IoannisKat1/bge-m3-ft-new

bge_m3 Finetuned on Data

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-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: BAAI/bge-m3
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• 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': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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 = [
    'What may impede authorities in the discharge of their responsibilities under Union law?',
    'The objectives and principles of Directive 95/46/EC remain sound, but it has not prevented fragmentation in the implementation of data protection across the Union, legal uncertainty or a widespread public perception that there are significant risks to the protection of natural persons, in particular with regard to online activity. Differences in the level of protection of the rights and freedoms of natural persons, in particular the right to the protection of personal data, with regard to the processing of personal data in the Member States may prevent the free flow of personal data throughout the Union. Those differences may therefore constitute an obstacle to the pursuit of economic activities at the level of the Union, distort competition and impede authorities in the discharge of their responsibilities under Union law. Such a difference in levels of protection is due to the existence of differences in the implementation and application of Directive 95/46/EC.',
    'The processing of personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes should be subject to appropriate safeguards for the rights and freedoms of the data subject pursuant to this Regulation. Those safeguards should ensure that technical and organisational measures are in place in order to ensure, in particular, the principle of data minimisation. The further processing of personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes is to be carried out when the controller has assessed the feasibility to fulfil those purposes by processing data which do not permit or no longer permit the identification of data subjects, provided that appropriate safeguards exist (such as, for instance, pseudonymisation of the data). Member States should provide for appropriate safeguards for the processing of personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes. Member States should be authorised to provide, under specific conditions and subject to appropriate safeguards for data subjects, specifications and derogations with regard to the information requirements and rights to rectification, to erasure, to be forgotten, to restriction of processing, to data portability, and to object when processing personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes. The conditions and safeguards in question may entail specific procedures for data subjects to exercise those rights if this is appropriate in the light of the purposes sought by the specific processing along with technical and organisational measures aimed at minimising the processing of personal data in pursuance of the proportionality and necessity principles. The processing of personal data for scientific purposes should also comply with other relevant legislation such as on clinical trials.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6716, 0.2994],
#         [0.6716, 1.0000, 0.3108],
#         [0.2994, 0.3108, 1.0000]])

Evaluation

Metrics

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.4373
cosine_accuracy@3	0.4717
cosine_accuracy@5	0.4939
cosine_accuracy@10	0.5233
cosine_precision@1	0.4373
cosine_precision@3	0.4259
cosine_precision@5	0.3985
cosine_precision@10	0.3337
cosine_recall@1	0.0863
cosine_recall@3	0.2209
cosine_recall@5	0.2991
cosine_recall@10	0.3932
cosine_ndcg@10	0.4785
cosine_mrr@10	0.4594
cosine_map@100	0.5295

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.4324
cosine_accuracy@3	0.457
cosine_accuracy@5	0.4865
cosine_accuracy@10	0.5184
cosine_precision@1	0.4324
cosine_precision@3	0.4169
cosine_precision@5	0.3907
cosine_precision@10	0.3312
cosine_recall@1	0.0843
cosine_recall@3	0.213
cosine_recall@5	0.2881
cosine_recall@10	0.3851
cosine_ndcg@10	0.4711
cosine_mrr@10	0.452
cosine_map@100	0.5216

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.4152
cosine_accuracy@3	0.4496
cosine_accuracy@5	0.4644
cosine_accuracy@10	0.5037
cosine_precision@1	0.4152
cosine_precision@3	0.4054
cosine_precision@5	0.3799
cosine_precision@10	0.3219
cosine_recall@1	0.0803
cosine_recall@3	0.2055
cosine_recall@5	0.2793
cosine_recall@10	0.3745
cosine_ndcg@10	0.4555
cosine_mrr@10	0.4365
cosine_map@100	0.5042

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.3907
cosine_accuracy@3	0.4349
cosine_accuracy@5	0.4521
cosine_accuracy@10	0.484
cosine_precision@1	0.3907
cosine_precision@3	0.3849
cosine_precision@5	0.3661
cosine_precision@10	0.3128
cosine_recall@1	0.0746
cosine_recall@3	0.1916
cosine_recall@5	0.2642
cosine_recall@10	0.358
cosine_ndcg@10	0.4362
cosine_mrr@10	0.4142
cosine_map@100	0.4853

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.3735
cosine_accuracy@3	0.4103
cosine_accuracy@5	0.4275
cosine_accuracy@10	0.484
cosine_precision@1	0.3735
cosine_precision@3	0.3653
cosine_precision@5	0.3455
cosine_precision@10	0.3037
cosine_recall@1	0.0716
cosine_recall@3	0.1824
cosine_recall@5	0.2494
cosine_recall@10	0.3544
cosine_ndcg@10	0.4222
cosine_mrr@10	0.3975
cosine_map@100	0.4701

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,627 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 7 tokens mean: 17.42 tokens max: 43 tokens	min: 36 tokens mean: 729.41 tokens max: 2824 tokens

• Samples:

  anchor	positive
  Who is responsible for monitoring the application of this Regulation?	1.The Board shall ensure the consistent application of this Regulation. To that end, the Board shall, on its own initiative or, where relevant, at the request of the Commission, in particular: (a) monitor and ensure the correct application of this Regulation in the cases provided for in Articles 64 and 65 without prejudice to the tasks of national supervisory authorities; 4.5.2016 L 119/76 (b) advise the Commission on any issue related to the protection of personal data in the Union, including on any proposed amendment of this Regulation; (c) advise the Commission on the format and procedures for the exchange of information between controllers, processors and supervisory authorities for binding corporate rules; (d) issue guidelines, recommendations, and best practices on procedures for erasing links, copies or replications of personal data from publicly available communication services as referred to in Article 17(2); (e) examine, on its own initiative, on request of one of its ...
