all-MiniLM-L6-v59-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v47 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_v47
• 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 = [
    'quick clumping cat litter',
    'soup bowl olive wave olive green bowl olive green olive gi designs bowl 14 cm 6 cm soup serving handcrafted hand-painted dishwasher safe microwave safe pottery olive green egypt art and elegance meet everyday practicality in this wave-inspired meticulously-handcrafted and hand-painted pottery . material pottery color olive green dishwasher and microwave safe dimensions 14 cm x 6 cm available to buy as a single bowl or as multiple bowls. you can choose the number of bowls you want in the quantity field this is a handmade item. slight variations in color and shape might happen. this adds to the authenticity of each item and is a result of the artist s creative variations. proudly made in egypt.',
    'gardening tool seed starting tray plastic seeds tray plant growth tray growpro tray 25 cm 50 cm rectangular seed starting plastic black introducing our seed starting tray plastic perfectly suited for egypt s gardening enthusiasts with dimensions of 25 x 50cm this tray is meticulously crafted to facilitate optimal seed germination and early plant growth. whether you re starting a small herb garden or sowing seeds for your favorite vegetables this plastic seed starting tray provides the ideal environment for healthy seedlings. elevate your gardening experience with our high-quality plastic seed starting tray designed to meet the needs of egyptian gardeners and promote successful seed propagation. specifications plastic dimensions 25 x 50cm',
]
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, -0.1557, -0.0738],
#         [-0.1557,  1.0000, -0.1355],
#         [-0.0738, -0.1355,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v47

• Dataset: pairs_with_scores_v47 at f34e122
• Size: 71,347,383 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: 6.64 tokens max: 26 tokens	min: 4 tokens mean: 64.0 tokens max: 256 tokens	min: 0.0 mean: 0.26 max: 1.0

• Samples:

  sentence1	sentence2	score
  hair addict leave in	tongue we aspire to be the first choice for online food shoppers seeking fresh and tasty products with reasonable prices.	0.0
  summer prawn rolls	spring roll shrimp spring rolls prawn spring rolls shrimp spring rolls with garlic and basil	1.0
  wabisabi creamer	evil eye charm pendant women urban jewel charm evil eye multicolor add a touch of charm to your style with our prettiest evil eye charm. protect yourself from negative energy with this eye-catching accessory that is sure to bring some positive vibes into your life	0.0

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v47

• Dataset: pairs_with_scores_v47 at f34e122
• Size: 358,530 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: 6.57 tokens max: 22 tokens	min: 3 tokens mean: 61.4 tokens max: 252 tokens	min: 0.0 mean: 0.27 max: 1.0

• Samples:

  sentence1	sentence2	score
  dalgona coffee boba	fifa 22 xbox series x/s xbox game fifa 22 xbox game xbox s game xbox series game xbox x game	0.0
  pistache	london tea pot - pottery teapot handmade tea pot pottery tea pot mymayz tea pot london 15 cm 10 cm brewing serving tea hand-made pottery black egypt proudly made in egypt introducing the hand-made tea pot black - a must-have for all tea lovers add a touch of elegance to your tea time with this beautiful pottery creation. it s a sleek black design. material pottery dimensions 15 cm x 10 cm 1 l care hand-wash	0.0
  dago adand ahrloss shamp	hair shampoo sunsilk instant repair shampoo 600 ml offer 20 600 millilitre	1.0

• 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
0.4993	556600	0.5083	-
0.4994	556700	0.7898	-
0.4995	556800	0.6504	-
0.4996	556900	0.5997	-
0.4996	557000	0.4353	-
0.4997	557100	0.477	-
