rufimelo/Legal-BERTimbau-sts-base-ma-v2
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Model: rufimelo/Legal-BERTimbau-sts-base-ma-v2
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{
"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
}

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---
language:
- pt
thumbnail: "Portuguese BERT for the Legal Domain"
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
datasets:
- assin
- assin2
- stsb_multi_mt
- rufimelo/PortugueseLegalSentences-v0
widget:
- source_sentence: "O advogado apresentou as provas ao juíz."
sentences:
- "O juíz leu as provas."
- "O juíz leu o recurso."
- "O juíz atirou uma pedra."
example_title: "Example 1"
model-index:
- name: BERTimbau
results:
- task:
name: STS
type: STS
metrics:
- name: Pearson Correlation - assin Dataset
type: Pearson Correlation
value: 0.75481
- name: Pearson Correlation - assin2 Dataset
type: Pearson Correlation
value: 0.80262
- name: Pearson Correlation - stsb_multi_mt pt Dataset
type: Pearson Correlation
value: 0.82178
---
# rufimelo/Legal-BERTimbau-sts-base-ma
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/rufimelo/Legal-BERTimbau-sts-base-ma is based on Legal-BERTimbau-base which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
model = SentenceTransformer('rufimelo/Legal-BERTimbau-sts-base-ma-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-BERTimbau-sts-base-ma-v2')
model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-sts-base-ma-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results STS
| Model| Assin | Assin2|stsb_multi_mt pt| avg|
| ---------------------------------------- | ---------- | ---------- |---------- |---------- |
| Legal-BERTimbau-sts-base| 0.71457| 0.73545 | 0.72383|0.72462|
| Legal-BERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |0.78886|
| Legal-BERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|0.79307|
| Legal-BERTimbau-base-TSDAE-sts|0.78814 |0.81380 |0.75777|0.78657|
| Legal-BERTimbau-sts-large| 0.76629| 0.82357 | 0.79120|0.79369|
| Legal-BERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |0.79715|
| Legal-BERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|0.80142|
| Legal-BERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 0.81863|
| Legal-BERTimbau-sts-large-ma-v3| 0.7749| **0.8470**| 0.8364| **0.81943**|
| Legal-BERTimbau-large-v2-sts| 0.71665| 0.80106| 0.73724| 0.75165|
| Legal-BERTimbau-large-TSDAE-sts| 0.72376| 0.79261| 0.73635| 0.75090|
| Legal-BERTimbau-large-TSDAE-sts-v2| 0.81326| 0.83130| 0.786314| 0.81029|
| Legal-BERTimbau-large-TSDAE-sts-v3|0.80703 |0.82270 |0.77638 |0.80204 |
| ---------------------------------------- | ---------- |---------- |---------- |---------- |
| BERTimbau base Fine-tuned for STS|**0.78455** | 0.80626|0.82841|0.80640|
| BERTimbau large Fine-tuned for STS|0.78193 | 0.81758|0.83784|0.81245|
| ---------------------------------------- | ---------- |---------- |---------- |---------- |
| paraphrase-multilingual-mpnet-base-v2| 0.71457| 0.79831 |0.83999 |0.78429|
| paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831 |**0.84575**|0.80682|
## Training
rufimelo/Legal-BERTimbau-sts-base-ma-v2 is based on Legal-BERTimbau-base which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) base.
Firstly, due to the lack of portuguese datasets, it was trained using multilingual knowledge distillation.
For the Multilingual Knowledge Distillation process, the teacher model was 'sentence-transformers/paraphrase-xlm-r-multilingual-v1', the supposed supported language as English and the language to learn was portuguese.
It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) datasets.
