1.0532	64400	7.1524
1.0548	64500	7.202
1.0564	64600	7.2632
1.0581	64700	7.2627
1.0597	64800	7.2534
1.0613	64900	7.3059
1.0630	65000	7.3292
1.0646	65100	7.3172
1.0662	65200	7.2423
1.0679	65300	7.2458
1.0695	65400	7.2681
1.0712	65500	7.2974
1.0728	65600	7.3866
1.0744	65700	7.2716
1.0761	65800	7.3154
1.0777	65900	7.1821
1.0793	66000	7.3136
1.0810	66100	7.2049
1.0826	66200	7.2742
1.0842	66300	7.238
1.0859	66400	7.2447
1.0875	66500	7.1865
1.0891	66600	7.3527
1.0908	66700	7.2212
1.0924	66800	7.2352
1.0940	66900	7.2332
1.0957	67000	7.2792
1.0973	67100	7.2603
1.0990	67200	7.1715
1.1006	67300	7.2222
1.1022	67400	7.2713
1.1039	67500	7.3203
1.1055	67600	7.2419
1.1071	67700	7.3449
1.1088	67800	7.3536
1.1104	67900	7.2384
1.1120	68000	7.311
1.1137	68100	7.2888
1.1153	68200	7.1715
1.1169	68300	7.2935
1.1186	68400	7.3365
1.1202	68500	7.327
1.1218	68600	7.175
1.1235	68700	7.2658
1.1251	68800	7.2048
1.1268	68900	7.304
1.1284	69000	7.2298
1.1300	69100	7.2357
1.1317	69200	7.2441
1.1333	69300	7.2333
1.1349	69400	7.2207
1.1366	69500	7.3227
1.1382	69600	7.2532
1.1398	69700	7.2096
1.1415	69800	7.2943
1.1431	69900	7.2858
1.1447	70000	7.2294
1.1464	70100	7.2744
1.1480	70200	7.2431
1.1497	70300	7.2513
1.1513	70400	7.291
1.1529	70500	7.2074
1.1546	70600	7.2191
1.1562	70700	7.2685
1.1578	70800	7.2441
1.1595	70900	7.4062
1.1611	71000	7.1991
1.1627	71100	7.2128
1.1644	71200	7.3431
1.1660	71300	7.3997
1.1676	71400	7.2139
1.1693	71500	7.2408
1.1709	71600	7.2873
1.1725	71700	7.2057
1.1742	71800	7.1855
1.1758	71900	7.2633
1.1775	72000	7.3066
1.1791	72100	7.3253
1.1807	72200	7.2647
1.1824	72300	7.181
1.1840	72400	7.2918
1.1856	72500	7.2447
1.1873	72600	7.2283
1.1889	72700	7.2672
1.1905	72800	7.2748
1.1922	72900	7.2319
1.1938	73000	7.2038
1.1954	73100	7.2999
1.1971	73200	7.3346
1.1987	73300	7.2448
1.2003	73400	7.2399
1.2020	73500	7.277
1.2036	73600	7.2367
1.2053	73700	7.2246
1.2069	73800	7.2548
1.2085	73900	7.3035
1.2102	74000	7.2042
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1.2134	74200	7.2431
1.2151	74300	7.2699
1.2167	74400	7.3081
1.2183	74500	7.3925
1.2200	74600	7.2347
1.2216	74700	7.4031
1.2232	74800	7.2845
1.2249	74900	7.2402
1.2265	75000	7.2312
1.2281	75100	7.2786
1.2298	75200	7.2651
1.2314	75300	7.2238
1.2331	75400	7.2677
1.2347	75500	7.358
1.2363	75600	7.1785
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1.2412	75900	7.3464
1.2429	76000	7.1859
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1.2461	76200	7.2781
1.2478	76300	7.2722
1.2494	76400	7.1868
1.2510	76500	7.1762
1.2527	76600	7.1755
1.2543	76700	7.2375
1.2559	76800	7.2447
1.2576	76900	7.1957
1.2592	77000	7.2081
1.2609	77100	7.3489
1.2625	77200	7.2171
1.2641	77300	7.2283
1.2658	77400	7.2665
1.2674	77500	7.2027
1.2690	77600	7.2112
1.2707	77700	7.3322
1.2723	77800	7.1551
1.2739	77900	7.312
1.2756	78000	7.2082
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1.2788	78200	7.1676
1.2805	78300	7.1858
1.2821	78400	7.2436
1.2837	78500	7.2016
1.2854	78600	7.2311
1.2870	78700	7.1834
1.2887	78800	7.2474
1.2903	78900	7.3177
