---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: distilbert-base-uncased-finetuned-cola
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: cola
    metric:
      name: Matthews Correlation
      type: matthews_correlation
      value: 0.520875943143754
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-base-uncased-finetuned-cola

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8486
- Matthews Correlation: 0.5209

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5265        | 1.0   | 535  | 0.5479          | 0.4049               |
| 0.3571        | 2.0   | 1070 | 0.5002          | 0.5164               |
| 0.2432        | 3.0   | 1605 | 0.6242          | 0.5091               |
| 0.173         | 4.0   | 2140 | 0.7559          | 0.5120               |
| 0.1352        | 5.0   | 2675 | 0.8486          | 0.5209               |

### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3


---
tags:
- conversational
---

We love owen


---
tags:
- conversational
---

- We love asher


---
tags:
- conversational
---

#Jake Peralta DialoGPT Model


---
tags:
- conversational
---

@Harry Potter DialoGPT model


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: portuguese-archival-finding-aids
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9617770479839446
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# portuguese-archival-finding-aids

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1812
- Precision: 0.8624
- Recall: 0.9557
- F1: 0.9067
- Accuracy: 0.9618

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 192  | 0.1565          | 0.8511    | 0.9327 | 0.8900 | 0.9563   |
| 0.1849        | 2.0   | 384  | 0.1594          | 0.8634    | 0.9543 | 0.9065 | 0.9619   |
| 0.0454        | 3.0   | 576  | 0.1812          | 0.8624    | 0.9557 | 0.9067 | 0.9618   |

### Framework versions

- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.3


---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-large-pt-archive
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9766762474673703
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-large-pt-archive

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0869
- Precision: 0.9280
- Recall: 0.9541
- F1: 0.9409
- Accuracy: 0.9767

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0665        | 1.0   | 765  | 0.1020          | 0.8928    | 0.9566 | 0.9236 | 0.9696   |
| 0.0392        | 2.0   | 1530 | 0.0781          | 0.9229    | 0.9586 | 0.9404 | 0.9757   |
| 0.0201        | 3.0   | 2295 | 0.0809          | 0.9278    | 0.9550 | 0.9412 | 0.9767   |
| 0.0152        | 4.0   | 3060 | 0.0869          | 0.9280    | 0.9541 | 0.9409 | 0.9767   |

### Framework versions

- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.3


---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-base-pt-archive
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9700325118974698
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-base-pt-archive

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1140
- Precision: 0.9147
- Recall: 0.9483
- F1: 0.9312
- Accuracy: 0.9700

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 192  | 0.1438          | 0.8917    | 0.9392 | 0.9148 | 0.9633   |
| 0.2454        | 2.0   | 384  | 0.1222          | 0.8985    | 0.9417 | 0.9196 | 0.9671   |
| 0.0526        | 3.0   | 576  | 0.1098          | 0.9150    | 0.9481 | 0.9312 | 0.9698   |
| 0.0372        | 4.0   | 768  | 0.1140          | 0.9147    | 0.9483 | 0.9312 | 0.9700   |

### Framework versions

- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-en-ru-finetuned-v2-en-to-ru
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# opus-mt-en-ru-finetuned-v2-en-to-ru

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2182
- Bleu: 29.1013
- Gen Len: 29.0582

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.7662        | 1.0   | 33135 | 1.2395          | 28.5144 | 29.0305 |
| 1.6612        | 2.0   | 66270 | 1.2245          | 28.7764 | 29.1395 |
| 1.6275        | 3.0   | 99405 | 1.2182          | 29.1013 | 29.0582 |

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mt5-small
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# mt5-small

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6823
- Bleu: 5.7578
- Gen Len: 17.9322

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Bleu   | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|
| 2.2139        | 1.0   | 298848 | 1.6823          | 5.7578 | 17.9322 |

### Framework versions

- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-en-ru-finetuned-v3-en-to-ru
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# opus-mt-en-ru-finetuned-v3-en-to-ru

This model is a fine-tuned version of [kazandaev/opus-mt-en-ru-finetuned-v3-en-to-ru](https://huggingface.co/kazandaev/opus-mt-en-ru-finetuned-v3-en-to-ru) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8237
- Bleu: 37.4514
- Gen Len: 29.2779

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 50
- eval_batch_size: 50
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 0.9367        | 1.0   | 42137  | 0.8649          | 36.2025 | 29.3907 |
| 0.9346        | 2.0   | 84274  | 0.8470          | 36.4315 | 29.484  |
| 0.9143        | 3.0   | 126411 | 0.8330          | 36.989  | 29.3385 |
| 0.9119        | 4.0   | 168548 | 0.8237          | 37.4514 | 29.2779 |

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-en-ru-finetuned-en-to-ru
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: wmt16
      type: wmt16
      args: ru-en
    metrics:
    - name: Bleu
      type: bleu
      value: 28.4589
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# opus-mt-en-ru-finetuned-en-to-ru

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2852
- Bleu: 28.4589
- Gen Len: 29.5852

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.9547        | 1.0   | 94761 | 1.2852          | 28.4589 | 29.5852 |

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


Swedish bert multilingual model trained on a machine translated (MS neural translation) SQUAD 1.1 dataset


---
tags:
- conversational
---

# My Awesome Model


---
language: es

tags:
- summarization

widget:
- text: "La Agencia Valenciana de la Innovación (AVI) financia el desarrollo de un software que integra diferentes modelos y tecnologías para la monitorización y análisis multilingüe de las redes sociales. A través de técnicas de 'deep learning' y procesamiento del lenguaje natural es capaz de interpretar la ironía y las emociones en los textos, incluso en aquellos escritos en idiomas menos extendidos, a menudo no contemplados por las herramientas comerciales. La iniciativa, bautizada como 'Guaita', está liderada por el Instituto Valenciano de Investigación en Inteligencia Artificial (VRAIN), adscrito a la Universidad Politécnica de Valencia (UPV), que cuenta a su vez para su desarrollo con la colaboración del Instituto Valenciano de Informática (ITI) y la Corporación Valenciana de Mitjans de Comunicación (CVMC).De este modo, y a solicitud del usuario o usuaria, monitorizará las redes sociales para obtener la información asociada a los temas objeto de interés y ofrecerá los resultados de forma gráfica, bien a través de una interfaz web, bien mediante la generación de informes. El programa será, además, capaz de determinar la reputación de una empresa o institución a partir de dichos análisis gracias a la combinación de distintas tecnologías de procesamiento e interpretación, destaca la agencia en un comunicado."
---

# NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish

Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish.

# The NASes model

News Abstractive Summarization for Spanish (NASES) is a Transformer encoder-decoder model, with the same hyper-parameters than BART, to perform summarization of Spanish news articles. It is pre-trained on a combination of several self-supervised tasks that help to increase the abstractivity of the generated summaries. Four pre-training tasks have been combined: sentence permutation, text infilling, Gap Sentence Generation, and Next Segment Generation. Spanish newspapers, and Wikipedia articles in Spanish were used for pre-training the model (21GB of raw text -8.5 millions of documents-).

NASES is finetuned for the summarization task on 1.802.919 (document, summary) pairs from the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA).

### BibTeX entry
```bibtex
@Article{app11219872,
AUTHOR = {Ahuir, Vicent and Hurtado, Lluís-F. and González, José Ángel and Segarra, Encarna},
TITLE = {NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish},
JOURNAL = {Applied Sciences},
VOLUME = {11},
YEAR = {2021},
NUMBER = {21},
ARTICLE-NUMBER = {9872},
URL = {https://www.mdpi.com/2076-3417/11/21/9872},
ISSN = {2076-3417},
DOI = {10.3390/app11219872}
}
```


---
language: ca

tags:
- summarization

widget:
- text: "Microsoft ha triat Barcelona com a base d’operacions per al seu nou hub de R+D especialitzat en l’aplicació de tecnologies d’Intel·ligència Artificial per a la millora de l’experiència d’usuari a la web. El centre situat a Barcelona serà un dels vuit centres d’investigació amb què compta a nivell mundial la divisió WebXT (Web Experiences Team) de Microsoft, una organització de més de 5.000 persones que presideix Mikhaïl Parakhin, centrada en el desenvolupament d’experiències d’usuari avançades basades en l’ús de tecnologies d’Intel·ligència Artificial i aprenentatge profund. El centre d’excel·lència pretén atraure talent de tots els països d’Europa i s’enquadra dins de l’equip de Search & AI que lidera Jordi Ribas, vicepresident corporatiu de Microsoft Corporation als Estats Units. Aquest equip especialitzat en aplicació d’Intel·ligència Artificial és el grup més gran de WebXT de la companyia i contribueix al desenvolupament de múltiples productes de Microsoft, incloent Windows, Azure i, per descomptat, Bing.  En aquesta primera fase, la inversió inclou l’actual procés de selecció obert que contempla la contractació d’una trentena d’enginyers de ‘software i científics especialitzats en àrees avançades d’enginyeria de ‘software’ incloent Intel·ligència Artificial, Machine Learning i Deep Learning, que podrien superar el centenar en els pròxims anys. L’objectiu de Microsoft és crear al voltant d’aquest equip d’enginyers un vector d’innovació en Intel·ligència Artificial –en col·laboració amb universitats, centres d’investigació i empreses de tecnologia–, reforçant els esforços per impulsar el talent digital a Espanya i la capacitació en tecnologies de ‘machine learning’."
---

# NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish

Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish.

# The NASca model
News Abstractive Summarization for Catalan (NASCA) is a Transformer encoder-decoder model, with the same hyper-parameters than BART, to perform summarization of Catalan news articles. It is pre-trained on a combination of several self-supervised tasks that help to increase the abstractivity of the generated summaries. Four pre-training tasks have been combined: sentence permutation, text infilling, Gap Sentence Generation, and Next Segment Generation. Catalan newspapers, the Catalan subset of the OSCAR corpus and Wikipedia articles in Catalan were used for pre-training the model (9.3GB of raw text -2.5 millions of documents-).

NASCA is finetuned for the summarization task on 636.596 (document, summary) pairs from the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA).

