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all-MiniLM-L6-v66-pair_score

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:12818275
loss:CoSENTLoss
text-embeddings-inference
Model card Files
xet
 Community

all-MiniLM-L6-v66-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v53 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 Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'colorful marble tote',
    'baygon crawling insects killer 400m baygon ant killer baygon insect killer baygon roach killer roaches killer baygon bug killer baygon crawling bugs killer',
    'linen kaftan inlaid with indian silk breathable kaftan inlaid with indian linen galebaya inlaid with indian a linen kaftan adorned with indian silk combines elegance with luxury. this kaftan features linen fabric providing comfort and breathability ideal for warmer days along with indian silk detailing that adds a touch of opulence and beauty. the indian silk embellishments may adorn the edges neckline or as elegant motifs throughout the kaftan. this garment is perfect for special occasions or times when a sophisticated and distinctive look is desired.',
]
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, -0.0657,  0.0665],
#         [-0.0657,  1.0000, -0.0702],
#         [ 0.0665, -0.0702,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v53

  • Dataset: pairs_with_scores_v53 at 5056e53
  • Size: 12,818,275 training samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 score
    type string string float
    details
    • min: 4 tokens
    • mean: 7.46 tokens
    • max: 23 tokens
    • min: 4 tokens
    • mean: 58.78 tokens
    • max: 256 tokens
    • min: 0.0
    • mean: 0.27
    • max: 1.0
  • Samples:
    sentence1 sentence2 score
    casting creme gloss hair color dabur vatika burgundy henna dabur vatika henna 1.0
    wooden wick candle twist drink - apple - 0.2 l apple soda twist and drink soda twist - apple twist and soda 0.0
    hugo boss pure edt vision invincible edt 100 ml tonka bean eau de toilette long lasting eau de toilette invincible eau de toilette vision eau de toilette introducing vision invincible edt 100ml a luxurious fragrance that will awaken your senses and leave you feeling invincible. this exquisite scent is a medley of italian lemon ceylon cinnamon dry amber tonka bean vanilla and moss creating a luscious and irresistible aroma. indulge in the splendor of this long-lasting fragrance perfect for any occasion. the top notes of italy lemon bergamot and green apple will captivate your senses while the heart notes of dry amber tonka beans and plum add a touch of warmth and depth. as the fragrance settles the base notes of vanilla amber moss and dry wood will leave a lingering and alluring scent that will make you feel c 1.0
  • Loss: CoSENTLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Dataset

