This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_three_scores_v4 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_v4
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 = [
    'buttons lounge set',
    'crispy fried shrimp wrap',
    'cold americano',
]
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, -0.3910, -0.3160],
#         [-0.3910,  1.0000,  0.9032],
#         [-0.3160,  0.9032,  1.0000]])

Training Details

Training Dataset

pairs_three_scores_v4

Dataset: pairs_three_scores_v4 at dee4115
Size: 9,471,735 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: 5.71 tokens
max: 43 tokens
min: 3 tokens
mean: 6.37 tokens
max: 110 tokens
min: 0.0
mean: 0.45
max: 1.0
Samples:
sentence1 sentence2 score
christmas box multiple purposes bowl 0.6536345183849335
linen kimono winter sweatshirts 0.5329802930355072
lanalou flashcards squatting leggings 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_v4

Dataset: pairs_three_scores_v4 at dee4115
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: 5.85 tokens
max: 119 tokens
min: 3 tokens
mean: 5.94 tokens
max: 115 tokens
min: 0.0
mean: 0.43
max: 1.0
Samples:
sentence1 sentence2 score
back pockets joggers nourishing face sheet mask 0.0
women water toy colorful print mat 0.0
handmade trays 6 deep plates 0.6035053730010986
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: 64
per_device_eval_batch_size: 64
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: 64
per_device_eval_batch_size: 64
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
0.0007	100	11.2772
0.0014	200	11.2045
0.0020	300	11.1008
0.0027	400	10.8444
0.0034	500	10.6532
0.0041	600	10.6076
0.0047	700	9.8378
0.0054	800	9.333
0.0061	900	9.0476
0.0068	1000	8.9552
0.0074	1100	8.3106
0.0081	1200	8.0685
0.0088	1300	7.7457
0.0095	1400	7.4884
0.0101	1500	7.2935
0.0108	1600	7.0525
0.0115	1700	6.9246
0.0122	1800	6.7854
0.0128	1900	6.6203
0.0135	2000	6.5445
0.0142	2100	6.4282
0.0149	2200	6.4666
0.0155	2300	6.3606
0.0162	2400	6.3206
0.0169	2500	6.3462
0.0176	2600	6.275
0.0182	2700	6.2399
0.0189	2800	6.1829
0.0196	2900	6.1858
0.0203	3000	6.0981
0.0209	3100	6.1599
0.0216	3200	6.0871
0.0223	3300	6.0986
0.0230	3400	6.0525
0.0236	3500	6.0007
0.0243	3600	6.0141
0.0250	3700	6.0264
0.0257	3800	6.0122
0.0264	3900	5.9951
0.0270	4000	5.9811
0.0277	4100	5.9728
0.0284	4200	6.0579
0.0291	4300	5.8964
0.0297	4400	6.0182
0.0304	4500	5.9026
0.0311	4600	5.9503
0.0318	4700	5.9141
0.0324	4800	5.9996
0.0331	4900	6.0455
0.0338	5000	5.8218
0.0345	5100	5.9668
0.0351	5200	5.8882
0.0358	5300	5.8238
0.0365	5400	5.8314
0.0372	5500	5.844
0.0378	5600	5.8349
0.0385	5700	5.7913
0.0392	5800	5.8043
0.0399	5900	5.7953
0.0405	6000	5.8511
0.0412	6100	5.7745
0.0419	6200	5.8096
0.0426	6300	5.8099
0.0432	6400	5.8212
0.0439	6500	5.7664
0.0446	6600	5.8258
0.0453	6700	5.8041
0.0459	6800	5.8314
0.0466	6900	5.7278
0.0473	7000	5.7442
0.0480	7100	5.7527
0.0486	7200	5.7967
0.0493	7300	5.7709
0.0500	7400	5.7246
0.0507	7500	5.7395
0.0514	7600	5.6816
0.0520	7700	5.7243
0.0527	7800	5.6907
0.0534	7900	5.6183
0.0541	8000	5.683
0.0547	8100	5.7337
0.0554	8200	5.7362
0.0561	8300	5.6575
0.0568	8400	5.6788
0.0574	8500	5.7192
0.0581	8600	5.7224
0.0588	8700	5.7161
0.0595	8800	5.6479
0.0601	8900	5.608
0.0608	9000	5.6661
0.0615	9100	5.6845
0.0622	9200	5.6438
0.0628	9300	5.607
0.0635	9400	5.5686
0.0642	9500	5.657
0.0649	9600	5.7203
0.0655	9700	5.6961
0.0662	9800	5.713
0.0669	9900	5.6134
0.0676	10000	5.6128
0.0682	10100	5.6158
0.0689	10200	5.5794
0.0696	10300	5.6728
0.0703	10400	5.6101
0.0709	10500	5.5899
0.0716	10600	5.6118
0.0723	10700	5.6399
0.0730	10800	5.519
0.0737	10900	5.5481
0.0743	11000	5.6637
0.0750	11100	5.5567
0.0757	11200	5.5738
0.0764	11300	5.5703
0.0770	11400	5.5796
0.0777	11500	5.5475
0.0784	11600	5.5255
0.0791	11700	5.5157
0.0797	11800	5.5204
0.0804	11900	5.6315
0.0811	12000	5.5876
0.0818	12100	5.4836
0.0824	12200	5.5909
0.0831	12300	5.5746
