Training in progress, step 915, checkpoint

8b4d39b verified about 1 month ago

156 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: A picture of a white gas range with figurines above.
	sentences:
	- A nerdy woman brushing her teeth with a friend nearby.
	- a white stove turned off with a digital clock
	- The plasma membrane also contains other molecules, primarily other lipids and
	proteins. The green molecules in Figure above , for example, are the lipid cholesterol.
	Molecules of cholesterol help the plasma membrane keep its shape. Many of the
	proteins in the plasma membrane assist other substances in crossing the membrane.
	- source_sentence: who makes the kentucky derby garland of roses
	sentences:
	- Accrington strengthened their position in the play-off places with a hard-fought
	win over struggling Dagenham.
	- "tidal energy can be used to produce electricity. Ocean thermal is energy derived\
	\ from waves and also from tidal waves. \n Ocean thermal energy can be used to\
	\ produce electricity."
	- Kentucky Derby Trophy The Kroger Company has been the official florist of the
	Kentucky Derby since 1987. After taking over the duties from the Kingsley Walker
	florist, Kroger began constructing the prestigious garland in one of its local
	stores for the public to view on Derby Eve. The preservation of the garland and
	crowds of spectators watching its construction are a testament to the prestige
	and mystique of the Garland of Roses.
	- source_sentence: what is the difference between a general sense and a special sense?
	sentences:
	- 'Ian Curtis ( of Touching from a distance) Ian Kevin Curtis was an English musician
	and singer-songwriter. He is best known as the lead singer and lyricist of the
	post-punk band Joy Division. Joy Division released its debut album, Unknown Pleasures,
	in 1979 and recorded its follow-up, Closer, in 1980. Curtis, who suffered from
	epilepsy and depression, committed suicide on 18 May 1980, on the eve of Joy Division''s
	first North American tour, resulting in the band''s dissolution and the subsequent
	formation of New Order. Curtis was known for his baritone voice, dance style,
	and songwriting filled with imagery of desolation, emptiness and alienation. In
	1995, Curtis''s widow Deborah published Touching from a Distance: Ian Curtis and
	Joy Division, a biography of the singer. His life and death Ian Kevin Curtis was
	an English musician and singer-songwriter. He is best known as the lead singer
	and lyricist of the post-punk band Joy Division. Joy Division released its debut
	album, Unknown Pleasures, in 1979 and recorded its follow-up, Closer, in 1980.
	Curtis, who suffered from epilepsy and depression, committed suicide on 18 May
	1980, on the eve of Joy Division''s first North American tour, resulting in the
	band''s dissolution and the subsequent formation of New Order. Curtis was known
	for his baritone voice, dance style, and songwriting filled with imagery of desolation,
	emptiness and alienation. In 1995, Curtis''s widow Deborah published Touching
	from a Distance: Ian Curtis and Joy Division, a biography of the singer. His life
	and death have been dramatised in the films 24 Hour Party People (2002) and Control
	(2007). ...more'
	- The human body has two basic types of senses, called special senses and general
	senses. Special senses have specialized sense organs that gather sensory information
	and change it into nerve impulses. ... General senses, in contrast, are all associated
	with the sense of touch. They lack special sense organs.
	- Captain Hook Barrie states in the novel that "Hook was not his true name. To reveal
	who he really was would even at this date set the country in a blaze", and relates
	that Peter Pan began their rivalry by feeding the pirate's hand to the crocodile.
	He is said to be "Blackbeard's bo'sun" and "the only man of whom Barbecue was
	afraid".[5] (In Robert Louis Stevenson's Treasure Island, one of the names Long
	John Silver goes by is Barbecue.)[6]
	- source_sentence: Retzius was born in Stockholm , son of the anatomist Anders Jahan
	Retzius ( and grandson of the naturalist and chemist Anders Retzius ) .
	sentences:
	- Retzius was born in Stockholm , the son of anatomist Anders Jahan Retzius ( and
	grandson of the naturalist and chemist Anders Retzius ) .
	- As of 14 March , over 156,000 cases of COVID-19 have been reported in around 140
	countries and territories ; more than 5,800 people have died from the disease
	and around 75,000 have recovered .
	- A person sitting on a stool on the street.
	- source_sentence: who was the first person who made the violin
	sentences:
	- Alice in Chains Alice in Chains is an American rock band from Seattle, Washington,
	formed in 1987 by guitarist and vocalist Jerry Cantrell and drummer Sean Kinney,[1]
	who recruited bassist Mike Starr[1] and lead vocalist Layne Staley.[1][2][3] Starr
	was replaced by Mike Inez in 1993.[4] After Staley's death in 2002, William DuVall
	joined in 2006 as co-lead vocalist and rhythm guitarist. The band took its name
	from Staley's previous group, the glam metal band Alice N' Chains.[5][2]
	- as distance from an object decreases , that object will appear larger
	- Violin The first makers of violins probably borrowed from various developments
	of the Byzantine lira. These included the rebec;[13] the Arabic rebab; the vielle
	(also known as the fidel or viuola); and the lira da braccio[11][14] The violin
	in its present form emerged in early 16th-century northern Italy. The earliest
	pictures of violins, albeit with three strings, are seen in northern Italy around
	1530, at around the same time as the words "violino" and "vyollon" are seen in
	Italian and French documents. One of the earliest explicit descriptions of the
	instrument, including its tuning, is from the Epitome musical by Jambe de Fer,
	published in Lyon in 1556.[15] By this time, the violin had already begun to spread
	throughout Europe.
	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: 0.4008630217130614
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.45136395222060033
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.43690206761360884
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.4609070244585321
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.42558645063013534
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.45137661682757413
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.39680226288992326
	name: Pearson Dot
	- type: spearman_dot
	value: 0.4467416839340055
	name: Spearman Dot
	- type: pearson_max
	value: 0.43690206761360884
	name: Pearson Max
	- type: spearman_max
	value: 0.4609070244585321
	name: Spearman Max
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: allNLI dev
	type: allNLI-dev
	metrics:
	- type: cosine_accuracy
	value: 0.685546875
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.9454631209373474
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.529531568228106
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.881622314453125
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.4088050314465409
	name: Cosine Precision
	- type: cosine_recall
	value: 0.7514450867052023
	name: Cosine Recall
	- type: cosine_ap
	value: 0.4792378857888552
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.685546875
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 726.0874633789062
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.5287356321839081
	name: Dot F1
	- type: dot_f1_threshold
	value: 667.7103271484375
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.3954154727793696
	name: Dot Precision
	- type: dot_recall
	value: 0.7976878612716763
	name: Dot Recall
	- type: dot_ap
	value: 0.4773554395168431
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.681640625
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 179.65867614746094
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.5375
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 272.7696838378906
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.4201954397394137
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.7456647398843931
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.4774414549549294
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.685546875