pairs_with_scores_v53

  • Dataset: pairs_with_scores_v53 at 5056e53
  • Size: 64,414 evaluation samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 score
    type string string float
    details
    • min: 4 tokens
    • mean: 7.43 tokens
    • max: 22 tokens
    • min: 4 tokens
    • mean: 57.88 tokens
    • max: 256 tokens
    • min: 0.0
    • mean: 0.25
    • max: 1.0
  • Samples:
    sentence1 sentence2 score
    kaya wood dinnerware vascomedra 600mg 30/f.c.tab 0.0
    mothers day bracelet round circle bracelet men bracelet 1.0
    rubber sole slipper switchbot curtain u rail smart home robot curtain motorized robot electrical home tool curtain automated robot electrical curtain rail home automation device switchbot curtain is a smart home robot that makes your existing curtain motorized and automated with 30 second installation schedule and touch go. u rail is required. 0.0
  • Loss: CoSENTLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 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 Validation Loss
0.0005 100 6.5005 -
0.0010 200 6.8196 -
0.0015 300 6.6551 -
0.0020 400 6.7104 -
0.0025 500 6.2763 -
0.0030 600 6.5017 -
0.0035 700 6.336 -
0.0040 800 6.1367 -
0.0045 900 6.194 -
0.0050 1000 6.4518 -
0.0055 1100 6.2857 -
0.0060 1200 6.0774 -
0.0065 1300 6.1036 -
0.0070 1400 6.2154 -
0.0075 1500 6.3046 -
0.0080 1600 5.9755 -
0.0085 1700 5.8602 -
0.0090 1800 5.8308 -
0.0095 1900 6.0314 -
0.0100 2000 5.86 -
0.0105 2100 5.8287 -
0.0110 2200 5.7462 -
0.0115 2300 5.7909 -
0.0120 2400 5.9804 -
0.0125 2500 5.4674 -
0.0130 2600 5.4586 -
0.0135 2700 5.7749 -
0.0140 2800 5.6838 -
0.0145 2900 5.7307 -
0.0150 3000 5.4713 -
0.0155 3100 5.5025 -
0.0160 3200 5.5276 -
0.0165 3300 5.3185 -
0.0170 3400 5.4734 -
0.0175 3500 5.4202 -
0.0180 3600 5.1365 -
0.0185 3700 5.3873 -
0.0190 3800 5.1091 -
0.0195 3900 5.2056 -
0.0200 4000 5.147 -
0.0205 4100 5.3614 -
0.0210 4200 5.0783 -
0.0215 4300 5.2087 -
0.0220 4400 5.2127 -
0.0225 4500 4.9735 -
0.0230 4600 5.0916 -
0.0235 4700 4.9882 -
0.0240 4800 5.1061 -
0.0245 4900 4.8584 -
0.0250 5000 4.9934 -
0.0255 5100 4.9541 -
0.0260 5200 4.9378 -
0.0265 5300 5.0161 -
0.0270 5400 4.8734 -
0.0275 5500 4.8832 -
0.0280 5600 4.9206 -
0.0285 5700 4.7206 -
0.0290 5800 4.7193 -
0.0295 5900 4.8795 -
0.0300 6000 4.6044 -
0.0305 6100 4.7623 -
0.0310 6200 4.7092 -
0.0315 6300 4.6061 -
0.0320 6400 4.6183 -
0.0325 6500 4.6138 -
0.0330 6600 4.554 -
0.0335 6700 4.6689 -
0.0340 6800 4.6988 -
0.0345 6900 4.6118 -
0.0350 7000 4.7152 -
0.0354 7100 4.5205 -
0.0359 7200 4.4475 -
0.0364 7300 4.6547 -
0.0369 7400 4.5081 -
0.0374 7500 4.508 -
0.0379 7600 4.7145 -
0.0384 7700 4.4167 -
0.0389 7800 4.631 -
0.0394 7900 4.5232 -
0.0399 8000 4.4811 -
0.0404 8100 4.5806 -
0.0409 8200 4.5535 -
0.0414 8300 4.3058 -
0.0419 8400 4.5954 -
0.0424 8500 4.4802 -
0.0429 8600 4.3596 -
0.0434 8700 4.4825 -
0.0439 8800 4.3692 -
0.0444 8900 4.3255 -
0.0449 9000 4.4243 -
0.0454 9100 4.2821 -
0.0459 9200 4.4329 -
0.0464 9300 4.309 -
0.0469 9400 4.3382 -
0.0474 9500 4.4366 -
0.0479 9600 4.3235 -
0.0484 9700 4.4367 -
0.0489 9800 4.4723 -
0.0494 9900 4.4395 -
0.0499 10000 4.4479 -
0.0504 10100 4.2866 -
0.0509 10200 4.2042 -
0.0514 10300 4.1899 -
0.0519 10400 4.4943 -
0.0524 10500 4.1929 -
0.0529 10600 4.1884 -
0.0534 10700 4.2187 -
0.0539 10800 4.2138 -
0.0544 10900 4.1536 -
0.0549 11000 4.3411 -
