AlexWortega/qwen_emb_6k

SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct

This is a sentence-transformers model finetuned from Qwen/Qwen2.5-0.5B-Instruct. It maps sentences & paragraphs to a 896-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: Qwen/Qwen2.5-0.5B-Instruct
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 896 dimensions
• Similarity Function: Cosine Similarity

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': 1024, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 896, '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})
)

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("AlexWortega/qwen_emb_6k")
# Run inference
sentences = [
    'query: \n\nHuman: 2 cars take 4 hours to drive from Stockholm to Copenhagen. Explain how long it takes for 6 cars to drive the same distance.\n\nAssistant: ',
    "The number of cars doesn't affect the time it takes to travel from Stockholm to Copenhagen, assuming each car is traveling independently and under the same conditions. If it takes one car 4 hours to make the journey, it will also take each of the other cars the same amount of time, 4 hours, to travel the same distance. So, whether you have 2 cars or 6 cars, each car would still take 4 hours to travel from Stockholm to Copenhagen.",
    '6 cars would take 2 hours and 40 minutes to drive from Stockholm to Copenhagen. This is because the total distance remains the same, but with more cars, the time is divided by the number of cars. So, 4 hours divided by 2 cars is 2 hours, and then divided by 3 (since there are now 6 cars) is 40 minutes.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 896]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

• Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
• Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.34	0.62	0.5	0.2	0.56	0.26	0.34	0.24	0.58	0.36	0.1	0.34	0.5102
cosine_accuracy@3	0.5	0.82	0.7	0.46	0.66	0.44	0.54	0.54	0.8	0.6	0.38	0.58	0.7551
cosine_accuracy@5	0.56	0.86	0.76	0.48	0.74	0.5	0.54	0.6	0.82	0.68	0.48	0.62	0.8571
cosine_accuracy@10	0.66	0.92	0.86	0.6	0.86	0.68	0.54	0.66	0.86	0.8	0.62	0.68	0.9796
cosine_precision@1	0.34	0.62	0.5	0.2	0.56	0.26	0.34	0.24	0.58	0.36	0.1	0.34	0.5102
cosine_precision@3	0.1867	0.5333	0.2333	0.1867	0.3	0.1467	0.3333	0.18	0.2867	0.28	0.1267	0.2	0.4626
cosine_precision@5	0.144	0.452	0.156	0.124	0.204	0.1	0.284	0.12	0.188	0.212	0.096	0.132	0.4204
cosine_precision@10	0.094	0.356	0.088	0.088	0.13	0.068	0.222	0.07	0.108	0.142	0.062	0.076	0.3653
cosine_recall@1	0.1757	0.0551	0.48	0.1083	0.28	0.26	0.0208	0.22	0.524	0.072	0.1	0.305	0.037
cosine_recall@3	0.2573	0.1282	0.68	0.2929	0.45	0.44	0.0622	0.51	0.7307	0.1737	0.38	0.55	0.0974
cosine_recall@5	0.314	0.1804	0.74	0.3269	0.51	0.5	0.0758	0.55	0.7613	0.2187	0.48	0.585	0.1441
cosine_recall@10	0.3773	0.247	0.83	0.4267	0.65	0.68	0.1035	0.62	0.8287	0.2937	0.62	0.665	0.2426
cosine_ndcg@10	0.3322	0.4606	0.6623	0.3084	0.5552	0.4463	0.2714	0.4404	0.7084	0.2888	0.3529	0.4924	0.4138
cosine_mrr@10	0.4347	0.7196	0.621	0.3335	0.6367	0.3753	0.4267	0.396	0.6884	0.4936	0.2687	0.454	0.6687
cosine_map@100	0.2716	0.3188	0.6078	0.2464	0.48	0.3896	0.1078	0.389	0.6682	0.2126	0.2836	0.4366	0.3139

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.3808
cosine_accuracy@3	0.5981
cosine_accuracy@5	0.6536
cosine_accuracy@10	0.7477
cosine_precision@1	0.3808
cosine_precision@3	0.2658
cosine_precision@5	0.2025
cosine_precision@10	0.1438
cosine_recall@1	0.2029
cosine_recall@3	0.3656
cosine_recall@5	0.4143
cosine_recall@10	0.5065
cosine_ndcg@10	0.441
cosine_mrr@10	0.5013
cosine_map@100	0.3635

