all-MiniLM-L6-v63-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v51 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_with_scores_v51
• 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 = [
    'squared bibs',
    'racing car with remote control racing car men racing car rc racing car black racing car blue racing car remote control car this toy is a miniature and realistic version of the famous bugatti veyron. it works with remote control allowing children and adults to control its movement and play with it. the car has an attractive design bright colors and a front light that works when it is turned on. functions moving forward. move backwards. turning left. turn right. front lighting headlights . operated by wireless remote control. operating front lighting. suitable for indoor and outdoor play. product measures 33 x 12 x 13 cm. product weight 0.475 k. product color black blue.',
    'apricot leopard basic sports bra elastic bust band sports bra compression fit sports bra removable cup sports bra central logo sports bra nylon sports bra elastane sports bra women bra leopard sports bra high support elastic bust band compression fit removable cup central logo 80 nylon 20 elastane',
]
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, -0.1496, -0.0242],
#         [-0.1496,  1.0000,  0.1042],
#         [-0.0242,  0.1042,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v51

• Dataset: pairs_with_scores_v51 at 2c9bce6
• Size: 43,374,097 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: 7.4 tokens max: 23 tokens	min: 3 tokens mean: 55.27 tokens max: 256 tokens	min: 0.0 mean: 0.21 max: 1.0

• Samples:

  sentence1	sentence2	score
  training cones essential -	alonzo immune stimulant immune stimulan cat immune stimulant dog immune stimulant pet immune stimulant vitamin c stimulant supplements stimulant alonzo stimulant cat supplements dog supplements immune pet stimulant pet supplements each 100 ml contains echinacea purpurea 5 gr. saccharomyces boulardii 7.5 gr. l- lysine 3.5 gr. n n-dimethylglycine 1.2 gr. vitamin c 3.8 gr. carrier up to 100 ml.	0.0
  super fresh aqua home scent	oreo with cream pie fetir pie oreo pie	0.0
  bionnex preventiva sscrspf50 sp	butterfly pearl ear set ear candy butterfly piercing set the prettiest ear candy	0.0

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v51

• Dataset: pairs_with_scores_v51 at 2c9bce6
• Size: 217,961 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: 7.47 tokens max: 25 tokens	min: 3 tokens mean: 57.17 tokens max: 218 tokens	min: 0.0 mean: 0.2 max: 1.0

• Samples:

  sentence1	sentence2	score
  improves blood circulation leavein	um ali oriental pot freshest oriental pot with a twist.	0.0
