all-MiniLM-L6-v24-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v23-pair_score on the pairs_with_scores_v120_tag_true_positives_and_false_negatives_description 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: KhaledReda/all-MiniLM-L6-v23-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v120_tag_true_positives_and_false_negatives_description
• 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'laces boot',
    'nursing covers mustard flowers category kids baby care breastfeeding aid breastfeeding aid tags breathable nursing cover full coverage nursing cover foldable nursing cover pouch nursing cover colorful nursing cover flowers nursing covers mustard nursing covers nursing covers keywords flowers nursing covers mustard nursing covers nursing covers description breastfeeding is one of the most special yet challenging things in motherhood we just wanted to add some more colors to this special moment with all its colors product details soft light breathable fabric machine washable full coverage comes with its pouch foldable in seconds',
    'raw african coffee soap category beauty skincare face soap face soap tags shea butter soap coconut oil soap antioxidant soap firming skin soap dark spots soap coffee soap raw african raw african soap soap ahwa soap kahwa soap kahwah soap qahwa soap raw african ahwa soap raw african kahwa soap raw african kahwah soap raw african qahwa soap keywords coffee soap raw african raw african soap soap ahwa soap kahwa soap kahwah soap qahwa soap raw african ahwa soap raw african kahwa soap raw african kahwah soap raw african qahwa soap description our coffee-based soap bar gives you a boosting and energizing sensation this soap is rich in antioxidants and nutrients that fight age signs firms and tighten the skin and gives you a youthful look. it helps in reducing dark spots and acne scars. for all skin types. this product is free of harsh chemicals like parabens sulphates or mineral oils. we never test our products on animals and we don t deal with suppliers who test their products on animals. ingredients shea butter coconut oil olive oil coffee.',
]
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, -0.0817, -0.0683],
#         [-0.0817,  1.0000, -0.0380],
#         [-0.0683, -0.0380,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v120_tag_true_positives_and_false_negatives_description

• Dataset: pairs_with_scores_v120_tag_true_positives_and_false_negatives_description at 25785dc
• Size: 12,582,766 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: 5.59 tokens max: 22 tokens	min: 11 tokens mean: 104.43 tokens max: 256 tokens	min: 0.0 mean: 0.1 max: 1.0

• Samples:

  sentence1	sentence2	score
  bergamot eau de toilette	knitted crop top in beige category fashion casual wear top top tags women top beige top comfort top breathable top fabric top stretch top knitted sets crop top top keywords crop top top attrs gender women brand psych generic name top size s features high-waisted soft breathable cut cropped material knitted color beige occasion beach description elevate your style with our chic knitted set featuring a matching pair of knitted pants and a knitted crop top. designed for comfort and effortless elegance this set is perfect for any occasion. the knitted crop top offers a flattering fit with a touch of stretch while the high-waisted knitted pants provide a sleek silhouette and ultimate comfort. made from soft breathable fabric this set is perfect for both enjoying a day at the beach and stepping out in style. versatile and stylish this knitted set is a must-have addition to your wardrobe. mix and match with your favorite accessories for a look that s uniquely you. model is 177 cm wearing size...	0.0
  wide leg pants	titania solingen no/1063 category beauty cosmetics make-up tool tweezers tags solingen tweezers titania titania tweezers tweezers keywords solingen tweezers titania titania tweezers tweezers	0.0
  women pumps	neurimax 30/cap 2 ex.new category health and nutrition dietary supplements joint supplement joint supplement tags neurimax neurimax supplement keywords neurimax neurimax supplement attrs pharmacies form cap	0.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

pairs_with_scores_v120_tag_true_positives_and_false_negatives_description

• Dataset: pairs_with_scores_v120_tag_true_positives_and_false_negatives_description at 25785dc
• Size: 63,230 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: 5.51 tokens max: 20 tokens	min: 13 tokens mean: 104.12 tokens max: 256 tokens	min: 0.0 mean: 0.1 max: 1.0

• Samples:

  sentence1	sentence2	score
  always maxi long	american - buffalo chicken hot category restaurants pizza deli pizza tags cheesy american pizza thick pizza mozzarella pizza cheese pizza hot chicken pizza american pizza buffalo chicken pizza chicken pizza pizza keywords american pizza buffalo chicken pizza chicken pizza pizza description a thick pizza with a generous amount of cheese mozzarella tomato sauce chicken buffalo sauce	0.0
  sushi	lemon fluffy set category fashion casual wear outfit outfit tags linen outfit summer outfit women outfit blouse skirt fluffy outfit lemon outfit outfit outfit set keywords fluffy outfit lemon outfit outfit outfit set attrs gender women brand dovera generic name outfit size one size features fluffy outfit style skirt top material linen color lemon season summer description modest comfyand summery set with fluffy skirt and top comes in 3 colors apple green - brown - blue one size. outside materials linen. blouse length 55 cm width 65 cm shoulder 25 cm skirt length 100 cm	0.0
  eyefree lid wipes	disposable - 5 ml syringe - latex free - 1 pcs category pharmacies first aid and medical equipment medical accessory medical accessory tags disposable syringe syringe keywords disposable syringe syringe attrs units 1 pcs 5 millilitre	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: 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
0.0010	100	5.7173
0.0020	200	5.7311
0.0031	300	5.4355
0.0041	400	5.4133
0.0051	500	5.1707
0.0061	600	5.1954
0.0071	700	4.9123
0.0081	800	4.852
0.0092	900	4.8442
0.0102	1000	4.4788
0.0112	1100	4.5735
0.0122	1200	4.3171
0.0132	1300	4.3714
0.0142	1400	4.2536
0.0153	1500	4.1945
0.0163	1600	4.0586
0.0173	1700	3.8934
0.0183	1800	3.9997
0.0193	1900	3.6822
0.0203	2000	3.6699
0.0214	2100	3.7657
0.0224	2200	3.6693
0.0234	2300	3.52
0.0244	2400	3.5535
0.0254	2500	3.3532
0.0264	2600	3.3329
0.0275	2700	3.3375
0.0285	2800	3.1954
0.0295	2900	3.1045
0.0305	3000	3.2362
0.0315	3100	3.1298
0.0326	3200	2.9862
0.0336	3300	2.9966
0.0346	3400	2.9131
0.0356	3500	3.0058
0.0366	3600	2.5357
0.0376	3700	2.7563
0.0387	3800	2.8085
0.0397	3900	2.6729
0.0407	4000	2.5785
0.0417	4100	2.7879
0.0427	4200	2.502
0.0437	4300	2.3824
0.0448	4400	2.4391
0.0458	4500	2.3122
0.0468	4600	2.2223
0.0478	4700	2.4876
0.0488	4800	2.5127
0.0498	4900	2.3576
0.0509	5000	1.961
0.0519	5100	2.4402
0.0529	5200	2.145
0.0539	5300	2.2863
0.0549	5400	2.2647
0.0559	5500	2.1835
0.0570	5600	2.0451
0.0580	5700	2.0484
0.0590	5800	2.1578
0.0600	5900	2.1455
0.0610	6000	2.0281
0.0621	6100	2.0751
0.0631	6200	1.9221
0.0641	6300	1.8355
0.0651	6400	1.9353
0.0661	6500	1.8617
0.0671	6600	1.8399
0.0682	6700	1.927
0.0692	6800	1.6166
0.0702	6900	2.1288
0.0712	7000	1.7884
0.0722	7100	1.8565
0.0732	7200	1.85
0.0743	7300	1.7127
0.0753	7400	1.7836
0.0763	7500	1.6113
0.0773	7600	1.8484
0.0783	7700	1.8673
0.0793	7800	1.6261
0.0804	7900	1.6207
0.0814	8000	2.0533
0.0824	8100	1.729
0.0834	8200	1.5739
0.0844	8300	1.7526
0.0855	8400	1.7466
0.0865	8500	1.6939
0.0875	8600	1.4806
0.0885	8700	1.6851
0.0895	8800	1.6117
0.0905	8900	1.5053
0.0916	9000	1.6736
0.0926	9100	1.5396
0.0936	9200	1.5309
0.0946	9300	1.5081
0.0956	9400	1.34
0.0966	9500	1.5146
0.0977	9600	1.3838
0.0987	9700	1.559
0.0997	9800	1.5523
0.1007	9900	1.3153
0.1017	10000	1.277
0.1027	10100	1.5285
0.1038	10200	1.3658