  What should European statistics be developed, produced, and disseminated in accordance with?	The confidential information which the Union and national statistical authorities collect for the production of official European and official national statistics should be protected. European statistics should be developed, produced and disseminated in accordance with the statistical principles as set out in Article 338(2) TFEU, while national statistics should also comply with Member State law. Regulation (EC) No 223/2009 of the European Parliament and of the Council (2) provides further specifications on statistical confidentiality for European statistics.
  What is evaluated in terms of adherence to approved codes of conduct?	1.Each supervisory authority shall ensure that the imposition of administrative fines pursuant to this Article in respect of infringements of this Regulation referred to in paragraphs 4, 5 and 6 shall in each individual case be effective, proportionate and dissuasive. 2.Administrative fines shall, depending on the circumstances of each individual case, be imposed in addition to, or instead of, measures referred to in points (a) to (h) and (j) of Article 58(2). When deciding whether to impose an administrative fine and deciding on the amount of the administrative fine in each individual case due regard shall be given to the following: (a) the nature, gravity and duration of the infringement taking into account the nature scope or purpose of the processing concerned as well as the number of data subjects affected and the level of damage suffered by them; (b) the intentional or negligent character of the infringement; (c) any action taken by the controller or processor to mitigate the ...

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 2
• per_device_eval_batch_size: 4
• gradient_accumulation_steps: 4
• learning_rate: 2e-05
• num_train_epochs: 10
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 2
• per_device_eval_batch_size: 4
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 4
• 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: 10
• max_steps: -1
• lr_scheduler_type: cosine
• 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: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: True
• 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: True
• 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}
• tp_size: 0
• 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_fused
• 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
• 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
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.0049	1	2.1126	-	-	-	-	-
0.0098	2	8.6334	-	-	-	-	-
0.0147	3	8.2284	-	-	-	-	-
0.0197	4	8.0839	-	-	-	-	-
0.0246	5	9.525	-	-	-	-	-
0.0295	6	1.5607	-	-	-	-	-
0.0344	7	4.5191	-	-	-	-	-
0.0393	8	4.0471	-	-	-	-	-
0.0442	9	1.2372	-	-	-	-	-
0.0491	10	7.5335	-	-	-	-	-
0.0541	11	2.9568	-	-	-	-	-
0.0590	12	9.9398	-	-	-	-	-
0.0639	13	3.0405	-	-	-	-	-
0.0688	14	3.304	-	-	-	-	-
0.0737	15	1.7575	-	-	-	-	-
0.0786	16	5.9459	-	-	-	-	-
0.0835	17	6.5538	-	-	-	-	-
0.0885	18	1.1162	-	-	-	-	-
0.0934	19	5.7308	-	-	-	-	-
0.0983	20	2.9328	-	-	-	-	-
0.1032	21	1.4039	-	-	-	-	-
0.1081	22	1.3671	-	-	-	-	-
0.1130	23	1.0179	-	-	-	-	-
0.1179	24	3.9188	-	-	-	-	-
0.1229	25	2.4424	-	-	-	-	-
0.1278	26	2.6898	-	-	-	-	-
0.1327	27	1.3133	-	-	-	-	-
0.1376	28	2.9771	-	-	-	-	-
0.1425	29	3.1579	-	-	-	-	-
0.1474	30	0.9947	-	-	-	-	-
0.1523	31	3.7173	-	-	-	-	-
0.1572	32	8.2647	-	-	-	-	-
0.1622	33	2.6619	-	-	-	-	-
0.1671	34	1.4925	-	-	-	-	-
0.1720	35	3.4065	-	-	-	-	-
0.1769	36	1.7833	-	-	-	-	-
0.1818	37	3.2862	-	-	-	-	-
0.1867	38	8.176	-	-	-	-	-
0.1916	39	2.8571	-	-	-	-	-
0.1966	40	4.3703	-	-	-	-	-
0.2015	41	1.3122	-	-	-	-	-
0.2064	42	6.6555	-	-	-	-	-
0.2113	43	9.1111	-	-	-	-	-
0.2162	44	6.1262	-	-	-	-	-
0.2211	45	0.7347	-	-	-	-	-
0.2260	46	1.0265	-	-	-	-	-
0.2310	47	6.0457	-	-	-	-	-
0.2359	48	9.6469	-	-	-	-	-
0.2408	49	1.8979	-	-	-	-	-
0.2457	50	3.5441	-	-	-	-	-
0.2506	51	2.7489	-	-	-	-	-
0.2555	52	2.1327	-	-	-	-	-
0.2604	53	1.642	-	-	-	-	-
0.2654	54	2.2627	-	-	-	-	-
0.2703	55	0.3302	-	-	-	-	-
0.2752	56	4.9844	-	-	-	-	-
0.2801	57	7.8189	-	-	-	-	-
0.2850	58	1.6654	-	-	-	-	-
0.2899	59	9.5835	-	-	-	-	-
0.2948	60	15.5611	-	-	-	-	-
0.2998	61	6.318	-	-	-	-	-
0.3047	62	0.7203	-	-	-	-	-
0.3096	63	7.7936	-	-	-	-	-
0.3145	64	0.2338	-	-	-	-	-
0.3194	65	3.5607	-	-	-	-	-
0.3243	66	3.2031	-	-	-	-	-
0.3292	67	2.0295	-	-	-	-	-
0.3342	68	1.2046	-	-	-	-	-
0.3391	69	1.9642	-	-	-	-	-
0.3440	70	3.3386	-	-	-	-	-
0.3489	71	0.2486	-	-	-	-	-
0.3538	72	3.8262	-	-	-	-	-
0.3587	73	5.0852	-	-	-	-	-
0.3636	74	6.9813	-	-	-	-	-
0.3686	75	1.6531	-	-	-	-	-
0.3735	76	3.2036	-	-	-	-	-
0.3784	77	1.6394	-	-	-	-	-
0.3833	78	0.5295	-	-	-	-	-
0.3882	79	4.7569	-	-	-	-	-
0.3931	80	0.4613	-	-	-	-	-
0.3980	81	3.2973	-	-	-	-	-
0.4029	82	4.2086	-	-	-	-	-
0.4079	83	1.8324	-	-	-	-	-
0.4128	84	1.4644	-	-	-	-	-
0.4177	85	8.7691	-	-	-	-	-
0.4226	86	2.0521	-	-	-	-	-
0.4275	87	1.1568	-	-	-	-	-
0.4324	88	4.8268	-	-	-	-	-
0.4373	89	3.0676	-	-	-	-	-
0.4423	90	1.4163	-	-	-	-	-
0.4472	91	1.809	-	-	-	-	-
0.4521	92	5.6695	-	-	-	-	-
0.4570	93	8.6196	-	-	-	-	-
0.4619	94	3.991	-	-	-	-	-
0.4668	95	1.4334	-	-	-	-	-
0.4717	96	0.843	-	-	-	-	-
0.4767	97	2.0174	-	-	-	-	-
0.4816	98	1.41	-	-	-	-	-
0.4865	99	1.474	-	-	-	-	-
0.4914	100	1.0071	-	-	-	-	-
0.4963	101	9.759	-	-	-	-	-
0.5012	102	1.3183	-	-	-	-	-
0.5061	103	6.5495	-	-	-	-	-
0.5111	104	6.7574	-	-	-	-	-
0.5160	105	5.2306	-	-	-	-	-
0.5209	106	9.8882	-	-	-	-	-
0.5258	107	5.6908	-	-	-	-	-
0.5307	108	0.204	-	-	-	-	-
0.5356	109	0.8722	-	-	-	-	-
0.5405	110	7.1935	-	-	-	-	-
0.5455	111	1.0109	-	-	-	-	-
0.5504	112	10.1742	-	-	-	-	-
0.5553	113	2.2728	-	-	-	-	-
0.5602	114	5.1402	-	-	-	-	-
0.5651	115	5.2589	-	-	-	-	-
0.5700	116	2.4842	-	-	-	-	-
