all-MiniLM-L6-v19-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v18-pair_score on the pairs_with_scores_v15_tag_sim_synonyms_typo_augmented 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-v18-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v15_tag_sim_synonyms_typo_augmented
• 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 = [
    'rubber sole slipper',
    'lotus flower incense burner',
    'steak knives with pp plastic handles',
]
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, 0.3841, 0.4347],
#         [0.3841, 1.0000, 0.2730],
#         [0.4347, 0.2730, 1.0000]])

Training Details

Training Dataset

pairs_with_scores_v15_tag_sim_synonyms_typo_augmented

• Dataset: pairs_with_scores_v15_tag_sim_synonyms_typo_augmented at 477293d
• Size: 42,533,977 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: 6.26 tokens max: 43 tokens	min: 3 tokens mean: 6.38 tokens max: 53 tokens	min: 0.13 mean: 0.3 max: 1.0

• Samples:

  sentence1	sentence2	score
  clutch clutch	internal bag	0.48
  suit	flawless lip stain	0.2
  cast iron skillet	and dinner plates	0.29

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v15_tag_sim_synonyms_typo_augmented

• Dataset: pairs_with_scores_v15_tag_sim_synonyms_typo_augmented at 477293d
• Size: 213,739 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: 6.27 tokens max: 57 tokens	min: 3 tokens mean: 6.75 tokens max: 131 tokens	min: 0.14 mean: 0.3 max: 1.0

• Samples:

  sentence1	sentence2	score
  comfort skirt	sporting activities tshirt	0.26
  flowy jumpuit	workwear pullover	0.25
  jeans	wide waistband shorts	0.23

• 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	Validation Loss
0.0003	100	9.2195	-
0.0006	200	9.2554	-
0.0009	300	9.2941	-
0.0012	400	9.3714	-
0.0015	500	9.2423	9.2898
0.0018	600	9.1357	-
0.0021	700	9.1476	-
0.0024	800	9.1307	-
0.0027	900	9.1277	-
0.0030	1000	9.0814	9.1435
0.0033	1100	9.0618	-
0.0036	1200	9.0388	-
0.0039	1300	9.0119	-
0.0042	1400	8.9708	-
0.0045	1500	8.8394	9.0078
0.0048	1600	8.9639	-
0.0051	1700	8.9005	-
0.0054	1800	8.9235	-
0.0057	1900	8.8485	-
0.0060	2000	8.86	8.8748
0.0063	2100	8.8658	-
0.0066	2200	8.8272	-
0.0069	2300	8.8384	-
0.0072	2400	8.6707	-
0.0075	2500	8.691	8.7429
0.0078	2600	8.7484	-
0.0081	2700	8.7436	-
0.0084	2800	8.5808	-
0.0087	2900	8.6106	-
0.0090	3000	8.5786	8.6151
0.0093	3100	8.6133	-
0.0096	3200	8.5827	-
0.0099	3300	8.5032	-
0.0102	3400	8.5051	-
0.0105	3500	8.4791	8.4838
0.0108	3600	8.4469	-
0.0111	3700	8.4512	-
0.0114	3800	8.4136	-
0.0117	3900	8.3726	-
0.0120	4000	8.4261	8.3693
0.0123	4100	8.3772	-
0.0126	4200	8.3776	-
0.0129	4300	8.3421	-
0.0132	4400	8.3089	-
0.0135	4500	8.3067	8.2796
0.0138	4600	8.2758	-
0.0141	4700	8.2731	-
0.0144	4800	8.2682	-
0.0147	4900	8.2707	-
0.0150	5000	8.2524	8.2163
0.0153	5100	8.2341	-
0.0156	5200	8.2339	-
0.0159	5300	8.2501	-
0.0163	5400	8.2266	-
0.0166	5500	8.1997	8.1804
0.0169	5600	8.2125	-
0.0172	5700	8.1884	-
0.0175	5800	8.198	-
0.0178	5900	8.2139	-
0.0181	6000	8.1961	8.1527
0.0184	6100	8.1933	-
0.0187	6200	8.1732	-
0.0190	6300	8.1564	-
0.0193	6400	8.2064	-
0.0196	6500	8.1803	8.1312
0.0199	6600	8.1576	-
0.0202	6700	8.167	-
0.0205	6800	8.1886	-
0.0208	6900	8.1835	-
0.0211	7000	8.1378	8.1136
0.0214	7100	8.1482	-
0.0217	7200	8.1377	-
0.0220	7300	8.142	-
0.0223	7400	8.1633	-
0.0226	7500	8.1463	8.0957
0.0229	7600	8.1507	-
0.0232	7700	8.1447	-
0.0235	7800	8.1385	-
0.0238	7900	8.1281	-
0.0241	8000	8.1412	8.0838
0.0244	8100	8.1217	-
0.0247	8200	8.137	-
0.0250	8300	8.1328	-
0.0253	8400	8.1143	-
0.0256	8500	8.115	8.0708
0.0259	8600	8.1242	-
0.0262	8700	8.1041	-
0.0265	8800	8.1022	-
0.0268	8900	8.1205	-
0.0271	9000	8.1097	8.0598
0.0274	9100	8.0984	-
0.0277	9200	8.1001	-
0.0280	9300	8.1195	-
0.0283	9400	8.08	-
0.0286	9500	8.088	8.0490
0.0289	9600	8.1075	-
0.0292	9700	8.0916	-
0.0295	9800	8.0814	-
0.0298	9900	8.085	-
0.0301	10000	8.0824	8.0380
0.0304	10100	8.0769	-
0.0307	10200	8.0979	-
0.0310	10300	8.0771	-
0.0313	10400	8.0856	-
0.0316	10500	8.0675	8.0275
0.0319	10600	8.0881	-
0.0322	10700	8.0742	-
0.0325	10800	8.0808	-
0.0328	10900	8.0549	-
0.0331	11000	8.0677	8.0186
0.0334	11100	8.0727	-
0.0337	11200	8.0653	-
0.0340	11300	8.0803	-
0.0343	11400	8.0633	-
0.0346	11500	8.0665	8.0083
0.0349	11600	8.0463	-
0.0352	11700	8.0562	-
0.0355	11800	8.0346	-