0.4998	557200	0.8305	-
0.4999	557300	0.8032	-
0.5000	557400	0.7214	-
0.5001	557500	0.5467	-
0.5002	557600	0.3062	-
0.5003	557700	0.4162	-
0.5004	557800	0.5257	-
0.5004	557900	0.5874	-
0.5005	558000	0.4694	-
0.5006	558100	0.716	-
0.5007	558200	0.4004	-
0.5008	558300	0.6425	-
0.5009	558400	0.487	-
0.5010	558500	0.5195	-
0.5011	558600	0.4806	-
0.5012	558700	0.3796	-
0.5013	558800	0.6516	-
0.5013	558900	0.3521	-
0.5014	559000	0.8511	-
0.5015	559100	0.5082	-
0.5016	559200	0.7745	-
0.5017	559300	0.4985	-
0.5018	559400	0.5577	-
0.5019	559500	0.5447	-
0.5020	559600	0.6291	-
0.5021	559700	0.5711	-
0.5022	559800	0.6433	-
0.5022	559900	0.6713	-
0.5023	560000	0.6461	-
0.5024	560100	0.5947	-
0.5025	560200	0.7536	-
0.5026	560300	0.5234	-
0.5027	560400	0.4918	-
0.5028	560500	0.5543	-
0.5029	560600	0.397	-
0.5030	560700	0.7445	-
0.5030	560800	0.5017	-
0.5031	560900	0.5311	-
0.5032	561000	0.3991	-
0.5033	561100	0.6642	-
0.5034	561200	0.6391	-
0.5035	561300	0.5653	-
0.5036	561400	0.8643	-
0.5037	561500	0.7407	-
0.5038	561600	0.6801	-
0.5039	561700	0.725	-
0.5039	561800	0.5667	-
0.5040	561900	0.6641	-
0.5041	562000	0.5062	-
0.5042	562100	0.4163	-
0.5043	562200	0.6029	-
0.5044	562300	0.4337	-
0.5045	562400	0.6002	-
0.5046	562500	0.6324	-
0.5047	562600	0.7035	-
0.5048	562700	0.5879	-
0.5048	562800	0.7908	-
0.5049	562900	0.6906	-
0.5050	563000	0.4653	-
0.5051	563100	0.4894	-
0.5052	563200	0.3802	-
0.5053	563300	0.3624	-
0.5054	563400	0.4071	-
0.5055	563500	0.5486	-
0.5056	563600	0.5296	-
0.5056	563700	0.7643	-
0.5057	563800	0.6102	-
0.5058	563900	0.7484	-
0.5059	564000	0.2816	-
0.5060	564100	0.4984	-
0.5061	564200	0.455	-
0.5062	564300	0.2962	-
0.5063	564400	0.7555	-
0.5064	564500	0.609	-
0.5065	564600	0.5858	-
0.5065	564700	0.5937	-
0.5066	564800	0.6498	-
0.5067	564900	0.4745	-
0.5068	565000	0.5162	-
0.5069	565100	0.6378	-
0.5070	565200	0.5279	-
0.5071	565300	0.634	-
0.5072	565400	0.7944	-
0.5073	565500	0.6329	-
0.5074	565600	0.2002	-
0.5074	565700	0.5195	-
0.5075	565800	0.5545	-
0.5076	565900	0.8067	-
0.5077	566000	0.4185	-
0.5078	566100	0.5953	-
0.5079	566200	0.3942	-
0.5080	566300	0.3349	-
0.5081	566400	0.4908	-
0.5082	566500	0.5728	-
0.5083	566600	0.682	-
0.5083	566700	0.5022	-
0.5084	566800	0.3356	-
0.5085	566900	0.7291	-
0.5086	567000	0.5946	-
0.5087	567100	0.4814	-
0.5088	567200	0.6113	-
0.5089	567300	0.572	-
0.5090	567400	0.372	-
0.5091	567500	0.4859	-
0.5091	567600	0.742	-
0.5092	567700	0.6736	-
0.5093	567800	0.4961	-
0.5094	567900	0.564	-
0.5095	568000	0.5346	-
0.5096	568100	0.4023	-
0.5097	568200	0.7005	-
0.5098	568300	0.7733	-
0.5099	568400	0.3769	-
0.5100	568500	0.8756	-
0.5100	568600	0.6677	-
0.5101	568700	0.7519	-
0.5102	568800	0.7338	-
0.5103	568900	0.4682	-
0.5104	569000	0.5299	-
0.5105	569100	0.5876	-
0.5106	569200	0.7548	-
0.5107	569300	0.6949	-
0.5108	569400	0.4521	-
0.5109	569500	0.5094	-
0.5109	569600	0.7162	-
0.5110	569700	0.685	-
0.5111	569800	0.5033	-
0.5112	569900	0.663	-
0.5113	570000	0.5575	-
0.5114	570100	0.4157	-
0.5115	570200	0.8242	-
0.5116	570300	0.5112	-
0.5117	570400	0.9198	-
0.5117	570500	0.7152	-
0.5118	570600	0.4793	-
0.5119	570700	0.5459	-
0.5120	570800	0.5783	-
0.5121	570900	0.696	-
0.5122	571000	0.6424	-
0.5123	571100	0.4009	-
0.5124	571200	0.6058	-
0.5125	571300	0.728	-
0.5126	571400	0.7629	-
0.5126	571500	0.9031	-
0.5127	571600	0.6718	-
0.5128	571700	0.3697	-
0.5129	571800	0.3879	-
0.5130	571900	0.6409	-
0.5131	572000	0.6762	-
0.5132	572100	0.4792	-
0.5133	572200	0.445	-
0.5134	572300	0.6002	-
0.5135	572400	0.842	-
0.5135	572500	0.6371	-
0.5136	572600	0.5527	-
0.5137	572700	0.7978	-
0.5138	572800	0.66	-
0.5139	572900	0.7922	-
0.5140	573000	0.5827	-
0.5141	573100	0.5476	-
0.5142	573200	0.6349	-
0.5143	573300	0.6947	-
0.5144	573400	0.4235	-
0.5144	573500	0.3395	-
0.5145	573600	0.7488	-
0.5146	573700	0.5014	-
0.5147	573800	0.8059	-
0.5148	573900	0.8777	-
0.5149	574000	0.4133	-
0.5150	574100	0.4036	-