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,580,101 training samples
• Columns: query, response, and negative
• Approximate statistics based on the first 1000 samples:

  	query	response	negative
  type	string	string	string
  details	min: 14 tokens mean: 149.45 tokens max: 1024 tokens	min: 2 tokens mean: 249.27 tokens max: 1024 tokens	min: 1 tokens mean: 174.24 tokens max: 1024 tokens

• Samples:

  query	response	negative
  query: Human: 0.002 = 1000 1 = x? Assistant:	To find the value of x, we can set up a proportion using the given information: 0.002/1000 = 1/x To solve for x, we can cross multiply: 0.002 * x = 1000 * 1 0.002x = 1000 Dividing both sides by 0.002: x = 1000 / 0.002 x = 500,000 Therefore, 1 is equal to 500,000 in this proportion.	I apologize, but the equation you provided is not correct. 0.002 is equal to 0.002, not 1000. 1 is equal to x, not a number. Can you please provide a correct equation or problem for me to solve?
  query: Human: 0:00 what's going on guys it's NAME_1 from 0:01 NAME_2 and today we're going to 0:03 talk about cns fatigue 0:05 so going over uh cns fatigue central 0:07 nervous system fatigue it's something 0:08 that people talk about and bring up a 0:10 lot 0:10 there's a bit of science to it and 0:12 there's also a lot of bro 0:13 culture myth to it and i want to get 0:15 into that and i'm i'm going to talk 0:17 really quick about where it comes from 0:19 why we talk about it this way and then 0:21 how it kind of gets used and misused 0:23 to describe a certain training 0:25 phenomenon that we all experience 0:27 so i'm going to start out by kind of 0:29 fleshing out what we're talking about or 0:31 how it kind of originally became 0:34 discussed about so we observe a few 0:35 things when it comes to weight training 0:37 specifically with lifting heavy 0:38 the first thing we observe is that true 0:40 maximal attempts at 90 0:42 and up rpe nine to ten so that's really 0:45 hard tri...	Hello! It seems like you're sharing a transcript about Central Nervous System (CNS) fatigue, particularly in relation to weight training. CNS fatigue is a topic that's often discussed in the context of high-intensity workouts, such as weightlifting. The transcript you've shared discusses the concept of CNS fatigue, its origins, and how it's often misused to describe certain training phenomena. It also talks about the effects of lifting heavy weights at maximum capacity in every session, which can lead to short-term regression. This is a common experience among weightlifters who push their limits consistently without giving their bodies adequate time to recover. The speaker also mentions the common practice of spacing out heavy lifting sessions or incorporating deload periods to prevent this regression. The time between the last heavy attempt and a contest day is also noted to be longer, presumably to allow for adequate recovery and prevent CNS fatigue. If you have any specific ques...	0:00 in the past 0:00 with the same lift 0:00 and the same weight 0:00 and the same load 0:00 then 0:00 if you have a deload day and you come back 0:00 and you're feeling like you can handle 0:00 heavier loads 0:00 with a little bit more energy 0:00 then the load is typically a little bit 0:01 lighter than the previous day's load 0:02 and the time between contest day and the 0:03 last heavy attempt 0:03 is typically a lot shorter 0:04 than the time between the previous day's 0:05 heavy attempt and the contest day 0:06 and that's a general observation that has 0:08 also been seen in other strength training 0:09 styles 0:10 and it's something that's been uh 0:12 accounted for in the wisdom of 0:13 trainers and coaches over the years 0:14 and it's a good idea to keep in mind when 0:16 you're lifting heavy or you're training in 0:17 a deloads schedule 0:18 and you have a lot of energy and you're 0:20 able to handle heavier loads 0:21 the load is typically a little bit 0:22 lighter than...
  query: Human: 0:00 what's going on guys it's NAME_1 from 0:01 NAME_2 and today we're going to 0:03 talk about cns fatigue 0:05 so going over uh cns fatigue central 0:07 nervous system fatigue it's something 0:08 that people talk about and bring up a 0:10 lot 0:10 there's a bit of science to it and 0:12 there's also a lot of bro 0:13 culture myth to it and i want to get 0:15 into that and i'm i'm going to talk 0:17 really quick about where it comes from 0:19 why we talk about it this way and then 0:21 how it kind of gets used and misused 0:23 to describe a certain training 0:25 phenomenon that we all experience 0:27 so i'm going to start out by kind of 0:29 fleshing out what we're talking about or 0:31 how it kind of originally became 0:34 discussed about so we observe a few 0:35 things when it comes to weight training 0:37 specifically with lifting heavy 0:38 the first thing we observe is that true 0:40 maximal attempts at 90 0:42 and up rpe nine to ten so that's really 0:45 hard tri...	0:00 Hey there! Today we're going to talk about CNS fatigue, or central nervous system fatigue. This is a term that is often used in the fitness community, but there is some confusion and misinformation surrounding it. So let's dive in and discuss what it really means and how it affects our training. First, let's define what we mean by CNS fatigue. This is the idea that our central nervous system, which controls our brain and spinal cord, can become fatigued from intense physical activity. This can manifest as a decrease in performance, strength, and coordination. One of the main observations that has led to the concept of CNS fatigue is that when we lift heavy weights at a high intensity, such as a 9 or 10 on the RPE scale, we tend to see short-term regression in our performance. This means that if we continue to lift at this intensity every session, we may eventually see a decrease in our strength and ability to handle heavy loads. Additionally, we also see that the time between a...	Hi, I'm an AI language model. How can I assist you today?