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 9.15554428100586
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.5303643724696356
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 13.481329917907715
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.40809968847352024
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.7572254335260116
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.479012627080778
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.685546875
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 726.0874633789062
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.5375
	name: Max F1
	- type: max_f1_threshold
	value: 667.7103271484375
	name: Max F1 Threshold
	- type: max_precision
	value: 0.4201954397394137
	name: Max Precision
	- type: max_recall
	value: 0.7976878612716763
	name: Max Recall
	- type: max_ap
	value: 0.4792378857888552
	name: Max Ap
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: Qnli dev
	type: Qnli-dev
	metrics:
	- type: cosine_accuracy
	value: 0.666015625
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.8375362157821655
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.663594470046083
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.776394248008728
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.5204819277108433
	name: Cosine Precision
	- type: cosine_recall
	value: 0.9152542372881356
	name: Cosine Recall
	- type: cosine_ap
	value: 0.6688267117653419
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.666015625
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 642.4522705078125
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.6636085626911314
	name: Dot F1
	- type: dot_f1_threshold
	value: 591.8570556640625
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.5191387559808612
	name: Dot Precision
	- type: dot_recall
	value: 0.9194915254237288
	name: Dot Recall
	- type: dot_ap
	value: 0.667969160193219
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.666015625
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 322.94683837890625
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.6762360446570973
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 371.5932922363281
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.5421994884910486
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.8983050847457628
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.6781736723001364
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.66796875
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 15.774551391601562
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.662557781201849
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 18.4862060546875
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.5205811138014528
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.9110169491525424
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.6688954739628344
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.66796875
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 642.4522705078125
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.6762360446570973
	name: Max F1
	- type: max_f1_threshold
	value: 591.8570556640625
	name: Max F1 Threshold
	- type: max_precision
	value: 0.5421994884910486
	name: Max Precision
	- type: max_recall
	value: 0.9194915254237288
	name: Max Recall
	- type: max_ap
	value: 0.6781736723001364
	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(
	(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-toytest-step1-checkpoints-tmp")
	# Run inference
	sentences = [
	'who was the first person who made the violin',
	'Violin The first makers of violins probably borrowed from various developments of the Byzantine lira. These included the rebec;[13] the Arabic rebab; the vielle (also known as the fidel or viuola); and the lira da braccio[11][14] The violin in its present form emerged in early 16th-century northern Italy. The earliest pictures of violins, albeit with three strings, are seen in northern Italy around 1530, at around the same time as the words "violino" and "vyollon" are seen in Italian and French documents. One of the earliest explicit descriptions of the instrument, including its tuning, is from the Epitome musical by Jambe de Fer, published in Lyon in 1556.[15] By this time, the violin had already begun to spread throughout Europe.',
	"Alice in Chains Alice in Chains is an American rock band from Seattle, Washington, formed in 1987 by guitarist and vocalist Jerry Cantrell and drummer Sean Kinney,[1] who recruited bassist Mike Starr[1] and lead vocalist Layne Staley.[1][2][3] Starr was replaced by Mike Inez in 1993.[4] After Staley's death in 2002, William DuVall joined in 2006 as co-lead vocalist and rhythm guitarist. The band took its name from Staley's previous group, the glam metal band Alice N' Chains.[5][2]",
	]
	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 \| 0.4009 \|
	\| spearman_cosine \| 0.4514 \|
	\| pearson_manhattan \| 0.4369 \|
	\| spearman_manhattan \| 0.4609 \|
	\| pearson_euclidean \| 0.4256 \|
	\| spearman_euclidean \| 0.4514 \|
	\| pearson_dot \| 0.3968 \|
	\| spearman_dot \| 0.4467 \|
	\| pearson_max \| 0.4369 \|
	\| spearman_max \| 0.4609 \|

	#### 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 \| 0.6855 \|
	\| cosine_accuracy_threshold \| 0.9455 \|
	\| cosine_f1 \| 0.5295 \|
	\| cosine_f1_threshold \| 0.8816 \|
	\| cosine_precision \| 0.4088 \|
	\| cosine_recall \| 0.7514 \|
	\| cosine_ap \| 0.4792 \|
	\| dot_accuracy \| 0.6855 \|
	\| dot_accuracy_threshold \| 726.0875 \|
	\| dot_f1 \| 0.5287 \|
	\| dot_f1_threshold \| 667.7103 \|
	\| dot_precision \| 0.3954 \|
	\| dot_recall \| 0.7977 \|
	\| dot_ap \| 0.4774 \|
	\| manhattan_accuracy \| 0.6816 \|
	\| manhattan_accuracy_threshold \| 179.6587 \|
	\| manhattan_f1 \| 0.5375 \|
	\| manhattan_f1_threshold \| 272.7697 \|
	\| manhattan_precision \| 0.4202 \|
	\| manhattan_recall \| 0.7457 \|
	\| manhattan_ap \| 0.4774 \|
	\| euclidean_accuracy \| 0.6855 \|
	\| euclidean_accuracy_threshold \| 9.1555 \|
	\| euclidean_f1 \| 0.5304 \|
	\| euclidean_f1_threshold \| 13.4813 \|
	\| euclidean_precision \| 0.4081 \|
	\| euclidean_recall \| 0.7572 \|
	\| euclidean_ap \| 0.479 \|
	\| max_accuracy \| 0.6855 \|
	\| max_accuracy_threshold \| 726.0875 \|
	\| max_f1 \| 0.5375 \|
	\| max_f1_threshold \| 667.7103 \|
	\| max_precision \| 0.4202 \|
	\| max_recall \| 0.7977 \|
	\| max_ap \| 0.4792 \|

	#### 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 \| 0.666 \|
	\| cosine_accuracy_threshold \| 0.8375 \|
	\| cosine_f1 \| 0.6636 \|
	\| cosine_f1_threshold \| 0.7764 \|
	\| cosine_precision \| 0.5205 \|
	\| cosine_recall \| 0.9153 \|
	\| cosine_ap \| 0.6688 \|
	\| dot_accuracy \| 0.666 \|
	\| dot_accuracy_threshold \| 642.4523 \|
	\| dot_f1 \| 0.6636 \|
	\| dot_f1_threshold \| 591.8571 \|
	\| dot_precision \| 0.5191 \|
	\| dot_recall \| 0.9195 \|
	\| dot_ap \| 0.668 \|
	\| manhattan_accuracy \| 0.666 \|
	\| manhattan_accuracy_threshold \| 322.9468 \|
	\| manhattan_f1 \| 0.6762 \|
	\| manhattan_f1_threshold \| 371.5933 \|
	\| manhattan_precision \| 0.5422 \|
	\| manhattan_recall \| 0.8983 \|
	\| manhattan_ap \| 0.6782 \|
	\| euclidean_accuracy \| 0.668 \|
	\| euclidean_accuracy_threshold \| 15.7746 \|
	\| euclidean_f1 \| 0.6626 \|
	\| euclidean_f1_threshold \| 18.4862 \|
	\| euclidean_precision \| 0.5206 \|
	\| euclidean_recall \| 0.911 \|
	\| euclidean_ap \| 0.6689 \|
	\| max_accuracy \| 0.668 \|
	\| max_accuracy_threshold \| 642.4523 \|
	\| max_f1 \| 0.6762 \|
	\| max_f1_threshold \| 591.8571 \|
	\| max_precision \| 0.5422 \|
	\| max_recall \| 0.9195 \|
	\| max_ap \| 0.6782 \|

	<!--
