---
tags:
- generated_from_trainer
model-index:
- name: roberta-tiny-cased-dtlm
  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. -->

# roberta-tiny-cased-dtlm

This model is a fine-tuned version of [haisongzhang/roberta-tiny-cased](https://huggingface.co/haisongzhang/roberta-tiny-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7666

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.8352        | 0.02  | 1000  | 1.5408          |
| 1.5206        | 0.04  | 2000  | 1.3241          |
| 1.3632        | 0.07  | 3000  | 1.2009          |
| 1.2699        | 0.09  | 4000  | 1.1458          |
| 1.1912        | 0.11  | 5000  | 1.0766          |
| 1.139         | 0.13  | 6000  | 1.0399          |
| 1.0983        | 0.16  | 7000  | 0.9807          |
| 1.0713        | 0.18  | 8000  | 0.9636          |
| 1.0458        | 0.2   | 9000  | 0.9266          |
| 1.0226        | 0.22  | 10000 | 0.9323          |
| 1.0037        | 0.24  | 11000 | 0.9036          |
| 0.9845        | 0.27  | 12000 | 0.9034          |
| 0.9746        | 0.29  | 13000 | 0.8766          |
| 0.964         | 0.31  | 14000 | 0.8716          |
| 0.9495        | 0.33  | 15000 | 0.8442          |
| 0.9396        | 0.35  | 16000 | 0.8558          |
| 0.9287        | 0.38  | 17000 | 0.8312          |
| 0.9161        | 0.4   | 18000 | 0.8311          |
| 0.9123        | 0.42  | 19000 | 0.8413          |
| 0.9054        | 0.44  | 20000 | 0.8079          |
| 0.893         | 0.47  | 21000 | 0.8140          |
| 0.8921        | 0.49  | 22000 | 0.8022          |
| 0.8856        | 0.51  | 23000 | 0.8084          |
| 0.8792        | 0.53  | 24000 | 0.8184          |
| 0.8731        | 0.55  | 25000 | 0.8000          |
| 0.8684        | 0.58  | 26000 | 0.7703          |
| 0.8616        | 0.6   | 27000 | 0.7917          |
| 0.8637        | 0.62  | 28000 | 0.7894          |
| 0.8592        | 0.64  | 29000 | 0.7800          |
| 0.8517        | 0.66  | 30000 | 0.7636          |
| 0.8501        | 0.69  | 31000 | 0.7405          |
| 0.8503        | 0.71  | 32000 | 0.7620          |
| 0.8422        | 0.73  | 33000 | 0.7395          |
| 0.8364        | 0.75  | 34000 | 0.7688          |
| 0.8394        | 0.78  | 35000 | 0.7653          |
| 0.8392        | 0.8   | 36000 | 0.7412          |
| 0.834         | 0.82  | 37000 | 0.7300          |
| 0.8377        | 0.84  | 38000 | 0.7695          |
| 0.8313        | 0.86  | 39000 | 0.7598          |
| 0.8266        | 0.89  | 40000 | 0.7659          |
| 0.8288        | 0.91  | 41000 | 0.7277          |
| 0.8271        | 0.93  | 42000 | 0.7491          |
| 0.8251        | 0.95  | 43000 | 0.7375          |
| 0.8246        | 0.97  | 44000 | 0.7190          |
| 0.8271        | 1.0   | 45000 | 0.7318          |

### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.15.1
- Tokenizers 0.10.3


---
license: mit
tags:
- generated_from_trainer
model-index:
- name: dev-lm-fast
  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. -->

# dev-lm-fast

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8148

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.7242        | 0.02  | 15000  | 1.6151          |
| 1.4916        | 0.04  | 30000  | 1.4046          |
| 1.3764        | 0.06  | 45000  | 1.2586          |
| 1.2939        | 0.08  | 60000  | 1.2500          |
| 1.2529        | 0.1   | 75000  | 1.1612          |
| 1.1913        | 0.12  | 90000  | 1.1410          |
| 1.1712        | 0.15  | 105000 | 1.0923          |
| 1.1435        | 0.17  | 120000 | 1.0421          |
| 1.1156        | 0.19  | 135000 | 1.0778          |
| 1.1105        | 0.21  | 150000 | 1.0409          |
| 1.0968        | 0.23  | 165000 | 1.0334          |
| 1.0829        | 0.25  | 180000 | 1.0151          |
| 1.0591        | 0.27  | 195000 | 0.9855          |
| 1.0606        | 0.29  | 210000 | 0.9567          |
| 1.0556        | 0.31  | 225000 | 0.9461          |
| 1.0349        | 0.33  | 240000 | 0.9628          |
| 1.0323        | 0.35  | 255000 | 0.9434          |
| 1.0055        | 0.37  | 270000 | 0.9502          |
| 1.0044        | 0.39  | 285000 | 0.9646          |
| 0.9886        | 0.42  | 300000 | 0.9216          |
| 0.9783        | 0.44  | 315000 | 0.9166          |
| 0.9646        | 0.46  | 330000 | 0.9277          |
| 0.9767        | 0.48  | 345000 | 0.8874          |
| 0.9653        | 0.5   | 360000 | 0.9212          |
| 0.95          | 0.52  | 375000 | 0.8993          |
| 0.947         | 0.54  | 390000 | 0.8900          |
| 0.9501        | 0.56  | 405000 | 0.8936          |
| 0.9505        | 0.58  | 420000 | 0.8920          |
| 0.9534        | 0.6   | 435000 | 0.8688          |
| 0.9412        | 0.62  | 450000 | 0.8820          |
| 0.9269        | 0.64  | 465000 | 0.8507          |
| 0.9237        | 0.66  | 480000 | 0.8848          |
| 0.9153        | 0.69  | 495000 | 0.8481          |
| 0.9132        | 0.71  | 510000 | 0.8403          |
| 0.8967        | 0.73  | 525000 | 0.8634          |
| 0.901         | 0.75  | 540000 | 0.8334          |
| 0.8941        | 0.77  | 555000 | 0.8441          |
| 0.9084        | 0.79  | 570000 | 0.8088          |
| 0.9238        | 0.81  | 585000 | 0.8470          |
| 0.8642        | 0.83  | 600000 | 0.8108          |
| 0.8887        | 0.85  | 615000 | 0.8180          |
| 0.8819        | 0.87  | 630000 | 0.8174          |
| 0.8807        | 0.89  | 645000 | 0.8235          |
| 0.896         | 0.91  | 660000 | 0.8298          |
| 0.8735        | 0.93  | 675000 | 0.8041          |
| 0.8616        | 0.96  | 690000 | 0.8353          |
| 0.8727        | 0.98  | 705000 | 0.7769          |
| 0.8737        | 1.0   | 720000 | 0.7964          |

### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.15.1
- Tokenizers 0.10.3


---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-ptc
  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. -->

# deberta-v3-base-ptc

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0174
- Accuracy: 0.9945
- F1: 0.9885
- Precision: 0.9864
- Recall: 0.9906

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0834        | 0.1   | 300  | 0.0815          | 0.9733   | 0.9365 | 0.9256    | 0.9498 |
| 0.0853        | 0.2   | 600  | 0.0600          | 0.9817   | 0.9576 | 0.9525    | 0.9637 |
| 0.0427        | 0.29  | 900  | 0.0386          | 0.9874   | 0.9701 | 0.9661    | 0.9747 |
| 0.0427        | 0.39  | 1200 | 0.0408          | 0.9888   | 0.9746 | 0.9763    | 0.9730 |
| 0.0432        | 0.49  | 1500 | 0.0311          | 0.9896   | 0.9761 | 0.9770    | 0.9756 |
| 0.0314        | 0.59  | 1800 | 0.0305          | 0.9899   | 0.9794 | 0.9767    | 0.9824 |
| 0.0167        | 0.69  | 2100 | 0.0236          | 0.9925   | 0.9820 | 0.9793    | 0.9851 |
| 0.0243        | 0.78  | 2400 | 0.0206          | 0.9934   | 0.9852 | 0.9838    | 0.9866 |
| 0.0228        | 0.88  | 2700 | 0.0183          | 0.9941   | 0.9859 | 0.9822    | 0.9898 |
| 0.009         | 0.98  | 3000 | 0.0170          | 0.9945   | 0.9879 | 0.9862    | 0.9896 |

### Framework versions

- Transformers 4.12.5
- Pytorch 1.8.1+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag-e1-b16-l5e5
  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. -->

# bert-base-uncased-finetuned-swag-e1-b16-l5e5

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5202
- Accuracy: 0.7997

## 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: 5e-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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.701         | 1.0   | 4597 | 0.5202          | 0.7997   |

### Framework versions

- Transformers 4.12.2
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
  name: bert-base-uncased-finetuned-semeval2020-task4b-append
---

<!-- 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-uncased-finetuned-semeval2020-task4b

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ComVE dataset which was part of SemEval 2020 Task 4.
It achieves the following results on the test set:
- Loss: 0.6760
- Accuracy: 0.8760

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5016        | 1.0   | 688  | 0.3502          | 0.8600   |
| 0.2528        | 2.0   | 1376 | 0.5769          | 0.8620   |
| 0.0598        | 3.0   | 2064 | 0.6720          | 0.8700   |
| 0.0197        | 4.0   | 2752 | 0.6760          | 0.8760   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
  name: bert-base-uncased-finetuned-semeval2020-task4a-append-e2-b32-l5e5
---

<!-- 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-uncased-finetuned-semeval2020-task4a-append-e2-b32-l5e5

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5466
- Accuracy: 0.8890

## 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: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 344  | 0.3057          | 0.8630   |
| 0.4091        | 2.0   | 688  | 0.2964          | 0.8880   |
| 0.1322        | 3.0   | 1032 | 0.4465          | 0.8820   |
| 0.1322        | 4.0   | 1376 | 0.5466          | 0.8890   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
  name: bert-base-uncased-finetuned-semeval2020-task4b-append-e3-b32-l4e5
---

<!-- 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-uncased-finetuned-semeval2020-task4b-append-e3-b32-l4e5

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5121
- Accuracy: 0.8700

## 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: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 344  | 0.3603          | 0.8550   |
| 0.3894        | 2.0   | 688  | 0.4011          | 0.8630   |
| 0.1088        | 3.0   | 1032 | 0.5121          | 0.8700   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
  name: bert-base-uncased-finetuned-semeval2020-task4b-base-e2-b32-l3e5
---

<!-- 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-uncased-finetuned-semeval2020-task4b-base-e2-b32-l3e5

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4114
- Accuracy: 0.8700

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 344  | 0.3773          | 0.8490   |
| 0.3812        | 2.0   | 688  | 0.4114          | 0.8700   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
  name: bert-base-uncased-finetuned-semeval2020-task4a-e2-b32-l5e5
---

<!-- 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-uncased-finetuned-semeval2020-task4a

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ComVE dataset which was part of SemEval 2020 Task 4.
It achieves the following results on the test set:
- Loss: 0.2782
- Accuracy: 0.9040

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 344  | 0.2700          | 0.8940   |
| 0.349         | 2.0   | 688  | 0.2782          | 0.9040   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3


This is Brain Piano

---
inference:
  parameters:
    temperature: 0.7
---


---

language: en

tags:

- sentence correction

- text2text-generation

license: cc-by-nc-sa-4.0

datasets:

- jfleg

---

# Model
This model utilises T5-base sentence correction pre-trained model. It was fine tuned using a modified version of the [JFLEG](https://arxiv.org/abs/1702.04066) dataset and [Happy Transformer framework](https://github.com/EricFillion/happy-transformer). This model was pre-trained for educational purposes only for correction on local caribbean dialect.
.
___

# Re-training/Fine Tuning

The results of fine-tuning resulted in a final accuracy of 90%

# Usage

```python

from happytransformer import HappyTextToText, TTSettings

pre_trained_model="T5"
model = HappyTextToText(pre_trained_model, "KES/T5-KES")

arguments = TTSettings(num_beams=4, min_length=1)
sentence = "Wat iz your nam"

correction = model.generate_text("grammar: "+sentence, args=arguments)
if(correction.text.find(" .")):
    correction.text=correction.text.replace(" .", ".")

print(correction.text) # Correction: "What is your name?".