  nigiri tuna	x8 ultra smart watch bluetooth wireless charging white wireless charging smart watch heart rate smart watch blood pressure smart watch sleep monitor smart watch indoor sports smart watch outdoor sports smart watch waterproof smart watch music control smart watch lithium polymer battery smart watch white smart watch x 8 smart watch about this item wireless charging. heart rate ecg blood pressure / oxygen sleep monitor. bluetooth push calls sms email facebook whatsapp wechat etc. steps / calories / distance indoor sports running / cycling / sit-ups / push ups / free movement outdoor sports on foot / mountainering / other sports . make and answer calls directly via a smartwatch. ip68 waterproof stopwatch music control vibration support nfc payments. lithium ion polymer battery.	0.0
  ticanase plus nasal spray	luna h2o2 20 vol.100ml	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
0.4893	331600	0.7528	-
0.4894	331700	0.796	-
0.4896	331800	0.9709	-
0.4897	331900	0.8568	-
0.4899	332000	0.6406	-
0.4900	332100	0.9254	-
0.4902	332200	0.9083	-
0.4903	332300	0.6775	-
0.4905	332400	0.637	-
0.4906	332500	0.5528	-
0.4908	332600	0.3745	-
0.4909	332700	0.772	-
0.4911	332800	0.629	-
0.4912	332900	0.6789	-
0.4914	333000	0.7483	-
0.4915	333100	0.6194	-
0.4916	333200	0.7905	-
0.4918	333300	0.5616	-
0.4919	333400	0.8379	-
0.4921	333500	0.744	-
0.4922	333600	0.3896	-
0.4924	333700	0.7723	-
0.4925	333800	0.502	-
0.4927	333900	0.602	-
0.4928	334000	0.5625	-
0.4930	334100	0.8533	-
0.4931	334200	0.6598	-
0.4933	334300	0.8498	-
0.4934	334400	0.6971	-
0.4936	334500	0.8485	-
0.4937	334600	0.8198	-
0.4939	334700	0.5705	-
0.4940	334800	0.6186	-
0.4942	334900	0.7309	-
0.4943	335000	0.802	-
0.4945	335100	0.638	-
0.4946	335200	0.5123	-
0.4947	335300	0.7191	-
0.4949	335400	0.7719	-
0.4950	335500	0.7885	-
0.4952	335600	0.7394	-
0.4953	335700	0.6737	-
0.4955	335800	0.5566	-
0.4956	335900	0.6606	-
0.4958	336000	0.4766	-
0.4959	336100	0.5193	-
0.4961	336200	0.7687	-
0.4962	336300	0.7256	-
0.4964	336400	0.7949	-
0.4965	336500	0.7515	-
0.4967	336600	1.0314	-
0.4968	336700	0.6048	-
0.4970	336800	0.8276	-
0.4971	336900	1.1823	-
0.4973	337000	0.8977	-
0.4974	337100	0.8226	-
0.4975	337200	0.7706	-
0.4977	337300	0.8421	-
0.4978	337400	0.4297	-
0.4980	337500	0.9785	-
0.4981	337600	0.7637	-
0.4983	337700	0.7587	-
0.4984	337800	0.692	-
0.4986	337900	0.5402	-
0.4987	338000	0.7104	-
0.4989	338100	0.6767	-
0.4990	338200	0.6803	-
0.4992	338300	0.429	-
0.4993	338400	0.7744	-
0.4995	338500	0.7677	-
0.4996	338600	0.6422	-
0.4998	338700	0.6876	-
0.4999	338800	0.4882	-
0.5001	338900	0.521	-
0.5002	339000	0.68	-
0.5004	339100	1.1991	-
0.5005	339200	0.5454	-
0.5006	339300	0.5639	-
0.5008	339400	0.6827	-
0.5009	339500	0.4903	-
0.5011	339600	0.7437	-
0.5012	339700	0.6053	-
0.5014	339800	0.7901	-
0.5015	339900	0.6331	-
0.5017	340000	0.6239	-
0.5018	340100	0.6015	-
0.5020	340200	0.7789	-
0.5021	340300	1.1998	-
0.5023	340400	0.4458	-
0.5024	340500	0.8625	-
0.5026	340600	0.969	-
0.5027	340700	0.3464	-
0.5029	340800	0.6069	-
0.5030	340900	0.6856	-
0.5032	341000	0.2861	-
0.5033	341100	0.7037	-
0.5035	341200	0.7408	-
0.5036	341300	0.812	-
0.5037	341400	0.5695	-
