Add new CrossEncoder model

5e03204 verified about 1 month ago

116 kB

	---
	language:
	- en
	tags:
	- sentence-transformers
	- cross-encoder
	- reranker
	- generated_from_trainer
	- dataset_size:39770704
	- loss:MarginMSELoss
	base_model: jhu-clsp/ettin-encoder-17m
	datasets:
	- sentence-transformers/msmarco
	pipeline_tag: text-ranking
	library_name: sentence-transformers
	metrics:
	- map
	- mrr@10
	- ndcg@10
	co2_eq_emissions:
	emissions: 1604.894253932601
	energy_consumed: 4.347723620604183
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: AMD EPYC 7R13 Processor
	ram_total_size: 1999.9855194091797
	hours_used: 1.576
	hardware_used: 8 x NVIDIA H100 80GB HBM3
	model-index:
	- name: CrossEncoder based on jhu-clsp/ettin-encoder-17m
	results:
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoMSMARCO R100
	type: NanoMSMARCO_R100
	metrics:
	- type: map
	value: 0.6239
	name: Map
	- type: mrr@10
	value: 0.6129
	name: Mrr@10
	- type: ndcg@10
	value: 0.6576
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoNFCorpus R100
	type: NanoNFCorpus_R100
	metrics:
	- type: map
	value: 0.3478
	name: Map
	- type: mrr@10
	value: 0.5462
	name: Mrr@10
	- type: ndcg@10
	value: 0.3996
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoNQ R100
	type: NanoNQ_R100
	metrics:
	- type: map
	value: 0.6263
	name: Map
	- type: mrr@10
	value: 0.6337
	name: Mrr@10
	- type: ndcg@10
	value: 0.672
	name: Ndcg@10
	- task:
	type: cross-encoder-nano-beir
	name: Cross Encoder Nano BEIR
	dataset:
	name: NanoBEIR R100 mean
	type: NanoBEIR_R100_mean
	metrics:
	- type: map
	value: 0.5327
	name: Map
	- type: mrr@10
	value: 0.5976
	name: Mrr@10
	- type: ndcg@10
	value: 0.5764
	name: Ndcg@10
	---

	# CrossEncoder based on jhu-clsp/ettin-encoder-17m

	This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

	## Model Details

	### Model Description
	- Model Type: Cross Encoder
	- Base model: [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) <!-- at revision 987607455c61e7a5bbc85f7758e0512ea6d0ae4c -->
	- Maximum Sequence Length: 512 tokens
	- Number of Output Labels: 1 label
	- Training Dataset:
	- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco)
	- Language: en
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Documentation: [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
	- Repository: [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
	- Hugging Face: [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import CrossEncoder

	# Download from the 🤗 Hub
	model = CrossEncoder("tomaarsen/ms-marco-ettin-17m-reranker")
	# Get scores for pairs of texts
	pairs = [
	['the name olmec means', 'The name Olmec comes from the Nahuatl word for the Olmecs: Å\x8clmÄ\x93catl [oË\x90lË\x88meË\x90katÍ¡É¬] (singular) or Å\x8clmÄ\x93cah [oË\x90lË\x88meË\x90kaÊ\x94] (plural). This word is composed of the two words Å\x8dlli [Ë\x88oË\x90lË\x90i] , meaning rubber, and mÄ\x93catl [Ë\x88meË\x90katÍ¡É¬] , meaning people, so the word means rubber people.hey lived in the tropical lowlands of south-central Mexico, in the present-day states of Veracruz and Tabasco. It has been speculated that Olmec derive in part from neighboring Mokaya and/or Mixeâ\x80\x93Zoque.'],
	['what causes hissing in the ears', 'The following medical conditions are some of the possible causes of Hissing in ears. There are likely to be other possible causes, so ask your doctor about your symptoms. 1 Acute ear infection. Chronic ear infection.'],
	['multiple birth sibling definition', 'multiple birth(Noun) a birth in which more than one baby are born. Multiple birth. A multiple birth occurs when more than one fetus is carried to term in a single pregnancy. Different names for multiple births are used, depending on the number of offspring.'],
	['what cause fluid build up on the knee', 'No: The joint should not have any appreciable fluid in it normally. Fluid can be there from many reasons. Arthritis can cause fluid. Also surgery can cause some bleeding in the knee which can cause the fluid to build up. The fluid makes the joint stiff and makes it hard to get a good quad function. ...Read more.'],
	['how long is the wait list for kidney transplant', 'At the center of this is the simple fact that organ transplantation is built upon altruism and public trust. Gift of Life works hard to ensure that this trust is maintained, through its commitment both to the donor family as well as to those on the waiting list. Average Median Wait Time to Transplant. Kidney â\x80\x93 5 years.'],
	]
	scores = model.predict(pairs)
	print(scores.shape)
	# (5,)

	# Or rank different texts based on similarity to a single text
	ranks = model.rank(
	'the name olmec means',
	[
	'The name Olmec comes from the Nahuatl word for the Olmecs: Å\x8clmÄ\x93catl [oË\x90lË\x88meË\x90katÍ¡É¬] (singular) or Å\x8clmÄ\x93cah [oË\x90lË\x88meË\x90kaÊ\x94] (plural). This word is composed of the two words Å\x8dlli [Ë\x88oË\x90lË\x90i] , meaning rubber, and mÄ\x93catl [Ë\x88meË\x90katÍ¡É¬] , meaning people, so the word means rubber people.hey lived in the tropical lowlands of south-central Mexico, in the present-day states of Veracruz and Tabasco. It has been speculated that Olmec derive in part from neighboring Mokaya and/or Mixeâ\x80\x93Zoque.',
	'The following medical conditions are some of the possible causes of Hissing in ears. There are likely to be other possible causes, so ask your doctor about your symptoms. 1 Acute ear infection. Chronic ear infection.',
	'multiple birth(Noun) a birth in which more than one baby are born. Multiple birth. A multiple birth occurs when more than one fetus is carried to term in a single pregnancy. Different names for multiple births are used, depending on the number of offspring.',
	'No: The joint should not have any appreciable fluid in it normally. Fluid can be there from many reasons. Arthritis can cause fluid. Also surgery can cause some bleeding in the knee which can cause the fluid to build up. The fluid makes the joint stiff and makes it hard to get a good quad function. ...Read more.',
	'At the center of this is the simple fact that organ transplantation is built upon altruism and public trust. Gift of Life works hard to ensure that this trust is maintained, through its commitment both to the donor family as well as to those on the waiting list. Average Median Wait Time to Transplant. Kidney â\x80\x93 5 years.',
	]
	)
	# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Cross Encoder Reranking

	* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
	* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
	```json
	{
	"at_k": 10,
	"always_rerank_positives": true
	}
	```

	\| Metric \| NanoMSMARCO_R100 \| NanoNFCorpus_R100 \| NanoNQ_R100 \|
	\|:------------\|:---------------------\|:---------------------\|:---------------------\|
	\| map \| 0.6239 (+0.1343) \| 0.3478 (+0.0868) \| 0.6263 (+0.2067) \|
	\| mrr@10 \| 0.6129 (+0.1354) \| 0.5462 (+0.0464) \| 0.6337 (+0.2070) \|
	\| ndcg@10 \| 0.6576 (+0.1172) \| 0.3996 (+0.0745) \| 0.6720 (+0.1714) \|

	#### Cross Encoder Nano BEIR

	* Dataset: `NanoBEIR_R100_mean`
	* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
	```json
	{
	"dataset_names": [
	"msmarco",
	"nfcorpus",
	"nq"
	],
	"rerank_k": 100,
	"at_k": 10,
	"always_rerank_positives": true
	}
	```

	\| Metric \| Value \|
	\|:------------\|:---------------------\|
	\| map \| 0.5327 (+0.1426) \|
	\| mrr@10 \| 0.5976 (+0.1296) \|
	\| ndcg@10 \| 0.5764 (+0.1210) \|

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### msmarco

	* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
	* Size: 39,770,704 training samples
	* Columns: <code>query_id</code>, <code>positive_id</code>, <code>negative_id</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query_id \| positive_id \| negative_id \| score \|
	\|:--------\|:----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| type \| string \| string \| string \| float \|
	\| details \| <ul><li>min: 11 characters</li><li>mean: 33.5 characters</li><li>max: 98 characters</li></ul> \| <ul><li>min: 68 characters</li><li>mean: 358.26 characters</li><li>max: 988 characters</li></ul> \| <ul><li>min: 50 characters</li><li>mean: 346.46 characters</li><li>max: 969 characters</li></ul> \| <ul><li>min: -3.69</li><li>mean: 13.4</li><li>max: 22.15</li></ul> \|
	* Samples:
	\| query_id \| positive_id \| negative_id \| score \|
	\|:------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>how do you attach a sanding screen to a floor buffer</code> \| <code>Lay the buffer on the floor so you can get underneath the housing and hook the screening brush to the central spindle. Center a sanding screen on the brush and push on it so it stays in place, then right the buffer and allow the housing to rest on the floor. Plug in the machine and grasp the handles with both hands before you turn it on.</code> \| <code>The random orbital sander used an offset drive bearing that causes the pad to also move in an elliptical pattern. These pad movements are used in combination, randomly of course to help reduce the swirls that a non-random sheet sander might leave behind. Mechanics aside, a mojor difference is in the sanding medium. Most random orbital sanders use sanding disks, typically in a 5-inch diameter. They are attached to the sander's pad using hook & loop connections. The sanding disks have holes in them that match-up with the dust collection holes in the sander's pad.</code> \| <code>15.936323801676433</code> \|
	\| <code>define method overriding</code> \| <code>Method overriding is when a child class redefines the same method as a parent class, with the same parameters. For example, the standard Java class java.util.LinkedHashSet eâ¦xtends java.util.HashSet. The method add() is overridden in LinkedHashSet.</code> \| <code>In spite of its limitations, the MET concept provides a convenient method to describe the functional capacity or exercise tolerance of an individual as determined from progressive exercise testing and to define a repertoire of physical activities in which a person may participate safely, without exceeding a prescribed intensity level. PMID: 2204507</code> \| <code>19.40437730153402</code> \|
	\| <code>toxic effects of constipation</code> \| <code>by Jane Kriese. The colon is the part of the large intestine extending from the cecum to the rectum. And the most common sign of having a toxic colon is a condition called constipation. An accumulation of waste food byproducts in the colon can lead to constipation, toxic build up, weight gain and low energy.</code> \| <code>In Summary. Commonly reported side effects of tizanidine include: bradycardia, dizziness, drowsiness, hypotension, asthenia, fatigue, and xerostomia. Other side effects include: constipation, and increased liver enzymes. See below for a comprehensive list of adverse effects.</code> \| <code>7.669035658240318</code> \|
	* Loss: [<code>MarginMSELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#marginmseloss) with these parameters:
	```json
	{
	"activation_fn": "torch.nn.modules.linear.Identity"
	}
	```

