slimaneMakh/student-multilang-XLMR-14jun

SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base on the en-fr, en-fi, en-pl, en-sv, en-de, en-it, en-pt, en-no, en-nb, en-de-de, en-es, en-cs, en-nl, en-da, en-lt, en-is, en-sl, en-sv-se, en-fi-fi, en-en-gb, en-lv, en-el and en-et datasets. 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: FacebookAI/xlm-roberta-base
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Datasets:
  • en-fr
  • en-fi
  • en-pl
  • en-sv
  • en-de
  • en-it
  • en-pt
  • en-no
  • en-nb
  • en-de-de
  • en-es
  • en-cs
  • en-nl
  • en-da
  • en-lt
  • en-is
  • en-sl
  • en-sv-se
  • en-fi-fi
  • en-en-gb
  • en-lv
  • en-el
  • en-et

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("slimaneMakh/student-multilang-XLMR-14jun")
# Run inference
sentences = [
    'Financial asset investments',
    'Financne nalozbe',
    'activities',
]
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]

Evaluation

Metrics

Knowledge Distillation

• Dataset: en-fr
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-18.7979

Translation

• Dataset: en-fr
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0026
trg2src_accuracy	0.0022
mean_accuracy	0.0024

Knowledge Distillation

• Dataset: en-fi
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.079

Translation

• Dataset: en-fi
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0055
trg2src_accuracy	0.0048
mean_accuracy	0.0052

Knowledge Distillation

• Dataset: en-pl
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-18.9324

Translation

• Dataset: en-pl
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0031
trg2src_accuracy	0.0027
mean_accuracy	0.0029

Knowledge Distillation

• Dataset: en-sv
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.0325

Translation

• Dataset: en-sv
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0037
trg2src_accuracy	0.004
mean_accuracy	0.0038

Knowledge Distillation

• Dataset: en-de
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.2001

Translation

• Dataset: en-de
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0026
trg2src_accuracy	0.0027
mean_accuracy	0.0027

Knowledge Distillation

• Dataset: en-it
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.0771

Translation

• Dataset: en-it
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0035
trg2src_accuracy	0.0036
mean_accuracy	0.0036

Knowledge Distillation

• Dataset: en-pt
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.0009

Translation

• Dataset: en-pt
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0084
trg2src_accuracy	0.0081
mean_accuracy	0.0083

Knowledge Distillation

• Dataset: en-no
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-20.6052

Translation

• Dataset: en-no
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.011
trg2src_accuracy	0.0123
mean_accuracy	0.0117

Knowledge Distillation

• Dataset: en-nb
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-20.6013

Translation

• Dataset: en-nb
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0127
trg2src_accuracy	0.0127
mean_accuracy	0.0127

Knowledge Distillation

• Dataset: en-de-de
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-20.8682

Translation

• Dataset: en-de-de
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0282
trg2src_accuracy	0.0282
mean_accuracy	0.0282

Knowledge Distillation

• Dataset: en-es
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-18.8438

Translation

• Dataset: en-es
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0051
trg2src_accuracy	0.0047
mean_accuracy	0.0049

Knowledge Distillation

• Dataset: en-cs
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.1286

Translation

• Dataset: en-cs
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0112
trg2src_accuracy	0.0145
mean_accuracy	0.0129

Knowledge Distillation

• Dataset: en-nl
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.8483

Translation

• Dataset: en-nl
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.007
trg2src_accuracy	0.0082
mean_accuracy	0.0076

Knowledge Distillation

• Dataset: en-da
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.3856

Translation

• Dataset: en-da
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0116
trg2src_accuracy	0.0126
mean_accuracy	0.0121

Knowledge Distillation

• Dataset: en-lt
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-20.485

Translation

• Dataset: en-lt
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0109
trg2src_accuracy	0.0109
mean_accuracy	0.0109

Knowledge Distillation

• Dataset: en-is
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.2169

Translation

• Dataset: en-is
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0072
trg2src_accuracy	0.0093
mean_accuracy	0.0083

Knowledge Distillation

• Dataset: en-sl
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-18.1531

Translation

• Dataset: en-sl
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0112
trg2src_accuracy	0.014
mean_accuracy	0.0126

Knowledge Distillation

• Dataset: en-sv-se
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-17.6476

Translation

• Dataset: en-sv-se
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0234
trg2src_accuracy	0.0208
mean_accuracy	0.0221

Knowledge Distillation

• Dataset: en-fi-fi
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-19.282

Translation

• Dataset: en-fi-fi
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.018
trg2src_accuracy	0.018
mean_accuracy	0.018

Knowledge Distillation

• Dataset: en-en-gb
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-23.5088

Translation

• Dataset: en-en-gb
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0126
trg2src_accuracy	0.0167
mean_accuracy	0.0146

Knowledge Distillation

• Dataset: en-lv
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-18.0377

Translation

• Dataset: en-lv
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0048
trg2src_accuracy	0.0095
mean_accuracy	0.0071

Knowledge Distillation

• Dataset: en-el
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-23.5207

Translation

• Dataset: en-el
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0513
trg2src_accuracy	0.0513
mean_accuracy	0.0513

Knowledge Distillation

• Dataset: en-et
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-17.5146

Translation

• Dataset: en-et
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.0192
trg2src_accuracy	0.0192
mean_accuracy	0.0192

Training Details

Training Datasets

en-fr

• Dataset: en-fr
• Size: 63,449 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 10.09 tokens max: 30 tokens	min: 3 tokens mean: 9.1 tokens max: 24 tokens

• Samples:

  label	english	non_english
  [-0.0459553524851799, 0.36456549167633057, 0.36365264654159546, 0.6452828645706177, -0.4019026756286621, ...]	Net income for the period attributable to shareholders	Resultat de lexercice
  [0.44971197843551636, 0.9621334075927734, -0.0879441499710083, -0.08917804807424545, 0.002839124295860529, ...]	Podatek dochodowy	Impots
  [0.3880807161331177, 0.19511738419532776, -0.13357722759246826, 0.25993096828460693, 0.0716109424829483, ...]	AttributabletotheshareholdersofKvikabankihf	aux actionnaires de la Societe

• Loss: MSELoss

en-fi

• Dataset: en-fi
• Size: 18,428 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.67 tokens max: 31 tokens	min: 3 tokens mean: 7.69 tokens max: 17 tokens

• Samples:

  label	english	non_english
  [-0.24573877453804016, 0.5694760680198669, 0.45771917700767517, -0.13942377269268036, -0.2597014904022217, ...]	Shareholders of Copenhagen Airports AS	Emoyhtion osakkeenomistajille
  [0.5077632665634155, 0.8774086236953735, -0.3499397933483124, -0.6389203667640686, 0.026370976120233536, ...]	Income tax benefit expense	Income taxes
  [0.9414718747138977, -0.24161840975284576, 0.41289815306663513, 0.10003143548965454, -1.092337965965271, ...]	Result	Emoyrityksen osakkeenomistajille kuuluvasta tuloksesta laskettu

