modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
DeepChem/ChemBERTa-77M-MTR
|
[
"pytorch",
"roberta",
"transformers"
] | null |
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| 7,169 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0528
- Train Accuracy: 0.9846
- Validation Loss: 0.6565
- Validation Accuracy: 0.8284
- Train Matthews Correlation: 0.5781
- Epoch: 4
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 4.3005903050037654e-05, 'decay_steps': 665, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5140 | 0.7592 | 0.4282 | 0.8159 | 0.5488 | 0 |
| 0.2880 | 0.8857 | 0.4530 | 0.8284 | 0.5785 | 1 |
| 0.1597 | 0.9449 | 0.4893 | 0.8226 | 0.5632 | 2 |
| 0.0897 | 0.9698 | 0.5782 | 0.8332 | 0.5920 | 3 |
| 0.0528 | 0.9846 | 0.6565 | 0.8284 | 0.5781 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DeepESP/gpt2-spanish-medium
|
[
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] |
text-generation
|
{
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| 340 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-3
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5675
- Train Accuracy: 0.7211
- Validation Loss: 0.5168
- Validation Accuracy: 0.7795
- Train Matthews Correlation: 0.4416
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2.0487381772212153e-05, 'decay_steps': 532, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5675 | 0.7211 | 0.5168 | 0.7795 | 0.4416 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] |
feature-extraction
|
{
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],
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| 1,614 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-4
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1922
- Train Accuracy: 0.9310
- Validation Loss: 0.5247
- Validation Accuracy: 0.8303
- Train Matthews Correlation: 0.5830
- Epoch: 4
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 9.858432402113778e-06, 'decay_steps': 665, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.6040 | 0.7007 | 0.5308 | 0.7191 | 0.2443 | 0 |
| 0.4246 | 0.8114 | 0.4163 | 0.8188 | 0.5525 | 1 |
| 0.2897 | 0.8848 | 0.5054 | 0.8121 | 0.5343 | 2 |
| 0.2224 | 0.9146 | 0.4868 | 0.8274 | 0.5754 | 3 |
| 0.1922 | 0.9310 | 0.5247 | 0.8303 | 0.5830 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DeepPavlov/distilrubert-tiny-cased-conversational
|
[
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null |
{
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| 5,993 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-5
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-5
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5040
- Train Accuracy: 0.7576
- Validation Loss: 0.4532
- Validation Accuracy: 0.7996
- Train Matthews Correlation: 0.4996
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3.189326671791535e-05, 'decay_steps': 399, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5040 | 0.7576 | 0.4532 | 0.7996 | 0.4996 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DeepPavlov/roberta-large-winogrande
|
[
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:winogrande",
"arxiv:1907.11692",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
"model_type": "roberta",
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}
| 348 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.56 +/- 17.89
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Deniskin/emailer_medium_300
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
| 14 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="huanvo88/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Deniskin/essays_small_2000
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-9
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-9
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5360
- Train Accuracy: 0.7339
- Validation Loss: 0.4551
- Validation Accuracy: 0.8188
- Train Matthews Correlation: 0.5521
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2.049971462512023e-05, 'decay_steps': 532, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5360 | 0.7339 | 0.4551 | 0.8188 | 0.5521 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Deniskin/essays_small_2000i
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- image-to-image
---
# These are a set of VAEEncoder.mlmodelc bundles that will enable the image2image feature with Mochi Diffusion 3.2 when using incompatible older CoreML converted models.<br>
## They are provided in a single zip file, containing five VAEEncoder.mlmodelc files, noted by their file names for use as follows:
**- for split_einsim 515x515 SD-1.5 type models**<br>
**- for original 512x512 SD-1.5 type models**<br>
**- for original 512x768 SD-1.5 type models**<br>
**- for original 768x512 SD-1.5 type models**<br>
**- for original 768x768 SD-1.5 type models**
### They should enable image2image for ANY model trained or merged from the Stable Diffusion v1.5 base model. They will not work with models derived from Stable Diffusion v2.0 or v2.1 base models.<br><br>
### INSTRUCTIONS
- If the model folder that you are upgrading already has a VAEEncoder.mlmodelc file inside, rename that file to VAEEncoder.mlmodelc.bak first, to keep it in case you want to teturn to it later. It is fine to leave it in the folder. It only needs to be renamed.
- Copy the appropriate new VAEEncoder.mlmodelc to the model folder of the model you are upgrading. The new VAEEncoder.mlmodelc file needs to match the original model in size and compute unit type.
- Examples: If the model is an original 512x768 model, copy the file named NEW-ORIG-512x768-VAEEncoder.mlmodelc. If the model is a split_einsum 512x512 model, copy the file named NEW-SPLIT-512x512-VAEEncoder.mlmodelc.
- After copying the appropriate VAE file, you must rename it to VAEEncoder.mlmodelc. That means removing the NEW-ORIG-512x768-, NEW-SPLIT-512x512-, etc., from the copied file's name.
- The upgraded model should now work with both image2image and text2image.
- IMPORTANT: Remeber that in image2image, your STARTING IMAGE must always be the same size as your model. A 512x512 model will only work with a 512x512 starting image, a 768x512 model will only work with a 786x512 starting image., etc
- Both the model and the starting image sizes are listed as WIDTH x HEIGHT. 512x768 is a "portrait" orientation. 768x512 is a "landscape" oriemtation.
- Please report any issues you encounter so that I can try resolve them.
|
Denny29/DialoGPT-medium-asunayuuki
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 9 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-10
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-10
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1630
- Train Accuracy: 0.9456
- Validation Loss: 0.4945
- Validation Accuracy: 0.8274
- Train Matthews Correlation: 0.5767
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1.4055261996332381e-05, 'decay_steps': 2136, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.4915 | 0.7741 | 0.4149 | 0.8159 | 0.5452 | 0 |
| 0.2792 | 0.8922 | 0.4300 | 0.8274 | 0.5756 | 1 |
| 0.1630 | 0.9456 | 0.4945 | 0.8274 | 0.5767 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DeskDown/MarianMixFT_en-ja
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 9 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
- chrf
model-index:
- name: es_fi_orig_quy
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. -->
# es_fi_orig_quy
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-fi](https://huggingface.co/Helsinki-NLP/opus-mt-es-fi) on the AmericasNLP2023 Original Task's dataset for Quechua.
It achieves the following results on the evaluation set:
- Loss: 0.5414
- Bleu: 2.4777
- Chrf: 35.3072
- Gen Len: 33.2767
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|:-------:|
| 0.9069 | 0.06 | 500 | 0.7130 | 0.7729 | 17.9749 | 44.4678 |
| 0.539 | 0.12 | 1000 | 0.6747 | 0.7711 | 20.5427 | 40.504 |
| 0.4832 | 0.18 | 1500 | 0.6461 | 0.8605 | 21.6266 | 40.7505 |
| 0.45 | 0.24 | 2000 | 0.6304 | 0.9152 | 22.8153 | 35.6489 |
| 0.4192 | 0.31 | 2500 | 0.6239 | 0.9358 | 23.6102 | 34.1469 |
| 0.4061 | 0.37 | 3000 | 0.6108 | 1.183 | 25.1705 | 34.0684 |
| 0.3864 | 0.43 | 3500 | 0.5939 | 1.2459 | 26.0102 | 33.6217 |
| 0.3762 | 0.49 | 4000 | 0.5876 | 1.3822 | 26.3468 | 33.7304 |
| 0.3669 | 0.55 | 4500 | 0.5796 | 1.6164 | 26.7314 | 32.5785 |
| 0.3591 | 0.61 | 5000 | 0.5795 | 1.2637 | 26.7829 | 31.6338 |
| 0.3471 | 0.67 | 5500 | 0.5758 | 1.6099 | 28.2004 | 33.7042 |
| 0.3368 | 0.73 | 6000 | 0.5678 | 1.5843 | 28.6728 | 36.1087 |
| 0.332 | 0.8 | 6500 | 0.5623 | 1.4817 | 28.141 | 33.2062 |
| 0.3305 | 0.86 | 7000 | 0.5592 | 1.6164 | 28.5089 | 33.7968 |
| 0.325 | 0.92 | 7500 | 0.5554 | 1.7755 | 29.4873 | 33.659 |
| 0.3148 | 0.98 | 8000 | 0.5534 | 1.4771 | 29.2475 | 32.9517 |
| 0.306 | 1.04 | 8500 | 0.5503 | 1.6723 | 30.3463 | 34.3541 |
| 0.2999 | 1.1 | 9000 | 0.5498 | 1.7604 | 30.4026 | 34.3531 |
| 0.2979 | 1.16 | 9500 | 0.5470 | 1.6658 | 30.6424 | 34.3038 |
| 0.2875 | 1.22 | 10000 | 0.5447 | 1.6232 | 30.6178 | 32.7374 |
| 0.2885 | 1.29 | 10500 | 0.5468 | 1.4892 | 30.536 | 33.5704 |
| 0.2914 | 1.35 | 11000 | 0.5397 | 1.7088 | 30.4463 | 32.665 |
| 0.2873 | 1.41 | 11500 | 0.5385 | 1.4307 | 31.359 | 32.6006 |
| 0.2821 | 1.47 | 12000 | 0.5391 | 1.6375 | 31.2201 | 33.7616 |
| 0.2813 | 1.53 | 12500 | 0.5343 | 1.7664 | 31.2716 | 32.8048 |
| 0.2795 | 1.59 | 13000 | 0.5390 | 1.6373 | 31.4692 | 33.2354 |
| 0.2766 | 1.65 | 13500 | 0.5333 | 1.6973 | 31.9842 | 34.3099 |
| 0.2726 | 1.71 | 14000 | 0.5342 | 1.9934 | 32.0932 | 34.7022 |
| 0.2696 | 1.77 | 14500 | 0.5316 | 1.7776 | 32.1299 | 33.3531 |
| 0.2732 | 1.84 | 15000 | 0.5274 | 1.5068 | 32.156 | 33.7404 |
| 0.2732 | 1.9 | 15500 | 0.5281 | 1.8983 | 32.123 | 32.7274 |
| 0.2695 | 1.96 | 16000 | 0.5285 | 1.7102 | 32.3456 | 34.7596 |
| 0.2609 | 2.02 | 16500 | 0.5299 | 1.8289 | 32.4311 | 32.169 |
| 0.2566 | 2.08 | 17000 | 0.5259 | 2.3307 | 32.4029 | 32.6801 |
| 0.2546 | 2.14 | 17500 | 0.5262 | 1.7847 | 32.4796 | 32.5594 |
| 0.2575 | 2.2 | 18000 | 0.5278 | 1.7378 | 32.6314 | 33.5493 |
| 0.2537 | 2.26 | 18500 | 0.5256 | 1.8299 | 32.4598 | 32.7515 |
| 0.2496 | 2.33 | 19000 | 0.5269 | 1.4243 | 32.4538 | 32.6368 |
| 0.2492 | 2.39 | 19500 | 0.5264 | 1.7878 | 32.4759 | 32.7958 |
| 0.2511 | 2.45 | 20000 | 0.5230 | 1.7877 | 32.7906 | 33.0956 |
| 0.2502 | 2.51 | 20500 | 0.5211 | 1.8735 | 33.2824 | 34.0503 |
| 0.2468 | 2.57 | 21000 | 0.5227 | 2.0487 | 33.1002 | 32.9849 |
| 0.2445 | 2.63 | 21500 | 0.5206 | 2.0547 | 33.3626 | 32.7867 |
| 0.2455 | 2.69 | 22000 | 0.5202 | 1.968 | 33.0031 | 32.9658 |
| 0.2489 | 2.75 | 22500 | 0.5195 | 2.0145 | 32.9789 | 32.5926 |
| 0.2385 | 2.82 | 23000 | 0.5180 | 2.0486 | 33.2563 | 33.337 |
| 0.2421 | 2.88 | 23500 | 0.5162 | 2.1097 | 33.0014 | 33.4105 |
| 0.2414 | 2.94 | 24000 | 0.5148 | 1.9033 | 33.1448 | 33.4175 |
| 0.2445 | 3.0 | 24500 | 0.5207 | 1.8791 | 32.9875 | 32.8018 |
| 0.2329 | 3.06 | 25000 | 0.5216 | 2.2176 | 33.6869 | 33.8642 |
| 0.2289 | 3.12 | 25500 | 0.5160 | 1.8891 | 33.3863 | 32.2857 |
| 0.2336 | 3.18 | 26000 | 0.5144 | 2.0862 | 33.4626 | 33.6097 |
| 0.2279 | 3.24 | 26500 | 0.5163 | 2.1618 | 33.7683 | 32.8773 |
| 0.2308 | 3.3 | 27000 | 0.5188 | 2.3628 | 34.06 | 33.7133 |
| 0.2313 | 3.37 | 27500 | 0.5188 | 1.6833 | 33.9596 | 33.7032 |
| 0.2308 | 3.43 | 28000 | 0.5165 | 2.1626 | 33.651 | 33.4507 |
| 0.2238 | 3.49 | 28500 | 0.5162 | 1.8525 | 33.2934 | 32.3511 |
| 0.2263 | 3.55 | 29000 | 0.5163 | 2.0621 | 33.4168 | 32.8561 |
| 0.2265 | 3.61 | 29500 | 0.5134 | 2.361 | 34.0822 | 33.0272 |
| 0.2256 | 3.67 | 30000 | 0.5128 | 2.289 | 33.938 | 33.6016 |
| 0.2277 | 3.73 | 30500 | 0.5153 | 1.8415 | 33.7406 | 34.1761 |
| 0.227 | 3.79 | 31000 | 0.5130 | 2.0473 | 33.2757 | 33.3571 |
| 0.2296 | 3.86 | 31500 | 0.5140 | 1.9687 | 33.6899 | 33.1459 |
| 0.2256 | 3.92 | 32000 | 0.5145 | 2.1504 | 33.8796 | 33.6278 |
| 0.2279 | 3.98 | 32500 | 0.5101 | 1.802 | 33.982 | 33.2414 |
| 0.2172 | 4.04 | 33000 | 0.5180 | 2.3048 | 34.348 | 33.167 |
| 0.2135 | 4.1 | 33500 | 0.5154 | 2.2467 | 34.1301 | 33.0382 |
| 0.2154 | 4.16 | 34000 | 0.5159 | 1.9384 | 34.2688 | 33.5855 |
| 0.2169 | 4.22 | 34500 | 0.5177 | 2.3588 | 34.2068 | 33.3954 |
| 0.216 | 4.28 | 35000 | 0.5160 | 2.0115 | 33.7004 | 33.3612 |
| 0.216 | 4.35 | 35500 | 0.5139 | 1.5035 | 33.8102 | 32.6217 |
| 0.2139 | 4.41 | 36000 | 0.5126 | 2.359 | 34.1215 | 33.6761 |
| 0.2168 | 4.47 | 36500 | 0.5105 | 2.1176 | 33.7094 | 32.5352 |
| 0.2155 | 4.53 | 37000 | 0.5144 | 2.1564 | 33.3474 | 32.1881 |
| 0.2156 | 4.59 | 37500 | 0.5118 | 2.0 | 33.9916 | 32.9537 |
| 0.2135 | 4.65 | 38000 | 0.5124 | 2.3122 | 34.095 | 32.667 |
| 0.2138 | 4.71 | 38500 | 0.5115 | 1.9716 | 33.9118 | 32.5644 |
| 0.2107 | 4.77 | 39000 | 0.5145 | 2.2866 | 34.4234 | 33.2153 |
| 0.2083 | 4.83 | 39500 | 0.5122 | 2.0444 | 34.6246 | 33.8491 |
| 0.2141 | 4.9 | 40000 | 0.5125 | 2.0011 | 34.6505 | 33.7384 |
| 0.2087 | 4.96 | 40500 | 0.5149 | 2.3504 | 34.1777 | 33.339 |
| 0.2113 | 5.02 | 41000 | 0.5160 | 2.2727 | 34.0993 | 33.4779 |
| 0.2019 | 5.08 | 41500 | 0.5143 | 2.3324 | 34.613 | 33.4376 |
| 0.206 | 5.14 | 42000 | 0.5154 | 2.1462 | 34.758 | 33.4437 |
| 0.2013 | 5.2 | 42500 | 0.5175 | 2.3562 | 34.2422 | 32.3511 |
| 0.2032 | 5.26 | 43000 | 0.5160 | 2.3984 | 34.645 | 32.5604 |
| 0.2068 | 5.32 | 43500 | 0.5159 | 2.2794 | 34.4269 | 32.8742 |
| 0.204 | 5.39 | 44000 | 0.5132 | 2.3506 | 34.7618 | 33.9487 |
| 0.2028 | 5.45 | 44500 | 0.5153 | 2.4088 | 34.9393 | 33.5131 |
| 0.2004 | 5.51 | 45000 | 0.5172 | 2.4071 | 34.3887 | 33.1006 |
| 0.2021 | 5.57 | 45500 | 0.5106 | 2.0544 | 34.1071 | 33.7404 |
| 0.2058 | 5.63 | 46000 | 0.5126 | 1.9098 | 34.6397 | 33.7807 |
| 0.2015 | 5.69 | 46500 | 0.5115 | 2.3614 | 34.25 | 32.8571 |
| 0.2038 | 5.75 | 47000 | 0.5094 | 2.0321 | 34.1132 | 32.6932 |
| 0.2021 | 5.81 | 47500 | 0.5147 | 2.2524 | 34.7776 | 33.4276 |
| 0.2041 | 5.88 | 48000 | 0.5154 | 2.2605 | 34.323 | 32.9769 |
| 0.2009 | 5.94 | 48500 | 0.5137 | 2.2593 | 34.8502 | 33.499 |
| 0.2025 | 6.0 | 49000 | 0.5103 | 2.3048 | 34.4908 | 33.4789 |
| 0.193 | 6.06 | 49500 | 0.5170 | 2.1498 | 34.3672 | 33.3612 |
| 0.193 | 6.12 | 50000 | 0.5151 | 2.5941 | 34.9481 | 33.336 |
| 0.1936 | 6.18 | 50500 | 0.5169 | 2.2235 | 34.6028 | 32.5744 |
| 0.196 | 6.24 | 51000 | 0.5162 | 2.2306 | 34.6904 | 32.4517 |
| 0.1949 | 6.3 | 51500 | 0.5129 | 2.3903 | 35.1637 | 33.2113 |
| 0.194 | 6.36 | 52000 | 0.5134 | 2.1318 | 34.7602 | 33.5885 |
| 0.1941 | 6.43 | 52500 | 0.5152 | 2.3425 | 34.4858 | 32.9135 |
| 0.1935 | 6.49 | 53000 | 0.5145 | 2.6653 | 35.0061 | 32.8984 |
| 0.191 | 6.55 | 53500 | 0.5134 | 2.2906 | 34.8134 | 33.5412 |
| 0.1971 | 6.61 | 54000 | 0.5188 | 2.1944 | 34.8164 | 33.1378 |
| 0.1951 | 6.67 | 54500 | 0.5130 | 2.4214 | 34.5857 | 32.8249 |
| 0.1921 | 6.73 | 55000 | 0.5137 | 2.2722 | 34.8066 | 32.6298 |
| 0.1928 | 6.79 | 55500 | 0.5136 | 2.316 | 34.7798 | 34.0654 |
| 0.1945 | 6.85 | 56000 | 0.5114 | 2.1866 | 34.8094 | 32.6922 |
| 0.1936 | 6.92 | 56500 | 0.5133 | 2.3216 | 34.6351 | 33.0805 |
| 0.1957 | 6.98 | 57000 | 0.5123 | 2.2095 | 34.9906 | 33.8893 |
| 0.1888 | 7.04 | 57500 | 0.5172 | 2.2208 | 34.772 | 33.334 |
| 0.1842 | 7.1 | 58000 | 0.5167 | 1.9387 | 34.6291 | 32.6278 |
| 0.1866 | 7.16 | 58500 | 0.5154 | 2.1733 | 35.019 | 32.8179 |
| 0.1869 | 7.22 | 59000 | 0.5187 | 2.0185 | 34.7933 | 33.4386 |
| 0.1851 | 7.28 | 59500 | 0.5158 | 2.2565 | 34.9985 | 33.3934 |
| 0.1846 | 7.34 | 60000 | 0.5182 | 2.087 | 35.3032 | 33.6207 |
| 0.1844 | 7.41 | 60500 | 0.5163 | 2.2813 | 34.9024 | 33.1127 |
| 0.1888 | 7.47 | 61000 | 0.5140 | 2.3832 | 35.0925 | 33.4527 |
| 0.1883 | 7.53 | 61500 | 0.5140 | 2.4389 | 35.3911 | 33.0121 |
| 0.1866 | 7.59 | 62000 | 0.5186 | 2.1768 | 35.0476 | 33.331 |
| 0.1854 | 7.65 | 62500 | 0.5182 | 2.1011 | 34.9619 | 33.7274 |
| 0.1895 | 7.71 | 63000 | 0.5166 | 2.2003 | 35.1485 | 33.2384 |
| 0.1907 | 7.77 | 63500 | 0.5167 | 2.1704 | 34.7886 | 33.1499 |
| 0.1868 | 7.83 | 64000 | 0.5177 | 2.4642 | 35.3304 | 33.494 |
| 0.1845 | 7.89 | 64500 | 0.5185 | 1.8674 | 35.2574 | 33.5372 |
| 0.1853 | 7.96 | 65000 | 0.5139 | 2.2624 | 35.2208 | 33.5604 |
| 0.1843 | 8.02 | 65500 | 0.5198 | 2.3718 | 35.0122 | 33.2485 |
| 0.1792 | 8.08 | 66000 | 0.5195 | 2.2219 | 34.6203 | 33.008 |
| 0.1813 | 8.14 | 66500 | 0.5213 | 2.2907 | 34.9642 | 33.4889 |
| 0.1783 | 8.2 | 67000 | 0.5181 | 2.4924 | 35.0622 | 32.996 |
| 0.1793 | 8.26 | 67500 | 0.5177 | 2.521 | 35.1152 | 33.3652 |
| 0.1815 | 8.32 | 68000 | 0.5197 | 2.186 | 34.9455 | 32.8964 |
| 0.1807 | 8.38 | 68500 | 0.5203 | 2.0672 | 34.8115 | 32.9658 |
| 0.1793 | 8.45 | 69000 | 0.5209 | 1.8363 | 35.0798 | 33.0674 |
| 0.1799 | 8.51 | 69500 | 0.5196 | 2.3825 | 35.3234 | 33.9738 |
| 0.179 | 8.57 | 70000 | 0.5219 | 2.275 | 34.7929 | 32.8239 |
| 0.1783 | 8.63 | 70500 | 0.5197 | 2.3194 | 35.0171 | 33.6288 |
| 0.1813 | 8.69 | 71000 | 0.5171 | 2.2258 | 35.1631 | 33.3873 |
| 0.1809 | 8.75 | 71500 | 0.5190 | 2.5308 | 35.1173 | 33.34 |
| 0.1805 | 8.81 | 72000 | 0.5171 | 1.9332 | 35.0571 | 32.9678 |
| 0.1786 | 8.87 | 72500 | 0.5157 | 2.2382 | 35.0783 | 32.9406 |
| 0.1811 | 8.94 | 73000 | 0.5201 | 2.2583 | 35.0626 | 33.4859 |
| 0.1804 | 9.0 | 73500 | 0.5177 | 2.278 | 34.693 | 33.1942 |
| 0.1745 | 9.06 | 74000 | 0.5210 | 2.2431 | 35.1854 | 33.6841 |
| 0.1731 | 9.12 | 74500 | 0.5219 | 2.3838 | 35.0609 | 33.7626 |
| 0.1745 | 9.18 | 75000 | 0.5205 | 2.3049 | 35.049 | 33.2394 |
| 0.1733 | 9.24 | 75500 | 0.5239 | 2.5352 | 35.2192 | 33.8048 |
| 0.1749 | 9.3 | 76000 | 0.5216 | 2.3165 | 34.7239 | 33.1087 |
| 0.1726 | 9.36 | 76500 | 0.5214 | 2.4599 | 34.9216 | 33.4487 |
| 0.175 | 9.42 | 77000 | 0.5245 | 2.5992 | 35.0381 | 33.1167 |
| 0.1753 | 9.49 | 77500 | 0.5248 | 2.2165 | 34.9097 | 33.1529 |
| 0.1751 | 9.55 | 78000 | 0.5208 | 2.7439 | 35.1761 | 33.2123 |
| 0.1751 | 9.61 | 78500 | 0.5207 | 2.5099 | 35.1488 | 32.8642 |
| 0.1729 | 9.67 | 79000 | 0.5239 | 2.534 | 35.0639 | 33.3682 |
| 0.1763 | 9.73 | 79500 | 0.5228 | 2.3696 | 34.5217 | 32.669 |
| 0.1719 | 9.79 | 80000 | 0.5241 | 2.4858 | 34.8637 | 33.2716 |
| 0.1753 | 9.85 | 80500 | 0.5204 | 2.3656 | 34.9314 | 32.7535 |
| 0.177 | 9.91 | 81000 | 0.5197 | 2.2903 | 35.0575 | 33.0634 |
| 0.1752 | 9.98 | 81500 | 0.5238 | 2.1415 | 34.9044 | 33.0392 |
| 0.1695 | 10.04 | 82000 | 0.5237 | 2.1674 | 34.9343 | 33.0926 |
| 0.1696 | 10.1 | 82500 | 0.5254 | 2.5423 | 34.91 | 33.0322 |
| 0.1667 | 10.16 | 83000 | 0.5278 | 1.9003 | 34.8527 | 32.7414 |
| 0.1701 | 10.22 | 83500 | 0.5283 | 2.4784 | 34.913 | 32.832 |
| 0.1689 | 10.28 | 84000 | 0.5232 | 2.492 | 35.2142 | 33.2374 |
| 0.1706 | 10.34 | 84500 | 0.5262 | 2.5049 | 35.2206 | 33.2817 |
| 0.1698 | 10.4 | 85000 | 0.5239 | 2.3618 | 34.9215 | 32.8119 |
| 0.1717 | 10.47 | 85500 | 0.5265 | 2.4575 | 34.9778 | 32.6026 |
| 0.1678 | 10.53 | 86000 | 0.5249 | 2.3199 | 34.8019 | 33.3028 |
| 0.1703 | 10.59 | 86500 | 0.5219 | 2.4114 | 34.9716 | 33.3089 |
| 0.1708 | 10.65 | 87000 | 0.5239 | 2.0047 | 34.8404 | 32.7777 |
| 0.1704 | 10.71 | 87500 | 0.5253 | 2.5167 | 35.2791 | 33.4588 |
| 0.1692 | 10.77 | 88000 | 0.5242 | 2.5192 | 35.1455 | 33.006 |
| 0.172 | 10.83 | 88500 | 0.5218 | 2.2699 | 35.1763 | 33.326 |
| 0.1712 | 10.89 | 89000 | 0.5246 | 2.332 | 35.2355 | 33.1247 |
| 0.1669 | 10.95 | 89500 | 0.5227 | 2.2954 | 35.279 | 33.2746 |
| 0.1663 | 11.02 | 90000 | 0.5250 | 2.4965 | 35.2613 | 33.3421 |
| 0.1653 | 11.08 | 90500 | 0.5278 | 2.324 | 35.1194 | 33.328 |
| 0.1656 | 11.14 | 91000 | 0.5257 | 2.2204 | 35.0059 | 33.1922 |
| 0.1643 | 11.2 | 91500 | 0.5261 | 2.2693 | 34.9625 | 33.2324 |
| 0.1654 | 11.26 | 92000 | 0.5271 | 2.5063 | 35.3606 | 33.4738 |
| 0.1634 | 11.32 | 92500 | 0.5255 | 2.303 | 35.0965 | 33.4688 |
| 0.1653 | 11.38 | 93000 | 0.5259 | 2.4993 | 34.8866 | 33.164 |
| 0.1662 | 11.44 | 93500 | 0.5257 | 2.0667 | 35.0151 | 33.505 |
| 0.1632 | 11.51 | 94000 | 0.5254 | 2.5568 | 35.0299 | 33.3883 |
| 0.1652 | 11.57 | 94500 | 0.5278 | 2.5814 | 34.992 | 32.5664 |
| 0.1639 | 11.63 | 95000 | 0.5304 | 2.41 | 34.9007 | 33.161 |
| 0.1622 | 11.69 | 95500 | 0.5247 | 2.4703 | 34.993 | 32.9457 |
| 0.1673 | 11.75 | 96000 | 0.5268 | 2.3927 | 35.0372 | 33.2938 |
| 0.1677 | 11.81 | 96500 | 0.5281 | 2.6505 | 34.8506 | 32.494 |
| 0.165 | 11.87 | 97000 | 0.5303 | 2.4796 | 35.0554 | 33.2183 |
| 0.1662 | 11.93 | 97500 | 0.5254 | 2.5202 | 35.1451 | 33.5986 |
| 0.1658 | 12.0 | 98000 | 0.5277 | 2.2551 | 35.2497 | 33.4869 |
| 0.1603 | 12.06 | 98500 | 0.5288 | 2.5281 | 35.103 | 33.338 |
| 0.1614 | 12.12 | 99000 | 0.5304 | 2.3583 | 35.3678 | 33.008 |
| 0.1585 | 12.18 | 99500 | 0.5289 | 2.1341 | 34.9736 | 33.2284 |
| 0.1616 | 12.24 | 100000 | 0.5299 | 2.2275 | 35.3401 | 33.5322 |
| 0.1592 | 12.3 | 100500 | 0.5268 | 2.1927 | 35.1932 | 33.5322 |
| 0.1606 | 12.36 | 101000 | 0.5315 | 2.2762 | 34.8855 | 33.2002 |
| 0.1617 | 12.42 | 101500 | 0.5304 | 1.919 | 35.2037 | 33.4688 |
| 0.1617 | 12.48 | 102000 | 0.5276 | 2.3777 | 35.0109 | 33.0724 |
| 0.1618 | 12.55 | 102500 | 0.5320 | 2.4878 | 35.1188 | 33.0674 |
| 0.163 | 12.61 | 103000 | 0.5296 | 2.6783 | 35.09 | 33.3632 |
| 0.1601 | 12.67 | 103500 | 0.5295 | 2.5972 | 34.8251 | 32.7837 |
| 0.1625 | 12.73 | 104000 | 0.5308 | 2.5953 | 34.816 | 33.2223 |
| 0.1628 | 12.79 | 104500 | 0.5291 | 2.6627 | 34.9021 | 33.1046 |
| 0.1594 | 12.85 | 105000 | 0.5320 | 2.185 | 35.2024 | 33.499 |
| 0.1641 | 12.91 | 105500 | 0.5282 | 2.2681 | 35.1077 | 33.2918 |
| 0.1632 | 12.97 | 106000 | 0.5267 | 2.6085 | 34.9718 | 33.0453 |
| 0.1592 | 13.04 | 106500 | 0.5314 | 2.6758 | 34.8983 | 33.1539 |
| 0.1593 | 13.1 | 107000 | 0.5336 | 2.431 | 34.8698 | 33.0624 |
| 0.1567 | 13.16 | 107500 | 0.5336 | 2.4034 | 34.7205 | 32.9245 |
| 0.1613 | 13.22 | 108000 | 0.5319 | 2.4685 | 35.1304 | 33.0493 |
| 0.1572 | 13.28 | 108500 | 0.5319 | 2.701 | 34.8217 | 33.0201 |
| 0.1586 | 13.34 | 109000 | 0.5324 | 2.6884 | 34.9147 | 32.8109 |
| 0.1565 | 13.4 | 109500 | 0.5327 | 1.9804 | 35.0132 | 33.2978 |
| 0.1578 | 13.46 | 110000 | 0.5310 | 2.4774 | 35.0983 | 33.4085 |
| 0.1593 | 13.53 | 110500 | 0.5310 | 2.5479 | 35.4449 | 33.7062 |
| 0.1559 | 13.59 | 111000 | 0.5338 | 2.3537 | 34.9088 | 33.0433 |
| 0.1592 | 13.65 | 111500 | 0.5321 | 2.2951 | 34.9337 | 32.7264 |
| 0.1599 | 13.71 | 112000 | 0.5323 | 2.1814 | 35.0657 | 33.4316 |
| 0.1549 | 13.77 | 112500 | 0.5343 | 2.4706 | 35.0882 | 33.4628 |
| 0.1583 | 13.83 | 113000 | 0.5296 | 2.5468 | 35.0331 | 32.9386 |
| 0.1579 | 13.89 | 113500 | 0.5303 | 2.6679 | 35.309 | 32.996 |
| 0.1582 | 13.95 | 114000 | 0.5323 | 2.4064 | 35.1723 | 33.2555 |
| 0.1563 | 14.01 | 114500 | 0.5348 | 2.4503 | 35.111 | 33.4195 |
| 0.1517 | 14.08 | 115000 | 0.5355 | 2.5443 | 35.3299 | 33.5372 |
| 0.1537 | 14.14 | 115500 | 0.5350 | 2.354 | 34.975 | 33.3089 |
| 0.1549 | 14.2 | 116000 | 0.5354 | 2.6566 | 34.9697 | 33.3561 |
| 0.154 | 14.26 | 116500 | 0.5342 | 2.5811 | 34.9067 | 33.338 |
| 0.1562 | 14.32 | 117000 | 0.5334 | 2.8386 | 35.1147 | 33.0634 |
| 0.1555 | 14.38 | 117500 | 0.5356 | 2.621 | 35.235 | 33.332 |
| 0.1551 | 14.44 | 118000 | 0.5345 | 2.6434 | 34.9156 | 32.9759 |
| 0.1553 | 14.5 | 118500 | 0.5343 | 2.8222 | 35.0846 | 33.336 |
| 0.1548 | 14.57 | 119000 | 0.5341 | 2.5853 | 34.8771 | 32.9306 |
| 0.1545 | 14.63 | 119500 | 0.5334 | 2.608 | 35.1324 | 33.1569 |
| 0.1565 | 14.69 | 120000 | 0.5333 | 2.3449 | 35.2465 | 33.3008 |
| 0.1578 | 14.75 | 120500 | 0.5334 | 2.4331 | 35.0261 | 33.1268 |
| 0.1565 | 14.81 | 121000 | 0.5338 | 2.5126 | 35.1174 | 33.0855 |
| 0.1576 | 14.87 | 121500 | 0.5319 | 2.643 | 35.2291 | 33.3461 |
| 0.1535 | 14.93 | 122000 | 0.5318 | 2.6643 | 35.0675 | 33.0835 |
| 0.1544 | 14.99 | 122500 | 0.5328 | 2.7986 | 35.221 | 33.4517 |
| 0.1527 | 15.06 | 123000 | 0.5364 | 2.433 | 35.3005 | 33.6217 |
| 0.1531 | 15.12 | 123500 | 0.5367 | 2.4569 | 35.1446 | 33.2706 |
| 0.1512 | 15.18 | 124000 | 0.5369 | 2.5325 | 34.9011 | 32.994 |
| 0.1511 | 15.24 | 124500 | 0.5378 | 2.4247 | 35.0081 | 32.9517 |
| 0.1494 | 15.3 | 125000 | 0.5354 | 2.6675 | 35.2802 | 33.3451 |
| 0.1513 | 15.36 | 125500 | 0.5380 | 2.706 | 35.3454 | 32.9779 |
| 0.1556 | 15.42 | 126000 | 0.5349 | 2.6606 | 35.2983 | 33.2384 |
| 0.152 | 15.48 | 126500 | 0.5374 | 2.7716 | 35.1135 | 33.1429 |
| 0.1539 | 15.54 | 127000 | 0.5357 | 2.4622 | 35.1187 | 33.0302 |
| 0.1522 | 15.61 | 127500 | 0.5376 | 2.7958 | 35.2546 | 33.1489 |
| 0.155 | 15.67 | 128000 | 0.5366 | 2.4966 | 34.9292 | 32.7143 |
| 0.1523 | 15.73 | 128500 | 0.5347 | 2.5537 | 34.9162 | 33.1046 |
| 0.1554 | 15.79 | 129000 | 0.5349 | 2.7393 | 35.1069 | 33.2082 |
| 0.1527 | 15.85 | 129500 | 0.5361 | 2.3551 | 35.2359 | 33.3893 |
| 0.1532 | 15.91 | 130000 | 0.5373 | 2.3327 | 35.0024 | 33.0141 |
| 0.1505 | 15.97 | 130500 | 0.5386 | 2.4325 | 34.8422 | 32.827 |
| 0.1519 | 16.03 | 131000 | 0.5370 | 2.7083 | 35.196 | 33.0744 |
| 0.1492 | 16.1 | 131500 | 0.5382 | 2.4176 | 35.2017 | 33.1509 |
| 0.1502 | 16.16 | 132000 | 0.5382 | 1.9908 | 34.9532 | 33.1026 |
| 0.1505 | 16.22 | 132500 | 0.5378 | 2.3938 | 34.8257 | 33.1398 |
| 0.1497 | 16.28 | 133000 | 0.5394 | 2.5119 | 35.1199 | 33.3139 |
| 0.15 | 16.34 | 133500 | 0.5381 | 2.5172 | 35.0647 | 32.9718 |
| 0.1501 | 16.4 | 134000 | 0.5387 | 2.6483 | 35.1301 | 33.3139 |
| 0.1496 | 16.46 | 134500 | 0.5387 | 2.3792 | 35.0936 | 33.1358 |
| 0.1507 | 16.52 | 135000 | 0.5383 | 2.2621 | 35.3078 | 33.3763 |
| 0.1493 | 16.59 | 135500 | 0.5389 | 2.744 | 35.3558 | 33.2525 |
| 0.1494 | 16.65 | 136000 | 0.5386 | 2.3218 | 35.0754 | 33.2425 |
| 0.1516 | 16.71 | 136500 | 0.5395 | 2.3908 | 34.9863 | 33.0302 |
| 0.1491 | 16.77 | 137000 | 0.5397 | 2.4509 | 35.0017 | 33.4044 |
| 0.1512 | 16.83 | 137500 | 0.5395 | 2.5314 | 35.0975 | 33.2042 |
| 0.1509 | 16.89 | 138000 | 0.5389 | 2.4688 | 34.8711 | 33.0493 |
| 0.1517 | 16.95 | 138500 | 0.5388 | 2.3796 | 35.1383 | 33.1982 |
| 0.1516 | 17.01 | 139000 | 0.5396 | 2.5719 | 34.9446 | 33.4195 |
| 0.1467 | 17.07 | 139500 | 0.5406 | 2.76 | 34.9953 | 33.2525 |
| 0.1498 | 17.14 | 140000 | 0.5410 | 2.4061 | 35.1196 | 33.0946 |
| 0.1491 | 17.2 | 140500 | 0.5396 | 2.6469 | 35.2868 | 33.5473 |
| 0.1492 | 17.26 | 141000 | 0.5411 | 2.5395 | 35.1808 | 33.4225 |
| 0.1506 | 17.32 | 141500 | 0.5405 | 2.5481 | 35.1489 | 33.5111 |
| 0.1461 | 17.38 | 142000 | 0.5417 | 2.3111 | 35.0102 | 33.1278 |
| 0.1505 | 17.44 | 142500 | 0.5401 | 2.4497 | 35.0826 | 33.4175 |
| 0.1477 | 17.5 | 143000 | 0.5389 | 2.5452 | 34.9631 | 33.3109 |
| 0.1476 | 17.56 | 143500 | 0.5418 | 2.7374 | 34.8662 | 32.9044 |
| 0.1489 | 17.63 | 144000 | 0.5403 | 2.5838 | 35.0227 | 33.0513 |
| 0.1499 | 17.69 | 144500 | 0.5397 | 2.4883 | 35.3753 | 33.3028 |
| 0.1459 | 17.75 | 145000 | 0.5393 | 2.3722 | 35.0427 | 33.1268 |
| 0.1473 | 17.81 | 145500 | 0.5401 | 2.6967 | 34.9388 | 33.3672 |
| 0.1484 | 17.87 | 146000 | 0.5406 | 2.5086 | 34.9813 | 33.0734 |
| 0.1489 | 17.93 | 146500 | 0.5403 | 2.7184 | 35.1614 | 33.1137 |
| 0.1488 | 17.99 | 147000 | 0.5413 | 2.7788 | 35.2434 | 33.5201 |
| 0.1462 | 18.05 | 147500 | 0.5415 | 2.7858 | 35.1306 | 33.3521 |
| 0.148 | 18.12 | 148000 | 0.5406 | 2.7313 | 34.9823 | 32.9588 |
| 0.1459 | 18.18 | 148500 | 0.5414 | 2.4159 | 35.047 | 33.2455 |
| 0.1492 | 18.24 | 149000 | 0.5412 | 2.4617 | 35.0451 | 33.2213 |
| 0.1466 | 18.3 | 149500 | 0.5411 | 2.3902 | 35.0494 | 33.1901 |
| 0.1475 | 18.36 | 150000 | 0.5406 | 2.6175 | 35.0336 | 32.9909 |
| 0.1453 | 18.42 | 150500 | 0.5407 | 2.4948 | 35.0448 | 32.9366 |
| 0.1443 | 18.48 | 151000 | 0.5403 | 2.6749 | 35.1871 | 33.1861 |
| 0.1473 | 18.54 | 151500 | 0.5401 | 2.5487 | 35.2026 | 33.0604 |
| 0.1456 | 18.6 | 152000 | 0.5398 | 2.5826 | 35.0012 | 32.9879 |
| 0.1467 | 18.67 | 152500 | 0.5398 | 2.4385 | 35.0235 | 33.0201 |
| 0.1483 | 18.73 | 153000 | 0.5410 | 2.5969 | 34.9183 | 33.0543 |
| 0.1484 | 18.79 | 153500 | 0.5414 | 2.707 | 35.163 | 33.2093 |
| 0.1486 | 18.85 | 154000 | 0.5400 | 2.4765 | 35.0631 | 33.0573 |
| 0.1477 | 18.91 | 154500 | 0.5403 | 2.5421 | 35.1549 | 32.9346 |
| 0.148 | 18.97 | 155000 | 0.5414 | 2.4963 | 34.9687 | 33.1298 |
| 0.1457 | 19.03 | 155500 | 0.5418 | 2.5232 | 34.9675 | 32.9577 |
| 0.1464 | 19.09 | 156000 | 0.5416 | 2.4857 | 35.1609 | 33.2978 |
| 0.1462 | 19.16 | 156500 | 0.5414 | 2.6489 | 35.1326 | 33.2233 |
| 0.1443 | 19.22 | 157000 | 0.5421 | 2.6008 | 35.218 | 33.2555 |
| 0.1447 | 19.28 | 157500 | 0.5415 | 2.4443 | 34.9947 | 33.1328 |
| 0.146 | 19.34 | 158000 | 0.5415 | 2.6343 | 35.1218 | 33.159 |
| 0.146 | 19.4 | 158500 | 0.5419 | 2.6944 | 35.2223 | 33.3722 |
| 0.1458 | 19.46 | 159000 | 0.5419 | 2.4237 | 35.1222 | 33.2646 |
| 0.1459 | 19.52 | 159500 | 0.5422 | 2.6489 | 35.0196 | 33.1811 |
| 0.1481 | 19.58 | 160000 | 0.5423 | 2.5228 | 35.1774 | 33.3209 |
| 0.145 | 19.65 | 160500 | 0.5419 | 2.6893 | 35.2431 | 33.1841 |
| 0.1463 | 19.71 | 161000 | 0.5415 | 2.5176 | 35.1588 | 33.2847 |
| 0.1465 | 19.77 | 161500 | 0.5413 | 2.6651 | 35.295 | 33.2686 |
| 0.1463 | 19.83 | 162000 | 0.5413 | 2.472 | 35.3013 | 33.2968 |
| 0.1455 | 19.89 | 162500 | 0.5415 | 2.4636 | 35.2344 | 33.2455 |
| 0.1473 | 19.95 | 163000 | 0.5414 | 2.4777 | 35.3072 | 33.2767 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
DevsIA/Devs_IA
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- Composer
- MosaicML
- llm-foundry
datasets:
- the_pile_books3
inference: false
---
# MPT-7B-StoryWriter-65k+
MPT-7B-StoryWriter-65k+ is a model designed to read and write fictional stories with super long context lengths.
It was built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3).
At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens.
We demonstrate generations as long as 84k tokens on a single node of 8 A100-80GB GPUs in our [blogpost](www.mosaicml.com/blog/mpt-7b).
* License: _Apache-2.0_ (commercial use permitted)
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
May 5, 2023
## Model License
Apache-2.0 (commercial use permitted)
## Documentation
* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-1btms90mc-GipE2ufuPkKY0QBrmF3LSA)!
## How to Use
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture that is not yet part of the `transformers` package.
It includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-storywriter',
trust_remote_code=True
)
```
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`:
```python
config = transformers.AutoConfig.from_pretrained(
'mosaicml/mpt-7b-storywriter',
trust_remote_code=True
)
config.attn_config['attn_impl'] = 'triton'
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-storywriter',
config=config,
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
model.to(device='cuda:0')
```
Although the model was trained with a sequence length of 2048 and finetuned with a sequence length of 65536,
ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
```python
config = transformers.AutoConfig.from_pretrained(
'mosaicml/mpt-7b-storywriter',
trust_remote_code=True
)
config.update({"max_seq_len": 83968})
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-storywriter',
config=config,
trust_remote_code=True
)
```
This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
```
## Example Epilogue
The full text of the _The Great Gatsby_ (67873 tokens) was fed to the model, followed by the text "EPILOGUE"
**TheRealFitzgerald233**:
> ...
>
> So we beat on, boats against the current, borne back ceaselessly into the past
>
> EPILOGUE
**MPT-7B-StoryWriter**:
>I have lived so long in the shadow of this dream that I may never see the bright sunlight of reality. I may look up some morning and find that I am six feet under, and that Gatsby, who gave me the dream and everything else, will have vanished behind me.
>
>And so it was. I had hardly crossed the threshold of the old world when my eye caught the headlines of a newspaper, and I knew at once that my dream was over. I knew that Gatsby was dead.
>
>And I knew that day that I didn't love her.
>
>That night I took the last train for West Egg, and when I reached home I went directly to my attic and brought down all Gatsby's letters.
>
>I have sorted them by date and placed them in the order in which they were written. They are very few in number.
>
>They are dated between April of nineteen eighteen and January of nineteen nineteen, and the last one I have here is the one that told me he was dead.
>
>There is something in that last letter that frightens me. It is the sense of a finality, of a completeness. I never heard of him after that, and I was glad.
>
>But a few months ago I read somewhere that he had been seen in New York. He had become a great man.
>
>And I knew that he had not changed at all.
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 6.7B |
|n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | **65536** |
## PreTraining Data
For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b).
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-7B-StoryWriter can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-StoryWriter was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## Acknowledgements
This model was finetuned by Alex Trott and the MosaicML NLP team
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo).
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
```
|
DicoTiar/wisdomfiy
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-qg-LearningQ-tarek-test
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. -->
# flan-t5-qg-LearningQ-tarek-test
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5936
- Rouge1: 22.4956
- Rouge2: 5.8552
- Rougel: 20.2758
- Rougelsum: 20.2629
- Gen Len: 16.2121
## 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: 8
- eval_batch_size: 8
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.6422 | 1.0 | 23583 | 1.5936 | 22.4956 | 5.8552 | 20.2758 | 20.2629 | 16.2121 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
DiegoBalam12/institute_classification
|
[] | null |
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}
}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: data2vec-audio-base-960h-digit-mask-ft
results: []
datasets:
- mazkooleg/digit_mask_augmented_raw
---
<!-- 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. -->
# data2vec-audio-base-960h-digit-mask-ft
This model is a fine-tuned version of [facebook/data2vec-audio-base-960h](https://huggingface.co/facebook/data2vec-audio-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Accuracy: 0.9991
- F1: 0.9991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|
| 0.0167 | 1.0 | 14264 | 0.9975 | 0.9975 | 0.0108 |
| 0.0016 | 2.0 | 28528 | 0.9991 | 0.9991 | 0.0067 |
| 0.0063 | 3.0 | 42792 | 0.9987 | 0.9987 | 0.0078 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.0+cpu
- Datasets 2.12.0
- Tokenizers 0.13.2
|
Dilmk2/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
}
| 13 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-19
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-19
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4989
- Train Accuracy: 0.7652
- Validation Loss: 0.4317
- Validation Accuracy: 0.8111
- Train Matthews Correlation: 0.5332
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3.570334620395596e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.4989 | 0.7652 | 0.4317 | 0.8111 | 0.5332 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Dkwkk/Da
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: finetuned-Sentiment-classfication-ROBERTA-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-Sentiment-classfication-ROBERTA-model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5882
- Rmse: 0.6283
## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6407 | 4.0 | 500 | 0.5882 | 0.6283 |
| 0.2172 | 8.0 | 1000 | 0.8376 | 0.5919 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
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. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Donghyun/L2_BERT
|
[] | null |
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| 0 | null |
---
metrics:
- accuracy
pipeline_tag: text-classification
---
|
Dongjae/mrc2reader
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
}
| 3 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# m9rbgas9t4w API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "m9rbgas9t4w"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/m9rbgas9t4w)
Credits: [View credits](https://civitai.com/?query=m9rbgas9t4w)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "m9rbgas9t4w",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
Waynehillsdev/Wayne_NLP_mT5
|
[
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 11 | null |
---
license: openrail
datasets:
- databricks/databricks-dolly-15k
- s3nh/alpaca-dolly-instruction-only-polish
language:
- pl
---
### Introduction
These repository consist of microsoft/DialoGPT-large finetuned to Polish language on translated alpaca-dolly dataset.
Main task is to perform accurate answers to instruction asked.
Below you can find an instruction of how to infer with that model.
These repository does not contain an tokenizer object, at the moment (#TODO).
### Evaluation part
```python
import pandas as pd
import torch
from torch.utils.data import AutTokenizer
from typing import List, Dict, Union
from typing import Any, TypeVar
import pandas as pd
import pickle
MODEL_NAME: str = 's3nh/DialoGPT-large-instruct-polish-3000-steps'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCasualLM.from_pretrained(MODEL_NAME).cuda()
#Resize model for tokenizer size
n_tokens: int = len(tokenizer)
model.resize_token_embeddings(n_tokens)
def _generate_prompt(instruction, input=None):
if input:
return f"""Poniżej znajduje się instrukcja opisująca zadanie, połączona z danymi wejściowymi, które zapewniają dalszy konktekst. Napisz odpowiedź, która odpowiednio odpowie na pytanie.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
manual_instruction: str = "Napisz mi proszę jakie są rodzaje telefonów komórkowych"
manual_input: str = "Telefony komórkowe, w przeciwieństwie do np. satelitarnych, charakteryzuje to, że działają w obrębie naziemnych fal radiowych w technologii GSM (i w różnych jej wariantach: 3G, 4G czy niebawem 5G). Zasadniczo można jednak wyróżnić wiele ich rodzajów i podzielić je na różne kryteria. I tak, ze względu na rodzaj obudowy, można mówić o telefonach jednobryłowych, rozsuwanych, obrotowych czy też z klapką. Obecnie jednak najbardziej popularne i – ze względu na posiadane parametry – najlepsze telefony komórkowe to smartfony dotykowe."
print(f"Valueation for {manual_instruction} \n\n\n {manual_input}\n\n")
evaluate(instruction = manual_instruction,
input = manual_input)
```
|
Waynehillsdev/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 5 | null |
---
tags:
- generated_from_trainer
model-index:
- name: bangla-para-v2-test-2
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. -->
# bangla-para-v2-test-2
This model is a fine-tuned version of [csebuetnlp/banglat5_banglaparaphrase](https://huggingface.co/csebuetnlp/banglat5_banglaparaphrase) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 113 | 2.5554 | 0.0 | 0.0 | 0.0 | 0.0 | 15.75 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Doohae/p_encoder
|
[
"pytorch"
] | null |
{
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: MeanPoolingBert-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.4340990431285672
---
<!-- 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. -->
# MeanPoolingBert-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4949
- Matthews Correlation: 0.4341
## 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: 1.018367046954782e-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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 268 | 0.4949 | 0.4341 |
### Framework versions
- Transformers 4.12.2
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.10.3
|
Doohae/roberta
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.4965380296929026
---
<!-- 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-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4656
- Matthews Correlation: 0.4965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4965 | 1.0 | 535 | 0.4656 | 0.4965 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Doxophobia/DialoGPT-medium-celeste
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 11 | null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: huanvo88/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DoyyingFace/bert-COVID-HATE-finetuned-test
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
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| 29 | null |
---
tags:
- fastai
pipeline_tag: image-classification
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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| 29 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-common-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5521390429003941
---
<!-- 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-common-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6196
- Matthews Correlation: 0.5521
## 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: 3.0535648029673025e-05
- train_batch_size: 4
- 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5514 | 1.0 | 2138 | 0.6196 | 0.5521 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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| 28 | null |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bangla-para-v3-30000
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. -->
# bangla-para-v3-30000
This model is a fine-tuned version of [csebuetnlp/banglat5_banglaparaphrase](https://huggingface.co/csebuetnlp/banglat5_banglaparaphrase) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2800
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 12.0373
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.5243 | 1.0 | 3375 | 1.2800 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0373 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DoyyingFace/bert-asian-hate-tweets-asonam-unclean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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},
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| 30 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-20
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-20
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5057
- Train Accuracy: 0.7599
- Validation Loss: 0.4599
- Validation Accuracy: 0.7756
- Train Matthews Correlation: 0.5087
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3.855526635827775e-05, 'decay_steps': 2136, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5057 | 0.7599 | 0.4599 | 0.7756 | 0.5087 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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| 25 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-lrc-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5729657494988228
---
<!-- 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-lrc-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9583
- Matthews Correlation: 0.5730
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.0638 | 1.0 | 535 | 0.9583 | 0.5730 |
| 0.0486 | 2.0 | 1070 | 1.1459 | 0.5496 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DoyyingFace/bert-asian-hate-tweets-concat-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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| 25 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-21
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-21
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5074
- Train Accuracy: 0.7630
- Validation Loss: 0.4764
- Validation Accuracy: 0.7824
- Train Matthews Correlation: 0.4520
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3.0706298415203186e-05, 'decay_steps': 1335, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5074 | 0.7630 | 0.4764 | 0.7824 | 0.4520 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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}
| 38,156 | 2023-05-07T20:20:17Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: PanoEvJ/PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
albert-base-v2
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 4,785,283 | 2023-05-07T20:21:19Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-22
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-22
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4871
- Train Accuracy: 0.7724
- Validation Loss: 0.4388
- Validation Accuracy: 0.7967
- Train Matthews Correlation: 0.4911
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3.238236188209261e-05, 'decay_steps': 1335, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.4871 | 0.7724 | 0.4388 | 0.7967 | 0.4911 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
albert-large-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
}
| 26,792 | 2023-05-07T20:22:50Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8615402154705581
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1388
- F1: 0.8615
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.27 | 1.0 | 525 | 0.1636 | 0.8214 |
| 0.1291 | 2.0 | 1050 | 0.1357 | 0.8442 |
| 0.0826 | 3.0 | 1575 | 0.1388 | 0.8615 |
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 341 | 2023-05-07T20:23:02Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-23
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-23
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5468
- Train Accuracy: 0.7325
- Validation Loss: 0.5034
- Validation Accuracy: 0.7709
- Train Matthews Correlation: 0.4201
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2.557243736349855e-05, 'decay_steps': 1335, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5468 | 0.7325 | 0.5034 | 0.7709 | 0.4201 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
albert-xxlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7,091 | 2023-05-07T20:24:45Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-24
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-24
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5004
- Train Accuracy: 0.7596
- Validation Loss: 0.4416
- Validation Accuracy: 0.8025
- Train Matthews Correlation: 0.5076
- Epoch: 0
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3.116044055688935e-05, 'decay_steps': 1335, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5004 | 0.7596 | 0.4416 | 0.8025 | 0.5076 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bert-base-multilingual-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 328,585 | 2023-05-07T20:29:56Z |
---
license: openrail
datasets:
- sdlfkjsdflkjds/clothing_dataset
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
library_name: adapter-transformers
---
|
bert-large-cased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8,214 | 2023-05-07T20:32:15Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.64 +/- 21.81
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bert-large-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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},
"translation_en_to_fr": {
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}
}
}
| 388,769 | 2023-05-07T20:34:18Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bert-large-uncased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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"early_stopping": null,
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}
}
}
| 76,685 | 2023-05-07T20:41:35Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Grammar_Error_Corretion_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Grammar_Error_Corretion_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8280
- Validation Loss: 0.7465
- Epoch: 4
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7815, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0037 | 0.8273 | 0 |
| 0.8891 | 0.7876 | 1 |
| 0.8605 | 0.7682 | 2 |
| 0.8374 | 0.7567 | 3 |
| 0.8280 | 0.7465 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
camembert-base
|
[
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
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}
| 1,440,898 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-bs-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5609903802347734
---
<!-- 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-bs-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1887
- Matthews Correlation: 0.5610
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1909 | 1.0 | 1069 | 0.8341 | 0.5565 |
| 0.0898 | 2.0 | 2138 | 1.1887 | 0.5610 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
distilbert-base-cased-distilled-squad
|
[
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 257,745 | 2023-05-07T20:47:01Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: exist-2023-task2
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. -->
# exist-2023-task2
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4756
- F1: 0.7027
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 97 | 1.0175 | 0.4991 |
| No log | 2.0 | 194 | 0.8374 | 0.5695 |
| No log | 3.0 | 291 | 0.7967 | 0.5876 |
| No log | 4.0 | 388 | 0.7797 | 0.5982 |
| No log | 5.0 | 485 | 0.7161 | 0.6424 |
| 0.8645 | 6.0 | 582 | 0.6662 | 0.6302 |
| 0.8645 | 7.0 | 679 | 0.6580 | 0.6385 |
| 0.8645 | 8.0 | 776 | 0.6465 | 0.6491 |
| 0.8645 | 9.0 | 873 | 0.8620 | 0.5650 |
| 0.8645 | 10.0 | 970 | 0.5704 | 0.6852 |
| 0.6764 | 11.0 | 1067 | 0.5434 | 0.6806 |
| 0.6764 | 12.0 | 1164 | 0.7109 | 0.6192 |
| 0.6764 | 13.0 | 1261 | 0.5411 | 0.6708 |
| 0.6764 | 14.0 | 1358 | 0.5557 | 0.6675 |
| 0.6764 | 15.0 | 1455 | 0.5483 | 0.6701 |
| 0.56 | 16.0 | 1552 | 0.5155 | 0.6817 |
| 0.56 | 17.0 | 1649 | 0.5375 | 0.6750 |
| 0.56 | 18.0 | 1746 | 0.4858 | 0.6984 |
| 0.56 | 19.0 | 1843 | 0.4571 | 0.7091 |
| 0.56 | 20.0 | 1940 | 0.4756 | 0.7027 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
distilbert-base-multilingual-cased
|
[
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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"DistilBertForMaskedLM"
],
"model_type": "distilbert",
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}
}
| 8,339,633 | 2023-05-07T21:01:18Z |
---
tags:
- generated_from_trainer
model-index:
- name: tmp_trainer
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. -->
# tmp_trainer
This model was trained from scratch on an unknown dataset.
## 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: 3.0
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
Ab2021/bookst5
|
[] | null |
{
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}
| 0 | 2023-05-08T04:16:47Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bangla-para-v3-120000
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. -->
# bangla-para-v3-120000
This model is a fine-tuned version of [mHossain/bangla-para-v3-90000](https://huggingface.co/mHossain/bangla-para-v3-90000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2301
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 12.0293
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.4278 | 1.0 | 1688 | 1.2301 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0293 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Akashpb13/Kabyle_xlsr
|
[
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kab",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"sw",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 3 | 2023-05-08T09:53:45Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - Chris7777777/path-to-save-model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
Akashpb13/Swahili_xlsr
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"sw",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
}
| 10 | 2023-05-08T09:56:12Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="labicquette/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Aklily/Lilys
|
[] | null |
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}
| 0 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartpoleV1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 479.32 +/- 70.58
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AkshatSurolia/BEiT-FaceMask-Finetuned
|
[
"pytorch",
"beit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
image-classification
|
{
"architectures": [
"BeitForImageClassification"
],
"model_type": "beit",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 239 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="labicquette/taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AkshatSurolia/ConvNeXt-FaceMask-Finetuned
|
[
"pytorch",
"safetensors",
"convnext",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
image-classification
|
{
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"ConvNextForImageClassification"
],
"model_type": "convnext",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 56 | null |
---
license: apache-2.0
language:
- en
datasets:
- togethercomputer/RedPajama-Data-1T
- OpenAssistant/oasst1
- databricks/databricks-dolly-15k
widget:
- text: "<human>: Write an email to my friends inviting them to come to my home on Friday for a dinner party, bring their own food to share.\n<bot>:"
example_title: "Email Writing"
- text: "<human>: Create a list of things to do in San Francisco\n<bot>:"
example_title: "Brainstorming"
inference:
parameters:
temperature: 0.7
top_p: 0.7
top_k: 50
max_new_tokens: 128
---
# # Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [togethercomputer/RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1)
```bash
pip install hf-hub-ctranslate2>=2.0.6 ctranslate2>=3.13.0
```
Converted on 2023-05-19 using
```
ct2-transformers-converter --model togethercomputer/RedPajama-INCITE-Chat-3B-v1 --output_dir /home/michael/tmp-ct2fast-RedPajama-INCITE-Chat-3B-v1 --force --copy_files tokenizer.json README.md tokenizer_config.json generation_config.json special_tokens_map.json .gitattributes --quantization float16
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-RedPajama-INCITE-Chat-3B-v1"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = GeneratorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
# RedPajama-INCITE-Chat-3B-v1
RedPajama-INCITE-Chat-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
It is fine-tuned on OASST1 and Dolly2 to enhance chatting ability.
- Base Model: [RedPajama-INCITE-Base-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1)
- Instruction-tuned Version: [RedPajama-INCITE-Instruct-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1)
- Chat Version: [RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1)
## Model Details
- **Developed by**: Together Computer.
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 2.8B parameter pretrained language model.
# Quick Start
Please note that the model requires `transformers` version >= 4.25.1.
To prompt the chat model, use the following format:
```
<human>: [Instruction]
<bot>:
```
## GPU Inference
This requires a GPU with 8GB memory.
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", torch_dtype=torch.float16)
model = model.to('cuda:0')
# infer
prompt = "<human>: Who is Alan Turing?\n<bot>:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Alan Turing was a British mathematician, logician, cryptologist, and computer scientist. He is widely regarded as the father of computer science and artificial intelligence.
"""
```
## GPU Inference in Int8
This requires a GPU with 6GB memory.
To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command:
```bash
pip install accelerate
pip install bitsandbytes
```
Then you can run inference with int8 as follows:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)
# infer
prompt = "<human>: Who is Alan Turing?\n<bot>:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Alan Turing was a British mathematician and computer scientist who made important contributions to computer science and mathematical logic. He is widely regarded as the father of computer science and artificial intelligence for his work on the Turing machine and Turing test.
"""
```
## CPU Inference
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", torch_dtype=torch.bfloat16)
# infer
prompt = "<human>: Who is Alan Turing?\n<bot>:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Alan Turing was a British mathematician and computer scientist who made important contributions to the fields of mathematics, cryptography, and computer science. He is widely regarded as the father of computer science and artificial intelligence.
"""
```
Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference.
# Uses
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.
#### Out-of-Scope Use
`RedPajama-INCITE-Chat-3B-v1` is a language model and may not perform well for other use cases outside of its intended scope.
For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society.
It is important to consider the limitations of the model and to only use it for its intended purpose.
#### Misuse and Malicious Use
`RedPajama-INCITE-Chat-3B-v1` is designed for language modeling.
Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project.
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating fake news, misinformation, or propaganda
- Promoting hate speech, discrimination, or violence against individuals or groups
- Impersonating individuals or organizations without their consent
- Engaging in cyberbullying or harassment
- Defamatory content
- Spamming or scamming
- Sharing confidential or sensitive information without proper authorization
- Violating the terms of use of the model or the data used to train it
- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming
## Limitations
`RedPajama-INCITE-Chat-3B-v1`, like other language models, has limitations that should be taken into consideration.
For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data.
We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.
## Training
**Training Data**
Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
**Training Procedure**
- **Hardware:** 8 A100
- **Optimizer:** Adam
- **Gradient Accumulations**: 1
- **Num of Tokens:** 131M tokens
- **Learning rate:** 1e-5
## Community
Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
|
AkshayDev/BERT_Fine_Tuning
|
[] | null |
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}
| 0 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="kujaomega/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AkshaySg/GrammarCorrection
|
[] | null |
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}
| 0 | null |
---
language: ja
license: apache-2.0
tags:
- speech
- speaker-diarization
datasets:
- callhome
---
# Fine-tuned XLSR-53 large model for speech diarization in Japanese phone-call
2 speakers diarization model which was fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using phone-call data [CallHome](https://media.talkbank.org/ca/CallHome/jpn/).
## Usage
The model can be used directly as follows.
```python
import numpy as np
import torch
from pydub import AudioSegment
from transformers import Wav2Vec2ForAudioFrameClassification, Wav2Vec2FeatureExtractor
def _make_timegrid(sound_duration: float, total_len: int):
start_timegrid = np.linspace(0, sound_duration, total_len + 1)
dt = start_timegrid[1] - start_timegrid[0]
end_timegrid = start_timegrid + dt
return start_timegrid[:total_len], end_timegrid[:total_len]
feature_extractor = Wav2Vec2FeatureExtractor(
feature_size=1,
sampling_rate=16_000,
padding_value=0.0,
do_normalize=True,
return_attention_mask=True,
)
model = Wav2Vec2ForAudioFrameClassification.from_pretrained("Ivydata/wav2vec2-large-speech-diarization-jp")
filepath = "/path/to/file.wav"
sound = AudioSegment.from_file(filepath)
sound = sound.set_frame_rate(16_000)
sound_duration = sound.duration_seconds
feature = feature_extractor(np.array(sound.get_array_of_samples())).input_values[0]
input_values = torch.tensor(feature, dtype=torch.float32).unsqueeze(0)
with torch.no_grad():
logits = model(input_values).logits
pred = logits.argmax(dim=-1).squeeze(0)
start_timegrid, end_timegrid = _make_timegrid(sound_duration, len(pred))
print("sec speaker_label")
for p, start_time in zip(pred, start_timegrid):
print(f"{start_time:.4f} {p}")
```
## Training
The model was trained on Japanese phone-call corpus [CallHome](https://media.talkbank.org/ca/CallHome/jpn/).