0.0838	12400	5.4464
0.0845	12500	5.4335
0.0851	12600	5.4754
0.0858	12700	5.5356
0.0865	12800	5.5325
0.0872	12900	5.5575
0.0878	13000	5.4516
0.0885	13100	5.5869
0.0892	13200	5.5381
0.0899	13300	5.508
0.0905	13400	5.4274
0.0912	13500	5.4552
0.0919	13600	5.5884
0.0926	13700	5.5348
0.0932	13800	5.444
0.0939	13900	5.4749
0.0946	14000	5.4637
0.0953	14100	5.5203
0.0959	14200	5.5285
0.0966	14300	5.4755
0.0973	14400	5.5051
0.0980	14500	5.4729
0.0987	14600	5.3955
0.0993	14700	5.5881
0.1000	14800	5.4181
0.1007	14900	5.3651
0.1014	15000	5.4613
0.1020	15100	5.5557
0.1027	15200	5.4996
0.1034	15300	5.3881
0.1041	15400	5.4835
0.1047	15500	5.5401
0.1054	15600	5.5961
0.1061	15700	5.4128
0.1068	15800	5.3917
0.1074	15900	5.5711
0.1081	16000	5.4357
0.1088	16100	5.4621
0.1095	16200	5.47
0.1101	16300	5.4925
0.1108	16400	5.3248
0.1115	16500	5.4251
0.1122	16600	5.4021
0.1128	16700	5.5096
0.1135	16800	5.4241
0.1142	16900	5.3936
0.1149	17000	5.3079
0.1155	17100	5.4653
0.1162	17200	5.4459
0.1169	17300	5.4444
0.1176	17400	5.3659
0.1182	17500	5.3743
0.1189	17600	5.3234
0.1196	17700	5.4278
0.1203	17800	5.4297
0.1209	17900	5.3561
0.1216	18000	5.3988
0.1223	18100	5.3245
0.1230	18200	5.3588
0.1237	18300	5.3977
0.1243	18400	5.2909
0.1250	18500	5.3745
0.1257	18600	5.3337
0.1264	18700	5.4919
0.1270	18800	5.2805
0.1277	18900	5.3844
0.1284	19000	5.4128
0.1291	19100	5.2554
0.1297	19200	5.408
0.1304	19300	5.2776
0.1311	19400	5.6267
0.1318	19500	5.3124
0.1324	19600	5.3846
0.1331	19700	5.384
0.1338	19800	5.3246
0.1345	19900	5.3179
0.1351	20000	5.3278
0.1358	20100	5.3834
0.1365	20200	5.3308
0.1372	20300	5.357
0.1378	20400	5.3896
0.1385	20500	5.2867
0.1392	20600	5.2974
0.1399	20700	5.313
0.1405	20800	5.3967
0.1412	20900	5.2301
0.1419	21000	5.4356
0.1426	21100	5.2693
0.1432	21200	5.3217
0.1439	21300	5.2899
0.1446	21400	5.3245
0.1453	21500	5.2185
0.1459	21600	5.2687
0.1466	21700	5.2723
0.1473	21800	5.4166
0.1480	21900	5.2839
0.1487	22000	5.2795
0.1493	22100	5.1613
0.1500	22200	5.3687
0.1507	22300	5.202
0.1514	22400	5.2485
0.1520	22500	5.2115
0.1527	22600	5.2436
0.1534	22700	5.4055
0.1541	22800	5.2773
0.1547	22900	5.2959
0.1554	23000	5.3102
0.1561	23100	5.3255
0.1568	23200	5.2784
0.1574	23300	5.1271
0.1581	23400	5.1952
0.1588	23500	5.3454
0.1595	23600	5.2846
0.1601	23700	5.2996
0.1608	23800	5.1608
0.1615	23900	5.2627
0.1622	24000	5.2594
0.1628	24100	5.2198
0.1635	24200	5.2236
0.1642	24300	5.175
0.1649	24400	5.3334
0.1655	24500	5.2199
0.1662	24600	5.1433
0.1669	24700	5.1984
0.1676	24800	5.2249
0.1682	24900	5.2563
0.1689	25000	5.2767
0.1696	25100	5.2708
0.1703	25200	5.1738
0.1710	25300	5.3836
0.1716	25400	5.292
0.1723	25500	5.2074
0.1730	25600	5.0917
0.1737	25700	5.2109
0.1743	25800	5.2041
0.1750	25900	5.2541
0.1757	26000	5.2361
0.1764	26100	5.377
0.1770	26200	5.1856
0.1777	26300	5.2809
0.1784	26400	5.2775
0.1791	26500	5.2574
0.1797	26600	5.2671
0.1804	26700	5.104
0.1811	26800	5.33
0.1818	26900	5.1559
0.1824	27000	5.0913
0.1831	27100	5.135
0.1838	27200	5.1912
0.1845	27300	5.1868
0.1851	27400	5.2395
0.1858	27500	5.1384
0.1865	27600	5.2198
0.1872	27700	5.2135
0.1878	27800	5.3694
0.1885	27900	5.2076
0.1892	28000	5.1509
0.1899	28100	5.1497
0.1905	28200	5.1712
0.1912	28300	5.1797
0.1919	28400	5.2396
0.1926	28500	5.2325
0.1932	28600	5.1888
0.1939	28700	5.2095
0.1946	28800	5.1883
0.1953	28900	5.1459
0.1960	29000	5.1744
0.1966	29100	5.1556
0.1973	29200	5.3217
0.1980	29300	5.2099
0.1987	29400	5.1099
0.1993	29500	5.2021
0.2000	29600	5.1876
0.2007	29700	5.1511
0.2014	29800	5.2566
0.2020	29900	5.1873
0.2027	30000	5.0897
0.2034	30100	5.1176
0.2041	30200	5.1516
0.2047	30300	5.0758
0.2054	30400	5.1937
0.2061	30500	5.2929
0.2068	30600	5.1915
0.2074	30700	5.3714
0.2081	30800	5.1536
0.2088	30900	5.0422
0.2095	31000	5.2734
0.2101	31100	5.1468
0.2108	31200	5.3353
0.2115	31300	5.2293
0.2122	31400	5.1361
0.2128	31500	5.2482
0.2135	31600	5.2306
0.2142	31700	5.2098
0.2149	31800	5.1354
0.2155	31900	5.2223