1.2919	79000	7.3093
1.2936	79100	7.2566
1.2952	79200	7.1785
1.2968	79300	7.2344
1.2985	79400	7.2815
1.3001	79500	7.1929
1.3017	79600	7.2579
1.3034	79700	7.2056
1.3050	79800	7.3351
1.3066	79900	7.1994
1.3083	80000	7.2705
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1.3132	80300	7.1795
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1.3165	80500	7.3024
1.3181	80600	7.2076
1.3197	80700	7.2667
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1.3230	80900	7.2894
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1.3312	81400	7.2047
1.3328	81500	7.2645
1.3344	81600	7.2628
1.3361	81700	7.363
1.3377	81800	7.2298
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1.3410	82000	7.2034
1.3426	82100	7.2825
1.3443	82200	7.2385
1.3459	82300	7.3717
1.3475	82400	7.2601
1.3492	82500	7.14
1.3508	82600	7.1989
1.3524	82700	7.3454
1.3541	82800	7.2326
1.3557	82900	7.1448
1.3573	83000	7.3331
1.3590	83100	7.3108
1.3606	83200	7.1545
1.3622	83300	7.2615
1.3639	83400	7.2073
1.3655	83500	7.2805
1.3672	83600	7.1949
1.3688	83700	7.3958
1.3704	83800	7.3567
1.3721	83900	7.2097
1.3737	84000	7.3873
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1.3770	84200	7.1561
1.3786	84300	7.2695
1.3802	84400	7.2659
1.3819	84500	7.1914
1.3835	84600	7.3513
1.3851	84700	7.2083
1.3868	84800	7.1815
1.3884	84900	7.1791
1.3900	85000	7.2966
1.3917	85100	7.1682
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1.3950	85300	7.2384
1.3966	85400	7.2641
1.3982	85500	7.2897
1.3999	85600	7.2003
1.4015	85700	7.1445
1.4031	85800	7.2939
1.4048	85900	7.2109
1.4064	86000	7.3391
1.4080	86100	7.2455
1.4097	86200	7.3384
1.4113	86300	7.2789
1.4129	86400	7.2253
1.4146	86500	7.2045
1.4162	86600	7.2715
1.4178	86700	7.1557
1.4195	86800	7.2064
1.4211	86900	7.2233
1.4228	87000	7.1618
1.4244	87100	7.259
1.4260	87200	7.1844
1.4277	87300	7.2255
1.4293	87400	7.2212
1.4309	87500	7.2605
1.4326	87600	7.287
1.4342	87700	7.2306
1.4358	87800	7.1867
1.4375	87900	7.163
1.4391	88000	7.2034
1.4407	88100	7.25
1.4424	88200	7.2781
1.4440	88300	7.1707
1.4456	88400	7.2171
1.4473	88500	7.2225
1.4489	88600	7.2039
1.4506	88700	7.2183
1.4522	88800	7.3452
1.4538	88900	7.2731
1.4555	89000	7.2704
1.4571	89100	7.2491
1.4587	89200	7.1967
1.4604	89300	7.1273
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1.4636	89500	7.1202
1.4653	89600	7.2365
1.4669	89700	7.1436
1.4685	89800	7.117
1.4702	89900	7.1736
1.4718	90000	7.2387
1.4735	90100	7.2144
1.4751	90200	7.1819
1.4767	90300	7.355
1.4784	90400	7.2249
1.4800	90500	7.2295
1.4816	90600	7.3626
1.4833	90700	7.2701
1.4849	90800	7.2152
1.4865	90900	7.2432
1.4882	91000	7.166
1.4898	91100	7.144
1.4914	91200	7.2671
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1.4947	91400	7.2628
1.4963	91500	7.258
1.4980	91600	7.191
1.4996	91700	7.2458
1.5013	91800	7.2966
1.5029	91900	7.1938
1.5045	92000	7.1933
1.5062	92100	7.1925
1.5078	92200	7.2848
1.5094	92300	7.191
1.5111	92400	7.2225
1.5127	92500	7.2102
1.5143	92600	7.1933
1.5160	92700	7.0943
1.5176	92800	7.2869
1.5192	92900	7.263
1.5209	93000	7.1362
1.5225	93100	7.2756