0.5151	574200	0.706	-
0.5152	574300	0.6489	-
0.5152	574400	0.817	-
0.5153	574500	0.5976	-
0.5154	574600	0.6957	-
0.5155	574700	0.518	-
0.5156	574800	0.5399	-
0.5157	574900	0.7831	-
0.5158	575000	0.596	-
0.5159	575100	0.4825	-
0.5160	575200	0.4703	-
0.5161	575300	0.6643	-
0.5161	575400	0.6445	-
0.5162	575500	0.3979	-
0.5163	575600	0.6053	-
0.5164	575700	0.3658	-
0.5165	575800	0.403	-
0.5166	575900	0.5212	-
0.5167	576000	0.4314	-
0.5168	576100	0.5718	-
0.5169	576200	0.5983	-
0.5170	576300	0.6087	-
0.5170	576400	0.4489	-
0.5171	576500	0.2841	-
0.5172	576600	0.6791	-
0.5173	576700	0.555	-
0.5174	576800	0.6396	-
0.5175	576900	0.5355	-
0.5176	577000	0.4586	-
0.5177	577100	0.7105	-
0.5178	577200	0.7221	-
0.5178	577300	0.4142	-
0.5179	577400	0.5611	-
0.5180	577500	0.4719	-
0.5181	577600	0.3925	-
0.5182	577700	0.524	-
0.5183	577800	0.6142	-
0.5184	577900	0.3612	-
0.5185	578000	0.3967	-
0.5186	578100	0.7159	-
0.5187	578200	0.7901	-
0.5187	578300	0.4645	-
0.5188	578400	0.4022	-
0.5189	578500	0.932	-
0.5190	578600	0.5906	-
0.5191	578700	0.5912	-
0.5192	578800	0.7033	-
0.5193	578900	0.495	-
0.5194	579000	0.5165	-
0.5195	579100	0.687	-
0.5196	579200	0.6197	-
0.5196	579300	0.426	-
0.5197	579400	0.6593	-
0.5198	579500	0.8591	-
0.5199	579600	0.7914	-
0.5200	579700	0.4899	-
0.5201	579800	0.9559	-
0.5202	579900	0.5519	-
0.5203	580000	0.8055	-
0.5204	580100	0.5902	-
0.5205	580200	0.4877	-
0.5205	580300	0.7933	-
0.5206	580400	0.531	-
0.5207	580500	0.7148	-
0.5208	580600	0.3736	-
0.5209	580700	0.4799	-
0.5210	580800	0.26	-
0.5211	580900	0.5141	-
0.5212	581000	0.6782	-
0.5213	581100	0.6091	-
0.5213	581200	0.656	-
0.5214	581300	0.5749	-
0.5215	581400	1.1483	-
0.5216	581500	0.5323	-
0.5217	581600	0.4865	-
0.5218	581700	0.501	-
0.5219	581800	0.755	-
0.5220	581900	0.6197	-
0.5221	582000	0.7878	-
0.5222	582100	0.793	-
0.5222	582200	0.7876	-
0.5223	582300	0.5592	-
0.5224	582400	0.6591	-
0.5225	582500	0.6277	-
0.5226	582600	0.5339	-
0.5227	582700	0.4041	-
0.5228	582800	0.5786	-
0.5229	582900	0.3433	-
0.5230	583000	0.4103	-
0.5231	583100	0.39	-
0.5231	583200	0.4523	-
0.5232	583300	0.4347	-
0.5233	583400	0.552	-
0.5234	583500	0.6534	-
0.5235	583600	0.4385	-
0.5236	583700	0.5987	-
0.5237	583800	0.4693	-
0.5238	583900	0.7193	-
0.5239	584000	0.8147	-
0.5239	584100	0.7138	-
0.5240	584200	0.5724	-
0.5241	584300	0.3809	-
0.5242	584400	0.4029	-
0.5243	584500	0.6374	-
0.5244	584600	0.6924	-
0.5245	584700	0.8854	-
0.5246	584800	0.4586	-
0.5247	584900	0.4548	-
0.5248	585000	0.3328	-
0.5248	585100	0.243	-
0.5249	585200	0.4911	-
0.5250	585300	0.5451	-
0.5251	585400	0.832	-
0.5252	585500	0.4245	-
0.5253	585600	0.5159	-
0.5254	585700	0.283	-
0.5255	585800	0.5253	-
0.5256	585900	0.6025	-
0.5257	586000	0.5957	-
0.5257	586100	0.4693	-
0.5258	586200	0.4696	-
0.5259	586300	0.4949	-
0.5260	586400	0.5939	-
0.5261	586500	0.6342	-
0.5262	586600	0.4945	-
0.5263	586700	0.5231	-
0.5264	586800	0.5646	-
0.5265	586900	0.6353	-
0.5266	587000	0.8096	-
0.5266	587100	0.8211	-
0.5267	587200	0.398	-
0.5268	587300	0.2992	-
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0.5716	637200	0.6595	-
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0.7495	835600	0.2708	-
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0.7935	884600	0.4873	-
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0.7939	885000	0.428	-
0.7940	885100	0.3848	-
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0.8380	934200	0.4873	-
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0.9262	1032500	0.2118	-
0.9263	1032600	0.5851	-
0.9264	1032700	0.4764	-
0.9264	1032800	0.2561	-
0.9265	1032900	0.3635	-
0.9266	1033000	0.2452	-
0.9267	1033100	0.2338	-
0.9268	1033200	0.2644	-
0.9269	1033300	0.2581	-
0.9270	1033400	0.2987	-
0.9271	1033500	0.29	-
0.9272	1033600	0.3638	-
0.9272	1033700	0.3565	-
0.9273	1033800	0.2571	-
0.9274	1033900	0.3715	-
0.9275	1034000	0.305	-
0.9276	1034100	0.6192	-