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(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})
)
```
## Citing & Authors
## Citing & Authors
If you use this work, please cite:
```bibtex
@inproceedings{souza2020bertimbau,
author = {F{\'a}bio Souza and
Rodrigo Nogueira and
Roberto Lotufo},
title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
year = {2020}
}
@inproceedings{fonseca2016assin,
title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
pages={13--15},
year={2016}
}
@inproceedings{real2020assin,
title={The assin 2 shared task: a quick overview},
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={406--412},
year={2020},
organization={Springer}
}
@InProceedings{huggingface:dataset:stsb_multi_mt,
title = {Machine translated multilingual STS benchmark dataset.},
author={Philip May},
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}
```

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{
"_name_or_path": "/home/ruimelo/.cache/torch/sentence_transformers/rufimelo_Legal-BERTimbau-base",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"directionality": "bidi",
"hidden_act": "gelu",
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"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
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"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 0,
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"pooler_num_attention_heads": 12,
"pooler_num_fc_layers": 3,
"pooler_size_per_head": 128,
"pooler_type": "first_token_transform",
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.20.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 29794
}

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{
"__version__": {
"sentence_transformers": "2.2.0",
"transformers": "4.20.1",
"pytorch": "1.10.1+cu111"
}
}

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epoch,steps,MSE
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1 epoch steps MSE
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51 4 -1 2.272995188832283

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3,1000,0.7540327394851281,0.7658997493078986,0.749836472873649,0.7480572145967006,0.7495837523658369,0.7481729179585739,0.6808002467334254,0.6826728988452317
3,2000,0.7570223312833305,0.7698993954218226,0.7540758631739267,0.753295770794736,0.7534239299289165,0.7520422536416839,0.6804191095329432,0.6824718594490202
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3,4000,0.7647207462854896,0.7760793387734382,0.7589105955969245,0.7577332445803333,0.7581578363689768,0.7571112909606956,0.6990299162467802,0.7017689507094818
3,5000,0.7648546144596025,0.7767082115309949,0.759655555046868,0.7593823057844403,0.7589242973646805,0.7579473534592813,0.6948561132830983,0.6960387513226229
3,6000,0.7651312360894998,0.7768604325652202,0.7613454557550654,0.7607815092303494,0.7602880440921177,0.7582152778553799,0.7019760063631014,0.7042006432915755
3,7000,0.7700810652664646,0.7811245435062832,0.7632005336042457,0.7634911205188694,0.7622691247480861,0.7624163477620668,0.7048378363188619,0.7093423315587402
3,8000,0.7735263359060158,0.7842835143630087,0.7652218749789204,0.7660219874111401,0.7639986737869758,0.7644951643102745,0.7130537943626254,0.7156110705136536
3,9000,0.7739488663284068,0.7862128006528485,0.7677560690354177,0.7681807584419712,0.7667809699348831,0.7655088180154564,0.7106389926111314,0.7118989530199328
3,-1,0.7749646355659017,0.7859306535843604,0.7668847071412118,0.76678078620296,0.7660379175603176,0.764305272414044,0.7158777617312314,0.7186727890429572
4,1000,0.7749262499900197,0.786532234186791,0.7683776275278195,0.7683794914588764,0.7672588326412471,0.7651355689643636,0.7162472357262631,0.7189245687839106
4,2000,0.7775372924636713,0.7886944647865819,0.7707564332014942,0.7711459934369553,0.7698450681065371,0.7695349874914046,0.7177465093084331,0.7211675226594269