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• gradient_accumulation_steps: 32
• learning_rate: 2e-05
• max_grad_norm: 0.4
• num_train_epochs: 1
• warmup_ratio: 0.4
• bf16: True
• prompts: {'query': 'query: ', 'answer': 'document: '}
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 32
• 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: 0.4
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.4
• 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: 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: False
• 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
• dispatch_batches: None
• split_batches: 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: {'query': 'query: ', 'answer': 'document: '}
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0.0016	10	2.3323	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0032	20	2.2923	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0049	30	2.2011	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0065	40	2.4198	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0081	50	2.4304	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0097	60	2.35	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0113	70	2.4141	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0130	80	2.4043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0146	90	2.2222	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0162	100	2.4379	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0178	110	2.4722	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0194	120	2.9719	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0211	130	2.5376	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0227	140	2.4272	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0243	150	2.1056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0259	160	2.1292	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0275	170	1.9443	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0292	180	1.8512	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0308	190	1.7141	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0324	200	1.8382	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0340	210	1.7891	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0356	220	1.6014	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0373	230	1.5022	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0389	240	1.412	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0405	250	1.3756	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0421	260	2.6414	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0437	270	1.6938	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0454	280	2.953	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0470	290	2.9116	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0486	300	1.273	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0502	310	1.4269	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0518	320	1.5998	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0535	330	1.5939	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0551	340	1.4772	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0567	350	1.162	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0583	360	1.4587	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0599	370	1.5296	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0616	380	1.6156	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0632	390	1.3018	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0648	400	1.5415	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0664	410	1.5115	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0680	420	2.435	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0697	430	1.7281	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0713	440	2.0099	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0729	450	1.2842	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0745	460	1.4389	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0761	470	1.3	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0778	480	1.3392	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0794	490	1.0975	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0810	500	1.2641	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0826	510	1.2011	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0842	520	1.3416	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0859	530	1.3424	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0875	540	1.29	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0891	550	1.383	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0907	560	0.971	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0923	570	1.0089	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0940	580	0.974	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0956	590	0.9482	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0972	600	1.2337	0.1808	0.1027	0.2711	0.0854	0.1819	0.0510	0.0569	0.0039	0.6725	0.0714	0.3804	0.3058	0.3047	0.2053
0.0988	610	1.1811	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1004	620	1.0868	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1021	630	1.1908	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1037	640	1.0508	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1053	650	1.097	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1069	660	0.9266	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1085	670	1.2172	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1102	680	1.1388	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1118	690	1.1859	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1134	700	0.8618	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1150	710	1.0641	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1167	720	1.1092	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1183	730	0.7565	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1199	740	0.7026	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1215	750	1.0661	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1231	760	1.3258	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1248	770	1.5056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1264	780	1.0812	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1280	790	1.0357	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1296	800	1.2638	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1312	810	1.7064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1329	820	1.4948	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1345	830	1.0338	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1361	840	0.9158	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1377	850	0.9544	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1393	860	1.8469	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1410	870	1.3733	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1426	880	0.8882	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1442	890	1.0591	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1458	900	1.0214	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1474	910	1.0111	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1491	920	0.783	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1507	930	0.9901	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1523	940	1.0508	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1539	950	1.6198	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1555	960	1.4054	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1572	970	2.0936	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1588	980	2.0536	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1604	990	1.595	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1620	1000	1.0133	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1636	1010	0.8841	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1653	1020	0.8795	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1669	1030	0.821	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1685	1040	0.9551	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1701	1050	0.8831	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1717	1060	0.8877	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1734	1070	0.9293	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1750	1080	1.1628	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1766	1090	1.0334	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1782	1100	0.9041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1798	1110	0.8715	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1815	1120	0.6835	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1831	1130	0.9067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1847	1140	0.9845	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1863	1150	0.9605	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1879	1160	0.9137	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1896	1170	0.8297	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1912	1180	0.9854	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1928	1190	1.0456	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1944	1200	0.8366	0.2868	0.2325	0.5528	0.1413	0.2869	0.0953	0.1302	0.0794	0.7002	0.1748	0.4492	0.3688	0.3810	0.2984