0.9277	1034200	0.2763	-
0.9278	1034300	0.2905	-
0.9279	1034400	0.282	-
0.9280	1034500	0.237	-
0.9281	1034600	0.4767	-
0.9281	1034700	0.4816	-
0.9282	1034800	0.2687	-
0.9283	1034900	0.2705	-
0.9284	1035000	0.3191	-
0.9285	1035100	0.3569	-
0.9286	1035200	0.1631	-
0.9287	1035300	0.3708	-
0.9288	1035400	0.222	-
0.9289	1035500	0.42	-
0.9290	1035600	0.4174	-
0.9290	1035700	0.3099	-
0.9291	1035800	0.4129	-
0.9292	1035900	0.3338	-
0.9293	1036000	0.5509	-
0.9294	1036100	0.1706	-
0.9295	1036200	0.2832	-
0.9296	1036300	0.4019	-
0.9297	1036400	0.2332	-
0.9298	1036500	0.3449	-
0.9299	1036600	0.1583	-
0.9299	1036700	0.1798	-
0.9300	1036800	0.3	-
0.9301	1036900	0.3364	-
0.9302	1037000	0.4857	-
0.9303	1037100	0.3094	-
0.9304	1037200	0.255	-
0.9305	1037300	0.3331	-
0.9306	1037400	0.2691	-
0.9307	1037500	0.2368	-
0.9307	1037600	0.3184	-
0.9308	1037700	0.4042	-
0.9309	1037800	0.3473	-
0.9310	1037900	0.4033	-
0.9311	1038000	0.3491	-
0.9312	1038100	0.2948	-
0.9313	1038200	0.6157	-
0.9314	1038300	0.4798	-
0.9315	1038400	0.3515	-
0.9316	1038500	0.2239	-
0.9316	1038600	0.212	-
0.9317	1038700	0.3291	-
0.9318	1038800	0.3186	-
0.9319	1038900	0.2653	-
0.9320	1039000	0.2065	-
0.9321	1039100	0.3668	-
0.9322	1039200	0.2484	-
0.9323	1039300	0.2522	-
0.9324	1039400	0.2558	-
0.9325	1039500	0.4124	-
0.9325	1039600	0.257	-
0.9326	1039700	0.3697	-
0.9327	1039800	0.2441	-
0.9328	1039900	0.2825	-
0.9329	1040000	0.1976	-
0.9330	1040100	0.2714	-
0.9331	1040200	0.2612	-
0.9332	1040300	0.1418	-
0.9333	1040400	0.1662	-
0.9333	1040500	0.3383	-
0.9334	1040600	0.4494	-
0.9335	1040700	0.3032	-
0.9336	1040800	0.5232	-
0.9337	1040900	0.261	-
0.9338	1041000	0.3289	-
0.9339	1041100	0.2779	-
0.9340	1041200	0.2032	-
0.9341	1041300	0.3114	-
0.9342	1041400	0.3217	-
0.9342	1041500	0.452	-
0.9343	1041600	0.3545	-
0.9344	1041700	0.2831	-
0.9345	1041800	0.2262	-
0.9346	1041900	0.5149	-
0.9347	1042000	0.3994	-
0.9348	1042100	0.2862	-
0.9349	1042200	0.1692	-
0.9350	1042300	0.3282	-
0.9351	1042400	0.2815	-
0.9351	1042500	0.2229	-
0.9352	1042600	0.5147	-
0.9353	1042700	0.2995	-
0.9354	1042800	0.3517	-
0.9355	1042900	0.4973	-
0.9356	1043000	0.2913	-
0.9357	1043100	0.3506	-
0.9358	1043200	0.426	-
0.9359	1043300	0.2747	-
0.9360	1043400	0.3377	-
0.9360	1043500	0.237	-
0.9361	1043600	0.1899	-
0.9362	1043700	0.2929	-
0.9363	1043800	0.3885	-
0.9364	1043900	0.1682	-
0.9365	1044000	0.3001	-
0.9366	1044100	0.3977	-
0.9367	1044200	0.3282	-
0.9368	1044300	0.5513	-
0.9368	1044400	0.1602	-
0.9369	1044500	0.2188	-
0.9370	1044600	0.383	-
0.9371	1044700	0.1854	-
0.9372	1044800	0.4856	-
0.9373	1044900	0.1767	-
0.9374	1045000	0.5054	-
0.9375	1045100	0.3563	-
0.9376	1045200	0.3891	-
0.9377	1045300	0.2851	-
0.9377	1045400	0.3794	-
0.9378	1045500	0.3705	-
0.9379	1045600	0.3282	-
0.9380	1045700	0.3846	-
0.9381	1045800	0.3664	-
0.9382	1045900	0.322	-
0.9383	1046000	0.1662	-
0.9384	1046100	0.222	-
0.9385	1046200	0.3874	-
0.9386	1046300	0.3163	-
0.9386	1046400	0.3861	-
0.9387	1046500	0.3233	-
0.9388	1046600	0.3801	-
0.9389	1046700	0.3479	-
0.9390	1046800	0.4669	-
0.9391	1046900	0.1027	-
0.9392	1047000	0.3069	-
0.9393	1047100	0.4568	-
0.9394	1047200	0.2854	-
0.9394	1047300	0.4674	-
0.9395	1047400	0.299	-
0.9396	1047500	0.3248	-
0.9397	1047600	0.2029	-
0.9398	1047700	0.4203	-
0.9399	1047800	0.3333	-
0.9400	1047900	0.4618	-
0.9401	1048000	0.2944	-
0.9402	1048100	0.462	-
0.9403	1048200	0.4109	-
0.9403	1048300	0.3775	-
0.9404	1048400	0.6008	-
0.9405	1048500	0.4735	-
0.9406	1048600	0.2079	-
0.9407	1048700	0.4816	-
0.9408	1048800	0.3558	-
0.9409	1048900	0.1681	-
0.9410	1049000	0.2889	-
0.9411	1049100	0.363	-
0.9412	1049200	0.2275	-
0.9412	1049300	0.3305	-
0.9413	1049400	0.4101	-
0.9414	1049500	0.2178	-
0.9415	1049600	0.3201	-
0.9416	1049700	0.2809	-
0.9417	1049800	0.2974	-
0.9418	1049900	0.4359	-
0.9419	1050000	0.3486	-
0.9420	1050100	0.4458	-
0.9420	1050200	0.3176	-
0.9421	1050300	0.4449	-
