This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_three_scores_v6 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- pairs_three_scores_v6
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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'cotton hoodies',
    'relaxed fitting sweatshirt',
    'soft satin wrap skirt',
]
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, 0.7509, 0.7368],
#         [0.7509, 1.0000, 0.7357],
#         [0.7368, 0.7357, 1.0000]])

Training Details

Training Dataset

pairs_three_scores_v6

Dataset: pairs_three_scores_v6 at f2543d0
Size: 9,471,736 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: 5.65 tokens
max: 56 tokens
min: 3 tokens
mean: 6.09 tokens
max: 103 tokens
min: 0.0
mean: 0.4
max: 1.0
Samples:
sentence1 sentence2 score
candle holder trio sculptures decore 1.0
seashells hat celery tomato soup 0.0
abou shakra coffee table 0.0
Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Evaluation Dataset

pairs_three_scores_v6

Dataset: pairs_three_scores_v6 at f2543d0
Size: 47,597 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: 5.6 tokens
max: 43 tokens
min: 3 tokens
mean: 5.88 tokens
max: 56 tokens
min: 0.0
mean: 0.44
max: 1.0
Samples:
sentence1 sentence2 score
short sleeves swimsuit female swimsuit 1.0
instant soup chicken appetizer 0.0044313876238872
wc plunger ecofriendly cloth 0.4
Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