```


---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-es-demo
  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. -->

# wav2vec2-common_voice-es-demo

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - ES dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1788
- Wer: 1.0239

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log        | 0.02  | 100   | 6.6465          | 1.0    |
| No log        | 0.04  | 200   | 3.0150          | 1.0    |
| No log        | 0.05  | 300   | 2.8622          | 1.0003 |
| No log        | 0.07  | 400   | 0.9506          | 0.9771 |
| 5.1598        | 0.09  | 500   | 0.4883          | 1.0009 |
| 5.1598        | 0.11  | 600   | 0.3893          | 1.0203 |
| 5.1598        | 0.13  | 700   | 0.3417          | 1.0283 |
| 5.1598        | 0.14  | 800   | 0.3352          | 1.0335 |
| 5.1598        | 0.16  | 900   | 0.2987          | 1.0168 |
| 0.3671        | 0.18  | 1000  | 0.2921          | 1.0159 |
| 0.3671        | 0.2   | 1100  | 0.2770          | 1.0096 |
| 0.3671        | 0.22  | 1200  | 0.2790          | 1.0398 |
| 0.3671        | 0.24  | 1300  | 0.2659          | 1.0190 |
| 0.3671        | 0.25  | 1400  | 0.2657          | 1.0528 |
| 0.289         | 0.27  | 1500  | 0.2556          | 1.0301 |
| 0.289         | 0.29  | 1600  | 0.2514          | 1.0193 |
| 0.289         | 0.31  | 1700  | 0.2708          | 1.0699 |
| 0.289         | 0.33  | 1800  | 0.2455          | 1.0723 |
| 0.289         | 0.34  | 1900  | 0.2456          | 1.0100 |
| 0.271         | 0.36  | 2000  | 0.2338          | 1.0533 |
| 0.271         | 0.38  | 2100  | 0.2479          | 1.0128 |
| 0.271         | 0.4   | 2200  | 0.2483          | 1.0386 |
| 0.271         | 0.42  | 2300  | 0.2436          | 1.0528 |
| 0.271         | 0.43  | 2400  | 0.2382          | 1.0476 |
| 0.2634        | 0.45  | 2500  | 0.2329          | 1.0680 |
| 0.2634        | 0.47  | 2600  | 0.2433          | 1.0581 |
| 0.2634        | 0.49  | 2700  | 0.2354          | 1.0641 |
| 0.2634        | 0.51  | 2800  | 0.2318          | 1.0504 |
| 0.2634        | 0.52  | 2900  | 0.2325          | 1.0500 |
| 0.2522        | 0.54  | 3000  | 0.2344          | 1.0380 |
| 0.2522        | 0.56  | 3100  | 0.2244          | 1.0663 |
| 0.2522        | 0.58  | 3200  | 0.2340          | 1.0647 |
| 0.2522        | 0.6   | 3300  | 0.2288          | 1.0538 |
| 0.2522        | 0.61  | 3400  | 0.2212          | 1.0614 |
| 0.2468        | 0.63  | 3500  | 0.2487          | 1.0557 |
| 0.2468        | 0.65  | 3600  | 0.2330          | 1.0510 |
| 0.2468        | 0.67  | 3700  | 0.2308          | 1.0506 |
| 0.2468        | 0.69  | 3800  | 0.2320          | 1.0451 |
| 0.2468        | 0.71  | 3900  | 0.2261          | 1.0701 |
| 0.2505        | 0.72  | 4000  | 0.2281          | 1.0713 |
| 0.2505        | 0.74  | 4100  | 0.2277          | 1.0741 |
| 0.2505        | 0.76  | 4200  | 0.2253          | 1.0814 |
| 0.2505        | 0.78  | 4300  | 0.2215          | 1.0437 |
| 0.2505        | 0.8   | 4400  | 0.2220          | 1.0557 |
| 0.2434        | 0.81  | 4500  | 0.2184          | 1.0533 |
| 0.2434        | 0.83  | 4600  | 0.2222          | 1.0819 |
| 0.2434        | 0.85  | 4700  | 0.2162          | 1.0238 |
| 0.2434        | 0.87  | 4800  | 0.2132          | 1.0457 |
| 0.2434        | 0.89  | 4900  | 0.2068          | 1.0611 |
| 0.2347        | 0.9   | 5000  | 0.2166          | 1.0332 |
| 0.2347        | 0.92  | 5100  | 0.2087          | 1.0433 |
| 0.2347        | 0.94  | 5200  | 0.2100          | 1.0292 |
| 0.2347        | 0.96  | 5300  | 0.2067          | 1.0734 |
| 0.2347        | 0.98  | 5400  | 0.2148          | 1.0279 |
| 0.2333        | 0.99  | 5500  | 0.2125          | 1.0277 |
| 0.2333        | 1.01  | 5600  | 0.2054          | 1.0453 |
| 0.2333        | 1.03  | 5700  | 0.2091          | 1.0557 |
| 0.2333        | 1.05  | 5800  | 0.2086          | 1.0239 |
| 0.2333        | 1.07  | 5900  | 0.2051          | 1.0645 |
| 0.2087        | 1.09  | 6000  | 0.2103          | 1.0240 |
| 0.2087        | 1.1   | 6100  | 0.2145          | 1.0197 |
| 0.2087        | 1.12  | 6200  | 0.2136          | 1.0248 |
| 0.2087        | 1.14  | 6300  | 0.2045          | 1.0443 |
| 0.2087        | 1.16  | 6400  | 0.2089          | 1.0397 |
| 0.2013        | 1.18  | 6500  | 0.2012          | 1.0654 |
| 0.2013        | 1.19  | 6600  | 0.2054          | 1.0414 |
| 0.2013        | 1.21  | 6700  | 0.2081          | 1.0632 |
| 0.2013        | 1.23  | 6800  | 0.2104          | 1.0190 |
| 0.2013        | 1.25  | 6900  | 0.2045          | 1.0813 |
| 0.2092        | 1.27  | 7000  | 0.2096          | 1.0751 |
| 0.2092        | 1.28  | 7100  | 0.2103          | 1.0328 |
| 0.2092        | 1.3   | 7200  | 0.2044          | 1.0011 |
| 0.2092        | 1.32  | 7300  | 0.2089          | 1.0260 |
| 0.2092        | 1.34  | 7400  | 0.2063          | 1.0551 |
| 0.2076        | 1.36  | 7500  | 0.2029          | 1.0075 |
| 0.2076        | 1.37  | 7600  | 0.2040          | 1.0528 |
| 0.2076        | 1.39  | 7700  | 0.2075          | 1.0398 |
| 0.2076        | 1.41  | 7800  | 0.2023          | 1.0231 |
| 0.2076        | 1.43  | 7900  | 0.2049          | 1.0318 |
| 0.2028        | 1.45  | 8000  | 0.2072          | 1.0763 |
| 0.2028        | 1.47  | 8100  | 0.2075          | 1.0762 |
| 0.2028        | 1.48  | 8200  | 0.2052          | 1.0838 |
| 0.2028        | 1.5   | 8300  | 0.2053          | 1.0407 |
| 0.2028        | 1.52  | 8400  | 0.2066          | 1.0266 |
| 0.2025        | 1.54  | 8500  | 0.2037          | 1.0628 |
| 0.2025        | 1.56  | 8600  | 0.2010          | 1.0351 |
| 0.2025        | 1.57  | 8700  | 0.1961          | 1.0812 |
| 0.2025        | 1.59  | 8800  | 0.1963          | 1.0868 |
| 0.2025        | 1.61  | 8900  | 0.2022          | 1.0710 |
| 0.1997        | 1.63  | 9000  | 0.2051          | 1.0764 |
| 0.1997        | 1.65  | 9100  | 0.1987          | 1.0581 |
| 0.1997        | 1.66  | 9200  | 0.2051          | 1.0611 |
| 0.1997        | 1.68  | 9300  | 0.1999          | 1.0808 |
| 0.1997        | 1.7   | 9400  | 0.1972          | 1.0703 |
| 0.1983        | 1.72  | 9500  | 0.1961          | 1.0584 |
| 0.1983        | 1.74  | 9600  | 0.2031          | 1.0938 |
| 0.1983        | 1.75  | 9700  | 0.2019          | 1.0891 |
| 0.1983        | 1.77  | 9800  | 0.2006          | 1.0542 |
| 0.1983        | 1.79  | 9900  | 0.1925          | 1.0627 |
| 0.1961        | 1.81  | 10000 | 0.1976          | 1.0751 |
| 0.1961        | 1.83  | 10100 | 0.2051          | 1.0611 |
| 0.1961        | 1.85  | 10200 | 0.2037          | 1.0656 |
| 0.1961        | 1.86  | 10300 | 0.2025          | 1.0291 |
| 0.1961        | 1.88  | 10400 | 0.1977          | 1.0525 |
| 0.2025        | 1.9   | 10500 | 0.2030          | 1.0670 |
| 0.2025        | 1.92  | 10600 | 0.1980          | 1.0765 |
| 0.2025        | 1.94  | 10700 | 0.1975          | 1.0254 |
| 0.2025        | 1.95  | 10800 | 0.1986          | 1.0636 |
| 0.2025        | 1.97  | 10900 | 0.1956          | 1.0352 |
| 0.2025        | 1.99  | 11000 | 0.1954          | 1.0265 |
| 0.2025        | 2.01  | 11100 | 0.1957          | 1.0752 |
| 0.2025        | 2.03  | 11200 | 0.1943          | 1.0784 |
| 0.2025        | 2.04  | 11300 | 0.1898          | 1.0341 |
| 0.2025        | 2.06  | 11400 | 0.1921          | 1.0301 |
| 0.1805        | 2.08  | 11500 | 0.1910          | 1.0230 |
| 0.1805        | 2.1   | 11600 | 0.1961          | 1.0203 |
| 0.1805        | 2.12  | 11700 | 0.1973          | 1.0776 |
| 0.1805        | 2.13  | 11800 | 0.1876          | 1.0788 |
| 0.1805        | 2.15  | 11900 | 0.1934          | 1.0251 |
| 0.177         | 2.17  | 12000 | 0.1967          | 1.0340 |
| 0.177         | 2.19  | 12100 | 0.1932          | 1.0131 |
| 0.177         | 2.21  | 12200 | 0.1926          | 1.0078 |
| 0.177         | 2.23  | 12300 | 0.1947          | 0.9991 |
| 0.177         | 2.24  | 12400 | 0.1914          | 1.0213 |
| 0.1782        | 2.26  | 12500 | 0.1962          | 0.9882 |
| 0.1782        | 2.28  | 12600 | 0.1960          | 1.0562 |
| 0.1782        | 2.3   | 12700 | 0.2006          | 1.0401 |
| 0.1782        | 2.32  | 12800 | 0.1950          | 1.0688 |
| 0.1782        | 2.33  | 12900 | 0.1920          | 1.0435 |
| 0.1796        | 2.35  | 13000 | 0.1926          | 1.0667 |
| 0.1796        | 2.37  | 13100 | 0.1949          | 1.0859 |
| 0.1796        | 2.39  | 13200 | 0.1932          | 1.0670 |
| 0.1796        | 2.41  | 13300 | 0.1882          | 1.0663 |