0.1960	1210	0.7654	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1977	1220	0.977	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1993	1230	0.64	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2009	1240	1.3624	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2025	1250	1.2971	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2041	1260	1.1123	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2058	1270	0.9836	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2074	1280	0.7819	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2090	1290	0.8977	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2106	1300	0.9156	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2122	1310	0.8029	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2139	1320	1.1394	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2155	1330	0.9088	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2171	1340	0.8174	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2187	1350	1.3159	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2203	1360	1.0255	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2220	1370	1.1159	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2236	1380	0.9766	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2252	1390	0.9058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2268	1400	0.88	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2284	1410	0.8224	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2301	1420	0.6394	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2317	1430	0.7517	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2333	1440	0.8308	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2349	1450	0.811	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2365	1460	0.8963	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2382	1470	0.9781	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2398	1480	0.8422	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2414	1490	0.8144	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2430	1500	0.7655	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2446	1510	0.6322	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2463	1520	0.6661	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2479	1530	0.7723	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2495	1540	0.7734	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2511	1550	0.8246	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2527	1560	0.7604	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2544	1570	0.8196	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2560	1580	0.7278	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2576	1590	0.7076	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2592	1600	0.6913	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2608	1610	0.6974	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2625	1620	0.7015	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2641	1630	0.677	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2657	1640	0.7185	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2673	1650	0.665	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2689	1660	0.7026	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2706	1670	0.6374	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2722	1680	0.652	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2738	1690	0.7426	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2754	1700	0.6444	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2770	1710	0.663	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2787	1720	0.6476	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2803	1730	0.6857	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2819	1740	0.6229	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2835	1750	0.5756	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2851	1760	0.6839	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2868	1770	0.8267	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2884	1780	0.8146	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2900	1790	0.7093	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2916	1800	0.7307	0.2597	0.2742	0.6859	0.2218	0.4912	0.2921	0.1728	0.3219	0.7381	0.2529	0.4898	0.4819	0.5037	0.3989
0.2932	1810	0.606	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2949	1820	0.6338	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2965	1830	0.5849	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2981	1840	0.699	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2997	1850	0.6164	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3013	1860	0.574	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3030	1870	0.5819	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3046	1880	0.5177	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3062	1890	0.6006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3078	1900	0.6981	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3094	1910	0.885	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3111	1920	1.2742	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3127	1930	0.7133	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3143	1940	0.7271	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3159	1950	1.3258	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3175	1960	1.2689	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3192	1970	0.6723	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3208	1980	0.3596	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3224	1990	0.4078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3240	2000	0.287	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3256	2010	0.2375	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3273	2020	0.2259	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3289	2030	0.3889	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3305	2040	0.7391	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3321	2050	0.5417	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3338	2060	0.4933	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3354	2070	0.426	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3370	2080	0.4222	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3386	2090	0.4132	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3402	2100	0.4133	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3419	2110	0.3989	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3435	2120	0.4035	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3451	2130	0.3804	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3467	2140	0.3597	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3483	2150	0.3793	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3500	2160	0.3633	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3516	2170	0.3504	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3532	2180	0.3475	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3548	2190	0.3467	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3564	2200	0.3412	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3581	2210	0.3665	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3597	2220	0.3585	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3613	2230	0.3335	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3629	2240	0.329	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3645	2250	0.3193	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3662	2260	0.3256	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3678	2270	0.325	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3694	2280	0.3312	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3710	2290	0.3323	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3726	2300	0.3192	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3743	2310	0.3366	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3759	2320	0.3247	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3775	2330	0.3207	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3791	2340	0.3238	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3807	2350	0.3217	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3824	2360	0.336	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3840	2370	0.3043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3856	2380	0.3043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3872	2390	0.3193	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3888	2400	0.3145	0.2338	0.4041	0.7329	0.2612	0.4511	0.3624	0.2742	0.3903	0.2020	0.2560	0.3127	0.5038	0.4262	0.3701