## License
[The Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0)
|
Akuva2001/SocialGraph
|
[
"has_space"
] | null |
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| 0 | null |
---
license: bigscience-openrail-m
datasets:
- PanoEvJ/job_postings_GPT
library_name: adapter-transformers
pipeline_tag: text2text-generation
---
|
Al/mymodel
|
[] | null |
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: baseline_review_generation2
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. -->
# baseline_review_generation2
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7525
## 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: 2
- eval_batch_size: 2
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1478 | 1.0 | 500 | 1.8205 |
| 1.9546 | 2.0 | 1000 | 1.7812 |
| 1.8783 | 3.0 | 1500 | 1.7639 |
| 1.8171 | 4.0 | 2000 | 1.7553 |
| 1.7736 | 5.0 | 2500 | 1.7519 |
| 1.7481 | 6.0 | 3000 | 1.7525 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlErysvi/Erys
|
[] | null |
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}
| 0 | null |
---
tags:
- ctranslate2
- int8
- float16
license: apache-2.0
language:
- en
datasets:
- togethercomputer/RedPajama-Data-1T
- Muennighoff/P3
- Muennighoff/natural-instructions
widget:
- text: "Label the tweets as either 'positive', 'negative', 'mixed', or 'neutral': \n\nTweet: I can say that there isn't anything I would change.\nLabel: positive\n\nTweet: I'm not sure about this.\nLabel: neutral\n\nTweet: I liked some parts but I didn't like other parts.\nLabel: mixed\n\nTweet: I think the background image could have been better.\nLabel: negative\n\nTweet: I really like it.\nLabel:"
example_title: "Sentiment Analysis"
- text: "Please answer the following question:\n\nQuestion: What is the capital of Canada?\nAnswer: Ottawa\n\nQuestion: What is the currency of Switzerland?\nAnswer: Swiss franc\n\nQuestion: In which country is Wisconsin located?\nAnswer:"
example_title: "Question Answering"
- text: "Given a news article, classify its topic.\nPossible labels: 1. World 2. Sports 3. Business 4. Sci/Tech\n\nArticle: A nearby star thought to harbor comets and asteroids now appears to be home to planets, too.\nLabel: Sci/Tech\n\nArticle: Soaring crude prices plus worries about the economy and the outlook for earnings are expected to hang over the stock market next week during the depth of the summer doldrums.\nLabel: Business\n\nArticle: Murtagh a stickler for success Northeastern field hockey coach Cheryl Murtagh doesn't want the glare of the spotlight that shines on her to detract from a team that has been the America East champion for the past three years and has been to the NCAA tournament 13 times.\nLabel::"
example_title: "Topic Classification"
- text: "Paraphrase the given sentence into a different sentence.\n\nInput: Can you recommend some upscale restaurants in New York?\nOutput: What upscale restaurants do you recommend in New York?\n\nInput: What are the famous places we should not miss in Paris?\nOutput: Recommend some of the best places to visit in Paris?\n\nInput: Could you recommend some hotels that have cheap price in Zurich?\nOutput:"
example_title: "Paraphrasing"
- text: "Given a review from Amazon's food products, the task is to generate a short summary of the given review in the input.\n\nInput: I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than most.\nOutput: Good Quality Dog Food\n\nInput: Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as 'Jumbo'.\nOutput: Not as Advertised\n\nInput: My toddler loves this game to a point where he asks for it. That's a big thing for me. Secondly, no glitching unlike one of their competitors (PlayShifu). Any tech I don’t have to reach out to support for help is a good tech for me. I even enjoy some of the games and activities in this. Overall, this is a product that shows that the developers took their time and made sure people would not be asking for refund. I’ve become bias regarding this product and honestly I look forward to buying more of this company’s stuff. Please keep up the great work.\nOutput:"
example_title: "Text Summarization"
- text: "Identify which sense of a word is meant in a given context.\n\nContext: The river overflowed the bank.\nWord: bank\nSense: river bank\n\nContext: A mouse takes much more room than a trackball.\nWord: mouse\nSense: computer mouse\n\nContext: The bank will not be accepting cash on Saturdays.\nWord: bank\nSense: commercial (finance) banks\n\nContext: Bill killed the project\nWord: kill\nSense:"
example_title: "Word Sense Disambiguation"
- text: "Given a pair of sentences, choose whether the two sentences agree (entailment)/disagree (contradiction) with each other.\nPossible labels: 1. entailment 2. contradiction\n\nSentence 1: The skier was on the edge of the ramp. Sentence 2: The skier was dressed in winter clothes.\nLabel: entailment\n\nSentence 1: The boy skated down the staircase railing. Sentence 2: The boy is a newbie skater.\nLabel: contradiction\n\nSentence 1: Two middle-aged people stand by a golf hole. Sentence 2: A couple riding in a golf cart.\nLabel:"
example_title: "Natural Language Inference"
inference:
parameters:
temperature: 0.7
top_p: 0.7
top_k: 50
max_new_tokens: 128
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [togethercomputer/RedPajama-INCITE-Instruct-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1)
```bash
pip install hf-hub-ctranslate2>=2.0.6
```
Converted on 2023-05-19 using
```
ct2-transformers-converter --model togethercomputer/RedPajama-INCITE-Instruct-3B-v1 --output_dir /home/michael/tmp-ct2fast-RedPajama-INCITE-Instruct-3B-v1 --force --copy_files tokenizer.json README.md tokenizer_config.json generation_config.json special_tokens_map.json .gitattributes --quantization float16
```
Checkpoint compatible to [ctranslate2>=3.13.0](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.0.6](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-RedPajama-INCITE-Instruct-3B-v1"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = GeneratorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing? Bot:"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
tags:
- ctranslate2
- int8
- float16
# RedPajama-INCITE-Instruct-3B-v1
RedPajama-INCITE-Instruct-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
The model was fine-tuned for few-shot applications on the data of [GPT-JT](https://huggingface.co/togethercomputer/GPT-JT-6B-v1), with exclusion of tasks that overlap with the HELM core scenarios.
- Base Model: [RedPajama-INCITE-Base-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1)
- Instruction-tuned Version: [RedPajama-INCITE-Instruct-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1)
- Chat Version: [RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1)
## Model Details
- **Developed by**: Together Computer.
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 2.8B parameter pretrained language model.
# Quick Start
Please note that the model requires `transformers` version >= 4.25.1.
## GPU Inference
This requires a GPU with 8GB memory.
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1", torch_dtype=torch.float16)
model = model.to('cuda:0')
# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```
## GPU Inference in Int8
This requires a GPU with 6GB memory.
To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command:
```bash
pip install accelerate
pip install bitsandbytes
```
Then you can run inference with int8 as follows:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)
# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```
## CPU Inference
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1", torch_dtype=torch.bfloat16)
# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```
Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference.
# Uses
## Direct Use
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.
#### Out-of-Scope Use
RedPajama-INCITE-Instruct-3B-v1 is a language model and may not perform well for other use cases outside of its intended scope.
For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society.
It is important to consider the limitations of the model and to only use it for its intended purpose.
#### Misuse and Malicious Use
RedPajama-INCITE-Instruct-3B-v1 is designed for language modeling.
Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project.
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating fake news, misinformation, or propaganda
- Promoting hate speech, discrimination, or violence against individuals or groups
- Impersonating individuals or organizations without their consent
- Engaging in cyberbullying or harassment
- Defamatory content
- Spamming or scamming
- Sharing confidential or sensitive information without proper authorization
- Violating the terms of use of the model or the data used to train it
- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming
## Limitations
RedPajama-INCITE-Instruct-3B-v1, like other language models, has limitations that should be taken into consideration.
For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data.
We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.
## Training
**Training Data**
Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
**Training Procedure**
- **Hardware:** 8 A100
- **Optimizer:** Adam
- **Gradient Accumulations**: 1
- **Num of Tokens:** 131M tokens
- **Learning rate:** 1e-5
## Community
Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
|
Alaeddin/convbert-base-turkish-ner-cased
|
[
"pytorch",
"convbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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"ConvBertForTokenClassification"
],
"model_type": "convbert",
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}
| 9 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.77
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="kujaomega/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AlanDev/DallEMiniButBetter
|
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}
}
}
| 0 | null |
## Checkpoints and conversion scripts for Nemo cpkt files to Huggingface
This repo contains two checkpoints (`.ckpt` files) for UL2 models we have started pretraining with Nemo. The checkpoints are found in `nemo_checkpoints/`. The Nemo config files used to train these models can be found in `nemo_config/ul2-base-nl36`.
`megatron_ul2--val_loss=2.54-step=7000-consumed_samples=14557920.0.ckpt` was trained with `megatron_legacy: False` in the config, whereas the other checkpoint was trained with `megatron_legacy: True`.
Nvidia have created a conversion script that converts T5, T5v1.1 and UL2 models on Huggingface Hub to Nemo format. The script can be found [here](https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/hf_t5-v1_1_to_nemo.py). It is also included in this repo.
We thought that adapting a T5/UL2 model trained with Nemo to a Huggingface format would simply be a manner of reversing the conversion that was performed by the script above. Our conversion script does work assuming we operate directly on the `pt` state dict weight files produced by running the above Nvidia script. I.e. it works when going directly `Huggingface -> Nemo -> Huggingface`. However, it does not work when attempting to go `Nemo -> Huggingface`. An UL2 model that was initialized with Nemo Megatron, and pretrained with Nemo, does not produce same output when converted to Huggingface format.
### Dependencies
We use Nemo docker containers (tag `23.02`) via Singularity when running the code in this repo. We have included a definition file to build the container.
To build the container:
```bash
sudo singularity build nemo2302.sif nemo_singularity.def
```
We provide bash scripts to execute with singularity. However, to debug easier you can also run singularity in interactive mode via:
```bash
singularity shell --nv nemo2302.sif
```
### Converting Nemo checkpoints to Huggingface
We have included our conversion script in this repo. It can be found in `convert_nemo_ul2_checkpoint.py`.
We manually created a Huggingface config file for UL2 that to the best of our knowledge matches the settings used when we trained with Nemo (see `config_ul2_base_nl36.json`).
To replicate our weights conversion, simply run:
```bash
singularity exec --nv nemo2302.sif bash convert_nemo_to_hf.sh
```
The resulting Huggingface model will be saved to `ul2-base-nl36-swedish/`.
We are aware that [Megatron-LM uses different ordering of QKV](https://github.com/NVIDIA/Megatron-LM/blob/42c1cf4279acea5a554500dcb552211f44cbec45/megatron/checkpointing.py#L209-L237) in the attention layers depending on the version of Megatron-LM used. We are also aware of an existing conversion script that Huggingface have created for converting Megatron-BERT to Huggingface, where they adapt the ordering of QKV in Megatron to [match the ordering used in Huggingface](https://github.com/NVIDIA/Megatron-LM/blob/42c1cf4279acea5a554500dcb552211f44cbec45/megatron/checkpointing.py#L209-L237). As such we have an optional `--fix_qkv` parameter in our conversion script that applies the same reordering of QKV as Huggingface does. See the lines that are commented out in `convert_nemo_to_hf.sh` for an example of how to use this parameter and set the `checkpoint_version`.
Unfortunately, none of the above solves the issue we have with the conversion script.
We have a test script that predicts both with the original Nemo model and with the converted Huggingface model. The output unfortunately isn't the same. We used the same identical tokenizer for both models. To run:
```bash
singularity exec --nv nemo2302.sif python test_ul2_hf.py
```
Or explore in interactive mode with `singularity shell --nv nemo2302.sif`.
### Confirming the conversion script can reverse Nvidia's conversion script
In order to confirm the conversion script is valid enough in the sense that it is able to reverse Nvidia's conversion script, we here include instructions to convert a UL2 model from Huggingface to Nemo, via Nvidia's conversion script, and then back to Huggingface via our conversion script.
Instructions:
1. Run `singularity exec --nv nemo2302.sif bash convert_hf_to_nemo.sh` to convert the existing [Finnish-NLP/ul2-base-nl36-finnish](https://huggingface.co/Finnish-NLP/ul2-base-nl36-finnish) from Huggingface to Nemo format via Nvidia's conversion script. The resultning model weights will be saved to the folder `ul2-base-nl36-finnish/`.
2. To perform the reverse conversion, and to perform a check whether the re-converted weights are identical, run `python convert_finnish_ul2_model.py`. Or via singularity: `singularity exec --nv nemo2302.sif python convert_finnish_ul2_model.py`.
The resuling model re-converted to Huggingface will be found in `ul2-base-nl36-finnish/hf_t5_ul2`.
This conversion produces a model that is identical to the original model.
|
AlanDev/dall-e-better
|
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| 0 | null |
---
language:
- en
datasets:
- natural_instructions
- the_pile
- cot
- Muennighoff/P3
tags:
- ctranslate2
- int8
- float16
- gpt
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.1
widget:
- text: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy. Answer:"
example_title: "Sentiment analysis"
- text: "Where is Zurich? Ans:"
example_title: "Question Answering"
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0)
```bash
pip install hf-hub-ctranslate2>=2.0.6
```
Converted on 2023-05-19 using
```
ct2-transformers-converter --model togethercomputer/GPT-JT-6B-v0 --output_dir /home/michael/tmp-ct2fast-GPT-JT-6B-v0 --force --copy_files merges.txt tokenizer.json README.md tokenizer_config.json vocab.json special_tokens_map.json added_tokens.json .gitattributes --quantization float16
```
Checkpoint compatible to [ctranslate2>=3.13.0](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.0.6](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-GPT-JT-6B-v0"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = GeneratorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v0")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing? Bot:"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
# Quick Start
```python
from transformers import pipeline
pipe = pipeline(model='togethercomputer/GPT-JT-6B-v0')
pipe("Where is Zurich? Ans:")
```
|
AlanDev/test
|
[] | null |
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}
| 0 | null |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bangla-para-v3-450000
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. -->
# bangla-para-v3-450000
This model is a fine-tuned version of [mHossain/bangla-para-v3-420000](https://huggingface.co/mHossain/bangla-para-v3-420000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1197
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 11.9723
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.2908 | 1.0 | 1688 | 1.1252 | 0.0 | 0.0 | 0.0 | 0.0 | 11.973 |
| 1.2755 | 2.0 | 3376 | 1.1197 | 0.0 | 0.0 | 0.0 | 0.0 | 11.9723 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Aleksandar1932/gpt2-hip-hop
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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}
| 8 | null |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Aleksandar1932/gpt2-rock-124439808
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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},
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}
| 11 | null |
---
license: apache-2.0
---
This is the OPT 6.7B model finetuned on english quotes.
|
Aleksandar1932/gpt2-soul
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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| 10 | null |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bangla-para-v3-480000
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. -->
# bangla-para-v3-480000
This model is a fine-tuned version of [mHossain/bangla-para-v3-450000](https://huggingface.co/mHossain/bangla-para-v3-450000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1055
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 11.8703
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.2716 | 1.0 | 1688 | 1.1093 | 0.0 | 0.0 | 0.0 | 0.0 | 11.8683 |
| 1.2611 | 2.0 | 3376 | 1.1055 | 0.0 | 0.0 | 0.0 | 0.0 | 11.8703 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"en",
"ru",
"multilingual",
"arxiv:1912.09723",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
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}
| 10,012 | null |
---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ner-hrl-ner-finetuning
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-ner-hrl-ner-finetuning
This model is a fine-tuned version of [Davlan/xlm-roberta-base-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1135
- Precision: 0.9290
- Recall: 0.9367
- F1: 0.9328
- Accuracy: 0.9801
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1534 | 1.0 | 477 | 0.0870 | 0.9001 | 0.9124 | 0.9062 | 0.9740 |
| 0.077 | 2.0 | 954 | 0.0764 | 0.9187 | 0.9321 | 0.9253 | 0.9789 |
| 0.0529 | 3.0 | 1431 | 0.0845 | 0.9178 | 0.9313 | 0.9245 | 0.9791 |
| 0.0377 | 4.0 | 1908 | 0.0805 | 0.9200 | 0.9310 | 0.9255 | 0.9795 |
| 0.0292 | 5.0 | 2385 | 0.0918 | 0.9278 | 0.9346 | 0.9312 | 0.9795 |
| 0.0204 | 6.0 | 2862 | 0.1016 | 0.9222 | 0.9323 | 0.9273 | 0.9790 |
| 0.0167 | 7.0 | 3339 | 0.1066 | 0.9271 | 0.9327 | 0.9299 | 0.9790 |
| 0.0134 | 8.0 | 3816 | 0.1088 | 0.9253 | 0.9358 | 0.9305 | 0.9797 |
| 0.0101 | 9.0 | 4293 | 0.1134 | 0.9289 | 0.9357 | 0.9323 | 0.9798 |
| 0.0079 | 10.0 | 4770 | 0.1135 | 0.9290 | 0.9367 | 0.9328 | 0.9801 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlexMaclean/sentence-compression-roberta
|
[
"pytorch",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] |
token-classification
|
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"RobertaForTokenClassification"
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| 13 | null |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
N3N3K0-Spl4T
Anime styled model inspired by Final Fantasy XIV, Gshade and Neneko's ColorS presets.
Extremey complicated lora squish.
https://civitai.com/models/62189?modelVersionId=66728
If you got requests, or concerns, We're still looking for beta testers: JOIN THE DISCORD AND DEMAND THINGS OF US: https://discord.gg/Da7s8d3KJ7
Listen to the music that we've made that goes with our art: https://open.spotify.com/playlist/00R8x00YktB4u541imdSSf?si=b60d209385a74b38
We stream a lot of our testing on twitch: https://www.twitch.tv/duskfallcrew
any chance you can spare a coffee or three? https://ko-fi.com/DUSKFALLcrew
1. Generation sites MAY use this as long as request/credit is given.
2. We are NOT RESPONSIBLE FOR YOUR use/downstream anything with this model.
3. DO NOT PRODUCE ILLEGAL CONTENT WITH THIS MODEL - we're still not responsible, we just said don't do it.
4. DO USE THIS.
5. Do feel free to ask for the safe tensors in another repo!
|
Alicanke/Wyau
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 690.50 +/- 299.30
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dawoz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dawoz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dawoz
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Alireza1044/albert-base-v2-qnli
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
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"AlbertForSequenceClassification"
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}
| 41 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-kl_01_05-hs_cn
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. -->
# gpt2-kl_01_05-hs_cn
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5387
## 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: 8
- eval_batch_size: 4
- seed: 21
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 73.5669 | 0.02 | 10 | 69.5838 |
| 46.1192 | 0.04 | 20 | 32.9319 |
| 13.5763 | 0.06 | 30 | 10.6437 |
| 5.6862 | 0.08 | 40 | 4.3509 |
| 2.8355 | 0.1 | 50 | 1.9914 |
| 1.4127 | 0.12 | 60 | 1.0386 |
| 1.139 | 0.14 | 70 | 0.8992 |
| 0.9191 | 0.16 | 80 | 0.7150 |
| 0.7454 | 0.18 | 90 | 0.7040 |
| 0.7465 | 0.2 | 100 | 0.6307 |
| 0.6444 | 0.22 | 110 | 0.6424 |
| 0.6783 | 0.24 | 120 | 0.6040 |
| 0.6724 | 0.26 | 130 | 0.6014 |
| 0.6898 | 0.28 | 140 | 0.6155 |
| 0.6583 | 0.3 | 150 | 0.5748 |
| 0.6234 | 0.32 | 160 | 0.5870 |
| 0.5572 | 0.34 | 170 | 0.5669 |
| 0.6596 | 0.36 | 180 | 0.5635 |
| 0.6763 | 0.38 | 190 | 0.5650 |
| 0.6112 | 0.4 | 200 | 0.5616 |
| 0.7173 | 0.42 | 210 | 0.5608 |
| 0.6714 | 0.44 | 220 | 0.5604 |
| 0.5898 | 0.46 | 230 | 0.5624 |
| 0.5849 | 0.48 | 240 | 0.5570 |
| 0.5825 | 0.5 | 250 | 0.5556 |
| 0.6123 | 0.52 | 260 | 0.5440 |
| 0.5956 | 0.54 | 270 | 0.5397 |
| 0.634 | 0.56 | 280 | 0.5404 |
| 0.6152 | 0.58 | 290 | 0.5387 |
| 0.5719 | 0.6 | 300 | 0.5396 |
| 0.587 | 0.62 | 310 | 0.5363 |
| 0.6913 | 0.64 | 320 | 0.5357 |
| 0.5504 | 0.66 | 330 | 0.5409 |
| 0.545 | 0.68 | 340 | 0.5359 |
| 0.558 | 0.7 | 350 | 0.5387 |
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.12.0a0+bd13bc6
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Alireza1044/albert-base-v2-rte
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
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| 30 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: Marc-Elie/Pyramidsv0
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Amirosein/roberta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
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| 6 | null |
---
license: apache-2.0
tags:
- Composer
- MosaicML
- llm-foundry
- StreamingDatasets
datasets:
- mc4
- c4
- togethercomputer/RedPajama-Data-1T
- bigcode/the-stack
- allenai/s2orc
inference: false
---
# MPT-7B
MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code.
This model was trained by [MosaicML](https://www.mosaicml.com).
MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.
These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing
positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)).
Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence.
MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer).
This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference.
### How is this model different?
MPT-7B is
* **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)).
* **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)).
* **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409) (we finetuned [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter) on up to 65k inputs and can handle up to 84k vs. 2k-4k for other open source models).
* **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer))
* **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry)
### Models finetuned off MPT-7B:
The following models are finetuned on MPT-7B:
* [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter): a model designed to read and write fictional stories with super long context lengths.
Built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3).
At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens.
We demonstrate generations as long as 80k tokens on a single A100-80GB GPU in our [blogpost](www.mosaicml.com/blog/mpt-7b).
* License: Apache 2.0
* [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): a model for short-form instruction following.
Built by finetuning MPT-7B on a [dataset](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) we also release, derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets.
* License: _CC-By-SA-3.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct)
* [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat): a chatbot-like model for dialogue generation.
Built by finetuning MPT-7B on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3),
[Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets.
* License: _CC-By-NC-SA-4.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-chat)
## Model Date
May 5, 2023
## Model License
Apache-2.0
## Documentation
* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-1btms90mc-GipE2ufuPkKY0QBrmF3LSA)!
## How to Use
This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b',
trust_remote_code=True
)
```
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`:
```python
config = transformers.AutoConfig.from_pretrained(
'mosaicml/mpt-7b',
trust_remote_code=True
)
config.attn_config['attn_impl'] = 'triton'
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b',
config=config,
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
model.to(device='cuda:0')
```
Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
```python
config = transformers.AutoConfig.from_pretrained(
'mosaicml/mpt-7b',
trust_remote_code=True
)
config.update({"max_seq_len": 4096})
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b',
config=config,
trust_remote_code=True
)
```
This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
```
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 6.7B |
|n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | 2048 |
## Training Data
### Streaming Datasets
Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training.
StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset.
### Data Mix
The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix:
| Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs |
|-------------|----------------------------|------------|----------------------------|--------|
| mC4 3.1.0 - English | 417.99 B | 0.33 | 330 B | 0.14 |
| C4 - English - SemDedup 80% | 100.42 B | 0.299 | 299 B | 2.98 |
| RedPajama - CommonCrawl | 878.45 B | 0.1 | 100 B | 0.11 |
| The Stack - Selected Languages | 463.78 B | 0.1 | 100 B | 0.22 |
| RedPajama - Wikipedia - En | 4.87 B | 0.04 | 40 B | 8.21 |
| The Stack - Markdown | 107.07 B | 0.035 | 35 B | 0.33 |
| S2ORC | 48.85 B | 0.033 | 33 B | 0.68 |
| RedPajama - Books | 26.02 B | 0.03 | 30B | 1.15 |
| RedPajama - arXiv | 28.10 B | 0.019 | 19 B | 0.68 |
| RedPajama - StackExchange | 20.54 B | 0.014 | 14 B |0.68 |
Samples for each batch were selected from one of the datasets with the probability specified above.
The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics,
most of which are relevant for tokenizing code:
(1) It was trained on a diverse mix of data that includes code (The Pile)
(2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces
(3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters.
The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points.
### Training Configuration
This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform).
The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-7B (Base) is **not** intended for deployment without finetuning.
It should not be used for human-facing interactions without further guardrails and user consent.
MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b).
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source,
ly Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
```
|
Anji/roberta-base-squad2-finetuned-squad
|
[] | null |
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| 0 | null |
---
license: openrail
language:
- en
library_name: transformers
pipeline_tag: text-to-image
tags:
- image-generation
- dall-e
---
# Overview
[[Blog]](https://openai.com/blog/dall-e/) [[Paper]](https://arxiv.org/abs/2102.12092) [[Model Card]](model_card.md) [[Usage]](notebooks/usage.ipynb)
This is the official PyTorch package for the discrete VAE used for DALL·E. The transformer used to generate the images from the text is not part of this code release.
# Installation
Before running [the example notebook](notebooks/usage.ipynb), you will need to install the package using
pip install DALL-E
|
AnonymousSub/AR_cline
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
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}
}
| 2 | null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: Arindam75/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/AR_rule_based_roberta_bert_triplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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}
| 4 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 250.82 +/- 21.03
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"RobertaModel"
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}
| 6 | null |
---
pipeline_tag: image-classification
metrics:
- accuracy
---
|
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"RobertaModel"
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| 2 | null |
---
license: mit
datasets:
- databricks/databricks-dolly-15k
language:
- en
library_name: ggml
---
Unofficial ggml Dolly-v2-3b models. These are intended to use with the ggml dolly-v2 example: https://github.com/ggerganov/ggml/tree/master/examples/dolly-v2
This requires more testing (both the ggml example and the ggml model conversions), use at your own risk.
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
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}
| 8 | null |
---
language:
- pt
tags:
- albertina-pt*
- albertina-ptpt
- albertina-ptbr
- fill-mask
- bert
- deberta
- portuguese
- encoder
- foundation model
license: other
datasets:
- brwac
- PORTULAN/glue-ptpt
- assin2
- dlb/plue
widget:
- text: >-
A culinária brasileira é rica em sabores e [MASK], tornando-se um dos
maiores patrimônios do país.
---
# Albertina PT-BR
---
<img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png">
<p style="text-align: center;"> We will soon release distilled models and <b>Albertina PT-BR v2</b>,
trained on a data set with most permissive license.</p>
---
**Albertina PT-*** is a foundation, large language model for the **Portuguese language**.
It is an **encoder** of the BERT family, based on the neural architecture Transformer and
developed over the DeBERTa model, and with most competitive performance for this language.
It has different versions that were trained for different variants of Portuguese (PT),
namely the European variant from Portugal (**PT-PT**) and the American variant from Brazil (**PT-BR**),
and it is distributed free of charge and under a most permissible license.
**Albertina PT-BR** is the version for American **Portuguese** from **Brazil**,
and to the best of our knowledge, at the time of its initial distribution,
it is an encoder specifically for this language and variant
that sets a new state of the art for it, and is made publicly available
and distributed for reuse.
It is developed by a joint team from the University of Lisbon and the University of Porto, Portugal.
For further details, check the respective [publication](https://arxiv.org/abs/2305.06721):
``` latex
@misc{albertina-pt,
title={Advancing Neural Encoding of Portuguese
with Transformer Albertina PT-*},
author={João Rodrigues and Luís Gomes and João Silva and
António Branco and Rodrigo Santos and
Henrique Lopes Cardoso and Tomás Osório},
year={2023},
eprint={2305.06721},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please use the above cannonical reference when using or citing this model.
<br>
# Model Description
**This model card is for Albertina-PT-BR**, with 900M parameters, 24 layers and a hidden size of 1536.
This model is distributed respecting the license granted
by the [BrWac](https://huggingface.co/datasets/brwac) data set on which it was trained,
namely that it is "available solely for academic research purposes,
and you agreed not to use it for any commercial applications".
<br>
# Training Data
**Albertina PT-BR** was trained over the 2.7 billion token [BrWac](https://huggingface.co/datasets/brwac) data set.
[**Albertina PT-PT**](https://huggingface.co/PORTULAN/albertina-ptpt), in turn, was trained over a 2.2 billion token data set that resulted from gathering some openly available corpora of European Portuguese from the following sources:
- [OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301): the OSCAR data set includes documents in more than one hundred languages, including Portuguese, and it is widely used in the literature. It is the result of a selection performed over the [Common Crawl](https://commoncrawl.org/) data set, crawled from the Web, that retains only pages whose metadata indicates permission to be crawled, that performs deduplication, and that removes some boilerplate, among other filters. Given that it does not discriminate between the Portuguese variants, we performed extra filtering by retaining only documents whose meta-data indicate the Internet country code top-level domain of Portugal. We used the January 2023 version of OSCAR, which is based on the November/December 2022 version of Common Crawl.
- [DCEP](https://joint-research-centre.ec.europa.eu/language-technology-resources/dcep-digital-corpus-european-parliament_en): the Digital Corpus of the European Parliament is a multilingual corpus including documents in all official EU languages published on the European Parliament's official website. We retained its European Portuguese portion.
- [Europarl](https://www.statmt.org/europarl/): the European Parliament Proceedings Parallel Corpus is extracted from the proceedings of the European Parliament from 1996 to 2011. We retained its European Portuguese portion.
- [ParlamentoPT](https://huggingface.co/datasets/PORTULAN/parlamento-pt): the ParlamentoPT is a data set we obtained by gathering the publicly available documents with the transcription of the debates in the Portuguese Parliament.
## Preprocessing
We filtered the PT-PT corpora using the [BLOOM pre-processing](https://github.com/bigscience-workshop/data-preparation) pipeline, resulting in a data set of 8 million documents, containing around 2.2 billion tokens.
We skipped the default filtering of stopwords since it would disrupt the syntactic structure, and also the filtering for language identification given the corpus was pre-selected as Portuguese.
## Training
As codebase, we resorted to the [DeBERTa V2 XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge), for English.
To train **Albertina-PT-BR** the BrWac data set was tokenized with the original DeBERTA tokenizer with a 128 token sequence truncation and dynamic padding.
The model was trained using the maximum available memory capacity resulting in a batch size of 896 samples (56 samples per GPU without gradient accumulation steps).
We chose a learning rate of 1e-5 with linear decay and 10k warm-up steps based on the results of exploratory experiments.
In total, around 200k training steps were taken across 50 epochs.
The model was trained for 1 day and 11 hours on a2-megagpu-16gb Google Cloud A2 VMs with 16 GPUs, 96 vCPUs and 1.360 GB of RAM.
To train [**Albertina PT-PT**](https://huggingface.co/PORTULAN/albertina-ptpt), the data set was tokenized with the original DeBERTa tokenizer with a 128 token sequence truncation and dynamic padding.
The model was trained using the maximum available memory capacity resulting in a batch size of 832 samples (52 samples per GPU and applying gradient accumulation in order to approximate the batch size of the PT-BR model).
Similarly to the PT-BR variant above, we opted for a learning rate of 1e-5 with linear decay and 10k warm-up steps.
However, since the number of training examples is approximately twice of that in the PT-BR variant, we reduced the number of training epochs to half and completed only 25 epochs, which resulted in approximately 245k steps.
The model was trained for 3 days on a2-highgpu-8gb Google Cloud A2 VMs with 8 GPUs, 96 vCPUs and 680 GB of RAM.
<br>
# Evaluation
The two model versions were evaluated on downstream tasks organized into two groups.
In one group, we have the two data sets from the [ASSIN 2 benchmark](https://huggingface.co/datasets/assin2), namely STS and RTE, that were used to evaluate the previous state-of-the-art model [BERTimbau Large](https://huggingface.co/neuralmind/bert-large-portuguese-cased).
In the other group of data sets, we have the translations into PT-BR and PT-PT of the English data sets used for a few of the tasks in the widely-used [GLUE benchmark](https://huggingface.co/datasets/glue), which allowed us to test both Albertina-PT-* variants on a wider variety of downstream tasks.
## ASSIN 2
[ASSIN 2](https://huggingface.co/datasets/assin2) is a **PT-BR data** set of approximately 10.000 sentence pairs, split into 6.500 for training, 500 for validation, and 2.448 for testing, annotated with semantic relatedness scores (range 1 to 5) and with binary entailment judgments.
This data set supports the task of semantic textual similarity (STS), which consists of assigning a score of how semantically related two sentences are; and the task of recognizing textual entailment (RTE), which given a pair of sentences, consists of determining whether the first entails the second.
| Model | RTE (Accuracy) | STS (Pearson)|
|---------------------|----------------|--------------|
| **Albertina-PT-BR** | **0.9130** | **0.8676** |
| BERTimbau-large | 0.8913 | 0.8531 |
## GLUE tasks translated
We resort to [PLUE](https://huggingface.co/datasets/dlb/plue) (Portuguese Language Understanding Evaluation), a data set that was obtained by automatically translating GLUE into **PT-BR**.
We address four tasks from those in PLUE, namely:
- two similarity tasks: MRPC, for detecting whether two sentences are paraphrases of each other, and STS-B, for semantic textual similarity;
- and two inference tasks: RTE, for recognizing textual entailment and WNLI, for coreference and natural language inference.
| Model | RTE (Accuracy) | WNLI (Accuracy)| MRPC (F1) | STS-B (Pearson) |
|---------------------|----------------|----------------|-----------|-----------------|
| **Albertina-PT-BR** | 0.7545 | 0.4601 | 0.9071 | **0.8910** |
| BERTimbau-large | 0.6546 | **0.5634** | 0.887 | 0.8842 |
| | | | | |
| **Albertina-PT-PT** | **0.7960** | 0.4507 | **0.9151**| 0.8799 |
We resorted to [GLUE-PT](https://huggingface.co/datasets/PORTULAN/glue-ptpt), a **PT-PT version of the GLUE** benchmark.
We automatically translated the same four tasks from GLUE using [DeepL Translate](https://www.deepl.com/), which specifically provides translation from English to PT-PT as an option.
| Model | RTE (Accuracy) | WNLI (Accuracy)| MRPC (F1) | STS-B (Pearson) |
|---------------------|----------------|----------------|-----------|-----------------|
| **Albertina-PT-PT** | **0.8339** | **0.4225** | **0.9171**| 0.8801 |
| | | | | |
| **Albertina-PT-BR** | 0.7942 | 0.4085 | 0.9048 | **0.8847** |
<br>
# How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='PORTULAN/albertina-ptbr')
>>> unmasker("A culinária brasileira é rica em sabores e [MASK], tornando-se um dos maiores patrimônios do país.")
[{'score': 0.6145166158676147, 'token': 23395, 'token_str': 'aromas', 'sequence': 'A culinária brasileira é rica em sabores e aromas, tornando-se um dos maiores patrimônios do país.'},
{'score': 0.1720353364944458, 'token': 21925, 'token_str': 'cores', 'sequence': 'A culinária brasileira é rica em sabores e cores, tornando-se um dos maiores patrimônios do país.'},
{'score': 0.1438736468553543, 'token': 10392, 'token_str': 'costumes', 'sequence': 'A culinária brasileira é rica em sabores e costumes, tornando-se um dos maiores patrimônios do país.'},
{'score': 0.02997930906713009, 'token': 117371, 'token_str': 'cultura', 'sequence': 'A culinária brasileira é rica em sabores e cultura, tornando-se um dos maiores patrimônios do país.'},
{'score': 0.015540072694420815, 'token': 22647, 'token_str': 'nuances', 'sequence': 'A culinária brasileira é rica em sabores e nuances, tornando-se um dos maiores patrimônios do país.'}]
```
The model can be used by fine-tuning it for a specific task:
```python
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
>>> from datasets import load_dataset
>>> model = AutoModelForSequenceClassification.from_pretrained("PORTULAN/albertina-ptbr", num_labels=2)
>>> tokenizer = AutoTokenizer.from_pretrained("PORTULAN/albertina-ptbr")
>>> dataset = load_dataset("PORTULAN/glue-ptpt", "rte")
>>> def tokenize_function(examples):
... return tokenizer(examples["sentence1"], examples["sentence2"], padding="max_length", truncation=True)
>>> tokenized_datasets = dataset.map(tokenize_function, batched=True)
>>> training_args = TrainingArguments(output_dir="albertina-ptbr-rte", evaluation_strategy="epoch")
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_datasets["train"],
... eval_dataset=tokenized_datasets["validation"],
... )
>>> trainer.train()
```
<br>
# Citation
When using or citing this model, kindly cite the following [publication](https://arxiv.org/abs/2305.06721):
``` latex
@misc{albertina-pt,
title={Advancing Neural Encoding of Portuguese
with Transformer Albertina PT-*},
author={João Rodrigues and Luís Gomes and João Silva and
António Branco and Rodrigo Santos and
Henrique Lopes Cardoso and Tomás Osório},
year={2023},
eprint={2305.06721},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<br>
# Acknowledgments
The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language,
funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the
grant PINFRA/22117/2016; research project ALBERTINA - Foundation Encoder Model for Portuguese and AI, funded by FCT—Fundação para a Ciência e Tecnologia under the
grant CPCA-IAC/AV/478394/2022; innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização; and LIACC - Laboratory for AI and Computer Science, funded by FCT—Fundação para a Ciência e Tecnologia under the grant FCT/UID/CEC/0027/2020.
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"prefix": null
}
}
}
| 8 | 2023-05-08T16:52:11Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Udit191/autotrain-data-summarization-led_base
co2_eq_emissions:
emissions: 20.77094576685784
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 56565131119
- CO2 Emissions (in grams): 20.7709
## Validation Metrics
- Loss: 2.506
- Rouge1: 48.873
- Rouge2: 20.930
- RougeL: 26.731
- RougeLsum: 43.847
- Gen Len: 230.300
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Udit191/autotrain-summarization-led_base-56565131119
```
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
language:
- uk
tags:
- text2text-generation
- flair
library_name: generic
license: mit
metrics:
- perplexity
datasets:
- ubertext2.0
widget:
- text: "Росія зазнає поразки"
- text: "Достеменно відомо, що Україна перемагає"
---
# Ukrainian flair embeddings (forward, large)
Trained for 10 epochs on the texts from ubertext2.0 and corpus of Ukrainian scraped texts from Stefan Schweter (54GB in total).
This is the **forward** version of the embeddings. You can find the backward version [here](https://huggingface.co/lang-uk/flair-uk-backward-large/)
The characters dictionary used for training is in `flair_dictionary.pkl` file
The model params are:
```python
is_forward_lm=True,
hidden_size=2048,
sequence_length=250,
mini_batch_size=1024,
max_epochs=30
```
For smaller size flair embeddings of the Ukrainian language please check [uk-forward](https://huggingface.co/lang-uk/flair-uk-forward)
For more information on flair embeddings, see [the article](https://github.com/flairNLP/flair/blob/master/resources/docs/embeddings/FLAIR_EMBEDDINGS.md) or the paper below:
```bibtex
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
For more information on UberText 2.0 please see:
```bibtex
@inproceedings{chaplynskyi-2023-introducing,
title = "Introducing {U}ber{T}ext 2.0: A Corpus of {M}odern {U}krainian at Scale",
author = "Chaplynskyi, Dmytro",
booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.unlp-1.1",
pages = "1--10",
abstract = "This paper addresses the need for massive corpora for a low-resource language and presents the publicly available UberText 2.0 corpus for the Ukrainian language and discusses the methodology of its construction. While the collection and maintenance of such a corpus is more of a data extraction and data engineering task, the corpus itself provides a solid foundation for natural language processing tasks. It can enable the creation of contemporary language models and word embeddings, resulting in a better performance of numerous downstream tasks for the Ukrainian language. In addition, the paper and software developed can be used as a guidance and model solution for other low-resource languages. The resulting corpus is available for download on the project page. It has 3.274 billion tokens, consists of 8.59 million texts and takes up 32 gigabytes of space.",
}
```
Copyright: [Dmytro Chaplynskyi](https://twitter.com/dchaplinsky), [lang-uk](https://lang.org.ua) project, 2023
|
AnonymousSub/SR_rule_based_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3 | 2023-05-08T17:02:54Z |
---
language:
- uk
tags:
- text2text-generation
- flair
library_name: generic
license: mit
metrics:
- perplexity
datasets:
- ubertext2.0
widget:
- text: "підсумував він."
- text: "Україна переможе!"
---
# Ukrainian flair embeddings (backward, large)
Trained for 8 epochs on the texts from ubertext2.0 and corpus of Ukrainian scraped texts from Stefan Schweter (54GB in total).
This is the **backward** version of the embeddings. You can find the forward version [here](https://huggingface.co/lang-uk/flair-uk-forward-large/)
The characters dictionary used for training is in `flair_dictionary.pkl` file
The model params are:
```python
is_forward_lm=False,
hidden_size=2048,
sequence_length=250,
mini_batch_size=1024,
max_epochs=30
```
For smaller size flair embeddings of the Ukrainian language please check [uk-backward](https://huggingface.co/lang-uk/flair-uk-backward)
For more information on flair embeddings, see [the article](https://github.com/flairNLP/flair/blob/master/resources/docs/embeddings/FLAIR_EMBEDDINGS.md) or the paper below:
```bibtex
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
For more information on UberText 2.0 please see:
```bibtex
@inproceedings{chaplynskyi-2023-introducing,
title = "Introducing {U}ber{T}ext 2.0: A Corpus of {M}odern {U}krainian at Scale",
author = "Chaplynskyi, Dmytro",
booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.unlp-1.1",
pages = "1--10",
abstract = "This paper addresses the need for massive corpora for a low-resource language and presents the publicly available UberText 2.0 corpus for the Ukrainian language and discusses the methodology of its construction. While the collection and maintenance of such a corpus is more of a data extraction and data engineering task, the corpus itself provides a solid foundation for natural language processing tasks. It can enable the creation of contemporary language models and word embeddings, resulting in a better performance of numerous downstream tasks for the Ukrainian language. In addition, the paper and software developed can be used as a guidance and model solution for other low-resource languages. The resulting corpus is available for download on the project page. It has 3.274 billion tokens, consists of 8.59 million texts and takes up 32 gigabytes of space.",
}
```
Copyright: [Dmytro Chaplynskyi](https://twitter.com/dchaplinsky), [lang-uk](https://lang.org.ua) project, 2023
|
AnonymousSub/bert-base-uncased_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 3 | null |
Access to model Christopher0603/eve is restricted and you are not in the authorized list. Visit https://huggingface.co/Christopher0603/eve to ask for access.
|
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
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"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | 2023-05-08T17:12:04Z |
---
license: other
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: opt-2.7b-realtime-chat-v2
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. -->
# opt-2.7b-realtime-chat-v2
This model is a fine-tuned version of [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0888
- Accuracy: 0.6870
## 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: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0974 | 0.5 | 51 | 2.1267 | 0.6826 |
| 2.0842 | 1.0 | 102 | 2.0968 | 0.6859 |
| 1.9624 | 1.49 | 153 | 2.0936 | 0.6863 |
| 1.9476 | 1.99 | 204 | 2.0888 | 0.6870 |
| 1.888 | 2.49 | 255 | 2.0993 | 0.6864 |
| 1.8687 | 2.99 | 306 | 2.0994 | 0.6865 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.12.1
|
AnonymousSub/bert_mean_diff_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
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},
"translation_en_to_ro": {
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}
}
}
| 6 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1299.15 +/- 208.60
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/bert_mean_diff_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.96 +/- 16.77
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/bert_triplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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"num_beams": null,
"prefix": null
},
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| 2 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# gszabo/distiluse-base-multilingual-cased-v2-epoch30-only-train
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('gszabo/distiluse-base-multilingual-cased-v2-epoch30-only-train')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=gszabo/distiluse-base-multilingual-cased-v2-epoch30-only-train)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 751 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss`
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/cline-papers-roberta-0.585
|
[
"pytorch",
"roberta",
"transformers"
] | null |
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| 1 | null |
---
license: apache-2.0
inference: false
---
**NOTE: This "delta model" cannot be used directly.**
Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights.
See https://github.com/haotian-liu/LLaVA#llava-weights for instructions.
<br>
<br>
# LLaVA Model Card
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA was trained in May 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
**License:**
Apache License 2.0
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
595K filtered image-text pairs from CC3M.
150K GPT-generated multimodal instruction-following data.
## Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs.
We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset.
See https://llava-vl.github.io/ for more details.
|
AnonymousSub/cline_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"RobertaForSequenceClassification"
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| 27 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### inpaint_furniture Dreambooth model trained by rohan1221 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
AnonymousSub/dummy_1
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
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}
| 33 | null |
---
tags:
- generated_from_keras_callback
model-index:
- name: Bert_class_1e-07
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Bert_class_1e-07
This model is a fine-tuned version of [guoluo/Bert_1.5e_07](https://huggingface.co/guoluo/Bert_1.5e_07) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0102
- Train Accuracy: 1.0
- Validation Loss: 1.7238
- Validation Accuracy: 0.6972
- Train Lr: 4.4946695e-08
- Epoch: 3999
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 4.4946695e-08, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Lr | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-------------:|:-----:|
| 1.4508 | 0.1647 | 1.4468 | 0.1408 | 1e-07 | 0 |
| 1.4039 | 0.1953 | 1.3961 | 0.1901 | 9.9999994e-08 | 1 |
| 1.3625 | 0.2612 | 1.3495 | 0.2817 | 9.999997e-08 | 2 |
| 1.3186 | 0.3788 | 1.3070 | 0.4930 | 9.9999944e-08 | 3 |
| 1.2774 | 0.5129 | 1.2667 | 0.6197 | 9.99999e-08 | 4 |
| 1.2385 | 0.5976 | 1.2318 | 0.6549 | 9.999985e-08 | 5 |
| 1.2131 | 0.6329 | 1.2005 | 0.6761 | 9.9999795e-08 | 6 |
| 1.1789 | 0.6565 | 1.1730 | 0.6761 | 9.9999724e-08 | 7 |
| 1.1624 | 0.6753 | 1.1462 | 0.6761 | 9.9999646e-08 | 8 |
| 1.1323 | 0.6753 | 1.1232 | 0.6761 | 9.999955e-08 | 9 |
| 1.1121 | 0.6776 | 1.1041 | 0.6761 | 9.9999454e-08 | 10 |
| 1.0925 | 0.6776 | 1.0864 | 0.6761 | 9.999935e-08 | 11 |
| 1.0686 | 0.6776 | 1.0705 | 0.6761 | 9.999923e-08 | 12 |
| 1.0624 | 0.6776 | 1.0586 | 0.6761 | 9.99991e-08 | 13 |
| 1.0507 | 0.6776 | 1.0460 | 0.6761 | 9.999896e-08 | 14 |
| 1.0419 | 0.6776 | 1.0358 | 0.6761 | 9.999881e-08 | 15 |
| 1.0323 | 0.6776 | 1.0266 | 0.6761 | 9.9998644e-08 | 16 |
| 1.0233 | 0.6776 | 1.0185 | 0.6761 | 9.999847e-08 | 17 |
| 1.0176 | 0.6776 | 1.0113 | 0.6761 | 9.9998296e-08 | 18 |
| 1.0026 | 0.6776 | 1.0049 | 0.6761 | 9.9998104e-08 | 19 |
| 1.0017 | 0.6776 | 0.9997 | 0.6761 | 9.9997905e-08 | 20 |
| 0.9869 | 0.6776 | 0.9946 | 0.6761 | 9.999769e-08 | 21 |
| 0.9874 | 0.6776 | 0.9902 | 0.6761 | 9.999747e-08 | 22 |
| 0.9813 | 0.6776 | 0.9862 | 0.6761 | 9.9997244e-08 | 23 |
| 0.9751 | 0.6776 | 0.9827 | 0.6761 | 9.9997e-08 | 24 |
| 0.9752 | 0.6776 | 0.9799 | 0.6761 | 9.9996754e-08 | 25 |
| 0.9753 | 0.6776 | 0.9771 | 0.6761 | 9.999649e-08 | 26 |
| 0.9704 | 0.6776 | 0.9752 | 0.6761 | 9.999622e-08 | 27 |
| 0.9629 | 0.6776 | 0.9731 | 0.6761 | 9.9995944e-08 | 28 |
| 0.9688 | 0.6776 | 0.9716 | 0.6761 | 9.999565e-08 | 29 |
| 0.9558 | 0.6776 | 0.9698 | 0.6761 | 9.9995354e-08 | 30 |
| 0.9666 | 0.6776 | 0.9681 | 0.6761 | 9.999504e-08 | 31 |
| 0.9599 | 0.6776 | 0.9667 | 0.6761 | 9.999472e-08 | 32 |
| 0.9532 | 0.6776 | 0.9653 | 0.6761 | 9.9994395e-08 | 33 |
| 0.9484 | 0.6776 | 0.9640 | 0.6761 | 9.9994054e-08 | 34 |
| 0.9447 | 0.6776 | 0.9629 | 0.6761 | 9.9993706e-08 | 35 |
| 0.9481 | 0.6776 | 0.9619 | 0.6761 | 9.999334e-08 | 36 |
| 0.9440 | 0.6776 | 0.9609 | 0.6761 | 9.9992974e-08 | 37 |
| 0.9474 | 0.6776 | 0.9599 | 0.6761 | 9.99926e-08 | 38 |
| 0.9468 | 0.6776 | 0.9591 | 0.6761 | 9.999221e-08 | 39 |
| 0.9512 | 0.6776 | 0.9582 | 0.6761 | 9.999181e-08 | 40 |
| 0.9437 | 0.6776 | 0.9574 | 0.6761 | 9.99914e-08 | 41 |
| 0.9430 | 0.6776 | 0.9566 | 0.6761 | 9.999098e-08 | 42 |
| 0.9372 | 0.6776 | 0.9560 | 0.6761 | 9.9990544e-08 | 43 |
| 0.9351 | 0.6776 | 0.9552 | 0.6761 | 9.99901e-08 | 44 |
| 0.9323 | 0.6776 | 0.9545 | 0.6761 | 9.9989656e-08 | 45 |
| 0.9300 | 0.6776 | 0.9538 | 0.6761 | 9.9989194e-08 | 46 |
| 0.9310 | 0.6776 | 0.9532 | 0.6761 | 9.9988725e-08 | 47 |
| 0.9332 | 0.6776 | 0.9527 | 0.6761 | 9.998824e-08 | 48 |
| 0.9280 | 0.6776 | 0.9521 | 0.6761 | 9.998775e-08 | 49 |
| 0.9335 | 0.6776 | 0.9515 | 0.6761 | 9.9987254e-08 | 50 |
| 0.9278 | 0.6776 | 0.9509 | 0.6761 | 9.998674e-08 | 51 |
| 0.9259 | 0.6776 | 0.9503 | 0.6761 | 9.9986224e-08 | 52 |
| 0.9329 | 0.6776 | 0.9496 | 0.6761 | 9.998569e-08 | 53 |
| 0.9235 | 0.6776 | 0.9491 | 0.6761 | 9.998515e-08 | 54 |
| 0.9306 | 0.6776 | 0.9485 | 0.6761 | 9.9984604e-08 | 55 |
| 0.9229 | 0.6776 | 0.9480 | 0.6761 | 9.998404e-08 | 56 |
| 0.9215 | 0.6776 | 0.9475 | 0.6761 | 9.9983474e-08 | 57 |
| 0.9220 | 0.6776 | 0.9469 | 0.6761 | 9.998289e-08 | 58 |
| 0.9236 | 0.6776 | 0.9464 | 0.6761 | 9.99823e-08 | 59 |
| 0.9212 | 0.6776 | 0.9460 | 0.6761 | 9.9981705e-08 | 60 |
| 0.9134 | 0.6776 | 0.9454 | 0.6761 | 9.9981094e-08 | 61 |
| 0.9215 | 0.6776 | 0.9448 | 0.6761 | 9.9980475e-08 | 62 |
| 0.9192 | 0.6776 | 0.9442 | 0.6761 | 9.997984e-08 | 63 |
| 0.9167 | 0.6776 | 0.9439 | 0.6761 | 9.9979204e-08 | 64 |
| 0.9194 | 0.6776 | 0.9433 | 0.6761 | 9.997856e-08 | 65 |
| 0.9142 | 0.6776 | 0.9428 | 0.6761 | 9.9977896e-08 | 66 |
| 0.9135 | 0.6776 | 0.9423 | 0.6761 | 9.997723e-08 | 67 |
| 0.9058 | 0.6776 | 0.9419 | 0.6761 | 9.9976546e-08 | 68 |
| 0.9134 | 0.6776 | 0.9415 | 0.6761 | 9.997586e-08 | 69 |
| 0.9129 | 0.6776 | 0.9411 | 0.6761 | 9.997516e-08 | 70 |
| 0.9128 | 0.6776 | 0.9407 | 0.6761 | 9.997445e-08 | 71 |
| 0.9099 | 0.6776 | 0.9404 | 0.6761 | 9.997373e-08 | 72 |
| 0.9110 | 0.6776 | 0.9400 | 0.6761 | 9.9973e-08 | 73 |
| 0.8994 | 0.6776 | 0.9394 | 0.6761 | 9.997226e-08 | 74 |
| 0.9065 | 0.6776 | 0.9388 | 0.6761 | 9.997151e-08 | 75 |
| 0.9038 | 0.6776 | 0.9382 | 0.6761 | 9.997075e-08 | 76 |
| 0.9062 | 0.6776 | 0.9376 | 0.6761 | 9.996998e-08 | 77 |
| 0.9011 | 0.6776 | 0.9370 | 0.6761 | 9.99692e-08 | 78 |
| 0.9015 | 0.6776 | 0.9366 | 0.6761 | 9.996841e-08 | 79 |
| 0.8978 | 0.6776 | 0.9361 | 0.6761 | 9.996761e-08 | 80 |
| 0.9003 | 0.6776 | 0.9355 | 0.6761 | 9.99668e-08 | 81 |
| 0.9023 | 0.6776 | 0.9349 | 0.6761 | 9.996598e-08 | 82 |
| 0.9083 | 0.6776 | 0.9345 | 0.6761 | 9.996515e-08 | 83 |
| 0.8979 | 0.6776 | 0.9341 | 0.6761 | 9.996431e-08 | 84 |
| 0.8943 | 0.6776 | 0.9334 | 0.6761 | 9.996346e-08 | 85 |
| 0.8877 | 0.6776 | 0.9328 | 0.6761 | 9.99626e-08 | 86 |
| 0.8946 | 0.6776 | 0.9322 | 0.6761 | 9.996173e-08 | 87 |
| 0.8964 | 0.6776 | 0.9318 | 0.6761 | 9.996085e-08 | 88 |
| 0.8905 | 0.6776 | 0.9313 | 0.6761 | 9.995996e-08 | 89 |
| 0.8941 | 0.6776 | 0.9307 | 0.6761 | 9.995906e-08 | 90 |
| 0.8883 | 0.6776 | 0.9302 | 0.6761 | 9.995815e-08 | 91 |
| 0.8906 | 0.6776 | 0.9297 | 0.6761 | 9.9957234e-08 | 92 |
| 0.8901 | 0.6776 | 0.9291 | 0.6761 | 9.99563e-08 | 93 |
| 0.8811 | 0.6776 | 0.9287 | 0.6761 | 9.9955365e-08 | 94 |
| 0.8866 | 0.6800 | 0.9283 | 0.6761 | 9.995441e-08 | 95 |
| 0.8830 | 0.6800 | 0.9278 | 0.6761 | 9.995345e-08 | 96 |
| 0.8810 | 0.6800 | 0.9272 | 0.6761 | 9.995249e-08 | 97 |
| 0.8823 | 0.6776 | 0.9266 | 0.6761 | 9.995151e-08 | 98 |
| 0.8852 | 0.6776 | 0.9259 | 0.6761 | 9.995052e-08 | 99 |
| 0.8770 | 0.6776 | 0.9253 | 0.6761 | 9.994952e-08 | 100 |
| 0.8847 | 0.6800 | 0.9246 | 0.6761 | 9.994851e-08 | 101 |
| 0.8823 | 0.6776 | 0.9241 | 0.6761 | 9.994749e-08 | 102 |
| 0.8843 | 0.6776 | 0.9237 | 0.6761 | 9.994646e-08 | 103 |
| 0.8753 | 0.6800 | 0.9229 | 0.6761 | 9.9945424e-08 | 104 |
| 0.8781 | 0.6824 | 0.9224 | 0.6761 | 9.994437e-08 | 105 |
| 0.8729 | 0.6800 | 0.9221 | 0.6761 | 9.9943314e-08 | 106 |
| 0.8797 | 0.6776 | 0.9217 | 0.6761 | 9.994224e-08 | 107 |
| 0.8728 | 0.6776 | 0.9211 | 0.6761 | 9.994116e-08 | 108 |
| 0.8768 | 0.6776 | 0.9207 | 0.6761 | 9.9940074e-08 | 109 |
| 0.8686 | 0.6776 | 0.9204 | 0.6761 | 9.993897e-08 | 110 |
| 0.8737 | 0.6824 | 0.9197 | 0.6761 | 9.9937864e-08 | 111 |
| 0.8722 | 0.6776 | 0.9190 | 0.6761 | 9.993674e-08 | 112 |
| 0.8702 | 0.6800 | 0.9185 | 0.6761 | 9.993561e-08 | 113 |
| 0.8663 | 0.6776 | 0.9179 | 0.6761 | 9.9934475e-08 | 114 |
| 0.8674 | 0.6800 | 0.9175 | 0.6761 | 9.9933324e-08 | 115 |
| 0.8639 | 0.6800 | 0.9171 | 0.6761 | 9.9932166e-08 | 116 |
| 0.8687 | 0.6800 | 0.9165 | 0.6761 | 9.993099e-08 | 117 |
| 0.8636 | 0.6800 | 0.9159 | 0.6761 | 9.9929814e-08 | 118 |
| 0.8623 | 0.6824 | 0.9156 | 0.6761 | 9.992863e-08 | 119 |
| 0.8685 | 0.6800 | 0.9154 | 0.6761 | 9.9927426e-08 | 120 |
| 0.8619 | 0.6800 | 0.9148 | 0.6761 | 9.992622e-08 | 121 |
| 0.8645 | 0.6800 | 0.9143 | 0.6761 | 9.9924996e-08 | 122 |
| 0.8535 | 0.6800 | 0.9135 | 0.6761 | 9.992377e-08 | 123 |
| 0.8547 | 0.6824 | 0.9131 | 0.6761 | 9.992253e-08 | 124 |
| 0.8631 | 0.6824 | 0.9126 | 0.6761 | 9.992128e-08 | 125 |
| 0.8538 | 0.6824 | 0.9118 | 0.6761 | 9.992002e-08 | 126 |
| 0.8532 | 0.6800 | 0.9112 | 0.6761 | 9.991875e-08 | 127 |
| 0.8595 | 0.6847 | 0.9107 | 0.6761 | 9.991747e-08 | 128 |
| 0.8527 | 0.6800 | 0.9100 | 0.6761 | 9.9916186e-08 | 129 |
| 0.8518 | 0.6776 | 0.9095 | 0.6761 | 9.9914885e-08 | 130 |
| 0.8459 | 0.6800 | 0.9088 | 0.6761 | 9.991358e-08 | 131 |
| 0.8501 | 0.6847 | 0.9082 | 0.6761 | 9.9912256e-08 | 132 |
| 0.8385 | 0.6824 | 0.9077 | 0.6761 | 9.991093e-08 | 133 |
| 0.8455 | 0.6776 | 0.9072 | 0.6761 | 9.990959e-08 | 134 |
| 0.8504 | 0.6824 | 0.9064 | 0.6761 | 9.990824e-08 | 135 |
| 0.8367 | 0.6824 | 0.9057 | 0.6761 | 9.9906885e-08 | 136 |
| 0.8402 | 0.6871 | 0.9054 | 0.6761 | 9.990551e-08 | 137 |
| 0.8430 | 0.6824 | 0.9047 | 0.6761 | 9.9904135e-08 | 138 |
| 0.8416 | 0.6847 | 0.9042 | 0.6761 | 9.990275e-08 | 139 |
| 0.8371 | 0.6824 | 0.9035 | 0.6761 | 9.990135e-08 | 140 |
| 0.8411 | 0.6871 | 0.9029 | 0.6761 | 9.989994e-08 | 141 |
| 0.8430 | 0.6824 | 0.9023 | 0.6761 | 9.989852e-08 | 142 |
| 0.8304 | 0.6847 | 0.9016 | 0.6761 | 9.989709e-08 | 143 |
| 0.8276 | 0.6847 | 0.9010 | 0.6761 | 9.989566e-08 | 144 |
| 0.8342 | 0.6847 | 0.9005 | 0.6761 | 9.989421e-08 | 145 |
| 0.8314 | 0.6824 | 0.9000 | 0.6761 | 9.989275e-08 | 146 |
| 0.8338 | 0.6847 | 0.8994 | 0.6761 | 9.989128e-08 | 147 |
| 0.8327 | 0.6847 | 0.8990 | 0.6761 | 9.98898e-08 | 148 |
| 0.8327 | 0.6847 | 0.8984 | 0.6761 | 9.988832e-08 | 149 |
| 0.8322 | 0.6847 | 0.8978 | 0.6761 | 9.988682e-08 | 150 |
| 0.8231 | 0.6894 | 0.8971 | 0.6761 | 9.988531e-08 | 151 |
| 0.8240 | 0.6871 | 0.8967 | 0.6761 | 9.988379e-08 | 152 |
| 0.8270 | 0.6847 | 0.8962 | 0.6761 | 9.9882264e-08 | 153 |
| 0.8216 | 0.6894 | 0.8958 | 0.6761 | 9.988073e-08 | 154 |
| 0.8283 | 0.6847 | 0.8953 | 0.6761 | 9.987918e-08 | 155 |
| 0.8211 | 0.6871 | 0.8944 | 0.6761 | 9.9877624e-08 | 156 |
| 0.8297 | 0.6918 | 0.8942 | 0.6761 | 9.9876054e-08 | 157 |
| 0.8211 | 0.6894 | 0.8936 | 0.6761 | 9.987448e-08 | 158 |
| 0.8155 | 0.6871 | 0.8929 | 0.6761 | 9.987289e-08 | 159 |
| 0.8119 | 0.6918 | 0.8927 | 0.6761 | 9.987129e-08 | 160 |
| 0.8152 | 0.6918 | 0.8919 | 0.6761 | 9.986969e-08 | 161 |
| 0.8116 | 0.6941 | 0.8913 | 0.6761 | 9.986807e-08 | 162 |
| 0.8142 | 0.6847 | 0.8906 | 0.6761 | 9.986644e-08 | 163 |
| 0.8187 | 0.6918 | 0.8901 | 0.6761 | 9.9864806e-08 | 164 |
| 0.8054 | 0.6918 | 0.8894 | 0.6761 | 9.986316e-08 | 165 |
| 0.8195 | 0.6894 | 0.8890 | 0.6761 | 9.98615e-08 | 166 |
| 0.8124 | 0.6894 | 0.8884 | 0.6761 | 9.985983e-08 | 167 |
| 0.8099 | 0.6847 | 0.8878 | 0.6761 | 9.9858156e-08 | 168 |
| 0.8060 | 0.6847 | 0.8872 | 0.6761 | 9.9856464e-08 | 169 |
| 0.8052 | 0.6918 | 0.8867 | 0.6761 | 9.9854766e-08 | 170 |
| 0.8073 | 0.6894 | 0.8864 | 0.6761 | 9.985306e-08 | 171 |
| 0.8077 | 0.6894 | 0.8858 | 0.6761 | 9.985134e-08 | 172 |
| 0.8022 | 0.6918 | 0.8853 | 0.6761 | 9.9849615e-08 | 173 |
| 0.8017 | 0.6894 | 0.8850 | 0.6761 | 9.9847874e-08 | 174 |
| 0.8025 | 0.6871 | 0.8846 | 0.6761 | 9.9846126e-08 | 175 |
| 0.7963 | 0.6965 | 0.8841 | 0.6761 | 9.984437e-08 | 176 |
| 0.8057 | 0.6941 | 0.8834 | 0.6690 | 9.98426e-08 | 177 |
| 0.7980 | 0.6871 | 0.8830 | 0.6690 | 9.9840825e-08 | 178 |
| 0.7916 | 0.6965 | 0.8823 | 0.6690 | 9.9839035e-08 | 179 |
| 0.7986 | 0.6988 | 0.8819 | 0.6690 | 9.983724e-08 | 180 |
| 0.7940 | 0.6941 | 0.8814 | 0.6690 | 9.983543e-08 | 181 |
| 0.7916 | 0.7035 | 0.8809 | 0.6690 | 9.983361e-08 | 182 |
| 0.7955 | 0.6941 | 0.8804 | 0.6690 | 9.983179e-08 | 183 |
| 0.7826 | 0.6871 | 0.8800 | 0.6690 | 9.982995e-08 | 184 |
| 0.7890 | 0.6965 | 0.8796 | 0.6690 | 9.98281e-08 | 185 |
| 0.7806 | 0.6894 | 0.8790 | 0.6690 | 9.9826245e-08 | 186 |
| 0.7863 | 0.6988 | 0.8787 | 0.6690 | 9.9824376e-08 | 187 |
| 0.7858 | 0.6941 | 0.8782 | 0.6690 | 9.98225e-08 | 188 |
| 0.7882 | 0.6988 | 0.8778 | 0.6690 | 9.982061e-08 | 189 |
| 0.7893 | 0.7012 | 0.8773 | 0.6690 | 9.981871e-08 | 190 |
| 0.7867 | 0.7012 | 0.8769 | 0.6690 | 9.981681e-08 | 191 |
| 0.7854 | 0.6941 | 0.8763 | 0.6690 | 9.981489e-08 | 192 |
| 0.7790 | 0.6894 | 0.8757 | 0.6761 | 9.9812965e-08 | 193 |
| 0.7874 | 0.7129 | 0.8752 | 0.6761 | 9.9811025e-08 | 194 |
| 0.7837 | 0.7012 | 0.8748 | 0.6761 | 9.980908e-08 | 195 |
| 0.7807 | 0.7035 | 0.8742 | 0.6761 | 9.9807124e-08 | 196 |
| 0.7797 | 0.7012 | 0.8738 | 0.6761 | 9.9805156e-08 | 197 |
| 0.7833 | 0.7106 | 0.8735 | 0.6761 | 9.980318e-08 | 198 |
| 0.7762 | 0.6988 | 0.8729 | 0.6761 | 9.980119e-08 | 199 |
| 0.7678 | 0.6988 | 0.8725 | 0.6761 | 9.9799195e-08 | 200 |
| 0.7771 | 0.7012 | 0.8722 | 0.6761 | 9.979719e-08 | 201 |
| 0.7729 | 0.7059 | 0.8717 | 0.6761 | 9.979517e-08 | 202 |
| 0.7729 | 0.7035 | 0.8714 | 0.6761 | 9.979315e-08 | 203 |
| 0.7722 | 0.7012 | 0.8710 | 0.6761 | 9.979111e-08 | 204 |
| 0.7705 | 0.7035 | 0.8706 | 0.6761 | 9.978906e-08 | 205 |
| 0.7588 | 0.7082 | 0.8704 | 0.6761 | 9.978701e-08 | 206 |
| 0.7616 | 0.7153 | 0.8699 | 0.6761 | 9.978494e-08 | 207 |
| 0.7722 | 0.7059 | 0.8695 | 0.6761 | 9.9782866e-08 | 208 |
| 0.7729 | 0.6988 | 0.8692 | 0.6761 | 9.9780785e-08 | 209 |
| 0.7601 | 0.6988 | 0.8687 | 0.6761 | 9.977869e-08 | 210 |
| 0.7627 | 0.7153 | 0.8684 | 0.6901 | 9.9776585e-08 | 211 |
| 0.7708 | 0.7059 | 0.8680 | 0.6901 | 9.977447e-08 | 212 |
| 0.7554 | 0.7153 | 0.8677 | 0.6901 | 9.977234e-08 | 213 |
| 0.7584 | 0.7059 | 0.8673 | 0.6901 | 9.977021e-08 | 214 |
| 0.7575 | 0.7176 | 0.8669 | 0.6901 | 9.9768066e-08 | 215 |
| 0.7501 | 0.7153 | 0.8665 | 0.6901 | 9.976591e-08 | 216 |
| 0.7515 | 0.7129 | 0.8661 | 0.6901 | 9.9763746e-08 | 217 |
| 0.7647 | 0.7176 | 0.8658 | 0.6831 | 9.976157e-08 | 218 |
| 0.7605 | 0.7318 | 0.8654 | 0.6831 | 9.975939e-08 | 219 |
| 0.7572 | 0.7129 | 0.8651 | 0.6831 | 9.9757195e-08 | 220 |
| 0.7531 | 0.7153 | 0.8647 | 0.6831 | 9.975499e-08 | 221 |
| 0.7501 | 0.7200 | 0.8644 | 0.6831 | 9.9752775e-08 | 222 |
| 0.7514 | 0.7129 | 0.8640 | 0.6831 | 9.975055e-08 | 223 |
| 0.7427 | 0.7318 | 0.8637 | 0.6831 | 9.974832e-08 | 224 |
| 0.7493 | 0.7106 | 0.8633 | 0.6831 | 9.9746075e-08 | 225 |
| 0.7533 | 0.7129 | 0.8628 | 0.6831 | 9.974382e-08 | 226 |
| 0.7429 | 0.7153 | 0.8625 | 0.6831 | 9.9741555e-08 | 227 |
| 0.7452 | 0.7294 | 0.8620 | 0.6831 | 9.973928e-08 | 228 |
| 0.7398 | 0.7200 | 0.8618 | 0.6901 | 9.9737e-08 | 229 |
| 0.7365 | 0.7271 | 0.8618 | 0.6972 | 9.9734706e-08 | 230 |
| 0.7439 | 0.7176 | 0.8614 | 0.6972 | 9.9732404e-08 | 231 |
| 0.7409 | 0.7271 | 0.8609 | 0.6972 | 9.973009e-08 | 232 |
| 0.7357 | 0.7271 | 0.8606 | 0.6901 | 9.9727764e-08 | 233 |
| 0.7455 | 0.7247 | 0.8602 | 0.6972 | 9.972543e-08 | 234 |
| 0.7384 | 0.7318 | 0.8598 | 0.6972 | 9.972309e-08 | 235 |
| 0.7438 | 0.7224 | 0.8595 | 0.6972 | 9.972074e-08 | 236 |
| 0.7346 | 0.7271 | 0.8592 | 0.6972 | 9.971837e-08 | 237 |
| 0.7324 | 0.7294 | 0.8588 | 0.6972 | 9.9716e-08 | 238 |
| 0.7358 | 0.7271 | 0.8585 | 0.6901 | 9.971362e-08 | 239 |
| 0.7464 | 0.7200 | 0.8583 | 0.6901 | 9.971122e-08 | 240 |
| 0.7282 | 0.7365 | 0.8580 | 0.6901 | 9.970882e-08 | 241 |
| 0.7292 | 0.7224 | 0.8577 | 0.6901 | 9.9706405e-08 | 242 |
| 0.7377 | 0.7294 | 0.8574 | 0.6901 | 9.970398e-08 | 243 |
| 0.7248 | 0.7412 | 0.8569 | 0.6901 | 9.970155e-08 | 244 |
| 0.7262 | 0.7365 | 0.8565 | 0.7042 | 9.969911e-08 | 245 |
| 0.7229 | 0.7200 | 0.8560 | 0.6972 | 9.9696656e-08 | 246 |
| 0.7181 | 0.7341 | 0.8557 | 0.6972 | 9.969419e-08 | 247 |
| 0.7273 | 0.7341 | 0.8554 | 0.7113 | 9.969172e-08 | 248 |
| 0.7272 | 0.7412 | 0.8550 | 0.7113 | 9.968924e-08 | 249 |
| 0.7245 | 0.7388 | 0.8547 | 0.7042 | 9.9686744e-08 | 250 |
| 0.7307 | 0.7271 | 0.8543 | 0.7113 | 9.968424e-08 | 251 |
| 0.7147 | 0.7388 | 0.8541 | 0.7113 | 9.968173e-08 | 252 |
| 0.7275 | 0.7435 | 0.8539 | 0.7183 | 9.9679205e-08 | 253 |
| 0.7246 | 0.7341 | 0.8538 | 0.7183 | 9.9676676e-08 | 254 |
| 0.7178 | 0.7412 | 0.8532 | 0.7183 | 9.967413e-08 | 255 |
| 0.7236 | 0.7365 | 0.8528 | 0.7183 | 9.967158e-08 | 256 |
| 0.7230 | 0.7365 | 0.8524 | 0.7183 | 9.966902e-08 | 257 |
| 0.7262 | 0.7294 | 0.8518 | 0.7183 | 9.966645e-08 | 258 |
| 0.7197 | 0.7365 | 0.8516 | 0.7183 | 9.966387e-08 | 259 |
| 0.7114 | 0.7388 | 0.8516 | 0.7183 | 9.966128e-08 | 260 |
| 0.7203 | 0.7294 | 0.8513 | 0.7183 | 9.965868e-08 | 261 |
| 0.7127 | 0.7506 | 0.8509 | 0.7183 | 9.965607e-08 | 262 |
| 0.7184 | 0.7294 | 0.8507 | 0.7183 | 9.965345e-08 | 263 |
| 0.7090 | 0.7529 | 0.8505 | 0.7183 | 9.965082e-08 | 264 |
| 0.7010 | 0.7388 | 0.8501 | 0.7183 | 9.9648176e-08 | 265 |
| 0.7103 | 0.7506 | 0.8497 | 0.7183 | 9.9645526e-08 | 266 |
| 0.7133 | 0.7435 | 0.8495 | 0.7183 | 9.964287e-08 | 267 |
| 0.7045 | 0.7576 | 0.8490 | 0.7183 | 9.96402e-08 | 268 |
| 0.7045 | 0.7318 | 0.8487 | 0.7183 | 9.963752e-08 | 269 |
| 0.7072 | 0.7271 | 0.8485 | 0.7183 | 9.9634825e-08 | 270 |
| 0.7033 | 0.7459 | 0.8483 | 0.7183 | 9.9632125e-08 | 271 |
| 0.7050 | 0.7553 | 0.8480 | 0.7183 | 9.962942e-08 | 272 |
| 0.7084 | 0.7388 | 0.8476 | 0.7183 | 9.9626696e-08 | 273 |
| 0.7123 | 0.7435 | 0.8476 | 0.7183 | 9.962397e-08 | 274 |
| 0.7054 | 0.7576 | 0.8480 | 0.7183 | 9.9621225e-08 | 275 |
| 0.6990 | 0.7459 | 0.8474 | 0.7254 | 9.9618475e-08 | 276 |
| 0.6995 | 0.7435 | 0.8472 | 0.7254 | 9.961572e-08 | 277 |
| 0.6885 | 0.7553 | 0.8471 | 0.7254 | 9.961295e-08 | 278 |
| 0.6993 | 0.7506 | 0.8469 | 0.7183 | 9.961017e-08 | 279 |
| 0.7039 | 0.7600 | 0.8465 | 0.7183 | 9.960738e-08 | 280 |
| 0.6966 | 0.7506 | 0.8457 | 0.7183 | 9.960458e-08 | 281 |
| 0.6908 | 0.7671 | 0.8453 | 0.7183 | 9.960177e-08 | 282 |
| 0.7020 | 0.7459 | 0.8453 | 0.7183 | 9.959895e-08 | 283 |
| 0.7047 | 0.7224 | 0.8449 | 0.7183 | 9.959612e-08 | 284 |
| 0.6943 | 0.7388 | 0.8449 | 0.7183 | 9.959329e-08 | 285 |
| 0.6984 | 0.7553 | 0.8448 | 0.7183 | 9.959044e-08 | 286 |
| 0.6862 | 0.7553 | 0.8445 | 0.7183 | 9.958758e-08 | 287 |
| 0.6907 | 0.7506 | 0.8444 | 0.7183 | 9.958471e-08 | 288 |
| 0.7013 | 0.7365 | 0.8441 | 0.7183 | 9.958183e-08 | 289 |
| 0.6907 | 0.7459 | 0.8440 | 0.7113 | 9.957895e-08 | 290 |
| 0.6824 | 0.7647 | 0.8438 | 0.7113 | 9.957605e-08 | 291 |
| 0.6784 | 0.7506 | 0.8433 | 0.7183 | 9.957314e-08 | 292 |
| 0.6933 | 0.7553 | 0.8429 | 0.7183 | 9.957022e-08 | 293 |
| 0.6799 | 0.7506 | 0.8428 | 0.7183 | 9.9567295e-08 | 294 |
| 0.6886 | 0.7600 | 0.8430 | 0.7113 | 9.956436e-08 | 295 |
| 0.6766 | 0.7600 | 0.8428 | 0.7113 | 9.956141e-08 | 296 |
| 0.6825 | 0.7482 | 0.8427 | 0.7113 | 9.9558456e-08 | 297 |
| 0.6797 | 0.7529 | 0.8428 | 0.7113 | 9.9555486e-08 | 298 |
| 0.6800 | 0.7576 | 0.8431 | 0.7183 | 9.955251e-08 | 299 |
| 0.6791 | 0.7553 | 0.8424 | 0.7183 | 9.9549524e-08 | 300 |
| 0.6857 | 0.7482 | 0.8419 | 0.7113 | 9.9546526e-08 | 301 |
| 0.6802 | 0.7482 | 0.8420 | 0.7183 | 9.954352e-08 | 302 |
| 0.6684 | 0.7482 | 0.8418 | 0.7183 | 9.954051e-08 | 303 |
| 0.6822 | 0.7482 | 0.8413 | 0.7113 | 9.953748e-08 | 304 |
| 0.6771 | 0.7600 | 0.8411 | 0.7113 | 9.953445e-08 | 305 |
| 0.6775 | 0.7553 | 0.8408 | 0.7113 | 9.95314e-08 | 306 |
| 0.6808 | 0.7600 | 0.8406 | 0.7113 | 9.952834e-08 | 307 |
| 0.6794 | 0.7529 | 0.8406 | 0.7113 | 9.952528e-08 | 308 |
| 0.6684 | 0.7718 | 0.8407 | 0.7183 | 9.9522204e-08 | 309 |
| 0.6757 | 0.7671 | 0.8408 | 0.7183 | 9.951912e-08 | 310 |
| 0.6698 | 0.7529 | 0.8407 | 0.7183 | 9.951602e-08 | 311 |
| 0.6625 | 0.7600 | 0.8403 | 0.7183 | 9.951292e-08 | 312 |
| 0.6626 | 0.7624 | 0.8398 | 0.7183 | 9.9509805e-08 | 313 |
| 0.6691 | 0.7529 | 0.8401 | 0.7183 | 9.950668e-08 | 314 |
| 0.6706 | 0.7718 | 0.8403 | 0.7113 | 9.9503545e-08 | 315 |
| 0.6716 | 0.7624 | 0.8401 | 0.7113 | 9.95004e-08 | 316 |
| 0.6713 | 0.7576 | 0.8399 | 0.7113 | 9.949724e-08 | 317 |
| 0.6576 | 0.7506 | 0.8398 | 0.7113 | 9.949408e-08 | 318 |
| 0.6596 | 0.7576 | 0.8392 | 0.7113 | 9.9490904e-08 | 319 |
| 0.6537 | 0.7788 | 0.8391 | 0.7113 | 9.948772e-08 | 320 |
| 0.6604 | 0.7624 | 0.8392 | 0.7113 | 9.948453e-08 | 321 |
| 0.6736 | 0.7600 | 0.8390 | 0.7113 | 9.9481326e-08 | 322 |
| 0.6524 | 0.7765 | 0.8386 | 0.7113 | 9.9478115e-08 | 323 |
| 0.6555 | 0.7741 | 0.8388 | 0.7042 | 9.947489e-08 | 324 |
| 0.6543 | 0.7741 | 0.8394 | 0.7042 | 9.9471656e-08 | 325 |
| 0.6643 | 0.7600 | 0.8384 | 0.7042 | 9.9468416e-08 | 326 |
| 0.6537 | 0.7671 | 0.8383 | 0.7042 | 9.946516e-08 | 327 |
| 0.6601 | 0.7718 | 0.8380 | 0.7042 | 9.94619e-08 | 328 |
| 0.6618 | 0.7647 | 0.8378 | 0.7042 | 9.9458624e-08 | 329 |
| 0.6571 | 0.7553 | 0.8377 | 0.7042 | 9.945534e-08 | 330 |
| 0.6575 | 0.7624 | 0.8379 | 0.7042 | 9.945205e-08 | 331 |
| 0.6616 | 0.7741 | 0.8373 | 0.7042 | 9.944875e-08 | 332 |
| 0.6515 | 0.7576 | 0.8372 | 0.7042 | 9.944544e-08 | 333 |
| 0.6510 | 0.7859 | 0.8369 | 0.7042 | 9.944212e-08 | 334 |
| 0.6486 | 0.7624 | 0.8364 | 0.7042 | 9.9438786e-08 | 335 |
| 0.6542 | 0.7624 | 0.8361 | 0.7042 | 9.943545e-08 | 336 |
| 0.6462 | 0.7694 | 0.8360 | 0.7042 | 9.943209e-08 | 337 |
| 0.6562 | 0.7576 | 0.8366 | 0.7042 | 9.942873e-08 | 338 |
| 0.6482 | 0.7741 | 0.8366 | 0.7042 | 9.9425364e-08 | 339 |
| 0.6529 | 0.7741 | 0.8363 | 0.7042 | 9.942198e-08 | 340 |
| 0.6430 | 0.7647 | 0.8354 | 0.7042 | 9.941859e-08 | 341 |
| 0.6554 | 0.7671 | 0.8354 | 0.7042 | 9.941519e-08 | 342 |
| 0.6419 | 0.7694 | 0.8356 | 0.7042 | 9.941178e-08 | 343 |
| 0.6402 | 0.7647 | 0.8355 | 0.7042 | 9.940836e-08 | 344 |
| 0.6568 | 0.7647 | 0.8355 | 0.7042 | 9.940493e-08 | 345 |
| 0.6463 | 0.7671 | 0.8364 | 0.6972 | 9.940149e-08 | 346 |
| 0.6481 | 0.7647 | 0.8360 | 0.7042 | 9.9398044e-08 | 347 |
| 0.6414 | 0.7694 | 0.8363 | 0.6972 | 9.939458e-08 | 348 |
| 0.6439 | 0.7647 | 0.8362 | 0.6972 | 9.9391116e-08 | 349 |
| 0.6385 | 0.7835 | 0.8360 | 0.6972 | 9.9387634e-08 | 350 |
| 0.6433 | 0.7671 | 0.8363 | 0.6972 | 9.9384145e-08 | 351 |
| 0.6433 | 0.7718 | 0.8370 | 0.6972 | 9.938065e-08 | 352 |
| 0.6339 | 0.7812 | 0.8365 | 0.6972 | 9.937714e-08 | 353 |
| 0.6388 | 0.7718 | 0.8362 | 0.6972 | 9.937362e-08 | 354 |
| 0.6290 | 0.7882 | 0.8354 | 0.6972 | 9.93701e-08 | 355 |
| 0.6343 | 0.7718 | 0.8354 | 0.6972 | 9.936656e-08 | 356 |
| 0.6247 | 0.7741 | 0.8355 | 0.6972 | 9.9363014e-08 | 357 |
| 0.6323 | 0.7741 | 0.8350 | 0.7113 | 9.9359454e-08 | 358 |
| 0.6401 | 0.7718 | 0.8351 | 0.6972 | 9.935589e-08 | 359 |
| 0.6339 | 0.7741 | 0.8348 | 0.6972 | 9.935231e-08 | 360 |
| 0.6250 | 0.7741 | 0.8352 | 0.6972 | 9.9348725e-08 | 361 |
| 0.6288 | 0.7788 | 0.8352 | 0.6972 | 9.934513e-08 | 362 |
| 0.6255 | 0.7765 | 0.8346 | 0.6972 | 9.934152e-08 | 363 |
| 0.6246 | 0.7788 | 0.8343 | 0.6972 | 9.93379e-08 | 364 |
| 0.6267 | 0.7765 | 0.8349 | 0.6972 | 9.933428e-08 | 365 |
| 0.6260 | 0.7859 | 0.8359 | 0.6972 | 9.933064e-08 | 366 |
| 0.6259 | 0.7788 | 0.8350 | 0.6972 | 9.9326996e-08 | 367 |
| 0.6224 | 0.7835 | 0.8343 | 0.6972 | 9.9323344e-08 | 368 |
| 0.6251 | 0.7882 | 0.8342 | 0.6972 | 9.931968e-08 | 369 |
| 0.6258 | 0.7906 | 0.8348 | 0.6972 | 9.9316004e-08 | 370 |
| 0.6202 | 0.7812 | 0.8356 | 0.6972 | 9.931232e-08 | 371 |
| 0.6260 | 0.7765 | 0.8349 | 0.6972 | 9.930862e-08 | 372 |
| 0.6243 | 0.7765 | 0.8344 | 0.6972 | 9.930492e-08 | 373 |
| 0.6274 | 0.7788 | 0.8339 | 0.6972 | 9.9301204e-08 | 374 |
| 0.6138 | 0.7788 | 0.8340 | 0.6972 | 9.929748e-08 | 375 |
| 0.6146 | 0.7788 | 0.8340 | 0.6972 | 9.929375e-08 | 376 |
| 0.6163 | 0.7741 | 0.8338 | 0.6972 | 9.9290006e-08 | 377 |
| 0.6137 | 0.7788 | 0.8341 | 0.6901 | 9.9286254e-08 | 378 |
| 0.6191 | 0.7765 | 0.8346 | 0.6972 | 9.928249e-08 | 379 |
| 0.6184 | 0.7835 | 0.8342 | 0.6901 | 9.9278715e-08 | 380 |
| 0.6177 | 0.8024 | 0.8337 | 0.6901 | 9.9274935e-08 | 381 |
| 0.6233 | 0.7741 | 0.8333 | 0.6901 | 9.927114e-08 | 382 |
| 0.6168 | 0.7953 | 0.8332 | 0.6901 | 9.926734e-08 | 383 |
| 0.6084 | 0.7953 | 0.8331 | 0.6901 | 9.926353e-08 | 384 |
| 0.6162 | 0.7812 | 0.8328 | 0.6901 | 9.925971e-08 | 385 |
| 0.6226 | 0.7906 | 0.8327 | 0.7042 | 9.925588e-08 | 386 |
| 0.6151 | 0.7835 | 0.8321 | 0.6901 | 9.9252034e-08 | 387 |
| 0.6160 | 0.7765 | 0.8316 | 0.6901 | 9.924818e-08 | 388 |
| 0.6201 | 0.7859 | 0.8317 | 0.6901 | 9.9244325e-08 | 389 |
| 0.6161 | 0.7812 | 0.8318 | 0.6972 | 9.924045e-08 | 390 |
| 0.6107 | 0.7765 | 0.8315 | 0.6972 | 9.923657e-08 | 391 |
| 0.6141 | 0.7765 | 0.8316 | 0.7042 | 9.9232686e-08 | 392 |
| 0.6166 | 0.7835 | 0.8322 | 0.7113 | 9.9228785e-08 | 393 |
| 0.6043 | 0.7882 | 0.8314 | 0.7113 | 9.922488e-08 | 394 |
| 0.6064 | 0.7788 | 0.8325 | 0.7183 | 9.9220955e-08 | 395 |
| 0.6040 | 0.7835 | 0.8323 | 0.7183 | 9.9217026e-08 | 396 |
| 0.6046 | 0.7812 | 0.8325 | 0.7183 | 9.921309e-08 | 397 |
| 0.6007 | 0.8071 | 0.8324 | 0.7183 | 9.920914e-08 | 398 |
| 0.6078 | 0.7835 | 0.8309 | 0.7113 | 9.920518e-08 | 399 |
| 0.6051 | 0.7929 | 0.8306 | 0.7042 | 9.9201216e-08 | 400 |
| 0.5952 | 0.7812 | 0.8306 | 0.7183 | 9.919724e-08 | 401 |
| 0.5973 | 0.7929 | 0.8310 | 0.7183 | 9.919325e-08 | 402 |
| 0.6055 | 0.7929 | 0.8311 | 0.7183 | 9.918925e-08 | 403 |
| 0.5996 | 0.7906 | 0.8302 | 0.7042 | 9.918524e-08 | 404 |
| 0.5921 | 0.7953 | 0.8299 | 0.7042 | 9.918123e-08 | 405 |
| 0.6025 | 0.7953 | 0.8311 | 0.7254 | 9.91772e-08 | 406 |
| 0.6109 | 0.7835 | 0.8311 | 0.7254 | 9.9173164e-08 | 407 |
| 0.6025 | 0.7906 | 0.8311 | 0.7254 | 9.916912e-08 | 408 |
| 0.5965 | 0.7882 | 0.8311 | 0.7254 | 9.9165064e-08 | 409 |
| 0.5990 | 0.7835 | 0.8306 | 0.7254 | 9.9161e-08 | 410 |
| 0.5870 | 0.7906 | 0.8307 | 0.7254 | 9.915692e-08 | 411 |
| 0.5908 | 0.7906 | 0.8302 | 0.7254 | 9.9152835e-08 | 412 |
| 0.5990 | 0.7929 | 0.8307 | 0.7324 | 9.914874e-08 | 413 |
| 0.5885 | 0.7976 | 0.8303 | 0.7324 | 9.9144636e-08 | 414 |
| 0.5916 | 0.7976 | 0.8300 | 0.7254 | 9.914052e-08 | 415 |
| 0.5923 | 0.7882 | 0.8302 | 0.7324 | 9.91364e-08 | 416 |
| 0.6001 | 0.7788 | 0.8302 | 0.7324 | 9.9132265e-08 | 417 |
| 0.5871 | 0.7859 | 0.8300 | 0.7324 | 9.912812e-08 | 418 |
| 0.5939 | 0.7929 | 0.8303 | 0.7324 | 9.9123966e-08 | 419 |
| 0.5956 | 0.7976 | 0.8298 | 0.7183 | 9.91198e-08 | 420 |
| 0.5913 | 0.7835 | 0.8295 | 0.7183 | 9.911563e-08 | 421 |
| 0.5963 | 0.7859 | 0.8299 | 0.7254 | 9.9111446e-08 | 422 |
| 0.5967 | 0.7765 | 0.8295 | 0.7254 | 9.9107254e-08 | 423 |
| 0.5910 | 0.7741 | 0.8297 | 0.7254 | 9.9103055e-08 | 424 |
| 0.5875 | 0.7835 | 0.8295 | 0.7254 | 9.909884e-08 | 425 |
| 0.5872 | 0.7906 | 0.8299 | 0.7254 | 9.909462e-08 | 426 |
| 0.5876 | 0.7882 | 0.8296 | 0.7254 | 9.909039e-08 | 427 |
| 0.5791 | 0.7906 | 0.8297 | 0.7254 | 9.908615e-08 | 428 |
| 0.6050 | 0.7788 | 0.8287 | 0.7254 | 9.90819e-08 | 429 |
| 0.5830 | 0.7906 | 0.8287 | 0.7254 | 9.907764e-08 | 430 |
| 0.5901 | 0.7906 | 0.8287 | 0.7254 | 9.907337e-08 | 431 |
| 0.5885 | 0.8000 | 0.8294 | 0.7254 | 9.906909e-08 | 432 |
| 0.5826 | 0.7859 | 0.8297 | 0.7254 | 9.90648e-08 | 433 |
| 0.5680 | 0.7906 | 0.8307 | 0.7254 | 9.90605e-08 | 434 |
| 0.5878 | 0.7906 | 0.8298 | 0.7324 | 9.9056194e-08 | 435 |
| 0.5839 | 0.7976 | 0.8295 | 0.7254 | 9.9051874e-08 | 436 |
| 0.5836 | 0.7835 | 0.8291 | 0.7324 | 9.904755e-08 | 437 |
| 0.5877 | 0.7976 | 0.8291 | 0.7324 | 9.9043206e-08 | 438 |
| 0.5726 | 0.7953 | 0.8280 | 0.7324 | 9.903886e-08 | 439 |
| 0.5726 | 0.8000 | 0.8285 | 0.7254 | 9.90345e-08 | 440 |
| 0.5738 | 0.7929 | 0.8288 | 0.7254 | 9.903013e-08 | 441 |
| 0.5836 | 0.7929 | 0.8294 | 0.7254 | 9.9025755e-08 | 442 |
| 0.5769 | 0.7953 | 0.8292 | 0.7254 | 9.902137e-08 | 443 |
| 0.5747 | 0.7953 | 0.8288 | 0.7254 | 9.901697e-08 | 444 |
| 0.5700 | 0.7976 | 0.8290 | 0.7254 | 9.901257e-08 | 445 |
| 0.5756 | 0.8094 | 0.8289 | 0.7254 | 9.9008155e-08 | 446 |
| 0.5776 | 0.7976 | 0.8281 | 0.7324 | 9.900373e-08 | 447 |
| 0.5757 | 0.7835 | 0.8287 | 0.7324 | 9.8999294e-08 | 448 |
| 0.5735 | 0.8000 | 0.8288 | 0.7324 | 9.8994846e-08 | 449 |
| 0.5719 | 0.7929 | 0.8287 | 0.7324 | 9.899039e-08 | 450 |
| 0.5804 | 0.8000 | 0.8283 | 0.7324 | 9.898593e-08 | 451 |
| 0.5756 | 0.8000 | 0.8280 | 0.7324 | 9.898145e-08 | 452 |
| 0.5651 | 0.8024 | 0.8280 | 0.7324 | 9.897697e-08 | 453 |
| 0.5587 | 0.8000 | 0.8290 | 0.7324 | 9.897248e-08 | 454 |
| 0.5730 | 0.7976 | 0.8309 | 0.7254 | 9.896797e-08 | 455 |
| 0.5596 | 0.8094 | 0.8304 | 0.7254 | 9.896346e-08 | 456 |
| 0.5719 | 0.8094 | 0.8297 | 0.7254 | 9.895894e-08 | 457 |
| 0.5621 | 0.8000 | 0.8299 | 0.7254 | 9.895441e-08 | 458 |
| 0.5619 | 0.8000 | 0.8298 | 0.7254 | 9.894987e-08 | 459 |
| 0.5708 | 0.7882 | 0.8289 | 0.7254 | 9.8945314e-08 | 460 |
| 0.5629 | 0.7859 | 0.8281 | 0.7254 | 9.894075e-08 | 461 |
| 0.5627 | 0.8094 | 0.8292 | 0.7254 | 9.8936184e-08 | 462 |
| 0.5616 | 0.8071 | 0.8297 | 0.7254 | 9.89316e-08 | 463 |
| 0.5652 | 0.8024 | 0.8302 | 0.7254 | 9.892701e-08 | 464 |
| 0.5720 | 0.8000 | 0.8305 | 0.7254 | 9.892241e-08 | 465 |
| 0.5713 | 0.7906 | 0.8297 | 0.7254 | 9.89178e-08 | 466 |
| 0.5643 | 0.8024 | 0.8294 | 0.7254 | 9.891318e-08 | 467 |
| 0.5478 | 0.8141 | 0.8288 | 0.7254 | 9.890856e-08 | 468 |
| 0.5510 | 0.8071 | 0.8287 | 0.7254 | 9.890392e-08 | 469 |
| 0.5560 | 0.8071 | 0.8290 | 0.7254 | 9.889927e-08 | 470 |
| 0.5532 | 0.8141 | 0.8279 | 0.7254 | 9.889461e-08 | 471 |
| 0.5564 | 0.8094 | 0.8294 | 0.7254 | 9.888994e-08 | 472 |
| 0.5629 | 0.7953 | 0.8301 | 0.7254 | 9.8885266e-08 | 473 |
| 0.5590 | 0.7976 | 0.8301 | 0.7254 | 9.888058e-08 | 474 |
| 0.5504 | 0.8071 | 0.8288 | 0.7254 | 9.887588e-08 | 475 |
| 0.5650 | 0.8047 | 0.8283 | 0.7254 | 9.8871176e-08 | 476 |
| 0.5545 | 0.8024 | 0.8280 | 0.7254 | 9.886646e-08 | 477 |
| 0.5631 | 0.7929 | 0.8282 | 0.7254 | 9.886173e-08 | 478 |
| 0.5557 | 0.8024 | 0.8272 | 0.7254 | 9.8857e-08 | 479 |
| 0.5582 | 0.8071 | 0.8282 | 0.7254 | 9.8852254e-08 | 480 |
| 0.5461 | 0.8094 | 0.8285 | 0.7254 | 9.88475e-08 | 481 |
| 0.5453 | 0.8071 | 0.8291 | 0.7254 | 9.884273e-08 | 482 |
| 0.5453 | 0.8071 | 0.8296 | 0.7254 | 9.883796e-08 | 483 |
| 0.5530 | 0.7976 | 0.8297 | 0.7254 | 9.8833176e-08 | 484 |
| 0.5531 | 0.8165 | 0.8307 | 0.7254 | 9.882838e-08 | 485 |
| 0.5662 | 0.8094 | 0.8309 | 0.7254 | 9.882358e-08 | 486 |
| 0.5379 | 0.8071 | 0.8291 | 0.7254 | 9.881877e-08 | 487 |
| 0.5464 | 0.8000 | 0.8280 | 0.7254 | 9.881394e-08 | 488 |
| 0.5493 | 0.7976 | 0.8294 | 0.7254 | 9.880911e-08 | 489 |
| 0.5465 | 0.7976 | 0.8303 | 0.7254 | 9.880427e-08 | 490 |
| 0.5508 | 0.8118 | 0.8305 | 0.7254 | 9.879942e-08 | 491 |
| 0.5359 | 0.8165 | 0.8303 | 0.7254 | 9.879456e-08 | 492 |
| 0.5356 | 0.8141 | 0.8314 | 0.7254 | 9.878969e-08 | 493 |
| 0.5428 | 0.8071 | 0.8310 | 0.7254 | 9.878481e-08 | 494 |
| 0.5380 | 0.8188 | 0.8304 | 0.7254 | 9.877992e-08 | 495 |
| 0.5548 | 0.7953 | 0.8293 | 0.7254 | 9.877502e-08 | 496 |
| 0.5428 | 0.8000 | 0.8290 | 0.7254 | 9.877011e-08 | 497 |
| 0.5586 | 0.7906 | 0.8293 | 0.7254 | 9.876519e-08 | 498 |
| 0.5342 | 0.8024 | 0.8290 | 0.7254 | 9.876026e-08 | 499 |
| 0.5394 | 0.8141 | 0.8294 | 0.7254 | 9.875532e-08 | 500 |
| 0.5517 | 0.8000 | 0.8293 | 0.7254 | 9.875038e-08 | 501 |
| 0.5428 | 0.8024 | 0.8288 | 0.7254 | 9.874542e-08 | 502 |
| 0.5427 | 0.8094 | 0.8302 | 0.7254 | 9.874045e-08 | 503 |
| 0.5443 | 0.8000 | 0.8297 | 0.7254 | 9.873548e-08 | 504 |