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
0.0014	100	13.1607
0.0027	200	13.0132
0.0041	300	12.7055
0.0054	400	12.5304
0.0068	500	11.462
0.0081	600	10.6911
0.0095	700	10.2024
0.0108	800	9.5896
0.0122	900	9.0485
0.0135	1000	8.6746
0.0149	1100	8.3508
0.0162	1200	8.1851
0.0176	1300	8.0442
0.0189	1400	7.9434
0.0203	1500	7.9051
0.0216	1600	7.8207
0.0230	1700	7.7898
0.0243	1800	7.7743
0.0257	1900	7.6743
0.0270	2000	7.6582
0.0284	2100	7.672
0.0297	2200	7.6531
0.0311	2300	7.586
0.0324	2400	7.5649
0.0338	2500	7.5575
0.0351	2600	7.5247
0.0365	2700	7.5267
0.0378	2800	7.5478
0.0392	2900	7.4814
0.0405	3000	7.4513
0.0419	3100	7.4606
0.0432	3200	7.4012
0.0446	3300	7.4187
0.0459	3400	7.4525
0.0473	3500	7.3908
0.0486	3600	7.438
0.0500	3700	7.4329
0.0514	3800	7.3937
0.0527	3900	7.3501
0.0541	4000	7.3666
0.0554	4100	7.3452
0.0568	4200	7.3323
0.0581	4300	7.3632
0.0595	4400	7.3342
0.0608	4500	7.3199
0.0622	4600	7.3332
0.0635	4700	7.2988
0.0649	4800	7.3495
0.0662	4900	7.2607
0.0676	5000	7.265
0.0689	5100	7.3003
0.0703	5200	7.2345
0.0716	5300	7.2833
0.0730	5400	7.2551
0.0743	5500	7.2355
0.0757	5600	7.2197
0.0770	5700	7.1716
0.0784	5800	7.2087
0.0797	5900	7.2112
0.0811	6000	7.2242
0.0824	6100	7.2274
0.0838	6200	7.1614
0.0851	6300	7.178
0.0865	6400	7.1488
0.0878	6500	7.1554
0.0892	6600	7.1598
0.0905	6700	7.1435
0.0919	6800	7.1935
0.0932	6900	7.2029
0.0946	7000	7.1339
0.0959	7100	7.1535
0.0973	7200	7.1551
0.0987	7300	7.1315
0.1000	7400	7.1762
0.1014	7500	7.143
0.1027	7600	7.1646
0.1041	7700	7.0627
0.1054	7800	7.1305
0.1068	7900	7.0544
0.1081	8000	7.1239
0.1095	8100	7.1719
0.1108	8200	7.076
0.1122	8300	7.0572
0.1135	8400	7.0625
0.1149	8500	7.1108
0.1162	8600	7.0755
0.1176	8700	7.0571
0.1189	8800	7.0745
0.1203	8900	7.0542
0.1216	9000	7.0892
0.1230	9100	7.0475
0.1243	9200	7.1386
0.1257	9300	7.0642
0.1270	9400	7.0968
0.1284	9500	7.0167
0.1297	9600	7.06
0.1311	9700	7.0398
0.1324	9800	6.9206
0.1338	9900	7.0453
0.1351	10000	7.0248
0.1365	10100	6.9912
0.1378	10200	6.9527
0.1392	10300	6.982
0.1405	10400	6.9526
0.1419	10500	6.9766
0.1432	10600	7.0039
0.1446	10700	7.0336
0.1459	10800	6.9593
0.1473	10900	7.0014
0.1487	11000	6.956
0.1500	11100	7.0147
0.1514	11200	7.0725
0.1527	11300	6.9998
0.1541	11400	7.016
0.1554	11500	6.9543
0.1568	11600	6.9661
0.1581	11700	7.0283
0.1595	11800	6.9542
0.1608	11900	6.9001
0.1622	12000	7.0087
0.1635	12100	6.9802
0.1649	12200	6.9321
0.1662	12300	6.939
0.1676	12400	6.9245
0.1689	12500	7.0119
0.1703	12600	6.9337
0.1716	12700	6.889
0.1730	12800	6.9475
0.1743	12900	6.9493
0.1757	13000	6.9676
0.1770	13100	6.9841
0.1784	13200	6.9705
0.1797	13300	6.9824
0.1811	13400	6.9591
0.1824	13500	6.9495
0.1838	13600	6.9184
0.1851	13700	6.9672
0.1865	13800	6.8777
0.1878	13900	6.9329
0.1892	14000	6.9289
0.1905	14100	7.0087
0.1919	14200	6.8774
0.1932	14300	6.9702
0.1946	14400	6.9593
0.1960	14500	6.906
0.1973	14600	6.9182
0.1987	14700	6.8959
0.2000	14800	6.9071
0.2014	14900	6.9425
0.2027	15000	6.8693
0.2041	15100	6.9177
0.2054	15200	6.9314
0.2068	15300	6.8845
0.2081	15400	6.8537
0.2095	15500	6.9147
0.2108	15600	6.8383
0.2122	15700	6.9094
0.2135	15800	6.8964
0.2149	15900	6.8568
0.2162	16000	6.8969