	## 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: 29.3 tokens</li><li>max: 343 tokens</li></ul> \| <ul><li>min: 2 tokens</li><li>mean: 57.53 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>A Slippery Dick is what type of creature?</code> \| <code>The Slippery Dick (Juvenile) - Whats That Fish! Description Also known as Sand-reef Wrasses and Slippery Dick Wrasse. Found singly or in pairs or in groups constantly circling around reefs, sea grass beds and sandy areas. Colours highly variable especially between juvenile to adult. They feed on hard shell invertebrates. Length - 18cm Depth - 2-12m Widespread Western Atlantic & Caribbean Most reef fish seen by divers during the day are grazers, that cruise around just above the surface of the coral or snoop into crevices looking for algae, worms and small crustaceans. Wrasses have small protruding teeth and graze the bottom taking in a variety of snails, worms, crabs, shrimps and eggs. Any hard coats or thick shells are then ground down by their pharyngeal jaws and the delicacies inside digested. From juvenile to adult wrasses dramatically alter their colour and body shapes. Wrasses are always on the go during the day, but are the first to go to bed and the last to rise. Small wrasses dive below the sand to sleep and larger wrasses wedge themselves in crevasses. Related creatures Heads up! Many creatures change during their life. Juvenile fish become adults and some change shape or their colour. Some species change sex and others just get older. The following creature(s) are known relatives of the Slippery Dick (Juvenile). Click the image(s) to explore further or hover over to get a better view! Slippery Dick</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>In December 2015 , the film was ranked # 192 on IMDb .</code> \| <code>As of December 2015 , it is the # 192 highest rated film on IMDb.</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: 28.74 tokens</li><li>max: 330 tokens</li></ul> \| <ul><li>min: 2 tokens</li><li>mean: 56.55 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What component of an organism, made up of many cells, in turn makes up an organ?</code> \| <code></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 the 1966 movie The Good, The Bad And The Ugly, Clint Eastwood played the Good" and Lee van Cleef played "the Bad", but who played "the Ugly"?</code> \| <code>View All Photos (10) Movie Info In the last and the best installment of his so-called "Dollars" trilogy of Sergio Leone-directed "spaghetti westerns," Clint Eastwood reprised the role of a taciturn, enigmatic loner. Here he searches for a cache of stolen gold against rivals the Bad (Lee Van Cleef), a ruthless bounty hunter, and the Ugly (Eli Wallach), a Mexican bandit. Though dubbed "the Good," Eastwood's character is not much better than his opponents -- he is just smarter and shoots faster. The film's title reveals its ironic attitude toward the canonized heroes of the classical western. "The real West was the world of violence, fear, and brutal instincts," claimed Leone. "In pursuit of profit there is no such thing as good and evil, generosity or deviousness; everything depends on chance, and not the best wins but the luckiest." Immensely entertaining and beautifully shot in Techniscope by Tonino Delli Colli, the movie is a virtually definitive "spaghetti western," rivaled only by Leone's own Once Upon a Time in the West (1968). The main musical theme by Ennio Morricone hit #1 on the British pop charts. Originally released in Italy at 177 minutes, the movie was later cut for its international release. ~ Yuri German, Rovi Rating:</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-toytest-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-toytest-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 \| 4.9603 \| - \| - \| - \| - \|
	\| 0.0020 \| 2 \| 28.2529 \| - \| - \| - \| - \|
	\| 0.0030 \| 3 \| 27.6365 \| - \| - \| - \| - \|
	\| 0.0039 \| 4 \| 6.1387 \| - \| - \| - \| - \|
	\| 0.0049 \| 5 \| 5.5753 \| - \| - \| - \| - \|
	\| 0.0059 \| 6 \| 5.6951 \| - \| - \| - \| - \|
	\| 0.0069 \| 7 \| 6.3533 \| - \| - \| - \| - \|
	\| 0.0079 \| 8 \| 27.3848 \| - \| - \| - \| - \|
	\| 0.0089 \| 9 \| 3.8501 \| - \| - \| - \| - \|
	\| 0.0098 \| 10 \| 27.911 \| - \| - \| - \| - \|
	\| 0.0108 \| 11 \| 4.9042 \| - \| - \| - \| - \|
	\| 0.0118 \| 12 \| 6.8003 \| - \| - \| - \| - \|
	\| 0.0128 \| 13 \| 5.7317 \| - \| - \| - \| - \|
	\| 0.0138 \| 14 \| 20.261 \| - \| - \| - \| - \|
	\| 0.0148 \| 15 \| 27.9051 \| - \| - \| - \| - \|
	\| 0.0157 \| 16 \| 5.5959 \| - \| - \| - \| - \|
	\| 0.0167 \| 17 \| 5.8052 \| - \| - \| - \| - \|
	\| 0.0177 \| 18 \| 4.5088 \| - \| - \| - \| - \|
	\| 0.0187 \| 19 \| 7.3472 \| - \| - \| - \| - \|
	\| 0.0197 \| 20 \| 5.8668 \| - \| - \| - \| - \|
	\| 0.0207 \| 21 \| 6.4083 \| - \| - \| - \| - \|
	\| 0.0217 \| 22 \| 6.011 \| - \| - \| - \| - \|
	\| 0.0226 \| 23 \| 5.2394 \| - \| - \| - \| - \|
	\| 0.0236 \| 24 \| 4.2966 \| - \| - \| - \| - \|
	\| 0.0246 \| 25 \| 26.605 \| - \| - \| - \| - \|
	\| 0.0256 \| 26 \| 6.2067 \| - \| - \| - \| - \|
	\| 0.0266 \| 27 \| 6.0346 \| - \| - \| - \| - \|
	\| 0.0276 \| 28 \| 5.4676 \| - \| - \| - \| - \|
	\| 0.0285 \| 29 \| 6.4292 \| - \| - \| - \| - \|
	\| 0.0295 \| 30 \| 26.6452 \| - \| - \| - \| - \|
	\| 0.0305 \| 31 \| 18.8401 \| - \| - \| - \| - \|
	\| 0.0315 \| 32 \| 7.4531 \| - \| - \| - \| - \|
	\| 0.0325 \| 33 \| 4.8286 \| - \| - \| - \| - \|
	\| 0.0335 \| 34 \| 5.0078 \| - \| - \| - \| - \|
	\| 0.0344 \| 35 \| 5.4115 \| - \| - \| - \| - \|
	\| 0.0354 \| 36 \| 5.4196 \| - \| - \| - \| - \|
	\| 0.0364 \| 37 \| 4.5023 \| - \| - \| - \| - \|
	\| 0.0374 \| 38 \| 5.376 \| - \| - \| - \| - \|
	\| 0.0384 \| 39 \| 5.2303 \| - \| - \| - \| - \|
	\| 0.0394 \| 40 \| 5.6694 \| - \| - \| - \| - \|
	\| 0.0404 \| 41 \| 4.7825 \| - \| - \| - \| - \|
	\| 0.0413 \| 42 \| 4.6507 \| - \| - \| - \| - \|
	\| 0.0423 \| 43 \| 24.2072 \| - \| - \| - \| - \|
	\| 0.0433 \| 44 \| 4.9285 \| - \| - \| - \| - \|
	\| 0.0443 \| 45 \| 6.326 \| - \| - \| - \| - \|
	\| 0.0453 \| 46 \| 4.5724 \| - \| - \| - \| - \|
	\| 0.0463 \| 47 \| 4.754 \| - \| - \| - \| - \|
	\| 0.0472 \| 48 \| 5.5443 \| - \| - \| - \| - \|
	\| 0.0482 \| 49 \| 4.5764 \| - \| - \| - \| - \|
	\| 0.0492 \| 50 \| 5.1434 \| - \| - \| - \| - \|
	\| 0.0502 \| 51 \| 22.6991 \| - \| - \| - \| - \|
	\| 0.0512 \| 52 \| 5.4277 \| - \| - \| - \| - \|
	\| 0.0522 \| 53 \| 5.0178 \| - \| - \| - \| - \|
	\| 0.0531 \| 54 \| 4.8779 \| - \| - \| - \| - \|
	\| 0.0541 \| 55 \| 4.2884 \| - \| - \| - \| - \|
	\| 0.0551 \| 56 \| 16.0994 \| - \| - \| - \| - \|
	\| 0.0561 \| 57 \| 21.31 \| - \| - \| - \| - \|
	\| 0.0571 \| 58 \| 4.9721 \| - \| - \| - \| - \|
	\| 0.0581 \| 59 \| 5.143 \| - \| - \| - \| - \|
	\| 0.0591 \| 60 \| 3.5933 \| - \| - \| - \| - \|
	\| 0.0600 \| 61 \| 5.2559 \| - \| - \| - \| - \|
	\| 0.0610 \| 62 \| 4.0757 \| - \| - \| - \| - \|
	\| 0.0620 \| 63 \| 3.6612 \| - \| - \| - \| - \|
	\| 0.0630 \| 64 \| 4.7505 \| - \| - \| - \| - \|
	\| 0.0640 \| 65 \| 4.1979 \| - \| - \| - \| - \|
	\| 0.0650 \| 66 \| 3.9982 \| - \| - \| - \| - \|
	\| 0.0659 \| 67 \| 4.7065 \| - \| - \| - \| - \|
	\| 0.0669 \| 68 \| 5.3413 \| - \| - \| - \| - \|
	\| 0.0679 \| 69 \| 3.6964 \| - \| - \| - \| - \|
	\| 0.0689 \| 70 \| 17.8774 \| - \| - \| - \| - \|
	\| 0.0699 \| 71 \| 4.8154 \| - \| - \| - \| - \|
	\| 0.0709 \| 72 \| 4.8356 \| - \| - \| - \| - \|
	\| 0.0719 \| 73 \| 4.568 \| - \| - \| - \| - \|
	\| 0.0728 \| 74 \| 4.0898 \| - \| - \| - \| - \|
	\| 0.0738 \| 75 \| 3.4502 \| - \| - \| - \| - \|
	\| 0.0748 \| 76 \| 3.7733 \| - \| - \| - \| - \|
	\| 0.0758 \| 77 \| 4.5204 \| - \| - \| - \| - \|
	\| 0.0768 \| 78 \| 4.2526 \| - \| - \| - \| - \|
	\| 0.0778 \| 79 \| 4.4398 \| - \| - \| - \| - \|
	\| 0.0787 \| 80 \| 4.0988 \| - \| - \| - \| - \|
	\| 0.0797 \| 81 \| 3.9704 \| - \| - \| - \| - \|
	\| 0.0807 \| 82 \| 4.3343 \| - \| - \| - \| - \|
	\| 0.0817 \| 83 \| 4.2587 \| - \| - \| - \| - \|
	\| 0.0827 \| 84 \| 15.0149 \| - \| - \| - \| - \|
	\| 0.0837 \| 85 \| 14.6599 \| - \| - \| - \| - \|
	\| 0.0846 \| 86 \| 4.0623 \| - \| - \| - \| - \|
	\| 0.0856 \| 87 \| 3.7597 \| - \| - \| - \| - \|
	\| 0.0866 \| 88 \| 4.3433 \| - \| - \| - \| - \|
	\| 0.0876 \| 89 \| 4.0287 \| - \| - \| - \| - \|
	\| 0.0886 \| 90 \| 4.6257 \| - \| - \| - \| - \|
	\| 0.0896 \| 91 \| 13.4689 \| - \| - \| - \| - \|
	\| 0.0906 \| 92 \| 4.6583 \| - \| - \| - \| - \|
	\| 0.0915 \| 93 \| 4.2682 \| - \| - \| - \| - \|
	\| 0.0925 \| 94 \| 4.468 \| - \| - \| - \| - \|
	\| 0.0935 \| 95 \| 3.4333 \| - \| - \| - \| - \|
	\| 0.0945 \| 96 \| 12.7654 \| - \| - \| - \| - \|
	\| 0.0955 \| 97 \| 3.5577 \| - \| - \| - \| - \|
	\| 0.0965 \| 98 \| 12.5875 \| - \| - \| - \| - \|
	\| 0.0974 \| 99 \| 4.2206 \| - \| - \| - \| - \|
	\| 0.0984 \| 100 \| 3.5981 \| - \| - \| - \| - \|
	\| 0.0994 \| 101 \| 3.5575 \| - \| - \| - \| - \|
	\| 0.1004 \| 102 \| 4.0271 \| - \| - \| - \| - \|
	\| 0.1014 \| 103 \| 4.0803 \| - \| - \| - \| - \|