0.5749	117	1.3957	-	-	-	-	-
0.5799	118	7.7296	-	-	-	-	-
0.5848	119	4.5689	-	-	-	-	-
0.5897	120	2.8115	-	-	-	-	-
0.5946	121	2.498	-	-	-	-	-
0.5995	122	0.4326	-	-	-	-	-
0.6044	123	1.403	-	-	-	-	-
0.6093	124	2.4042	-	-	-	-	-
0.6143	125	1.7056	-	-	-	-	-
0.6192	126	0.4784	-	-	-	-	-
0.6241	127	14.7394	-	-	-	-	-
0.6290	128	4.8974	-	-	-	-	-
0.6339	129	9.9614	-	-	-	-	-
0.6388	130	6.626	-	-	-	-	-
0.6437	131	2.1262	-	-	-	-	-
0.6486	132	5.0936	-	-	-	-	-
0.6536	133	4.5314	-	-	-	-	-
0.6585	134	0.6033	-	-	-	-	-
0.6634	135	4.8067	-	-	-	-	-
0.6683	136	0.3481	-	-	-	-	-
0.6732	137	1.2218	-	-	-	-	-
0.6781	138	1.0191	-	-	-	-	-
0.6830	139	0.366	-	-	-	-	-
0.6880	140	3.331	-	-	-	-	-
0.6929	141	0.5188	-	-	-	-	-
0.6978	142	5.6721	-	-	-	-	-
0.7027	143	1.5885	-	-	-	-	-
0.7076	144	2.7388	-	-	-	-	-
0.7125	145	11.0374	-	-	-	-	-
0.7174	146	3.0557	-	-	-	-	-
0.7224	147	0.0759	-	-	-	-	-
0.7273	148	0.8796	-	-	-	-	-
0.7322	149	4.3239	-	-	-	-	-
0.7371	150	6.7367	-	-	-	-	-
0.7420	151	0.5102	-	-	-	-	-
0.7469	152	2.8752	-	-	-	-	-
0.7518	153	3.2807	-	-	-	-	-
0.7568	154	0.7898	-	-	-	-	-
0.7617	155	2.3934	-	-	-	-	-
0.7666	156	6.4737	-	-	-	-	-
0.7715	157	10.7761	-	-	-	-	-
0.7764	158	1.5748	-	-	-	-	-
0.7813	159	2.9404	-	-	-	-	-
0.7862	160	3.3701	-	-	-	-	-
0.7912	161	1.4911	-	-	-	-	-
0.7961	162	1.059	-	-	-	-	-
0.8010	163	0.2009	-	-	-	-	-
0.8059	164	4.3053	-	-	-	-	-
0.8108	165	8.1984	-	-	-	-	-
0.8157	166	1.4482	-	-	-	-	-
0.8206	167	6.9033	-	-	-	-	-
0.8256	168	0.1755	-	-	-	-	-
0.8305	169	3.1569	-	-	-	-	-
0.8354	170	10.1853	-	-	-	-	-
0.8403	171	4.021	-	-	-	-	-
0.8452	172	1.2394	-	-	-	-	-
0.8501	173	2.2133	-	-	-	-	-
0.8550	174	2.8847	-	-	-	-	-
0.8600	175	3.6311	-	-	-	-	-
0.8649	176	0.3839	-	-	-	-	-
0.8698	177	4.1139	-	-	-	-	-
0.8747	178	1.5477	-	-	-	-	-
0.8796	179	14.3119	-	-	-	-	-
0.8845	180	2.9921	-	-	-	-	-
0.8894	181	2.6819	-	-	-	-	-
0.8943	182	0.4497	-	-	-	-	-
0.8993	183	0.9949	-	-	-	-	-
0.9042	184	0.1105	-	-	-	-	-
0.9091	185	1.6702	-	-	-	-	-
0.9140	186	3.0496	-	-	-	-	-
0.9189	187	13.805	-	-	-	-	-
0.9238	188	4.3785	-	-	-	-	-
0.9287	189	3.3977	-	-	-	-	-
0.9337	190	9.0982	-	-	-	-	-
0.9386	191	3.6429	-	-	-	-	-
0.9435	192	0.0417	-	-	-	-	-
0.9484	193	6.3926	-	-	-	-	-
0.9533	194	1.2612	-	-	-	-	-
0.9582	195	4.2172	-	-	-	-	-
0.9631	196	1.7142	-	-	-	-	-
0.9681	197	3.8045	-	-	-	-	-
0.9730	198	5.5675	-	-	-	-	-
0.9779	199	2.0159	-	-	-	-	-
0.9828	200	0.3509	-	-	-	-	-
0.9877	201	3.0548	-	-	-	-	-
0.9926	202	6.3828	-	-	-	-	-
0.9975	203	1.7689	-	-	-	-	-
1.0	204	0.0099	0.4091	0.4105	0.3859	0.3417	0.2651
1.0049	205	3.7857	-	-	-	-	-
1.0098	206	3.7477	-	-	-	-	-
1.0147	207	0.1039	-	-	-	-	-
1.0197	208	1.9289	-	-	-	-	-
1.0246	209	4.0245	-	-	-	-	-
1.0295	210	0.0435	-	-	-	-	-
1.0344	211	0.1228	-	-	-	-	-
1.0393	212	4.9706	-	-	-	-	-
1.0442	213	0.0138	-	-	-	-	-
1.0491	214	0.2759	-	-	-	-	-
1.0541	215	2.7098	-	-	-	-	-
1.0590	216	2.1365	-	-	-	-	-
1.0639	217	1.4398	-	-	-	-	-
1.0688	218	0.9605	-	-	-	-	-
1.0737	219	0.4798	-	-	-	-	-
1.0786	220	0.8345	-	-	-	-	-
1.0835	221	0.1061	-	-	-	-	-
1.0885	222	1.7815	-	-	-	-	-
1.0934	223	2.3127	-	-	-	-	-
1.0983	224	1.9179	-	-	-	-	-
1.1032	225	1.4213	-	-	-	-	-
1.1081	226	1.0013	-	-	-	-	-
1.1130	227	0.3178	-	-	-	-	-
1.1179	228	0.365	-	-	-	-	-
1.1229	229	1.2156	-	-	-	-	-
1.1278	230	5.9569	-	-	-	-	-
1.1327	231	12.2159	-	-	-	-	-
1.1376	232	0.4907	-	-	-	-	-
1.1425	233	4.4903	-	-	-	-	-
1.1474	234	0.0977	-	-	-	-	-
1.1523	235	3.0879	-	-	-	-	-
1.1572	236	3.836	-	-	-	-	-
1.1622	237	1.3545	-	-	-	-	-
1.1671	238	2.8771	-	-	-	-	-
1.1720	239	2.591	-	-	-	-	-
1.1769	240	0.3288	-	-	-	-	-
1.1818	241	3.2974	-	-	-	-	-
1.1867	242	0.2526	-	-	-	-	-
1.1916	243	0.65	-	-	-	-	-
1.1966	244	0.3477	-	-	-	-	-
1.2015	245	0.6088	-	-	-	-	-
1.2064	246	0.5337	-	-	-	-	-
1.2113	247	0.1657	-	-	-	-	-
1.2162	248	0.1702	-	-	-	-	-
1.2211	249	0.724	-	-	-	-	-
1.2260	250	1.6809	-	-	-	-	-
1.2310	251	2.1363	-	-	-	-	-
1.2359	252	0.5129	-	-	-	-	-
1.2408	253	2.8669	-	-	-	-	-
1.2457	254	5.3834	-	-	-	-	-
1.2506	255	0.2463	-	-	-	-	-
1.2555	256	1.6799	-	-	-	-	-
1.2604	257	3.9336	-	-	-	-	-
1.2654	258	1.3616	-	-	-	-	-
1.2703	259	8.8923	-	-	-	-	-
1.2752	260	0.1433	-	-	-	-	-
1.2801	261	5.4949	-	-	-	-	-
1.2850	262	0.0395	-	-	-	-	-
1.2899	263	0.828	-	-	-	-	-
1.2948	264	4.5095	-	-	-	-	-
1.2998	265	0.3675	-	-	-	-	-
1.3047	266	0.8749	-	-	-	-	-
1.3096	267	0.9569	-	-	-	-	-
1.3145	268	3.8587	-	-	-	-	-
1.3194	269	0.2796	-	-	-	-	-
1.3243	270	0.0195	-	-	-	-	-
1.3292	271	3.5645	-	-	-	-	-
1.3342	272	0.0594	-	-	-	-	-
1.3391	273	5.8937	-	-	-	-	-
1.3440	274	0.3646	-	-	-	-	-
1.3489	275	7.9112	-	-	-	-	-
1.3538	276	1.943	-	-	-	-	-
1.3587	277	0.8289	-	-	-	-	-
1.3636	278	0.2369	-	-	-	-	-
1.3686	279	2.7562	-	-	-	-	-
1.3735	280	0.0293	-	-	-	-	-
1.3784	281	4.2874	-	-	-	-	-
1.3833	282	0.5958	-	-	-	-	-
1.3882	283	12.9377	-	-	-	-	-
1.3931	284	4.748	-	-	-	-	-
1.3980	285	1.9095	-	-	-	-	-
1.4029	286	2.2069	-	-	-	-	-
1.4079	287	2.627	-	-	-	-	-
1.4128	288	0.0352	-	-	-	-	-
1.4177	289	0.8522	-	-	-	-	-
1.4226	290	0.2362	-	-	-	-	-
1.4275	291	0.5066	-	-	-	-	-
1.4324	292	0.1919	-	-	-	-	-
1.4373	293	2.9645	-	-	-	-	-
1.4423	294	1.2498	-	-	-	-	-
1.4472	295	0.2644	-	-	-	-	-
1.4521	296	3.2783	-	-	-	-	-
1.4570	297	0.8226	-	-	-	-	-
1.4619	298	0.2221	-	-	-	-	-
1.4668	299	1.2149	-	-	-	-	-
1.4717	300	0.5072	-	-	-	-	-
1.4767	301	0.5181	-	-	-	-	-
1.4816	302	0.937	-	-	-	-	-
1.4865	303	2.1448	-	-	-	-	-
1.4914	304	0.1876	-	-	-	-	-
1.4963	305	6.4399	-	-	-	-	-
1.5012	306	0.0395	-	-	-	-	-
1.5061	307	1.9102	-	-	-	-	-
1.5111	308	0.0729	-	-	-	-	-
1.5160	309	0.2241	-	-	-	-	-
1.5209	310	6.0667	-	-	-	-	-
1.5258	311	0.0848	-	-	-	-	-
1.5307	312	0.8551	-	-	-	-	-
1.5356	313	0.0216	-	-	-	-	-
1.5405	314	0.025	-	-	-	-	-
1.5455	315	17.4796	-	-	-	-	-
1.5504	316	0.2316	-	-	-	-	-
1.5553	317	2.9799	-	-	-	-	-
1.5602	318	6.4386	-	-	-	-	-
1.5651	319	1.1931	-	-	-	-	-
1.5700	320	1.9184	-	-	-	-	-
1.5749	321	12.6412	-	-	-	-	-
1.5799	322	4.9206	-	-	-	-	-
1.5848	323	0.1121	-	-	-	-	-
1.5897	324	10.8083	-	-	-	-	-
1.5946	325	3.1304	-	-	-	-	-
1.5995	326	4.435	-	-	-	-	-
1.6044	327	17.1776	-	-	-	-	-
1.6093	328	0.0288	-	-	-	-	-
1.6143	329	5.829	-	-	-	-	-
1.6192	330	6.4497	-	-	-	-	-
1.6241	331	0.518	-	-	-	-	-
1.6290	332	0.27	-	-	-	-	-
1.6339	333	0.0485	-	-	-	-	-
1.6388	334	0.1782	-	-	-	-	-
1.6437	335	0.0779	-	-	-	-	-
1.6486	336	1.8905	-	-	-	-	-
1.6536	337	11.1906	-	-	-	-	-
1.6585	338	0.0944	-	-	-	-	-
1.6634	339	9.5066	-	-	-	-	-