0.2176	16100	6.9299
0.2189	16200	6.8756
0.2203	16300	6.8966
0.2216	16400	6.939
0.2230	16500	6.8675
0.2243	16600	6.8635
0.2257	16700	6.8574
0.2270	16800	6.8506
0.2284	16900	6.8472
0.2297	17000	6.8619
0.2311	17100	6.8367
0.2324	17200	6.9259
0.2338	17300	6.8878
0.2351	17400	6.8356
0.2365	17500	6.8109
0.2378	17600	6.819
0.2392	17700	6.8587
0.2405	17800	6.8697
0.2419	17900	6.8772
0.2432	18000	6.8792
0.2446	18100	6.833
0.2460	18200	6.8876
0.2473	18300	6.8189
0.2487	18400	6.8884
0.2500	18500	6.8711
0.2514	18600	6.7725
0.2527	18700	6.8676
0.2541	18800	6.808
0.2554	18900	6.8564
0.2568	19000	6.8588
0.2581	19100	6.8501
0.2595	19200	6.8219
0.2608	19300	6.8175
0.2622	19400	6.8997
0.2635	19500	6.8199
0.2649	19600	6.8018
0.2662	19700	6.793
0.2676	19800	6.7399
0.2689	19900	6.8103
0.2703	20000	6.9001
0.2716	20100	6.783
0.2730	20200	6.8215
0.2743	20300	6.7769
0.2757	20400	6.845
0.2770	20500	6.8142
0.2784	20600	6.7481
0.2797	20700	6.904
0.2811	20800	6.8675
0.2824	20900	6.89
0.2838	21000	6.8667
0.2851	21100	6.749
0.2865	21200	6.872
0.2878	21300	6.7523
0.2892	21400	6.758
0.2905	21500	6.7681
0.2919	21600	6.7825
0.2933	21700	6.8364
0.2946	21800	6.8005
0.2960	21900	6.8361
0.2973	22000	6.7539
0.2987	22100	6.7623
0.3000	22200	6.7517
0.3014	22300	6.7892
0.3027	22400	6.6968
0.3041	22500	6.7873
0.3054	22600	6.838
0.3068	22700	6.833
0.3081	22800	6.7933
0.3095	22900	6.7481
0.3108	23000	6.7271
0.3122	23100	6.7163
0.3135	23200	6.7966
0.3149	23300	6.8368
0.3162	23400	6.8421
0.3176	23500	6.7855
0.3189	23600	6.7645
0.3203	23700	6.8296
0.3216	23800	6.7468
0.3230	23900	6.7604
0.3243	24000	6.7929
0.3257	24100	6.7689
0.3270	24200	6.7878
0.3284	24300	6.7126
0.3297	24400	6.6862
0.3311	24500	6.763
0.3324	24600	6.7559
0.3338	24700	6.7727
0.3351	24800	6.7062
0.3365	24900	6.7667
0.3378	25000	6.693
0.3392	25100	6.7147
0.3405	25200	6.7297
0.3419	25300	6.7853
0.3433	25400	6.785
0.3446	25500	6.7014
0.3460	25600	6.7242
0.3473	25700	6.7492
0.3487	25800	6.6989
0.3500	25900	6.672
0.3514	26000	6.6778
0.3527	26100	6.7596
0.3541	26200	6.7604
0.3554	26300	6.5954
0.3568	26400	6.6577
0.3581	26500	6.7412
0.3595	26600	6.7144
0.3608	26700	6.7136
0.3622	26800	6.7032
0.3635	26900	6.6934
0.3649	27000	6.8086
0.3662	27100	6.71
0.3676	27200	6.7704
0.3689	27300	6.6313
0.3703	27400	6.717
0.3716	27500	6.7133
0.3730	27600	6.7486
0.3743	27700	6.6821
0.3757	27800	6.7048
0.3770	27900	6.7223
0.3784	28000	6.7429
0.3797	28100	6.7134
0.3811	28200	6.7402
0.3824	28300	6.6841
0.3838	28400	6.738
0.3851	28500	6.6747
0.3865	28600	6.7045
0.3878	28700	6.7258
0.3892	28800	6.7096
0.3906	28900	6.7068
0.3919	29000	6.8006
0.3933	29100	6.7229
0.3946	29200	6.6395
0.3960	29300	6.6551
0.3973	29400	6.65
0.3987	29500	6.7203
0.4000	29600	6.659
0.4014	29700	6.6643
0.4027	29800	6.6619
0.4041	29900	6.7384
0.4054	30000	6.7994
0.4068	30100	6.6682
0.4081	30200	6.7452
0.4095	30300	6.625
0.4108	30400	6.6993
0.4122	30500	6.702
0.4135	30600	6.654
0.4149	30700	6.6563
0.4162	30800	6.6692
0.4176	30900	6.6547
0.4189	31000	6.6859
0.4203	31100	6.5456
0.4216	31200	6.6314
0.4230	31300	6.7221
0.4243	31400	6.6892
0.4257	31500	6.6671
0.4270	31600	6.6691
0.4284	31700	6.846
0.4297	31800	6.6761
0.4311	31900	6.7338