0.0554 11100 4.1162 -
0.0559 11200 4.2277 -
0.0564 11300 4.1056 -
0.0569 11400 4.2191 -
0.0574 11500 4.0704 -
0.0579 11600 4.2494 -
0.0584 11700 4.1014 -
0.0589 11800 4.0936 -
0.0594 11900 3.8393 -
0.0599 12000 4.052 -
0.0604 12100 3.9942 -
0.0609 12200 4.1937 -
0.0614 12300 4.213 -
0.0619 12400 4.1348 -
0.0624 12500 3.9975 -
0.0629 12600 4.1062 -
0.0634 12700 4.121 -
0.0639 12800 3.8878 -
0.0644 12900 4.0766 -
0.0649 13000 3.9884 -
0.0654 13100 4.0049 -
0.0659 13200 4.0452 -
0.0664 13300 3.9827 -
0.0669 13400 3.9904 -
0.0674 13500 3.9601 -
0.0679 13600 4.052 -
0.0684 13700 3.8402 -
0.0689 13800 3.9445 -
0.0694 13900 3.8026 -
0.0699 14000 3.9697 -
0.0704 14100 3.869 -
0.0709 14200 4.0541 -
0.0714 14300 3.8637 -
0.0719 14400 4.1086 -
0.0724 14500 3.8104 -
0.0729 14600 3.9208 -
0.0734 14700 3.8052 -
0.0739 14800 4.0067 -
0.0744 14900 3.9121 -
0.0749 15000 4.0612 -
0.0754 15100 3.8813 -
0.0759 15200 3.8245 -
0.0764 15300 3.8588 -
0.0769 15400 3.6933 -
0.0774 15500 3.8568 -
0.0779 15600 3.6819 -
0.0784 15700 3.7928 -
0.0789 15800 4.0747 -
0.0794 15900 3.6708 -
0.0799 16000 3.7275 -
0.0804 16100 3.7768 -
0.0809 16200 3.8808 -
0.0814 16300 3.648 -
0.0819 16400 3.7793 -
0.0824 16500 3.9536 -
0.0829 16600 3.7201 -
0.0834 16700 3.5479 -
0.0839 16800 3.4113 -
0.0844 16900 4.265 -
0.0849 17000 3.7576 -
0.0854 17100 3.948 -
0.0859 17200 3.5882 -
0.0864 17300 3.8988 -
0.0869 17400 3.6749 -
0.0874 17500 3.8741 -
0.0879 17600 3.5572 -
0.0884 17700 3.8278 -
0.0889 17800 3.6886 -
0.0894 17900 3.5759 -
0.0899 18000 3.515 -
0.0904 18100 3.7087 -
0.0909 18200 3.7857 -
0.0914 18300 3.5894 -
0.0919 18400 3.8247 -
0.0924 18500 3.5725 -
0.0929 18600 3.4782 -
0.0934 18700 3.6195 -
0.0939 18800 3.7224 -
0.0944 18900 3.6477 -
0.0949 19000 3.6129 -
0.0954 19100 3.6367 -
0.0959 19200 3.702 -
0.0964 19300 3.5366 -
0.0969 19400 3.7397 -
0.0974 19500 3.7948 -
0.0979 19600 3.5529 -
0.0984 19700 3.7017 -
0.0989 19800 3.3169 -
0.0994 19900 3.7506 -
0.0999 20000 3.4023 -
0.1004 20100 3.5573 -
0.1009 20200 3.6026 -
0.1014 20300 3.5585 -
0.1019 20400 3.4484 -
0.1024 20500 3.8137 -
0.1029 20600 3.6434 -
0.1034 20700 3.3974 -
0.1039 20800 3.5116 -
0.1044 20900 3.9141 -
0.1049 21000 3.5298 -
0.1053 21100 3.4814 -
0.1058 21200 3.4113 -
0.1063 21300 3.4321 -
0.1068 21400 3.4263 -
0.1073 21500 3.4734 -
0.1078 21600 3.096 -
0.1083 21700 3.3228 -
0.1088 21800 3.335 -
0.1093 21900 3.4392 -
0.1098 22000 3.2421 -
0.1103 22100 3.2693 -
0.1108 22200 3.6501 -
0.1113 22300 3.1809 -
0.1118 22400 3.3698 -
0.1123 22500 3.6283 -
0.1128 22600 3.2968 -
0.1133 22700 3.359 -
0.1138 22800 3.3095 -
0.1143 22900 3.5046 -
0.1148 23000 3.3438 -
0.1153 23100 3.2553 -
0.1158 23200 3.1866 -
0.1163 23300 3.5381 -
0.1168 23400 3.1583 -
0.1173 23500 3.5116 -
0.1178 23600 3.2659 -
0.1183 23700 3.7753 -
0.1188 23800 3.3164 -
0.1193 23900 3.4583 -
0.1198 24000 3.5863 -
0.1203 24100 3.1361 -
0.1208 24200 3.114 -
0.1213 24300 3.3258 -
0.1218 24400 3.3718 -
0.1223 24500 3.3788 -
0.1228 24600 3.2925 -
0.1233 24700 3.4382 -
0.1238 24800 3.1105 -
0.1243 24900 3.0611 -
0.1248 25000 3.2392 -
0.1253 25100 3.3668 -
0.1258 25200 3.026 -
0.1263 25300 2.9381 -
0.1268 25400 3.0699 -
0.1273 25500 3.4645 -
0.1278 25600 3.1359 -
0.1283 25700 3.1008 -
0.1288 25800 3.2122 -
0.1293 25900 3.2104 -
0.1298 26000 3.3811 -
0.1303 26100 3.3549 -
0.1308 26200 3.391 -