| 0.1796        | 2.42  | 13400 | 0.1877          | 1.0760 |
| 0.1775        | 2.44  | 13500 | 0.1893          | 1.0859 |
| 0.1775        | 2.46  | 13600 | 0.1936          | 1.0702 |
| 0.1775        | 2.48  | 13700 | 0.1871          | 1.0414 |
| 0.1775        | 2.5   | 13800 | 0.1917          | 1.0430 |
| 0.1775        | 2.51  | 13900 | 0.1922          | 1.0422 |
| 0.1778        | 2.53  | 14000 | 0.1875          | 1.0585 |
| 0.1778        | 2.55  | 14100 | 0.1876          | 1.0603 |
| 0.1778        | 2.57  | 14200 | 0.1888          | 1.0628 |
| 0.1778        | 2.59  | 14300 | 0.1948          | 1.0782 |
| 0.1778        | 2.6   | 14400 | 0.1942          | 1.0695 |
| 0.1784        | 2.62  | 14500 | 0.1842          | 1.0863 |
| 0.1784        | 2.64  | 14600 | 0.1850          | 1.0543 |
| 0.1784        | 2.66  | 14700 | 0.1824          | 1.0683 |
| 0.1784        | 2.68  | 14800 | 0.1888          | 1.0693 |
| 0.1784        | 2.7   | 14900 | 0.1871          | 1.0175 |
| 0.1753        | 2.71  | 15000 | 0.1889          | 1.0549 |
| 0.1753        | 2.73  | 15100 | 0.1865          | 1.0544 |
| 0.1753        | 2.75  | 15200 | 0.1918          | 1.0726 |
| 0.1753        | 2.77  | 15300 | 0.1964          | 1.0915 |
| 0.1753        | 2.79  | 15400 | 0.1900          | 1.0610 |
| 0.1768        | 2.8   | 15500 | 0.1894          | 1.0763 |
| 0.1768        | 2.82  | 15600 | 0.1882          | 1.0548 |
| 0.1768        | 2.84  | 15700 | 0.1861          | 1.0902 |
| 0.1768        | 2.86  | 15800 | 0.1860          | 1.0551 |
| 0.1768        | 2.88  | 15900 | 0.1879          | 1.0581 |
| 0.1761        | 2.89  | 16000 | 0.1899          | 1.0544 |
| 0.1761        | 2.91  | 16100 | 0.1860          | 1.0530 |
| 0.1761        | 2.93  | 16200 | 0.1894          | 1.0596 |
| 0.1761        | 2.95  | 16300 | 0.1835          | 1.0394 |
| 0.1761        | 2.97  | 16400 | 0.1852          | 1.0445 |
| 0.1754        | 2.98  | 16500 | 0.1847          | 1.0390 |
| 0.1754        | 3.0   | 16600 | 0.1828          | 1.0440 |
| 0.1754        | 3.02  | 16700 | 0.1869          | 1.0560 |
| 0.1754        | 3.04  | 16800 | 0.1882          | 1.0573 |
| 0.1754        | 3.06  | 16900 | 0.1912          | 1.0600 |
| 0.1592        | 3.08  | 17000 | 0.1921          | 1.0529 |
| 0.1592        | 3.09  | 17100 | 0.1881          | 1.0175 |
| 0.1592        | 3.11  | 17200 | 0.1891          | 1.0654 |
| 0.1592        | 3.13  | 17300 | 0.1889          | 1.0687 |
| 0.1592        | 3.15  | 17400 | 0.1916          | 1.0642 |
| 0.1556        | 3.17  | 17500 | 0.1850          | 1.0295 |
| 0.1556        | 3.18  | 17600 | 0.1875          | 1.0273 |
| 0.1556        | 3.2   | 17700 | 0.1894          | 1.0051 |
| 0.1556        | 3.22  | 17800 | 0.1870          | 1.0462 |
| 0.1556        | 3.24  | 17900 | 0.1831          | 1.0308 |
| 0.1557        | 3.26  | 18000 | 0.1878          | 1.0603 |
| 0.1557        | 3.27  | 18100 | 0.1850          | 1.0566 |
| 0.1557        | 3.29  | 18200 | 0.1843          | 1.0629 |
| 0.1557        | 3.31  | 18300 | 0.1886          | 1.0378 |
| 0.1557        | 3.33  | 18400 | 0.1892          | 1.0381 |
| 0.159         | 3.35  | 18500 | 0.1942          | 1.0519 |
| 0.159         | 3.36  | 18600 | 0.1829          | 1.0622 |
| 0.159         | 3.38  | 18700 | 0.1894          | 1.0557 |
| 0.159         | 3.4   | 18800 | 0.1895          | 1.0627 |
| 0.159         | 3.42  | 18900 | 0.1863          | 1.0362 |
| 0.1582        | 3.44  | 19000 | 0.1888          | 1.0491 |
| 0.1582        | 3.46  | 19100 | 0.1854          | 1.0483 |
| 0.1582        | 3.47  | 19200 | 0.1797          | 0.9787 |
| 0.1582        | 3.49  | 19300 | 0.1785          | 1.0086 |
| 0.1582        | 3.51  | 19400 | 0.1797          | 0.9915 |
| 0.1507        | 3.53  | 19500 | 0.1873          | 1.0266 |
| 0.1507        | 3.55  | 19600 | 0.1838          | 1.0299 |
| 0.1507        | 3.56  | 19700 | 0.1817          | 1.0355 |
| 0.1507        | 3.58  | 19800 | 0.1819          | 1.0271 |
| 0.1507        | 3.6   | 19900 | 0.1883          | 1.0248 |
| 0.1601        | 3.62  | 20000 | 0.1823          | 1.0406 |
| 0.1601        | 3.64  | 20100 | 0.1801          | 1.0261 |
| 0.1601        | 3.65  | 20200 | 0.1783          | 1.0329 |
| 0.1601        | 3.67  | 20300 | 0.1857          | 1.0162 |
| 0.1601        | 3.69  | 20400 | 0.1814          | 1.0212 |
| 0.1552        | 3.71  | 20500 | 0.1837          | 1.0232 |
| 0.1552        | 3.73  | 20600 | 0.1843          | 1.0314 |
| 0.1552        | 3.74  | 20700 | 0.1842          | 1.0258 |
| 0.1552        | 3.76  | 20800 | 0.1821          | 1.0479 |
| 0.1552        | 3.78  | 20900 | 0.1864          | 1.0459 |
| 0.1576        | 3.8   | 21000 | 0.1831          | 1.0364 |
| 0.1576        | 3.82  | 21100 | 0.1852          | 1.0271 |
| 0.1576        | 3.83  | 21200 | 0.1865          | 1.0204 |
| 0.1576        | 3.85  | 21300 | 0.1794          | 1.0324 |
| 0.1576        | 3.87  | 21400 | 0.1826          | 1.0315 |
| 0.1585        | 3.89  | 21500 | 0.1824          | 1.0327 |
| 0.1585        | 3.91  | 21600 | 0.1838          | 1.0208 |
| 0.1585        | 3.93  | 21700 | 0.1850          | 1.0199 |
| 0.1585        | 3.94  | 21800 | 0.1841          | 1.0050 |
| 0.1585        | 3.96  | 21900 | 0.1783          | 1.0003 |
| 0.1572        | 3.98  | 22000 | 0.1787          | 1.0115 |
| 0.1572        | 4.0   | 22100 | 0.1810          | 1.0235 |
| 0.1572        | 4.02  | 22200 | 0.1763          | 1.0191 |
| 0.1572        | 4.03  | 22300 | 0.1764          | 1.0332 |
| 0.1572        | 4.05  | 22400 | 0.1794          | 1.0429 |
| 0.1406        | 4.07  | 22500 | 0.1905          | 1.0288 |
| 0.1406        | 4.09  | 22600 | 0.1776          | 1.0244 |
| 0.1406        | 4.11  | 22700 | 0.1782          | 1.0451 |
| 0.1406        | 4.12  | 22800 | 0.1771          | 1.0387 |
| 0.1406        | 4.14  | 22900 | 0.1788          | 1.0435 |
| 0.14          | 4.16  | 23000 | 0.1792          | 1.0421 |
| 0.14          | 4.18  | 23100 | 0.1841          | 1.0241 |
| 0.14          | 4.2   | 23200 | 0.1769          | 1.0546 |
| 0.14          | 4.21  | 23300 | 0.1815          | 1.0602 |
| 0.14          | 4.23  | 23400 | 0.1784          | 1.0369 |
| 0.1394        | 4.25  | 23500 | 0.1809          | 1.0406 |
| 0.1394        | 4.27  | 23600 | 0.1744          | 1.0133 |
| 0.1394        | 4.29  | 23700 | 0.1771          | 1.0214 |
| 0.1394        | 4.31  | 23800 | 0.1765          | 1.0064 |
| 0.1394        | 4.32  | 23900 | 0.1793          | 1.0200 |
| 0.14          | 4.34  | 24000 | 0.1776          | 1.0352 |
| 0.14          | 4.36  | 24100 | 0.1775          | 1.0294 |
| 0.14          | 4.38  | 24200 | 0.1763          | 1.0213 |
| 0.14          | 4.4   | 24300 | 0.1697          | 1.0302 |
| 0.14          | 4.41  | 24400 | 0.1771          | 1.0259 |
| 0.1408        | 4.43  | 24500 | 0.1747          | 1.0409 |
| 0.1408        | 4.45  | 24600 | 0.1769          | 1.0278 |
| 0.1408        | 4.47  | 24700 | 0.1767          | 1.0190 |
| 0.1408        | 4.49  | 24800 | 0.1745          | 1.0281 |
| 0.1408        | 4.5   | 24900 | 0.1738          | 1.0356 |
| 0.1391        | 4.52  | 25000 | 0.1781          | 1.0429 |
| 0.1391        | 4.54  | 25100 | 0.1784          | 1.0076 |
| 0.1391        | 4.56  | 25200 | 0.1771          | 1.0157 |
| 0.1391        | 4.58  | 25300 | 0.1758          | 1.0337 |
| 0.1391        | 4.59  | 25400 | 0.1758          | 1.0466 |
| 0.1398        | 4.61  | 25500 | 0.1724          | 1.0403 |
| 0.1398        | 4.63  | 25600 | 0.1765          | 1.0481 |
| 0.1398        | 4.65  | 25700 | 0.1757          | 1.0320 |
| 0.1398        | 4.67  | 25800 | 0.1814          | 1.0479 |
| 0.1398        | 4.69  | 25900 | 0.1713          | 1.0251 |
| 0.1427        | 4.7   | 26000 | 0.1735          | 1.0340 |
| 0.1427        | 4.72  | 26100 | 0.1765          | 1.0358 |
| 0.1427        | 4.74  | 26200 | 0.1731          | 1.0220 |
| 0.1427        | 4.76  | 26300 | 0.1769          | 1.0261 |
| 0.1427        | 4.78  | 26400 | 0.1747          | 1.0139 |
| 0.1424        | 4.79  | 26500 | 0.1791          | 1.0406 |
| 0.1424        | 4.81  | 26600 | 0.1735          | 1.0497 |
| 0.1424        | 4.83  | 26700 | 0.1710          | 1.0433 |
| 0.1424        | 4.85  | 26800 | 0.1771          | 1.0002 |
| 0.1424        | 4.87  | 26900 | 0.1748          | 1.0046 |
| 0.1419        | 4.88  | 27000 | 0.1794          | 1.0332 |
| 0.1419        | 4.9   | 27100 | 0.1772          | 1.0558 |
| 0.1419        | 4.92  | 27200 | 0.1757          | 1.0477 |
| 0.1419        | 4.94  | 27300 | 0.1735          | 1.0324 |