0.2162	32000	5.0355
0.2169	32100	5.094
0.2176	32200	5.1195
0.2182	32300	5.1196
0.2189	32400	5.138
0.2196	32500	5.2377
0.2203	32600	5.1725
0.2210	32700	5.0781
0.2216	32800	5.18
0.2223	32900	5.1088
0.2230	33000	5.2161
0.2237	33100	5.1438
0.2243	33200	5.1116
0.2250	33300	5.1823
0.2257	33400	5.079
0.2264	33500	5.2694
0.2270	33600	5.1641
0.2277	33700	5.2138
0.2284	33800	5.1935
0.2291	33900	5.1723
0.2297	34000	5.1072
0.2304	34100	5.186
0.2311	34200	5.0348
0.2318	34300	5.2384
0.2324	34400	5.1755
0.2331	34500	5.0979
0.2338	34600	5.0663
0.2345	34700	5.0989
0.2351	34800	5.2739
0.2358	34900	5.1265
0.2365	35000	5.1322
0.2372	35100	5.2121
0.2378	35200	5.1385
0.2385	35300	5.2434
0.2392	35400	5.0476
0.2399	35500	5.2256
0.2405	35600	5.2383
0.2412	35700	5.2071
0.2419	35800	5.0999
0.2426	35900	5.1044
0.2432	36000	5.1613
0.2439	36100	5.1462
0.2446	36200	5.0728
0.2453	36300	5.0023
0.2460	36400	5.1733
0.2466	36500	5.3145
0.2473	36600	5.0838
0.2480	36700	5.1461
0.2487	36800	5.1862
0.2493	36900	5.188
0.2500	37000	5.1184
0.2507	37100	5.0856
0.2514	37200	5.2862
0.2520	37300	4.9814
0.2527	37400	5.19
0.2534	37500	5.0314
0.2541	37600	5.1364
0.2547	37700	5.1157
0.2554	37800	5.114
0.2561	37900	5.1727
0.2568	38000	4.9733
0.2574	38100	5.0293
0.2581	38200	5.0877
0.2588	38300	5.219
0.2595	38400	5.0985
0.2601	38500	5.2359
0.2608	38600	5.1565
0.2615	38700	5.0726
0.2622	38800	5.0726
0.2628	38900	5.0614
0.2635	39000	5.2868
0.2642	39100	5.0182
0.2649	39200	5.3397
0.2655	39300	5.0637
0.2662	39400	5.1195
0.2669	39500	5.2523
0.2676	39600	5.1122
0.2683	39700	5.0181
0.2689	39800	5.1299
0.2696	39900	5.1427
0.2703	40000	5.0565
0.2710	40100	5.26
0.2716	40200	5.0041
0.2723	40300	5.0455
0.2730	40400	5.1708
0.2737	40500	5.2165
0.2743	40600	5.1393
0.2750	40700	5.0108
0.2757	40800	5.0889
0.2764	40900	5.1834
0.2770	41000	5.028
0.2777	41100	4.9961
0.2784	41200	5.1672
0.2791	41300	5.0564
0.2797	41400	5.081
0.2804	41500	5.1974
0.2811	41600	5.1264
0.2818	41700	5.1353
0.2824	41800	5.1746
0.2831	41900	5.1971
0.2838	42000	5.0538
0.2845	42100	5.0128
0.2851	42200	5.1144
0.2858	42300	4.9608
0.2865	42400	5.1669
0.2872	42500	5.0291
0.2878	42600	5.021
0.2885	42700	5.1213
0.2892	42800	5.1709
0.2899	42900	5.0978
0.2905	43000	5.1681
0.2912	43100	5.1087
0.2919	43200	5.1294
0.2926	43300	5.051
0.2933	43400	5.0416
0.2939	43500	5.0433
0.2946	43600	4.9924
0.2953	43700	5.1638
0.2960	43800	5.063
0.2966	43900	5.0403
0.2973	44000	4.9952
0.2980	44100	5.11
0.2987	44200	5.0319
0.2993	44300	5.1957
0.3000	44400	5.0856
0.3007	44500	4.9665
0.3014	44600	5.0926
0.3020	44700	4.9115
0.3027	44800	5.0353
0.3034	44900	5.1113
0.3041	45000	5.2239
0.3047	45100	5.0391
0.3054	45200	4.9572
0.3061	45300	5.1975
0.3068	45400	5.0444
0.3074	45500	5.0685
0.3081	45600	5.0488
0.3088	45700	5.0968
0.3095	45800	5.0757
0.3101	45900	5.0493
0.3108	46000	5.073
0.3115	46100	5.047
0.3122	46200	4.9862
0.3128	46300	5.0781
0.3135	46400	5.0331
0.3142	46500	5.056
0.3149	46600	5.0055
0.3155	46700	5.0088
0.3162	46800	5.0066
0.3169	46900	4.9467
0.3176	47000	5.2226
0.3183	47100	5.0494
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0.3196	47300	5.0008
0.3203	47400	5.1185
0.3210	47500	5.1898
0.3216	47600	5.0068
0.3223	47700	5.005
0.3230	47800	5.0591
0.3237	47900	5.0649
0.3243	48000	5.1205
0.3250	48100	5.065
0.3257	48200	4.911
0.3264	48300	5.1139
0.3270	48400	4.9509
0.3277	48500	5.0446
0.3284	48600	4.9778
0.3291	48700	5.1231
0.3297	48800	4.9543
0.3304	48900	5.0999
0.3311	49000	5.1623
0.3318	49100	5.0506
0.3324	49200	5.1689
0.3331	49300	5.0546
0.3338	49400	4.9665
0.3345	49500	5.0791
0.3351	49600	5.0729
0.3358	49700	4.88
0.3365	49800	5.0973
0.3372	49900	4.9499
0.3378	50000	4.9133
0.3385	50100	5.0726
0.3392	50200	5.1231
0.3399	50300	5.1781
0.3405	50400	5.0056
0.3412	50500	5.0575
0.3419	50600	4.99
0.3426	50700	5.0301
0.3433	50800	5.0807
0.3439	50900	5.0426
0.3446	51000	5.2096
0.3453	51100	5.0524
0.3460	51200	4.8141
0.3466	51300	5.0286