	\| 0.1024 \| 104 \| 4.0886 \| - \| - \| - \| - \|
	\| 0.1033 \| 105 \| 4.176 \| - \| - \| - \| - \|
	\| 0.1043 \| 106 \| 4.6653 \| - \| - \| - \| - \|
	\| 0.1053 \| 107 \| 4.3076 \| - \| - \| - \| - \|
	\| 0.1063 \| 108 \| 8.7282 \| - \| - \| - \| - \|
	\| 0.1073 \| 109 \| 3.4192 \| - \| - \| - \| - \|
	\| 0.1083 \| 110 \| 10.6027 \| - \| - \| - \| - \|
	\| 0.1093 \| 111 \| 4.0959 \| - \| - \| - \| - \|
	\| 0.1102 \| 112 \| 4.2785 \| - \| - \| - \| - \|
	\| 0.1112 \| 113 \| 3.9945 \| - \| - \| - \| - \|
	\| 0.1122 \| 114 \| 10.0652 \| - \| - \| - \| - \|
	\| 0.1132 \| 115 \| 3.8621 \| - \| - \| - \| - \|
	\| 0.1142 \| 116 \| 4.3975 \| - \| - \| - \| - \|
	\| 0.1152 \| 117 \| 9.7899 \| - \| - \| - \| - \|
	\| 0.1161 \| 118 \| 4.3812 \| - \| - \| - \| - \|
	\| 0.1171 \| 119 \| 3.8715 \| - \| - \| - \| - \|
	\| 0.1181 \| 120 \| 3.8327 \| - \| - \| - \| - \|
	\| 0.1191 \| 121 \| 3.5103 \| - \| - \| - \| - \|
	\| 0.1201 \| 122 \| 9.3158 \| - \| - \| - \| - \|
	\| 0.1211 \| 123 \| 3.7201 \| - \| - \| - \| - \|
	\| 0.1220 \| 124 \| 3.4311 \| - \| - \| - \| - \|
	\| 0.1230 \| 125 \| 3.7946 \| - \| - \| - \| - \|
	\| 0.1240 \| 126 \| 4.0456 \| - \| - \| - \| - \|
	\| 0.125 \| 127 \| 3.482 \| - \| - \| - \| - \|
	\| 0.1260 \| 128 \| 3.1901 \| - \| - \| - \| - \|
	\| 0.1270 \| 129 \| 3.414 \| - \| - \| - \| - \|
	\| 0.1280 \| 130 \| 3.4967 \| - \| - \| - \| - \|
	\| 0.1289 \| 131 \| 3.6594 \| - \| - \| - \| - \|
	\| 0.1299 \| 132 \| 8.066 \| - \| - \| - \| - \|
	\| 0.1309 \| 133 \| 3.7872 \| - \| - \| - \| - \|
	\| 0.1319 \| 134 \| 4.0023 \| - \| - \| - \| - \|
	\| 0.1329 \| 135 \| 3.7728 \| - \| - \| - \| - \|
	\| 0.1339 \| 136 \| 3.1893 \| - \| - \| - \| - \|
	\| 0.1348 \| 137 \| 3.3635 \| - \| - \| - \| - \|
	\| 0.1358 \| 138 \| 4.0195 \| - \| - \| - \| - \|
	\| 0.1368 \| 139 \| 4.1097 \| - \| - \| - \| - \|
	\| 0.1378 \| 140 \| 3.7903 \| - \| - \| - \| - \|
	\| 0.1388 \| 141 \| 3.5748 \| - \| - \| - \| - \|
	\| 0.1398 \| 142 \| 3.8104 \| - \| - \| - \| - \|
	\| 0.1407 \| 143 \| 8.0411 \| - \| - \| - \| - \|
	\| 0.1417 \| 144 \| 3.4819 \| - \| - \| - \| - \|
	\| 0.1427 \| 145 \| 3.452 \| - \| - \| - \| - \|
	\| 0.1437 \| 146 \| 3.5861 \| - \| - \| - \| - \|
	\| 0.1447 \| 147 \| 3.4324 \| - \| - \| - \| - \|
	\| 0.1457 \| 148 \| 3.521 \| - \| - \| - \| - \|
	\| 0.1467 \| 149 \| 3.8868 \| - \| - \| - \| - \|
	\| 0.1476 \| 150 \| 8.1191 \| - \| - \| - \| - \|
	\| 0.1486 \| 151 \| 3.6447 \| - \| - \| - \| - \|
	\| 0.1496 \| 152 \| 2.9436 \| - \| - \| - \| - \|
	\| 0.1506 \| 153 \| 8.1535 \| 2.2032 \| 0.2236 \| 0.4009 \| 0.5892 \|
	\| 0.1516 \| 154 \| 3.9619 \| - \| - \| - \| - \|
	\| 0.1526 \| 155 \| 3.1301 \| - \| - \| - \| - \|
	\| 0.1535 \| 156 \| 3.0478 \| - \| - \| - \| - \|
	\| 0.1545 \| 157 \| 3.2986 \| - \| - \| - \| - \|
	\| 0.1555 \| 158 \| 3.2847 \| - \| - \| - \| - \|
	\| 0.1565 \| 159 \| 3.6599 \| - \| - \| - \| - \|
	\| 0.1575 \| 160 \| 3.2238 \| - \| - \| - \| - \|
	\| 0.1585 \| 161 \| 2.8897 \| - \| - \| - \| - \|
	\| 0.1594 \| 162 \| 3.9443 \| - \| - \| - \| - \|
	\| 0.1604 \| 163 \| 3.3733 \| - \| - \| - \| - \|
	\| 0.1614 \| 164 \| 3.7444 \| - \| - \| - \| - \|
	\| 0.1624 \| 165 \| 3.4813 \| - \| - \| - \| - \|
	\| 0.1634 \| 166 \| 2.6865 \| - \| - \| - \| - \|
	\| 0.1644 \| 167 \| 2.7587 \| - \| - \| - \| - \|
	\| 0.1654 \| 168 \| 3.3628 \| - \| - \| - \| - \|
	\| 0.1663 \| 169 \| 3.0035 \| - \| - \| - \| - \|
	\| 0.1673 \| 170 \| 10.1591 \| - \| - \| - \| - \|
	\| 0.1683 \| 171 \| 3.5366 \| - \| - \| - \| - \|
	\| 0.1693 \| 172 \| 8.4047 \| - \| - \| - \| - \|
	\| 0.1703 \| 173 \| 3.8643 \| - \| - \| - \| - \|
	\| 0.1713 \| 174 \| 3.3529 \| - \| - \| - \| - \|
	\| 0.1722 \| 175 \| 3.7143 \| - \| - \| - \| - \|
	\| 0.1732 \| 176 \| 3.3323 \| - \| - \| - \| - \|
	\| 0.1742 \| 177 \| 3.1206 \| - \| - \| - \| - \|
	\| 0.1752 \| 178 \| 3.1348 \| - \| - \| - \| - \|
	\| 0.1762 \| 179 \| 7.6011 \| - \| - \| - \| - \|
	\| 0.1772 \| 180 \| 3.7025 \| - \| - \| - \| - \|
	\| 0.1781 \| 181 \| 10.5662 \| - \| - \| - \| - \|
	\| 0.1791 \| 182 \| 8.966 \| - \| - \| - \| - \|
	\| 0.1801 \| 183 \| 9.426 \| - \| - \| - \| - \|
	\| 0.1811 \| 184 \| 3.0025 \| - \| - \| - \| - \|
	\| 0.1821 \| 185 \| 7.0984 \| - \| - \| - \| - \|
	\| 0.1831 \| 186 \| 7.3808 \| - \| - \| - \| - \|
	\| 0.1841 \| 187 \| 2.8657 \| - \| - \| - \| - \|
	\| 0.1850 \| 188 \| 6.5636 \| - \| - \| - \| - \|
	\| 0.1860 \| 189 \| 3.4702 \| - \| - \| - \| - \|
	\| 0.1870 \| 190 \| 5.9302 \| - \| - \| - \| - \|
	\| 0.1880 \| 191 \| 3.2406 \| - \| - \| - \| - \|
	\| 0.1890 \| 192 \| 3.4459 \| - \| - \| - \| - \|
	\| 0.1900 \| 193 \| 5.269 \| - \| - \| - \| - \|
	\| 0.1909 \| 194 \| 4.8605 \| - \| - \| - \| - \|
	\| 0.1919 \| 195 \| 2.9891 \| - \| - \| - \| - \|
	\| 0.1929 \| 196 \| 3.6681 \| - \| - \| - \| - \|
	\| 0.1939 \| 197 \| 3.1589 \| - \| - \| - \| - \|
	\| 0.1949 \| 198 \| 3.1835 \| - \| - \| - \| - \|
	\| 0.1959 \| 199 \| 3.7561 \| - \| - \| - \| - \|
	\| 0.1969 \| 200 \| 4.0891 \| - \| - \| - \| - \|
	\| 0.1978 \| 201 \| 3.563 \| - \| - \| - \| - \|
	\| 0.1988 \| 202 \| 3.7433 \| - \| - \| - \| - \|
	\| 0.1998 \| 203 \| 3.3813 \| - \| - \| - \| - \|
	\| 0.2008 \| 204 \| 5.2311 \| - \| - \| - \| - \|
	\| 0.2018 \| 205 \| 3.3494 \| - \| - \| - \| - \|
	\| 0.2028 \| 206 \| 3.3533 \| - \| - \| - \| - \|
	\| 0.2037 \| 207 \| 3.688 \| - \| - \| - \| - \|
	\| 0.2047 \| 208 \| 3.5342 \| - \| - \| - \| - \|
	\| 0.2057 \| 209 \| 4.9381 \| - \| - \| - \| - \|
	\| 0.2067 \| 210 \| 3.1839 \| - \| - \| - \| - \|
	\| 0.2077 \| 211 \| 3.0465 \| - \| - \| - \| - \|
	\| 0.2087 \| 212 \| 3.1232 \| - \| - \| - \| - \|
	\| 0.2096 \| 213 \| 4.6297 \| - \| - \| - \| - \|
	\| 0.2106 \| 214 \| 2.9834 \| - \| - \| - \| - \|
	\| 0.2116 \| 215 \| 4.2231 \| - \| - \| - \| - \|
	\| 0.2126 \| 216 \| 3.1458 \| - \| - \| - \| - \|
	\| 0.2136 \| 217 \| 3.2525 \| - \| - \| - \| - \|
	\| 0.2146 \| 218 \| 3.5971 \| - \| - \| - \| - \|
	\| 0.2156 \| 219 \| 3.5616 \| - \| - \| - \| - \|
	\| 0.2165 \| 220 \| 3.2378 \| - \| - \| - \| - \|
	\| 0.2175 \| 221 \| 2.9075 \| - \| - \| - \| - \|
	\| 0.2185 \| 222 \| 3.0391 \| - \| - \| - \| - \|
	\| 0.2195 \| 223 \| 3.5573 \| - \| - \| - \| - \|
	\| 0.2205 \| 224 \| 3.2092 \| - \| - \| - \| - \|
	\| 0.2215 \| 225 \| 3.2646 \| - \| - \| - \| - \|
	\| 0.2224 \| 226 \| 3.0886 \| - \| - \| - \| - \|
	\| 0.2234 \| 227 \| 3.5241 \| - \| - \| - \| - \|
	\| 0.2244 \| 228 \| 3.0111 \| - \| - \| - \| - \|
	\| 0.2254 \| 229 \| 3.707 \| - \| - \| - \| - \|
	\| 0.2264 \| 230 \| 5.3822 \| - \| - \| - \| - \|
	\| 0.2274 \| 231 \| 3.2646 \| - \| - \| - \| - \|
	\| 0.2283 \| 232 \| 2.7021 \| - \| - \| - \| - \|
	\| 0.2293 \| 233 \| 3.5131 \| - \| - \| - \| - \|
	\| 0.2303 \| 234 \| 3.103 \| - \| - \| - \| - \|
	\| 0.2313 \| 235 \| 2.9535 \| - \| - \| - \| - \|
	\| 0.2323 \| 236 \| 2.9631 \| - \| - \| - \| - \|
	\| 0.2333 \| 237 \| 2.8068 \| - \| - \| - \| - \|
	\| 0.2343 \| 238 \| 3.4251 \| - \| - \| - \| - \|
	\| 0.2352 \| 239 \| 2.8495 \| - \| - \| - \| - \|
	\| 0.2362 \| 240 \| 2.9972 \| - \| - \| - \| - \|
	\| 0.2372 \| 241 \| 3.3509 \| - \| - \| - \| - \|
	\| 0.2382 \| 242 \| 2.9234 \| - \| - \| - \| - \|
	\| 0.2392 \| 243 \| 2.4086 \| - \| - \| - \| - \|
	\| 0.2402 \| 244 \| 3.1282 \| - \| - \| - \| - \|
	\| 0.2411 \| 245 \| 2.3352 \| - \| - \| - \| - \|
	\| 0.2421 \| 246 \| 2.4706 \| - \| - \| - \| - \|
	\| 0.2431 \| 247 \| 3.5449 \| - \| - \| - \| - \|
	\| 0.2441 \| 248 \| 2.8963 \| - \| - \| - \| - \|
	\| 0.2451 \| 249 \| 2.773 \| - \| - \| - \| - \|
	\| 0.2461 \| 250 \| 2.355 \| - \| - \| - \| - \|
	\| 0.2470 \| 251 \| 2.656 \| - \| - \| - \| - \|
	\| 0.2480 \| 252 \| 2.6221 \| - \| - \| - \| - \|
	\| 0.2490 \| 253 \| 8.6739 \| - \| - \| - \| - \|
	\| 0.25 \| 254 \| 10.8242 \| - \| - \| - \| - \|
	\| 0.2510 \| 255 \| 2.3408 \| - \| - \| - \| - \|
	\| 0.2520 \| 256 \| 2.1221 \| - \| - \| - \| - \|
	\| 0.2530 \| 257 \| 3.295 \| - \| - \| - \| - \|
	\| 0.2539 \| 258 \| 2.5896 \| - \| - \| - \| - \|
	\| 0.2549 \| 259 \| 2.1215 \| - \| - \| - \| - \|
	\| 0.2559 \| 260 \| 9.4851 \| - \| - \| - \| - \|
	\| 0.2569 \| 261 \| 2.1982 \| - \| - \| - \| - \|
	\| 0.2579 \| 262 \| 3.0568 \| - \| - \| - \| - \|
	\| 0.2589 \| 263 \| 2.6269 \| - \| - \| - \| - \|
	\| 0.2598 \| 264 \| 2.4792 \| - \| - \| - \| - \|
	\| 0.2608 \| 265 \| 1.9445 \| - \| - \| - \| - \|
	\| 0.2618 \| 266 \| 2.4061 \| - \| - \| - \| - \|