0.4324	32000	6.65
0.4338	32100	6.6716
0.4351	32200	6.694
0.4365	32300	6.6953
0.4378	32400	6.8016
0.4392	32500	6.6266
0.4406	32600	6.6896
0.4419	32700	6.7279
0.4433	32800	6.7346
0.4446	32900	6.6012
0.4460	33000	6.6795
0.4473	33100	6.7093
0.4487	33200	6.6804
0.4500	33300	6.65
0.4514	33400	6.705
0.4527	33500	6.6218
0.4541	33600	6.6393
0.4554	33700	6.69
0.4568	33800	6.6352
0.4581	33900	6.5862
0.4595	34000	6.5596
0.4608	34100	6.5927
0.4622	34200	6.5934
0.4635	34300	6.6412
0.4649	34400	6.7579
0.4662	34500	6.723
0.4676	34600	6.6634
0.4689	34700	6.5774
0.4703	34800	6.7144
0.4716	34900	6.5977
0.4730	35000	6.5791
0.4743	35100	6.5678
0.4757	35200	6.6226
0.4770	35300	6.6758
0.4784	35400	6.6791
0.4797	35500	6.6256
0.4811	35600	6.6707
0.4824	35700	6.6792
0.4838	35800	6.6337
0.4851	35900	6.7528
0.4865	36000	6.6277
0.4879	36100	6.7062
0.4892	36200	6.6413
0.4906	36300	6.643
0.4919	36400	6.6886
0.4933	36500	6.6273
0.4946	36600	6.6248
0.4960	36700	6.6166
0.4973	36800	6.7319
0.4987	36900	6.6325
0.5000	37000	6.6615
0.5014	37100	6.6962
0.5027	37200	6.6409
0.5041	37300	6.7092
0.5054	37400	6.5776
0.5068	37500	6.6661
0.5081	37600	6.605
0.5095	37700	6.6499
0.5108	37800	6.6626
0.5122	37900	6.6674
0.5135	38000	6.6125
0.5149	38100	6.6443
0.5162	38200	6.6957
0.5176	38300	6.5364
0.5189	38400	6.5796
0.5203	38500	6.5717
0.5216	38600	6.5697
0.5230	38700	6.6763
0.5243	38800	6.6214
0.5257	38900	6.6345
0.5270	39000	6.6536
0.5284	39100	6.591
0.5297	39200	6.5896
0.5311	39300	6.6633
0.5324	39400	6.7338
0.5338	39500	6.5586
0.5351	39600	6.6323
0.5365	39700	6.6563
0.5379	39800	6.6083
0.5392	39900	6.6002
0.5406	40000	6.5946
0.5419	40100	6.6664
0.5433	40200	6.5792
0.5446	40300	6.6031
0.5460	40400	6.5577
0.5473	40500	6.5857
0.5487	40600	6.5744
0.5500	40700	6.5541
0.5514	40800	6.702
0.5527	40900	6.6044
0.5541	41000	6.5603
0.5554	41100	6.565
0.5568	41200	6.6005
0.5581	41300	6.555
0.5595	41400	6.6081
0.5608	41500	6.6207
0.5622	41600	6.6522
0.5635	41700	6.5458
0.5649	41800	6.6064
0.5662	41900	6.5423
0.5676	42000	6.5808
0.5689	42100	6.7097
0.5703	42200	6.6089
0.5716	42300	6.7112
0.5730	42400	6.5389
0.5743	42500	6.5221
0.5757	42600	6.6074
0.5770	42700	6.5791
0.5784	42800	6.5936
0.5797	42900	6.5641
0.5811	43000	6.54
0.5824	43100	6.5362
0.5838	43200	6.5212
0.5852	43300	6.562
0.5865	43400	6.504
0.5879	43500	6.5234
0.5892	43600	6.5897
0.5906	43700	6.5014
0.5919	43800	6.6355
0.5933	43900	6.5833
0.5946	44000	6.5856
0.5960	44100	6.5507
0.5973	44200	6.6285
0.5987	44300	6.5778
0.6000	44400	6.59
0.6014	44500	6.6623
0.6027	44600	6.6402
0.6041	44700	6.589
0.6054	44800	6.5292
0.6068	44900	6.5621
0.6081	45000	6.5848
0.6095	45100	6.5743
0.6108	45200	6.5757
0.6122	45300	6.5166
0.6135	45400	6.5547
0.6149	45500	6.5087
0.6162	45600	6.5411
0.6176	45700	6.4707
0.6189	45800	6.5859
0.6203	45900	6.4987
0.6216	46000	6.6401
0.6230	46100	6.4804
0.6243	46200	6.5736
0.6257	46300	6.5887
0.6270	46400	6.5927
0.6284	46500	6.5571
0.6297	46600	6.556
0.6311	46700	6.503
0.6324	46800	6.587
0.6338	46900	6.5449
0.6352	47000	6.511
0.6365	47100	6.5148
0.6379	47200	6.5622
0.6392	47300	6.4928
0.6406	47400	6.555
0.6419	47500	6.5695
0.6433	47600	6.6136
0.6446	47700	6.5741