### BibTeX entry
```bibtex
@Article{app11219872,
AUTHOR = {Ahuir, Vicent and Hurtado, Lluís-F. and González, José Ángel and Segarra, Encarna},
TITLE = {NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish},
JOURNAL = {Applied Sciences},
VOLUME = {11},
YEAR = {2021},
NUMBER = {21},
ARTICLE-NUMBER = {9872},
URL = {https://www.mdpi.com/2076-3417/11/21/9872},
ISSN = {2076-3417},
DOI = {10.3390/app11219872}
}
```


---
language:
- Tagalog
thumbnail:
tags:
- Tagalog
- Mang Bert
license: apache-2.0
datasets:
- OSCAR tl
---
# Mang Bert

## Model description

Fine-Tuned Roberta Model using RobertaForMaskedLM
Tagalog Dataset from OSCAR tl

## Training data

458206 text dataset from OSCAR


---
tags:
- conversational
---

# Family Guy (Peter) DialoGPT Model


---
tags:
- conversational
---

# Harry Potter DialoGPT Model


---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xlm-roberta-base-finetuned-marc-en

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9302
- Mae: 0.5

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mae    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1253        | 1.0   | 235  | 0.9756          | 0.5488 |
| 0.9465        | 2.0   | 470  | 0.9302          | 0.5    |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3


---
tags :
- conversational
---

#Rick Sanchez DialoGPT Model


---
language: "en"
tags:
- gpt2
- exbert
- commonsense
- semeval2020
- comve
license: "mit"
datasets:
- https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation
metrics:
- bleu
widget:
- text: "Chicken can swim in water. <|continue|>"
---

# ComVE-gpt2-large

## Model description

Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.

## Intended uses & limitations

You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.

#### How to use

You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.

*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.

#### Limitations and bias

The model biased to negate the entered sentence usually instead of producing a factual reason.

## Training data

The model is initialized from the [gpt2-large](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.

## Training procedure

Each natural language statement that against commonsense is concatenated with its reference reason with `<|conteniue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.

## Eval results

The model achieved 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.

### BibTeX entry and citation info

```bibtex
@article{fadel2020justers,
  title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
  author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
  year={2020}
}
```


---
language: "en"
tags:
- gpt2
- exbert
- commonsense
- semeval2020
- comve
license: "mit"
datasets:
- ComVE
metrics:
- bleu
widget:
- text: "Chicken can swim in water. <|continue|>"
---

# ComVE-gpt2-medium

## Model description

Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.

## Intended uses & limitations

You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.

#### How to use

You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.

*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.

#### Limitations and bias

The model biased to negate the entered sentence usually instead of producing a factual reason.

## Training data

The model is initialized from the [gpt2-medium](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.

## Training procedure

Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.

## Eval results

The model achieved fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.

These are some examples generated by the model:
|             Against Commonsense Statement             |               Generated Reason               |
|:-----------------------------------------------------:|:--------------------------------------------:|
| Chicken can swim in water.                            | Chicken can't swim.                          |
| shoes can fly                                         | Shoes are not able to fly.                   |
| Chocolate can be used to make a coffee pot            | Chocolate is not used to make coffee pots.   |
| you can also buy tickets online with an identity card | You can't buy tickets with an identity card. |
| a ball is square and can roll                         | A ball is round and cannot roll.             |
| You can use detergent to dye your hair.               | Detergent is used to wash clothes.           |
| you can eat mercury                                   | mercury is poisonous                         |
| A gardener can follow a suspect                       | gardener is not a police officer             |
| cars can float in the ocean just like a boat          | Cars are too heavy to float in the ocean.    |
| I am going to work so I can lose money.               | Working is not a way to lose money.          |

### BibTeX entry and citation info

```bibtex
@article{fadel2020justers,
  title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
  author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
  year={2020}
}
```


---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
- classification
license: "mit"
datasets:
- ai-soco
metrics:
- accuracy
---

# ai-soco-c++-roberta-small-clas

## Model description

`ai-soco-c++-roberta-small` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.

#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.

#### Limitations and bias

The model is limited to C++ programming language only.

## Training data

The model initialized from [`ai-soco-c++-roberta-small`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-small) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.

## Training procedure

The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.

## Eval results

The model achieved 93.19%/92.88% accuracy on AI-SOCO task and ranked in the 4th place.

### BibTeX entry and citation info

```bibtex
@inproceedings{ai-soco-2020-fire,
    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
    year = "2020"
}
```


---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
license: "mit"
datasets:
- ai-soco
metrics:
- perplexity
---

# ai-soco-c++-roberta-tiny

## Model description

From scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.

## Intended uses & limitations

The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.

#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.

#### Limitations and bias

The model is limited to C++ programming language only.

## Training data

The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.

## Training procedure

The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.

### BibTeX entry and citation info

```bibtex
@inproceedings{ai-soco-2020-fire,
    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
    year = "2020"
}
```


---
language: "en"
tags:
- exbert
- commonsense
- semeval2020
- comve
license: "mit"
datasets:
- ComVE
metrics:
- bleu
widget:
- text: "Chicken can swim in water. <|continue|>"
---

# ComVE-distilgpt2

## Model description

Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.

## Intended uses & limitations

You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.

#### How to use

You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.

*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.

#### Limitations and bias

The model biased to negate the entered sentence usually instead of producing a factual reason.

## Training data

The model is initialized from the [distilgpt2](https://github.com/huggingface/transformers/blob/master/model_cards/distilgpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.

## Training procedure

Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size.

## Eval results

The model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.

### BibTeX entry and citation info

```bibtex
@article{fadel2020justers,
  title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
  author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
  year={2020}
}
```


---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
- classification
license: "mit"
datasets:
- ai-soco
metrics:
- accuracy
---

# ai-soco-c++-roberta-tiny-clas

## Model description

`ai-soco-c++-roberta-tiny` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.

#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.

#### Limitations and bias

The model is limited to C++ programming language only.

## Training data

The model initialized from [`ai-soco-c++-roberta-tiny`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.

## Training procedure

The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.

## Eval results

The model achieved 87.66%/87.46% accuracy on AI-SOCO task and ranked in the 9th place.

### BibTeX entry and citation info

```bibtex
@inproceedings{ai-soco-2020-fire,
    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
    year = "2020"
}
```


---
language: "en"
tags:
- exbert
- commonsense
- semeval2020
- comve
license: "mit"
datasets:
- ComVE
metrics:
- bleu
widget:
- text: "Chicken can swim in water. <|continue|>"
---

# ComVE-gpt2

## Model description

Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.

## Intended uses & limitations

You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.

#### How to use

You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.

*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.

#### Limitations and bias

The model biased to negate the entered sentence usually instead of producing a factual reason.

## Training data

The model is initialized from the [gpt2](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.

## Training procedure

Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.

## Eval results

The model achieved 14.0547/13.6534 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.

### BibTeX entry and citation info

```bibtex
@article{fadel2020justers,
  title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
  author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
  year={2020}
}
```


---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
license: "mit"
datasets:
- ai-soco
metrics:
- perplexity
---

# ai-soco-c++-roberta-tiny-96

## Model description

From scratch pre-trained RoBERTa model with 1 layers and 96 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.

## Intended uses & limitations

The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.

#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.

#### Limitations and bias

The model is limited to C++ programming language only.

## Training data

The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.

## Training procedure

The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.

### BibTeX entry and citation info

```bibtex
@inproceedings{ai-soco-2020-fire,
    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
    year = "2020"
}
```


---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
license: "mit"
datasets:
- ai-soco
metrics:
- perplexity
---

# ai-soco-c++-roberta-small

## Model description

From scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.

## Intended uses & limitations

The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.

#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.

#### Limitations and bias

The model is limited to C++ programming language only.

## Training data

The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.

## Training procedure

The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.

### BibTeX entry and citation info

```bibtex
@inproceedings{ai-soco-2020-fire,
    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
    year = "2020"
}
```


---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
- classification
license: "mit"
datasets:
- ai-soco
metrics:
- accuracy
---

# ai-soco-c++-roberta-tiny-96-clas

## Model description

`ai-soco-c++-roberta-tiny-96` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.

#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.

#### Limitations and bias

The model is limited to C++ programming language only.

## Training data

The model initialized from [`ai-soco-c++-roberta-tiny-96`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny-96) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.

## Training procedure

The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.

## Eval results

The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.

### BibTeX entry and citation info

```bibtex
@inproceedings{ai-soco-2020-fire,
    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
    year = "2020"
}
```


---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned-bert-bounti
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# finetuned-bert-bounti

This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) on the BounTi Turkish Twitter sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1188
- Accuracy: 0.7246
- F1: 0.6845
- Precision: 0.6892
- Recall: 0.6806