4,3000,0.7773850252493334,0.7881536188392207,0.772183724897433,0.7717883200737999,0.7712699567266517,0.7709161242994133,0.7187237988981621,0.7223937476573528
4,4000,0.777775327985964,0.7893621615957971,0.7716378455124066,0.7722138470558387,0.7709049470179239,0.7709922348165259,0.7176467231063159,0.7201719355921455
4,5000,0.7763059687016113,0.7876235359952898,0.7698424419369689,0.7698820975770244,0.7689586641660842,0.7687961773202405,0.7197057873099552,0.7245286861020173
4,6000,0.7794826897000439,0.7905141980593657,0.7720120513908072,0.7726462931757969,0.7714463473656613,0.7704221747312833,0.7226559974833779,0.7265990459261015
4,7000,0.778285341526584,0.7896335455608554,0.77200273274823,0.7723626085211043,0.7713809544539256,0.7717356577463027,0.7204467718381531,0.724500625153789
4,8000,0.778450136549209,0.7893325630613646,0.7721776463065276,0.7722626654178252,0.771422818962253,0.7717952392117192,0.7215457163794511,0.7250733760148891
4,9000,0.7784703374820181,0.7894732521990575,0.7726776751649114,0.773120254123271,0.7719272768083242,0.7713374229193902,0.7210072930243006,0.7252590395490577
4,-1,0.7787882635851495,0.789720034784847,0.7728008205428657,0.7732217348127545,0.7720684298045568,0.7715788239534646,0.7213522278358117,0.7256630403242363
1 epoch steps cosine_pearson cosine_spearman euclidean_pearson euclidean_spearman manhattan_pearson manhattan_spearman dot_pearson dot_spearman
2 0 1000 0.517801837376822 0.5927498604628574 0.5626056283814881 0.5947325778732967 0.5483264326845998 0.5859883251294673 0.23162225976519732 0.23201906745235812
3 0 2000 0.44806823321693495 0.5571128406093457 0.5180349070283493 0.5641853527525548 0.5165027287876152 0.5635880005121858 -0.03452826425581184 -0.0432895863922912
4 0 3000 0.433520107513419 0.5461290935589597 0.511366853016028 0.5491873525193037 0.5116493313071497 0.5462251926967281 0.07126694981170532 0.035360638733289526
5 0 4000 0.48062137883402073 0.5730049472203947 0.544857385161381 0.5860482909914347 0.5440379004589636 0.5829423668587576 0.16089119466468452 0.17975766555800177
6 0 5000 0.52956541125226 0.5763791801457215 0.5692791350686931 0.5868570613348942 0.5676877437900325 0.5846068039249076 0.271357969453925 0.2856427707237205
7 0 6000 0.553818706587758 0.5820717086705751 0.5777327816006972 0.581899499015694 0.5776770168455237 0.5822243141013514 0.3043967926716216 0.28029350831897587
8 0 7000 0.5446056633357405 0.5698348288636882 0.5673459868377165 0.5672251606784472 0.5702414573979611 0.5691925022268436 0.28701828287031017 0.2736030863475344
9 0 8000 0.5792358045204946 0.5983720444188645 0.5818917049240697 0.5800659274670714 0.5831890633374769 0.5820959256532927 0.31313325242691503 0.3153285631735058
10 0 9000 0.601108450257991 0.6167169854223161 0.5969917017988638 0.5977251050234071 0.5967274984844755 0.5979695812298902 0.30333549584709796 0.3133719847285396
11 0 -1 0.6280522190532274 0.6406195317611942 0.6227122902564645 0.6197779351585178 0.6222082717382086 0.6196268673139457 0.36291338197354595 0.3751545047964458
12 1 1000 0.6441822639355907 0.6659512644769654 0.6429577685600287 0.6412045833119289 0.6432852285988369 0.6433975656358055 0.41298893952764454 0.4275673589319253
13 1 2000 0.6559511145597557 0.6795427577298369 0.6515445072356066 0.6485446354546853 0.6516716895687359 0.6493933830394565 0.47552172351909894 0.49381310735435074
14 1 3000 0.6527469478536292 0.684905474013869 0.655923943892963 0.6548279814785396 0.6553730945939851 0.6549210054438994 0.48203680194355364 0.507563740779367
15 1 4000 0.669088698727519 0.6937462102920181 0.670854432566903 0.6659097496494495 0.6701009267853754 0.6655422665466227 0.5304511185054699 0.5427909939094337
16 1 5000 0.672516667814659 0.6941490578775437 0.674045628530324 0.6672585971471678 0.6736569476628284 0.6680327718010307 0.5476425334723899 0.5575533590568764
17 1 6000 0.6752432037594164 0.7003174693326267 0.6838270035504933 0.6740485778253374 0.6828421410239922 0.6749730515306702 0.5463083655615174 0.5502217636382437