0.9422	1050400	0.5533	-
0.9423	1050500	0.255	-
0.9424	1050600	0.3661	-
0.9425	1050700	0.4372	-
0.9426	1050800	0.371	-
0.9427	1050900	0.3553	-
0.9428	1051000	0.5474	-
0.9429	1051100	0.2891	-
0.9429	1051200	0.3707	-
0.9430	1051300	0.2005	-
0.9431	1051400	0.4769	-
0.9432	1051500	0.2794	-
0.9433	1051600	0.2935	-
0.9434	1051700	0.3213	-
0.9435	1051800	0.3003	-
0.9436	1051900	0.4429	-
0.9437	1052000	0.3022	-
0.9438	1052100	0.2611	-
0.9438	1052200	0.1595	-
0.9439	1052300	0.3606	-
0.9440	1052400	0.4529	-
0.9441	1052500	0.2014	-
0.9442	1052600	0.2407	-
0.9443	1052700	0.2882	-
0.9444	1052800	0.3038	-
0.9445	1052900	0.186	-
0.9446	1053000	0.3846	-
0.9447	1053100	0.3813	-
0.9447	1053200	0.2961	-
0.9448	1053300	0.3472	-
0.9449	1053400	0.2533	-
0.9450	1053500	0.1629	-
0.9451	1053600	0.3308	-
0.9452	1053700	0.1923	-
0.9453	1053800	0.3929	-
0.9454	1053900	0.2935	-
0.9455	1054000	0.4262	-
0.9455	1054100	0.0961	-
0.9456	1054200	0.4672	-
0.9457	1054300	0.1619	-
0.9458	1054400	0.1331	-
0.9459	1054500	0.4913	-
0.9460	1054600	0.2981	-
0.9461	1054700	0.2362	-
0.9462	1054800	0.3853	-
0.9463	1054900	0.3657	-
0.9464	1055000	0.3242	-
0.9464	1055100	0.3075	-
0.9465	1055200	0.1931	-
0.9466	1055300	0.2084	-
0.9467	1055400	0.3255	-
0.9468	1055500	0.3883	-
0.9469	1055600	0.3258	-
0.9470	1055700	0.4619	-
0.9471	1055800	0.419	-
0.9472	1055900	0.2569	-
0.9473	1056000	0.2944	-
0.9473	1056100	0.4665	-
0.9474	1056200	0.3843	-
0.9475	1056300	0.4336	-
0.9476	1056400	0.3755	-
0.9477	1056500	0.2136	-
0.9478	1056600	0.3638	-
0.9479	1056700	0.5993	-
0.9480	1056800	0.5425	-
0.9481	1056900	0.3799	-
0.9481	1057000	0.5572	-
0.9482	1057100	0.3449	-
0.9483	1057200	0.4291	-
0.9484	1057300	0.1816	-
0.9485	1057400	0.3149	-
0.9486	1057500	0.4907	-
0.9487	1057600	0.392	-
0.9488	1057700	0.549	-
0.9489	1057800	0.2668	-
0.9490	1057900	0.3613	-
0.9490	1058000	0.3275	-
0.9491	1058100	0.2072	-
0.9492	1058200	0.6009	-
0.9493	1058300	0.3809	-
0.9494	1058400	0.3429	-
0.9495	1058500	0.4719	-
0.9496	1058600	0.2399	-
0.9497	1058700	0.2672	-
0.9498	1058800	0.3912	-
0.9499	1058900	0.3323	-
0.9499	1059000	0.3062	-
0.9500	1059100	0.4842	-
0.9501	1059200	0.3715	-
0.9502	1059300	0.4191	-
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0.9504	1059500	0.2064	-
0.9505	1059600	0.5192	-
0.9506	1059700	0.3938	-
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0.9508	1059900	0.4249	-
0.9508	1060000	0.2226	-
0.9509	1060100	0.4415	-
0.9510	1060200	0.2928	-
0.9511	1060300	0.2678	-
0.9512	1060400	0.5721	-
0.9513	1060500	0.3299	-
0.9514	1060600	0.3247	-
0.9515	1060700	0.2902	-
0.9516	1060800	0.5281	-
0.9516	1060900	0.3856	-
0.9517	1061000	0.2604	-
0.9518	1061100	0.273	-
0.9519	1061200	0.0893	-
0.9520	1061300	0.1874	-
0.9521	1061400	0.3412	-
0.9522	1061500	0.4351	-
0.9523	1061600	0.5312	-
0.9524	1061700	0.8303	-
0.9525	1061800	0.5599	-
0.9525	1061900	0.3543	-
0.9526	1062000	0.3954	-
0.9527	1062100	0.2213	-
0.9528	1062200	0.3194	-
0.9529	1062300	0.3265	-
0.9530	1062400	0.4724	-
0.9531	1062500	0.3427	-
0.9532	1062600	0.3574	-
0.9533	1062700	0.3581	-
0.9534	1062800	0.4188	-
0.9534	1062900	0.4446	-
0.9535	1063000	0.4661	-
0.9536	1063100	0.4455	-
0.9537	1063200	0.2565	-
0.9538	1063300	0.2975	-
0.9539	1063400	0.4014	-
0.9540	1063500	0.3576	-
0.9541	1063600	0.225	-
0.9542	1063700	0.4153	-
0.9542	1063800	0.2254	-
0.9543	1063900	0.2314	-
0.9544	1064000	0.3539	-
0.9545	1064100	0.7109	-
0.9546	1064200	0.3831	-
0.9547	1064300	0.2679	-
0.9548	1064400	0.2115	-
0.9549	1064500	0.4648	-
0.9550	1064600	0.3278	-
0.9551	1064700	0.4404	-
0.9551	1064800	0.2973	-
0.9552	1064900	0.3365	-
0.9553	1065000	0.5359	-
0.9554	1065100	0.3575	-
0.9555	1065200	0.224	-
0.9556	1065300	0.2976	-
0.9557	1065400	0.1995	-
0.9558	1065500	0.3974	-
0.9559	1065600	0.2808	-
0.9560	1065700	0.1401	-
0.9560	1065800	0.3327	-
0.9561	1065900	0.2388	-
0.9562	1066000	0.3689	-
0.9563	1066100	0.2231	-
0.9564	1066200	0.2018	-
0.9565	1066300	0.2659	-