0.5039	341500	0.9312	-
0.5040	341600	0.6359	-
0.5042	341700	0.882	-
0.5043	341800	0.8864	-
0.5045	341900	0.4277	-
0.5046	342000	0.7647	-
0.5048	342100	0.9842	-
0.5049	342200	0.6323	-
0.5051	342300	0.7169	-
0.5052	342400	0.7255	-
0.5054	342500	0.8665	-
0.5055	342600	0.6691	-
0.5057	342700	0.6942	-
0.5058	342800	0.6271	-
0.5060	342900	0.8327	-
0.5061	343000	0.8994	-
0.5063	343100	0.8273	-
0.5064	343200	0.5692	-
0.5066	343300	0.6404	-
0.5067	343400	0.5199	-
0.5068	343500	0.4697	-
0.5070	343600	0.9024	-
0.5071	343700	0.3914	-
0.5073	343800	0.6407	-
0.5074	343900	0.8356	-
0.5076	344000	0.6714	-
0.5077	344100	0.7138	-
0.5079	344200	0.6824	-
0.5080	344300	0.8684	-
0.5082	344400	0.7711	-
0.5083	344500	1.0353	-
0.5085	344600	0.686	-
0.5086	344700	0.9273	-
0.5088	344800	0.6784	-
0.5089	344900	0.7471	-
0.5091	345000	0.7151	-
0.5092	345100	0.8589	-
0.5094	345200	0.6324	-
0.5095	345300	0.6613	-
0.5096	345400	0.4967	-
0.5098	345500	0.7982	-
0.5099	345600	0.5961	-
0.5101	345700	1.1026	-
0.5102	345800	0.9158	-
0.5104	345900	0.3781	-
0.5105	346000	1.0324	-
0.5107	346100	0.6719	-
0.5108	346200	0.6441	-
0.5110	346300	0.7193	-
0.5111	346400	0.7076	-
0.5113	346500	0.5447	-
0.5114	346600	0.4472	-
0.5116	346700	0.8031	-
0.5117	346800	0.9831	-
0.5119	346900	0.8875	-
0.5120	347000	0.5189	-
0.5122	347100	0.8193	-
0.5123	347200	0.8664	-
0.5125	347300	0.4646	-
0.5126	347400	0.5154	-
0.5127	347500	0.7426	-
0.5129	347600	0.7213	-
0.5130	347700	0.9373	-
0.5132	347800	0.9253	-
0.5133	347900	0.8619	-
0.5135	348000	0.8777	-
0.5136	348100	1.0144	-
0.5138	348200	0.6336	-
0.5139	348300	0.6397	-
0.5141	348400	0.8579	-
0.5142	348500	0.6395	-
0.5144	348600	0.6173	-
0.5145	348700	0.7748	-
0.5147	348800	0.7562	-
0.5148	348900	0.6533	-
0.5150	349000	0.8722	-
0.5151	349100	0.7388	-
0.5153	349200	0.761	-
0.5154	349300	0.6714	-
0.5156	349400	0.5233	-
0.5157	349500	1.048	-
0.5158	349600	0.6416	-
0.5160	349700	0.6295	-
0.5161	349800	0.5747	-
0.5163	349900	0.754	-
0.5164	350000	0.8363	-
0.5166	350100	0.8067	-
0.5167	350200	0.4478	-
0.5169	350300	0.6605	-
0.5170	350400	0.6008	-
0.5172	350500	0.573	-
0.5173	350600	0.6811	-
0.5175	350700	0.4765	-
0.5176	350800	0.8203	-
0.5178	350900	0.7194	-
0.5179	351000	0.6099	-
0.5181	351100	0.8868	-
0.5182	351200	0.7051	-
0.5184	351300	0.6381	-
0.5185	351400	0.7378	-
0.5187	351500	1.1666	-
0.5188	351600	0.9276	-
0.5189	351700	0.6129	-
0.5191	351800	0.6222	-
0.5192	351900	0.5124	-
0.5194	352000	0.5675	-
0.5195	352100	0.7017	-
0.5197	352200	0.5927	-
0.5198	352300	0.5707	-
0.5200	352400	0.9473	-
0.5201	352500	0.5522	-
0.5203	352600	0.8133	-
0.5204	352700	0.9207	-
0.5206	352800	0.5245	-
0.5207	352900	0.2517	-
0.5209	353000	0.876	-
0.5210	353100	0.8532	-
0.5212	353200	0.6717	-
0.5213	353300	0.6545	-
0.5215	353400	0.7209	-
0.5216	353500	0.7655	-
0.5217	353600	0.6601	-
0.5219	353700	0.6086	-
0.5220	353800	0.4999	-
0.5222	353900	0.647	-
0.5223	354000	0.986	-
0.5225	354100	0.7322	-
0.5226	354200	0.5899	-