0.3905	2410	0.319	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3921	2420	0.3097	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3937	2430	0.2817	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3953	2440	0.3168	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3969	2450	0.2941	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3986	2460	0.2902	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4002	2470	0.3095	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4018	2480	0.3149	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4034	2490	0.2949	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4050	2500	0.3057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4067	2510	0.2982	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4083	2520	0.3064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4099	2530	0.3169	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4115	2540	0.2922	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4131	2550	0.2999	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4148	2560	0.2803	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4164	2570	0.3118	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.4293	2650	0.3245	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4310	2660	0.316	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.4342	2680	0.3046	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.4374	2700	0.2542	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4391	2710	0.3079	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4407	2720	0.2821	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4423	2730	0.2863	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4439	2740	0.2889	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4455	2750	0.282	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4472	2760	0.29	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4488	2770	0.2973	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4504	2780	0.3018	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4520	2790	0.2938	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4536	2800	0.2835	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4553	2810	0.2773	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4569	2820	0.2867	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4585	2830	0.2954	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4601	2840	0.3035	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4617	2850	0.2905	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4634	2860	0.2821	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4650	2870	0.2815	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4666	2880	0.298	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4682	2890	0.2905	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4698	2900	0.2821	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.4731	2920	0.2992	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.4763	2940	0.2855	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4779	2950	0.2775	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4796	2960	0.2994	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4812	2970	0.2939	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4828	2980	0.2999	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4844	2990	0.2935	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4860	3000	0.2714	0.2471	0.3962	0.7912	0.2469	0.4488	0.3739	0.2677	0.3976	0.1890	0.2485	0.2962	0.4538	0.4259	0.3679
0.4877	3010	0.2819	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4893	3020	0.2679	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4909	3030	0.2789	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4925	3040	0.2865	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4941	3050	0.2852	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4958	3060	0.2706	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4974	3070	0.2935	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4990	3080	0.272	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5006	3090	0.2915	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5022	3100	0.2826	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5039	3110	0.2652	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5055	3120	0.2887	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5071	3130	0.2613	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5087	3140	0.283	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5103	3150	0.2945	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5120	3160	0.2877	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5136	3170	0.2889	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5152	3180	0.268	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5168	3190	0.2911	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5184	3200	0.2785	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5201	3210	0.2711	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5217	3220	0.2911	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5233	3230	0.2649	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5249	3240	0.3054	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5265	3250	0.2531	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5282	3260	0.2767	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5298	3270	0.2853	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5314	3280	0.2731	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5330	3290	0.2776	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5346	3300	0.2725	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5363	3310	0.281	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5379	3320	0.2666	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5395	3330	0.2654	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5411	3340	0.2909	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5427	3350	0.2598	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5444	3360	0.2837	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5460	3370	0.2855	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5476	3380	0.2601	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5492	3390	0.268	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5508	3400	0.2681	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5525	3410	0.2663	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5541	3420	0.2837	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5557	3430	0.259	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5573	3440	0.2622	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5590	3450	0.2825	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5606	3460	0.2921	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5622	3470	0.2721	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5638	3480	0.2797	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5654	3490	0.2899	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5671	3500	0.2745	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5687	3510	0.2665	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5703	3520	0.2908	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5719	3530	0.2492	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5735	3540	0.2562	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5752	3550	0.2616	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5768	3560	0.2775	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5784	3570	0.2736	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5800	3580	0.2862	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5816	3590	0.2582	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5833	3600	0.2547	0.2371	0.3994	0.7786	0.2418	0.4072	0.3469	0.2615	0.4070	0.1551	0.2294	0.2533	0.4270	0.4161	0.3508