0.3473	51400	4.9065
0.3480	51500	5.0237
0.3487	51600	4.9762
0.3493	51700	4.9031
0.3500	51800	5.0163
0.3507	51900	5.0948
0.3514	52000	5.0748
0.3520	52100	5.1252
0.3527	52200	5.0668
0.3534	52300	5.0756
0.3541	52400	4.9194
0.3547	52500	5.0224
0.3554	52600	4.9381
0.3561	52700	5.084
0.3568	52800	5.1556
0.3574	52900	5.0096
0.3581	53000	4.9221
0.3588	53100	4.9921
0.3595	53200	4.9809
0.3601	53300	4.8309
0.3608	53400	4.99
0.3615	53500	5.0263
0.3622	53600	5.047
0.3628	53700	5.0267
0.3635	53800	5.0454
0.3642	53900	5.0048
0.3649	54000	5.1308
0.3656	54100	5.0573
0.3662	54200	5.0132
0.3669	54300	5.0754
0.3676	54400	5.0739
0.3683	54500	5.0495
0.3689	54600	4.9821
0.3696	54700	5.0457
0.3703	54800	5.0674
0.3710	54900	5.0398
0.3716	55000	4.9616
0.3723	55100	5.0519
0.3730	55200	5.1302
0.3737	55300	4.9325
0.3743	55400	4.9855
0.3750	55500	5.0143
0.3757	55600	4.9813
0.3764	55700	4.9939
0.3770	55800	4.9754
0.3777	55900	4.9555
0.3784	56000	5.0472
0.3791	56100	4.8199
0.3797	56200	5.0489
0.3804	56300	5.0116
0.3811	56400	4.8692
0.3818	56500	4.9782
0.3824	56600	5.0982
0.3831	56700	4.9478
0.3838	56800	5.0515
0.3845	56900	4.9851
0.3851	57000	4.9369
0.3858	57100	5.0503
0.3865	57200	5.1519
0.3872	57300	4.8901
0.3878	57400	4.9972
0.3885	57500	5.0872
0.3892	57600	5.0471
0.3899	57700	5.0104
0.3906	57800	4.9545
0.3912	57900	5.1393
0.3919	58000	5.1265
0.3926	58100	5.0318
0.3933	58200	4.9786
0.3939	58300	4.9941
0.3946	58400	4.9208
0.3953	58500	4.917
0.3960	58600	5.1085
0.3966	58700	5.0643
0.3973	58800	4.9786
0.3980	58900	5.083
0.3987	59000	4.9903
0.3993	59100	4.9337
0.4000	59200	4.999
0.4007	59300	5.0316
0.4014	59400	4.9835
0.4020	59500	4.9135
0.4027	59600	4.871
0.4034	59700	4.9476
0.4041	59800	4.9628
0.4047	59900	4.9808
0.4054	60000	5.0067
0.4061	60100	4.9598
0.4068	60200	5.1294
0.4074	60300	4.98
0.4081	60400	4.8716
0.4088	60500	4.9405
0.4095	60600	4.9857
0.4101	60700	4.8523
0.4108	60800	5.0364
0.4115	60900	4.9431
0.4122	61000	4.9074
0.4128	61100	5.0504
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0.4142	61300	5.0064
0.4149	61400	5.0509
0.4156	61500	4.871
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0.4169	61700	5.0781
0.4176	61800	5.0707
0.4183	61900	5.1243
0.4189	62000	4.9073
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0.4203	62200	4.9865
0.4210	62300	5.1234
0.4216	62400	4.8452
0.4223	62500	4.875
0.4230	62600	4.8942
0.4237	62700	4.8696
0.4243	62800	5.0397
0.4250	62900	4.9888
0.4257	63000	5.0882
0.4264	63100	4.975
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0.4277	63300	5.1847
0.4284	63400	4.8951
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0.4324	64000	4.9858
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0.4358	64500	4.9761
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0.4372	64700	5.1057
0.4378	64800	5.0384
0.4385	64900	5.1029
0.4392	65000	5.0692
0.4399	65100	4.9154
0.4406	65200	5.0107
0.4412	65300	4.9505
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0.4426	65500	5.0706
0.4433	65600	5.077
0.4439	65700	5.0827
0.4446	65800	4.8848
0.4453	65900	4.908
0.4460	66000	4.9257
0.4466	66100	5.0686
0.4473	66200	5.0702
0.4480	66300	4.8844
0.4487	66400	4.9588
0.4493	66500	4.9962
0.4500	66600	4.9503
0.4507	66700	4.9838
0.4514	66800	4.9846
0.4520	66900	4.9147
0.4527	67000	4.7638
0.4534	67100	4.942
0.4541	67200	5.0893
0.4547	67300	4.965
0.4554	67400	5.0695
0.4561	67500	4.9575
0.4568	67600	5.0108
0.4574	67700	4.852
0.4581	67800	5.085
0.4588	67900	4.9382
0.4595	68000	4.8926
0.4601	68100	4.912
0.4608	68200	4.9563
0.4615	68300	4.8529
0.4622	68400	4.9267
0.4629	68500	4.9673
0.4635	68600	5.1755
0.4642	68700	5.0644
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0.4656	68900	5.0092
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0.4669	69100	4.8676
0.4676	69200	5.0287
0.4683	69300	5.0049
0.4689	69400	4.9011
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0.4703	69600	4.9382
0.4710	69700	4.994