	\| 0.2628 \| 267 \| 8.3116 \| - \| - \| - \| - \|
	\| 0.2638 \| 268 \| 8.0804 \| - \| - \| - \| - \|
	\| 0.2648 \| 269 \| 2.1674 \| - \| - \| - \| - \|
	\| 0.2657 \| 270 \| 7.1975 \| - \| - \| - \| - \|
	\| 0.2667 \| 271 \| 5.9104 \| - \| - \| - \| - \|
	\| 0.2677 \| 272 \| 2.498 \| - \| - \| - \| - \|
	\| 0.2687 \| 273 \| 2.5249 \| - \| - \| - \| - \|
	\| 0.2697 \| 274 \| 2.7152 \| - \| - \| - \| - \|
	\| 0.2707 \| 275 \| 2.7904 \| - \| - \| - \| - \|
	\| 0.2717 \| 276 \| 2.7745 \| - \| - \| - \| - \|
	\| 0.2726 \| 277 \| 2.9741 \| - \| - \| - \| - \|
	\| 0.2736 \| 278 \| 1.8215 \| - \| - \| - \| - \|
	\| 0.2746 \| 279 \| 4.6844 \| - \| - \| - \| - \|
	\| 0.2756 \| 280 \| 2.8613 \| - \| - \| - \| - \|
	\| 0.2766 \| 281 \| 2.7147 \| - \| - \| - \| - \|
	\| 0.2776 \| 282 \| 2.814 \| - \| - \| - \| - \|
	\| 0.2785 \| 283 \| 2.3569 \| - \| - \| - \| - \|
	\| 0.2795 \| 284 \| 2.672 \| - \| - \| - \| - \|
	\| 0.2805 \| 285 \| 3.2052 \| - \| - \| - \| - \|
	\| 0.2815 \| 286 \| 2.8056 \| - \| - \| - \| - \|
	\| 0.2825 \| 287 \| 2.6268 \| - \| - \| - \| - \|
	\| 0.2835 \| 288 \| 2.5641 \| - \| - \| - \| - \|
	\| 0.2844 \| 289 \| 2.4475 \| - \| - \| - \| - \|
	\| 0.2854 \| 290 \| 2.7377 \| - \| - \| - \| - \|
	\| 0.2864 \| 291 \| 2.3831 \| - \| - \| - \| - \|
	\| 0.2874 \| 292 \| 8.8069 \| - \| - \| - \| - \|
	\| 0.2884 \| 293 \| 2.186 \| - \| - \| - \| - \|
	\| 0.2894 \| 294 \| 2.3389 \| - \| - \| - \| - \|
	\| 0.2904 \| 295 \| 1.9744 \| - \| - \| - \| - \|
	\| 0.2913 \| 296 \| 2.4491 \| - \| - \| - \| - \|
	\| 0.2923 \| 297 \| 2.5668 \| - \| - \| - \| - \|
	\| 0.2933 \| 298 \| 2.1939 \| - \| - \| - \| - \|
	\| 0.2943 \| 299 \| 2.2832 \| - \| - \| - \| - \|
	\| 0.2953 \| 300 \| 2.7508 \| - \| - \| - \| - \|
	\| 0.2963 \| 301 \| 2.5206 \| - \| - \| - \| - \|
	\| 0.2972 \| 302 \| 2.3522 \| - \| - \| - \| - \|
	\| 0.2982 \| 303 \| 2.7186 \| - \| - \| - \| - \|
	\| 0.2992 \| 304 \| 2.1369 \| - \| - \| - \| - \|
	\| 0.3002 \| 305 \| 9.7972 \| - \| - \| - \| - \|
	\| 0.3012 \| 306 \| 1.9378 \| 1.5786 \| 0.2924 \| 0.4272 \| 0.6159 \|
	\| 0.3022 \| 307 \| 2.5365 \| - \| - \| - \| - \|
	\| 0.3031 \| 308 \| 2.0346 \| - \| - \| - \| - \|
	\| 0.3041 \| 309 \| 2.0721 \| - \| - \| - \| - \|
	\| 0.3051 \| 310 \| 2.6966 \| - \| - \| - \| - \|
	\| 0.3061 \| 311 \| 2.6757 \| - \| - \| - \| - \|
	\| 0.3071 \| 312 \| 10.6395 \| - \| - \| - \| - \|
	\| 0.3081 \| 313 \| 2.8671 \| - \| - \| - \| - \|
	\| 0.3091 \| 314 \| 2.0144 \| - \| - \| - \| - \|
	\| 0.3100 \| 315 \| 9.9338 \| - \| - \| - \| - \|
	\| 0.3110 \| 316 \| 2.6167 \| - \| - \| - \| - \|
	\| 0.3120 \| 317 \| 2.1342 \| - \| - \| - \| - \|
	\| 0.3130 \| 318 \| 9.0369 \| - \| - \| - \| - \|
	\| 0.3140 \| 319 \| 2.0182 \| - \| - \| - \| - \|
	\| 0.3150 \| 320 \| 2.2189 \| - \| - \| - \| - \|
	\| 0.3159 \| 321 \| 1.9667 \| - \| - \| - \| - \|
	\| 0.3169 \| 322 \| 2.3371 \| - \| - \| - \| - \|
	\| 0.3179 \| 323 \| 6.9866 \| - \| - \| - \| - \|
	\| 0.3189 \| 324 \| 1.6119 \| - \| - \| - \| - \|
	\| 0.3199 \| 325 \| 1.8615 \| - \| - \| - \| - \|
	\| 0.3209 \| 326 \| 2.1708 \| - \| - \| - \| - \|
	\| 0.3219 \| 327 \| 2.0174 \| - \| - \| - \| - \|
	\| 0.3228 \| 328 \| 6.7891 \| - \| - \| - \| - \|
	\| 0.3238 \| 329 \| 2.155 \| - \| - \| - \| - \|
	\| 0.3248 \| 330 \| 2.4636 \| - \| - \| - \| - \|
	\| 0.3258 \| 331 \| 1.9844 \| - \| - \| - \| - \|
	\| 0.3268 \| 332 \| 1.9035 \| - \| - \| - \| - \|
	\| 0.3278 \| 333 \| 2.0729 \| - \| - \| - \| - \|
	\| 0.3287 \| 334 \| 1.5715 \| - \| - \| - \| - \|
	\| 0.3297 \| 335 \| 2.7211 \| - \| - \| - \| - \|
	\| 0.3307 \| 336 \| 2.0351 \| - \| - \| - \| - \|
	\| 0.3317 \| 337 \| 2.4049 \| - \| - \| - \| - \|
	\| 0.3327 \| 338 \| 2.3939 \| - \| - \| - \| - \|
	\| 0.3337 \| 339 \| 1.7353 \| - \| - \| - \| - \|
	\| 0.3346 \| 340 \| 1.8393 \| - \| - \| - \| - \|
	\| 0.3356 \| 341 \| 2.2874 \| - \| - \| - \| - \|
	\| 0.3366 \| 342 \| 1.8566 \| - \| - \| - \| - \|
	\| 0.3376 \| 343 \| 2.2676 \| - \| - \| - \| - \|
	\| 0.3386 \| 344 \| 1.7895 \| - \| - \| - \| - \|
	\| 0.3396 \| 345 \| 2.2506 \| - \| - \| - \| - \|
	\| 0.3406 \| 346 \| 1.5613 \| - \| - \| - \| - \|
	\| 0.3415 \| 347 \| 2.3531 \| - \| - \| - \| - \|
	\| 0.3425 \| 348 \| 1.99 \| - \| - \| - \| - \|
	\| 0.3435 \| 349 \| 12.0831 \| - \| - \| - \| - \|
	\| 0.3445 \| 350 \| 2.0959 \| - \| - \| - \| - \|
	\| 0.3455 \| 351 \| 2.0641 \| - \| - \| - \| - \|
	\| 0.3465 \| 352 \| 1.9197 \| - \| - \| - \| - \|
	\| 0.3474 \| 353 \| 1.9382 \| - \| - \| - \| - \|
	\| 0.3484 \| 354 \| 2.3819 \| - \| - \| - \| - \|
	\| 0.3494 \| 355 \| 1.6053 \| - \| - \| - \| - \|
	\| 0.3504 \| 356 \| 2.4719 \| - \| - \| - \| - \|
	\| 0.3514 \| 357 \| 1.5602 \| - \| - \| - \| - \|
	\| 0.3524 \| 358 \| 2.1675 \| - \| - \| - \| - \|
	\| 0.3533 \| 359 \| 11.5856 \| - \| - \| - \| - \|
	\| 0.3543 \| 360 \| 9.3718 \| - \| - \| - \| - \|
	\| 0.3553 \| 361 \| 1.8952 \| - \| - \| - \| - \|
	\| 0.3563 \| 362 \| 1.701 \| - \| - \| - \| - \|
	\| 0.3573 \| 363 \| 1.46 \| - \| - \| - \| - \|
	\| 0.3583 \| 364 \| 1.7913 \| - \| - \| - \| - \|
	\| 0.3593 \| 365 \| 9.1152 \| - \| - \| - \| - \|
	\| 0.3602 \| 366 \| 9.2681 \| - \| - \| - \| - \|
	\| 0.3612 \| 367 \| 2.2932 \| - \| - \| - \| - \|
	\| 0.3622 \| 368 \| 1.7176 \| - \| - \| - \| - \|
	\| 0.3632 \| 369 \| 2.2559 \| - \| - \| - \| - \|
	\| 0.3642 \| 370 \| 1.9846 \| - \| - \| - \| - \|
	\| 0.3652 \| 371 \| 1.8022 \| - \| - \| - \| - \|
	\| 0.3661 \| 372 \| 8.1128 \| - \| - \| - \| - \|
	\| 0.3671 \| 373 \| 6.929 \| - \| - \| - \| - \|
	\| 0.3681 \| 374 \| 1.9038 \| - \| - \| - \| - \|
	\| 0.3691 \| 375 \| 1.3899 \| - \| - \| - \| - \|
	\| 0.3701 \| 376 \| 1.5677 \| - \| - \| - \| - \|
	\| 0.3711 \| 377 \| 5.2357 \| - \| - \| - \| - \|
	\| 0.3720 \| 378 \| 2.2304 \| - \| - \| - \| - \|
	\| 0.3730 \| 379 \| 2.1727 \| - \| - \| - \| - \|
	\| 0.3740 \| 380 \| 2.2941 \| - \| - \| - \| - \|
	\| 0.375 \| 381 \| 2.2257 \| - \| - \| - \| - \|
	\| 0.3760 \| 382 \| 1.7489 \| - \| - \| - \| - \|
	\| 0.3770 \| 383 \| 1.5027 \| - \| - \| - \| - \|
	\| 0.3780 \| 384 \| 1.6917 \| - \| - \| - \| - \|
	\| 0.3789 \| 385 \| 5.7867 \| - \| - \| - \| - \|
	\| 0.3799 \| 386 \| 1.6871 \| - \| - \| - \| - \|
	\| 0.3809 \| 387 \| 1.5652 \| - \| - \| - \| - \|
	\| 0.3819 \| 388 \| 2.1691 \| - \| - \| - \| - \|
	\| 0.3829 \| 389 \| 1.869 \| - \| - \| - \| - \|
	\| 0.3839 \| 390 \| 2.1934 \| - \| - \| - \| - \|
	\| 0.3848 \| 391 \| 7.0152 \| - \| - \| - \| - \|
	\| 0.3858 \| 392 \| 2.0454 \| - \| - \| - \| - \|
	\| 0.3868 \| 393 \| 1.8098 \| - \| - \| - \| - \|
	\| 0.3878 \| 394 \| 5.7529 \| - \| - \| - \| - \|
	\| 0.3888 \| 395 \| 1.3949 \| - \| - \| - \| - \|
	\| 0.3898 \| 396 \| 1.5962 \| - \| - \| - \| - \|
	\| 0.3907 \| 397 \| 6.1436 \| - \| - \| - \| - \|
	\| 0.3917 \| 398 \| 5.2979 \| - \| - \| - \| - \|
	\| 0.3927 \| 399 \| 1.2422 \| - \| - \| - \| - \|
	\| 0.3937 \| 400 \| 2.1152 \| - \| - \| - \| - \|
	\| 0.3947 \| 401 \| 1.6679 \| - \| - \| - \| - \|
	\| 0.3957 \| 402 \| 4.2978 \| - \| - \| - \| - \|
	\| 0.3967 \| 403 \| 1.624 \| - \| - \| - \| - \|
	\| 0.3976 \| 404 \| 2.0267 \| - \| - \| - \| - \|
	\| 0.3986 \| 405 \| 1.3975 \| - \| - \| - \| - \|
	\| 0.3996 \| 406 \| 1.905 \| - \| - \| - \| - \|
	\| 0.4006 \| 407 \| 5.4419 \| - \| - \| - \| - \|
	\| 0.4016 \| 408 \| 2.0008 \| - \| - \| - \| - \|
	\| 0.4026 \| 409 \| 1.8387 \| - \| - \| - \| - \|
	\| 0.4035 \| 410 \| 2.2391 \| - \| - \| - \| - \|
	\| 0.4045 \| 411 \| 1.7153 \| - \| - \| - \| - \|
	\| 0.4055 \| 412 \| 2.1533 \| - \| - \| - \| - \|
	\| 0.4065 \| 413 \| 1.788 \| - \| - \| - \| - \|
	\| 0.4075 \| 414 \| 3.482 \| - \| - \| - \| - \|
	\| 0.4085 \| 415 \| 1.8376 \| - \| - \| - \| - \|
	\| 0.4094 \| 416 \| 4.8811 \| - \| - \| - \| - \|
	\| 0.4104 \| 417 \| 1.9421 \| - \| - \| - \| - \|
	\| 0.4114 \| 418 \| 1.4796 \| - \| - \| - \| - \|
	\| 0.4124 \| 419 \| 1.6209 \| - \| - \| - \| - \|
	\| 0.4134 \| 420 \| 1.8734 \| - \| - \| - \| - \|
	\| 0.4144 \| 421 \| 1.9444 \| - \| - \| - \| - \|
	\| 0.4154 \| 422 \| 1.9581 \| - \| - \| - \| - \|
	\| 0.4163 \| 423 \| 1.5175 \| - \| - \| - \| - \|
	\| 0.4173 \| 424 \| 1.2831 \| - \| - \| - \| - \|
	\| 0.4183 \| 425 \| 1.1355 \| - \| - \| - \| - \|
	\| 0.4193 \| 426 \| 1.864 \| - \| - \| - \| - \|
	\| 0.4203 \| 427 \| 5.1574 \| - \| - \| - \| - \|
	\| 0.4213 \| 428 \| 5.323 \| - \| - \| - \| - \|
	\| 0.4222 \| 429 \| 1.385 \| - \| - \| - \| - \|
