Training in progress, step 1220, checkpoint

f47d16b verified about 2 months ago

185 kB

	---
	base_model: microsoft/deberta-v3-small
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	- cosine_accuracy
	- cosine_accuracy_threshold
	- cosine_f1
	- cosine_f1_threshold
	- cosine_precision
	- cosine_recall
	- cosine_ap
	- dot_accuracy
	- dot_accuracy_threshold
	- dot_f1
	- dot_f1_threshold
	- dot_precision
	- dot_recall
	- dot_ap
	- manhattan_accuracy
	- manhattan_accuracy_threshold
	- manhattan_f1
	- manhattan_f1_threshold
	- manhattan_precision
	- manhattan_recall
	- manhattan_ap
	- euclidean_accuracy
	- euclidean_accuracy_threshold
	- euclidean_f1
	- euclidean_f1_threshold
	- euclidean_precision
	- euclidean_recall
	- euclidean_ap
	- max_accuracy
	- max_accuracy_threshold
	- max_f1
	- max_f1_threshold
	- max_precision
	- max_recall
	- max_ap
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:32500
	- loss:GISTEmbedLoss
	widget:
	- source_sentence: What was the name of Jed's nephew in The Beverly Hillbillies?
	sentences:
	- Jed Clampett - The Beverly Hillbillies Characters - ShareTV Buddy Ebsen began
	his career as a dancer in the late 1920s in a Broadway chorus. He later formed
	a vaudeville ... Character Bio Although he had received little formal education,
	Jed Clampett had a good deal of common sense. A good-natured man, he is the apparent
	head of the family. Jed's wife (Elly May's mother) died, but is referred to in
	the episode "Duke Steals A Wife" as Rose Ellen. Jed was shown to be an expert
	marksman and was extremely loyal to his family and kinfolk. The huge oil pool
	in the swamp he owned was the beginning of his rags-to-riches journey to Beverly
	Hills. Although he longed for the old ways back in the hills, he made the best
	of being in Beverly Hills. Whenever he had anything on his mind, he would sit
	on the curbstone of his mansion and whittle until he came up with the answer.
	Jedediah, the version of Jed's name used in the 1993 Beverly Hillbillies theatrical
	movie, was never mentioned in the original television series (though coincidentally,
	on Ebsen's subsequent series, Barnaby Jones, Barnaby's nephew J.R. was also named
	Jedediah). In one episode Jed and Granny reminisce about seeing Buddy Ebsen and
	Vilma Ebsen—a joking reference to the Ebsens' song and dance act. Jed appears
	in all 274 episodes. Episode Screenshots
	- a stove generates heat for cooking usually
	- Miss Marple series by Agatha Christie Miss Marple series 43 works, 13 primary
	works Mystery series in order of publication. Miss Marple is introduced in The
	Murder at the Vicarage but the books can be read in any order. Mixed short story
	collections are included if some are Marple, often have horror, supernatural,
	maybe detective Poirot, Pyne, or Quin. Note that "Nemesis" should be read AFTER
	"A Caribbean Holiday"
	- source_sentence: A recording of folk songs done for the Columbia society in 1942
	was largely arranged by Pjetër Dungu .
	sentences:
	- Someone cooking drugs in a spoon over a candle
	- A recording of folk songs made for the Columbia society in 1942 was largely arranged
	by Pjetër Dungu .
	- A Murder of Crows, A Parliament of Owls What do You Call a Group of Birds? Do
	you know what a group of Ravens is called? What about a group of peacocks, snipe
	or hummingbirds? Here is a list of Bird Collectives, terms that you can use to
	describe a group of birds. Birds in general
	- source_sentence: A person in a kitchen looking at the oven.
	sentences:
	- "staying warm has a positive impact on an animal 's survival. Furry animals grow\
	\ thicker coats to keep warm in the winter. \n Furry animals grow thicker coats\
	\ which has a positive impact on their survival. "
	- A woman In the kitchen opening her oven.
	- EE has apologised after a fault left some of its customers unable to use the internet
	on their mobile devices.
	- source_sentence: Air can be separated into several elements.
	sentences:
	- Which of the following substances can be separated into several elements?
	- 'Funny Interesting Facts Humor Strange: Carl and the Passions changed band name
	to what Carl and the Passions changed band name to what Beach Boys Carl and the
	Passions - "So Tough" is the fifteenth studio album released by The Beach Boys
	in 1972. In its initial release, it was the second disc of a two-album set with
	Pet Sounds (which The Beach Boys were able to license from Capitol Records). Unfortunately,
	due to the fact that Carl and the Passions - "So Tough" was a transitional album
	that saw the departure of one member and the introduction of two new ones, making
	it wildly inconsistent in terms of type of material present, it paled next to
	their 1966 classic and was seen as something of a disappointment in its time of
	release. The title of the album itself was a reference to an early band Carl Wilson
	had been in as a teenager (some say a possible early name for the Beach Boys).
	It was also the first album released under a new deal with Warner Bros. that allowed
	the company to distribute all future Beach Boys product in foreign as well as
	domestic markets.'
	- Which statement correctly describes a relationship between two human body systems?
	- source_sentence: What do outdoor plants require to survive?
	sentences:
	- "a plants require water for survival. If no rain or watering, the plant dies.\
	\ \n Outdoor plants require rain to survive."
	- (Vegan) soups are nutritious. In addition to them being easy to digest, most the
	time, soups are made from nutrient-dense ingredients like herbs, spices, vegetables,
	and beans. Because the soup is full of those nutrients AND that it's easy to digest,
	your body is able to absorb more of those nutrients into your system.
	- If you do the math, there are 11,238,513 possible combinations of five white balls
	(without order mattering). Multiply that by the 26 possible red balls, and you
	get 292,201,338 possible Powerball number combinations. At $2 per ticket, you'd
	need $584,402,676 to buy every single combination and guarantee a win.
	model-index:
	- name: SentenceTransformer based on microsoft/deberta-v3-small
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.509950648911252
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.5372664597807683
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.5423369169040173
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.5495945820201323
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.5284216887479147
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.5375220789858359
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5045563506807493
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5314762283231191
	name: Spearman Dot
	- type: pearson_max
	value: 0.5423369169040173
	name: Pearson Max
	- type: spearman_max
	value: 0.5495945820201323
	name: Spearman Max
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: allNLI dev
	type: allNLI-dev
	metrics:
	- type: cosine_accuracy
	value: 0.69140625
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.9270304441452026
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.5363984674329503
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.8337951898574829
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.40114613180515757
	name: Cosine Precision
	- type: cosine_recall
	value: 0.8092485549132948
	name: Cosine Recall
	- type: cosine_ap
	value: 0.49228397783239236
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.69140625
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 709.9082641601562
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.5384615384615384
	name: Dot F1
	- type: dot_f1_threshold
	value: 638.5916748046875
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.4034582132564842
	name: Dot Precision
	- type: dot_recall
	value: 0.8092485549132948
	name: Dot Recall
	- type: dot_ap
	value: 0.4925267288107693
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.689453125
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 214.40182495117188
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.5391621129326047
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 346.060302734375
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.39361702127659576
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.8554913294797688
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.49086550398875856
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.69140625
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 10.571220397949219
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.5363984674329503
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 15.952140808105469
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.40114613180515757
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.8092485549132948
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.4923798851447917
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.69140625
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 709.9082641601562
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.5391621129326047
	name: Max F1
	- type: max_f1_threshold
	value: 638.5916748046875
	name: Max F1 Threshold
	- type: max_precision
	value: 0.4034582132564842
	name: Max Precision
	- type: max_recall
	value: 0.8554913294797688
	name: Max Recall
	- type: max_ap
	value: 0.4925267288107693
	name: Max Ap
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: Qnli dev
	type: Qnli-dev
	metrics:
	- type: cosine_accuracy
	value: 0.6640625
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.8653759956359863
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.6816608996539792
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.7897687554359436
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.5760233918128655
	name: Cosine Precision
	- type: cosine_recall
	value: 0.8347457627118644
	name: Cosine Recall
	- type: cosine_ap
	value: 0.6846632153971953
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.66015625
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 657.5789794921875
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.6814310051107325
	name: Dot F1
	- type: dot_f1_threshold
	value: 598.9862670898438
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.5698005698005698
	name: Dot Precision
	- type: dot_recall
	value: 0.847457627118644
	name: Dot Recall
	- type: dot_ap
	value: 0.6832739733257174
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.6796875
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 294.7474670410156
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.6860068259385665