| 0.1419        | 4.96  | 27400 | 0.1758          | 1.0260 |
| 0.1433        | 4.97  | 27500 | 0.1767          | 1.0422 |
| 0.1433        | 4.99  | 27600 | 0.1695          | 1.0386 |
| 0.1433        | 5.01  | 27700 | 0.1763          | 1.0571 |
| 0.1433        | 5.03  | 27800 | 0.1743          | 1.0367 |
| 0.1433        | 5.05  | 27900 | 0.1804          | 1.0255 |
| 0.1306        | 5.07  | 28000 | 0.1803          | 1.0377 |
| 0.1306        | 5.08  | 28100 | 0.1750          | 1.0552 |
| 0.1306        | 5.1   | 28200 | 0.1743          | 1.0512 |
| 0.1306        | 5.12  | 28300 | 0.1777          | 1.0584 |
| 0.1306        | 5.14  | 28400 | 0.1726          | 1.0374 |
| 0.123         | 5.16  | 28500 | 0.1776          | 1.0439 |
| 0.123         | 5.17  | 28600 | 0.1759          | 1.0682 |
| 0.123         | 5.19  | 28700 | 0.1724          | 1.0511 |
| 0.123         | 5.21  | 28800 | 0.1677          | 1.0560 |
| 0.123         | 5.23  | 28900 | 0.1699          | 1.0421 |
| 0.1217        | 5.25  | 29000 | 0.1803          | 1.0370 |
| 0.1217        | 5.26  | 29100 | 0.1770          | 1.0474 |
| 0.1217        | 5.28  | 29200 | 0.1733          | 1.0332 |
| 0.1217        | 5.3   | 29300 | 0.1746          | 1.0158 |
| 0.1217        | 5.32  | 29400 | 0.1763          | 1.0341 |
| 0.1246        | 5.34  | 29500 | 0.1775          | 1.0348 |
| 0.1246        | 5.35  | 29600 | 0.1730          | 1.0492 |
| 0.1246        | 5.37  | 29700 | 0.1730          | 1.0503 |
| 0.1246        | 5.39  | 29800 | 0.1727          | 1.0437 |
| 0.1246        | 5.41  | 29900 | 0.1744          | 1.0539 |
| 0.127         | 5.43  | 30000 | 0.1748          | 1.0463 |
| 0.127         | 5.44  | 30100 | 0.1746          | 1.0555 |
| 0.127         | 5.46  | 30200 | 0.1810          | 1.0558 |
| 0.127         | 5.48  | 30300 | 0.1773          | 1.0407 |
| 0.127         | 5.5   | 30400 | 0.1722          | 1.0489 |
| 0.1276        | 5.52  | 30500 | 0.1720          | 1.0520 |
| 0.1276        | 5.54  | 30600 | 0.1777          | 1.0347 |
| 0.1276        | 5.55  | 30700 | 0.1685          | 1.0347 |
| 0.1276        | 5.57  | 30800 | 0.1659          | 1.0338 |
| 0.1276        | 5.59  | 30900 | 0.1756          | 1.0228 |
| 0.1246        | 5.61  | 31000 | 0.1717          | 1.0409 |
| 0.1246        | 5.63  | 31100 | 0.1764          | 1.0202 |
| 0.1246        | 5.64  | 31200 | 0.1693          | 1.0314 |
| 0.1246        | 5.66  | 31300 | 0.1731          | 1.0319 |
| 0.1246        | 5.68  | 31400 | 0.1688          | 1.0380 |
| 0.1271        | 5.7   | 31500 | 0.1671          | 1.0350 |
| 0.1271        | 5.72  | 31600 | 0.1676          | 1.0430 |
| 0.1271        | 5.73  | 31700 | 0.1656          | 1.0441 |
| 0.1271        | 5.75  | 31800 | 0.1664          | 1.0403 |
| 0.1271        | 5.77  | 31900 | 0.1691          | 1.0152 |
| 0.1259        | 5.79  | 32000 | 0.1702          | 1.0018 |
| 0.1259        | 5.81  | 32100 | 0.1664          | 1.0246 |
| 0.1259        | 5.82  | 32200 | 0.1737          | 1.0340 |
| 0.1259        | 5.84  | 32300 | 0.1742          | 1.0449 |
| 0.1259        | 5.86  | 32400 | 0.1707          | 1.0279 |
| 0.1273        | 5.88  | 32500 | 0.1697          | 1.0471 |
| 0.1273        | 5.9   | 32600 | 0.1668          | 1.0322 |
| 0.1273        | 5.92  | 32700 | 0.1706          | 1.0378 |
| 0.1273        | 5.93  | 32800 | 0.1704          | 1.0350 |
| 0.1273        | 5.95  | 32900 | 0.1725          | 1.0244 |
| 0.123         | 5.97  | 33000 | 0.1678          | 1.0447 |
| 0.123         | 5.99  | 33100 | 0.1681          | 1.0438 |
| 0.123         | 6.01  | 33200 | 0.1689          | 1.0297 |
| 0.123         | 6.02  | 33300 | 0.1690          | 1.0333 |
| 0.123         | 6.04  | 33400 | 0.1734          | 1.0296 |
| 0.1163        | 6.06  | 33500 | 0.1748          | 1.0307 |
| 0.1163        | 6.08  | 33600 | 0.1715          | 1.0123 |
| 0.1163        | 6.1   | 33700 | 0.1668          | 1.0117 |
| 0.1163        | 6.11  | 33800 | 0.1690          | 1.0230 |
| 0.1163        | 6.13  | 33900 | 0.1693          | 1.0166 |
| 0.1101        | 6.15  | 34000 | 0.1728          | 1.0162 |
| 0.1101        | 6.17  | 34100 | 0.1683          | 1.0107 |
| 0.1101        | 6.19  | 34200 | 0.1703          | 0.9814 |
| 0.1101        | 6.2   | 34300 | 0.1692          | 1.0007 |
| 0.1101        | 6.22  | 34400 | 0.1690          | 1.0000 |
| 0.1118        | 6.24  | 34500 | 0.1734          | 0.9972 |
| 0.1118        | 6.26  | 34600 | 0.1739          | 1.0096 |
| 0.1118        | 6.28  | 34700 | 0.1749          | 1.0047 |
| 0.1118        | 6.3   | 34800 | 0.1709          | 1.0111 |
| 0.1118        | 6.31  | 34900 | 0.1717          | 1.0179 |
| 0.1153        | 6.33  | 35000 | 0.1690          | 1.0155 |
| 0.1153        | 6.35  | 35100 | 0.1710          | 1.0144 |
| 0.1153        | 6.37  | 35200 | 0.1719          | 1.0030 |
| 0.1153        | 6.39  | 35300 | 0.1690          | 1.0272 |
| 0.1153        | 6.4   | 35400 | 0.1673          | 1.0103 |
| 0.1106        | 6.42  | 35500 | 0.1710          | 1.0222 |
| 0.1106        | 6.44  | 35600 | 0.1747          | 1.0173 |
| 0.1106        | 6.46  | 35700 | 0.1721          | 0.9933 |
| 0.1106        | 6.48  | 35800 | 0.1670          | 1.0184 |
| 0.1106        | 6.49  | 35900 | 0.1714          | 1.0122 |
| 0.1116        | 6.51  | 36000 | 0.1717          | 1.0035 |
| 0.1116        | 6.53  | 36100 | 0.1685          | 1.0099 |
| 0.1116        | 6.55  | 36200 | 0.1687          | 1.0288 |
| 0.1116        | 6.57  | 36300 | 0.1664          | 1.0314 |
| 0.1116        | 6.58  | 36400 | 0.1665          | 1.0264 |
| 0.1128        | 6.6   | 36500 | 0.1681          | 1.0420 |
| 0.1128        | 6.62  | 36600 | 0.1682          | 1.0409 |
| 0.1128        | 6.64  | 36700 | 0.1717          | 1.0271 |
| 0.1128        | 6.66  | 36800 | 0.1717          | 1.0166 |
| 0.1128        | 6.68  | 36900 | 0.1755          | 1.0175 |
| 0.1134        | 6.69  | 37000 | 0.1623          | 1.0185 |
| 0.1134        | 6.71  | 37100 | 0.1674          | 1.0302 |
| 0.1134        | 6.73  | 37200 | 0.1633          | 1.0325 |
| 0.1134        | 6.75  | 37300 | 0.1628          | 1.0228 |
| 0.1134        | 6.77  | 37400 | 0.1636          | 1.0243 |
| 0.1102        | 6.78  | 37500 | 0.1667          | 1.0282 |
| 0.1102        | 6.8   | 37600 | 0.1623          | 1.0212 |
| 0.1102        | 6.82  | 37700 | 0.1639          | 1.0140 |
| 0.1102        | 6.84  | 37800 | 0.1587          | 1.0258 |
| 0.1102        | 6.86  | 37900 | 0.1610          | 1.0087 |
| 0.1113        | 6.87  | 38000 | 0.1647          | 1.0199 |
| 0.1113        | 6.89  | 38100 | 0.1609          | 1.0054 |
| 0.1113        | 6.91  | 38200 | 0.1602          | 1.0145 |
| 0.1113        | 6.93  | 38300 | 0.1602          | 1.0144 |
| 0.1113        | 6.95  | 38400 | 0.1602          | 1.0375 |
| 0.1071        | 6.96  | 38500 | 0.1592          | 1.0259 |
| 0.1071        | 6.98  | 38600 | 0.1612          | 1.0236 |
| 0.1071        | 7.0   | 38700 | 0.1621          | 1.0277 |
| 0.1071        | 7.02  | 38800 | 0.1669          | 1.0367 |
| 0.1071        | 7.04  | 38900 | 0.1742          | 1.0484 |
| 0.1062        | 7.05  | 39000 | 0.1752          | 1.0302 |
| 0.1062        | 7.07  | 39100 | 0.1676          | 1.0244 |
| 0.1062        | 7.09  | 39200 | 0.1723          | 1.0300 |
| 0.1062        | 7.11  | 39300 | 0.1727          | 1.0294 |
| 0.1062        | 7.13  | 39400 | 0.1711          | 1.0255 |
| 0.1021        | 7.15  | 39500 | 0.1699          | 1.0471 |
| 0.1021        | 7.16  | 39600 | 0.1682          | 1.0426 |
| 0.1021        | 7.18  | 39700 | 0.1713          | 1.0233 |
| 0.1021        | 7.2   | 39800 | 0.1682          | 1.0259 |
| 0.1021        | 7.22  | 39900 | 0.1710          | 1.0162 |
| 0.103         | 7.24  | 40000 | 0.1725          | 1.0283 |
| 0.103         | 7.25  | 40100 | 0.1729          | 1.0264 |
| 0.103         | 7.27  | 40200 | 0.1665          | 1.0451 |
| 0.103         | 7.29  | 40300 | 0.1671          | 1.0386 |
| 0.103         | 7.31  | 40400 | 0.1671          | 1.0316 |
| 0.0981        | 7.33  | 40500 | 0.1708          | 1.0257 |
| 0.0981        | 7.34  | 40600 | 0.1642          | 1.0152 |
| 0.0981        | 7.36  | 40700 | 0.1707          | 1.0110 |
| 0.0981        | 7.38  | 40800 | 0.1675          | 1.0186 |
| 0.0981        | 7.4   | 40900 | 0.1702          | 1.0123 |
| 0.1005        | 7.42  | 41000 | 0.1699          | 1.0159 |
| 0.1005        | 7.43  | 41100 | 0.1703          | 1.0219 |
| 0.1005        | 7.45  | 41200 | 0.1707          | 1.0194 |