	\| 0.4232 \| 430 \| 1.1691 \| - \| - \| - \| - \|
	\| 0.4242 \| 431 \| 1.8994 \| - \| - \| - \| - \|
	\| 0.4252 \| 432 \| 5.4254 \| - \| - \| - \| - \|
	\| 0.4262 \| 433 \| 1.9113 \| - \| - \| - \| - \|
	\| 0.4272 \| 434 \| 2.1108 \| - \| - \| - \| - \|
	\| 0.4281 \| 435 \| 1.7012 \| - \| - \| - \| - \|
	\| 0.4291 \| 436 \| 1.5722 \| - \| - \| - \| - \|
	\| 0.4301 \| 437 \| 1.5967 \| - \| - \| - \| - \|
	\| 0.4311 \| 438 \| 5.609 \| - \| - \| - \| - \|
	\| 0.4321 \| 439 \| 1.4444 \| - \| - \| - \| - \|
	\| 0.4331 \| 440 \| 5.3153 \| - \| - \| - \| - \|
	\| 0.4341 \| 441 \| 5.0934 \| - \| - \| - \| - \|
	\| 0.4350 \| 442 \| 1.3028 \| - \| - \| - \| - \|
	\| 0.4360 \| 443 \| 1.263 \| - \| - \| - \| - \|
	\| 0.4370 \| 444 \| 1.8462 \| - \| - \| - \| - \|
	\| 0.4380 \| 445 \| 2.1533 \| - \| - \| - \| - \|
	\| 0.4390 \| 446 \| 1.5467 \| - \| - \| - \| - \|
	\| 0.4400 \| 447 \| 1.4331 \| - \| - \| - \| - \|
	\| 0.4409 \| 448 \| 1.4416 \| - \| - \| - \| - \|
	\| 0.4419 \| 449 \| 1.5976 \| - \| - \| - \| - \|
	\| 0.4429 \| 450 \| 1.8723 \| - \| - \| - \| - \|
	\| 0.4439 \| 451 \| 1.1753 \| - \| - \| - \| - \|
	\| 0.4449 \| 452 \| 2.3205 \| - \| - \| - \| - \|
	\| 0.4459 \| 453 \| 1.6467 \| - \| - \| - \| - \|
	\| 0.4469 \| 454 \| 0.9322 \| - \| - \| - \| - \|
	\| 0.4478 \| 455 \| 1.958 \| - \| - \| - \| - \|
	\| 0.4488 \| 456 \| 1.8746 \| - \| - \| - \| - \|
	\| 0.4498 \| 457 \| 1.4546 \| - \| - \| - \| - \|
	\| 0.4508 \| 458 \| 0.9795 \| - \| - \| - \| - \|
	\| 0.4518 \| 459 \| 1.5458 \| 1.2676 \| 0.2751 \| 0.4485 \| 0.6433 \|
	\| 0.4528 \| 460 \| 1.6558 \| - \| - \| - \| - \|
	\| 0.4537 \| 461 \| 1.389 \| - \| - \| - \| - \|
	\| 0.4547 \| 462 \| 1.5608 \| - \| - \| - \| - \|
	\| 0.4557 \| 463 \| 1.6618 \| - \| - \| - \| - \|
	\| 0.4567 \| 464 \| 1.5122 \| - \| - \| - \| - \|
	\| 0.4577 \| 465 \| 1.3602 \| - \| - \| - \| - \|
	\| 0.4587 \| 466 \| 1.6714 \| - \| - \| - \| - \|
	\| 0.4596 \| 467 \| 1.0644 \| - \| - \| - \| - \|
	\| 0.4606 \| 468 \| 7.6421 \| - \| - \| - \| - \|
	\| 0.4616 \| 469 \| 1.2987 \| - \| - \| - \| - \|
	\| 0.4626 \| 470 \| 1.4231 \| - \| - \| - \| - \|
	\| 0.4636 \| 471 \| 7.7424 \| - \| - \| - \| - \|
	\| 0.4646 \| 472 \| 1.6811 \| - \| - \| - \| - \|
	\| 0.4656 \| 473 \| 1.1814 \| - \| - \| - \| - \|
	\| 0.4665 \| 474 \| 1.4486 \| - \| - \| - \| - \|
	\| 0.4675 \| 475 \| 1.3892 \| - \| - \| - \| - \|
	\| 0.4685 \| 476 \| 1.3681 \| - \| - \| - \| - \|
	\| 0.4695 \| 477 \| 1.3081 \| - \| - \| - \| - \|
	\| 0.4705 \| 478 \| 0.9102 \| - \| - \| - \| - \|
	\| 0.4715 \| 479 \| 1.0992 \| - \| - \| - \| - \|
	\| 0.4724 \| 480 \| 6.018 \| - \| - \| - \| - \|
	\| 0.4734 \| 481 \| 6.0908 \| - \| - \| - \| - \|
	\| 0.4744 \| 482 \| 1.2245 \| - \| - \| - \| - \|
	\| 0.4754 \| 483 \| 1.4825 \| - \| - \| - \| - \|
	\| 0.4764 \| 484 \| 1.8037 \| - \| - \| - \| - \|
	\| 0.4774 \| 485 \| 1.3611 \| - \| - \| - \| - \|
	\| 0.4783 \| 486 \| 1.7482 \| - \| - \| - \| - \|
	\| 0.4793 \| 487 \| 1.6385 \| - \| - \| - \| - \|
	\| 0.4803 \| 488 \| 1.3245 \| - \| - \| - \| - \|
	\| 0.4813 \| 489 \| 1.5638 \| - \| - \| - \| - \|
	\| 0.4823 \| 490 \| 1.566 \| - \| - \| - \| - \|
	\| 0.4833 \| 491 \| 1.9482 \| - \| - \| - \| - \|
	\| 0.4843 \| 492 \| 6.0859 \| - \| - \| - \| - \|
	\| 0.4852 \| 493 \| 5.8754 \| - \| - \| - \| - \|
	\| 0.4862 \| 494 \| 0.9964 \| - \| - \| - \| - \|
	\| 0.4872 \| 495 \| 1.5949 \| - \| - \| - \| - \|
	\| 0.4882 \| 496 \| 1.3167 \| - \| - \| - \| - \|
	\| 0.4892 \| 497 \| 3.9345 \| - \| - \| - \| - \|
	\| 0.4902 \| 498 \| 4.3886 \| - \| - \| - \| - \|
	\| 0.4911 \| 499 \| 1.6124 \| - \| - \| - \| - \|
	\| 0.4921 \| 500 \| 1.2145 \| - \| - \| - \| - \|
	\| 0.4931 \| 501 \| 3.5499 \| - \| - \| - \| - \|
	\| 0.4941 \| 502 \| 1.2999 \| - \| - \| - \| - \|
	\| 0.4951 \| 503 \| 1.2375 \| - \| - \| - \| - \|
	\| 0.4961 \| 504 \| 1.1606 \| - \| - \| - \| - \|
	\| 0.4970 \| 505 \| 1.4634 \| - \| - \| - \| - \|
	\| 0.4980 \| 506 \| 1.35 \| - \| - \| - \| - \|
	\| 0.4990 \| 507 \| 1.7187 \| - \| - \| - \| - \|
	\| 0.5 \| 508 \| 1.5915 \| - \| - \| - \| - \|
	\| 0.5010 \| 509 \| 1.2357 \| - \| - \| - \| - \|
	\| 0.5020 \| 510 \| 3.4122 \| - \| - \| - \| - \|
	\| 0.5030 \| 511 \| 4.244 \| - \| - \| - \| - \|
	\| 0.5039 \| 512 \| 0.9151 \| - \| - \| - \| - \|
	\| 0.5049 \| 513 \| 1.4323 \| - \| - \| - \| - \|
	\| 0.5059 \| 514 \| 1.4824 \| - \| - \| - \| - \|
	\| 0.5069 \| 515 \| 1.339 \| - \| - \| - \| - \|
	\| 0.5079 \| 516 \| 4.1658 \| - \| - \| - \| - \|
	\| 0.5089 \| 517 \| 1.3062 \| - \| - \| - \| - \|
	\| 0.5098 \| 518 \| 1.2905 \| - \| - \| - \| - \|
	\| 0.5108 \| 519 \| 1.1487 \| - \| - \| - \| - \|
	\| 0.5118 \| 520 \| 2.8652 \| - \| - \| - \| - \|
	\| 0.5128 \| 521 \| 1.2634 \| - \| - \| - \| - \|
	\| 0.5138 \| 522 \| 1.6745 \| - \| - \| - \| - \|
	\| 0.5148 \| 523 \| 1.6548 \| - \| - \| - \| - \|
	\| 0.5157 \| 524 \| 2.4204 \| - \| - \| - \| - \|
	\| 0.5167 \| 525 \| 1.7201 \| - \| - \| - \| - \|
	\| 0.5177 \| 526 \| 1.761 \| - \| - \| - \| - \|
	\| 0.5187 \| 527 \| 2.7098 \| - \| - \| - \| - \|
	\| 0.5197 \| 528 \| 1.6425 \| - \| - \| - \| - \|
	\| 0.5207 \| 529 \| 1.2466 \| - \| - \| - \| - \|
	\| 0.5217 \| 530 \| 1.3339 \| - \| - \| - \| - \|
	\| 0.5226 \| 531 \| 1.2398 \| - \| - \| - \| - \|
	\| 0.5236 \| 532 \| 3.5325 \| - \| - \| - \| - \|
	\| 0.5246 \| 533 \| 1.1303 \| - \| - \| - \| - \|
	\| 0.5256 \| 534 \| 1.2601 \| - \| - \| - \| - \|
	\| 0.5266 \| 535 \| 1.5762 \| - \| - \| - \| - \|
	\| 0.5276 \| 536 \| 1.3992 \| - \| - \| - \| - \|
	\| 0.5285 \| 537 \| 1.7125 \| - \| - \| - \| - \|
	\| 0.5295 \| 538 \| 3.6759 \| - \| - \| - \| - \|
	\| 0.5305 \| 539 \| 1.5468 \| - \| - \| - \| - \|
	\| 0.5315 \| 540 \| 1.4316 \| - \| - \| - \| - \|
	\| 0.5325 \| 541 \| 1.2797 \| - \| - \| - \| - \|
	\| 0.5335 \| 542 \| 1.9122 \| - \| - \| - \| - \|
	\| 0.5344 \| 543 \| 2.0367 \| - \| - \| - \| - \|
	\| 0.5354 \| 544 \| 3.3029 \| - \| - \| - \| - \|
	\| 0.5364 \| 545 \| 3.9263 \| - \| - \| - \| - \|
	\| 0.5374 \| 546 \| 3.0101 \| - \| - \| - \| - \|
	\| 0.5384 \| 547 \| 3.3555 \| - \| - \| - \| - \|
	\| 0.5394 \| 548 \| 1.2068 \| - \| - \| - \| - \|
	\| 0.5404 \| 549 \| 1.1566 \| - \| - \| - \| - \|
	\| 0.5413 \| 550 \| 1.2773 \| - \| - \| - \| - \|
	\| 0.5423 \| 551 \| 1.4047 \| - \| - \| - \| - \|
	\| 0.5433 \| 552 \| 1.6048 \| - \| - \| - \| - \|
	\| 0.5443 \| 553 \| 1.217 \| - \| - \| - \| - \|
	\| 0.5453 \| 554 \| 1.8104 \| - \| - \| - \| - \|
	\| 0.5463 \| 555 \| 1.687 \| - \| - \| - \| - \|
	\| 0.5472 \| 556 \| 1.6702 \| - \| - \| - \| - \|
	\| 0.5482 \| 557 \| 1.7011 \| - \| - \| - \| - \|
	\| 0.5492 \| 558 \| 1.7341 \| - \| - \| - \| - \|
	\| 0.5502 \| 559 \| 1.5006 \| - \| - \| - \| - \|
	\| 0.5512 \| 560 \| 1.2778 \| - \| - \| - \| - \|
	\| 0.5522 \| 561 \| 1.5081 \| - \| - \| - \| - \|
	\| 0.5531 \| 562 \| 1.2398 \| - \| - \| - \| - \|
	\| 0.5541 \| 563 \| 1.1054 \| - \| - \| - \| - \|
	\| 0.5551 \| 564 \| 4.0185 \| - \| - \| - \| - \|
	\| 0.5561 \| 565 \| 1.0427 \| - \| - \| - \| - \|
	\| 0.5571 \| 566 \| 1.3934 \| - \| - \| - \| - \|
	\| 0.5581 \| 567 \| 1.2378 \| - \| - \| - \| - \|
	\| 0.5591 \| 568 \| 1.022 \| - \| - \| - \| - \|
	\| 0.5600 \| 569 \| 0.9001 \| - \| - \| - \| - \|
	\| 0.5610 \| 570 \| 1.3279 \| - \| - \| - \| - \|
	\| 0.5620 \| 571 \| 1.2889 \| - \| - \| - \| - \|
	\| 0.5630 \| 572 \| 0.9383 \| - \| - \| - \| - \|
	\| 0.5640 \| 573 \| 1.749 \| - \| - \| - \| - \|
	\| 0.5650 \| 574 \| 0.7669 \| - \| - \| - \| - \|
	\| 0.5659 \| 575 \| 0.9355 \| - \| - \| - \| - \|
	\| 0.5669 \| 576 \| 1.3596 \| - \| - \| - \| - \|
	\| 0.5679 \| 577 \| 5.5102 \| - \| - \| - \| - \|
	\| 0.5689 \| 578 \| 0.7984 \| - \| - \| - \| - \|
	\| 0.5699 \| 579 \| 0.8871 \| - \| - \| - \| - \|
	\| 0.5709 \| 580 \| 1.1151 \| - \| - \| - \| - \|
	\| 0.5719 \| 581 \| 0.9502 \| - \| - \| - \| - \|
	\| 0.5728 \| 582 \| 3.6492 \| - \| - \| - \| - \|
	\| 0.5738 \| 583 \| 3.4262 \| - \| - \| - \| - \|
	\| 0.5748 \| 584 \| 1.3362 \| - \| - \| - \| - \|
	\| 0.5758 \| 585 \| 0.9015 \| - \| - \| - \| - \|
	\| 0.5768 \| 586 \| 1.5884 \| - \| - \| - \| - \|
	\| 0.5778 \| 587 \| 1.109 \| - \| - \| - \| - \|
	\| 0.5787 \| 588 \| 1.041 \| - \| - \| - \| - \|
	\| 0.5797 \| 589 \| 1.4892 \| - \| - \| - \| - \|
	\| 0.5807 \| 590 \| 1.2623 \| - \| - \| - \| - \|
	\| 0.5817 \| 591 \| 1.5302 \| - \| - \| - \| - \|
	\| 0.5827 \| 592 \| 1.3517 \| - \| - \| - \| - \|