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 376.3381652832031
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.5742857142857143
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.8516949152542372
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.6961691800504137
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.6640625
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 14.331533432006836
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.6804123711340205
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 17.989498138427734
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.5722543352601156
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.8389830508474576
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.6849916947033118
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.6796875
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 657.5789794921875
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.6860068259385665
	name: Max F1
	- type: max_f1_threshold
	value: 598.9862670898438
	name: Max F1 Threshold
	- type: max_precision
	value: 0.5760233918128655
	name: Max Precision
	- type: max_recall
	value: 0.8516949152542372
	name: Max Recall
	- type: max_ap
	value: 0.6961691800504137
	name: Max Ap
	---

	# SentenceTransformer based on microsoft/deberta-v3-small

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). It maps sentences & paragraphs to a 768-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: [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
	(1): AdvancedWeightedPooling(
	(alpha_dropout_layer): Dropout(p=0.01, inplace=False)
	(gate_dropout_layer): Dropout(p=0.05, inplace=False)
	(linear_cls_pj): Linear(in_features=768, out_features=768, bias=True)
	(linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True)
	(linear_mean_pj): Linear(in_features=768, out_features=768, bias=True)
	(linear_attnOut): Linear(in_features=768, out_features=768, bias=True)
	(mha): MultiheadAttention(
	(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
	)
	(layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
	(layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
	(layernorm_pjCls): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
	(layernorm_pjMean): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
	(layernorm_attnOut): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
	)
	)
	```

	## 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 SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-toytest2-step1-checkpoints-tmp")
	# Run inference
	sentences = [
	'What do outdoor plants require to survive?',
	'a plants require water for survival. If no rain or watering, the plant dies. \n Outdoor plants require rain to survive.',
	"(Vegan) soups are nutritious. In addition to them being easy to digest, most the time, soups are made from nutrient-dense ingredients like herbs, spices, vegetables, and beans. Because the soup is full of those nutrients AND that it's easy to digest, your body is able to absorb more of those nutrients into your system.",
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

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

	<!--
	### 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

	#### Semantic Similarity
	* Dataset: `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.51 \|
	\| spearman_cosine \| 0.5373 \|
	\| pearson_manhattan \| 0.5423 \|
	\| spearman_manhattan \| 0.5496 \|
	\| pearson_euclidean \| 0.5284 \|
	\| spearman_euclidean \| 0.5375 \|
	\| pearson_dot \| 0.5046 \|
	\| spearman_dot \| 0.5315 \|
	\| pearson_max \| 0.5423 \|
	\| spearman_max \| 0.5496 \|

	#### Binary Classification
	* Dataset: `allNLI-dev`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.6914 \|
	\| cosine_accuracy_threshold \| 0.927 \|
	\| cosine_f1 \| 0.5364 \|
	\| cosine_f1_threshold \| 0.8338 \|
	\| cosine_precision \| 0.4011 \|
	\| cosine_recall \| 0.8092 \|
	\| cosine_ap \| 0.4923 \|
	\| dot_accuracy \| 0.6914 \|
	\| dot_accuracy_threshold \| 709.9083 \|
	\| dot_f1 \| 0.5385 \|
	\| dot_f1_threshold \| 638.5917 \|
	\| dot_precision \| 0.4035 \|
	\| dot_recall \| 0.8092 \|
	\| dot_ap \| 0.4925 \|
	\| manhattan_accuracy \| 0.6895 \|
	\| manhattan_accuracy_threshold \| 214.4018 \|
	\| manhattan_f1 \| 0.5392 \|
	\| manhattan_f1_threshold \| 346.0603 \|
	\| manhattan_precision \| 0.3936 \|
	\| manhattan_recall \| 0.8555 \|
	\| manhattan_ap \| 0.4909 \|
	\| euclidean_accuracy \| 0.6914 \|
	\| euclidean_accuracy_threshold \| 10.5712 \|
	\| euclidean_f1 \| 0.5364 \|
	\| euclidean_f1_threshold \| 15.9521 \|
	\| euclidean_precision \| 0.4011 \|
	\| euclidean_recall \| 0.8092 \|
	\| euclidean_ap \| 0.4924 \|
	\| max_accuracy \| 0.6914 \|
	\| max_accuracy_threshold \| 709.9083 \|
	\| max_f1 \| 0.5392 \|
	\| max_f1_threshold \| 638.5917 \|
	\| max_precision \| 0.4035 \|
	\| max_recall \| 0.8555 \|
	\| max_ap \| 0.4925 \|

	#### Binary Classification
	* Dataset: `Qnli-dev`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.6641 \|
	\| cosine_accuracy_threshold \| 0.8654 \|
	\| cosine_f1 \| 0.6817 \|
	\| cosine_f1_threshold \| 0.7898 \|
	\| cosine_precision \| 0.576 \|
	\| cosine_recall \| 0.8347 \|
	\| cosine_ap \| 0.6847 \|
	\| dot_accuracy \| 0.6602 \|
	\| dot_accuracy_threshold \| 657.579 \|
	\| dot_f1 \| 0.6814 \|
	\| dot_f1_threshold \| 598.9863 \|
	\| dot_precision \| 0.5698 \|
	\| dot_recall \| 0.8475 \|
	\| dot_ap \| 0.6833 \|
	\| manhattan_accuracy \| 0.6797 \|
	\| manhattan_accuracy_threshold \| 294.7475 \|
	\| manhattan_f1 \| 0.686 \|
	\| manhattan_f1_threshold \| 376.3382 \|
	\| manhattan_precision \| 0.5743 \|
	\| manhattan_recall \| 0.8517 \|
	\| manhattan_ap \| 0.6962 \|
	\| euclidean_accuracy \| 0.6641 \|
	\| euclidean_accuracy_threshold \| 14.3315 \|
	\| euclidean_f1 \| 0.6804 \|
	\| euclidean_f1_threshold \| 17.9895 \|
	\| euclidean_precision \| 0.5723 \|
	\| euclidean_recall \| 0.839 \|
	\| euclidean_ap \| 0.685 \|
	\| max_accuracy \| 0.6797 \|
	\| max_accuracy_threshold \| 657.579 \|
	\| max_f1 \| 0.686 \|
	\| max_f1_threshold \| 598.9863 \|
	\| max_precision \| 0.576 \|
	\| max_recall \| 0.8517 \|
	\| max_ap \| 0.6962 \|

	<!--
	## 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

	#### Unnamed Dataset


	* Size: 32,500 training samples
	* Columns: <code>sentence1</code> and <code>sentence2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 29.43 tokens</li><li>max: 400 tokens</li></ul> \| <ul><li>min: 2 tokens</li><li>mean: 57.02 tokens</li><li>max: 389 tokens</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What is the chemical symbol for Silver?</code> \| <code>Chemical Elements.com - Silver (Ag) Bentor, Yinon. Chemical Element.com - Silver. <http://www.chemicalelements.com/elements/ag.html>. For more information about citing online sources, please visit the MLA's Website . This page was created by Yinon Bentor. Use of this web site is restricted by this site's license agreement . Copyright © 1996-2012 Yinon Bentor. All Rights Reserved.</code> \|
	\| <code>e. in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently.</code> \| <code>Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas</code> \|
	\| <code>Keanu Neal was born in 1995 .</code> \| <code>Keanu Neal ( born July 26 , 1995 ) is an American football safety for the Atlanta Falcons of the National Football League ( NFL ) .</code> \|
	* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
	```json
	{'guide': SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	), 'temperature': 0.025}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 1,664 evaluation samples
	* Columns: <code>sentence1</code> and <code>sentence2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 28.9 tokens</li><li>max: 348 tokens</li></ul> \| <ul><li>min: 2 tokens</li><li>mean: 57.31 tokens</li><li>max: 450 tokens</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Gene expression is regulated primarily at the what level?</code> \| <code>Gene expression is regulated primarily at the transcriptional level.</code> \|
	\| <code>Diffusion Diffusion is a process where atoms or molecules move from areas of high concentration to areas of low concentration.</code> \| <code>Diffusion is the process in which a substance naturally moves from an area of higher to lower concentration.</code> \|
	\| <code>In which James Bond film did Sean Connery wear the Bell Rocket Belt (Jet Pack)?</code> \| <code>Jet Pack - James Bond Gadgets 125lbs Summary James Bond used the Jetpack in 1965's Thunderball, to escape from gunmen after killing a SPECTRE agent. The Jetpack In the 1965 movie Thunderball, James Bond (Sean Connery) uses Q's Jetpack to escape from two gunmen after killing Jacques Bouvar, SPECTRE Agent No. 6. It was also used in the Thunderball movie posters, being the "Look Up" part of the "Look Up! Look Down! Look Out!" tagline. The Jetpack returned in the 2002 movie Die Another Day, in the Q scene that showcased many other classic gadgets. The Jetpack is a very popular Bond gadget and is a favorite among many fans due to its originality and uniqueness. The Bell Rocket Belt The Jetpack is actually a Bell Rocket Belt, a fully functional rocket pack device. It was designed for use in the army, but was rejected because of its short flying time of 21-22 seconds. Powered by hydrogen peroxide, it could fly about 250m and reach a maximum altitude of 18m, going 55km/h. Despite its impracticality in the real world, the Jetpack made a spectacular debut in Thunderball. Although Sean Connery is seen in the takeoff and landings, the main flight was piloted by Gordon Yeager and Bill Suitor.</code> \|
	* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
	```json
	{'guide': SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	), 'temperature': 0.025}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 256
	- `lr_scheduler_type`: cosine_with_min_lr
	- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
	- `warmup_ratio`: 0.33
	- `save_safetensors`: False
	- `fp16`: True
	- `push_to_hub`: True
	- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest2-step1-checkpoints-tmp
	- `hub_strategy`: all_checkpoints
	- `batch_sampler`: no_duplicates

	#### 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`: 32
	- `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`: 5e-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`: 3
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine_with_min_lr
	- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
	- `warmup_ratio`: 0.33
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: False