| 0.5440 | 0.8000 | 0.8300 | 0.7254 | 9.873049e-08 | 505 |
| 0.5308 | 0.8165 | 0.8299 | 0.7254 | 9.8725494e-08 | 506 |
| 0.5451 | 0.8024 | 0.8286 | 0.7254 | 9.872049e-08 | 507 |
| 0.5446 | 0.8141 | 0.8287 | 0.7254 | 9.8715475e-08 | 508 |
| 0.5460 | 0.8118 | 0.8290 | 0.7254 | 9.871045e-08 | 509 |
| 0.5279 | 0.8165 | 0.8292 | 0.7254 | 9.870542e-08 | 510 |
| 0.5259 | 0.8094 | 0.8294 | 0.7254 | 9.8700376e-08 | 511 |
| 0.5224 | 0.8165 | 0.8297 | 0.7254 | 9.8695324e-08 | 512 |
| 0.5349 | 0.8000 | 0.8295 | 0.7254 | 9.869026e-08 | 513 |
| 0.5475 | 0.8094 | 0.8290 | 0.7254 | 9.8685184e-08 | 514 |
| 0.5435 | 0.7906 | 0.8293 | 0.7254 | 9.8680104e-08 | 515 |
| 0.5251 | 0.8306 | 0.8287 | 0.7254 | 9.867501e-08 | 516 |
| 0.5340 | 0.8141 | 0.8290 | 0.7254 | 9.866991e-08 | 517 |
| 0.5263 | 0.8000 | 0.8287 | 0.7254 | 9.86648e-08 | 518 |
| 0.5279 | 0.8235 | 0.8291 | 0.7254 | 9.8659676e-08 | 519 |
| 0.5363 | 0.8118 | 0.8292 | 0.7254 | 9.8654546e-08 | 520 |
| 0.5272 | 0.8071 | 0.8291 | 0.7254 | 9.864941e-08 | 521 |
| 0.5168 | 0.8141 | 0.8288 | 0.7254 | 9.864426e-08 | 522 |
| 0.5306 | 0.8118 | 0.8292 | 0.7254 | 9.86391e-08 | 523 |
| 0.5360 | 0.8071 | 0.8304 | 0.7254 | 9.863393e-08 | 524 |
| 0.5358 | 0.8141 | 0.8295 | 0.7254 | 9.862875e-08 | 525 |
| 0.5307 | 0.8118 | 0.8285 | 0.7254 | 9.8623566e-08 | 526 |
| 0.5272 | 0.8047 | 0.8289 | 0.7254 | 9.861837e-08 | 527 |
| 0.5349 | 0.8212 | 0.8293 | 0.7254 | 9.8613164e-08 | 528 |
| 0.5281 | 0.8118 | 0.8302 | 0.7254 | 9.860795e-08 | 529 |
| 0.5248 | 0.8024 | 0.8297 | 0.7254 | 9.8602726e-08 | 530 |
| 0.5296 | 0.8047 | 0.8303 | 0.7254 | 9.859749e-08 | 531 |
| 0.5337 | 0.8141 | 0.8307 | 0.7183 | 9.8592245e-08 | 532 |
| 0.5235 | 0.8212 | 0.8310 | 0.7183 | 9.858699e-08 | 533 |
| 0.5081 | 0.8165 | 0.8299 | 0.7254 | 9.858172e-08 | 534 |
| 0.5359 | 0.8024 | 0.8291 | 0.7254 | 9.857645e-08 | 535 |
| 0.5138 | 0.8118 | 0.8292 | 0.7254 | 9.8571164e-08 | 536 |
| 0.5239 | 0.8071 | 0.8292 | 0.7254 | 9.856587e-08 | 537 |
| 0.5142 | 0.8047 | 0.8299 | 0.7254 | 9.856057e-08 | 538 |
| 0.5290 | 0.8094 | 0.8294 | 0.7254 | 9.8555255e-08 | 539 |
| 0.5135 | 0.8141 | 0.8292 | 0.7254 | 9.854993e-08 | 540 |
| 0.5158 | 0.8141 | 0.8304 | 0.7254 | 9.8544604e-08 | 541 |
| 0.5086 | 0.8141 | 0.8302 | 0.7254 | 9.853926e-08 | 542 |
| 0.5305 | 0.8094 | 0.8309 | 0.7254 | 9.853391e-08 | 543 |
| 0.5179 | 0.8047 | 0.8310 | 0.7254 | 9.852855e-08 | 544 |
| 0.5171 | 0.8141 | 0.8314 | 0.7183 | 9.852318e-08 | 545 |
| 0.5053 | 0.8212 | 0.8313 | 0.7183 | 9.85178e-08 | 546 |
| 0.5223 | 0.8212 | 0.8314 | 0.7183 | 9.8512416e-08 | 547 |
| 0.5084 | 0.8141 | 0.8308 | 0.7254 | 9.8507016e-08 | 548 |
| 0.5072 | 0.8212 | 0.8313 | 0.7254 | 9.850161e-08 | 549 |
| 0.5174 | 0.8071 | 0.8301 | 0.7254 | 9.8496194e-08 | 550 |
| 0.5128 | 0.8188 | 0.8295 | 0.7254 | 9.8490766e-08 | 551 |
| 0.5044 | 0.8071 | 0.8313 | 0.7183 | 9.848533e-08 | 552 |
| 0.4974 | 0.8259 | 0.8311 | 0.7254 | 9.847989e-08 | 553 |
| 0.5189 | 0.8165 | 0.8314 | 0.7183 | 9.847443e-08 | 554 |
| 0.5161 | 0.8141 | 0.8314 | 0.7183 | 9.8468966e-08 | 555 |
| 0.4974 | 0.8141 | 0.8316 | 0.7183 | 9.8463495e-08 | 556 |
| 0.5077 | 0.8282 | 0.8315 | 0.7183 | 9.845801e-08 | 557 |
| 0.5084 | 0.8094 | 0.8331 | 0.7113 | 9.845252e-08 | 558 |
| 0.4988 | 0.8259 | 0.8331 | 0.7113 | 9.844701e-08 | 559 |
| 0.5178 | 0.8188 | 0.8330 | 0.7113 | 9.84415e-08 | 560 |
| 0.5063 | 0.8259 | 0.8318 | 0.7183 | 9.8435976e-08 | 561 |
| 0.5036 | 0.8165 | 0.8322 | 0.7183 | 9.843044e-08 | 562 |
| 0.5046 | 0.8259 | 0.8317 | 0.7183 | 9.84249e-08 | 563 |
| 0.5053 | 0.8165 | 0.8301 | 0.7254 | 9.841935e-08 | 564 |
| 0.4978 | 0.8118 | 0.8310 | 0.7254 | 9.8413786e-08 | 565 |
| 0.4986 | 0.8165 | 0.8316 | 0.7183 | 9.8408215e-08 | 566 |
| 0.4996 | 0.8259 | 0.8318 | 0.7183 | 9.840264e-08 | 567 |
| 0.5046 | 0.8212 | 0.8323 | 0.7042 | 9.8397045e-08 | 568 |
| 0.5058 | 0.8188 | 0.8321 | 0.7113 | 9.8391446e-08 | 569 |
| 0.4927 | 0.8188 | 0.8327 | 0.7042 | 9.838584e-08 | 570 |
| 0.4856 | 0.8306 | 0.8335 | 0.7113 | 9.838022e-08 | 571 |
| 0.4980 | 0.8306 | 0.8328 | 0.7042 | 9.837459e-08 | 572 |
| 0.4948 | 0.8235 | 0.8324 | 0.7042 | 9.836896e-08 | 573 |
| 0.4987 | 0.8188 | 0.8322 | 0.7113 | 9.836331e-08 | 574 |
| 0.4920 | 0.8306 | 0.8326 | 0.7113 | 9.835765e-08 | 575 |
| 0.5005 | 0.8235 | 0.8327 | 0.7113 | 9.835199e-08 | 576 |
| 0.4951 | 0.8235 | 0.8321 | 0.7113 | 9.834631e-08 | 577 |
| 0.5081 | 0.8235 | 0.8315 | 0.7113 | 9.834063e-08 | 578 |
| 0.4888 | 0.8235 | 0.8314 | 0.7113 | 9.833494e-08 | 579 |
| 0.4969 | 0.8165 | 0.8310 | 0.7113 | 9.832923e-08 | 580 |
| 0.5023 | 0.8165 | 0.8315 | 0.7113 | 9.832352e-08 | 581 |
| 0.4897 | 0.8306 | 0.8317 | 0.7113 | 9.83178e-08 | 582 |
| 0.4984 | 0.8188 | 0.8325 | 0.7183 | 9.8312064e-08 | 583 |
| 0.5020 | 0.8259 | 0.8326 | 0.7183 | 9.830632e-08 | 584 |
| 0.4950 | 0.8188 | 0.8337 | 0.7113 | 9.8300575e-08 | 585 |
| 0.5045 | 0.8188 | 0.8350 | 0.7042 | 9.829481e-08 | 586 |
| 0.4893 | 0.8212 | 0.8347 | 0.7042 | 9.828904e-08 | 587 |
| 0.4852 | 0.8165 | 0.8331 | 0.7183 | 9.8283266e-08 | 588 |
| 0.4781 | 0.8306 | 0.8328 | 0.7183 | 9.8277475e-08 | 589 |
| 0.4934 | 0.8165 | 0.8332 | 0.7113 | 9.827168e-08 | 590 |
| 0.4840 | 0.8094 | 0.8330 | 0.7183 | 9.826587e-08 | 591 |
| 0.4915 | 0.8306 | 0.8322 | 0.7183 | 9.826005e-08 | 592 |
| 0.4846 | 0.8329 | 0.8341 | 0.7042 | 9.8254226e-08 | 593 |
| 0.4825 | 0.8235 | 0.8343 | 0.7042 | 9.824839e-08 | 594 |
| 0.4826 | 0.8353 | 0.8352 | 0.7042 | 9.8242545e-08 | 595 |
| 0.4741 | 0.8376 | 0.8354 | 0.7042 | 9.823669e-08 | 596 |
| 0.4946 | 0.8212 | 0.8346 | 0.7042 | 9.823083e-08 | 597 |
| 0.4850 | 0.8282 | 0.8333 | 0.7113 | 9.822495e-08 | 598 |
| 0.4932 | 0.8235 | 0.8341 | 0.7042 | 9.821907e-08 | 599 |
| 0.4809 | 0.8259 | 0.8336 | 0.7113 | 9.821318e-08 | 600 |
| 0.4901 | 0.8235 | 0.8349 | 0.7042 | 9.820727e-08 | 601 |
| 0.4806 | 0.8259 | 0.8333 | 0.7113 | 9.820136e-08 | 602 |
| 0.4831 | 0.8282 | 0.8328 | 0.7113 | 9.819544e-08 | 603 |
| 0.4845 | 0.8235 | 0.8319 | 0.7042 | 9.818951e-08 | 604 |
| 0.4851 | 0.8235 | 0.8330 | 0.7113 | 9.818357e-08 | 605 |
| 0.4920 | 0.8188 | 0.8330 | 0.7113 | 9.817762e-08 | 606 |
| 0.4853 | 0.8376 | 0.8341 | 0.7113 | 9.817166e-08 | 607 |
| 0.4862 | 0.8212 | 0.8345 | 0.7113 | 9.816569e-08 | 608 |
| 0.4754 | 0.8400 | 0.8349 | 0.7113 | 9.815972e-08 | 609 |
| 0.4828 | 0.8188 | 0.8360 | 0.7042 | 9.815373e-08 | 610 |
| 0.4769 | 0.8329 | 0.8363 | 0.7042 | 9.814773e-08 | 611 |
| 0.4778 | 0.8329 | 0.8368 | 0.7042 | 9.8141726e-08 | 612 |
| 0.4709 | 0.8353 | 0.8366 | 0.7042 | 9.813571e-08 | 613 |
| 0.4735 | 0.8306 | 0.8378 | 0.7042 | 9.812968e-08 | 614 |
| 0.4682 | 0.8353 | 0.8379 | 0.7042 | 9.812365e-08 | 615 |
| 0.4767 | 0.8329 | 0.8365 | 0.7042 | 9.81176e-08 | 616 |
| 0.4774 | 0.8259 | 0.8363 | 0.7042 | 9.811155e-08 | 617 |
| 0.4668 | 0.8353 | 0.8363 | 0.7042 | 9.810549e-08 | 618 |
| 0.4607 | 0.8329 | 0.8365 | 0.7042 | 9.809941e-08 | 619 |
| 0.4601 | 0.8447 | 0.8370 | 0.7042 | 9.809333e-08 | 620 |
| 0.4801 | 0.8282 | 0.8362 | 0.7113 | 9.808724e-08 | 621 |
| 0.4694 | 0.8376 | 0.8349 | 0.7042 | 9.808114e-08 | 622 |
| 0.4862 | 0.8400 | 0.8352 | 0.7113 | 9.807503e-08 | 623 |
| 0.4802 | 0.8259 | 0.8349 | 0.7042 | 9.806891e-08 | 624 |
| 0.4902 | 0.8141 | 0.8355 | 0.7042 | 9.806278e-08 | 625 |
| 0.4697 | 0.8447 | 0.8378 | 0.7042 | 9.805664e-08 | 626 |
| 0.4583 | 0.8494 | 0.8382 | 0.7042 | 9.805049e-08 | 627 |
| 0.4711 | 0.8376 | 0.8371 | 0.7042 | 9.804433e-08 | 628 |
| 0.4596 | 0.8376 | 0.8368 | 0.7042 | 9.8038164e-08 | 629 |
| 0.4716 | 0.8306 | 0.8360 | 0.7113 | 9.803199e-08 | 630 |
| 0.4625 | 0.8400 | 0.8371 | 0.7042 | 9.80258e-08 | 631 |
| 0.4625 | 0.8259 | 0.8373 | 0.7042 | 9.8019605e-08 | 632 |
| 0.4678 | 0.8353 | 0.8372 | 0.7042 | 9.80134e-08 | 633 |
| 0.4554 | 0.8424 | 0.8375 | 0.7042 | 9.800719e-08 | 634 |
| 0.4602 | 0.8424 | 0.8368 | 0.7113 | 9.800097e-08 | 635 |
| 0.4754 | 0.8141 | 0.8362 | 0.7042 | 9.7994736e-08 | 636 |
| 0.4659 | 0.8282 | 0.8364 | 0.7113 | 9.79885e-08 | 637 |
| 0.4613 | 0.8259 | 0.8383 | 0.7042 | 9.7982245e-08 | 638 |
| 0.4642 | 0.8400 | 0.8379 | 0.7042 | 9.7975985e-08 | 639 |
| 0.4566 | 0.8306 | 0.8401 | 0.7042 | 9.796972e-08 | 640 |
| 0.4574 | 0.8282 | 0.8396 | 0.7042 | 9.796344e-08 | 641 |
| 0.4641 | 0.8353 | 0.8401 | 0.7042 | 9.795715e-08 | 642 |
| 0.4656 | 0.8235 | 0.8390 | 0.7042 | 9.795085e-08 | 643 |
| 0.4536 | 0.8282 | 0.8398 | 0.7042 | 9.794454e-08 | 644 |
| 0.4539 | 0.8400 | 0.8398 | 0.7042 | 9.7938226e-08 | 645 |
| 0.4553 | 0.8353 | 0.8402 | 0.7042 | 9.79319e-08 | 646 |
| 0.4639 | 0.8424 | 0.8405 | 0.7042 | 9.7925565e-08 | 647 |
| 0.4593 | 0.8424 | 0.8397 | 0.7042 | 9.791922e-08 | 648 |
| 0.4550 | 0.8471 | 0.8398 | 0.7042 | 9.791287e-08 | 649 |
| 0.4437 | 0.8471 | 0.8378 | 0.7042 | 9.79065e-08 | 650 |
| 0.4563 | 0.8494 | 0.8388 | 0.7042 | 9.790013e-08 | 651 |
| 0.4554 | 0.8376 | 0.8378 | 0.7042 | 9.7893746e-08 | 652 |
| 0.4592 | 0.8353 | 0.8392 | 0.7042 | 9.788735e-08 | 653 |
| 0.4589 | 0.8306 | 0.8395 | 0.7042 | 9.788095e-08 | 654 |
| 0.4574 | 0.8376 | 0.8395 | 0.7042 | 9.787454e-08 | 655 |
| 0.4632 | 0.8282 | 0.8404 | 0.6972 | 9.786812e-08 | 656 |
| 0.4576 | 0.8376 | 0.8405 | 0.6972 | 9.786169e-08 | 657 |
| 0.4461 | 0.8306 | 0.8403 | 0.7042 | 9.785525e-08 | 658 |
| 0.4552 | 0.8376 | 0.8402 | 0.7042 | 9.78488e-08 | 659 |
| 0.4497 | 0.8447 | 0.8408 | 0.7042 | 9.784234e-08 | 660 |
| 0.4513 | 0.8447 | 0.8404 | 0.7042 | 9.783587e-08 | 661 |
| 0.4519 | 0.8447 | 0.8403 | 0.7042 | 9.78294e-08 | 662 |
| 0.4727 | 0.8329 | 0.8405 | 0.7042 | 9.782291e-08 | 663 |
| 0.4550 | 0.8353 | 0.8428 | 0.7042 | 9.781642e-08 | 664 |
| 0.4558 | 0.8353 | 0.8429 | 0.7042 | 9.780992e-08 | 665 |
| 0.4412 | 0.8376 | 0.8443 | 0.7113 | 9.78034e-08 | 666 |
| 0.4488 | 0.8376 | 0.8418 | 0.6972 | 9.779688e-08 | 667 |
| 0.4579 | 0.8376 | 0.8421 | 0.7042 | 9.779035e-08 | 668 |
| 0.4394 | 0.8306 | 0.8425 | 0.6972 | 9.7783804e-08 | 669 |
| 0.4387 | 0.8494 | 0.8414 | 0.7042 | 9.777725e-08 | 670 |
| 0.4549 | 0.8329 | 0.8417 | 0.7042 | 9.7770695e-08 | 671 |
| 0.4465 | 0.8424 | 0.8423 | 0.6972 | 9.776412e-08 | 672 |
| 0.4462 | 0.8447 | 0.8415 | 0.7042 | 9.775754e-08 | 673 |
| 0.4538 | 0.8353 | 0.8410 | 0.7042 | 9.7750956e-08 | 674 |
| 0.4575 | 0.8376 | 0.8427 | 0.6972 | 9.7744355e-08 | 675 |
| 0.4509 | 0.8353 | 0.8430 | 0.6972 | 9.773775e-08 | 676 |
| 0.4323 | 0.8424 | 0.8422 | 0.7042 | 9.773113e-08 | 677 |
| 0.4323 | 0.8518 | 0.8406 | 0.7042 | 9.772451e-08 | 678 |
| 0.4442 | 0.8212 | 0.8417 | 0.7042 | 9.771787e-08 | 679 |
| 0.4421 | 0.8471 | 0.8429 | 0.7042 | 9.771123e-08 | 680 |
| 0.4448 | 0.8376 | 0.8438 | 0.7042 | 9.770458e-08 | 681 |
| 0.4349 | 0.8400 | 0.8440 | 0.7042 | 9.7697914e-08 | 682 |
| 0.4410 | 0.8424 | 0.8448 | 0.6972 | 9.769124e-08 | 683 |
| 0.4390 | 0.8282 | 0.8459 | 0.6972 | 9.768456e-08 | 684 |
| 0.4446 | 0.8565 | 0.8463 | 0.6972 | 9.767787e-08 | 685 |
| 0.4330 | 0.8518 | 0.8436 | 0.7042 | 9.767117e-08 | 686 |
| 0.4463 | 0.8400 | 0.8427 | 0.7042 | 9.766446e-08 | 687 |
| 0.4541 | 0.8424 | 0.8433 | 0.7042 | 9.765774e-08 | 688 |
| 0.4355 | 0.8400 | 0.8419 | 0.7042 | 9.765101e-08 | 689 |
| 0.4466 | 0.8329 | 0.8427 | 0.7042 | 9.7644275e-08 | 690 |
| 0.4253 | 0.8400 | 0.8434 | 0.7042 | 9.7637525e-08 | 691 |
| 0.4356 | 0.8400 | 0.8444 | 0.7042 | 9.763077e-08 | 692 |
| 0.4318 | 0.8518 | 0.8448 | 0.7042 | 9.7624e-08 | 693 |
| 0.4417 | 0.8447 | 0.8442 | 0.7042 | 9.761723e-08 | 694 |
| 0.4277 | 0.8518 | 0.8456 | 0.7042 | 9.7610446e-08 | 695 |
| 0.4415 | 0.8400 | 0.8452 | 0.7042 | 9.760365e-08 | 696 |
| 0.4317 | 0.8471 | 0.8451 | 0.7042 | 9.759685e-08 | 697 |
| 0.4297 | 0.8400 | 0.8449 | 0.7042 | 9.759004e-08 | 698 |
| 0.4178 | 0.8494 | 0.8463 | 0.7042 | 9.758322e-08 | 699 |
| 0.4357 | 0.8400 | 0.8465 | 0.7042 | 9.757639e-08 | 700 |
| 0.4407 | 0.8376 | 0.8471 | 0.7042 | 9.756955e-08 | 701 |
| 0.4238 | 0.8565 | 0.8475 | 0.7113 | 9.75627e-08 | 702 |
| 0.4273 | 0.8518 | 0.8490 | 0.7042 | 9.755584e-08 | 703 |
| 0.4220 | 0.8447 | 0.8484 | 0.7113 | 9.754897e-08 | 704 |
| 0.4213 | 0.8588 | 0.8462 | 0.7042 | 9.754209e-08 | 705 |
| 0.4352 | 0.8494 | 0.8466 | 0.7042 | 9.753521e-08 | 706 |
| 0.4237 | 0.8447 | 0.8479 | 0.7113 | 9.7528314e-08 | 707 |
| 0.4331 | 0.8447 | 0.8463 | 0.7042 | 9.752141e-08 | 708 |
| 0.4306 | 0.8447 | 0.8460 | 0.7042 | 9.7514494e-08 | 709 |
| 0.4230 | 0.8494 | 0.8452 | 0.7042 | 9.7507574e-08 | 710 |
| 0.4268 | 0.8541 | 0.8454 | 0.7042 | 9.750064e-08 | 711 |
| 0.4261 | 0.8612 | 0.8454 | 0.7042 | 9.74937e-08 | 712 |
| 0.4398 | 0.8376 | 0.8463 | 0.7042 | 9.748675e-08 | 713 |
| 0.4180 | 0.8424 | 0.8475 | 0.7042 | 9.7479784e-08 | 714 |
| 0.4239 | 0.8471 | 0.8470 | 0.7042 | 9.7472814e-08 | 715 |
| 0.4353 | 0.8424 | 0.8480 | 0.7113 | 9.7465836e-08 | 716 |
| 0.4131 | 0.8447 | 0.8491 | 0.7113 | 9.745885e-08 | 717 |
| 0.4324 | 0.8424 | 0.8525 | 0.7113 | 9.745185e-08 | 718 |
| 0.4242 | 0.8518 | 0.8513 | 0.7183 | 9.744485e-08 | 719 |
| 0.4216 | 0.8400 | 0.8493 | 0.7113 | 9.7437834e-08 | 720 |
| 0.4212 | 0.8400 | 0.8482 | 0.7113 | 9.743081e-08 | 721 |
| 0.4161 | 0.8518 | 0.8482 | 0.7113 | 9.742377e-08 | 722 |
| 0.4133 | 0.8494 | 0.8489 | 0.7113 | 9.741673e-08 | 723 |
| 0.4118 | 0.8518 | 0.8508 | 0.7113 | 9.7409675e-08 | 724 |
| 0.4073 | 0.8659 | 0.8509 | 0.7113 | 9.740261e-08 | 725 |
| 0.4153 | 0.8494 | 0.8502 | 0.7113 | 9.739554e-08 | 726 |
| 0.4097 | 0.8541 | 0.8500 | 0.7113 | 9.7388465e-08 | 727 |
| 0.4221 | 0.8400 | 0.8493 | 0.7113 | 9.7381374e-08 | 728 |
| 0.4040 | 0.8635 | 0.8506 | 0.7113 | 9.7374276e-08 | 729 |
| 0.4070 | 0.8612 | 0.8508 | 0.7113 | 9.736717e-08 | 730 |
| 0.4144 | 0.8565 | 0.8493 | 0.7113 | 9.736005e-08 | 731 |
| 0.4260 | 0.8494 | 0.8496 | 0.7113 | 9.7352924e-08 | 732 |
| 0.4081 | 0.8612 | 0.8497 | 0.7113 | 9.734579e-08 | 733 |
| 0.4242 | 0.8494 | 0.8500 | 0.7113 | 9.733864e-08 | 734 |
| 0.4070 | 0.8565 | 0.8501 | 0.7113 | 9.733149e-08 | 735 |
| 0.4194 | 0.8518 | 0.8512 | 0.7113 | 9.7324325e-08 | 736 |
| 0.4279 | 0.8518 | 0.8519 | 0.7113 | 9.7317155e-08 | 737 |
| 0.4119 | 0.8588 | 0.8517 | 0.7113 | 9.730997e-08 | 738 |
| 0.4126 | 0.8471 | 0.8529 | 0.7113 | 9.730278e-08 | 739 |
| 0.4193 | 0.8400 | 0.8523 | 0.7113 | 9.729558e-08 | 740 |
| 0.4114 | 0.8447 | 0.8529 | 0.7113 | 9.728837e-08 | 741 |
| 0.4142 | 0.8447 | 0.8543 | 0.7183 | 9.728115e-08 | 742 |
| 0.4097 | 0.8612 | 0.8547 | 0.7183 | 9.7273926e-08 | 743 |
| 0.4014 | 0.8635 | 0.8531 | 0.7113 | 9.726669e-08 | 744 |
| 0.3902 | 0.8635 | 0.8525 | 0.7113 | 9.7259445e-08 | 745 |
| 0.4114 | 0.8494 | 0.8539 | 0.7113 | 9.725219e-08 | 746 |
| 0.4179 | 0.8565 | 0.8542 | 0.7183 | 9.724493e-08 | 747 |
| 0.3993 | 0.8753 | 0.8546 | 0.7183 | 9.723765e-08 | 748 |
| 0.4003 | 0.8541 | 0.8559 | 0.7113 | 9.723037e-08 | 749 |
| 0.4246 | 0.8400 | 0.8561 | 0.7113 | 9.722308e-08 | 750 |
| 0.3973 | 0.8612 | 0.8551 | 0.7183 | 9.7215775e-08 | 751 |
| 0.4115 | 0.8494 | 0.8544 | 0.7113 | 9.720846e-08 | 752 |
| 0.4088 | 0.8424 | 0.8545 | 0.7113 | 9.7201145e-08 | 753 |
| 0.4154 | 0.8400 | 0.8543 | 0.7113 | 9.719382e-08 | 754 |
| 0.4215 | 0.8518 | 0.8549 | 0.7113 | 9.718648e-08 | 755 |
| 0.4047 | 0.8565 | 0.8547 | 0.7113 | 9.717913e-08 | 756 |
| 0.4058 | 0.8424 | 0.8560 | 0.7183 | 9.717178e-08 | 757 |
| 0.4080 | 0.8376 | 0.8558 | 0.7183 | 9.716441e-08 | 758 |
| 0.4080 | 0.8541 | 0.8562 | 0.7113 | 9.7157034e-08 | 759 |
| 0.3968 | 0.8635 | 0.8570 | 0.7113 | 9.714965e-08 | 760 |
| 0.3936 | 0.8612 | 0.8557 | 0.7183 | 9.714226e-08 | 761 |
| 0.4100 | 0.8565 | 0.8570 | 0.7183 | 9.713486e-08 | 762 |
| 0.3994 | 0.8588 | 0.8564 | 0.7113 | 9.712745e-08 | 763 |
| 0.4114 | 0.8400 | 0.8548 | 0.7183 | 9.712003e-08 | 764 |
| 0.4050 | 0.8518 | 0.8562 | 0.7113 | 9.71126e-08 | 765 |
| 0.3991 | 0.8588 | 0.8579 | 0.7113 | 9.710516e-08 | 766 |
| 0.3984 | 0.8659 | 0.8582 | 0.7113 | 9.709771e-08 | 767 |
| 0.3865 | 0.8659 | 0.8597 | 0.7113 | 9.709026e-08 | 768 |
| 0.4004 | 0.8541 | 0.8581 | 0.7183 | 9.708279e-08 | 769 |
| 0.4130 | 0.8471 | 0.8582 | 0.7254 | 9.7075315e-08 | 770 |
| 0.4086 | 0.8565 | 0.8576 | 0.7254 | 9.706783e-08 | 771 |
| 0.3977 | 0.8612 | 0.8579 | 0.7254 | 9.706034e-08 | 772 |
| 0.3905 | 0.8471 | 0.8592 | 0.7113 | 9.705283e-08 | 773 |
| 0.3977 | 0.8682 | 0.8596 | 0.7183 | 9.704532e-08 | 774 |
| 0.3773 | 0.8682 | 0.8586 | 0.7254 | 9.7037805e-08 | 775 |
| 0.3895 | 0.8612 | 0.8593 | 0.7183 | 9.7030274e-08 | 776 |
| 0.3903 | 0.8635 | 0.8601 | 0.7183 | 9.7022735e-08 | 777 |
| 0.3972 | 0.8494 | 0.8599 | 0.7183 | 9.701519e-08 | 778 |
| 0.3899 | 0.8588 | 0.8598 | 0.7254 | 9.700763e-08 | 779 |
| 0.3972 | 0.8635 | 0.8599 | 0.7254 | 9.700006e-08 | 780 |
| 0.3873 | 0.8612 | 0.8599 | 0.7254 | 9.699249e-08 | 781 |
| 0.3941 | 0.8541 | 0.8604 | 0.7183 | 9.6984905e-08 | 782 |
| 0.3858 | 0.8682 | 0.8599 | 0.7254 | 9.697731e-08 | 783 |
| 0.3691 | 0.8635 | 0.8602 | 0.7183 | 9.696971e-08 | 784 |
| 0.3879 | 0.8682 | 0.8609 | 0.7183 | 9.69621e-08 | 785 |
| 0.3892 | 0.8565 | 0.8612 | 0.7183 | 9.695447e-08 | 786 |
| 0.3818 | 0.8753 | 0.8620 | 0.7113 | 9.694684e-08 | 787 |
| 0.3798 | 0.8706 | 0.8625 | 0.7113 | 9.69392e-08 | 788 |
| 0.3828 | 0.8612 | 0.8627 | 0.7183 | 9.693156e-08 | 789 |
| 0.4055 | 0.8447 | 0.8618 | 0.7183 | 9.69239e-08 | 790 |
| 0.4016 | 0.8635 | 0.8625 | 0.7183 | 9.691623e-08 | 791 |
| 0.3952 | 0.8659 | 0.8629 | 0.7183 | 9.690856e-08 | 792 |
| 0.3878 | 0.8753 | 0.8649 | 0.7042 | 9.690088e-08 | 793 |
| 0.3724 | 0.8871 | 0.8650 | 0.7042 | 9.689318e-08 | 794 |
| 0.3746 | 0.8682 | 0.8640 | 0.7183 | 9.688548e-08 | 795 |
| 0.3752 | 0.8682 | 0.8635 | 0.7183 | 9.687777e-08 | 796 |
| 0.3817 | 0.8682 | 0.8638 | 0.7183 | 9.6870046e-08 | 797 |
| 0.3891 | 0.8729 | 0.8636 | 0.7183 | 9.6862316e-08 | 798 |
| 0.3775 | 0.8635 | 0.8626 | 0.7183 | 9.685458e-08 | 799 |
| 0.3968 | 0.8447 | 0.8634 | 0.7183 | 9.684683e-08 | 800 |
| 0.3826 | 0.8635 | 0.8633 | 0.7183 | 9.6839074e-08 | 801 |
| 0.3809 | 0.8471 | 0.8632 | 0.7183 | 9.683131e-08 | 802 |
| 0.3811 | 0.8659 | 0.8636 | 0.7183 | 9.6823534e-08 | 803 |
| 0.3647 | 0.8682 | 0.8636 | 0.7183 | 9.6815754e-08 | 804 |
| 0.3752 | 0.8800 | 0.8632 | 0.7254 | 9.680796e-08 | 805 |
| 0.3823 | 0.8753 | 0.8636 | 0.7183 | 9.680016e-08 | 806 |
| 0.4058 | 0.8424 | 0.8643 | 0.7183 | 9.679235e-08 | 807 |
| 0.3703 | 0.8871 | 0.8650 | 0.7183 | 9.6784525e-08 | 808 |
| 0.3668 | 0.8871 | 0.8660 | 0.7183 | 9.6776695e-08 | 809 |
| 0.3709 | 0.8729 | 0.8677 | 0.7183 | 9.676886e-08 | 810 |
| 0.3715 | 0.8776 | 0.8698 | 0.7042 | 9.676101e-08 | 811 |
| 0.3838 | 0.8729 | 0.8687 | 0.7183 | 9.6753155e-08 | 812 |
| 0.3827 | 0.8706 | 0.8676 | 0.7183 | 9.674529e-08 | 813 |
| 0.3873 | 0.8682 | 0.8661 | 0.7183 | 9.6737416e-08 | 814 |
| 0.3668 | 0.8659 | 0.8672 | 0.7183 | 9.6729536e-08 | 815 |
| 0.3785 | 0.8776 | 0.8667 | 0.7183 | 9.672164e-08 | 816 |
| 0.3693 | 0.8729 | 0.8669 | 0.7183 | 9.671374e-08 | 817 |
| 0.3739 | 0.8729 | 0.8673 | 0.7183 | 9.670583e-08 | 818 |
| 0.3728 | 0.8800 | 0.8679 | 0.7183 | 9.669791e-08 | 819 |
| 0.3747 | 0.8706 | 0.8673 | 0.7183 | 9.668998e-08 | 820 |
| 0.3659 | 0.8635 | 0.8676 | 0.7183 | 9.6682044e-08 | 821 |
| 0.3742 | 0.8612 | 0.8686 | 0.7183 | 9.66741e-08 | 822 |
| 0.3672 | 0.8753 | 0.8702 | 0.7113 | 9.666614e-08 | 823 |
| 0.3876 | 0.8635 | 0.8702 | 0.7113 | 9.665818e-08 | 824 |
| 0.3816 | 0.8706 | 0.8700 | 0.7183 | 9.6650204e-08 | 825 |
| 0.3764 | 0.8682 | 0.8706 | 0.7183 | 9.6642225e-08 | 826 |
| 0.3863 | 0.8682 | 0.8716 | 0.7183 | 9.663423e-08 | 827 |
| 0.3608 | 0.8682 | 0.8719 | 0.7113 | 9.662623e-08 | 828 |
| 0.3592 | 0.8729 | 0.8713 | 0.7113 | 9.661822e-08 | 829 |
| 0.3594 | 0.8565 | 0.8719 | 0.7113 | 9.66102e-08 | 830 |
| 0.3772 | 0.8659 | 0.8714 | 0.7183 | 9.660217e-08 | 831 |
| 0.3771 | 0.8541 | 0.8726 | 0.7113 | 9.6594135e-08 | 832 |
| 0.3803 | 0.8565 | 0.8735 | 0.7113 | 9.658609e-08 | 833 |
| 0.3558 | 0.8871 | 0.8728 | 0.7183 | 9.6578034e-08 | 834 |
| 0.3758 | 0.8659 | 0.8718 | 0.7183 | 9.656997e-08 | 835 |
| 0.3712 | 0.8706 | 0.8722 | 0.7183 | 9.65619e-08 | 836 |
| 0.3721 | 0.8565 | 0.8731 | 0.7113 | 9.655382e-08 | 837 |
| 0.3659 | 0.8871 | 0.8736 | 0.7113 | 9.6545726e-08 | 838 |
| 0.3747 | 0.8659 | 0.8717 | 0.7183 | 9.6537626e-08 | 839 |
| 0.3522 | 0.8871 | 0.8715 | 0.7183 | 9.652952e-08 | 840 |
| 0.3715 | 0.8659 | 0.8717 | 0.7183 | 9.6521404e-08 | 841 |
| 0.3718 | 0.8706 | 0.8724 | 0.7183 | 9.6513276e-08 | 842 |
| 0.3643 | 0.8682 | 0.8729 | 0.7183 | 9.650514e-08 | 843 |
| 0.3596 | 0.8729 | 0.8750 | 0.7113 | 9.6497e-08 | 844 |
| 0.3653 | 0.8776 | 0.8752 | 0.7113 | 9.648885e-08 | 845 |
| 0.3606 | 0.8776 | 0.8741 | 0.7183 | 9.648068e-08 | 846 |
| 0.3604 | 0.8659 | 0.8737 | 0.7113 | 9.647251e-08 | 847 |
| 0.3661 | 0.8776 | 0.8746 | 0.7113 | 9.646433e-08 | 848 |
| 0.3663 | 0.8659 | 0.8740 | 0.7183 | 9.645614e-08 | 849 |
| 0.3568 | 0.8847 | 0.8745 | 0.7113 | 9.644794e-08 | 850 |
| 0.3718 | 0.8565 | 0.8758 | 0.7113 | 9.6439734e-08 | 851 |
| 0.3603 | 0.8659 | 0.8750 | 0.7183 | 9.643152e-08 | 852 |
| 0.3610 | 0.8918 | 0.8767 | 0.7113 | 9.642329e-08 | 853 |
| 0.3629 | 0.8706 | 0.8752 | 0.7183 | 9.641506e-08 | 854 |
| 0.3577 | 0.8800 | 0.8744 | 0.7183 | 9.6406815e-08 | 855 |
| 0.3556 | 0.8659 | 0.8745 | 0.7254 | 9.6398566e-08 | 856 |
| 0.3613 | 0.8776 | 0.8748 | 0.7183 | 9.63903e-08 | 857 |
| 0.3626 | 0.8659 | 0.8749 | 0.7254 | 9.638203e-08 | 858 |
| 0.3538 | 0.8729 | 0.8748 | 0.7254 | 9.637375e-08 | 859 |
| 0.3545 | 0.8706 | 0.8746 | 0.7254 | 9.636547e-08 | 860 |
| 0.3545 | 0.8824 | 0.8749 | 0.7254 | 9.635717e-08 | 861 |
| 0.3431 | 0.8776 | 0.8754 | 0.7254 | 9.634886e-08 | 862 |
| 0.3612 | 0.8706 | 0.8766 | 0.7183 | 9.634055e-08 | 863 |
| 0.3533 | 0.8729 | 0.8782 | 0.7113 | 9.633223e-08 | 864 |
| 0.3695 | 0.8659 | 0.8779 | 0.7183 | 9.6323895e-08 | 865 |
| 0.3466 | 0.8847 | 0.8776 | 0.7183 | 9.631555e-08 | 866 |
| 0.3493 | 0.8753 | 0.8790 | 0.7042 | 9.6307204e-08 | 867 |
| 0.3409 | 0.8847 | 0.8785 | 0.7042 | 9.629885e-08 | 868 |
| 0.3423 | 0.8894 | 0.8800 | 0.7042 | 9.629048e-08 | 869 |
| 0.3529 | 0.8753 | 0.8810 | 0.6972 | 9.62821e-08 | 870 |
| 0.3539 | 0.8682 | 0.8800 | 0.6972 | 9.6273716e-08 | 871 |
| 0.3528 | 0.8706 | 0.8793 | 0.7183 | 9.6265325e-08 | 872 |
| 0.3525 | 0.8729 | 0.8784 | 0.7254 | 9.625692e-08 | 873 |
| 0.3503 | 0.8824 | 0.8777 | 0.7254 | 9.6248506e-08 | 874 |
| 0.3529 | 0.8824 | 0.8783 | 0.7254 | 9.6240086e-08 | 875 |
| 0.3444 | 0.8918 | 0.8797 | 0.7183 | 9.623166e-08 | 876 |
| 0.3491 | 0.8800 | 0.8791 | 0.7254 | 9.622322e-08 | 877 |
| 0.3457 | 0.8871 | 0.8797 | 0.7183 | 9.621477e-08 | 878 |
| 0.3449 | 0.8824 | 0.8792 | 0.7254 | 9.6206314e-08 | 879 |
| 0.3548 | 0.8847 | 0.8803 | 0.7183 | 9.619785e-08 | 880 |
| 0.3499 | 0.8776 | 0.8810 | 0.7183 | 9.6189375e-08 | 881 |
| 0.3426 | 0.9012 | 0.8843 | 0.6972 | 9.618089e-08 | 882 |
| 0.3376 | 0.8894 | 0.8836 | 0.7042 | 9.61724e-08 | 883 |
| 0.3337 | 0.8800 | 0.8828 | 0.7113 | 9.61639e-08 | 884 |
| 0.3528 | 0.8729 | 0.8842 | 0.7113 | 9.615539e-08 | 885 |
| 0.3576 | 0.8682 | 0.8831 | 0.7183 | 9.614687e-08 | 886 |
| 0.3467 | 0.8894 | 0.8841 | 0.7183 | 9.613834e-08 | 887 |
| 0.3433 | 0.8824 | 0.8834 | 0.7183 | 9.612981e-08 | 888 |
| 0.3427 | 0.8871 | 0.8835 | 0.7254 | 9.612126e-08 | 889 |
| 0.3516 | 0.8753 | 0.8836 | 0.7183 | 9.611271e-08 | 890 |
| 0.3336 | 0.8824 | 0.8837 | 0.7254 | 9.6104145e-08 | 891 |
| 0.3516 | 0.8753 | 0.8836 | 0.7254 | 9.6095576e-08 | 892 |
| 0.3448 | 0.8824 | 0.8838 | 0.7254 | 9.608699e-08 | 893 |
| 0.3412 | 0.8847 | 0.8838 | 0.7254 | 9.60784e-08 | 894 |
| 0.3568 | 0.8776 | 0.8845 | 0.7254 | 9.6069805e-08 | 895 |
| 0.3175 | 0.8941 | 0.8856 | 0.7183 | 9.60612e-08 | 896 |
| 0.3414 | 0.8871 | 0.8857 | 0.7113 | 9.605258e-08 | 897 |
| 0.3430 | 0.8847 | 0.8865 | 0.7113 | 9.6043955e-08 | 898 |
| 0.3461 | 0.8776 | 0.8877 | 0.7042 | 9.603532e-08 | 899 |
| 0.3415 | 0.8894 | 0.8856 | 0.7254 | 9.602668e-08 | 900 |
| 0.3332 | 0.8847 | 0.8854 | 0.7254 | 9.601803e-08 | 901 |
| 0.3473 | 0.8776 | 0.8856 | 0.7254 | 9.6009366e-08 | 902 |
| 0.3374 | 0.8941 | 0.8870 | 0.7254 | 9.60007e-08 | 903 |
| 0.3351 | 0.8729 | 0.8881 | 0.7113 | 9.599202e-08 | 904 |
| 0.3468 | 0.8706 | 0.8887 | 0.7113 | 9.598333e-08 | 905 |
| 0.3393 | 0.8941 | 0.8882 | 0.7254 | 9.5974634e-08 | 906 |
| 0.3379 | 0.8800 | 0.8872 | 0.7254 | 9.596593e-08 | 907 |
| 0.3416 | 0.8894 | 0.8872 | 0.7254 | 9.595722e-08 | 908 |
| 0.3199 | 0.8965 | 0.8881 | 0.7254 | 9.59485e-08 | 909 |
| 0.3392 | 0.8776 | 0.8877 | 0.7254 | 9.593977e-08 | 910 |
| 0.3356 | 0.8871 | 0.8896 | 0.7113 | 9.593103e-08 | 911 |
| 0.3379 | 0.8729 | 0.8892 | 0.7113 | 9.592228e-08 | 912 |
| 0.3472 | 0.8918 | 0.8906 | 0.7113 | 9.591353e-08 | 913 |
| 0.3394 | 0.8776 | 0.8927 | 0.6972 | 9.590476e-08 | 914 |
| 0.3438 | 0.8729 | 0.8928 | 0.6972 | 9.5895984e-08 | 915 |
| 0.3303 | 0.8800 | 0.8912 | 0.7183 | 9.58872e-08 | 916 |
| 0.3288 | 0.8894 | 0.8921 | 0.6972 | 9.587841e-08 | 917 |
| 0.3187 | 0.8988 | 0.8910 | 0.7183 | 9.586961e-08 | 918 |
| 0.3390 | 0.8800 | 0.8907 | 0.7183 | 9.58608e-08 | 919 |
| 0.3385 | 0.8776 | 0.8911 | 0.7183 | 9.585198e-08 | 920 |
| 0.3257 | 0.8871 | 0.8903 | 0.7183 | 9.5843156e-08 | 921 |
| 0.3233 | 0.8847 | 0.8908 | 0.7183 | 9.583432e-08 | 922 |
| 0.3289 | 0.8847 | 0.8899 | 0.7254 | 9.582547e-08 | 923 |
| 0.3232 | 0.8894 | 0.8916 | 0.7183 | 9.581662e-08 | 924 |
| 0.3434 | 0.8659 | 0.8942 | 0.7113 | 9.5807756e-08 | 925 |
| 0.3175 | 0.8965 | 0.8936 | 0.7183 | 9.579889e-08 | 926 |
| 0.3317 | 0.8941 | 0.8947 | 0.7042 | 9.579001e-08 | 927 |
| 0.3095 | 0.9059 | 0.8930 | 0.7183 | 9.578112e-08 | 928 |
| 0.3422 | 0.8753 | 0.8912 | 0.7254 | 9.577222e-08 | 929 |
| 0.3369 | 0.8918 | 0.8919 | 0.7183 | 9.576332e-08 | 930 |
| 0.3316 | 0.8753 | 0.8933 | 0.7183 | 9.57544e-08 | 931 |
| 0.3050 | 0.9106 | 0.8939 | 0.7183 | 9.574548e-08 | 932 |
| 0.3229 | 0.8894 | 0.8941 | 0.7183 | 9.5736546e-08 | 933 |
| 0.3361 | 0.8941 | 0.8931 | 0.7183 | 9.572761e-08 | 934 |
| 0.3267 | 0.8941 | 0.8952 | 0.7183 | 9.5718654e-08 | 935 |
| 0.3158 | 0.8965 | 0.8962 | 0.7042 | 9.5709694e-08 | 936 |
| 0.3282 | 0.8847 | 0.8957 | 0.7113 | 9.570073e-08 | 937 |
| 0.3287 | 0.8800 | 0.8958 | 0.7113 | 9.569175e-08 | 938 |
| 0.3242 | 0.8988 | 0.8963 | 0.7042 | 9.568277e-08 | 939 |
| 0.3318 | 0.8753 | 0.8957 | 0.7183 | 9.567378e-08 | 940 |
| 0.3343 | 0.8800 | 0.8965 | 0.7183 | 9.5664774e-08 | 941 |
| 0.3278 | 0.8871 | 0.8958 | 0.7183 | 9.5655764e-08 | 942 |
| 0.3299 | 0.8824 | 0.8955 | 0.7183 | 9.564675e-08 | 943 |
| 0.3231 | 0.8918 | 0.8963 | 0.7183 | 9.5637716e-08 | 944 |
| 0.3265 | 0.8941 | 0.8969 | 0.7042 | 9.562868e-08 | 945 |
| 0.3301 | 0.8847 | 0.8957 | 0.7113 | 9.561963e-08 | 946 |
| 0.3099 | 0.9035 | 0.8963 | 0.7183 | 9.561058e-08 | 947 |
| 0.3200 | 0.9012 | 0.8969 | 0.7183 | 9.5601514e-08 | 948 |
| 0.3235 | 0.8847 | 0.8963 | 0.7113 | 9.559244e-08 | 949 |
| 0.3194 | 0.8753 | 0.8963 | 0.7113 | 9.558336e-08 | 950 |
| 0.3224 | 0.8800 | 0.8968 | 0.7113 | 9.557427e-08 | 951 |
| 0.3229 | 0.8871 | 0.8976 | 0.7183 | 9.556518e-08 | 952 |
| 0.3283 | 0.8800 | 0.9004 | 0.7042 | 9.555607e-08 | 953 |
| 0.3196 | 0.8824 | 0.9018 | 0.6972 | 9.554695e-08 | 954 |
| 0.3207 | 0.8894 | 0.9019 | 0.6901 | 9.553783e-08 | 955 |
| 0.3244 | 0.8824 | 0.9030 | 0.6901 | 9.55287e-08 | 956 |
| 0.3301 | 0.8988 | 0.8994 | 0.7183 | 9.551955e-08 | 957 |
| 0.3086 | 0.9012 | 0.8994 | 0.7183 | 9.55104e-08 | 958 |
| 0.3111 | 0.9059 | 0.8996 | 0.7183 | 9.550124e-08 | 959 |
| 0.3198 | 0.8800 | 0.8997 | 0.7113 | 9.549208e-08 | 960 |
| 0.3367 | 0.8824 | 0.9017 | 0.7042 | 9.54829e-08 | 961 |
| 0.3287 | 0.8871 | 0.9016 | 0.7042 | 9.5473716e-08 | 962 |
| 0.3195 | 0.8941 | 0.9029 | 0.6972 | 9.546452e-08 | 963 |
| 0.3192 | 0.8941 | 0.9037 | 0.6831 | 9.545532e-08 | 964 |
| 0.3191 | 0.8988 | 0.9035 | 0.6831 | 9.544611e-08 | 965 |
| 0.3378 | 0.8824 | 0.9007 | 0.7113 | 9.5436896e-08 | 966 |
| 0.3276 | 0.8871 | 0.9021 | 0.7042 | 9.5427666e-08 | 967 |
| 0.3155 | 0.8871 | 0.9007 | 0.7113 | 9.541843e-08 | 968 |
| 0.3221 | 0.8776 | 0.9006 | 0.7113 | 9.5409185e-08 | 969 |
| 0.3085 | 0.9035 | 0.9023 | 0.7042 | 9.539993e-08 | 970 |
| 0.3081 | 0.9035 | 0.9031 | 0.7042 | 9.539067e-08 | 971 |
| 0.3084 | 0.9012 | 0.9023 | 0.7113 | 9.5381395e-08 | 972 |
| 0.3048 | 0.8918 | 0.9026 | 0.6972 | 9.5372116e-08 | 973 |
| 0.3216 | 0.8847 | 0.9040 | 0.6901 | 9.536283e-08 | 974 |
| 0.3060 | 0.8965 | 0.9033 | 0.6972 | 9.5353535e-08 | 975 |
| 0.3197 | 0.8706 | 0.9025 | 0.7113 | 9.534423e-08 | 976 |
| 0.3110 | 0.8894 | 0.9038 | 0.6972 | 9.533491e-08 | 977 |
| 0.3092 | 0.8965 | 0.9055 | 0.6831 | 9.532559e-08 | 978 |
| 0.3142 | 0.8871 | 0.9067 | 0.6901 | 9.531626e-08 | 979 |
| 0.3116 | 0.8988 | 0.9044 | 0.6831 | 9.530692e-08 | 980 |
| 0.3130 | 0.8965 | 0.9052 | 0.6831 | 9.529757e-08 | 981 |
| 0.3138 | 0.8988 | 0.9049 | 0.7042 | 9.5288215e-08 | 982 |
| 0.2931 | 0.8965 | 0.9047 | 0.7042 | 9.527885e-08 | 983 |
| 0.3097 | 0.8941 | 0.9052 | 0.7042 | 9.526948e-08 | 984 |
| 0.3083 | 0.8941 | 0.9047 | 0.7042 | 9.526009e-08 | 985 |
| 0.2876 | 0.9106 | 0.9053 | 0.7042 | 9.52507e-08 | 986 |
| 0.2991 | 0.8965 | 0.9055 | 0.7042 | 9.52413e-08 | 987 |
| 0.3027 | 0.9035 | 0.9063 | 0.7113 | 9.523189e-08 | 988 |
| 0.3063 | 0.8894 | 0.9077 | 0.7042 | 9.5222475e-08 | 989 |
| 0.3036 | 0.8941 | 0.9075 | 0.6972 | 9.5213046e-08 | 990 |
| 0.3033 | 0.9082 | 0.9088 | 0.6901 | 9.520361e-08 | 991 |
| 0.3197 | 0.8753 | 0.9079 | 0.7042 | 9.519417e-08 | 992 |
| 0.3021 | 0.9035 | 0.9092 | 0.6972 | 9.518472e-08 | 993 |
| 0.3144 | 0.8847 | 0.9107 | 0.6972 | 9.517526e-08 | 994 |
| 0.3085 | 0.8918 | 0.9085 | 0.6972 | 9.516579e-08 | 995 |
| 0.2938 | 0.9012 | 0.9079 | 0.7042 | 9.515631e-08 | 996 |
| 0.3006 | 0.9059 | 0.9085 | 0.7042 | 9.5146824e-08 | 997 |
| 0.3031 | 0.8965 | 0.9091 | 0.6972 | 9.513733e-08 | 998 |
| 0.3031 | 0.9035 | 0.9112 | 0.6831 | 9.512783e-08 | 999 |
| 0.2973 | 0.9012 | 0.9105 | 0.6831 | 9.511832e-08 | 1000 |
| 0.2860 | 0.9012 | 0.9103 | 0.6901 | 9.5108796e-08 | 1001 |
| 0.2966 | 0.9106 | 0.9122 | 0.6831 | 9.509927e-08 | 1002 |
| 0.2915 | 0.9012 | 0.9114 | 0.6901 | 9.508973e-08 | 1003 |
| 0.2913 | 0.9059 | 0.9105 | 0.7042 | 9.508019e-08 | 1004 |
| 0.3020 | 0.9082 | 0.9118 | 0.6901 | 9.507063e-08 | 1005 |
| 0.2910 | 0.9082 | 0.9124 | 0.6831 | 9.506107e-08 | 1006 |
| 0.3047 | 0.8965 | 0.9112 | 0.6972 | 9.50515e-08 | 1007 |
| 0.2942 | 0.8894 | 0.9103 | 0.7042 | 9.504192e-08 | 1008 |
| 0.2864 | 0.9200 | 0.9124 | 0.6901 | 9.5032334e-08 | 1009 |
| 0.2805 | 0.9224 | 0.9128 | 0.6901 | 9.5022735e-08 | 1010 |
| 0.2943 | 0.8918 | 0.9116 | 0.7042 | 9.501313e-08 | 1011 |
| 0.3138 | 0.8824 | 0.9122 | 0.7042 | 9.5003514e-08 | 1012 |
| 0.2957 | 0.8965 | 0.9130 | 0.7042 | 9.4993894e-08 | 1013 |
| 0.2907 | 0.9012 | 0.9166 | 0.6901 | 9.4984266e-08 | 1014 |
| 0.2776 | 0.9106 | 0.9167 | 0.6831 | 9.4974624e-08 | 1015 |
| 0.3045 | 0.9012 | 0.9147 | 0.6972 | 9.4964975e-08 | 1016 |
| 0.2965 | 0.9059 | 0.9151 | 0.6901 | 9.495532e-08 | 1017 |
| 0.2927 | 0.9082 | 0.9160 | 0.6901 | 9.4945655e-08 | 1018 |
| 0.3016 | 0.8988 | 0.9162 | 0.6901 | 9.4935984e-08 | 1019 |
| 0.2937 | 0.9012 | 0.9166 | 0.6901 | 9.49263e-08 | 1020 |
| 0.2989 | 0.9035 | 0.9173 | 0.6831 | 9.491661e-08 | 1021 |
| 0.2873 | 0.9035 | 0.9181 | 0.6901 | 9.490691e-08 | 1022 |
| 0.3089 | 0.8941 | 0.9200 | 0.6901 | 9.48972e-08 | 1023 |
| 0.2910 | 0.9035 | 0.9191 | 0.6972 | 9.488749e-08 | 1024 |
| 0.2783 | 0.9106 | 0.9193 | 0.6972 | 9.487776e-08 | 1025 |
| 0.2792 | 0.9035 | 0.9183 | 0.6901 | 9.486803e-08 | 1026 |
| 0.2868 | 0.9082 | 0.9171 | 0.6972 | 9.485829e-08 | 1027 |
| 0.2870 | 0.9129 | 0.9168 | 0.6972 | 9.484854e-08 | 1028 |
| 0.2867 | 0.9106 | 0.9161 | 0.6972 | 9.483878e-08 | 1029 |
| 0.2814 | 0.8988 | 0.9159 | 0.6972 | 9.482901e-08 | 1030 |
| 0.2835 | 0.9106 | 0.9154 | 0.7042 | 9.4819235e-08 | 1031 |
| 0.2868 | 0.9059 | 0.9163 | 0.7042 | 9.480945e-08 | 1032 |
| 0.2995 | 0.8941 | 0.9172 | 0.6972 | 9.479966e-08 | 1033 |
| 0.2943 | 0.9012 | 0.9186 | 0.6972 | 9.478986e-08 | 1034 |
| 0.2939 | 0.9012 | 0.9232 | 0.6972 | 9.478005e-08 | 1035 |
| 0.2913 | 0.9012 | 0.9204 | 0.7113 | 9.477023e-08 | 1036 |
| 0.2953 | 0.9082 | 0.9197 | 0.7042 | 9.47604e-08 | 1037 |
| 0.2967 | 0.8918 | 0.9193 | 0.7042 | 9.475057e-08 | 1038 |
| 0.2780 | 0.9012 | 0.9210 | 0.7113 | 9.474073e-08 | 1039 |
| 0.2915 | 0.9059 | 0.9217 | 0.7113 | 9.473087e-08 | 1040 |
| 0.3084 | 0.8894 | 0.9219 | 0.7113 | 9.472101e-08 | 1041 |
| 0.2769 | 0.9106 | 0.9219 | 0.7113 | 9.471114e-08 | 1042 |
| 0.2918 | 0.9035 | 0.9219 | 0.7113 | 9.4701264e-08 | 1043 |
| 0.2802 | 0.9106 | 0.9230 | 0.7113 | 9.469138e-08 | 1044 |
| 0.2767 | 0.9200 | 0.9225 | 0.7113 | 9.468149e-08 | 1045 |
| 0.2888 | 0.8918 | 0.9215 | 0.7113 | 9.4671584e-08 | 1046 |
| 0.2719 | 0.9082 | 0.9215 | 0.7042 | 9.466167e-08 | 1047 |
| 0.2806 | 0.9153 | 0.9223 | 0.7113 | 9.465175e-08 | 1048 |
| 0.2766 | 0.9129 | 0.9241 | 0.7042 | 9.464183e-08 | 1049 |
| 0.2850 | 0.9106 | 0.9232 | 0.7113 | 9.463189e-08 | 1050 |
| 0.2749 | 0.9106 | 0.9229 | 0.7113 | 9.4621946e-08 | 1051 |
| 0.2945 | 0.8918 | 0.9200 | 0.6972 | 9.461199e-08 | 1052 |
| 0.2927 | 0.8988 | 0.9216 | 0.6972 | 9.460203e-08 | 1053 |
| 0.2851 | 0.9012 | 0.9221 | 0.7042 | 9.459206e-08 | 1054 |
| 0.2741 | 0.9035 | 0.9221 | 0.7042 | 9.4582084e-08 | 1055 |
| 0.2769 | 0.9082 | 0.9254 | 0.7113 | 9.4572094e-08 | 1056 |
| 0.2841 | 0.9059 | 0.9251 | 0.7113 | 9.45621e-08 | 1057 |
| 0.2817 | 0.9012 | 0.9262 | 0.7113 | 9.455209e-08 | 1058 |
| 0.2920 | 0.8988 | 0.9266 | 0.7042 | 9.454208e-08 | 1059 |
| 0.2618 | 0.9129 | 0.9264 | 0.7113 | 9.453206e-08 | 1060 |
| 0.2861 | 0.9012 | 0.9252 | 0.7113 | 9.4522036e-08 | 1061 |
| 0.2805 | 0.9153 | 0.9279 | 0.7113 | 9.4512e-08 | 1062 |
| 0.2810 | 0.9200 | 0.9284 | 0.7113 | 9.450195e-08 | 1063 |
| 0.2737 | 0.9106 | 0.9277 | 0.7113 | 9.4491895e-08 | 1064 |
| 0.2802 | 0.9059 | 0.9270 | 0.7113 | 9.4481834e-08 | 1065 |
| 0.2756 | 0.9082 | 0.9259 | 0.7113 | 9.4471766e-08 | 1066 |
| 0.2669 | 0.9200 | 0.9262 | 0.7113 | 9.446168e-08 | 1067 |
| 0.2906 | 0.9106 | 0.9263 | 0.7113 | 9.445159e-08 | 1068 |
| 0.2823 | 0.9035 | 0.9258 | 0.7042 | 9.4441496e-08 | 1069 |
| 0.2815 | 0.9129 | 0.9277 | 0.7113 | 9.443139e-08 | 1070 |
| 0.2768 | 0.9082 | 0.9287 | 0.7113 | 9.442128e-08 | 1071 |
| 0.2663 | 0.9129 | 0.9294 | 0.7113 | 9.441116e-08 | 1072 |
| 0.2664 | 0.9200 | 0.9296 | 0.7113 | 9.440103e-08 | 1073 |
| 0.2668 | 0.9153 | 0.9294 | 0.7113 | 9.439089e-08 | 1074 |
| 0.2728 | 0.9129 | 0.9297 | 0.7113 | 9.4380745e-08 | 1075 |
| 0.2684 | 0.9106 | 0.9313 | 0.7113 | 9.437059e-08 | 1076 |
| 0.2757 | 0.9224 | 0.9321 | 0.7113 | 9.436043e-08 | 1077 |
| 0.2775 | 0.9082 | 0.9306 | 0.7113 | 9.435026e-08 | 1078 |
| 0.2593 | 0.9224 | 0.9317 | 0.7113 | 9.434008e-08 | 1079 |
| 0.2745 | 0.8988 | 0.9317 | 0.7113 | 9.432989e-08 | 1080 |
| 0.2679 | 0.9224 | 0.9320 | 0.7113 | 9.4319695e-08 | 1081 |
| 0.2713 | 0.9059 | 0.9311 | 0.7113 | 9.430949e-08 | 1082 |
| 0.2679 | 0.8918 | 0.9352 | 0.7113 | 9.429928e-08 | 1083 |
| 0.2847 | 0.9224 | 0.9355 | 0.7113 | 9.4289064e-08 | 1084 |
| 0.2707 | 0.9059 | 0.9338 | 0.7113 | 9.427883e-08 | 1085 |
| 0.2781 | 0.9082 | 0.9337 | 0.7113 | 9.426859e-08 | 1086 |
| 0.2635 | 0.9129 | 0.9347 | 0.7113 | 9.425835e-08 | 1087 |
| 0.2748 | 0.9082 | 0.9348 | 0.7113 | 9.4248094e-08 | 1088 |
| 0.2536 | 0.9365 | 0.9344 | 0.7113 | 9.423783e-08 | 1089 |
| 0.2537 | 0.9153 | 0.9361 | 0.7113 | 9.4227566e-08 | 1090 |
| 0.2717 | 0.9082 | 0.9372 | 0.7113 | 9.4217285e-08 | 1091 |
| 0.2643 | 0.9224 | 0.9385 | 0.7113 | 9.4206996e-08 | 1092 |
| 0.2681 | 0.9082 | 0.9365 | 0.7113 | 9.41967e-08 | 1093 |
| 0.2651 | 0.9153 | 0.9363 | 0.7113 | 9.41864e-08 | 1094 |
| 0.2702 | 0.9247 | 0.9352 | 0.7113 | 9.417609e-08 | 1095 |
| 0.2628 | 0.9176 | 0.9373 | 0.7113 | 9.416577e-08 | 1096 |
| 0.2636 | 0.9200 | 0.9363 | 0.7113 | 9.415544e-08 | 1097 |
| 0.2675 | 0.9082 | 0.9374 | 0.7113 | 9.41451e-08 | 1098 |
| 0.2577 | 0.9271 | 0.9392 | 0.7113 | 9.4134755e-08 | 1099 |
| 0.2600 | 0.9247 | 0.9403 | 0.7042 | 9.41244e-08 | 1100 |
| 0.2653 | 0.9153 | 0.9413 | 0.7042 | 9.411404e-08 | 1101 |
| 0.2505 | 0.9247 | 0.9396 | 0.7113 | 9.4103676e-08 | 1102 |
| 0.2722 | 0.9035 | 0.9419 | 0.6972 | 9.4093295e-08 | 1103 |
| 0.2658 | 0.9129 | 0.9390 | 0.7113 | 9.408291e-08 | 1104 |
| 0.2596 | 0.9271 | 0.9416 | 0.7042 | 9.407251e-08 | 1105 |
| 0.2642 | 0.9224 | 0.9413 | 0.7113 | 9.406211e-08 | 1106 |
| 0.2773 | 0.9059 | 0.9435 | 0.6972 | 9.40517e-08 | 1107 |
| 0.2484 | 0.9224 | 0.9425 | 0.7113 | 9.404128e-08 | 1108 |
| 0.2715 | 0.9106 | 0.9410 | 0.7113 | 9.403085e-08 | 1109 |
| 0.2612 | 0.9176 | 0.9406 | 0.7113 | 9.4020415e-08 | 1110 |
| 0.2572 | 0.9035 | 0.9406 | 0.7113 | 9.400997e-08 | 1111 |
| 0.2633 | 0.9153 | 0.9406 | 0.7113 | 9.399952e-08 | 1112 |
| 0.2381 | 0.9294 | 0.9427 | 0.7113 | 9.398906e-08 | 1113 |
| 0.2642 | 0.9035 | 0.9419 | 0.7113 | 9.397859e-08 | 1114 |
| 0.2674 | 0.8988 | 0.9416 | 0.7113 | 9.396811e-08 | 1115 |
| 0.2556 | 0.9035 | 0.9432 | 0.7113 | 9.3957624e-08 | 1116 |
| 0.2655 | 0.9200 | 0.9442 | 0.7113 | 9.394713e-08 | 1117 |
| 0.2529 | 0.9271 | 0.9428 | 0.7113 | 9.393663e-08 | 1118 |
| 0.2625 | 0.9106 | 0.9428 | 0.7113 | 9.392612e-08 | 1119 |
| 0.2498 | 0.9106 | 0.9429 | 0.7113 | 9.39156e-08 | 1120 |
| 0.2595 | 0.9129 | 0.9438 | 0.7113 | 9.390507e-08 | 1121 |
| 0.2535 | 0.9176 | 0.9449 | 0.7113 | 9.3894535e-08 | 1122 |
| 0.2571 | 0.9176 | 0.9443 | 0.7113 | 9.388399e-08 | 1123 |
| 0.2678 | 0.9129 | 0.9439 | 0.7113 | 9.387344e-08 | 1124 |
| 0.2471 | 0.9176 | 0.9451 | 0.7324 | 9.386288e-08 | 1125 |
| 0.2562 | 0.9153 | 0.9471 | 0.7113 | 9.3852314e-08 | 1126 |
| 0.2471 | 0.9200 | 0.9470 | 0.7113 | 9.384174e-08 | 1127 |
| 0.2644 | 0.9200 | 0.9479 | 0.7113 | 9.3831154e-08 | 1128 |
| 0.2619 | 0.9012 | 0.9461 | 0.7113 | 9.382056e-08 | 1129 |
| 0.2551 | 0.9271 | 0.9464 | 0.7113 | 9.380996e-08 | 1130 |
| 0.2423 | 0.9388 | 0.9464 | 0.7113 | 9.379935e-08 | 1131 |
| 0.2455 | 0.9176 | 0.9468 | 0.7113 | 9.3788735e-08 | 1132 |
| 0.2505 | 0.9153 | 0.9474 | 0.7113 | 9.377811e-08 | 1133 |
| 0.2494 | 0.9200 | 0.9478 | 0.7113 | 9.376748e-08 | 1134 |
| 0.2559 | 0.9153 | 0.9494 | 0.7113 | 9.375684e-08 | 1135 |
| 0.2606 | 0.9082 | 0.9528 | 0.6972 | 9.374619e-08 | 1136 |
| 0.2511 | 0.9200 | 0.9529 | 0.6972 | 9.373553e-08 | 1137 |
| 0.2521 | 0.9176 | 0.9516 | 0.7042 | 9.3724864e-08 | 1138 |
| 0.2458 | 0.9082 | 0.9527 | 0.7042 | 9.371419e-08 | 1139 |
| 0.2501 | 0.9153 | 0.9510 | 0.7113 | 9.370351e-08 | 1140 |
| 0.2432 | 0.9200 | 0.9507 | 0.7113 | 9.369282e-08 | 1141 |
| 0.2555 | 0.9059 | 0.9501 | 0.7183 | 9.368212e-08 | 1142 |
| 0.2393 | 0.9271 | 0.9499 | 0.7113 | 9.367141e-08 | 1143 |
| 0.2549 | 0.9200 | 0.9496 | 0.7183 | 9.3660695e-08 | 1144 |
| 0.2536 | 0.9153 | 0.9511 | 0.7113 | 9.364997e-08 | 1145 |
| 0.2327 | 0.9271 | 0.9532 | 0.7113 | 9.3639244e-08 | 1146 |
| 0.2494 | 0.9247 | 0.9572 | 0.7042 | 9.362851e-08 | 1147 |
| 0.2580 | 0.9153 | 0.9569 | 0.7042 | 9.361776e-08 | 1148 |
| 0.2483 | 0.9153 | 0.9547 | 0.7113 | 9.3607e-08 | 1149 |
| 0.2426 | 0.9318 | 0.9547 | 0.7113 | 9.3596235e-08 | 1150 |
| 0.2398 | 0.9271 | 0.9513 | 0.7254 | 9.358546e-08 | 1151 |
| 0.2547 | 0.9059 | 0.9517 | 0.7183 | 9.3574684e-08 | 1152 |
| 0.2446 | 0.9200 | 0.9543 | 0.7113 | 9.35639e-08 | 1153 |
| 0.2435 | 0.9224 | 0.9539 | 0.7113 | 9.3553105e-08 | 1154 |
| 0.2454 | 0.9129 | 0.9544 | 0.7113 | 9.35423e-08 | 1155 |
| 0.2479 | 0.9153 | 0.9540 | 0.7113 | 9.353148e-08 | 1156 |
| 0.2547 | 0.9129 | 0.9547 | 0.7113 | 9.352066e-08 | 1157 |
| 0.2590 | 0.9035 | 0.9549 | 0.7113 | 9.350983e-08 | 1158 |
| 0.2516 | 0.9200 | 0.9567 | 0.7113 | 9.3499e-08 | 1159 |
| 0.2468 | 0.9082 | 0.9582 | 0.7113 | 9.3488154e-08 | 1160 |
| 0.2355 | 0.9388 | 0.9594 | 0.7113 | 9.3477304e-08 | 1161 |
| 0.2323 | 0.9388 | 0.9574 | 0.7183 | 9.346644e-08 | 1162 |
| 0.2483 | 0.9059 | 0.9581 | 0.7113 | 9.345557e-08 | 1163 |
| 0.2390 | 0.9224 | 0.9585 | 0.7113 | 9.344469e-08 | 1164 |
| 0.2611 | 0.9129 | 0.9594 | 0.7113 | 9.3433805e-08 | 1165 |
| 0.2302 | 0.9200 | 0.9591 | 0.7113 | 9.342291e-08 | 1166 |
| 0.2513 | 0.9129 | 0.9588 | 0.7113 | 9.341201e-08 | 1167 |
| 0.2431 | 0.9271 | 0.9593 | 0.7113 | 9.3401106e-08 | 1168 |
| 0.2486 | 0.9082 | 0.9609 | 0.7113 | 9.339019e-08 | 1169 |
| 0.2446 | 0.9176 | 0.9599 | 0.7113 | 9.337926e-08 | 1170 |
| 0.2397 | 0.9176 | 0.9605 | 0.7113 | 9.336833e-08 | 1171 |
| 0.2423 | 0.9224 | 0.9629 | 0.7042 | 9.3357386e-08 | 1172 |
| 0.2190 | 0.9553 | 0.9634 | 0.6972 | 9.3346436e-08 | 1173 |
| 0.2391 | 0.9294 | 0.9605 | 0.7113 | 9.333548e-08 | 1174 |
| 0.2438 | 0.9200 | 0.9617 | 0.7113 | 9.3324516e-08 | 1175 |
| 0.2436 | 0.9176 | 0.9644 | 0.7042 | 9.3313545e-08 | 1176 |
| 0.2474 | 0.9153 | 0.9624 | 0.7113 | 9.330256e-08 | 1177 |
| 0.2578 | 0.9153 | 0.9625 | 0.7113 | 9.329157e-08 | 1178 |
| 0.2458 | 0.9200 | 0.9613 | 0.7113 | 9.328057e-08 | 1179 |
| 0.2436 | 0.9318 | 0.9637 | 0.7113 | 9.326956e-08 | 1180 |
| 0.2387 | 0.9247 | 0.9627 | 0.7113 | 9.325855e-08 | 1181 |
| 0.2460 | 0.9224 | 0.9629 | 0.7113 | 9.324753e-08 | 1182 |
| 0.2386 | 0.9224 | 0.9627 | 0.7254 | 9.32365e-08 | 1183 |
| 0.2290 | 0.9247 | 0.9640 | 0.7183 | 9.322547e-08 | 1184 |
| 0.2250 | 0.9294 | 0.9636 | 0.7254 | 9.321442e-08 | 1185 |
| 0.2285 | 0.9412 | 0.9653 | 0.7113 | 9.320336e-08 | 1186 |
| 0.2429 | 0.9247 | 0.9657 | 0.7183 | 9.31923e-08 | 1187 |
| 0.2284 | 0.9294 | 0.9655 | 0.7254 | 9.318123e-08 | 1188 |
| 0.2303 | 0.9365 | 0.9651 | 0.7254 | 9.317015e-08 | 1189 |
| 0.2245 | 0.9247 | 0.9655 | 0.7254 | 9.3159066e-08 | 1190 |
| 0.2342 | 0.9365 | 0.9677 | 0.7113 | 9.3147975e-08 | 1191 |
| 0.2419 | 0.9247 | 0.9683 | 0.7113 | 9.3136876e-08 | 1192 |
| 0.2358 | 0.9271 | 0.9665 | 0.7254 | 9.312576e-08 | 1193 |
| 0.2376 | 0.9200 | 0.9678 | 0.7254 | 9.311464e-08 | 1194 |
| 0.2253 | 0.9365 | 0.9688 | 0.7183 | 9.3103516e-08 | 1195 |
| 0.2237 | 0.9365 | 0.9689 | 0.7113 | 9.309238e-08 | 1196 |
| 0.2383 | 0.9200 | 0.9685 | 0.7183 | 9.308124e-08 | 1197 |
| 0.2505 | 0.9012 | 0.9701 | 0.7113 | 9.307009e-08 | 1198 |
| 0.2348 | 0.9365 | 0.9707 | 0.7113 | 9.305894e-08 | 1199 |
| 0.2364 | 0.9082 | 0.9715 | 0.7113 | 9.3047774e-08 | 1200 |
| 0.2289 | 0.9412 | 0.9727 | 0.7113 | 9.30366e-08 | 1201 |
| 0.2374 | 0.9318 | 0.9732 | 0.7113 | 9.302541e-08 | 1202 |
| 0.2459 | 0.9294 | 0.9730 | 0.7113 | 9.301422e-08 | 1203 |
| 0.2354 | 0.9271 | 0.9720 | 0.7113 | 9.3003024e-08 | 1204 |