1.6683	340	0.0802	-	-	-	-	-
1.6732	341	5.3741	-	-	-	-	-
1.6781	342	0.3266	-	-	-	-	-
1.6830	343	9.735	-	-	-	-	-
1.6880	344	8.7255	-	-	-	-	-
1.6929	345	4.9033	-	-	-	-	-
1.6978	346	0.6812	-	-	-	-	-
1.7027	347	0.4711	-	-	-	-	-
1.7076	348	0.6801	-	-	-	-	-
1.7125	349	9.1073	-	-	-	-	-
1.7174	350	1.8518	-	-	-	-	-
1.7224	351	3.0041	-	-	-	-	-
1.7273	352	0.074	-	-	-	-	-
1.7322	353	0.3182	-	-	-	-	-
1.7371	354	1.0006	-	-	-	-	-
1.7420	355	6.0453	-	-	-	-	-
1.7469	356	10.938	-	-	-	-	-
1.7518	357	0.3152	-	-	-	-	-
1.7568	358	3.1026	-	-	-	-	-
1.7617	359	1.0964	-	-	-	-	-
1.7666	360	2.2737	-	-	-	-	-
1.7715	361	0.8961	-	-	-	-	-
1.7764	362	5.6047	-	-	-	-	-
1.7813	363	10.9563	-	-	-	-	-
1.7862	364	5.2848	-	-	-	-	-
1.7912	365	0.0042	-	-	-	-	-
1.7961	366	1.3142	-	-	-	-	-
1.8010	367	4.5506	-	-	-	-	-
1.8059	368	0.7704	-	-	-	-	-
1.8108	369	5.4817	-	-	-	-	-
1.8157	370	1.8137	-	-	-	-	-
1.8206	371	1.4185	-	-	-	-	-
1.8256	372	11.2898	-	-	-	-	-
1.8305	373	2.7519	-	-	-	-	-
1.8354	374	0.5657	-	-	-	-	-
1.8403	375	1.7001	-	-	-	-	-
1.8452	376	0.6031	-	-	-	-	-
1.8501	377	0.1446	-	-	-	-	-
1.8550	378	3.34	-	-	-	-	-
1.8600	379	3.4178	-	-	-	-	-
1.8649	380	0.3415	-	-	-	-	-
1.8698	381	0.6753	-	-	-	-	-
1.8747	382	0.0068	-	-	-	-	-
1.8796	383	14.0264	-	-	-	-	-
1.8845	384	0.2598	-	-	-	-	-
1.8894	385	0.0627	-	-	-	-	-
1.8943	386	0.1462	-	-	-	-	-
1.8993	387	0.0358	-	-	-	-	-
1.9042	388	2.2702	-	-	-	-	-
1.9091	389	17.183	-	-	-	-	-
1.9140	390	0.0035	-	-	-	-	-
1.9189	391	7.0306	-	-	-	-	-
1.9238	392	3.5389	-	-	-	-	-
1.9287	393	3.0237	-	-	-	-	-
1.9337	394	1.9053	-	-	-	-	-
1.9386	395	0.3276	-	-	-	-	-
1.9435	396	1.8516	-	-	-	-	-
1.9484	397	0.1166	-	-	-	-	-
1.9533	398	3.4987	-	-	-	-	-
1.9582	399	1.5579	-	-	-	-	-
1.9631	400	0.0375	-	-	-	-	-
1.9681	401	1.8356	-	-	-	-	-
1.9730	402	1.6129	-	-	-	-	-
1.9779	403	0.0597	-	-	-	-	-
1.9828	404	2.4842	-	-	-	-	-
1.9877	405	0.9398	-	-	-	-	-
1.9926	406	4.4135	-	-	-	-	-
1.9975	407	0.8529	-	-	-	-	-
2.0	408	0.0555	0.4050	0.4000	0.3685	0.3534	0.2996
2.0049	409	6.4779	-	-	-	-	-
2.0098	410	0.1078	-	-	-	-	-
2.0147	411	0.9369	-	-	-	-	-
2.0197	412	1.3398	-	-	-	-	-
2.0246	413	3.2537	-	-	-	-	-
2.0295	414	0.7063	-	-	-	-	-
2.0344	415	1.4912	-	-	-	-	-
2.0393	416	0.5246	-	-	-	-	-
2.0442	417	2.5067	-	-	-	-	-
2.0491	418	0.1324	-	-	-	-	-
2.0541	419	2.7333	-	-	-	-	-
2.0590	420	0.1247	-	-	-	-	-
2.0639	421	0.1794	-	-	-	-	-
2.0688	422	5.055	-	-	-	-	-
2.0737	423	0.1422	-	-	-	-	-
2.0786	424	1.511	-	-	-	-	-
2.0835	425	0.5098	-	-	-	-	-
2.0885	426	9.1543	-	-	-	-	-
2.0934	427	0.0017	-	-	-	-	-
2.0983	428	0.8768	-	-	-	-	-
2.1032	429	0.6347	-	-	-	-	-
2.1081	430	0.062	-	-	-	-	-
2.1130	431	1.6098	-	-	-	-	-
2.1179	432	2.6084	-	-	-	-	-
2.1229	433	4.9172	-	-	-	-	-
2.1278	434	0.02	-	-	-	-	-
2.1327	435	1.5805	-	-	-	-	-
2.1376	436	3.6176	-	-	-	-	-
2.1425	437	2.8269	-	-	-	-	-
2.1474	438	1.4134	-	-	-	-	-
2.1523	439	0.8755	-	-	-	-	-
2.1572	440	2.6434	-	-	-	-	-
2.1622	441	10.6355	-	-	-	-	-
2.1671	442	0.0964	-	-	-	-	-
2.1720	443	0.0223	-	-	-	-	-
2.1769	444	5.9773	-	-	-	-	-
2.1818	445	2.8012	-	-	-	-	-
2.1867	446	0.0054	-	-	-	-	-
2.1916	447	0.0303	-	-	-	-	-
2.1966	448	0.8663	-	-	-	-	-
2.2015	449	0.0423	-	-	-	-	-
2.2064	450	8.415	-	-	-	-	-
2.2113	451	8.1867	-	-	-	-	-
2.2162	452	1.0138	-	-	-	-	-
2.2211	453	6.004	-	-	-	-	-
2.2260	454	1.2642	-	-	-	-	-
2.2310	455	1.0424	-	-	-	-	-
2.2359	456	2.9422	-	-	-	-	-
2.2408	457	0.2013	-	-	-	-	-
2.2457	458	1.1042	-	-	-	-	-
2.2506	459	1.7342	-	-	-	-	-
2.2555	460	0.0039	-	-	-	-	-
2.2604	461	0.5644	-	-	-	-	-
2.2654	462	6.9326	-	-	-	-	-
2.2703	463	0.1054	-	-	-	-	-
2.2752	464	0.1134	-	-	-	-	-
2.2801	465	0.281	-	-	-	-	-
2.2850	466	0.0216	-	-	-	-	-
2.2899	467	1.5359	-	-	-	-	-
2.2948	468	0.066	-	-	-	-	-
2.2998	469	0.0401	-	-	-	-	-
2.3047	470	0.9936	-	-	-	-	-
2.3096	471	0.0539	-	-	-	-	-
2.3145	472	0.5133	-	-	-	-	-
2.3194	473	6.1059	-	-	-	-	-
2.3243	474	0.038	-	-	-	-	-
2.3292	475	0.0854	-	-	-	-	-
2.3342	476	0.0276	-	-	-	-	-
2.3391	477	0.039	-	-	-	-	-
2.3440	478	0.0163	-	-	-	-	-
2.3489	479	0.0204	-	-	-	-	-
2.3538	480	0.1532	-	-	-	-	-
2.3587	481	1.7098	-	-	-	-	-
2.3636	482	0.2531	-	-	-	-	-
2.3686	483	0.3448	-	-	-	-	-
2.3735	484	0.0023	-	-	-	-	-
2.3784	485	0.0148	-	-	-	-	-
2.3833	486	25.1183	-	-	-	-	-
2.3882	487	0.1733	-	-	-	-	-
2.3931	488	0.0948	-	-	-	-	-
2.3980	489	0.0019	-	-	-	-	-
2.4029	490	5.0696	-	-	-	-	-
2.4079	491	0.0803	-	-	-	-	-
2.4128	492	2.7643	-	-	-	-	-
2.4177	493	0.0711	-	-	-	-	-
2.4226	494	0.3009	-	-	-	-	-
2.4275	495	1.9427	-	-	-	-	-
2.4324	496	0.2456	-	-	-	-	-
2.4373	497	2.7995	-	-	-	-	-
2.4423	498	0.0666	-	-	-	-	-
2.4472	499	0.5429	-	-	-	-	-
2.4521	500	0.7347	-	-	-	-	-
2.4570	501	1.507	-	-	-	-	-
2.4619	502	0.1055	-	-	-	-	-
2.4668	503	1.0503	-	-	-	-	-
2.4717	504	0.0046	-	-	-	-	-
2.4767	505	0.0089	-	-	-	-	-
2.4816	506	3.0963	-	-	-	-	-
2.4865	507	0.0141	-	-	-	-	-
2.4914	508	0.0219	-	-	-	-	-
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2.5111	512	0.5769	-	-	-	-	-
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2.5258	515	24.2047	-	-	-	-	-
2.5307	516	0.0749	-	-	-	-	-
2.5356	517	0.0788	-	-	-	-	-
2.5405	518	0.8379	-	-	-	-	-
2.5455	519	3.0144	-	-	-	-	-
2.5504	520	4.8283	-	-	-	-	-
2.5553	521	0.4814	-	-	-	-	-
2.5602	522	2.7476	-	-	-	-	-
2.5651	523	0.2397	-	-	-	-	-
2.5700	524	0.0043	-	-	-	-	-
2.5749	525	6.0072	-	-	-	-	-
2.5799	526	0.3147	-	-	-	-	-
2.5848	527	1.1259	-	-	-	-	-
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2.5946	529	4.9853	-	-	-	-	-
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2.6044	531	0.143	-	-	-	-	-
2.6093	532	0.031	-	-	-	-	-
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2.6290	536	0.0042	-	-	-	-	-
2.6339	537	0.1536	-	-	-	-	-
2.6388	538	4.659	-	-	-	-	-
2.6437	539	4.3203	-	-	-	-	-
2.6486	540	8.4674	-	-	-	-	-
2.6536	541	0.1089	-	-	-	-	-
2.6585	542	0.0138	-	-	-	-	-
2.6634	543	2.3114	-	-	-	-	-
2.6683	544	0.0151	-	-	-	-	-
2.6732	545	5.4184	-	-	-	-	-
2.6781	546	3.0697	-	-	-	-	-
2.6830	547	8.4918	-	-	-	-	-
2.6880	548	0.09	-	-	-	-	-
2.6929	549	0.7371	-	-	-	-	-
2.6978	550	0.0025	-	-	-	-	-
2.7027	551	0.0232	-	-	-	-	-
2.7076	552	1.4486	-	-	-	-	-
2.7125	553	0.7637	-	-	-	-	-
2.7174	554	0.5324	-	-	-	-	-
2.7224	555	15.0489	-	-	-	-	-
2.7273	556	0.0422	-	-	-	-	-
2.7322	557	5.4301	-	-	-	-	-
2.7371	558	0.3509	-	-	-	-	-
2.7420	559	0.1936	-	-	-	-	-
2.7469	560	0.1582	-	-	-	-	-
2.7518	561	3.1946	-	-	-	-	-
2.7568	562	0.0097	-	-	-	-	-