0.9566	1066400	0.2296	-
0.9567	1066500	0.1624	-
0.9568	1066600	0.313	-
0.9569	1066700	0.1221	-
0.9569	1066800	0.3092	-
0.9570	1066900	0.1855	-
0.9571	1067000	0.0844	-
0.9572	1067100	0.4548	-
0.9573	1067200	0.3572	-
0.9574	1067300	0.2043	-
0.9575	1067400	0.1572	-
0.9576	1067500	0.1633	-
0.9577	1067600	0.2995	-
0.9577	1067700	0.5757	-
0.9578	1067800	0.6299	-
0.9579	1067900	0.3257	-
0.9580	1068000	0.5032	-
0.9581	1068100	0.2309	-
0.9582	1068200	0.4633	-
0.9583	1068300	0.1759	-
0.9584	1068400	0.3408	-
0.9585	1068500	0.3277	-
0.9586	1068600	0.363	-
0.9586	1068700	0.3121	-
0.9587	1068800	0.3741	-
0.9588	1068900	0.2347	-
0.9589	1069000	0.4715	-
0.9590	1069100	0.3029	-
0.9591	1069200	0.3394	-
0.9592	1069300	0.6942	-
0.9593	1069400	0.3492	-
0.9594	1069500	0.2841	-
0.9595	1069600	0.5165	-
0.9595	1069700	0.4002	-
0.9596	1069800	0.2364	-
0.9597	1069900	0.5599	-
0.9598	1070000	0.2036	-
0.9599	1070100	0.2294	-
0.9600	1070200	0.2217	-
0.9601	1070300	0.221	-
0.9602	1070400	0.3886	-
0.9603	1070500	0.1101	-
0.9603	1070600	0.4677	-
0.9604	1070700	0.2365	-
0.9605	1070800	0.2855	-
0.9606	1070900	0.41	-
0.9607	1071000	0.2686	-
0.9608	1071100	0.2662	-
0.9609	1071200	0.3016	-
0.9610	1071300	0.3907	-
0.9611	1071400	0.2129	-
0.9612	1071500	0.4431	-
0.9612	1071600	0.2617	-
0.9613	1071700	0.3253	-
0.9614	1071800	0.3815	-
0.9615	1071900	0.4207	-
0.9616	1072000	0.2134	-
0.9617	1072100	0.4344	-
0.9618	1072200	0.4862	-
0.9619	1072300	0.2817	-
0.9620	1072400	0.2951	-
0.9621	1072500	0.3788	-
0.9621	1072600	0.3695	-
0.9622	1072700	0.3645	-
0.9623	1072800	0.4439	-
0.9624	1072900	0.5042	-
0.9625	1073000	0.2349	-
0.9626	1073100	0.2577	-
0.9627	1073200	0.3034	-
0.9628	1073300	0.3495	-
0.9629	1073400	0.2134	-
0.9630	1073500	0.477	-
0.9630	1073600	0.5259	-
0.9631	1073700	0.3326	-
0.9632	1073800	0.1878	-
0.9633	1073900	0.4654	-
0.9634	1074000	0.2115	-
0.9635	1074100	0.286	-
0.9636	1074200	0.3179	-
0.9637	1074300	0.3436	-
0.9638	1074400	0.1825	-
0.9638	1074500	0.3012	-
0.9639	1074600	0.277	-
0.9640	1074700	0.5933	-
0.9641	1074800	0.1305	-
0.9642	1074900	0.4671	-
0.9643	1075000	0.3331	-
0.9644	1075100	0.327	-
0.9645	1075200	0.2705	-
0.9646	1075300	0.3396	-
0.9647	1075400	0.32	-
0.9647	1075500	0.4822	-
0.9648	1075600	0.6522	-
0.9649	1075700	0.144	-
0.9650	1075800	0.2514	-
0.9651	1075900	0.3139	-
0.9652	1076000	0.3388	-
0.9653	1076100	0.3063	-
0.9654	1076200	0.3685	-
0.9655	1076300	0.3709	-
0.9656	1076400	0.3395	-
0.9656	1076500	0.2859	-
0.9657	1076600	0.2558	-
0.9658	1076700	0.3967	-
0.9659	1076800	0.6581	-
0.9660	1076900	0.1694	-
0.9661	1077000	0.2497	-
0.9662	1077100	0.2169	-
0.9663	1077200	0.2693	-
0.9664	1077300	0.3346	-
0.9664	1077400	0.239	-
0.9665	1077500	0.2674	-
0.9666	1077600	0.1818	-
0.9667	1077700	0.5452	-
0.9668	1077800	0.374	-
0.9669	1077900	0.3463	-
0.9670	1078000	0.4481	-
0.9671	1078100	0.398	-
0.9672	1078200	0.3297	-
0.9673	1078300	0.5071	-
0.9673	1078400	0.5089	-
0.9674	1078500	0.2929	-
0.9675	1078600	0.3246	-
0.9676	1078700	0.2123	-
0.9677	1078800	0.2186	-
0.9678	1078900	0.242	-
0.9679	1079000	0.3926	-
0.9680	1079100	0.231	-
0.9681	1079200	0.2996	-
0.9682	1079300	0.2313	-
0.9682	1079400	0.3201	-
0.9683	1079500	0.251	-
0.9684	1079600	0.3251	-
0.9685	1079700	0.3328	-
0.9686	1079800	0.4934	-
0.9687	1079900	0.3764	-
0.9688	1080000	0.3385	-
0.9689	1080100	0.4231	-
0.9690	1080200	0.2147	-
0.9691	1080300	0.3602	-
0.9691	1080400	0.3217	-
0.9692	1080500	0.3212	-
0.9693	1080600	0.5269	-
0.9694	1080700	0.3764	-
0.9695	1080800	0.1406	-
0.9696	1080900	0.2264	-
0.9697	1081000	0.1943	-
0.9698	1081100	0.363	-
0.9699	1081200	0.3331	-
0.9699	1081300	0.2375	-
0.9700	1081400	0.5521	-
0.9701	1081500	0.3771	-
0.9702	1081600	0.2504	-
0.9703	1081700	0.4404	-
0.9704	1081800	0.1975	-
0.9705	1081900	0.4834	-
0.9706	1082000	0.3578	-
0.9707	1082100	0.3117	-
0.9708	1082200	0.456	-
0.9708	1082300	0.1274	-
0.9709	1082400	0.6647	-
0.9710	1082500	0.3808	-