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0.4743	70200	4.8646
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0.4764	70500	4.9157
0.4770	70600	4.8941
0.4777	70700	5.046
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0.4791	70900	4.9428
0.4797	71000	5.0245
0.4804	71100	4.8914
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0.4818	71300	4.8793
0.4824	71400	4.999
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0.4845	71700	5.0097
0.4851	71800	4.8948
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0.4865	72000	4.8481
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0.4879	72200	4.8968
0.4885	72300	4.909
0.4892	72400	4.9397
0.4899	72500	5.0138
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0.4912	72700	5.0896
0.4919	72800	4.9579
0.4926	72900	4.9736
0.4933	73000	4.881
0.4939	73100	5.0581
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0.4953	73300	4.8367
0.4960	73400	4.8918
0.4966	73500	4.7634
0.4973	73600	4.7917
0.4980	73700	5.0236
0.4987	73800	4.9218
0.4993	73900	4.9303
0.5000	74000	5.0704
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0.5054	74800	4.9314
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0.5068	75000	4.9102
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0.5493	81300	5.1237
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0.5588	82700	4.8966
0.5595	82800	5.0352
0.5602	82900	4.8495
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0.5622	83200	4.9868
0.5629	83300	4.8476
0.5635	83400	4.8922
0.5642	83500	4.9305
0.5649	83600	5.0012
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0.5662	83800	4.8592
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0.5676	84000	4.9188
0.5683	84100	4.9274
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0.5703	84400	4.8974
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0.5716	84600	5.0959
0.5723	84700	4.8588
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0.5811	86000	4.8216
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0.5838	86400	4.9687
0.5845	86500	5.0368
0.5852	86600	4.9792
0.5858	86700	4.9185
0.5865	86800	4.7731
0.5872	86900	4.8259
0.5879	87000	4.8377
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0.5906	87400	4.7984
0.5912	87500	4.9468
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0.5926	87700	5.0067
0.5933	87800	4.9026
0.5939	87900	4.9694
0.5946	88000	5.0212
0.5953	88100	4.9471
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0.5987	88600	4.8674
0.5993	88700	4.7978
0.6000	88800	4.9072
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0.6014	89000	4.9705
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0.6074	89900	4.9385
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0.6088	90100	5.006
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0.6102	90300	4.9149
0.6108	90400	4.8718
0.6115	90500	4.9802
0.6122	90600	5.0188
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0.6135	90800	4.8555
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0.6149	91000	4.9388
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0.6176	91400	4.9955
0.6183	91500	4.8431
0.6189	91600	5.0583
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0.6223	92100	4.8347
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0.6237	92300	4.9706
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0.6264	92700	4.7354
0.6270	92800	4.8125
0.6277	92900	4.9224
0.6284	93000	4.8623
0.6291	93100	4.8557
0.6297	93200	5.0586
0.6304	93300	4.7108
0.6311	93400	4.8773
0.6318	93500	4.9871
0.6324	93600	4.7584
0.6331	93700	4.7968
0.6338	93800	4.8444
0.6345	93900	4.8546
0.6352	94000	4.919
0.6358	94100	4.9734
0.6365	94200	4.9805
0.6372	94300	4.7908
0.6379	94400	4.8707
0.6385	94500	4.8426
0.6392	94600	4.8301
0.6399	94700	4.8238
0.6406	94800	4.8391
0.6412	94900	4.8319
0.6419	95000	4.9225
0.6426	95100	4.8148
0.6433	95200	4.7127
0.6439	95300	4.8146
0.6446	95400	4.8545
0.6453	95500	4.9341
0.6460	95600	4.811
0.6466	95700	4.7797