1.5241	93200	7.315
1.5258	93300	7.2681
1.5274	93400	7.2584
1.5291	93500	7.1334
1.5307	93600	7.1853
1.5323	93700	7.0984
1.5340	93800	7.2932
1.5356	93900	7.1664
1.5372	94000	7.2162
1.5389	94100	7.2584
1.5405	94200	7.2293
1.5421	94300	7.212
1.5438	94400	7.1278
1.5454	94500	7.2018
1.5470	94600	7.1641
1.5487	94700	7.2488
1.5503	94800	7.2033
1.5519	94900	7.2038
1.5536	95000	7.3503
1.5552	95100	7.231
1.5569	95200	7.2417
1.5585	95300	7.3997
1.5601	95400	7.195
1.5618	95500	7.2201
1.5634	95600	7.2433
1.5650	95700	7.3198
1.5667	95800	7.2291
1.5683	95900	7.257
1.5699	96000	7.2843
1.5716	96100	7.1688
1.5732	96200	7.2803
1.5748	96300	7.2952
1.5765	96400	7.1823
1.5781	96500	7.2153
1.5797	96600	7.28
1.5814	96700	7.3577
1.5830	96800	7.1236
1.5847	96900	7.1463
1.5863	97000	7.1706
1.5879	97100	7.128
1.5896	97200	7.2604
1.5912	97300	7.1904
1.5928	97400	7.1805
1.5945	97500	7.2696
1.5961	97600	7.2077
1.5977	97700	7.1146
1.5994	97800	7.1935
1.6010	97900	7.2175
1.6026	98000	7.1328
1.6043	98100	7.1975
1.6059	98200	7.214
1.6075	98300	7.209
1.6092	98400	7.3163
1.6108	98500	7.2076
1.6125	98600	7.2207
1.6141	98700	7.2498
1.6157	98800	7.1402
1.6174	98900	7.1557
1.6190	99000	7.2513
1.6206	99100	7.1799
1.6223	99200	7.1575
1.6239	99300	7.2611
1.6255	99400	7.2935
1.6272	99500	7.2455
1.6288	99600	7.2989
1.6304	99700	7.1742
1.6321	99800	7.2553
1.6337	99900	7.198
1.6353	100000	7.2551
1.6370	100100	7.2795
1.6386	100200	7.1472
1.6403	100300	7.2149
1.6419	100400	7.2685
1.6435	100500	7.2504
1.6452	100600	7.205
1.6468	100700	7.1591
1.6484	100800	7.2839
1.6501	100900	7.257
1.6517	101000	7.2206
1.6533	101100	7.1539
1.6550	101200	7.1568
1.6566	101300	7.2602
1.6582	101400	7.3935
1.6599	101500	7.1626
1.6615	101600	7.1664
1.6632	101700	7.1269
1.6648	101800	7.2743
1.6664	101900	7.1909
1.6681	102000	7.1663
1.6697	102100	7.1403
1.6713	102200	7.2911
1.6730	102300	7.1812
1.6746	102400	7.2591
1.6762	102500	7.1834
1.6779	102600	7.3092
1.6795	102700	7.1196
1.6811	102800	7.2028
1.6828	102900	7.263
1.6844	103000	7.1883
1.6860	103100	7.2059
1.6877	103200	7.1645
1.6893	103300	7.2974
1.6910	103400	7.2004
1.6926	103500	7.2515
1.6942	103600	7.2385
1.6959	103700	7.2995
1.6975	103800	7.3026
1.6991	103900	7.192
1.7008	104000	7.2307
1.7024	104100	7.3336
1.7040	104200	7.2262
1.7057	104300	7.2228
1.7073	104400	7.1587
1.7089	104500	7.2922
1.7106	104600	7.1341
1.7122	104700	7.1274
1.7138	104800	7.0825
1.7155	104900	7.143
1.7171	105000	7.1935
1.7188	105100	7.1841
1.7204	105200	7.1301
1.7220	105300	7.2685
1.7237	105400	7.1137
1.7253	105500	7.1538
1.7269	105600	7.211
1.7286	105700	7.2827
1.7302	105800	7.2475
1.7318	105900	7.2251
1.7335	106000	7.1754
1.7351	106100	7.2269
1.7367	106200	7.2586
1.7384	106300	7.1901
1.7400	106400	7.3009
1.7416	106500	7.245
1.7433	106600	7.2958
1.7449	106700	7.251
1.7466	106800	7.2217
1.7482	106900	7.2433
1.7498	107000	7.2166
1.7515	107100	7.2322
1.7531	107200	7.2559
1.7547	107300	7.1228
1.7564	107400	7.1692
1.7580	107500	7.1959
1.7596	107600	7.2923
1.7613	107700	7.2951
1.7629	107800	7.3977
1.7645	107900	7.1821