0.9711	1082600	0.3472	-
0.9712	1082700	0.2165	-
0.9713	1082800	0.256	-
0.9714	1082900	0.3391	-
0.9715	1083000	0.2443	-
0.9716	1083100	0.5084	-
0.9717	1083200	0.3572	-
0.9717	1083300	0.19	-
0.9718	1083400	0.1591	-
0.9719	1083500	0.227	-
0.9720	1083600	0.1546	-
0.9721	1083700	0.3678	-
0.9722	1083800	0.3767	-
0.9723	1083900	0.3589	-
0.9724	1084000	0.203	-
0.9725	1084100	0.3592	-
0.9725	1084200	0.3143	-
0.9726	1084300	0.4648	-
0.9727	1084400	0.2079	-
0.9728	1084500	0.341	-
0.9729	1084600	0.196	-
0.9730	1084700	0.4323	-
0.9731	1084800	0.601	-
0.9732	1084900	0.3834	-
0.9733	1085000	0.2679	-
0.9734	1085100	0.3493	-
0.9734	1085200	0.2591	-
0.9735	1085300	0.3947	-
0.9736	1085400	0.4283	-
0.9737	1085500	0.3459	-
0.9738	1085600	0.276	-
0.9739	1085700	0.4801	-
0.9740	1085800	0.2583	-
0.9741	1085900	0.253	-
0.9742	1086000	0.1733	-
0.9743	1086100	0.33	-
0.9743	1086200	0.2157	-
0.9744	1086300	0.3481	-
0.9745	1086400	0.4052	-
0.9746	1086500	0.386	-
0.9747	1086600	0.6015	-
0.9748	1086700	0.3691	-
0.9749	1086800	0.2677	-
0.9750	1086900	0.5305	-
0.9751	1087000	0.5815	-
0.9751	1087100	0.5636	-
0.9752	1087200	0.3213	-
0.9753	1087300	0.4031	-
0.9754	1087400	0.1237	-
0.9755	1087500	0.2784	-
0.9756	1087600	0.4277	-
0.9757	1087700	0.4368	-
0.9758	1087800	0.4269	-
0.9759	1087900	0.243	-
0.9760	1088000	0.2957	-
0.9760	1088100	0.3187	-
0.9761	1088200	0.3353	-
0.9762	1088300	0.377	-
0.9763	1088400	0.2993	-
0.9764	1088500	0.2928	-
0.9765	1088600	0.3526	-
0.9766	1088700	0.364	-
0.9767	1088800	0.3336	-
0.9768	1088900	0.21	-
0.9769	1089000	0.2332	-
0.9769	1089100	0.1033	-
0.9770	1089200	0.1943	-
0.9771	1089300	0.4226	-
0.9772	1089400	0.5164	-
0.9773	1089500	0.416	-
0.9774	1089600	0.4162	-
0.9775	1089700	0.3876	-
0.9776	1089800	0.2798	-
0.9777	1089900	0.2295	-
0.9778	1090000	0.3831	-
0.9778	1090100	0.3505	-
0.9779	1090200	0.1386	-
0.9780	1090300	0.4642	-
0.9781	1090400	0.4033	-
0.9782	1090500	0.2429	-
0.9783	1090600	0.3646	-
0.9784	1090700	0.4189	-
0.9785	1090800	0.4724	-
0.9786	1090900	0.2555	-
0.9786	1091000	0.2056	-
0.9787	1091100	0.1832	-
0.9788	1091200	0.313	-
0.9789	1091300	0.3695	-
0.9790	1091400	0.2176	-
0.9791	1091500	0.2718	-
0.9792	1091600	0.261	-
0.9793	1091700	0.3911	-
0.9794	1091800	0.2258	-
0.9795	1091900	0.13	-
0.9795	1092000	0.2245	-
0.9796	1092100	0.3009	-
0.9797	1092200	0.3841	-
0.9798	1092300	0.4755	-
0.9799	1092400	0.311	-
0.9800	1092500	0.2432	-
0.9801	1092600	0.1288	-
0.9802	1092700	0.3067	-
0.9803	1092800	0.4961	-
0.9804	1092900	0.2265	-
0.9804	1093000	0.2715	-
0.9805	1093100	0.2644	-
0.9806	1093200	0.2754	-
0.9807	1093300	0.2161	-
0.9808	1093400	0.3725	-
0.9809	1093500	0.389	-
0.9810	1093600	0.3514	-
0.9811	1093700	0.1492	-
0.9812	1093800	0.3608	-
0.9812	1093900	0.4302	-
0.9813	1094000	0.2083	-
0.9814	1094100	0.3702	-
0.9815	1094200	0.3754	-
0.9816	1094300	0.1673	-
0.9817	1094400	0.3138	-
0.9818	1094500	0.4096	-
0.9819	1094600	0.3243	-
0.9820	1094700	0.3098	-
0.9821	1094800	0.2956	-
0.9821	1094900	0.5137	-
0.9822	1095000	0.2052	-
0.9823	1095100	0.3049	-
0.9824	1095200	0.2169	-
0.9825	1095300	0.3291	-
0.9826	1095400	0.4625	-
0.9827	1095500	0.496	-
0.9828	1095600	0.598	-
0.9829	1095700	0.2539	-
0.9830	1095800	0.3146	-
0.9830	1095900	0.4799	-
0.9831	1096000	0.4698	-
0.9832	1096100	0.3824	-
0.9833	1096200	0.3436	-
0.9834	1096300	0.2606	-
0.9835	1096400	0.4234	-
0.9836	1096500	0.3168	-
0.9837	1096600	0.5522	-
0.9838	1096700	0.5665	-
0.9839	1096800	0.1775	-
0.9839	1096900	0.672	-
0.9840	1097000	0.4151	-
0.9841	1097100	0.4973	-
0.9842	1097200	0.2384	-
0.9843	1097300	0.3381	-
0.9844	1097400	0.3364	-
0.9845	1097500	0.3815	-
0.9846	1097600	0.4994	-
0.9847	1097700	0.1988	-
0.9847	1097800	0.2776	-
0.9848	1097900	0.4003	-
0.9849	1098000	0.4225	-
0.9850	1098100	0.1525	-
0.9851	1098200	0.2866	-
0.9852	1098300	0.1399	-
0.9853	1098400	0.446	-
0.9854	1098500	0.4018	-
0.9855	1098600	0.2331	-
0.9856	1098700	0.4617	-
0.9856	1098800	0.5922	-