0.6473	95800	4.8518
0.6480	95900	4.9098
0.6487	96000	4.8293
0.6493	96100	4.9575
0.6500	96200	4.9823
0.6507	96300	4.8951
0.6514	96400	4.9203
0.6520	96500	4.949
0.6527	96600	4.7515
0.6534	96700	4.841
0.6541	96800	5.0006
0.6547	96900	4.9503
0.6554	97000	4.858
0.6561	97100	4.8247
0.6568	97200	4.8647
0.6575	97300	4.8945
0.6581	97400	4.915
0.6588	97500	4.8665
0.6595	97600	4.988
0.6602	97700	4.9165
0.6608	97800	4.9017
0.6615	97900	4.8478
0.6622	98000	4.9352
0.6629	98100	4.9424
0.6635	98200	4.8742
0.6642	98300	4.8291
0.6649	98400	4.7546
0.6656	98500	4.9225
0.6662	98600	4.9349
0.6669	98700	5.039
0.6676	98800	4.807
0.6683	98900	4.8662
0.6689	99000	4.8141
0.6696	99100	4.8987
0.6703	99200	5.0198
0.6710	99300	4.812
0.6716	99400	4.8604
0.6723	99500	4.7991
0.6730	99600	4.8771
0.6737	99700	4.8452
0.6743	99800	4.8962
0.6750	99900	4.9228
0.6757	100000	4.8508
0.6764	100100	4.9226
0.6770	100200	4.8876
0.6777	100300	4.8183
0.6784	100400	4.9764
0.6791	100500	4.8009
0.6797	100600	4.8364
0.6804	100700	4.9191
0.6811	100800	4.9095
0.6818	100900	4.8875
0.6825	101000	4.8351
0.6831	101100	4.8123
0.6838	101200	4.8977
0.6845	101300	4.8961
0.6852	101400	4.8829
0.6858	101500	5.0646
0.6865	101600	4.882
0.6872	101700	4.7914
0.6879	101800	4.8949
0.6885	101900	5.0041
0.6892	102000	4.8903
0.6899	102100	4.787
0.6906	102200	4.9285
0.6912	102300	4.8532
0.6919	102400	4.714
0.6926	102500	4.7915
0.6933	102600	4.8374
0.6939	102700	4.9381
0.6946	102800	4.6831
0.6953	102900	4.9734
0.6960	103000	4.8589
0.6966	103100	4.7956
0.6973	103200	4.9256
0.6980	103300	4.8837
0.6987	103400	4.811
0.6993	103500	4.7817
0.7000	103600	4.8917
0.7007	103700	4.9237
0.7014	103800	5.1242
0.7020	103900	4.9205
0.7027	104000	4.7802
0.7034	104100	4.747
0.7041	104200	4.8402
0.7047	104300	4.8237
0.7054	104400	4.891
0.7061	104500	4.9716
0.7068	104600	4.782
0.7075	104700	4.8045
0.7081	104800	4.8766
0.7088	104900	4.6696
0.7095	105000	4.966
0.7102	105100	4.7423
0.7108	105200	4.8792
0.7115	105300	4.9519
0.7122	105400	4.8432
0.7129	105500	4.747
0.7135	105600	4.9843
0.7142	105700	4.8073
0.7149	105800	4.8489
0.7156	105900	4.8077
0.7162	106000	4.8104
0.7169	106100	4.8456
0.7176	106200	4.7071
0.7183	106300	4.8306
0.7189	106400	4.898
0.7196	106500	4.984
0.7203	106600	4.7332
0.7210	106700	4.8607
0.7216	106800	4.7275
0.7223	106900	4.9019
0.7230	107000	5.0538
0.7237	107100	4.9044
0.7243	107200	4.8562
0.7250	107300	4.7629
0.7257	107400	4.9326
0.7264	107500	4.9372
0.7270	107600	4.9545
0.7277	107700	4.8538
0.7284	107800	4.8037
0.7291	107900	4.8438
0.7297	108000	4.8519
0.7304	108100	4.8531
0.7311	108200	4.9255
0.7318	108300	4.9294
0.7325	108400	5.0015
0.7331	108500	4.8106
0.7338	108600	4.7808
0.7345	108700	4.8153
0.7352	108800	4.8395
0.7358	108900	4.9764
0.7365	109000	4.9352
0.7372	109100	4.8532
0.7379	109200	4.8949
0.7385	109300	4.7463
0.7392	109400	4.9339
0.7399	109500	4.8068
0.7406	109600	4.7261
0.7412	109700	4.8311
0.7419	109800	4.8155
0.7426	109900	4.8212
0.7433	110000	4.8287
0.7439	110100	4.9467
0.7446	110200	4.919
0.7453	110300	4.9262
0.7460	110400	4.7839
0.7466	110500	4.5588
0.7473	110600	4.8132
0.7480	110700	4.7664
0.7487	110800	4.8463
0.7493	110900	4.7304
0.7500	111000	4.7074
0.7507	111100	4.8316
0.7514	111200	4.801
0.7520	111300	4.9375
0.7527	111400	4.8528
0.7534	111500	4.8829
0.7541	111600	4.8974
0.7548	111700	4.799
0.7554	111800	4.855
0.7561	111900	4.7261
0.7568	112000	4.7997
0.7575	112100	4.7468
0.7581	112200	4.8724
0.7588	112300	5.0014
0.7595	112400	4.8915
0.7602	112500	4.9614
0.7608	112600	4.8972
0.7615	112700	4.9147
0.7622	112800	4.7014
0.7629	112900	4.8888
0.7635	113000	4.7892
0.7642	113100	4.842
0.7649	113200	4.9121
0.7656	113300	4.8818
0.7662	113400	4.7736
0.7669	113500	4.8422
0.7676	113600	4.8474
0.7683	113700	4.8108
0.7689	113800	4.8007
0.7696	113900	4.7973
0.7703	114000	4.835
0.7710	114100	4.8499
0.7716	114200	4.8245
0.7723	114300	4.8621
0.7730	114400	4.9664
0.7737	114500	4.7552
0.7743	114600	4.889