2.7617	563	0.0353	-	-	-	-	-
2.7666	564	3.6379	-	-	-	-	-
2.7715	565	0.0123	-	-	-	-	-
2.7764	566	0.0222	-	-	-	-	-
2.7813	567	0.0079	-	-	-	-	-
2.7862	568	0.1142	-	-	-	-	-
2.7912	569	3.3224	-	-	-	-	-
2.7961	570	1.5419	-	-	-	-	-
2.8010	571	0.2215	-	-	-	-	-
2.8059	572	5.9916	-	-	-	-	-
2.8108	573	1.9149	-	-	-	-	-
2.8157	574	12.2268	-	-	-	-	-
2.8206	575	0.1859	-	-	-	-	-
2.8256	576	8.719	-	-	-	-	-
2.8305	577	0.1879	-	-	-	-	-
2.8354	578	0.0797	-	-	-	-	-
2.8403	579	0.3781	-	-	-	-	-
2.8452	580	0.047	-	-	-	-	-
2.8501	581	0.6769	-	-	-	-	-
2.8550	582	0.11	-	-	-	-	-
2.8600	583	8.1354	-	-	-	-	-
2.8649	584	1.125	-	-	-	-	-
2.8698	585	3.3	-	-	-	-	-
2.8747	586	3.2444	-	-	-	-	-
2.8796	587	0.0642	-	-	-	-	-
2.8845	588	0.017	-	-	-	-	-
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2.8943	590	0.885	-	-	-	-	-
2.8993	591	5.437	-	-	-	-	-
2.9042	592	12.8624	-	-	-	-	-
2.9091	593	0.0331	-	-	-	-	-
2.9140	594	1.9041	-	-	-	-	-
2.9189	595	0.0406	-	-	-	-	-
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2.9435	600	0.0244	-	-	-	-	-
2.9484	601	0.2647	-	-	-	-	-
2.9533	602	4.1754	-	-	-	-	-
2.9582	603	0.9577	-	-	-	-	-
2.9631	604	2.1507	-	-	-	-	-
2.9681	605	11.0173	-	-	-	-	-
2.9730	606	0.0023	-	-	-	-	-
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2.9828	608	0.9823	-	-	-	-	-
2.9877	609	1.4711	-	-	-	-	-
2.9926	610	0.6055	-	-	-	-	-
2.9975	611	0.4423	-	-	-	-	-
3.0	612	4.3458	0.4805	0.4756	0.4405	0.4046	0.3865
3.0049	613	0.0673	-	-	-	-	-
3.0098	614	0.4593	-	-	-	-	-
3.0147	615	0.045	-	-	-	-	-
3.0197	616	1.473	-	-	-	-	-
3.0246	617	5.4872	-	-	-	-	-
3.0295	618	0.0125	-	-	-	-	-
3.0344	619	4.1117	-	-	-	-	-
3.0393	620	0.5648	-	-	-	-	-
3.0442	621	0.2546	-	-	-	-	-
3.0491	622	1.8912	-	-	-	-	-
3.0541	623	0.0366	-	-	-	-	-
3.0590	624	0.0343	-	-	-	-	-
3.0639	625	2.3127	-	-	-	-	-
3.0688	626	0.0717	-	-	-	-	-
3.0737	627	0.6625	-	-	-	-	-
3.0786	628	0.016	-	-	-	-	-
3.0835	629	23.2323	-	-	-	-	-
3.0885	630	0.1324	-	-	-	-	-
3.0934	631	0.1937	-	-	-	-	-
3.0983	632	6.0085	-	-	-	-	-
3.1032	633	0.0487	-	-	-	-	-
3.1081	634	15.3035	-	-	-	-	-
3.1130	635	0.1268	-	-	-	-	-
3.1179	636	0.3022	-	-	-	-	-
3.1229	637	2.0766	-	-	-	-	-
3.1278	638	2.0618	-	-	-	-	-
3.1327	639	5.9734	-	-	-	-	-
3.1376	640	8.3862	-	-	-	-	-
3.1425	641	0.0284	-	-	-	-	-
3.1474	642	0.1016	-	-	-	-	-
3.1523	643	0.3523	-	-	-	-	-
3.1572	644	0.2117	-	-	-	-	-
3.1622	645	2.2211	-	-	-	-	-
3.1671	646	0.5002	-	-	-	-	-
3.1720	647	0.0016	-	-	-	-	-
3.1769	648	0.0077	-	-	-	-	-
3.1818	649	0.4822	-	-	-	-	-
3.1867	650	0.0004	-	-	-	-	-
3.1916	651	0.0275	-	-	-	-	-
3.1966	652	0.0907	-	-	-	-	-
3.2015	653	0.0751	-	-	-	-	-
3.2064	654	0.0147	-	-	-	-	-
3.2113	655	0.4161	-	-	-	-	-
3.2162	656	3.217	-	-	-	-	-
3.2211	657	0.0028	-	-	-	-	-
3.2260	658	0.0028	-	-	-	-	-
3.2310	659	0.0079	-	-	-	-	-
3.2359	660	0.0083	-	-	-	-	-
3.2408	661	0.0379	-	-	-	-	-
3.2457	662	0.0294	-	-	-	-	-
3.2506	663	0.1602	-	-	-	-	-
3.2555	664	0.0018	-	-	-	-	-
3.2604	665	0.0035	-	-	-	-	-
3.2654	666	0.387	-	-	-	-	-
3.2703	667	0.0984	-	-	-	-	-
3.2752	668	4.7913	-	-	-	-	-
3.2801	669	0.1947	-	-	-	-	-
3.2850	670	3.7142	-	-	-	-	-
3.2899	671	0.096	-	-	-	-	-
3.2948	672	0.0027	-	-	-	-	-
3.2998	673	0.0359	-	-	-	-	-
3.3047	674	0.6312	-	-	-	-	-
3.3096	675	8.0573	-	-	-	-	-
3.3145	676	2.9999	-	-	-	-	-
3.3194	677	0.1601	-	-	-	-	-
3.3243	678	3.4294	-	-	-	-	-
3.3292	679	0.0017	-	-	-	-	-
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3.3391	681	1.7632	-	-	-	-	-
3.3440	682	0.0085	-	-	-	-	-
3.3489	683	0.7914	-	-	-	-	-
3.3538	684	0.0857	-	-	-	-	-
3.3587	685	0.0445	-	-	-	-	-
3.3636	686	0.0049	-	-	-	-	-
3.3686	687	0.0697	-	-	-	-	-
3.3735	688	0.1631	-	-	-	-	-
3.3784	689	0.0157	-	-	-	-	-
3.3833	690	0.0021	-	-	-	-	-
3.3882	691	0.0015	-	-	-	-	-
3.3931	692	0.3901	-	-	-	-	-
3.3980	693	1.7331	-	-	-	-	-
3.4029	694	6.0614	-	-	-	-	-
3.4079	695	0.0287	-	-	-	-	-
3.4128	696	2.9112	-	-	-	-	-
3.4177	697	0.0373	-	-	-	-	-
3.4226	698	0.0298	-	-	-	-	-
3.4275	699	3.69	-	-	-	-	-
3.4324	700	0.0247	-	-	-	-	-
3.4373	701	0.0097	-	-	-	-	-
3.4423	702	7.6551	-	-	-	-	-
3.4472	703	1.6144	-	-	-	-	-
3.4521	704	0.0964	-	-	-	-	-
3.4570	705	0.1756	-	-	-	-	-
3.4619	706	0.0834	-	-	-	-	-
3.4668	707	0.0264	-	-	-	-	-
3.4717	708	0.7804	-	-	-	-	-
3.4767	709	0.0252	-	-	-	-	-
3.4816	710	9.4975	-	-	-	-	-
3.4865	711	1.8196	-	-	-	-	-
3.4914	712	0.0874	-	-	-	-	-
3.4963	713	0.0515	-	-	-	-	-
3.5012	714	2.1845	-	-	-	-	-
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3.5111	716	0.0005	-	-	-	-	-
3.5160	717	0.0246	-	-	-	-	-
3.5209	718	0.0042	-	-	-	-	-
3.5258	719	0.2764	-	-	-	-	-
3.5307	720	0.0604	-	-	-	-	-
3.5356	721	0.5046	-	-	-	-	-
3.5405	722	0.1577	-	-	-	-	-
3.5455	723	0.3033	-	-	-	-	-
3.5504	724	0.0348	-	-	-	-	-
3.5553	725	0.1488	-	-	-	-	-
3.5602	726	0.0189	-	-	-	-	-
3.5651	727	0.0688	-	-	-	-	-
3.5700	728	0.001	-	-	-	-	-
3.5749	729	4.4539	-	-	-	-	-
3.5799	730	1.9252	-	-	-	-	-
3.5848	731	0.1565	-	-	-	-	-
3.5897	732	0.0041	-	-	-	-	-
3.5946	733	0.0026	-	-	-	-	-
3.5995	734	0.0044	-	-	-	-	-
3.6044	735	0.0666	-	-	-	-	-
3.6093	736	0.0383	-	-	-	-	-
3.6143	737	11.7611	-	-	-	-	-
3.6192	738	12.9361	-	-	-	-	-
3.6241	739	0.001	-	-	-	-	-
3.6290	740	3.1032	-	-	-	-	-
3.6339	741	0.0002	-	-	-	-	-
3.6388	742	0.1582	-	-	-	-	-
3.6437	743	0.4429	-	-	-	-	-
3.6486	744	2.7306	-	-	-	-	-
3.6536	745	0.1293	-	-	-	-	-
3.6585	746	1.9508	-	-	-	-	-
3.6634	747	0.0719	-	-	-	-	-
3.6683	748	0.0586	-	-	-	-	-
3.6732	749	3.3595	-	-	-	-	-
3.6781	750	0.8918	-	-	-	-	-
3.6830	751	0.0548	-	-	-	-	-
3.6880	752	0.2089	-	-	-	-	-
3.6929	753	4.3746	-	-	-	-	-
3.6978	754	1.923	-	-	-	-	-
3.7027	755	0.1344	-	-	-	-	-
3.7076	756	0.002	-	-	-	-	-
3.7125	757	6.0042	-	-	-	-	-
3.7174	758	0.0317	-	-	-	-	-
3.7224	759	21.2955	-	-	-	-	-
3.7273	760	0.5189	-	-	-	-	-
3.7322	761	2.1175	-	-	-	-	-
3.7371	762	7.9324	-	-	-	-	-
3.7420	763	0.0259	-	-	-	-	-
3.7469	764	0.1636	-	-	-	-	-
3.7518	765	0.037	-	-	-	-	-
3.7568	766	0.4433	-	-	-	-	-
3.7617	767	0.3925	-	-	-	-	-
3.7666	768	0.0118	-	-	-	-	-
3.7715	769	0.0039	-	-	-	-	-
3.7764	770	12.2304	-	-	-	-	-
3.7813	771	0.0159	-	-	-	-	-
3.7862	772	0.0369	-	-	-	-	-
3.7912	773	0.0076	-	-	-	-	-
3.7961	774	0.0014	-	-	-	-	-
3.8010	775	0.0441	-	-	-	-	-
3.8059	776	0.0469	-	-	-	-	-
3.8108	777	0.1142	-	-	-	-	-
3.8157	778	0.4633	-	-	-	-	-
3.8206	779	0.4934	-	-	-	-	-
3.8256	780	0.2579	-	-	-	-	-
3.8305	781	0.0166	-	-	-	-	-
3.8354	782	0.05	-	-	-	-	-
3.8403	783	3.154	-	-	-	-	-
3.8452	784	0.5013	-	-	-	-	-