	### Evaluation Dataset

	#### msmarco

	* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
	* Size: 10,000 evaluation samples
	* Columns: <code>query_id</code>, <code>positive_id</code>, <code>negative_id</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query_id \| positive_id \| negative_id \| score \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------\|
	\| type \| string \| string \| string \| float \|
	\| details \| <ul><li>min: 10 characters</li><li>mean: 34.37 characters</li><li>max: 116 characters</li></ul> \| <ul><li>min: 61 characters</li><li>mean: 341.63 characters</li><li>max: 952 characters</li></ul> \| <ul><li>min: 73 characters</li><li>mean: 346.27 characters</li><li>max: 1350 characters</li></ul> \| <ul><li>min: -0.85</li><li>mean: 13.13</li><li>max: 22.52</li></ul> \|
	* Samples:
	\| query_id \| positive_id \| negative_id \| score \|
	\|:-----------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>the name olmec means</code> \| <code>The name Olmec comes from the Nahuatl word for the Olmecs: ÅlmÄcatl [oËlËmeËkatÍ¡É¬] (singular) or ÅlmÄcah [oËlËmeËkaÊ] (plural). This word is composed of the two words Ålli [ËoËlËi] , meaning rubber, and mÄcatl [ËmeËkatÍ¡É¬] , meaning people, so the word means rubber people.hey lived in the tropical lowlands of south-central Mexico, in the present-day states of Veracruz and Tabasco. It has been speculated that Olmec derive in part from neighboring Mokaya and/or MixeâZoque.</code> \| <code>Jai: Glory to, Joy, Hail. Guru; Gu - means darkness, and Ru - means a remover. So, Guru means a remover of darkness. Deva; means a shining one and is the source of the English word, Divine. So when we say Jai Guru Deva we are giving thanks to whoever we feel is the source of knowledge to us.</code> \| <code>19.567786693572998</code> \|
	\| <code>what causes hissing in the ears</code> \| <code>The following medical conditions are some of the possible causes of Hissing in ears. There are likely to be other possible causes, so ask your doctor about your symptoms. 1 Acute ear infection. Chronic ear infection.</code> \| <code>There are many possible causes of itchy ears. Although the annoyance of itchy ears is not usually found to be serious, the condition is very real no matter the reason for the uncomfortable nuisance. Poking objects like cotton swabs, pencils, and paperclips in the ears to relieve the irritation is a dangerous and non-effective method for relief.</code> \| <code>10.332231203715006</code> \|
	\| <code>multiple birth sibling definition</code> \| <code>multiple birth(Noun) a birth in which more than one baby are born. Multiple birth. A multiple birth occurs when more than one fetus is carried to term in a single pregnancy. Different names for multiple births are used, depending on the number of offspring.</code> \| <code>From Wikipedia, the free encyclopedia. Not to be confused with Life unworthy of life or Wrongful birth. Wrongful life is the name given to a legal action in which someone is sued by a severely disabled child (through the child's legal guardian) for failing to prevent the child's birth.1 1 Definition.rongful life is the name given to a legal action in which someone is sued by a severely disabled child (through the child's legal guardian) for failing to prevent the child's birth. Contents.</code> \| <code>16.912671327590942</code> \|
	* Loss: [<code>MarginMSELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#marginmseloss) with these parameters:
	```json
	{
	"activation_fn": "torch.nn.modules.linear.Identity"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 256
	- `per_device_eval_batch_size`: 256
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `seed`: 12
	- `bf16`: True
	- `load_best_model_at_end`: True

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 256
	- `per_device_eval_batch_size`: 256
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 1
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 12
	- `data_seed`: None
	- `jit_mode_eval`: 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`: True
	- `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}
	- `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}
	- `parallelism_config`: 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
	- `project`: huggingface
	- `trackio_space_id`: trackio
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: None
	- `hub_always_push`: False
	- `hub_revision`: None
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: no
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `liger_kernel_config`: None
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: True
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| Validation Loss \| NanoMSMARCO_R100_ndcg@10 \| NanoNFCorpus_R100_ndcg@10 \| NanoNQ_R100_ndcg@10 \| NanoBEIR_R100_mean_ndcg@10 \|
	\|:----------:\|:---------:\|:-------------:\|:---------------:\|:------------------------:\|:-------------------------:\|:--------------------:\|:--------------------------:\|
	\| -1 \| -1 \| - \| - \| 0.0277 (-0.5127) \| 0.3408 (+0.0157) \| 0.0189 (-0.4817) \| 0.1291 (-0.3262) \|
	\| 0.0001 \| 1 \| 203.5639 \| - \| - \| - \| - \| - \|
	\| 0.0020 \| 39 \| 206.4834 \| - \| - \| - \| - \| - \|
	\| 0.0040 \| 78 \| 202.3608 \| - \| - \| - \| - \| - \|
	\| 0.0060 \| 117 \| 197.2066 \| - \| - \| - \| - \| - \|
	\| 0.0080 \| 156 \| 189.4029 \| - \| - \| - \| - \| - \|
	\| 0.0100 \| 195 \| 180.9973 \| 174.2619 \| 0.0536 (-0.4868) \| 0.2671 (-0.0580) \| 0.0676 (-0.4330) \| 0.1294 (-0.3259) \|
	\| 0.0121 \| 234 \| 172.5803 \| - \| - \| - \| - \| - \|
	\| 0.0141 \| 273 \| 165.2367 \| - \| - \| - \| - \| - \|
	\| 0.0161 \| 312 \| 157.6653 \| - \| - \| - \| - \| - \|
	\| 0.0181 \| 351 \| 147.8596 \| - \| - \| - \| - \| - \|
	\| 0.0201 \| 390 \| 129.2817 \| 113.0435 \| 0.1823 (-0.3581) \| 0.2122 (-0.1128) \| 0.2045 (-0.2961) \| 0.1997 (-0.2557) \|