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: True
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: True
	- `resume_from_checkpoint`: None
	- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest2-step1-checkpoints-tmp
	- `hub_strategy`: all_checkpoints
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `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
	- `eval_use_gather_object`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| Validation Loss \| sts-test_spearman_cosine \| allNLI-dev_max_ap \| Qnli-dev_max_ap \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:------------------------:\|:-----------------:\|:---------------:\|
	\| 0.0010 \| 1 \| 18.7427 \| - \| - \| - \| - \|
	\| 0.0020 \| 2 \| 11.6434 \| - \| - \| - \| - \|
	\| 0.0030 \| 3 \| 7.4859 \| - \| - \| - \| - \|
	\| 0.0039 \| 4 \| 7.3779 \| - \| - \| - \| - \|
	\| 0.0049 \| 5 \| 17.5878 \| - \| - \| - \| - \|
	\| 0.0059 \| 6 \| 8.4984 \| - \| - \| - \| - \|
	\| 0.0069 \| 7 \| 8.375 \| - \| - \| - \| - \|
	\| 0.0079 \| 8 \| 7.3241 \| - \| - \| - \| - \|
	\| 0.0089 \| 9 \| 10.3081 \| - \| - \| - \| - \|
	\| 0.0098 \| 10 \| 8.5363 \| - \| - \| - \| - \|
	\| 0.0108 \| 11 \| 17.2241 \| - \| - \| - \| - \|
	\| 0.0118 \| 12 \| 7.575 \| - \| - \| - \| - \|
	\| 0.0128 \| 13 \| 9.1905 \| - \| - \| - \| - \|
	\| 0.0138 \| 14 \| 11.7727 \| - \| - \| - \| - \|
	\| 0.0148 \| 15 \| 9.5827 \| - \| - \| - \| - \|
	\| 0.0157 \| 16 \| 7.4432 \| - \| - \| - \| - \|
	\| 0.0167 \| 17 \| 7.1573 \| - \| - \| - \| - \|
	\| 0.0177 \| 18 \| 19.8016 \| - \| - \| - \| - \|
	\| 0.0187 \| 19 \| 19.5118 \| - \| - \| - \| - \|
	\| 0.0197 \| 20 \| 7.9062 \| - \| - \| - \| - \|
	\| 0.0207 \| 21 \| 8.6791 \| - \| - \| - \| - \|
	\| 0.0217 \| 22 \| 7.7318 \| - \| - \| - \| - \|
	\| 0.0226 \| 23 \| 7.9319 \| - \| - \| - \| - \|
	\| 0.0236 \| 24 \| 7.192 \| - \| - \| - \| - \|
	\| 0.0246 \| 25 \| 15.5799 \| - \| - \| - \| - \|
	\| 0.0256 \| 26 \| 9.7859 \| - \| - \| - \| - \|
	\| 0.0266 \| 27 \| 9.9259 \| - \| - \| - \| - \|
	\| 0.0276 \| 28 \| 6.3076 \| - \| - \| - \| - \|
	\| 0.0285 \| 29 \| 7.4471 \| - \| - \| - \| - \|
	\| 0.0295 \| 30 \| 7.1246 \| - \| - \| - \| - \|
	\| 0.0305 \| 31 \| 6.5505 \| - \| - \| - \| - \|
	\| 0.0315 \| 32 \| 18.5194 \| - \| - \| - \| - \|
	\| 0.0325 \| 33 \| 7.0747 \| - \| - \| - \| - \|
	\| 0.0335 \| 34 \| 14.9456 \| - \| - \| - \| - \|
	\| 0.0344 \| 35 \| 6.608 \| - \| - \| - \| - \|
	\| 0.0354 \| 36 \| 8.4672 \| - \| - \| - \| - \|
	\| 0.0364 \| 37 \| 6.8853 \| - \| - \| - \| - \|
	\| 0.0374 \| 38 \| 13.6063 \| - \| - \| - \| - \|
	\| 0.0384 \| 39 \| 7.2625 \| - \| - \| - \| - \|
	\| 0.0394 \| 40 \| 6.2234 \| - \| - \| - \| - \|
	\| 0.0404 \| 41 \| 14.9675 \| - \| - \| - \| - \|
	\| 0.0413 \| 42 \| 6.6038 \| - \| - \| - \| - \|
	\| 0.0423 \| 43 \| 13.1173 \| - \| - \| - \| - \|
	\| 0.0433 \| 44 \| 16.6992 \| - \| - \| - \| - \|
	\| 0.0443 \| 45 \| 6.4828 \| - \| - \| - \| - \|
	\| 0.0453 \| 46 \| 5.9815 \| - \| - \| - \| - \|
	\| 0.0463 \| 47 \| 6.1738 \| - \| - \| - \| - \|
	\| 0.0472 \| 48 \| 7.134 \| - \| - \| - \| - \|
	\| 0.0482 \| 49 \| 9.3933 \| - \| - \| - \| - \|
	\| 0.0492 \| 50 \| 10.8085 \| - \| - \| - \| - \|
	\| 0.0502 \| 51 \| 11.4172 \| - \| - \| - \| - \|
	\| 0.0512 \| 52 \| 7.3397 \| - \| - \| - \| - \|
	\| 0.0522 \| 53 \| 5.8851 \| - \| - \| - \| - \|
	\| 0.0531 \| 54 \| 6.8105 \| - \| - \| - \| - \|
	\| 0.0541 \| 55 \| 5.3637 \| - \| - \| - \| - \|
	\| 0.0551 \| 56 \| 6.2628 \| - \| - \| - \| - \|
	\| 0.0561 \| 57 \| 6.0039 \| - \| - \| - \| - \|
	\| 0.0571 \| 58 \| 7.5859 \| - \| - \| - \| - \|
	\| 0.0581 \| 59 \| 6.0802 \| - \| - \| - \| - \|
	\| 0.0591 \| 60 \| 5.5822 \| - \| - \| - \| - \|
	\| 0.0600 \| 61 \| 5.8773 \| - \| - \| - \| - \|
	\| 0.0610 \| 62 \| 6.0814 \| - \| - \| - \| - \|
	\| 0.0620 \| 63 \| 5.4483 \| - \| - \| - \| - \|
	\| 0.0630 \| 64 \| 10.2506 \| - \| - \| - \| - \|
	\| 0.0640 \| 65 \| 10.5976 \| - \| - \| - \| - \|
	\| 0.0650 \| 66 \| 6.9942 \| - \| - \| - \| - \|
	\| 0.0659 \| 67 \| 5.4813 \| - \| - \| - \| - \|
	\| 0.0669 \| 68 \| 7.045 \| - \| - \| - \| - \|
	\| 0.0679 \| 69 \| 5.8549 \| - \| - \| - \| - \|
	\| 0.0689 \| 70 \| 8.8514 \| - \| - \| - \| - \|
	\| 0.0699 \| 71 \| 5.2557 \| - \| - \| - \| - \|
	\| 0.0709 \| 72 \| 5.1181 \| - \| - \| - \| - \|
	\| 0.0719 \| 73 \| 5.5331 \| - \| - \| - \| - \|
	\| 0.0728 \| 74 \| 5.5944 \| - \| - \| - \| - \|
	\| 0.0738 \| 75 \| 4.6332 \| - \| - \| - \| - \|
	\| 0.0748 \| 76 \| 4.9532 \| - \| - \| - \| - \|
	\| 0.0758 \| 77 \| 5.055 \| - \| - \| - \| - \|
	\| 0.0768 \| 78 \| 4.5005 \| - \| - \| - \| - \|
	\| 0.0778 \| 79 \| 5.1997 \| - \| - \| - \| - \|
	\| 0.0787 \| 80 \| 5.1479 \| - \| - \| - \| - \|
	\| 0.0797 \| 81 \| 5.1777 \| - \| - \| - \| - \|
	\| 0.0807 \| 82 \| 5.5565 \| - \| - \| - \| - \|
	\| 0.0817 \| 83 \| 4.6999 \| - \| - \| - \| - \|
	\| 0.0827 \| 84 \| 5.0681 \| - \| - \| - \| - \|
	\| 0.0837 \| 85 \| 5.2208 \| - \| - \| - \| - \|
	\| 0.0846 \| 86 \| 4.56 \| - \| - \| - \| - \|
	\| 0.0856 \| 87 \| 4.6793 \| - \| - \| - \| - \|
	\| 0.0866 \| 88 \| 4.4611 \| - \| - \| - \| - \|
	\| 0.0876 \| 89 \| 9.623 \| - \| - \| - \| - \|
	\| 0.0886 \| 90 \| 5.0316 \| - \| - \| - \| - \|
	\| 0.0896 \| 91 \| 4.1771 \| - \| - \| - \| - \|
	\| 0.0906 \| 92 \| 4.9652 \| - \| - \| - \| - \|
	\| 0.0915 \| 93 \| 8.7432 \| - \| - \| - \| - \|
	\| 0.0925 \| 94 \| 4.6234 \| - \| - \| - \| - \|
	\| 0.0935 \| 95 \| 4.4016 \| - \| - \| - \| - \|
	\| 0.0945 \| 96 \| 4.9903 \| - \| - \| - \| - \|
	\| 0.0955 \| 97 \| 4.5606 \| - \| - \| - \| - \|
	\| 0.0965 \| 98 \| 4.9534 \| - \| - \| - \| - \|
	\| 0.0974 \| 99 \| 8.1838 \| - \| - \| - \| - \|
	\| 0.0984 \| 100 \| 4.9736 \| - \| - \| - \| - \|
	\| 0.0994 \| 101 \| 4.4733 \| - \| - \| - \| - \|
	\| 0.1004 \| 102 \| 4.9725 \| - \| - \| - \| - \|
	\| 0.1014 \| 103 \| 4.5861 \| - \| - \| - \| - \|
	\| 0.1024 \| 104 \| 7.7634 \| - \| - \| - \| - \|
	\| 0.1033 \| 105 \| 4.9915 \| - \| - \| - \| - \|
	\| 0.1043 \| 106 \| 5.1391 \| - \| - \| - \| - \|
	\| 0.1053 \| 107 \| 5.0157 \| - \| - \| - \| - \|
	\| 0.1063 \| 108 \| 4.0982 \| - \| - \| - \| - \|
	\| 0.1073 \| 109 \| 4.2178 \| - \| - \| - \| - \|
	\| 0.1083 \| 110 \| 4.6193 \| - \| - \| - \| - \|
	\| 0.1093 \| 111 \| 4.7638 \| - \| - \| - \| - \|
	\| 0.1102 \| 112 \| 4.1207 \| - \| - \| - \| - \|
	\| 0.1112 \| 113 \| 5.2034 \| - \| - \| - \| - \|
	\| 0.1122 \| 114 \| 5.0693 \| - \| - \| - \| - \|
	\| 0.1132 \| 115 \| 4.7895 \| - \| - \| - \| - \|
	\| 0.1142 \| 116 \| 4.9486 \| - \| - \| - \| - \|
	\| 0.1152 \| 117 \| 4.6552 \| - \| - \| - \| - \|
	\| 0.1161 \| 118 \| 4.4555 \| - \| - \| - \| - \|
	\| 0.1171 \| 119 \| 4.8977 \| - \| - \| - \| - \|
	\| 0.1181 \| 120 \| 7.6836 \| - \| - \| - \| - \|
	\| 0.1191 \| 121 \| 4.8106 \| - \| - \| - \| - \|
	\| 0.1201 \| 122 \| 4.9958 \| - \| - \| - \| - \|
	\| 0.1211 \| 123 \| 4.4585 \| - \| - \| - \| - \|
	\| 0.1220 \| 124 \| 7.5559 \| - \| - \| - \| - \|
	\| 0.1230 \| 125 \| 4.2636 \| - \| - \| - \| - \|
	\| 0.1240 \| 126 \| 4.0436 \| - \| - \| - \| - \|
	\| 0.125 \| 127 \| 4.7416 \| - \| - \| - \| - \|
	\| 0.1260 \| 128 \| 4.2215 \| - \| - \| - \| - \|
	\| 0.1270 \| 129 \| 6.3561 \| - \| - \| - \| - \|
	\| 0.1280 \| 130 \| 6.2299 \| - \| - \| - \| - \|
	\| 0.1289 \| 131 \| 4.3492 \| - \| - \| - \| - \|
	\| 0.1299 \| 132 \| 4.0216 \| - \| - \| - \| - \|
	\| 0.1309 \| 133 \| 6.963 \| - \| - \| - \| - \|
	\| 0.1319 \| 134 \| 3.9474 \| - \| - \| - \| - \|
	\| 0.1329 \| 135 \| 4.3437 \| - \| - \| - \| - \|
	\| 0.1339 \| 136 \| 3.6267 \| - \| - \| - \| - \|
	\| 0.1348 \| 137 \| 3.9896 \| - \| - \| - \| - \|
	\| 0.1358 \| 138 \| 4.8156 \| - \| - \| - \| - \|
	\| 0.1368 \| 139 \| 4.9751 \| - \| - \| - \| - \|
	\| 0.1378 \| 140 \| 4.4144 \| - \| - \| - \| - \|
	\| 0.1388 \| 141 \| 4.7213 \| - \| - \| - \| - \|
	\| 0.1398 \| 142 \| 6.6081 \| - \| - \| - \| - \|
	\| 0.1407 \| 143 \| 4.2929 \| - \| - \| - \| - \|
	\| 0.1417 \| 144 \| 4.2537 \| - \| - \| - \| - \|
	\| 0.1427 \| 145 \| 4.0647 \| - \| - \| - \| - \|
	\| 0.1437 \| 146 \| 3.937 \| - \| - \| - \| - \|
	\| 0.1447 \| 147 \| 5.6582 \| - \| - \| - \| - \|
	\| 0.1457 \| 148 \| 4.2648 \| - \| - \| - \| - \|
	\| 0.1467 \| 149 \| 4.4429 \| - \| - \| - \| - \|
	\| 0.1476 \| 150 \| 3.6197 \| - \| - \| - \| - \|
	\| 0.1486 \| 151 \| 3.7953 \| - \| - \| - \| - \|
	\| 0.1496 \| 152 \| 3.8175 \| - \| - \| - \| - \|
	\| 0.1506 \| 153 \| 4.5137 \| 3.3210 \| 0.1806 \| 0.3919 \| 0.5750 \|
	\| 0.1516 \| 154 \| 4.3528 \| - \| - \| - \| - \|
	\| 0.1526 \| 155 \| 3.6573 \| - \| - \| - \| - \|
	\| 0.1535 \| 156 \| 3.5248 \| - \| - \| - \| - \|
	\| 0.1545 \| 157 \| 3.9275 \| - \| - \| - \| - \|
	\| 0.1555 \| 158 \| 7.1868 \| - \| - \| - \| - \|
	\| 0.1565 \| 159 \| 3.6294 \| - \| - \| - \| - \|
	\| 0.1575 \| 160 \| 3.6886 \| - \| - \| - \| - \|
	\| 0.1585 \| 161 \| 3.1873 \| - \| - \| - \| - \|
	\| 0.1594 \| 162 \| 6.1951 \| - \| - \| - \| - \|
	\| 0.1604 \| 163 \| 3.9747 \| - \| - \| - \| - \|
	\| 0.1614 \| 164 \| 7.004 \| - \| - \| - \| - \|
	\| 0.1624 \| 165 \| 4.3221 \| - \| - \| - \| - \|
	\| 0.1634 \| 166 \| 3.5963 \| - \| - \| - \| - \|
	\| 0.1644 \| 167 \| 3.1988 \| - \| - \| - \| - \|
	\| 0.1654 \| 168 \| 3.8236 \| - \| - \| - \| - \|
	\| 0.1663 \| 169 \| 3.5063 \| - \| - \| - \| - \|
	\| 0.1673 \| 170 \| 5.9843 \| - \| - \| - \| - \|
	\| 0.1683 \| 171 \| 5.884 \| - \| - \| - \| - \|
	\| 0.1693 \| 172 \| 4.1317 \| - \| - \| - \| - \|
	\| 0.1703 \| 173 \| 3.9255 \| - \| - \| - \| - \|
	\| 0.1713 \| 174 \| 4.1121 \| - \| - \| - \| - \|
	\| 0.1722 \| 175 \| 3.7748 \| - \| - \| - \| - \|
	\| 0.1732 \| 176 \| 5.1602 \| - \| - \| - \| - \|
	\| 0.1742 \| 177 \| 4.8807 \| - \| - \| - \| - \|
	\| 0.1752 \| 178 \| 3.4643 \| - \| - \| - \| - \|
	\| 0.1762 \| 179 \| 3.4937 \| - \| - \| - \| - \|
	\| 0.1772 \| 180 \| 5.2731 \| - \| - \| - \| - \|
	\| 0.1781 \| 181 \| 4.6416 \| - \| - \| - \| - \|
	\| 0.1791 \| 182 \| 3.5226 \| - \| - \| - \| - \|
	\| 0.1801 \| 183 \| 4.7794 \| - \| - \| - \| - \|
	\| 0.1811 \| 184 \| 3.8504 \| - \| - \| - \| - \|
	\| 0.1821 \| 185 \| 3.5391 \| - \| - \| - \| - \|
	\| 0.1831 \| 186 \| 4.0291 \| - \| - \| - \| - \|
	\| 0.1841 \| 187 \| 3.5606 \| - \| - \| - \| - \|
	\| 0.1850 \| 188 \| 3.8957 \| - \| - \| - \| - \|
	\| 0.1860 \| 189 \| 4.3657 \| - \| - \| - \| - \|
	\| 0.1870 \| 190 \| 5.0173 \| - \| - \| - \| - \|
	\| 0.1880 \| 191 \| 4.3915 \| - \| - \| - \| - \|
	\| 0.1890 \| 192 \| 3.4613 \| - \| - \| - \| - \|
	\| 0.1900 \| 193 \| 3.2005 \| - \| - \| - \| - \|
	\| 0.1909 \| 194 \| 3.3986 \| - \| - \| - \| - \|
	\| 0.1919 \| 195 \| 3.7937 \| - \| - \| - \| - \|
	\| 0.1929 \| 196 \| 3.8981 \| - \| - \| - \| - \|