3.8501	785	3.0774	-	-	-	-	-
3.8550	786	13.6522	-	-	-	-	-
3.8600	787	0.0302	-	-	-	-	-
3.8649	788	10.857	-	-	-	-	-
3.8698	789	0.0159	-	-	-	-	-
3.8747	790	2.6406	-	-	-	-	-
3.8796	791	0.0053	-	-	-	-	-
3.8845	792	0.0013	-	-	-	-	-
3.8894	793	0.0158	-	-	-	-	-
3.8943	794	0.1813	-	-	-	-	-
3.8993	795	0.0177	-	-	-	-	-
3.9042	796	7.1747	-	-	-	-	-
3.9091	797	8.2372	-	-	-	-	-
3.9140	798	0.1498	-	-	-	-	-
3.9189	799	2.8086	-	-	-	-	-
3.9238	800	0.0003	-	-	-	-	-
3.9287	801	2.1916	-	-	-	-	-
3.9337	802	0.0079	-	-	-	-	-
3.9386	803	0.0199	-	-	-	-	-
3.9435	804	0.0023	-	-	-	-	-
3.9484	805	0.017	-	-	-	-	-
3.9533	806	0.0047	-	-	-	-	-
3.9582	807	0.119	-	-	-	-	-
3.9631	808	3.4038	-	-	-	-	-
3.9681	809	0.0024	-	-	-	-	-
3.9730	810	0.0113	-	-	-	-	-
3.9779	811	3.7138	-	-	-	-	-
3.9828	812	1.1807	-	-	-	-	-
3.9877	813	7.1167	-	-	-	-	-
3.9926	814	0.6866	-	-	-	-	-
3.9975	815	0.0302	-	-	-	-	-
4.0	816	0.0	0.4828	0.4783	0.4511	0.4436	0.4257
4.0049	817	0.2861	-	-	-	-	-
4.0098	818	0.2564	-	-	-	-	-
4.0147	819	3.7804	-	-	-	-	-
4.0197	820	0.002	-	-	-	-	-
4.0246	821	0.064	-	-	-	-	-
4.0295	822	0.0054	-	-	-	-	-
4.0344	823	6.2346	-	-	-	-	-
4.0393	824	0.0931	-	-	-	-	-
4.0442	825	0.234	-	-	-	-	-
4.0491	826	0.0019	-	-	-	-	-
4.0541	827	17.2578	-	-	-	-	-
4.0590	828	0.6095	-	-	-	-	-
4.0639	829	0.1835	-	-	-	-	-
4.0688	830	1.2404	-	-	-	-	-
4.0737	831	0.0181	-	-	-	-	-
4.0786	832	0.0269	-	-	-	-	-
4.0835	833	0.0089	-	-	-	-	-
4.0885	834	5.2607	-	-	-	-	-
4.0934	835	0.0021	-	-	-	-	-
4.0983	836	0.0073	-	-	-	-	-
4.1032	837	0.0131	-	-	-	-	-
4.1081	838	0.2196	-	-	-	-	-
4.1130	839	0.0002	-	-	-	-	-
4.1179	840	1.7468	-	-	-	-	-
4.1229	841	0.0695	-	-	-	-	-
4.1278	842	0.0069	-	-	-	-	-
4.1327	843	0.0283	-	-	-	-	-
4.1376	844	0.0886	-	-	-	-	-
4.1425	845	0.0936	-	-	-	-	-
4.1474	846	0.5142	-	-	-	-	-
4.1523	847	5.2388	-	-	-	-	-
4.1572	848	0.0043	-	-	-	-	-
4.1622	849	3.6164	-	-	-	-	-
4.1671	850	0.7693	-	-	-	-	-
4.1720	851	0.0729	-	-	-	-	-
4.1769	852	8.5971	-	-	-	-	-
4.1818	853	2.8356	-	-	-	-	-
4.1867	854	0.0057	-	-	-	-	-
4.1916	855	0.0037	-	-	-	-	-
4.1966	856	0.0564	-	-	-	-	-
4.2015	857	0.0603	-	-	-	-	-
4.2064	858	3.4425	-	-	-	-	-
4.2113	859	0.0108	-	-	-	-	-
4.2162	860	0.0418	-	-	-	-	-
4.2211	861	0.0125	-	-	-	-	-
4.2260	862	0.0053	-	-	-	-	-
4.2310	863	0.0561	-	-	-	-	-
4.2359	864	0.1855	-	-	-	-	-
4.2408	865	0.0101	-	-	-	-	-
4.2457	866	4.9659	-	-	-	-	-
4.2506	867	0.0055	-	-	-	-	-
4.2555	868	8.4273	-	-	-	-	-
4.2604	869	0.0005	-	-	-	-	-
4.2654	870	0.737	-	-	-	-	-
4.2703	871	0.0038	-	-	-	-	-
4.2752	872	0.0763	-	-	-	-	-
4.2801	873	0.2328	-	-	-	-	-
4.2850	874	1.5894	-	-	-	-	-
4.2899	875	0.0121	-	-	-	-	-
4.2948	876	0.008	-	-	-	-	-
4.2998	877	1.2244	-	-	-	-	-
4.3047	878	4.2282	-	-	-	-	-
4.3096	879	0.0064	-	-	-	-	-
4.3145	880	0.9835	-	-	-	-	-
4.3194	881	0.0671	-	-	-	-	-
4.3243	882	0.003	-	-	-	-	-
4.3292	883	0.0287	-	-	-	-	-
4.3342	884	0.0061	-	-	-	-	-
4.3391	885	0.0044	-	-	-	-	-
4.3440	886	0.0705	-	-	-	-	-
4.3489	887	2.2214	-	-	-	-	-
4.3538	888	0.0004	-	-	-	-	-
4.3587	889	0.0082	-	-	-	-	-
4.3636	890	0.1958	-	-	-	-	-
4.3686	891	1.7235	-	-	-	-	-
4.3735	892	0.0167	-	-	-	-	-
4.3784	893	0.6845	-	-	-	-	-
4.3833	894	0.0001	-	-	-	-	-
4.3882	895	1.0436	-	-	-	-	-
4.3931	896	0.0112	-	-	-	-	-
4.3980	897	0.0404	-	-	-	-	-
4.4029	898	0.0005	-	-	-	-	-
4.4079	899	0.1872	-	-	-	-	-
4.4128	900	0.0002	-	-	-	-	-
4.4177	901	1.8915	-	-	-	-	-
4.4226	902	1.7094	-	-	-	-	-
4.4275	903	0.1402	-	-	-	-	-
4.4324	904	0.0643	-	-	-	-	-
4.4373	905	0.0776	-	-	-	-	-
4.4423	906	0.0005	-	-	-	-	-
4.4472	907	0.8611	-	-	-	-	-
4.4521	908	0.0003	-	-	-	-	-
4.4570	909	0.1705	-	-	-	-	-
4.4619	910	0.1037	-	-	-	-	-
4.4668	911	0.0134	-	-	-	-	-
4.4717	912	0.173	-	-	-	-	-
4.4767	913	0.0914	-	-	-	-	-
4.4816	914	0.5567	-	-	-	-	-
4.4865	915	0.0187	-	-	-	-	-
4.4914	916	0.0121	-	-	-	-	-
4.4963	917	1.0578	-	-	-	-	-
4.5012	918	0.0656	-	-	-	-	-
4.5061	919	0.0447	-	-	-	-	-
4.5111	920	0.0164	-	-	-	-	-
4.5160	921	0.0009	-	-	-	-	-
4.5209	922	1.1752	-	-	-	-	-
4.5258	923	0.0125	-	-	-	-	-
4.5307	924	0.3298	-	-	-	-	-
4.5356	925	0.0022	-	-	-	-	-
4.5405	926	0.9016	-	-	-	-	-
4.5455	927	0.0154	-	-	-	-	-
4.5504	928	0.0028	-	-	-	-	-
4.5553	929	0.0229	-	-	-	-	-
4.5602	930	0.8454	-	-	-	-	-
4.5651	931	5.024	-	-	-	-	-
4.5700	932	0.0031	-	-	-	-	-
4.5749	933	1.1479	-	-	-	-	-
4.5799	934	0.0018	-	-	-	-	-
4.5848	935	0.0457	-	-	-	-	-
4.5897	936	11.4855	-	-	-	-	-
4.5946	937	0.1458	-	-	-	-	-
4.5995	938	0.0159	-	-	-	-	-
4.6044	939	1.3953	-	-	-	-	-
4.6093	940	0.0497	-	-	-	-	-
4.6143	941	0.0192	-	-	-	-	-
4.6192	942	1.2701	-	-	-	-	-
4.6241	943	1.5943	-	-	-	-	-
4.6290	944	0.2584	-	-	-	-	-
4.6339	945	0.0015	-	-	-	-	-
4.6388	946	0.0079	-	-	-	-	-
4.6437	947	0.0221	-	-	-	-	-
4.6486	948	0.0049	-	-	-	-	-
4.6536	949	0.1198	-	-	-	-	-
4.6585	950	1.2655	-	-	-	-	-
4.6634	951	0.2401	-	-	-	-	-
4.6683	952	0.0001	-	-	-	-	-
4.6732	953	0.0	-	-	-	-	-
4.6781	954	0.203	-	-	-	-	-
4.6830	955	0.0066	-	-	-	-	-
4.6880	956	0.002	-	-	-	-	-
4.6929	957	0.0057	-	-	-	-	-
4.6978	958	0.0004	-	-	-	-	-
4.7027	959	3.0077	-	-	-	-	-
4.7076	960	0.0558	-	-	-	-	-
4.7125	961	0.0018	-	-	-	-	-
4.7174	962	0.0001	-	-	-	-	-
4.7224	963	0.818	-	-	-	-	-
4.7273	964	0.0587	-	-	-	-	-
4.7322	965	0.0785	-	-	-	-	-
4.7371	966	0.3031	-	-	-	-	-
4.7420	967	0.0018	-	-	-	-	-
4.7469	968	0.15	-	-	-	-	-
4.7518	969	1.1299	-	-	-	-	-
4.7568	970	4.6018	-	-	-	-	-
4.7617	971	0.0022	-	-	-	-	-
4.7666	972	0.0006	-	-	-	-	-
4.7715	973	0.0295	-	-	-	-	-
4.7764	974	0.4112	-	-	-	-	-
4.7813	975	3.3922	-	-	-	-	-
4.7862	976	0.0108	-	-	-	-	-
4.7912	977	0.0003	-	-	-	-	-
4.7961	978	0.003	-	-	-	-	-
4.8010	979	4.5611	-	-	-	-	-
4.8059	980	1.8257	-	-	-	-	-
4.8108	981	0.0129	-	-	-	-	-
4.8157	982	0.6645	-	-	-	-	-
4.8206	983	0.0207	-	-	-	-	-
4.8256	984	0.0032	-	-	-	-	-
4.8305	985	0.0077	-	-	-	-	-
4.8354	986	7.022	-	-	-	-	-
4.8403	987	17.4472	-	-	-	-	-
4.8452	988	0.0	-	-	-	-	-
4.8501	989	0.0042	-	-	-	-	-
4.8550	990	6.4386	-	-	-	-	-
4.8600	991	0.0182	-	-	-	-	-
4.8649	992	0.0191	-	-	-	-	-
4.8698	993	0.2168	-	-	-	-	-
4.8747	994	0.0071	-	-	-	-	-
4.8796	995	0.004	-	-	-	-	-
4.8845	996	0.0026	-	-	-	-	-
4.8894	997	0.6601	-	-	-	-	-
4.8943	998	0.0045	-	-	-	-	-
4.8993	999	0.0239	-	-	-	-	-
4.9042	1000	0.0069	-	-	-	-	-
4.9091	1001	0.0215	-	-	-	-	-
4.9140	1002	0.1398	-	-	-	-	-
4.9189	1003	0.2671	-	-	-	-	-
4.9238	1004	0.0045	-	-	-	-	-
4.9287	1005	0.0487	-	-	-	-	-
4.9337	1006	0.0024	-	-	-	-	-