	\| 0.5837 \| 593 \| 0.6166 \| - \| - \| - \| - \|
	\| 0.5846 \| 594 \| 1.6761 \| - \| - \| - \| - \|
	\| 0.5856 \| 595 \| 1.1115 \| - \| - \| - \| - \|
	\| 0.5866 \| 596 \| 1.2945 \| - \| - \| - \| - \|
	\| 0.5876 \| 597 \| 1.4378 \| - \| - \| - \| - \|
	\| 0.5886 \| 598 \| 0.9928 \| - \| - \| - \| - \|
	\| 0.5896 \| 599 \| 0.9898 \| - \| - \| - \| - \|
	\| 0.5906 \| 600 \| 4.6887 \| - \| - \| - \| - \|
	\| 0.5915 \| 601 \| 1.2254 \| - \| - \| - \| - \|
	\| 0.5925 \| 602 \| 1.2707 \| - \| - \| - \| - \|
	\| 0.5935 \| 603 \| 1.8289 \| - \| - \| - \| - \|
	\| 0.5945 \| 604 \| 0.7801 \| - \| - \| - \| - \|
	\| 0.5955 \| 605 \| 0.9111 \| - \| - \| - \| - \|
	\| 0.5965 \| 606 \| 1.1405 \| - \| - \| - \| - \|
	\| 0.5974 \| 607 \| 1.0497 \| - \| - \| - \| - \|
	\| 0.5984 \| 608 \| 1.0792 \| - \| - \| - \| - \|
	\| 0.5994 \| 609 \| 0.9699 \| - \| - \| - \| - \|
	\| 0.6004 \| 610 \| 0.9398 \| - \| - \| - \| - \|
	\| 0.6014 \| 611 \| 1.5483 \| - \| - \| - \| - \|
	\| 0.6024 \| 612 \| 0.997 \| 1.0047 \| 0.3980 \| 0.4554 \| 0.6701 \|
	\| 0.6033 \| 613 \| 0.8358 \| - \| - \| - \| - \|
	\| 0.6043 \| 614 \| 1.211 \| - \| - \| - \| - \|
	\| 0.6053 \| 615 \| 6.7813 \| - \| - \| - \| - \|
	\| 0.6063 \| 616 \| 1.1229 \| - \| - \| - \| - \|
	\| 0.6073 \| 617 \| 1.0317 \| - \| - \| - \| - \|
	\| 0.6083 \| 618 \| 1.2123 \| - \| - \| - \| - \|
	\| 0.6093 \| 619 \| 1.4073 \| - \| - \| - \| - \|
	\| 0.6102 \| 620 \| 0.9951 \| - \| - \| - \| - \|
	\| 0.6112 \| 621 \| 1.3166 \| - \| - \| - \| - \|
	\| 0.6122 \| 622 \| 4.5204 \| - \| - \| - \| - \|
	\| 0.6132 \| 623 \| 0.6539 \| - \| - \| - \| - \|
	\| 0.6142 \| 624 \| 1.1959 \| - \| - \| - \| - \|
	\| 0.6152 \| 625 \| 4.2551 \| - \| - \| - \| - \|
	\| 0.6161 \| 626 \| 1.2459 \| - \| - \| - \| - \|
	\| 0.6171 \| 627 \| 1.3758 \| - \| - \| - \| - \|
	\| 0.6181 \| 628 \| 1.0524 \| - \| - \| - \| - \|
	\| 0.6191 \| 629 \| 1.5197 \| - \| - \| - \| - \|
	\| 0.6201 \| 630 \| 1.0201 \| - \| - \| - \| - \|
	\| 0.6211 \| 631 \| 0.9007 \| - \| - \| - \| - \|
	\| 0.6220 \| 632 \| 0.8418 \| - \| - \| - \| - \|
	\| 0.6230 \| 633 \| 1.4343 \| - \| - \| - \| - \|
	\| 0.6240 \| 634 \| 0.5292 \| - \| - \| - \| - \|
	\| 0.625 \| 635 \| 0.8549 \| - \| - \| - \| - \|
	\| 0.6260 \| 636 \| 0.8703 \| - \| - \| - \| - \|
	\| 0.6270 \| 637 \| 0.9911 \| - \| - \| - \| - \|
	\| 0.6280 \| 638 \| 1.3342 \| - \| - \| - \| - \|
	\| 0.6289 \| 639 \| 1.1332 \| - \| - \| - \| - \|
	\| 0.6299 \| 640 \| 3.9965 \| - \| - \| - \| - \|
	\| 0.6309 \| 641 \| 0.7236 \| - \| - \| - \| - \|
	\| 0.6319 \| 642 \| 0.9079 \| - \| - \| - \| - \|
	\| 0.6329 \| 643 \| 1.0967 \| - \| - \| - \| - \|
	\| 0.6339 \| 644 \| 1.4183 \| - \| - \| - \| - \|
	\| 0.6348 \| 645 \| 1.3841 \| - \| - \| - \| - \|
	\| 0.6358 \| 646 \| 1.2982 \| - \| - \| - \| - \|
	\| 0.6368 \| 647 \| 0.9048 \| - \| - \| - \| - \|
	\| 0.6378 \| 648 \| 0.7918 \| - \| - \| - \| - \|
	\| 0.6388 \| 649 \| 0.3685 \| - \| - \| - \| - \|
	\| 0.6398 \| 650 \| 0.6949 \| - \| - \| - \| - \|
	\| 0.6407 \| 651 \| 5.1568 \| - \| - \| - \| - \|
	\| 0.6417 \| 652 \| 1.3943 \| - \| - \| - \| - \|
	\| 0.6427 \| 653 \| 0.8608 \| - \| - \| - \| - \|
	\| 0.6437 \| 654 \| 0.8197 \| - \| - \| - \| - \|
	\| 0.6447 \| 655 \| 0.822 \| - \| - \| - \| - \|
	\| 0.6457 \| 656 \| 3.2918 \| - \| - \| - \| - \|
	\| 0.6467 \| 657 \| 0.5596 \| - \| - \| - \| - \|
	\| 0.6476 \| 658 \| 4.1499 \| - \| - \| - \| - \|
	\| 0.6486 \| 659 \| 1.0279 \| - \| - \| - \| - \|
	\| 0.6496 \| 660 \| 1.1506 \| - \| - \| - \| - \|
	\| 0.6506 \| 661 \| 1.1673 \| - \| - \| - \| - \|
	\| 0.6516 \| 662 \| 0.96 \| - \| - \| - \| - \|
	\| 0.6526 \| 663 \| 3.5414 \| - \| - \| - \| - \|
	\| 0.6535 \| 664 \| 0.6599 \| - \| - \| - \| - \|
	\| 0.6545 \| 665 \| 3.5518 \| - \| - \| - \| - \|
	\| 0.6555 \| 666 \| 1.1906 \| - \| - \| - \| - \|
	\| 0.6565 \| 667 \| 2.1353 \| - \| - \| - \| - \|
	\| 0.6575 \| 668 \| 0.7083 \| - \| - \| - \| - \|
	\| 0.6585 \| 669 \| 2.9425 \| - \| - \| - \| - \|
	\| 0.6594 \| 670 \| 0.9433 \| - \| - \| - \| - \|
	\| 0.6604 \| 671 \| 1.8499 \| - \| - \| - \| - \|
	\| 0.6614 \| 672 \| 1.1614 \| - \| - \| - \| - \|
	\| 0.6624 \| 673 \| 1.0474 \| - \| - \| - \| - \|
	\| 0.6634 \| 674 \| 1.2895 \| - \| - \| - \| - \|
	\| 0.6644 \| 675 \| 0.9789 \| - \| - \| - \| - \|
	\| 0.6654 \| 676 \| 0.7719 \| - \| - \| - \| - \|
	\| 0.6663 \| 677 \| 1.2203 \| - \| - \| - \| - \|
	\| 0.6673 \| 678 \| 1.0516 \| - \| - \| - \| - \|
	\| 0.6683 \| 679 \| 2.5514 \| - \| - \| - \| - \|
	\| 0.6693 \| 680 \| 0.7346 \| - \| - \| - \| - \|
	\| 0.6703 \| 681 \| 1.0245 \| - \| - \| - \| - \|
	\| 0.6713 \| 682 \| 2.8005 \| - \| - \| - \| - \|
	\| 0.6722 \| 683 \| 1.3212 \| - \| - \| - \| - \|
	\| 0.6732 \| 684 \| 0.95 \| - \| - \| - \| - \|
	\| 0.6742 \| 685 \| 1.0483 \| - \| - \| - \| - \|
	\| 0.6752 \| 686 \| 0.8504 \| - \| - \| - \| - \|
	\| 0.6762 \| 687 \| 2.281 \| - \| - \| - \| - \|
	\| 0.6772 \| 688 \| 1.8153 \| - \| - \| - \| - \|
	\| 0.6781 \| 689 \| 1.3652 \| - \| - \| - \| - \|
	\| 0.6791 \| 690 \| 1.0949 \| - \| - \| - \| - \|
	\| 0.6801 \| 691 \| 1.2196 \| - \| - \| - \| - \|
	\| 0.6811 \| 692 \| 0.7995 \| - \| - \| - \| - \|
	\| 0.6821 \| 693 \| 1.5108 \| - \| - \| - \| - \|
	\| 0.6831 \| 694 \| 0.7933 \| - \| - \| - \| - \|
	\| 0.6841 \| 695 \| 1.2367 \| - \| - \| - \| - \|
	\| 0.6850 \| 696 \| 1.0352 \| - \| - \| - \| - \|
	\| 0.6860 \| 697 \| 1.1709 \| - \| - \| - \| - \|
	\| 0.6870 \| 698 \| 1.452 \| - \| - \| - \| - \|
	\| 0.6880 \| 699 \| 0.8497 \| - \| - \| - \| - \|
	\| 0.6890 \| 700 \| 2.8109 \| - \| - \| - \| - \|
	\| 0.6900 \| 701 \| 2.6196 \| - \| - \| - \| - \|
	\| 0.6909 \| 702 \| 1.4556 \| - \| - \| - \| - \|
	\| 0.6919 \| 703 \| 1.3494 \| - \| - \| - \| - \|
	\| 0.6929 \| 704 \| 1.6624 \| - \| - \| - \| - \|
	\| 0.6939 \| 705 \| 1.6169 \| - \| - \| - \| - \|
	\| 0.6949 \| 706 \| 0.5565 \| - \| - \| - \| - \|
	\| 0.6959 \| 707 \| 0.8594 \| - \| - \| - \| - \|
	\| 0.6969 \| 708 \| 0.8551 \| - \| - \| - \| - \|
	\| 0.6978 \| 709 \| 1.1693 \| - \| - \| - \| - \|
	\| 0.6988 \| 710 \| 1.0514 \| - \| - \| - \| - \|
	\| 0.6998 \| 711 \| 1.1862 \| - \| - \| - \| - \|
	\| 0.7008 \| 712 \| 0.8359 \| - \| - \| - \| - \|
	\| 0.7018 \| 713 \| 0.7692 \| - \| - \| - \| - \|
	\| 0.7028 \| 714 \| 1.815 \| - \| - \| - \| - \|
	\| 0.7037 \| 715 \| 1.0751 \| - \| - \| - \| - \|
	\| 0.7047 \| 716 \| 0.6526 \| - \| - \| - \| - \|
	\| 0.7057 \| 717 \| 1.1617 \| - \| - \| - \| - \|
	\| 0.7067 \| 718 \| 1.0783 \| - \| - \| - \| - \|
	\| 0.7077 \| 719 \| 0.7916 \| - \| - \| - \| - \|
	\| 0.7087 \| 720 \| 1.3039 \| - \| - \| - \| - \|
	\| 0.7096 \| 721 \| 1.1156 \| - \| - \| - \| - \|
	\| 0.7106 \| 722 \| 1.0529 \| - \| - \| - \| - \|
	\| 0.7116 \| 723 \| 0.8265 \| - \| - \| - \| - \|
	\| 0.7126 \| 724 \| 0.8019 \| - \| - \| - \| - \|
	\| 0.7136 \| 725 \| 0.6116 \| - \| - \| - \| - \|
	\| 0.7146 \| 726 \| 1.135 \| - \| - \| - \| - \|
	\| 0.7156 \| 727 \| 0.7692 \| - \| - \| - \| - \|
	\| 0.7165 \| 728 \| 2.3559 \| - \| - \| - \| - \|
	\| 0.7175 \| 729 \| 1.352 \| - \| - \| - \| - \|
	\| 0.7185 \| 730 \| 2.823 \| - \| - \| - \| - \|
	\| 0.7195 \| 731 \| 1.0067 \| - \| - \| - \| - \|
	\| 0.7205 \| 732 \| 0.9077 \| - \| - \| - \| - \|
	\| 0.7215 \| 733 \| 1.0933 \| - \| - \| - \| - \|
	\| 0.7224 \| 734 \| 0.8174 \| - \| - \| - \| - \|
	\| 0.7234 \| 735 \| 1.2212 \| - \| - \| - \| - \|
	\| 0.7244 \| 736 \| 1.1557 \| - \| - \| - \| - \|
	\| 0.7254 \| 737 \| 0.6191 \| - \| - \| - \| - \|
	\| 0.7264 \| 738 \| 1.7437 \| - \| - \| - \| - \|
	\| 0.7274 \| 739 \| 0.8977 \| - \| - \| - \| - \|
	\| 0.7283 \| 740 \| 1.0782 \| - \| - \| - \| - \|
	\| 0.7293 \| 741 \| 0.8985 \| - \| - \| - \| - \|
	\| 0.7303 \| 742 \| 1.4867 \| - \| - \| - \| - \|
	\| 0.7313 \| 743 \| 0.7497 \| - \| - \| - \| - \|
	\| 0.7323 \| 744 \| 0.6433 \| - \| - \| - \| - \|
	\| 0.7333 \| 745 \| 1.4175 \| - \| - \| - \| - \|
	\| 0.7343 \| 746 \| 1.1896 \| - \| - \| - \| - \|
	\| 0.7352 \| 747 \| 1.9867 \| - \| - \| - \| - \|
	\| 0.7362 \| 748 \| 0.8968 \| - \| - \| - \| - \|
	\| 0.7372 \| 749 \| 0.7265 \| - \| - \| - \| - \|
	\| 0.7382 \| 750 \| 0.9418 \| - \| - \| - \| - \|
	\| 0.7392 \| 751 \| 1.3717 \| - \| - \| - \| - \|
	\| 0.7402 \| 752 \| 2.1774 \| - \| - \| - \| - \|
	\| 0.7411 \| 753 \| 1.0854 \| - \| - \| - \| - \|
	\| 0.7421 \| 754 \| 0.9777 \| - \| - \| - \| - \|