| 0.1005        | 7.47  | 41300 | 0.1644          | 1.0016 |
| 0.1005        | 7.49  | 41400 | 0.1716          | 0.9941 |
| 0.1021        | 7.51  | 41500 | 0.1670          | 1.0159 |
| 0.1021        | 7.53  | 41600 | 0.1667          | 1.0033 |
| 0.1021        | 7.54  | 41700 | 0.1667          | 1.0176 |
| 0.1021        | 7.56  | 41800 | 0.1679          | 1.0194 |
| 0.1021        | 7.58  | 41900 | 0.1632          | 1.0418 |
| 0.0963        | 7.6   | 42000 | 0.1712          | 1.0152 |
| 0.0963        | 7.62  | 42100 | 0.1632          | 1.0364 |
| 0.0963        | 7.63  | 42200 | 0.1702          | 1.0229 |
| 0.0963        | 7.65  | 42300 | 0.1655          | 1.0179 |
| 0.0963        | 7.67  | 42400 | 0.1698          | 1.0329 |
| 0.1014        | 7.69  | 42500 | 0.1691          | 1.0398 |
| 0.1014        | 7.71  | 42600 | 0.1638          | 1.0487 |
| 0.1014        | 7.72  | 42700 | 0.1617          | 1.0210 |
| 0.1014        | 7.74  | 42800 | 0.1648          | 1.0124 |
| 0.1014        | 7.76  | 42900 | 0.1608          | 1.0202 |
| 0.1008        | 7.78  | 43000 | 0.1611          | 1.0353 |
| 0.1008        | 7.8   | 43100 | 0.1633          | 1.0319 |
| 0.1008        | 7.81  | 43200 | 0.1640          | 1.0032 |
| 0.1008        | 7.83  | 43300 | 0.1589          | 0.9985 |
| 0.1008        | 7.85  | 43400 | 0.1630          | 0.9975 |
| 0.0988        | 7.87  | 43500 | 0.1604          | 1.0053 |
| 0.0988        | 7.89  | 43600 | 0.1687          | 1.0063 |
| 0.0988        | 7.91  | 43700 | 0.1619          | 1.0096 |
| 0.0988        | 7.92  | 43800 | 0.1565          | 0.9901 |
| 0.0988        | 7.94  | 43900 | 0.1619          | 0.9742 |
| 0.102         | 7.96  | 44000 | 0.1598          | 0.9593 |
| 0.102         | 7.98  | 44100 | 0.1635          | 0.9718 |
| 0.102         | 8.0   | 44200 | 0.1624          | 0.9903 |
| 0.102         | 8.01  | 44300 | 0.1605          | 0.9882 |
| 0.102         | 8.03  | 44400 | 0.1657          | 1.0128 |
| 0.0961        | 8.05  | 44500 | 0.1651          | 1.0155 |
| 0.0961        | 8.07  | 44600 | 0.1680          | 1.0194 |
| 0.0961        | 8.09  | 44700 | 0.1694          | 1.0112 |
| 0.0961        | 8.1   | 44800 | 0.1665          | 1.0073 |
| 0.0961        | 8.12  | 44900 | 0.1612          | 1.0200 |
| 0.0894        | 8.14  | 45000 | 0.1652          | 1.0337 |
| 0.0894        | 8.16  | 45100 | 0.1626          | 1.0086 |
| 0.0894        | 8.18  | 45200 | 0.1639          | 1.0083 |
| 0.0894        | 8.19  | 45300 | 0.1634          | 1.0223 |
| 0.0894        | 8.21  | 45400 | 0.1631          | 1.0339 |
| 0.0887        | 8.23  | 45500 | 0.1640          | 1.0311 |
| 0.0887        | 8.25  | 45600 | 0.1661          | 1.0264 |
| 0.0887        | 8.27  | 45700 | 0.1650          | 1.0315 |
| 0.0887        | 8.29  | 45800 | 0.1624          | 1.0390 |
| 0.0887        | 8.3   | 45900 | 0.1624          | 1.0350 |
| 0.0884        | 8.32  | 46000 | 0.1615          | 1.0318 |
| 0.0884        | 8.34  | 46100 | 0.1628          | 1.0410 |
| 0.0884        | 8.36  | 46200 | 0.1627          | 1.0429 |
| 0.0884        | 8.38  | 46300 | 0.1644          | 1.0320 |
| 0.0884        | 8.39  | 46400 | 0.1633          | 1.0177 |
| 0.0893        | 8.41  | 46500 | 0.1654          | 1.0189 |
| 0.0893        | 8.43  | 46600 | 0.1598          | 1.0154 |
| 0.0893        | 8.45  | 46700 | 0.1618          | 1.0250 |
| 0.0893        | 8.47  | 46800 | 0.1639          | 1.0402 |
| 0.0893        | 8.48  | 46900 | 0.1616          | 1.0336 |
| 0.0869        | 8.5   | 47000 | 0.1613          | 1.0296 |
| 0.0869        | 8.52  | 47100 | 0.1648          | 1.0568 |
| 0.0869        | 8.54  | 47200 | 0.1625          | 1.0256 |
| 0.0869        | 8.56  | 47300 | 0.1609          | 1.0390 |
| 0.0869        | 8.57  | 47400 | 0.1606          | 1.0450 |
| 0.0894        | 8.59  | 47500 | 0.1605          | 1.0445 |
| 0.0894        | 8.61  | 47600 | 0.1660          | 1.0402 |
| 0.0894        | 8.63  | 47700 | 0.1618          | 1.0444 |
| 0.0894        | 8.65  | 47800 | 0.1669          | 1.0333 |
| 0.0894        | 8.66  | 47900 | 0.1627          | 1.0364 |
| 0.0885        | 8.68  | 48000 | 0.1616          | 1.0334 |
| 0.0885        | 8.7   | 48100 | 0.1626          | 1.0564 |
| 0.0885        | 8.72  | 48200 | 0.1624          | 1.0396 |
| 0.0885        | 8.74  | 48300 | 0.1623          | 1.0396 |
| 0.0885        | 8.76  | 48400 | 0.1612          | 1.0112 |
| 0.0888        | 8.77  | 48500 | 0.1638          | 1.0292 |
| 0.0888        | 8.79  | 48600 | 0.1639          | 0.9988 |
| 0.0888        | 8.81  | 48700 | 0.1618          | 1.0127 |
| 0.0888        | 8.83  | 48800 | 0.1584          | 1.0042 |
| 0.0888        | 8.85  | 48900 | 0.1615          | 1.0041 |
| 0.0887        | 8.86  | 49000 | 0.1637          | 1.0269 |
| 0.0887        | 8.88  | 49100 | 0.1627          | 0.9989 |
| 0.0887        | 8.9   | 49200 | 0.1583          | 1.0104 |
| 0.0887        | 8.92  | 49300 | 0.1600          | 1.0214 |
| 0.0887        | 8.94  | 49400 | 0.1599          | 1.0126 |
| 0.0893        | 8.95  | 49500 | 0.1595          | 1.0516 |
| 0.0893        | 8.97  | 49600 | 0.1625          | 1.0464 |
| 0.0893        | 8.99  | 49700 | 0.1595          | 1.0361 |
| 0.0893        | 9.01  | 49800 | 0.1614          | 1.0469 |
| 0.0893        | 9.03  | 49900 | 0.1612          | 1.0304 |
| 0.0834        | 9.04  | 50000 | 0.1643          | 1.0335 |
| 0.0834        | 9.06  | 50100 | 0.1640          | 1.0175 |
| 0.0834        | 9.08  | 50200 | 0.1655          | 1.0264 |
| 0.0834        | 9.1   | 50300 | 0.1678          | 1.0243 |
| 0.0834        | 9.12  | 50400 | 0.1659          | 1.0145 |
| 0.079         | 9.14  | 50500 | 0.1644          | 1.0316 |
| 0.079         | 9.15  | 50600 | 0.1630          | 1.0326 |
| 0.079         | 9.17  | 50700 | 0.1634          | 1.0154 |
| 0.079         | 9.19  | 50800 | 0.1697          | 1.0095 |
| 0.079         | 9.21  | 50900 | 0.1678          | 1.0050 |
| 0.078         | 9.23  | 51000 | 0.1626          | 1.0159 |
| 0.078         | 9.24  | 51100 | 0.1666          | 1.0238 |
| 0.078         | 9.26  | 51200 | 0.1644          | 1.0244 |
| 0.078         | 9.28  | 51300 | 0.1655          | 1.0345 |
| 0.078         | 9.3   | 51400 | 0.1615          | 1.0237 |
| 0.0776        | 9.32  | 51500 | 0.1664          | 1.0180 |
| 0.0776        | 9.33  | 51600 | 0.1603          | 1.0208 |
| 0.0776        | 9.35  | 51700 | 0.1594          | 1.0230 |
| 0.0776        | 9.37  | 51800 | 0.1622          | 1.0201 |
| 0.0776        | 9.39  | 51900 | 0.1596          | 1.0039 |
| 0.0782        | 9.41  | 52000 | 0.1645          | 1.0204 |
| 0.0782        | 9.42  | 52100 | 0.1640          | 1.0318 |
| 0.0782        | 9.44  | 52200 | 0.1621          | 1.0290 |
| 0.0782        | 9.46  | 52300 | 0.1638          | 1.0318 |
| 0.0782        | 9.48  | 52400 | 0.1613          | 1.0217 |
| 0.0782        | 9.5   | 52500 | 0.1609          | 1.0261 |
| 0.0782        | 9.52  | 52600 | 0.1625          | 1.0101 |
| 0.0782        | 9.53  | 52700 | 0.1613          | 1.0058 |
| 0.0782        | 9.55  | 52800 | 0.1599          | 1.0068 |
| 0.0782        | 9.57  | 52900 | 0.1600          | 1.0110 |
| 0.0797        | 9.59  | 53000 | 0.1594          | 1.0171 |
| 0.0797        | 9.61  | 53100 | 0.1583          | 1.0124 |
| 0.0797        | 9.62  | 53200 | 0.1646          | 1.0093 |
| 0.0797        | 9.64  | 53300 | 0.1580          | 1.0201 |
| 0.0797        | 9.66  | 53400 | 0.1599          | 1.0207 |
| 0.0783        | 9.68  | 53500 | 0.1577          | 1.0226 |
| 0.0783        | 9.7   | 53600 | 0.1593          | 1.0160 |
| 0.0783        | 9.71  | 53700 | 0.1570          | 1.0173 |
| 0.0783        | 9.73  | 53800 | 0.1614          | 1.0299 |
| 0.0783        | 9.75  | 53900 | 0.1610          | 1.0184 |
| 0.0779        | 9.77  | 54000 | 0.1606          | 1.0173 |
| 0.0779        | 9.79  | 54100 | 0.1577          | 1.0032 |
| 0.0779        | 9.8   | 54200 | 0.1590          | 1.0070 |
| 0.0779        | 9.82  | 54300 | 0.1580          | 1.0257 |
| 0.0779        | 9.84  | 54400 | 0.1592          | 1.0108 |
| 0.0778        | 9.86  | 54500 | 0.1617          | 0.9907 |
| 0.0778        | 9.88  | 54600 | 0.1605          | 1.0189 |
| 0.0778        | 9.89  | 54700 | 0.1605          | 1.0177 |
| 0.0778        | 9.91  | 54800 | 0.1536          | 1.0275 |
| 0.0778        | 9.93  | 54900 | 0.1658          | 1.0282 |
| 0.0777        | 9.95  | 55000 | 0.1543          | 1.0385 |
| 0.0777        | 9.97  | 55100 | 0.1559          | 1.0375 |