0.0358	11900	8.0622	-
0.0361	12000	8.0315	8.0014
0.0364	12100	8.0496	-
0.0367	12200	8.0537	-
0.0370	12300	8.0292	-
0.0373	12400	8.0503	-
0.0376	12500	8.0539	7.9922
0.0379	12600	8.0558	-
0.0382	12700	8.0598	-
0.0385	12800	8.0461	-
0.0388	12900	8.0351	-
0.0391	13000	8.0377	7.9866
0.0394	13100	8.0318	-
0.0397	13200	8.0385	-
0.0400	13300	8.0309	-
0.0403	13400	8.053	-
0.0406	13500	8.0517	7.9773
0.0409	13600	8.0569	-
0.0412	13700	8.0342	-
0.0415	13800	8.0372	-
0.0418	13900	8.0299	-
0.0421	14000	8.0227	7.9671
0.0424	14100	8.024	-
0.0427	14200	8.0181	-
0.0430	14300	8.021	-
0.0433	14400	8.0289	-
0.0436	14500	8.0121	7.9627
0.0439	14600	8.0022	-
0.0442	14700	8.018	-
0.0445	14800	8.0217	-
0.0448	14900	8.0032	-
0.0451	15000	7.9904	7.9550
0.0454	15100	8.001	-
0.0457	15200	8.0234	-
0.0460	15300	8.0099	-
0.0463	15400	8.0209	-
0.0466	15500	8.0258	7.9501
0.0469	15600	8.0236	-
0.0472	15700	7.9996	-
0.0475	15800	8.0003	-
0.0478	15900	7.9923	-
0.0481	16000	7.9767	7.9454
0.0485	16100	8.0209	-
0.0488	16200	7.9976	-
0.0491	16300	7.9744	-
0.0494	16400	8.0049	-
0.0497	16500	7.9714	7.9390
0.0500	16600	7.9806	-
0.0503	16700	7.9866	-
0.0506	16800	8.012	-
0.0509	16900	7.9786	-
0.0512	17000	7.988	7.9346
0.0515	17100	7.994	-
0.0518	17200	7.9825	-
0.0521	17300	7.9885	-
0.0524	17400	7.9759	-
0.0527	17500	7.9957	7.9253
0.0530	17600	7.9872	-
0.0533	17700	7.9968	-
0.0536	17800	7.9837	-
0.0539	17900	7.9825	-
0.0542	18000	7.9761	7.9198
0.0545	18100	7.9897	-
0.0548	18200	7.9753	-
0.0551	18300	7.981	-
0.0554	18400	7.9952	-
0.0557	18500	7.9855	7.9135
0.0560	18600	7.9611	-
0.0563	18700	7.9873	-
0.0566	18800	7.9698	-
0.0569	18900	7.9624	-
0.0572	19000	7.9609	7.9116
0.0575	19100	7.9468	-
0.0578	19200	7.9468	-
0.0581	19300	7.9854	-
0.0584	19400	7.9667	-
0.0587	19500	7.9704	7.9077
0.0590	19600	7.9891	-
0.0593	19700	7.9447	-
0.0596	19800	7.9796	-
0.0599	19900	7.9772	-
0.0602	20000	7.9535	7.9035
0.0605	20100	7.9572	-
0.0608	20200	7.942	-
0.0611	20300	7.9585	-
0.0614	20400	7.9566	-
0.0617	20500	7.9764	7.8973
0.0620	20600	7.9641	-
0.0623	20700	7.9466	-
0.0626	20800	7.9724	-
0.0629	20900	7.9626	-
0.0632	21000	7.9514	7.8923
0.0635	21100	7.9613	-
0.0638	21200	7.9605	-
0.0641	21300	7.9471	-
0.0644	21400	7.9536	-
0.0647	21500	7.9514	7.8928
0.0650	21600	7.9597	-
0.0653	21700	7.9602	-
0.0656	21800	7.9814	-
0.0659	21900	7.9484	-
0.0662	22000	7.9654	7.8829
0.0665	22100	7.9332	-
0.0668	22200	7.9295	-
0.0671	22300	7.9426	-
0.0674	22400	7.9645	-
0.0677	22500	7.9302	7.8803
0.0680	22600	7.971	-
0.0683	22700	7.9665	-
0.0686	22800	7.9438	-
0.0689	22900	7.9217	-
0.0692	23000	7.9203	7.8783
0.0695	23100	7.9361	-
0.0698	23200	7.9392	-
0.0701	23300	7.9892	-
0.0704	23400	7.9394	-
0.0707	23500	7.9632	7.8761
0.0710	23600	7.9428	-
0.0713	23700	7.9558	-
0.0716	23800	7.9211	-
0.0719	23900	7.9415	-
0.0722	24000	7.9552	7.8678
0.0725	24100	7.9299	-
0.0728	24200	7.9303	-
0.0731	24300	7.9317	-
0.0734	24400	7.9295	-
0.0737	24500	7.9534	7.8668
0.0740	24600	7.9258	-
0.0743	24700	7.9085	-
0.0746	24800	7.9391	-
0.0749	24900	7.9114	-
0.0752	25000	7.9439	7.8620
0.0755	25100	7.9325	-
0.0758	25200	7.9127	-
0.0761	25300	7.909	-
0.0764	25400	7.917	-
0.0767	25500	7.9294	7.8585
0.0770	25600	7.9403	-
0.0773	25700	7.9321	-
0.0776	25800	7.9238	-
0.0779	25900	7.9294	-
0.0782	26000	7.927	7.8533
0.0785	26100	7.9021	-
0.0788	26200	7.9053	-
0.0791	26300	7.9552	-
0.0794	26400	7.9127	-
0.0797	26500	7.9255	7.8501
0.0800	26600	7.9264	-
0.0803	26700	7.927	-
0.0807	26800	7.8984	-
0.0810	26900	7.92	-
0.0813	27000	7.9212	7.8480
0.0816	27100	7.9003	-
0.0819	27200	7.9146	-
0.0822	27300	7.9081	-
0.0825	27400	7.9205	-
0.0828	27500	7.9046	7.8454
0.0831	27600	7.8894	-
0.0834	27700	7.9592	-
0.0837	27800	7.895	-
0.0840	27900	7.8933	-
0.0843	28000	7.9272	7.8428
0.0846	28100	7.8853	-
0.0849	28200	7.9394	-
0.0852	28300	7.8962	-
0.0855	28400	7.8911	-
0.0858	28500	7.9226	7.8410
0.0861	28600	7.9079	-
0.0864	28700	7.911	-
0.0867	28800	7.8932	-
0.0870	28900	7.9071	-
0.0873	29000	7.9328	7.8342
0.0876	29100	7.8917	-
0.0879	29200	7.9271	-
0.0882	29300	7.9248	-
0.0885	29400	7.9039	-
0.0888	29500	7.886	7.8308
0.0891	29600	7.8755	-
0.0894	29700	7.9035	-
0.0897	29800	7.8967	-
0.0900	29900	7.9157	-
0.0903	30000	7.8794	7.8313
0.0906	30100	7.9032	-
0.0909	30200	7.9283	-
0.0912	30300	7.8745	-
0.0915	30400	7.9002	-