• Loss: MSELoss

en-pl

• Dataset: en-pl
• Size: 45,054 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 10.09 tokens max: 29 tokens	min: 4 tokens mean: 12.42 tokens max: 39 tokens

• Samples:

  label	english	non_english
  [0.09482160955667496, 0.7886450886726379, 0.23035818338394165, 0.21230120956897736, 0.33353161811828613, ...]	Changes in deferred taxes directly recognized in other comprehensive income	Podatek dochodowy dotyczacy innych calkowitych dochodow
  [-0.15856720507144928, 0.6147034168243408, -0.25085723400115967, -0.5494844913482666, -0.526219367980957, ...]	Diluted from continuing operations	Rozwodniony zysk strata na jedna akcje
  [-0.1696387380361557, -0.23339493572711945, -0.7045446038246155, -0.3721548914909363, -0.36909934878349304, ...]	CASH FLOW RESULTING FROM OPERATING ACTIVITIES	Srodki pieniezne netto z dzialalnosci operacyjnej

• Loss: MSELoss

en-sv

• Dataset: en-sv
• Size: 37,354 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.67 tokens max: 36 tokens	min: 3 tokens mean: 8.34 tokens max: 21 tokens

• Samples:

  label	english	non_english
  [-0.2742433547973633, -0.4345971345901489, -0.28529638051986694, -0.06954757869243622, -1.822569489479065, ...]	grupe	moderbolagets aktieagare
  [0.04750566929578781, 0.2545453608036041, 0.3464582860469818, 0.22448834776878357, -0.0583755262196064, ...]	Total comprehensive income for the year attributable to owners of the parent Company	Moderbolagets aktieagare
  [0.045431576669216156, 0.3078455924987793, -0.06083355098962784, -0.5454118847846985, 0.5727013349533081, ...]	Repayment of obligations under lease arrangements	Amortering av skuld

• Loss: MSELoss

en-de

• Dataset: en-de
• Size: 45,253 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.92 tokens max: 31 tokens	min: 3 tokens mean: 9.18 tokens max: 23 tokens

• Samples:

  label	english	non_english
  [-0.086859792470932, 0.7745860815048218, -0.08605925738811493, 0.37508440017700195, -0.9738988876342773, ...]	adjustments of investments in subsidiaries	Wahrungsumrechnungsdifferenzen
  [-0.05315065383911133, 0.0072781918570399284, -0.2516656517982483, -0.4747457504272461, -1.1008282899856567, ...]	LOSS FROM CONTINUING OPERATIONS	Ergebnis nach Ertragsteuern
  [0.14867287874221802, 1.0406593084335327, -0.17914682626724243, -0.6161922812461853, 0.14850790798664093, ...]	Taxation paid received	Ertragsteueraufwand ertrag

• Loss: MSELoss

en-it

• Dataset: en-it
• Size: 34,682 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.98 tokens max: 26 tokens	min: 3 tokens mean: 8.39 tokens max: 20 tokens

• Samples:

  label	english	non_english
  [0.5695832371711731, 0.02826128527522087, 0.1920386552810669, 0.40783414244651794, -1.2495031356811523, ...]	Current financial receivables	Titoli in portafoglio
  [0.662227988243103, 0.6725629568099976, 0.22833657264709473, 0.054810211062431335, -0.40215858817100525, ...]	Proceeds from sale of assets	Attivita destinate alla vendita
  [0.1357184797525406, 0.7814697623252869, 0.3390173614025116, -0.10204766690731049, -0.3055779039859772, ...]	Profit before income tax	Risultato netto

• Loss: MSELoss

en-pt

• Dataset: en-pt
• Size: 7,300 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.84 tokens max: 27 tokens	min: 3 tokens mean: 6.71 tokens max: 12 tokens

• Samples:

  label	english	non_english
  [-0.008626206777989864, 0.6093286275863647, 0.08171450346708298, 1.162959337234497, 0.6411553025245667, ...]	Interest received by the Barclays Bank Group was m	Juros recebidos
  [-0.27057403326034546, 0.2500847578048706, -0.07353457063436508, 0.5000247955322266, -0.07040926814079285, ...]	Other liabilities	Outros passivos
  [-0.03809820115566254, 0.1842460036277771, -0.08849599212408066, -0.844947338104248, 0.7437804341316223, ...]	Payment of obligations under leases	Passivos de locacao

• Loss: MSELoss

en-no

• Dataset: en-no
• Size: 3,602 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.04 tokens max: 25 tokens	min: 3 tokens mean: 5.4 tokens max: 14 tokens

• Samples:

  label	english	non_english
  [0.19592446088790894, 0.5323967337608337, 0.21345381438732147, -0.4241628348827362, -0.0008733272552490234, ...]	of the parent company	income
  [-0.05730602145195007, 0.16925856471061707, -0.16081246733665466, -1.6013731956481934, 0.6432715654373169, ...]	Employee charges and benefits expenses	Personalkostnader
  [0.053435444831848145, -0.08411762863397598, 0.7841566801071167, 0.822182834148407, -0.3946605324745178, ...]	in expected credit losses net	totalresultat

• Loss: MSELoss

en-nb

• Dataset: en-nb
• Size: 3,446 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.39 tokens max: 34 tokens	min: 3 tokens mean: 5.83 tokens max: 14 tokens

• Samples:

  label	english	non_english
  [0.6152929663658142, 1.0328565835952759, -0.48867374658584595, 0.6196318864822388, -1.0412869453430176, ...]	Note b	Andre driftskostnader
  [-0.08955559879541397, 0.07031169533729553, -0.4530458450317383, 0.6429653763771057, -0.17220227420330048, ...]	Profitloss for the period	Resultat
  [-0.2092481404542923, 0.8907342553138733, -0.2213028073310852, 0.19046330451965332, 0.36781418323516846, ...]	Tax on profitloss	Skattekostnad

• Loss: MSELoss

en-de-de

• Dataset: en-de-de
• Size: 623 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.53 tokens max: 31 tokens	min: 5 tokens mean: 9.83 tokens max: 14 tokens

• Samples:

  label	english	non_english
  [-0.15285512804985046, 0.24292221665382385, -0.21986141800880432, -0.12183597683906555, -0.8729998469352722, ...]	Ikkekontrollerende eierinteresse	davon den nicht beherrschenden Anteilen zuzurechnen
  [0.7105820178985596, 0.6940978765487671, 0.29005366563796997, 0.33401334285736084, 0.05582822486758232, ...]	Total net revenue	Umsatzerlose
  [-0.20316101610660553, 0.9045584797859192, -0.2203243523836136, -1.074849247932434, -0.4881342351436615, ...]	Caixa e equivalentes de caixa	Zahlungsmittel und Zahlungsmittelaquivalente

• Loss: MSELoss

en-es

• Dataset: en-es
• Size: 28,719 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.47 tokens max: 28 tokens	min: 3 tokens mean: 9.48 tokens max: 23 tokens