18 1 7000 0.683567200442913 0.7059803993230477 0.6906067465159954 0.686100947287709 0.690382106531219 0.6868170780623597 0.5699645042777446 0.5723637737732034
19 1 8000 0.7036926133871262 0.7257372288585937 0.7078482102922881 0.7057120905303981 0.7076590987201354 0.7059942375988862 0.5934997113002226 0.5971093017485867
20 1 9000 0.7154936325287922 0.7367717162537215 0.7180129717719661 0.7165839781844215 0.717671531550584 0.7165097896500643 0.6133281247581909 0.6199732086064632
21 1 -1 0.7196875035717942 0.736280073064898 0.7160184824695199 0.7144390454294293 0.7165942734656012 0.7160250655991601 0.6225343657806216 0.6294332077283923
22 2 1000 0.720899402697572 0.7415174760732801 0.7195892119980556 0.7164744251673655 0.7196496138770179 0.7170771589594495 0.630202637897733 0.635334463580479
23 2 2000 0.7250465844610594 0.7404323246095984 0.7260894530415754 0.7210352902458576 0.7259676751222017 0.7216122694690196 0.636508874611686 0.6438696046005243
24 2 3000 0.7262776654731603 0.7426172346059026 0.7261976982608093 0.7199013204201895 0.7257484003658375 0.7207635218842483 0.6362831430635101 0.6430681377915353
25 2 4000 0.7289594513535187 0.7460222192553153 0.7265128423312566 0.7219920532614806 0.7253344413328074 0.7219551511925775 0.6456632097565471 0.6464765820099075
26 2 5000 0.7382714011547186 0.7537920267421724 0.7337106120585335 0.728817398422349 0.7328743912757635 0.7280182379926664 0.6652114382782512 0.6682976210247206
27 2 6000 0.7402044783316905 0.7559581013074739 0.7401263074099426 0.7364987947024589 0.7391512971643294 0.7368728125466539 0.6548352584625278 0.6586757909547893
28 2 7000 0.7449855277046175 0.7586204318202119 0.742407037478152 0.7406210633161757 0.7419037859609786 0.7401351860756182 0.6658503652340054 0.6707327731757738
29 2 8000 0.746684321788316 0.7587492046648215 0.7420907061986265 0.737954504441376 0.7414911948572276 0.7378837754759784 0.6676518777357797 0.6709895500718912
30 2 9000 0.752118628883576 0.7652508879296858 0.7481933017748738 0.746660701926649 0.7481565280073592 0.7468771171849038 0.6829612724188509 0.6833717317750841
31 2 -1 0.7537535743394218 0.7676153111153414 0.7483667618080623 0.7467821712367885 0.7484240035786225 0.7475082963217671 0.6801060385066588 0.6816596295366009
32 3 1000 0.7540327394851281 0.7658997493078986 0.749836472873649 0.7480572145967006 0.7495837523658369 0.7481729179585739 0.6808002467334254 0.6826728988452317
33 3 2000 0.7570223312833305 0.7698993954218226 0.7540758631739267 0.753295770794736 0.7534239299289165 0.7520422536416839 0.6804191095329432 0.6824718594490202
34 3 3000 0.7599567018278637 0.7720689295560839 0.7556908343726982 0.7529013799333341 0.755271373708525 0.7528248850196704 0.6851683555897045 0.6876523717678433
35 3 4000 0.7647207462854896 0.7760793387734382 0.7589105955969245 0.7577332445803333 0.7581578363689768 0.7571112909606956 0.6990299162467802 0.7017689507094818
36 3 5000 0.7648546144596025 0.7767082115309949 0.759655555046868 0.7593823057844403 0.7589242973646805 0.7579473534592813 0.6948561132830983 0.6960387513226229
37 3 6000 0.7651312360894998 0.7768604325652202 0.7613454557550654 0.7607815092303494 0.7602880440921177 0.7582152778553799 0.7019760063631014 0.7042006432915755
38 3 7000 0.7700810652664646 0.7811245435062832 0.7632005336042457 0.7634911205188694 0.7622691247480861 0.7624163477620668 0.7048378363188619 0.7093423315587402
39 3 8000 0.7735263359060158 0.7842835143630087 0.7652218749789204 0.7660219874111401 0.7639986737869758 0.7644951643102745 0.7130537943626254 0.7156110705136536
40 3 9000 0.7739488663284068 0.7862128006528485 0.7677560690354177 0.7681807584419712 0.7667809699348831 0.7655088180154564 0.7106389926111314 0.7118989530199328
41 3 -1 0.7749646355659017 0.7859306535843604 0.7668847071412118 0.76678078620296 0.7660379175603176 0.764305272414044 0.7158777617312314 0.7186727890429572