0.0918	30500	7.8913	7.8281
0.0921	30600	7.9054	-
0.0924	30700	7.8684	-
0.0927	30800	7.9018	-
0.0930	30900	7.9275	-
0.0933	31000	7.8863	7.8275
0.0936	31100	7.8863	-
0.0939	31200	7.9136	-
0.0942	31300	7.9023	-
0.0945	31400	7.8738	-
0.0948	31500	7.9142	7.8250
0.0951	31600	7.8838	-
0.0954	31700	7.9111	-
0.0957	31800	7.8597	-
0.0960	31900	7.8978	-
0.0963	32000	7.9103	7.8209
0.0966	32100	7.8902	-
0.0969	32200	7.8954	-
0.0972	32300	7.9272	-
0.0975	32400	7.9109	-
0.0978	32500	7.884	7.8162
0.0981	32600	7.8984	-
0.0984	32700	7.911	-
0.0987	32800	7.8997	-
0.0990	32900	7.8883	-
0.0993	33000	7.9024	7.8181
0.0996	33100	7.8655	-
0.0999	33200	7.886	-
0.1002	33300	7.8859	-
0.1005	33400	7.8907	-
0.1008	33500	7.8811	7.8115
0.1011	33600	7.8518	-
0.1014	33700	7.8774	-
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0.1020	33900	7.9091	-
0.1023	34000	7.9174	7.8109
0.1026	34100	7.8799	-
0.1029	34200	7.8869	-
0.1032	34300	7.8871	-
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0.1038	34500	7.8975	7.8079
0.1041	34600	7.8645	-
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0.1053	35000	7.8775	7.8020
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0.1080	35900	7.883	-
0.1083	36000	7.8489	7.7972
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0.1122	37300	7.8841	-
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0.1144	38000	7.8762	7.8007
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0.1159	38500	7.8827	7.7885
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0.1171	38900	7.8445	-
0.1174	39000	7.8617	7.7833
0.1177	39100	7.8599	-
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0.1189	39500	7.8625	7.7828
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0.1198	39800	7.8422	-
0.1201	39900	7.8643	-
0.1204	40000	7.8584	7.7837
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0.1210	40200	7.8376	-
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0.1216	40400	7.8455	-
0.1219	40500	7.826	7.7842
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0.1225	40700	7.8384	-
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0.1231	40900	7.8611	-
0.1234	41000	7.8374	7.7774
0.1237	41100	7.8695	-
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0.1243	41300	7.8393	-
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0.1249	41500	7.8589	7.7765
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0.1255	41700	7.8719	-
0.1258	41800	7.8604	-
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0.1264	42000	7.8528	7.7723
0.1267	42100	7.8799	-
0.1270	42200	7.8502	-
0.1273	42300	7.8411	-
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0.1279	42500	7.8277	7.7745
0.1282	42600	7.8291	-
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0.1288	42800	7.8465	-
0.1291	42900	7.8409	-
0.1294	43000	7.7991	7.7753
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0.1300	43200	7.8264	-
0.1303	43300	7.8299	-
0.1306	43400	7.848	-
0.1309	43500	7.8445	7.7695
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0.1324	44000	7.8662	7.7714
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0.1330	44200	7.8605	-
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0.1339	44500	7.8401	7.7644
0.1342	44600	7.8359	-
0.1345	44700	7.8215	-
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0.1354	45000	7.851	7.7648
0.1357	45100	7.8203	-
0.1360	45200	7.8218	-
0.1363	45300	7.8632	-
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0.1369	45500	7.8406	7.7650
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0.1384	46000	7.8291	7.7652
0.1387	46100	7.8481	-
0.1390	46200	7.8295	-
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0.1399	46500	7.859	7.7617
0.1402	46600	7.8308	-
0.1405	46700	7.8456	-
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0.1414	47000	7.8244	7.7589
0.1417	47100	7.8467	-
0.1420	47200	7.814	-
0.1423	47300	7.83	-
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0.1429	47500	7.8451	7.7533
0.1432	47600	7.8201	-
0.1435	47700	7.8063	-
0.1438	47800	7.8391	-
0.1441	47900	7.818	-
0.1444	48000	7.8246	7.7625
0.1448	48100	7.8518	-
0.1451	48200	7.8287	-
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0.1457	48400	7.8592	-
0.1460	48500	7.847	7.7505
0.1463	48600	7.8434	-
0.1466	48700	7.8204	-
0.1469	48800	7.8363	-
0.1472	48900	7.844	-
0.1475	49000	7.8024	7.7530