	\| 0.0221 \| 429 \| 97.5978 \| - \| - \| - \| - \| - \|
	\| 0.0241 \| 468 \| 67.4161 \| - \| - \| - \| - \| - \|
	\| 0.0261 \| 507 \| 52.7053 \| - \| - \| - \| - \| - \|
	\| 0.0281 \| 546 \| 45.7155 \| - \| - \| - \| - \| - \|
	\| 0.0301 \| 585 \| 41.9731 \| 41.3247 \| 0.4712 (-0.0692) \| 0.2975 (-0.0275) \| 0.4695 (-0.0311) \| 0.4127 (-0.0426) \|
	\| 0.0321 \| 624 \| 39.3177 \| - \| - \| - \| - \| - \|
	\| 0.0341 \| 663 \| 36.7606 \| - \| - \| - \| - \| - \|
	\| 0.0362 \| 702 \| 34.6954 \| - \| - \| - \| - \| - \|
	\| 0.0382 \| 741 \| 33.4444 \| - \| - \| - \| - \| - \|
	\| 0.0402 \| 780 \| 31.8238 \| 31.8207 \| 0.5084 (-0.0320) \| 0.3346 (+0.0096) \| 0.5137 (+0.0131) \| 0.4522 (-0.0031) \|
	\| 0.0422 \| 819 \| 30.6819 \| - \| - \| - \| - \| - \|
	\| 0.0442 \| 858 \| 29.4152 \| - \| - \| - \| - \| - \|
	\| 0.0462 \| 897 \| 28.4563 \| - \| - \| - \| - \| - \|
	\| 0.0482 \| 936 \| 27.38 \| - \| - \| - \| - \| - \|
	\| 0.0502 \| 975 \| 26.8008 \| 27.3043 \| 0.4982 (-0.0422) \| 0.3449 (+0.0198) \| 0.5200 (+0.0194) \| 0.4544 (-0.0010) \|
	\| 0.0522 \| 1014 \| 26.0336 \| - \| - \| - \| - \| - \|
	\| 0.0542 \| 1053 \| 25.3375 \| - \| - \| - \| - \| - \|
	\| 0.0562 \| 1092 \| 24.9918 \| - \| - \| - \| - \| - \|
	\| 0.0582 \| 1131 \| 24.1657 \| - \| - \| - \| - \| - \|
	\| 0.0603 \| 1170 \| 24.4028 \| 24.2684 \| 0.5504 (+0.0100) \| 0.3672 (+0.0422) \| 0.5478 (+0.0471) \| 0.4885 (+0.0331) \|
	\| 0.0623 \| 1209 \| 23.3062 \| - \| - \| - \| - \| - \|
	\| 0.0643 \| 1248 \| 22.7702 \| - \| - \| - \| - \| - \|
	\| 0.0663 \| 1287 \| 22.4086 \| - \| - \| - \| - \| - \|
	\| 0.0683 \| 1326 \| 21.9251 \| - \| - \| - \| - \| - \|
	\| 0.0703 \| 1365 \| 21.9175 \| 21.9635 \| 0.5852 (+0.0448) \| 0.3549 (+0.0299) \| 0.5656 (+0.0649) \| 0.5019 (+0.0465) \|
	\| 0.0723 \| 1404 \| 20.9865 \| - \| - \| - \| - \| - \|
	\| 0.0743 \| 1443 \| 20.8581 \| - \| - \| - \| - \| - \|
	\| 0.0763 \| 1482 \| 20.584 \| - \| - \| - \| - \| - \|
	\| 0.0783 \| 1521 \| 19.8788 \| - \| - \| - \| - \| - \|
	\| 0.0803 \| 1560 \| 19.6365 \| 19.9889 \| 0.5988 (+0.0583) \| 0.3533 (+0.0283) \| 0.5369 (+0.0362) \| 0.4963 (+0.0409) \|
	\| 0.0823 \| 1599 \| 19.2009 \| - \| - \| - \| - \| - \|
	\| 0.0844 \| 1638 \| 18.9352 \| - \| - \| - \| - \| - \|
	\| 0.0864 \| 1677 \| 18.7044 \| - \| - \| - \| - \| - \|
	\| 0.0884 \| 1716 \| 18.3696 \| - \| - \| - \| - \| - \|
	\| 0.0904 \| 1755 \| 18.223 \| 18.6559 \| 0.6300 (+0.0896) \| 0.3561 (+0.0311) \| 0.5659 (+0.0652) \| 0.5173 (+0.0620) \|
	\| 0.0924 \| 1794 \| 18.1149 \| - \| - \| - \| - \| - \|
	\| 0.0944 \| 1833 \| 17.5771 \| - \| - \| - \| - \| - \|
	\| 0.0964 \| 1872 \| 17.2442 \| - \| - \| - \| - \| - \|
	\| 0.0984 \| 1911 \| 16.8317 \| - \| - \| - \| - \| - \|
	\| 0.1004 \| 1950 \| 16.8462 \| 17.0786 \| 0.6294 (+0.0890) \| 0.3552 (+0.0302) \| 0.5643 (+0.0637) \| 0.5163 (+0.0609) \|
	\| 0.1024 \| 1989 \| 16.5298 \| - \| - \| - \| - \| - \|
	\| 0.1044 \| 2028 \| 16.4289 \| - \| - \| - \| - \| - \|
	\| 0.1064 \| 2067 \| 16.0292 \| - \| - \| - \| - \| - \|
	\| 0.1085 \| 2106 \| 15.8428 \| - \| - \| - \| - \| - \|
	\| 0.1105 \| 2145 \| 15.7437 \| 16.0163 \| 0.6269 (+0.0865) \| 0.3538 (+0.0287) \| 0.5752 (+0.0746) \| 0.5186 (+0.0633) \|
	\| 0.1125 \| 2184 \| 15.7076 \| - \| - \| - \| - \| - \|
	\| 0.1145 \| 2223 \| 15.3865 \| - \| - \| - \| - \| - \|
	\| 0.1165 \| 2262 \| 15.174 \| - \| - \| - \| - \| - \|
	\| 0.1185 \| 2301 \| 15.0104 \| - \| - \| - \| - \| - \|
	\| 0.1205 \| 2340 \| 14.8128 \| 14.9314 \| 0.6112 (+0.0708) \| 0.3625 (+0.0375) \| 0.5936 (+0.0929) \| 0.5225 (+0.0671) \|
	\| 0.1225 \| 2379 \| 14.407 \| - \| - \| - \| - \| - \|
	\| 0.1245 \| 2418 \| 14.3549 \| - \| - \| - \| - \| - \|
	\| 0.1265 \| 2457 \| 14.3104 \| - \| - \| - \| - \| - \|
	\| 0.1285 \| 2496 \| 14.0911 \| - \| - \| - \| - \| - \|
	\| 0.1305 \| 2535 \| 13.8692 \| 14.5249 \| 0.6153 (+0.0749) \| 0.3644 (+0.0393) \| 0.5976 (+0.0969) \| 0.5258 (+0.0704) \|
	\| 0.1326 \| 2574 \| 13.9886 \| - \| - \| - \| - \| - \|
	\| 0.1346 \| 2613 \| 13.5694 \| - \| - \| - \| - \| - \|
	\| 0.1366 \| 2652 \| 13.6723 \| - \| - \| - \| - \| - \|
	\| 0.1386 \| 2691 \| 13.5047 \| - \| - \| - \| - \| - \|
	\| 0.1406 \| 2730 \| 13.3759 \| 13.6388 \| 0.6318 (+0.0914) \| 0.3578 (+0.0327) \| 0.5819 (+0.0812) \| 0.5238 (+0.0685) \|
	\| 0.1426 \| 2769 \| 13.042 \| - \| - \| - \| - \| - \|
	\| 0.1446 \| 2808 \| 13.2039 \| - \| - \| - \| - \| - \|
	\| 0.1466 \| 2847 \| 12.9865 \| - \| - \| - \| - \| - \|
	\| 0.1486 \| 2886 \| 13.0293 \| - \| - \| - \| - \| - \|
	\| 0.1506 \| 2925 \| 12.7206 \| 12.6907 \| 0.6392 (+0.0988) \| 0.3649 (+0.0399) \| 0.5919 (+0.0912) \| 0.5320 (+0.0766) \|
	\| 0.1526 \| 2964 \| 12.5802 \| - \| - \| - \| - \| - \|
	\| 0.1546 \| 3003 \| 12.7734 \| - \| - \| - \| - \| - \|
	\| 0.1567 \| 3042 \| 12.4192 \| - \| - \| - \| - \| - \|
	\| 0.1587 \| 3081 \| 12.3157 \| - \| - \| - \| - \| - \|
	\| 0.1607 \| 3120 \| 12.384 \| 12.5061 \| 0.6382 (+0.0978) \| 0.3784 (+0.0533) \| 0.5907 (+0.0901) \| 0.5358 (+0.0804) \|
	\| 0.1627 \| 3159 \| 12.0562 \| - \| - \| - \| - \| - \|
	\| 0.1647 \| 3198 \| 12.1539 \| - \| - \| - \| - \| - \|
	\| 0.1667 \| 3237 \| 12.0754 \| - \| - \| - \| - \| - \|
	\| 0.1687 \| 3276 \| 11.8814 \| - \| - \| - \| - \| - \|
	\| 0.1707 \| 3315 \| 11.7829 \| 11.9039 \| 0.6371 (+0.0967) \| 0.3818 (+0.0568) \| 0.5998 (+0.0992) \| 0.5396 (+0.0842) \|
	\| 0.1727 \| 3354 \| 11.8772 \| - \| - \| - \| - \| - \|
	\| 0.1747 \| 3393 \| 11.8422 \| - \| - \| - \| - \| - \|
	\| 0.1767 \| 3432 \| 11.6353 \| - \| - \| - \| - \| - \|
	\| 0.1787 \| 3471 \| 11.584 \| - \| - \| - \| - \| - \|
	\| 0.1808 \| 3510 \| 11.443 \| 11.8674 \| 0.6317 (+0.0912) \| 0.3828 (+0.0578) \| 0.6080 (+0.1073) \| 0.5408 (+0.0854) \|
	\| 0.1828 \| 3549 \| 11.475 \| - \| - \| - \| - \| - \|
	\| 0.1848 \| 3588 \| 11.1514 \| - \| - \| - \| - \| - \|
	\| 0.1868 \| 3627 \| 11.3053 \| - \| - \| - \| - \| - \|
	\| 0.1888 \| 3666 \| 11.0035 \| - \| - \| - \| - \| - \|