| 0.2285 | 0.9341 | 0.9721 | 0.7113 | 9.299182e-08 | 1205 |
| 0.2364 | 0.9318 | 0.9718 | 0.7113 | 9.2980606e-08 | 1206 |
| 0.2338 | 0.9318 | 0.9739 | 0.7113 | 9.296939e-08 | 1207 |
| 0.2227 | 0.9388 | 0.9731 | 0.7113 | 9.295816e-08 | 1208 |
| 0.2391 | 0.9012 | 0.9723 | 0.7113 | 9.294692e-08 | 1209 |
| 0.2329 | 0.9153 | 0.9725 | 0.7113 | 9.293567e-08 | 1210 |
| 0.2191 | 0.9459 | 0.9739 | 0.7113 | 9.292442e-08 | 1211 |
| 0.2319 | 0.9271 | 0.9733 | 0.7113 | 9.2913155e-08 | 1212 |
| 0.2258 | 0.9271 | 0.9725 | 0.7113 | 9.2901885e-08 | 1213 |
| 0.2352 | 0.9318 | 0.9718 | 0.7183 | 9.289061e-08 | 1214 |
| 0.2363 | 0.9153 | 0.9740 | 0.7113 | 9.2879326e-08 | 1215 |
| 0.2253 | 0.9200 | 0.9765 | 0.7113 | 9.2868035e-08 | 1216 |
| 0.2248 | 0.9224 | 0.9735 | 0.7113 | 9.285674e-08 | 1217 |
| 0.2306 | 0.9224 | 0.9745 | 0.7113 | 9.2845426e-08 | 1218 |
| 0.2360 | 0.9200 | 0.9761 | 0.7113 | 9.283411e-08 | 1219 |
| 0.2379 | 0.9153 | 0.9748 | 0.7113 | 9.282278e-08 | 1220 |
| 0.2225 | 0.9247 | 0.9765 | 0.7113 | 9.281145e-08 | 1221 |
| 0.2213 | 0.9459 | 0.9778 | 0.7113 | 9.280011e-08 | 1222 |
| 0.2238 | 0.9341 | 0.9751 | 0.7254 | 9.278876e-08 | 1223 |
| 0.2351 | 0.9153 | 0.9754 | 0.7254 | 9.2777405e-08 | 1224 |
| 0.2278 | 0.9200 | 0.9763 | 0.7113 | 9.2766044e-08 | 1225 |
| 0.2249 | 0.9271 | 0.9776 | 0.7113 | 9.2754675e-08 | 1226 |
| 0.2130 | 0.9271 | 0.9767 | 0.7113 | 9.274329e-08 | 1227 |
| 0.2119 | 0.9341 | 0.9769 | 0.7113 | 9.27319e-08 | 1228 |
| 0.2259 | 0.9318 | 0.9777 | 0.7113 | 9.2720505e-08 | 1229 |
| 0.2307 | 0.9318 | 0.9775 | 0.7113 | 9.27091e-08 | 1230 |
| 0.2153 | 0.9224 | 0.9777 | 0.7113 | 9.269769e-08 | 1231 |
| 0.2193 | 0.9388 | 0.9772 | 0.7113 | 9.268627e-08 | 1232 |
| 0.2136 | 0.9247 | 0.9779 | 0.7113 | 9.2674846e-08 | 1233 |
| 0.2272 | 0.9153 | 0.9805 | 0.7113 | 9.266341e-08 | 1234 |
| 0.2243 | 0.9318 | 0.9814 | 0.7113 | 9.265197e-08 | 1235 |
| 0.2124 | 0.9365 | 0.9803 | 0.7113 | 9.2640526e-08 | 1236 |
| 0.2327 | 0.9271 | 0.9790 | 0.7183 | 9.2629065e-08 | 1237 |
| 0.2261 | 0.9365 | 0.9806 | 0.7113 | 9.26176e-08 | 1238 |
| 0.2088 | 0.9365 | 0.9827 | 0.7113 | 9.260612e-08 | 1239 |
| 0.2325 | 0.9224 | 0.9826 | 0.7113 | 9.259464e-08 | 1240 |
| 0.2165 | 0.9412 | 0.9795 | 0.7254 | 9.258315e-08 | 1241 |
| 0.2066 | 0.9412 | 0.9809 | 0.7254 | 9.257165e-08 | 1242 |
| 0.1951 | 0.9482 | 0.9822 | 0.7254 | 9.256015e-08 | 1243 |
| 0.2166 | 0.9365 | 0.9821 | 0.7254 | 9.254864e-08 | 1244 |
| 0.2245 | 0.9247 | 0.9822 | 0.7254 | 9.253712e-08 | 1245 |
| 0.2042 | 0.9435 | 0.9830 | 0.7254 | 9.252559e-08 | 1246 |
| 0.2177 | 0.9365 | 0.9855 | 0.7113 | 9.251405e-08 | 1247 |
| 0.2168 | 0.9341 | 0.9850 | 0.7113 | 9.25025e-08 | 1248 |
| 0.2245 | 0.9294 | 0.9852 | 0.7254 | 9.249095e-08 | 1249 |
| 0.2080 | 0.9365 | 0.9843 | 0.7254 | 9.247939e-08 | 1250 |
| 0.2174 | 0.9365 | 0.9839 | 0.7254 | 9.246782e-08 | 1251 |
| 0.2246 | 0.9247 | 0.9867 | 0.7113 | 9.245625e-08 | 1252 |
| 0.2139 | 0.9365 | 0.9870 | 0.7113 | 9.2444665e-08 | 1253 |
| 0.2153 | 0.9388 | 0.9846 | 0.7254 | 9.2433076e-08 | 1254 |
| 0.2191 | 0.9365 | 0.9842 | 0.7254 | 9.242148e-08 | 1255 |
| 0.2219 | 0.9247 | 0.9858 | 0.7254 | 9.240988e-08 | 1256 |
| 0.2072 | 0.9412 | 0.9888 | 0.7113 | 9.239826e-08 | 1257 |
| 0.2312 | 0.9200 | 0.9862 | 0.7254 | 9.2386635e-08 | 1258 |
| 0.2133 | 0.9294 | 0.9870 | 0.7254 | 9.2375004e-08 | 1259 |
| 0.2126 | 0.9388 | 0.9889 | 0.7113 | 9.2363365e-08 | 1260 |
| 0.2068 | 0.9271 | 0.9927 | 0.7113 | 9.235172e-08 | 1261 |
| 0.1979 | 0.9482 | 0.9914 | 0.7042 | 9.2340066e-08 | 1262 |
| 0.1986 | 0.9341 | 0.9886 | 0.7113 | 9.2328406e-08 | 1263 |
| 0.2181 | 0.9341 | 0.9892 | 0.7113 | 9.231674e-08 | 1264 |
| 0.2152 | 0.9294 | 0.9888 | 0.7113 | 9.2305065e-08 | 1265 |
| 0.2085 | 0.9247 | 0.9884 | 0.7254 | 9.2293384e-08 | 1266 |
| 0.2147 | 0.9294 | 0.9894 | 0.7183 | 9.228169e-08 | 1267 |
| 0.2213 | 0.9318 | 0.9927 | 0.7042 | 9.2269985e-08 | 1268 |
| 0.2132 | 0.9365 | 0.9934 | 0.7042 | 9.2258276e-08 | 1269 |
| 0.2294 | 0.9341 | 0.9925 | 0.7113 | 9.224656e-08 | 1270 |
| 0.2104 | 0.9318 | 0.9930 | 0.7042 | 9.2234835e-08 | 1271 |
| 0.1949 | 0.9459 | 0.9918 | 0.7113 | 9.2223104e-08 | 1272 |
| 0.2225 | 0.9294 | 0.9916 | 0.7113 | 9.2211366e-08 | 1273 |
| 0.2177 | 0.9294 | 0.9896 | 0.7254 | 9.219962e-08 | 1274 |
| 0.1972 | 0.9482 | 0.9891 | 0.7254 | 9.218787e-08 | 1275 |
| 0.2041 | 0.9412 | 0.9913 | 0.7254 | 9.217611e-08 | 1276 |
| 0.2056 | 0.9341 | 0.9935 | 0.7254 | 9.216434e-08 | 1277 |
| 0.1910 | 0.9553 | 0.9922 | 0.7254 | 9.215257e-08 | 1278 |
| 0.2137 | 0.9247 | 0.9917 | 0.7254 | 9.214078e-08 | 1279 |
| 0.2177 | 0.9247 | 0.9928 | 0.7254 | 9.2128985e-08 | 1280 |
| 0.2114 | 0.9388 | 0.9939 | 0.7254 | 9.211718e-08 | 1281 |
| 0.2036 | 0.9388 | 0.9956 | 0.7113 | 9.2105374e-08 | 1282 |
| 0.2217 | 0.9412 | 0.9960 | 0.7113 | 9.209356e-08 | 1283 |
| 0.1949 | 0.9435 | 0.9953 | 0.7113 | 9.2081734e-08 | 1284 |
| 0.1983 | 0.9365 | 0.9955 | 0.7254 | 9.2069904e-08 | 1285 |
| 0.2023 | 0.9482 | 0.9951 | 0.7254 | 9.2058066e-08 | 1286 |
| 0.2109 | 0.9247 | 0.9956 | 0.7254 | 9.204622e-08 | 1287 |
| 0.2113 | 0.9224 | 0.9979 | 0.7113 | 9.203437e-08 | 1288 |
| 0.2112 | 0.9365 | 0.9979 | 0.7254 | 9.202251e-08 | 1289 |
| 0.2085 | 0.9294 | 0.9973 | 0.7254 | 9.2010644e-08 | 1290 |
| 0.1924 | 0.9529 | 0.9955 | 0.7254 | 9.1998764e-08 | 1291 |
| 0.1916 | 0.9388 | 0.9967 | 0.7254 | 9.198688e-08 | 1292 |
| 0.2088 | 0.9412 | 0.9973 | 0.7254 | 9.197498e-08 | 1293 |
| 0.2008 | 0.9529 | 0.9973 | 0.7254 | 9.196308e-08 | 1294 |
| 0.2044 | 0.9341 | 0.9979 | 0.7254 | 9.195117e-08 | 1295 |
| 0.2097 | 0.9388 | 0.9997 | 0.7254 | 9.1939256e-08 | 1296 |
| 0.1950 | 0.9412 | 1.0000 | 0.7254 | 9.192733e-08 | 1297 |
| 0.2109 | 0.9365 | 0.9989 | 0.7254 | 9.19154e-08 | 1298 |
| 0.2064 | 0.9365 | 0.9989 | 0.7254 | 9.1903466e-08 | 1299 |
| 0.2026 | 0.9412 | 0.9991 | 0.7254 | 9.189152e-08 | 1300 |
| 0.2060 | 0.9341 | 1.0000 | 0.7254 | 9.187957e-08 | 1301 |
| 0.1943 | 0.9435 | 1.0036 | 0.7183 | 9.186761e-08 | 1302 |
| 0.2008 | 0.9388 | 1.0042 | 0.7183 | 9.185565e-08 | 1303 |
| 0.2004 | 0.9435 | 1.0036 | 0.7254 | 9.184367e-08 | 1304 |
| 0.2002 | 0.9365 | 1.0023 | 0.7254 | 9.183168e-08 | 1305 |
| 0.1976 | 0.9435 | 1.0007 | 0.7254 | 9.1819686e-08 | 1306 |
| 0.1907 | 0.9412 | 1.0020 | 0.7254 | 9.1807685e-08 | 1307 |
| 0.1964 | 0.9435 | 1.0034 | 0.7254 | 9.179568e-08 | 1308 |
| 0.1935 | 0.9388 | 1.0040 | 0.7254 | 9.178366e-08 | 1309 |
| 0.2107 | 0.9271 | 1.0063 | 0.7254 | 9.177164e-08 | 1310 |
| 0.1962 | 0.9388 | 1.0065 | 0.7254 | 9.175961e-08 | 1311 |
| 0.2016 | 0.9506 | 1.0056 | 0.7254 | 9.174757e-08 | 1312 |
| 0.2024 | 0.9294 | 1.0051 | 0.7254 | 9.173553e-08 | 1313 |
| 0.1935 | 0.9341 | 1.0057 | 0.7254 | 9.172348e-08 | 1314 |
| 0.1939 | 0.9412 | 1.0076 | 0.7183 | 9.171142e-08 | 1315 |
| 0.1883 | 0.9435 | 1.0083 | 0.7183 | 9.1699356e-08 | 1316 |
| 0.2000 | 0.9247 | 1.0071 | 0.7254 | 9.168728e-08 | 1317 |
| 0.2031 | 0.9224 | 1.0069 | 0.7254 | 9.16752e-08 | 1318 |
| 0.1831 | 0.9553 | 1.0081 | 0.7254 | 9.1663104e-08 | 1319 |
| 0.1891 | 0.9459 | 1.0100 | 0.7254 | 9.1651e-08 | 1320 |
| 0.1932 | 0.9412 | 1.0093 | 0.7254 | 9.1638896e-08 | 1321 |
| 0.1950 | 0.9247 | 1.0084 | 0.7254 | 9.162678e-08 | 1322 |
| 0.1996 | 0.9271 | 1.0092 | 0.7254 | 9.161466e-08 | 1323 |
| 0.1958 | 0.9365 | 1.0095 | 0.7254 | 9.160253e-08 | 1324 |
| 0.1900 | 0.9412 | 1.0106 | 0.7254 | 9.1590394e-08 | 1325 |
| 0.1812 | 0.9529 | 1.0127 | 0.7324 | 9.157825e-08 | 1326 |
| 0.1889 | 0.9388 | 1.0112 | 0.7254 | 9.15661e-08 | 1327 |
| 0.1918 | 0.9412 | 1.0123 | 0.7254 | 9.155394e-08 | 1328 |
| 0.2004 | 0.9388 | 1.0136 | 0.7254 | 9.154178e-08 | 1329 |
| 0.2025 | 0.9341 | 1.0151 | 0.7183 | 9.152961e-08 | 1330 |
| 0.1811 | 0.9459 | 1.0149 | 0.7254 | 9.151743e-08 | 1331 |
| 0.1892 | 0.9388 | 1.0145 | 0.7324 | 9.150524e-08 | 1332 |
| 0.1909 | 0.9365 | 1.0140 | 0.7254 | 9.149305e-08 | 1333 |
| 0.1840 | 0.9553 | 1.0139 | 0.7324 | 9.148085e-08 | 1334 |
| 0.1746 | 0.9553 | 1.0149 | 0.7324 | 9.1468635e-08 | 1335 |
| 0.1936 | 0.9412 | 1.0162 | 0.7324 | 9.1456414e-08 | 1336 |
| 0.1862 | 0.9506 | 1.0184 | 0.7042 | 9.1444186e-08 | 1337 |
| 0.1906 | 0.9365 | 1.0184 | 0.7183 | 9.143195e-08 | 1338 |
| 0.1874 | 0.9553 | 1.0147 | 0.7254 | 9.141971e-08 | 1339 |
| 0.1932 | 0.9435 | 1.0158 | 0.7254 | 9.140746e-08 | 1340 |
| 0.1944 | 0.9412 | 1.0173 | 0.7254 | 9.13952e-08 | 1341 |
| 0.1976 | 0.9294 | 1.0169 | 0.7254 | 9.138294e-08 | 1342 |
| 0.1951 | 0.9388 | 1.0180 | 0.7324 | 9.1370666e-08 | 1343 |
| 0.1801 | 0.9412 | 1.0165 | 0.7254 | 9.135839e-08 | 1344 |
| 0.2004 | 0.9412 | 1.0172 | 0.7254 | 9.13461e-08 | 1345 |
| 0.1866 | 0.9435 | 1.0198 | 0.7324 | 9.133381e-08 | 1346 |
| 0.1853 | 0.9412 | 1.0211 | 0.7254 | 9.132151e-08 | 1347 |
| 0.1965 | 0.9435 | 1.0243 | 0.7042 | 9.1309204e-08 | 1348 |
| 0.1969 | 0.9365 | 1.0242 | 0.7113 | 9.129689e-08 | 1349 |
| 0.1845 | 0.9506 | 1.0226 | 0.7183 | 9.128457e-08 | 1350 |
| 0.1907 | 0.9459 | 1.0214 | 0.7324 | 9.127224e-08 | 1351 |
| 0.1808 | 0.9459 | 1.0203 | 0.7254 | 9.1259906e-08 | 1352 |
| 0.1736 | 0.9553 | 1.0219 | 0.7324 | 9.124756e-08 | 1353 |
| 0.1864 | 0.9435 | 1.0236 | 0.7254 | 9.12352e-08 | 1354 |
| 0.1728 | 0.9459 | 1.0229 | 0.7324 | 9.122284e-08 | 1355 |
| 0.1958 | 0.9365 | 1.0232 | 0.7254 | 9.121047e-08 | 1356 |
| 0.1869 | 0.9412 | 1.0203 | 0.7254 | 9.119809e-08 | 1357 |
| 0.1802 | 0.9482 | 1.0218 | 0.7254 | 9.1185704e-08 | 1358 |
| 0.1880 | 0.9388 | 1.0218 | 0.7254 | 9.117331e-08 | 1359 |
| 0.1771 | 0.9459 | 1.0234 | 0.7324 | 9.116091e-08 | 1360 |
| 0.1952 | 0.9506 | 1.0243 | 0.7324 | 9.114851e-08 | 1361 |
| 0.1929 | 0.9506 | 1.0240 | 0.7324 | 9.1136094e-08 | 1362 |
| 0.1711 | 0.9624 | 1.0228 | 0.7254 | 9.1123674e-08 | 1363 |
| 0.1873 | 0.9435 | 1.0248 | 0.7324 | 9.111125e-08 | 1364 |
| 0.1767 | 0.9459 | 1.0286 | 0.7254 | 9.109881e-08 | 1365 |
| 0.1765 | 0.9529 | 1.0275 | 0.7254 | 9.108637e-08 | 1366 |
| 0.1737 | 0.9529 | 1.0265 | 0.7254 | 9.107392e-08 | 1367 |
| 0.1832 | 0.9412 | 1.0277 | 0.7254 | 9.1061466e-08 | 1368 |
| 0.1941 | 0.9388 | 1.0270 | 0.7324 | 9.1049e-08 | 1369 |
| 0.1786 | 0.9506 | 1.0287 | 0.7254 | 9.103653e-08 | 1370 |
| 0.1782 | 0.9506 | 1.0302 | 0.7254 | 9.1024056e-08 | 1371 |
| 0.1734 | 0.9529 | 1.0296 | 0.7254 | 9.101157e-08 | 1372 |
| 0.1692 | 0.9553 | 1.0286 | 0.7324 | 9.099908e-08 | 1373 |
| 0.1765 | 0.9459 | 1.0303 | 0.7254 | 9.098658e-08 | 1374 |
| 0.1754 | 0.9412 | 1.0304 | 0.7254 | 9.0974076e-08 | 1375 |
| 0.1664 | 0.9553 | 1.0325 | 0.7254 | 9.096156e-08 | 1376 |
| 0.1919 | 0.9412 | 1.0308 | 0.7183 | 9.094903e-08 | 1377 |
| 0.1773 | 0.9529 | 1.0319 | 0.7254 | 9.0936496e-08 | 1378 |
| 0.1794 | 0.9412 | 1.0310 | 0.7324 | 9.0923955e-08 | 1379 |
| 0.1799 | 0.9482 | 1.0301 | 0.7254 | 9.0911406e-08 | 1380 |
| 0.1820 | 0.9412 | 1.0300 | 0.7254 | 9.089885e-08 | 1381 |
| 0.1707 | 0.9459 | 1.0346 | 0.7254 | 9.088629e-08 | 1382 |
| 0.1738 | 0.9529 | 1.0366 | 0.7183 | 9.087372e-08 | 1383 |
| 0.1762 | 0.9459 | 1.0378 | 0.7042 | 9.086114e-08 | 1384 |
| 0.1683 | 0.9435 | 1.0380 | 0.6972 | 9.084856e-08 | 1385 |
| 0.1785 | 0.9506 | 1.0364 | 0.7183 | 9.083597e-08 | 1386 |
| 0.1845 | 0.9459 | 1.0360 | 0.7254 | 9.082337e-08 | 1387 |
| 0.1769 | 0.9459 | 1.0362 | 0.7254 | 9.0810765e-08 | 1388 |
| 0.1754 | 0.9459 | 1.0375 | 0.7183 | 9.079815e-08 | 1389 |
| 0.1753 | 0.9459 | 1.0390 | 0.7183 | 9.0785534e-08 | 1390 |
| 0.1765 | 0.9482 | 1.0408 | 0.7113 | 9.077291e-08 | 1391 |
| 0.1650 | 0.9506 | 1.0416 | 0.7113 | 9.0760274e-08 | 1392 |
| 0.1967 | 0.9435 | 1.0399 | 0.7254 | 9.074763e-08 | 1393 |
| 0.1748 | 0.9506 | 1.0352 | 0.7254 | 9.0734986e-08 | 1394 |
| 0.1779 | 0.9506 | 1.0348 | 0.7254 | 9.072233e-08 | 1395 |
| 0.1720 | 0.9459 | 1.0367 | 0.7254 | 9.070967e-08 | 1396 |
| 0.1583 | 0.9624 | 1.0407 | 0.7254 | 9.0697e-08 | 1397 |
| 0.1808 | 0.9459 | 1.0443 | 0.7113 | 9.0684324e-08 | 1398 |
| 0.1708 | 0.9529 | 1.0441 | 0.7254 | 9.067164e-08 | 1399 |
| 0.1833 | 0.9553 | 1.0443 | 0.7183 | 9.065895e-08 | 1400 |
| 0.1805 | 0.9435 | 1.0441 | 0.7183 | 9.064625e-08 | 1401 |
| 0.1692 | 0.9482 | 1.0414 | 0.7324 | 9.063355e-08 | 1402 |
| 0.1686 | 0.9553 | 1.0412 | 0.7324 | 9.062084e-08 | 1403 |
| 0.1690 | 0.9482 | 1.0416 | 0.7254 | 9.060812e-08 | 1404 |
| 0.1886 | 0.9388 | 1.0438 | 0.7183 | 9.059539e-08 | 1405 |
| 0.1642 | 0.9506 | 1.0460 | 0.7113 | 9.058266e-08 | 1406 |
| 0.1801 | 0.9529 | 1.0468 | 0.7113 | 9.056992e-08 | 1407 |
| 0.1819 | 0.9529 | 1.0474 | 0.7113 | 9.055717e-08 | 1408 |
| 0.1622 | 0.9600 | 1.0458 | 0.7113 | 9.054442e-08 | 1409 |
| 0.1557 | 0.9647 | 1.0429 | 0.7254 | 9.053165e-08 | 1410 |
| 0.1789 | 0.9388 | 1.0432 | 0.7324 | 9.0518874e-08 | 1411 |
| 0.1712 | 0.9435 | 1.0430 | 0.7324 | 9.050609e-08 | 1412 |
| 0.1741 | 0.9435 | 1.0438 | 0.7324 | 9.04933e-08 | 1413 |
| 0.1649 | 0.9553 | 1.0453 | 0.7324 | 9.0480505e-08 | 1414 |
| 0.1648 | 0.9529 | 1.0475 | 0.7254 | 9.04677e-08 | 1415 |
| 0.1668 | 0.9459 | 1.0482 | 0.7254 | 9.045489e-08 | 1416 |
| 0.1659 | 0.9576 | 1.0463 | 0.7324 | 9.044207e-08 | 1417 |
| 0.1602 | 0.9600 | 1.0448 | 0.7324 | 9.0429246e-08 | 1418 |
| 0.1707 | 0.9412 | 1.0457 | 0.7324 | 9.0416414e-08 | 1419 |
| 0.1730 | 0.9459 | 1.0466 | 0.7324 | 9.0403574e-08 | 1420 |
| 0.1536 | 0.9647 | 1.0476 | 0.7254 | 9.039073e-08 | 1421 |
| 0.1781 | 0.9388 | 1.0515 | 0.7183 | 9.0377874e-08 | 1422 |
| 0.1720 | 0.9388 | 1.0485 | 0.7324 | 9.036501e-08 | 1423 |
| 0.1746 | 0.9482 | 1.0511 | 0.7183 | 9.0352145e-08 | 1424 |
| 0.1659 | 0.9435 | 1.0528 | 0.7113 | 9.033927e-08 | 1425 |
| 0.1643 | 0.9647 | 1.0544 | 0.7042 | 9.032639e-08 | 1426 |
| 0.1786 | 0.9459 | 1.0533 | 0.7183 | 9.03135e-08 | 1427 |
| 0.1646 | 0.9482 | 1.0516 | 0.7183 | 9.03006e-08 | 1428 |
| 0.1749 | 0.9388 | 1.0539 | 0.7183 | 9.02877e-08 | 1429 |
| 0.1636 | 0.9529 | 1.0529 | 0.7183 | 9.027479e-08 | 1430 |
| 0.1692 | 0.9506 | 1.0542 | 0.7183 | 9.026187e-08 | 1431 |
| 0.1616 | 0.9529 | 1.0531 | 0.7183 | 9.0248946e-08 | 1432 |
| 0.1764 | 0.9459 | 1.0513 | 0.7254 | 9.0236014e-08 | 1433 |
| 0.1660 | 0.9529 | 1.0528 | 0.7183 | 9.0223075e-08 | 1434 |
| 0.1613 | 0.9506 | 1.0531 | 0.7183 | 9.021013e-08 | 1435 |
| 0.1502 | 0.9600 | 1.0546 | 0.7183 | 9.0197176e-08 | 1436 |
| 0.1513 | 0.9671 | 1.0550 | 0.7183 | 9.0184216e-08 | 1437 |
| 0.1745 | 0.9482 | 1.0541 | 0.7254 | 9.017125e-08 | 1438 |
| 0.1661 | 0.9482 | 1.0567 | 0.7183 | 9.0158274e-08 | 1439 |
| 0.1683 | 0.9553 | 1.0572 | 0.7183 | 9.014529e-08 | 1440 |
| 0.1560 | 0.9671 | 1.0564 | 0.7254 | 9.01323e-08 | 1441 |
| 0.1726 | 0.9459 | 1.0539 | 0.7324 | 9.011931e-08 | 1442 |
| 0.1599 | 0.9553 | 1.0587 | 0.7113 | 9.0106305e-08 | 1443 |
| 0.1592 | 0.9576 | 1.0603 | 0.7113 | 9.0093295e-08 | 1444 |
| 0.1693 | 0.9506 | 1.0643 | 0.7042 | 9.008028e-08 | 1445 |
| 0.1633 | 0.9600 | 1.0648 | 0.7113 | 9.006725e-08 | 1446 |
| 0.1589 | 0.9624 | 1.0624 | 0.7113 | 9.005422e-08 | 1447 |
| 0.1641 | 0.9576 | 1.0601 | 0.7254 | 9.004118e-08 | 1448 |
| 0.1573 | 0.9529 | 1.0570 | 0.7254 | 9.002814e-08 | 1449 |
| 0.1656 | 0.9412 | 1.0562 | 0.7324 | 9.0015085e-08 | 1450 |
| 0.1560 | 0.9600 | 1.0579 | 0.7324 | 9.0002025e-08 | 1451 |
| 0.1703 | 0.9482 | 1.0593 | 0.7324 | 8.998896e-08 | 1452 |
| 0.1633 | 0.9482 | 1.0581 | 0.7324 | 8.9975885e-08 | 1453 |
| 0.1763 | 0.9435 | 1.0597 | 0.7324 | 8.99628e-08 | 1454 |
| 0.1617 | 0.9482 | 1.0603 | 0.7254 | 8.9949715e-08 | 1455 |
| 0.1767 | 0.9482 | 1.0615 | 0.7254 | 8.993662e-08 | 1456 |
| 0.1545 | 0.9694 | 1.0614 | 0.7254 | 8.992352e-08 | 1457 |
| 0.1516 | 0.9600 | 1.0628 | 0.7183 | 8.991041e-08 | 1458 |
| 0.1547 | 0.9529 | 1.0636 | 0.7183 | 8.989729e-08 | 1459 |
| 0.1487 | 0.9718 | 1.0634 | 0.7183 | 8.988417e-08 | 1460 |
| 0.1627 | 0.9529 | 1.0644 | 0.7183 | 8.987104e-08 | 1461 |
| 0.1572 | 0.9529 | 1.0635 | 0.7254 | 8.98579e-08 | 1462 |
| 0.1525 | 0.9553 | 1.0649 | 0.7183 | 8.9844754e-08 | 1463 |
| 0.1567 | 0.9576 | 1.0652 | 0.7183 | 8.98316e-08 | 1464 |
| 0.1742 | 0.9412 | 1.0648 | 0.7254 | 8.981844e-08 | 1465 |
| 0.1678 | 0.9506 | 1.0660 | 0.7183 | 8.9805276e-08 | 1466 |
| 0.1418 | 0.9671 | 1.0667 | 0.7183 | 8.97921e-08 | 1467 |
| 0.1671 | 0.9365 | 1.0673 | 0.7183 | 8.977892e-08 | 1468 |
| 0.1572 | 0.9459 | 1.0664 | 0.7324 | 8.9765734e-08 | 1469 |
| 0.1621 | 0.9529 | 1.0665 | 0.7324 | 8.975254e-08 | 1470 |
| 0.1604 | 0.9624 | 1.0671 | 0.7254 | 8.973934e-08 | 1471 |
| 0.1701 | 0.9435 | 1.0681 | 0.7254 | 8.972613e-08 | 1472 |
| 0.1569 | 0.9529 | 1.0696 | 0.7183 | 8.971291e-08 | 1473 |
| 0.1551 | 0.9624 | 1.0700 | 0.7183 | 8.969969e-08 | 1474 |
| 0.1599 | 0.9482 | 1.0732 | 0.7113 | 8.968646e-08 | 1475 |
| 0.1634 | 0.9529 | 1.0745 | 0.7183 | 8.967322e-08 | 1476 |
| 0.1454 | 0.9671 | 1.0722 | 0.7183 | 8.965998e-08 | 1477 |
| 0.1454 | 0.9553 | 1.0715 | 0.7183 | 8.9646726e-08 | 1478 |
| 0.1540 | 0.9576 | 1.0700 | 0.7254 | 8.963347e-08 | 1479 |
| 0.1474 | 0.9647 | 1.0707 | 0.7254 | 8.96202e-08 | 1480 |
| 0.1478 | 0.9553 | 1.0728 | 0.7183 | 8.960693e-08 | 1481 |
| 0.1599 | 0.9506 | 1.0724 | 0.7183 | 8.959365e-08 | 1482 |
| 0.1524 | 0.9600 | 1.0742 | 0.7183 | 8.958036e-08 | 1483 |
| 0.1530 | 0.9506 | 1.0745 | 0.7183 | 8.956707e-08 | 1484 |
| 0.1543 | 0.9506 | 1.0729 | 0.7254 | 8.9553765e-08 | 1485 |
| 0.1465 | 0.9600 | 1.0729 | 0.7254 | 8.954046e-08 | 1486 |
| 0.1555 | 0.9553 | 1.0745 | 0.7183 | 8.952714e-08 | 1487 |
| 0.1644 | 0.9553 | 1.0752 | 0.7183 | 8.951382e-08 | 1488 |
| 0.1644 | 0.9435 | 1.0752 | 0.7183 | 8.950049e-08 | 1489 |
| 0.1445 | 0.9647 | 1.0755 | 0.7183 | 8.948715e-08 | 1490 |
| 0.1544 | 0.9600 | 1.0757 | 0.7183 | 8.947381e-08 | 1491 |
| 0.1517 | 0.9624 | 1.0758 | 0.7183 | 8.9460464e-08 | 1492 |
| 0.1486 | 0.9718 | 1.0755 | 0.7254 | 8.944711e-08 | 1493 |
| 0.1765 | 0.9388 | 1.0777 | 0.7183 | 8.9433755e-08 | 1494 |
| 0.1448 | 0.9576 | 1.0780 | 0.7183 | 8.942039e-08 | 1495 |
| 0.1549 | 0.9506 | 1.0777 | 0.7183 | 8.940702e-08 | 1496 |
| 0.1570 | 0.9576 | 1.0770 | 0.7254 | 8.939364e-08 | 1497 |
| 0.1568 | 0.9576 | 1.0757 | 0.7254 | 8.938025e-08 | 1498 |
| 0.1500 | 0.9482 | 1.0762 | 0.7254 | 8.936686e-08 | 1499 |
| 0.1397 | 0.9647 | 1.0781 | 0.7183 | 8.9353456e-08 | 1500 |
| 0.1537 | 0.9506 | 1.0780 | 0.7254 | 8.934005e-08 | 1501 |
| 0.1521 | 0.9624 | 1.0799 | 0.7183 | 8.932663e-08 | 1502 |
| 0.1587 | 0.9482 | 1.0813 | 0.7183 | 8.931321e-08 | 1503 |
| 0.1529 | 0.9600 | 1.0790 | 0.7254 | 8.929978e-08 | 1504 |
| 0.1551 | 0.9482 | 1.0797 | 0.7254 | 8.9286345e-08 | 1505 |
| 0.1576 | 0.9459 | 1.0813 | 0.7183 | 8.92729e-08 | 1506 |
| 0.1568 | 0.9576 | 1.0845 | 0.7254 | 8.925945e-08 | 1507 |
| 0.1631 | 0.9459 | 1.0865 | 0.7254 | 8.9245994e-08 | 1508 |
| 0.1432 | 0.9671 | 1.0861 | 0.7254 | 8.923253e-08 | 1509 |
| 0.1363 | 0.9647 | 1.0856 | 0.7254 | 8.921906e-08 | 1510 |
| 0.1366 | 0.9624 | 1.0863 | 0.7254 | 8.920558e-08 | 1511 |
| 0.1444 | 0.9647 | 1.0839 | 0.7254 | 8.919209e-08 | 1512 |
| 0.1530 | 0.9576 | 1.0846 | 0.7183 | 8.91786e-08 | 1513 |
| 0.1471 | 0.9529 | 1.0859 | 0.7183 | 8.91651e-08 | 1514 |
| 0.1505 | 0.9694 | 1.0888 | 0.7254 | 8.915159e-08 | 1515 |
| 0.1629 | 0.9529 | 1.0886 | 0.7254 | 8.913808e-08 | 1516 |
| 0.1630 | 0.9529 | 1.0866 | 0.7254 | 8.9124555e-08 | 1517 |
| 0.1591 | 0.9506 | 1.0862 | 0.7254 | 8.9111026e-08 | 1518 |
| 0.1472 | 0.9553 | 1.0850 | 0.7254 | 8.909749e-08 | 1519 |
| 0.1482 | 0.9624 | 1.0862 | 0.7254 | 8.908395e-08 | 1520 |
| 0.1501 | 0.9553 | 1.0870 | 0.7183 | 8.90704e-08 | 1521 |
| 0.1469 | 0.9529 | 1.0870 | 0.7254 | 8.905684e-08 | 1522 |
| 0.1413 | 0.9576 | 1.0865 | 0.7254 | 8.9043276e-08 | 1523 |
| 0.1402 | 0.9647 | 1.0860 | 0.7183 | 8.9029704e-08 | 1524 |
| 0.1320 | 0.9624 | 1.0878 | 0.7254 | 8.9016126e-08 | 1525 |
| 0.1528 | 0.9553 | 1.0905 | 0.7254 | 8.900255e-08 | 1526 |
| 0.1335 | 0.9694 | 1.0899 | 0.7183 | 8.898896e-08 | 1527 |
| 0.1478 | 0.9600 | 1.0919 | 0.7254 | 8.897537e-08 | 1528 |
| 0.1374 | 0.9671 | 1.0929 | 0.7254 | 8.896177e-08 | 1529 |
| 0.1417 | 0.9600 | 1.0931 | 0.7254 | 8.894816e-08 | 1530 |
| 0.1387 | 0.9647 | 1.0934 | 0.7254 | 8.893455e-08 | 1531 |
| 0.1373 | 0.9671 | 1.0955 | 0.7254 | 8.892093e-08 | 1532 |
| 0.1383 | 0.9576 | 1.0947 | 0.7254 | 8.89073e-08 | 1533 |
| 0.1452 | 0.9482 | 1.0946 | 0.7254 | 8.8893664e-08 | 1534 |
| 0.1411 | 0.9506 | 1.0939 | 0.7254 | 8.888002e-08 | 1535 |
| 0.1574 | 0.9482 | 1.0936 | 0.7254 | 8.886637e-08 | 1536 |
| 0.1365 | 0.9671 | 1.0917 | 0.7254 | 8.8852715e-08 | 1537 |
| 0.1452 | 0.9624 | 1.0925 | 0.7254 | 8.883905e-08 | 1538 |
| 0.1477 | 0.9482 | 1.0937 | 0.7254 | 8.882538e-08 | 1539 |
| 0.1412 | 0.9671 | 1.0956 | 0.7394 | 8.88117e-08 | 1540 |
| 0.1447 | 0.9624 | 1.0952 | 0.7324 | 8.879802e-08 | 1541 |
| 0.1358 | 0.9647 | 1.0966 | 0.7254 | 8.8784326e-08 | 1542 |
| 0.1489 | 0.9506 | 1.0997 | 0.7254 | 8.8770626e-08 | 1543 |
| 0.1573 | 0.9506 | 1.0987 | 0.7254 | 8.875692e-08 | 1544 |
| 0.1374 | 0.9624 | 1.0982 | 0.7254 | 8.874321e-08 | 1545 |
| 0.1322 | 0.9718 | 1.0994 | 0.7254 | 8.8729486e-08 | 1546 |
| 0.1292 | 0.9718 | 1.0992 | 0.7254 | 8.871576e-08 | 1547 |
| 0.1480 | 0.9576 | 1.1002 | 0.7254 | 8.870203e-08 | 1548 |
| 0.1340 | 0.9718 | 1.1005 | 0.7254 | 8.8688296e-08 | 1549 |
| 0.1332 | 0.9671 | 1.0997 | 0.7254 | 8.8674554e-08 | 1550 |
| 0.1416 | 0.9624 | 1.0983 | 0.7254 | 8.8660805e-08 | 1551 |
| 0.1288 | 0.9624 | 1.1002 | 0.7324 | 8.864705e-08 | 1552 |
| 0.1382 | 0.9671 | 1.0999 | 0.7254 | 8.8633286e-08 | 1553 |
| 0.1328 | 0.9576 | 1.1012 | 0.7254 | 8.8619515e-08 | 1554 |
| 0.1306 | 0.9694 | 1.1011 | 0.7183 | 8.860574e-08 | 1555 |
| 0.1248 | 0.9694 | 1.1021 | 0.7254 | 8.859195e-08 | 1556 |
| 0.1341 | 0.9600 | 1.1020 | 0.7254 | 8.857816e-08 | 1557 |
| 0.1343 | 0.9600 | 1.1034 | 0.7324 | 8.856436e-08 | 1558 |
| 0.1347 | 0.9647 | 1.1069 | 0.7254 | 8.855056e-08 | 1559 |
| 0.1447 | 0.9529 | 1.1065 | 0.7254 | 8.8536744e-08 | 1560 |
| 0.1443 | 0.9553 | 1.1063 | 0.7254 | 8.8522924e-08 | 1561 |
| 0.1355 | 0.9788 | 1.1063 | 0.7254 | 8.85091e-08 | 1562 |
| 0.1538 | 0.9506 | 1.1061 | 0.7254 | 8.849526e-08 | 1563 |
| 0.1308 | 0.9694 | 1.1082 | 0.7254 | 8.848142e-08 | 1564 |
| 0.1412 | 0.9600 | 1.1090 | 0.7254 | 8.846757e-08 | 1565 |
| 0.1550 | 0.9459 | 1.1087 | 0.7254 | 8.8453724e-08 | 1566 |
| 0.1511 | 0.9506 | 1.1094 | 0.7254 | 8.843987e-08 | 1567 |
| 0.1532 | 0.9506 | 1.1089 | 0.7254 | 8.8426006e-08 | 1568 |
| 0.1265 | 0.9671 | 1.1068 | 0.7324 | 8.8412136e-08 | 1569 |
| 0.1408 | 0.9600 | 1.1067 | 0.7324 | 8.839826e-08 | 1570 |
| 0.1349 | 0.9671 | 1.1071 | 0.7324 | 8.8384375e-08 | 1571 |
| 0.1224 | 0.9624 | 1.1064 | 0.7394 | 8.8370484e-08 | 1572 |
| 0.1375 | 0.9553 | 1.1103 | 0.7254 | 8.8356586e-08 | 1573 |
| 0.1281 | 0.9671 | 1.1114 | 0.7254 | 8.834268e-08 | 1574 |
| 0.1262 | 0.9671 | 1.1130 | 0.7254 | 8.832877e-08 | 1575 |
| 0.1472 | 0.9624 | 1.1121 | 0.7254 | 8.831485e-08 | 1576 |
| 0.1381 | 0.9600 | 1.1114 | 0.7254 | 8.830092e-08 | 1577 |
| 0.1331 | 0.9694 | 1.1113 | 0.7254 | 8.828699e-08 | 1578 |
| 0.1401 | 0.9506 | 1.1104 | 0.7324 | 8.827305e-08 | 1579 |
| 0.1446 | 0.9600 | 1.1117 | 0.7254 | 8.82591e-08 | 1580 |
| 0.1349 | 0.9647 | 1.1115 | 0.7254 | 8.8245145e-08 | 1581 |
| 0.1345 | 0.9576 | 1.1125 | 0.7183 | 8.823119e-08 | 1582 |
| 0.1328 | 0.9694 | 1.1152 | 0.7254 | 8.821723e-08 | 1583 |
| 0.1387 | 0.9576 | 1.1151 | 0.7254 | 8.820326e-08 | 1584 |
| 0.1325 | 0.9671 | 1.1147 | 0.7254 | 8.818928e-08 | 1585 |
| 0.1310 | 0.9624 | 1.1132 | 0.7324 | 8.81753e-08 | 1586 |
| 0.1347 | 0.9718 | 1.1140 | 0.7254 | 8.816131e-08 | 1587 |
| 0.1217 | 0.9741 | 1.1141 | 0.7254 | 8.814731e-08 | 1588 |
| 0.1282 | 0.9694 | 1.1152 | 0.7254 | 8.8133305e-08 | 1589 |
| 0.1285 | 0.9647 | 1.1169 | 0.7254 | 8.811929e-08 | 1590 |
| 0.1195 | 0.9671 | 1.1163 | 0.7254 | 8.8105274e-08 | 1591 |
| 0.1294 | 0.9694 | 1.1152 | 0.7324 | 8.809125e-08 | 1592 |
| 0.1335 | 0.9624 | 1.1145 | 0.7254 | 8.8077215e-08 | 1593 |
| 0.1324 | 0.9647 | 1.1148 | 0.7254 | 8.8063175e-08 | 1594 |
| 0.1263 | 0.9671 | 1.1165 | 0.7254 | 8.8049134e-08 | 1595 |
| 0.1281 | 0.9671 | 1.1191 | 0.7254 | 8.803509e-08 | 1596 |
| 0.1297 | 0.9671 | 1.1209 | 0.7254 | 8.802103e-08 | 1597 |
| 0.1220 | 0.9765 | 1.1206 | 0.7254 | 8.800697e-08 | 1598 |
| 0.1384 | 0.9647 | 1.1212 | 0.7254 | 8.79929e-08 | 1599 |
| 0.1315 | 0.9600 | 1.1241 | 0.7254 | 8.7978826e-08 | 1600 |
| 0.1456 | 0.9624 | 1.1247 | 0.7254 | 8.796474e-08 | 1601 |
| 0.1328 | 0.9576 | 1.1258 | 0.7254 | 8.795065e-08 | 1602 |
| 0.1232 | 0.9671 | 1.1241 | 0.7254 | 8.7936556e-08 | 1603 |
| 0.1323 | 0.9624 | 1.1219 | 0.7254 | 8.792245e-08 | 1604 |
| 0.1262 | 0.9671 | 1.1219 | 0.7254 | 8.790834e-08 | 1605 |
| 0.1256 | 0.9624 | 1.1227 | 0.7254 | 8.789422e-08 | 1606 |
| 0.1276 | 0.9576 | 1.1235 | 0.7254 | 8.7880096e-08 | 1607 |
| 0.1399 | 0.9624 | 1.1283 | 0.7183 | 8.786597e-08 | 1608 |
| 0.1276 | 0.9671 | 1.1302 | 0.7183 | 8.785184e-08 | 1609 |
| 0.1258 | 0.9718 | 1.1299 | 0.7183 | 8.78377e-08 | 1610 |
| 0.1364 | 0.9624 | 1.1261 | 0.7254 | 8.782355e-08 | 1611 |
| 0.1127 | 0.9765 | 1.1252 | 0.7254 | 8.78094e-08 | 1612 |
| 0.1248 | 0.9647 | 1.1253 | 0.7254 | 8.7795236e-08 | 1613 |
| 0.1292 | 0.9694 | 1.1265 | 0.7254 | 8.778107e-08 | 1614 |
| 0.1249 | 0.9529 | 1.1285 | 0.7183 | 8.776689e-08 | 1615 |
| 0.1284 | 0.9647 | 1.1278 | 0.7254 | 8.775271e-08 | 1616 |
| 0.1259 | 0.9624 | 1.1269 | 0.7254 | 8.773852e-08 | 1617 |
| 0.1256 | 0.9718 | 1.1267 | 0.7254 | 8.7724324e-08 | 1618 |
| 0.1254 | 0.9765 | 1.1273 | 0.7254 | 8.771012e-08 | 1619 |
| 0.1293 | 0.9624 | 1.1324 | 0.7183 | 8.7695916e-08 | 1620 |
| 0.1189 | 0.9647 | 1.1301 | 0.7254 | 8.7681705e-08 | 1621 |
| 0.1284 | 0.9600 | 1.1281 | 0.7254 | 8.766749e-08 | 1622 |
| 0.1182 | 0.9741 | 1.1276 | 0.7254 | 8.765326e-08 | 1623 |
| 0.1270 | 0.9624 | 1.1270 | 0.7254 | 8.763903e-08 | 1624 |
| 0.1270 | 0.9624 | 1.1285 | 0.7254 | 8.762479e-08 | 1625 |
| 0.1169 | 0.9741 | 1.1295 | 0.7254 | 8.7610545e-08 | 1626 |
| 0.1223 | 0.9694 | 1.1292 | 0.7254 | 8.759629e-08 | 1627 |
| 0.1205 | 0.9671 | 1.1298 | 0.7254 | 8.758203e-08 | 1628 |
| 0.1441 | 0.9600 | 1.1322 | 0.7254 | 8.756776e-08 | 1629 |
| 0.1316 | 0.9647 | 1.1325 | 0.7254 | 8.7553495e-08 | 1630 |
| 0.1219 | 0.9694 | 1.1322 | 0.7254 | 8.753922e-08 | 1631 |
| 0.1128 | 0.9765 | 1.1316 | 0.7254 | 8.752494e-08 | 1632 |
| 0.1249 | 0.9765 | 1.1334 | 0.7254 | 8.751065e-08 | 1633 |
| 0.1221 | 0.9624 | 1.1344 | 0.7254 | 8.749635e-08 | 1634 |
| 0.1132 | 0.9741 | 1.1352 | 0.7254 | 8.748205e-08 | 1635 |
| 0.1342 | 0.9647 | 1.1360 | 0.7183 | 8.746774e-08 | 1636 |
| 0.1208 | 0.9718 | 1.1358 | 0.7254 | 8.745342e-08 | 1637 |
| 0.1263 | 0.9718 | 1.1344 | 0.7324 | 8.74391e-08 | 1638 |
| 0.1176 | 0.9671 | 1.1344 | 0.7254 | 8.7424766e-08 | 1639 |
| 0.1344 | 0.9647 | 1.1350 | 0.7254 | 8.741043e-08 | 1640 |
| 0.1163 | 0.9694 | 1.1371 | 0.7254 | 8.739609e-08 | 1641 |
| 0.1142 | 0.9718 | 1.1379 | 0.7254 | 8.738174e-08 | 1642 |
| 0.1274 | 0.9624 | 1.1398 | 0.7183 | 8.736739e-08 | 1643 |
| 0.1384 | 0.9624 | 1.1408 | 0.7183 | 8.735303e-08 | 1644 |
| 0.1294 | 0.9600 | 1.1395 | 0.7183 | 8.733866e-08 | 1645 |
| 0.1344 | 0.9600 | 1.1396 | 0.7183 | 8.732429e-08 | 1646 |
| 0.1055 | 0.9741 | 1.1396 | 0.7183 | 8.730991e-08 | 1647 |
| 0.1294 | 0.9647 | 1.1404 | 0.7183 | 8.729552e-08 | 1648 |
| 0.1117 | 0.9741 | 1.1413 | 0.7254 | 8.728112e-08 | 1649 |
| 0.1131 | 0.9671 | 1.1411 | 0.7183 | 8.726673e-08 | 1650 |
| 0.1155 | 0.9741 | 1.1447 | 0.7254 | 8.7252324e-08 | 1651 |
| 0.1164 | 0.9671 | 1.1462 | 0.7183 | 8.7237915e-08 | 1652 |
| 0.1061 | 0.9694 | 1.1447 | 0.7254 | 8.72235e-08 | 1653 |
| 0.1167 | 0.9741 | 1.1431 | 0.7183 | 8.7209074e-08 | 1654 |
| 0.1205 | 0.9671 | 1.1433 | 0.7183 | 8.719464e-08 | 1655 |
| 0.1234 | 0.9647 | 1.1452 | 0.7183 | 8.7180204e-08 | 1656 |
| 0.1212 | 0.9647 | 1.1477 | 0.7183 | 8.716576e-08 | 1657 |
| 0.1243 | 0.9718 | 1.1460 | 0.7183 | 8.715131e-08 | 1658 |
| 0.1169 | 0.9694 | 1.1454 | 0.7183 | 8.713685e-08 | 1659 |
| 0.1128 | 0.9718 | 1.1461 | 0.7183 | 8.712239e-08 | 1660 |
| 0.1165 | 0.9718 | 1.1470 | 0.7183 | 8.710792e-08 | 1661 |
| 0.1372 | 0.9576 | 1.1459 | 0.7183 | 8.709345e-08 | 1662 |
| 0.1095 | 0.9765 | 1.1452 | 0.7254 | 8.7078966e-08 | 1663 |
| 0.1182 | 0.9694 | 1.1475 | 0.7254 | 8.706448e-08 | 1664 |
| 0.1093 | 0.9788 | 1.1476 | 0.7254 | 8.704998e-08 | 1665 |
| 0.1180 | 0.9765 | 1.1477 | 0.7254 | 8.703548e-08 | 1666 |
| 0.1383 | 0.9553 | 1.1497 | 0.7254 | 8.702097e-08 | 1667 |
| 0.1147 | 0.9694 | 1.1503 | 0.7254 | 8.700646e-08 | 1668 |
| 0.1254 | 0.9647 | 1.1498 | 0.7183 | 8.6991946e-08 | 1669 |
| 0.1217 | 0.9624 | 1.1503 | 0.7183 | 8.697742e-08 | 1670 |
| 0.1093 | 0.9694 | 1.1515 | 0.7183 | 8.696289e-08 | 1671 |
| 0.1196 | 0.9671 | 1.1515 | 0.7183 | 8.6948354e-08 | 1672 |
| 0.1185 | 0.9718 | 1.1535 | 0.7183 | 8.693381e-08 | 1673 |
| 0.1162 | 0.9647 | 1.1548 | 0.7183 | 8.691926e-08 | 1674 |
| 0.1096 | 0.9788 | 1.1548 | 0.7183 | 8.69047e-08 | 1675 |
| 0.1241 | 0.9624 | 1.1546 | 0.7183 | 8.689013e-08 | 1676 |
| 0.1371 | 0.9506 | 1.1569 | 0.7183 | 8.6875566e-08 | 1677 |
| 0.1200 | 0.9741 | 1.1535 | 0.7254 | 8.686099e-08 | 1678 |
| 0.1197 | 0.9671 | 1.1534 | 0.7254 | 8.684641e-08 | 1679 |
| 0.1072 | 0.9671 | 1.1534 | 0.7183 | 8.6831825e-08 | 1680 |
| 0.1119 | 0.9694 | 1.1550 | 0.7183 | 8.681723e-08 | 1681 |
| 0.1153 | 0.9671 | 1.1550 | 0.7183 | 8.680263e-08 | 1682 |
| 0.1147 | 0.9671 | 1.1544 | 0.7183 | 8.678802e-08 | 1683 |
| 0.1067 | 0.9741 | 1.1551 | 0.7183 | 8.6773404e-08 | 1684 |
| 0.1204 | 0.9671 | 1.1575 | 0.7183 | 8.675879e-08 | 1685 |
| 0.1113 | 0.9694 | 1.1581 | 0.7183 | 8.6744166e-08 | 1686 |
| 0.1184 | 0.9671 | 1.1563 | 0.7183 | 8.6729536e-08 | 1687 |
| 0.1134 | 0.9718 | 1.1573 | 0.7183 | 8.67149e-08 | 1688 |
| 0.1157 | 0.9765 | 1.1575 | 0.7183 | 8.6700254e-08 | 1689 |
| 0.1277 | 0.9600 | 1.1586 | 0.7183 | 8.66856e-08 | 1690 |
| 0.1144 | 0.9741 | 1.1589 | 0.7183 | 8.6670944e-08 | 1691 |
| 0.1180 | 0.9718 | 1.1618 | 0.7183 | 8.665628e-08 | 1692 |
| 0.1184 | 0.9671 | 1.1631 | 0.7183 | 8.664161e-08 | 1693 |
| 0.1012 | 0.9718 | 1.1629 | 0.7183 | 8.662694e-08 | 1694 |
| 0.1065 | 0.9694 | 1.1624 | 0.7183 | 8.661226e-08 | 1695 |
| 0.0955 | 0.9812 | 1.1622 | 0.7183 | 8.6597574e-08 | 1696 |
| 0.1075 | 0.9718 | 1.1630 | 0.7183 | 8.658288e-08 | 1697 |
| 0.1079 | 0.9765 | 1.1652 | 0.7183 | 8.656818e-08 | 1698 |
| 0.1002 | 0.9788 | 1.1654 | 0.7183 | 8.655347e-08 | 1699 |
| 0.1092 | 0.9718 | 1.1663 | 0.7183 | 8.653876e-08 | 1700 |
| 0.1168 | 0.9624 | 1.1648 | 0.7183 | 8.652405e-08 | 1701 |
| 0.0993 | 0.9765 | 1.1609 | 0.7183 | 8.6509324e-08 | 1702 |
| 0.1193 | 0.9647 | 1.1626 | 0.7254 | 8.6494595e-08 | 1703 |
| 0.1105 | 0.9718 | 1.1644 | 0.7254 | 8.647986e-08 | 1704 |
| 0.1191 | 0.9671 | 1.1664 | 0.7183 | 8.6465114e-08 | 1705 |
| 0.1205 | 0.9671 | 1.1678 | 0.7183 | 8.645036e-08 | 1706 |
| 0.1081 | 0.9718 | 1.1692 | 0.7113 | 8.6435605e-08 | 1707 |
| 0.1091 | 0.9718 | 1.1682 | 0.7183 | 8.642085e-08 | 1708 |
| 0.0995 | 0.9906 | 1.1648 | 0.7254 | 8.640608e-08 | 1709 |
| 0.1073 | 0.9788 | 1.1651 | 0.7254 | 8.639131e-08 | 1710 |
| 0.1133 | 0.9741 | 1.1668 | 0.7183 | 8.637653e-08 | 1711 |
| 0.1127 | 0.9671 | 1.1681 | 0.7183 | 8.6361744e-08 | 1712 |
| 0.1104 | 0.9718 | 1.1657 | 0.7254 | 8.634695e-08 | 1713 |
| 0.1188 | 0.9694 | 1.1656 | 0.7254 | 8.633215e-08 | 1714 |
| 0.1248 | 0.9624 | 1.1665 | 0.7254 | 8.631735e-08 | 1715 |
| 0.1108 | 0.9647 | 1.1716 | 0.7254 | 8.630254e-08 | 1716 |
| 0.1136 | 0.9718 | 1.1730 | 0.7254 | 8.628773e-08 | 1717 |
| 0.1114 | 0.9741 | 1.1722 | 0.7254 | 8.6272905e-08 | 1718 |
| 0.1103 | 0.9694 | 1.1723 | 0.7254 | 8.6258076e-08 | 1719 |
| 0.1132 | 0.9718 | 1.1724 | 0.7254 | 8.624324e-08 | 1720 |
| 0.1183 | 0.9694 | 1.1750 | 0.7254 | 8.62284e-08 | 1721 |
| 0.1138 | 0.9718 | 1.1744 | 0.7254 | 8.6213554e-08 | 1722 |
| 0.1091 | 0.9788 | 1.1716 | 0.7254 | 8.61987e-08 | 1723 |
| 0.1051 | 0.9765 | 1.1718 | 0.7254 | 8.6183846e-08 | 1724 |
| 0.1128 | 0.9671 | 1.1709 | 0.7183 | 8.616898e-08 | 1725 |
| 0.1221 | 0.9624 | 1.1717 | 0.7183 | 8.615411e-08 | 1726 |
| 0.0965 | 0.9812 | 1.1758 | 0.7254 | 8.613923e-08 | 1727 |
| 0.1055 | 0.9788 | 1.1758 | 0.7183 | 8.612435e-08 | 1728 |
| 0.1183 | 0.9671 | 1.1750 | 0.7183 | 8.6109466e-08 | 1729 |
| 0.0998 | 0.9741 | 1.1719 | 0.7254 | 8.609457e-08 | 1730 |
| 0.1215 | 0.9624 | 1.1728 | 0.7183 | 8.607967e-08 | 1731 |
| 0.1011 | 0.9741 | 1.1742 | 0.7254 | 8.6064766e-08 | 1732 |
| 0.1023 | 0.9741 | 1.1732 | 0.7183 | 8.604985e-08 | 1733 |
| 0.1019 | 0.9718 | 1.1748 | 0.7183 | 8.603493e-08 | 1734 |
| 0.0984 | 0.9859 | 1.1740 | 0.7183 | 8.602001e-08 | 1735 |
| 0.1067 | 0.9718 | 1.1731 | 0.7254 | 8.600508e-08 | 1736 |
| 0.1113 | 0.9671 | 1.1741 | 0.7254 | 8.5990145e-08 | 1737 |
| 0.0981 | 0.9812 | 1.1755 | 0.7183 | 8.59752e-08 | 1738 |
| 0.1106 | 0.9694 | 1.1766 | 0.7183 | 8.596025e-08 | 1739 |
| 0.1000 | 0.9859 | 1.1774 | 0.7183 | 8.5945295e-08 | 1740 |
| 0.1190 | 0.9671 | 1.1794 | 0.7183 | 8.593034e-08 | 1741 |
| 0.1181 | 0.9671 | 1.1783 | 0.7183 | 8.5915374e-08 | 1742 |
| 0.1085 | 0.9812 | 1.1777 | 0.7183 | 8.59004e-08 | 1743 |
| 0.0958 | 0.9812 | 1.1776 | 0.7183 | 8.5885425e-08 | 1744 |
| 0.1121 | 0.9624 | 1.1790 | 0.7183 | 8.587044e-08 | 1745 |
| 0.1087 | 0.9671 | 1.1797 | 0.7254 | 8.585545e-08 | 1746 |
| 0.1130 | 0.9647 | 1.1797 | 0.7254 | 8.584045e-08 | 1747 |
| 0.0981 | 0.9765 | 1.1813 | 0.7254 | 8.582545e-08 | 1748 |
| 0.1090 | 0.9741 | 1.1826 | 0.7254 | 8.581044e-08 | 1749 |
| 0.1047 | 0.9718 | 1.1836 | 0.7183 | 8.579543e-08 | 1750 |
| 0.0960 | 0.9812 | 1.1824 | 0.7183 | 8.578041e-08 | 1751 |
| 0.1100 | 0.9694 | 1.1837 | 0.7183 | 8.576538e-08 | 1752 |
| 0.1124 | 0.9694 | 1.1875 | 0.7113 | 8.5750344e-08 | 1753 |
| 0.0986 | 0.9741 | 1.1892 | 0.7113 | 8.573531e-08 | 1754 |
| 0.0981 | 0.9812 | 1.1873 | 0.7113 | 8.5720266e-08 | 1755 |
| 0.0941 | 0.9835 | 1.1854 | 0.7183 | 8.570522e-08 | 1756 |
| 0.1150 | 0.9671 | 1.1839 | 0.7183 | 8.569016e-08 | 1757 |
| 0.1111 | 0.9671 | 1.1851 | 0.7183 | 8.56751e-08 | 1758 |
| 0.1151 | 0.9647 | 1.1849 | 0.7183 | 8.566003e-08 | 1759 |
| 0.0966 | 0.9718 | 1.1892 | 0.7183 | 8.5644956e-08 | 1760 |
| 0.1063 | 0.9741 | 1.1869 | 0.7183 | 8.562988e-08 | 1761 |
| 0.1054 | 0.9765 | 1.1854 | 0.7183 | 8.561479e-08 | 1762 |
| 0.1007 | 0.9718 | 1.1866 | 0.7183 | 8.55997e-08 | 1763 |
| 0.1112 | 0.9741 | 1.1861 | 0.7183 | 8.55846e-08 | 1764 |
| 0.1025 | 0.9694 | 1.1846 | 0.7254 | 8.5569496e-08 | 1765 |
| 0.1048 | 0.9718 | 1.1858 | 0.7183 | 8.555439e-08 | 1766 |
| 0.0897 | 0.9835 | 1.1882 | 0.7183 | 8.553928e-08 | 1767 |
| 0.1030 | 0.9765 | 1.1886 | 0.7254 | 8.552416e-08 | 1768 |
| 0.0918 | 0.9812 | 1.1914 | 0.7254 | 8.550903e-08 | 1769 |
| 0.1144 | 0.9671 | 1.1914 | 0.7254 | 8.5493895e-08 | 1770 |
| 0.1045 | 0.9741 | 1.1873 | 0.7254 | 8.547875e-08 | 1771 |
| 0.1035 | 0.9812 | 1.1865 | 0.7254 | 8.546361e-08 | 1772 |
| 0.1219 | 0.9694 | 1.1878 | 0.7183 | 8.544846e-08 | 1773 |
| 0.1037 | 0.9718 | 1.1900 | 0.7254 | 8.543331e-08 | 1774 |
| 0.0928 | 0.9788 | 1.1913 | 0.7254 | 8.5418144e-08 | 1775 |
| 0.1003 | 0.9788 | 1.1905 | 0.7183 | 8.5402974e-08 | 1776 |
| 0.1115 | 0.9694 | 1.1938 | 0.7183 | 8.53878e-08 | 1777 |
| 0.1067 | 0.9718 | 1.1975 | 0.7183 | 8.5372626e-08 | 1778 |
| 0.0940 | 0.9788 | 1.1979 | 0.7113 | 8.535744e-08 | 1779 |
| 0.1098 | 0.9694 | 1.1959 | 0.7183 | 8.534225e-08 | 1780 |
| 0.1068 | 0.9671 | 1.1955 | 0.7183 | 8.532705e-08 | 1781 |
| 0.1053 | 0.9671 | 1.1960 | 0.7183 | 8.531185e-08 | 1782 |
| 0.0973 | 0.9788 | 1.1968 | 0.7183 | 8.529664e-08 | 1783 |
| 0.1030 | 0.9741 | 1.1955 | 0.7183 | 8.528143e-08 | 1784 |
| 0.1202 | 0.9553 | 1.1940 | 0.7183 | 8.526621e-08 | 1785 |
| 0.0957 | 0.9788 | 1.1942 | 0.7183 | 8.525098e-08 | 1786 |
| 0.1077 | 0.9694 | 1.1944 | 0.7183 | 8.523575e-08 | 1787 |
| 0.0904 | 0.9835 | 1.1951 | 0.7183 | 8.522051e-08 | 1788 |
| 0.0935 | 0.9835 | 1.1948 | 0.7183 | 8.520527e-08 | 1789 |
| 0.0964 | 0.9812 | 1.1955 | 0.7183 | 8.5190024e-08 | 1790 |
| 0.1150 | 0.9647 | 1.1950 | 0.7183 | 8.517477e-08 | 1791 |
| 0.0885 | 0.9812 | 1.1955 | 0.7183 | 8.5159506e-08 | 1792 |
| 0.1001 | 0.9741 | 1.1946 | 0.7183 | 8.514424e-08 | 1793 |
| 0.0932 | 0.9741 | 1.1954 | 0.7254 | 8.512897e-08 | 1794 |
| 0.1023 | 0.9765 | 1.1982 | 0.7254 | 8.511369e-08 | 1795 |
| 0.1076 | 0.9718 | 1.1984 | 0.7254 | 8.509841e-08 | 1796 |
| 0.1005 | 0.9741 | 1.1996 | 0.7254 | 8.5083116e-08 | 1797 |
| 0.1028 | 0.9788 | 1.1999 | 0.7254 | 8.506782e-08 | 1798 |
| 0.1075 | 0.9647 | 1.1995 | 0.7254 | 8.505252e-08 | 1799 |
| 0.1058 | 0.9718 | 1.2006 | 0.7183 | 8.5037215e-08 | 1800 |
| 0.0910 | 0.9741 | 1.2030 | 0.7254 | 8.50219e-08 | 1801 |
| 0.0918 | 0.9882 | 1.2045 | 0.7183 | 8.500658e-08 | 1802 |
| 0.1041 | 0.9671 | 1.2036 | 0.7254 | 8.499126e-08 | 1803 |
| 0.0912 | 0.9812 | 1.2029 | 0.7254 | 8.497593e-08 | 1804 |
| 0.0925 | 0.9835 | 1.2017 | 0.7183 | 8.49606e-08 | 1805 |
| 0.0930 | 0.9788 | 1.2012 | 0.7183 | 8.4945256e-08 | 1806 |
| 0.1033 | 0.9694 | 1.2011 | 0.7183 | 8.492991e-08 | 1807 |
| 0.0992 | 0.9765 | 1.2032 | 0.7183 | 8.4914554e-08 | 1808 |
| 0.0961 | 0.9765 | 1.2036 | 0.7183 | 8.48992e-08 | 1809 |
| 0.0942 | 0.9788 | 1.2033 | 0.7254 | 8.488384e-08 | 1810 |
| 0.1041 | 0.9671 | 1.2038 | 0.7183 | 8.486847e-08 | 1811 |
| 0.1002 | 0.9718 | 1.2040 | 0.7183 | 8.485309e-08 | 1812 |
| 0.0921 | 0.9835 | 1.2031 | 0.7183 | 8.483771e-08 | 1813 |
| 0.1028 | 0.9812 | 1.2046 | 0.7254 | 8.4822325e-08 | 1814 |
| 0.0939 | 0.9741 | 1.2086 | 0.7254 | 8.4806935e-08 | 1815 |
| 0.0991 | 0.9788 | 1.2083 | 0.7254 | 8.479154e-08 | 1816 |
| 0.0981 | 0.9718 | 1.2079 | 0.7254 | 8.477613e-08 | 1817 |
| 0.0953 | 0.9835 | 1.2078 | 0.7183 | 8.476072e-08 | 1818 |
| 0.0890 | 0.9835 | 1.2085 | 0.7183 | 8.474531e-08 | 1819 |
| 0.0923 | 0.9788 | 1.2094 | 0.7183 | 8.472989e-08 | 1820 |
| 0.0927 | 0.9765 | 1.2110 | 0.7254 | 8.4714465e-08 | 1821 |
| 0.0839 | 0.9835 | 1.2129 | 0.7254 | 8.469903e-08 | 1822 |
| 0.0831 | 0.9906 | 1.2110 | 0.7254 | 8.468359e-08 | 1823 |
| 0.0926 | 0.9788 | 1.2087 | 0.7183 | 8.466815e-08 | 1824 |
| 0.0997 | 0.9765 | 1.2077 | 0.7254 | 8.4652704e-08 | 1825 |
| 0.0971 | 0.9741 | 1.2079 | 0.7254 | 8.463725e-08 | 1826 |
| 0.1017 | 0.9765 | 1.2100 | 0.7183 | 8.462179e-08 | 1827 |
| 0.0886 | 0.9882 | 1.2122 | 0.7254 | 8.460632e-08 | 1828 |
| 0.0899 | 0.9859 | 1.2121 | 0.7183 | 8.459085e-08 | 1829 |
| 0.0827 | 0.9812 | 1.2126 | 0.7183 | 8.4575376e-08 | 1830 |
| 0.0977 | 0.9694 | 1.2131 | 0.7183 | 8.455989e-08 | 1831 |
| 0.0988 | 0.9671 | 1.2135 | 0.7254 | 8.45444e-08 | 1832 |
| 0.0905 | 0.9765 | 1.2140 | 0.7254 | 8.452891e-08 | 1833 |
| 0.0929 | 0.9835 | 1.2167 | 0.7254 | 8.451341e-08 | 1834 |
| 0.0998 | 0.9671 | 1.2179 | 0.7254 | 8.4497906e-08 | 1835 |
| 0.0968 | 0.9812 | 1.2156 | 0.7254 | 8.4482394e-08 | 1836 |
| 0.0953 | 0.9812 | 1.2147 | 0.7254 | 8.4466876e-08 | 1837 |
| 0.0848 | 0.9859 | 1.2145 | 0.7183 | 8.445135e-08 | 1838 |
| 0.1127 | 0.9647 | 1.2152 | 0.7183 | 8.4435825e-08 | 1839 |
| 0.0901 | 0.9765 | 1.2183 | 0.7254 | 8.442029e-08 | 1840 |
| 0.0891 | 0.9812 | 1.2220 | 0.7183 | 8.440475e-08 | 1841 |
| 0.0981 | 0.9812 | 1.2195 | 0.7254 | 8.438921e-08 | 1842 |
| 0.0860 | 0.9835 | 1.2187 | 0.7254 | 8.437366e-08 | 1843 |
| 0.0817 | 0.9953 | 1.2200 | 0.7254 | 8.4358106e-08 | 1844 |
| 0.0979 | 0.9812 | 1.2199 | 0.7254 | 8.4342545e-08 | 1845 |
| 0.0927 | 0.9694 | 1.2205 | 0.7183 | 8.432698e-08 | 1846 |
| 0.0883 | 0.9788 | 1.2203 | 0.7183 | 8.43114e-08 | 1847 |
| 0.0852 | 0.9859 | 1.2216 | 0.7183 | 8.429583e-08 | 1848 |
| 0.1044 | 0.9671 | 1.2238 | 0.7254 | 8.4280245e-08 | 1849 |
| 0.0927 | 0.9741 | 1.2242 | 0.7254 | 8.4264656e-08 | 1850 |
| 0.0919 | 0.9859 | 1.2267 | 0.7183 | 8.424906e-08 | 1851 |
| 0.0812 | 0.9859 | 1.2271 | 0.7254 | 8.4233456e-08 | 1852 |
| 0.0993 | 0.9718 | 1.2266 | 0.7254 | 8.421785e-08 | 1853 |
| 0.0876 | 0.9812 | 1.2244 | 0.7254 | 8.420224e-08 | 1854 |
| 0.0826 | 0.9882 | 1.2230 | 0.7183 | 8.4186624e-08 | 1855 |
| 0.0960 | 0.9671 | 1.2238 | 0.7183 | 8.4171e-08 | 1856 |
| 0.0936 | 0.9718 | 1.2229 | 0.7183 | 8.4155374e-08 | 1857 |
| 0.0957 | 0.9741 | 1.2228 | 0.7183 | 8.413974e-08 | 1858 |
| 0.0848 | 0.9835 | 1.2247 | 0.7254 | 8.41241e-08 | 1859 |
| 0.1037 | 0.9671 | 1.2267 | 0.7183 | 8.410846e-08 | 1860 |
| 0.0859 | 0.9859 | 1.2276 | 0.7183 | 8.409281e-08 | 1861 |
| 0.0933 | 0.9765 | 1.2270 | 0.7254 | 8.407716e-08 | 1862 |
| 0.0779 | 0.9906 | 1.2265 | 0.7183 | 8.40615e-08 | 1863 |
| 0.0819 | 0.9835 | 1.2279 | 0.7183 | 8.404583e-08 | 1864 |
| 0.0806 | 0.9859 | 1.2278 | 0.7183 | 8.4030155e-08 | 1865 |
| 0.1020 | 0.9765 | 1.2291 | 0.7183 | 8.401448e-08 | 1866 |
| 0.0780 | 0.9906 | 1.2308 | 0.7183 | 8.39988e-08 | 1867 |
| 0.0890 | 0.9788 | 1.2303 | 0.7183 | 8.398311e-08 | 1868 |
| 0.0889 | 0.9812 | 1.2288 | 0.7183 | 8.3967414e-08 | 1869 |
| 0.0976 | 0.9812 | 1.2302 | 0.7183 | 8.395172e-08 | 1870 |
| 0.0848 | 0.9788 | 1.2323 | 0.7183 | 8.3936015e-08 | 1871 |
| 0.0785 | 0.9906 | 1.2332 | 0.7183 | 8.3920305e-08 | 1872 |
| 0.0878 | 0.9835 | 1.2305 | 0.7183 | 8.390459e-08 | 1873 |
| 0.0847 | 0.9788 | 1.2298 | 0.7183 | 8.3888864e-08 | 1874 |
| 0.0854 | 0.9835 | 1.2308 | 0.7183 | 8.387314e-08 | 1875 |
| 0.0861 | 0.9835 | 1.2319 | 0.7183 | 8.385741e-08 | 1876 |
| 0.0829 | 0.9788 | 1.2333 | 0.7183 | 8.384167e-08 | 1877 |
| 0.0953 | 0.9741 | 1.2326 | 0.7183 | 8.3825924e-08 | 1878 |
| 0.0973 | 0.9788 | 1.2319 | 0.7183 | 8.381018e-08 | 1879 |
| 0.0877 | 0.9835 | 1.2335 | 0.7183 | 8.3794426e-08 | 1880 |
| 0.0945 | 0.9788 | 1.2325 | 0.7183 | 8.3778666e-08 | 1881 |
| 0.0817 | 0.9812 | 1.2318 | 0.7183 | 8.37629e-08 | 1882 |
| 0.0900 | 0.9741 | 1.2334 | 0.7183 | 8.374713e-08 | 1883 |
| 0.0810 | 0.9835 | 1.2341 | 0.7183 | 8.373136e-08 | 1884 |
| 0.0914 | 0.9788 | 1.2348 | 0.7183 | 8.371558e-08 | 1885 |
| 0.0856 | 0.9788 | 1.2351 | 0.7183 | 8.369979e-08 | 1886 |
| 0.0715 | 0.9906 | 1.2362 | 0.7183 | 8.3684e-08 | 1887 |
| 0.0904 | 0.9835 | 1.2371 | 0.7183 | 8.3668205e-08 | 1888 |
| 0.0836 | 0.9835 | 1.2371 | 0.7183 | 8.36524e-08 | 1889 |
| 0.0984 | 0.9671 | 1.2374 | 0.7183 | 8.363659e-08 | 1890 |
| 0.0823 | 0.9859 | 1.2383 | 0.7183 | 8.362078e-08 | 1891 |
| 0.0843 | 0.9882 | 1.2395 | 0.7183 | 8.360497e-08 | 1892 |
| 0.0832 | 0.9859 | 1.2398 | 0.7183 | 8.358914e-08 | 1893 |
| 0.0929 | 0.9694 | 1.2391 | 0.7183 | 8.357331e-08 | 1894 |
| 0.0854 | 0.9788 | 1.2447 | 0.7183 | 8.355748e-08 | 1895 |