0.1048	10300	1.4931
0.1058	10400	1.3631
0.1068	10500	1.3536
0.1078	10600	1.4563
0.1088	10700	1.4296
0.1099	10800	1.4555
0.1109	10900	1.5459
0.1119	11000	1.4178
0.1129	11100	1.4425
0.1139	11200	1.3951
0.1150	11300	1.2531
0.1160	11400	1.4435
0.1170	11500	1.168
0.1180	11600	1.3839
0.1190	11700	1.4541
0.1200	11800	1.2666
0.1211	11900	1.3136
0.1221	12000	1.3001
0.1231	12100	1.1904
0.1241	12200	1.2617
0.1251	12300	1.2397
0.1261	12400	1.5342
0.1272	12500	1.3735
0.1282	12600	1.2123
0.1292	12700	1.28
0.1302	12800	1.3773
0.1312	12900	1.3931
0.1322	13000	1.4614
0.1333	13100	1.3945
0.1343	13200	1.4541
0.1353	13300	1.2571
0.1363	13400	1.1574
0.1373	13500	1.2597
0.1383	13600	1.2595
0.1394	13700	1.218
0.1404	13800	1.262
0.1414	13900	1.0565
0.1424	14000	1.1767
0.1434	14100	1.2089
0.1445	14200	1.211
0.1455	14300	0.8943
0.1465	14400	1.2541
0.1475	14500	1.1358
0.1485	14600	0.9817
0.1495	14700	1.1535
0.1506	14800	1.2066
0.1516	14900	1.2272
0.1526	15000	0.9362
0.1536	15100	1.3058
0.1546	15200	1.2812
0.1556	15300	1.1447
0.1567	15400	1.2213
0.1577	15500	1.1535
0.1587	15600	1.5273
0.1597	15700	1.0432
0.1607	15800	1.3215
0.1617	15900	1.0787
0.1628	16000	1.1641
0.1638	16100	1.0483
0.1648	16200	1.3148
0.1658	16300	1.0111
0.1668	16400	1.1823
0.1678	16500	1.2526
0.1689	16600	0.8983
0.1699	16700	1.1997
0.1709	16800	1.1394
0.1719	16900	1.1923
0.1729	17000	1.1439
0.1740	17100	1.259
0.1750	17200	1.3803
0.1760	17300	1.1672
0.1770	17400	1.149
0.1780	17500	1.0019
0.1790	17600	0.9692
0.1801	17700	1.1611
0.1811	17800	1.111
0.1821	17900	0.9874
0.1831	18000	1.2028
0.1841	18100	0.9416
0.1851	18200	1.1619
0.1862	18300	1.17
0.1872	18400	1.003
0.1882	18500	0.9409
0.1892	18600	0.9224
0.1902	18700	0.9215
0.1912	18800	1.2007
0.1923	18900	1.0021
0.1933	19000	1.0305
0.1943	19100	1.1084
0.1953	19200	0.961
0.1963	19300	0.9769
0.1973	19400	1.218
0.1984	19500	1.043
0.1994	19600	1.0366
0.2004	19700	0.9459
0.2014	19800	1.0557
0.2024	19900	1.0953
0.2035	20000	1.0327
0.2045	20100	1.0284
0.2055	20200	0.9376
0.2065	20300	1.1122
0.2075	20400	0.9807
0.2085	20500	0.9054
0.2096	20600	1.069
0.2106	20700	1.0802
0.2116	20800	0.9857
0.2126	20900	1.1127
0.2136	21000	1.2601
0.2146	21100	0.9709
0.2157	21200	0.9984
0.2167	21300	1.1281
0.2177	21400	0.8692
0.2187	21500	1.1773
0.2197	21600	0.9221
0.2207	21700	0.9007
0.2218	21800	1.0686
0.2228	21900	1.1078
0.2238	22000	0.999
0.2248	22100	0.8577
0.2258	22200	1.0215
0.2268	22300	0.9952
0.2279	22400	0.9597
0.2289	22500	0.79
0.2299	22600	1.1086
0.2309	22700	1.1255
0.2319	22800	1.0515
0.2330	22900	0.9184
0.2340	23000	1.0096
0.2350	23100	1.0243
0.2360	23200	1.0578
0.2370	23300	0.9486
0.2380	23400	1.0553
0.2391	23500	0.9279
0.2401	23600	0.9487
0.2411	23700	1.0134
0.2421	23800	0.7462
0.2431	23900	0.7586
0.2441	24000	0.9968
0.2452	24100	1.1576
0.2462	24200	0.8984
0.2472	24300	1.0449
0.2482	24400	0.886
0.2492	24500	0.9021
0.2502	24600	1.1053
0.2513	24700	0.9241
0.2523	24800	1.0178
0.2533	24900	1.0758
0.2543	25000	0.8807
0.2553	25100	0.9876
0.2564	25200	1.0116
0.2574	25300	0.7735
0.2584	25400	1.0378
0.2594	25500	1.0
0.2604	25600	0.8934
0.2614	25700	0.9769
0.2625	25800	1.2004
0.2635	25900	0.9047
0.2645	26000	0.8331
0.2655	26100	1.0331
0.2665	26200	1.0265
0.2675	26300	0.8131
0.2686	26400	1.0083
0.2696	26500	1.0486
0.2706	26600	0.8721
0.2716	26700	0.9227
0.2726	26800	1.0438
0.2736	26900	0.6701