0.6460	47800	6.485
0.6473	47900	6.5418
0.6487	48000	6.5917
0.6500	48100	6.5553
0.6514	48200	6.6585
0.6527	48300	6.628
0.6541	48400	6.5494
0.6554	48500	6.5024
0.6568	48600	6.5283
0.6581	48700	6.5837
0.6595	48800	6.6447
0.6608	48900	6.5722
0.6622	49000	6.6493
0.6635	49100	6.5176
0.6649	49200	6.6154
0.6662	49300	6.4741
0.6676	49400	6.6054
0.6689	49500	6.5655
0.6703	49600	6.5484
0.6716	49700	6.5374
0.6730	49800	6.5894
0.6743	49900	6.4872
0.6757	50000	6.5193
0.6770	50100	6.369
0.6784	50200	6.5489
0.6797	50300	6.6689
0.6811	50400	6.5446
0.6825	50500	6.5719
0.6838	50600	6.6133
0.6852	50700	6.542
0.6865	50800	6.5595
0.6879	50900	6.4925
0.6892	51000	6.5256
0.6906	51100	6.5235
0.6919	51200	6.5254
0.6933	51300	6.4789
0.6946	51400	6.5811
0.6960	51500	6.542
0.6973	51600	6.5616
0.6987	51700	6.4711
0.7000	51800	6.5253
0.7014	51900	6.5302
0.7027	52000	6.6016
0.7041	52100	6.5745
0.7054	52200	6.616
0.7068	52300	6.5754
0.7081	52400	6.5775
0.7095	52500	6.4937
0.7108	52600	6.5871
0.7122	52700	6.5232
0.7135	52800	6.5317
0.7149	52900	6.4712
0.7162	53000	6.575
0.7176	53100	6.5826
0.7189	53200	6.411
0.7203	53300	6.5005
0.7216	53400	6.5052
0.7230	53500	6.5396
0.7243	53600	6.5062
0.7257	53700	6.4628
0.7270	53800	6.6049
0.7284	53900	6.48
0.7297	54000	6.6043
0.7311	54100	6.4534
0.7325	54200	6.4317
0.7338	54300	6.4979
0.7352	54400	6.6124
0.7365	54500	6.5964
0.7379	54600	6.475
0.7392	54700	6.5992
0.7406	54800	6.5138
0.7419	54900	6.6341
0.7433	55000	6.4336
0.7446	55100	6.5501
0.7460	55200	6.4973
0.7473	55300	6.4827
0.7487	55400	6.5459
0.7500	55500	6.471
0.7514	55600	6.5341
0.7527	55700	6.4344
0.7541	55800	6.6941
0.7554	55900	6.4856
0.7568	56000	6.4986
0.7581	56100	6.6032
0.7595	56200	6.4769
0.7608	56300	6.4915
0.7622	56400	6.5936
0.7635	56500	6.4872
0.7649	56600	6.5023
0.7662	56700	6.4641
0.7676	56800	6.5152
0.7689	56900	6.5421
0.7703	57000	6.5301
0.7716	57100	6.4144
0.7730	57200	6.5937
0.7743	57300	6.5222
0.7757	57400	6.4715
0.7770	57500	6.545
0.7784	57600	6.5088
0.7798	57700	6.6281
0.7811	57800	6.4805
0.7825	57900	6.5291
0.7838	58000	6.687
0.7852	58100	6.4391
0.7865	58200	6.5797
0.7879	58300	6.5248
0.7892	58400	6.4844
0.7906	58500	6.5358
0.7919	58600	6.5392
0.7933	58700	6.528
0.7946	58800	6.4534
0.7960	58900	6.5813
0.7973	59000	6.5447
0.7987	59100	6.4923
0.8000	59200	6.5267
0.8014	59300	6.5874
0.8027	59400	6.5121
0.8041	59500	6.4735
0.8054	59600	6.5215
0.8068	59700	6.556
0.8081	59800	6.5138
0.8095	59900	6.5183
0.8108	60000	6.5109
0.8122	60100	6.4328
0.8135	60200	6.5213
0.8149	60300	6.5595
0.8162	60400	6.4816
0.8176	60500	6.4968
0.8189	60600	6.4402
0.8203	60700	6.5293
0.8216	60800	6.4756
0.8230	60900	6.5606
0.8243	61000	6.5504
0.8257	61100	6.5294
0.8270	61200	6.5323
0.8284	61300	6.4996
0.8298	61400	6.4573
0.8311	61500	6.4867
0.8325	61600	6.5564
0.8338	61700	6.5622
0.8352	61800	6.5379
0.8365	61900	6.5351
0.8379	62000	6.4532
0.8392	62100	6.5494
0.8406	62200	6.5348
0.8419	62300	6.4821
0.8433	62400	6.4776
0.8446	62500	6.4212
0.8460	62600	6.4415
0.8473	62700	6.5892
0.8487	62800	6.5264
0.8500	62900	6.4653
0.8514	63000	6.4696
0.8527	63100	6.4663
0.8541	63200	6.5449
0.8554	63300	6.5057
0.8568	63400	6.5861
0.8581	63500	6.5672