0.5849	3610	0.2822	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5865	3620	0.2622	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5881	3630	0.2691	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5897	3640	0.2585	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5914	3650	0.2927	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5930	3660	0.2593	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5946	3670	0.2501	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5962	3680	0.2796	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5978	3690	0.2622	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5995	3700	0.2508	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6011	3710	0.2891	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6027	3720	0.274	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6043	3730	0.2769	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6059	3740	0.2617	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6076	3750	0.2557	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6092	3760	0.2634	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6108	3770	0.262	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6124	3780	0.2696	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6140	3790	0.2608	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6157	3800	0.2592	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6173	3810	0.2757	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6189	3820	0.2672	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6205	3830	0.2523	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6221	3840	0.2775	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6238	3850	0.2621	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6254	3860	0.275	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6270	3870	0.2727	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6286	3880	0.2709	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6302	3890	0.2749	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6319	3900	0.2844	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6335	3910	0.2713	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6351	3920	0.2711	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6367	3930	0.2523	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6383	3940	0.2789	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6400	3950	0.2639	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6416	3960	0.2609	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6432	3970	0.2699	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6448	3980	0.2614	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6464	3990	0.2567	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6481	4000	1.2987	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6497	4010	1.4783	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6513	4020	1.7162	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6529	4030	1.2907	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6545	4040	1.2583	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6562	4050	1.0498	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6578	4060	1.8076	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6594	4070	1.215	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6610	4080	1.1462	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6626	4090	0.9511	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6643	4100	0.6151	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6659	4110	0.7482	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6675	4120	0.8572	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6691	4130	0.7722	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6707	4140	0.6085	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6724	4150	0.6644	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6740	4160	0.6423	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6756	4170	0.7482	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6772	4180	0.9649	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6788	4190	0.9205	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6805	4200	0.7746	0.2822	0.4484	0.7622	0.2944	0.5133	0.4592	0.2717	0.4451	0.3682	0.2594	0.2342	0.5123	0.4209	0.4055
0.6821	4210	0.5752	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6837	4220	0.6221	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6853	4230	0.526	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6869	4240	0.455	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6886	4250	0.4964	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6902	4260	0.935	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6918	4270	0.6227	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6934	4280	0.5594	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6950	4290	0.496	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6967	4300	0.5907	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6983	4310	0.5163	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6999	4320	0.468	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7015	4330	0.5214	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7031	4340	0.625	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7048	4350	0.593	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7064	4360	0.5852	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7080	4370	0.5648	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7096	4380	0.6791	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7112	4390	0.7008	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7129	4400	0.6731	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7145	4410	0.654	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7161	4420	0.6135	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7177	4430	0.6206	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7193	4440	0.5056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7210	4450	0.5201	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7226	4460	0.5894	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7242	4470	0.5571	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7258	4480	0.5979	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7274	4490	0.6202	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7291	4500	0.5544	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7307	4510	0.6122	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7323	4520	0.5631	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7339	4530	0.5284	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7355	4540	0.6899	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7372	4550	0.5838	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7388	4560	0.6806	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7404	4570	0.5413	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7420	4580	0.5956	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7436	4590	0.6044	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7453	4600	0.5857	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7469	4610	0.5664	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7485	4620	0.5097	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7501	4630	0.4912	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7517	4640	0.6049	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7534	4650	0.5389	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7550	4660	0.555	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7566	4670	0.6238	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7582	4680	0.6447	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7598	4690	0.5606	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7615	4700	0.5165	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7631	4710	0.5839	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7647	4720	0.5189	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7663	4730	0.584	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7679	4740	0.5744	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7696	4750	0.5351	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7712	4760	0.5953	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7728	4770	0.5725	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7744	4780	0.5688	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7761	4790	0.5004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7777	4800	0.5378	0.3514	0.4652	0.7429	0.3103	0.5406	0.4361	0.2797	0.4267	0.3843	0.2727	0.3474	0.5341	0.4249	0.4243