| 0.0819 | 0.9906 | 1.2443 | 0.7254 | 8.354164e-08 | 1896 |
| 0.0875 | 0.9788 | 1.2423 | 0.7183 | 8.35258e-08 | 1897 |
| 0.0835 | 0.9882 | 1.2406 | 0.7183 | 8.3509946e-08 | 1898 |
| 0.0815 | 0.9835 | 1.2399 | 0.7183 | 8.349409e-08 | 1899 |
| 0.0791 | 0.9835 | 1.2404 | 0.7183 | 8.3478234e-08 | 1900 |
| 0.0846 | 0.9765 | 1.2402 | 0.7183 | 8.346237e-08 | 1901 |
| 0.0810 | 0.9882 | 1.2416 | 0.7183 | 8.3446494e-08 | 1902 |
| 0.0846 | 0.9812 | 1.2424 | 0.7183 | 8.343062e-08 | 1903 |
| 0.0887 | 0.9671 | 1.2420 | 0.7183 | 8.341474e-08 | 1904 |
| 0.0898 | 0.9741 | 1.2435 | 0.7183 | 8.339885e-08 | 1905 |
| 0.0778 | 0.9859 | 1.2449 | 0.7254 | 8.338296e-08 | 1906 |
| 0.0772 | 0.9812 | 1.2441 | 0.7183 | 8.336706e-08 | 1907 |
| 0.0885 | 0.9788 | 1.2436 | 0.7183 | 8.335116e-08 | 1908 |
| 0.0807 | 0.9835 | 1.2467 | 0.7183 | 8.333525e-08 | 1909 |
| 0.0850 | 0.9788 | 1.2471 | 0.7113 | 8.3319335e-08 | 1910 |
| 0.0760 | 0.9859 | 1.2456 | 0.7183 | 8.330342e-08 | 1911 |
| 0.0865 | 0.9741 | 1.2483 | 0.7183 | 8.3287496e-08 | 1912 |
| 0.0805 | 0.9835 | 1.2490 | 0.7183 | 8.3271566e-08 | 1913 |
| 0.0904 | 0.9788 | 1.2473 | 0.7183 | 8.325563e-08 | 1914 |
| 0.0812 | 0.9812 | 1.2474 | 0.7183 | 8.323969e-08 | 1915 |
| 0.0674 | 0.9882 | 1.2488 | 0.7254 | 8.3223746e-08 | 1916 |
| 0.0879 | 0.9812 | 1.2514 | 0.7183 | 8.3207794e-08 | 1917 |
| 0.0770 | 0.9788 | 1.2515 | 0.7183 | 8.3191836e-08 | 1918 |
| 0.0675 | 0.9906 | 1.2508 | 0.7254 | 8.317588e-08 | 1919 |
| 0.0881 | 0.9718 | 1.2498 | 0.7183 | 8.315991e-08 | 1920 |
| 0.0787 | 0.9906 | 1.2505 | 0.7183 | 8.314394e-08 | 1921 |
| 0.0801 | 0.9859 | 1.2533 | 0.7183 | 8.312796e-08 | 1922 |
| 0.0973 | 0.9671 | 1.2523 | 0.7183 | 8.311198e-08 | 1923 |
| 0.0864 | 0.9812 | 1.2511 | 0.7183 | 8.309599e-08 | 1924 |
| 0.0942 | 0.9765 | 1.2510 | 0.7183 | 8.3079996e-08 | 1925 |
| 0.0778 | 0.9835 | 1.2508 | 0.7183 | 8.3063995e-08 | 1926 |
| 0.0801 | 0.9835 | 1.2497 | 0.7183 | 8.304799e-08 | 1927 |
| 0.0804 | 0.9812 | 1.2504 | 0.7183 | 8.3031985e-08 | 1928 |
| 0.0725 | 0.9929 | 1.2520 | 0.7183 | 8.301597e-08 | 1929 |
| 0.0746 | 0.9835 | 1.2530 | 0.7183 | 8.2999954e-08 | 1930 |
| 0.0825 | 0.9835 | 1.2525 | 0.7183 | 8.298393e-08 | 1931 |
| 0.0810 | 0.9835 | 1.2516 | 0.7183 | 8.29679e-08 | 1932 |
| 0.0805 | 0.9812 | 1.2540 | 0.7113 | 8.2951864e-08 | 1933 |
| 0.0879 | 0.9788 | 1.2549 | 0.7183 | 8.293583e-08 | 1934 |
| 0.0761 | 0.9859 | 1.2549 | 0.7254 | 8.291978e-08 | 1935 |
| 0.0768 | 0.9835 | 1.2558 | 0.7113 | 8.290373e-08 | 1936 |
| 0.0790 | 0.9812 | 1.2540 | 0.7183 | 8.2887674e-08 | 1937 |
| 0.0741 | 0.9835 | 1.2558 | 0.7254 | 8.2871615e-08 | 1938 |
| 0.0691 | 0.9882 | 1.2576 | 0.7183 | 8.285555e-08 | 1939 |
| 0.0770 | 0.9859 | 1.2559 | 0.7183 | 8.283948e-08 | 1940 |
| 0.0875 | 0.9812 | 1.2546 | 0.7254 | 8.2823405e-08 | 1941 |
| 0.0768 | 0.9859 | 1.2556 | 0.7183 | 8.2807325e-08 | 1942 |
| 0.0727 | 0.9812 | 1.2570 | 0.7183 | 8.279124e-08 | 1943 |
| 0.0671 | 0.9929 | 1.2603 | 0.7113 | 8.2775145e-08 | 1944 |
| 0.0686 | 0.9812 | 1.2653 | 0.7113 | 8.275905e-08 | 1945 |
| 0.0852 | 0.9835 | 1.2625 | 0.7113 | 8.274295e-08 | 1946 |
| 0.0645 | 0.9906 | 1.2605 | 0.7113 | 8.272684e-08 | 1947 |
| 0.0769 | 0.9882 | 1.2588 | 0.7183 | 8.271073e-08 | 1948 |
| 0.0807 | 0.9812 | 1.2587 | 0.7183 | 8.269461e-08 | 1949 |
| 0.0788 | 0.9835 | 1.2594 | 0.7183 | 8.267849e-08 | 1950 |
| 0.0785 | 0.9741 | 1.2601 | 0.7183 | 8.266236e-08 | 1951 |
| 0.0764 | 0.9765 | 1.2581 | 0.7183 | 8.264623e-08 | 1952 |
| 0.0792 | 0.9859 | 1.2593 | 0.7183 | 8.2630095e-08 | 1953 |
| 0.0792 | 0.9859 | 1.2619 | 0.7113 | 8.261395e-08 | 1954 |
| 0.0757 | 0.9882 | 1.2619 | 0.7113 | 8.25978e-08 | 1955 |
| 0.0787 | 0.9835 | 1.2616 | 0.7113 | 8.258165e-08 | 1956 |
| 0.0961 | 0.9671 | 1.2641 | 0.7113 | 8.256549e-08 | 1957 |
| 0.0743 | 0.9859 | 1.2646 | 0.7113 | 8.254933e-08 | 1958 |
| 0.0814 | 0.9835 | 1.2670 | 0.7113 | 8.253316e-08 | 1959 |
| 0.0819 | 0.9788 | 1.2684 | 0.7113 | 8.251699e-08 | 1960 |
| 0.0925 | 0.9741 | 1.2658 | 0.7042 | 8.250081e-08 | 1961 |
| 0.0850 | 0.9812 | 1.2643 | 0.7113 | 8.2484625e-08 | 1962 |
| 0.0805 | 0.9835 | 1.2657 | 0.7113 | 8.246844e-08 | 1963 |
| 0.0613 | 0.9906 | 1.2652 | 0.7113 | 8.2452246e-08 | 1964 |
| 0.0787 | 0.9882 | 1.2666 | 0.7113 | 8.2436046e-08 | 1965 |
| 0.0803 | 0.9835 | 1.2675 | 0.7113 | 8.2419845e-08 | 1966 |
| 0.0806 | 0.9859 | 1.2683 | 0.7042 | 8.240364e-08 | 1967 |
| 0.0795 | 0.9882 | 1.2685 | 0.7113 | 8.238742e-08 | 1968 |
| 0.0652 | 0.9906 | 1.2693 | 0.7113 | 8.23712e-08 | 1969 |
| 0.0670 | 0.9906 | 1.2710 | 0.7042 | 8.235498e-08 | 1970 |
| 0.0769 | 0.9859 | 1.2701 | 0.7042 | 8.233875e-08 | 1971 |
| 0.0608 | 0.9929 | 1.2701 | 0.7113 | 8.2322515e-08 | 1972 |
| 0.0761 | 0.9859 | 1.2703 | 0.7113 | 8.230628e-08 | 1973 |
| 0.0731 | 0.9882 | 1.2690 | 0.7254 | 8.2290036e-08 | 1974 |
| 0.0838 | 0.9765 | 1.2682 | 0.7254 | 8.2273786e-08 | 1975 |
| 0.0782 | 0.9812 | 1.2705 | 0.7113 | 8.225753e-08 | 1976 |
| 0.0816 | 0.9859 | 1.2728 | 0.7113 | 8.224127e-08 | 1977 |
| 0.0890 | 0.9741 | 1.2715 | 0.7113 | 8.222501e-08 | 1978 |
| 0.0768 | 0.9882 | 1.2706 | 0.7183 | 8.2208736e-08 | 1979 |
| 0.0807 | 0.9835 | 1.2697 | 0.7183 | 8.2192464e-08 | 1980 |
| 0.0710 | 0.9859 | 1.2710 | 0.7183 | 8.2176186e-08 | 1981 |
| 0.0676 | 0.9859 | 1.2704 | 0.7183 | 8.21599e-08 | 1982 |
| 0.0772 | 0.9812 | 1.2725 | 0.7183 | 8.2143615e-08 | 1983 |
| 0.0657 | 0.9859 | 1.2722 | 0.7183 | 8.212732e-08 | 1984 |
| 0.0799 | 0.9835 | 1.2713 | 0.7183 | 8.211102e-08 | 1985 |
| 0.0771 | 0.9765 | 1.2729 | 0.7183 | 8.2094715e-08 | 1986 |
| 0.0823 | 0.9788 | 1.2759 | 0.7113 | 8.207841e-08 | 1987 |
| 0.0583 | 0.9953 | 1.2759 | 0.7113 | 8.2062094e-08 | 1988 |
| 0.0907 | 0.9741 | 1.2761 | 0.7113 | 8.204577e-08 | 1989 |
| 0.0768 | 0.9859 | 1.2784 | 0.7042 | 8.202945e-08 | 1990 |
| 0.0784 | 0.9835 | 1.2766 | 0.7113 | 8.201312e-08 | 1991 |
| 0.0698 | 0.9906 | 1.2775 | 0.7042 | 8.199679e-08 | 1992 |
| 0.0667 | 0.9929 | 1.2795 | 0.7113 | 8.198045e-08 | 1993 |
| 0.0776 | 0.9812 | 1.2771 | 0.7183 | 8.196411e-08 | 1994 |
| 0.0679 | 0.9882 | 1.2786 | 0.7183 | 8.194776e-08 | 1995 |
| 0.0876 | 0.9812 | 1.2775 | 0.7183 | 8.1931404e-08 | 1996 |
| 0.0700 | 0.9929 | 1.2792 | 0.7042 | 8.191505e-08 | 1997 |
| 0.0844 | 0.9882 | 1.2782 | 0.7183 | 8.189868e-08 | 1998 |
| 0.0633 | 0.9929 | 1.2764 | 0.7183 | 8.188231e-08 | 1999 |
| 0.0684 | 0.9859 | 1.2758 | 0.7183 | 8.186594e-08 | 2000 |
| 0.0805 | 0.9788 | 1.2777 | 0.7183 | 8.1849564e-08 | 2001 |
| 0.0798 | 0.9812 | 1.2814 | 0.7113 | 8.183318e-08 | 2002 |
| 0.0764 | 0.9882 | 1.2825 | 0.7113 | 8.181679e-08 | 2003 |
| 0.0751 | 0.9788 | 1.2831 | 0.7042 | 8.18004e-08 | 2004 |
| 0.0769 | 0.9812 | 1.2842 | 0.7113 | 8.1784e-08 | 2005 |
| 0.0677 | 0.9859 | 1.2839 | 0.7113 | 8.17676e-08 | 2006 |
| 0.0704 | 0.9859 | 1.2794 | 0.7183 | 8.1751196e-08 | 2007 |
| 0.0780 | 0.9812 | 1.2786 | 0.7183 | 8.173478e-08 | 2008 |
| 0.0730 | 0.9812 | 1.2796 | 0.7183 | 8.171836e-08 | 2009 |
| 0.0773 | 0.9859 | 1.2811 | 0.7183 | 8.170194e-08 | 2010 |
| 0.0649 | 0.9882 | 1.2815 | 0.7183 | 8.168551e-08 | 2011 |
| 0.0808 | 0.9765 | 1.2819 | 0.7183 | 8.166908e-08 | 2012 |
| 0.0789 | 0.9788 | 1.2814 | 0.7183 | 8.1652644e-08 | 2013 |
| 0.0715 | 0.9906 | 1.2819 | 0.7183 | 8.16362e-08 | 2014 |
| 0.0733 | 0.9835 | 1.2792 | 0.7183 | 8.161975e-08 | 2015 |
| 0.0769 | 0.9859 | 1.2813 | 0.7183 | 8.1603304e-08 | 2016 |
| 0.0681 | 0.9953 | 1.2835 | 0.7183 | 8.158685e-08 | 2017 |
| 0.0734 | 0.9788 | 1.2861 | 0.7113 | 8.1570384e-08 | 2018 |
| 0.0707 | 0.9859 | 1.2861 | 0.7183 | 8.155392e-08 | 2019 |
| 0.0554 | 0.9953 | 1.2854 | 0.7183 | 8.153745e-08 | 2020 |
| 0.0736 | 0.9859 | 1.2844 | 0.7183 | 8.152097e-08 | 2021 |
| 0.0737 | 0.9882 | 1.2856 | 0.7113 | 8.1504496e-08 | 2022 |
| 0.0881 | 0.9788 | 1.2847 | 0.7183 | 8.148801e-08 | 2023 |
| 0.0658 | 0.9882 | 1.2827 | 0.7254 | 8.147152e-08 | 2024 |
| 0.0681 | 0.9882 | 1.2837 | 0.7183 | 8.145503e-08 | 2025 |
| 0.0870 | 0.9647 | 1.2882 | 0.7042 | 8.143853e-08 | 2026 |
| 0.0755 | 0.9906 | 1.2898 | 0.7113 | 8.142202e-08 | 2027 |
| 0.0725 | 0.9835 | 1.2910 | 0.7113 | 8.140552e-08 | 2028 |
| 0.0681 | 0.9882 | 1.2878 | 0.7113 | 8.1389004e-08 | 2029 |
| 0.0624 | 0.9953 | 1.2879 | 0.7113 | 8.1372484e-08 | 2030 |
| 0.0680 | 0.9812 | 1.2883 | 0.7113 | 8.1355964e-08 | 2031 |
| 0.0769 | 0.9812 | 1.2898 | 0.7113 | 8.133944e-08 | 2032 |
| 0.0693 | 0.9859 | 1.2886 | 0.7113 | 8.13229e-08 | 2033 |
| 0.0643 | 0.9929 | 1.2885 | 0.7113 | 8.130637e-08 | 2034 |
| 0.0774 | 0.9812 | 1.2874 | 0.7183 | 8.1289826e-08 | 2035 |
| 0.0694 | 0.9882 | 1.2884 | 0.7183 | 8.127328e-08 | 2036 |
| 0.0764 | 0.9835 | 1.2885 | 0.7254 | 8.125673e-08 | 2037 |
| 0.0589 | 0.9906 | 1.2907 | 0.7113 | 8.1240174e-08 | 2038 |
| 0.0656 | 0.9859 | 1.2915 | 0.7113 | 8.122361e-08 | 2039 |
| 0.0698 | 0.9882 | 1.2918 | 0.7113 | 8.120705e-08 | 2040 |
| 0.0750 | 0.9788 | 1.2938 | 0.7113 | 8.119048e-08 | 2041 |
| 0.0747 | 0.9835 | 1.2937 | 0.7113 | 8.11739e-08 | 2042 |
| 0.0698 | 0.9906 | 1.2928 | 0.7113 | 8.1157324e-08 | 2043 |
| 0.0725 | 0.9812 | 1.2921 | 0.7113 | 8.114074e-08 | 2044 |
| 0.0624 | 0.9929 | 1.2934 | 0.7042 | 8.112415e-08 | 2045 |
| 0.0746 | 0.9859 | 1.2946 | 0.7042 | 8.110756e-08 | 2046 |
| 0.0788 | 0.9835 | 1.2967 | 0.7042 | 8.109096e-08 | 2047 |
| 0.0611 | 0.9859 | 1.2972 | 0.7042 | 8.1074354e-08 | 2048 |
| 0.0642 | 0.9812 | 1.2972 | 0.7042 | 8.105775e-08 | 2049 |
| 0.0681 | 0.9765 | 1.2955 | 0.7183 | 8.104114e-08 | 2050 |
| 0.0692 | 0.9882 | 1.2943 | 0.7183 | 8.102452e-08 | 2051 |
| 0.0643 | 0.9882 | 1.2965 | 0.7113 | 8.10079e-08 | 2052 |
| 0.0754 | 0.9812 | 1.2960 | 0.7113 | 8.099127e-08 | 2053 |
| 0.0682 | 0.9882 | 1.2980 | 0.7113 | 8.097464e-08 | 2054 |
| 0.0663 | 0.9882 | 1.2971 | 0.7183 | 8.0958e-08 | 2055 |
| 0.0572 | 0.9906 | 1.2984 | 0.7113 | 8.094136e-08 | 2056 |
| 0.0672 | 0.9906 | 1.2991 | 0.7113 | 8.0924714e-08 | 2057 |
| 0.0625 | 0.9859 | 1.2997 | 0.7183 | 8.0908066e-08 | 2058 |
| 0.0870 | 0.9741 | 1.3026 | 0.7042 | 8.089141e-08 | 2059 |
| 0.0721 | 0.9835 | 1.3025 | 0.7042 | 8.087475e-08 | 2060 |
| 0.0618 | 0.9906 | 1.3037 | 0.7042 | 8.085809e-08 | 2061 |
| 0.0636 | 0.9929 | 1.3033 | 0.7042 | 8.084142e-08 | 2062 |
| 0.0699 | 0.9859 | 1.3026 | 0.7042 | 8.082474e-08 | 2063 |
| 0.0624 | 0.9906 | 1.3002 | 0.7183 | 8.0808064e-08 | 2064 |
| 0.0711 | 0.9812 | 1.2998 | 0.7183 | 8.079138e-08 | 2065 |
| 0.0677 | 0.9859 | 1.3018 | 0.7042 | 8.077469e-08 | 2066 |
| 0.0697 | 0.9882 | 1.3029 | 0.7042 | 8.0758e-08 | 2067 |
| 0.0633 | 0.9882 | 1.3034 | 0.7042 | 8.07413e-08 | 2068 |
| 0.0754 | 0.9835 | 1.3045 | 0.7042 | 8.07246e-08 | 2069 |
| 0.0662 | 0.9882 | 1.3070 | 0.7042 | 8.070789e-08 | 2070 |
| 0.0679 | 0.9788 | 1.3067 | 0.7042 | 8.069118e-08 | 2071 |
| 0.0577 | 0.9976 | 1.3043 | 0.7042 | 8.067446e-08 | 2072 |
| 0.0568 | 0.9906 | 1.3047 | 0.7042 | 8.065774e-08 | 2073 |
| 0.0652 | 0.9882 | 1.3017 | 0.7183 | 8.0641016e-08 | 2074 |
| 0.0726 | 0.9812 | 1.3021 | 0.7183 | 8.062429e-08 | 2075 |
| 0.0643 | 0.9882 | 1.3056 | 0.7183 | 8.0607556e-08 | 2076 |
| 0.0670 | 0.9906 | 1.3073 | 0.7113 | 8.0590816e-08 | 2077 |
| 0.0646 | 0.9882 | 1.3067 | 0.7183 | 8.0574075e-08 | 2078 |
| 0.0639 | 0.9859 | 1.3094 | 0.7113 | 8.055733e-08 | 2079 |
| 0.0625 | 0.9882 | 1.3094 | 0.7113 | 8.054057e-08 | 2080 |
| 0.0595 | 0.9859 | 1.3091 | 0.7113 | 8.052382e-08 | 2081 |
| 0.0671 | 0.9812 | 1.3097 | 0.7113 | 8.050706e-08 | 2082 |
| 0.0712 | 0.9835 | 1.3100 | 0.7113 | 8.049029e-08 | 2083 |
| 0.0724 | 0.9882 | 1.3090 | 0.7113 | 8.047352e-08 | 2084 |
| 0.0790 | 0.9718 | 1.3077 | 0.7042 | 8.045674e-08 | 2085 |
| 0.0605 | 0.9953 | 1.3084 | 0.7042 | 8.043996e-08 | 2086 |
| 0.0706 | 0.9882 | 1.3118 | 0.7113 | 8.042318e-08 | 2087 |
| 0.0582 | 0.9906 | 1.3094 | 0.7042 | 8.040639e-08 | 2088 |
| 0.0719 | 0.9859 | 1.3097 | 0.7113 | 8.03896e-08 | 2089 |
| 0.0569 | 1.0 | 1.3099 | 0.7113 | 8.03728e-08 | 2090 |
| 0.0649 | 0.9859 | 1.3102 | 0.7113 | 8.0355996e-08 | 2091 |
| 0.0643 | 0.9859 | 1.3094 | 0.7183 | 8.033919e-08 | 2092 |
| 0.0588 | 0.9882 | 1.3114 | 0.7113 | 8.032238e-08 | 2093 |
| 0.0601 | 0.9906 | 1.3115 | 0.7183 | 8.030556e-08 | 2094 |
| 0.0656 | 0.9859 | 1.3112 | 0.7183 | 8.028874e-08 | 2095 |
| 0.0703 | 0.9882 | 1.3108 | 0.7113 | 8.027192e-08 | 2096 |
| 0.0527 | 0.9929 | 1.3096 | 0.7183 | 8.0255084e-08 | 2097 |
| 0.0795 | 0.9812 | 1.3113 | 0.7113 | 8.023825e-08 | 2098 |
| 0.0713 | 0.9859 | 1.3125 | 0.7113 | 8.022141e-08 | 2099 |
| 0.0682 | 0.9859 | 1.3134 | 0.7183 | 8.020457e-08 | 2100 |
| 0.0623 | 0.9882 | 1.3136 | 0.7113 | 8.0187725e-08 | 2101 |
| 0.0596 | 0.9906 | 1.3140 | 0.7183 | 8.017087e-08 | 2102 |
| 0.0650 | 0.9859 | 1.3144 | 0.7183 | 8.015402e-08 | 2103 |
| 0.0691 | 0.9882 | 1.3157 | 0.7113 | 8.0137156e-08 | 2104 |
| 0.0619 | 0.9906 | 1.3159 | 0.7113 | 8.012029e-08 | 2105 |
| 0.0561 | 0.9953 | 1.3164 | 0.7113 | 8.010342e-08 | 2106 |
| 0.0566 | 0.9929 | 1.3170 | 0.7113 | 8.0086544e-08 | 2107 |
| 0.0585 | 0.9953 | 1.3171 | 0.7113 | 8.006967e-08 | 2108 |
| 0.0632 | 0.9906 | 1.3188 | 0.7113 | 8.0052786e-08 | 2109 |
| 0.0615 | 0.9859 | 1.3182 | 0.7113 | 8.0035896e-08 | 2110 |
| 0.0640 | 0.9859 | 1.3187 | 0.7042 | 8.001901e-08 | 2111 |
| 0.0715 | 0.9859 | 1.3183 | 0.7042 | 8.000211e-08 | 2112 |
| 0.0628 | 0.9882 | 1.3194 | 0.7113 | 7.9985206e-08 | 2113 |
| 0.0549 | 0.9953 | 1.3206 | 0.7042 | 7.99683e-08 | 2114 |
| 0.0640 | 0.9906 | 1.3187 | 0.7113 | 7.995139e-08 | 2115 |
| 0.0592 | 0.9906 | 1.3203 | 0.7042 | 7.993448e-08 | 2116 |
| 0.0750 | 0.9788 | 1.3215 | 0.7042 | 7.991756e-08 | 2117 |
| 0.0636 | 0.9882 | 1.3202 | 0.7042 | 7.990064e-08 | 2118 |
| 0.0608 | 0.9882 | 1.3218 | 0.7113 | 7.988371e-08 | 2119 |
| 0.0583 | 0.9929 | 1.3231 | 0.7113 | 7.986678e-08 | 2120 |
| 0.0693 | 0.9835 | 1.3221 | 0.7113 | 7.984984e-08 | 2121 |
| 0.0671 | 0.9906 | 1.3234 | 0.7113 | 7.98329e-08 | 2122 |
| 0.0618 | 0.9906 | 1.3280 | 0.7113 | 7.9815955e-08 | 2123 |
| 0.0594 | 0.9929 | 1.3257 | 0.7042 | 7.979901e-08 | 2124 |
| 0.0596 | 0.9929 | 1.3248 | 0.7042 | 7.9782055e-08 | 2125 |
| 0.0587 | 0.9882 | 1.3236 | 0.7113 | 7.9765094e-08 | 2126 |
| 0.0664 | 0.9788 | 1.3235 | 0.7113 | 7.974813e-08 | 2127 |
| 0.0581 | 0.9906 | 1.3232 | 0.7042 | 7.9731166e-08 | 2128 |
| 0.0577 | 0.9929 | 1.3241 | 0.7042 | 7.97142e-08 | 2129 |
| 0.0694 | 0.9882 | 1.3255 | 0.7113 | 7.969722e-08 | 2130 |
| 0.0514 | 0.9929 | 1.3261 | 0.7042 | 7.968024e-08 | 2131 |
| 0.0710 | 0.9812 | 1.3289 | 0.7113 | 7.966326e-08 | 2132 |
| 0.0647 | 0.9882 | 1.3307 | 0.7113 | 7.964627e-08 | 2133 |
| 0.0602 | 0.9882 | 1.3305 | 0.7113 | 7.9629274e-08 | 2134 |
| 0.0686 | 0.9859 | 1.3281 | 0.7042 | 7.961228e-08 | 2135 |
| 0.0629 | 0.9835 | 1.3262 | 0.7042 | 7.9595274e-08 | 2136 |
| 0.0672 | 0.9859 | 1.3295 | 0.7042 | 7.957827e-08 | 2137 |
| 0.0675 | 0.9859 | 1.3329 | 0.7113 | 7.956126e-08 | 2138 |
| 0.0629 | 0.9859 | 1.3337 | 0.7113 | 7.954424e-08 | 2139 |
| 0.0546 | 0.9929 | 1.3347 | 0.7113 | 7.9527226e-08 | 2140 |
| 0.0556 | 0.9953 | 1.3341 | 0.7042 | 7.95102e-08 | 2141 |
| 0.0591 | 0.9906 | 1.3350 | 0.7113 | 7.949318e-08 | 2142 |
| 0.0517 | 0.9882 | 1.3349 | 0.7113 | 7.9476145e-08 | 2143 |
| 0.0573 | 0.9929 | 1.3339 | 0.7042 | 7.9459106e-08 | 2144 |
| 0.0563 | 0.9953 | 1.3348 | 0.7042 | 7.944207e-08 | 2145 |
| 0.0553 | 0.9929 | 1.3339 | 0.7042 | 7.942502e-08 | 2146 |
| 0.0676 | 0.9812 | 1.3345 | 0.7042 | 7.9407975e-08 | 2147 |
| 0.0609 | 0.9835 | 1.3360 | 0.7042 | 7.939092e-08 | 2148 |
| 0.0688 | 0.9812 | 1.3366 | 0.7042 | 7.937386e-08 | 2149 |
| 0.0672 | 0.9835 | 1.3385 | 0.7042 | 7.93568e-08 | 2150 |
| 0.0607 | 0.9882 | 1.3368 | 0.7113 | 7.9339735e-08 | 2151 |
| 0.0538 | 0.9953 | 1.3372 | 0.7113 | 7.932267e-08 | 2152 |
| 0.0641 | 0.9882 | 1.3347 | 0.7042 | 7.930559e-08 | 2153 |
| 0.0638 | 0.9835 | 1.3338 | 0.7183 | 7.928851e-08 | 2154 |
| 0.0579 | 0.9906 | 1.3341 | 0.7183 | 7.927143e-08 | 2155 |
| 0.0595 | 0.9882 | 1.3339 | 0.7183 | 7.925434e-08 | 2156 |
| 0.0714 | 0.9812 | 1.3342 | 0.7183 | 7.923725e-08 | 2157 |
| 0.0512 | 0.9929 | 1.3373 | 0.7113 | 7.922016e-08 | 2158 |
| 0.0562 | 0.9906 | 1.3392 | 0.7113 | 7.9203055e-08 | 2159 |
| 0.0662 | 0.9906 | 1.3368 | 0.7113 | 7.918595e-08 | 2160 |
| 0.0462 | 0.9976 | 1.3371 | 0.7113 | 7.916884e-08 | 2161 |
| 0.0641 | 0.9812 | 1.3370 | 0.7042 | 7.915173e-08 | 2162 |
| 0.0705 | 0.9906 | 1.3381 | 0.7042 | 7.9134615e-08 | 2163 |
| 0.0548 | 0.9929 | 1.3397 | 0.7042 | 7.911749e-08 | 2164 |
| 0.0559 | 0.9835 | 1.3404 | 0.7113 | 7.910037e-08 | 2165 |
| 0.0635 | 0.9835 | 1.3411 | 0.7113 | 7.9083236e-08 | 2166 |
| 0.0510 | 0.9906 | 1.3402 | 0.7113 | 7.9066105e-08 | 2167 |
| 0.0629 | 0.9835 | 1.3397 | 0.7113 | 7.904897e-08 | 2168 |
| 0.0580 | 0.9929 | 1.3420 | 0.7113 | 7.903182e-08 | 2169 |
| 0.0529 | 0.9929 | 1.3432 | 0.7042 | 7.9014676e-08 | 2170 |
| 0.0585 | 0.9906 | 1.3456 | 0.7113 | 7.899752e-08 | 2171 |
| 0.0650 | 0.9835 | 1.3463 | 0.7113 | 7.898037e-08 | 2172 |
| 0.0547 | 0.9906 | 1.3444 | 0.7042 | 7.896321e-08 | 2173 |
| 0.0546 | 0.9906 | 1.3416 | 0.7042 | 7.894605e-08 | 2174 |
| 0.0577 | 0.9929 | 1.3406 | 0.7183 | 7.8928885e-08 | 2175 |
| 0.0550 | 0.9906 | 1.3422 | 0.7113 | 7.891171e-08 | 2176 |
| 0.0559 | 0.9953 | 1.3447 | 0.7042 | 7.889454e-08 | 2177 |
| 0.0670 | 0.9835 | 1.3443 | 0.7042 | 7.8877356e-08 | 2178 |
| 0.0601 | 0.9906 | 1.3424 | 0.7113 | 7.8860175e-08 | 2179 |
| 0.0573 | 0.9835 | 1.3436 | 0.7042 | 7.884299e-08 | 2180 |
| 0.0521 | 0.9906 | 1.3461 | 0.7042 | 7.882579e-08 | 2181 |
| 0.0600 | 0.9835 | 1.3468 | 0.7042 | 7.88086e-08 | 2182 |
| 0.0748 | 0.9788 | 1.3462 | 0.7042 | 7.8791395e-08 | 2183 |
| 0.0523 | 0.9976 | 1.3450 | 0.7113 | 7.877419e-08 | 2184 |
| 0.0522 | 0.9882 | 1.3444 | 0.7042 | 7.875698e-08 | 2185 |
| 0.0578 | 0.9882 | 1.3476 | 0.7042 | 7.8739774e-08 | 2186 |
| 0.0579 | 0.9953 | 1.3475 | 0.7042 | 7.872256e-08 | 2187 |
| 0.0511 | 0.9929 | 1.3468 | 0.7042 | 7.8705334e-08 | 2188 |
| 0.0578 | 0.9953 | 1.3475 | 0.7113 | 7.868811e-08 | 2189 |
| 0.0639 | 0.9859 | 1.3472 | 0.7113 | 7.867088e-08 | 2190 |
| 0.0540 | 0.9882 | 1.3463 | 0.7042 | 7.865365e-08 | 2191 |
| 0.0509 | 0.9882 | 1.3478 | 0.7042 | 7.863641e-08 | 2192 |
| 0.0534 | 0.9906 | 1.3484 | 0.7113 | 7.861917e-08 | 2193 |
| 0.0694 | 0.9835 | 1.3481 | 0.7113 | 7.860193e-08 | 2194 |
| 0.0606 | 0.9882 | 1.3523 | 0.7113 | 7.858468e-08 | 2195 |
| 0.0502 | 0.9953 | 1.3529 | 0.7113 | 7.8567425e-08 | 2196 |
| 0.0549 | 0.9835 | 1.3533 | 0.7113 | 7.8550165e-08 | 2197 |
| 0.0476 | 0.9953 | 1.3537 | 0.7113 | 7.8532906e-08 | 2198 |
| 0.0604 | 0.9882 | 1.3544 | 0.7113 | 7.851564e-08 | 2199 |
| 0.0593 | 0.9882 | 1.3533 | 0.7042 | 7.8498374e-08 | 2200 |
| 0.0522 | 0.9953 | 1.3541 | 0.7042 | 7.84811e-08 | 2201 |
| 0.0559 | 0.9882 | 1.3519 | 0.7042 | 7.846382e-08 | 2202 |
| 0.0570 | 0.9906 | 1.3507 | 0.7042 | 7.844654e-08 | 2203 |
| 0.0473 | 1.0 | 1.3498 | 0.7042 | 7.842925e-08 | 2204 |
| 0.0541 | 0.9929 | 1.3494 | 0.7042 | 7.8411965e-08 | 2205 |
| 0.0543 | 0.9953 | 1.3493 | 0.6972 | 7.839467e-08 | 2206 |
| 0.0603 | 0.9882 | 1.3477 | 0.7042 | 7.8377376e-08 | 2207 |
| 0.0464 | 0.9929 | 1.3478 | 0.7113 | 7.8360074e-08 | 2208 |
| 0.0518 | 0.9859 | 1.3502 | 0.7113 | 7.8342765e-08 | 2209 |
| 0.0526 | 0.9882 | 1.3520 | 0.7113 | 7.8325456e-08 | 2210 |
| 0.0518 | 0.9906 | 1.3545 | 0.7042 | 7.830814e-08 | 2211 |
| 0.0495 | 0.9882 | 1.3552 | 0.7042 | 7.8290824e-08 | 2212 |
| 0.0514 | 0.9929 | 1.3561 | 0.7042 | 7.82735e-08 | 2213 |
| 0.0484 | 0.9953 | 1.3546 | 0.7042 | 7.825618e-08 | 2214 |
| 0.0538 | 0.9929 | 1.3544 | 0.7042 | 7.823885e-08 | 2215 |
| 0.0515 | 0.9906 | 1.3560 | 0.7042 | 7.822151e-08 | 2216 |
| 0.0540 | 0.9882 | 1.3571 | 0.7042 | 7.8204174e-08 | 2217 |
| 0.0488 | 0.9953 | 1.3586 | 0.7042 | 7.818683e-08 | 2218 |
| 0.0573 | 0.9859 | 1.3571 | 0.7042 | 7.8169485e-08 | 2219 |
| 0.0529 | 0.9906 | 1.3556 | 0.7042 | 7.8152134e-08 | 2220 |
| 0.0570 | 0.9906 | 1.3568 | 0.7113 | 7.813478e-08 | 2221 |
| 0.0598 | 0.9882 | 1.3590 | 0.7113 | 7.8117424e-08 | 2222 |
| 0.0422 | 0.9929 | 1.3608 | 0.7113 | 7.8100065e-08 | 2223 |
| 0.0513 | 0.9906 | 1.3605 | 0.7113 | 7.80827e-08 | 2224 |
| 0.0484 | 0.9976 | 1.3572 | 0.7042 | 7.806533e-08 | 2225 |
| 0.0623 | 0.9859 | 1.3574 | 0.7042 | 7.8047954e-08 | 2226 |
| 0.0551 | 0.9882 | 1.3580 | 0.7042 | 7.8030574e-08 | 2227 |
| 0.0503 | 0.9976 | 1.3593 | 0.7042 | 7.8013194e-08 | 2228 |
| 0.0529 | 0.9929 | 1.3611 | 0.7042 | 7.799581e-08 | 2229 |
| 0.0467 | 0.9929 | 1.3630 | 0.7113 | 7.797842e-08 | 2230 |
| 0.0593 | 0.9906 | 1.3625 | 0.7113 | 7.7961026e-08 | 2231 |
| 0.0585 | 0.9812 | 1.3612 | 0.7042 | 7.794363e-08 | 2232 |
| 0.0516 | 0.9882 | 1.3612 | 0.7113 | 7.792623e-08 | 2233 |
| 0.0543 | 0.9953 | 1.3637 | 0.7113 | 7.790882e-08 | 2234 |
| 0.0474 | 0.9953 | 1.3675 | 0.7042 | 7.7891414e-08 | 2235 |
| 0.0555 | 0.9929 | 1.3666 | 0.7042 | 7.7874e-08 | 2236 |
| 0.0514 | 0.9906 | 1.3662 | 0.7042 | 7.785658e-08 | 2237 |
| 0.0546 | 0.9882 | 1.3652 | 0.7042 | 7.783916e-08 | 2238 |
| 0.0584 | 0.9929 | 1.3642 | 0.7113 | 7.782174e-08 | 2239 |
| 0.0469 | 0.9929 | 1.3636 | 0.7113 | 7.780431e-08 | 2240 |
| 0.0508 | 0.9906 | 1.3669 | 0.7113 | 7.778688e-08 | 2241 |
| 0.0519 | 0.9929 | 1.3674 | 0.7113 | 7.776944e-08 | 2242 |
| 0.0503 | 0.9929 | 1.3689 | 0.7113 | 7.7752006e-08 | 2243 |
| 0.0483 | 0.9953 | 1.3715 | 0.7113 | 7.773456e-08 | 2244 |
| 0.0473 | 0.9953 | 1.3722 | 0.7113 | 7.771711e-08 | 2245 |
| 0.0540 | 0.9906 | 1.3708 | 0.7042 | 7.769966e-08 | 2246 |
| 0.0540 | 0.9929 | 1.3685 | 0.7042 | 7.76822e-08 | 2247 |
| 0.0494 | 0.9953 | 1.3672 | 0.7042 | 7.7664744e-08 | 2248 |
| 0.0490 | 0.9929 | 1.3681 | 0.7042 | 7.764728e-08 | 2249 |
| 0.0544 | 0.9882 | 1.3669 | 0.7113 | 7.7629814e-08 | 2250 |
| 0.0507 | 0.9929 | 1.3658 | 0.7113 | 7.761234e-08 | 2251 |
| 0.0596 | 0.9859 | 1.3644 | 0.6972 | 7.759487e-08 | 2252 |
| 0.0498 | 0.9929 | 1.3634 | 0.7042 | 7.757739e-08 | 2253 |
| 0.0471 | 0.9953 | 1.3654 | 0.6972 | 7.755991e-08 | 2254 |
| 0.0539 | 0.9906 | 1.3651 | 0.6972 | 7.7542424e-08 | 2255 |
| 0.0513 | 0.9882 | 1.3645 | 0.7113 | 7.752494e-08 | 2256 |
| 0.0582 | 0.9859 | 1.3662 | 0.7113 | 7.7507444e-08 | 2257 |
| 0.0417 | 0.9953 | 1.3686 | 0.7113 | 7.748994e-08 | 2258 |
| 0.0502 | 0.9882 | 1.3675 | 0.7042 | 7.747244e-08 | 2259 |
| 0.0526 | 0.9859 | 1.3690 | 0.6972 | 7.7454935e-08 | 2260 |
| 0.0583 | 0.9835 | 1.3704 | 0.7042 | 7.743743e-08 | 2261 |
| 0.0581 | 0.9929 | 1.3704 | 0.7042 | 7.741991e-08 | 2262 |
| 0.0458 | 0.9929 | 1.3715 | 0.7042 | 7.74024e-08 | 2263 |
| 0.0523 | 0.9859 | 1.3736 | 0.7042 | 7.7384875e-08 | 2264 |
| 0.0538 | 0.9929 | 1.3741 | 0.7042 | 7.736735e-08 | 2265 |
| 0.0633 | 0.9788 | 1.3705 | 0.6972 | 7.7349824e-08 | 2266 |
| 0.0626 | 0.9859 | 1.3691 | 0.6972 | 7.7332295e-08 | 2267 |
| 0.0521 | 0.9929 | 1.3705 | 0.6972 | 7.731476e-08 | 2268 |
| 0.0519 | 0.9882 | 1.3732 | 0.6972 | 7.729722e-08 | 2269 |
| 0.0485 | 0.9953 | 1.3742 | 0.7042 | 7.727968e-08 | 2270 |
| 0.0472 | 0.9929 | 1.3732 | 0.7042 | 7.7262136e-08 | 2271 |
| 0.0476 | 0.9953 | 1.3754 | 0.7042 | 7.7244586e-08 | 2272 |
| 0.0464 | 0.9906 | 1.3760 | 0.7042 | 7.7227035e-08 | 2273 |
| 0.0531 | 0.9906 | 1.3728 | 0.7042 | 7.720948e-08 | 2274 |
| 0.0520 | 0.9906 | 1.3718 | 0.6972 | 7.719191e-08 | 2275 |
| 0.0410 | 1.0 | 1.3713 | 0.6972 | 7.717435e-08 | 2276 |
| 0.0593 | 0.9859 | 1.3729 | 0.6972 | 7.715678e-08 | 2277 |
| 0.0533 | 0.9882 | 1.3760 | 0.7042 | 7.7139205e-08 | 2278 |
| 0.0572 | 0.9906 | 1.3765 | 0.7042 | 7.7121626e-08 | 2279 |
| 0.0490 | 0.9929 | 1.3762 | 0.7042 | 7.710405e-08 | 2280 |
| 0.0628 | 0.9812 | 1.3796 | 0.7042 | 7.708646e-08 | 2281 |
| 0.0528 | 0.9929 | 1.3807 | 0.7042 | 7.7068876e-08 | 2282 |
| 0.0521 | 0.9906 | 1.3820 | 0.7042 | 7.705128e-08 | 2283 |
| 0.0432 | 0.9953 | 1.3823 | 0.7042 | 7.703369e-08 | 2284 |
| 0.0514 | 0.9906 | 1.3827 | 0.7042 | 7.701609e-08 | 2285 |
| 0.0542 | 0.9929 | 1.3880 | 0.7042 | 7.699849e-08 | 2286 |
| 0.0509 | 0.9906 | 1.3876 | 0.7042 | 7.698088e-08 | 2287 |
| 0.0492 | 0.9929 | 1.3850 | 0.7042 | 7.6963275e-08 | 2288 |
| 0.0427 | 0.9953 | 1.3844 | 0.7042 | 7.694566e-08 | 2289 |
| 0.0496 | 0.9906 | 1.3854 | 0.7042 | 7.6928046e-08 | 2290 |
| 0.0478 | 0.9929 | 1.3868 | 0.7113 | 7.6910425e-08 | 2291 |
| 0.0484 | 0.9953 | 1.3886 | 0.7113 | 7.68928e-08 | 2292 |
| 0.0492 | 0.9976 | 1.3871 | 0.7113 | 7.6875175e-08 | 2293 |
| 0.0430 | 0.9929 | 1.3844 | 0.7042 | 7.6857546e-08 | 2294 |
| 0.0466 | 0.9906 | 1.3831 | 0.6972 | 7.683991e-08 | 2295 |
| 0.0431 | 0.9882 | 1.3832 | 0.6972 | 7.6822275e-08 | 2296 |
| 0.0508 | 0.9906 | 1.3828 | 0.6972 | 7.680463e-08 | 2297 |
| 0.0465 | 0.9953 | 1.3844 | 0.6972 | 7.678699e-08 | 2298 |
| 0.0510 | 0.9906 | 1.3852 | 0.6972 | 7.676934e-08 | 2299 |
| 0.0623 | 0.9859 | 1.3868 | 0.7042 | 7.675169e-08 | 2300 |
| 0.0503 | 0.9882 | 1.3860 | 0.7113 | 7.673403e-08 | 2301 |
| 0.0420 | 0.9976 | 1.3875 | 0.7113 | 7.6716375e-08 | 2302 |
| 0.0478 | 0.9953 | 1.3875 | 0.7113 | 7.669871e-08 | 2303 |
| 0.0427 | 0.9976 | 1.3880 | 0.7113 | 7.668105e-08 | 2304 |
| 0.0555 | 0.9906 | 1.3861 | 0.6972 | 7.6663376e-08 | 2305 |
| 0.0446 | 0.9953 | 1.3860 | 0.6972 | 7.6645705e-08 | 2306 |
| 0.0447 | 0.9906 | 1.3864 | 0.6972 | 7.662803e-08 | 2307 |
| 0.0599 | 0.9859 | 1.3861 | 0.6972 | 7.661035e-08 | 2308 |
| 0.0502 | 0.9906 | 1.3878 | 0.6972 | 7.659266e-08 | 2309 |
| 0.0386 | 0.9976 | 1.3887 | 0.7113 | 7.657498e-08 | 2310 |
| 0.0453 | 0.9929 | 1.3881 | 0.7113 | 7.6557285e-08 | 2311 |
| 0.0514 | 0.9906 | 1.3902 | 0.7113 | 7.653959e-08 | 2312 |
| 0.0543 | 0.9859 | 1.3923 | 0.7113 | 7.652189e-08 | 2313 |
| 0.0428 | 0.9906 | 1.3903 | 0.7113 | 7.650419e-08 | 2314 |
| 0.0569 | 0.9859 | 1.3908 | 0.7113 | 7.648649e-08 | 2315 |
| 0.0451 | 0.9929 | 1.3923 | 0.7113 | 7.646878e-08 | 2316 |
| 0.0440 | 0.9929 | 1.3906 | 0.7113 | 7.6451066e-08 | 2317 |
| 0.0505 | 0.9859 | 1.3903 | 0.7042 | 7.643335e-08 | 2318 |
| 0.0413 | 0.9882 | 1.3912 | 0.7113 | 7.641563e-08 | 2319 |
| 0.0554 | 0.9906 | 1.3932 | 0.7113 | 7.639791e-08 | 2320 |
| 0.0488 | 0.9976 | 1.3925 | 0.7113 | 7.638018e-08 | 2321 |
| 0.0461 | 0.9906 | 1.3901 | 0.7042 | 7.6362454e-08 | 2322 |
| 0.0535 | 0.9835 | 1.3919 | 0.7113 | 7.634472e-08 | 2323 |
| 0.0502 | 0.9882 | 1.3934 | 0.7113 | 7.6326984e-08 | 2324 |
| 0.0542 | 0.9812 | 1.3912 | 0.7113 | 7.630924e-08 | 2325 |
| 0.0454 | 0.9929 | 1.3928 | 0.7113 | 7.62915e-08 | 2326 |
| 0.0471 | 0.9882 | 1.3932 | 0.7113 | 7.627375e-08 | 2327 |
| 0.0441 | 0.9906 | 1.3928 | 0.7042 | 7.6256e-08 | 2328 |
| 0.0479 | 0.9929 | 1.3915 | 0.7042 | 7.6238244e-08 | 2329 |
| 0.0496 | 0.9835 | 1.3916 | 0.7042 | 7.622049e-08 | 2330 |
| 0.0548 | 0.9882 | 1.3945 | 0.7113 | 7.6202724e-08 | 2331 |
| 0.0441 | 0.9906 | 1.3987 | 0.7113 | 7.618496e-08 | 2332 |
| 0.0526 | 0.9835 | 1.3970 | 0.7113 | 7.616719e-08 | 2333 |
| 0.0496 | 0.9906 | 1.3923 | 0.7042 | 7.614942e-08 | 2334 |
| 0.0392 | 0.9953 | 1.3918 | 0.6972 | 7.613164e-08 | 2335 |
| 0.0454 | 0.9906 | 1.3929 | 0.7042 | 7.6113864e-08 | 2336 |
| 0.0462 | 0.9882 | 1.3938 | 0.7042 | 7.609608e-08 | 2337 |
| 0.0435 | 0.9953 | 1.3937 | 0.7042 | 7.6078294e-08 | 2338 |
| 0.0497 | 0.9906 | 1.3923 | 0.7042 | 7.60605e-08 | 2339 |
| 0.0402 | 0.9976 | 1.3921 | 0.6972 | 7.604271e-08 | 2340 |
| 0.0446 | 0.9953 | 1.3958 | 0.7113 | 7.602491e-08 | 2341 |
| 0.0548 | 0.9859 | 1.4004 | 0.7113 | 7.600711e-08 | 2342 |
| 0.0439 | 0.9953 | 1.4009 | 0.7113 | 7.5989306e-08 | 2343 |
| 0.0493 | 0.9929 | 1.3986 | 0.7113 | 7.59715e-08 | 2344 |
| 0.0466 | 0.9906 | 1.3981 | 0.7113 | 7.5953686e-08 | 2345 |
| 0.0474 | 0.9976 | 1.3965 | 0.7042 | 7.593587e-08 | 2346 |
| 0.0505 | 0.9859 | 1.3971 | 0.7042 | 7.591805e-08 | 2347 |
| 0.0426 | 0.9953 | 1.3992 | 0.7113 | 7.590023e-08 | 2348 |
| 0.0433 | 0.9953 | 1.4004 | 0.7113 | 7.5882404e-08 | 2349 |
| 0.0464 | 0.9976 | 1.4011 | 0.7113 | 7.586458e-08 | 2350 |
| 0.0420 | 0.9906 | 1.4017 | 0.7113 | 7.584674e-08 | 2351 |
| 0.0397 | 0.9953 | 1.3991 | 0.7042 | 7.582891e-08 | 2352 |
| 0.0425 | 0.9953 | 1.3964 | 0.7113 | 7.5811066e-08 | 2353 |
| 0.0587 | 0.9788 | 1.3970 | 0.7042 | 7.5793224e-08 | 2354 |
| 0.0475 | 0.9929 | 1.3989 | 0.7042 | 7.577538e-08 | 2355 |
| 0.0430 | 0.9929 | 1.3995 | 0.7183 | 7.5757534e-08 | 2356 |
| 0.0495 | 0.9882 | 1.4017 | 0.7042 | 7.5739685e-08 | 2357 |
| 0.0375 | 0.9976 | 1.4049 | 0.7042 | 7.572183e-08 | 2358 |
| 0.0443 | 0.9976 | 1.4070 | 0.7113 | 7.570397e-08 | 2359 |
| 0.0410 | 0.9976 | 1.4074 | 0.7113 | 7.568611e-08 | 2360 |
| 0.0384 | 0.9976 | 1.4064 | 0.6972 | 7.566825e-08 | 2361 |
| 0.0479 | 0.9953 | 1.4059 | 0.7042 | 7.565038e-08 | 2362 |
| 0.0491 | 0.9906 | 1.4063 | 0.7042 | 7.563251e-08 | 2363 |
| 0.0483 | 0.9882 | 1.4074 | 0.7113 | 7.561463e-08 | 2364 |
| 0.0356 | 0.9929 | 1.4076 | 0.7113 | 7.559675e-08 | 2365 |
| 0.0391 | 0.9929 | 1.4090 | 0.7042 | 7.557887e-08 | 2366 |
| 0.0472 | 0.9929 | 1.4105 | 0.7113 | 7.556098e-08 | 2367 |
| 0.0425 | 0.9906 | 1.4104 | 0.7042 | 7.554309e-08 | 2368 |
| 0.0535 | 0.9882 | 1.4095 | 0.7042 | 7.55252e-08 | 2369 |
| 0.0409 | 0.9953 | 1.4091 | 0.6972 | 7.55073e-08 | 2370 |
| 0.0457 | 0.9929 | 1.4100 | 0.7042 | 7.54894e-08 | 2371 |
| 0.0487 | 0.9859 | 1.4112 | 0.7042 | 7.5471505e-08 | 2372 |
| 0.0450 | 0.9929 | 1.4109 | 0.7042 | 7.54536e-08 | 2373 |
| 0.0464 | 0.9906 | 1.4094 | 0.6972 | 7.543569e-08 | 2374 |
| 0.0417 | 0.9929 | 1.4091 | 0.7042 | 7.541778e-08 | 2375 |
| 0.0423 | 0.9976 | 1.4093 | 0.7042 | 7.539987e-08 | 2376 |
| 0.0453 | 0.9906 | 1.4116 | 0.7042 | 7.538195e-08 | 2377 |
| 0.0479 | 0.9882 | 1.4164 | 0.7113 | 7.536403e-08 | 2378 |
| 0.0486 | 0.9906 | 1.4164 | 0.7113 | 7.53461e-08 | 2379 |
| 0.0343 | 0.9976 | 1.4162 | 0.7113 | 7.5328174e-08 | 2380 |
| 0.0511 | 0.9859 | 1.4165 | 0.7113 | 7.531024e-08 | 2381 |
| 0.0361 | 0.9953 | 1.4174 | 0.7113 | 7.5292306e-08 | 2382 |
| 0.0437 | 0.9929 | 1.4181 | 0.7113 | 7.5274365e-08 | 2383 |
| 0.0430 | 0.9953 | 1.4166 | 0.7113 | 7.525642e-08 | 2384 |
| 0.0459 | 0.9953 | 1.4175 | 0.7113 | 7.523848e-08 | 2385 |
| 0.0434 | 0.9953 | 1.4197 | 0.7113 | 7.5220534e-08 | 2386 |
| 0.0373 | 0.9953 | 1.4183 | 0.7113 | 7.5202585e-08 | 2387 |
| 0.0412 | 0.9929 | 1.4172 | 0.7113 | 7.518463e-08 | 2388 |
| 0.0620 | 0.9859 | 1.4162 | 0.7113 | 7.5166675e-08 | 2389 |
| 0.0441 | 0.9929 | 1.4185 | 0.7113 | 7.514871e-08 | 2390 |
| 0.0469 | 0.9929 | 1.4209 | 0.7113 | 7.513075e-08 | 2391 |
| 0.0552 | 0.9882 | 1.4205 | 0.7113 | 7.511278e-08 | 2392 |
| 0.0426 | 0.9929 | 1.4175 | 0.7042 | 7.509481e-08 | 2393 |
| 0.0513 | 0.9859 | 1.4156 | 0.7042 | 7.5076834e-08 | 2394 |
| 0.0468 | 0.9929 | 1.4142 | 0.7042 | 7.505886e-08 | 2395 |
| 0.0472 | 0.9882 | 1.4155 | 0.7042 | 7.504088e-08 | 2396 |
| 0.0465 | 0.9929 | 1.4168 | 0.7042 | 7.5022896e-08 | 2397 |
| 0.0402 | 0.9906 | 1.4161 | 0.7042 | 7.500491e-08 | 2398 |
| 0.0371 | 0.9953 | 1.4141 | 0.6972 | 7.498692e-08 | 2399 |
| 0.0425 | 0.9953 | 1.4168 | 0.6972 | 7.496893e-08 | 2400 |
| 0.0594 | 0.9835 | 1.4179 | 0.7042 | 7.495093e-08 | 2401 |
| 0.0439 | 0.9929 | 1.4180 | 0.7042 | 7.4932935e-08 | 2402 |
| 0.0365 | 0.9976 | 1.4180 | 0.7042 | 7.491493e-08 | 2403 |
| 0.0396 | 0.9953 | 1.4185 | 0.7042 | 7.4896924e-08 | 2404 |
| 0.0361 | 0.9976 | 1.4195 | 0.7042 | 7.487892e-08 | 2405 |
| 0.0421 | 0.9953 | 1.4204 | 0.7042 | 7.486091e-08 | 2406 |
| 0.0418 | 0.9906 | 1.4191 | 0.7042 | 7.4842895e-08 | 2407 |
| 0.0471 | 0.9859 | 1.4186 | 0.7042 | 7.4824875e-08 | 2408 |
| 0.0432 | 0.9906 | 1.4182 | 0.7042 | 7.4806856e-08 | 2409 |
| 0.0382 | 0.9953 | 1.4175 | 0.7042 | 7.478883e-08 | 2410 |
| 0.0433 | 0.9906 | 1.4191 | 0.7042 | 7.47708e-08 | 2411 |
| 0.0427 | 0.9929 | 1.4188 | 0.7042 | 7.475277e-08 | 2412 |
| 0.0438 | 0.9929 | 1.4186 | 0.7042 | 7.4734736e-08 | 2413 |
| 0.0594 | 0.9906 | 1.4207 | 0.7042 | 7.47167e-08 | 2414 |
| 0.0418 | 0.9906 | 1.4235 | 0.7042 | 7.469866e-08 | 2415 |
| 0.0402 | 0.9906 | 1.4261 | 0.7113 | 7.468062e-08 | 2416 |
| 0.0396 | 0.9953 | 1.4255 | 0.7113 | 7.466257e-08 | 2417 |
| 0.0449 | 0.9882 | 1.4254 | 0.7042 | 7.4644525e-08 | 2418 |
| 0.0335 | 0.9976 | 1.4243 | 0.7042 | 7.462647e-08 | 2419 |
| 0.0460 | 0.9929 | 1.4234 | 0.6972 | 7.4608415e-08 | 2420 |
| 0.0477 | 0.9906 | 1.4235 | 0.7042 | 7.459036e-08 | 2421 |
| 0.0428 | 0.9882 | 1.4227 | 0.7042 | 7.45723e-08 | 2422 |
| 0.0429 | 0.9953 | 1.4237 | 0.7042 | 7.455424e-08 | 2423 |
| 0.0304 | 1.0 | 1.4240 | 0.7042 | 7.453617e-08 | 2424 |
| 0.0435 | 0.9906 | 1.4211 | 0.7113 | 7.45181e-08 | 2425 |
| 0.0400 | 0.9953 | 1.4213 | 0.7113 | 7.450002e-08 | 2426 |
| 0.0416 | 0.9929 | 1.4235 | 0.7042 | 7.4481946e-08 | 2427 |
| 0.0426 | 0.9953 | 1.4258 | 0.6972 | 7.446386e-08 | 2428 |
| 0.0434 | 0.9953 | 1.4273 | 0.6972 | 7.444578e-08 | 2429 |
| 0.0360 | 0.9929 | 1.4296 | 0.7113 | 7.4427696e-08 | 2430 |
| 0.0391 | 0.9976 | 1.4308 | 0.7113 | 7.4409606e-08 | 2431 |
| 0.0473 | 0.9882 | 1.4342 | 0.7113 | 7.4391515e-08 | 2432 |
| 0.0430 | 0.9929 | 1.4344 | 0.7113 | 7.437342e-08 | 2433 |
| 0.0416 | 0.9929 | 1.4334 | 0.7113 | 7.435532e-08 | 2434 |
| 0.0454 | 0.9882 | 1.4323 | 0.6972 | 7.4337215e-08 | 2435 |
| 0.0358 | 0.9929 | 1.4311 | 0.6972 | 7.431911e-08 | 2436 |
| 0.0472 | 0.9859 | 1.4336 | 0.7113 | 7.4301006e-08 | 2437 |
| 0.0534 | 0.9812 | 1.4365 | 0.7113 | 7.4282895e-08 | 2438 |
| 0.0400 | 0.9929 | 1.4349 | 0.7113 | 7.426478e-08 | 2439 |
| 0.0381 | 0.9953 | 1.4328 | 0.7042 | 7.4246664e-08 | 2440 |
| 0.0298 | 1.0 | 1.4326 | 0.7042 | 7.4228545e-08 | 2441 |
| 0.0431 | 0.9906 | 1.4333 | 0.7042 | 7.4210426e-08 | 2442 |
| 0.0356 | 0.9906 | 1.4348 | 0.7113 | 7.41923e-08 | 2443 |
| 0.0382 | 0.9953 | 1.4344 | 0.7113 | 7.4174174e-08 | 2444 |
| 0.0381 | 0.9929 | 1.4344 | 0.7113 | 7.415604e-08 | 2445 |
| 0.0442 | 0.9953 | 1.4352 | 0.7113 | 7.413791e-08 | 2446 |
| 0.0410 | 0.9906 | 1.4367 | 0.7113 | 7.411977e-08 | 2447 |
| 0.0297 | 0.9976 | 1.4368 | 0.7113 | 7.410163e-08 | 2448 |
| 0.0443 | 0.9906 | 1.4354 | 0.7113 | 7.408349e-08 | 2449 |
| 0.0428 | 0.9906 | 1.4348 | 0.6972 | 7.406534e-08 | 2450 |
| 0.0313 | 1.0 | 1.4360 | 0.6972 | 7.404719e-08 | 2451 |
| 0.0465 | 0.9929 | 1.4382 | 0.7113 | 7.402904e-08 | 2452 |
| 0.0445 | 0.9929 | 1.4378 | 0.7042 | 7.4010885e-08 | 2453 |
| 0.0416 | 0.9953 | 1.4384 | 0.7113 | 7.399273e-08 | 2454 |
| 0.0511 | 0.9882 | 1.4385 | 0.7113 | 7.397457e-08 | 2455 |
| 0.0502 | 0.9882 | 1.4355 | 0.7042 | 7.395641e-08 | 2456 |
| 0.0410 | 0.9953 | 1.4355 | 0.7042 | 7.393824e-08 | 2457 |
| 0.0355 | 0.9976 | 1.4360 | 0.6972 | 7.392007e-08 | 2458 |
| 0.0512 | 0.9906 | 1.4390 | 0.7042 | 7.3901894e-08 | 2459 |
| 0.0485 | 0.9859 | 1.4450 | 0.7113 | 7.388372e-08 | 2460 |
| 0.0341 | 0.9976 | 1.4449 | 0.7113 | 7.386554e-08 | 2461 |
| 0.0361 | 0.9953 | 1.4436 | 0.7113 | 7.384736e-08 | 2462 |
| 0.0376 | 0.9953 | 1.4422 | 0.7113 | 7.382918e-08 | 2463 |
| 0.0373 | 0.9976 | 1.4394 | 0.7042 | 7.381099e-08 | 2464 |
| 0.0475 | 0.9929 | 1.4379 | 0.7042 | 7.37928e-08 | 2465 |
| 0.0419 | 0.9882 | 1.4371 | 0.7042 | 7.377461e-08 | 2466 |
| 0.0346 | 0.9976 | 1.4375 | 0.7042 | 7.375641e-08 | 2467 |
| 0.0406 | 0.9906 | 1.4381 | 0.7042 | 7.3738214e-08 | 2468 |
| 0.0369 | 0.9929 | 1.4391 | 0.7042 | 7.372001e-08 | 2469 |
| 0.0428 | 0.9882 | 1.4401 | 0.7042 | 7.3701806e-08 | 2470 |
| 0.0453 | 0.9906 | 1.4413 | 0.7042 | 7.36836e-08 | 2471 |
| 0.0351 | 0.9929 | 1.4410 | 0.7042 | 7.366539e-08 | 2472 |
| 0.0317 | 1.0 | 1.4413 | 0.7042 | 7.364718e-08 | 2473 |
| 0.0381 | 0.9953 | 1.4419 | 0.7113 | 7.362896e-08 | 2474 |
| 0.0418 | 0.9906 | 1.4394 | 0.7042 | 7.361074e-08 | 2475 |
| 0.0484 | 0.9859 | 1.4397 | 0.7042 | 7.3592524e-08 | 2476 |
| 0.0379 | 0.9906 | 1.4448 | 0.7113 | 7.35743e-08 | 2477 |
| 0.0395 | 0.9929 | 1.4451 | 0.7113 | 7.355607e-08 | 2478 |
| 0.0403 | 0.9929 | 1.4451 | 0.7042 | 7.353784e-08 | 2479 |
| 0.0482 | 0.9906 | 1.4461 | 0.7042 | 7.351961e-08 | 2480 |
| 0.0329 | 0.9976 | 1.4462 | 0.7113 | 7.3501376e-08 | 2481 |
| 0.0506 | 0.9859 | 1.4456 | 0.7042 | 7.3483136e-08 | 2482 |
| 0.0407 | 0.9929 | 1.4476 | 0.7113 | 7.3464896e-08 | 2483 |
| 0.0396 | 0.9953 | 1.4461 | 0.7042 | 7.344665e-08 | 2484 |
| 0.0426 | 0.9929 | 1.4461 | 0.6972 | 7.34284e-08 | 2485 |
| 0.0345 | 0.9929 | 1.4488 | 0.7113 | 7.3410156e-08 | 2486 |
| 0.0525 | 0.9882 | 1.4476 | 0.7113 | 7.33919e-08 | 2487 |
| 0.0413 | 0.9976 | 1.4451 | 0.7042 | 7.337365e-08 | 2488 |
| 0.0347 | 0.9976 | 1.4443 | 0.7042 | 7.335539e-08 | 2489 |
| 0.0362 | 0.9953 | 1.4443 | 0.7042 | 7.333713e-08 | 2490 |
| 0.0395 | 0.9882 | 1.4452 | 0.7042 | 7.3318866e-08 | 2491 |
| 0.0414 | 0.9906 | 1.4454 | 0.7042 | 7.33006e-08 | 2492 |
| 0.0478 | 0.9906 | 1.4461 | 0.7042 | 7.328233e-08 | 2493 |
| 0.0324 | 0.9976 | 1.4459 | 0.7042 | 7.3264054e-08 | 2494 |
| 0.0363 | 0.9953 | 1.4457 | 0.7042 | 7.324578e-08 | 2495 |
| 0.0360 | 0.9976 | 1.4448 | 0.7042 | 7.3227504e-08 | 2496 |
| 0.0369 | 0.9953 | 1.4454 | 0.7042 | 7.320922e-08 | 2497 |
| 0.0352 | 0.9953 | 1.4465 | 0.6972 | 7.319094e-08 | 2498 |
| 0.0428 | 0.9953 | 1.4479 | 0.7042 | 7.317265e-08 | 2499 |
| 0.0317 | 0.9953 | 1.4485 | 0.7113 | 7.315436e-08 | 2500 |
| 0.0337 | 0.9953 | 1.4488 | 0.7113 | 7.313607e-08 | 2501 |
| 0.0383 | 0.9929 | 1.4485 | 0.7042 | 7.3117775e-08 | 2502 |
| 0.0382 | 0.9953 | 1.4500 | 0.7042 | 7.309948e-08 | 2503 |
| 0.0361 | 0.9953 | 1.4529 | 0.7113 | 7.308118e-08 | 2504 |
| 0.0442 | 0.9859 | 1.4520 | 0.6972 | 7.306288e-08 | 2505 |
| 0.0372 | 0.9929 | 1.4497 | 0.6972 | 7.3044575e-08 | 2506 |
| 0.0463 | 0.9882 | 1.4504 | 0.7113 | 7.3026264e-08 | 2507 |
| 0.0315 | 1.0 | 1.4516 | 0.7113 | 7.300795e-08 | 2508 |
| 0.0412 | 0.9906 | 1.4508 | 0.7113 | 7.298964e-08 | 2509 |
| 0.0355 | 0.9976 | 1.4508 | 0.7042 | 7.2971325e-08 | 2510 |
| 0.0392 | 0.9929 | 1.4525 | 0.7113 | 7.295301e-08 | 2511 |
| 0.0420 | 0.9929 | 1.4547 | 0.7113 | 7.293469e-08 | 2512 |
| 0.0423 | 0.9906 | 1.4553 | 0.7113 | 7.2916365e-08 | 2513 |
| 0.0489 | 0.9882 | 1.4544 | 0.7113 | 7.289804e-08 | 2514 |
| 0.0355 | 0.9953 | 1.4535 | 0.7042 | 7.287971e-08 | 2515 |
| 0.0373 | 0.9929 | 1.4516 | 0.7042 | 7.2861376e-08 | 2516 |
| 0.0388 | 0.9929 | 1.4512 | 0.7042 | 7.2843044e-08 | 2517 |
| 0.0311 | 0.9976 | 1.4511 | 0.7042 | 7.2824704e-08 | 2518 |
| 0.0359 | 0.9953 | 1.4517 | 0.7042 | 7.2806365e-08 | 2519 |
| 0.0374 | 0.9929 | 1.4507 | 0.7042 | 7.2788026e-08 | 2520 |
| 0.0403 | 0.9906 | 1.4497 | 0.7042 | 7.276968e-08 | 2521 |
| 0.0358 | 0.9976 | 1.4507 | 0.7042 | 7.2751334e-08 | 2522 |
| 0.0348 | 0.9976 | 1.4500 | 0.7042 | 7.273298e-08 | 2523 |
| 0.0344 | 0.9953 | 1.4512 | 0.7042 | 7.271463e-08 | 2524 |
| 0.0319 | 1.0 | 1.4524 | 0.7042 | 7.2696274e-08 | 2525 |
| 0.0360 | 0.9953 | 1.4515 | 0.7042 | 7.267791e-08 | 2526 |
| 0.0336 | 0.9929 | 1.4507 | 0.7042 | 7.265955e-08 | 2527 |
| 0.0363 | 0.9953 | 1.4505 | 0.7042 | 7.264119e-08 | 2528 |
| 0.0407 | 0.9953 | 1.4518 | 0.7042 | 7.2622825e-08 | 2529 |
| 0.0310 | 0.9976 | 1.4515 | 0.7042 | 7.260446e-08 | 2530 |
| 0.0541 | 0.9859 | 1.4531 | 0.7042 | 7.258608e-08 | 2531 |
| 0.0403 | 0.9953 | 1.4541 | 0.7042 | 7.256771e-08 | 2532 |
| 0.0460 | 0.9859 | 1.4547 | 0.7042 | 7.2549334e-08 | 2533 |
| 0.0460 | 0.9882 | 1.4545 | 0.6972 | 7.253095e-08 | 2534 |
| 0.0342 | 0.9953 | 1.4545 | 0.7042 | 7.251257e-08 | 2535 |
| 0.0423 | 0.9859 | 1.4538 | 0.6972 | 7.249419e-08 | 2536 |
| 0.0391 | 0.9929 | 1.4551 | 0.7042 | 7.24758e-08 | 2537 |
| 0.0340 | 0.9953 | 1.4572 | 0.7042 | 7.245741e-08 | 2538 |
| 0.0318 | 0.9929 | 1.4587 | 0.7042 | 7.243902e-08 | 2539 |
| 0.0367 | 0.9953 | 1.4596 | 0.7042 | 7.2420626e-08 | 2540 |
| 0.0476 | 0.9812 | 1.4581 | 0.6972 | 7.240223e-08 | 2541 |
| 0.0472 | 0.9906 | 1.4591 | 0.7042 | 7.238383e-08 | 2542 |
| 0.0396 | 0.9929 | 1.4582 | 0.7042 | 7.2365424e-08 | 2543 |
| 0.0445 | 0.9882 | 1.4591 | 0.7042 | 7.234702e-08 | 2544 |
| 0.0363 | 0.9929 | 1.4601 | 0.7042 | 7.232861e-08 | 2545 |
| 0.0339 | 0.9953 | 1.4687 | 0.7113 | 7.23102e-08 | 2546 |
| 0.0410 | 0.9929 | 1.4697 | 0.7113 | 7.229179e-08 | 2547 |
| 0.0365 | 0.9929 | 1.4675 | 0.7113 | 7.227337e-08 | 2548 |
| 0.0404 | 0.9929 | 1.4646 | 0.7113 | 7.2254956e-08 | 2549 |
| 0.0424 | 0.9953 | 1.4630 | 0.7042 | 7.223654e-08 | 2550 |
| 0.0389 | 0.9929 | 1.4618 | 0.7042 | 7.2218114e-08 | 2551 |
| 0.0433 | 0.9929 | 1.4594 | 0.7042 | 7.219969e-08 | 2552 |
| 0.0378 | 0.9953 | 1.4582 | 0.6972 | 7.2181265e-08 | 2553 |
| 0.0397 | 0.9906 | 1.4564 | 0.6972 | 7.2162834e-08 | 2554 |
| 0.0359 | 0.9929 | 1.4583 | 0.6972 | 7.21444e-08 | 2555 |
| 0.0311 | 0.9976 | 1.4586 | 0.7042 | 7.2125964e-08 | 2556 |
| 0.0381 | 0.9906 | 1.4574 | 0.6972 | 7.2107525e-08 | 2557 |
| 0.0436 | 0.9882 | 1.4593 | 0.7042 | 7.208909e-08 | 2558 |
| 0.0293 | 1.0 | 1.4607 | 0.7042 | 7.207064e-08 | 2559 |
| 0.0293 | 1.0 | 1.4615 | 0.7042 | 7.2052195e-08 | 2560 |
| 0.0536 | 0.9812 | 1.4608 | 0.7042 | 7.203375e-08 | 2561 |
| 0.0313 | 0.9953 | 1.4609 | 0.6972 | 7.20153e-08 | 2562 |
| 0.0266 | 0.9976 | 1.4617 | 0.6972 | 7.1996844e-08 | 2563 |
| 0.0474 | 0.9859 | 1.4642 | 0.7042 | 7.197839e-08 | 2564 |
| 0.0354 | 1.0 | 1.4656 | 0.7113 | 7.195993e-08 | 2565 |
| 0.0295 | 1.0 | 1.4657 | 0.7042 | 7.194147e-08 | 2566 |
| 0.0414 | 0.9906 | 1.4650 | 0.7042 | 7.192301e-08 | 2567 |
| 0.0360 | 0.9929 | 1.4622 | 0.7042 | 7.1904545e-08 | 2568 |
| 0.0414 | 0.9906 | 1.4636 | 0.7042 | 7.188608e-08 | 2569 |
| 0.0300 | 0.9976 | 1.4664 | 0.7042 | 7.186761e-08 | 2570 |
| 0.0278 | 0.9976 | 1.4645 | 0.7042 | 7.1849136e-08 | 2571 |
| 0.0364 | 0.9929 | 1.4642 | 0.6972 | 7.183066e-08 | 2572 |
| 0.0347 | 0.9929 | 1.4645 | 0.7042 | 7.181219e-08 | 2573 |
| 0.0345 | 0.9953 | 1.4658 | 0.6972 | 7.179371e-08 | 2574 |
| 0.0318 | 0.9976 | 1.4707 | 0.7042 | 7.1775226e-08 | 2575 |
| 0.0375 | 0.9929 | 1.4720 | 0.7113 | 7.1756745e-08 | 2576 |
| 0.0259 | 1.0 | 1.4720 | 0.7113 | 7.1738256e-08 | 2577 |
| 0.0322 | 0.9953 | 1.4685 | 0.7042 | 7.171977e-08 | 2578 |
| 0.0400 | 0.9882 | 1.4688 | 0.7042 | 7.170128e-08 | 2579 |
| 0.0399 | 0.9953 | 1.4727 | 0.7042 | 7.1682784e-08 | 2580 |
| 0.0293 | 0.9976 | 1.4699 | 0.6972 | 7.166429e-08 | 2581 |
| 0.0326 | 0.9929 | 1.4692 | 0.6972 | 7.164579e-08 | 2582 |
| 0.0304 | 0.9976 | 1.4674 | 0.7042 | 7.162729e-08 | 2583 |
| 0.0430 | 0.9953 | 1.4691 | 0.7042 | 7.160879e-08 | 2584 |
| 0.0316 | 1.0 | 1.4718 | 0.7042 | 7.159028e-08 | 2585 |
| 0.0382 | 0.9929 | 1.4703 | 0.6972 | 7.157177e-08 | 2586 |