| 0.0777        | 9.99  | 55200 | 0.1590          | 1.0215 |
| 0.0777        | 10.0  | 55300 | 0.1624          | 1.0242 |
| 0.0777        | 10.02 | 55400 | 0.1635          | 1.0244 |
| 0.0712        | 10.04 | 55500 | 0.1629          | 1.0298 |
| 0.0712        | 10.06 | 55600 | 0.1601          | 1.0299 |
| 0.0712        | 10.08 | 55700 | 0.1625          | 1.0117 |
| 0.0712        | 10.09 | 55800 | 0.1650          | 1.0233 |
| 0.0712        | 10.11 | 55900 | 0.1631          | 1.0061 |
| 0.0667        | 10.13 | 56000 | 0.1637          | 1.0226 |
| 0.0667        | 10.15 | 56100 | 0.1607          | 1.0042 |
| 0.0667        | 10.17 | 56200 | 0.1599          | 1.0117 |
| 0.0667        | 10.18 | 56300 | 0.1623          | 1.0246 |
| 0.0667        | 10.2  | 56400 | 0.1639          | 1.0294 |
| 0.0695        | 10.22 | 56500 | 0.1650          | 1.0232 |
| 0.0695        | 10.24 | 56600 | 0.1620          | 1.0289 |
| 0.0695        | 10.26 | 56700 | 0.1667          | 1.0209 |
| 0.0695        | 10.27 | 56800 | 0.1580          | 1.0163 |
| 0.0695        | 10.29 | 56900 | 0.1646          | 1.0293 |
| 0.0686        | 10.31 | 57000 | 0.1636          | 1.0106 |
| 0.0686        | 10.33 | 57100 | 0.1586          | 1.0044 |
| 0.0686        | 10.35 | 57200 | 0.1582          | 1.0213 |
| 0.0686        | 10.37 | 57300 | 0.1627          | 1.0151 |
| 0.0686        | 10.38 | 57400 | 0.1619          | 1.0248 |
| 0.0686        | 10.4  | 57500 | 0.1596          | 1.0098 |
| 0.0686        | 10.42 | 57600 | 0.1606          | 1.0031 |
| 0.0686        | 10.44 | 57700 | 0.1620          | 1.0046 |
| 0.0686        | 10.46 | 57800 | 0.1592          | 1.0018 |
| 0.0686        | 10.47 | 57900 | 0.1592          | 1.0058 |
| 0.0669        | 10.49 | 58000 | 0.1605          | 0.9961 |
| 0.0669        | 10.51 | 58100 | 0.1632          | 1.0102 |
| 0.0669        | 10.53 | 58200 | 0.1593          | 1.0061 |
| 0.0669        | 10.55 | 58300 | 0.1586          | 1.0091 |
| 0.0669        | 10.56 | 58400 | 0.1603          | 1.0085 |
| 0.068         | 10.58 | 58500 | 0.1579          | 1.0031 |
| 0.068         | 10.6  | 58600 | 0.1591          | 1.0021 |
| 0.068         | 10.62 | 58700 | 0.1590          | 1.0163 |
| 0.068         | 10.64 | 58800 | 0.1584          | 1.0045 |
| 0.068         | 10.65 | 58900 | 0.1594          | 1.0158 |
| 0.0693        | 10.67 | 59000 | 0.1568          | 1.0052 |
| 0.0693        | 10.69 | 59100 | 0.1581          | 0.9955 |
| 0.0693        | 10.71 | 59200 | 0.1622          | 0.9917 |
| 0.0693        | 10.73 | 59300 | 0.1580          | 1.0018 |
| 0.0693        | 10.75 | 59400 | 0.1601          | 1.0077 |
| 0.0699        | 10.76 | 59500 | 0.1605          | 0.9997 |
| 0.0699        | 10.78 | 59600 | 0.1585          | 1.0009 |
| 0.0699        | 10.8  | 59700 | 0.1541          | 1.0058 |
| 0.0699        | 10.82 | 59800 | 0.1583          | 1.0026 |
| 0.0699        | 10.84 | 59900 | 0.1592          | 0.9992 |
| 0.0671        | 10.85 | 60000 | 0.1590          | 1.0004 |
| 0.0671        | 10.87 | 60100 | 0.1585          | 1.0060 |
| 0.0671        | 10.89 | 60200 | 0.1579          | 1.0063 |
| 0.0671        | 10.91 | 60300 | 0.1582          | 0.9949 |
| 0.0671        | 10.93 | 60400 | 0.1562          | 1.0004 |
| 0.0661        | 10.94 | 60500 | 0.1560          | 0.9950 |
| 0.0661        | 10.96 | 60600 | 0.1564          | 0.9990 |
| 0.0661        | 10.98 | 60700 | 0.1552          | 0.9982 |
| 0.0661        | 11.0  | 60800 | 0.1596          | 1.0018 |
| 0.0661        | 11.02 | 60900 | 0.1618          | 0.9905 |
| 0.0634        | 11.03 | 61000 | 0.1652          | 0.9890 |
| 0.0634        | 11.05 | 61100 | 0.1649          | 0.9886 |
| 0.0634        | 11.07 | 61200 | 0.1668          | 0.9870 |
| 0.0634        | 11.09 | 61300 | 0.1663          | 0.9921 |
| 0.0634        | 11.11 | 61400 | 0.1650          | 0.9919 |
| 0.0587        | 11.13 | 61500 | 0.1674          | 0.9831 |
| 0.0587        | 11.14 | 61600 | 0.1633          | 0.9793 |
| 0.0587        | 11.16 | 61700 | 0.1665          | 0.9781 |
| 0.0587        | 11.18 | 61800 | 0.1642          | 0.9821 |
| 0.0587        | 11.2  | 61900 | 0.1638          | 0.9797 |
| 0.0581        | 11.22 | 62000 | 0.1628          | 0.9727 |
| 0.0581        | 11.23 | 62100 | 0.1661          | 0.9796 |
| 0.0581        | 11.25 | 62200 | 0.1641          | 0.9830 |
| 0.0581        | 11.27 | 62300 | 0.1601          | 0.9867 |
| 0.0581        | 11.29 | 62400 | 0.1626          | 0.9757 |
| 0.0584        | 11.31 | 62500 | 0.1632          | 1.0014 |
| 0.0584        | 11.32 | 62600 | 0.1626          | 1.0052 |
| 0.0584        | 11.34 | 62700 | 0.1586          | 1.0098 |
| 0.0584        | 11.36 | 62800 | 0.1597          | 1.0151 |
| 0.0584        | 11.38 | 62900 | 0.1624          | 1.0054 |
| 0.0589        | 11.4  | 63000 | 0.1618          | 1.0018 |
| 0.0589        | 11.41 | 63100 | 0.1635          | 1.0032 |
| 0.0589        | 11.43 | 63200 | 0.1654          | 1.0142 |
| 0.0589        | 11.45 | 63300 | 0.1646          | 1.0031 |
| 0.0589        | 11.47 | 63400 | 0.1618          | 1.0118 |
| 0.0579        | 11.49 | 63500 | 0.1634          | 1.0218 |
| 0.0579        | 11.51 | 63600 | 0.1616          | 1.0179 |
| 0.0579        | 11.52 | 63700 | 0.1603          | 1.0036 |
| 0.0579        | 11.54 | 63800 | 0.1610          | 1.0150 |
| 0.0579        | 11.56 | 63900 | 0.1605          | 1.0285 |
| 0.0572        | 11.58 | 64000 | 0.1621          | 1.0261 |
| 0.0572        | 11.6  | 64100 | 0.1625          | 1.0252 |
| 0.0572        | 11.61 | 64200 | 0.1677          | 1.0257 |
| 0.0572        | 11.63 | 64300 | 0.1656          | 1.0243 |
| 0.0572        | 11.65 | 64400 | 0.1669          | 1.0270 |
| 0.0592        | 11.67 | 64500 | 0.1605          | 1.0305 |
| 0.0592        | 11.69 | 64600 | 0.1633          | 1.0277 |
| 0.0592        | 11.7  | 64700 | 0.1606          | 1.0176 |
| 0.0592        | 11.72 | 64800 | 0.1618          | 1.0249 |
| 0.0592        | 11.74 | 64900 | 0.1609          | 1.0113 |
| 0.0595        | 11.76 | 65000 | 0.1609          | 1.0254 |
| 0.0595        | 11.78 | 65100 | 0.1662          | 1.0275 |
| 0.0595        | 11.79 | 65200 | 0.1652          | 1.0164 |
| 0.0595        | 11.81 | 65300 | 0.1638          | 1.0266 |
| 0.0595        | 11.83 | 65400 | 0.1589          | 1.0274 |
| 0.0588        | 11.85 | 65500 | 0.1607          | 1.0136 |
| 0.0588        | 11.87 | 65600 | 0.1592          | 1.0136 |
| 0.0588        | 11.88 | 65700 | 0.1581          | 1.0183 |
| 0.0588        | 11.9  | 65800 | 0.1587          | 1.0133 |
| 0.0588        | 11.92 | 65900 | 0.1596          | 1.0170 |
| 0.0558        | 11.94 | 66000 | 0.1590          | 1.0161 |
| 0.0558        | 11.96 | 66100 | 0.1597          | 1.0193 |
| 0.0558        | 11.98 | 66200 | 0.1590          | 1.0193 |
| 0.0558        | 11.99 | 66300 | 0.1608          | 1.0242 |
| 0.0558        | 12.01 | 66400 | 0.1642          | 1.0231 |
| 0.0555        | 12.03 | 66500 | 0.1679          | 1.0168 |
| 0.0555        | 12.05 | 66600 | 0.1674          | 1.0083 |
| 0.0555        | 12.07 | 66700 | 0.1658          | 1.0069 |
| 0.0555        | 12.08 | 66800 | 0.1661          | 1.0134 |
| 0.0555        | 12.1  | 66900 | 0.1682          | 1.0274 |
| 0.0508        | 12.12 | 67000 | 0.1702          | 1.0219 |
| 0.0508        | 12.14 | 67100 | 0.1694          | 1.0219 |
| 0.0508        | 12.16 | 67200 | 0.1667          | 1.0236 |
| 0.0508        | 12.17 | 67300 | 0.1672          | 1.0253 |
| 0.0508        | 12.19 | 67400 | 0.1640          | 1.0215 |
| 0.0513        | 12.21 | 67500 | 0.1649          | 1.0242 |
| 0.0513        | 12.23 | 67600 | 0.1687          | 1.0262 |
| 0.0513        | 12.25 | 67700 | 0.1655          | 1.0231 |
| 0.0513        | 12.26 | 67800 | 0.1692          | 1.0176 |
| 0.0513        | 12.28 | 67900 | 0.1675          | 1.0202 |
| 0.0519        | 12.3  | 68000 | 0.1644          | 1.0241 |
| 0.0519        | 12.32 | 68100 | 0.1651          | 1.0297 |
| 0.0519        | 12.34 | 68200 | 0.1661          | 1.0287 |
| 0.0519        | 12.36 | 68300 | 0.1665          | 1.0257 |
| 0.0519        | 12.37 | 68400 | 0.1685          | 1.0233 |
| 0.0522        | 12.39 | 68500 | 0.1636          | 1.0177 |
| 0.0522        | 12.41 | 68600 | 0.1709          | 1.0200 |
| 0.0522        | 12.43 | 68700 | 0.1684          | 1.0164 |
| 0.0522        | 12.45 | 68800 | 0.1666          | 1.0119 |
| 0.0522        | 12.46 | 68900 | 0.1683          | 1.0136 |
| 0.05          | 12.48 | 69000 | 0.1696          | 1.0127 |