0.8595	63600	6.5386
0.8608	63700	6.6146
0.8622	63800	6.5084
0.8635	63900	6.4613
0.8649	64000	6.4439
0.8662	64100	6.4719
0.8676	64200	6.5528
0.8689	64300	6.4415
0.8703	64400	6.5744
0.8716	64500	6.4752
0.8730	64600	6.4576
0.8743	64700	6.6003
0.8757	64800	6.5032
0.8771	64900	6.5095
0.8784	65000	6.5355
0.8798	65100	6.5429
0.8811	65200	6.4531
0.8825	65300	6.4813
0.8838	65400	6.4481
0.8852	65500	6.4387
0.8865	65600	6.4488
0.8879	65700	6.4652
0.8892	65800	6.5577
0.8906	65900	6.5483
0.8919	66000	6.5038
0.8933	66100	6.4359
0.8946	66200	6.5768
0.8960	66300	6.5042
0.8973	66400	6.4881
0.8987	66500	6.6159
0.9000	66600	6.5436
0.9014	66700	6.4862
0.9027	66800	6.3755
0.9041	66900	6.4245
0.9054	67000	6.4585
0.9068	67100	6.461
0.9081	67200	6.4796
0.9095	67300	6.4851
0.9108	67400	6.5083
0.9122	67500	6.6178
0.9135	67600	6.4687
0.9149	67700	6.4314
0.9162	67800	6.5557
0.9176	67900	6.4496
0.9189	68000	6.5808
0.9203	68100	6.3931
0.9216	68200	6.4423
0.9230	68300	6.4833
0.9243	68400	6.6081
0.9257	68500	6.4838
0.9271	68600	6.4108
0.9284	68700	6.4401
0.9298	68800	6.4756
0.9311	68900	6.4104
0.9325	69000	6.4449
0.9338	69100	6.5963
0.9352	69200	6.403
0.9365	69300	6.4618
0.9379	69400	6.6078
0.9392	69500	6.5498
0.9406	69600	6.4001
0.9419	69700	6.5358
0.9433	69800	6.3801
0.9446	69900	6.4694
0.9460	70000	6.382
0.9473	70100	6.534
0.9487	70200	6.4584
0.9500	70300	6.4675
0.9514	70400	6.4597
0.9527	70500	6.4955
0.9541	70600	6.4007
0.9554	70700	6.4553
0.9568	70800	6.575
0.9581	70900	6.5662
0.9595	71000	6.4833
0.9608	71100	6.5095
0.9622	71200	6.5039
0.9635	71300	6.5929
0.9649	71400	6.4759
0.9662	71500	6.4356
0.9676	71600	6.547
0.9689	71700	6.4707
0.9703	71800	6.4305
0.9716	71900	6.4988
0.9730	72000	6.4564
0.9744	72100	6.4538
0.9757	72200	6.49
0.9771	72300	6.4462
0.9784	72400	6.4843
0.9798	72500	6.6274
0.9811	72600	6.5567
0.9825	72700	6.4825
0.9838	72800	6.4677
0.9852	72900	6.5521
0.9865	73000	6.508
0.9879	73100	6.6502
0.9892	73200	6.3627
0.9906	73300	6.5945
0.9919	73400	6.4639
0.9933	73500	6.4663
0.9946	73600	6.4172
0.9960	73700	6.5404
0.9973	73800	6.4847
0.9987	73900	6.5225

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.1.0
Transformers: 4.55.4
PyTorch: 2.5.1+cu121
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.21.4

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

bibtex

@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},
}

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 5.65 tokens max: 56 tokens	min: 3 tokens mean: 6.09 tokens max: 103 tokens	min: 0.0 mean: 0.4 max: 1.0

sentence1	sentence2	score
`candle holder`	`trio sculptures decore`	`1.0`
`seashells hat`	`celery tomato soup`	`0.0`
`abou shakra`	`coffee table`	`0.0`

sentence1	sentence2	score
`short sleeves swimsuit`	`female swimsuit`	`1.0`
`instant soup`	`chicken appetizer`	`0.0044313876238872`
`wc plunger`	`ecofriendly cloth`	`0.4`

KhaledReda/all-MiniLM-L6-v6-pair_score

all-MiniLM-L6-v6-pair_score

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

pairs_three_scores_v6

Evaluation Dataset

pairs_three_scores_v6

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CoSENTLoss

KhaledReda/
all-MiniLM-L6-v6-pair_score