0.1478	49100	7.8513	-
0.1481	49200	7.7986	-
0.1484	49300	7.8237	-
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0.1490	49500	7.8204	7.7463
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0.1505	50000	7.845	7.7461
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0.1565	52000	7.8503	7.7383
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0.1625	54000	7.8199	7.7373
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0.1634	54300	7.814	-
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0.1640	54500	7.8037	7.7336
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0.1655	55000	7.8009	7.7334
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0.1670	55500	7.8092	7.7359
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0.1685	56000	7.7899	7.7356
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0.1691	56200	7.8261	-
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0.1700	56500	7.841	7.7318
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0.1706	56700	7.8078	-
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0.1715	57000	7.7987	7.7303
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0.1730	57500	7.7861	7.7374
0.1733	57600	7.8413	-
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0.1757	58400	7.7933	-
0.1760	58500	7.796	7.7258
0.1763	58600	7.793	-
0.1766	58700	7.7967	-
0.1770	58800	7.809	-
0.1773	58900	7.7903	-
0.1776	59000	7.8022	7.7280
0.1779	59100	7.7889	-
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0.1791	59500	7.8329	7.7265
0.1794	59600	7.8027	-
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0.1800	59800	7.8013	-
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0.1806	60000	7.7883	7.7265
0.1809	60100	7.7663	-
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0.1815	60300	7.7757	-
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0.1821	60500	7.85	7.7209
0.1824	60600	7.8304	-
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0.1830	60800	7.8312	-
0.1833	60900	7.8201	-
0.1836	61000	7.7789	7.7219
0.1839	61100	7.8029	-
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0.1845	61300	7.799	-
0.1848	61400	7.8205	-
0.1851	61500	7.7813	7.7158
0.1854	61600	7.7814	-
0.1857	61700	7.7861	-
0.1860	61800	7.8206	-
0.1863	61900	7.8004	-
0.1866	62000	7.787	7.7182
0.1869	62100	7.782	-
0.1872	62200	7.7744	-
0.1875	62300	7.8141	-
0.1878	62400	7.7884	-
0.1881	62500	7.7938	7.7198
0.1884	62600	7.795	-
0.1887	62700	7.7929	-
0.1890	62800	7.803	-
0.1893	62900	7.8215	-
0.1896	63000	7.7985	7.7248
0.1899	63100	7.7649	-
0.1902	63200	7.8331	-
0.1905	63300	7.8061	-
0.1908	63400	7.8021	-
0.1911	63500	7.7879	7.7183
0.1914	63600	7.7947	-
0.1917	63700	7.7958	-
0.1920	63800	7.7715	-
0.1923	63900	7.7999	-
0.1926	64000	7.8087	7.7165
0.1929	64100	7.7872	-
0.1932	64200	7.7966	-
0.1935	64300	7.792	-
0.1938	64400	7.8052	-
0.1941	64500	7.7755	7.7124
0.1944	64600	7.7807	-
0.1947	64700	7.82	-
0.1950	64800	7.7907	-
0.1953	64900	7.7652	-
0.1956	65000	7.7848	7.7166
0.1959	65100	7.7666	-
0.1962	65200	7.7954	-
0.1965	65300	7.8276	-
0.1968	65400	7.8041	-
0.1971	65500	7.7864	7.7131
0.1974	65600	7.7844	-
0.1977	65700	7.7807	-
0.1980	65800	7.7914	-
0.1983	65900	7.8016	-
0.1986	66000	7.7748	7.7112
0.1989	66100	7.7783	-
0.1992	66200	7.8318	-
0.1995	66300	7.7996	-
0.1998	66400	7.7516	-
0.2001	66500	7.7947	7.7081
0.2004	66600	7.7975	-
0.2007	66700	7.8284	-
0.2010	66800	7.7719	-
0.2013	66900	7.7709	-
0.2016	67000	7.7658	7.7109
0.2019	67100	7.8013	-
0.2022	67200	7.8149	-
0.2025	67300	7.8212	-
0.2028	67400	7.793	-
0.2031	67500	7.7718	7.7085
0.2034	67600	7.8089	-
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0.2040	67800	7.7951	-
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0.2046	68000	7.7964	7.7064
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0.2052	68200	7.7938	-
0.2055	68300	7.8016	-
0.2058	68400	7.7777	-
0.2061	68500	7.7661	7.7135
0.2064	68600	7.8053	-
0.2067	68700	7.8036	-
0.2070	68800	7.8031	-
0.2073	68900	7.7772	-
0.2076	69000	7.7987	7.7050
0.2079	69100	7.7723	-
0.2082	69200	7.792	-
0.2085	69300	7.8232	-
0.2088	69400	7.7857	-
0.2092	69500	7.7539	7.7079
0.2095	69600	7.7893	-
0.2098	69700	7.7819	-
0.2101	69800	7.7774	-
0.2104	69900	7.8106	-
0.2107	70000	7.7997	7.7035
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0.2113	70200	7.7867	-