	\| 0.1908 \| 3705 \| 11.1834 \| 11.4955 \| 0.6333 (+0.0928) \| 0.3642 (+0.0392) \| 0.6094 (+0.1087) \| 0.5356 (+0.0803) \|
	\| 0.1928 \| 3744 \| 10.8977 \| - \| - \| - \| - \| - \|
	\| 0.1948 \| 3783 \| 10.9302 \| - \| - \| - \| - \| - \|
	\| 0.1968 \| 3822 \| 10.8946 \| - \| - \| - \| - \| - \|
	\| 0.1988 \| 3861 \| 10.821 \| - \| - \| - \| - \| - \|
	\| 0.2008 \| 3900 \| 10.8044 \| 11.0295 \| 0.6262 (+0.0858) \| 0.3784 (+0.0533) \| 0.6098 (+0.1091) \| 0.5381 (+0.0828) \|
	\| 0.2028 \| 3939 \| 10.6379 \| - \| - \| - \| - \| - \|
	\| 0.2049 \| 3978 \| 10.6515 \| - \| - \| - \| - \| - \|
	\| 0.2069 \| 4017 \| 10.4407 \| - \| - \| - \| - \| - \|
	\| 0.2089 \| 4056 \| 10.4732 \| - \| - \| - \| - \| - \|
	\| 0.2109 \| 4095 \| 10.3832 \| 10.6019 \| 0.6286 (+0.0882) \| 0.3631 (+0.0381) \| 0.6167 (+0.1161) \| 0.5362 (+0.0808) \|
	\| 0.2129 \| 4134 \| 10.4307 \| - \| - \| - \| - \| - \|
	\| 0.2149 \| 4173 \| 10.3511 \| - \| - \| - \| - \| - \|
	\| 0.2169 \| 4212 \| 10.2797 \| - \| - \| - \| - \| - \|
	\| 0.2189 \| 4251 \| 10.2157 \| - \| - \| - \| - \| - \|
	\| 0.2209 \| 4290 \| 10.2122 \| 10.3425 \| 0.6281 (+0.0877) \| 0.3868 (+0.0617) \| 0.5901 (+0.0895) \| 0.5350 (+0.0796) \|
	\| 0.2229 \| 4329 \| 10.185 \| - \| - \| - \| - \| - \|
	\| 0.2249 \| 4368 \| 10.163 \| - \| - \| - \| - \| - \|
	\| 0.2269 \| 4407 \| 10.1477 \| - \| - \| - \| - \| - \|
	\| 0.2290 \| 4446 \| 9.9438 \| - \| - \| - \| - \| - \|
	\| 0.2310 \| 4485 \| 9.9041 \| 10.1282 \| 0.6452 (+0.1048) \| 0.3772 (+0.0522) \| 0.6136 (+0.1130) \| 0.5454 (+0.0900) \|
	\| 0.2330 \| 4524 \| 9.8034 \| - \| - \| - \| - \| - \|
	\| 0.2350 \| 4563 \| 9.7994 \| - \| - \| - \| - \| - \|
	\| 0.2370 \| 4602 \| 9.9711 \| - \| - \| - \| - \| - \|
	\| 0.2390 \| 4641 \| 9.7652 \| - \| - \| - \| - \| - \|
	\| 0.2410 \| 4680 \| 9.6757 \| 9.7409 \| 0.6364 (+0.0960) \| 0.3800 (+0.0550) \| 0.6095 (+0.1088) \| 0.5420 (+0.0866) \|
	\| 0.2430 \| 4719 \| 9.558 \| - \| - \| - \| - \| - \|
	\| 0.2450 \| 4758 \| 9.6791 \| - \| - \| - \| - \| - \|
	\| 0.2470 \| 4797 \| 9.5759 \| - \| - \| - \| - \| - \|
	\| 0.2490 \| 4836 \| 9.4958 \| - \| - \| - \| - \| - \|
	\| 0.2510 \| 4875 \| 9.487 \| 9.5897 \| 0.6479 (+0.1075) \| 0.3734 (+0.0484) \| 0.6261 (+0.1255) \| 0.5492 (+0.0938) \|
	\| 0.2531 \| 4914 \| 9.4796 \| - \| - \| - \| - \| - \|
	\| 0.2551 \| 4953 \| 9.3878 \| - \| - \| - \| - \| - \|
	\| 0.2571 \| 4992 \| 9.3888 \| - \| - \| - \| - \| - \|
	\| 0.2591 \| 5031 \| 9.2657 \| - \| - \| - \| - \| - \|
	\| 0.2611 \| 5070 \| 9.1936 \| 9.2371 \| 0.6296 (+0.0892) \| 0.3857 (+0.0607) \| 0.6220 (+0.1213) \| 0.5458 (+0.0904) \|
	\| 0.2631 \| 5109 \| 9.2403 \| - \| - \| - \| - \| - \|
	\| 0.2651 \| 5148 \| 9.1963 \| - \| - \| - \| - \| - \|
	\| 0.2671 \| 5187 \| 9.066 \| - \| - \| - \| - \| - \|
	\| 0.2691 \| 5226 \| 9.1684 \| - \| - \| - \| - \| - \|
	\| 0.2711 \| 5265 \| 9.1036 \| 9.1932 \| 0.6188 (+0.0784) \| 0.3803 (+0.0552) \| 0.6280 (+0.1274) \| 0.5424 (+0.0870) \|
	\| 0.2731 \| 5304 \| 9.0206 \| - \| - \| - \| - \| - \|
	\| 0.2751 \| 5343 \| 9.2051 \| - \| - \| - \| - \| - \|
	\| 0.2772 \| 5382 \| 9.1335 \| - \| - \| - \| - \| - \|
	\| 0.2792 \| 5421 \| 8.9798 \| - \| - \| - \| - \| - \|
	\| 0.2812 \| 5460 \| 8.9199 \| 9.0195 \| 0.6210 (+0.0805) \| 0.3939 (+0.0688) \| 0.6178 (+0.1172) \| 0.5442 (+0.0888) \|
	\| 0.2832 \| 5499 \| 8.8599 \| - \| - \| - \| - \| - \|
	\| 0.2852 \| 5538 \| 8.7977 \| - \| - \| - \| - \| - \|
	\| 0.2872 \| 5577 \| 8.7378 \| - \| - \| - \| - \| - \|
	\| 0.2892 \| 5616 \| 8.7338 \| - \| - \| - \| - \| - \|
	\| 0.2912 \| 5655 \| 8.6566 \| 8.8711 \| 0.6308 (+0.0904) \| 0.3885 (+0.0635) \| 0.6194 (+0.1188) \| 0.5463 (+0.0909) \|
	\| 0.2932 \| 5694 \| 8.8228 \| - \| - \| - \| - \| - \|
	\| 0.2952 \| 5733 \| 8.6837 \| - \| - \| - \| - \| - \|
	\| 0.2972 \| 5772 \| 8.6967 \| - \| - \| - \| - \| - \|
	\| 0.2992 \| 5811 \| 8.6721 \| - \| - \| - \| - \| - \|
	\| 0.3013 \| 5850 \| 8.5856 \| 8.4996 \| 0.6424 (+0.1020) \| 0.3820 (+0.0569) \| 0.6268 (+0.1261) \| 0.5504 (+0.0950) \|
	\| 0.3033 \| 5889 \| 8.5552 \| - \| - \| - \| - \| - \|
	\| 0.3053 \| 5928 \| 8.4364 \| - \| - \| - \| - \| - \|
	\| 0.3073 \| 5967 \| 8.4726 \| - \| - \| - \| - \| - \|
	\| 0.3093 \| 6006 \| 8.6169 \| - \| - \| - \| - \| - \|
	\| 0.3113 \| 6045 \| 8.4303 \| 8.4561 \| 0.6142 (+0.0738) \| 0.3720 (+0.0470) \| 0.6226 (+0.1220) \| 0.5363 (+0.0809) \|
	\| 0.3133 \| 6084 \| 8.5336 \| - \| - \| - \| - \| - \|
	\| 0.3153 \| 6123 \| 8.3723 \| - \| - \| - \| - \| - \|
	\| 0.3173 \| 6162 \| 8.358 \| - \| - \| - \| - \| - \|
	\| 0.3193 \| 6201 \| 8.3435 \| - \| - \| - \| - \| - \|
	\| 0.3213 \| 6240 \| 8.2638 \| 8.3670 \| 0.6346 (+0.0941) \| 0.3838 (+0.0587) \| 0.6229 (+0.1222) \| 0.5471 (+0.0917) \|
	\| 0.3233 \| 6279 \| 8.2994 \| - \| - \| - \| - \| - \|
	\| 0.3254 \| 6318 \| 8.1733 \| - \| - \| - \| - \| - \|
	\| 0.3274 \| 6357 \| 8.2564 \| - \| - \| - \| - \| - \|
	\| 0.3294 \| 6396 \| 8.212 \| - \| - \| - \| - \| - \|
	\| 0.3314 \| 6435 \| 8.1111 \| 8.3150 \| 0.6385 (+0.0981) \| 0.3828 (+0.0578) \| 0.6353 (+0.1346) \| 0.5522 (+0.0968) \|
	\| 0.3334 \| 6474 \| 8.1655 \| - \| - \| - \| - \| - \|
	\| 0.3354 \| 6513 \| 8.0391 \| - \| - \| - \| - \| - \|
	\| 0.3374 \| 6552 \| 8.0784 \| - \| - \| - \| - \| - \|
	\| 0.3394 \| 6591 \| 8.0369 \| - \| - \| - \| - \| - \|
	\| 0.3414 \| 6630 \| 8.012 \| 8.0672 \| 0.6191 (+0.0787) \| 0.3899 (+0.0648) \| 0.6482 (+0.1475) \| 0.5524 (+0.0970) \|
	\| 0.3434 \| 6669 \| 7.9809 \| - \| - \| - \| - \| - \|
	\| 0.3454 \| 6708 \| 8.0001 \| - \| - \| - \| - \| - \|
	\| 0.3474 \| 6747 \| 8.009 \| - \| - \| - \| - \| - \|
	\| 0.3495 \| 6786 \| 7.9692 \| - \| - \| - \| - \| - \|
	\| 0.3515 \| 6825 \| 7.8565 \| 7.9072 \| 0.6220 (+0.0815) \| 0.3866 (+0.0616) \| 0.6154 (+0.1147) \| 0.5413 (+0.0859) \|
	\| 0.3535 \| 6864 \| 7.9108 \| - \| - \| - \| - \| - \|
	\| 0.3555 \| 6903 \| 7.7341 \| - \| - \| - \| - \| - \|
	\| 0.3575 \| 6942 \| 7.8442 \| - \| - \| - \| - \| - \|