0.7750	114700	4.8633
0.7757	114800	4.945
0.7764	114900	4.8014
0.7770	115000	5.0571
0.7777	115100	4.8442
0.7784	115200	4.891
0.7791	115300	4.8179
0.7798	115400	4.8536
0.7804	115500	4.8337
0.7811	115600	4.889
0.7818	115700	4.8317
0.7825	115800	4.954
0.7831	115900	4.6911
0.7838	116000	4.6755
0.7845	116100	4.7733
0.7852	116200	4.7477
0.7858	116300	4.7943
0.7865	116400	4.9386
0.7872	116500	4.9404
0.7879	116600	4.8741
0.7885	116700	4.828
0.7892	116800	4.6601
0.7899	116900	4.8839
0.7906	117000	4.7382
0.7912	117100	4.9639
0.7919	117200	4.8114
0.7926	117300	4.8981
0.7933	117400	4.6133
0.7939	117500	4.9746
0.7946	117600	4.806
0.7953	117700	4.8657
0.7960	117800	4.7481
0.7966	117900	4.9489
0.7973	118000	4.8918
0.7980	118100	4.8091
0.7987	118200	4.8468
0.7993	118300	4.8915
0.8000	118400	4.8232
0.8007	118500	4.8094
0.8014	118600	4.8406
0.8020	118700	4.6952
0.8027	118800	4.9493
0.8034	118900	4.8409
0.8041	119000	4.8076
0.8048	119100	4.7683
0.8054	119200	4.7711
0.8061	119300	4.8403
0.8068	119400	4.8552
0.8075	119500	4.7858
0.8081	119600	4.7949
0.8088	119700	4.857
0.8095	119800	4.8493
0.8102	119900	4.8138
0.8108	120000	4.8924
0.8115	120100	4.9199
0.8122	120200	4.7959
0.8129	120300	4.9105
0.8135	120400	4.7398
0.8142	120500	4.7417
0.8149	120600	4.8675
0.8156	120700	4.8286
0.8162	120800	4.9776
0.8169	120900	4.7633
0.8176	121000	4.8754
0.8183	121100	4.6804
0.8189	121200	4.9887
0.8196	121300	4.7755
0.8203	121400	4.8534
0.8210	121500	4.796
0.8216	121600	4.7772
0.8223	121700	4.7257
0.8230	121800	4.7923
0.8237	121900	4.9664
0.8243	122000	4.9028
0.8250	122100	4.735
0.8257	122200	4.8558
0.8264	122300	4.6377
0.8270	122400	4.7363
0.8277	122500	4.8146
0.8284	122600	4.8014
0.8291	122700	4.9073
0.8298	122800	4.885
0.8304	122900	4.8979
0.8311	123000	4.6831
0.8318	123100	4.8764
0.8325	123200	4.7262
0.8331	123300	4.8478
0.8338	123400	4.7653
0.8345	123500	4.747
0.8352	123600	4.862
0.8358	123700	4.9815
0.8365	123800	4.8782
0.8372	123900	4.9795
0.8379	124000	4.9657
0.8385	124100	4.7268
0.8392	124200	4.8524
0.8399	124300	4.7545
0.8406	124400	4.7671
0.8412	124500	4.9008
0.8419	124600	4.8795
0.8426	124700	4.7094
0.8433	124800	4.8436
0.8439	124900	4.8447
0.8446	125000	4.9346
0.8453	125100	4.8161
0.8460	125200	4.9257
0.8466	125300	4.7665
0.8473	125400	4.7542
0.8480	125500	4.7743
0.8487	125600	4.8028
0.8493	125700	4.7752
0.8500	125800	4.8159
0.8507	125900	4.684
0.8514	126000	4.7639
0.8521	126100	4.8525
0.8527	126200	4.6599
0.8534	126300	4.8479
0.8541	126400	4.7353
0.8548	126500	4.8794
0.8554	126600	4.8526
0.8561	126700	4.9318
0.8568	126800	4.7838
0.8575	126900	4.7219
0.8581	127000	4.6088
0.8588	127100	4.9252
0.8595	127200	4.8616
0.8602	127300	4.8704
0.8608	127400	4.9092
0.8615	127500	4.8068
0.8622	127600	4.7678
0.8629	127700	4.9085
0.8635	127800	4.9128
0.8642	127900	4.788
0.8649	128000	4.8339
0.8656	128100	4.8148
0.8662	128200	4.85
0.8669	128300	4.7569
0.8676	128400	4.6677
0.8683	128500	4.7757
0.8689	128600	4.8681
0.8696	128700	4.7805
0.8703	128800	4.7842
0.8710	128900	4.7266
0.8716	129000	4.8235
0.8723	129100	4.675
0.8730	129200	4.8449
0.8737	129300	4.8369
0.8743	129400	4.6927
0.8750	129500	4.8986
0.8757	129600	4.8778
0.8764	129700	4.8664
0.8771	129800	4.8076
0.8777	129900	4.9578
0.8784	130000	4.9221
0.8791	130100	4.7611
0.8798	130200	4.7326
0.8804	130300	4.873
0.8811	130400	4.7681
0.8818	130500	4.748
0.8825	130600	4.9144
0.8831	130700	4.9244
0.8838	130800	4.6843
0.8845	130900	4.7198
0.8852	131000	4.7994
0.8858	131100	4.6881
0.8865	131200	4.8807
0.8872	131300	4.7782
0.8879	131400	4.6872
0.8885	131500	4.8619
0.8892	131600	4.9362
0.8899	131700	4.8273
0.8906	131800	4.6813
0.8912	131900	4.7488
0.8919	132000	5.0823
0.8926	132100	4.759
0.8933	132200	4.8894
0.8939	132300	4.8348
0.8946	132400	4.7979
0.8953	132500	4.9633
0.8960	132600	4.6837
0.8966	132700	4.827
0.8973	132800	4.7684
0.8980	132900	4.7666
0.8987	133000	4.6846
0.8993	133100	4.7952
0.9000	133200	4.932