0.2116	70300	7.7895	-
0.2119	70400	7.8106	-
0.2122	70500	7.7935	7.7028
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0.2137	71000	7.7612	7.7010
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0.2146	71300	7.8051	-
0.2149	71400	7.7699	-
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0.2194	72900	7.7809	-
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0.2206	73300	7.7916	-
0.2209	73400	7.7564	-
0.2212	73500	7.8262	7.6972
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0.2218	73700	7.7741	-
0.2221	73800	7.793	-
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0.2227	74000	7.7732	7.6972
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0.2239	74400	7.8092	-
0.2242	74500	7.7723	7.6960
0.2245	74600	7.7652	-
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0.2251	74800	7.7894	-
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0.2257	75000	7.7685	7.6971
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0.2266	75300	7.772	-
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0.2272	75500	7.7442	7.6937
0.2275	75600	7.7727	-
0.2278	75700	7.793	-
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0.2287	76000	7.8281	7.6972
0.2290	76100	7.7687	-
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0.2302	76500	7.7828	7.6924
0.2305	76600	7.7833	-
0.2308	76700	7.7629	-
0.2311	76800	7.8034	-
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0.2317	77000	7.7765	7.6907
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0.2326	77300	7.7604	-
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0.2332	77500	7.7833	7.6890
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0.2341	77800	7.7731	-
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0.2347	78000	7.7852	7.6928
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0.2359	78400	7.7591	-
0.2362	78500	7.7188	7.6912
0.2365	78600	7.7674	-
0.2368	78700	7.7617	-
0.2371	78800	7.7725	-
0.2374	78900	7.7548	-
0.2377	79000	7.7867	7.6880
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0.2392	79500	7.8075	7.6867
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0.2407	80000	7.7779	7.6835
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0.2423	80500	7.7801	7.6865
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0.2438	81000	7.7526	7.6864
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0.2468	82000	7.729	7.6861
0.2471	82100	7.7721	-
0.2474	82200	7.7644	-
0.2477	82300	7.7806	-
0.2480	82400	7.7697	-
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0.3936	130800	7.7324	-
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0.5351	177800	7.6599	-
0.5354	177900	7.6892	-
0.5357	178000	7.7109	7.5970
0.5360	178100	7.6846	-
0.5363	178200	7.6587	-
0.5366	178300	7.7157	-
0.5369	178400	7.6436	-
0.5372	178500	7.6884	7.5976
0.5375	178600	7.6597	-
0.5378	178700	7.6975	-
0.5381	178800	7.6678	-
0.5384	178900	7.6719	-
0.5387	179000	7.6797	7.5958
0.5390	179100	7.6697	-
0.5393	179200	7.6569	-
0.5396	179300	7.6761	-
0.5399	179400	7.6858	-
0.5402	179500	7.722	7.5975
0.5405	179600	7.6765	-
0.5408	179700	7.6882	-
0.5411	179800	7.6642	-
0.5414	179900	7.7041	-
0.5417	180000	7.6746	7.5968
0.5420	180100	7.6815	-
0.5423	180200	7.7476	-
0.5426	180300	7.6941	-
0.5429	180400	7.6968	-
0.5432	180500	7.6923	7.5923
0.5435	180600	7.6768	-
0.5438	180700	7.6784	-
0.5441	180800	7.6595	-
0.5444	180900	7.6803	-
0.5447	181000	7.6819	7.5976
0.5450	181100	7.6796	-
0.5453	181200	7.6605	-
0.5456	181300	7.6716	-
0.5459	181400	7.6823	-
0.5462	181500	7.7118	7.5940
0.5465	181600	7.7154	-
0.5468	181700	7.6968	-
0.5471	181800	7.6934	-
0.5474	181900	7.6931	-
0.5477	182000	7.7171	7.5923
0.5480	182100	7.7198	-
0.5483	182200	7.7081	-
0.5486	182300	7.6644	-
0.5489	182400	7.6725	-
0.5492	182500	7.7201	7.5938
0.5495	182600	7.7109	-
0.5498	182700	7.6675	-
0.5501	182800	7.682	-
0.5504	182900	7.6896	-
0.5507	183000	7.6493	7.5929
0.5510	183100	7.6684	-
0.5513	183200	7.719	-
0.5516	183300	7.6729	-
0.5519	183400	7.7123	-
0.5522	183500	7.7063	7.5910
0.5525	183600	7.6667	-
0.5528	183700	7.6738	-
0.5531	183800	7.6715	-
0.5534	183900	7.6496	-
0.5537	184000	7.688	7.5928
0.5540	184100	7.7022	-
0.5543	184200	7.7228	-
0.5546	184300	7.6918	-
0.5549	184400	7.7124	-
0.5552	184500	7.6649	7.5915
0.5555	184600	7.7871	-
0.5558	184700	7.6725	-