	\| 0.7431 \| 755 \| 1.2721 \| - \| - \| - \| - \|
	\| 0.7441 \| 756 \| 0.7114 \| - \| - \| - \| - \|
	\| 0.7451 \| 757 \| 1.4036 \| - \| - \| - \| - \|
	\| 0.7461 \| 758 \| 1.1742 \| - \| - \| - \| - \|
	\| 0.7470 \| 759 \| 0.9351 \| - \| - \| - \| - \|
	\| 0.7480 \| 760 \| 0.5537 \| - \| - \| - \| - \|
	\| 0.7490 \| 761 \| 0.8688 \| - \| - \| - \| - \|
	\| 0.75 \| 762 \| 3.0053 \| - \| - \| - \| - \|
	\| 0.7510 \| 763 \| 3.3743 \| - \| - \| - \| - \|
	\| 0.7520 \| 764 \| 1.9928 \| - \| - \| - \| - \|
	\| 0.7530 \| 765 \| 1.5118 \| 0.9342 \| 0.4514 \| 0.4792 \| 0.6782 \|
	\| 0.7539 \| 766 \| 1.1213 \| - \| - \| - \| - \|
	\| 0.7549 \| 767 \| 2.1312 \| - \| - \| - \| - \|
	\| 0.7559 \| 768 \| 1.3739 \| - \| - \| - \| - \|
	\| 0.7569 \| 769 \| 0.8819 \| - \| - \| - \| - \|
	\| 0.7579 \| 770 \| 0.9069 \| - \| - \| - \| - \|
	\| 0.7589 \| 771 \| 0.935 \| - \| - \| - \| - \|
	\| 0.7598 \| 772 \| 0.7874 \| - \| - \| - \| - \|
	\| 0.7608 \| 773 \| 1.9942 \| - \| - \| - \| - \|
	\| 0.7618 \| 774 \| 1.1711 \| - \| - \| - \| - \|
	\| 0.7628 \| 775 \| 0.8407 \| - \| - \| - \| - \|
	\| 0.7638 \| 776 \| 1.5171 \| - \| - \| - \| - \|
	\| 0.7648 \| 777 \| 0.5308 \| - \| - \| - \| - \|
	\| 0.7657 \| 778 \| 1.4107 \| - \| - \| - \| - \|
	\| 0.7667 \| 779 \| 1.1766 \| - \| - \| - \| - \|
	\| 0.7677 \| 780 \| 1.326 \| - \| - \| - \| - \|
	\| 0.7687 \| 781 \| 0.7371 \| - \| - \| - \| - \|
	\| 0.7697 \| 782 \| 1.0504 \| - \| - \| - \| - \|
	\| 0.7707 \| 783 \| 1.1458 \| - \| - \| - \| - \|
	\| 0.7717 \| 784 \| 0.7242 \| - \| - \| - \| - \|
	\| 0.7726 \| 785 \| 0.8113 \| - \| - \| - \| - \|
	\| 0.7736 \| 786 \| 1.3808 \| - \| - \| - \| - \|
	\| 0.7746 \| 787 \| 0.7584 \| - \| - \| - \| - \|
	\| 0.7756 \| 788 \| 1.226 \| - \| - \| - \| - \|
	\| 0.7766 \| 789 \| 1.0599 \| - \| - \| - \| - \|
	\| 0.7776 \| 790 \| 2.9348 \| - \| - \| - \| - \|
	\| 0.7785 \| 791 \| 1.0849 \| - \| - \| - \| - \|
	\| 0.7795 \| 792 \| 0.5362 \| - \| - \| - \| - \|
	\| 0.7805 \| 793 \| 1.3765 \| - \| - \| - \| - \|
	\| 0.7815 \| 794 \| 0.6824 \| - \| - \| - \| - \|
	\| 0.7825 \| 795 \| 0.6009 \| - \| - \| - \| - \|
	\| 0.7835 \| 796 \| 2.3853 \| - \| - \| - \| - \|
	\| 0.7844 \| 797 \| 1.0571 \| - \| - \| - \| - \|
	\| 0.7854 \| 798 \| 0.9172 \| - \| - \| - \| - \|
	\| 0.7864 \| 799 \| 0.7915 \| - \| - \| - \| - \|
	\| 0.7874 \| 800 \| 0.827 \| - \| - \| - \| - \|
	\| 0.7884 \| 801 \| 0.8465 \| - \| - \| - \| - \|
	\| 0.7894 \| 802 \| 2.3489 \| - \| - \| - \| - \|
	\| 0.7904 \| 803 \| 0.6506 \| - \| - \| - \| - \|
	\| 0.7913 \| 804 \| 0.8346 \| - \| - \| - \| - \|
	\| 0.7923 \| 805 \| 0.6249 \| - \| - \| - \| - \|
	\| 0.7933 \| 806 \| 1.0557 \| - \| - \| - \| - \|
	\| 0.7943 \| 807 \| 0.7552 \| - \| - \| - \| - \|
	\| 0.7953 \| 808 \| 1.281 \| - \| - \| - \| - \|
	\| 0.7963 \| 809 \| 0.7846 \| - \| - \| - \| - \|
	\| 0.7972 \| 810 \| 2.6403 \| - \| - \| - \| - \|
	\| 0.7982 \| 811 \| 0.3679 \| - \| - \| - \| - \|
	\| 0.7992 \| 812 \| 1.9118 \| - \| - \| - \| - \|
	\| 0.8002 \| 813 \| 2.5911 \| - \| - \| - \| - \|
	\| 0.8012 \| 814 \| 1.1783 \| - \| - \| - \| - \|
	\| 0.8022 \| 815 \| 0.9347 \| - \| - \| - \| - \|
	\| 0.8031 \| 816 \| 0.5311 \| - \| - \| - \| - \|
	\| 0.8041 \| 817 \| 0.7092 \| - \| - \| - \| - \|
	\| 0.8051 \| 818 \| 0.8384 \| - \| - \| - \| - \|
	\| 0.8061 \| 819 \| 0.514 \| - \| - \| - \| - \|
	\| 0.8071 \| 820 \| 0.3638 \| - \| - \| - \| - \|
	\| 0.8081 \| 821 \| 1.9376 \| - \| - \| - \| - \|
	\| 0.8091 \| 822 \| 0.9177 \| - \| - \| - \| - \|
	\| 0.8100 \| 823 \| 0.8293 \| - \| - \| - \| - \|
	\| 0.8110 \| 824 \| 0.7269 \| - \| - \| - \| - \|
	\| 0.8120 \| 825 \| 0.664 \| - \| - \| - \| - \|
	\| 0.8130 \| 826 \| 0.6205 \| - \| - \| - \| - \|
	\| 0.8140 \| 827 \| 0.6562 \| - \| - \| - \| - \|
	\| 0.8150 \| 828 \| 0.6576 \| - \| - \| - \| - \|
	\| 0.8159 \| 829 \| 0.9931 \| - \| - \| - \| - \|
	\| 0.8169 \| 830 \| 1.1707 \| - \| - \| - \| - \|
	\| 0.8179 \| 831 \| 0.8635 \| - \| - \| - \| - \|
	\| 0.8189 \| 832 \| 0.7274 \| - \| - \| - \| - \|
	\| 0.8199 \| 833 \| 1.6808 \| - \| - \| - \| - \|
	\| 0.8209 \| 834 \| 1.8309 \| - \| - \| - \| - \|
	\| 0.8219 \| 835 \| 0.6191 \| - \| - \| - \| - \|
	\| 0.8228 \| 836 \| 1.0789 \| - \| - \| - \| - \|
	\| 0.8238 \| 837 \| 1.1637 \| - \| - \| - \| - \|
	\| 0.8248 \| 838 \| 0.7813 \| - \| - \| - \| - \|
	\| 0.8258 \| 839 \| 1.0403 \| - \| - \| - \| - \|
	\| 0.8268 \| 840 \| 0.7656 \| - \| - \| - \| - \|
	\| 0.8278 \| 841 \| 0.9994 \| - \| - \| - \| - \|
	\| 0.8287 \| 842 \| 1.009 \| - \| - \| - \| - \|
	\| 0.8297 \| 843 \| 0.8585 \| - \| - \| - \| - \|
	\| 0.8307 \| 844 \| 0.8847 \| - \| - \| - \| - \|
	\| 0.8317 \| 845 \| 0.8321 \| - \| - \| - \| - \|
	\| 0.8327 \| 846 \| 1.2605 \| - \| - \| - \| - \|
	\| 0.8337 \| 847 \| 1.0609 \| - \| - \| - \| - \|
	\| 0.8346 \| 848 \| 2.0115 \| - \| - \| - \| - \|
	\| 0.8356 \| 849 \| 1.2952 \| - \| - \| - \| - \|
	\| 0.8366 \| 850 \| 0.6999 \| - \| - \| - \| - \|
	\| 0.8376 \| 851 \| 0.7006 \| - \| - \| - \| - \|
	\| 0.8386 \| 852 \| 0.927 \| - \| - \| - \| - \|
	\| 0.8396 \| 853 \| 1.2083 \| - \| - \| - \| - \|
	\| 0.8406 \| 854 \| 0.608 \| - \| - \| - \| - \|
	\| 0.8415 \| 855 \| 0.8478 \| - \| - \| - \| - \|
	\| 0.8425 \| 856 \| 1.5731 \| - \| - \| - \| - \|
	\| 0.8435 \| 857 \| 1.6353 \| - \| - \| - \| - \|
	\| 0.8445 \| 858 \| 0.7862 \| - \| - \| - \| - \|
	\| 0.8455 \| 859 \| 0.8909 \| - \| - \| - \| - \|
	\| 0.8465 \| 860 \| 1.1719 \| - \| - \| - \| - \|
	\| 0.8474 \| 861 \| 1.2722 \| - \| - \| - \| - \|
	\| 0.8484 \| 862 \| 1.0022 \| - \| - \| - \| - \|
	\| 0.8494 \| 863 \| 1.5307 \| - \| - \| - \| - \|
	\| 0.8504 \| 864 \| 1.0162 \| - \| - \| - \| - \|
	\| 0.8514 \| 865 \| 0.6827 \| - \| - \| - \| - \|
	\| 0.8524 \| 866 \| 0.7744 \| - \| - \| - \| - \|
	\| 0.8533 \| 867 \| 1.2011 \| - \| - \| - \| - \|
	\| 0.8543 \| 868 \| 0.9219 \| - \| - \| - \| - \|
	\| 0.8553 \| 869 \| 0.7636 \| - \| - \| - \| - \|
	\| 0.8563 \| 870 \| 1.5061 \| - \| - \| - \| - \|
	\| 0.8573 \| 871 \| 1.5569 \| - \| - \| - \| - \|
	\| 0.8583 \| 872 \| 0.5896 \| - \| - \| - \| - \|
	\| 0.8593 \| 873 \| 1.1918 \| - \| - \| - \| - \|
	\| 0.8602 \| 874 \| 0.8572 \| - \| - \| - \| - \|
	\| 0.8612 \| 875 \| 1.0421 \| - \| - \| - \| - \|
	\| 0.8622 \| 876 \| 2.4599 \| - \| - \| - \| - \|
	\| 0.8632 \| 877 \| 0.55 \| - \| - \| - \| - \|
	\| 0.8642 \| 878 \| 1.2829 \| - \| - \| - \| - \|
	\| 0.8652 \| 879 \| 0.7808 \| - \| - \| - \| - \|
	\| 0.8661 \| 880 \| 1.7712 \| - \| - \| - \| - \|
	\| 0.8671 \| 881 \| 0.7456 \| - \| - \| - \| - \|
	\| 0.8681 \| 882 \| 1.2805 \| - \| - \| - \| - \|
	\| 0.8691 \| 883 \| 2.1927 \| - \| - \| - \| - \|
	\| 0.8701 \| 884 \| 0.855 \| - \| - \| - \| - \|
	\| 0.8711 \| 885 \| 0.667 \| - \| - \| - \| - \|
	\| 0.8720 \| 886 \| 1.1097 \| - \| - \| - \| - \|
	\| 0.8730 \| 887 \| 1.8795 \| - \| - \| - \| - \|
	\| 0.8740 \| 888 \| 0.6767 \| - \| - \| - \| - \|
	\| 0.875 \| 889 \| 0.7549 \| - \| - \| - \| - \|
	\| 0.8760 \| 890 \| 0.8616 \| - \| - \| - \| - \|
	\| 0.8770 \| 891 \| 1.9461 \| - \| - \| - \| - \|
	\| 0.8780 \| 892 \| 1.2694 \| - \| - \| - \| - \|
	\| 0.8789 \| 893 \| 1.825 \| - \| - \| - \| - \|
	\| 0.8799 \| 894 \| 0.9218 \| - \| - \| - \| - \|
	\| 0.8809 \| 895 \| 1.0297 \| - \| - \| - \| - \|
	\| 0.8819 \| 896 \| 0.609 \| - \| - \| - \| - \|
	\| 0.8829 \| 897 \| 0.9638 \| - \| - \| - \| - \|
	\| 0.8839 \| 898 \| 0.5521 \| - \| - \| - \| - \|
	\| 0.8848 \| 899 \| 1.3365 \| - \| - \| - \| - \|
	\| 0.8858 \| 900 \| 0.8443 \| - \| - \| - \| - \|
	\| 0.8868 \| 901 \| 0.7848 \| - \| - \| - \| - \|
	\| 0.8878 \| 902 \| 1.0733 \| - \| - \| - \| - \|
	\| 0.8888 \| 903 \| 0.5657 \| - \| - \| - \| - \|
	\| 0.8898 \| 904 \| 1.8081 \| - \| - \| - \| - \|
	\| 0.8907 \| 905 \| 0.8232 \| - \| - \| - \| - \|
	\| 0.8917 \| 906 \| 0.6159 \| - \| - \| - \| - \|
	\| 0.8927 \| 907 \| 0.9832 \| - \| - \| - \| - \|
	\| 0.8937 \| 908 \| 1.1375 \| - \| - \| - \| - \|
	\| 0.8947 \| 909 \| 1.4182 \| - \| - \| - \| - \|
	\| 0.8957 \| 910 \| 1.2287 \| - \| - \| - \| - \|
	\| 0.8967 \| 911 \| 1.0915 \| - \| - \| - \| - \|
	\| 0.8976 \| 912 \| 0.8116 \| - \| - \| - \| - \|
	\| 0.8986 \| 913 \| 0.6824 \| - \| - \| - \| - \|
	\| 0.8996 \| 914 \| 0.8888 \| - \| - \| - \| - \|
	\| 0.9006 \| 915 \| 0.5974 \| - \| - \| - \| - \|

	</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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