• Samples:

  label	english	non_english
  [0.172838494181633, 0.43473777174949646, 0.3958137333393097, 0.1424863040447235, -0.8349866271018982, ...]	Increase in trade receivables and other assets	Clientes y otras cuentas a cobrar
  [0.5418481826782227, 0.5917099714279175, 0.1668325960636139, 0.3066450357437134, -1.260878324508667, ...]	Increase in trade and other receivables and advances paid	Clientes y otras cuentas a cobrar
  [-0.2715812921524048, 0.05829544737935066, -0.4542696177959442, -0.029009468853473663, -0.7529364824295044, ...]	Total Comprehensive Loss for the year wholly attributable to Equity Holders of the Parent Company	Atribuible a la sociedad dominante

• Loss: MSELoss

en-cs

• Dataset: en-cs
• Size: 2,203 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 10.1 tokens max: 27 tokens	min: 4 tokens mean: 7.9 tokens max: 23 tokens

• Samples:

  label	english	non_english
  [0.06290890276432037, 0.5762706398963928, -0.024871770292520523, 0.22431252896785736, -0.6742631196975708, ...]	Udzialy niekontrolujace	Nekontrolnim podilum
  [0.39093080163002014, -0.009997962974011898, 0.24490250647068024, 0.9013416171073914, -0.796424388885498, ...]	Profit for the year attributable to ordinary Shareholders	Akcionarum materske spolecnosti
  [-0.23978163301944733, 0.484517902135849, -0.3151543438434601, 0.1443774700164795, -0.16455821692943573, ...]	Avsetning for forpliktelser	Rezervy

• Loss: MSELoss

en-nl

• Dataset: en-nl
• Size: 8,101 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.39 tokens max: 34 tokens	min: 3 tokens mean: 7.01 tokens max: 12 tokens

• Samples:

  label	english	non_english
  [0.43313074111938477, -0.23663929104804993, -0.0008638567524030805, 0.21914006769657135, -1.1042245626449585, ...]	Shareholders of FGC UES	Aandeelhouders van de moedermaatschappij
  [0.5194972157478333, 0.45368078351020813, 0.5302746295928955, 0.2755521535873413, -0.3021118640899658, ...]	Noncontrolling interest	Belang van derden
  [0.9302910566329956, 0.7344815731048584, 0.6589862108230591, 0.1774829477071762, 0.528937578201294, ...]	Debt	Leningen

• Loss: MSELoss

en-da

• Dataset: en-da
• Size: 4,554 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.91 tokens max: 31 tokens	min: 4 tokens mean: 7.54 tokens max: 26 tokens

• Samples:

  label	english	non_english
  [-0.016798147931694984, 0.7280638813972473, 0.1259734034538269, -0.07660696655511856, -0.20033679902553558, ...]	Provisions current portion	Hensatte forpligtelser
  [-0.07381738722324371, -0.07786396145820618, -0.21328210830688477, 0.18608279526233673, -0.3095148205757141, ...]	or loss	Kursreguleringer
  [-0.4245157241821289, 0.4695541262626648, 0.05997037887573242, 0.2986871004104614, 0.011750679463148117, ...]	assets depreciation	Af og nedskrivninger

• Loss: MSELoss

en-lt

• Dataset: en-lt
• Size: 2,998 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.39 tokens max: 31 tokens	min: 5 tokens mean: 8.65 tokens max: 12 tokens

• Samples:

  label	english	non_english
  [0.2119722217321396, 0.5226094722747803, -0.3225395679473877, 0.6458964347839355, -0.22873802483081818, ...]	NOTE	Atsargos
  [0.5478602647781372, 0.3326689302921295, -0.14589856564998627, 0.5814526677131653, 0.5692975521087646, ...]	Repayment of loan	Paskolu grazinimas
  [0.2744126319885254, 0.5255246162414551, 0.05724802985787392, 0.25815054774284363, -0.766740620136261, ...]	Attributable to the owners of the Company	Bendroves akcininkams

• Loss: MSELoss

en-is

• Dataset: en-is
• Size: 2,138 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.65 tokens max: 29 tokens	min: 4 tokens mean: 10.54 tokens max: 15 tokens

• Samples:

  label	english	non_english
  [-0.037829890847206116, 1.1669130325317383, 0.2974126636981964, 0.16161930561065674, 0.022792719304561615, ...]	Tax expenses	Tekjuskattur
  [0.11290981620550156, 0.3291318714618683, -0.6060066819190979, 0.029671549797058105, -0.4738736152648926, ...]	Share of profit from Hyundai Glovis	Ahrif hlutdeildarfelaga
  [-0.1636863499879837, -0.4239570200443268, 0.2055961787700653, -1.1946961879730225, 0.13549365103244781, ...]	Changes in working capital requirements	Veltufe fra rekstri

• Loss: MSELoss

en-sl

• Dataset: en-sl
• Size: 834 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.04 tokens max: 25 tokens	min: 3 tokens mean: 5.48 tokens max: 9 tokens

• Samples:

  label	english	non_english
  [0.020984871312975883, -0.31524133682250977, 0.10546927899122238, 1.0089449882507324, -0.592142641544342, ...]	Net cash flows tofrom investing activities	activities
  [0.1349133551120758, -0.2043939232826233, 0.2521047592163086, -0.04384709894657135, -0.5578309893608093, ...]	Net cash ows from investing activities	activities
  [-0.16783905029296875, 1.331608533859253, 0.9504968523979187, 0.402763694524765, -0.8187195658683777, ...]	Foreign currency translations	Prevedbena rezerva

• Loss: MSELoss

en-sv-se

• Dataset: en-sv-se
• Size: 847 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.93 tokens max: 33 tokens	min: 4 tokens mean: 7.15 tokens max: 11 tokens

• Samples:

  label	english	non_english
  [-0.14358974993228912, 0.12112939357757568, 0.152898907661438, 0.2965115010738373, -0.6465349197387695, ...]	Cash flow from investing activities	Kassaflode fran investeringsverksamheten
  [-0.3012215495109558, -0.6284143924713135, 0.952661395072937, 0.6150138974189758, 1.3908427953720093, ...]	reporting year	Likvida medel
  [0.7741854190826416, 0.9692693948745728, -0.48180654644966125, -0.3358636796474457, -1.0314745903015137, ...]	Note c	Personalkostnader

• Loss: MSELoss

en-fi-fi

• Dataset: en-fi-fi
• Size: 874 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.97 tokens max: 27 tokens	min: 6 tokens mean: 8.69 tokens max: 10 tokens