4.9386	1007	0.0464	-	-	-	-	-
4.9435	1008	0.1883	-	-	-	-	-
4.9484	1009	0.0332	-	-	-	-	-
4.9533	1010	0.0235	-	-	-	-	-
4.9582	1011	0.0074	-	-	-	-	-
4.9631	1012	2.0417	-	-	-	-	-
4.9681	1013	0.0087	-	-	-	-	-
4.9730	1014	0.0375	-	-	-	-	-
4.9779	1015	0.0096	-	-	-	-	-
4.9828	1016	1.0317	-	-	-	-	-
4.9877	1017	9.0656	-	-	-	-	-
4.9926	1018	0.4875	-	-	-	-	-
4.9975	1019	0.2363	-	-	-	-	-
5.0	1020	6.9161	0.4730	0.4752	0.4446	0.4208	0.4295
5.0049	1021	0.0076	-	-	-	-	-
5.0098	1022	0.0059	-	-	-	-	-
5.0147	1023	1.8155	-	-	-	-	-
5.0197	1024	0.0035	-	-	-	-	-
5.0246	1025	0.0095	-	-	-	-	-
5.0295	1026	0.265	-	-	-	-	-
5.0344	1027	0.0682	-	-	-	-	-
5.0393	1028	0.0011	-	-	-	-	-
5.0442	1029	0.0073	-	-	-	-	-
5.0491	1030	0.002	-	-	-	-	-
5.0541	1031	0.0749	-	-	-	-	-
5.0590	1032	0.0143	-	-	-	-	-
5.0639	1033	0.1008	-	-	-	-	-
5.0688	1034	0.0003	-	-	-	-	-
5.0737	1035	2.8253	-	-	-	-	-
5.0786	1036	4.2712	-	-	-	-	-
5.0835	1037	0.0008	-	-	-	-	-
5.0885	1038	0.0056	-	-	-	-	-
5.0934	1039	1.2385	-	-	-	-	-
5.0983	1040	0.0372	-	-	-	-	-
5.1032	1041	0.4262	-	-	-	-	-
5.1081	1042	0.0012	-	-	-	-	-
5.1130	1043	0.0461	-	-	-	-	-
5.1179	1044	0.0335	-	-	-	-	-
5.1229	1045	0.0008	-	-	-	-	-
5.1278	1046	0.8225	-	-	-	-	-
5.1327	1047	0.0027	-	-	-	-	-
5.1376	1048	0.0278	-	-	-	-	-
5.1425	1049	0.0106	-	-	-	-	-
5.1474	1050	0.0086	-	-	-	-	-
5.1523	1051	0.4186	-	-	-	-	-
5.1572	1052	3.0836	-	-	-	-	-
5.1622	1053	0.1715	-	-	-	-	-
5.1671	1054	0.0007	-	-	-	-	-
5.1720	1055	0.0002	-	-	-	-	-
5.1769	1056	0.6005	-	-	-	-	-
5.1818	1057	0.0003	-	-	-	-	-
5.1867	1058	0.9117	-	-	-	-	-
5.1916	1059	0.0078	-	-	-	-	-
5.1966	1060	0.1735	-	-	-	-	-
5.2015	1061	0.013	-	-	-	-	-
5.2064	1062	0.0005	-	-	-	-	-
5.2113	1063	0.0115	-	-	-	-	-
5.2162	1064	2.2331	-	-	-	-	-
5.2211	1065	0.7744	-	-	-	-	-
5.2260	1066	0.0006	-	-	-	-	-
5.2310	1067	0.2042	-	-	-	-	-
5.2359	1068	0.0503	-	-	-	-	-
5.2408	1069	0.0079	-	-	-	-	-
5.2457	1070	0.1597	-	-	-	-	-
5.2506	1071	0.026	-	-	-	-	-
5.2555	1072	0.2249	-	-	-	-	-
5.2604	1073	0.0014	-	-	-	-	-
5.2654	1074	0.6355	-	-	-	-	-
5.2703	1075	2.9834	-	-	-	-	-
5.2752	1076	0.0379	-	-	-	-	-
5.2801	1077	0.0002	-	-	-	-	-
5.2850	1078	0.8298	-	-	-	-	-
5.2899	1079	0.0611	-	-	-	-	-
5.2948	1080	0.0058	-	-	-	-	-
5.2998	1081	0.0023	-	-	-	-	-
5.3047	1082	0.0152	-	-	-	-	-
5.3096	1083	0.0938	-	-	-	-	-
5.3145	1084	5.463	-	-	-	-	-
5.3194	1085	0.0048	-	-	-	-	-
5.3243	1086	0.005	-	-	-	-	-
5.3292	1087	0.0029	-	-	-	-	-
5.3342	1088	0.0302	-	-	-	-	-
5.3391	1089	0.0001	-	-	-	-	-
5.3440	1090	0.4089	-	-	-	-	-
5.3489	1091	1.078	-	-	-	-	-
5.3538	1092	0.2965	-	-	-	-	-
5.3587	1093	0.0014	-	-	-	-	-
5.3636	1094	0.0001	-	-	-	-	-
5.3686	1095	0.0187	-	-	-	-	-
5.3735	1096	9.4833	-	-	-	-	-
5.3784	1097	0.6337	-	-	-	-	-
5.3833	1098	0.0061	-	-	-	-	-
5.3882	1099	0.0008	-	-	-	-	-
5.3931	1100	0.0197	-	-	-	-	-
5.3980	1101	0.0034	-	-	-	-	-
5.4029	1102	8.6963	-	-	-	-	-
5.4079	1103	0.227	-	-	-	-	-
5.4128	1104	2.3266	-	-	-	-	-
5.4177	1105	0.8878	-	-	-	-	-
5.4226	1106	0.0199	-	-	-	-	-
5.4275	1107	0.4296	-	-	-	-	-
5.4324	1108	0.1091	-	-	-	-	-
5.4373	1109	0.0005	-	-	-	-	-
5.4423	1110	2.1527	-	-	-	-	-
5.4472	1111	0.204	-	-	-	-	-
5.4521	1112	0.0012	-	-	-	-	-
5.4570	1113	0.2691	-	-	-	-	-
5.4619	1114	1.5596	-	-	-	-	-
5.4668	1115	0.0004	-	-	-	-	-
5.4717	1116	0.0687	-	-	-	-	-
5.4767	1117	1.7525	-	-	-	-	-
5.4816	1118	2.2527	-	-	-	-	-
5.4865	1119	0.0545	-	-	-	-	-
5.4914	1120	0.0004	-	-	-	-	-
5.4963	1121	0.2455	-	-	-	-	-
5.5012	1122	3.9042	-	-	-	-	-
5.5061	1123	0.0332	-	-	-	-	-
5.5111	1124	0.7148	-	-	-	-	-
5.5160	1125	0.0329	-	-	-	-	-
5.5209	1126	0.4336	-	-	-	-	-
5.5258	1127	0.0007	-	-	-	-	-
5.5307	1128	0.0351	-	-	-	-	-
5.5356	1129	0.0071	-	-	-	-	-
5.5405	1130	0.0021	-	-	-	-	-
5.5455	1131	7.8513	-	-	-	-	-
5.5504	1132	9.1421	-	-	-	-	-
5.5553	1133	0.0222	-	-	-	-	-
5.5602	1134	0.009	-	-	-	-	-
5.5651	1135	0.3206	-	-	-	-	-
5.5700	1136	2.1536	-	-	-	-	-
5.5749	1137	0.7952	-	-	-	-	-
5.5799	1138	4.8399	-	-	-	-	-
5.5848	1139	0.0121	-	-	-	-	-
5.5897	1140	1.8385	-	-	-	-	-
5.5946	1141	0.0033	-	-	-	-	-
5.5995	1142	5.1626	-	-	-	-	-
5.6044	1143	0.0003	-	-	-	-	-
5.6093	1144	1.3345	-	-	-	-	-
5.6143	1145	0.0044	-	-	-	-	-
5.6192	1146	15.9512	-	-	-	-	-
5.6241	1147	0.0001	-	-	-	-	-
5.6290	1148	1.8027	-	-	-	-	-
5.6339	1149	0.0027	-	-	-	-	-
5.6388	1150	3.0103	-	-	-	-	-
5.6437	1151	0.2128	-	-	-	-	-
5.6486	1152	0.0291	-	-	-	-	-
5.6536	1153	0.0032	-	-	-	-	-
5.6585	1154	0.3131	-	-	-	-	-
5.6634	1155	0.0403	-	-	-	-	-
5.6683	1156	0.0001	-	-	-	-	-
5.6732	1157	0.3199	-	-	-	-	-
5.6781	1158	2.783	-	-	-	-	-
5.6830	1159	0.0048	-	-	-	-	-
5.6880	1160	0.0171	-	-	-	-	-
5.6929	1161	0.0001	-	-	-	-	-
5.6978	1162	0.0003	-	-	-	-	-
5.7027	1163	0.0836	-	-	-	-	-
5.7076	1164	4.8424	-	-	-	-	-
5.7125	1165	0.1689	-	-	-	-	-
5.7174	1166	0.0332	-	-	-	-	-
5.7224	1167	0.0071	-	-	-	-	-
5.7273	1168	0.9302	-	-	-	-	-
5.7322	1169	0.0009	-	-	-	-	-
5.7371	1170	0.131	-	-	-	-	-
5.7420	1171	0.0011	-	-	-	-	-
5.7469	1172	0.3116	-	-	-	-	-
5.7518	1173	3.2493	-	-	-	-	-
5.7568	1174	0.0012	-	-	-	-	-
5.7617	1175	0.9874	-	-	-	-	-
5.7666	1176	12.0563	-	-	-	-	-
5.7715	1177	0.0221	-	-	-	-	-
5.7764	1178	0.0045	-	-	-	-	-
5.7813	1179	0.0017	-	-	-	-	-
5.7862	1180	0.1269	-	-	-	-	-
5.7912	1181	0.0629	-	-	-	-	-
5.7961	1182	0.0015	-	-	-	-	-
5.8010	1183	2.6041	-	-	-	-	-
5.8059	1184	3.2558	-	-	-	-	-
5.8108	1185	0.0874	-	-	-	-	-
5.8157	1186	0.0006	-	-	-	-	-
5.8206	1187	0.0013	-	-	-	-	-
5.8256	1188	0.2076	-	-	-	-	-
5.8305	1189	0.0002	-	-	-	-	-
5.8354	1190	0.0013	-	-	-	-	-
5.8403	1191	0.087	-	-	-	-	-
5.8452	1192	0.0856	-	-	-	-	-
5.8501	1193	0.0009	-	-	-	-	-
5.8550	1194	0.0002	-	-	-	-	-
5.8600	1195	0.0718	-	-	-	-	-
5.8649	1196	0.0359	-	-	-	-	-
5.8698	1197	0.0155	-	-	-	-	-
5.8747	1198	2.4011	-	-	-	-	-
5.8796	1199	1.7882	-	-	-	-	-
5.8845	1200	0.003	-	-	-	-	-
5.8894	1201	0.0003	-	-	-	-	-
5.8943	1202	0.0301	-	-	-	-	-
5.8993	1203	0.0016	-	-	-	-	-
5.9042	1204	0.0026	-	-	-	-	-
5.9091	1205	0.0567	-	-	-	-	-
5.9140	1206	1.6303	-	-	-	-	-
5.9189	1207	0.0001	-	-	-	-	-
5.9238	1208	17.0385	-	-	-	-	-
5.9287	1209	1.1137	-	-	-	-	-
5.9337	1210	0.0022	-	-	-	-	-
5.9386	1211	0.0436	-	-	-	-	-
5.9435	1212	11.5906	-	-	-	-	-
5.9484	1213	0.0012	-	-	-	-	-
5.9533	1214	0.0027	-	-	-	-	-
5.9582	1215	2.7394	-	-	-	-	-
5.9631	1216	0.0005	-	-	-	-	-
5.9681	1217	0.0033	-	-	-	-	-
5.9730	1218	0.0949	-	-	-	-	-
5.9779	1219	0.0002	-	-	-	-	-
5.9828	1220	0.1714	-	-	-	-	-
5.9877	1221	12.5216	-	-	-	-	-