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 36
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.0974        | 0.02  | 5    | 1.0790          | 0.3756   | 0.3064 | 0.3255    | 0.3232 |
| 1.1345        | 0.04  | 10   | 1.0784          | 0.3725   | 0.3037 | 0.3219    | 0.3197 |
| 1.1441        | 0.06  | 15   | 1.0776          | 0.3772   | 0.3072 | 0.3250    | 0.3234 |
| 1.122         | 0.08  | 20   | 1.0774          | 0.3787   | 0.3077 | 0.3244    | 0.3228 |
| 1.1201        | 0.1   | 25   | 1.0776          | 0.3787   | 0.3047 | 0.3193    | 0.3216 |
| 1.1489        | 0.13  | 30   | 1.0783          | 0.3787   | 0.3012 | 0.3120    | 0.3189 |
| 1.0716        | 0.15  | 35   | 1.0783          | 0.3897   | 0.3093 | 0.3212    | 0.3282 |
| 1.082         | 0.17  | 40   | 1.0767          | 0.3865   | 0.3060 | 0.3203    | 0.3238 |
| 1.1113        | 0.19  | 45   | 1.0738          | 0.3897   | 0.3058 | 0.3219    | 0.3211 |
| 1.0892        | 0.21  | 50   | 1.0715          | 0.4069   | 0.3290 | 0.3475    | 0.3374 |
| 1.0913        | 0.23  | 55   | 1.0719          | 0.4178   | 0.3283 | 0.3398    | 0.3361 |
| 1.1114        | 0.25  | 60   | 1.0694          | 0.4397   | 0.3479 | 0.3605    | 0.3538 |
| 1.1129        | 0.27  | 65   | 1.0682          | 0.4491   | 0.3593 | 0.3731    | 0.3648 |
| 1.1283        | 0.29  | 70   | 1.0671          | 0.4664   | 0.3719 | 0.3775    | 0.3780 |
| 1.1267        | 0.31  | 75   | 1.0714          | 0.4507   | 0.3826 | 0.3834    | 0.3835 |
| 1.1325        | 0.33  | 80   | 1.0762          | 0.4335   | 0.3909 | 0.3918    | 0.3954 |
| 1.0919        | 0.36  | 85   | 1.0723          | 0.4335   | 0.3930 | 0.3937    | 0.3982 |
| 1.0545        | 0.38  | 90   | 1.0694          | 0.4507   | 0.4161 | 0.4180    | 0.4279 |
| 1.1121        | 0.4   | 95   | 1.0698          | 0.4491   | 0.4151 | 0.4280    | 0.4324 |
| 1.0675        | 0.42  | 100  | 1.0711          | 0.4382   | 0.4005 | 0.4349    | 0.4494 |
| 1.0954        | 0.44  | 105  | 1.0720          | 0.4085   | 0.3690 | 0.4233    | 0.4326 |
| 1.1087        | 0.46  | 110  | 1.0562          | 0.4820   | 0.4463 | 0.4762    | 0.4841 |
| 1.0669        | 0.48  | 115  | 1.0459          | 0.5086   | 0.4746 | 0.4844    | 0.4997 |
| 1.0529        | 0.5   | 120  | 1.0364          | 0.5243   | 0.4935 | 0.4946    | 0.5119 |
| 1.0348        | 0.52  | 125  | 1.0248          | 0.5321   | 0.4953 | 0.4977    | 0.5067 |
| 1.0454        | 0.54  | 130  | 1.0169          | 0.5415   | 0.5089 | 0.5084    | 0.5232 |
| 1.0366        | 0.56  | 135  | 1.0071          | 0.5493   | 0.5176 | 0.5156    | 0.5344 |
| 1.0197        | 0.59  | 140  | 1.0010          | 0.5446   | 0.5132 | 0.5150    | 0.5350 |
| 1.0459        | 0.61  | 145  | 0.9966          | 0.5399   | 0.5094 | 0.5184    | 0.5383 |
| 1.0059        | 0.63  | 150  | 1.0011          | 0.5477   | 0.5222 | 0.5394    | 0.5617 |
| 0.9455        | 0.65  | 155  | 0.9898          | 0.5399   | 0.5173 | 0.5390    | 0.5583 |
| 0.9732        | 0.67  | 160  | 0.9750          | 0.5477   | 0.5207 | 0.5406    | 0.5601 |
| 1.0215        | 0.69  | 165  | 0.9494          | 0.5790   | 0.5495 | 0.5511    | 0.5759 |
| 0.99          | 0.71  | 170  | 0.9331          | 0.5696   | 0.5355 | 0.5372    | 0.5500 |
| 1.0102        | 0.73  | 175  | 0.9284          | 0.5759   | 0.5425 | 0.5488    | 0.5567 |
| 0.9633        | 0.75  | 180  | 0.9313          | 0.5837   | 0.5571 | 0.5726    | 0.5758 |
| 0.9388        | 0.77  | 185  | 0.9262          | 0.5869   | 0.5625 | 0.5830    | 0.5817 |
| 0.9606        | 0.79  | 190  | 0.9140          | 0.5915   | 0.5638 | 0.5728    | 0.5835 |
| 0.969         | 0.82  | 195  | 0.9170          | 0.5978   | 0.5712 | 0.5769    | 0.5964 |
| 0.8779        | 0.84  | 200  | 0.9089          | 0.5947   | 0.5696 | 0.5790    | 0.5925 |
| 0.9041        | 0.86  | 205  | 0.9013          | 0.6166   | 0.5874 | 0.5894    | 0.6083 |
| 0.8643        | 0.88  | 210  | 0.8783          | 0.6275   | 0.5961 | 0.5972    | 0.6140 |
| 0.8864        | 0.9   | 215  | 0.8651          | 0.6307   | 0.5984 | 0.6060    | 0.6152 |
| 0.9075        | 0.92  | 220  | 0.8562          | 0.6401   | 0.6107 | 0.6096    | 0.6313 |
| 0.8659        | 0.94  | 225  | 0.8407          | 0.6244   | 0.5896 | 0.5864    | 0.6085 |
| 0.8921        | 0.96  | 230  | 0.8171          | 0.6385   | 0.6014 | 0.5955    | 0.6138 |
| 0.9176        | 0.98  | 235  | 0.8120          | 0.6432   | 0.6052 | 0.6001    | 0.6183 |
| 0.8124        | 1.0   | 240  | 0.8084          | 0.6479   | 0.6087 | 0.6058    | 0.6229 |
| 0.7606        | 1.03  | 245  | 0.7978          | 0.6588   | 0.6198 | 0.6166    | 0.6258 |
| 0.7879        | 1.05  | 250  | 0.8361          | 0.6322   | 0.6002 | 0.6090    | 0.6310 |
| 0.8515        | 1.07  | 255  | 0.8527          | 0.6307   | 0.6063 | 0.6070    | 0.6368 |
| 0.7861        | 1.09  | 260  | 0.8300          | 0.6510   | 0.6229 | 0.6172    | 0.6449 |
| 0.8782        | 1.11  | 265  | 0.8068          | 0.6588   | 0.6262 | 0.6195    | 0.6412 |
| 0.6993        | 1.13  | 270  | 0.8127          | 0.6573   | 0.6245 | 0.6186    | 0.6414 |
| 0.7961        | 1.15  | 275  | 0.8302          | 0.6448   | 0.6129 | 0.6142    | 0.6382 |
| 0.829         | 1.17  | 280  | 0.8130          | 0.6416   | 0.6068 | 0.6047    | 0.6264 |
| 0.7315        | 1.19  | 285  | 0.8127          | 0.6714   | 0.6414 | 0.6348    | 0.6609 |
| 0.7115        | 1.21  | 290  | 0.8074          | 0.6651   | 0.6367 | 0.6297    | 0.6577 |
| 0.7937        | 1.23  | 295  | 0.8018          | 0.6667   | 0.6405 | 0.6338    | 0.6595 |
| 0.8213        | 1.26  | 300  | 0.7846          | 0.6651   | 0.6317 | 0.6313    | 0.6424 |
| 0.9309        | 1.28  | 305  | 0.7801          | 0.6651   | 0.6267 | 0.6314    | 0.6357 |
| 0.7616        | 1.3   | 310  | 0.8000          | 0.6635   | 0.6403 | 0.6352    | 0.6657 |
| 0.7075        | 1.32  | 315  | 0.8006          | 0.6635   | 0.6395 | 0.6354    | 0.6642 |
| 0.8925        | 1.34  | 320  | 0.8418          | 0.6385   | 0.6185 | 0.6205    | 0.6531 |
| 0.7579        | 1.36  | 325  | 0.8114          | 0.6541   | 0.6308 | 0.6281    | 0.6602 |
| 0.6983        | 1.38  | 330  | 0.7589          | 0.6745   | 0.6424 | 0.6356    | 0.6538 |
| 0.756         | 1.4   | 335  | 0.7540          | 0.6870   | 0.6423 | 0.6454    | 0.6436 |
| 0.8183        | 1.42  | 340  | 0.7762          | 0.6651   | 0.6304 | 0.6248    | 0.6486 |
| 0.7386        | 1.44  | 345  | 0.8212          | 0.6510   | 0.6244 | 0.6229    | 0.6535 |
| 0.7175        | 1.46  | 350  | 0.8002          | 0.6573   | 0.6269 | 0.6229    | 0.6512 |
| 0.7076        | 1.49  | 355  | 0.7799          | 0.6682   | 0.6310 | 0.6281    | 0.6506 |
| 0.7115        | 1.51  | 360  | 0.7525          | 0.6886   | 0.6576 | 0.6510    | 0.6697 |
| 0.7092        | 1.53  | 365  | 0.7882          | 0.6714   | 0.6272 | 0.6513    | 0.6330 |
| 0.6852        | 1.55  | 370  | 0.7909          | 0.6698   | 0.6287 | 0.6548    | 0.6363 |
| 0.673         | 1.57  | 375  | 0.7396          | 0.6901   | 0.6523 | 0.6536    | 0.6542 |
| 0.7115        | 1.59  | 380  | 0.7270          | 0.6933   | 0.6539 | 0.6532    | 0.6546 |
| 0.6391        | 1.61  | 385  | 0.7389          | 0.6964   | 0.6654 | 0.6576    | 0.6790 |
| 0.6018        | 1.63  | 390  | 0.7619          | 0.6886   | 0.6628 | 0.6571    | 0.6835 |
| 0.743         | 1.65  | 395  | 0.7635          | 0.6854   | 0.6579 | 0.6546    | 0.6780 |
| 0.6865        | 1.67  | 400  | 0.7457          | 0.7011   | 0.6709 | 0.6681    | 0.6855 |
| 0.6629        | 1.69  | 405  | 0.7309          | 0.7058   | 0.6752 | 0.6717    | 0.6861 |
| 0.6887        | 1.72  | 410  | 0.7389          | 0.6933   | 0.6628 | 0.6555    | 0.6809 |
| 0.6494        | 1.74  | 415  | 0.7742          | 0.6823   | 0.6565 | 0.6519    | 0.6831 |
| 0.6798        | 1.76  | 420  | 0.7751          | 0.6667   | 0.6337 | 0.6345    | 0.6614 |
| 0.6825        | 1.78  | 425  | 0.7798          | 0.6604   | 0.6269 | 0.6375    | 0.6594 |
| 0.7926        | 1.8   | 430  | 0.7085          | 0.7105   | 0.6726 | 0.6670    | 0.6804 |
| 0.6508        | 1.82  | 435  | 0.7455          | 0.6964   | 0.6439 | 0.6653    | 0.6460 |
| 0.7772        | 1.84  | 440  | 0.7669          | 0.6964   | 0.6531 | 0.6780    | 0.6594 |
| 0.7265        | 1.86  | 445  | 0.7454          | 0.7089   | 0.6722 | 0.6800    | 0.6826 |
| 0.5965        | 1.88  | 450  | 0.7700          | 0.6933   | 0.6670 | 0.6623    | 0.6931 |
| 0.6436        | 1.9   | 455  | 0.7910          | 0.6901   | 0.6654 | 0.6620    | 0.6951 |
| 0.6887        | 1.92  | 460  | 0.7752          | 0.6870   | 0.6590 | 0.6552    | 0.6872 |
| 0.7574        | 1.95  | 465  | 0.7511          | 0.6980   | 0.6686 | 0.6621    | 0.6925 |
| 0.6853        | 1.97  | 470  | 0.7446          | 0.7074   | 0.6775 | 0.6711    | 0.6981 |
| 0.7416        | 1.99  | 475  | 0.7151          | 0.7105   | 0.6783 | 0.6703    | 0.6938 |