| 0.0304 | 0.9953 | 1.4711 | 0.7042 | 7.155326e-08 | 2587 |
| 0.0364 | 0.9882 | 1.4720 | 0.7042 | 7.153474e-08 | 2588 |
| 0.0308 | 0.9953 | 1.4786 | 0.7113 | 7.1516226e-08 | 2589 |
| 0.0314 | 0.9976 | 1.4783 | 0.7113 | 7.149771e-08 | 2590 |
| 0.0476 | 0.9906 | 1.4732 | 0.7042 | 7.147919e-08 | 2591 |
| 0.0371 | 0.9929 | 1.4695 | 0.7042 | 7.146067e-08 | 2592 |
| 0.0394 | 0.9929 | 1.4693 | 0.7042 | 7.1442145e-08 | 2593 |
| 0.0372 | 0.9953 | 1.4712 | 0.7042 | 7.142362e-08 | 2594 |
| 0.0383 | 0.9929 | 1.4698 | 0.7042 | 7.140509e-08 | 2595 |
| 0.0450 | 0.9929 | 1.4699 | 0.7042 | 7.138656e-08 | 2596 |
| 0.0350 | 0.9906 | 1.4731 | 0.7042 | 7.136803e-08 | 2597 |
| 0.0320 | 0.9976 | 1.4712 | 0.6972 | 7.134949e-08 | 2598 |
| 0.0299 | 0.9976 | 1.4713 | 0.6972 | 7.133095e-08 | 2599 |
| 0.0350 | 0.9953 | 1.4717 | 0.6972 | 7.1312414e-08 | 2600 |
| 0.0425 | 0.9882 | 1.4753 | 0.7042 | 7.129387e-08 | 2601 |
| 0.0339 | 0.9976 | 1.4776 | 0.7042 | 7.127532e-08 | 2602 |
| 0.0278 | 0.9953 | 1.4765 | 0.6972 | 7.125678e-08 | 2603 |
| 0.0370 | 0.9882 | 1.4761 | 0.6972 | 7.1238226e-08 | 2604 |
| 0.0361 | 0.9882 | 1.4773 | 0.7042 | 7.1219674e-08 | 2605 |
| 0.0396 | 0.9906 | 1.4777 | 0.7042 | 7.120112e-08 | 2606 |
| 0.0335 | 0.9929 | 1.4774 | 0.7042 | 7.118256e-08 | 2607 |
| 0.0364 | 0.9929 | 1.4762 | 0.6972 | 7.1164e-08 | 2608 |
| 0.0459 | 0.9835 | 1.4738 | 0.7042 | 7.114544e-08 | 2609 |
| 0.0397 | 0.9929 | 1.4733 | 0.7042 | 7.112688e-08 | 2610 |
| 0.0291 | 0.9953 | 1.4749 | 0.7042 | 7.110831e-08 | 2611 |
| 0.0322 | 1.0 | 1.4785 | 0.7042 | 7.1089744e-08 | 2612 |
| 0.0362 | 0.9976 | 1.4791 | 0.7042 | 7.107117e-08 | 2613 |
| 0.0329 | 0.9976 | 1.4802 | 0.7042 | 7.10526e-08 | 2614 |
| 0.0303 | 0.9976 | 1.4789 | 0.7042 | 7.103402e-08 | 2615 |
| 0.0328 | 0.9953 | 1.4781 | 0.7042 | 7.101544e-08 | 2616 |
| 0.0288 | 0.9976 | 1.4794 | 0.7042 | 7.099686e-08 | 2617 |
| 0.0348 | 0.9929 | 1.4791 | 0.7042 | 7.097828e-08 | 2618 |
| 0.0442 | 0.9929 | 1.4779 | 0.7042 | 7.095969e-08 | 2619 |
| 0.0284 | 0.9976 | 1.4784 | 0.7042 | 7.0941105e-08 | 2620 |
| 0.0369 | 0.9929 | 1.4800 | 0.7042 | 7.092252e-08 | 2621 |
| 0.0448 | 0.9882 | 1.4790 | 0.7042 | 7.090393e-08 | 2622 |
| 0.0324 | 0.9953 | 1.4775 | 0.7042 | 7.0885335e-08 | 2623 |
| 0.0295 | 0.9929 | 1.4775 | 0.7042 | 7.086674e-08 | 2624 |
| 0.0381 | 0.9882 | 1.4823 | 0.7042 | 7.0848145e-08 | 2625 |
| 0.0356 | 0.9953 | 1.4820 | 0.7042 | 7.082954e-08 | 2626 |
| 0.0337 | 0.9953 | 1.4814 | 0.7042 | 7.081094e-08 | 2627 |
| 0.0354 | 0.9929 | 1.4804 | 0.7042 | 7.079234e-08 | 2628 |
| 0.0354 | 0.9953 | 1.4823 | 0.7113 | 7.077373e-08 | 2629 |
| 0.0270 | 0.9976 | 1.4818 | 0.7113 | 7.075512e-08 | 2630 |
| 0.0291 | 0.9976 | 1.4817 | 0.7113 | 7.073651e-08 | 2631 |
| 0.0383 | 0.9976 | 1.4806 | 0.7042 | 7.0717896e-08 | 2632 |
| 0.0333 | 0.9976 | 1.4808 | 0.7042 | 7.069928e-08 | 2633 |
| 0.0329 | 0.9929 | 1.4775 | 0.7042 | 7.068066e-08 | 2634 |
| 0.0340 | 0.9953 | 1.4751 | 0.7113 | 7.066205e-08 | 2635 |
| 0.0271 | 0.9976 | 1.4767 | 0.7113 | 7.0643424e-08 | 2636 |
| 0.0291 | 0.9953 | 1.4785 | 0.7042 | 7.06248e-08 | 2637 |
| 0.0375 | 0.9953 | 1.4796 | 0.7042 | 7.060618e-08 | 2638 |
| 0.0337 | 0.9929 | 1.4813 | 0.7113 | 7.058755e-08 | 2639 |
| 0.0297 | 0.9953 | 1.4827 | 0.7042 | 7.0568916e-08 | 2640 |
| 0.0281 | 0.9976 | 1.4850 | 0.7042 | 7.0550286e-08 | 2641 |
| 0.0411 | 0.9882 | 1.4833 | 0.7042 | 7.053165e-08 | 2642 |
| 0.0354 | 0.9929 | 1.4831 | 0.6972 | 7.051301e-08 | 2643 |
| 0.0290 | 1.0 | 1.4810 | 0.7113 | 7.049437e-08 | 2644 |
| 0.0342 | 0.9953 | 1.4806 | 0.7113 | 7.0475735e-08 | 2645 |
| 0.0295 | 0.9953 | 1.4830 | 0.7042 | 7.045709e-08 | 2646 |
| 0.0356 | 0.9906 | 1.4837 | 0.7042 | 7.0438446e-08 | 2647 |
| 0.0342 | 0.9929 | 1.4843 | 0.7042 | 7.04198e-08 | 2648 |
| 0.0348 | 0.9953 | 1.4852 | 0.7042 | 7.040115e-08 | 2649 |
| 0.0288 | 0.9953 | 1.4871 | 0.7042 | 7.03825e-08 | 2650 |
| 0.0298 | 0.9976 | 1.4870 | 0.7042 | 7.0363846e-08 | 2651 |
| 0.0284 | 0.9953 | 1.4881 | 0.7042 | 7.034519e-08 | 2652 |
| 0.0364 | 0.9906 | 1.4890 | 0.7042 | 7.032653e-08 | 2653 |
| 0.0296 | 0.9976 | 1.4880 | 0.7042 | 7.030787e-08 | 2654 |
| 0.0319 | 0.9953 | 1.4859 | 0.7042 | 7.028921e-08 | 2655 |
| 0.0428 | 0.9882 | 1.4869 | 0.7042 | 7.0270545e-08 | 2656 |
| 0.0423 | 0.9859 | 1.4916 | 0.7042 | 7.025188e-08 | 2657 |
| 0.0365 | 0.9882 | 1.4941 | 0.7042 | 7.023321e-08 | 2658 |
| 0.0416 | 0.9835 | 1.4920 | 0.7042 | 7.021454e-08 | 2659 |
| 0.0269 | 1.0 | 1.4917 | 0.7042 | 7.019587e-08 | 2660 |
| 0.0330 | 0.9906 | 1.4945 | 0.7042 | 7.0177194e-08 | 2661 |
| 0.0268 | 1.0 | 1.4948 | 0.7042 | 7.015852e-08 | 2662 |
| 0.0371 | 0.9906 | 1.4948 | 0.7042 | 7.013984e-08 | 2663 |
| 0.0377 | 0.9929 | 1.4947 | 0.7042 | 7.012116e-08 | 2664 |
| 0.0334 | 0.9953 | 1.4939 | 0.7113 | 7.010248e-08 | 2665 |
| 0.0369 | 0.9929 | 1.4914 | 0.7042 | 7.008379e-08 | 2666 |
| 0.0444 | 0.9929 | 1.4921 | 0.7042 | 7.0065106e-08 | 2667 |
| 0.0460 | 0.9859 | 1.4876 | 0.7042 | 7.004642e-08 | 2668 |
| 0.0272 | 0.9953 | 1.4876 | 0.7042 | 7.002773e-08 | 2669 |
| 0.0371 | 0.9929 | 1.4904 | 0.7042 | 7.000904e-08 | 2670 |
| 0.0291 | 1.0 | 1.4919 | 0.7042 | 6.999034e-08 | 2671 |
| 0.0340 | 0.9953 | 1.4948 | 0.7042 | 6.997165e-08 | 2672 |
| 0.0292 | 1.0 | 1.4970 | 0.7042 | 6.995295e-08 | 2673 |
| 0.0383 | 0.9906 | 1.4995 | 0.7113 | 6.9934245e-08 | 2674 |
| 0.0294 | 1.0 | 1.4985 | 0.7042 | 6.9915544e-08 | 2675 |
| 0.0255 | 1.0 | 1.4988 | 0.7042 | 6.989684e-08 | 2676 |
| 0.0286 | 0.9953 | 1.4980 | 0.7042 | 6.987813e-08 | 2677 |
| 0.0345 | 0.9906 | 1.4977 | 0.7042 | 6.9859425e-08 | 2678 |
| 0.0271 | 0.9976 | 1.4986 | 0.7042 | 6.9840716e-08 | 2679 |
| 0.0414 | 0.9882 | 1.4968 | 0.7042 | 6.9822e-08 | 2680 |
| 0.0371 | 0.9929 | 1.4951 | 0.7042 | 6.9803285e-08 | 2681 |
| 0.0371 | 0.9929 | 1.4915 | 0.7042 | 6.978457e-08 | 2682 |
| 0.0312 | 0.9953 | 1.4902 | 0.7042 | 6.976585e-08 | 2683 |
| 0.0289 | 1.0 | 1.4906 | 0.7042 | 6.974713e-08 | 2684 |
| 0.0282 | 0.9953 | 1.4924 | 0.7042 | 6.972841e-08 | 2685 |
| 0.0318 | 0.9953 | 1.4939 | 0.7042 | 6.9709685e-08 | 2686 |
| 0.0222 | 1.0 | 1.4928 | 0.7042 | 6.969096e-08 | 2687 |
| 0.0368 | 0.9859 | 1.4925 | 0.7042 | 6.967223e-08 | 2688 |
| 0.0376 | 0.9882 | 1.4934 | 0.7042 | 6.96535e-08 | 2689 |
| 0.0285 | 0.9976 | 1.4886 | 0.7113 | 6.963477e-08 | 2690 |
| 0.0327 | 0.9906 | 1.4889 | 0.7113 | 6.9616036e-08 | 2691 |
| 0.0262 | 0.9976 | 1.4907 | 0.7113 | 6.95973e-08 | 2692 |
| 0.0298 | 0.9953 | 1.4937 | 0.7113 | 6.957856e-08 | 2693 |
| 0.0406 | 0.9929 | 1.4981 | 0.7042 | 6.9559825e-08 | 2694 |
| 0.0461 | 0.9929 | 1.4967 | 0.7113 | 6.954108e-08 | 2695 |
| 0.0305 | 0.9953 | 1.4969 | 0.7042 | 6.9522336e-08 | 2696 |
| 0.0382 | 0.9953 | 1.4962 | 0.7042 | 6.950359e-08 | 2697 |
| 0.0298 | 0.9929 | 1.4962 | 0.7042 | 6.948485e-08 | 2698 |
| 0.0304 | 0.9976 | 1.4998 | 0.7042 | 6.94661e-08 | 2699 |
| 0.0289 | 0.9976 | 1.4997 | 0.7042 | 6.9447346e-08 | 2700 |
| 0.0356 | 0.9906 | 1.4992 | 0.7042 | 6.9428594e-08 | 2701 |
| 0.0264 | 0.9976 | 1.4993 | 0.7042 | 6.940984e-08 | 2702 |
| 0.0272 | 0.9976 | 1.4992 | 0.7042 | 6.9391085e-08 | 2703 |
| 0.0299 | 0.9953 | 1.4979 | 0.7042 | 6.937233e-08 | 2704 |
| 0.0312 | 0.9976 | 1.4967 | 0.7113 | 6.935357e-08 | 2705 |
| 0.0280 | 0.9953 | 1.4971 | 0.7113 | 6.93348e-08 | 2706 |
| 0.0282 | 0.9953 | 1.4998 | 0.7113 | 6.931604e-08 | 2707 |
| 0.0307 | 0.9953 | 1.4990 | 0.7113 | 6.929727e-08 | 2708 |
| 0.0291 | 0.9929 | 1.5012 | 0.7113 | 6.927851e-08 | 2709 |
| 0.0283 | 0.9976 | 1.5018 | 0.7113 | 6.9259734e-08 | 2710 |
| 0.0400 | 0.9882 | 1.5010 | 0.7113 | 6.924096e-08 | 2711 |
| 0.0298 | 0.9953 | 1.5007 | 0.7042 | 6.922219e-08 | 2712 |
| 0.0246 | 1.0 | 1.5021 | 0.7042 | 6.9203416e-08 | 2713 |
| 0.0317 | 0.9953 | 1.5030 | 0.7042 | 6.918464e-08 | 2714 |
| 0.0337 | 0.9953 | 1.5037 | 0.7042 | 6.916586e-08 | 2715 |
| 0.0373 | 0.9929 | 1.5027 | 0.7042 | 6.914708e-08 | 2716 |
| 0.0273 | 0.9976 | 1.5050 | 0.7042 | 6.91283e-08 | 2717 |
| 0.0372 | 0.9906 | 1.5090 | 0.7042 | 6.910951e-08 | 2718 |
| 0.0292 | 0.9976 | 1.5107 | 0.7042 | 6.9090724e-08 | 2719 |
| 0.0275 | 1.0 | 1.5095 | 0.7042 | 6.907194e-08 | 2720 |
| 0.0238 | 0.9976 | 1.5095 | 0.7042 | 6.905315e-08 | 2721 |
| 0.0225 | 1.0 | 1.5093 | 0.7042 | 6.903436e-08 | 2722 |
| 0.0264 | 0.9953 | 1.5085 | 0.7042 | 6.901556e-08 | 2723 |
| 0.0288 | 0.9953 | 1.5077 | 0.7042 | 6.899677e-08 | 2724 |
| 0.0350 | 0.9929 | 1.5119 | 0.7042 | 6.8977975e-08 | 2725 |
| 0.0354 | 0.9906 | 1.5117 | 0.7042 | 6.8959174e-08 | 2726 |
| 0.0218 | 1.0 | 1.5104 | 0.7042 | 6.894037e-08 | 2727 |
| 0.0285 | 0.9953 | 1.5088 | 0.7042 | 6.892157e-08 | 2728 |
| 0.0286 | 0.9953 | 1.5082 | 0.7042 | 6.890277e-08 | 2729 |
| 0.0323 | 0.9929 | 1.5104 | 0.7042 | 6.888396e-08 | 2730 |
| 0.0259 | 0.9976 | 1.5126 | 0.7042 | 6.8865155e-08 | 2731 |
| 0.0232 | 1.0 | 1.5153 | 0.7042 | 6.884635e-08 | 2732 |
| 0.0253 | 0.9976 | 1.5143 | 0.7042 | 6.882754e-08 | 2733 |
| 0.0278 | 0.9953 | 1.5109 | 0.7042 | 6.8808724e-08 | 2734 |
| 0.0470 | 0.9882 | 1.5076 | 0.7042 | 6.878991e-08 | 2735 |
| 0.0350 | 0.9953 | 1.5092 | 0.7042 | 6.8771094e-08 | 2736 |
| 0.0325 | 0.9953 | 1.5088 | 0.7042 | 6.875228e-08 | 2737 |
| 0.0239 | 1.0 | 1.5068 | 0.7042 | 6.8733456e-08 | 2738 |
| 0.0340 | 0.9929 | 1.5053 | 0.7113 | 6.8714634e-08 | 2739 |
| 0.0266 | 0.9976 | 1.5057 | 0.7113 | 6.869581e-08 | 2740 |
| 0.0287 | 0.9929 | 1.5069 | 0.7042 | 6.867699e-08 | 2741 |
| 0.0351 | 0.9929 | 1.5092 | 0.7042 | 6.865816e-08 | 2742 |
| 0.0309 | 0.9929 | 1.5101 | 0.7113 | 6.863933e-08 | 2743 |
| 0.0284 | 0.9929 | 1.5136 | 0.7042 | 6.86205e-08 | 2744 |
| 0.0222 | 1.0 | 1.5161 | 0.7042 | 6.860167e-08 | 2745 |
| 0.0229 | 0.9976 | 1.5154 | 0.7042 | 6.858284e-08 | 2746 |
| 0.0288 | 0.9976 | 1.5156 | 0.7042 | 6.8564006e-08 | 2747 |
| 0.0388 | 0.9882 | 1.5170 | 0.7042 | 6.854517e-08 | 2748 |
| 0.0320 | 0.9976 | 1.5173 | 0.7042 | 6.852633e-08 | 2749 |
| 0.0332 | 0.9929 | 1.5174 | 0.7042 | 6.85075e-08 | 2750 |
| 0.0387 | 0.9882 | 1.5183 | 0.7042 | 6.848865e-08 | 2751 |
| 0.0342 | 0.9953 | 1.5193 | 0.7042 | 6.846981e-08 | 2752 |
| 0.0465 | 0.9882 | 1.5215 | 0.7042 | 6.8450966e-08 | 2753 |
| 0.0238 | 1.0 | 1.5241 | 0.6972 | 6.843212e-08 | 2754 |
| 0.0328 | 0.9953 | 1.5258 | 0.6972 | 6.841327e-08 | 2755 |
| 0.0316 | 0.9929 | 1.5235 | 0.7042 | 6.839442e-08 | 2756 |
| 0.0315 | 0.9906 | 1.5230 | 0.7042 | 6.837557e-08 | 2757 |
| 0.0267 | 0.9976 | 1.5221 | 0.7042 | 6.835672e-08 | 2758 |
| 0.0330 | 0.9929 | 1.5201 | 0.7042 | 6.833786e-08 | 2759 |
| 0.0232 | 0.9953 | 1.5179 | 0.6972 | 6.8319004e-08 | 2760 |
| 0.0304 | 0.9929 | 1.5186 | 0.6972 | 6.8300146e-08 | 2761 |
| 0.0274 | 0.9953 | 1.5196 | 0.7042 | 6.828129e-08 | 2762 |
| 0.0290 | 0.9976 | 1.5225 | 0.7042 | 6.826243e-08 | 2763 |
| 0.0271 | 0.9953 | 1.5199 | 0.7042 | 6.8243565e-08 | 2764 |
| 0.0227 | 0.9976 | 1.5190 | 0.7042 | 6.82247e-08 | 2765 |
| 0.0297 | 0.9953 | 1.5210 | 0.7042 | 6.8205836e-08 | 2766 |
| 0.0331 | 0.9929 | 1.5224 | 0.7042 | 6.818697e-08 | 2767 |
| 0.0269 | 1.0 | 1.5210 | 0.7042 | 6.81681e-08 | 2768 |
| 0.0247 | 0.9976 | 1.5213 | 0.7042 | 6.814923e-08 | 2769 |
| 0.0222 | 1.0 | 1.5227 | 0.7042 | 6.8130355e-08 | 2770 |
| 0.0219 | 1.0 | 1.5231 | 0.7042 | 6.811148e-08 | 2771 |
| 0.0451 | 0.9882 | 1.5247 | 0.7042 | 6.809261e-08 | 2772 |
| 0.0298 | 0.9929 | 1.5262 | 0.6972 | 6.807373e-08 | 2773 |
| 0.0319 | 0.9906 | 1.5273 | 0.6972 | 6.805485e-08 | 2774 |
| 0.0335 | 0.9953 | 1.5282 | 0.6972 | 6.803597e-08 | 2775 |
| 0.0253 | 0.9976 | 1.5257 | 0.6972 | 6.8017094e-08 | 2776 |
| 0.0318 | 0.9953 | 1.5245 | 0.6972 | 6.799821e-08 | 2777 |
| 0.0220 | 1.0 | 1.5257 | 0.6972 | 6.797932e-08 | 2778 |
| 0.0331 | 0.9953 | 1.5289 | 0.7042 | 6.7960436e-08 | 2779 |
| 0.0316 | 0.9929 | 1.5282 | 0.7042 | 6.794155e-08 | 2780 |
| 0.0287 | 0.9953 | 1.5259 | 0.7042 | 6.792266e-08 | 2781 |
| 0.0301 | 0.9929 | 1.5272 | 0.7042 | 6.790377e-08 | 2782 |
| 0.0239 | 0.9976 | 1.5266 | 0.7042 | 6.7884876e-08 | 2783 |
| 0.0297 | 0.9953 | 1.5273 | 0.6972 | 6.786598e-08 | 2784 |
| 0.0290 | 0.9929 | 1.5283 | 0.6972 | 6.784709e-08 | 2785 |
| 0.0464 | 0.9812 | 1.5314 | 0.7042 | 6.782819e-08 | 2786 |
| 0.0281 | 0.9929 | 1.5319 | 0.7042 | 6.780929e-08 | 2787 |
| 0.0231 | 0.9976 | 1.5294 | 0.7042 | 6.779039e-08 | 2788 |
| 0.0302 | 0.9976 | 1.5278 | 0.7042 | 6.777149e-08 | 2789 |
| 0.0228 | 1.0 | 1.5277 | 0.7042 | 6.775259e-08 | 2790 |
| 0.0233 | 0.9953 | 1.5292 | 0.7042 | 6.773368e-08 | 2791 |
| 0.0305 | 0.9976 | 1.5300 | 0.7042 | 6.771477e-08 | 2792 |
| 0.0199 | 1.0 | 1.5283 | 0.7042 | 6.7695865e-08 | 2793 |
| 0.0301 | 0.9976 | 1.5281 | 0.7042 | 6.767696e-08 | 2794 |
| 0.0224 | 0.9976 | 1.5279 | 0.7042 | 6.765805e-08 | 2795 |
| 0.0271 | 0.9976 | 1.5290 | 0.7042 | 6.7639135e-08 | 2796 |
| 0.0244 | 0.9976 | 1.5322 | 0.7042 | 6.762022e-08 | 2797 |
| 0.0227 | 1.0 | 1.5322 | 0.7042 | 6.7601306e-08 | 2798 |
| 0.0294 | 0.9976 | 1.5291 | 0.7042 | 6.758239e-08 | 2799 |
| 0.0298 | 0.9906 | 1.5262 | 0.7042 | 6.756348e-08 | 2800 |
| 0.0272 | 0.9953 | 1.5263 | 0.7042 | 6.7544555e-08 | 2801 |
| 0.0237 | 0.9976 | 1.5257 | 0.7042 | 6.752563e-08 | 2802 |
| 0.0261 | 0.9976 | 1.5243 | 0.7042 | 6.750671e-08 | 2803 |
| 0.0311 | 0.9976 | 1.5248 | 0.7042 | 6.748779e-08 | 2804 |
| 0.0305 | 0.9953 | 1.5240 | 0.6972 | 6.746887e-08 | 2805 |
| 0.0268 | 0.9976 | 1.5263 | 0.7113 | 6.744994e-08 | 2806 |
| 0.0284 | 0.9929 | 1.5289 | 0.6972 | 6.743101e-08 | 2807 |
| 0.0324 | 0.9929 | 1.5298 | 0.6972 | 6.741208e-08 | 2808 |
| 0.0228 | 1.0 | 1.5306 | 0.6972 | 6.739315e-08 | 2809 |
| 0.0386 | 0.9906 | 1.5334 | 0.7042 | 6.737422e-08 | 2810 |
| 0.0401 | 0.9882 | 1.5346 | 0.7042 | 6.735529e-08 | 2811 |
| 0.0219 | 1.0 | 1.5354 | 0.7042 | 6.733635e-08 | 2812 |
| 0.0318 | 0.9929 | 1.5357 | 0.7042 | 6.7317416e-08 | 2813 |
| 0.0391 | 0.9859 | 1.5395 | 0.7042 | 6.729848e-08 | 2814 |
| 0.0250 | 0.9976 | 1.5395 | 0.7042 | 6.7279544e-08 | 2815 |
| 0.0289 | 0.9929 | 1.5376 | 0.7042 | 6.72606e-08 | 2816 |
| 0.0210 | 1.0 | 1.5386 | 0.7042 | 6.724166e-08 | 2817 |
| 0.0231 | 0.9976 | 1.5392 | 0.7042 | 6.7222715e-08 | 2818 |
| 0.0263 | 0.9929 | 1.5364 | 0.7042 | 6.720377e-08 | 2819 |
| 0.0318 | 0.9976 | 1.5336 | 0.7042 | 6.718483e-08 | 2820 |
| 0.0333 | 0.9953 | 1.5309 | 0.7042 | 6.716588e-08 | 2821 |
| 0.0225 | 1.0 | 1.5313 | 0.7042 | 6.714693e-08 | 2822 |
| 0.0318 | 0.9929 | 1.5315 | 0.7042 | 6.712798e-08 | 2823 |
| 0.0262 | 0.9953 | 1.5291 | 0.7042 | 6.710903e-08 | 2824 |
| 0.0226 | 0.9976 | 1.5294 | 0.7042 | 6.709008e-08 | 2825 |
| 0.0287 | 0.9953 | 1.5344 | 0.7042 | 6.707112e-08 | 2826 |
| 0.0297 | 0.9929 | 1.5354 | 0.7042 | 6.705216e-08 | 2827 |
| 0.0173 | 1.0 | 1.5344 | 0.7042 | 6.7033206e-08 | 2828 |
| 0.0239 | 0.9976 | 1.5343 | 0.7042 | 6.701425e-08 | 2829 |
| 0.0335 | 0.9906 | 1.5365 | 0.7042 | 6.699529e-08 | 2830 |
| 0.0332 | 0.9929 | 1.5391 | 0.7042 | 6.697633e-08 | 2831 |
| 0.0260 | 0.9953 | 1.5386 | 0.7042 | 6.695736e-08 | 2832 |
| 0.0242 | 0.9953 | 1.5355 | 0.7042 | 6.69384e-08 | 2833 |
| 0.0247 | 0.9953 | 1.5344 | 0.7042 | 6.691943e-08 | 2834 |
| 0.0217 | 0.9953 | 1.5335 | 0.7042 | 6.690047e-08 | 2835 |
| 0.0271 | 0.9953 | 1.5339 | 0.7042 | 6.6881505e-08 | 2836 |
| 0.0227 | 0.9976 | 1.5343 | 0.7042 | 6.686253e-08 | 2837 |
| 0.0210 | 1.0 | 1.5352 | 0.7042 | 6.684356e-08 | 2838 |
| 0.0206 | 1.0 | 1.5355 | 0.7042 | 6.682459e-08 | 2839 |
| 0.0260 | 0.9953 | 1.5354 | 0.7042 | 6.680562e-08 | 2840 |
| 0.0359 | 0.9859 | 1.5371 | 0.7042 | 6.678665e-08 | 2841 |
| 0.0285 | 0.9953 | 1.5392 | 0.7042 | 6.676767e-08 | 2842 |
| 0.0225 | 0.9976 | 1.5407 | 0.7042 | 6.674869e-08 | 2843 |
| 0.0271 | 1.0 | 1.5383 | 0.7042 | 6.672971e-08 | 2844 |
| 0.0219 | 1.0 | 1.5361 | 0.7042 | 6.671073e-08 | 2845 |
| 0.0262 | 0.9953 | 1.5358 | 0.7042 | 6.6691754e-08 | 2846 |
| 0.0221 | 1.0 | 1.5353 | 0.7042 | 6.6672776e-08 | 2847 |
| 0.0244 | 0.9976 | 1.5355 | 0.7042 | 6.665379e-08 | 2848 |
| 0.0271 | 0.9929 | 1.5369 | 0.7042 | 6.6634804e-08 | 2849 |
| 0.0255 | 0.9976 | 1.5378 | 0.7042 | 6.661582e-08 | 2850 |
| 0.0260 | 0.9953 | 1.5374 | 0.7042 | 6.659683e-08 | 2851 |
| 0.0225 | 1.0 | 1.5390 | 0.7042 | 6.657785e-08 | 2852 |
| 0.0293 | 0.9929 | 1.5385 | 0.7042 | 6.655886e-08 | 2853 |
| 0.0195 | 1.0 | 1.5399 | 0.6972 | 6.653987e-08 | 2854 |
| 0.0277 | 0.9953 | 1.5421 | 0.6972 | 6.6520876e-08 | 2855 |
| 0.0228 | 0.9976 | 1.5421 | 0.6972 | 6.650188e-08 | 2856 |
| 0.0254 | 0.9976 | 1.5421 | 0.7042 | 6.648289e-08 | 2857 |
| 0.0228 | 0.9976 | 1.5420 | 0.7042 | 6.64639e-08 | 2858 |
| 0.0328 | 0.9906 | 1.5433 | 0.7042 | 6.64449e-08 | 2859 |
| 0.0263 | 0.9953 | 1.5458 | 0.7042 | 6.64259e-08 | 2860 |
| 0.0337 | 0.9953 | 1.5457 | 0.7042 | 6.64069e-08 | 2861 |
| 0.0334 | 0.9929 | 1.5441 | 0.7042 | 6.63879e-08 | 2862 |
| 0.0239 | 1.0 | 1.5414 | 0.7042 | 6.63689e-08 | 2863 |
| 0.0255 | 0.9953 | 1.5408 | 0.7042 | 6.63499e-08 | 2864 |
| 0.0324 | 0.9953 | 1.5414 | 0.7042 | 6.633089e-08 | 2865 |
| 0.0290 | 0.9906 | 1.5408 | 0.7042 | 6.6311884e-08 | 2866 |
| 0.0275 | 0.9906 | 1.5397 | 0.7042 | 6.629288e-08 | 2867 |
| 0.0203 | 1.0 | 1.5384 | 0.7042 | 6.627387e-08 | 2868 |
| 0.0269 | 0.9953 | 1.5389 | 0.7042 | 6.625486e-08 | 2869 |
| 0.0226 | 1.0 | 1.5399 | 0.7042 | 6.6235856e-08 | 2870 |
| 0.0283 | 0.9882 | 1.5416 | 0.7042 | 6.621684e-08 | 2871 |
| 0.0222 | 1.0 | 1.5446 | 0.7042 | 6.619783e-08 | 2872 |
| 0.0285 | 0.9953 | 1.5438 | 0.7042 | 6.617881e-08 | 2873 |
| 0.0297 | 0.9953 | 1.5454 | 0.7042 | 6.61598e-08 | 2874 |
| 0.0216 | 0.9976 | 1.5473 | 0.7042 | 6.6140785e-08 | 2875 |
| 0.0228 | 0.9976 | 1.5481 | 0.7042 | 6.612177e-08 | 2876 |
| 0.0309 | 0.9929 | 1.5479 | 0.7042 | 6.610275e-08 | 2877 |
| 0.0295 | 0.9906 | 1.5439 | 0.7113 | 6.608373e-08 | 2878 |
| 0.0323 | 0.9906 | 1.5386 | 0.7113 | 6.606471e-08 | 2879 |
| 0.0212 | 0.9976 | 1.5400 | 0.7113 | 6.6045686e-08 | 2880 |
| 0.0277 | 0.9953 | 1.5424 | 0.7113 | 6.6026665e-08 | 2881 |
| 0.0291 | 0.9976 | 1.5455 | 0.7042 | 6.6007644e-08 | 2882 |
| 0.0231 | 0.9953 | 1.5454 | 0.7042 | 6.598862e-08 | 2883 |
| 0.0235 | 1.0 | 1.5451 | 0.7042 | 6.5969594e-08 | 2884 |
| 0.0354 | 0.9882 | 1.5456 | 0.7042 | 6.5950566e-08 | 2885 |
| 0.0261 | 0.9953 | 1.5468 | 0.7042 | 6.593154e-08 | 2886 |
| 0.0270 | 0.9976 | 1.5461 | 0.7042 | 6.591251e-08 | 2887 |
| 0.0289 | 0.9906 | 1.5445 | 0.7042 | 6.589348e-08 | 2888 |
| 0.0285 | 0.9929 | 1.5447 | 0.7042 | 6.587445e-08 | 2889 |
| 0.0209 | 0.9976 | 1.5444 | 0.7042 | 6.585542e-08 | 2890 |
| 0.0279 | 0.9929 | 1.5441 | 0.7042 | 6.583638e-08 | 2891 |
| 0.0227 | 1.0 | 1.5459 | 0.7042 | 6.5817346e-08 | 2892 |
| 0.0293 | 0.9976 | 1.5454 | 0.7113 | 6.579831e-08 | 2893 |
| 0.0390 | 0.9929 | 1.5466 | 0.7113 | 6.5779275e-08 | 2894 |
| 0.0247 | 0.9976 | 1.5494 | 0.7042 | 6.576024e-08 | 2895 |
| 0.0245 | 0.9953 | 1.5504 | 0.7042 | 6.5741204e-08 | 2896 |
| 0.0266 | 0.9953 | 1.5526 | 0.7042 | 6.572216e-08 | 2897 |
| 0.0252 | 0.9976 | 1.5532 | 0.7042 | 6.570312e-08 | 2898 |
| 0.0292 | 0.9976 | 1.5518 | 0.7042 | 6.568408e-08 | 2899 |
| 0.0236 | 0.9976 | 1.5521 | 0.7042 | 6.5665034e-08 | 2900 |
| 0.0257 | 0.9929 | 1.5531 | 0.7042 | 6.564599e-08 | 2901 |
| 0.0219 | 1.0 | 1.5523 | 0.7042 | 6.562695e-08 | 2902 |
| 0.0242 | 0.9976 | 1.5499 | 0.7113 | 6.560791e-08 | 2903 |
| 0.0219 | 0.9953 | 1.5490 | 0.7042 | 6.558886e-08 | 2904 |
| 0.0259 | 0.9976 | 1.5521 | 0.7042 | 6.556981e-08 | 2905 |
| 0.0233 | 0.9953 | 1.5514 | 0.7042 | 6.555076e-08 | 2906 |
| 0.0256 | 0.9929 | 1.5529 | 0.7042 | 6.553171e-08 | 2907 |
| 0.0234 | 0.9976 | 1.5540 | 0.7042 | 6.551266e-08 | 2908 |
| 0.0275 | 0.9953 | 1.5549 | 0.7042 | 6.549361e-08 | 2909 |
| 0.0261 | 0.9953 | 1.5542 | 0.7042 | 6.547456e-08 | 2910 |
| 0.0200 | 1.0 | 1.5542 | 0.7042 | 6.54555e-08 | 2911 |
| 0.0309 | 0.9929 | 1.5504 | 0.7042 | 6.5436446e-08 | 2912 |
| 0.0231 | 0.9929 | 1.5485 | 0.7042 | 6.541739e-08 | 2913 |
| 0.0209 | 1.0 | 1.5486 | 0.7042 | 6.539833e-08 | 2914 |
| 0.0193 | 1.0 | 1.5482 | 0.7042 | 6.5379275e-08 | 2915 |
| 0.0204 | 1.0 | 1.5492 | 0.7042 | 6.536022e-08 | 2916 |
| 0.0294 | 0.9929 | 1.5508 | 0.7042 | 6.534116e-08 | 2917 |
| 0.0212 | 0.9976 | 1.5510 | 0.7042 | 6.53221e-08 | 2918 |
| 0.0275 | 0.9929 | 1.5523 | 0.7042 | 6.5303034e-08 | 2919 |
| 0.0255 | 0.9953 | 1.5501 | 0.7042 | 6.528397e-08 | 2920 |
| 0.0262 | 0.9929 | 1.5493 | 0.7042 | 6.5264906e-08 | 2921 |
| 0.0227 | 0.9953 | 1.5474 | 0.7113 | 6.524584e-08 | 2922 |
| 0.0295 | 0.9906 | 1.5479 | 0.7113 | 6.522678e-08 | 2923 |
| 0.0254 | 1.0 | 1.5471 | 0.7183 | 6.5207715e-08 | 2924 |
| 0.0259 | 0.9976 | 1.5492 | 0.7113 | 6.5188644e-08 | 2925 |
| 0.0265 | 0.9953 | 1.5547 | 0.7042 | 6.516957e-08 | 2926 |
| 0.0328 | 0.9929 | 1.5575 | 0.7042 | 6.51505e-08 | 2927 |
| 0.0240 | 0.9953 | 1.5583 | 0.7042 | 6.513143e-08 | 2928 |
| 0.0280 | 0.9953 | 1.5587 | 0.7042 | 6.511236e-08 | 2929 |
| 0.0216 | 0.9976 | 1.5574 | 0.7042 | 6.509329e-08 | 2930 |
| 0.0301 | 0.9953 | 1.5566 | 0.6972 | 6.507422e-08 | 2931 |
| 0.0285 | 0.9906 | 1.5564 | 0.7042 | 6.505515e-08 | 2932 |
| 0.0204 | 1.0 | 1.5551 | 0.7042 | 6.503607e-08 | 2933 |
| 0.0264 | 0.9929 | 1.5549 | 0.7113 | 6.501699e-08 | 2934 |
| 0.0196 | 1.0 | 1.5559 | 0.7042 | 6.499791e-08 | 2935 |
| 0.0238 | 0.9953 | 1.5567 | 0.7042 | 6.4978835e-08 | 2936 |
| 0.0297 | 0.9906 | 1.5578 | 0.7042 | 6.495976e-08 | 2937 |
| 0.0216 | 0.9953 | 1.5577 | 0.7042 | 6.494068e-08 | 2938 |
| 0.0270 | 0.9976 | 1.5653 | 0.7042 | 6.49216e-08 | 2939 |
| 0.0238 | 0.9976 | 1.5679 | 0.7042 | 6.490252e-08 | 2940 |
| 0.0374 | 0.9906 | 1.5689 | 0.7042 | 6.488344e-08 | 2941 |
| 0.0254 | 0.9976 | 1.5661 | 0.7042 | 6.486435e-08 | 2942 |
| 0.0262 | 0.9953 | 1.5643 | 0.7042 | 6.484527e-08 | 2943 |
| 0.0206 | 0.9976 | 1.5643 | 0.7042 | 6.482618e-08 | 2944 |
| 0.0220 | 0.9976 | 1.5654 | 0.7042 | 6.48071e-08 | 2945 |
| 0.0338 | 0.9906 | 1.5634 | 0.7042 | 6.478801e-08 | 2946 |
| 0.0233 | 0.9976 | 1.5618 | 0.7042 | 6.4768926e-08 | 2947 |
| 0.0217 | 1.0 | 1.5624 | 0.7042 | 6.474984e-08 | 2948 |
| 0.0251 | 0.9953 | 1.5674 | 0.7042 | 6.473075e-08 | 2949 |
| 0.0205 | 0.9953 | 1.5705 | 0.7042 | 6.471166e-08 | 2950 |
| 0.0175 | 1.0 | 1.5699 | 0.7042 | 6.4692564e-08 | 2951 |
| 0.0248 | 0.9976 | 1.5694 | 0.7042 | 6.467347e-08 | 2952 |
| 0.0279 | 0.9929 | 1.5654 | 0.7042 | 6.465438e-08 | 2953 |
| 0.0219 | 0.9976 | 1.5651 | 0.7042 | 6.463529e-08 | 2954 |
| 0.0279 | 0.9929 | 1.5667 | 0.7042 | 6.4616195e-08 | 2955 |
| 0.0252 | 0.9953 | 1.5681 | 0.7042 | 6.45971e-08 | 2956 |
| 0.0197 | 1.0 | 1.5678 | 0.7042 | 6.457801e-08 | 2957 |
| 0.0262 | 0.9929 | 1.5657 | 0.7042 | 6.455891e-08 | 2958 |
| 0.0244 | 0.9929 | 1.5637 | 0.7042 | 6.453981e-08 | 2959 |
| 0.0197 | 0.9976 | 1.5661 | 0.7042 | 6.452071e-08 | 2960 |
| 0.0294 | 0.9929 | 1.5672 | 0.7042 | 6.450161e-08 | 2961 |
| 0.0261 | 0.9976 | 1.5690 | 0.7042 | 6.448251e-08 | 2962 |
| 0.0214 | 0.9976 | 1.5684 | 0.7042 | 6.4463414e-08 | 2963 |
| 0.0274 | 0.9976 | 1.5684 | 0.7042 | 6.4444315e-08 | 2964 |
| 0.0302 | 0.9906 | 1.5698 | 0.7042 | 6.4425215e-08 | 2965 |
| 0.0189 | 0.9976 | 1.5691 | 0.7042 | 6.4406116e-08 | 2966 |
| 0.0179 | 1.0 | 1.5683 | 0.7042 | 6.438701e-08 | 2967 |
| 0.0254 | 0.9976 | 1.5666 | 0.7042 | 6.43679e-08 | 2968 |
| 0.0179 | 1.0 | 1.5652 | 0.7042 | 6.43488e-08 | 2969 |
| 0.0202 | 0.9976 | 1.5658 | 0.7042 | 6.432969e-08 | 2970 |
| 0.0228 | 0.9953 | 1.5657 | 0.7042 | 6.431058e-08 | 2971 |
| 0.0242 | 0.9953 | 1.5676 | 0.7042 | 6.429148e-08 | 2972 |
| 0.0219 | 0.9976 | 1.5694 | 0.7042 | 6.427237e-08 | 2973 |
| 0.0208 | 1.0 | 1.5710 | 0.7042 | 6.4253264e-08 | 2974 |
| 0.0244 | 0.9953 | 1.5718 | 0.7042 | 6.423416e-08 | 2975 |
| 0.0201 | 1.0 | 1.5735 | 0.7042 | 6.4215044e-08 | 2976 |
| 0.0258 | 0.9976 | 1.5738 | 0.7042 | 6.419593e-08 | 2977 |
| 0.0170 | 0.9976 | 1.5720 | 0.7042 | 6.417682e-08 | 2978 |
| 0.0177 | 1.0 | 1.5713 | 0.7042 | 6.41577e-08 | 2979 |
| 0.0297 | 0.9953 | 1.5680 | 0.7042 | 6.413859e-08 | 2980 |
| 0.0247 | 0.9953 | 1.5656 | 0.7113 | 6.4119476e-08 | 2981 |
| 0.0256 | 0.9953 | 1.5648 | 0.7042 | 6.410036e-08 | 2982 |
| 0.0220 | 0.9976 | 1.5634 | 0.7042 | 6.408125e-08 | 2983 |
| 0.0187 | 0.9976 | 1.5656 | 0.7042 | 6.4062135e-08 | 2984 |
| 0.0194 | 0.9976 | 1.5669 | 0.7042 | 6.404302e-08 | 2985 |
| 0.0220 | 0.9976 | 1.5656 | 0.7042 | 6.40239e-08 | 2986 |
| 0.0342 | 0.9882 | 1.5654 | 0.7042 | 6.400478e-08 | 2987 |
| 0.0305 | 0.9929 | 1.5653 | 0.7042 | 6.398566e-08 | 2988 |
| 0.0238 | 0.9976 | 1.5650 | 0.7113 | 6.396654e-08 | 2989 |
| 0.0261 | 0.9929 | 1.5661 | 0.7113 | 6.394742e-08 | 2990 |
| 0.0240 | 0.9929 | 1.5657 | 0.7113 | 6.39283e-08 | 2991 |
| 0.0182 | 0.9976 | 1.5654 | 0.7113 | 6.390918e-08 | 2992 |
| 0.0236 | 0.9953 | 1.5683 | 0.7042 | 6.3890056e-08 | 2993 |
| 0.0255 | 0.9953 | 1.5691 | 0.7042 | 6.3870935e-08 | 2994 |
| 0.0221 | 0.9976 | 1.5674 | 0.7042 | 6.3851815e-08 | 2995 |
| 0.0261 | 0.9929 | 1.5680 | 0.7042 | 6.383269e-08 | 2996 |
| 0.0216 | 0.9976 | 1.5703 | 0.7042 | 6.381356e-08 | 2997 |
| 0.0192 | 1.0 | 1.5711 | 0.7042 | 6.379443e-08 | 2998 |
| 0.0220 | 0.9976 | 1.5697 | 0.7042 | 6.37753e-08 | 2999 |
| 0.0152 | 1.0 | 1.5693 | 0.7042 | 6.3756175e-08 | 3000 |
| 0.0292 | 0.9953 | 1.5721 | 0.7042 | 6.373705e-08 | 3001 |
| 0.0169 | 1.0 | 1.5713 | 0.7042 | 6.371792e-08 | 3002 |
| 0.0209 | 0.9976 | 1.5696 | 0.7042 | 6.369879e-08 | 3003 |
| 0.0278 | 0.9906 | 1.5706 | 0.7042 | 6.3679664e-08 | 3004 |
| 0.0218 | 0.9976 | 1.5743 | 0.7042 | 6.3660536e-08 | 3005 |
| 0.0187 | 0.9976 | 1.5770 | 0.7042 | 6.364141e-08 | 3006 |
| 0.0263 | 0.9953 | 1.5793 | 0.7042 | 6.362228e-08 | 3007 |
| 0.0228 | 0.9976 | 1.5813 | 0.7042 | 6.3603146e-08 | 3008 |
| 0.0270 | 0.9976 | 1.5784 | 0.7042 | 6.358401e-08 | 3009 |
| 0.0206 | 1.0 | 1.5749 | 0.7042 | 6.3564876e-08 | 3010 |
| 0.0196 | 1.0 | 1.5756 | 0.7042 | 6.354574e-08 | 3011 |
| 0.0181 | 0.9976 | 1.5768 | 0.7042 | 6.3526606e-08 | 3012 |
| 0.0210 | 1.0 | 1.5753 | 0.7042 | 6.350747e-08 | 3013 |
| 0.0181 | 1.0 | 1.5739 | 0.7042 | 6.3488336e-08 | 3014 |
| 0.0209 | 0.9976 | 1.5761 | 0.7042 | 6.34692e-08 | 3015 |
| 0.0208 | 0.9953 | 1.5771 | 0.7042 | 6.345007e-08 | 3016 |
| 0.0231 | 0.9929 | 1.5767 | 0.7042 | 6.343093e-08 | 3017 |
| 0.0227 | 0.9929 | 1.5784 | 0.7042 | 6.34118e-08 | 3018 |
| 0.0154 | 1.0 | 1.5773 | 0.7042 | 6.339266e-08 | 3019 |
| 0.0202 | 1.0 | 1.5778 | 0.7042 | 6.337352e-08 | 3020 |
| 0.0270 | 0.9906 | 1.5791 | 0.7042 | 6.335438e-08 | 3021 |
| 0.0231 | 0.9976 | 1.5802 | 0.7042 | 6.3335236e-08 | 3022 |
| 0.0226 | 0.9976 | 1.5824 | 0.7042 | 6.3316094e-08 | 3023 |
| 0.0238 | 0.9976 | 1.5832 | 0.7042 | 6.329695e-08 | 3024 |
| 0.0249 | 1.0 | 1.5845 | 0.7042 | 6.327781e-08 | 3025 |
| 0.0250 | 0.9953 | 1.5791 | 0.7042 | 6.325867e-08 | 3026 |
| 0.0279 | 0.9929 | 1.5778 | 0.7042 | 6.3239526e-08 | 3027 |
| 0.0216 | 0.9976 | 1.5812 | 0.7042 | 6.3220384e-08 | 3028 |
| 0.0250 | 0.9953 | 1.5805 | 0.6972 | 6.320124e-08 | 3029 |
| 0.0179 | 1.0 | 1.5804 | 0.6972 | 6.31821e-08 | 3030 |
| 0.0179 | 0.9953 | 1.5803 | 0.7042 | 6.316296e-08 | 3031 |
| 0.0188 | 1.0 | 1.5821 | 0.7042 | 6.3143816e-08 | 3032 |
| 0.0227 | 0.9953 | 1.5826 | 0.7042 | 6.3124666e-08 | 3033 |
| 0.0310 | 0.9906 | 1.5825 | 0.7042 | 6.310552e-08 | 3034 |
| 0.0312 | 0.9929 | 1.5809 | 0.6972 | 6.308637e-08 | 3035 |
| 0.0236 | 0.9976 | 1.5800 | 0.7042 | 6.306722e-08 | 3036 |
| 0.0216 | 1.0 | 1.5792 | 0.7042 | 6.304807e-08 | 3037 |
| 0.0305 | 0.9953 | 1.5807 | 0.7042 | 6.302892e-08 | 3038 |
| 0.0205 | 0.9976 | 1.5825 | 0.7042 | 6.300977e-08 | 3039 |
| 0.0222 | 0.9953 | 1.5833 | 0.7042 | 6.299062e-08 | 3040 |
| 0.0220 | 0.9953 | 1.5839 | 0.7042 | 6.297147e-08 | 3041 |
| 0.0211 | 1.0 | 1.5863 | 0.7042 | 6.2952324e-08 | 3042 |
| 0.0188 | 0.9976 | 1.5858 | 0.7042 | 6.2933175e-08 | 3043 |
| 0.0203 | 0.9976 | 1.5860 | 0.7042 | 6.2914026e-08 | 3044 |
| 0.0200 | 0.9976 | 1.5858 | 0.7042 | 6.289488e-08 | 3045 |
| 0.0260 | 0.9953 | 1.5863 | 0.7042 | 6.287573e-08 | 3046 |
| 0.0188 | 1.0 | 1.5862 | 0.7042 | 6.285658e-08 | 3047 |
| 0.0253 | 0.9953 | 1.5838 | 0.7042 | 6.283742e-08 | 3048 |
| 0.0242 | 0.9953 | 1.5823 | 0.7042 | 6.2818266e-08 | 3049 |
| 0.0222 | 0.9953 | 1.5814 | 0.7042 | 6.279911e-08 | 3050 |
| 0.0266 | 0.9953 | 1.5819 | 0.7042 | 6.2779954e-08 | 3051 |
| 0.0195 | 0.9976 | 1.5831 | 0.7042 | 6.27608e-08 | 3052 |
| 0.0235 | 0.9953 | 1.5840 | 0.7042 | 6.274164e-08 | 3053 |
| 0.0200 | 0.9953 | 1.5828 | 0.7042 | 6.2722485e-08 | 3054 |
| 0.0263 | 0.9953 | 1.5834 | 0.7042 | 6.270333e-08 | 3055 |
| 0.0185 | 0.9976 | 1.5836 | 0.7042 | 6.268417e-08 | 3056 |
| 0.0239 | 0.9953 | 1.5785 | 0.7042 | 6.2665016e-08 | 3057 |
| 0.0174 | 1.0 | 1.5779 | 0.7042 | 6.264586e-08 | 3058 |
| 0.0220 | 0.9953 | 1.5795 | 0.7042 | 6.2626704e-08 | 3059 |
| 0.0203 | 0.9976 | 1.5835 | 0.7042 | 6.260755e-08 | 3060 |
| 0.0180 | 0.9976 | 1.5856 | 0.7042 | 6.258839e-08 | 3061 |
| 0.0231 | 0.9929 | 1.5846 | 0.7042 | 6.2569235e-08 | 3062 |
| 0.0172 | 0.9976 | 1.5834 | 0.7042 | 6.255008e-08 | 3063 |
| 0.0320 | 0.9906 | 1.5802 | 0.7042 | 6.253092e-08 | 3064 |
| 0.0206 | 0.9953 | 1.5824 | 0.7042 | 6.251176e-08 | 3065 |
| 0.0175 | 1.0 | 1.5833 | 0.7042 | 6.2492596e-08 | 3066 |
| 0.0206 | 0.9976 | 1.5819 | 0.7042 | 6.247343e-08 | 3067 |
| 0.0227 | 0.9976 | 1.5810 | 0.7042 | 6.245427e-08 | 3068 |
| 0.0212 | 0.9953 | 1.5808 | 0.7042 | 6.2435106e-08 | 3069 |
| 0.0303 | 0.9929 | 1.5806 | 0.7042 | 6.241594e-08 | 3070 |
| 0.0224 | 0.9976 | 1.5812 | 0.7042 | 6.239678e-08 | 3071 |
| 0.0286 | 0.9906 | 1.5819 | 0.7042 | 6.2377616e-08 | 3072 |
| 0.0262 | 0.9929 | 1.5820 | 0.7042 | 6.235845e-08 | 3073 |
| 0.0258 | 0.9929 | 1.5832 | 0.7042 | 6.233929e-08 | 3074 |
| 0.0322 | 0.9906 | 1.5823 | 0.7042 | 6.2320126e-08 | 3075 |
| 0.0223 | 0.9929 | 1.5809 | 0.7042 | 6.230096e-08 | 3076 |
| 0.0244 | 0.9953 | 1.5805 | 0.7042 | 6.22818e-08 | 3077 |
| 0.0189 | 1.0 | 1.5809 | 0.7042 | 6.2262636e-08 | 3078 |
| 0.0213 | 0.9953 | 1.5810 | 0.7042 | 6.224347e-08 | 3079 |
| 0.0161 | 1.0 | 1.5811 | 0.7042 | 6.222431e-08 | 3080 |
| 0.0238 | 0.9976 | 1.5832 | 0.7042 | 6.2205146e-08 | 3081 |
| 0.0166 | 0.9976 | 1.5837 | 0.7042 | 6.218598e-08 | 3082 |
| 0.0165 | 1.0 | 1.5821 | 0.7042 | 6.216682e-08 | 3083 |
| 0.0192 | 1.0 | 1.5795 | 0.7042 | 6.2147656e-08 | 3084 |
| 0.0202 | 0.9976 | 1.5796 | 0.7042 | 6.212849e-08 | 3085 |
| 0.0193 | 0.9976 | 1.5809 | 0.7042 | 6.210932e-08 | 3086 |
| 0.0157 | 1.0 | 1.5821 | 0.7042 | 6.209015e-08 | 3087 |
| 0.0218 | 0.9929 | 1.5834 | 0.7042 | 6.207098e-08 | 3088 |
| 0.0196 | 0.9976 | 1.5903 | 0.7042 | 6.205181e-08 | 3089 |
| 0.0267 | 0.9976 | 1.5917 | 0.7042 | 6.203264e-08 | 3090 |
| 0.0165 | 0.9976 | 1.5937 | 0.7042 | 6.201347e-08 | 3091 |
| 0.0209 | 1.0 | 1.5921 | 0.7042 | 6.19943e-08 | 3092 |
| 0.0234 | 0.9976 | 1.5901 | 0.7042 | 6.197513e-08 | 3093 |
| 0.0178 | 0.9976 | 1.5892 | 0.7042 | 6.195596e-08 | 3094 |
| 0.0203 | 0.9953 | 1.5885 | 0.7042 | 6.193679e-08 | 3095 |
| 0.0254 | 0.9953 | 1.5869 | 0.7042 | 6.191762e-08 | 3096 |
| 0.0192 | 0.9976 | 1.5868 | 0.7042 | 6.189845e-08 | 3097 |
| 0.0183 | 1.0 | 1.5885 | 0.7042 | 6.187928e-08 | 3098 |
| 0.0249 | 0.9929 | 1.5913 | 0.7042 | 6.1860106e-08 | 3099 |
| 0.0240 | 0.9953 | 1.5962 | 0.7042 | 6.1840936e-08 | 3100 |
| 0.0252 | 0.9976 | 1.5994 | 0.7042 | 6.1821765e-08 | 3101 |
| 0.0342 | 0.9929 | 1.5971 | 0.7042 | 6.1802595e-08 | 3102 |
| 0.0197 | 1.0 | 1.5882 | 0.7042 | 6.1783425e-08 | 3103 |
| 0.0151 | 1.0 | 1.5865 | 0.7113 | 6.1764254e-08 | 3104 |
| 0.0210 | 0.9976 | 1.5883 | 0.7042 | 6.1745084e-08 | 3105 |
| 0.0307 | 0.9929 | 1.5905 | 0.7042 | 6.172591e-08 | 3106 |
| 0.0204 | 0.9953 | 1.5939 | 0.7042 | 6.170674e-08 | 3107 |
| 0.0321 | 0.9953 | 1.5964 | 0.7042 | 6.168757e-08 | 3108 |
| 0.0277 | 0.9953 | 1.5979 | 0.7042 | 6.16684e-08 | 3109 |
| 0.0199 | 0.9953 | 1.5996 | 0.7042 | 6.164923e-08 | 3110 |
| 0.0182 | 0.9976 | 1.5997 | 0.7042 | 6.163006e-08 | 3111 |
| 0.0152 | 1.0 | 1.5990 | 0.7042 | 6.161089e-08 | 3112 |
| 0.0288 | 0.9929 | 1.5964 | 0.7042 | 6.159172e-08 | 3113 |
| 0.0195 | 0.9953 | 1.5957 | 0.7042 | 6.157255e-08 | 3114 |
| 0.0217 | 0.9976 | 1.5977 | 0.7042 | 6.155338e-08 | 3115 |
| 0.0169 | 1.0 | 1.5977 | 0.7042 | 6.15342e-08 | 3116 |
| 0.0194 | 0.9976 | 1.5990 | 0.7042 | 6.1515024e-08 | 3117 |
| 0.0174 | 0.9976 | 1.5982 | 0.7042 | 6.149585e-08 | 3118 |
| 0.0208 | 0.9953 | 1.5984 | 0.7042 | 6.147667e-08 | 3119 |
| 0.0231 | 0.9929 | 1.5987 | 0.7042 | 6.145749e-08 | 3120 |
| 0.0255 | 0.9953 | 1.5984 | 0.7042 | 6.1438314e-08 | 3121 |
| 0.0153 | 1.0 | 1.5981 | 0.7042 | 6.1419136e-08 | 3122 |
| 0.0146 | 1.0 | 1.5975 | 0.7042 | 6.139996e-08 | 3123 |
| 0.0185 | 0.9976 | 1.5973 | 0.7042 | 6.138078e-08 | 3124 |
| 0.0243 | 0.9953 | 1.5971 | 0.7042 | 6.1361604e-08 | 3125 |
| 0.0141 | 1.0 | 1.5967 | 0.7042 | 6.1342426e-08 | 3126 |
| 0.0187 | 0.9976 | 1.5980 | 0.7042 | 6.132325e-08 | 3127 |
| 0.0231 | 0.9953 | 1.5974 | 0.7042 | 6.130407e-08 | 3128 |
| 0.0240 | 0.9929 | 1.5972 | 0.7042 | 6.1284894e-08 | 3129 |
| 0.0227 | 0.9976 | 1.5964 | 0.7042 | 6.1265716e-08 | 3130 |
| 0.0151 | 1.0 | 1.5934 | 0.7042 | 6.124654e-08 | 3131 |
| 0.0163 | 1.0 | 1.5929 | 0.7042 | 6.122736e-08 | 3132 |
| 0.0282 | 0.9953 | 1.5949 | 0.7042 | 6.120818e-08 | 3133 |
| 0.0186 | 1.0 | 1.5959 | 0.7042 | 6.1189006e-08 | 3134 |
| 0.0183 | 1.0 | 1.5969 | 0.7042 | 6.116983e-08 | 3135 |
| 0.0171 | 1.0 | 1.5965 | 0.7042 | 6.115065e-08 | 3136 |
| 0.0155 | 0.9976 | 1.5973 | 0.7042 | 6.113147e-08 | 3137 |
| 0.0177 | 0.9976 | 1.5995 | 0.7042 | 6.1112296e-08 | 3138 |
| 0.0233 | 0.9929 | 1.5984 | 0.7042 | 6.109312e-08 | 3139 |
| 0.0206 | 0.9976 | 1.5999 | 0.7042 | 6.107394e-08 | 3140 |
| 0.0246 | 0.9953 | 1.6000 | 0.7042 | 6.105476e-08 | 3141 |
| 0.0155 | 1.0 | 1.6010 | 0.7042 | 6.1035585e-08 | 3142 |
| 0.0152 | 1.0 | 1.6014 | 0.7042 | 6.101641e-08 | 3143 |
| 0.0212 | 0.9953 | 1.6012 | 0.7042 | 6.099723e-08 | 3144 |
| 0.0228 | 0.9976 | 1.6000 | 0.7042 | 6.097805e-08 | 3145 |
| 0.0193 | 0.9976 | 1.5975 | 0.6972 | 6.0958875e-08 | 3146 |
| 0.0174 | 0.9976 | 1.5964 | 0.6972 | 6.09397e-08 | 3147 |
| 0.0202 | 0.9953 | 1.5985 | 0.7042 | 6.092052e-08 | 3148 |
| 0.0223 | 0.9976 | 1.5987 | 0.7042 | 6.090134e-08 | 3149 |
| 0.0249 | 0.9906 | 1.6020 | 0.7042 | 6.0882165e-08 | 3150 |
| 0.0148 | 1.0 | 1.6035 | 0.7042 | 6.086299e-08 | 3151 |
| 0.0195 | 1.0 | 1.6044 | 0.7042 | 6.084381e-08 | 3152 |
| 0.0175 | 0.9976 | 1.6041 | 0.7042 | 6.082463e-08 | 3153 |
| 0.0171 | 0.9976 | 1.6032 | 0.7042 | 6.0805455e-08 | 3154 |
| 0.0256 | 0.9906 | 1.6012 | 0.7042 | 6.078628e-08 | 3155 |
| 0.0189 | 0.9953 | 1.6011 | 0.7042 | 6.07671e-08 | 3156 |
| 0.0228 | 0.9953 | 1.6034 | 0.7042 | 6.074792e-08 | 3157 |
| 0.0171 | 1.0 | 1.6059 | 0.7042 | 6.0728745e-08 | 3158 |
| 0.0159 | 1.0 | 1.6050 | 0.7042 | 6.070957e-08 | 3159 |
| 0.0228 | 0.9953 | 1.6049 | 0.7042 | 6.069039e-08 | 3160 |
| 0.0228 | 0.9953 | 1.6055 | 0.7042 | 6.067121e-08 | 3161 |
| 0.0153 | 1.0 | 1.6031 | 0.7042 | 6.0652035e-08 | 3162 |
| 0.0224 | 0.9953 | 1.6020 | 0.7042 | 6.063286e-08 | 3163 |
| 0.0190 | 0.9953 | 1.6020 | 0.7042 | 6.061368e-08 | 3164 |
| 0.0172 | 0.9976 | 1.6047 | 0.7042 | 6.05945e-08 | 3165 |
| 0.0285 | 0.9929 | 1.6061 | 0.7042 | 6.0575324e-08 | 3166 |
| 0.0193 | 0.9976 | 1.6061 | 0.7042 | 6.055615e-08 | 3167 |
| 0.0196 | 0.9976 | 1.6072 | 0.7042 | 6.053697e-08 | 3168 |
| 0.0166 | 1.0 | 1.6068 | 0.7042 | 6.051779e-08 | 3169 |
| 0.0270 | 0.9953 | 1.6051 | 0.7042 | 6.0498614e-08 | 3170 |
| 0.0121 | 1.0 | 1.6047 | 0.7042 | 6.047944e-08 | 3171 |
| 0.0140 | 1.0 | 1.6039 | 0.7042 | 6.046026e-08 | 3172 |
| 0.0258 | 0.9953 | 1.6023 | 0.7042 | 6.044108e-08 | 3173 |
| 0.0148 | 1.0 | 1.6021 | 0.7042 | 6.0421904e-08 | 3174 |
| 0.0208 | 0.9929 | 1.6035 | 0.7042 | 6.0402726e-08 | 3175 |
| 0.0152 | 0.9976 | 1.6037 | 0.6972 | 6.038355e-08 | 3176 |
| 0.0131 | 1.0 | 1.6036 | 0.7042 | 6.036437e-08 | 3177 |
| 0.0144 | 1.0 | 1.6053 | 0.7042 | 6.0345194e-08 | 3178 |
| 0.0199 | 0.9953 | 1.6067 | 0.7042 | 6.0326016e-08 | 3179 |
| 0.0162 | 0.9976 | 1.6076 | 0.7042 | 6.030684e-08 | 3180 |
| 0.0212 | 0.9929 | 1.6092 | 0.7042 | 6.028766e-08 | 3181 |
| 0.0171 | 1.0 | 1.6099 | 0.7042 | 6.026848e-08 | 3182 |
| 0.0153 | 1.0 | 1.6085 | 0.7042 | 6.0249306e-08 | 3183 |
| 0.0182 | 0.9953 | 1.6058 | 0.7042 | 6.023013e-08 | 3184 |
| 0.0211 | 0.9976 | 1.6054 | 0.7042 | 6.021095e-08 | 3185 |
| 0.0206 | 0.9953 | 1.6082 | 0.7042 | 6.019177e-08 | 3186 |
| 0.0227 | 0.9976 | 1.6114 | 0.7042 | 6.0172596e-08 | 3187 |
| 0.0177 | 1.0 | 1.6120 | 0.7042 | 6.015342e-08 | 3188 |
| 0.0216 | 0.9953 | 1.6101 | 0.7042 | 6.013424e-08 | 3189 |
| 0.0261 | 0.9929 | 1.6102 | 0.7042 | 6.011506e-08 | 3190 |
| 0.0174 | 1.0 | 1.6115 | 0.7042 | 6.0095886e-08 | 3191 |
| 0.0227 | 0.9906 | 1.6116 | 0.7042 | 6.007671e-08 | 3192 |
| 0.0169 | 1.0 | 1.6111 | 0.7042 | 6.005753e-08 | 3193 |
| 0.0214 | 0.9953 | 1.6103 | 0.7042 | 6.003835e-08 | 3194 |
| 0.0167 | 0.9976 | 1.6090 | 0.7042 | 6.0019175e-08 | 3195 |
| 0.0201 | 0.9953 | 1.6073 | 0.7113 | 6e-08 | 3196 |
| 0.0215 | 0.9953 | 1.6073 | 0.7042 | 5.998082e-08 | 3197 |
| 0.0129 | 1.0 | 1.6066 | 0.7042 | 5.996164e-08 | 3198 |
| 0.0166 | 1.0 | 1.6077 | 0.7042 | 5.9942465e-08 | 3199 |
| 0.0269 | 0.9906 | 1.6103 | 0.7042 | 5.992329e-08 | 3200 |
| 0.0189 | 0.9976 | 1.6106 | 0.7042 | 5.990411e-08 | 3201 |
| 0.0276 | 0.9882 | 1.6134 | 0.7042 | 5.988493e-08 | 3202 |
| 0.0189 | 1.0 | 1.6132 | 0.7042 | 5.9865755e-08 | 3203 |
| 0.0177 | 1.0 | 1.6115 | 0.7042 | 5.984658e-08 | 3204 |
| 0.0222 | 0.9976 | 1.6126 | 0.7042 | 5.98274e-08 | 3205 |
| 0.0159 | 0.9976 | 1.6141 | 0.7042 | 5.980822e-08 | 3206 |
| 0.0247 | 0.9976 | 1.6151 | 0.7042 | 5.9789045e-08 | 3207 |
| 0.0163 | 1.0 | 1.6147 | 0.7042 | 5.976987e-08 | 3208 |
| 0.0239 | 0.9976 | 1.6149 | 0.7042 | 5.975069e-08 | 3209 |
| 0.0212 | 0.9953 | 1.6163 | 0.7042 | 5.973152e-08 | 3210 |
| 0.0213 | 0.9953 | 1.6160 | 0.7042 | 5.971235e-08 | 3211 |
| 0.0252 | 0.9953 | 1.6169 | 0.7042 | 5.969318e-08 | 3212 |
| 0.0275 | 0.9929 | 1.6165 | 0.7042 | 5.967401e-08 | 3213 |
| 0.0344 | 0.9906 | 1.6146 | 0.7042 | 5.965484e-08 | 3214 |
| 0.0161 | 1.0 | 1.6134 | 0.7042 | 5.963567e-08 | 3215 |
| 0.0178 | 0.9953 | 1.6140 | 0.7042 | 5.96165e-08 | 3216 |
| 0.0275 | 0.9953 | 1.6145 | 0.7042 | 5.9597323e-08 | 3217 |
| 0.0176 | 0.9976 | 1.6159 | 0.7042 | 5.957815e-08 | 3218 |
| 0.0243 | 0.9953 | 1.6185 | 0.7042 | 5.9558978e-08 | 3219 |
| 0.0140 | 1.0 | 1.6189 | 0.7042 | 5.9539808e-08 | 3220 |
| 0.0255 | 0.9929 | 1.6199 | 0.7042 | 5.9520637e-08 | 3221 |
| 0.0212 | 0.9953 | 1.6208 | 0.7042 | 5.9501467e-08 | 3222 |
| 0.0169 | 0.9953 | 1.6166 | 0.7042 | 5.9482296e-08 | 3223 |
| 0.0192 | 0.9976 | 1.6130 | 0.7042 | 5.9463126e-08 | 3224 |
| 0.0152 | 0.9976 | 1.6122 | 0.7042 | 5.9443956e-08 | 3225 |
| 0.0156 | 1.0 | 1.6142 | 0.7042 | 5.9424785e-08 | 3226 |
| 0.0206 | 0.9953 | 1.6129 | 0.7042 | 5.9405615e-08 | 3227 |
| 0.0174 | 0.9976 | 1.6129 | 0.7042 | 5.9386444e-08 | 3228 |
| 0.0191 | 0.9976 | 1.6132 | 0.7042 | 5.9367274e-08 | 3229 |
| 0.0170 | 0.9976 | 1.6128 | 0.7042 | 5.9348103e-08 | 3230 |
| 0.0195 | 0.9953 | 1.6134 | 0.7042 | 5.9328933e-08 | 3231 |
| 0.0232 | 0.9953 | 1.6164 | 0.7042 | 5.9309762e-08 | 3232 |
| 0.0136 | 1.0 | 1.6190 | 0.7042 | 5.9290596e-08 | 3233 |
| 0.0175 | 0.9976 | 1.6188 | 0.7042 | 5.927143e-08 | 3234 |
| 0.0269 | 0.9953 | 1.6198 | 0.7042 | 5.925226e-08 | 3235 |
| 0.0171 | 1.0 | 1.6212 | 0.7042 | 5.9233095e-08 | 3236 |
| 0.0170 | 0.9976 | 1.6188 | 0.7042 | 5.9213928e-08 | 3237 |
| 0.0175 | 0.9976 | 1.6155 | 0.7042 | 5.919476e-08 | 3238 |
| 0.0230 | 0.9953 | 1.6146 | 0.7042 | 5.9175594e-08 | 3239 |
| 0.0160 | 0.9976 | 1.6140 | 0.7042 | 5.9156427e-08 | 3240 |
| 0.0300 | 0.9953 | 1.6164 | 0.7042 | 5.913726e-08 | 3241 |
| 0.0124 | 1.0 | 1.6196 | 0.7042 | 5.9118094e-08 | 3242 |
| 0.0193 | 0.9976 | 1.6208 | 0.7042 | 5.9098927e-08 | 3243 |
| 0.0183 | 0.9976 | 1.6180 | 0.7042 | 5.907976e-08 | 3244 |
| 0.0170 | 1.0 | 1.6171 | 0.7042 | 5.9060593e-08 | 3245 |
| 0.0155 | 1.0 | 1.6188 | 0.7042 | 5.904143e-08 | 3246 |
| 0.0183 | 1.0 | 1.6221 | 0.7042 | 5.9022266e-08 | 3247 |
| 0.0240 | 0.9929 | 1.6219 | 0.7042 | 5.9003103e-08 | 3248 |
| 0.0119 | 1.0 | 1.6225 | 0.7042 | 5.898394e-08 | 3249 |
| 0.0195 | 0.9976 | 1.6234 | 0.7042 | 5.8964776e-08 | 3250 |
| 0.0154 | 0.9976 | 1.6232 | 0.7042 | 5.8945613e-08 | 3251 |
| 0.0244 | 0.9953 | 1.6210 | 0.7042 | 5.892645e-08 | 3252 |
| 0.0135 | 1.0 | 1.6218 | 0.7042 | 5.8907286e-08 | 3253 |
| 0.0154 | 1.0 | 1.6221 | 0.7042 | 5.8888123e-08 | 3254 |
| 0.0137 | 0.9976 | 1.6216 | 0.7042 | 5.886896e-08 | 3255 |
| 0.0213 | 0.9976 | 1.6230 | 0.7042 | 5.88498e-08 | 3256 |
| 0.0257 | 0.9929 | 1.6263 | 0.7042 | 5.883064e-08 | 3257 |
| 0.0224 | 0.9976 | 1.6261 | 0.7042 | 5.881148e-08 | 3258 |
| 0.0137 | 1.0 | 1.6205 | 0.7042 | 5.879232e-08 | 3259 |
| 0.0129 | 1.0 | 1.6210 | 0.7042 | 5.877316e-08 | 3260 |
| 0.0137 | 1.0 | 1.6223 | 0.7042 | 5.8754e-08 | 3261 |
| 0.0168 | 0.9976 | 1.6234 | 0.7042 | 5.873484e-08 | 3262 |
| 0.0210 | 0.9976 | 1.6238 | 0.7042 | 5.871568e-08 | 3263 |
| 0.0206 | 0.9953 | 1.6252 | 0.7042 | 5.869652e-08 | 3264 |
| 0.0167 | 1.0 | 1.6263 | 0.7042 | 5.867736e-08 | 3265 |
| 0.0130 | 1.0 | 1.6257 | 0.7042 | 5.8658205e-08 | 3266 |
| 0.0127 | 1.0 | 1.6243 | 0.7042 | 5.863905e-08 | 3267 |
| 0.0163 | 0.9976 | 1.6250 | 0.7042 | 5.8619893e-08 | 3268 |
| 0.0140 | 1.0 | 1.6255 | 0.7042 | 5.8600737e-08 | 3269 |
| 0.0236 | 0.9976 | 1.6237 | 0.7042 | 5.858158e-08 | 3270 |
| 0.0217 | 0.9953 | 1.6246 | 0.7042 | 5.8562424e-08 | 3271 |
| 0.0154 | 0.9976 | 1.6250 | 0.7042 | 5.8543268e-08 | 3272 |
| 0.0170 | 0.9976 | 1.6254 | 0.7042 | 5.8524112e-08 | 3273 |
| 0.0198 | 0.9953 | 1.6252 | 0.7042 | 5.8504956e-08 | 3274 |
| 0.0107 | 1.0 | 1.6283 | 0.7042 | 5.8485803e-08 | 3275 |
| 0.0174 | 0.9976 | 1.6295 | 0.7042 | 5.846665e-08 | 3276 |
| 0.0192 | 0.9976 | 1.6310 | 0.7042 | 5.8447498e-08 | 3277 |
| 0.0176 | 0.9976 | 1.6304 | 0.7042 | 5.8428345e-08 | 3278 |
| 0.0190 | 0.9953 | 1.6302 | 0.7042 | 5.8409192e-08 | 3279 |
| 0.0165 | 1.0 | 1.6307 | 0.7042 | 5.839004e-08 | 3280 |
| 0.0189 | 0.9953 | 1.6311 | 0.7042 | 5.8370887e-08 | 3281 |
| 0.0176 | 1.0 | 1.6288 | 0.7042 | 5.8351734e-08 | 3282 |
| 0.0220 | 0.9976 | 1.6265 | 0.7042 | 5.8332585e-08 | 3283 |
| 0.0229 | 0.9953 | 1.6270 | 0.7042 | 5.8313436e-08 | 3284 |