| 0.05          | 12.5  | 69100 | 0.1708          | 1.0184 |
| 0.05          | 12.52 | 69200 | 0.1654          | 1.0282 |
| 0.05          | 12.54 | 69300 | 0.1700          | 1.0235 |
| 0.05          | 12.55 | 69400 | 0.1688          | 1.0257 |
| 0.0513        | 12.57 | 69500 | 0.1646          | 1.0274 |
| 0.0513        | 12.59 | 69600 | 0.1660          | 1.0247 |
| 0.0513        | 12.61 | 69700 | 0.1657          | 1.0188 |
| 0.0513        | 12.63 | 69800 | 0.1654          | 1.0087 |
| 0.0513        | 12.64 | 69900 | 0.1681          | 1.0146 |
| 0.0512        | 12.66 | 70000 | 0.1660          | 1.0185 |
| 0.0512        | 12.68 | 70100 | 0.1690          | 1.0214 |
| 0.0512        | 12.7  | 70200 | 0.1683          | 1.0160 |
| 0.0512        | 12.72 | 70300 | 0.1695          | 1.0198 |
| 0.0512        | 12.74 | 70400 | 0.1666          | 1.0193 |
| 0.0484        | 12.75 | 70500 | 0.1654          | 1.0142 |
| 0.0484        | 12.77 | 70600 | 0.1598          | 1.0154 |
| 0.0484        | 12.79 | 70700 | 0.1623          | 1.0139 |
| 0.0484        | 12.81 | 70800 | 0.1662          | 1.0180 |
| 0.0484        | 12.83 | 70900 | 0.1659          | 1.0232 |
| 0.0501        | 12.84 | 71000 | 0.1662          | 1.0202 |
| 0.0501        | 12.86 | 71100 | 0.1639          | 1.0161 |
| 0.0501        | 12.88 | 71200 | 0.1666          | 1.0151 |
| 0.0501        | 12.9  | 71300 | 0.1644          | 1.0129 |
| 0.0501        | 12.92 | 71400 | 0.1642          | 1.0171 |
| 0.0482        | 12.93 | 71500 | 0.1635          | 1.0162 |
| 0.0482        | 12.95 | 71600 | 0.1637          | 1.0186 |
| 0.0482        | 12.97 | 71700 | 0.1639          | 1.0142 |
| 0.0482        | 12.99 | 71800 | 0.1643          | 1.0122 |
| 0.0482        | 13.01 | 71900 | 0.1679          | 1.0156 |
| 0.0483        | 13.02 | 72000 | 0.1717          | 1.0224 |
| 0.0483        | 13.04 | 72100 | 0.1742          | 1.0229 |
| 0.0483        | 13.06 | 72200 | 0.1718          | 1.0237 |
| 0.0483        | 13.08 | 72300 | 0.1742          | 1.0266 |
| 0.0483        | 13.1  | 72400 | 0.1736          | 1.0257 |
| 0.0443        | 13.12 | 72500 | 0.1741          | 1.0275 |
| 0.0443        | 13.13 | 72600 | 0.1745          | 1.0325 |
| 0.0443        | 13.15 | 72700 | 0.1737          | 1.0296 |
| 0.0443        | 13.17 | 72800 | 0.1722          | 1.0303 |
| 0.0443        | 13.19 | 72900 | 0.1702          | 1.0305 |
| 0.0424        | 13.21 | 73000 | 0.1733          | 1.0241 |
| 0.0424        | 13.22 | 73100 | 0.1748          | 1.0243 |
| 0.0424        | 13.24 | 73200 | 0.1760          | 1.0231 |
| 0.0424        | 13.26 | 73300 | 0.1745          | 1.0241 |
| 0.0424        | 13.28 | 73400 | 0.1772          | 1.0217 |
| 0.0424        | 13.3  | 73500 | 0.1755          | 1.0206 |
| 0.0424        | 13.31 | 73600 | 0.1743          | 1.0242 |
| 0.0424        | 13.33 | 73700 | 0.1738          | 1.0208 |
| 0.0424        | 13.35 | 73800 | 0.1736          | 1.0249 |
| 0.0424        | 13.37 | 73900 | 0.1747          | 1.0271 |
| 0.0437        | 13.39 | 74000 | 0.1707          | 1.0241 |
| 0.0437        | 13.4  | 74100 | 0.1731          | 1.0269 |
| 0.0437        | 13.42 | 74200 | 0.1743          | 1.0290 |
| 0.0437        | 13.44 | 74300 | 0.1739          | 1.0266 |
| 0.0437        | 13.46 | 74400 | 0.1763          | 1.0246 |
| 0.0443        | 13.48 | 74500 | 0.1724          | 1.0209 |
| 0.0443        | 13.49 | 74600 | 0.1744          | 1.0244 |
| 0.0443        | 13.51 | 74700 | 0.1717          | 1.0232 |
| 0.0443        | 13.53 | 74800 | 0.1754          | 1.0217 |
| 0.0443        | 13.55 | 74900 | 0.1721          | 1.0234 |
| 0.0435        | 13.57 | 75000 | 0.1751          | 1.0197 |
| 0.0435        | 13.59 | 75100 | 0.1727          | 1.0285 |
| 0.0435        | 13.6  | 75200 | 0.1715          | 1.0221 |
| 0.0435        | 13.62 | 75300 | 0.1746          | 1.0247 |
| 0.0435        | 13.64 | 75400 | 0.1712          | 1.0231 |
| 0.0436        | 13.66 | 75500 | 0.1719          | 1.0228 |
| 0.0436        | 13.68 | 75600 | 0.1727          | 1.0197 |
| 0.0436        | 13.69 | 75700 | 0.1750          | 1.0252 |
| 0.0436        | 13.71 | 75800 | 0.1702          | 1.0241 |
| 0.0436        | 13.73 | 75900 | 0.1720          | 1.0250 |
| 0.0433        | 13.75 | 76000 | 0.1744          | 1.0210 |
| 0.0433        | 13.77 | 76100 | 0.1735          | 1.0211 |
| 0.0433        | 13.78 | 76200 | 0.1727          | 1.0205 |
| 0.0433        | 13.8  | 76300 | 0.1706          | 1.0218 |
| 0.0433        | 13.82 | 76400 | 0.1709          | 1.0238 |
| 0.0431        | 13.84 | 76500 | 0.1705          | 1.0197 |
| 0.0431        | 13.86 | 76600 | 0.1734          | 1.0223 |
| 0.0431        | 13.87 | 76700 | 0.1695          | 1.0250 |
| 0.0431        | 13.89 | 76800 | 0.1734          | 1.0232 |
| 0.0431        | 13.91 | 76900 | 0.1724          | 1.0219 |
| 0.041         | 13.93 | 77000 | 0.1706          | 1.0236 |
| 0.041         | 13.95 | 77100 | 0.1689          | 1.0220 |
| 0.041         | 13.97 | 77200 | 0.1738          | 1.0230 |
| 0.041         | 13.98 | 77300 | 0.1727          | 1.0254 |
| 0.041         | 14.0  | 77400 | 0.1721          | 1.0261 |
| 0.041         | 14.02 | 77500 | 0.1760          | 1.0261 |
| 0.041         | 14.04 | 77600 | 0.1772          | 1.0202 |
| 0.041         | 14.06 | 77700 | 0.1782          | 1.0202 |
| 0.041         | 14.07 | 77800 | 0.1777          | 1.0222 |
| 0.041         | 14.09 | 77900 | 0.1787          | 1.0203 |
| 0.0383        | 14.11 | 78000 | 0.1790          | 1.0236 |
| 0.0383        | 14.13 | 78100 | 0.1812          | 1.0245 |
| 0.0383        | 14.15 | 78200 | 0.1778          | 1.0224 |
| 0.0383        | 14.16 | 78300 | 0.1771          | 1.0231 |
| 0.0383        | 14.18 | 78400 | 0.1782          | 1.0242 |
| 0.0391        | 14.2  | 78500 | 0.1785          | 1.0262 |
| 0.0391        | 14.22 | 78600 | 0.1791          | 1.0261 |
| 0.0391        | 14.24 | 78700 | 0.1770          | 1.0254 |
| 0.0391        | 14.25 | 78800 | 0.1810          | 1.0257 |
| 0.0391        | 14.27 | 78900 | 0.1794          | 1.0241 |
| 0.0387        | 14.29 | 79000 | 0.1774          | 1.0256 |
| 0.0387        | 14.31 | 79100 | 0.1774          | 1.0236 |
| 0.0387        | 14.33 | 79200 | 0.1759          | 1.0222 |
| 0.0387        | 14.35 | 79300 | 0.1787          | 1.0237 |
| 0.0387        | 14.36 | 79400 | 0.1788          | 1.0227 |
| 0.0372        | 14.38 | 79500 | 0.1789          | 1.0232 |
| 0.0372        | 14.4  | 79600 | 0.1771          | 1.0254 |
| 0.0372        | 14.42 | 79700 | 0.1777          | 1.0244 |
| 0.0372        | 14.44 | 79800 | 0.1791          | 1.0225 |
| 0.0372        | 14.45 | 79900 | 0.1786          | 1.0237 |
| 0.0385        | 14.47 | 80000 | 0.1782          | 1.0243 |
| 0.0385        | 14.49 | 80100 | 0.1770          | 1.0236 |
| 0.0385        | 14.51 | 80200 | 0.1782          | 1.0240 |
| 0.0385        | 14.53 | 80300 | 0.1764          | 1.0243 |
| 0.0385        | 14.54 | 80400 | 0.1748          | 1.0248 |
| 0.039         | 14.56 | 80500 | 0.1758          | 1.0232 |
| 0.039         | 14.58 | 80600 | 0.1763          | 1.0246 |
| 0.039         | 14.6  | 80700 | 0.1770          | 1.0220 |
| 0.039         | 14.62 | 80800 | 0.1788          | 1.0225 |
| 0.039         | 14.63 | 80900 | 0.1781          | 1.0230 |
| 0.039         | 14.65 | 81000 | 0.1779          | 1.0230 |
| 0.039         | 14.67 | 81100 | 0.1755          | 1.0212 |
| 0.039         | 14.69 | 81200 | 0.1765          | 1.0226 |
| 0.039         | 14.71 | 81300 | 0.1787          | 1.0241 |
| 0.039         | 14.72 | 81400 | 0.1782          | 1.0250 |
| 0.0368        | 14.74 | 81500 | 0.1780          | 1.0248 |
| 0.0368        | 14.76 | 81600 | 0.1782          | 1.0242 |
| 0.0368        | 14.78 | 81700 | 0.1782          | 1.0242 |
| 0.0368        | 14.8  | 81800 | 0.1792          | 1.0241 |
| 0.0368        | 14.82 | 81900 | 0.1796          | 1.0238 |
| 0.0378        | 14.83 | 82000 | 0.1795          | 1.0236 |
| 0.0378        | 14.85 | 82100 | 0.1796          | 1.0239 |
| 0.0378        | 14.87 | 82200 | 0.1792          | 1.0236 |
| 0.0378        | 14.89 | 82300 | 0.1789          | 1.0239 |
| 0.0378        | 14.91 | 82400 | 0.1788          | 1.0238 |
| 0.0386        | 14.92 | 82500 | 0.1787          | 1.0239 |
| 0.0386        | 14.94 | 82600 | 0.1786          | 1.0236 |
| 0.0386        | 14.96 | 82700 | 0.1786          | 1.0237 |
| 0.0386        | 14.98 | 82800 | 0.1787          | 1.0239 |
| 0.0386        | 15.0  | 82900 | 0.1788          | 1.0238 |