| 0.723         | 2.01  | 480  | 0.6886          | 0.7105   | 0.6727 | 0.6691    | 0.6776 |
| 0.5993        | 2.03  | 485  | 0.6947          | 0.7152   | 0.6767 | 0.6711    | 0.6865 |
| 0.549         | 2.05  | 490  | 0.7140          | 0.7167   | 0.6833 | 0.6764    | 0.6969 |
| 0.5739        | 2.07  | 495  | 0.7372          | 0.7136   | 0.6843 | 0.6828    | 0.6961 |
| 0.6444        | 2.09  | 500  | 0.7733          | 0.7089   | 0.6796 | 0.6943    | 0.6920 |
| 0.5526        | 2.11  | 505  | 0.7368          | 0.7277   | 0.6954 | 0.6927    | 0.7074 |
| 0.5429        | 2.13  | 510  | 0.7194          | 0.7246   | 0.6886 | 0.6879    | 0.6913 |
| 0.5838        | 2.15  | 515  | 0.7465          | 0.7214   | 0.6818 | 0.6933    | 0.6866 |
| 0.6746        | 2.18  | 520  | 0.7644          | 0.7152   | 0.6865 | 0.6819    | 0.7054 |
| 0.7252        | 2.2   | 525  | 0.7564          | 0.7042   | 0.6713 | 0.6645    | 0.6918 |
| 0.5443        | 2.22  | 530  | 0.7337          | 0.7027   | 0.6636 | 0.6598    | 0.6782 |
| 0.5526        | 2.24  | 535  | 0.7324          | 0.7183   | 0.6795 | 0.6831    | 0.6865 |
| 0.692         | 2.26  | 540  | 0.7622          | 0.7121   | 0.6826 | 0.6841    | 0.6971 |
| 0.5897        | 2.28  | 545  | 0.7525          | 0.7089   | 0.6771 | 0.6708    | 0.6951 |
| 0.708         | 2.3   | 550  | 0.7366          | 0.7105   | 0.6763 | 0.6690    | 0.6938 |
| 0.6009        | 2.32  | 555  | 0.7232          | 0.7136   | 0.6741 | 0.6690    | 0.6843 |
| 0.6622        | 2.34  | 560  | 0.7104          | 0.7136   | 0.6763 | 0.6727    | 0.6816 |
| 0.8816        | 2.36  | 565  | 0.7150          | 0.7183   | 0.6830 | 0.6775    | 0.6932 |
| 0.6642        | 2.38  | 570  | 0.7545          | 0.6980   | 0.6681 | 0.6652    | 0.6961 |
| 0.5929        | 2.41  | 575  | 0.7167          | 0.7136   | 0.6778 | 0.6704    | 0.6930 |
| 0.6612        | 2.43  | 580  | 0.7078          | 0.7277   | 0.6912 | 0.6858    | 0.7023 |
| 0.4924        | 2.45  | 585  | 0.7138          | 0.7167   | 0.6809 | 0.6753    | 0.6938 |
| 0.544         | 2.47  | 590  | 0.7088          | 0.7183   | 0.6807 | 0.6749    | 0.6901 |
| 0.4047        | 2.49  | 595  | 0.7210          | 0.7199   | 0.6843 | 0.6775    | 0.6965 |
| 0.5416        | 2.51  | 600  | 0.7199          | 0.7214   | 0.6845 | 0.6777    | 0.6952 |
| 0.5407        | 2.53  | 605  | 0.7159          | 0.7293   | 0.6934 | 0.6873    | 0.7017 |
| 0.5775        | 2.55  | 610  | 0.7354          | 0.7308   | 0.6975 | 0.6902    | 0.7133 |
| 0.6107        | 2.57  | 615  | 0.7402          | 0.7261   | 0.6932 | 0.6863    | 0.7103 |
| 0.5679        | 2.59  | 620  | 0.7266          | 0.7293   | 0.6946 | 0.6869    | 0.7091 |
| 0.5599        | 2.62  | 625  | 0.7049          | 0.7136   | 0.6736 | 0.6716    | 0.6757 |
| 0.6608        | 2.64  | 630  | 0.7150          | 0.7183   | 0.6834 | 0.6761    | 0.6952 |
| 0.6886        | 2.66  | 635  | 0.7334          | 0.7230   | 0.6925 | 0.6856    | 0.7107 |
| 0.6524        | 2.68  | 640  | 0.7106          | 0.7324   | 0.6955 | 0.6907    | 0.7060 |
| 0.5027        | 2.7   | 645  | 0.7031          | 0.7261   | 0.6871 | 0.6896    | 0.6883 |
| 0.5327        | 2.72  | 650  | 0.7033          | 0.7230   | 0.6824 | 0.6863    | 0.6812 |
| 0.6561        | 2.74  | 655  | 0.7188          | 0.7183   | 0.6846 | 0.6770    | 0.6979 |
| 0.591         | 2.76  | 660  | 0.7449          | 0.7136   | 0.6844 | 0.6793    | 0.7087 |
| 0.4584        | 2.78  | 665  | 0.7220          | 0.7074   | 0.6732 | 0.6661    | 0.6855 |
| 0.501         | 2.8   | 670  | 0.7212          | 0.7199   | 0.6829 | 0.6830    | 0.6879 |
| 0.7118        | 2.82  | 675  | 0.7327          | 0.7167   | 0.6827 | 0.6775    | 0.6962 |
| 0.5037        | 2.85  | 680  | 0.7544          | 0.7121   | 0.6818 | 0.6742    | 0.7042 |
| 0.4921        | 2.87  | 685  | 0.7265          | 0.7136   | 0.6791 | 0.6714    | 0.6926 |
| 0.5255        | 2.89  | 690  | 0.7278          | 0.7074   | 0.6706 | 0.6659    | 0.6855 |
| 0.509         | 2.91  | 695  | 0.7334          | 0.7027   | 0.6654 | 0.6599    | 0.6806 |
| 0.4321        | 2.93  | 700  | 0.7358          | 0.7152   | 0.6805 | 0.6728    | 0.6944 |
| 0.6196        | 2.95  | 705  | 0.7406          | 0.7293   | 0.6971 | 0.6895    | 0.7119 |
| 0.5289        | 2.97  | 710  | 0.7363          | 0.7324   | 0.7017 | 0.6944    | 0.7162 |
| 0.6204        | 2.99  | 715  | 0.7401          | 0.7324   | 0.7024 | 0.6949    | 0.7182 |
| 0.5459        | 3.01  | 720  | 0.7360          | 0.7308   | 0.7010 | 0.6937    | 0.7152 |
| 0.4793        | 3.03  | 725  | 0.7363          | 0.7324   | 0.7007 | 0.6966    | 0.7123 |
| 0.5157        | 3.05  | 730  | 0.7330          | 0.7355   | 0.7026 | 0.6999    | 0.7107 |
| 0.4863        | 3.08  | 735  | 0.7231          | 0.7199   | 0.6842 | 0.6803    | 0.6887 |
| 0.423         | 3.1   | 740  | 0.7313          | 0.7230   | 0.6873 | 0.6816    | 0.6950 |
| 0.4879        | 3.12  | 745  | 0.7546          | 0.7199   | 0.6895 | 0.6828    | 0.7064 |
| 0.2499        | 3.14  | 750  | 0.7727          | 0.7214   | 0.6934 | 0.6913    | 0.7093 |
| 0.487         | 3.16  | 755  | 0.7621          | 0.7230   | 0.6906 | 0.6832    | 0.7052 |
| 0.3501        | 3.18  | 760  | 0.7966          | 0.7027   | 0.6689 | 0.6664    | 0.6919 |
| 0.5762        | 3.2   | 765  | 0.7694          | 0.7121   | 0.6747 | 0.6708    | 0.6896 |
| 0.4491        | 3.22  | 770  | 0.7482          | 0.7230   | 0.6873 | 0.6860    | 0.6887 |
| 0.4803        | 3.24  | 775  | 0.7584          | 0.7261   | 0.6895 | 0.6910    | 0.6934 |
| 0.3349        | 3.26  | 780  | 0.7874          | 0.7183   | 0.6870 | 0.6929    | 0.6956 |
| 0.5481        | 3.28  | 785  | 0.8124          | 0.7105   | 0.6856 | 0.6831    | 0.7075 |
| 0.3695        | 3.31  | 790  | 0.7935          | 0.7089   | 0.6798 | 0.6714    | 0.6995 |
| 0.3998        | 3.33  | 795  | 0.7702          | 0.7152   | 0.6811 | 0.6748    | 0.6912 |
| 0.5214        | 3.35  | 800  | 0.7705          | 0.7152   | 0.6765 | 0.6772    | 0.6759 |
| 0.4914        | 3.37  | 805  | 0.7796          | 0.7293   | 0.6954 | 0.6887    | 0.7048 |
| 0.4096        | 3.39  | 810  | 0.7912          | 0.7121   | 0.6818 | 0.6732    | 0.6999 |
| 0.4346        | 3.41  | 815  | 0.7758          | 0.7293   | 0.6958 | 0.6887    | 0.7060 |
| 0.4933        | 3.43  | 820  | 0.7802          | 0.7136   | 0.6795 | 0.6719    | 0.6942 |
| 0.4561        | 3.45  | 825  | 0.7670          | 0.7261   | 0.6929 | 0.6863    | 0.7020 |
| 0.5619        | 3.47  | 830  | 0.7656          | 0.7293   | 0.6916 | 0.6950    | 0.6915 |
| 0.4934        | 3.49  | 835  | 0.7875          | 0.7277   | 0.6872 | 0.7002    | 0.6866 |
| 0.545         | 3.51  | 840  | 0.7675          | 0.7199   | 0.6733 | 0.6852    | 0.6663 |
| 0.4279        | 3.54  | 845  | 0.7582          | 0.7136   | 0.6709 | 0.6735    | 0.6690 |
| 0.351         | 3.56  | 850  | 0.7599          | 0.7136   | 0.6728 | 0.6724    | 0.6741 |
| 0.3701        | 3.58  | 855  | 0.7602          | 0.7293   | 0.6922 | 0.6940    | 0.6915 |
| 0.5307        | 3.6   | 860  | 0.7689          | 0.7308   | 0.6936 | 0.6968    | 0.6940 |
| 0.3895        | 3.62  | 865  | 0.7657          | 0.7246   | 0.6897 | 0.6852    | 0.6952 |
| 0.4676        | 3.64  | 870  | 0.7715          | 0.7230   | 0.6875 | 0.6811    | 0.6965 |
| 0.4124        | 3.66  | 875  | 0.7795          | 0.7230   | 0.6899 | 0.6822    | 0.7024 |
| 0.464         | 3.68  | 880  | 0.7933          | 0.7214   | 0.6893 | 0.6829    | 0.7022 |
| 0.4911        | 3.7   | 885  | 0.8201          | 0.7324   | 0.6947 | 0.6999    | 0.7029 |
| 0.4753        | 3.72  | 890  | 0.7907          | 0.7324   | 0.6978 | 0.6928    | 0.7060 |
| 0.3981        | 3.74  | 895  | 0.7811          | 0.7214   | 0.6832 | 0.6823    | 0.6842 |
| 0.5685        | 3.77  | 900  | 0.7806          | 0.7277   | 0.6899 | 0.6880    | 0.6920 |
| 0.4643        | 3.79  | 905  | 0.7792          | 0.7308   | 0.6961 | 0.6942    | 0.6995 |
| 0.4609        | 3.81  | 910  | 0.7886          | 0.7152   | 0.6814 | 0.6738    | 0.6940 |
| 0.5575        | 3.83  | 915  | 0.8158          | 0.7011   | 0.6688 | 0.6656    | 0.6925 |
| 0.4409        | 3.85  | 920  | 0.7921          | 0.7074   | 0.6717 | 0.6657    | 0.6890 |
| 0.5152        | 3.87  | 925  | 0.7839          | 0.7214   | 0.6859 | 0.6783    | 0.7003 |
| 0.4547        | 3.89  | 930  | 0.7646          | 0.7387   | 0.7034 | 0.6998    | 0.7111 |
| 0.32          | 3.91  | 935  | 0.7502          | 0.7277   | 0.6885 | 0.6893    | 0.6881 |
| 0.2742        | 3.93  | 940  | 0.7583          | 0.7167   | 0.6734 | 0.6794    | 0.6686 |
| 0.5842        | 3.95  | 945  | 0.7613          | 0.7261   | 0.6885 | 0.6842    | 0.6942 |
| 0.4406        | 3.97  | 950  | 0.7951          | 0.7387   | 0.7056 | 0.7011    | 0.7178 |
| 0.5251        | 4.0   | 955  | 0.7932          | 0.7261   | 0.6918 | 0.6851    | 0.7056 |