1.7662	108000	7.2629
1.7678	108100	7.1958
1.7694	108200	7.0956
1.7711	108300	7.279
1.7727	108400	7.1452
1.7744	108500	7.2905
1.7760	108600	7.1713
1.7776	108700	7.1666
1.7793	108800	7.2494
1.7809	108900	7.243
1.7825	109000	7.2904
1.7842	109100	7.2237
1.7858	109200	7.2001
1.7874	109300	7.2414
1.7891	109400	7.2922
1.7907	109500	7.2264
1.7923	109600	7.1515
1.7940	109700	7.1905
1.7956	109800	7.1717
1.7972	109900	7.2072
1.7989	110000	7.1213
1.8005	110100	7.2218
1.8022	110200	7.1974
1.8038	110300	7.0928
1.8054	110400	7.1477
1.8071	110500	7.1406
1.8087	110600	7.2329
1.8103	110700	7.1725
1.8120	110800	7.2708
1.8136	110900	7.2481
1.8152	111000	7.1359
1.8169	111100	7.2038
1.8185	111200	7.2292
1.8201	111300	7.2504
1.8218	111400	7.2571
1.8234	111500	7.1655
1.8251	111600	7.3047
1.8267	111700	7.142
1.8283	111800	7.2475
1.8300	111900	7.2443
1.8316	112000	7.2015
1.8332	112100	7.1179
1.8349	112200	7.123
1.8365	112300	7.3373
1.8381	112400	7.1966
1.8398	112500	7.2326
1.8414	112600	7.1472
1.8430	112700	7.1406
1.8447	112800	7.313
1.8463	112900	7.2112
1.8479	113000	7.2402
1.8496	113100	7.2575
1.8512	113200	7.1365
1.8529	113300	7.1803
1.8545	113400	7.2568
1.8561	113500	7.3168
1.8578	113600	7.1657
1.8594	113700	7.2552
1.8610	113800	7.1183
1.8627	113900	7.1829
1.8643	114000	7.2194
1.8659	114100	7.2145
1.8676	114200	7.1671
1.8692	114300	7.1594
1.8708	114400	7.0844
1.8725	114500	7.1534
1.8741	114600	7.1934
1.8757	114700	7.1685
1.8774	114800	7.2746
1.8790	114900	7.1504
1.8807	115000	7.1447
1.8823	115100	7.1682
1.8839	115200	7.2072
1.8856	115300	7.098
1.8872	115400	7.1204
1.8888	115500	7.0923
1.8905	115600	7.183
1.8921	115700	7.1502
1.8937	115800	7.369
1.8954	115900	7.314
1.8970	116000	7.1633
1.8986	116100	7.1789
1.9003	116200	7.1506
1.9019	116300	7.1516
1.9035	116400	7.2381
1.9052	116500	7.147
1.9068	116600	7.184
1.9085	116700	7.2773
1.9101	116800	7.1546
1.9117	116900	7.1855
1.9134	117000	7.2159
1.9150	117100	7.2668
1.9166	117200	7.2658
1.9183	117300	7.2767
1.9199	117400	7.101
1.9215	117500	7.2031
1.9232	117600	7.2688
1.9248	117700	7.1934
1.9264	117800	7.1508
1.9281	117900	7.1479
1.9297	118000	7.3306
1.9313	118100	7.2065
1.9330	118200	7.1814
1.9346	118300	7.1969
1.9363	118400	7.1315
1.9379	118500	7.2007
1.9395	118600	7.2288
1.9412	118700	7.191
1.9428	118800	7.2268
1.9444	118900	7.1451
1.9461	119000	7.1345
1.9477	119100	7.2011
1.9493	119200	7.1849
1.9510	119300	7.2218
1.9526	119400	7.1971
1.9542	119500	7.3396
1.9559	119600	7.2104
1.9575	119700	7.2039
1.9591	119800	7.2464
1.9608	119900	7.3043
1.9624	120000	7.4122
1.9641	120100	7.1111
1.9657	120200	7.2129
1.9673	120300	7.1588
1.9690	120400	7.1101
1.9706	120500	7.352
1.9722	120600	7.1776
1.9739	120700	7.2707
1.9755	120800	7.1185
1.9771	120900	7.1938
1.9788	121000	7.2589
1.9804	121100	7.2122
1.9820	121200	7.2125
1.9837	121300	7.095
1.9853	121400	7.1098
1.9869	121500	7.2205
1.9886	121600	7.1645
1.9902	121700	7.3004
1.9919	121800	7.2379
1.9935	121900	7.1142
1.9951	122000	7.2685
1.9968	122100	7.214
1.9984	122200	7.1726

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