0.5561	184800	7.7041	-
0.5564	184900	7.6976	-
0.5567	185000	7.6807	7.5928
0.5570	185100	7.6497	-
0.5573	185200	7.6883	-
0.5576	185300	7.6869	-
0.5579	185400	7.6619	-
0.5582	185500	7.693	7.5915
0.5585	185600	7.7663	-
0.5588	185700	7.6989	-
0.5591	185800	7.7354	-
0.5594	185900	7.6724	-
0.5597	186000	7.6698	7.5909
0.5600	186100	7.713	-
0.5603	186200	7.7196	-
0.5606	186300	7.6675	-
0.5609	186400	7.6629	-
0.5612	186500	7.6612	7.5907
0.5615	186600	7.6689	-
0.5618	186700	7.6528	-
0.5621	186800	7.6769	-
0.5624	186900	7.6749	-
0.5627	187000	7.7126	7.5883
0.5631	187100	7.6677	-
0.5634	187200	7.7477	-
0.5637	187300	7.656	-
0.5640	187400	7.6924	-
0.5643	187500	7.6602	7.5901
0.5646	187600	7.7201	-
0.5649	187700	7.7367	-
0.5652	187800	7.6985	-
0.5655	187900	7.6667	-
0.5658	188000	7.7135	7.5905
0.5661	188100	7.7184	-
0.5664	188200	7.7492	-
0.5667	188300	7.6714	-
0.5670	188400	7.6932	-
0.5673	188500	7.6776	7.5862
0.5676	188600	7.6506	-
0.5679	188700	7.6858	-
0.5682	188800	7.7153	-
0.5685	188900	7.7207	-
0.5688	189000	7.7491	7.5878
0.5691	189100	7.6914	-
0.5694	189200	7.6943	-
0.5697	189300	7.6875	-
0.5700	189400	7.648	-
0.5703	189500	7.6988	7.5886
0.5706	189600	7.6348	-
0.5709	189700	7.678	-
0.5712	189800	7.6792	-
0.5715	189900	7.6998	-
0.5718	190000	7.7254	7.5886
0.5721	190100	7.7141	-
0.5724	190200	7.6751	-
0.5727	190300	7.6625	-
0.5730	190400	7.6952	-
0.5733	190500	7.6582	7.5874
0.5736	190600	7.6632	-
0.5739	190700	7.6796	-
0.5742	190800	7.6824	-
0.5745	190900	7.6711	-
0.5748	191000	7.7495	7.5871
0.5751	191100	7.6546	-
0.5754	191200	7.6899	-
0.5757	191300	7.6594	-
0.5760	191400	7.6454	-
0.5763	191500	7.6662	7.5885
0.5766	191600	7.7023	-
0.5769	191700	7.7482	-
0.5772	191800	7.6611	-
0.5775	191900	7.6992	-
0.5778	192000	7.7316	7.5867
0.5781	192100	7.6921	-
0.5784	192200	7.6353	-
0.5787	192300	7.671	-
0.5790	192400	7.6734	-
0.5793	192500	7.6687	7.5894
0.5796	192600	7.6966	-
0.5799	192700	7.6918	-
0.5802	192800	7.6853	-
0.5805	192900	7.6982	-
0.5808	193000	7.7306	7.5889
0.5811	193100	7.7224	-
0.5814	193200	7.6963	-
0.5817	193300	7.653	-
0.5820	193400	7.6532	-
0.5823	193500	7.6591	7.5866
0.5826	193600	7.6843	-
0.5829	193700	7.681	-
0.5832	193800	7.6442	-
0.5835	193900	7.6714	-
0.5838	194000	7.6865	7.5869
0.5841	194100	7.6672	-
0.5844	194200	7.7018	-
0.5847	194300	7.7001	-
0.5850	194400	7.7055	-
0.5853	194500	7.679	7.5906
0.5856	194600	7.7004	-
0.5859	194700	7.6425	-
0.5862	194800	7.6825	-
0.5865	194900	7.705	-
0.5868	195000	7.6839	7.5862
0.5871	195100	7.6694	-
0.5874	195200	7.6922	-
0.5877	195300	7.7101	-
0.5880	195400	7.6658	-
0.5883	195500	7.6821	7.5856
0.5886	195600	7.6678	-
0.5889	195700	7.6853	-
0.5892	195800	7.6397	-
0.5895	195900	7.6829	-
0.5898	196000	7.6925	7.5871
0.5901	196100	7.6683	-
0.5904	196200	7.654	-
0.5907	196300	7.689	-
0.5910	196400	7.6479	-
0.5913	196500	7.6693	7.5879
0.5916	196600	7.6727	-
0.5919	196700	7.6631	-
0.5922	196800	7.7053	-
0.5925	196900	7.6718	-
0.5928	197000	7.6854	7.5866
0.5931	197100	7.6912	-
0.5934	197200	7.7091	-
0.5937	197300	7.701	-
0.5940	197400	7.6945	-
0.5943	197500	7.6621	7.5849
0.5946	197600	7.774	-
0.5949	197700	7.6958	-
0.5953	197800	7.6502	-
0.5956	197900	7.645	-
0.5959	198000	7.6795	7.5850
0.5962	198100	7.7529	-
0.5965	198200	7.6813	-
0.5968	198300	7.6982	-
0.5971	198400	7.694	-
0.5974	198500	7.7046	7.5862
0.5977	198600	7.6417	-
0.5980	198700	7.6559	-
0.5983	198800	7.6824	-
0.5986	198900	7.67	-
0.5989	199000	7.6814	7.5862
0.5992	199100	7.6648	-
0.5995	199200	7.6966	-
0.5998	199300	7.6964	-
0.6001	199400	7.6862	-
0.6004	199500	7.6932	7.5828
0.6007	199600	7.7052	-
0.6010	199700	7.6853	-
0.6013	199800	7.6448	-
0.6016	199900	7.6655	-
0.6019	200000	7.6645	7.5830
0.6022	200100	7.7028	-
0.6025	200200	7.659	-
0.6028	200300	7.7176	-
0.6031	200400	7.7161	-
0.6034	200500	7.6601	7.5842
0.6037	200600	7.6828	-
0.6040	200700	7.6849	-
0.6043	200800	7.6594	-
0.6046	200900	7.6656	-
0.6049	201000	7.679	7.5826
0.6052	201100	7.6655	-
0.6055	201200	7.6963	-
0.6058	201300	7.6017	-
0.6061	201400	7.6704	-
0.6064	201500	7.7047	7.5808
0.6067	201600	7.6787	-
0.6070	201700	7.6834	-
0.6073	201800	7.693	-
0.6076	201900	7.6787	-
0.6079	202000	7.7106	7.5813
0.6082	202100	7.7021	-
0.6085	202200	7.7021	-
0.6088	202300	7.6642	-
0.6091	202400	7.6759	-
0.6094	202500	7.6721	7.5843
0.6097	202600	7.6729	-