	\| 0.3595 \| 6981 \| 7.912 \| - \| - \| - \| - \| - \|
	\| 0.3615 \| 7020 \| 7.7133 \| 8.0526 \| 0.6389 (+0.0985) \| 0.3991 (+0.0740) \| 0.6408 (+0.1402) \| 0.5596 (+0.1042) \|
	\| 0.3635 \| 7059 \| 7.8985 \| - \| - \| - \| - \| - \|
	\| 0.3655 \| 7098 \| 7.6834 \| - \| - \| - \| - \| - \|
	\| 0.3675 \| 7137 \| 7.7494 \| - \| - \| - \| - \| - \|
	\| 0.3695 \| 7176 \| 7.7146 \| - \| - \| - \| - \| - \|
	\| 0.3715 \| 7215 \| 7.6553 \| 7.8366 \| 0.6338 (+0.0934) \| 0.3992 (+0.0742) \| 0.6253 (+0.1247) \| 0.5528 (+0.0974) \|
	\| 0.3736 \| 7254 \| 7.612 \| - \| - \| - \| - \| - \|
	\| 0.3756 \| 7293 \| 7.6707 \| - \| - \| - \| - \| - \|
	\| 0.3776 \| 7332 \| 7.716 \| - \| - \| - \| - \| - \|
	\| 0.3796 \| 7371 \| 7.7436 \| - \| - \| - \| - \| - \|
	\| 0.3816 \| 7410 \| 7.6003 \| 7.6881 \| 0.6307 (+0.0902) \| 0.4029 (+0.0779) \| 0.6334 (+0.1327) \| 0.5556 (+0.1003) \|
	\| 0.3836 \| 7449 \| 7.5153 \| - \| - \| - \| - \| - \|
	\| 0.3856 \| 7488 \| 7.5351 \| - \| - \| - \| - \| - \|
	\| 0.3876 \| 7527 \| 7.5687 \| - \| - \| - \| - \| - \|
	\| 0.3896 \| 7566 \| 7.5155 \| - \| - \| - \| - \| - \|
	\| 0.3916 \| 7605 \| 7.4451 \| 7.6049 \| 0.6321 (+0.0916) \| 0.3914 (+0.0664) \| 0.6358 (+0.1352) \| 0.5531 (+0.0977) \|
	\| 0.3936 \| 7644 \| 7.5113 \| - \| - \| - \| - \| - \|
	\| 0.3956 \| 7683 \| 7.5135 \| - \| - \| - \| - \| - \|
	\| 0.3977 \| 7722 \| 7.4258 \| - \| - \| - \| - \| - \|
	\| 0.3997 \| 7761 \| 7.458 \| - \| - \| - \| - \| - \|
	\| 0.4017 \| 7800 \| 7.3602 \| 7.4536 \| 0.6472 (+0.1067) \| 0.4034 (+0.0783) \| 0.6372 (+0.1365) \| 0.5626 (+0.1072) \|
	\| 0.4037 \| 7839 \| 7.4779 \| - \| - \| - \| - \| - \|
	\| 0.4057 \| 7878 \| 7.4154 \| - \| - \| - \| - \| - \|
	\| 0.4077 \| 7917 \| 7.2897 \| - \| - \| - \| - \| - \|
	\| 0.4097 \| 7956 \| 7.3614 \| - \| - \| - \| - \| - \|
	\| 0.4117 \| 7995 \| 7.2537 \| 7.4876 \| 0.6298 (+0.0893) \| 0.3999 (+0.0749) \| 0.6318 (+0.1312) \| 0.5538 (+0.0985) \|
	\| 0.4137 \| 8034 \| 7.2474 \| - \| - \| - \| - \| - \|
	\| 0.4157 \| 8073 \| 7.2166 \| - \| - \| - \| - \| - \|
	\| 0.4177 \| 8112 \| 7.2344 \| - \| - \| - \| - \| - \|
	\| 0.4197 \| 8151 \| 7.2647 \| - \| - \| - \| - \| - \|
	\| 0.4218 \| 8190 \| 7.2618 \| 7.4345 \| 0.6347 (+0.0943) \| 0.4057 (+0.0806) \| 0.6289 (+0.1282) \| 0.5564 (+0.1010) \|
	\| 0.4238 \| 8229 \| 7.2227 \| - \| - \| - \| - \| - \|
	\| 0.4258 \| 8268 \| 7.2384 \| - \| - \| - \| - \| - \|
	\| 0.4278 \| 8307 \| 7.2133 \| - \| - \| - \| - \| - \|
	\| 0.4298 \| 8346 \| 7.1558 \| - \| - \| - \| - \| - \|
	\| 0.4318 \| 8385 \| 7.0712 \| 7.2899 \| 0.6523 (+0.1119) \| 0.4027 (+0.0777) \| 0.6341 (+0.1335) \| 0.5631 (+0.1077) \|
	\| 0.4338 \| 8424 \| 7.1063 \| - \| - \| - \| - \| - \|
	\| 0.4358 \| 8463 \| 7.0908 \| - \| - \| - \| - \| - \|
	\| 0.4378 \| 8502 \| 7.1122 \| - \| - \| - \| - \| - \|
	\| 0.4398 \| 8541 \| 7.1549 \| - \| - \| - \| - \| - \|
	\| 0.4418 \| 8580 \| 7.0516 \| 7.2487 \| 0.6553 (+0.1148) \| 0.3955 (+0.0705) \| 0.6399 (+0.1392) \| 0.5635 (+0.1082) \|
	\| 0.4438 \| 8619 \| 7.0792 \| - \| - \| - \| - \| - \|
	\| 0.4459 \| 8658 \| 7.0351 \| - \| - \| - \| - \| - \|
	\| 0.4479 \| 8697 \| 7.0315 \| - \| - \| - \| - \| - \|
	\| 0.4499 \| 8736 \| 6.9912 \| - \| - \| - \| - \| - \|
	\| 0.4519 \| 8775 \| 7.0102 \| 7.0829 \| 0.6569 (+0.1165) \| 0.3949 (+0.0698) \| 0.6534 (+0.1527) \| 0.5684 (+0.1130) \|
	\| 0.4539 \| 8814 \| 6.9478 \| - \| - \| - \| - \| - \|
	\| 0.4559 \| 8853 \| 6.9741 \| - \| - \| - \| - \| - \|
	\| 0.4579 \| 8892 \| 7.0344 \| - \| - \| - \| - \| - \|
	\| 0.4599 \| 8931 \| 6.907 \| - \| - \| - \| - \| - \|
	\| 0.4619 \| 8970 \| 6.9089 \| 7.0948 \| 0.6369 (+0.0964) \| 0.3976 (+0.0725) \| 0.6367 (+0.1360) \| 0.5570 (+0.1017) \|
	\| 0.4639 \| 9009 \| 6.9295 \| - \| - \| - \| - \| - \|
	\| 0.4659 \| 9048 \| 6.863 \| - \| - \| - \| - \| - \|
	\| 0.4679 \| 9087 \| 6.9167 \| - \| - \| - \| - \| - \|
	\| 0.4700 \| 9126 \| 6.9123 \| - \| - \| - \| - \| - \|
	\| 0.4720 \| 9165 \| 6.8659 \| 6.9749 \| 0.6478 (+0.1073) \| 0.3977 (+0.0727) \| 0.6420 (+0.1414) \| 0.5625 (+0.1071) \|
	\| 0.4740 \| 9204 \| 6.8238 \| - \| - \| - \| - \| - \|
	\| 0.4760 \| 9243 \| 6.7847 \| - \| - \| - \| - \| - \|
	\| 0.4780 \| 9282 \| 6.7687 \| - \| - \| - \| - \| - \|
	\| 0.4800 \| 9321 \| 6.8748 \| - \| - \| - \| - \| - \|
	\| 0.4820 \| 9360 \| 6.7392 \| 6.9672 \| 0.6469 (+0.1064) \| 0.4037 (+0.0787) \| 0.6375 (+0.1369) \| 0.5627 (+0.1073) \|
	\| 0.4840 \| 9399 \| 6.6911 \| - \| - \| - \| - \| - \|
	\| 0.4860 \| 9438 \| 6.6688 \| - \| - \| - \| - \| - \|
	\| 0.4880 \| 9477 \| 6.7891 \| - \| - \| - \| - \| - \|
	\| 0.4900 \| 9516 \| 6.7704 \| - \| - \| - \| - \| - \|
	\| 0.4920 \| 9555 \| 6.7022 \| 6.8938 \| 0.6519 (+0.1115) \| 0.4040 (+0.0789) \| 0.6484 (+0.1477) \| 0.5681 (+0.1127) \|
	\| 0.4941 \| 9594 \| 6.7143 \| - \| - \| - \| - \| - \|
	\| 0.4961 \| 9633 \| 6.6628 \| - \| - \| - \| - \| - \|
	\| 0.4981 \| 9672 \| 6.7368 \| - \| - \| - \| - \| - \|
	\| 0.5001 \| 9711 \| 6.5205 \| - \| - \| - \| - \| - \|
	\| 0.5021 \| 9750 \| 6.6955 \| 6.8142 \| 0.6485 (+0.1081) \| 0.3968 (+0.0718) \| 0.6302 (+0.1296) \| 0.5585 (+0.1032) \|
	\| 0.5041 \| 9789 \| 6.6244 \| - \| - \| - \| - \| - \|
	\| 0.5061 \| 9828 \| 6.6949 \| - \| - \| - \| - \| - \|
	\| 0.5081 \| 9867 \| 6.5489 \| - \| - \| - \| - \| - \|
	\| 0.5101 \| 9906 \| 6.6067 \| - \| - \| - \| - \| - \|
	\| 0.5121 \| 9945 \| 6.5962 \| 6.7924 \| 0.6574 (+0.1170) \| 0.3910 (+0.0660) \| 0.6459 (+0.1452) \| 0.5648 (+0.1094) \|
	\| 0.5141 \| 9984 \| 6.5248 \| - \| - \| - \| - \| - \|
	\| 0.5161 \| 10023 \| 6.5204 \| - \| - \| - \| - \| - \|
	\| 0.5182 \| 10062 \| 6.5291 \| - \| - \| - \| - \| - \|
	\| 0.5202 \| 10101 \| 6.5512 \| - \| - \| - \| - \| - \|
	\| 0.5222 \| 10140 \| 6.4731 \| 6.7636 \| 0.6638 (+0.1234) \| 0.3973 (+0.0722) \| 0.6433 (+0.1426) \| 0.5681 (+0.1128) \|
	\| 0.5242 \| 10179 \| 6.5327 \| - \| - \| - \| - \| - \|
	\| 0.5262 \| 10218 \| 6.5019 \| - \| - \| - \| - \| - \|