5.9926	1222	0.0057	-	-	-	-	-
5.9975	1223	0.0776	-	-	-	-	-
6.0	1224	0.0	0.4675	0.4648	0.4481	0.4329	0.4236
6.0049	1225	6.2431	-	-	-	-	-
6.0098	1226	0.0034	-	-	-	-	-
6.0147	1227	1.607	-	-	-	-	-
6.0197	1228	0.0011	-	-	-	-	-
6.0246	1229	5.6846	-	-	-	-	-
6.0295	1230	0.9689	-	-	-	-	-
6.0344	1231	0.0115	-	-	-	-	-
6.0393	1232	0.0332	-	-	-	-	-
6.0442	1233	0.0716	-	-	-	-	-
6.0491	1234	0.0059	-	-	-	-	-
6.0541	1235	0.0111	-	-	-	-	-
6.0590	1236	1.3194	-	-	-	-	-
6.0639	1237	0.0113	-	-	-	-	-
6.0688	1238	0.1392	-	-	-	-	-
6.0737	1239	0.0007	-	-	-	-	-
6.0786	1240	0.0016	-	-	-	-	-
6.0835	1241	0.0001	-	-	-	-	-
6.0885	1242	0.5488	-	-	-	-	-
6.0934	1243	0.0016	-	-	-	-	-
6.0983	1244	0.0145	-	-	-	-	-
6.1032	1245	4.2422	-	-	-	-	-
6.1081	1246	6.2805	-	-	-	-	-
6.1130	1247	0.1299	-	-	-	-	-
6.1179	1248	0.068	-	-	-	-	-
6.1229	1249	0.146	-	-	-	-	-
6.1278	1250	8.7093	-	-	-	-	-
6.1327	1251	0.001	-	-	-	-	-
6.1376	1252	0.0002	-	-	-	-	-
6.1425	1253	0.0298	-	-	-	-	-
6.1474	1254	0.0006	-	-	-	-	-
6.1523	1255	0.7816	-	-	-	-	-
6.1572	1256	0.001	-	-	-	-	-
6.1622	1257	0.1088	-	-	-	-	-
6.1671	1258	5.3505	-	-	-	-	-
6.1720	1259	0.0071	-	-	-	-	-
6.1769	1260	0.0185	-	-	-	-	-
6.1818	1261	2.3218	-	-	-	-	-
6.1867	1262	0.0776	-	-	-	-	-
6.1916	1263	0.0021	-	-	-	-	-
6.1966	1264	2.0907	-	-	-	-	-
6.2015	1265	1.4855	-	-	-	-	-
6.2064	1266	0.0069	-	-	-	-	-
6.2113	1267	0.0003	-	-	-	-	-
6.2162	1268	0.001	-	-	-	-	-
6.2211	1269	0.107	-	-	-	-	-
6.2260	1270	4.1798	-	-	-	-	-
6.2310	1271	0.0176	-	-	-	-	-
6.2359	1272	2.6422	-	-	-	-	-
6.2408	1273	0.0045	-	-	-	-	-
6.2457	1274	0.0018	-	-	-	-	-
6.2506	1275	0.2778	-	-	-	-	-
6.2555	1276	0.5032	-	-	-	-	-
6.2604	1277	0.1316	-	-	-	-	-
6.2654	1278	0.854	-	-	-	-	-
6.2703	1279	0.0043	-	-	-	-	-
6.2752	1280	0.094	-	-	-	-	-
6.2801	1281	0.0005	-	-	-	-	-
6.2850	1282	0.0003	-	-	-	-	-
6.2899	1283	1.8024	-	-	-	-	-
6.2948	1284	0.0791	-	-	-	-	-
6.2998	1285	0.0052	-	-	-	-	-
6.3047	1286	0.0861	-	-	-	-	-
6.3096	1287	0.1681	-	-	-	-	-
6.3145	1288	0.0005	-	-	-	-	-
6.3194	1289	0.0107	-	-	-	-	-
6.3243	1290	0.0074	-	-	-	-	-
6.3292	1291	0.0073	-	-	-	-	-
6.3342	1292	0.0395	-	-	-	-	-
6.3391	1293	0.0093	-	-	-	-	-
6.3440	1294	8.5639	-	-	-	-	-
6.3489	1295	0.0007	-	-	-	-	-
6.3538	1296	0.0001	-	-	-	-	-
6.3587	1297	1.1961	-	-	-	-	-
6.3636	1298	0.0187	-	-	-	-	-
6.3686	1299	9.2708	-	-	-	-	-
6.3735	1300	0.2093	-	-	-	-	-
6.3784	1301	8.735	-	-	-	-	-
6.3833	1302	0.0024	-	-	-	-	-
6.3882	1303	0.0029	-	-	-	-	-
6.3931	1304	0.0005	-	-	-	-	-
6.3980	1305	0.0764	-	-	-	-	-
6.4029	1306	1.1146	-	-	-	-	-
6.4079	1307	0.0007	-	-	-	-	-
6.4128	1308	0.3375	-	-	-	-	-
6.4177	1309	4.8914	-	-	-	-	-
6.4226	1310	0.0013	-	-	-	-	-
6.4275	1311	0.0684	-	-	-	-	-
6.4324	1312	2.272	-	-	-	-	-
6.4373	1313	0.9252	-	-	-	-	-
6.4423	1314	0.0099	-	-	-	-	-
6.4472	1315	0.01	-	-	-	-	-
6.4521	1316	0.1832	-	-	-	-	-
6.4570	1317	1.0361	-	-	-	-	-
6.4619	1318	0.0175	-	-	-	-	-
6.4668	1319	0.0443	-	-	-	-	-
6.4717	1320	0.0266	-	-	-	-	-
6.4767	1321	0.0004	-	-	-	-	-
6.4816	1322	2.6529	-	-	-	-	-
6.4865	1323	0.8222	-	-	-	-	-
6.4914	1324	0.0001	-	-	-	-	-
6.4963	1325	0.0055	-	-	-	-	-
6.5012	1326	0.0002	-	-	-	-	-
6.5061	1327	0.0003	-	-	-	-	-
6.5111	1328	0.009	-	-	-	-	-
6.5160	1329	3.8228	-	-	-	-	-
6.5209	1330	0.4528	-	-	-	-	-
6.5258	1331	0.4198	-	-	-	-	-
6.5307	1332	0.0007	-	-	-	-	-
6.5356	1333	0.1281	-	-	-	-	-
6.5405	1334	0.0131	-	-	-	-	-
6.5455	1335	3.3781	-	-	-	-	-
6.5504	1336	0.1504	-	-	-	-	-
6.5553	1337	0.0128	-	-	-	-	-
6.5602	1338	0.0427	-	-	-	-	-
6.5651	1339	0.1236	-	-	-	-	-
6.5700	1340	0.0005	-	-	-	-	-
6.5749	1341	8.4445	-	-	-	-	-
6.5799	1342	3.8744	-	-	-	-	-
6.5848	1343	0.0001	-	-	-	-	-
6.5897	1344	0.1478	-	-	-	-	-
6.5946	1345	0.0463	-	-	-	-	-
6.5995	1346	0.0005	-	-	-	-	-
6.6044	1347	0.4847	-	-	-	-	-
6.6093	1348	0.0008	-	-	-	-	-
6.6143	1349	1.2222	-	-	-	-	-
6.6192	1350	0.8561	-	-	-	-	-
6.6241	1351	0.5016	-	-	-	-	-
6.6290	1352	0.8134	-	-	-	-	-
6.6339	1353	0.663	-	-	-	-	-
6.6388	1354	0.0373	-	-	-	-	-
6.6437	1355	4.6129	-	-	-	-	-
6.6486	1356	0.0003	-	-	-	-	-
6.6536	1357	0.036	-	-	-	-	-
6.6585	1358	0.0591	-	-	-	-	-
6.6634	1359	0.0724	-	-	-	-	-
6.6683	1360	2.0866	-	-	-	-	-
6.6732	1361	0.0015	-	-	-	-	-
6.6781	1362	0.0446	-	-	-	-	-
6.6830	1363	0.0003	-	-	-	-	-
6.6880	1364	3.0299	-	-	-	-	-
6.6929	1365	0.0068	-	-	-	-	-
6.6978	1366	0.0257	-	-	-	-	-
6.7027	1367	0.2567	-	-	-	-	-
6.7076	1368	0.6689	-	-	-	-	-
6.7125	1369	3.2128	-	-	-	-	-
6.7174	1370	0.0011	-	-	-	-	-
6.7224	1371	0.0004	-	-	-	-	-
6.7273	1372	0.0383	-	-	-	-	-
6.7322	1373	0.0005	-	-	-	-	-
6.7371	1374	1.4068	-	-	-	-	-
6.7420	1375	0.1166	-	-	-	-	-
6.7469	1376	0.8068	-	-	-	-	-
6.7518	1377	0.8235	-	-	-	-	-
6.7568	1378	0.7885	-	-	-	-	-
6.7617	1379	0.0022	-	-	-	-	-
6.7666	1380	0.0	-	-	-	-	-
6.7715	1381	0.0026	-	-	-	-	-
6.7764	1382	0.0015	-	-	-	-	-
6.7813	1383	0.0848	-	-	-	-	-
6.7862	1384	0.1958	-	-	-	-	-
6.7912	1385	0.0173	-	-	-	-	-
6.7961	1386	0.0008	-	-	-	-	-
6.8010	1387	7.3964	-	-	-	-	-
6.8059	1388	0.0005	-	-	-	-	-
6.8108	1389	0.3121	-	-	-	-	-
6.8157	1390	0.0226	-	-	-	-	-
6.8206	1391	0.0007	-	-	-	-	-
6.8256	1392	0.0072	-	-	-	-	-
6.8305	1393	0.0001	-	-	-	-	-
6.8354	1394	0.459	-	-	-	-	-
6.8403	1395	0.0009	-	-	-	-	-
6.8452	1396	4.7422	-	-	-	-	-
6.8501	1397	0.0004	-	-	-	-	-
6.8550	1398	0.0028	-	-	-	-	-
6.8600	1399	0.0011	-	-	-	-	-
6.8649	1400	0.0	-	-	-	-	-
6.8698	1401	0.0005	-	-	-	-	-
6.8747	1402	0.0033	-	-	-	-	-
6.8796	1403	0.0	-	-	-	-	-
6.8845	1404	4.9801	-	-	-	-	-
6.8894	1405	3.454	-	-	-	-	-
6.8943	1406	5.912	-	-	-	-	-
6.8993	1407	0.0053	-	-	-	-	-
6.9042	1408	0.0001	-	-	-	-	-
6.9091	1409	0.0064	-	-	-	-	-
6.9140	1410	0.0747	-	-	-	-	-
6.9189	1411	0.1264	-	-	-	-	-
6.9238	1412	5.4115	-	-	-	-	-
6.9287	1413	0.0373	-	-	-	-	-
6.9337	1414	0.5234	-	-	-	-	-
6.9386	1415	0.743	-	-	-	-	-
6.9435	1416	0.0004	-	-	-	-	-
6.9484	1417	0.0039	-	-	-	-	-
6.9533	1418	0.008	-	-	-	-	-
6.9582	1419	0.005	-	-	-	-	-
6.9631	1420	0.2574	-	-	-	-	-
6.9681	1421	0.0001	-	-	-	-	-
6.9730	1422	0.0001	-	-	-	-	-
6.9779	1423	0.0018	-	-	-	-	-
6.9828	1424	0.0186	-	-	-	-	-
6.9877	1425	0.0113	-	-	-	-	-
6.9926	1426	0.0048	-	-	-	-	-
6.9975	1427	1.5498	-	-	-	-	-
7.0	1428	0.0001	0.4785	0.4711	0.4555	0.4362	0.4222

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.1.2
• Transformers: 4.51.3
• PyTorch: 2.8.0+cu126
• Accelerate: 1.11.0
• 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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}