0.9857	1098900	0.1296	-
0.9858	1099000	0.3323	-
0.9859	1099100	0.3673	-
0.9860	1099200	0.2697	-
0.9861	1099300	0.5017	-
0.9862	1099400	0.3939	-
0.9863	1099500	0.34	-
0.9864	1099600	0.2165	-
0.9865	1099700	0.3288	-
0.9865	1099800	0.3327	-
0.9866	1099900	0.192	-
0.9867	1100000	0.253	-
0.9868	1100100	0.5576	-
0.9869	1100200	0.3833	-
0.9870	1100300	0.2703	-
0.9871	1100400	0.2764	-
0.9872	1100500	0.3334	-
0.9873	1100600	0.3293	-
0.9873	1100700	0.3319	-
0.9874	1100800	0.1744	-
0.9875	1100900	0.4169	-
0.9876	1101000	0.4396	-
0.9877	1101100	0.3015	-
0.9878	1101200	0.424	-
0.9879	1101300	0.3331	-
0.9880	1101400	0.5067	-
0.9881	1101500	0.2382	-
0.9882	1101600	0.2266	-
0.9882	1101700	0.3753	-
0.9883	1101800	0.3491	-
0.9884	1101900	0.3574	-
0.9885	1102000	0.2345	-
0.9886	1102100	0.3118	-
0.9887	1102200	0.3493	-
0.9888	1102300	0.5065	-
0.9889	1102400	0.3483	-
0.9890	1102500	0.2171	-
0.9891	1102600	0.3111	-
0.9891	1102700	0.3035	-
0.9892	1102800	0.5352	-
0.9893	1102900	0.3173	-
0.9894	1103000	0.1621	-
0.9895	1103100	0.2635	-
0.9896	1103200	0.4804	-
0.9897	1103300	0.4016	-
0.9898	1103400	0.3427	-
0.9899	1103500	0.2715	-
0.9900	1103600	0.701	-
0.9900	1103700	0.4755	-
0.9901	1103800	0.2619	-
0.9902	1103900	0.2452	-
0.9903	1104000	0.5381	-
0.9904	1104100	0.5633	-
0.9905	1104200	0.4997	-
0.9906	1104300	0.546	-
0.9907	1104400	0.4594	-
0.9908	1104500	0.4808	-
0.9908	1104600	0.5694	-
0.9909	1104700	0.236	-
0.9910	1104800	0.3949	-
0.9911	1104900	0.2692	-
0.9912	1105000	0.1735	-
0.9913	1105100	0.334	-
0.9914	1105200	0.3887	-
0.9915	1105300	0.2865	-
0.9916	1105400	0.4051	-
0.9917	1105500	0.2151	-
0.9917	1105600	0.3811	-
0.9918	1105700	0.2514	-
0.9919	1105800	0.2922	-
0.9920	1105900	0.1497	-
0.9921	1106000	0.3255	-
0.9922	1106100	0.5318	-
0.9923	1106200	0.5127	-
0.9924	1106300	0.1471	-
0.9925	1106400	0.1821	-
0.9926	1106500	0.4398	-
0.9926	1106600	0.1684	-
0.9927	1106700	0.3663	-
0.9928	1106800	0.4744	-
0.9929	1106900	0.2701	-
0.9930	1107000	0.6137	-
0.9931	1107100	0.1517	-
0.9932	1107200	0.2657	-
0.9933	1107300	0.4412	-
0.9934	1107400	0.2801	-
0.9934	1107500	0.2577	-
0.9935	1107600	0.4944	-
0.9936	1107700	0.2839	-
0.9937	1107800	0.3122	-
0.9938	1107900	0.3043	-
0.9939	1108000	0.2724	-
0.9940	1108100	0.3579	-
0.9941	1108200	0.2736	-
0.9942	1108300	0.5603	-
0.9943	1108400	0.3101	-
0.9943	1108500	0.2701	-
0.9944	1108600	0.194	-
0.9945	1108700	0.3	-
0.9946	1108800	0.4925	-
0.9947	1108900	0.2374	-
0.9948	1109000	0.5701	-
0.9949	1109100	0.5467	-
0.9950	1109200	0.303	-
0.9951	1109300	0.3182	-
0.9952	1109400	0.4199	-
0.9952	1109500	0.3153	-
0.9953	1109600	0.3527	-
0.9954	1109700	0.2657	-
0.9955	1109800	0.3758	-
0.9956	1109900	0.3998	-
0.9957	1110000	0.2812	-
0.9958	1110100	0.34	-
0.9959	1110200	0.4542	-
0.9960	1110300	0.3163	-
0.9961	1110400	0.3079	-
0.9961	1110500	0.1982	-
0.9962	1110600	0.3001	-
0.9963	1110700	0.2772	-
0.9964	1110800	0.3411	-
0.9965	1110900	0.3667	-
0.9966	1111000	0.447	-
0.9967	1111100	0.5308	-
0.9968	1111200	0.3906	-
0.9969	1111300	0.3294	-
0.9969	1111400	0.3073	-
0.9970	1111500	0.2757	-
0.9971	1111600	0.4113	-
0.9972	1111700	0.4787	-
0.9973	1111800	0.3898	-
0.9974	1111900	0.3418	-
0.9975	1112000	0.5384	-
0.9976	1112100	0.282	-
0.9977	1112200	0.4432	-
0.9978	1112300	0.3447	-
0.9978	1112400	0.286	-
0.9979	1112500	0.359	-
0.9980	1112600	0.2608	-
0.9981	1112700	0.3024	-
0.9982	1112800	0.3303	-
0.9983	1112900	0.399	-
0.9984	1113000	0.2647	-
0.9985	1113100	0.3463	-
0.9986	1113200	0.4274	-
0.9987	1113300	0.2059	-
0.9987	1113400	0.2192	-
0.9988	1113500	0.3566	-
0.9989	1113600	0.1455	-
0.9990	1113700	0.3032	-
0.9991	1113800	0.5018	-
0.9992	1113900	0.3447	-
0.9993	1114000	0.2207	-
0.9994	1114100	0.3546	-
0.9995	1114200	0.1606	-
0.9995	1114300	0.136	-
0.9996	1114400	0.3791	-
0.9997	1114500	0.3987	-
0.9998	1114600	0.2861	-
0.9999	1114700	0.2293	-
1.0000	1114800	0.2376	-

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},
}