• Samples:

  label	english	non_english
  [-0.0959925726056099, -0.0646059587597847, -0.5595968961715698, 0.40048298239707947, -0.0345945879817009, ...]	Soci della controllante	Emoyhtion osakkeenomistajille
  [0.07576075196266174, 0.13357341289520264, 0.2546372711658478, 0.0818142369389534, -0.08272691816091537, ...]	ordinary shareholders of the parent company	Emoyhtion osakkeenomistajille
  [-0.1580277979373932, 0.6337043642997742, 0.21239566802978516, 0.5370602011680603, -1.064493179321289, ...]	Net gains losses on investments in foreign operations	Muuntoerot

• Loss: MSELoss

en-en-gb

• Dataset: en-en-gb
• Size: 551 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.86 tokens max: 38 tokens	min: 5 tokens mean: 6.33 tokens max: 8 tokens

• Samples:

  label	english	non_english
  [0.18707695603370667, 0.7752551436424255, 0.12487845122814178, 0.7609840631484985, 0.21821437776088715, ...]	Shortterm and current portion of longterm debt	Borrowings
  [-0.24947500228881836, 1.0999057292938232, 0.3973265290260315, 0.551521897315979, -0.20870772004127502, ...]	Trade and other	Trade and other payables
  [0.16158847510814667, 0.9547826647758484, 0.5619722604751587, 1.3562628030776978, -0.42042723298072815, ...]	Interest rate derivatives	Derivative financial instruments

• Loss: MSELoss

en-lv

• Dataset: en-lv
• Size: 487 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.71 tokens max: 25 tokens	min: 5 tokens mean: 8.63 tokens max: 14 tokens

• Samples:

  label	english	non_english
  [0.5849851369857788, 0.12363594025373459, -0.019146278500556946, 0.223326176404953, 0.3553294241428375, ...]	Noncurrent interestbearing loans	Aiznemumi no kreditiestadem
  [-0.4405641555786133, 0.6129574179649353, 0.3001856207847595, 0.2243034392595291, 0.3611409366130829, ...]	Loans long term	Aiznemumi no kreditiestadem
  [0.4723680913448334, 0.5573369860649109, -0.02968907356262207, -0.17952217161655426, -0.6545169949531555, ...]	Proceeds from dividends	No meitassabiedribam sanemtas dividendes

• Loss: MSELoss

en-el

• Dataset: en-el
• Size: 104 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 7.91 tokens max: 22 tokens	min: 4 tokens mean: 5.54 tokens max: 8 tokens

• Samples:

  label	english	non_english
  [-0.4922516345977783, -0.07638876140117645, 0.27244681119918823, -0.03274909406900406, -0.44587045907974243, ...]	other reserves	Reserves
  [0.02690565586090088, 0.5322003960609436, -0.22316685318946838, 1.4094343185424805, -1.2200299501419067, ...]	Derivativesliabilities	Derivative financial instruments
  [-0.4285869002342224, -1.2929456233978271, -0.05507340282201767, -0.9150614142417908, -1.67551589012146, ...]	Invested unrestricted equity fund	Reserves

• Loss: MSELoss

en-et

• Dataset: en-et
• Size: 136 training samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 10.24 tokens max: 25 tokens	min: 7 tokens mean: 7.0 tokens max: 7 tokens

• Samples:

  label	english	non_english
  [-0.23401905596256256, 0.947270393371582, -0.3706150949001312, 0.32394295930862427, -0.10204663872718811, ...]	Depreciation and amortisation including impairment charges	Pohivara kulum
  [0.5078503489494324, 0.9610038995742798, 0.028378624469041824, 0.5917476415634155, -1.4292068481445312, ...]	vii	Pohivara kulum
  [-0.39173853397369385, 0.42254066467285156, -0.6972977519035339, 0.13764289021492004, 0.11351882666349411, ...]	Total depreciation	Pohivara kulum

• Loss: MSELoss

Evaluation Datasets

en-fr

• Dataset: en-fr
• Size: 27,038 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.77 tokens max: 21 tokens	min: 3 tokens mean: 8.48 tokens max: 21 tokens

• Samples:

  label	english	non_english
  [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]	Revenue	Ventes
  [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]	Revenue	Produits des activites ordinaires
  [-0.7187896966934204, 0.300822377204895, -0.038356583565473557, 1.0221939086914062, -0.07130642980337143, ...]	Distribution costs	Frais commerciaux

• Loss: MSELoss

en-fi

• Dataset: en-fi
• Size: 7,849 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.34 tokens max: 34 tokens	min: 3 tokens mean: 7.66 tokens max: 17 tokens

• Samples:

  label	english	non_english
  [-0.044488366693258286, 0.4498324394226074, 0.35706791281700134, 0.5602209568023682, -0.1801929622888565, ...]	Tax on profit for the year	Tuloverot
  [-0.044488366693258286, 0.4498324394226074, 0.35706791281700134, 0.5602209568023682, -0.1801929622888565, ...]	Tax on profit for the year	Income taxes
  [-0.10370840132236481, 0.5262670516967773, -0.1583852767944336, 0.05357339233160019, 0.7700905799865723, ...]	Remeasurements of defined benefit plans	Etuuspohjaisen nettovelan uudelleen maarittamisesta johtuvat erat

• Loss: MSELoss

en-pl

• Dataset: en-pl
• Size: 19,308 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.3 tokens max: 22 tokens	min: 4 tokens mean: 12.43 tokens max: 39 tokens

• Samples:

  label	english	non_english
  [0.012203109450638294, 0.6782587766647339, 0.11951778084039688, -0.30175572633743286, -0.6870222091674805, ...]	Administrative expenses	Ogolne koszty administracyjne
  [0.11572737991809845, 1.1026246547698975, 0.1337483674287796, 0.13492430746555328, -0.2561548352241516, ...]	Other operating income	Pozostale przychody
  [-0.012237715534865856, 0.7524855136871338, 0.0722682923078537, -0.1759086549282074, -0.8265506625175476, ...]	Other operating expenses	Pozostale koszty operacyjne

• Loss: MSELoss

en-sv

• Dataset: en-sv
• Size: 15,902 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.03 tokens max: 24 tokens	min: 3 tokens mean: 8.03 tokens max: 21 tokens

• Samples:

  label	english	non_english
  [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]	Revenue	Nettoomsattning
  [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]	Revenue	Summa rorelsens intakter
  [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]	Revenue	Summa intakter

• Loss: MSELoss

en-de

• Dataset: en-de
• Size: 19,441 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.14 tokens max: 24 tokens	min: 3 tokens mean: 9.0 tokens max: 23 tokens

• Samples:

  label	english	non_english
  [0.405586302280426, 0.545492947101593, 0.5445799231529236, 0.5528497695922852, 0.3698521554470062, ...]	Financial income	Finanzertrage
  [0.405586302280426, 0.545492947101593, 0.5445799231529236, 0.5528497695922852, 0.3698521554470062, ...]	Financial income	IIIB
  [0.10624096542596817, 0.2766471207141876, 0.6653332114219666, 0.09570542722940445, -0.5832860469818115, ...]	Financial expenses	Finanzaufwendungen