0.7793	4810	0.5244	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7809	4820	0.6241	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7825	4830	0.4844	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7842	4840	0.4401	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7858	4850	0.499	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7874	4860	0.5326	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7890	4870	0.4981	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7906	4880	0.5659	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7923	4890	0.5364	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7939	4900	0.5479	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7955	4910	0.4653	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7971	4920	0.5005	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7987	4930	0.5624	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8004	4940	0.4399	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8020	4950	0.4859	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8036	4960	0.5087	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8052	4970	0.511	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8068	4980	0.5819	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8085	4990	0.4462	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8101	5000	0.4882	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8117	5010	0.5306	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8133	5020	0.507	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8149	5030	0.4471	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8166	5040	0.5333	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8182	5050	0.4353	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8198	5060	0.5615	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8214	5070	0.5629	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8230	5080	0.5131	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8247	5090	0.4789	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8263	5100	0.4934	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8279	5110	0.5285	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8295	5120	0.4414	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8311	5130	0.5262	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8328	5140	0.4645	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8344	5150	0.4532	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8360	5160	0.4421	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8376	5170	0.4375	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8392	5180	0.5234	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8409	5190	0.4803	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8425	5200	0.4872	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8441	5210	0.451	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8457	5220	0.4388	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8473	5230	0.5182	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8490	5240	0.5302	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8506	5250	0.4643	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8522	5260	0.5581	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8538	5270	0.4643	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8554	5280	0.5288	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8571	5290	0.4133	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8587	5300	0.4664	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8603	5310	0.4814	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8619	5320	0.5256	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8635	5330	0.4904	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8652	5340	0.4495	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8668	5350	0.5389	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8684	5360	0.4497	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8700	5370	0.4776	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8716	5380	0.5441	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8733	5390	0.4473	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8749	5400	0.5598	0.3381	0.4668	0.7306	0.3137	0.5415	0.4550	0.2840	0.4169	0.4719	0.2735	0.3582	0.5311	0.4076	0.4299
0.8765	5410	0.4726	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8781	5420	0.4966	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8797	5430	0.4644	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8814	5440	0.4084	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8830	5450	0.4913	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8846	5460	0.5708	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8862	5470	0.5577	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8878	5480	0.4839	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8895	5490	0.461	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8911	5500	0.4799	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8927	5510	0.5608	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8943	5520	0.4625	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8959	5530	0.4765	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8976	5540	0.4348	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8992	5550	0.4424	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9008	5560	0.4147	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9024	5570	0.433	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9040	5580	0.4628	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9057	5590	0.4466	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9073	5600	0.4563	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9089	5610	0.4508	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9105	5620	0.4619	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9121	5630	0.4264	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9138	5640	0.5157	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9154	5650	0.4721	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9170	5660	0.4518	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9186	5670	0.4101	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9202	5680	0.4092	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9219	5690	0.4042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9235	5700	0.3852	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9251	5710	0.375	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9267	5720	0.3548	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9283	5730	0.3461	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9300	5740	0.3396	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9316	5750	0.3465	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9332	5760	0.347	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9348	5770	0.3365	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9364	5780	0.3299	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9381	5790	0.3417	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9397	5800	0.3423	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9413	5810	0.3512	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9429	5820	0.3353	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9445	5830	0.3291	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9462	5840	0.3162	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9478	5850	0.3326	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9494	5860	0.345	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9510	5870	0.2998	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9526	5880	0.307	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9543	5890	0.3019	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9559	5900	0.3169	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9575	5910	0.2857	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9591	5920	0.3018	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9607	5930	0.2954	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9624	5940	0.2953	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9640	5950	0.2861	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9656	5960	0.3384	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9672	5970	0.2968	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9688	5980	0.3191	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9705	5990	0.3069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9721	6000	0.3025	0.3322	0.4606	0.6623	0.3084	0.5552	0.4463	0.2714	0.4404	0.7084	0.2888	0.3529	0.4924	0.4138	0.4410
0.9737	6010	0.2891	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9753	6020	0.3038	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9769	6030	0.2931	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9786	6040	0.3145	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9802	6050	0.3046	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9818	6060	0.2896	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9834	6070	0.2926	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9850	6080	0.3025	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9867	6090	0.2798	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9883	6100	0.3006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9899	6110	0.2695	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9915	6120	0.3017	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9931	6130	0.2955	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9948	6140	0.2699	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9964	6150	0.2955	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9980	6160	0.2963	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9996	6170	0.2988	-	-	-	-	-	-	-	-	-	-	-	-	-	-

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.0
• Transformers: 4.46.2
• PyTorch: 2.1.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.1.0
• Tokenizers: 0.20.3

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",
}

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}
}