| 0.4235        | 4.02  | 960  | 0.7839          | 0.7167   | 0.6818 | 0.6745    | 0.6949 |
| 0.3876        | 4.04  | 965  | 0.7668          | 0.7277   | 0.6918 | 0.6864    | 0.6987 |
| 0.4244        | 4.06  | 970  | 0.7622          | 0.7246   | 0.6851 | 0.6872    | 0.6834 |
| 0.3872        | 4.08  | 975  | 0.7696          | 0.7261   | 0.6879 | 0.6903    | 0.6867 |
| 0.3878        | 4.1   | 980  | 0.7760          | 0.7183   | 0.6781 | 0.6779    | 0.6787 |
| 0.3029        | 4.12  | 985  | 0.7897          | 0.7340   | 0.6971 | 0.6933    | 0.7027 |
| 0.3147        | 4.14  | 990  | 0.7987          | 0.7308   | 0.6946 | 0.6903    | 0.7003 |
| 0.3531        | 4.16  | 995  | 0.8009          | 0.7167   | 0.6750 | 0.6746    | 0.6753 |
| 0.393         | 4.18  | 1000 | 0.8072          | 0.7136   | 0.6724 | 0.6730    | 0.6718 |
| 0.5162        | 4.21  | 1005 | 0.8105          | 0.7277   | 0.6902 | 0.6861    | 0.6952 |
| 0.4582        | 4.23  | 1010 | 0.8124          | 0.7293   | 0.6919 | 0.6873    | 0.6977 |
| 0.4746        | 4.25  | 1015 | 0.8130          | 0.7340   | 0.7015 | 0.6944    | 0.7125 |
| 0.453         | 4.27  | 1020 | 0.8024          | 0.7418   | 0.7083 | 0.7019    | 0.7174 |
| 0.3852        | 4.29  | 1025 | 0.7856          | 0.7183   | 0.6778 | 0.6763    | 0.6798 |
| 0.3614        | 4.31  | 1030 | 0.7797          | 0.7167   | 0.6766 | 0.6757    | 0.6781 |
| 0.3222        | 4.33  | 1035 | 0.7949          | 0.7293   | 0.6897 | 0.6983    | 0.6899 |
| 0.3769        | 4.35  | 1040 | 0.8036          | 0.7246   | 0.6853 | 0.6974    | 0.6826 |
| 0.3626        | 4.37  | 1045 | 0.7951          | 0.7340   | 0.6947 | 0.7033    | 0.6925 |
| 0.335         | 4.39  | 1050 | 0.8133          | 0.7293   | 0.6999 | 0.6923    | 0.7139 |
| 0.4664        | 4.41  | 1055 | 0.8644          | 0.7074   | 0.6818 | 0.6747    | 0.7095 |
| 0.3939        | 4.44  | 1060 | 0.8280          | 0.7246   | 0.6949 | 0.6859    | 0.7140 |
| 0.3793        | 4.46  | 1065 | 0.7876          | 0.7293   | 0.6919 | 0.6879    | 0.6966 |
| 0.4559        | 4.48  | 1070 | 0.7933          | 0.7277   | 0.6837 | 0.6939    | 0.6787 |
| 0.362         | 4.5   | 1075 | 0.7908          | 0.7308   | 0.6886 | 0.6955    | 0.6862 |
| 0.3833        | 4.52  | 1080 | 0.8061          | 0.7246   | 0.6894 | 0.6912    | 0.6948 |
| 0.2983        | 4.54  | 1085 | 0.8001          | 0.7371   | 0.6958 | 0.7029    | 0.6956 |
| 0.4279        | 4.56  | 1090 | 0.7939          | 0.7340   | 0.6985 | 0.6970    | 0.7007 |
| 0.371         | 4.58  | 1095 | 0.8178          | 0.7355   | 0.7047 | 0.6957    | 0.7213 |
| 0.2119        | 4.6   | 1100 | 0.8276          | 0.7277   | 0.6953 | 0.6877    | 0.7129 |
| 0.4231        | 4.62  | 1105 | 0.8099          | 0.7402   | 0.7089 | 0.7007    | 0.7219 |
| 0.1754        | 4.64  | 1110 | 0.8107          | 0.7340   | 0.6973 | 0.7013    | 0.6991 |
| 0.2922        | 4.67  | 1115 | 0.8135          | 0.7324   | 0.6945 | 0.6989    | 0.6954 |
| 0.3584        | 4.69  | 1120 | 0.8163          | 0.7433   | 0.7120 | 0.7076    | 0.7192 |
| 0.3186        | 4.71  | 1125 | 0.8135          | 0.7449   | 0.7120 | 0.7076    | 0.7178 |
| 0.2247        | 4.73  | 1130 | 0.8224          | 0.7418   | 0.7103 | 0.7060    | 0.7166 |
| 0.5324        | 4.75  | 1135 | 0.8359          | 0.7402   | 0.7119 | 0.7071    | 0.7216 |
| 0.3348        | 4.77  | 1140 | 0.8277          | 0.7340   | 0.6964 | 0.6981    | 0.6991 |
| 0.2568        | 4.79  | 1145 | 0.8138          | 0.7340   | 0.6960 | 0.6974    | 0.6956 |
| 0.3209        | 4.81  | 1150 | 0.8127          | 0.7293   | 0.6892 | 0.6901    | 0.6883 |
| 0.4479        | 4.83  | 1155 | 0.8081          | 0.7340   | 0.6962 | 0.6930    | 0.6999 |
| 0.3882        | 4.85  | 1160 | 0.8195          | 0.7371   | 0.7053 | 0.6981    | 0.7156 |
| 0.3669        | 4.87  | 1165 | 0.8290          | 0.7293   | 0.6967 | 0.6885    | 0.7107 |
| 0.3157        | 4.9   | 1170 | 0.8288          | 0.7355   | 0.7019 | 0.6943    | 0.7135 |
| 0.4165        | 4.92  | 1175 | 0.8225          | 0.7340   | 0.6982 | 0.6948    | 0.7039 |
| 0.2225        | 4.94  | 1180 | 0.8172          | 0.7293   | 0.6896 | 0.6894    | 0.6903 |
| 0.3322        | 4.96  | 1185 | 0.8276          | 0.7246   | 0.6833 | 0.6856    | 0.6814 |
| 0.3355        | 4.98  | 1190 | 0.8414          | 0.7214   | 0.6813 | 0.6819    | 0.6838 |
| 0.3134        | 5.0   | 1195 | 0.8560          | 0.7324   | 0.6976 | 0.6927    | 0.7103 |
| 0.2255        | 5.02  | 1200 | 0.8507          | 0.7308   | 0.6970 | 0.6901    | 0.7070 |
| 0.3257        | 5.04  | 1205 | 0.8506          | 0.7214   | 0.6806 | 0.6834    | 0.6814 |
| 0.2508        | 5.06  | 1210 | 0.8652          | 0.7261   | 0.6840 | 0.6932    | 0.6805 |
| 0.2465        | 5.08  | 1215 | 0.8663          | 0.7246   | 0.6814 | 0.6902    | 0.6771 |
| 0.273         | 5.1   | 1220 | 0.8629          | 0.7199   | 0.6769 | 0.6790    | 0.6765 |
| 0.2377        | 5.13  | 1225 | 0.8664          | 0.7355   | 0.6996 | 0.6956    | 0.7052 |
| 0.2537        | 5.15  | 1230 | 0.8793          | 0.7324   | 0.6998 | 0.6947    | 0.7088 |
| 0.2031        | 5.17  | 1235 | 0.8715          | 0.7261   | 0.6928 | 0.6877    | 0.7005 |
| 0.2148        | 5.19  | 1240 | 0.8654          | 0.7355   | 0.6980 | 0.6962    | 0.7001 |
| 0.2889        | 5.21  | 1245 | 0.8712          | 0.7261   | 0.6872 | 0.6881    | 0.6863 |
| 0.368         | 5.23  | 1250 | 0.8732          | 0.7308   | 0.6917 | 0.6929    | 0.6913 |
| 0.2998        | 5.25  | 1255 | 0.8758          | 0.7293   | 0.6927 | 0.6905    | 0.6958 |
| 0.3705        | 5.27  | 1260 | 0.8713          | 0.7308   | 0.6939 | 0.6906    | 0.6975 |
| 0.2486        | 5.29  | 1265 | 0.8734          | 0.7277   | 0.6929 | 0.6872    | 0.7003 |
| 0.2424        | 5.31  | 1270 | 0.8772          | 0.7214   | 0.6847 | 0.6820    | 0.6909 |
| 0.3169        | 5.33  | 1275 | 0.8768          | 0.7230   | 0.6828 | 0.6847    | 0.6856 |
| 0.2918        | 5.36  | 1280 | 0.8836          | 0.7246   | 0.6856 | 0.6839    | 0.6913 |
| 0.2464        | 5.38  | 1285 | 0.8798          | 0.7246   | 0.6859 | 0.6835    | 0.6909 |
| 0.3308        | 5.4   | 1290 | 0.8762          | 0.7340   | 0.6947 | 0.6909    | 0.6995 |
| 0.2678        | 5.42  | 1295 | 0.8799          | 0.7340   | 0.6952 | 0.6900    | 0.7019 |
| 0.3768        | 5.44  | 1300 | 0.8762          | 0.7293   | 0.6880 | 0.6862    | 0.6907 |
| 0.3272        | 5.46  | 1305 | 0.8741          | 0.7246   | 0.6816 | 0.6831    | 0.6806 |
| 0.2762        | 5.48  | 1310 | 0.8801          | 0.7308   | 0.6872 | 0.6914    | 0.6850 |
| 0.3292        | 5.5   | 1315 | 0.8855          | 0.7324   | 0.6884 | 0.6922    | 0.6868 |
| 0.2974        | 5.52  | 1320 | 0.8856          | 0.7324   | 0.6879 | 0.6911    | 0.6868 |
| 0.3522        | 5.54  | 1325 | 0.8799          | 0.7214   | 0.6767 | 0.6759    | 0.6775 |
| 0.2946        | 5.56  | 1330 | 0.8815          | 0.7199   | 0.6783 | 0.6769    | 0.6804 |
| 0.2064        | 5.59  | 1335 | 0.8876          | 0.7293   | 0.6894 | 0.6839    | 0.6970 |
| 0.2353        | 5.61  | 1340 | 0.9266          | 0.7261   | 0.6938 | 0.6878    | 0.7087 |
| 0.2696        | 5.63  | 1345 | 0.9339          | 0.7152   | 0.6817 | 0.6789    | 0.6956 |
| 0.4084        | 5.65  | 1350 | 0.8897          | 0.7308   | 0.6886 | 0.6897    | 0.6901 |
| 0.3375        | 5.67  | 1355 | 0.8848          | 0.7246   | 0.6812 | 0.6874    | 0.6775 |
| 0.2449        | 5.69  | 1360 | 0.8848          | 0.7230   | 0.6789 | 0.6850    | 0.6749 |
| 0.2459        | 5.71  | 1365 | 0.8859          | 0.7246   | 0.6815 | 0.6832    | 0.6806 |
| 0.3471        | 5.73  | 1370 | 0.8895          | 0.7230   | 0.6818 | 0.6805    | 0.6832 |
| 0.3112        | 5.75  | 1375 | 0.9040          | 0.7261   | 0.6881 | 0.6876    | 0.6919 |
| 0.3404        | 5.77  | 1380 | 0.9397          | 0.7214   | 0.6836 | 0.6910    | 0.6897 |
| 0.2509        | 5.79  | 1385 | 0.9319          | 0.7277   | 0.6852 | 0.6963    | 0.6878 |
| 0.367         | 5.82  | 1390 | 0.8828          | 0.7261   | 0.6839 | 0.6861    | 0.6832 |
| 0.3158        | 5.84  | 1395 | 0.8770          | 0.7167   | 0.6741 | 0.6770    | 0.6729 |
| 0.1901        | 5.86  | 1400 | 0.8789          | 0.7183   | 0.6771 | 0.6783    | 0.6779 |
| 0.2183        | 5.88  | 1405 | 0.8804          | 0.7261   | 0.6845 | 0.6838    | 0.6856 |
| 0.3058        | 5.9   | 1410 | 0.8927          | 0.7277   | 0.6877 | 0.6921    | 0.6866 |
| 0.1906        | 5.92  | 1415 | 0.8929          | 0.7261   | 0.6859 | 0.6889    | 0.6856 |
| 0.2887        | 5.94  | 1420 | 0.8876          | 0.7293   | 0.6904 | 0.6908    | 0.6915 |
| 0.2236        | 5.96  | 1425 | 0.8900          | 0.7261   | 0.6866 | 0.6823    | 0.6918 |
| 0.3345        | 5.98  | 1430 | 0.8948          | 0.7293   | 0.6902 | 0.6884    | 0.6930 |
| 0.3004        | 6.0   | 1435 | 0.8938          | 0.7277   | 0.6871 | 0.6868    | 0.6873 |