• Loss: MSELoss

en-it

• Dataset: en-it
• Size: 15,109 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.12 tokens max: 22 tokens	min: 3 tokens mean: 8.6 tokens max: 20 tokens

• Samples:

  label	english	non_english
  [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]	Revenue	Ricavi
  [0.11572737991809845, 1.1026246547698975, 0.1337483674287796, 0.13492430746555328, -0.2561548352241516, ...]	Other operating income	Altri proventi
  [-0.012237218208611012, 0.7524856925010681, 0.0722685381770134, -0.17590798437595367, -0.8265498876571655, ...]	Other operating expenses	Altri oneri

• Loss: MSELoss

en-pt

• Dataset: en-pt
• Size: 3,206 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.62 tokens max: 36 tokens	min: 3 tokens mean: 6.54 tokens max: 12 tokens

• Samples:

  label	english	non_english
  [0.0850653275847435, 0.5872150659561157, 0.3560439944267273, -0.4916071593761444, -0.5272688269615173, ...]	Investments in intangible assets	Ativos intangiveis
  [-0.29471272230148315, 0.912581205368042, -0.22577235102653503, 0.051218513399362564, -0.2710682451725006, ...]	Other provisions	Provisoes
  [0.03657735511660576, 0.3423381447792053, -0.249881774187088, -0.22646693885326385, 0.7550634145736694, ...]	Remeasurements of defined benefit schemes	Ganhos perdas atuariais

• Loss: MSELoss

en-no

• Dataset: en-no
• Size: 1,541 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.91 tokens max: 32 tokens	min: 3 tokens mean: 5.48 tokens max: 14 tokens

• Samples:

  label	english	non_english
  [0.11572737991809845, 1.1026246547698975, 0.1337483674287796, 0.13492430746555328, -0.2561548352241516, ...]	Other operating income	Andre driftsinntekter
  [0.6171316504478455, 0.09544796496629715, 0.3045019507408142, 1.3532874584197998, -0.5360710024833679, ...]	Net profit for the year	Arets resultat
  [0.31753233075141907, 0.9272720813751221, -0.13628403842449188, -0.618966817855835, -0.11626463383436203, ...]	Income tax paid	Betalte skatter

• Loss: MSELoss

en-nb

• Dataset: en-nb
• Size: 1,496 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.35 tokens max: 23 tokens	min: 3 tokens mean: 5.79 tokens max: 14 tokens

• Samples:

  label	english	non_english
  [0.7072435021400452, 0.33462974429130554, -0.25377699732780457, 0.554284393787384, -0.9292709231376648, ...]	Operating profit EBIT	Resultat etter skatt
  [0.6171316504478455, 0.09544817358255386, 0.3045021593570709, 1.3532869815826416, -0.5360713601112366, ...]	Net profit for the year	Resultat etter skatt
  [0.6171316504478455, 0.09544817358255386, 0.3045021593570709, 1.3532869815826416, -0.5360713601112366, ...]	Net profit for the year	Resultat

• Loss: MSELoss

en-de-de

• Dataset: en-de-de
• Size: 284 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.87 tokens max: 27 tokens	min: 5 tokens mean: 9.26 tokens max: 14 tokens

• Samples:

  label	english	non_english
  [0.005065362900495529, 0.7755594253540039, -0.4470923840999603, -0.1863422989845276, 0.39092710614204407, ...]	Revenue	Umsatzerlose
  [0.6505127549171448, 0.502105712890625, 0.05527564138174057, 0.031440261751413345, -0.10601992905139923, ...]	Interest received	Erhaltene Zinsen
  [0.5774980783462524, 0.4874580204486847, -0.11888153851032257, 0.025767352432012558, 0.07453231513500214, ...]	Total revenue	Umsatzerlose

• Loss: MSELoss

en-es

• Dataset: en-es
• Size: 12,190 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.24 tokens max: 22 tokens	min: 3 tokens mean: 9.77 tokens max: 23 tokens

• Samples:

  label	english	non_english
  [-0.011251086369156837, 0.17945028841495514, -0.23512840270996094, 0.601173996925354, 0.3077372610569, ...]	Gross profit	MARGEN BRUTO
  [0.11572762578725815, 1.1026241779327393, 0.13374821841716766, 0.13492360711097717, -0.2561551034450531, ...]	Other operating income	Ingresos accesorios y otros de gestion corriente
  [0.7072424292564392, 0.3346295654773712, -0.25377705693244934, 0.5542840361595154, -0.9292711615562439, ...]	Operating profit EBIT	MARGEN BRUTO

• Loss: MSELoss

en-cs

• Dataset: en-cs
• Size: 894 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 10.17 tokens max: 28 tokens	min: 4 tokens mean: 7.86 tokens max: 23 tokens

• Samples:

  label	english	non_english
  [0.405586302280426, 0.545492947101593, 0.5445799231529236, 0.5528497695922852, 0.3698521554470062, ...]	Financial income	Financni vynosy
  [0.8856601715087891, 0.7636779546737671, -0.22451487183570862, 0.9918713569641113, 0.730712890625, ...]	Finance income	Financni vynosy
  [0.35414567589759827, 0.484447717666626, 0.41246268153190613, 0.26654252409935, -0.46763384342193604, ...]	Noncontrolling interests	Nekontrolnim podilum

• Loss: MSELoss

en-nl

• Dataset: en-nl
• Size: 3,429 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.8 tokens max: 27 tokens	min: 3 tokens mean: 6.99 tokens max: 12 tokens

• Samples:

  label	english	non_english
  [0.5604397058486938, 0.9408637285232544, 0.12189843505620956, -0.34225529432296753, -0.11250410228967667, ...]	Interest paid etc	Betaalde rente
  [0.31753233075141907, 0.9272720813751221, -0.13628403842449188, -0.618966817855835, -0.11626463383436203, ...]	Income tax paid	Betaalde winstbelastingen
  [0.39916926622390747, 0.20327667891979218, 0.41986599564552307, -0.6084388494491577, -0.4903983175754547, ...]	Intangible assets	Immateriele vaste activa

• Loss: MSELoss

en-da

• Dataset: en-da
• Size: 1,901 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.79 tokens max: 27 tokens	min: 4 tokens mean: 7.42 tokens max: 26 tokens

• Samples:

  label	english	non_english
  [0.405586302280426, 0.545492947101593, 0.5445799231529236, 0.5528497695922852, 0.3698521554470062, ...]	Financial income	Finansielle indtaegter
  [0.5749809145927429, 0.25882387161254883, 0.06829871982336044, 0.3255525231361389, -0.193973109126091, ...]	Movements on credit facilities	Kreditinstitutter
  [-0.5068938136100769, 0.421630859375, 0.4049156904220581, -0.48719698190689087, -0.10700821876525879, ...]	Share capital	Aktiekapital

• Loss: MSELoss

en-lt

• Dataset: en-lt
• Size: 1,377 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.32 tokens max: 27 tokens	min: 5 tokens mean: 8.68 tokens max: 12 tokens