0.6100	202700	7.6876	-
0.6103	202800	7.6475	-
0.6106	202900	7.6691	-
0.6109	203000	7.6499	7.5845
0.6112	203100	7.7046	-
0.6115	203200	7.6855	-
0.6118	203300	7.6842	-
0.6121	203400	7.7014	-
0.6124	203500	7.7111	7.5828
0.6127	203600	7.6905	-
0.6130	203700	7.6395	-
0.6133	203800	7.6887	-
0.6136	203900	7.6617	-
0.6139	204000	7.7338	7.5845
0.6142	204100	7.6793	-
0.6145	204200	7.6684	-
0.6148	204300	7.7074	-
0.6151	204400	7.6896	-
0.6154	204500	7.6765	7.5823
0.6157	204600	7.6605	-
0.6160	204700	7.6673	-
0.6163	204800	7.6989	-
0.6166	204900	7.6628	-
0.6169	205000	7.7015	7.5824
0.6172	205100	7.6759	-
0.6175	205200	7.6733	-
0.6178	205300	7.7762	-
0.6181	205400	7.6792	-
0.6184	205500	7.6453	7.5826
0.6187	205600	7.6523	-
0.6190	205700	7.6937	-
0.6193	205800	7.7061	-
0.6196	205900	7.6499	-
0.6199	206000	7.6669	7.5834
0.6202	206100	7.6707	-
0.6205	206200	7.6847	-
0.6208	206300	7.6854	-
0.6211	206400	7.6875	-
0.6214	206500	7.662	7.5813
0.6217	206600	7.6886	-
0.6220	206700	7.6452	-
0.6223	206800	7.6489	-
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0.6232	207100	7.6775	-
0.6235	207200	7.6734	-
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0.6241	207400	7.6756	-
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0.6247	207600	7.7447	-
0.6250	207700	7.6783	-
0.6253	207800	7.6593	-
0.6256	207900	7.6687	-
0.6259	208000	7.6691	7.5804
0.6262	208100	7.6689	-
0.6265	208200	7.6418	-
0.6268	208300	7.6646	-
0.6271	208400	7.6875	-
0.6275	208500	7.6627	7.5815
0.6278	208600	7.6247	-
0.6281	208700	7.6919	-
0.6284	208800	7.6948	-
0.6287	208900	7.6818	-
0.6290	209000	7.6786	7.5800
0.6293	209100	7.6631	-
0.6296	209200	7.6757	-
0.6299	209300	7.6556	-
0.6302	209400	7.6559	-
0.6305	209500	7.6549	7.5823
0.6308	209600	7.694	-
0.6311	209700	7.6485	-
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0.6320	210000	7.7053	7.5816
0.6323	210100	7.6549	-
0.6326	210200	7.6744	-
0.6329	210300	7.6706	-
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0.6335	210500	7.6337	7.5814
0.6338	210600	7.668	-
0.6341	210700	7.6767	-
0.6344	210800	7.6614	-
0.6347	210900	7.684	-
0.6350	211000	7.6867	7.5782
0.6353	211100	7.6662	-
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0.6359	211300	7.6542	-
0.6362	211400	7.7198	-
0.6365	211500	7.6544	7.5770
0.6368	211600	7.674	-
0.6371	211700	7.6444	-
0.6374	211800	7.6819	-
0.6377	211900	7.685	-
0.6380	212000	7.6812	7.5780
0.6383	212100	7.656	-
0.6386	212200	7.7385	-
0.6389	212300	7.6688	-
0.6392	212400	7.7493	-
0.6395	212500	7.6972	7.5783
0.6398	212600	7.6659	-
0.6401	212700	7.6579	-
0.6404	212800	7.6733	-
0.6407	212900	7.6572	-
0.6410	213000	7.6808	7.5786
0.6413	213100	7.6544	-
0.6416	213200	7.7368	-
0.6419	213300	7.6696	-
0.6422	213400	7.6797	-
0.6425	213500	7.7105	7.5775
0.6428	213600	7.6842	-
0.6431	213700	7.6596	-
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0.6437	213900	7.6495	-
0.6440	214000	7.7391	7.5763
0.6443	214100	7.7195	-
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0.6449	214300	7.7048	-
0.6452	214400	7.7347	-
0.6455	214500	7.6819	7.5782
0.6458	214600	7.6673	-
0.6461	214700	7.6906	-
0.6464	214800	7.676	-
0.6467	214900	7.6618	-
0.6470	215000	7.6721	7.5790
0.6473	215100	7.6724	-
0.6476	215200	7.6452	-
0.6479	215300	7.6888	-
0.6482	215400	7.682	-
0.6485	215500	7.6554	7.5770
0.6488	215600	7.6845	-
0.6491	215700	7.6575	-
0.6494	215800	7.6726	-
0.6497	215900	7.6613	-
0.6500	216000	7.7045	7.5772
0.6503	216100	7.6606	-
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0.6509	216300	7.6689	-
0.6512	216400	7.6549	-
0.6515	216500	7.6804	7.5779
0.6518	216600	7.6734	-
0.6521	216700	7.6499	-
0.6524	216800	7.6599	-
0.6527	216900	7.6791	-
0.6530	217000	7.6639	7.5761
0.6533	217100	7.638	-
0.6536	217200	7.6736	-
0.6539	217300	7.684	-
0.6542	217400	7.6536	-
0.6545	217500	7.6492	7.5794
0.6548	217600	7.6508	-
0.6551	217700	7.6741	-
0.6554	217800	7.6715	-
0.6557	217900	7.6895	-
0.6560	218000	7.6991	7.5755
0.6563	218100	7.6403	-
0.6566	218200	7.6815	-
0.6569	218300	7.6956	-
0.6572	218400	7.6541	-
0.6575	218500	7.6822	7.5765
0.6578	218600	7.641	-
0.6581	218700	7.6857	-
0.6584	218800	7.7011	-
0.6587	218900	7.6986	-
0.6590	219000	7.6908	7.5759
0.6593	219100	7.7081	-
0.6597	219200	7.6622	-
0.6600	219300	7.6666	-
0.6603	219400	7.6529	-
0.6606	219500	7.6799	7.5748