0.9007	133300	4.904
0.9014	133400	4.8865
0.9021	133500	4.882
0.9027	133600	4.8556
0.9034	133700	4.9084
0.9041	133800	4.7161
0.9048	133900	4.8533
0.9054	134000	4.7563
0.9061	134100	4.8941
0.9068	134200	4.7466
0.9075	134300	4.8495
0.9081	134400	4.6427
0.9088	134500	4.7976
0.9095	134600	4.7338
0.9102	134700	4.7859
0.9108	134800	4.7682
0.9115	134900	4.7073
0.9122	135000	4.7621
0.9129	135100	4.7238
0.9135	135200	4.8175
0.9142	135300	4.7787
0.9149	135400	4.8235
0.9156	135500	4.7365
0.9162	135600	4.9519
0.9169	135700	4.9351
0.9176	135800	4.7475
0.9183	135900	4.7262
0.9189	136000	4.7777
0.9196	136100	5.0142
0.9203	136200	4.7907
0.9210	136300	4.8532
0.9216	136400	4.8485
0.9223	136500	4.7198
0.9230	136600	4.7489
0.9237	136700	4.7924
0.9243	136800	4.9665
0.9250	136900	4.8142
0.9257	137000	4.7417
0.9264	137100	4.7902
0.9271	137200	4.6992
0.9277	137300	4.8546
0.9284	137400	4.912
0.9291	137500	4.8318
0.9298	137600	4.8206
0.9304	137700	4.8262
0.9311	137800	4.8196
0.9318	137900	4.8031
0.9325	138000	4.7586
0.9331	138100	4.9838
0.9338	138200	4.8376
0.9345	138300	4.8503
0.9352	138400	4.791
0.9358	138500	4.8049
0.9365	138600	4.763
0.9372	138700	4.7151
0.9379	138800	4.7011
0.9385	138900	4.8396
0.9392	139000	4.8832
0.9399	139100	4.8726
0.9406	139200	4.7795
0.9412	139300	4.9655
0.9419	139400	4.7534
0.9426	139500	4.6648
0.9433	139600	4.7469
0.9439	139700	5.0025
0.9446	139800	4.6819
0.9453	139900	4.7776
0.9460	140000	4.7588
0.9466	140100	4.8632
0.9473	140200	4.7968
0.9480	140300	4.71
0.9487	140400	4.9049
0.9493	140500	4.854
0.9500	140600	4.7854
0.9507	140700	4.6892
0.9514	140800	4.8114
0.9521	140900	4.7882
0.9527	141000	4.7983
0.9534	141100	4.7194
0.9541	141200	4.6948
0.9548	141300	4.7143
0.9554	141400	4.8061
0.9561	141500	4.8325
0.9568	141600	5.0124
0.9575	141700	4.8147
0.9581	141800	4.7599
0.9588	141900	4.7414
0.9595	142000	4.7544
0.9602	142100	4.7658
0.9608	142200	4.9544
0.9615	142300	4.7399
0.9622	142400	4.9067
0.9629	142500	4.7801
0.9635	142600	4.9353
0.9642	142700	4.7814
0.9649	142800	4.7622
0.9656	142900	4.6572
0.9662	143000	4.8606
0.9669	143100	4.6498
0.9676	143200	4.8319
0.9683	143300	4.6856
0.9689	143400	4.8436
0.9696	143500	4.8899
0.9703	143600	4.9186
0.9710	143700	4.8192
0.9716	143800	4.829
0.9723	143900	4.7806
0.9730	144000	4.8361
0.9737	144100	4.8053
0.9744	144200	4.7178
0.9750	144300	4.706
0.9757	144400	4.696
0.9764	144500	4.768
0.9771	144600	4.9138
0.9777	144700	4.8175
0.9784	144800	4.7693
0.9791	144900	4.8059
0.9798	145000	4.8145
0.9804	145100	4.7919
0.9811	145200	4.8201
0.9818	145300	4.7305
0.9825	145400	4.8962
0.9831	145500	4.78
0.9838	145600	4.8389
0.9845	145700	4.7632
0.9852	145800	4.7947
0.9858	145900	4.7513
0.9865	146000	4.8301
0.9872	146100	4.7331
0.9879	146200	4.8216
0.9885	146300	4.7698
0.9892	146400	4.7947
0.9899	146500	4.6226
0.9906	146600	4.8399
0.9912	146700	4.727
0.9919	146800	4.8684
0.9926	146900	4.7456
0.9933	147000	4.9196
0.9939	147100	4.7849
0.9946	147200	4.7178
0.9953	147300	4.7022
0.9960	147400	4.7802
0.9966	147500	4.9208
0.9973	147600	4.732
0.9980	147700	4.9044
0.9987	147800	4.8702
0.9994	147900	4.8843

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: 5.71 tokens max: 43 tokens	min: 3 tokens mean: 6.37 tokens max: 110 tokens	min: 0.0 mean: 0.45 max: 1.0

sentence1	sentence2	score
`christmas box`	`multiple purposes bowl`	`0.6536345183849335`
`linen kimono`	`winter sweatshirts`	`0.5329802930355072`
`lanalou flashcards`	`squatting leggings`	`0.0`

sentence1	sentence2	score
`back pockets joggers`	`nourishing face sheet mask`	`0.0`
`women water toy`	`colorful print mat`	`0.0`
`handmade trays`	`6 deep plates`	`0.6035053730010986`

KhaledReda/all-MiniLM-L6-v4-pair_score

all-MiniLM-L6-v4-pair_score

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

pairs_three_scores_v4

Evaluation Dataset

pairs_three_scores_v4

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CoSENTLoss

KhaledReda/
all-MiniLM-L6-v4-pair_score