• Samples:

  label	english	non_english
  [-0.04448840767145157, 0.44983237981796265, 0.3570672273635864, 0.5602210760116577, -0.18019315600395203, ...]	Tax on profit for the year	Pelno mokescio sanaudos
  [0.053332049399614334, 0.6696042418479919, 0.218048557639122, 0.22305572032928467, -0.7841112017631531, ...]	Other receivables	Kitos gautinos sumos
  [-0.5280259251594543, 0.39407506585121155, -0.17667946219444275, -0.9611474871635437, -1.0850781202316284, ...]	Inventories	Atsargos

• Loss: MSELoss

en-is

• Dataset: en-is
• Size: 966 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.89 tokens max: 34 tokens	min: 4 tokens mean: 10.5 tokens max: 15 tokens

• Samples:

  label	english	non_english
  [-0.28052818775177, 0.5305177569389343, -0.2726171910762787, -0.6555124521255493, -1.195023775100708, ...]	Property plant and equipment	Rekstrarfjarmunir
  [0.5614703893661499, 0.7126756906509399, -0.7462524175643921, -0.8577789068222046, -0.2560833990573883, ...]	Decrease increase in payables	Vidskiptaskuldir og adrar skammtimaskuldir
  [0.6009606122970581, 1.0522949695587158, 0.024701133370399475, -0.4767942428588867, -0.27263158559799194, ...]	Income tax	Tekjuskattur

• Loss: MSELoss

en-sl

• Dataset: en-sl
• Size: 357 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.12 tokens max: 23 tokens	min: 3 tokens mean: 5.49 tokens max: 9 tokens

• Samples:

  label	english	non_english
  [-0.044488903135061264, 0.44983190298080444, 0.35706815123558044, 0.560221791267395, -0.18019415438175201, ...]	Tax on profit for the year	Davek iz dobicka
  [0.10000382363796234, 0.1258276104927063, 0.48933619260787964, 0.4827534556388855, -1.07231605052948, ...]	Current asset investments	Financne nalozbe
  [0.00028255581855773926, -0.16900330781936646, -0.0987740308046341, 0.19973833858966827, -0.23712165653705597, ...]	Net cash outflow from investing activities	activities

• Loss: MSELoss

en-sv-se

• Dataset: en-sv-se
• Size: 385 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.98 tokens max: 27 tokens	min: 4 tokens mean: 7.06 tokens max: 11 tokens

• Samples:

  label	english	non_english
  [0.5604397058486938, 0.9408637285232544, 0.12189843505620956, -0.34225529432296753, -0.11250410228967667, ...]	Interest paid etc	Betald ranta
  [-0.15956099331378937, -0.104736328125, 0.17104840278625488, 0.3255482017993927, -0.4631202518939972, ...]	Cash flows from investing activities	Kassaflode fran investeringsverksamheten
  [0.11968827247619629, 0.7799925208091736, -0.08703255653381348, -1.228922724723816, -1.6603511571884155, ...]	Cash and cash equivalents	Likvida medel

• Loss: MSELoss

en-fi-fi

• Dataset: en-fi-fi
• Size: 389 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 9.7 tokens max: 25 tokens	min: 6 tokens mean: 8.6 tokens max: 10 tokens

• Samples:

  label	english	non_english
  [-0.08681110292673111, 0.06999394297599792, 0.16943465173244476, -0.6658964157104492, -1.3333454132080078, ...]	Equity shareholders	Emoyhtion osakkeenomistajille
  [-0.4602399170398712, 1.3417373895645142, 0.6107428073883057, 0.45281982421875, -0.7822347283363342, ...]	Exchange differences arising on translation of foreign operations	Muuntoerot
  [0.20600593090057373, 0.06086999550461769, 0.1364181935787201, 0.6713289618492126, -0.8476033210754395, ...]	Attributable to the shareholders	Emoyhtion osakkeenomistajille

• Loss: MSELoss

en-en-gb

• Dataset: en-en-gb
• Size: 239 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.98 tokens max: 33 tokens	min: 5 tokens mean: 6.49 tokens max: 8 tokens

• Samples:

  label	english	non_english
  [0.35414567589759827, 0.484447717666626, 0.41246268153190613, 0.26654252409935, -0.46763384342193604, ...]	Noncontrolling interests	Noncontrolling interests
  [-0.26346728205680847, 1.010565161705017, 0.25545963644981384, -0.09261462837457657, -0.5145906805992126, ...]	Trade and other payables	Trade and other payables
  [0.3337377905845642, 0.28091752529144287, 0.26623502373695374, 0.8748410940170288, -0.44941988587379456, ...]	Attributable to noncontrolling interest	Noncontrolling interests

• Loss: MSELoss

en-lv

• Dataset: en-lv
• Size: 210 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 8.75 tokens max: 21 tokens	min: 5 tokens mean: 8.05 tokens max: 14 tokens

• Samples:

  label	english	non_english
  [0.3419339656829834, -0.2543298006057739, 0.34351760149002075, 0.6980054378509521, 0.699012815952301, ...]	Interestbearing loans and borrowings	Aiznemumi
  [0.27617645263671875, 0.6733821630477905, 0.47860750555992126, 0.4202423095703125, 0.044836655259132385, ...]	Borrowings and bank overdrafts	Aiznemumi
  [0.36503127217292786, -0.47215989232063293, 0.6517267227172852, 0.6172035932540894, 1.0784108638763428, ...]	loans and borrowings	Aiznemumi no kreditiestadem

• Loss: MSELoss

en-el

• Dataset: en-el
• Size: 39 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 7.56 tokens max: 19 tokens	min: 4 tokens mean: 5.44 tokens max: 8 tokens

• Samples:

  label	english	non_english
  [-0.07889065891504288, -0.6466420888900757, 0.4228314757347107, -0.11737698316574097, -0.06180833652615547, ...]	Share premium account	Reserves
  [0.07863229513168335, 0.6249228119850159, -0.08239512890577316, 0.9754469990730286, 0.02359396405518055, ...]	Derivative liabilities note	Derivative financial instruments
  [-0.1764196902513504, 0.4463600814342499, 0.06581983715295792, 0.787315845489502, -0.7786881923675537, ...]	Derivatives liabilities	Derivative financial instruments

• Loss: MSELoss

en-et

• Dataset: en-et
• Size: 52 evaluation samples
• Columns: label, english, and non_english
• Approximate statistics based on the first 1000 samples:

  	label	english	non_english
  type	list	string	string
  details	size: 768 elements	min: 3 tokens mean: 10.31 tokens max: 21 tokens	min: 7 tokens mean: 7.0 tokens max: 7 tokens