| 0.3376        | 6.03  | 1440 | 0.8939          | 0.7308   | 0.6902 | 0.6895    | 0.6913 |
| 0.1774        | 6.05  | 1445 | 0.9019          | 0.7261   | 0.6893 | 0.6890    | 0.6915 |
| 0.1947        | 6.07  | 1450 | 0.8971          | 0.7308   | 0.6913 | 0.6917    | 0.6913 |
| 0.1641        | 6.09  | 1455 | 0.9135          | 0.7089   | 0.6639 | 0.6746    | 0.6574 |
| 0.3712        | 6.11  | 1460 | 0.9258          | 0.7089   | 0.6612 | 0.6755    | 0.6543 |
| 0.234         | 6.13  | 1465 | 0.8986          | 0.7261   | 0.6863 | 0.6868    | 0.6863 |
| 0.2605        | 6.15  | 1470 | 0.9004          | 0.7277   | 0.6875 | 0.6874    | 0.6881 |
| 0.1891        | 6.17  | 1475 | 0.9035          | 0.7293   | 0.6881 | 0.6867    | 0.6907 |
| 0.1988        | 6.19  | 1480 | 0.9032          | 0.7230   | 0.6807 | 0.6796    | 0.6824 |
| 0.1683        | 6.21  | 1485 | 0.9044          | 0.7293   | 0.6867 | 0.6876    | 0.6864 |
| 0.2669        | 6.23  | 1490 | 0.9156          | 0.7277   | 0.6879 | 0.6887    | 0.6885 |
| 0.2185        | 6.26  | 1495 | 0.9242          | 0.7324   | 0.6922 | 0.6927    | 0.6938 |
| 0.1485        | 6.28  | 1500 | 0.9264          | 0.7308   | 0.6916 | 0.6921    | 0.6925 |
| 0.1654        | 6.3   | 1505 | 0.9295          | 0.7308   | 0.6907 | 0.6913    | 0.6905 |
| 0.2177        | 6.32  | 1510 | 0.9347          | 0.7293   | 0.6884 | 0.6898    | 0.6871 |
| 0.1512        | 6.34  | 1515 | 0.9451          | 0.7261   | 0.6853 | 0.6842    | 0.6867 |
| 0.1006        | 6.36  | 1520 | 0.9623          | 0.7261   | 0.6869 | 0.6850    | 0.6911 |
| 0.1367        | 6.38  | 1525 | 0.9851          | 0.7277   | 0.6901 | 0.6916    | 0.6932 |
| 0.2743        | 6.4   | 1530 | 0.9740          | 0.7340   | 0.6958 | 0.6982    | 0.6960 |
| 0.2843        | 6.42  | 1535 | 0.9689          | 0.7261   | 0.6873 | 0.6892    | 0.6856 |
| 0.2563        | 6.44  | 1540 | 0.9781          | 0.7199   | 0.6757 | 0.6819    | 0.6706 |
| 0.2941        | 6.46  | 1545 | 0.9763          | 0.7246   | 0.6844 | 0.6915    | 0.6799 |
| 0.2245        | 6.49  | 1550 | 0.9718          | 0.7340   | 0.6948 | 0.6962    | 0.6952 |
| 0.1545        | 6.51  | 1555 | 0.9737          | 0.7324   | 0.6921 | 0.6921    | 0.6934 |
| 0.3361        | 6.53  | 1560 | 0.9692          | 0.7324   | 0.6944 | 0.6931    | 0.6966 |
| 0.162         | 6.55  | 1565 | 0.9704          | 0.7324   | 0.6946 | 0.6925    | 0.6982 |
| 0.2815        | 6.57  | 1570 | 0.9656          | 0.7340   | 0.6957 | 0.6962    | 0.6964 |
| 0.2087        | 6.59  | 1575 | 0.9639          | 0.7308   | 0.6927 | 0.6919    | 0.6952 |
| 0.2326        | 6.61  | 1580 | 0.9696          | 0.7324   | 0.6959 | 0.6929    | 0.7009 |
| 0.1923        | 6.63  | 1585 | 0.9611          | 0.7340   | 0.6981 | 0.6959    | 0.7019 |
| 0.1684        | 6.65  | 1590 | 0.9606          | 0.7355   | 0.6964 | 0.6978    | 0.6954 |
| 0.3993        | 6.67  | 1595 | 0.9609          | 0.7293   | 0.6888 | 0.6921    | 0.6860 |
| 0.3185        | 6.69  | 1600 | 0.9627          | 0.7355   | 0.6970 | 0.6974    | 0.6982 |
| 0.2099        | 6.72  | 1605 | 0.9814          | 0.7261   | 0.6910 | 0.6906    | 0.6962 |
| 0.1302        | 6.74  | 1610 | 0.9806          | 0.7308   | 0.6938 | 0.6922    | 0.6991 |
| 0.238         | 6.76  | 1615 | 0.9711          | 0.7324   | 0.6928 | 0.6940    | 0.6927 |
| 0.3351        | 6.78  | 1620 | 0.9749          | 0.7230   | 0.6788 | 0.6868    | 0.6738 |
| 0.3485        | 6.8   | 1625 | 0.9761          | 0.7308   | 0.6884 | 0.6937    | 0.6858 |
| 0.137         | 6.82  | 1630 | 0.9766          | 0.7324   | 0.6909 | 0.6947    | 0.6895 |
| 0.1751        | 6.84  | 1635 | 0.9776          | 0.7324   | 0.6932 | 0.6928    | 0.6946 |
| 0.1701        | 6.86  | 1640 | 0.9787          | 0.7355   | 0.6977 | 0.6954    | 0.7005 |
| 0.148         | 6.88  | 1645 | 0.9830          | 0.7387   | 0.7036 | 0.7001    | 0.7076 |
| 0.2204        | 6.9   | 1650 | 0.9860          | 0.7340   | 0.6949 | 0.6942    | 0.6960 |
| 0.1966        | 6.92  | 1655 | 0.9920          | 0.7214   | 0.6793 | 0.6817    | 0.6775 |
| 0.2242        | 6.95  | 1660 | 0.9979          | 0.7152   | 0.6727 | 0.6771    | 0.6688 |
| 0.157         | 6.97  | 1665 | 1.0002          | 0.7293   | 0.6876 | 0.6925    | 0.6852 |
| 0.2665        | 6.99  | 1670 | 1.0067          | 0.7230   | 0.6838 | 0.6860    | 0.6860 |
| 0.159         | 7.01  | 1675 | 1.0002          | 0.7230   | 0.6841 | 0.6834    | 0.6867 |
| 0.1399        | 7.03  | 1680 | 0.9954          | 0.7277   | 0.6887 | 0.6874    | 0.6909 |
| 0.16          | 7.05  | 1685 | 0.9981          | 0.7277   | 0.6878 | 0.6878    | 0.6889 |
| 0.1074        | 7.07  | 1690 | 1.0067          | 0.7277   | 0.6881 | 0.6886    | 0.6889 |
| 0.15          | 7.09  | 1695 | 1.0130          | 0.7261   | 0.6857 | 0.6860    | 0.6863 |
| 0.1956        | 7.11  | 1700 | 1.0177          | 0.7261   | 0.6858 | 0.6854    | 0.6871 |
| 0.0964        | 7.13  | 1705 | 1.0193          | 0.7277   | 0.6877 | 0.6884    | 0.6881 |
| 0.1922        | 7.15  | 1710 | 1.0224          | 0.7277   | 0.6867 | 0.6894    | 0.6854 |
| 0.1334        | 7.18  | 1715 | 1.0224          | 0.7261   | 0.6844 | 0.6883    | 0.6812 |
| 0.1071        | 7.2   | 1720 | 1.0252          | 0.7183   | 0.6746 | 0.6796    | 0.6704 |
| 0.1798        | 7.22  | 1725 | 1.0306          | 0.7214   | 0.6781 | 0.6851    | 0.6724 |
| 0.2293        | 7.24  | 1730 | 1.0302          | 0.7277   | 0.6878 | 0.6900    | 0.6865 |
| 0.1813        | 7.26  | 1735 | 1.0316          | 0.7261   | 0.6884 | 0.6898    | 0.6895 |
| 0.1884        | 7.28  | 1740 | 1.0327          | 0.7261   | 0.6884 | 0.6898    | 0.6895 |
| 0.1482        | 7.3   | 1745 | 1.0328          | 0.7261   | 0.6877 | 0.6900    | 0.6883 |
| 0.1044        | 7.32  | 1750 | 1.0387          | 0.7324   | 0.6947 | 0.6989    | 0.6946 |
| 0.3129        | 7.34  | 1755 | 1.0264          | 0.7261   | 0.6884 | 0.6905    | 0.6887 |
| 0.1136        | 7.36  | 1760 | 1.0226          | 0.7183   | 0.6789 | 0.6826    | 0.6759 |
| 0.1869        | 7.38  | 1765 | 1.0219          | 0.7214   | 0.6812 | 0.6852    | 0.6783 |
| 0.1363        | 7.41  | 1770 | 1.0230          | 0.7261   | 0.6865 | 0.6913    | 0.6836 |
| 0.0683        | 7.43  | 1775 | 1.0295          | 0.7230   | 0.6835 | 0.6885    | 0.6800 |
| 0.155         | 7.45  | 1780 | 1.0372          | 0.7214   | 0.6805 | 0.6870    | 0.6767 |
| 0.3063        | 7.47  | 1785 | 1.0365          | 0.7246   | 0.6849 | 0.6885    | 0.6834 |
| 0.0882        | 7.49  | 1790 | 1.0347          | 0.7214   | 0.6821 | 0.6856    | 0.6795 |
| 0.1951        | 7.51  | 1795 | 1.0363          | 0.7183   | 0.6786 | 0.6803    | 0.6771 |
| 0.1963        | 7.53  | 1800 | 1.0397          | 0.7261   | 0.6865 | 0.6878    | 0.6875 |
| 0.2286        | 7.55  | 1805 | 1.0406          | 0.7261   | 0.6868 | 0.6880    | 0.6883 |
| 0.1509        | 7.57  | 1810 | 1.0362          | 0.7293   | 0.6896 | 0.6930    | 0.6887 |
| 0.1184        | 7.59  | 1815 | 1.0418          | 0.7105   | 0.6661 | 0.6765    | 0.6584 |
| 0.1063        | 7.62  | 1820 | 1.0522          | 0.7105   | 0.6630 | 0.6777    | 0.6529 |
| 0.134         | 7.64  | 1825 | 1.0484          | 0.7199   | 0.6762 | 0.6882    | 0.6686 |
| 0.2583        | 7.66  | 1830 | 1.0450          | 0.7261   | 0.6826 | 0.6912    | 0.6789 |
| 0.1144        | 7.68  | 1835 | 1.0507          | 0.7277   | 0.6882 | 0.6944    | 0.6877 |
| 0.1107        | 7.7   | 1840 | 1.0511          | 0.7214   | 0.6839 | 0.6853    | 0.6877 |
| 0.2604        | 7.72  | 1845 | 1.0395          | 0.7246   | 0.6863 | 0.6858    | 0.6881 |
| 0.1464        | 7.74  | 1850 | 1.0398          | 0.7199   | 0.6787 | 0.6801    | 0.6777 |
| 0.2535        | 7.76  | 1855 | 1.0411          | 0.7246   | 0.6820 | 0.6869    | 0.6779 |
| 0.1572        | 7.78  | 1860 | 1.0406          | 0.7183   | 0.6765 | 0.6789    | 0.6743 |
| 0.1646        | 7.8   | 1865 | 1.0415          | 0.7183   | 0.6746 | 0.6796    | 0.6704 |
| 0.2349        | 7.82  | 1870 | 1.0426          | 0.7261   | 0.6844 | 0.6890    | 0.6816 |
| 0.2146        | 7.85  | 1875 | 1.0449          | 0.7277   | 0.6882 | 0.6907    | 0.6885 |
| 0.1505        | 7.87  | 1880 | 1.0456          | 0.7277   | 0.6915 | 0.6908    | 0.6944 |
| 0.2806        | 7.89  | 1885 | 1.0445          | 0.7261   | 0.6900 | 0.6894    | 0.6926 |
| 0.2245        | 7.91  | 1890 | 1.0402          | 0.7277   | 0.6908 | 0.6904    | 0.6916 |
| 0.1388        | 7.93  | 1895 | 1.0410          | 0.7293   | 0.6914 | 0.6919    | 0.6911 |
| 0.3175        | 7.95  | 1900 | 1.0403          | 0.7261   | 0.6876 | 0.6899    | 0.6856 |
| 0.2023        | 7.97  | 1905 | 1.0379          | 0.7230   | 0.6857 | 0.6885    | 0.6832 |
| 0.1165        | 7.99  | 1910 | 1.0389          | 0.7261   | 0.6881 | 0.6913    | 0.6852 |
| 0.1103        | 8.01  | 1915 | 1.0431          | 0.7246   | 0.6865 | 0.6899    | 0.6834 |