	\| 0.5282 \| 10257 \| 6.5113 \| - \| - \| - \| - \| - \|
	\| 0.5302 \| 10296 \| 6.5181 \| - \| - \| - \| - \| - \|
	\| 0.5322 \| 10335 \| 6.4987 \| 6.6856 \| 0.6656 (+0.1252) \| 0.3946 (+0.0695) \| 0.6501 (+0.1494) \| 0.5701 (+0.1147) \|
	\| 0.5342 \| 10374 \| 6.4575 \| - \| - \| - \| - \| - \|
	\| 0.5362 \| 10413 \| 6.4239 \| - \| - \| - \| - \| - \|
	\| 0.5382 \| 10452 \| 6.4181 \| - \| - \| - \| - \| - \|
	\| 0.5402 \| 10491 \| 6.3535 \| - \| - \| - \| - \| - \|
	\| 0.5423 \| 10530 \| 6.4066 \| 6.6634 \| 0.6482 (+0.1078) \| 0.3993 (+0.0743) \| 0.6449 (+0.1442) \| 0.5641 (+0.1088) \|
	\| 0.5443 \| 10569 \| 6.4005 \| - \| - \| - \| - \| - \|
	\| 0.5463 \| 10608 \| 6.4521 \| - \| - \| - \| - \| - \|
	\| 0.5483 \| 10647 \| 6.4178 \| - \| - \| - \| - \| - \|
	\| 0.5503 \| 10686 \| 6.3495 \| - \| - \| - \| - \| - \|
	\| 0.5523 \| 10725 \| 6.3246 \| 6.6161 \| 0.6434 (+0.1030) \| 0.3915 (+0.0664) \| 0.6426 (+0.1420) \| 0.5592 (+0.1038) \|
	\| 0.5543 \| 10764 \| 6.4175 \| - \| - \| - \| - \| - \|
	\| 0.5563 \| 10803 \| 6.3035 \| - \| - \| - \| - \| - \|
	\| 0.5583 \| 10842 \| 6.2432 \| - \| - \| - \| - \| - \|
	\| 0.5603 \| 10881 \| 6.3024 \| - \| - \| - \| - \| - \|
	\| 0.5623 \| 10920 \| 6.4134 \| 6.4813 \| 0.6441 (+0.1037) \| 0.3936 (+0.0685) \| 0.6392 (+0.1386) \| 0.5590 (+0.1036) \|
	\| 0.5643 \| 10959 \| 6.2943 \| - \| - \| - \| - \| - \|
	\| 0.5664 \| 10998 \| 6.2934 \| - \| - \| - \| - \| - \|
	\| 0.5684 \| 11037 \| 6.3379 \| - \| - \| - \| - \| - \|
	\| 0.5704 \| 11076 \| 6.2481 \| - \| - \| - \| - \| - \|
	\| 0.5724 \| 11115 \| 6.256 \| 6.4519 \| 0.6371 (+0.0967) \| 0.3985 (+0.0735) \| 0.6343 (+0.1336) \| 0.5566 (+0.1013) \|
	\| 0.5744 \| 11154 \| 6.2639 \| - \| - \| - \| - \| - \|
	\| 0.5764 \| 11193 \| 6.2727 \| - \| - \| - \| - \| - \|
	\| 0.5784 \| 11232 \| 6.2347 \| - \| - \| - \| - \| - \|
	\| 0.5804 \| 11271 \| 6.3073 \| - \| - \| - \| - \| - \|
	\| 0.5824 \| 11310 \| 6.2281 \| 6.3956 \| 0.6336 (+0.0932) \| 0.3973 (+0.0723) \| 0.6454 (+0.1448) \| 0.5588 (+0.1034) \|
	\| 0.5844 \| 11349 \| 6.0973 \| - \| - \| - \| - \| - \|
	\| 0.5864 \| 11388 \| 6.282 \| - \| - \| - \| - \| - \|
	\| 0.5884 \| 11427 \| 6.1308 \| - \| - \| - \| - \| - \|
	\| 0.5905 \| 11466 \| 6.0991 \| - \| - \| - \| - \| - \|
	\| 0.5925 \| 11505 \| 6.2648 \| 6.3232 \| 0.6362 (+0.0958) \| 0.3943 (+0.0693) \| 0.6527 (+0.1520) \| 0.5611 (+0.1057) \|
	\| 0.5945 \| 11544 \| 6.1303 \| - \| - \| - \| - \| - \|
	\| 0.5965 \| 11583 \| 6.2142 \| - \| - \| - \| - \| - \|
	\| 0.5985 \| 11622 \| 6.115 \| - \| - \| - \| - \| - \|
	\| 0.6005 \| 11661 \| 6.1109 \| - \| - \| - \| - \| - \|
	\| 0.6025 \| 11700 \| 6.2147 \| 6.3124 \| 0.6190 (+0.0785) \| 0.3905 (+0.0654) \| 0.6544 (+0.1538) \| 0.5546 (+0.0993) \|
	\| 0.6045 \| 11739 \| 6.1101 \| - \| - \| - \| - \| - \|
	\| 0.6065 \| 11778 \| 6.1246 \| - \| - \| - \| - \| - \|
	\| 0.6085 \| 11817 \| 6.0777 \| - \| - \| - \| - \| - \|
	\| 0.6105 \| 11856 \| 6.1565 \| - \| - \| - \| - \| - \|
	\| 0.6125 \| 11895 \| 5.9654 \| 6.2800 \| 0.6371 (+0.0967) \| 0.3990 (+0.0740) \| 0.6534 (+0.1528) \| 0.5632 (+0.1078) \|
	\| 0.6146 \| 11934 \| 6.0115 \| - \| - \| - \| - \| - \|
	\| 0.6166 \| 11973 \| 6.0402 \| - \| - \| - \| - \| - \|
	\| 0.6186 \| 12012 \| 6.1312 \| - \| - \| - \| - \| - \|
	\| 0.6206 \| 12051 \| 6.0977 \| - \| - \| - \| - \| - \|
	\| 0.6226 \| 12090 \| 6.1147 \| 6.2629 \| 0.6438 (+0.1033) \| 0.3997 (+0.0746) \| 0.6595 (+0.1589) \| 0.5676 (+0.1123) \|
	\| 0.6246 \| 12129 \| 6.0727 \| - \| - \| - \| - \| - \|
	\| 0.6266 \| 12168 \| 6.0468 \| - \| - \| - \| - \| - \|
	\| 0.6286 \| 12207 \| 6.022 \| - \| - \| - \| - \| - \|
	\| 0.6306 \| 12246 \| 5.9995 \| - \| - \| - \| - \| - \|
	\| 0.6326 \| 12285 \| 6.0553 \| 6.2714 \| 0.6511 (+0.1107) \| 0.3965 (+0.0714) \| 0.6490 (+0.1483) \| 0.5655 (+0.1102) \|
	\| 0.6346 \| 12324 \| 6.0713 \| - \| - \| - \| - \| - \|
	\| 0.6366 \| 12363 \| 5.9443 \| - \| - \| - \| - \| - \|
	\| 0.6387 \| 12402 \| 6.0045 \| - \| - \| - \| - \| - \|
	\| 0.6407 \| 12441 \| 5.9835 \| - \| - \| - \| - \| - \|
	\| 0.6427 \| 12480 \| 5.9936 \| 6.1741 \| 0.6330 (+0.0926) \| 0.3993 (+0.0743) \| 0.6452 (+0.1446) \| 0.5592 (+0.1038) \|
	\| 0.6447 \| 12519 \| 5.9637 \| - \| - \| - \| - \| - \|
	\| 0.6467 \| 12558 \| 5.9407 \| - \| - \| - \| - \| - \|
	\| 0.6487 \| 12597 \| 5.8556 \| - \| - \| - \| - \| - \|
	\| 0.6507 \| 12636 \| 6.0084 \| - \| - \| - \| - \| - \|
	\| 0.6527 \| 12675 \| 6.0038 \| 6.1379 \| 0.6555 (+0.1150) \| 0.3981 (+0.0731) \| 0.6501 (+0.1495) \| 0.5679 (+0.1125) \|
	\| 0.6547 \| 12714 \| 5.8648 \| - \| - \| - \| - \| - \|
	\| 0.6567 \| 12753 \| 5.9154 \| - \| - \| - \| - \| - \|
	\| 0.6587 \| 12792 \| 5.9591 \| - \| - \| - \| - \| - \|
	\| 0.6607 \| 12831 \| 5.9369 \| - \| - \| - \| - \| - \|
	\| 0.6628 \| 12870 \| 5.8238 \| 6.1443 \| 0.6430 (+0.1026) \| 0.3945 (+0.0694) \| 0.6621 (+0.1614) \| 0.5665 (+0.1111) \|
	\| 0.6648 \| 12909 \| 5.8622 \| - \| - \| - \| - \| - \|
	\| 0.6668 \| 12948 \| 5.9296 \| - \| - \| - \| - \| - \|
	\| 0.6688 \| 12987 \| 5.8676 \| - \| - \| - \| - \| - \|
	\| 0.6708 \| 13026 \| 5.8811 \| - \| - \| - \| - \| - \|
	\| 0.6728 \| 13065 \| 5.9176 \| 6.0863 \| 0.6422 (+0.1018) \| 0.3931 (+0.0681) \| 0.6651 (+0.1644) \| 0.5668 (+0.1114) \|
	\| 0.6748 \| 13104 \| 5.8536 \| - \| - \| - \| - \| - \|
	\| 0.6768 \| 13143 \| 5.8865 \| - \| - \| - \| - \| - \|
	\| 0.6788 \| 13182 \| 5.7893 \| - \| - \| - \| - \| - \|
	\| 0.6808 \| 13221 \| 5.8791 \| - \| - \| - \| - \| - \|
	\| 0.6828 \| 13260 \| 5.8686 \| 6.0588 \| 0.6384 (+0.0979) \| 0.3980 (+0.0730) \| 0.6555 (+0.1548) \| 0.5640 (+0.1086) \|
	\| 0.6848 \| 13299 \| 5.8052 \| - \| - \| - \| - \| - \|
	\| 0.6869 \| 13338 \| 5.8452 \| - \| - \| - \| - \| - \|
	\| 0.6889 \| 13377 \| 5.8033 \| - \| - \| - \| - \| - \|
	\| 0.6909 \| 13416 \| 5.7734 \| - \| - \| - \| - \| - \|
	\| 0.6929 \| 13455 \| 5.7619 \| 5.9937 \| 0.6524 (+0.1120) \| 0.3965 (+0.0714) \| 0.6505 (+0.1498) \| 0.5664 (+0.1111) \|