• Samples:

  label	english	non_english
  [0.5006873607635498, 0.9590571522712708, 0.5849384069442749, -0.725926399230957, -0.5808520317077637, ...]	impairment of noncurrent assets	Pohivara kulum
  [-0.12556228041648865, 0.2528606057167053, -0.2748187780380249, 0.25966036319732666, -0.31089597940444946, ...]	depreciation and amortisation	Pohivara kulum
  [0.458812415599823, 1.155530571937561, -0.515108585357666, 0.35893556475639343, 0.506560206413269, ...]	Amortyzacja	Pohivara kulum

• Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 2e-05
• num_train_epochs: 5
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 5
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 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: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	en-es loss	en-pl loss	en-is loss	en-sv loss	en-sv-se loss	en-da loss	en-en-gb loss	en-de loss	en-pt loss	en-fi loss	en-sl loss	en-el loss	en-nb loss	en-de-de loss	en-cs loss	en-et loss	en-nl loss	en-lt loss	en-no loss	en-it loss	en-fi-fi loss	en-lv loss	en-fr loss	en-cs_mean_accuracy	en-cs_negative_mse	en-da_mean_accuracy	en-da_negative_mse	en-de-de_mean_accuracy	en-de-de_negative_mse	en-de_mean_accuracy	en-de_negative_mse	en-el_mean_accuracy	en-el_negative_mse	en-en-gb_mean_accuracy	en-en-gb_negative_mse	en-es_mean_accuracy	en-es_negative_mse	en-et_mean_accuracy	en-et_negative_mse	en-fi-fi_mean_accuracy	en-fi-fi_negative_mse	en-fi_mean_accuracy	en-fi_negative_mse	en-fr_mean_accuracy	en-fr_negative_mse	en-is_mean_accuracy	en-is_negative_mse	en-it_mean_accuracy	en-it_negative_mse	en-lt_mean_accuracy	en-lt_negative_mse	en-lv_mean_accuracy	en-lv_negative_mse	en-nb_mean_accuracy	en-nb_negative_mse	en-nl_mean_accuracy	en-nl_negative_mse	en-no_mean_accuracy	en-no_negative_mse	en-pl_mean_accuracy	en-pl_negative_mse	en-pt_mean_accuracy	en-pt_negative_mse	en-sl_mean_accuracy	en-sl_negative_mse	en-sv-se_mean_accuracy	en-sv-se_negative_mse	en-sv_mean_accuracy	en-sv_negative_mse
0.0205	100	0.7598	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0410	200	0.5938	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0615	300	0.405	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0819	400	0.3145	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1024	500	0.2891	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1229	600	0.2762	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1434	700	0.2693	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1639	800	0.2655	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1844	900	0.2645	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2048	1000	0.2656	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2253	1100	0.2623	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2458	1200	0.2606	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2663	1300	0.2674	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2868	1400	0.2571	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3073	1500	0.252	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3277	1600	0.2464	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3482	1700	0.2396	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0205	100	0.2311	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0410	200	0.2294	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0615	300	0.2297	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0819	400	0.2282	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1024	500	0.2283	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1229	600	0.2251	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1434	700	0.2259	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1639	800	0.224	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1844	900	0.2213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2048	1000	0.2202	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2253	1100	0.219	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2458	1200	0.2162	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2663	1300	0.213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2868	1400	0.2097	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3073	1500	0.2069	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3277	1600	0.206	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3482	1700	0.2017	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3687	1800	0.1982	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3892	1900	0.1985	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4097	2000	0.1953	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4302	2100	0.1923	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4506	2200	0.1912	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4711	2300	0.1867	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4916	2400	0.1876	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5121	2500	0.1865	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5326	2600	0.1816	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5531	2700	0.1786	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5735	2800	0.1786	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5940	2900	0.1775	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6145	3000	0.175	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6350	3100	0.1735	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6555	3200	0.1731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6760	3300	0.1717	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6964	3400	0.1703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7169	3500	0.17	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7374	3600	0.1668	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7579	3700	0.1648	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7784	3800	0.1664	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7989	3900	0.1638	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8193	4000	0.1616	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8398	4100	0.1631	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8603	4200	0.1614	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8808	4300	0.1592	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9013	4400	0.1597	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9218	4500	0.1605	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9422	4600	0.1593	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9627	4700	0.1573	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9832	4800	0.1608	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0037	4900	0.1559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0242	5000	0.1567	0.1498	0.1470	0.1566	0.1527	0.1467	0.1618	0.1750	0.1491	0.1507	0.1475	0.1495	0.2114	0.1634	0.1609	0.1529	0.1600	0.1575	0.1526	0.1587	0.1497	0.1438	0.1393	0.1466	0.0112	-20.2568	0.0100	-21.6889	0.0246	-21.0408	0.0022	-19.8428	0.0513	-25.8657	0.0146	-25.2005	0.0048	-19.5834	0.0192	-20.9676	0.0180	-19.3767	0.0046	-19.5729	0.0019	-19.3683	0.0083	-20.4514	0.0031	-19.7759	0.0091	-20.1992	0.0095	-18.4323	0.0117	-21.5480	0.0063	-20.7031	0.0097	-21.0662	0.0023	-19.3807	0.0069	-19.5748	0.0112	-19.9180	0.0169	-18.6913	0.0031	-20.0760
1.0447	5100	0.1554	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0651	5200	0.1558	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0856	5300	0.1542	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1061	5400	0.1533	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1266	5500	0.1538	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1471	5600	0.1527	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1676	5700	0.1535	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1880	5800	0.1539	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2085	5900	0.1529	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2290	6000	0.1546	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2495	6100	0.1523	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2700	6200	0.1484	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2905	6300	0.1509	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3109	6400	0.1496	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3314	6500	0.1505	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3519	6600	0.148	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3724	6700	0.1477	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3929	6800	0.1482	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4134	6900	0.1473	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4338	7000	0.1479	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4543	7100	0.1476	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4748	7200	0.1449	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4953	7300	0.1469	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5158	7400	0.1486	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5363	7500	0.1457	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5567	7600	0.1448	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5772	7700	0.1449	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5977	7800	0.1433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6182	7900	0.1433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6387	8000	0.1433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6592	8100	0.1432	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6796	8200	0.1434	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7001	8300	0.1423	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7206	8400	0.1428	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7411	8500	0.1412	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7616	8600	0.1401	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7821	8700	0.142	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8025	8800	0.141	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8230	8900	0.1397	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8435	9000	0.1404	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8640	9100	0.1401	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8845	9200	0.1395	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9050	9300	0.1391	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9254	9400	0.1411	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9459	9500	0.1394	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9664	9600	0.1386	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9869	9700	0.1415	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0074	9800	0.1388	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0279	9900	0.1402	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0483	10000	0.1393	0.1328	0.1306	0.1365	0.1342	0.1282	0.1368	0.1601	0.1335	0.1335	0.1318	0.1305	0.1868	0.1486	0.1445	0.1349	0.1292	0.1395	0.1348	0.1462	0.1330	0.1301	0.1219	0.1304	0.0117	-19.4912	0.0121	-19.7982	0.0282	-20.8897	0.0025	-19.6494	0.0513	-24.6742	0.0167	-25.4686	0.0045	-19.1742	0.0192	-17.9511	0.0193	-19.3175	0.0050	-19.3365	0.0024	-19.0925	0.0083	-19.6830	0.0033	-19.4012	0.0109	-19.7036	0.0119	-18.3941	0.0107	-21.7453	0.0063	-20.2261	0.0114	-21.4993	0.0028	-19.0938	0.0073	-19.3771	0.0112	-18.8671	0.0195	-17.8846	0.0037	-19.4199