0.5228	354300	0.427	-
0.5229	354400	0.7038	-
0.5231	354500	0.3893	-
0.5232	354600	0.4361	-
0.5234	354700	0.7513	-
0.5235	354800	0.556	-
0.5237	354900	0.6916	-
0.5238	355000	0.655	-
0.5240	355100	0.8475	-
0.5241	355200	0.8681	-
0.5243	355300	0.5154	-
0.5244	355400	0.7862	-
0.5246	355500	0.8547	-
0.5247	355600	0.5172	-
0.5248	355700	0.4489	-
0.5250	355800	0.6504	-
0.5251	355900	0.7575	-
0.5253	356000	0.6361	-
0.5254	356100	0.5509	-
0.5256	356200	0.6757	-
0.5257	356300	0.9329	-
0.5259	356400	0.6399	-
0.5260	356500	0.6159	-
0.5262	356600	0.4458	-
0.5263	356700	0.9561	-
0.5265	356800	1.0397	-
0.5266	356900	0.8211	-
0.5268	357000	0.5666	-
0.5269	357100	0.3875	-
0.5271	357200	0.4396	-
0.5272	357300	0.504	-
0.5274	357400	0.7974	-
0.5275	357500	0.6802	-
0.5277	357600	0.5851	-
0.5278	357700	0.4811	-
0.5279	357800	0.912	-
0.5281	357900	0.8298	-
0.5282	358000	0.6697	-
0.5284	358100	0.6326	-
0.5285	358200	0.8838	-
0.5287	358300	0.7868	-
0.5288	358400	0.8091	-
0.5290	358500	0.707	-
0.5291	358600	0.733	-
0.5293	358700	1.0663	-
0.5294	358800	0.6936	-
0.5296	358900	0.7545	-
0.5297	359000	0.7779	-
0.5299	359100	0.5029	-
0.5300	359200	0.5014	-
0.5302	359300	0.5901	-
0.5303	359400	0.6381	-
0.5305	359500	0.6827	-
0.5306	359600	0.5001	-
0.5307	359700	0.6001	-
0.5309	359800	0.6414	-
0.5310	359900	0.9829	-
0.5312	360000	0.4543	-
0.5313	360100	1.0097	-
0.5315	360200	1.1911	-
0.5316	360300	0.744	-
0.5318	360400	0.7802	-
0.5319	360500	0.7291	-
0.5321	360600	0.7444	-
0.5322	360700	0.5867	-
0.5324	360800	0.7285	-
0.5325	360900	0.6147	-
0.5327	361000	0.7272	-
0.5328	361100	0.4248	-
0.5330	361200	0.5916	-
0.5331	361300	0.6154	-
0.5333	361400	0.7537	-
0.5334	361500	0.7938	-
0.5336	361600	0.9285	-
0.5337	361700	0.568	-
0.5338	361800	0.6397	-
0.5340	361900	0.7131	-
0.5341	362000	0.7104	-
0.5343	362100	0.7806	-
0.5344	362200	0.7737	-
0.5346	362300	0.8256	-
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0.5352	362700	0.79	-
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0.6000	406600	0.7361	-
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0.6070	411400	0.5797	-
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0.7547	511500	0.3696	-
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0.9768	662000	0.3661	-
0.9770	662100	0.3289	-
0.9771	662200	0.3146	-
0.9772	662300	0.3573	-
0.9774	662400	0.3491	-
0.9775	662500	0.3714	-
0.9777	662600	0.2249	-
0.9778	662700	0.4062	-
0.9780	662800	0.4177	-
0.9781	662900	0.1953	-
0.9783	663000	0.4181	-
0.9784	663100	0.1522	-
0.9786	663200	0.3051	-
0.9787	663300	0.3594	-
0.9789	663400	0.5108	-
0.9790	663500	0.2864	-
0.9792	663600	0.4652	-
0.9793	663700	0.2575	-
0.9795	663800	0.4504	-
0.9796	663900	0.3733	-
0.9798	664000	0.2809	-
0.9799	664100	0.3343	-
0.9800	664200	0.1744	-
0.9802	664300	0.1535	-
0.9803	664400	0.3481	-
0.9805	664500	0.3056	-
0.9806	664600	0.4076	-
0.9808	664700	0.1809	-
0.9809	664800	0.274	-
0.9811	664900	0.2575	-
0.9812	665000	0.2753	-
0.9814	665100	0.4235	-