42 4 1000 0.7749262499900197 0.786532234186791 0.7683776275278195 0.7683794914588764 0.7672588326412471 0.7651355689643636 0.7162472357262631 0.7189245687839106
43 4 2000 0.7775372924636713 0.7886944647865819 0.7707564332014942 0.7711459934369553 0.7698450681065371 0.7695349874914046 0.7177465093084331 0.7211675226594269
44 4 3000 0.7773850252493334 0.7881536188392207 0.772183724897433 0.7717883200737999 0.7712699567266517 0.7709161242994133 0.7187237988981621 0.7223937476573528
45 4 4000 0.777775327985964 0.7893621615957971 0.7716378455124066 0.7722138470558387 0.7709049470179239 0.7709922348165259 0.7176467231063159 0.7201719355921455
46 4 5000 0.7763059687016113 0.7876235359952898 0.7698424419369689 0.7698820975770244 0.7689586641660842 0.7687961773202405 0.7197057873099552 0.7245286861020173
47 4 6000 0.7794826897000439 0.7905141980593657 0.7720120513908072 0.7726462931757969 0.7714463473656613 0.7704221747312833 0.7226559974833779 0.7265990459261015
48 4 7000 0.778285341526584 0.7896335455608554 0.77200273274823 0.7723626085211043 0.7713809544539256 0.7717356577463027 0.7204467718381531 0.724500625153789
49 4 8000 0.778450136549209 0.7893325630613646 0.7721776463065276 0.7722626654178252 0.771422818962253 0.7717952392117192 0.7215457163794511 0.7250733760148891
50 4 9000 0.7784703374820181 0.7894732521990575 0.7726776751649114 0.773120254123271 0.7719272768083242 0.7713374229193902 0.7210072930243006 0.7252590395490577
51 4 -1 0.7787882635851495 0.789720034784847 0.7728008205428657 0.7732217348127545 0.7720684298045568 0.7715788239534646 0.7213522278358117 0.7256630403242363

51
eval/translation_evaluation_TED2020-en-pt-dev.tsv.gz_results.csv Normal file
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epoch,steps,src2trg,trg2src
0,1000,0.046,0.188
0,2000,0.058,0.114
0,3000,0.102,0.146
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0,5000,0.436,0.432
0,6000,0.588,0.621
0,7000,0.719,0.729
0,8000,0.81,0.818
0,9000,0.854,0.88
0,-1,0.889,0.899
1,1000,0.917,0.917
1,2000,0.93,0.932
1,3000,0.94,0.943
1,4000,0.943,0.947
1,5000,0.955,0.954
1,6000,0.958,0.952
1,7000,0.962,0.955
1,8000,0.961,0.961
1,9000,0.962,0.961
1,-1,0.964,0.961
2,1000,0.966,0.967
2,2000,0.968,0.967
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2,4000,0.972,0.966
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2,7000,0.971,0.972
2,8000,0.974,0.969
2,9000,0.974,0.97
2,-1,0.971,0.972
3,1000,0.974,0.973
3,2000,0.975,0.972
3,3000,0.974,0.974
3,4000,0.975,0.972
3,5000,0.974,0.972
3,6000,0.976,0.972
3,7000,0.975,0.971
3,8000,0.975,0.973
3,9000,0.975,0.974
3,-1,0.974,0.973
4,1000,0.975,0.974
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4,4000,0.975,0.973
4,5000,0.975,0.975
4,6000,0.974,0.974
4,7000,0.975,0.973
4,8000,0.974,0.973
4,9000,0.974,0.973
4,-1,0.974,0.973
1 epoch steps src2trg trg2src
2 0 1000 0.046 0.188
3 0 2000 0.058 0.114
4 0 3000 0.102 0.146
5 0 4000 0.202 0.252
6 0 5000 0.436 0.432
7 0 6000 0.588 0.621
8 0 7000 0.719 0.729
9 0 8000 0.81 0.818
10 0 9000 0.854 0.88
11 0 -1 0.889 0.899
12 1 1000 0.917 0.917
13 1 2000 0.93 0.932
14 1 3000 0.94 0.943
15 1 4000 0.943 0.947
16 1 5000 0.955 0.954
17 1 6000 0.958 0.952
18 1 7000 0.962 0.955
19 1 8000 0.961 0.961
20 1 9000 0.962 0.961
21 1 -1 0.964 0.961
22 2 1000 0.966 0.967
23 2 2000 0.968 0.967
24 2 3000 0.968 0.966
25 2 4000 0.972 0.966
26 2 5000 0.971 0.967
27 2 6000 0.971 0.967
28 2 7000 0.971 0.972
29 2 8000 0.974 0.969
30 2 9000 0.974 0.97
31 2 -1 0.971 0.972
32 3 1000 0.974 0.973
33 3 2000 0.975 0.972
34 3 3000 0.974 0.974
35 3 4000 0.975 0.972
36 3 5000 0.974 0.972
37 3 6000 0.976 0.972
38 3 7000 0.975 0.971
39 3 8000 0.975 0.973
40 3 9000 0.975 0.974
41 3 -1 0.974 0.973
42 4 1000 0.975 0.974
43 4 2000 0.975 0.972
44 4 3000 0.975 0.974
45 4 4000 0.975 0.973
46 4 5000 0.975 0.975
47 4 6000 0.974 0.974
48 4 7000 0.975 0.973
49 4 8000 0.974 0.973
50 4 9000 0.974 0.973
51 4 -1 0.974 0.973

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