2.0688	10100	0.1382	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0893	10200	0.1368	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1098	10300	0.1378	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1303	10400	0.137	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1508	10500	0.1369	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1712	10600	0.1369	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1917	10700	0.1382	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2122	10800	0.1372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2327	10900	0.1369	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2532	11000	0.1358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2737	11100	0.1343	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2941	11200	0.1372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3146	11300	0.1354	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3351	11400	0.1364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3556	11500	0.135	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3761	11600	0.1349	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3966	11700	0.1353	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4170	11800	0.1353	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4375	11900	0.1354	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4580	12000	0.1357	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4785	12100	0.1328	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4990	12200	0.1355	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5195	12300	0.1356	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5399	12400	0.1349	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5604	12500	0.1332	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5809	12600	0.1345	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6014	12700	0.1327	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6219	12800	0.1326	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6424	12900	0.1332	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6628	13000	0.1332	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6833	13100	0.1334	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7038	13200	0.1328	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7243	13300	0.1334	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7448	13400	0.1323	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7653	13500	0.132	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7857	13600	0.1318	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8062	13700	0.1324	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8267	13800	0.1323	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8472	13900	0.1313	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8677	14000	0.1318	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8882	14100	0.1311	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9086	14200	0.1312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9291	14300	0.1336	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9496	14400	0.1312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9701	14500	0.1312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9906	14600	0.1334	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.0111	14700	0.131	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.0315	14800	0.1316	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.0520	14900	0.1312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.0725	15000	0.1304	0.1256	0.1236	0.1278	0.1269	0.1225	0.1291	0.1491	0.1258	0.1254	0.1250	0.1222	0.1761	0.1366	0.1376	0.1273	0.1216	0.1316	0.1280	0.1349	0.1257	0.1253	0.1152	0.1233	0.0123	-19.1985	0.0124	-19.4425	0.0282	-20.7684	0.0025	-19.2806	0.0513	-24.1800	0.0146	-24.4860	0.0044	-18.9131	0.0192	-17.5769	0.0180	-19.5151	0.0049	-19.1971	0.0025	-18.8663	0.0083	-19.2975	0.0034	-19.1577	0.0102	-19.5784	0.0095	-18.1528	0.0117	-20.5703	0.0076	-19.9089	0.0114	-20.4863	0.0027	-18.9161	0.0083	-19.0866	0.0126	-18.3424	0.0208	-17.8123	0.0039	-19.1637
3.0930	15100	0.1304	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.1135	15200	0.1302	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.1340	15300	0.1296	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.1544	15400	0.1307	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.1749	15500	0.1308	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.1954	15600	0.1309	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.2159	15700	0.1312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.2364	15800	0.1299	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.2569	15900	0.1303	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.2773	16000	0.1288	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.2978	16100	0.131	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.3183	16200	0.1296	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.3388	16300	0.1308	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.3593	16400	0.1277	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.3798	16500	0.1309	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.4002	16600	0.1282	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.4207	16700	0.1298	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.4412	16800	0.1307	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.4617	16900	0.1293	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.4822	17000	0.1282	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.5027	17100	0.1307	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.5231	17200	0.1302	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.5436	17300	0.1305	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.5641	17400	0.129	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.5846	17500	0.1292	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.6051	17600	0.1286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.6256	17700	0.1282	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.6460	17800	0.1291	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.6665	17900	0.128	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.6870	18000	0.129	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.7075	18100	0.1289	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.7280	18200	0.1289	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.7485	18300	0.1268	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.7689	18400	0.128	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.7894	18500	0.128	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.8099	18600	0.1284	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.8304	18700	0.1278	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.8509	18800	0.1276	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.8714	18900	0.1279	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.8918	19000	0.1274	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.9123	19100	0.1277	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.9328	19200	0.1293	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.9533	19300	0.1277	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.9738	19400	0.1281	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.9943	19500	0.1294	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.0147	19600	0.1275	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.0352	19700	0.1289	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.0557	19800	0.1277	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.0762	19900	0.1269	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.0967	20000	0.1287	0.1223	0.1207	0.1242	0.1232	0.1192	0.1258	0.1411	0.1225	0.1219	0.1214	0.1184	0.1690	0.1335	0.1345	0.1239	0.1186	0.1281	0.1294	0.1321	0.1223	0.1211	0.1119	0.1200	0.0129	-19.1286	0.0121	-19.3856	0.0282	-20.8682	0.0027	-19.2001	0.0513	-23.5207	0.0146	-23.5088	0.0049	-18.8438	0.0192	-17.5146	0.0180	-19.2820	0.0052	-19.0790	0.0024	-18.7979	0.0083	-19.2169	0.0036	-19.0771	0.0109	-20.4850	0.0071	-18.0377	0.0127	-20.6013	0.0076	-19.8483	0.0117	-20.6052	0.0029	-18.9324	0.0083	-19.0009	0.0126	-18.1531	0.0221	-17.6476	0.0038	-19.0325
4.1172	20100	0.1262	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.1376	20200	0.1277	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.1581	20300	0.1276	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.1786	20400	0.1274	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.1991	20500	0.1278	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.2196	20600	0.1282	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.2401	20700	0.1272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.2605	20800	0.1284	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.2810	20900	0.1263	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.3015	21000	0.1283	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.3220	21100	0.128	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.3425	21200	0.1273	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.3630	21300	0.1256	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.3834	21400	0.1274	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.4039	21500	0.1264	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.4244	21600	0.1276	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.4449	21700	0.1281	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.4654	21800	0.1261	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.4859	21900	0.1269	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.5063	22000	0.1292	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.5268	22100	0.1271	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.5473	22200	0.1272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.5678	22300	0.1261	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.5883	22400	0.1262	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.6088	22500	0.1266	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.6293	22600	0.1256	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.6497	22700	0.1272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.6702	22800	0.126	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.6907	22900	0.1268	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.7112	23000	0.1277	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.7317	23100	0.1263	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.7522	23200	0.1254	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.7726	23300	0.1267	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.7931	23400	0.1263	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.8136	23500	0.1258	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.8341	23600	0.1266	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.8546	23700	0.1261	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.8751	23800	0.1254	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.8955	23900	0.126	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.9160	24000	0.1272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.9365	24100	0.1267	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.9570	24200	0.1266	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.9775	24300	0.1263	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.9980	24400	0.1279	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.3.1+cu121
• Accelerate: 0.31.0
• Datasets: 2.19.2
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}