	\| 0.1939 \| 197 \| 3.7051 \| - \| - \| - \| - \|
	\| 0.1949 \| 198 \| 3.8028 \| - \| - \| - \| - \|
	\| 0.1959 \| 199 \| 3.3294 \| - \| - \| - \| - \|
	\| 0.1969 \| 200 \| 4.1252 \| - \| - \| - \| - \|
	\| 0.1978 \| 201 \| 4.2564 \| - \| - \| - \| - \|
	\| 0.1988 \| 202 \| 3.8258 \| - \| - \| - \| - \|
	\| 0.1998 \| 203 \| 3.1025 \| - \| - \| - \| - \|
	\| 0.2008 \| 204 \| 3.5038 \| - \| - \| - \| - \|
	\| 0.2018 \| 205 \| 3.6021 \| - \| - \| - \| - \|
	\| 0.2028 \| 206 \| 3.7637 \| - \| - \| - \| - \|
	\| 0.2037 \| 207 \| 3.2563 \| - \| - \| - \| - \|
	\| 0.2047 \| 208 \| 3.9323 \| - \| - \| - \| - \|
	\| 0.2057 \| 209 \| 3.489 \| - \| - \| - \| - \|
	\| 0.2067 \| 210 \| 3.6549 \| - \| - \| - \| - \|
	\| 0.2077 \| 211 \| 3.1609 \| - \| - \| - \| - \|
	\| 0.2087 \| 212 \| 3.2467 \| - \| - \| - \| - \|
	\| 0.2096 \| 213 \| 3.4514 \| - \| - \| - \| - \|
	\| 0.2106 \| 214 \| 3.4945 \| - \| - \| - \| - \|
	\| 0.2116 \| 215 \| 3.5932 \| - \| - \| - \| - \|
	\| 0.2126 \| 216 \| 3.2289 \| - \| - \| - \| - \|
	\| 0.2136 \| 217 \| 3.3279 \| - \| - \| - \| - \|
	\| 0.2146 \| 218 \| 3.8141 \| - \| - \| - \| - \|
	\| 0.2156 \| 219 \| 3.1171 \| - \| - \| - \| - \|
	\| 0.2165 \| 220 \| 3.6287 \| - \| - \| - \| - \|
	\| 0.2175 \| 221 \| 3.8517 \| - \| - \| - \| - \|
	\| 0.2185 \| 222 \| 3.3836 \| - \| - \| - \| - \|
	\| 0.2195 \| 223 \| 3.425 \| - \| - \| - \| - \|
	\| 0.2205 \| 224 \| 3.6246 \| - \| - \| - \| - \|
	\| 0.2215 \| 225 \| 3.5682 \| - \| - \| - \| - \|
	\| 0.2224 \| 226 \| 3.3034 \| - \| - \| - \| - \|
	\| 0.2234 \| 227 \| 3.9251 \| - \| - \| - \| - \|
	\| 0.2244 \| 228 \| 3.146 \| - \| - \| - \| - \|
	\| 0.2254 \| 229 \| 3.8859 \| - \| - \| - \| - \|
	\| 0.2264 \| 230 \| 3.2977 \| - \| - \| - \| - \|
	\| 0.2274 \| 231 \| 3.2664 \| - \| - \| - \| - \|
	\| 0.2283 \| 232 \| 3.1275 \| - \| - \| - \| - \|
	\| 0.2293 \| 233 \| 3.2408 \| - \| - \| - \| - \|
	\| 0.2303 \| 234 \| 2.907 \| - \| - \| - \| - \|
	\| 0.2313 \| 235 \| 2.9178 \| - \| - \| - \| - \|
	\| 0.2323 \| 236 \| 3.324 \| - \| - \| - \| - \|
	\| 0.2333 \| 237 \| 2.9172 \| - \| - \| - \| - \|
	\| 0.2343 \| 238 \| 3.4324 \| - \| - \| - \| - \|
	\| 0.2352 \| 239 \| 4.0563 \| - \| - \| - \| - \|
	\| 0.2362 \| 240 \| 2.8736 \| - \| - \| - \| - \|
	\| 0.2372 \| 241 \| 4.7174 \| - \| - \| - \| - \|
	\| 0.2382 \| 242 \| 3.2025 \| - \| - \| - \| - \|
	\| 0.2392 \| 243 \| 2.7835 \| - \| - \| - \| - \|
	\| 0.2402 \| 244 \| 4.3158 \| - \| - \| - \| - \|
	\| 0.2411 \| 245 \| 2.8619 \| - \| - \| - \| - \|
	\| 0.2421 \| 246 \| 2.5156 \| - \| - \| - \| - \|
	\| 0.2431 \| 247 \| 3.2144 \| - \| - \| - \| - \|
	\| 0.2441 \| 248 \| 3.5927 \| - \| - \| - \| - \|
	\| 0.2451 \| 249 \| 2.6059 \| - \| - \| - \| - \|
	\| 0.2461 \| 250 \| 2.9758 \| - \| - \| - \| - \|
	\| 0.2470 \| 251 \| 3.9214 \| - \| - \| - \| - \|
	\| 0.2480 \| 252 \| 3.2892 \| - \| - \| - \| - \|
	\| 0.2490 \| 253 \| 2.9503 \| - \| - \| - \| - \|
	\| 0.25 \| 254 \| 2.5969 \| - \| - \| - \| - \|
	\| 0.2510 \| 255 \| 2.9908 \| - \| - \| - \| - \|
	\| 0.2520 \| 256 \| 2.8995 \| - \| - \| - \| - \|
	\| 0.2530 \| 257 \| 3.124 \| - \| - \| - \| - \|
	\| 0.2539 \| 258 \| 3.1197 \| - \| - \| - \| - \|
	\| 0.2549 \| 259 \| 2.3073 \| - \| - \| - \| - \|
	\| 0.2559 \| 260 \| 2.8441 \| - \| - \| - \| - \|
	\| 0.2569 \| 261 \| 1.9788 \| - \| - \| - \| - \|
	\| 0.2579 \| 262 \| 2.1442 \| - \| - \| - \| - \|
	\| 0.2589 \| 263 \| 4.9015 \| - \| - \| - \| - \|
	\| 0.2598 \| 264 \| 2.7866 \| - \| - \| - \| - \|
	\| 0.2608 \| 265 \| 2.4588 \| - \| - \| - \| - \|
	\| 0.2618 \| 266 \| 2.3909 \| - \| - \| - \| - \|
	\| 0.2628 \| 267 \| 4.7394 \| - \| - \| - \| - \|
	\| 0.2638 \| 268 \| 3.1581 \| - \| - \| - \| - \|
	\| 0.2648 \| 269 \| 3.973 \| - \| - \| - \| - \|
	\| 0.2657 \| 270 \| 4.1565 \| - \| - \| - \| - \|
	\| 0.2667 \| 271 \| 2.5183 \| - \| - \| - \| - \|
	\| 0.2677 \| 272 \| 3.614 \| - \| - \| - \| - \|
	\| 0.2687 \| 273 \| 2.6858 \| - \| - \| - \| - \|
	\| 0.2697 \| 274 \| 3.1182 \| - \| - \| - \| - \|
	\| 0.2707 \| 275 \| 2.9628 \| - \| - \| - \| - \|
	\| 0.2717 \| 276 \| 2.8376 \| - \| - \| - \| - \|
	\| 0.2726 \| 277 \| 2.7858 \| - \| - \| - \| - \|
	\| 0.2736 \| 278 \| 2.1037 \| - \| - \| - \| - \|
	\| 0.2746 \| 279 \| 3.0436 \| - \| - \| - \| - \|
	\| 0.2756 \| 280 \| 3.4125 \| - \| - \| - \| - \|
	\| 0.2766 \| 281 \| 2.5027 \| - \| - \| - \| - \|
	\| 0.2776 \| 282 \| 2.7922 \| - \| - \| - \| - \|
	\| 0.2785 \| 283 \| 2.9762 \| - \| - \| - \| - \|
	\| 0.2795 \| 284 \| 2.6458 \| - \| - \| - \| - \|
	\| 0.2805 \| 285 \| 2.962 \| - \| - \| - \| - \|
	\| 0.2815 \| 286 \| 2.5439 \| - \| - \| - \| - \|
	\| 0.2825 \| 287 \| 2.8437 \| - \| - \| - \| - \|
	\| 0.2835 \| 288 \| 3.2134 \| - \| - \| - \| - \|
	\| 0.2844 \| 289 \| 2.5655 \| - \| - \| - \| - \|
	\| 0.2854 \| 290 \| 2.9465 \| - \| - \| - \| - \|
	\| 0.2864 \| 291 \| 2.4653 \| - \| - \| - \| - \|
	\| 0.2874 \| 292 \| 3.1467 \| - \| - \| - \| - \|
	\| 0.2884 \| 293 \| 2.6551 \| - \| - \| - \| - \|
	\| 0.2894 \| 294 \| 2.5098 \| - \| - \| - \| - \|
	\| 0.2904 \| 295 \| 2.5988 \| - \| - \| - \| - \|
	\| 0.2913 \| 296 \| 3.778 \| - \| - \| - \| - \|
	\| 0.2923 \| 297 \| 2.6257 \| - \| - \| - \| - \|
	\| 0.2933 \| 298 \| 2.5142 \| - \| - \| - \| - \|
	\| 0.2943 \| 299 \| 2.3182 \| - \| - \| - \| - \|
	\| 0.2953 \| 300 \| 3.3505 \| - \| - \| - \| - \|
	\| 0.2963 \| 301 \| 2.9615 \| - \| - \| - \| - \|
	\| 0.2972 \| 302 \| 2.9136 \| - \| - \| - \| - \|
	\| 0.2982 \| 303 \| 2.6192 \| - \| - \| - \| - \|
	\| 0.2992 \| 304 \| 2.3255 \| - \| - \| - \| - \|
	\| 0.3002 \| 305 \| 2.7168 \| - \| - \| - \| - \|
	\| 0.3012 \| 306 \| 2.9137 \| 2.4280 \| 0.2507 \| 0.4103 \| 0.5948 \|
	\| 0.3022 \| 307 \| 2.6681 \| - \| - \| - \| - \|
	\| 0.3031 \| 308 \| 2.7219 \| - \| - \| - \| - \|
	\| 0.3041 \| 309 \| 2.4057 \| - \| - \| - \| - \|
	\| 0.3051 \| 310 \| 2.7402 \| - \| - \| - \| - \|
	\| 0.3061 \| 311 \| 2.5512 \| - \| - \| - \| - \|
	\| 0.3071 \| 312 \| 2.8553 \| - \| - \| - \| - \|
	\| 0.3081 \| 313 \| 2.598 \| - \| - \| - \| - \|
	\| 0.3091 \| 314 \| 2.6186 \| - \| - \| - \| - \|
	\| 0.3100 \| 315 \| 2.3678 \| - \| - \| - \| - \|
	\| 0.3110 \| 316 \| 2.886 \| - \| - \| - \| - \|
	\| 0.3120 \| 317 \| 2.1738 \| - \| - \| - \| - \|
	\| 0.3130 \| 318 \| 2.6619 \| - \| - \| - \| - \|
	\| 0.3140 \| 319 \| 2.1818 \| - \| - \| - \| - \|
	\| 0.3150 \| 320 \| 3.0407 \| - \| - \| - \| - \|
	\| 0.3159 \| 321 \| 2.464 \| - \| - \| - \| - \|
	\| 0.3169 \| 322 \| 2.7415 \| - \| - \| - \| - \|
	\| 0.3179 \| 323 \| 2.7455 \| - \| - \| - \| - \|
	\| 0.3189 \| 324 \| 2.4061 \| - \| - \| - \| - \|
	\| 0.3199 \| 325 \| 2.0491 \| - \| - \| - \| - \|
	\| 0.3209 \| 326 \| 3.3097 \| - \| - \| - \| - \|
	\| 0.3219 \| 327 \| 2.3587 \| - \| - \| - \| - \|
	\| 0.3228 \| 328 \| 1.9493 \| - \| - \| - \| - \|
	\| 0.3238 \| 329 \| 2.5399 \| - \| - \| - \| - \|
	\| 0.3248 \| 330 \| 2.3569 \| - \| - \| - \| - \|
	\| 0.3258 \| 331 \| 1.9024 \| - \| - \| - \| - \|
	\| 0.3268 \| 332 \| 2.3513 \| - \| - \| - \| - \|
	\| 0.3278 \| 333 \| 2.2488 \| - \| - \| - \| - \|
	\| 0.3287 \| 334 \| 1.9141 \| - \| - \| - \| - \|
	\| 0.3297 \| 335 \| 2.7065 \| - \| - \| - \| - \|
	\| 0.3307 \| 336 \| 2.139 \| - \| - \| - \| - \|
	\| 0.3317 \| 337 \| 2.2345 \| - \| - \| - \| - \|
	\| 0.3327 \| 338 \| 2.3612 \| - \| - \| - \| - \|
	\| 0.3337 \| 339 \| 2.1413 \| - \| - \| - \| - \|
	\| 0.3346 \| 340 \| 2.2214 \| - \| - \| - \| - \|
	\| 0.3356 \| 341 \| 2.9006 \| - \| - \| - \| - \|
	\| 0.3366 \| 342 \| 2.417 \| - \| - \| - \| - \|
	\| 0.3376 \| 343 \| 2.2348 \| - \| - \| - \| - \|
	\| 0.3386 \| 344 \| 2.4369 \| - \| - \| - \| - \|
	\| 0.3396 \| 345 \| 2.7623 \| - \| - \| - \| - \|
	\| 0.3406 \| 346 \| 2.6741 \| - \| - \| - \| - \|
	\| 0.3415 \| 347 \| 3.0515 \| - \| - \| - \| - \|
	\| 0.3425 \| 348 \| 2.4952 \| - \| - \| - \| - \|
	\| 0.3435 \| 349 \| 2.1265 \| - \| - \| - \| - \|
	\| 0.3445 \| 350 \| 2.0359 \| - \| - \| - \| - \|
	\| 0.3455 \| 351 \| 3.107 \| - \| - \| - \| - \|
	\| 0.3465 \| 352 \| 2.116 \| - \| - \| - \| - \|
	\| 0.3474 \| 353 \| 2.1996 \| - \| - \| - \| - \|
	\| 0.3484 \| 354 \| 2.9312 \| - \| - \| - \| - \|
	\| 0.3494 \| 355 \| 2.2885 \| - \| - \| - \| - \|
	\| 0.3504 \| 356 \| 3.0302 \| - \| - \| - \| - \|
	\| 0.3514 \| 357 \| 2.2163 \| - \| - \| - \| - \|
	\| 0.3524 \| 358 \| 2.8304 \| - \| - \| - \| - \|
	\| 0.3533 \| 359 \| 2.2715 \| - \| - \| - \| - \|
	\| 0.3543 \| 360 \| 2.3388 \| - \| - \| - \| - \|
	\| 0.3553 \| 361 \| 2.2098 \| - \| - \| - \| - \|
	\| 0.3563 \| 362 \| 2.0911 \| - \| - \| - \| - \|
	\| 0.3573 \| 363 \| 2.3582 \| - \| - \| - \| - \|
	\| 0.3583 \| 364 \| 1.8605 \| - \| - \| - \| - \|
	\| 0.3593 \| 365 \| 2.2252 \| - \| - \| - \| - \|
	\| 0.3602 \| 366 \| 2.2018 \| - \| - \| - \| - \|
	\| 0.3612 \| 367 \| 2.1099 \| - \| - \| - \| - \|
	\| 0.3622 \| 368 \| 2.1323 \| - \| - \| - \| - \|
	\| 0.3632 \| 369 \| 2.4203 \| - \| - \| - \| - \|
	\| 0.3642 \| 370 \| 2.7768 \| - \| - \| - \| - \|
	\| 0.3652 \| 371 \| 2.3359 \| - \| - \| - \| - \|
	\| 0.3661 \| 372 \| 2.3773 \| - \| - \| - \| - \|
	\| 0.3671 \| 373 \| 2.4424 \| - \| - \| - \| - \|
	\| 0.3681 \| 374 \| 1.9478 \| - \| - \| - \| - \|
	\| 0.3691 \| 375 \| 1.6047 \| - \| - \| - \| - \|
	\| 0.3701 \| 376 \| 1.7384 \| - \| - \| - \| - \|
	\| 0.3711 \| 377 \| 2.1147 \| - \| - \| - \| - \|
	\| 0.3720 \| 378 \| 1.8449 \| - \| - \| - \| - \|
	\| 0.3730 \| 379 \| 2.6009 \| - \| - \| - \| - \|
	\| 0.3740 \| 380 \| 2.4051 \| - \| - \| - \| - \|
	\| 0.375 \| 381 \| 2.3035 \| - \| - \| - \| - \|
	\| 0.3760 \| 382 \| 1.8955 \| - \| - \| - \| - \|
	\| 0.3770 \| 383 \| 2.287 \| - \| - \| - \| - \|
	\| 0.3780 \| 384 \| 1.9123 \| - \| - \| - \| - \|
	\| 0.3789 \| 385 \| 1.9369 \| - \| - \| - \| - \|
	\| 0.3799 \| 386 \| 2.1367 \| - \| - \| - \| - \|
	\| 0.3809 \| 387 \| 1.9437 \| - \| - \| - \| - \|
	\| 0.3819 \| 388 \| 2.3873 \| - \| - \| - \| - \|
	\| 0.3829 \| 389 \| 1.7463 \| - \| - \| - \| - \|
	\| 0.3839 \| 390 \| 2.8438 \| - \| - \| - \| - \|
	\| 0.3848 \| 391 \| 2.4875 \| - \| - \| - \| - \|
	\| 0.3858 \| 392 \| 2.0798 \| - \| - \| - \| - \|
	\| 0.3868 \| 393 \| 2.2242 \| - \| - \| - \| - \|
	\| 0.3878 \| 394 \| 1.8714 \| - \| - \| - \| - \|
	\| 0.3888 \| 395 \| 1.5893 \| - \| - \| - \| - \|
	\| 0.3898 \| 396 \| 1.5633 \| - \| - \| - \| - \|
	\| 0.3907 \| 397 \| 1.8645 \| - \| - \| - \| - \|
	\| 0.3917 \| 398 \| 1.8928 \| - \| - \| - \| - \|
	\| 0.3927 \| 399 \| 1.3352 \| - \| - \| - \| - \|
	\| 0.3937 \| 400 \| 3.3052 \| - \| - \| - \| - \|
	\| 0.3947 \| 401 \| 1.6256 \| - \| - \| - \| - \|
	\| 0.3957 \| 402 \| 1.8856 \| - \| - \| - \| - \|
	\| 0.3967 \| 403 \| 1.8355 \| - \| - \| - \| - \|
	\| 0.3976 \| 404 \| 1.8944 \| - \| - \| - \| - \|