	\| 0.6949 \| 13494 \| 5.7637 \| - \| - \| - \| - \| - \|
	\| 0.6969 \| 13533 \| 5.7513 \| - \| - \| - \| - \| - \|
	\| 0.6989 \| 13572 \| 5.8172 \| - \| - \| - \| - \| - \|
	\| 0.7009 \| 13611 \| 5.8323 \| - \| - \| - \| - \| - \|
	\| 0.7029 \| 13650 \| 5.76 \| 5.9823 \| 0.6451 (+0.1047) \| 0.4061 (+0.0810) \| 0.6428 (+0.1421) \| 0.5646 (+0.1093) \|
	\| 0.7049 \| 13689 \| 5.7541 \| - \| - \| - \| - \| - \|
	\| 0.7069 \| 13728 \| 5.7465 \| - \| - \| - \| - \| - \|
	\| 0.7089 \| 13767 \| 5.8207 \| - \| - \| - \| - \| - \|
	\| 0.7110 \| 13806 \| 5.7531 \| - \| - \| - \| - \| - \|
	\| 0.7130 \| 13845 \| 5.7436 \| 5.9584 \| 0.6417 (+0.1012) \| 0.4021 (+0.0771) \| 0.6469 (+0.1462) \| 0.5636 (+0.1082) \|
	\| 0.7150 \| 13884 \| 5.6921 \| - \| - \| - \| - \| - \|
	\| 0.7170 \| 13923 \| 5.6492 \| - \| - \| - \| - \| - \|
	\| 0.7190 \| 13962 \| 5.7005 \| - \| - \| - \| - \| - \|
	\| 0.7210 \| 14001 \| 5.7808 \| - \| - \| - \| - \| - \|
	\| 0.7230 \| 14040 \| 5.7058 \| 5.9436 \| 0.6403 (+0.0998) \| 0.3904 (+0.0654) \| 0.6602 (+0.1595) \| 0.5636 (+0.1083) \|
	\| 0.7250 \| 14079 \| 5.7288 \| - \| - \| - \| - \| - \|
	\| 0.7270 \| 14118 \| 5.6235 \| - \| - \| - \| - \| - \|
	\| 0.7290 \| 14157 \| 5.6438 \| - \| - \| - \| - \| - \|
	\| 0.7310 \| 14196 \| 5.6733 \| - \| - \| - \| - \| - \|
	\| 0.7330 \| 14235 \| 5.7588 \| 5.9466 \| 0.6583 (+0.1179) \| 0.3944 (+0.0693) \| 0.6479 (+0.1473) \| 0.5669 (+0.1115) \|
	\| 0.7351 \| 14274 \| 5.6172 \| - \| - \| - \| - \| - \|
	\| 0.7371 \| 14313 \| 5.649 \| - \| - \| - \| - \| - \|
	\| 0.7391 \| 14352 \| 5.7198 \| - \| - \| - \| - \| - \|
	\| 0.7411 \| 14391 \| 5.627 \| - \| - \| - \| - \| - \|
	\| 0.7431 \| 14430 \| 5.6958 \| 5.8615 \| 0.6463 (+0.1058) \| 0.3905 (+0.0654) \| 0.6619 (+0.1613) \| 0.5662 (+0.1109) \|
	\| 0.7451 \| 14469 \| 5.6216 \| - \| - \| - \| - \| - \|
	\| 0.7471 \| 14508 \| 5.5954 \| - \| - \| - \| - \| - \|
	\| 0.7491 \| 14547 \| 5.5794 \| - \| - \| - \| - \| - \|
	\| 0.7511 \| 14586 \| 5.6821 \| - \| - \| - \| - \| - \|
	\| 0.7531 \| 14625 \| 5.5987 \| 5.8702 \| 0.6436 (+0.1032) \| 0.3936 (+0.0685) \| 0.6563 (+0.1557) \| 0.5645 (+0.1091) \|
	\| 0.7551 \| 14664 \| 5.6134 \| - \| - \| - \| - \| - \|
	\| 0.7571 \| 14703 \| 5.6476 \| - \| - \| - \| - \| - \|
	\| 0.7592 \| 14742 \| 5.679 \| - \| - \| - \| - \| - \|
	\| 0.7612 \| 14781 \| 5.6292 \| - \| - \| - \| - \| - \|
	\| 0.7632 \| 14820 \| 5.6129 \| 5.8149 \| 0.6454 (+0.1049) \| 0.3949 (+0.0699) \| 0.6553 (+0.1546) \| 0.5652 (+0.1098) \|
	\| 0.7652 \| 14859 \| 5.5883 \| - \| - \| - \| - \| - \|
	\| 0.7672 \| 14898 \| 5.6431 \| - \| - \| - \| - \| - \|
	\| 0.7692 \| 14937 \| 5.5464 \| - \| - \| - \| - \| - \|
	\| 0.7712 \| 14976 \| 5.6273 \| - \| - \| - \| - \| - \|
	\| 0.7732 \| 15015 \| 5.6428 \| 5.8067 \| 0.6576 (+0.1172) \| 0.3996 (+0.0745) \| 0.6720 (+0.1714) \| 0.5764 (+0.1210) \|
	\| 0.7752 \| 15054 \| 5.4733 \| - \| - \| - \| - \| - \|
	\| 0.7772 \| 15093 \| 5.5618 \| - \| - \| - \| - \| - \|
	\| 0.7792 \| 15132 \| 5.5804 \| - \| - \| - \| - \| - \|
	\| 0.7812 \| 15171 \| 5.5858 \| - \| - \| - \| - \| - \|
	\| 0.7833 \| 15210 \| 5.4994 \| 5.7438 \| 0.6424 (+0.1019) \| 0.3973 (+0.0722) \| 0.6573 (+0.1566) \| 0.5656 (+0.1103) \|
	\| 0.7853 \| 15249 \| 5.5405 \| - \| - \| - \| - \| - \|
	\| 0.7873 \| 15288 \| 5.5032 \| - \| - \| - \| - \| - \|
	\| 0.7893 \| 15327 \| 5.5755 \| - \| - \| - \| - \| - \|
	\| 0.7913 \| 15366 \| 5.4816 \| - \| - \| - \| - \| - \|
	\| 0.7933 \| 15405 \| 5.4922 \| 5.7130 \| 0.6523 (+0.1118) \| 0.3851 (+0.0601) \| 0.6633 (+0.1626) \| 0.5669 (+0.1115) \|
	\| 0.7953 \| 15444 \| 5.5049 \| - \| - \| - \| - \| - \|
	\| 0.7973 \| 15483 \| 5.5061 \| - \| - \| - \| - \| - \|
	\| 0.7993 \| 15522 \| 5.5243 \| - \| - \| - \| - \| - \|
	\| 0.8013 \| 15561 \| 5.4995 \| - \| - \| - \| - \| - \|
	\| 0.8033 \| 15600 \| 5.5222 \| 5.7124 \| 0.6440 (+0.1035) \| 0.3981 (+0.0731) \| 0.6695 (+0.1689) \| 0.5705 (+0.1152) \|
	\| 0.8053 \| 15639 \| 5.5646 \| - \| - \| - \| - \| - \|
	\| 0.8074 \| 15678 \| 5.5963 \| - \| - \| - \| - \| - \|
	\| 0.8094 \| 15717 \| 5.5167 \| - \| - \| - \| - \| - \|
	\| 0.8114 \| 15756 \| 5.5645 \| - \| - \| - \| - \| - \|
	\| 0.8134 \| 15795 \| 5.4805 \| 5.7093 \| 0.6571 (+0.1167) \| 0.3948 (+0.0697) \| 0.6566 (+0.1560) \| 0.5695 (+0.1141) \|
	\| 0.8154 \| 15834 \| 5.5332 \| - \| - \| - \| - \| - \|
	\| 0.8174 \| 15873 \| 5.4952 \| - \| - \| - \| - \| - \|
	\| 0.8194 \| 15912 \| 5.5312 \| - \| - \| - \| - \| - \|
	\| 0.8214 \| 15951 \| 5.5023 \| - \| - \| - \| - \| - \|
	\| 0.8234 \| 15990 \| 5.3999 \| 5.6825 \| 0.6641 (+0.1237) \| 0.3984 (+0.0734) \| 0.6575 (+0.1569) \| 0.5733 (+0.1180) \|
	\| 0.8254 \| 16029 \| 5.4961 \| - \| - \| - \| - \| - \|
	\| 0.8274 \| 16068 \| 5.5271 \| - \| - \| - \| - \| - \|
	\| 0.8294 \| 16107 \| 5.4806 \| - \| - \| - \| - \| - \|
	\| 0.8315 \| 16146 \| 5.4955 \| - \| - \| - \| - \| - \|
	\| 0.8335 \| 16185 \| 5.4969 \| 5.6419 \| 0.6576 (+0.1172) \| 0.4014 (+0.0763) \| 0.6578 (+0.1572) \| 0.5723 (+0.1169) \|
	\| 0.8355 \| 16224 \| 5.4633 \| - \| - \| - \| - \| - \|
	\| 0.8375 \| 16263 \| 5.4822 \| - \| - \| - \| - \| - \|
	\| 0.8395 \| 16302 \| 5.4476 \| - \| - \| - \| - \| - \|
	\| 0.8415 \| 16341 \| 5.4727 \| - \| - \| - \| - \| - \|
	\| 0.8435 \| 16380 \| 5.4001 \| 5.5943 \| 0.6513 (+0.1109) \| 0.4057 (+0.0806) \| 0.6477 (+0.1471) \| 0.5682 (+0.1129) \|
	\| 0.8455 \| 16419 \| 5.4684 \| - \| - \| - \| - \| - \|
	\| 0.8475 \| 16458 \| 5.4463 \| - \| - \| - \| - \| - \|
	\| 0.8495 \| 16497 \| 5.4672 \| - \| - \| - \| - \| - \|
	\| 0.8515 \| 16536 \| 5.4415 \| - \| - \| - \| - \| - \|
	\| 0.8535 \| 16575 \| 5.4633 \| 5.6234 \| 0.6645 (+0.1241) \| 0.3943 (+0.0693) \| 0.6620 (+0.1614) \| 0.5736 (+0.1183) \|
	\| 0.8556 \| 16614 \| 5.3845 \| - \| - \| - \| - \| - \|
	\| 0.8576 \| 16653 \| 5.4805 \| - \| - \| - \| - \| - \|
	\| 0.8596 \| 16692 \| 5.4391 \| - \| - \| - \| - \| - \|
	\| 0.8616 \| 16731 \| 5.382 \| - \| - \| - \| - \| - \|