0.2747	27000	0.8246
0.2757	27100	0.8877
0.2767	27200	0.8974
0.2777	27300	0.9877
0.2787	27400	0.8809
0.2797	27500	0.8058
0.2808	27600	1.0499
0.2818	27700	1.0949
0.2828	27800	1.0794
0.2838	27900	0.7273
0.2848	28000	0.8775
0.2859	28100	0.7947
0.2869	28200	0.9967
0.2879	28300	1.0834
0.2889	28400	0.8397
0.2899	28500	0.9808
0.2909	28600	0.8525
0.2920	28700	0.6795
0.2930	28800	0.8213
0.2940	28900	0.7962
0.2950	29000	0.7181
0.2960	29100	0.7304
0.2970	29200	0.8983
0.2981	29300	0.8157
0.2991	29400	0.9902
0.3001	29500	1.106
0.3011	29600	0.9016
0.3021	29700	0.9756
0.3031	29800	0.9426
0.3042	29900	0.8033
0.3052	30000	0.7583
0.3062	30100	0.8602
0.3072	30200	0.8691
0.3082	30300	1.0453
0.3092	30400	0.9485
0.3103	30500	0.9637
0.3113	30600	0.8028
0.3123	30700	0.9261
0.3133	30800	0.7166
0.3143	30900	0.8809
0.3154	31000	0.8061
0.3164	31100	0.9817
0.3174	31200	0.94
0.3184	31300	0.7935
0.3194	31400	0.8372
0.3204	31500	1.1727
0.3215	31600	0.7606
0.3225	31700	0.9101
0.3235	31800	0.681
0.3245	31900	0.9235
0.3255	32000	0.7649
0.3265	32100	0.7917
0.3276	32200	0.9602
0.3286	32300	0.8561
0.3296	32400	0.7201
0.3306	32500	0.9261
0.3316	32600	0.9769
0.3326	32700	0.7281
0.3337	32800	0.8497
0.3347	32900	0.935
0.3357	33000	0.8837
0.3367	33100	0.6759
0.3377	33200	0.9258
0.3387	33300	0.8128
0.3398	33400	0.8352
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0.3459	34000	0.9483
0.3469	34100	0.6316
0.3479	34200	0.9778
0.3489	34300	1.2536
0.3499	34400	0.8554
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0.3530	34700	1.2065
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0.3571	35100	1.0686
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0.3794	37300	1.0938
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0.3845	37800	1.0998
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0.3896	38300	1.0219
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0.9308	91500	0.3872
0.9318	91600	0.622
0.9328	91700	0.6095
0.9338	91800	0.4772
0.9349	91900	0.4708
0.9359	92000	0.5463
0.9369	92100	0.7436
0.9379	92200	0.698
0.9389	92300	0.3119
0.9400	92400	0.4237
0.9410	92500	0.5579
0.9420	92600	0.6101
0.9430	92700	0.6106
0.9440	92800	0.614
0.9450	92900	0.6228
0.9461	93000	0.5155
0.9471	93100	0.6098
0.9481	93200	0.6685
0.9491	93300	0.3962
0.9501	93400	0.5151
0.9511	93500	0.4819
0.9522	93600	0.5941
0.9532	93700	0.5932
0.9542	93800	0.6307
0.9552	93900	0.6368
0.9562	94000	0.6799
0.9572	94100	0.5089
0.9583	94200	0.5623
0.9593	94300	0.4027
0.9603	94400	0.6181
0.9613	94500	0.5755
0.9623	94600	0.5631
0.9633	94700	0.4376
0.9644	94800	0.429
0.9654	94900	0.4997
0.9664	95000	0.5789
0.9674	95100	0.5636
0.9684	95200	0.6638
0.9695	95300	0.8632
0.9705	95400	0.5708
0.9715	95500	0.5817
0.9725	95600	0.5245
0.9735	95700	0.5836
0.9745	95800	0.5696
0.9756	95900	0.5988
0.9766	96000	0.5597
0.9776	96100	0.5968
0.9786	96200	0.7544
0.9796	96300	0.6484
0.9806	96400	0.3758
0.9817	96500	0.6732
0.9827	96600	0.5634
0.9837	96700	0.4491
0.9847	96800	0.349
0.9857	96900	0.6564
0.9867	97000	0.5724
0.9878	97100	0.6022
0.9888	97200	0.3853
0.9898	97300	0.6601
0.9908	97400	0.6511
0.9918	97500	0.4784
0.9928	97600	0.5943
0.9939	97700	0.8411
0.9949	97800	0.5165
0.9959	97900	0.4567
0.9969	98000	0.492
0.9979	98100	0.5838
0.9990	98200	0.5109
1.0000	98300	0.4494

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},
}