0.1313 26300 2.9513 -
0.1318 26400 3.4013 -
0.1323 26500 3.2946 -
0.1328 26600 3.1047 -
0.1333 26700 2.9188 -
0.1338 26800 3.4218 -
0.1343 26900 2.8501 -
0.1348 27000 3.2599 -
0.1353 27100 3.3038 -
0.1358 27200 3.1399 -
0.1363 27300 2.745 -
0.1368 27400 3.3719 -
0.1373 27500 3.41 -
0.1378 27600 2.7585 -
0.1383 27700 3.1948 -
0.1388 27800 3.0964 -
0.1393 27900 3.3553 -
0.1398 28000 3.1098 -
0.1403 28100 3.1651 -
0.1408 28200 3.0293 -
0.1413 28300 3.4216 -
0.1418 28400 3.3289 -
0.1423 28500 3.3303 -
0.1428 28600 3.2838 -
0.1433 28700 3.3382 -
0.1438 28800 3.3087 -
0.1443 28900 3.2504 -
0.1448 29000 3.0627 -
0.1453 29100 3.0779 -
0.1458 29200 3.0 -
0.1463 29300 3.1066 -
0.1468 29400 3.0483 -
0.1473 29500 2.841 -
0.1478 29600 3.0283 -
0.1483 29700 3.1668 -
0.1488 29800 3.1329 -
0.1493 29900 3.044 -
0.1498 30000 3.2942 -
0.1503 30100 3.0769 -
0.1508 30200 3.0751 -
0.1513 30300 2.848 -
0.1518 30400 2.9772 -
0.1523 30500 2.9561 -
0.1528 30600 3.0682 -
0.1533 30700 3.0736 -
0.1538 30800 2.9908 -
0.1543 30900 3.0628 -
0.1548 31000 2.863 -
0.1553 31100 2.7324 -
0.1558 31200 2.9683 -
0.1563 31300 3.062 -
0.1568 31400 2.9669 -
0.1573 31500 3.0353 -
0.1578 31600 2.7867 -
0.1583 31700 2.8247 -
0.1588 31800 3.0862 -
0.1593 31900 3.1947 -
0.1598 32000 2.6121 -
0.1603 32100 2.9946 -
0.1608 32200 2.7883 -
0.1613 32300 2.9826 -
0.1618 32400 2.9318 -
0.1623 32500 2.796 -
0.1628 32600 2.9169 -
0.1633 32700 2.9236 -
0.1638 32800 2.6384 -
0.1643 32900 3.1341 -
0.1648 33000 3.1256 -
0.1653 33100 2.7875 -
0.1658 33200 2.9638 -
0.1663 33300 3.0285 -
0.1668 33400 2.9998 -
0.1673 33500 2.9766 -
0.1678 33600 2.8865 -
0.1683 33700 3.0156 -
0.1688 33800 2.8313 -
0.1693 33900 2.8715 -
0.1698 34000 3.1022 -
0.1703 34100 3.1711 -
0.1708 34200 2.9908 -
0.1713 34300 3.0944 -
0.1718 34400 2.8759 -
0.1723 34500 2.9095 -
0.1728 34600 3.214 -
0.1733 34700 2.9522 -
0.1738 34800 3.0699 -
0.1743 34900 2.8568 -
0.1748 35000 2.6142 -
0.1752 35100 3.0088 -
0.1757 35200 2.8695 -
0.1762 35300 2.5945 -
0.1767 35400 2.7676 -
0.1772 35500 2.8531 -
0.1777 35600 2.7271 -
0.1782 35700 3.0287 -
0.1787 35800 2.9784 -
0.1792 35900 3.0512 -
0.1797 36000 3.0043 -
0.1802 36100 2.8445 -
0.1807 36200 2.8787 -
0.1812 36300 2.9373 -
0.1817 36400 2.7795 -
0.1822 36500 2.6245 -
0.1827 36600 2.638 -
0.1832 36700 2.6042 -
0.1837 36800 2.7934 -
0.1842 36900 2.7317 -
0.1847 37000 2.614 -
0.1852 37100 2.8794 -
0.1857 37200 2.6396 -
0.1862 37300 2.6917 -
0.1867 37400 2.52 -
0.1872 37500 2.5415 -
0.1877 37600 2.6995 -
0.1882 37700 3.013 -
0.1887 37800 2.8997 -
0.1892 37900 2.7595 -
0.1897 38000 2.9921 -
0.1902 38100 2.7121 -
0.1907 38200 2.5674 -
0.1912 38300 2.5434 -
0.1917 38400 2.6567 -
0.1922 38500 2.9682 -
0.1927 38600 2.7865 -
0.1932 38700 2.9794 -
0.1937 38800 2.933 -
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0.9946 199200 1.2127 -
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0.9981 199900 1.2037 -
0.9986 200000 1.0005 0.9529
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0.9996 200200 0.995 -

Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 5.1.0
  • Transformers: 4.55.4
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.10.1
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers 

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CoSENTLoss 

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