	\| 0.3986 \| 405 \| 1.7636 \| - \| - \| - \| - \|
	\| 0.3996 \| 406 \| 2.8097 \| - \| - \| - \| - \|
	\| 0.4006 \| 407 \| 1.9121 \| - \| - \| - \| - \|
	\| 0.4016 \| 408 \| 1.9233 \| - \| - \| - \| - \|
	\| 0.4026 \| 409 \| 1.543 \| - \| - \| - \| - \|
	\| 0.4035 \| 410 \| 1.7207 \| - \| - \| - \| - \|
	\| 0.4045 \| 411 \| 1.6344 \| - \| - \| - \| - \|
	\| 0.4055 \| 412 \| 2.4177 \| - \| - \| - \| - \|
	\| 0.4065 \| 413 \| 2.2995 \| - \| - \| - \| - \|
	\| 0.4075 \| 414 \| 1.7681 \| - \| - \| - \| - \|
	\| 0.4085 \| 415 \| 1.6562 \| - \| - \| - \| - \|
	\| 0.4094 \| 416 \| 1.8896 \| - \| - \| - \| - \|
	\| 0.4104 \| 417 \| 2.0671 \| - \| - \| - \| - \|
	\| 0.4114 \| 418 \| 1.6097 \| - \| - \| - \| - \|
	\| 0.4124 \| 419 \| 2.8126 \| - \| - \| - \| - \|
	\| 0.4134 \| 420 \| 1.7028 \| - \| - \| - \| - \|
	\| 0.4144 \| 421 \| 1.526 \| - \| - \| - \| - \|
	\| 0.4154 \| 422 \| 2.5029 \| - \| - \| - \| - \|
	\| 0.4163 \| 423 \| 1.7668 \| - \| - \| - \| - \|
	\| 0.4173 \| 424 \| 1.9065 \| - \| - \| - \| - \|
	\| 0.4183 \| 425 \| 1.6645 \| - \| - \| - \| - \|
	\| 0.4193 \| 426 \| 1.8075 \| - \| - \| - \| - \|
	\| 0.4203 \| 427 \| 1.872 \| - \| - \| - \| - \|
	\| 0.4213 \| 428 \| 2.0487 \| - \| - \| - \| - \|
	\| 0.4222 \| 429 \| 1.535 \| - \| - \| - \| - \|
	\| 0.4232 \| 430 \| 1.8046 \| - \| - \| - \| - \|
	\| 0.4242 \| 431 \| 2.2561 \| - \| - \| - \| - \|
	\| 0.4252 \| 432 \| 2.0306 \| - \| - \| - \| - \|
	\| 0.4262 \| 433 \| 2.1311 \| - \| - \| - \| - \|
	\| 0.4272 \| 434 \| 2.3013 \| - \| - \| - \| - \|
	\| 0.4281 \| 435 \| 1.6402 \| - \| - \| - \| - \|
	\| 0.4291 \| 436 \| 1.9572 \| - \| - \| - \| - \|
	\| 0.4301 \| 437 \| 1.6364 \| - \| - \| - \| - \|
	\| 0.4311 \| 438 \| 1.446 \| - \| - \| - \| - \|
	\| 0.4321 \| 439 \| 1.6009 \| - \| - \| - \| - \|
	\| 0.4331 \| 440 \| 1.9469 \| - \| - \| - \| - \|
	\| 0.4341 \| 441 \| 2.1951 \| - \| - \| - \| - \|
	\| 0.4350 \| 442 \| 1.675 \| - \| - \| - \| - \|
	\| 0.4360 \| 443 \| 1.4182 \| - \| - \| - \| - \|
	\| 0.4370 \| 444 \| 2.2317 \| - \| - \| - \| - \|
	\| 0.4380 \| 445 \| 2.1076 \| - \| - \| - \| - \|
	\| 0.4390 \| 446 \| 1.6691 \| - \| - \| - \| - \|
	\| 0.4400 \| 447 \| 1.6909 \| - \| - \| - \| - \|
	\| 0.4409 \| 448 \| 3.1056 \| - \| - \| - \| - \|
	\| 0.4419 \| 449 \| 1.4069 \| - \| - \| - \| - \|
	\| 0.4429 \| 450 \| 2.1639 \| - \| - \| - \| - \|
	\| 0.4439 \| 451 \| 1.5531 \| - \| - \| - \| - \|
	\| 0.4449 \| 452 \| 2.1895 \| - \| - \| - \| - \|
	\| 0.4459 \| 453 \| 1.9384 \| - \| - \| - \| - \|
	\| 0.4469 \| 454 \| 1.7761 \| - \| - \| - \| - \|
	\| 0.4478 \| 455 \| 2.8286 \| - \| - \| - \| - \|
	\| 0.4488 \| 456 \| 2.4877 \| - \| - \| - \| - \|
	\| 0.4498 \| 457 \| 1.7636 \| - \| - \| - \| - \|
	\| 0.4508 \| 458 \| 1.1849 \| - \| - \| - \| - \|
	\| 0.4518 \| 459 \| 1.8331 \| 1.9854 \| 0.3261 \| 0.4327 \| 0.6511 \|
	\| 0.4528 \| 460 \| 2.0416 \| - \| - \| - \| - \|
	\| 0.4537 \| 461 \| 2.1907 \| - \| - \| - \| - \|
	\| 0.4547 \| 462 \| 1.7478 \| - \| - \| - \| - \|
	\| 0.4557 \| 463 \| 1.9 \| - \| - \| - \| - \|
	\| 0.4567 \| 464 \| 1.6876 \| - \| - \| - \| - \|
	\| 0.4577 \| 465 \| 2.0035 \| - \| - \| - \| - \|
	\| 0.4587 \| 466 \| 1.4127 \| - \| - \| - \| - \|
	\| 0.4596 \| 467 \| 1.5593 \| - \| - \| - \| - \|
	\| 0.4606 \| 468 \| 1.7 \| - \| - \| - \| - \|
	\| 0.4616 \| 469 \| 1.5157 \| - \| - \| - \| - \|
	\| 0.4626 \| 470 \| 1.6554 \| - \| - \| - \| - \|
	\| 0.4636 \| 471 \| 1.7404 \| - \| - \| - \| - \|
	\| 0.4646 \| 472 \| 2.1432 \| - \| - \| - \| - \|
	\| 0.4656 \| 473 \| 1.7322 \| - \| - \| - \| - \|
	\| 0.4665 \| 474 \| 1.7281 \| - \| - \| - \| - \|
	\| 0.4675 \| 475 \| 1.5107 \| - \| - \| - \| - \|
	\| 0.4685 \| 476 \| 1.779 \| - \| - \| - \| - \|
	\| 0.4695 \| 477 \| 1.325 \| - \| - \| - \| - \|
	\| 0.4705 \| 478 \| 1.073 \| - \| - \| - \| - \|
	\| 0.4715 \| 479 \| 1.864 \| - \| - \| - \| - \|
	\| 0.4724 \| 480 \| 2.3645 \| - \| - \| - \| - \|
	\| 0.4734 \| 481 \| 1.181 \| - \| - \| - \| - \|
	\| 0.4744 \| 482 \| 1.4562 \| - \| - \| - \| - \|
	\| 0.4754 \| 483 \| 1.3105 \| - \| - \| - \| - \|
	\| 0.4764 \| 484 \| 2.8012 \| - \| - \| - \| - \|
	\| 0.4774 \| 485 \| 2.0114 \| - \| - \| - \| - \|
	\| 0.4783 \| 486 \| 1.6307 \| - \| - \| - \| - \|
	\| 0.4793 \| 487 \| 2.7733 \| - \| - \| - \| - \|
	\| 0.4803 \| 488 \| 1.8211 \| - \| - \| - \| - \|
	\| 0.4813 \| 489 \| 1.574 \| - \| - \| - \| - \|
	\| 0.4823 \| 490 \| 1.9713 \| - \| - \| - \| - \|
	\| 0.4833 \| 491 \| 1.2774 \| - \| - \| - \| - \|
	\| 0.4843 \| 492 \| 2.58 \| - \| - \| - \| - \|
	\| 0.4852 \| 493 \| 2.0594 \| - \| - \| - \| - \|
	\| 0.4862 \| 494 \| 1.5857 \| - \| - \| - \| - \|
	\| 0.4872 \| 495 \| 2.0028 \| - \| - \| - \| - \|
	\| 0.4882 \| 496 \| 1.8863 \| - \| - \| - \| - \|
	\| 0.4892 \| 497 \| 1.5171 \| - \| - \| - \| - \|
	\| 0.4902 \| 498 \| 1.9355 \| - \| - \| - \| - \|
	\| 0.4911 \| 499 \| 2.0675 \| - \| - \| - \| - \|
	\| 0.4921 \| 500 \| 1.6017 \| - \| - \| - \| - \|
	\| 0.4931 \| 501 \| 1.4089 \| - \| - \| - \| - \|
	\| 0.4941 \| 502 \| 1.3836 \| - \| - \| - \| - \|
	\| 0.4951 \| 503 \| 1.6033 \| - \| - \| - \| - \|
	\| 0.4961 \| 504 \| 1.0891 \| - \| - \| - \| - \|
	\| 0.4970 \| 505 \| 1.7119 \| - \| - \| - \| - \|
	\| 0.4980 \| 506 \| 1.3685 \| - \| - \| - \| - \|
	\| 0.4990 \| 507 \| 1.4252 \| - \| - \| - \| - \|
	\| 0.5 \| 508 \| 1.5538 \| - \| - \| - \| - \|
	\| 0.5010 \| 509 \| 1.7513 \| - \| - \| - \| - \|
	\| 0.5020 \| 510 \| 1.1831 \| - \| - \| - \| - \|
	\| 0.5030 \| 511 \| 1.7767 \| - \| - \| - \| - \|
	\| 0.5039 \| 512 \| 1.4324 \| - \| - \| - \| - \|
	\| 0.5049 \| 513 \| 2.1672 \| - \| - \| - \| - \|
	\| 0.5059 \| 514 \| 1.6348 \| - \| - \| - \| - \|
	\| 0.5069 \| 515 \| 1.7285 \| - \| - \| - \| - \|
	\| 0.5079 \| 516 \| 2.0186 \| - \| - \| - \| - \|
	\| 0.5089 \| 517 \| 1.382 \| - \| - \| - \| - \|
	\| 0.5098 \| 518 \| 1.4509 \| - \| - \| - \| - \|
	\| 0.5108 \| 519 \| 1.1043 \| - \| - \| - \| - \|
	\| 0.5118 \| 520 \| 1.3322 \| - \| - \| - \| - \|
	\| 0.5128 \| 521 \| 1.3267 \| - \| - \| - \| - \|
	\| 0.5138 \| 522 \| 1.3639 \| - \| - \| - \| - \|
	\| 0.5148 \| 523 \| 1.203 \| - \| - \| - \| - \|
	\| 0.5157 \| 524 \| 1.8583 \| - \| - \| - \| - \|
	\| 0.5167 \| 525 \| 2.267 \| - \| - \| - \| - \|
	\| 0.5177 \| 526 \| 1.2935 \| - \| - \| - \| - \|
	\| 0.5187 \| 527 \| 1.7431 \| - \| - \| - \| - \|
	\| 0.5197 \| 528 \| 1.8484 \| - \| - \| - \| - \|
	\| 0.5207 \| 529 \| 1.5626 \| - \| - \| - \| - \|
	\| 0.5217 \| 530 \| 2.2645 \| - \| - \| - \| - \|
	\| 0.5226 \| 531 \| 1.4313 \| - \| - \| - \| - \|
	\| 0.5236 \| 532 \| 1.8204 \| - \| - \| - \| - \|
	\| 0.5246 \| 533 \| 1.5659 \| - \| - \| - \| - \|
	\| 0.5256 \| 534 \| 1.2689 \| - \| - \| - \| - \|
	\| 0.5266 \| 535 \| 1.8193 \| - \| - \| - \| - \|
	\| 0.5276 \| 536 \| 2.2902 \| - \| - \| - \| - \|
	\| 0.5285 \| 537 \| 1.6936 \| - \| - \| - \| - \|
	\| 0.5295 \| 538 \| 1.7305 \| - \| - \| - \| - \|
	\| 0.5305 \| 539 \| 1.4449 \| - \| - \| - \| - \|
	\| 0.5315 \| 540 \| 1.5594 \| - \| - \| - \| - \|
	\| 0.5325 \| 541 \| 1.9678 \| - \| - \| - \| - \|
	\| 0.5335 \| 542 \| 2.0327 \| - \| - \| - \| - \|
	\| 0.5344 \| 543 \| 2.0456 \| - \| - \| - \| - \|
	\| 0.5354 \| 544 \| 2.0452 \| - \| - \| - \| - \|
	\| 0.5364 \| 545 \| 1.9435 \| - \| - \| - \| - \|
	\| 0.5374 \| 546 \| 1.8963 \| - \| - \| - \| - \|
	\| 0.5384 \| 547 \| 1.9536 \| - \| - \| - \| - \|
	\| 0.5394 \| 548 \| 1.0665 \| - \| - \| - \| - \|
	\| 0.5404 \| 549 \| 1.8067 \| - \| - \| - \| - \|
	\| 0.5413 \| 550 \| 1.6227 \| - \| - \| - \| - \|
	\| 0.5423 \| 551 \| 1.687 \| - \| - \| - \| - \|
	\| 0.5433 \| 552 \| 1.5937 \| - \| - \| - \| - \|
	\| 0.5443 \| 553 \| 0.9216 \| - \| - \| - \| - \|
	\| 0.5453 \| 554 \| 1.3895 \| - \| - \| - \| - \|
	\| 0.5463 \| 555 \| 1.7863 \| - \| - \| - \| - \|
	\| 0.5472 \| 556 \| 1.2574 \| - \| - \| - \| - \|
	\| 0.5482 \| 557 \| 2.108 \| - \| - \| - \| - \|
	\| 0.5492 \| 558 \| 1.2782 \| - \| - \| - \| - \|
	\| 0.5502 \| 559 \| 1.4959 \| - \| - \| - \| - \|
	\| 0.5512 \| 560 \| 1.9191 \| - \| - \| - \| - \|
	\| 0.5522 \| 561 \| 2.0049 \| - \| - \| - \| - \|
	\| 0.5531 \| 562 \| 1.2511 \| - \| - \| - \| - \|
	\| 0.5541 \| 563 \| 1.3912 \| - \| - \| - \| - \|
	\| 0.5551 \| 564 \| 1.371 \| - \| - \| - \| - \|
	\| 0.5561 \| 565 \| 1.6155 \| - \| - \| - \| - \|
	\| 0.5571 \| 566 \| 1.4625 \| - \| - \| - \| - \|
	\| 0.5581 \| 567 \| 0.86 \| - \| - \| - \| - \|
	\| 0.5591 \| 568 \| 1.5753 \| - \| - \| - \| - \|
	\| 0.5600 \| 569 \| 1.6126 \| - \| - \| - \| - \|
	\| 0.5610 \| 570 \| 1.3171 \| - \| - \| - \| - \|
	\| 0.5620 \| 571 \| 1.9378 \| - \| - \| - \| - \|
	\| 0.5630 \| 572 \| 1.2736 \| - \| - \| - \| - \|
	\| 0.5640 \| 573 \| 1.2368 \| - \| - \| - \| - \|
	\| 0.5650 \| 574 \| 1.1005 \| - \| - \| - \| - \|
	\| 0.5659 \| 575 \| 1.1765 \| - \| - \| - \| - \|
	\| 0.5669 \| 576 \| 1.3557 \| - \| - \| - \| - \|
	\| 0.5679 \| 577 \| 1.3224 \| - \| - \| - \| - \|
	\| 0.5689 \| 578 \| 1.7914 \| - \| - \| - \| - \|
	\| 0.5699 \| 579 \| 1.0633 \| - \| - \| - \| - \|
	\| 0.5709 \| 580 \| 1.3624 \| - \| - \| - \| - \|
	\| 0.5719 \| 581 \| 0.9804 \| - \| - \| - \| - \|
	\| 0.5728 \| 582 \| 1.8246 \| - \| - \| - \| - \|
	\| 0.5738 \| 583 \| 1.1806 \| - \| - \| - \| - \|
	\| 0.5748 \| 584 \| 1.6243 \| - \| - \| - \| - \|
	\| 0.5758 \| 585 \| 1.739 \| - \| - \| - \| - \|
	\| 0.5768 \| 586 \| 1.2502 \| - \| - \| - \| - \|
	\| 0.5778 \| 587 \| 1.6328 \| - \| - \| - \| - \|
	\| 0.5787 \| 588 \| 1.3618 \| - \| - \| - \| - \|
	\| 0.5797 \| 589 \| 1.1535 \| - \| - \| - \| - \|
	\| 0.5807 \| 590 \| 1.2214 \| - \| - \| - \| - \|
	\| 0.5817 \| 591 \| 1.4884 \| - \| - \| - \| - \|
	\| 0.5827 \| 592 \| 1.4029 \| - \| - \| - \| - \|
	\| 0.5837 \| 593 \| 1.0542 \| - \| - \| - \| - \|
	\| 0.5846 \| 594 \| 1.5848 \| - \| - \| - \| - \|
	\| 0.5856 \| 595 \| 1.405 \| - \| - \| - \| - \|
	\| 0.5866 \| 596 \| 1.6281 \| - \| - \| - \| - \|
	\| 0.5876 \| 597 \| 1.5228 \| - \| - \| - \| - \|
	\| 0.5886 \| 598 \| 1.8192 \| - \| - \| - \| - \|
	\| 0.5896 \| 599 \| 1.2403 \| - \| - \| - \| - \|
	\| 0.5906 \| 600 \| 1.9368 \| - \| - \| - \| - \|
	\| 0.5915 \| 601 \| 1.6623 \| - \| - \| - \| - \|
	\| 0.5925 \| 602 \| 1.495 \| - \| - \| - \| - \|
	\| 0.5935 \| 603 \| 1.7079 \| - \| - \| - \| - \|
	\| 0.5945 \| 604 \| 1.0651 \| - \| - \| - \| - \|
	\| 0.5955 \| 605 \| 1.2121 \| - \| - \| - \| - \|
	\| 0.5965 \| 606 \| 1.5385 \| - \| - \| - \| - \|
	\| 0.5974 \| 607 \| 1.1015 \| - \| - \| - \| - \|
	\| 0.5984 \| 608 \| 1.7596 \| - \| - \| - \| - \|
	\| 0.5994 \| 609 \| 1.5597 \| - \| - \| - \| - \|
	\| 0.6004 \| 610 \| 1.3254 \| - \| - \| - \| - \|
	\| 0.6014 \| 611 \| 1.3595 \| - \| - \| - \| - \|
	\| 0.6024 \| 612 \| 1.0326 \| 1.6315 \| 0.3597 \| 0.4478 \| 0.6598 \|