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3


---
language:
- zh
- en
- etc

tags:
- transformers
---


_deprecated_

This model is fine-tuned on data from https://www.reddit.com/r/WritingPrompts/
- The model is based on gpt2-xl
- The prompt responses to the top 1000 prompts (by upvote) are used to fine-tune the model.


This model was trained using prompt_responses_full.csv which you can read more about [here](https://huggingface.co/datasets/projectaligned/reddit_writingprompts_full).

All other training parameters and settings are accessible via the config.json and trainer_state.json files of the individual checkpoints


# kwang2049/TSDAE-scidocs2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain scidocs. Training procedure of this model:
 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
 2. Unsupervised training on scidocs with the TSDAE objective;
 3. Supervised training on the NLI data with cross-entropy loss;
 4. Supervised training on the STSb data with MSE loss.

 The pooling method is CLS-pooling.

 ## Usage
 To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
 ```bash
 pip install sentence-transformers
 ```
 And then load the model and use it to encode sentences:
 ```python
 from sentence_transformers import SentenceTransformer, models
 dataset = 'scidocs'
 model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
 model = SentenceTransformer(model_name_or_path)
 model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
 sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
 ```
 ## Evaluation
 To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
 ```bash
 pip install useb  # Or git clone and pip install .
 python -m useb.downloading all  # Download both training and evaluation data
 ```
 And then do the evaluation:
 ```python
 from sentence_transformers import SentenceTransformer, models
import torch
from useb import run_on
dataset = 'scidocs'
model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
model = SentenceTransformer(model_name_or_path)
model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
@torch.no_grad()
def semb_fn(sentences) -> torch.Tensor:
    return torch.Tensor(model.encode(sentences, show_progress_bar=False))
result = run_on(
    dataset,
    semb_fn=semb_fn,
    eval_type='test',
    data_eval_path='data-eval'
)
 ```

 ## Training
 Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.

 ## Cite & Authors
 If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
```bibtex
@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna",
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}
```


# kwang2049/TSDAE-askubuntu2nli_stsb

This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain AskUbuntu. Training procedure of this model:
 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
 2. Unsupervised training on AskUbuntu with the TSDAE objective;
 3. Supervised training on the NLI data with cross-entropy loss;
 4. Supervised training on the STSb data with MSE loss.

 The pooling method is CLS-pooling.

 ## Usage
 To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
 ```bash
 pip install sentence-transformers
 ```
 And then load the model and use it to encode sentences:
 ```python
 from sentence_transformers import SentenceTransformer, models
 dataset = 'askubuntu'
 model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
 model = SentenceTransformer(model_name_or_path)
 model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
 sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
 ```

 ## Evaluation
 To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
 ```bash
 pip install useb  # Or git clone and pip install .
 python -m useb.downloading all  # Download both training and evaluation data
 ```
 And then do the evaluation:
 ```python
 from sentence_transformers import SentenceTransformer, models
import torch
from useb import run_on

dataset = 'askubuntu'
model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
model = SentenceTransformer(model_name_or_path)
model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling

@torch.no_grad()
def semb_fn(sentences) -> torch.Tensor:
    return torch.Tensor(model.encode(sentences, show_progress_bar=False))

result = run_on(
    dataset,
    semb_fn=semb_fn,
    eval_type='test',
    data_eval_path='data-eval'
)
 ```

 ## Training
 Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.

 ## Cite & Authors
 If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
```bibtex
@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna",
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}
```


# kwang2049/TSDAE-cqadupstack2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain cqadupstack. Training procedure of this model:
 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
 2. Unsupervised training on cqadupstack with the TSDAE objective;
 3. Supervised training on the NLI data with cross-entropy loss;
 4. Supervised training on the STSb data with MSE loss.

 The pooling method is CLS-pooling.

 ## Usage
 To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
 ```bash
 pip install sentence-transformers
 ```
 And then load the model and use it to encode sentences:
 ```python
 from sentence_transformers import SentenceTransformer, models
 dataset = 'cqadupstack'
 model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
 model = SentenceTransformer(model_name_or_path)
 model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
 sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
 ```
 ## Evaluation
 To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
 ```bash
 pip install useb  # Or git clone and pip install .
 python -m useb.downloading all  # Download both training and evaluation data
 ```
 And then do the evaluation:
 ```python
 from sentence_transformers import SentenceTransformer, models
import torch
from useb import run_on
dataset = 'cqadupstack'
model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
model = SentenceTransformer(model_name_or_path)
model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
@torch.no_grad()
def semb_fn(sentences) -> torch.Tensor:
    return torch.Tensor(model.encode(sentences, show_progress_bar=False))
result = run_on(
    dataset,
    semb_fn=semb_fn,
    eval_type='test',
    data_eval_path='data-eval'
)
 ```

 ## Training
 Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.

 ## Cite & Authors
 If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
```bibtex
@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna",
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}
```


# kwang2049/TSDAE-twitterpara2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain twitterpara. Training procedure of this model:
 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
 2. Unsupervised training on twitterpara with the TSDAE objective;
 3. Supervised training on the NLI data with cross-entropy loss;
 4. Supervised training on the STSb data with MSE loss.

 The pooling method is CLS-pooling.

 ## Usage
 To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
 ```bash
 pip install sentence-transformers
 ```
 And then load the model and use it to encode sentences:
 ```python
 from sentence_transformers import SentenceTransformer, models
 dataset = 'twitterpara'
 model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
 model = SentenceTransformer(model_name_or_path)
 model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
 sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
 ```
 ## Evaluation
 To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
 ```bash
 pip install useb  # Or git clone and pip install .
 python -m useb.downloading all  # Download both training and evaluation data
 ```
 And then do the evaluation:
 ```python
 from sentence_transformers import SentenceTransformer, models
import torch
from useb import run_on
dataset = 'twitterpara'
model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
model = SentenceTransformer(model_name_or_path)
model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
@torch.no_grad()
def semb_fn(sentences) -> torch.Tensor:
    return torch.Tensor(model.encode(sentences, show_progress_bar=False))
result = run_on(
    dataset,
    semb_fn=semb_fn,
    eval_type='test',
    data_eval_path='data-eval'
)
 ```

 ## Training
 Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.

 ## Cite & Authors
 If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
```bibtex
@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna",
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}
```


# kwang2049/TSDAE-scidocs2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on scidocs in an unsupervised manner. Training procedure of this model:
 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
 2. Unsupervised training on scidocs with the TSDAE objective;

 The pooling method is CLS-pooling.

 ## Usage
 To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
 ```bash
 pip install sentence-transformers
 ```
 And then load the model and use it to encode sentences:
 ```python
 from sentence_transformers import SentenceTransformer, models
 dataset = 'scidocs'
 model_name_or_path = f'kwang2049/TSDAE-{dataset}'
 model = SentenceTransformer(model_name_or_path)
 model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
 sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
 ```
 ## Evaluation
 To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
 ```bash
 pip install useb  # Or git clone and pip install .
 python -m useb.downloading all  # Download both training and evaluation data
 ```
 And then do the evaluation:
 ```python
 from sentence_transformers import SentenceTransformer, models
import torch
from useb import run_on
dataset = 'scidocs'
model_name_or_path = f'kwang2049/TSDAE-{dataset}'
model = SentenceTransformer(model_name_or_path)
model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
@torch.no_grad()
def semb_fn(sentences) -> torch.Tensor:
    return torch.Tensor(model.encode(sentences, show_progress_bar=False))
result = run_on(
    dataset,
    semb_fn=semb_fn,
    eval_type='test',
    data_eval_path='data-eval'
)
 ```

 ## Training
 Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.

 ## Cite & Authors
 If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
```bibtex
@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna",
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}
```


# kwang2049/TSDAE-cqadupstack2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on cqadupstack in an unsupervised manner. Training procedure of this model:
 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
 2. Unsupervised training on cqadupstack with the TSDAE objective;

 The pooling method is CLS-pooling.

 ## Usage
 To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
 ```bash
 pip install sentence-transformers
 ```
 And then load the model and use it to encode sentences:
 ```python
 from sentence_transformers import SentenceTransformer, models
 dataset = 'cqadupstack'
 model_name_or_path = f'kwang2049/TSDAE-{dataset}'
 model = SentenceTransformer(model_name_or_path)
 model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
 sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
 ```
 ## Evaluation
 To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
 ```bash
 pip install useb  # Or git clone and pip install .
 python -m useb.downloading all  # Download both training and evaluation data
 ```
 And then do the evaluation:
 ```python
 from sentence_transformers import SentenceTransformer, models
import torch
from useb import run_on
dataset = 'cqadupstack'
model_name_or_path = f'kwang2049/TSDAE-{dataset}'
model = SentenceTransformer(model_name_or_path)
model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
@torch.no_grad()
def semb_fn(sentences) -> torch.Tensor:
    return torch.Tensor(model.encode(sentences, show_progress_bar=False))
result = run_on(
    dataset,
    semb_fn=semb_fn,
    eval_type='test',
    data_eval_path='data-eval'
)
 ```

 ## Training
 Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.

 ## Cite & Authors
 If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
```bibtex
@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna",
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}
```


# kwang2049/TSDAE-askubuntu2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on AskUbuntu in an unsupervised manner. Training procedure of this model:
 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
 2. Unsupervised training on AskUbuntu with the TSDAE objective;

 The pooling method is CLS-pooling.

 ## Usage
 To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
 ```bash
 pip install sentence-transformers
 ```
 And then load the model and use it to encode sentences:
 ```python
 from sentence_transformers import SentenceTransformer, models
 dataset = 'askubuntu'
 model_name_or_path = f'kwang2049/TSDAE-{dataset}'
 model = SentenceTransformer(model_name_or_path)
 model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
 sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
 ```
 ## Evaluation
 To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
 ```bash
 pip install useb  # Or git clone and pip install .
 python -m useb.downloading all  # Download both training and evaluation data
 ```
 And then do the evaluation:
 ```python
 from sentence_transformers import SentenceTransformer, models
import torch
from useb import run_on
dataset = 'askubuntu'
model_name_or_path = f'kwang2049/TSDAE-{dataset}'
model = SentenceTransformer(model_name_or_path)
model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
@torch.no_grad()
def semb_fn(sentences) -> torch.Tensor:
    return torch.Tensor(model.encode(sentences, show_progress_bar=False))
result = run_on(
    dataset,
    semb_fn=semb_fn,
    eval_type='test',
    data_eval_path='data-eval'
)
 ```

 ## Training
 Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.

 ## Cite & Authors
 If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
```bibtex
@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna",
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}
```


# kwang2049/TSDAE-twitterpara2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on twitterpara in an unsupervised manner. Training procedure of this model:
 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
 2. Unsupervised training on twitterpara with the TSDAE objective;

 The pooling method is CLS-pooling.

 ## Usage
 To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
 ```bash
 pip install sentence-transformers
 ```
 And then load the model and use it to encode sentences:
 ```python
 from sentence_transformers import SentenceTransformer, models
 dataset = 'twitterpara'
 model_name_or_path = f'kwang2049/TSDAE-{dataset}'
 model = SentenceTransformer(model_name_or_path)
 model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
 sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
 ```
 ## Evaluation
 To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
 ```bash
 pip install useb  # Or git clone and pip install .
 python -m useb.downloading all  # Download both training and evaluation data
 ```
 And then do the evaluation:
 ```python
 from sentence_transformers import SentenceTransformer, models
import torch
from useb import run_on
dataset = 'twitterpara'
model_name_or_path = f'kwang2049/TSDAE-{dataset}'
model = SentenceTransformer(model_name_or_path)
model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
@torch.no_grad()
def semb_fn(sentences) -> torch.Tensor:
    return torch.Tensor(model.encode(sentences, show_progress_bar=False))
result = run_on(
    dataset,
    semb_fn=semb_fn,
    eval_type='test',
    data_eval_path='data-eval'
)
 ```

 ## Training
 Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.

 ## Cite & Authors
 If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
```bibtex
@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna",
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}
```


---
tags:
- conversational
---

# Rick Sanchez DialoGPT Model