	\| 0.8636 \| 16770 \| 5.4113 \| 5.5914 \| 0.6645 (+0.1241) \| 0.3938 (+0.0688) \| 0.6598 (+0.1591) \| 0.5727 (+0.1173) \|
	\| 0.8656 \| 16809 \| 5.4142 \| - \| - \| - \| - \| - \|
	\| 0.8676 \| 16848 \| 5.3805 \| - \| - \| - \| - \| - \|
	\| 0.8696 \| 16887 \| 5.4068 \| - \| - \| - \| - \| - \|
	\| 0.8716 \| 16926 \| 5.392 \| - \| - \| - \| - \| - \|
	\| 0.8736 \| 16965 \| 5.4121 \| 5.5925 \| 0.6592 (+0.1188) \| 0.4029 (+0.0779) \| 0.6473 (+0.1466) \| 0.5698 (+0.1144) \|
	\| 0.8756 \| 17004 \| 5.3969 \| - \| - \| - \| - \| - \|
	\| 0.8776 \| 17043 \| 5.4349 \| - \| - \| - \| - \| - \|
	\| 0.8797 \| 17082 \| 5.3231 \| - \| - \| - \| - \| - \|
	\| 0.8817 \| 17121 \| 5.3217 \| - \| - \| - \| - \| - \|
	\| 0.8837 \| 17160 \| 5.3942 \| 5.5690 \| 0.6619 (+0.1215) \| 0.3941 (+0.0690) \| 0.6603 (+0.1597) \| 0.5721 (+0.1167) \|
	\| 0.8857 \| 17199 \| 5.3824 \| - \| - \| - \| - \| - \|
	\| 0.8877 \| 17238 \| 5.3817 \| - \| - \| - \| - \| - \|
	\| 0.8897 \| 17277 \| 5.3159 \| - \| - \| - \| - \| - \|
	\| 0.8917 \| 17316 \| 5.3866 \| - \| - \| - \| - \| - \|
	\| 0.8937 \| 17355 \| 5.3396 \| 5.5496 \| 0.6645 (+0.1241) \| 0.3996 (+0.0745) \| 0.6525 (+0.1518) \| 0.5722 (+0.1168) \|
	\| 0.8957 \| 17394 \| 5.4085 \| - \| - \| - \| - \| - \|
	\| 0.8977 \| 17433 \| 5.3788 \| - \| - \| - \| - \| - \|
	\| 0.8997 \| 17472 \| 5.3739 \| - \| - \| - \| - \| - \|
	\| 0.9017 \| 17511 \| 5.3322 \| - \| - \| - \| - \| - \|
	\| 0.9038 \| 17550 \| 5.3472 \| 5.5212 \| 0.6513 (+0.1109) \| 0.4041 (+0.0791) \| 0.6607 (+0.1601) \| 0.5721 (+0.1167) \|
	\| 0.9058 \| 17589 \| 5.3451 \| - \| - \| - \| - \| - \|
	\| 0.9078 \| 17628 \| 5.3297 \| - \| - \| - \| - \| - \|
	\| 0.9098 \| 17667 \| 5.3158 \| - \| - \| - \| - \| - \|
	\| 0.9118 \| 17706 \| 5.363 \| - \| - \| - \| - \| - \|
	\| 0.9138 \| 17745 \| 5.3346 \| 5.5014 \| 0.6650 (+0.1245) \| 0.3989 (+0.0739) \| 0.6602 (+0.1595) \| 0.5747 (+0.1193) \|
	\| 0.9158 \| 17784 \| 5.3702 \| - \| - \| - \| - \| - \|
	\| 0.9178 \| 17823 \| 5.4226 \| - \| - \| - \| - \| - \|
	\| 0.9198 \| 17862 \| 5.3099 \| - \| - \| - \| - \| - \|
	\| 0.9218 \| 17901 \| 5.3468 \| - \| - \| - \| - \| - \|
	\| 0.9238 \| 17940 \| 5.3903 \| 5.5093 \| 0.6523 (+0.1118) \| 0.4000 (+0.0750) \| 0.6624 (+0.1618) \| 0.5716 (+0.1162) \|
	\| 0.9258 \| 17979 \| 5.3288 \| - \| - \| - \| - \| - \|
	\| 0.9279 \| 18018 \| 5.3464 \| - \| - \| - \| - \| - \|
	\| 0.9299 \| 18057 \| 5.2696 \| - \| - \| - \| - \| - \|
	\| 0.9319 \| 18096 \| 5.3532 \| - \| - \| - \| - \| - \|
	\| 0.9339 \| 18135 \| 5.3093 \| 5.4815 \| 0.6518 (+0.1114) \| 0.4021 (+0.0771) \| 0.6689 (+0.1683) \| 0.5743 (+0.1189) \|
	\| 0.9359 \| 18174 \| 5.3378 \| - \| - \| - \| - \| - \|
	\| 0.9379 \| 18213 \| 5.3576 \| - \| - \| - \| - \| - \|
	\| 0.9399 \| 18252 \| 5.3102 \| - \| - \| - \| - \| - \|
	\| 0.9419 \| 18291 \| 5.3322 \| - \| - \| - \| - \| - \|
	\| 0.9439 \| 18330 \| 5.3015 \| 5.4963 \| 0.6645 (+0.1241) \| 0.3980 (+0.0729) \| 0.6573 (+0.1566) \| 0.5732 (+0.1179) \|
	\| 0.9459 \| 18369 \| 5.2332 \| - \| - \| - \| - \| - \|
	\| 0.9479 \| 18408 \| 5.2934 \| - \| - \| - \| - \| - \|
	\| 0.9499 \| 18447 \| 5.3187 \| - \| - \| - \| - \| - \|
	\| 0.9520 \| 18486 \| 5.2961 \| - \| - \| - \| - \| - \|
	\| 0.9540 \| 18525 \| 5.2884 \| 5.4628 \| 0.6654 (+0.1250) \| 0.3982 (+0.0731) \| 0.6628 (+0.1621) \| 0.5754 (+0.1201) \|
	\| 0.9560 \| 18564 \| 5.277 \| - \| - \| - \| - \| - \|
	\| 0.9580 \| 18603 \| 5.3364 \| - \| - \| - \| - \| - \|
	\| 0.9600 \| 18642 \| 5.2839 \| - \| - \| - \| - \| - \|
	\| 0.9620 \| 18681 \| 5.2503 \| - \| - \| - \| - \| - \|
	\| 0.9640 \| 18720 \| 5.2547 \| 5.4463 \| 0.6596 (+0.1192) \| 0.3981 (+0.0730) \| 0.6642 (+0.1635) \| 0.5740 (+0.1186) \|
	\| 0.9660 \| 18759 \| 5.2803 \| - \| - \| - \| - \| - \|
	\| 0.9680 \| 18798 \| 5.3167 \| - \| - \| - \| - \| - \|
	\| 0.9700 \| 18837 \| 5.2781 \| - \| - \| - \| - \| - \|
	\| 0.9720 \| 18876 \| 5.2809 \| - \| - \| - \| - \| - \|
	\| 0.9740 \| 18915 \| 5.2917 \| 5.4579 \| 0.6596 (+0.1192) \| 0.4000 (+0.0749) \| 0.6689 (+0.1683) \| 0.5762 (+0.1208) \|
	\| 0.9761 \| 18954 \| 5.2893 \| - \| - \| - \| - \| - \|
	\| 0.9781 \| 18993 \| 5.3301 \| - \| - \| - \| - \| - \|
	\| 0.9801 \| 19032 \| 5.2753 \| - \| - \| - \| - \| - \|
	\| 0.9821 \| 19071 \| 5.2079 \| - \| - \| - \| - \| - \|
	\| 0.9841 \| 19110 \| 5.2672 \| 5.4520 \| 0.6592 (+0.1188) \| 0.3989 (+0.0738) \| 0.6689 (+0.1683) \| 0.5757 (+0.1203) \|
	\| 0.9861 \| 19149 \| 5.268 \| - \| - \| - \| - \| - \|
	\| 0.9881 \| 19188 \| 5.224 \| - \| - \| - \| - \| - \|
	\| 0.9901 \| 19227 \| 5.3047 \| - \| - \| - \| - \| - \|
	\| 0.9921 \| 19266 \| 5.2771 \| - \| - \| - \| - \| - \|
	\| 0.9941 \| 19305 \| 5.233 \| 5.4545 \| 0.6592 (+0.1188) \| 0.4003 (+0.0752) \| 0.6689 (+0.1683) \| 0.5761 (+0.1208) \|
	\| 0.9961 \| 19344 \| 5.2459 \| - \| - \| - \| - \| - \|
	\| 0.9981 \| 19383 \| 5.3069 \| - \| - \| - \| - \| - \|
	\| -1 \| -1 \| - \| - \| 0.6576 (+0.1172) \| 0.3996 (+0.0745) \| 0.6720 (+0.1714) \| 0.5764 (+0.1210) \|

	* The bold row denotes the saved checkpoint.
	</details>

	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 4.348 kWh
	- Carbon Emitted: 1.605 kg of CO2
	- Hours Used: 1.576 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 8 x NVIDIA H100 80GB HBM3
	- CPU Model: AMD EPYC 7R13 Processor
	- RAM Size: 1999.99 GB

	### Framework Versions
	- Python: 3.10.14
	- Sentence Transformers: 5.1.2
	- Transformers: 4.57.1
	- PyTorch: 2.9.1+cu126
	- Accelerate: 1.12.0
	- Datasets: 4.4.1
	- Tokenizers: 0.22.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@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",
	}
	```

	#### MarginMSELoss
	```bibtex
	@misc{hofstätter2021improving,
	title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
	author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
	year={2021},
	eprint={2010.02666},
	archivePrefix={arXiv},
	primaryClass={cs.IR}
	}
	```

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