	\| 0.6033 \| 613 \| 1.4822 \| - \| - \| - \| - \|
	\| 0.6043 \| 614 \| 0.9997 \| - \| - \| - \| - \|
	\| 0.6053 \| 615 \| 1.4946 \| - \| - \| - \| - \|
	\| 0.6063 \| 616 \| 1.3491 \| - \| - \| - \| - \|
	\| 0.6073 \| 617 \| 1.2118 \| - \| - \| - \| - \|
	\| 0.6083 \| 618 \| 1.6948 \| - \| - \| - \| - \|
	\| 0.6093 \| 619 \| 2.0457 \| - \| - \| - \| - \|
	\| 0.6102 \| 620 \| 1.2724 \| - \| - \| - \| - \|
	\| 0.6112 \| 621 \| 1.2677 \| - \| - \| - \| - \|
	\| 0.6122 \| 622 \| 1.6639 \| - \| - \| - \| - \|
	\| 0.6132 \| 623 \| 1.139 \| - \| - \| - \| - \|
	\| 0.6142 \| 624 \| 1.2424 \| - \| - \| - \| - \|
	\| 0.6152 \| 625 \| 0.8214 \| - \| - \| - \| - \|
	\| 0.6161 \| 626 \| 1.4398 \| - \| - \| - \| - \|
	\| 0.6171 \| 627 \| 1.6008 \| - \| - \| - \| - \|
	\| 0.6181 \| 628 \| 1.3225 \| - \| - \| - \| - \|
	\| 0.6191 \| 629 \| 1.4452 \| - \| - \| - \| - \|
	\| 0.6201 \| 630 \| 1.7723 \| - \| - \| - \| - \|
	\| 0.6211 \| 631 \| 1.035 \| - \| - \| - \| - \|
	\| 0.6220 \| 632 \| 1.6132 \| - \| - \| - \| - \|
	\| 0.6230 \| 633 \| 1.4167 \| - \| - \| - \| - \|
	\| 0.6240 \| 634 \| 1.1764 \| - \| - \| - \| - \|
	\| 0.625 \| 635 \| 1.0702 \| - \| - \| - \| - \|
	\| 0.6260 \| 636 \| 1.3096 \| - \| - \| - \| - \|
	\| 0.6270 \| 637 \| 1.4174 \| - \| - \| - \| - \|
	\| 0.6280 \| 638 \| 1.8742 \| - \| - \| - \| - \|
	\| 0.6289 \| 639 \| 1.3372 \| - \| - \| - \| - \|
	\| 0.6299 \| 640 \| 1.4453 \| - \| - \| - \| - \|
	\| 0.6309 \| 641 \| 0.6792 \| - \| - \| - \| - \|
	\| 0.6319 \| 642 \| 1.2363 \| - \| - \| - \| - \|
	\| 0.6329 \| 643 \| 1.2037 \| - \| - \| - \| - \|
	\| 0.6339 \| 644 \| 1.105 \| - \| - \| - \| - \|
	\| 0.6348 \| 645 \| 1.8319 \| - \| - \| - \| - \|
	\| 0.6358 \| 646 \| 1.4261 \| - \| - \| - \| - \|
	\| 0.6368 \| 647 \| 1.0431 \| - \| - \| - \| - \|
	\| 0.6378 \| 648 \| 1.2132 \| - \| - \| - \| - \|
	\| 0.6388 \| 649 \| 1.1464 \| - \| - \| - \| - \|
	\| 0.6398 \| 650 \| 1.3076 \| - \| - \| - \| - \|
	\| 0.6407 \| 651 \| 0.9162 \| - \| - \| - \| - \|
	\| 0.6417 \| 652 \| 1.6739 \| - \| - \| - \| - \|
	\| 0.6427 \| 653 \| 1.3627 \| - \| - \| - \| - \|
	\| 0.6437 \| 654 \| 1.084 \| - \| - \| - \| - \|
	\| 0.6447 \| 655 \| 0.8349 \| - \| - \| - \| - \|
	\| 0.6457 \| 656 \| 1.5575 \| - \| - \| - \| - \|
	\| 0.6467 \| 657 \| 0.7503 \| - \| - \| - \| - \|
	\| 0.6476 \| 658 \| 1.5593 \| - \| - \| - \| - \|
	\| 0.6486 \| 659 \| 1.1788 \| - \| - \| - \| - \|
	\| 0.6496 \| 660 \| 1.5119 \| - \| - \| - \| - \|
	\| 0.6506 \| 661 \| 1.1793 \| - \| - \| - \| - \|
	\| 0.6516 \| 662 \| 1.1577 \| - \| - \| - \| - \|
	\| 0.6526 \| 663 \| 1.4391 \| - \| - \| - \| - \|
	\| 0.6535 \| 664 \| 1.2291 \| - \| - \| - \| - \|
	\| 0.6545 \| 665 \| 1.3118 \| - \| - \| - \| - \|
	\| 0.6555 \| 666 \| 1.1358 \| - \| - \| - \| - \|
	\| 0.6565 \| 667 \| 1.814 \| - \| - \| - \| - \|
	\| 0.6575 \| 668 \| 1.038 \| - \| - \| - \| - \|
	\| 0.6585 \| 669 \| 1.2344 \| - \| - \| - \| - \|
	\| 0.6594 \| 670 \| 1.1064 \| - \| - \| - \| - \|
	\| 0.6604 \| 671 \| 1.8745 \| - \| - \| - \| - \|
	\| 0.6614 \| 672 \| 1.8775 \| - \| - \| - \| - \|
	\| 0.6624 \| 673 \| 1.2582 \| - \| - \| - \| - \|
	\| 0.6634 \| 674 \| 1.3454 \| - \| - \| - \| - \|
	\| 0.6644 \| 675 \| 1.2374 \| - \| - \| - \| - \|
	\| 0.6654 \| 676 \| 0.7227 \| - \| - \| - \| - \|
	\| 0.6663 \| 677 \| 1.2036 \| - \| - \| - \| - \|
	\| 0.6673 \| 678 \| 1.5248 \| - \| - \| - \| - \|
	\| 0.6683 \| 679 \| 1.0584 \| - \| - \| - \| - \|
	\| 0.6693 \| 680 \| 0.7455 \| - \| - \| - \| - \|
	\| 0.6703 \| 681 \| 1.0616 \| - \| - \| - \| - \|
	\| 0.6713 \| 682 \| 1.1377 \| - \| - \| - \| - \|
	\| 0.6722 \| 683 \| 1.7022 \| - \| - \| - \| - \|
	\| 0.6732 \| 684 \| 1.4008 \| - \| - \| - \| - \|
	\| 0.6742 \| 685 \| 1.4894 \| - \| - \| - \| - \|
	\| 0.6752 \| 686 \| 1.5464 \| - \| - \| - \| - \|
	\| 0.6762 \| 687 \| 1.5018 \| - \| - \| - \| - \|
	\| 0.6772 \| 688 \| 1.5803 \| - \| - \| - \| - \|
	\| 0.6781 \| 689 \| 1.5654 \| - \| - \| - \| - \|
	\| 0.6791 \| 690 \| 1.2537 \| - \| - \| - \| - \|
	\| 0.6801 \| 691 \| 1.4901 \| - \| - \| - \| - \|
	\| 0.6811 \| 692 \| 0.9597 \| - \| - \| - \| - \|
	\| 0.6821 \| 693 \| 1.5515 \| - \| - \| - \| - \|
	\| 0.6831 \| 694 \| 1.1314 \| - \| - \| - \| - \|
	\| 0.6841 \| 695 \| 1.2442 \| - \| - \| - \| - \|
	\| 0.6850 \| 696 \| 1.5542 \| - \| - \| - \| - \|
	\| 0.6860 \| 697 \| 0.7617 \| - \| - \| - \| - \|
	\| 0.6870 \| 698 \| 1.4626 \| - \| - \| - \| - \|
	\| 0.6880 \| 699 \| 0.6546 \| - \| - \| - \| - \|
	\| 0.6890 \| 700 \| 1.1726 \| - \| - \| - \| - \|
	\| 0.6900 \| 701 \| 0.7522 \| - \| - \| - \| - \|
	\| 0.6909 \| 702 \| 1.6203 \| - \| - \| - \| - \|
	\| 0.6919 \| 703 \| 1.0822 \| - \| - \| - \| - \|
	\| 0.6929 \| 704 \| 0.9652 \| - \| - \| - \| - \|
	\| 0.6939 \| 705 \| 0.9884 \| - \| - \| - \| - \|
	\| 0.6949 \| 706 \| 1.3876 \| - \| - \| - \| - \|
	\| 0.6959 \| 707 \| 1.1726 \| - \| - \| - \| - \|
	\| 0.6969 \| 708 \| 1.1426 \| - \| - \| - \| - \|
	\| 0.6978 \| 709 \| 1.2303 \| - \| - \| - \| - \|
	\| 0.6988 \| 710 \| 1.3438 \| - \| - \| - \| - \|
	\| 0.6998 \| 711 \| 1.3823 \| - \| - \| - \| - \|
	\| 0.7008 \| 712 \| 1.5166 \| - \| - \| - \| - \|
	\| 0.7018 \| 713 \| 1.4148 \| - \| - \| - \| - \|
	\| 0.7028 \| 714 \| 0.9564 \| - \| - \| - \| - \|
	\| 0.7037 \| 715 \| 1.1031 \| - \| - \| - \| - \|
	\| 0.7047 \| 716 \| 1.6628 \| - \| - \| - \| - \|
	\| 0.7057 \| 717 \| 1.3693 \| - \| - \| - \| - \|
	\| 0.7067 \| 718 \| 1.0247 \| - \| - \| - \| - \|
	\| 0.7077 \| 719 \| 0.7778 \| - \| - \| - \| - \|
	\| 0.7087 \| 720 \| 1.3158 \| - \| - \| - \| - \|
	\| 0.7096 \| 721 \| 1.5329 \| - \| - \| - \| - \|
	\| 0.7106 \| 722 \| 1.2845 \| - \| - \| - \| - \|
	\| 0.7116 \| 723 \| 0.7895 \| - \| - \| - \| - \|
	\| 0.7126 \| 724 \| 0.9098 \| - \| - \| - \| - \|
	\| 0.7136 \| 725 \| 0.7648 \| - \| - \| - \| - \|
	\| 0.7146 \| 726 \| 1.0951 \| - \| - \| - \| - \|
	\| 0.7156 \| 727 \| 1.1351 \| - \| - \| - \| - \|
	\| 0.7165 \| 728 \| 1.0514 \| - \| - \| - \| - \|
	\| 0.7175 \| 729 \| 1.2328 \| - \| - \| - \| - \|
	\| 0.7185 \| 730 \| 1.0664 \| - \| - \| - \| - \|
	\| 0.7195 \| 731 \| 1.1058 \| - \| - \| - \| - \|
	\| 0.7205 \| 732 \| 0.6698 \| - \| - \| - \| - \|
	\| 0.7215 \| 733 \| 1.1438 \| - \| - \| - \| - \|
	\| 0.7224 \| 734 \| 1.0473 \| - \| - \| - \| - \|
	\| 0.7234 \| 735 \| 1.5307 \| - \| - \| - \| - \|
	\| 0.7244 \| 736 \| 1.2537 \| - \| - \| - \| - \|
	\| 0.7254 \| 737 \| 0.7006 \| - \| - \| - \| - \|
	\| 0.7264 \| 738 \| 0.8487 \| - \| - \| - \| - \|
	\| 0.7274 \| 739 \| 1.193 \| - \| - \| - \| - \|
	\| 0.7283 \| 740 \| 0.9711 \| - \| - \| - \| - \|
	\| 0.7293 \| 741 \| 0.5139 \| - \| - \| - \| - \|
	\| 0.7303 \| 742 \| 1.1185 \| - \| - \| - \| - \|
	\| 0.7313 \| 743 \| 0.9982 \| - \| - \| - \| - \|
	\| 0.7323 \| 744 \| 0.7987 \| - \| - \| - \| - \|
	\| 0.7333 \| 745 \| 1.161 \| - \| - \| - \| - \|
	\| 0.7343 \| 746 \| 0.9832 \| - \| - \| - \| - \|
	\| 0.7352 \| 747 \| 1.6041 \| - \| - \| - \| - \|
	\| 0.7362 \| 748 \| 1.1084 \| - \| - \| - \| - \|
	\| 0.7372 \| 749 \| 1.1264 \| - \| - \| - \| - \|
	\| 0.7382 \| 750 \| 0.6759 \| - \| - \| - \| - \|
	\| 0.7392 \| 751 \| 1.5241 \| - \| - \| - \| - \|
	\| 0.7402 \| 752 \| 1.6181 \| - \| - \| - \| - \|
	\| 0.7411 \| 753 \| 1.1862 \| - \| - \| - \| - \|
	\| 0.7421 \| 754 \| 1.0147 \| - \| - \| - \| - \|
	\| 0.7431 \| 755 \| 1.0435 \| - \| - \| - \| - \|
	\| 0.7441 \| 756 \| 0.9175 \| - \| - \| - \| - \|
	\| 0.7451 \| 757 \| 1.5608 \| - \| - \| - \| - \|
	\| 0.7461 \| 758 \| 1.7027 \| - \| - \| - \| - \|
	\| 0.7470 \| 759 \| 0.9282 \| - \| - \| - \| - \|
	\| 0.7480 \| 760 \| 0.8072 \| - \| - \| - \| - \|
	\| 0.7490 \| 761 \| 1.13 \| - \| - \| - \| - \|
	\| 0.75 \| 762 \| 1.153 \| - \| - \| - \| - \|
	\| 0.7510 \| 763 \| 1.7723 \| - \| - \| - \| - \|
	\| 0.7520 \| 764 \| 0.9833 \| - \| - \| - \| - \|
	\| 0.7530 \| 765 \| 1.1766 \| 1.3821 \| 0.4583 \| 0.4713 \| 0.6741 \|
	\| 0.7539 \| 766 \| 1.2351 \| - \| - \| - \| - \|
	\| 0.7549 \| 767 \| 0.9521 \| - \| - \| - \| - \|
	\| 0.7559 \| 768 \| 1.1981 \| - \| - \| - \| - \|
	\| 0.7569 \| 769 \| 0.8031 \| - \| - \| - \| - \|
	\| 0.7579 \| 770 \| 1.2502 \| - \| - \| - \| - \|
	\| 0.7589 \| 771 \| 0.9313 \| - \| - \| - \| - \|
	\| 0.7598 \| 772 \| 0.8738 \| - \| - \| - \| - \|
	\| 0.7608 \| 773 \| 1.0316 \| - \| - \| - \| - \|
	\| 0.7618 \| 774 \| 1.0679 \| - \| - \| - \| - \|
	\| 0.7628 \| 775 \| 1.0308 \| - \| - \| - \| - \|
	\| 0.7638 \| 776 \| 1.0663 \| - \| - \| - \| - \|
	\| 0.7648 \| 777 \| 1.3565 \| - \| - \| - \| - \|
	\| 0.7657 \| 778 \| 0.9103 \| - \| - \| - \| - \|
	\| 0.7667 \| 779 \| 1.477 \| - \| - \| - \| - \|
	\| 0.7677 \| 780 \| 1.2526 \| - \| - \| - \| - \|
	\| 0.7687 \| 781 \| 0.8647 \| - \| - \| - \| - \|
	\| 0.7697 \| 782 \| 0.8498 \| - \| - \| - \| - \|
	\| 0.7707 \| 783 \| 1.5257 \| - \| - \| - \| - \|
	\| 0.7717 \| 784 \| 0.5493 \| - \| - \| - \| - \|
	\| 0.7726 \| 785 \| 1.2321 \| - \| - \| - \| - \|
	\| 0.7736 \| 786 \| 1.4306 \| - \| - \| - \| - \|
	\| 0.7746 \| 787 \| 1.0428 \| - \| - \| - \| - \|
	\| 0.7756 \| 788 \| 1.3553 \| - \| - \| - \| - \|
	\| 0.7766 \| 789 \| 1.1102 \| - \| - \| - \| - \|
	\| 0.7776 \| 790 \| 0.9225 \| - \| - \| - \| - \|
	\| 0.7785 \| 791 \| 1.2585 \| - \| - \| - \| - \|
	\| 0.7795 \| 792 \| 1.1335 \| - \| - \| - \| - \|
	\| 0.7805 \| 793 \| 1.2781 \| - \| - \| - \| - \|
	\| 0.7815 \| 794 \| 1.0265 \| - \| - \| - \| - \|
	\| 0.7825 \| 795 \| 1.1846 \| - \| - \| - \| - \|
	\| 0.7835 \| 796 \| 1.2123 \| - \| - \| - \| - \|
	\| 0.7844 \| 797 \| 1.0861 \| - \| - \| - \| - \|
	\| 0.7854 \| 798 \| 1.1929 \| - \| - \| - \| - \|
	\| 0.7864 \| 799 \| 0.8202 \| - \| - \| - \| - \|
	\| 0.7874 \| 800 \| 1.0745 \| - \| - \| - \| - \|
	\| 0.7884 \| 801 \| 0.9162 \| - \| - \| - \| - \|
	\| 0.7894 \| 802 \| 1.2077 \| - \| - \| - \| - \|
	\| 0.7904 \| 803 \| 1.432 \| - \| - \| - \| - \|
	\| 0.7913 \| 804 \| 0.9384 \| - \| - \| - \| - \|
	\| 0.7923 \| 805 \| 1.4686 \| - \| - \| - \| - \|
	\| 0.7933 \| 806 \| 0.8565 \| - \| - \| - \| - \|
	\| 0.7943 \| 807 \| 1.1208 \| - \| - \| - \| - \|
	\| 0.7953 \| 808 \| 1.006 \| - \| - \| - \| - \|
	\| 0.7963 \| 809 \| 0.71 \| - \| - \| - \| - \|
	\| 0.7972 \| 810 \| 1.0219 \| - \| - \| - \| - \|
	\| 0.7982 \| 811 \| 1.0836 \| - \| - \| - \| - \|
	\| 0.7992 \| 812 \| 1.1318 \| - \| - \| - \| - \|
	\| 0.8002 \| 813 \| 1.5411 \| - \| - \| - \| - \|
	\| 0.8012 \| 814 \| 1.0063 \| - \| - \| - \| - \|
	\| 0.8022 \| 815 \| 1.277 \| - \| - \| - \| - \|
	\| 0.8031 \| 816 \| 1.0547 \| - \| - \| - \| - \|
	\| 0.8041 \| 817 \| 0.7084 \| - \| - \| - \| - \|
	\| 0.8051 \| 818 \| 1.2581 \| - \| - \| - \| - \|
	\| 0.8061 \| 819 \| 0.7857 \| - \| - \| - \| - \|