0.9815	665200	0.2491	-
0.9817	665300	0.1952	-
0.9818	665400	0.2001	-
0.9820	665500	0.2162	-
0.9821	665600	0.289	-
0.9823	665700	0.4272	-
0.9824	665800	0.3387	-
0.9826	665900	0.3779	-
0.9827	666000	0.3077	-
0.9829	666100	0.4155	-
0.9830	666200	0.2414	-
0.9831	666300	0.2842	-
0.9833	666400	0.4889	-
0.9834	666500	0.2761	-
0.9836	666600	0.4415	-
0.9837	666700	0.4023	-
0.9839	666800	0.2784	-
0.9840	666900	0.431	-
0.9842	667000	0.4483	-
0.9843	667100	0.3257	-
0.9845	667200	0.3003	-
0.9846	667300	0.2792	-
0.9848	667400	0.1376	-
0.9849	667500	0.4313	-
0.9851	667600	0.2053	-
0.9852	667700	0.1835	-
0.9854	667800	0.4765	-
0.9855	667900	0.4473	-
0.9857	668000	0.302	-
0.9858	668100	0.2327	-
0.9860	668200	0.3723	-
0.9861	668300	0.4699	-
0.9862	668400	0.3233	-
0.9864	668500	0.2365	-
0.9865	668600	0.3697	-
0.9867	668700	0.3888	-
0.9868	668800	0.3908	-
0.9870	668900	0.4824	-
0.9871	669000	0.3159	-
0.9873	669100	0.3152	-
0.9874	669200	0.3072	-
0.9876	669300	0.2832	-
0.9877	669400	0.6092	-
0.9879	669500	0.3462	-
0.9880	669600	0.3083	-
0.9882	669700	0.4286	-
0.9883	669800	0.2882	-
0.9885	669900	0.4853	-
0.9886	670000	0.3692	-
0.9888	670100	0.2957	-
0.9889	670200	0.2932	-
0.9891	670300	0.2576	-
0.9892	670400	0.2914	-
0.9893	670500	0.3413	-
0.9895	670600	0.4181	-
0.9896	670700	0.4905	-
0.9898	670800	0.3328	-
0.9899	670900	0.4027	-
0.9901	671000	0.2885	-
0.9902	671100	0.3688	-
0.9904	671200	0.4745	-
0.9905	671300	0.1864	-
0.9907	671400	0.5056	-
0.9908	671500	0.4579	-
0.9910	671600	0.2583	-
0.9911	671700	0.3365	-
0.9913	671800	0.178	-
0.9914	671900	0.429	-
0.9916	672000	0.236	-
0.9917	672100	0.2837	-
0.9919	672200	0.3106	-
0.9920	672300	0.5289	-
0.9921	672400	0.507	-
0.9923	672500	0.4705	-
0.9924	672600	0.3564	-
0.9926	672700	0.3445	-
0.9927	672800	0.4546	-
0.9929	672900	0.4546	-
0.9930	673000	0.3357	-
0.9932	673100	0.5831	-
0.9933	673200	0.2103	-
0.9935	673300	0.2072	-
0.9936	673400	0.188	-
0.9938	673500	0.6235	-
0.9939	673600	0.4453	-
0.9941	673700	0.1589	-
0.9942	673800	0.234	-
0.9944	673900	0.5613	-
0.9945	674000	0.1966	-
0.9947	674100	0.4023	-
0.9948	674200	0.206	-
0.9950	674300	0.3899	-
0.9951	674400	0.337	-
0.9952	674500	0.4391	-
0.9954	674600	0.261	-
0.9955	674700	0.4867	-
0.9957	674800	0.4385	-
0.9958	674900	0.3121	-
0.9960	675000	0.3895	-
0.9961	675100	0.4949	-
0.9963	675200	0.5844	-
0.9964	675300	0.4511	-
0.9966	675400	0.3611	-
0.9967	675500	0.2271	-
0.9969	675600	0.362	-
0.9970	675700	0.1125	-
0.9972	675800	0.6294	-
0.9973	675900	0.2618	-
0.9975	676000	0.3871	-
0.9976	676100	0.2613	-
0.9978	676200	0.4379	-
0.9979	676300	0.3276	-
0.9981	676400	0.1472	-
0.9982	676500	0.3242	-
0.9983	676600	0.2306	-
0.9985	676700	0.5339	-
0.9986	676800	0.4565	-
0.9988	676900	0.3196	-
0.9989	677000	0.3637	-
0.9991	677100	0.2689	-
0.9992	677200	0.4032	-
0.9994	677300	0.3168	-
0.9995	677400	0.2608	-
0.9997	677500	0.283	-
0.9998	677600	0.3362	-
1.0000	677700	0.4391	-

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},
}