| 0.1822        | 8.03  | 1920 | 1.0520          | 0.7214   | 0.6820 | 0.6872    | 0.6775 |
| 0.1773        | 8.05  | 1925 | 1.0600          | 0.7121   | 0.6690 | 0.6790    | 0.6614 |
| 0.1259        | 8.08  | 1930 | 1.0601          | 0.7183   | 0.6773 | 0.6843    | 0.6716 |
| 0.1737        | 8.1   | 1935 | 1.0619          | 0.7183   | 0.6804 | 0.6845    | 0.6775 |
| 0.1776        | 8.12  | 1940 | 1.0646          | 0.7277   | 0.6901 | 0.6921    | 0.6905 |
| 0.112         | 8.14  | 1945 | 1.0652          | 0.7324   | 0.6965 | 0.6968    | 0.6982 |
| 0.1649        | 8.16  | 1950 | 1.0650          | 0.7324   | 0.6962 | 0.6960    | 0.6982 |
| 0.1296        | 8.18  | 1955 | 1.0660          | 0.7308   | 0.6958 | 0.6954    | 0.6976 |
| 0.1325        | 8.2   | 1960 | 1.0651          | 0.7277   | 0.6897 | 0.6905    | 0.6901 |
| 0.1422        | 8.22  | 1965 | 1.0680          | 0.7199   | 0.6782 | 0.6839    | 0.6738 |
| 0.3486        | 8.24  | 1970 | 1.0723          | 0.7183   | 0.6729 | 0.6821    | 0.6661 |
| 0.2213        | 8.26  | 1975 | 1.0700          | 0.7121   | 0.6632 | 0.6738    | 0.6563 |
| 0.1206        | 8.28  | 1980 | 1.0671          | 0.7152   | 0.6673 | 0.6766    | 0.6622 |
| 0.1196        | 8.31  | 1985 | 1.0657          | 0.7183   | 0.6723 | 0.6796    | 0.6692 |
| 0.1955        | 8.33  | 1990 | 1.0568          | 0.7183   | 0.6745 | 0.6812    | 0.6696 |
| 0.1085        | 8.35  | 1995 | 1.0566          | 0.7152   | 0.6735 | 0.6813    | 0.6672 |
| 0.1359        | 8.37  | 2000 | 1.0549          | 0.7230   | 0.6862 | 0.6890    | 0.6836 |
| 0.2431        | 8.39  | 2005 | 1.0555          | 0.7308   | 0.6960 | 0.6976    | 0.6944 |
| 0.1512        | 8.41  | 2010 | 1.0570          | 0.7324   | 0.6966 | 0.6972    | 0.6970 |
| 0.1002        | 8.43  | 2015 | 1.0601          | 0.7355   | 0.6997 | 0.7000    | 0.7005 |
| 0.1529        | 8.45  | 2020 | 1.0601          | 0.7277   | 0.6913 | 0.6915    | 0.6913 |
| 0.1633        | 8.47  | 2025 | 1.0618          | 0.7261   | 0.6881 | 0.6882    | 0.6883 |
| 0.068         | 8.49  | 2030 | 1.0657          | 0.7199   | 0.6816 | 0.6826    | 0.6812 |
| 0.1883        | 8.51  | 2035 | 1.0644          | 0.7261   | 0.6885 | 0.6881    | 0.6891 |
| 0.1484        | 8.54  | 2040 | 1.0624          | 0.7324   | 0.6961 | 0.6952    | 0.6970 |
| 0.1438        | 8.56  | 2045 | 1.0642          | 0.7340   | 0.6983 | 0.6973    | 0.6995 |
| 0.1164        | 8.58  | 2050 | 1.0660          | 0.7308   | 0.6950 | 0.6948    | 0.6952 |
| 0.1523        | 8.6   | 2055 | 1.0702          | 0.7246   | 0.6875 | 0.6895    | 0.6857 |
| 0.0793        | 8.62  | 2060 | 1.0749          | 0.7230   | 0.6832 | 0.6874    | 0.6797 |
| 0.0752        | 8.64  | 2065 | 1.0783          | 0.7214   | 0.6797 | 0.6853    | 0.6755 |
| 0.0825        | 8.66  | 2070 | 1.0854          | 0.7230   | 0.6798 | 0.6868    | 0.6745 |
| 0.1463        | 8.68  | 2075 | 1.0937          | 0.7199   | 0.6748 | 0.6837    | 0.6686 |
| 0.1806        | 8.7   | 2080 | 1.0951          | 0.7199   | 0.6786 | 0.6854    | 0.6741 |
| 0.1354        | 8.72  | 2085 | 1.0925          | 0.7277   | 0.6885 | 0.6918    | 0.6877 |
| 0.1348        | 8.74  | 2090 | 1.0896          | 0.7324   | 0.6960 | 0.6958    | 0.6982 |
| 0.174         | 8.77  | 2095 | 1.0875          | 0.7261   | 0.6908 | 0.6900    | 0.6918 |
| 0.1424        | 8.79  | 2100 | 1.0902          | 0.7261   | 0.6896 | 0.6897    | 0.6895 |
| 0.1056        | 8.81  | 2105 | 1.0938          | 0.7261   | 0.6886 | 0.6906    | 0.6867 |
| 0.1662        | 8.83  | 2110 | 1.0952          | 0.7261   | 0.6866 | 0.6900    | 0.6836 |
| 0.1077        | 8.85  | 2115 | 1.0970          | 0.7246   | 0.6853 | 0.6887    | 0.6830 |
| 0.2363        | 8.87  | 2120 | 1.0967          | 0.7230   | 0.6832 | 0.6872    | 0.6808 |
| 0.1287        | 8.89  | 2125 | 1.0975          | 0.7261   | 0.6875 | 0.6916    | 0.6860 |
| 0.141         | 8.91  | 2130 | 1.0982          | 0.7277   | 0.6890 | 0.6930    | 0.6877 |
| 0.1411        | 8.93  | 2135 | 1.0962          | 0.7230   | 0.6824 | 0.6861    | 0.6800 |
| 0.1088        | 8.95  | 2140 | 1.0954          | 0.7230   | 0.6823 | 0.6880    | 0.6777 |
| 0.1032        | 8.97  | 2145 | 1.0942          | 0.7214   | 0.6807 | 0.6866    | 0.6759 |
| 0.0683        | 9.0   | 2150 | 1.0915          | 0.7230   | 0.6825 | 0.6877    | 0.6785 |
| 0.1402        | 9.02  | 2155 | 1.0894          | 0.7277   | 0.6894 | 0.6934    | 0.6861 |
| 0.0853        | 9.04  | 2160 | 1.0914          | 0.7246   | 0.6841 | 0.6891    | 0.6802 |
| 0.1155        | 9.06  | 2165 | 1.0937          | 0.7214   | 0.6787 | 0.6846    | 0.6743 |
| 0.0675        | 9.08  | 2170 | 1.0961          | 0.7230   | 0.6801 | 0.6869    | 0.6753 |
| 0.0754        | 9.1   | 2175 | 1.0959          | 0.7246   | 0.6828 | 0.6881    | 0.6791 |
| 0.0974        | 9.12  | 2180 | 1.0975          | 0.7293   | 0.6892 | 0.6926    | 0.6867 |
| 0.1567        | 9.14  | 2185 | 1.0993          | 0.7246   | 0.6850 | 0.6886    | 0.6822 |
| 0.1691        | 9.16  | 2190 | 1.0999          | 0.7261   | 0.6866 | 0.6917    | 0.6824 |
| 0.1026        | 9.18  | 2195 | 1.1006          | 0.7246   | 0.6850 | 0.6904    | 0.6806 |
| 0.0727        | 9.21  | 2200 | 1.1029          | 0.7246   | 0.6850 | 0.6904    | 0.6806 |
| 0.0834        | 9.23  | 2205 | 1.1046          | 0.7199   | 0.6783 | 0.6843    | 0.6738 |
| 0.1159        | 9.25  | 2210 | 1.1049          | 0.7230   | 0.6823 | 0.6880    | 0.6777 |
| 0.1586        | 9.27  | 2215 | 1.1046          | 0.7214   | 0.6808 | 0.6852    | 0.6775 |
| 0.1292        | 9.29  | 2220 | 1.1043          | 0.7230   | 0.6824 | 0.6865    | 0.6793 |
| 0.0743        | 9.31  | 2225 | 1.1035          | 0.7246   | 0.6851 | 0.6889    | 0.6822 |
| 0.06          | 9.33  | 2230 | 1.1022          | 0.7277   | 0.6912 | 0.6927    | 0.6901 |
| 0.1545        | 9.35  | 2235 | 1.1039          | 0.7293   | 0.6916 | 0.6932    | 0.6907 |
| 0.1546        | 9.37  | 2240 | 1.1058          | 0.7230   | 0.6833 | 0.6861    | 0.6812 |
| 0.2023        | 9.39  | 2245 | 1.1066          | 0.7214   | 0.6808 | 0.6852    | 0.6775 |
| 0.1607        | 9.41  | 2250 | 1.1077          | 0.7230   | 0.6818 | 0.6868    | 0.6777 |
| 0.0658        | 9.44  | 2255 | 1.1090          | 0.7230   | 0.6818 | 0.6868    | 0.6777 |
| 0.0417        | 9.46  | 2260 | 1.1107          | 0.7230   | 0.6818 | 0.6868    | 0.6777 |
| 0.063         | 9.48  | 2265 | 1.1129          | 0.7230   | 0.6818 | 0.6868    | 0.6777 |
| 0.0988        | 9.5   | 2270 | 1.1147          | 0.7230   | 0.6833 | 0.6886    | 0.6789 |
| 0.1082        | 9.52  | 2275 | 1.1155          | 0.7230   | 0.6833 | 0.6886    | 0.6789 |
| 0.1984        | 9.54  | 2280 | 1.1154          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1793        | 9.56  | 2285 | 1.1153          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1324        | 9.58  | 2290 | 1.1152          | 0.7230   | 0.6818 | 0.6868    | 0.6777 |
| 0.1059        | 9.6   | 2295 | 1.1157          | 0.7230   | 0.6818 | 0.6868    | 0.6777 |
| 0.0473        | 9.62  | 2300 | 1.1158          | 0.7230   | 0.6818 | 0.6868    | 0.6777 |
| 0.1065        | 9.64  | 2305 | 1.1166          | 0.7230   | 0.6818 | 0.6868    | 0.6777 |
| 0.1373        | 9.67  | 2310 | 1.1173          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1248        | 9.69  | 2315 | 1.1177          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.0966        | 9.71  | 2320 | 1.1183          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.0742        | 9.73  | 2325 | 1.1189          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.0827        | 9.75  | 2330 | 1.1193          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.143         | 9.77  | 2335 | 1.1202          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1623        | 9.79  | 2340 | 1.1201          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1495        | 9.81  | 2345 | 1.1197          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.0965        | 9.83  | 2350 | 1.1195          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1297        | 9.85  | 2355 | 1.1194          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1164        | 9.87  | 2360 | 1.1195          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1759        | 9.9   | 2365 | 1.1195          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.2404        | 9.92  | 2370 | 1.1192          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1467        | 9.94  | 2375 | 1.1189          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1969        | 9.96  | 2380 | 1.1187          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.1573        | 9.98  | 2385 | 1.1187          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |
| 0.2614        | 10.0  | 2390 | 1.1188          | 0.7246   | 0.6845 | 0.6892    | 0.6806 |