	\| 0.8071 \| 820 \| 0.7219 \| - \| - \| - \| - \|
	\| 0.8081 \| 821 \| 1.2658 \| - \| - \| - \| - \|
	\| 0.8091 \| 822 \| 1.4563 \| - \| - \| - \| - \|
	\| 0.8100 \| 823 \| 0.9098 \| - \| - \| - \| - \|
	\| 0.8110 \| 824 \| 0.8606 \| - \| - \| - \| - \|
	\| 0.8120 \| 825 \| 1.1116 \| - \| - \| - \| - \|
	\| 0.8130 \| 826 \| 0.952 \| - \| - \| - \| - \|
	\| 0.8140 \| 827 \| 0.6831 \| - \| - \| - \| - \|
	\| 0.8150 \| 828 \| 0.8267 \| - \| - \| - \| - \|
	\| 0.8159 \| 829 \| 0.934 \| - \| - \| - \| - \|
	\| 0.8169 \| 830 \| 1.7555 \| - \| - \| - \| - \|
	\| 0.8179 \| 831 \| 1.0109 \| - \| - \| - \| - \|
	\| 0.8189 \| 832 \| 1.4509 \| - \| - \| - \| - \|
	\| 0.8199 \| 833 \| 0.6548 \| - \| - \| - \| - \|
	\| 0.8209 \| 834 \| 0.7777 \| - \| - \| - \| - \|
	\| 0.8219 \| 835 \| 0.8929 \| - \| - \| - \| - \|
	\| 0.8228 \| 836 \| 1.282 \| - \| - \| - \| - \|
	\| 0.8238 \| 837 \| 0.6726 \| - \| - \| - \| - \|
	\| 0.8248 \| 838 \| 1.0643 \| - \| - \| - \| - \|
	\| 0.8258 \| 839 \| 1.01 \| - \| - \| - \| - \|
	\| 0.8268 \| 840 \| 1.0631 \| - \| - \| - \| - \|
	\| 0.8278 \| 841 \| 1.092 \| - \| - \| - \| - \|
	\| 0.8287 \| 842 \| 1.0219 \| - \| - \| - \| - \|
	\| 0.8297 \| 843 \| 1.0437 \| - \| - \| - \| - \|
	\| 0.8307 \| 844 \| 0.8789 \| - \| - \| - \| - \|
	\| 0.8317 \| 845 \| 1.5409 \| - \| - \| - \| - \|
	\| 0.8327 \| 846 \| 1.365 \| - \| - \| - \| - \|
	\| 0.8337 \| 847 \| 1.0567 \| - \| - \| - \| - \|
	\| 0.8346 \| 848 \| 0.59 \| - \| - \| - \| - \|
	\| 0.8356 \| 849 \| 0.839 \| - \| - \| - \| - \|
	\| 0.8366 \| 850 \| 0.727 \| - \| - \| - \| - \|
	\| 0.8376 \| 851 \| 0.8681 \| - \| - \| - \| - \|
	\| 0.8386 \| 852 \| 1.5186 \| - \| - \| - \| - \|
	\| 0.8396 \| 853 \| 0.8923 \| - \| - \| - \| - \|
	\| 0.8406 \| 854 \| 0.8248 \| - \| - \| - \| - \|
	\| 0.8415 \| 855 \| 0.9884 \| - \| - \| - \| - \|
	\| 0.8425 \| 856 \| 1.257 \| - \| - \| - \| - \|
	\| 0.8435 \| 857 \| 0.8841 \| - \| - \| - \| - \|
	\| 0.8445 \| 858 \| 1.232 \| - \| - \| - \| - \|
	\| 0.8455 \| 859 \| 1.2214 \| - \| - \| - \| - \|
	\| 0.8465 \| 860 \| 1.3332 \| - \| - \| - \| - \|
	\| 0.8474 \| 861 \| 0.8272 \| - \| - \| - \| - \|
	\| 0.8484 \| 862 \| 1.0901 \| - \| - \| - \| - \|
	\| 0.8494 \| 863 \| 1.2892 \| - \| - \| - \| - \|
	\| 0.8504 \| 864 \| 0.8817 \| - \| - \| - \| - \|
	\| 0.8514 \| 865 \| 0.944 \| - \| - \| - \| - \|
	\| 0.8524 \| 866 \| 0.6212 \| - \| - \| - \| - \|
	\| 0.8533 \| 867 \| 1.4346 \| - \| - \| - \| - \|
	\| 0.8543 \| 868 \| 0.7204 \| - \| - \| - \| - \|
	\| 0.8553 \| 869 \| 1.4009 \| - \| - \| - \| - \|
	\| 0.8563 \| 870 \| 1.1522 \| - \| - \| - \| - \|
	\| 0.8573 \| 871 \| 0.9558 \| - \| - \| - \| - \|
	\| 0.8583 \| 872 \| 0.8871 \| - \| - \| - \| - \|
	\| 0.8593 \| 873 \| 1.0025 \| - \| - \| - \| - \|
	\| 0.8602 \| 874 \| 0.8617 \| - \| - \| - \| - \|
	\| 0.8612 \| 875 \| 1.1982 \| - \| - \| - \| - \|
	\| 0.8622 \| 876 \| 1.2484 \| - \| - \| - \| - \|
	\| 0.8632 \| 877 \| 1.0181 \| - \| - \| - \| - \|
	\| 0.8642 \| 878 \| 0.8535 \| - \| - \| - \| - \|
	\| 0.8652 \| 879 \| 1.144 \| - \| - \| - \| - \|
	\| 0.8661 \| 880 \| 1.1927 \| - \| - \| - \| - \|
	\| 0.8671 \| 881 \| 0.7245 \| - \| - \| - \| - \|
	\| 0.8681 \| 882 \| 1.0118 \| - \| - \| - \| - \|
	\| 0.8691 \| 883 \| 1.4399 \| - \| - \| - \| - \|
	\| 0.8701 \| 884 \| 1.0666 \| - \| - \| - \| - \|
	\| 0.8711 \| 885 \| 1.1947 \| - \| - \| - \| - \|
	\| 0.8720 \| 886 \| 0.8908 \| - \| - \| - \| - \|
	\| 0.8730 \| 887 \| 1.394 \| - \| - \| - \| - \|
	\| 0.8740 \| 888 \| 0.802 \| - \| - \| - \| - \|
	\| 0.875 \| 889 \| 0.9879 \| - \| - \| - \| - \|
	\| 0.8760 \| 890 \| 0.9895 \| - \| - \| - \| - \|
	\| 0.8770 \| 891 \| 1.0927 \| - \| - \| - \| - \|
	\| 0.8780 \| 892 \| 1.4376 \| - \| - \| - \| - \|
	\| 0.8789 \| 893 \| 1.1509 \| - \| - \| - \| - \|
	\| 0.8799 \| 894 \| 0.7944 \| - \| - \| - \| - \|
	\| 0.8809 \| 895 \| 1.4574 \| - \| - \| - \| - \|
	\| 0.8819 \| 896 \| 1.2278 \| - \| - \| - \| - \|
	\| 0.8829 \| 897 \| 1.1974 \| - \| - \| - \| - \|
	\| 0.8839 \| 898 \| 1.0711 \| - \| - \| - \| - \|
	\| 0.8848 \| 899 \| 1.3942 \| - \| - \| - \| - \|
	\| 0.8858 \| 900 \| 1.1718 \| - \| - \| - \| - \|
	\| 0.8868 \| 901 \| 1.1274 \| - \| - \| - \| - \|
	\| 0.8878 \| 902 \| 0.9954 \| - \| - \| - \| - \|
	\| 0.8888 \| 903 \| 1.0155 \| - \| - \| - \| - \|
	\| 0.8898 \| 904 \| 1.4032 \| - \| - \| - \| - \|
	\| 0.8907 \| 905 \| 1.4135 \| - \| - \| - \| - \|
	\| 0.8917 \| 906 \| 1.1893 \| - \| - \| - \| - \|
	\| 0.8927 \| 907 \| 0.7243 \| - \| - \| - \| - \|
	\| 0.8937 \| 908 \| 1.1674 \| - \| - \| - \| - \|
	\| 0.8947 \| 909 \| 1.3777 \| - \| - \| - \| - \|
	\| 0.8957 \| 910 \| 1.0281 \| - \| - \| - \| - \|
	\| 0.8967 \| 911 \| 1.2869 \| - \| - \| - \| - \|
	\| 0.8976 \| 912 \| 1.0125 \| - \| - \| - \| - \|
	\| 0.8986 \| 913 \| 1.0047 \| - \| - \| - \| - \|
	\| 0.8996 \| 914 \| 0.8839 \| - \| - \| - \| - \|
	\| 0.9006 \| 915 \| 1.4279 \| - \| - \| - \| - \|
	\| 0.9016 \| 916 \| 1.053 \| - \| - \| - \| - \|
	\| 0.9026 \| 917 \| 0.7841 \| - \| - \| - \| - \|
	\| 0.9035 \| 918 \| 0.7738 \| 1.2464 \| 0.5249 \| 0.4860 \| 0.6758 \|
	\| 0.9045 \| 919 \| 0.8998 \| - \| - \| - \| - \|
	\| 0.9055 \| 920 \| 1.7733 \| - \| - \| - \| - \|
	\| 0.9065 \| 921 \| 0.8032 \| - \| - \| - \| - \|
	\| 0.9075 \| 922 \| 0.6311 \| - \| - \| - \| - \|
	\| 0.9085 \| 923 \| 0.9534 \| - \| - \| - \| - \|
	\| 0.9094 \| 924 \| 1.2538 \| - \| - \| - \| - \|
	\| 0.9104 \| 925 \| 0.5411 \| - \| - \| - \| - \|
	\| 0.9114 \| 926 \| 0.797 \| - \| - \| - \| - \|
	\| 0.9124 \| 927 \| 1.2115 \| - \| - \| - \| - \|
	\| 0.9134 \| 928 \| 0.8412 \| - \| - \| - \| - \|
	\| 0.9144 \| 929 \| 0.7325 \| - \| - \| - \| - \|
	\| 0.9154 \| 930 \| 1.6631 \| - \| - \| - \| - \|
	\| 0.9163 \| 931 \| 0.8573 \| - \| - \| - \| - \|
	\| 0.9173 \| 932 \| 0.8194 \| - \| - \| - \| - \|
	\| 0.9183 \| 933 \| 1.1627 \| - \| - \| - \| - \|
	\| 0.9193 \| 934 \| 1.3729 \| - \| - \| - \| - \|
	\| 0.9203 \| 935 \| 1.0679 \| - \| - \| - \| - \|
	\| 0.9213 \| 936 \| 1.2123 \| - \| - \| - \| - \|
	\| 0.9222 \| 937 \| 0.9217 \| - \| - \| - \| - \|
	\| 0.9232 \| 938 \| 0.6959 \| - \| - \| - \| - \|
	\| 0.9242 \| 939 \| 0.9306 \| - \| - \| - \| - \|
	\| 0.9252 \| 940 \| 1.2815 \| - \| - \| - \| - \|
	\| 0.9262 \| 941 \| 1.1188 \| - \| - \| - \| - \|
	\| 0.9272 \| 942 \| 0.8454 \| - \| - \| - \| - \|
	\| 0.9281 \| 943 \| 0.7369 \| - \| - \| - \| - \|
	\| 0.9291 \| 944 \| 1.2244 \| - \| - \| - \| - \|
	\| 0.9301 \| 945 \| 1.1402 \| - \| - \| - \| - \|
	\| 0.9311 \| 946 \| 0.4213 \| - \| - \| - \| - \|
	\| 0.9321 \| 947 \| 1.0047 \| - \| - \| - \| - \|
	\| 0.9331 \| 948 \| 1.1353 \| - \| - \| - \| - \|
	\| 0.9341 \| 949 \| 1.2862 \| - \| - \| - \| - \|
	\| 0.9350 \| 950 \| 0.6358 \| - \| - \| - \| - \|
	\| 0.9360 \| 951 \| 0.676 \| - \| - \| - \| - \|
	\| 0.9370 \| 952 \| 1.1628 \| - \| - \| - \| - \|
	\| 0.9380 \| 953 \| 0.861 \| - \| - \| - \| - \|
	\| 0.9390 \| 954 \| 0.7121 \| - \| - \| - \| - \|
	\| 0.9400 \| 955 \| 0.6625 \| - \| - \| - \| - \|
	\| 0.9409 \| 956 \| 0.9343 \| - \| - \| - \| - \|
	\| 0.9419 \| 957 \| 0.8103 \| - \| - \| - \| - \|
	\| 0.9429 \| 958 \| 1.047 \| - \| - \| - \| - \|
	\| 0.9439 \| 959 \| 0.6455 \| - \| - \| - \| - \|
	\| 0.9449 \| 960 \| 1.0978 \| - \| - \| - \| - \|
	\| 0.9459 \| 961 \| 0.9522 \| - \| - \| - \| - \|
	\| 0.9469 \| 962 \| 0.9151 \| - \| - \| - \| - \|
	\| 0.9478 \| 963 \| 1.2515 \| - \| - \| - \| - \|
	\| 0.9488 \| 964 \| 1.0809 \| - \| - \| - \| - \|
	\| 0.9498 \| 965 \| 0.6971 \| - \| - \| - \| - \|
	\| 0.9508 \| 966 \| 1.4086 \| - \| - \| - \| - \|
	\| 0.9518 \| 967 \| 1.3102 \| - \| - \| - \| - \|
	\| 0.9528 \| 968 \| 1.5008 \| - \| - \| - \| - \|
	\| 0.9537 \| 969 \| 0.9449 \| - \| - \| - \| - \|
	\| 0.9547 \| 970 \| 1.0705 \| - \| - \| - \| - \|
	\| 0.9557 \| 971 \| 0.7788 \| - \| - \| - \| - \|
	\| 0.9567 \| 972 \| 1.0007 \| - \| - \| - \| - \|
	\| 0.9577 \| 973 \| 0.9345 \| - \| - \| - \| - \|
	\| 0.9587 \| 974 \| 1.4525 \| - \| - \| - \| - \|
	\| 0.9596 \| 975 \| 0.5892 \| - \| - \| - \| - \|
	\| 0.9606 \| 976 \| 0.927 \| - \| - \| - \| - \|
	\| 0.9616 \| 977 \| 0.7473 \| - \| - \| - \| - \|
	\| 0.9626 \| 978 \| 0.8173 \| - \| - \| - \| - \|
	\| 0.9636 \| 979 \| 0.6888 \| - \| - \| - \| - \|
	\| 0.9646 \| 980 \| 0.8059 \| - \| - \| - \| - \|
	\| 0.9656 \| 981 \| 0.9087 \| - \| - \| - \| - \|
	\| 0.9665 \| 982 \| 1.5197 \| - \| - \| - \| - \|
	\| 0.9675 \| 983 \| 0.6625 \| - \| - \| - \| - \|
	\| 0.9685 \| 984 \| 1.0372 \| - \| - \| - \| - \|
	\| 0.9695 \| 985 \| 0.91 \| - \| - \| - \| - \|
	\| 0.9705 \| 986 \| 0.7797 \| - \| - \| - \| - \|
	\| 0.9715 \| 987 \| 0.838 \| - \| - \| - \| - \|
	\| 0.9724 \| 988 \| 0.9568 \| - \| - \| - \| - \|
	\| 0.9734 \| 989 \| 1.4704 \| - \| - \| - \| - \|
	\| 0.9744 \| 990 \| 0.8209 \| - \| - \| - \| - \|
	\| 0.9754 \| 991 \| 0.86 \| - \| - \| - \| - \|
	\| 0.9764 \| 992 \| 0.7749 \| - \| - \| - \| - \|
	\| 0.9774 \| 993 \| 0.7225 \| - \| - \| - \| - \|
	\| 0.9783 \| 994 \| 0.8488 \| - \| - \| - \| - \|
	\| 0.9793 \| 995 \| 1.4838 \| - \| - \| - \| - \|
	\| 0.9803 \| 996 \| 1.3282 \| - \| - \| - \| - \|
	\| 0.9813 \| 997 \| 0.9905 \| - \| - \| - \| - \|
	\| 0.9823 \| 998 \| 1.7902 \| - \| - \| - \| - \|
	\| 0.9833 \| 999 \| 0.7411 \| - \| - \| - \| - \|
	\| 0.9843 \| 1000 \| 1.2985 \| - \| - \| - \| - \|
	\| 0.9852 \| 1001 \| 0.9858 \| - \| - \| - \| - \|
	\| 0.9862 \| 1002 \| 0.8951 \| - \| - \| - \| - \|
	\| 0.9872 \| 1003 \| 0.5526 \| - \| - \| - \| - \|
	\| 0.9882 \| 1004 \| 1.466 \| - \| - \| - \| - \|
	\| 0.9892 \| 1005 \| 0.5767 \| - \| - \| - \| - \|
	\| 0.9902 \| 1006 \| 0.8946 \| - \| - \| - \| - \|
	\| 0.9911 \| 1007 \| 1.0825 \| - \| - \| - \| - \|
	\| 0.9921 \| 1008 \| 1.0152 \| - \| - \| - \| - \|
	\| 0.9931 \| 1009 \| 0.7586 \| - \| - \| - \| - \|
	\| 0.9941 \| 1010 \| 1.3353 \| - \| - \| - \| - \|
	\| 0.9951 \| 1011 \| 0.9555 \| - \| - \| - \| - \|
	\| 0.9961 \| 1012 \| 1.0585 \| - \| - \| - \| - \|
	\| 0.9970 \| 1013 \| 0.6774 \| - \| - \| - \| - \|
	\| 0.9980 \| 1014 \| 1.5326 \| - \| - \| - \| - \|
	\| 0.9990 \| 1015 \| 1.0034 \| - \| - \| - \| - \|
	\| 1.0 \| 1016 \| 0.8925 \| - \| - \| - \| - \|
	\| 1.0010 \| 1017 \| 1.1286 \| - \| - \| - \| - \|
	\| 1.0020 \| 1018 \| 0.9836 \| - \| - \| - \| - \|
	\| 1.0030 \| 1019 \| 0.6727 \| - \| - \| - \| - \|
	\| 1.0039 \| 1020 \| 0.8101 \| - \| - \| - \| - \|
	\| 1.0049 \| 1021 \| 0.7747 \| - \| - \| - \| - \|
	\| 1.0059 \| 1022 \| 0.6931 \| - \| - \| - \| - \|
	\| 1.0069 \| 1023 \| 1.2261 \| - \| - \| - \| - \|
	\| 1.0079 \| 1024 \| 0.7315 \| - \| - \| - \| - \|
	\| 1.0089 \| 1025 \| 0.61 \| - \| - \| - \| - \|
	\| 1.0098 \| 1026 \| 0.5402 \| - \| - \| - \| - \|