0.6609	219600	7.6441	-
0.6612	219700	7.6424	-
0.6615	219800	7.6745	-
0.6618	219900	7.7191	-
0.6621	220000	7.6554	7.5740
0.6624	220100	7.6537	-
0.6627	220200	7.6671	-
0.6630	220300	7.7171	-
0.6633	220400	7.6894	-
0.6636	220500	7.6659	7.5762
0.6639	220600	7.6935	-
0.6642	220700	7.6693	-
0.6645	220800	7.6984	-
0.6648	220900	7.6587	-
0.6651	221000	7.6333	7.5757
0.6654	221100	7.6493	-
0.6657	221200	7.6769	-
0.6660	221300	7.6759	-
0.6663	221400	7.6258	-
0.6666	221500	7.6807	7.5794
0.6669	221600	7.6443	-
0.6672	221700	7.6998	-
0.6675	221800	7.6937	-
0.6678	221900	7.6774	-
0.6681	222000	7.713	7.5727
0.6684	222100	7.7059	-
0.6687	222200	7.6585	-
0.6690	222300	7.7276	-
0.6693	222400	7.6887	-
0.6696	222500	7.6815	7.5746
0.6699	222600	7.6649	-
0.6702	222700	7.6783	-
0.6705	222800	7.6629	-
0.6708	222900	7.6906	-
0.6711	223000	7.6805	7.5756
0.6714	223100	7.6807	-
0.6717	223200	7.6627	-
0.6720	223300	7.7109	-
0.6723	223400	7.6616	-
0.6726	223500	7.6527	7.5723
0.6729	223600	7.6782	-
0.6732	223700	7.6232	-
0.6735	223800	7.6942	-
0.6738	223900	7.7046	-
0.6741	224000	7.6633	7.5711
0.6744	224100	7.6798	-
0.6747	224200	7.667	-
0.6750	224300	7.6959	-
0.6753	224400	7.6687	-
0.6756	224500	7.6708	7.5725
0.6759	224600	7.668	-
0.6762	224700	7.6998	-
0.6765	224800	7.696	-
0.6768	224900	7.6865	-
0.6771	225000	7.6414	7.5738
0.6774	225100	7.6596	-
0.6777	225200	7.6764	-
0.6780	225300	7.6758	-
0.6783	225400	7.6626	-
0.6786	225500	7.6929	7.5743
0.6789	225600	7.6722	-
0.6792	225700	7.6603	-
0.6795	225800	7.6612	-
0.6798	225900	7.6852	-
0.6801	226000	7.6541	7.5751
0.6804	226100	7.677	-
0.6807	226200	7.6533	-
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0.9678	321600	7.6108	-
0.9681	321700	7.6573	-
0.9684	321800	7.6231	-
0.9687	321900	7.6523	-
0.9690	322000	7.6363	7.5449
0.9693	322100	7.6469	-
0.9696	322200	7.6249	-
0.9699	322300	7.6216	-
0.9702	322400	7.6515	-
0.9705	322500	7.6478	7.5452
0.9708	322600	7.6392	-
0.9711	322700	7.6673	-
0.9714	322800	7.648	-
0.9717	322900	7.6765	-
0.9720	323000	7.6497	7.5451
0.9723	323100	7.6738	-
0.9726	323200	7.6326	-
0.9729	323300	7.6416	-
0.9732	323400	7.6316	-
0.9735	323500	7.6385	7.5446
0.9738	323600	7.6387	-
0.9741	323700	7.608	-
0.9744	323800	7.5724	-
0.9747	323900	7.6701	-
0.9750	324000	7.6025	7.5446
0.9753	324100	7.6805	-
0.9756	324200	7.6896	-
0.9759	324300	7.6587	-
0.9762	324400	7.627	-
0.9765	324500	7.6497	7.5448
0.9768	324600	7.6287	-
0.9771	324700	7.6441	-
0.9774	324800	7.6288	-
0.9777	324900	7.6803	-
0.9780	325000	7.6812	7.5449
0.9783	325100	7.6785	-
0.9786	325200	7.6291	-
0.9789	325300	7.697	-
0.9792	325400	7.6513	-
0.9795	325500	7.675	7.5448
0.9798	325600	7.6773	-
0.9801	325700	7.6552	-
0.9804	325800	7.6886	-
0.9807	325900	7.668	-
0.9811	326000	7.6818	7.5445
0.9814	326100	7.6498	-
0.9817	326200	7.66	-
0.9820	326300	7.6551	-
0.9823	326400	7.6237	-
0.9826	326500	7.6301	7.5444
0.9829	326600	7.6361	-
0.9832	326700	7.615	-
0.9835	326800	7.6122	-
0.9838	326900	7.6213	-
0.9841	327000	7.6419	7.5445
0.9844	327100	7.6199	-
0.9847	327200	7.6912	-
0.9850	327300	7.684	-
0.9853	327400	7.6976	-
0.9856	327500	7.6807	7.5445
0.9859	327600	7.6255	-
0.9862	327700	7.6491	-
0.9865	327800	7.709	-
0.9868	327900	7.7205	-
0.9871	328000	7.6338	7.5443
0.9874	328100	7.666	-
0.9877	328200	7.6669	-
0.9880	328300	7.6577	-
0.9883	328400	7.7123	-
0.9886	328500	7.6567	7.5443
0.9889	328600	7.6261	-
0.9892	328700	7.6265	-
0.9895	328800	7.6417	-
0.9898	328900	7.6991	-
0.9901	329000	7.6416	7.5444
0.9904	329100	7.6322	-
0.9907	329200	7.6549	-
0.9910	329300	7.652	-
0.9913	329400	7.6621	-
0.9916	329500	7.6511	7.5443
0.9919	329600	7.6308	-
0.9922	329700	7.669	-
0.9925	329800	7.629	-
0.9928	329900	7.6101	-
0.9931	330000	7.6029	7.5444
0.9934	330100	7.6469	-
0.9937	330200	7.6193	-
0.9940	330300	7.6113	-
0.9943	330400	7.6924	-
0.9946	330500	7.6566	7.5444
0.9949	330600	7.6293	-
0.9952	330700	7.6123	-
0.9955	330800	7.6498	-
0.9958	330900	7.6773	-
0.9961	331000	7.6576	7.5443
0.9964	331100	7.6749	-
0.9967	331200	7.6674	-
0.9970	331300	7.6574	-
0.9973	331400	7.6614	-
0.9976	331500	7.626	7.5443
0.9979	331600	7.6493	-
0.9982	331700	7.6448	-
0.9985	331800	7.6556	-
0.9988	331900	7.6869	-
0.9991	332000	7.6263	7.5443
0.9994	332100	7.6736	-
0.9997	332200	7.6308	-

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},
}