### Framework versions

- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Tokenizers 0.10.3


Informal to Formal:
Wordy to Concise:
Fill Missing Phrase:
```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln20")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln20")
```

```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time)
```

```
https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time)
```

```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.

informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.

informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.

informal english:
````

```
infill: increasing the number of sidewalks in suburban areas will [MASK].
Translated into the Style of Abraham Lincoln: increasing the number of sidewalks in suburban areas will ( ( enhance / maximize ) community cohesion / facilitate ( communal ties / the formation of neighborhood camaraderie ) / forge neighborly relations / lend themselves to the advancement of neighborly ties / plant the seeds of community building / flower anew the bonds of friendship / invite the budding of neighborhood rapport / enrich neighborhood life ).

infill: corn fields [MASK], [MASK] visibly as one ventures beyond chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), ( manifesting themselves ) visibly as one ventures beyond chicago.

infill: the [MASK] the SAT will soon be [MASK]. [MASK] an examination undertaken on one's laptop. [MASK] will allow students to retrieve test results promptly.
Translated into the Style of Abraham Lincoln: the ( conventional form of ) the SAT will soon be ( consigned to history ). ( replacing it will be ) an examination undertaken on one's laptop. ( so doing ) will allow students to retrieve test results promptly.

infill:
```
```
***

wordy: chancing upon a linux user is a rare occurrence in the present day.
Translate into Concise Text: present-day linux users are rare.

***

wordy: an interest in classical music is becoming more and more less popular.
Translate into Concise Text: classical music appreciation is dwindling.
Translate into Concise Text: waning interest in classic music persists.
Translate into Concise Text: interest in classic music is fading.

***

wordy: the ice cream was only one dollar, but it was not a good value for the size.
Translate into Concise Text: the one dollar ice cream was overpriced for its size.
Translate into Concise Text: overpriced, the one dollar ice cream was small.

***
wordy:
```


Converting Points to Paragraphs

Example Prompts:
```
###
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
###
- with 2,000,000 individual articles on everything
- wikipedia is the #8 site on the world wide web
- created by anyone with access to a computer
- growing at fast rate
- proof that collaborative community-based projects are the future
Text: encompassing a staggering 2,000,000 articles on every subject conceivable, wikipedia is the 8th most visited website in the world. borne of the collective efforts of anyone with an internet connection, its contents are increasing exponentially. most compellingly, however, this effort is an affirmation that community-based initiatives is the future.
###
-
```


- All credit goes to https://huggingface.co/philippelaban/keep_it_simple.
- This is a copy of their repository for future training purposes.
- It is supposed to simplify text.
- Their model card gives instructions on how to use it.


Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln13")
```

```
https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time)
```

```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.

informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.

informal english: meteors are much harder to see, because they are only there for a fraction of a second.
Translated into the Style of Abraham Lincoln: meteors are not ( easily / readily ) detectable, lasting for mere fractions of a second.

informal english:
````