	\| 1.0108 \| 1027 \| 1.3353 \| - \| - \| - \| - \|
	\| 1.0118 \| 1028 \| 0.9483 \| - \| - \| - \| - \|
	\| 1.0128 \| 1029 \| 0.9226 \| - \| - \| - \| - \|
	\| 1.0138 \| 1030 \| 0.6536 \| - \| - \| - \| - \|
	\| 1.0148 \| 1031 \| 0.7343 \| - \| - \| - \| - \|
	\| 1.0157 \| 1032 \| 0.7647 \| - \| - \| - \| - \|
	\| 1.0167 \| 1033 \| 0.7541 \| - \| - \| - \| - \|
	\| 1.0177 \| 1034 \| 1.1746 \| - \| - \| - \| - \|
	\| 1.0187 \| 1035 \| 1.3935 \| - \| - \| - \| - \|
	\| 1.0197 \| 1036 \| 1.5224 \| - \| - \| - \| - \|
	\| 1.0207 \| 1037 \| 0.9067 \| - \| - \| - \| - \|
	\| 1.0217 \| 1038 \| 0.7184 \| - \| - \| - \| - \|
	\| 1.0226 \| 1039 \| 0.5991 \| - \| - \| - \| - \|
	\| 1.0236 \| 1040 \| 0.8841 \| - \| - \| - \| - \|
	\| 1.0246 \| 1041 \| 0.7474 \| - \| - \| - \| - \|
	\| 1.0256 \| 1042 \| 0.7444 \| - \| - \| - \| - \|
	\| 1.0266 \| 1043 \| 1.4098 \| - \| - \| - \| - \|
	\| 1.0276 \| 1044 \| 0.6033 \| - \| - \| - \| - \|
	\| 1.0285 \| 1045 \| 0.93 \| - \| - \| - \| - \|
	\| 1.0295 \| 1046 \| 0.7308 \| - \| - \| - \| - \|
	\| 1.0305 \| 1047 \| 0.7339 \| - \| - \| - \| - \|
	\| 1.0315 \| 1048 \| 1.3459 \| - \| - \| - \| - \|
	\| 1.0325 \| 1049 \| 0.6866 \| - \| - \| - \| - \|
	\| 1.0335 \| 1050 \| 0.8323 \| - \| - \| - \| - \|
	\| 1.0344 \| 1051 \| 0.5301 \| - \| - \| - \| - \|
	\| 1.0354 \| 1052 \| 1.1091 \| - \| - \| - \| - \|
	\| 1.0364 \| 1053 \| 0.7301 \| - \| - \| - \| - \|
	\| 1.0374 \| 1054 \| 1.1041 \| - \| - \| - \| - \|
	\| 1.0384 \| 1055 \| 0.433 \| - \| - \| - \| - \|
	\| 1.0394 \| 1056 \| 0.8808 \| - \| - \| - \| - \|
	\| 1.0404 \| 1057 \| 0.5836 \| - \| - \| - \| - \|
	\| 1.0413 \| 1058 \| 0.6459 \| - \| - \| - \| - \|
	\| 1.0423 \| 1059 \| 0.4647 \| - \| - \| - \| - \|
	\| 1.0433 \| 1060 \| 1.4988 \| - \| - \| - \| - \|
	\| 1.0443 \| 1061 \| 1.2506 \| - \| - \| - \| - \|
	\| 1.0453 \| 1062 \| 0.5674 \| - \| - \| - \| - \|
	\| 1.0463 \| 1063 \| 0.8192 \| - \| - \| - \| - \|
	\| 1.0472 \| 1064 \| 1.443 \| - \| - \| - \| - \|
	\| 1.0482 \| 1065 \| 0.4771 \| - \| - \| - \| - \|
	\| 1.0492 \| 1066 \| 0.5886 \| - \| - \| - \| - \|
	\| 1.0502 \| 1067 \| 0.9119 \| - \| - \| - \| - \|
	\| 1.0512 \| 1068 \| 0.7875 \| - \| - \| - \| - \|
	\| 1.0522 \| 1069 \| 0.7041 \| - \| - \| - \| - \|
	\| 1.0531 \| 1070 \| 0.5232 \| - \| - \| - \| - \|
	\| 1.0541 \| 1071 \| 0.57 \| 1.1571 \| 0.5373 \| 0.4925 \| 0.6962 \|
	\| 1.0551 \| 1072 \| 0.8948 \| - \| - \| - \| - \|
	\| 1.0561 \| 1073 \| 1.0059 \| - \| - \| - \| - \|
	\| 1.0571 \| 1074 \| 0.9796 \| - \| - \| - \| - \|
	\| 1.0581 \| 1075 \| 0.9632 \| - \| - \| - \| - \|
	\| 1.0591 \| 1076 \| 1.1254 \| - \| - \| - \| - \|
	\| 1.0600 \| 1077 \| 0.8915 \| - \| - \| - \| - \|
	\| 1.0610 \| 1078 \| 0.5213 \| - \| - \| - \| - \|
	\| 1.0620 \| 1079 \| 0.6755 \| - \| - \| - \| - \|
	\| 1.0630 \| 1080 \| 0.6665 \| - \| - \| - \| - \|
	\| 1.0640 \| 1081 \| 0.729 \| - \| - \| - \| - \|
	\| 1.0650 \| 1082 \| 0.9047 \| - \| - \| - \| - \|
	\| 1.0659 \| 1083 \| 1.0514 \| - \| - \| - \| - \|
	\| 1.0669 \| 1084 \| 1.6729 \| - \| - \| - \| - \|
	\| 1.0679 \| 1085 \| 0.8698 \| - \| - \| - \| - \|
	\| 1.0689 \| 1086 \| 1.1209 \| - \| - \| - \| - \|
	\| 1.0699 \| 1087 \| 0.9425 \| - \| - \| - \| - \|
	\| 1.0709 \| 1088 \| 0.7151 \| - \| - \| - \| - \|
	\| 1.0719 \| 1089 \| 0.7336 \| - \| - \| - \| - \|
	\| 1.0728 \| 1090 \| 1.0971 \| - \| - \| - \| - \|
	\| 1.0738 \| 1091 \| 0.5334 \| - \| - \| - \| - \|
	\| 1.0748 \| 1092 \| 1.0171 \| - \| - \| - \| - \|
	\| 1.0758 \| 1093 \| 1.0153 \| - \| - \| - \| - \|
	\| 1.0768 \| 1094 \| 0.6662 \| - \| - \| - \| - \|
	\| 1.0778 \| 1095 \| 0.5451 \| - \| - \| - \| - \|
	\| 1.0787 \| 1096 \| 0.7743 \| - \| - \| - \| - \|
	\| 1.0797 \| 1097 \| 0.9187 \| - \| - \| - \| - \|
	\| 1.0807 \| 1098 \| 1.1919 \| - \| - \| - \| - \|
	\| 1.0817 \| 1099 \| 0.9405 \| - \| - \| - \| - \|
	\| 1.0827 \| 1100 \| 0.5361 \| - \| - \| - \| - \|
	\| 1.0837 \| 1101 \| 1.233 \| - \| - \| - \| - \|
	\| 1.0846 \| 1102 \| 0.8222 \| - \| - \| - \| - \|
	\| 1.0856 \| 1103 \| 0.906 \| - \| - \| - \| - \|
	\| 1.0866 \| 1104 \| 0.62 \| - \| - \| - \| - \|
	\| 1.0876 \| 1105 \| 0.887 \| - \| - \| - \| - \|
	\| 1.0886 \| 1106 \| 0.6135 \| - \| - \| - \| - \|
	\| 1.0896 \| 1107 \| 0.2279 \| - \| - \| - \| - \|
	\| 1.0906 \| 1108 \| 1.0091 \| - \| - \| - \| - \|
	\| 1.0915 \| 1109 \| 1.2457 \| - \| - \| - \| - \|
	\| 1.0925 \| 1110 \| 0.6233 \| - \| - \| - \| - \|
	\| 1.0935 \| 1111 \| 0.6004 \| - \| - \| - \| - \|
	\| 1.0945 \| 1112 \| 1.0716 \| - \| - \| - \| - \|
	\| 1.0955 \| 1113 \| 0.5854 \| - \| - \| - \| - \|
	\| 1.0965 \| 1114 \| 0.5708 \| - \| - \| - \| - \|
	\| 1.0974 \| 1115 \| 1.0223 \| - \| - \| - \| - \|
	\| 1.0984 \| 1116 \| 0.6434 \| - \| - \| - \| - \|
	\| 1.0994 \| 1117 \| 0.363 \| - \| - \| - \| - \|
	\| 1.1004 \| 1118 \| 1.2779 \| - \| - \| - \| - \|
	\| 1.1014 \| 1119 \| 0.8207 \| - \| - \| - \| - \|
	\| 1.1024 \| 1120 \| 0.6738 \| - \| - \| - \| - \|
	\| 1.1033 \| 1121 \| 1.0475 \| - \| - \| - \| - \|
	\| 1.1043 \| 1122 \| 0.9189 \| - \| - \| - \| - \|
	\| 1.1053 \| 1123 \| 1.0991 \| - \| - \| - \| - \|
	\| 1.1063 \| 1124 \| 0.7348 \| - \| - \| - \| - \|
	\| 1.1073 \| 1125 \| 0.7178 \| - \| - \| - \| - \|
	\| 1.1083 \| 1126 \| 0.893 \| - \| - \| - \| - \|
	\| 1.1093 \| 1127 \| 0.7549 \| - \| - \| - \| - \|
	\| 1.1102 \| 1128 \| 1.4074 \| - \| - \| - \| - \|
	\| 1.1112 \| 1129 \| 0.8321 \| - \| - \| - \| - \|
	\| 1.1122 \| 1130 \| 1.0289 \| - \| - \| - \| - \|
	\| 1.1132 \| 1131 \| 0.8296 \| - \| - \| - \| - \|
	\| 1.1142 \| 1132 \| 0.7587 \| - \| - \| - \| - \|
	\| 1.1152 \| 1133 \| 0.6198 \| - \| - \| - \| - \|
	\| 1.1161 \| 1134 \| 0.7474 \| - \| - \| - \| - \|
	\| 1.1171 \| 1135 \| 0.719 \| - \| - \| - \| - \|
	\| 1.1181 \| 1136 \| 0.517 \| - \| - \| - \| - \|
	\| 1.1191 \| 1137 \| 0.4452 \| - \| - \| - \| - \|
	\| 1.1201 \| 1138 \| 1.1683 \| - \| - \| - \| - \|
	\| 1.1211 \| 1139 \| 0.7705 \| - \| - \| - \| - \|
	\| 1.1220 \| 1140 \| 0.7873 \| - \| - \| - \| - \|
	\| 1.1230 \| 1141 \| 0.7939 \| - \| - \| - \| - \|
	\| 1.1240 \| 1142 \| 0.6513 \| - \| - \| - \| - \|
	\| 1.125 \| 1143 \| 1.4787 \| - \| - \| - \| - \|
	\| 1.1260 \| 1144 \| 0.7314 \| - \| - \| - \| - \|
	\| 1.1270 \| 1145 \| 0.4927 \| - \| - \| - \| - \|
	\| 1.1280 \| 1146 \| 0.9718 \| - \| - \| - \| - \|
	\| 1.1289 \| 1147 \| 0.6738 \| - \| - \| - \| - \|
	\| 1.1299 \| 1148 \| 1.2356 \| - \| - \| - \| - \|
	\| 1.1309 \| 1149 \| 0.902 \| - \| - \| - \| - \|
	\| 1.1319 \| 1150 \| 0.5957 \| - \| - \| - \| - \|
	\| 1.1329 \| 1151 \| 1.1595 \| - \| - \| - \| - \|
	\| 1.1339 \| 1152 \| 0.9146 \| - \| - \| - \| - \|
	\| 1.1348 \| 1153 \| 0.5402 \| - \| - \| - \| - \|
	\| 1.1358 \| 1154 \| 0.6177 \| - \| - \| - \| - \|
	\| 1.1368 \| 1155 \| 0.6705 \| - \| - \| - \| - \|
	\| 1.1378 \| 1156 \| 0.603 \| - \| - \| - \| - \|
	\| 1.1388 \| 1157 \| 0.733 \| - \| - \| - \| - \|
	\| 1.1398 \| 1158 \| 1.1947 \| - \| - \| - \| - \|
	\| 1.1407 \| 1159 \| 0.6454 \| - \| - \| - \| - \|
	\| 1.1417 \| 1160 \| 1.0318 \| - \| - \| - \| - \|
	\| 1.1427 \| 1161 \| 1.0791 \| - \| - \| - \| - \|
	\| 1.1437 \| 1162 \| 0.928 \| - \| - \| - \| - \|
	\| 1.1447 \| 1163 \| 0.5488 \| - \| - \| - \| - \|
	\| 1.1457 \| 1164 \| 0.7937 \| - \| - \| - \| - \|
	\| 1.1467 \| 1165 \| 0.6262 \| - \| - \| - \| - \|
	\| 1.1476 \| 1166 \| 0.6925 \| - \| - \| - \| - \|
	\| 1.1486 \| 1167 \| 0.6949 \| - \| - \| - \| - \|
	\| 1.1496 \| 1168 \| 0.2552 \| - \| - \| - \| - \|
	\| 1.1506 \| 1169 \| 0.7877 \| - \| - \| - \| - \|
	\| 1.1516 \| 1170 \| 0.7581 \| - \| - \| - \| - \|
	\| 1.1526 \| 1171 \| 0.4293 \| - \| - \| - \| - \|
	\| 1.1535 \| 1172 \| 0.3826 \| - \| - \| - \| - \|
	\| 1.1545 \| 1173 \| 0.9302 \| - \| - \| - \| - \|
	\| 1.1555 \| 1174 \| 0.9708 \| - \| - \| - \| - \|
	\| 1.1565 \| 1175 \| 0.4477 \| - \| - \| - \| - \|
	\| 1.1575 \| 1176 \| 0.7183 \| - \| - \| - \| - \|
	\| 1.1585 \| 1177 \| 0.6087 \| - \| - \| - \| - \|
	\| 1.1594 \| 1178 \| 0.8324 \| - \| - \| - \| - \|
	\| 1.1604 \| 1179 \| 0.3906 \| - \| - \| - \| - \|
	\| 1.1614 \| 1180 \| 1.5957 \| - \| - \| - \| - \|
	\| 1.1624 \| 1181 \| 1.2033 \| - \| - \| - \| - \|
	\| 1.1634 \| 1182 \| 0.473 \| - \| - \| - \| - \|
	\| 1.1644 \| 1183 \| 0.6048 \| - \| - \| - \| - \|
	\| 1.1654 \| 1184 \| 0.861 \| - \| - \| - \| - \|
	\| 1.1663 \| 1185 \| 0.9065 \| - \| - \| - \| - \|
	\| 1.1673 \| 1186 \| 0.6253 \| - \| - \| - \| - \|
	\| 1.1683 \| 1187 \| 1.227 \| - \| - \| - \| - \|
	\| 1.1693 \| 1188 \| 0.7943 \| - \| - \| - \| - \|
	\| 1.1703 \| 1189 \| 0.6892 \| - \| - \| - \| - \|
	\| 1.1713 \| 1190 \| 1.0856 \| - \| - \| - \| - \|
	\| 1.1722 \| 1191 \| 0.7547 \| - \| - \| - \| - \|
	\| 1.1732 \| 1192 \| 0.4079 \| - \| - \| - \| - \|
	\| 1.1742 \| 1193 \| 0.6927 \| - \| - \| - \| - \|
	\| 1.1752 \| 1194 \| 1.0321 \| - \| - \| - \| - \|
	\| 1.1762 \| 1195 \| 1.1502 \| - \| - \| - \| - \|
	\| 1.1772 \| 1196 \| 0.9344 \| - \| - \| - \| - \|
	\| 1.1781 \| 1197 \| 1.0466 \| - \| - \| - \| - \|
	\| 1.1791 \| 1198 \| 0.6004 \| - \| - \| - \| - \|
	\| 1.1801 \| 1199 \| 0.7064 \| - \| - \| - \| - \|
	\| 1.1811 \| 1200 \| 0.659 \| - \| - \| - \| - \|
	\| 1.1821 \| 1201 \| 0.6445 \| - \| - \| - \| - \|
	\| 1.1831 \| 1202 \| 1.1254 \| - \| - \| - \| - \|
	\| 1.1841 \| 1203 \| 0.7727 \| - \| - \| - \| - \|
	\| 1.1850 \| 1204 \| 0.8912 \| - \| - \| - \| - \|
	\| 1.1860 \| 1205 \| 1.3996 \| - \| - \| - \| - \|
	\| 1.1870 \| 1206 \| 0.6232 \| - \| - \| - \| - \|
	\| 1.1880 \| 1207 \| 1.2074 \| - \| - \| - \| - \|
	\| 1.1890 \| 1208 \| 0.7199 \| - \| - \| - \| - \|
	\| 1.1900 \| 1209 \| 0.5826 \| - \| - \| - \| - \|
	\| 1.1909 \| 1210 \| 0.7618 \| - \| - \| - \| - \|
	\| 1.1919 \| 1211 \| 1.1995 \| - \| - \| - \| - \|
	\| 1.1929 \| 1212 \| 0.6375 \| - \| - \| - \| - \|
	\| 1.1939 \| 1213 \| 0.5623 \| - \| - \| - \| - \|
	\| 1.1949 \| 1214 \| 0.7756 \| - \| - \| - \| - \|
	\| 1.1959 \| 1215 \| 0.8011 \| - \| - \| - \| - \|
	\| 1.1969 \| 1216 \| 0.4404 \| - \| - \| - \| - \|
	\| 1.1978 \| 1217 \| 0.8401 \| - \| - \| - \| - \|
	\| 1.1988 \| 1218 \| 0.8971 \| - \| - \| - \| - \|
	\| 1.1998 \| 1219 \| 1.0111 \| - \| - \| - \| - \|
	\| 1.2008 \| 1220 \| 0.8536 \| - \| - \| - \| - \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.2.1
	- Transformers: 4.44.2
	- PyTorch: 2.5.0+cu121
	- Accelerate: 0.34.2
	- Datasets: 3.0.2
	- Tokenizers: 0.19.1

	## 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",
	}
	```

	#### GISTEmbedLoss
	```bibtex
	@misc{solatorio2024gistembed,
	title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
	author={Aivin V. Solatorio},
	year={2024},
	eprint={2402.16829},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

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