| 0.0165 | 1.0 | 1.6271 | 0.7042 | 5.8294287e-08 | 3285 |
| 0.0140 | 1.0 | 1.6262 | 0.7042 | 5.8275138e-08 | 3286 |
| 0.0189 | 0.9976 | 1.6284 | 0.7042 | 5.825599e-08 | 3287 |
| 0.0142 | 1.0 | 1.6300 | 0.7042 | 5.823684e-08 | 3288 |
| 0.0159 | 1.0 | 1.6295 | 0.7042 | 5.821769e-08 | 3289 |
| 0.0255 | 0.9953 | 1.6270 | 0.7042 | 5.8198545e-08 | 3290 |
| 0.0195 | 0.9953 | 1.6277 | 0.7042 | 5.81794e-08 | 3291 |
| 0.0210 | 0.9953 | 1.6320 | 0.7042 | 5.8160254e-08 | 3292 |
| 0.0283 | 0.9906 | 1.6296 | 0.7042 | 5.8141108e-08 | 3293 |
| 0.0192 | 0.9953 | 1.6286 | 0.7042 | 5.8121962e-08 | 3294 |
| 0.0172 | 1.0 | 1.6278 | 0.7042 | 5.8102817e-08 | 3295 |
| 0.0136 | 1.0 | 1.6273 | 0.7042 | 5.808367e-08 | 3296 |
| 0.0131 | 1.0 | 1.6273 | 0.7042 | 5.8064526e-08 | 3297 |
| 0.0213 | 0.9953 | 1.6278 | 0.7042 | 5.8045384e-08 | 3298 |
| 0.0266 | 0.9929 | 1.6266 | 0.7113 | 5.802624e-08 | 3299 |
| 0.0145 | 1.0 | 1.6259 | 0.7183 | 5.80071e-08 | 3300 |
| 0.0191 | 0.9953 | 1.6265 | 0.7113 | 5.7987958e-08 | 3301 |
| 0.0210 | 0.9906 | 1.6311 | 0.7042 | 5.7968816e-08 | 3302 |
| 0.0200 | 0.9976 | 1.6336 | 0.7042 | 5.7949674e-08 | 3303 |
| 0.0179 | 1.0 | 1.6341 | 0.7042 | 5.793053e-08 | 3304 |
| 0.0245 | 0.9929 | 1.6349 | 0.7042 | 5.7911393e-08 | 3305 |
| 0.0226 | 0.9929 | 1.6357 | 0.7042 | 5.7892255e-08 | 3306 |
| 0.0131 | 1.0 | 1.6358 | 0.7042 | 5.7873116e-08 | 3307 |
| 0.0197 | 0.9976 | 1.6377 | 0.7042 | 5.7853978e-08 | 3308 |
| 0.0164 | 0.9976 | 1.6405 | 0.7042 | 5.783484e-08 | 3309 |
| 0.0157 | 1.0 | 1.6379 | 0.7042 | 5.78157e-08 | 3310 |
| 0.0184 | 0.9976 | 1.6327 | 0.7042 | 5.7796566e-08 | 3311 |
| 0.0123 | 1.0 | 1.6306 | 0.7042 | 5.777743e-08 | 3312 |
| 0.0155 | 0.9976 | 1.6306 | 0.7042 | 5.7758296e-08 | 3313 |
| 0.0200 | 0.9976 | 1.6305 | 0.7042 | 5.773916e-08 | 3314 |
| 0.0212 | 0.9953 | 1.6325 | 0.7042 | 5.7720026e-08 | 3315 |
| 0.0239 | 0.9953 | 1.6350 | 0.7042 | 5.770089e-08 | 3316 |
| 0.0163 | 0.9976 | 1.6345 | 0.7042 | 5.768176e-08 | 3317 |
| 0.0157 | 0.9976 | 1.6336 | 0.7042 | 5.766263e-08 | 3318 |
| 0.0140 | 1.0 | 1.6332 | 0.7042 | 5.7643497e-08 | 3319 |
| 0.0250 | 0.9953 | 1.6330 | 0.7042 | 5.7624366e-08 | 3320 |
| 0.0148 | 1.0 | 1.6336 | 0.7042 | 5.7605234e-08 | 3321 |
| 0.0181 | 0.9976 | 1.6326 | 0.7042 | 5.7586103e-08 | 3322 |
| 0.0145 | 1.0 | 1.6331 | 0.7042 | 5.7566975e-08 | 3323 |
| 0.0200 | 0.9953 | 1.6335 | 0.7042 | 5.7547847e-08 | 3324 |
| 0.0242 | 0.9929 | 1.6329 | 0.7042 | 5.752872e-08 | 3325 |
| 0.0116 | 1.0 | 1.6328 | 0.7042 | 5.750959e-08 | 3326 |
| 0.0185 | 0.9953 | 1.6336 | 0.7042 | 5.7490464e-08 | 3327 |
| 0.0220 | 0.9976 | 1.6328 | 0.7042 | 5.7471336e-08 | 3328 |
| 0.0164 | 0.9976 | 1.6323 | 0.7042 | 5.7452212e-08 | 3329 |
| 0.0154 | 0.9976 | 1.6316 | 0.7042 | 5.7433088e-08 | 3330 |
| 0.0114 | 1.0 | 1.6302 | 0.7113 | 5.7413963e-08 | 3331 |
| 0.0164 | 1.0 | 1.6320 | 0.7042 | 5.739484e-08 | 3332 |
| 0.0175 | 0.9976 | 1.6311 | 0.7042 | 5.7375715e-08 | 3333 |
| 0.0158 | 1.0 | 1.6308 | 0.7113 | 5.735659e-08 | 3334 |
| 0.0154 | 1.0 | 1.6341 | 0.7042 | 5.733747e-08 | 3335 |
| 0.0180 | 0.9929 | 1.6336 | 0.7042 | 5.731835e-08 | 3336 |
| 0.0167 | 0.9976 | 1.6338 | 0.7042 | 5.729923e-08 | 3337 |
| 0.0265 | 0.9882 | 1.6373 | 0.7042 | 5.7280108e-08 | 3338 |
| 0.0170 | 0.9953 | 1.6407 | 0.7042 | 5.7260987e-08 | 3339 |
| 0.0164 | 1.0 | 1.6418 | 0.7042 | 5.724187e-08 | 3340 |
| 0.0263 | 0.9929 | 1.6417 | 0.7042 | 5.7222753e-08 | 3341 |
| 0.0136 | 1.0 | 1.6414 | 0.7042 | 5.7203636e-08 | 3342 |
| 0.0167 | 0.9976 | 1.6404 | 0.7042 | 5.718452e-08 | 3343 |
| 0.0246 | 0.9953 | 1.6385 | 0.7042 | 5.71654e-08 | 3344 |
| 0.0200 | 0.9976 | 1.6406 | 0.7042 | 5.7146284e-08 | 3345 |
| 0.0192 | 0.9953 | 1.6387 | 0.7042 | 5.712717e-08 | 3346 |
| 0.0130 | 0.9976 | 1.6344 | 0.7042 | 5.7108057e-08 | 3347 |
| 0.0164 | 0.9953 | 1.6324 | 0.7113 | 5.7088943e-08 | 3348 |
| 0.0175 | 0.9976 | 1.6331 | 0.7113 | 5.706983e-08 | 3349 |
| 0.0225 | 0.9906 | 1.6357 | 0.7042 | 5.7050716e-08 | 3350 |
| 0.0127 | 1.0 | 1.6378 | 0.7042 | 5.7031606e-08 | 3351 |
| 0.0216 | 0.9953 | 1.6396 | 0.7042 | 5.7012496e-08 | 3352 |
| 0.0150 | 0.9976 | 1.6428 | 0.7042 | 5.6993386e-08 | 3353 |
| 0.0184 | 0.9976 | 1.6419 | 0.7042 | 5.6974276e-08 | 3354 |
| 0.0151 | 0.9953 | 1.6422 | 0.7042 | 5.6955166e-08 | 3355 |
| 0.0165 | 1.0 | 1.6421 | 0.7042 | 5.693606e-08 | 3356 |
| 0.0133 | 1.0 | 1.6421 | 0.7042 | 5.6916953e-08 | 3357 |
| 0.0154 | 0.9976 | 1.6422 | 0.7042 | 5.6897846e-08 | 3358 |
| 0.0146 | 1.0 | 1.6398 | 0.7042 | 5.687874e-08 | 3359 |
| 0.0181 | 0.9976 | 1.6390 | 0.7042 | 5.6859633e-08 | 3360 |
| 0.0177 | 0.9953 | 1.6372 | 0.7042 | 5.684053e-08 | 3361 |
| 0.0201 | 0.9953 | 1.6350 | 0.7183 | 5.6821428e-08 | 3362 |
| 0.0135 | 1.0 | 1.6351 | 0.7183 | 5.6802325e-08 | 3363 |
| 0.0169 | 0.9953 | 1.6353 | 0.7183 | 5.678322e-08 | 3364 |
| 0.0130 | 1.0 | 1.6341 | 0.7183 | 5.6764122e-08 | 3365 |
| 0.0149 | 1.0 | 1.6339 | 0.7183 | 5.6745023e-08 | 3366 |
| 0.0170 | 0.9953 | 1.6345 | 0.7183 | 5.6725924e-08 | 3367 |
| 0.0166 | 1.0 | 1.6339 | 0.7183 | 5.6706824e-08 | 3368 |
| 0.0250 | 0.9929 | 1.6328 | 0.7183 | 5.6687725e-08 | 3369 |
| 0.0179 | 0.9976 | 1.6330 | 0.7183 | 5.666863e-08 | 3370 |
| 0.0131 | 0.9976 | 1.6351 | 0.7183 | 5.6649533e-08 | 3371 |
| 0.0142 | 1.0 | 1.6363 | 0.7183 | 5.6630437e-08 | 3372 |
| 0.0107 | 1.0 | 1.6371 | 0.7183 | 5.661134e-08 | 3373 |
| 0.0243 | 0.9929 | 1.6372 | 0.7183 | 5.6592246e-08 | 3374 |
| 0.0268 | 0.9906 | 1.6385 | 0.7183 | 5.6573153e-08 | 3375 |
| 0.0170 | 0.9953 | 1.6396 | 0.7183 | 5.655406e-08 | 3376 |
| 0.0145 | 0.9976 | 1.6392 | 0.7183 | 5.653497e-08 | 3377 |
| 0.0191 | 0.9953 | 1.6399 | 0.7183 | 5.6515876e-08 | 3378 |
| 0.0180 | 0.9953 | 1.6404 | 0.7183 | 5.6496788e-08 | 3379 |
| 0.0244 | 0.9953 | 1.6403 | 0.7183 | 5.64777e-08 | 3380 |
| 0.0170 | 0.9976 | 1.6383 | 0.7183 | 5.645861e-08 | 3381 |
| 0.0148 | 1.0 | 1.6394 | 0.7113 | 5.643952e-08 | 3382 |
| 0.0213 | 0.9929 | 1.6427 | 0.7042 | 5.6420433e-08 | 3383 |
| 0.0140 | 0.9976 | 1.6442 | 0.7042 | 5.6401348e-08 | 3384 |
| 0.0198 | 0.9976 | 1.6451 | 0.7042 | 5.6382262e-08 | 3385 |
| 0.0269 | 0.9906 | 1.6443 | 0.7042 | 5.6363177e-08 | 3386 |
| 0.0146 | 1.0 | 1.6433 | 0.7042 | 5.6344092e-08 | 3387 |
| 0.0145 | 1.0 | 1.6426 | 0.7042 | 5.632501e-08 | 3388 |
| 0.0206 | 0.9929 | 1.6413 | 0.7042 | 5.630593e-08 | 3389 |
| 0.0139 | 1.0 | 1.6396 | 0.7042 | 5.6286847e-08 | 3390 |
| 0.0168 | 0.9976 | 1.6373 | 0.7113 | 5.6267766e-08 | 3391 |
| 0.0161 | 0.9976 | 1.6373 | 0.7183 | 5.6248687e-08 | 3392 |
| 0.0183 | 0.9976 | 1.6389 | 0.7113 | 5.622961e-08 | 3393 |
| 0.0183 | 0.9953 | 1.6404 | 0.7042 | 5.621053e-08 | 3394 |
| 0.0157 | 1.0 | 1.6430 | 0.7042 | 5.6191453e-08 | 3395 |
| 0.0159 | 1.0 | 1.6449 | 0.7042 | 5.617238e-08 | 3396 |
| 0.0172 | 0.9976 | 1.6460 | 0.7042 | 5.6153304e-08 | 3397 |
| 0.0138 | 1.0 | 1.6466 | 0.7042 | 5.613423e-08 | 3398 |
| 0.0135 | 0.9976 | 1.6483 | 0.7042 | 5.6115155e-08 | 3399 |
| 0.0195 | 0.9906 | 1.6468 | 0.7042 | 5.6096084e-08 | 3400 |
| 0.0170 | 1.0 | 1.6461 | 0.7042 | 5.6077013e-08 | 3401 |
| 0.0150 | 1.0 | 1.6467 | 0.7042 | 5.6057942e-08 | 3402 |
| 0.0132 | 0.9976 | 1.6472 | 0.7042 | 5.603887e-08 | 3403 |
| 0.0184 | 0.9953 | 1.6452 | 0.7042 | 5.6019804e-08 | 3404 |
| 0.0218 | 0.9976 | 1.6437 | 0.7042 | 5.6000736e-08 | 3405 |
| 0.0143 | 1.0 | 1.6438 | 0.7042 | 5.598167e-08 | 3406 |
| 0.0225 | 0.9953 | 1.6437 | 0.7042 | 5.59626e-08 | 3407 |
| 0.0158 | 0.9953 | 1.6429 | 0.7113 | 5.5943538e-08 | 3408 |
| 0.0143 | 0.9976 | 1.6464 | 0.7042 | 5.5924474e-08 | 3409 |
| 0.0211 | 0.9976 | 1.6472 | 0.7042 | 5.590541e-08 | 3410 |
| 0.0168 | 0.9976 | 1.6472 | 0.7042 | 5.5886346e-08 | 3411 |
| 0.0193 | 0.9976 | 1.6440 | 0.7183 | 5.5867286e-08 | 3412 |
| 0.0182 | 0.9976 | 1.6402 | 0.7183 | 5.5848226e-08 | 3413 |
| 0.0158 | 1.0 | 1.6408 | 0.7183 | 5.5829165e-08 | 3414 |
| 0.0126 | 0.9976 | 1.6412 | 0.7183 | 5.5810105e-08 | 3415 |
| 0.0164 | 0.9976 | 1.6412 | 0.7183 | 5.579105e-08 | 3416 |
| 0.0129 | 1.0 | 1.6404 | 0.7183 | 5.577199e-08 | 3417 |
| 0.0185 | 0.9929 | 1.6414 | 0.7183 | 5.5752935e-08 | 3418 |
| 0.0232 | 0.9953 | 1.6424 | 0.7183 | 5.5733878e-08 | 3419 |
| 0.0105 | 1.0 | 1.6438 | 0.7183 | 5.5714825e-08 | 3420 |
| 0.0222 | 0.9929 | 1.6439 | 0.7183 | 5.569577e-08 | 3421 |
| 0.0141 | 1.0 | 1.6444 | 0.7113 | 5.567672e-08 | 3422 |
| 0.0221 | 0.9929 | 1.6461 | 0.7042 | 5.5657665e-08 | 3423 |
| 0.0173 | 0.9976 | 1.6491 | 0.7042 | 5.5638615e-08 | 3424 |
| 0.0184 | 0.9976 | 1.6493 | 0.7042 | 5.5619566e-08 | 3425 |
| 0.0114 | 1.0 | 1.6493 | 0.7042 | 5.5600516e-08 | 3426 |
| 0.0130 | 1.0 | 1.6490 | 0.7042 | 5.558147e-08 | 3427 |
| 0.0153 | 0.9976 | 1.6496 | 0.7042 | 5.5562424e-08 | 3428 |
| 0.0122 | 1.0 | 1.6516 | 0.7042 | 5.5543378e-08 | 3429 |
| 0.0133 | 0.9976 | 1.6516 | 0.7042 | 5.5524332e-08 | 3430 |
| 0.0134 | 1.0 | 1.6497 | 0.7042 | 5.550529e-08 | 3431 |
| 0.0153 | 0.9953 | 1.6486 | 0.7042 | 5.5486247e-08 | 3432 |
| 0.0189 | 0.9953 | 1.6471 | 0.7042 | 5.5467204e-08 | 3433 |
| 0.0180 | 0.9953 | 1.6465 | 0.7042 | 5.544816e-08 | 3434 |
| 0.0155 | 0.9976 | 1.6452 | 0.7042 | 5.5429123e-08 | 3435 |
| 0.0213 | 0.9953 | 1.6484 | 0.7042 | 5.5410084e-08 | 3436 |
| 0.0169 | 0.9953 | 1.6490 | 0.7042 | 5.5391045e-08 | 3437 |
| 0.0174 | 0.9953 | 1.6486 | 0.7042 | 5.537201e-08 | 3438 |
| 0.0155 | 1.0 | 1.6473 | 0.7042 | 5.5352974e-08 | 3439 |
| 0.0195 | 0.9976 | 1.6452 | 0.7042 | 5.533394e-08 | 3440 |
| 0.0100 | 1.0 | 1.6441 | 0.7113 | 5.5314903e-08 | 3441 |
| 0.0160 | 0.9976 | 1.6442 | 0.7113 | 5.529587e-08 | 3442 |
| 0.0149 | 0.9976 | 1.6463 | 0.7042 | 5.527684e-08 | 3443 |
| 0.0169 | 1.0 | 1.6489 | 0.7042 | 5.5257807e-08 | 3444 |
| 0.0184 | 0.9953 | 1.6499 | 0.7042 | 5.523878e-08 | 3445 |
| 0.0164 | 0.9976 | 1.6491 | 0.7042 | 5.521975e-08 | 3446 |
| 0.0134 | 1.0 | 1.6498 | 0.7042 | 5.5200722e-08 | 3447 |
| 0.0169 | 1.0 | 1.6504 | 0.7042 | 5.5181694e-08 | 3448 |
| 0.0124 | 1.0 | 1.6503 | 0.7042 | 5.516267e-08 | 3449 |
| 0.0144 | 1.0 | 1.6490 | 0.7042 | 5.5143644e-08 | 3450 |
| 0.0169 | 0.9976 | 1.6502 | 0.7042 | 5.512462e-08 | 3451 |
| 0.0152 | 0.9976 | 1.6520 | 0.7042 | 5.51056e-08 | 3452 |
| 0.0151 | 0.9976 | 1.6506 | 0.7042 | 5.5086577e-08 | 3453 |
| 0.0115 | 1.0 | 1.6500 | 0.7042 | 5.5067556e-08 | 3454 |
| 0.0140 | 1.0 | 1.6501 | 0.7042 | 5.5048538e-08 | 3455 |
| 0.0154 | 1.0 | 1.6518 | 0.7042 | 5.502952e-08 | 3456 |
| 0.0267 | 0.9929 | 1.6521 | 0.7042 | 5.5010503e-08 | 3457 |
| 0.0198 | 0.9976 | 1.6541 | 0.7042 | 5.4991485e-08 | 3458 |
| 0.0245 | 0.9929 | 1.6519 | 0.7042 | 5.497247e-08 | 3459 |
| 0.0141 | 0.9976 | 1.6479 | 0.7042 | 5.4953457e-08 | 3460 |
| 0.0220 | 0.9953 | 1.6452 | 0.7113 | 5.4934443e-08 | 3461 |
| 0.0130 | 1.0 | 1.6465 | 0.7113 | 5.4915432e-08 | 3462 |
| 0.0167 | 0.9976 | 1.6475 | 0.7042 | 5.489642e-08 | 3463 |
| 0.0145 | 0.9953 | 1.6484 | 0.7042 | 5.487741e-08 | 3464 |
| 0.0170 | 1.0 | 1.6473 | 0.7042 | 5.4858404e-08 | 3465 |
| 0.0166 | 0.9976 | 1.6476 | 0.7042 | 5.4839397e-08 | 3466 |
| 0.0128 | 1.0 | 1.6513 | 0.7042 | 5.482039e-08 | 3467 |
| 0.0116 | 1.0 | 1.6535 | 0.7042 | 5.4801387e-08 | 3468 |
| 0.0155 | 1.0 | 1.6540 | 0.7042 | 5.4782383e-08 | 3469 |
| 0.0139 | 0.9976 | 1.6533 | 0.7042 | 5.476338e-08 | 3470 |
| 0.0135 | 0.9976 | 1.6546 | 0.7042 | 5.4744376e-08 | 3471 |
| 0.0156 | 0.9976 | 1.6570 | 0.7042 | 5.4725376e-08 | 3472 |
| 0.0193 | 0.9976 | 1.6592 | 0.7042 | 5.4706376e-08 | 3473 |
| 0.0139 | 0.9976 | 1.6579 | 0.7042 | 5.4687376e-08 | 3474 |
| 0.0149 | 1.0 | 1.6554 | 0.7042 | 5.466838e-08 | 3475 |
| 0.0192 | 0.9953 | 1.6534 | 0.7113 | 5.4649384e-08 | 3476 |
| 0.0115 | 1.0 | 1.6532 | 0.7113 | 5.4630387e-08 | 3477 |
| 0.0201 | 0.9976 | 1.6557 | 0.7042 | 5.4611395e-08 | 3478 |
| 0.0179 | 0.9953 | 1.6575 | 0.7042 | 5.45924e-08 | 3479 |
| 0.0122 | 1.0 | 1.6529 | 0.7113 | 5.457341e-08 | 3480 |
| 0.0155 | 0.9976 | 1.6551 | 0.7042 | 5.455442e-08 | 3481 |
| 0.0138 | 1.0 | 1.6578 | 0.7042 | 5.453543e-08 | 3482 |
| 0.0254 | 0.9906 | 1.6597 | 0.7042 | 5.451644e-08 | 3483 |
| 0.0177 | 0.9953 | 1.6583 | 0.7042 | 5.4497455e-08 | 3484 |
| 0.0185 | 0.9976 | 1.6585 | 0.7042 | 5.447847e-08 | 3485 |
| 0.0216 | 0.9976 | 1.6579 | 0.7042 | 5.4459484e-08 | 3486 |
| 0.0153 | 0.9976 | 1.6601 | 0.7042 | 5.4440502e-08 | 3487 |
| 0.0137 | 0.9976 | 1.6600 | 0.7042 | 5.442152e-08 | 3488 |
| 0.0135 | 1.0 | 1.6597 | 0.7042 | 5.4402538e-08 | 3489 |
| 0.0155 | 1.0 | 1.6602 | 0.7042 | 5.438356e-08 | 3490 |
| 0.0180 | 0.9953 | 1.6614 | 0.7042 | 5.436458e-08 | 3491 |
| 0.0183 | 0.9976 | 1.6599 | 0.7042 | 5.4345602e-08 | 3492 |
| 0.0145 | 1.0 | 1.6583 | 0.7113 | 5.4326627e-08 | 3493 |
| 0.0148 | 0.9953 | 1.6562 | 0.7113 | 5.430765e-08 | 3494 |
| 0.0112 | 1.0 | 1.6544 | 0.7113 | 5.4288677e-08 | 3495 |
| 0.0169 | 0.9976 | 1.6554 | 0.7113 | 5.4269705e-08 | 3496 |
| 0.0123 | 0.9976 | 1.6571 | 0.7113 | 5.4250734e-08 | 3497 |
| 0.0178 | 0.9976 | 1.6600 | 0.7042 | 5.4231762e-08 | 3498 |
| 0.0169 | 0.9976 | 1.6622 | 0.7042 | 5.4212794e-08 | 3499 |
| 0.0136 | 1.0 | 1.6631 | 0.7042 | 5.4193826e-08 | 3500 |
| 0.0117 | 1.0 | 1.6622 | 0.7042 | 5.417486e-08 | 3501 |
| 0.0156 | 0.9976 | 1.6602 | 0.7042 | 5.4155894e-08 | 3502 |
| 0.0162 | 0.9976 | 1.6599 | 0.7042 | 5.413693e-08 | 3503 |
| 0.0161 | 0.9976 | 1.6579 | 0.7042 | 5.4117965e-08 | 3504 |
| 0.0161 | 0.9976 | 1.6569 | 0.7042 | 5.4099004e-08 | 3505 |
| 0.0153 | 0.9976 | 1.6572 | 0.7042 | 5.4080044e-08 | 3506 |
| 0.0197 | 0.9976 | 1.6590 | 0.7042 | 5.4061083e-08 | 3507 |
| 0.0152 | 0.9976 | 1.6595 | 0.7042 | 5.4042125e-08 | 3508 |
| 0.0112 | 1.0 | 1.6600 | 0.7042 | 5.4023168e-08 | 3509 |
| 0.0106 | 1.0 | 1.6590 | 0.7042 | 5.4004214e-08 | 3510 |
| 0.0246 | 0.9929 | 1.6607 | 0.7042 | 5.398526e-08 | 3511 |
| 0.0135 | 0.9976 | 1.6623 | 0.7042 | 5.3966307e-08 | 3512 |
| 0.0182 | 0.9953 | 1.6601 | 0.7042 | 5.3947357e-08 | 3513 |
| 0.0116 | 1.0 | 1.6597 | 0.7042 | 5.3928407e-08 | 3514 |
| 0.0231 | 0.9976 | 1.6588 | 0.7042 | 5.3909456e-08 | 3515 |
| 0.0201 | 0.9906 | 1.6624 | 0.7042 | 5.389051e-08 | 3516 |
| 0.0138 | 0.9976 | 1.6626 | 0.7042 | 5.3871563e-08 | 3517 |
| 0.0260 | 0.9929 | 1.6620 | 0.7042 | 5.3852617e-08 | 3518 |
| 0.0301 | 0.9882 | 1.6597 | 0.7042 | 5.3833674e-08 | 3519 |
| 0.0172 | 0.9976 | 1.6587 | 0.7042 | 5.381473e-08 | 3520 |
| 0.0157 | 0.9976 | 1.6597 | 0.7042 | 5.3795787e-08 | 3521 |
| 0.0172 | 0.9976 | 1.6580 | 0.7042 | 5.3776848e-08 | 3522 |
| 0.0086 | 1.0 | 1.6565 | 0.7042 | 5.375791e-08 | 3523 |
| 0.0138 | 0.9976 | 1.6574 | 0.7042 | 5.3738972e-08 | 3524 |
| 0.0171 | 0.9976 | 1.6590 | 0.7042 | 5.3720036e-08 | 3525 |
| 0.0151 | 1.0 | 1.6580 | 0.7042 | 5.37011e-08 | 3526 |
| 0.0126 | 0.9976 | 1.6569 | 0.7113 | 5.3682168e-08 | 3527 |
| 0.0173 | 0.9976 | 1.6550 | 0.7113 | 5.3663236e-08 | 3528 |
| 0.0127 | 1.0 | 1.6540 | 0.7113 | 5.3644303e-08 | 3529 |
| 0.0115 | 1.0 | 1.6545 | 0.7113 | 5.3625374e-08 | 3530 |
| 0.0134 | 1.0 | 1.6543 | 0.7113 | 5.3606446e-08 | 3531 |
| 0.0154 | 0.9976 | 1.6551 | 0.7113 | 5.3587517e-08 | 3532 |
| 0.0185 | 0.9976 | 1.6560 | 0.7113 | 5.356859e-08 | 3533 |
| 0.0125 | 0.9976 | 1.6557 | 0.7113 | 5.3549666e-08 | 3534 |
| 0.0129 | 0.9976 | 1.6555 | 0.7113 | 5.3530744e-08 | 3535 |
| 0.0208 | 0.9976 | 1.6551 | 0.7113 | 5.3511823e-08 | 3536 |
| 0.0190 | 0.9953 | 1.6569 | 0.7113 | 5.34929e-08 | 3537 |
| 0.0165 | 0.9953 | 1.6594 | 0.7113 | 5.3473983e-08 | 3538 |
| 0.0134 | 1.0 | 1.6621 | 0.7042 | 5.3455064e-08 | 3539 |
| 0.0181 | 0.9953 | 1.6631 | 0.7042 | 5.343615e-08 | 3540 |
| 0.0133 | 1.0 | 1.6644 | 0.7042 | 5.3417235e-08 | 3541 |
| 0.0183 | 1.0 | 1.6636 | 0.7042 | 5.339832e-08 | 3542 |
| 0.0143 | 0.9976 | 1.6620 | 0.7042 | 5.337941e-08 | 3543 |
| 0.0143 | 0.9976 | 1.6591 | 0.7113 | 5.33605e-08 | 3544 |
| 0.0137 | 0.9976 | 1.6592 | 0.7183 | 5.3341587e-08 | 3545 |
| 0.0114 | 1.0 | 1.6601 | 0.7113 | 5.332268e-08 | 3546 |
| 0.0152 | 0.9976 | 1.6635 | 0.7113 | 5.3303772e-08 | 3547 |
| 0.0121 | 0.9976 | 1.6666 | 0.7042 | 5.3284868e-08 | 3548 |
| 0.0118 | 1.0 | 1.6666 | 0.7042 | 5.3265964e-08 | 3549 |
| 0.0149 | 1.0 | 1.6661 | 0.7042 | 5.324706e-08 | 3550 |
| 0.0157 | 1.0 | 1.6685 | 0.7042 | 5.322816e-08 | 3551 |
| 0.0183 | 0.9953 | 1.6709 | 0.7042 | 5.320926e-08 | 3552 |
| 0.0132 | 1.0 | 1.6718 | 0.7042 | 5.3190362e-08 | 3553 |
| 0.0094 | 1.0 | 1.6720 | 0.7042 | 5.3171465e-08 | 3554 |
| 0.0170 | 0.9976 | 1.6722 | 0.7042 | 5.315257e-08 | 3555 |
| 0.0118 | 1.0 | 1.6736 | 0.7042 | 5.3133675e-08 | 3556 |
| 0.0133 | 0.9976 | 1.6725 | 0.7042 | 5.3114782e-08 | 3557 |
| 0.0174 | 0.9953 | 1.6713 | 0.7042 | 5.3095892e-08 | 3558 |
| 0.0112 | 1.0 | 1.6713 | 0.7042 | 5.3077002e-08 | 3559 |
| 0.0189 | 0.9953 | 1.6703 | 0.7042 | 5.3058113e-08 | 3560 |
| 0.0116 | 0.9976 | 1.6679 | 0.7042 | 5.3039226e-08 | 3561 |
| 0.0126 | 1.0 | 1.6658 | 0.7042 | 5.302034e-08 | 3562 |
| 0.0188 | 0.9953 | 1.6678 | 0.7042 | 5.3001457e-08 | 3563 |
| 0.0102 | 1.0 | 1.6691 | 0.7042 | 5.2982575e-08 | 3564 |
| 0.0215 | 0.9929 | 1.6683 | 0.7042 | 5.2963692e-08 | 3565 |
| 0.0120 | 1.0 | 1.6663 | 0.7042 | 5.2944813e-08 | 3566 |
| 0.0116 | 1.0 | 1.6656 | 0.7042 | 5.2925934e-08 | 3567 |
| 0.0110 | 1.0 | 1.6641 | 0.7113 | 5.290706e-08 | 3568 |
| 0.0102 | 1.0 | 1.6641 | 0.7113 | 5.2888183e-08 | 3569 |
| 0.0132 | 1.0 | 1.6648 | 0.7113 | 5.2869307e-08 | 3570 |
| 0.0141 | 0.9976 | 1.6661 | 0.7042 | 5.2850435e-08 | 3571 |
| 0.0094 | 1.0 | 1.6642 | 0.7113 | 5.2831563e-08 | 3572 |
| 0.0127 | 0.9976 | 1.6649 | 0.7042 | 5.2812695e-08 | 3573 |
| 0.0181 | 0.9929 | 1.6665 | 0.7042 | 5.2793826e-08 | 3574 |
| 0.0166 | 0.9953 | 1.6635 | 0.7042 | 5.2774958e-08 | 3575 |
| 0.0134 | 0.9976 | 1.6627 | 0.7113 | 5.2756093e-08 | 3576 |
| 0.0138 | 1.0 | 1.6640 | 0.7042 | 5.2737228e-08 | 3577 |
| 0.0123 | 1.0 | 1.6661 | 0.7042 | 5.2718367e-08 | 3578 |
| 0.0119 | 1.0 | 1.6677 | 0.7042 | 5.2699505e-08 | 3579 |
| 0.0123 | 0.9976 | 1.6718 | 0.7042 | 5.2680644e-08 | 3580 |
| 0.0182 | 0.9976 | 1.6720 | 0.7042 | 5.2661786e-08 | 3581 |
| 0.0138 | 1.0 | 1.6707 | 0.7042 | 5.264293e-08 | 3582 |
| 0.0095 | 1.0 | 1.6701 | 0.7042 | 5.2624074e-08 | 3583 |
| 0.0129 | 1.0 | 1.6704 | 0.7113 | 5.260522e-08 | 3584 |
| 0.0169 | 0.9953 | 1.6676 | 0.7113 | 5.258637e-08 | 3585 |
| 0.0130 | 0.9976 | 1.6660 | 0.7113 | 5.256752e-08 | 3586 |
| 0.0173 | 0.9953 | 1.6669 | 0.7113 | 5.2548668e-08 | 3587 |
| 0.0157 | 0.9953 | 1.6691 | 0.7113 | 5.252982e-08 | 3588 |
| 0.0116 | 1.0 | 1.6711 | 0.7113 | 5.2510973e-08 | 3589 |
| 0.0138 | 0.9976 | 1.6725 | 0.7113 | 5.249213e-08 | 3590 |
| 0.0167 | 0.9976 | 1.6757 | 0.7042 | 5.2473286e-08 | 3591 |
| 0.0109 | 1.0 | 1.6777 | 0.7042 | 5.2454446e-08 | 3592 |
| 0.0101 | 1.0 | 1.6771 | 0.7042 | 5.2435606e-08 | 3593 |
| 0.0132 | 1.0 | 1.6758 | 0.7042 | 5.2416766e-08 | 3594 |
| 0.0113 | 1.0 | 1.6753 | 0.7042 | 5.239793e-08 | 3595 |
| 0.0144 | 1.0 | 1.6729 | 0.7042 | 5.2379093e-08 | 3596 |
| 0.0103 | 1.0 | 1.6715 | 0.7042 | 5.236026e-08 | 3597 |
| 0.0184 | 0.9976 | 1.6731 | 0.7042 | 5.2341427e-08 | 3598 |
| 0.0156 | 0.9953 | 1.6703 | 0.7113 | 5.2322598e-08 | 3599 |
| 0.0151 | 0.9976 | 1.6689 | 0.7113 | 5.230377e-08 | 3600 |
| 0.0113 | 0.9976 | 1.6668 | 0.7183 | 5.228494e-08 | 3601 |
| 0.0155 | 0.9976 | 1.6675 | 0.7113 | 5.2266113e-08 | 3602 |
| 0.0195 | 0.9953 | 1.6706 | 0.7113 | 5.2247287e-08 | 3603 |
| 0.0124 | 0.9976 | 1.6713 | 0.7113 | 5.2228465e-08 | 3604 |
| 0.0089 | 1.0 | 1.6717 | 0.7113 | 5.2209643e-08 | 3605 |
| 0.0137 | 0.9976 | 1.6714 | 0.7113 | 5.2190824e-08 | 3606 |
| 0.0131 | 1.0 | 1.6715 | 0.7113 | 5.2172005e-08 | 3607 |
| 0.0106 | 1.0 | 1.6721 | 0.7113 | 5.215319e-08 | 3608 |
| 0.0131 | 0.9976 | 1.6708 | 0.7113 | 5.2134375e-08 | 3609 |
| 0.0119 | 1.0 | 1.6708 | 0.7113 | 5.211556e-08 | 3610 |
| 0.0132 | 0.9976 | 1.6741 | 0.7042 | 5.209675e-08 | 3611 |
| 0.0177 | 0.9929 | 1.6763 | 0.7042 | 5.2077937e-08 | 3612 |
| 0.0096 | 1.0 | 1.6773 | 0.7042 | 5.205913e-08 | 3613 |
| 0.0146 | 0.9976 | 1.6771 | 0.7042 | 5.204032e-08 | 3614 |
| 0.0174 | 0.9976 | 1.6777 | 0.7042 | 5.2021516e-08 | 3615 |
| 0.0176 | 0.9976 | 1.6795 | 0.7042 | 5.200271e-08 | 3616 |
| 0.0117 | 0.9976 | 1.6807 | 0.7042 | 5.198391e-08 | 3617 |
| 0.0160 | 1.0 | 1.6768 | 0.7042 | 5.196511e-08 | 3618 |
| 0.0093 | 1.0 | 1.6757 | 0.7042 | 5.194631e-08 | 3619 |
| 0.0145 | 0.9953 | 1.6758 | 0.7042 | 5.192751e-08 | 3620 |
| 0.0127 | 0.9976 | 1.6761 | 0.7042 | 5.1908714e-08 | 3621 |
| 0.0134 | 0.9976 | 1.6780 | 0.7042 | 5.188992e-08 | 3622 |
| 0.0134 | 1.0 | 1.6789 | 0.7042 | 5.1871126e-08 | 3623 |
| 0.0174 | 0.9953 | 1.6824 | 0.7042 | 5.1852336e-08 | 3624 |
| 0.0236 | 0.9929 | 1.6828 | 0.7042 | 5.1833545e-08 | 3625 |
| 0.0101 | 1.0 | 1.6814 | 0.7042 | 5.181476e-08 | 3626 |
| 0.0210 | 0.9906 | 1.6819 | 0.7042 | 5.1795972e-08 | 3627 |
| 0.0177 | 0.9953 | 1.6830 | 0.7042 | 5.177719e-08 | 3628 |
| 0.0116 | 1.0 | 1.6849 | 0.7042 | 5.1758406e-08 | 3629 |
| 0.0101 | 1.0 | 1.6845 | 0.7042 | 5.1739622e-08 | 3630 |
| 0.0135 | 0.9953 | 1.6857 | 0.7042 | 5.1720843e-08 | 3631 |
| 0.0136 | 0.9976 | 1.6832 | 0.7042 | 5.1702063e-08 | 3632 |
| 0.0166 | 0.9953 | 1.6817 | 0.7042 | 5.1683287e-08 | 3633 |
| 0.0171 | 0.9976 | 1.6827 | 0.7042 | 5.166451e-08 | 3634 |
| 0.0176 | 0.9976 | 1.6815 | 0.7042 | 5.164574e-08 | 3635 |
| 0.0120 | 1.0 | 1.6806 | 0.7113 | 5.1626966e-08 | 3636 |
| 0.0113 | 1.0 | 1.6815 | 0.7042 | 5.1608197e-08 | 3637 |
| 0.0160 | 0.9976 | 1.6821 | 0.7113 | 5.1589428e-08 | 3638 |
| 0.0087 | 1.0 | 1.6823 | 0.7042 | 5.1570662e-08 | 3639 |
| 0.0139 | 1.0 | 1.6835 | 0.7042 | 5.1551897e-08 | 3640 |
| 0.0132 | 0.9976 | 1.6863 | 0.7042 | 5.1533135e-08 | 3641 |
| 0.0112 | 1.0 | 1.6859 | 0.7042 | 5.1514373e-08 | 3642 |
| 0.0142 | 0.9976 | 1.6829 | 0.7042 | 5.1495615e-08 | 3643 |
| 0.0168 | 0.9953 | 1.6841 | 0.7042 | 5.1476857e-08 | 3644 |
| 0.0116 | 0.9976 | 1.6851 | 0.7042 | 5.1458102e-08 | 3645 |
| 0.0130 | 1.0 | 1.6867 | 0.7042 | 5.1439347e-08 | 3646 |
| 0.0116 | 1.0 | 1.6899 | 0.7042 | 5.1420592e-08 | 3647 |
| 0.0092 | 1.0 | 1.6896 | 0.7042 | 5.140184e-08 | 3648 |
| 0.0134 | 1.0 | 1.6873 | 0.7042 | 5.138309e-08 | 3649 |
| 0.0147 | 0.9976 | 1.6886 | 0.7042 | 5.1364342e-08 | 3650 |
| 0.0110 | 1.0 | 1.6879 | 0.7042 | 5.1345594e-08 | 3651 |
| 0.0095 | 1.0 | 1.6881 | 0.7042 | 5.132685e-08 | 3652 |
| 0.0110 | 1.0 | 1.6886 | 0.7042 | 5.1308106e-08 | 3653 |
| 0.0175 | 0.9953 | 1.6850 | 0.7042 | 5.1289366e-08 | 3654 |
| 0.0159 | 0.9976 | 1.6830 | 0.7042 | 5.1270625e-08 | 3655 |
| 0.0176 | 0.9976 | 1.6870 | 0.7042 | 5.1251888e-08 | 3656 |
| 0.0089 | 1.0 | 1.6879 | 0.7042 | 5.123315e-08 | 3657 |
| 0.0110 | 1.0 | 1.6879 | 0.7042 | 5.1214418e-08 | 3658 |
| 0.0133 | 0.9953 | 1.6881 | 0.7042 | 5.1195684e-08 | 3659 |
| 0.0179 | 0.9976 | 1.6866 | 0.7042 | 5.1176954e-08 | 3660 |
| 0.0163 | 0.9953 | 1.6868 | 0.7042 | 5.1158224e-08 | 3661 |
| 0.0203 | 0.9953 | 1.6866 | 0.7042 | 5.1139498e-08 | 3662 |
| 0.0100 | 1.0 | 1.6869 | 0.7042 | 5.112077e-08 | 3663 |
| 0.0234 | 0.9953 | 1.6880 | 0.7042 | 5.110205e-08 | 3664 |
| 0.0103 | 1.0 | 1.6890 | 0.7042 | 5.1083326e-08 | 3665 |
| 0.0145 | 0.9976 | 1.6885 | 0.7042 | 5.1064607e-08 | 3666 |
| 0.0119 | 1.0 | 1.6862 | 0.6972 | 5.1045888e-08 | 3667 |
| 0.0190 | 0.9953 | 1.6864 | 0.7042 | 5.1027172e-08 | 3668 |
| 0.0141 | 0.9976 | 1.6889 | 0.7042 | 5.1008456e-08 | 3669 |
| 0.0141 | 0.9976 | 1.6905 | 0.7042 | 5.0989744e-08 | 3670 |
| 0.0141 | 0.9976 | 1.6915 | 0.7042 | 5.0971032e-08 | 3671 |
| 0.0105 | 1.0 | 1.6893 | 0.7042 | 5.0952323e-08 | 3672 |
| 0.0147 | 1.0 | 1.6901 | 0.7042 | 5.0933615e-08 | 3673 |
| 0.0142 | 0.9976 | 1.6877 | 0.7042 | 5.091491e-08 | 3674 |
| 0.0142 | 0.9976 | 1.6859 | 0.7042 | 5.0896205e-08 | 3675 |
| 0.0103 | 1.0 | 1.6859 | 0.7042 | 5.0877503e-08 | 3676 |
| 0.0121 | 1.0 | 1.6858 | 0.7042 | 5.08588e-08 | 3677 |
| 0.0182 | 0.9976 | 1.6856 | 0.7042 | 5.0840104e-08 | 3678 |
| 0.0252 | 0.9953 | 1.6828 | 0.7042 | 5.0821406e-08 | 3679 |
| 0.0190 | 0.9976 | 1.6802 | 0.7042 | 5.080271e-08 | 3680 |
| 0.0138 | 0.9976 | 1.6790 | 0.7042 | 5.0784017e-08 | 3681 |
| 0.0137 | 0.9976 | 1.6787 | 0.7042 | 5.0765326e-08 | 3682 |
| 0.0172 | 0.9976 | 1.6785 | 0.7042 | 5.0746635e-08 | 3683 |
| 0.0205 | 0.9929 | 1.6797 | 0.7042 | 5.0727948e-08 | 3684 |
| 0.0093 | 1.0 | 1.6815 | 0.7042 | 5.070926e-08 | 3685 |
| 0.0077 | 1.0 | 1.6828 | 0.7042 | 5.0690577e-08 | 3686 |
| 0.0134 | 0.9976 | 1.6823 | 0.7042 | 5.0671893e-08 | 3687 |
| 0.0139 | 0.9976 | 1.6820 | 0.7113 | 5.0653213e-08 | 3688 |
| 0.0124 | 1.0 | 1.6849 | 0.7042 | 5.0634533e-08 | 3689 |
| 0.0217 | 0.9953 | 1.6858 | 0.6972 | 5.0615856e-08 | 3690 |
| 0.0147 | 0.9976 | 1.6867 | 0.7042 | 5.059718e-08 | 3691 |
| 0.0139 | 1.0 | 1.6869 | 0.7042 | 5.0578507e-08 | 3692 |
| 0.0101 | 0.9976 | 1.6887 | 0.7042 | 5.0559834e-08 | 3693 |
| 0.0146 | 1.0 | 1.6893 | 0.7042 | 5.0541164e-08 | 3694 |
| 0.0126 | 0.9976 | 1.6889 | 0.7042 | 5.0522495e-08 | 3695 |
| 0.0151 | 0.9953 | 1.6916 | 0.7042 | 5.050383e-08 | 3696 |
| 0.0122 | 1.0 | 1.6930 | 0.7042 | 5.0485163e-08 | 3697 |
| 0.0117 | 1.0 | 1.6941 | 0.7042 | 5.04665e-08 | 3698 |
| 0.0131 | 1.0 | 1.6933 | 0.6972 | 5.0447838e-08 | 3699 |
| 0.0173 | 1.0 | 1.6943 | 0.7042 | 5.042918e-08 | 3700 |
| 0.0181 | 0.9953 | 1.6932 | 0.6972 | 5.0410524e-08 | 3701 |
| 0.0135 | 0.9976 | 1.6909 | 0.6972 | 5.039187e-08 | 3702 |
| 0.0193 | 0.9976 | 1.6904 | 0.7042 | 5.0373217e-08 | 3703 |
| 0.0099 | 1.0 | 1.6912 | 0.7042 | 5.0354565e-08 | 3704 |
| 0.0140 | 0.9976 | 1.6925 | 0.7042 | 5.0335917e-08 | 3705 |
| 0.0128 | 1.0 | 1.6933 | 0.7042 | 5.031727e-08 | 3706 |
| 0.0120 | 1.0 | 1.6934 | 0.7042 | 5.0298624e-08 | 3707 |
| 0.0166 | 0.9976 | 1.6924 | 0.7042 | 5.027998e-08 | 3708 |
| 0.0138 | 0.9953 | 1.6910 | 0.7042 | 5.0261338e-08 | 3709 |
| 0.0103 | 1.0 | 1.6912 | 0.7042 | 5.0242697e-08 | 3710 |
| 0.0124 | 0.9976 | 1.6915 | 0.7042 | 5.022406e-08 | 3711 |
| 0.0204 | 0.9953 | 1.6911 | 0.7042 | 5.0205422e-08 | 3712 |
| 0.0123 | 1.0 | 1.6921 | 0.6972 | 5.0186788e-08 | 3713 |
| 0.0104 | 1.0 | 1.6923 | 0.6972 | 5.0168154e-08 | 3714 |
| 0.0114 | 0.9976 | 1.6922 | 0.6972 | 5.0149524e-08 | 3715 |
| 0.0149 | 0.9976 | 1.6922 | 0.6972 | 5.0130897e-08 | 3716 |
| 0.0122 | 1.0 | 1.6917 | 0.7042 | 5.011227e-08 | 3717 |
| 0.0091 | 1.0 | 1.6929 | 0.7042 | 5.0093647e-08 | 3718 |
| 0.0123 | 1.0 | 1.6924 | 0.7042 | 5.0075023e-08 | 3719 |
| 0.0082 | 1.0 | 1.6922 | 0.7042 | 5.0056403e-08 | 3720 |
| 0.0186 | 0.9953 | 1.6946 | 0.7042 | 5.0037784e-08 | 3721 |
| 0.0106 | 1.0 | 1.6985 | 0.7042 | 5.0019167e-08 | 3722 |
| 0.0179 | 0.9976 | 1.6977 | 0.7042 | 5.000055e-08 | 3723 |
| 0.0126 | 1.0 | 1.6980 | 0.7042 | 4.998194e-08 | 3724 |
| 0.0173 | 0.9976 | 1.6950 | 0.7042 | 4.9963326e-08 | 3725 |
| 0.0231 | 0.9906 | 1.6966 | 0.7042 | 4.9944717e-08 | 3726 |
| 0.0157 | 1.0 | 1.6952 | 0.7042 | 4.9926108e-08 | 3727 |
| 0.0210 | 0.9953 | 1.6905 | 0.7042 | 4.9907502e-08 | 3728 |
| 0.0135 | 0.9976 | 1.6919 | 0.7042 | 4.98889e-08 | 3729 |
| 0.0100 | 1.0 | 1.6932 | 0.7042 | 4.9870298e-08 | 3730 |
| 0.0190 | 0.9953 | 1.6922 | 0.7042 | 4.98517e-08 | 3731 |
| 0.0105 | 1.0 | 1.6935 | 0.7042 | 4.98331e-08 | 3732 |
| 0.0084 | 1.0 | 1.6941 | 0.7042 | 4.9814506e-08 | 3733 |
| 0.0106 | 1.0 | 1.6923 | 0.7042 | 4.979591e-08 | 3734 |
| 0.0198 | 0.9953 | 1.6937 | 0.7042 | 4.977732e-08 | 3735 |
| 0.0109 | 1.0 | 1.6949 | 0.6972 | 4.975873e-08 | 3736 |
| 0.0129 | 1.0 | 1.6957 | 0.7042 | 4.974014e-08 | 3737 |
| 0.0095 | 1.0 | 1.6956 | 0.7042 | 4.9721557e-08 | 3738 |
| 0.0160 | 0.9976 | 1.6942 | 0.7042 | 4.9702972e-08 | 3739 |
| 0.0135 | 1.0 | 1.6938 | 0.7042 | 4.968439e-08 | 3740 |
| 0.0122 | 1.0 | 1.6941 | 0.7042 | 4.966581e-08 | 3741 |
| 0.0120 | 0.9976 | 1.6945 | 0.7042 | 4.9647234e-08 | 3742 |
| 0.0098 | 1.0 | 1.6946 | 0.7042 | 4.9628657e-08 | 3743 |
| 0.0104 | 0.9976 | 1.6949 | 0.7042 | 4.9610083e-08 | 3744 |
| 0.0271 | 0.9953 | 1.6965 | 0.7042 | 4.959151e-08 | 3745 |
| 0.0131 | 0.9953 | 1.6978 | 0.7042 | 4.957294e-08 | 3746 |
| 0.0148 | 0.9976 | 1.6994 | 0.7042 | 4.9554373e-08 | 3747 |
| 0.0175 | 0.9953 | 1.7007 | 0.7042 | 4.9535807e-08 | 3748 |
| 0.0091 | 1.0 | 1.7011 | 0.7042 | 4.9517244e-08 | 3749 |
| 0.0166 | 0.9953 | 1.7012 | 0.7042 | 4.949868e-08 | 3750 |
| 0.0118 | 0.9976 | 1.6992 | 0.7042 | 4.948012e-08 | 3751 |
| 0.0125 | 0.9976 | 1.6980 | 0.7042 | 4.9461562e-08 | 3752 |
| 0.0111 | 1.0 | 1.6968 | 0.7042 | 4.9443006e-08 | 3753 |
| 0.0114 | 1.0 | 1.6990 | 0.7042 | 4.9424454e-08 | 3754 |
| 0.0096 | 1.0 | 1.7000 | 0.7042 | 4.94059e-08 | 3755 |
| 0.0149 | 0.9953 | 1.7001 | 0.7042 | 4.9387353e-08 | 3756 |
| 0.0112 | 1.0 | 1.6959 | 0.7042 | 4.9368804e-08 | 3757 |
| 0.0096 | 1.0 | 1.6935 | 0.7042 | 4.935026e-08 | 3758 |
| 0.0121 | 1.0 | 1.6943 | 0.7042 | 4.9331714e-08 | 3759 |
| 0.0159 | 0.9976 | 1.6961 | 0.7042 | 4.9313172e-08 | 3760 |
| 0.0157 | 0.9976 | 1.6958 | 0.7042 | 4.9294634e-08 | 3761 |
| 0.0098 | 1.0 | 1.6955 | 0.7042 | 4.9276096e-08 | 3762 |
| 0.0128 | 1.0 | 1.6947 | 0.7042 | 4.925756e-08 | 3763 |
| 0.0155 | 0.9953 | 1.6930 | 0.7113 | 4.9239027e-08 | 3764 |
| 0.0193 | 0.9953 | 1.6946 | 0.7113 | 4.9220496e-08 | 3765 |
| 0.0210 | 0.9906 | 1.6952 | 0.7042 | 4.9201965e-08 | 3766 |
| 0.0151 | 0.9976 | 1.6931 | 0.7042 | 4.9183438e-08 | 3767 |
| 0.0099 | 0.9976 | 1.6921 | 0.7042 | 4.9164914e-08 | 3768 |
| 0.0128 | 0.9976 | 1.6914 | 0.7042 | 4.914639e-08 | 3769 |
| 0.0102 | 1.0 | 1.6911 | 0.7113 | 4.912787e-08 | 3770 |
| 0.0121 | 0.9976 | 1.6921 | 0.7113 | 4.910935e-08 | 3771 |
| 0.0118 | 1.0 | 1.6923 | 0.7113 | 4.9090833e-08 | 3772 |
| 0.0153 | 0.9976 | 1.6926 | 0.7113 | 4.9072316e-08 | 3773 |
| 0.0164 | 0.9953 | 1.6935 | 0.7042 | 4.9053803e-08 | 3774 |
| 0.0140 | 0.9976 | 1.6921 | 0.7113 | 4.9035293e-08 | 3775 |
| 0.0095 | 1.0 | 1.6941 | 0.7042 | 4.9016784e-08 | 3776 |
| 0.0185 | 0.9929 | 1.6982 | 0.7042 | 4.8998277e-08 | 3777 |
| 0.0185 | 0.9953 | 1.6993 | 0.7042 | 4.897977e-08 | 3778 |
| 0.0117 | 0.9976 | 1.6992 | 0.7042 | 4.896127e-08 | 3779 |
| 0.0117 | 0.9976 | 1.7002 | 0.7042 | 4.894277e-08 | 3780 |
| 0.0091 | 1.0 | 1.6998 | 0.7042 | 4.892427e-08 | 3781 |
| 0.0151 | 0.9976 | 1.7027 | 0.7042 | 4.8905775e-08 | 3782 |
| 0.0074 | 1.0 | 1.7034 | 0.7042 | 4.888728e-08 | 3783 |
| 0.0102 | 1.0 | 1.7041 | 0.7042 | 4.8868788e-08 | 3784 |
| 0.0190 | 0.9976 | 1.7050 | 0.7042 | 4.88503e-08 | 3785 |
| 0.0085 | 1.0 | 1.7073 | 0.7042 | 4.883181e-08 | 3786 |
| 0.0120 | 0.9976 | 1.7085 | 0.7042 | 4.8813327e-08 | 3787 |
| 0.0197 | 0.9929 | 1.7082 | 0.7042 | 4.8794842e-08 | 3788 |
| 0.0118 | 0.9976 | 1.7058 | 0.7042 | 4.877636e-08 | 3789 |
| 0.0113 | 1.0 | 1.7032 | 0.7042 | 4.8757883e-08 | 3790 |
| 0.0166 | 0.9953 | 1.7027 | 0.7042 | 4.8739405e-08 | 3791 |
| 0.0083 | 1.0 | 1.7025 | 0.7042 | 4.872093e-08 | 3792 |
| 0.0148 | 0.9976 | 1.7025 | 0.7042 | 4.8702457e-08 | 3793 |
| 0.0099 | 1.0 | 1.7041 | 0.7042 | 4.8683987e-08 | 3794 |
| 0.0108 | 0.9976 | 1.7050 | 0.7042 | 4.866552e-08 | 3795 |
| 0.0113 | 1.0 | 1.7052 | 0.7042 | 4.8647053e-08 | 3796 |
| 0.0119 | 1.0 | 1.7038 | 0.7042 | 4.862859e-08 | 3797 |
| 0.0091 | 1.0 | 1.7034 | 0.7042 | 4.8610126e-08 | 3798 |
| 0.0140 | 0.9976 | 1.7051 | 0.7042 | 4.8591666e-08 | 3799 |
| 0.0100 | 0.9976 | 1.7070 | 0.7042 | 4.857321e-08 | 3800 |
| 0.0125 | 1.0 | 1.7061 | 0.7042 | 4.8554753e-08 | 3801 |
| 0.0095 | 1.0 | 1.7014 | 0.7042 | 4.85363e-08 | 3802 |
| 0.0087 | 1.0 | 1.7005 | 0.7042 | 4.8517848e-08 | 3803 |
| 0.0107 | 0.9976 | 1.7000 | 0.7042 | 4.84994e-08 | 3804 |
| 0.0124 | 0.9976 | 1.6996 | 0.7042 | 4.8480953e-08 | 3805 |
| 0.0115 | 1.0 | 1.6997 | 0.7042 | 4.8462507e-08 | 3806 |
| 0.0122 | 1.0 | 1.7001 | 0.6972 | 4.8444065e-08 | 3807 |
| 0.0150 | 0.9953 | 1.7010 | 0.7042 | 4.8425623e-08 | 3808 |
| 0.0153 | 1.0 | 1.7032 | 0.7042 | 4.8407184e-08 | 3809 |
| 0.0108 | 1.0 | 1.7053 | 0.7042 | 4.838875e-08 | 3810 |
| 0.0121 | 1.0 | 1.7046 | 0.7042 | 4.8370314e-08 | 3811 |
| 0.0107 | 1.0 | 1.7026 | 0.7042 | 4.8351882e-08 | 3812 |
| 0.0096 | 1.0 | 1.7025 | 0.7042 | 4.8333455e-08 | 3813 |
| 0.0121 | 0.9976 | 1.7039 | 0.7042 | 4.8315027e-08 | 3814 |
| 0.0118 | 1.0 | 1.7075 | 0.7042 | 4.8296602e-08 | 3815 |
| 0.0111 | 1.0 | 1.7073 | 0.7042 | 4.8278178e-08 | 3816 |
| 0.0141 | 0.9953 | 1.7071 | 0.7042 | 4.8259757e-08 | 3817 |
| 0.0134 | 0.9953 | 1.7140 | 0.7042 | 4.824134e-08 | 3818 |
| 0.0141 | 0.9976 | 1.7140 | 0.7042 | 4.8222923e-08 | 3819 |
| 0.0096 | 1.0 | 1.7130 | 0.7042 | 4.820451e-08 | 3820 |
| 0.0098 | 1.0 | 1.7106 | 0.7042 | 4.81861e-08 | 3821 |
| 0.0189 | 0.9953 | 1.7082 | 0.7042 | 4.816769e-08 | 3822 |
| 0.0124 | 0.9976 | 1.7077 | 0.7042 | 4.8149282e-08 | 3823 |
| 0.0095 | 1.0 | 1.7083 | 0.7042 | 4.8130875e-08 | 3824 |
| 0.0103 | 0.9976 | 1.7077 | 0.7042 | 4.8112472e-08 | 3825 |
| 0.0182 | 0.9929 | 1.7075 | 0.7042 | 4.8094073e-08 | 3826 |
| 0.0194 | 0.9906 | 1.7100 | 0.7042 | 4.8075673e-08 | 3827 |
| 0.0112 | 1.0 | 1.7105 | 0.7042 | 4.8057277e-08 | 3828 |
| 0.0121 | 0.9976 | 1.7099 | 0.7042 | 4.8038885e-08 | 3829 |
| 0.0156 | 0.9976 | 1.7118 | 0.7042 | 4.8020492e-08 | 3830 |
| 0.0156 | 0.9976 | 1.7094 | 0.7042 | 4.8002104e-08 | 3831 |
| 0.0118 | 0.9976 | 1.7057 | 0.7042 | 4.7983715e-08 | 3832 |
| 0.0104 | 1.0 | 1.7046 | 0.7042 | 4.796533e-08 | 3833 |
| 0.0086 | 1.0 | 1.7042 | 0.7042 | 4.7946948e-08 | 3834 |
| 0.0107 | 1.0 | 1.7037 | 0.7042 | 4.7928566e-08 | 3835 |
| 0.0103 | 0.9976 | 1.7039 | 0.7042 | 4.7910188e-08 | 3836 |
| 0.0125 | 1.0 | 1.7051 | 0.7042 | 4.7891813e-08 | 3837 |
| 0.0168 | 0.9953 | 1.7068 | 0.7042 | 4.787344e-08 | 3838 |
| 0.0089 | 1.0 | 1.7079 | 0.7042 | 4.7855067e-08 | 3839 |
| 0.0155 | 0.9953 | 1.7069 | 0.7042 | 4.78367e-08 | 3840 |
| 0.0140 | 0.9953 | 1.7057 | 0.7042 | 4.7818332e-08 | 3841 |
| 0.0111 | 1.0 | 1.7052 | 0.7042 | 4.779997e-08 | 3842 |
| 0.0101 | 1.0 | 1.7024 | 0.7042 | 4.7781604e-08 | 3843 |
| 0.0119 | 1.0 | 1.6977 | 0.7113 | 4.7763244e-08 | 3844 |
| 0.0146 | 0.9953 | 1.6999 | 0.7113 | 4.7744887e-08 | 3845 |
| 0.0113 | 1.0 | 1.7034 | 0.7113 | 4.772653e-08 | 3846 |
| 0.0088 | 1.0 | 1.7040 | 0.7113 | 4.7708177e-08 | 3847 |
| 0.0146 | 0.9976 | 1.7042 | 0.7042 | 4.7689827e-08 | 3848 |
| 0.0082 | 1.0 | 1.7043 | 0.7042 | 4.7671477e-08 | 3849 |
| 0.0111 | 1.0 | 1.7054 | 0.7042 | 4.765313e-08 | 3850 |
| 0.0115 | 1.0 | 1.7058 | 0.7042 | 4.763479e-08 | 3851 |
| 0.0131 | 0.9976 | 1.7074 | 0.7042 | 4.7616446e-08 | 3852 |
| 0.0130 | 0.9976 | 1.7052 | 0.7042 | 4.7598107e-08 | 3853 |
| 0.0126 | 0.9976 | 1.7041 | 0.7113 | 4.757977e-08 | 3854 |
| 0.0120 | 1.0 | 1.7008 | 0.7113 | 4.7561436e-08 | 3855 |
| 0.0109 | 0.9976 | 1.7010 | 0.7113 | 4.7543104e-08 | 3856 |
| 0.0122 | 1.0 | 1.7029 | 0.7113 | 4.752477e-08 | 3857 |
| 0.0122 | 0.9976 | 1.7039 | 0.7042 | 4.7506443e-08 | 3858 |
| 0.0107 | 0.9976 | 1.7021 | 0.7113 | 4.748812e-08 | 3859 |
| 0.0158 | 0.9976 | 1.7011 | 0.7113 | 4.7469793e-08 | 3860 |
| 0.0085 | 1.0 | 1.7008 | 0.7183 | 4.7451472e-08 | 3861 |
| 0.0106 | 0.9976 | 1.7012 | 0.7113 | 4.7433154e-08 | 3862 |
| 0.0210 | 0.9976 | 1.7011 | 0.7113 | 4.7414837e-08 | 3863 |
| 0.0127 | 0.9976 | 1.7025 | 0.7113 | 4.7396522e-08 | 3864 |
| 0.0110 | 0.9976 | 1.7026 | 0.7113 | 4.737821e-08 | 3865 |
| 0.0105 | 1.0 | 1.7016 | 0.7113 | 4.73599e-08 | 3866 |
| 0.0126 | 0.9976 | 1.7035 | 0.7113 | 4.7341594e-08 | 3867 |
| 0.0083 | 1.0 | 1.7053 | 0.7042 | 4.732329e-08 | 3868 |
| 0.0147 | 0.9953 | 1.7069 | 0.7042 | 4.7304987e-08 | 3869 |
| 0.0220 | 0.9929 | 1.7074 | 0.7042 | 4.7286687e-08 | 3870 |
| 0.0084 | 1.0 | 1.7080 | 0.7042 | 4.726839e-08 | 3871 |
| 0.0170 | 0.9953 | 1.7067 | 0.7113 | 4.7250094e-08 | 3872 |
| 0.0102 | 1.0 | 1.7062 | 0.7113 | 4.72318e-08 | 3873 |
| 0.0121 | 1.0 | 1.7064 | 0.7113 | 4.721351e-08 | 3874 |
| 0.0151 | 0.9953 | 1.7068 | 0.7113 | 4.7195222e-08 | 3875 |
| 0.0112 | 1.0 | 1.7061 | 0.7113 | 4.7176936e-08 | 3876 |
| 0.0125 | 0.9976 | 1.7054 | 0.7113 | 4.7158654e-08 | 3877 |
| 0.0100 | 1.0 | 1.7056 | 0.7113 | 4.714037e-08 | 3878 |
| 0.0122 | 0.9976 | 1.7070 | 0.7113 | 4.7122093e-08 | 3879 |
| 0.0098 | 1.0 | 1.7059 | 0.7183 | 4.7103818e-08 | 3880 |
| 0.0097 | 1.0 | 1.7059 | 0.7183 | 4.7085543e-08 | 3881 |
| 0.0085 | 1.0 | 1.7071 | 0.7113 | 4.706727e-08 | 3882 |
| 0.0159 | 0.9953 | 1.7093 | 0.7113 | 4.7049003e-08 | 3883 |
| 0.0111 | 0.9976 | 1.7092 | 0.7113 | 4.7030735e-08 | 3884 |
| 0.0137 | 0.9976 | 1.7108 | 0.7113 | 4.701247e-08 | 3885 |
| 0.0111 | 0.9976 | 1.7123 | 0.7042 | 4.699421e-08 | 3886 |
| 0.0122 | 1.0 | 1.7122 | 0.7113 | 4.697595e-08 | 3887 |
| 0.0113 | 1.0 | 1.7117 | 0.7113 | 4.695769e-08 | 3888 |
| 0.0098 | 1.0 | 1.7116 | 0.7113 | 4.6939437e-08 | 3889 |
| 0.0101 | 1.0 | 1.7107 | 0.7113 | 4.6921183e-08 | 3890 |
| 0.0183 | 0.9929 | 1.7096 | 0.7113 | 4.6902933e-08 | 3891 |
| 0.0137 | 0.9976 | 1.7079 | 0.7113 | 4.6884686e-08 | 3892 |
| 0.0134 | 0.9953 | 1.7060 | 0.7113 | 4.686644e-08 | 3893 |
| 0.0084 | 1.0 | 1.7054 | 0.7113 | 4.6848196e-08 | 3894 |
| 0.0154 | 0.9953 | 1.7053 | 0.7113 | 4.6829957e-08 | 3895 |
| 0.0107 | 1.0 | 1.7050 | 0.7042 | 4.681172e-08 | 3896 |
| 0.0156 | 0.9976 | 1.7049 | 0.7113 | 4.6793485e-08 | 3897 |
| 0.0087 | 1.0 | 1.7044 | 0.7113 | 4.6775252e-08 | 3898 |
| 0.0134 | 0.9976 | 1.7051 | 0.7113 | 4.6757023e-08 | 3899 |
| 0.0108 | 0.9976 | 1.7078 | 0.7042 | 4.6738794e-08 | 3900 |
| 0.0103 | 0.9976 | 1.7076 | 0.7042 | 4.672057e-08 | 3901 |
| 0.0082 | 1.0 | 1.7081 | 0.7042 | 4.6702347e-08 | 3902 |
| 0.0118 | 0.9953 | 1.7087 | 0.7042 | 4.6684125e-08 | 3903 |
| 0.0249 | 0.9929 | 1.7105 | 0.7042 | 4.6665907e-08 | 3904 |
| 0.0126 | 1.0 | 1.7103 | 0.7042 | 4.6647692e-08 | 3905 |
| 0.0131 | 0.9976 | 1.7082 | 0.7113 | 4.6629477e-08 | 3906 |
| 0.0149 | 0.9953 | 1.7065 | 0.7113 | 4.6611266e-08 | 3907 |
| 0.0119 | 0.9953 | 1.7059 | 0.7113 | 4.659306e-08 | 3908 |
| 0.0159 | 0.9976 | 1.7072 | 0.7113 | 4.657485e-08 | 3909 |
| 0.0112 | 0.9976 | 1.7081 | 0.7113 | 4.6556647e-08 | 3910 |
| 0.0134 | 1.0 | 1.7092 | 0.7113 | 4.6538446e-08 | 3911 |
| 0.0097 | 0.9976 | 1.7090 | 0.7113 | 4.652025e-08 | 3912 |
| 0.0097 | 1.0 | 1.7080 | 0.7042 | 4.6502052e-08 | 3913 |
| 0.0171 | 0.9976 | 1.7108 | 0.7042 | 4.648386e-08 | 3914 |
| 0.0093 | 1.0 | 1.7123 | 0.7113 | 4.646567e-08 | 3915 |
| 0.0150 | 0.9953 | 1.7114 | 0.7113 | 4.644748e-08 | 3916 |
| 0.0164 | 0.9976 | 1.7105 | 0.7042 | 4.6429292e-08 | 3917 |
| 0.0113 | 0.9976 | 1.7104 | 0.7042 | 4.641111e-08 | 3918 |
| 0.0091 | 0.9976 | 1.7111 | 0.7042 | 4.6392927e-08 | 3919 |
| 0.0218 | 0.9953 | 1.7122 | 0.7042 | 4.6374748e-08 | 3920 |
| 0.0149 | 0.9953 | 1.7126 | 0.7042 | 4.6356572e-08 | 3921 |
| 0.0255 | 0.9953 | 1.7136 | 0.7113 | 4.6338396e-08 | 3922 |
| 0.0126 | 0.9976 | 1.7127 | 0.7042 | 4.6320224e-08 | 3923 |
| 0.0112 | 1.0 | 1.7121 | 0.7042 | 4.6302056e-08 | 3924 |
| 0.0104 | 0.9976 | 1.7126 | 0.7113 | 4.628389e-08 | 3925 |
| 0.0108 | 0.9976 | 1.7118 | 0.7113 | 4.6265725e-08 | 3926 |
| 0.0107 | 0.9976 | 1.7109 | 0.7113 | 4.6247564e-08 | 3927 |
| 0.0145 | 0.9976 | 1.7111 | 0.7042 | 4.6229406e-08 | 3928 |
| 0.0099 | 1.0 | 1.7105 | 0.7042 | 4.6211248e-08 | 3929 |
| 0.0096 | 1.0 | 1.7120 | 0.7113 | 4.6193094e-08 | 3930 |
| 0.0133 | 0.9976 | 1.7129 | 0.7042 | 4.6174943e-08 | 3931 |
| 0.0092 | 1.0 | 1.7156 | 0.7042 | 4.6156796e-08 | 3932 |
| 0.0127 | 0.9976 | 1.7163 | 0.7113 | 4.613865e-08 | 3933 |
| 0.0101 | 0.9976 | 1.7138 | 0.7042 | 4.6120505e-08 | 3934 |
| 0.0157 | 0.9953 | 1.7134 | 0.7042 | 4.6102365e-08 | 3935 |
| 0.0177 | 0.9953 | 1.7165 | 0.7042 | 4.6084224e-08 | 3936 |
| 0.0090 | 1.0 | 1.7178 | 0.7042 | 4.6066088e-08 | 3937 |
| 0.0099 | 1.0 | 1.7174 | 0.7042 | 4.6047955e-08 | 3938 |
| 0.0099 | 1.0 | 1.7168 | 0.7042 | 4.6029825e-08 | 3939 |
| 0.0239 | 0.9929 | 1.7137 | 0.7113 | 4.6011696e-08 | 3940 |
| 0.0085 | 1.0 | 1.7111 | 0.7042 | 4.599357e-08 | 3941 |
| 0.0100 | 1.0 | 1.7113 | 0.7042 | 4.5975447e-08 | 3942 |
| 0.0200 | 0.9929 | 1.7136 | 0.7042 | 4.5957325e-08 | 3943 |
| 0.0084 | 1.0 | 1.7144 | 0.7042 | 4.5939206e-08 | 3944 |
| 0.0096 | 1.0 | 1.7143 | 0.7042 | 4.592109e-08 | 3945 |
| 0.0075 | 1.0 | 1.7142 | 0.7042 | 4.590298e-08 | 3946 |
| 0.0137 | 0.9976 | 1.7135 | 0.7042 | 4.5884867e-08 | 3947 |
| 0.0075 | 1.0 | 1.7129 | 0.7042 | 4.586676e-08 | 3948 |
| 0.0217 | 0.9976 | 1.7115 | 0.7042 | 4.5848655e-08 | 3949 |
| 0.0159 | 0.9953 | 1.7112 | 0.7042 | 4.583055e-08 | 3950 |
| 0.0111 | 0.9976 | 1.7118 | 0.7042 | 4.581245e-08 | 3951 |
| 0.0096 | 0.9976 | 1.7121 | 0.7042 | 4.579435e-08 | 3952 |
| 0.0087 | 1.0 | 1.7134 | 0.7042 | 4.5776257e-08 | 3953 |
| 0.0113 | 1.0 | 1.7147 | 0.7042 | 4.5758163e-08 | 3954 |
| 0.0113 | 0.9976 | 1.7147 | 0.7042 | 4.5740073e-08 | 3955 |
| 0.0162 | 0.9953 | 1.7146 | 0.7042 | 4.5721986e-08 | 3956 |
| 0.0130 | 0.9976 | 1.7141 | 0.6972 | 4.5703903e-08 | 3957 |
| 0.0113 | 1.0 | 1.7147 | 0.6972 | 4.568582e-08 | 3958 |
| 0.0134 | 0.9976 | 1.7172 | 0.6972 | 4.566774e-08 | 3959 |
| 0.0094 | 1.0 | 1.7190 | 0.6972 | 4.5649664e-08 | 3960 |
| 0.0077 | 1.0 | 1.7183 | 0.6972 | 4.5631587e-08 | 3961 |
| 0.0100 | 1.0 | 1.7194 | 0.6972 | 4.5613515e-08 | 3962 |
| 0.0137 | 0.9976 | 1.7203 | 0.7042 | 4.5595446e-08 | 3963 |
| 0.0089 | 1.0 | 1.7210 | 0.6972 | 4.557738e-08 | 3964 |
| 0.0182 | 0.9976 | 1.7210 | 0.6972 | 4.5559315e-08 | 3965 |
| 0.0081 | 1.0 | 1.7207 | 0.6972 | 4.5541253e-08 | 3966 |
| 0.0096 | 1.0 | 1.7219 | 0.6972 | 4.5523194e-08 | 3967 |
| 0.0174 | 0.9953 | 1.7230 | 0.7042 | 4.550514e-08 | 3968 |
| 0.0103 | 1.0 | 1.7237 | 0.7042 | 4.5487084e-08 | 3969 |
| 0.0095 | 1.0 | 1.7239 | 0.7042 | 4.5469033e-08 | 3970 |
| 0.0140 | 0.9953 | 1.7220 | 0.7042 | 4.5450985e-08 | 3971 |
| 0.0101 | 1.0 | 1.7221 | 0.7042 | 4.543294e-08 | 3972 |
| 0.0120 | 1.0 | 1.7229 | 0.6972 | 4.5414897e-08 | 3973 |
| 0.0094 | 1.0 | 1.7232 | 0.6972 | 4.5396856e-08 | 3974 |
| 0.0161 | 0.9929 | 1.7230 | 0.7042 | 4.537882e-08 | 3975 |
| 0.0109 | 0.9976 | 1.7220 | 0.7042 | 4.5360782e-08 | 3976 |
| 0.0125 | 0.9976 | 1.7224 | 0.7042 | 4.5342748e-08 | 3977 |
| 0.0091 | 1.0 | 1.7219 | 0.7042 | 4.5324718e-08 | 3978 |
| 0.0113 | 1.0 | 1.7209 | 0.7042 | 4.530669e-08 | 3979 |
| 0.0149 | 0.9976 | 1.7196 | 0.7042 | 4.5288665e-08 | 3980 |
| 0.0193 | 0.9929 | 1.7173 | 0.7042 | 4.5270642e-08 | 3981 |
| 0.0111 | 0.9976 | 1.7190 | 0.7042 | 4.5252623e-08 | 3982 |
| 0.0113 | 1.0 | 1.7191 | 0.6972 | 4.5234607e-08 | 3983 |
| 0.0120 | 0.9976 | 1.7199 | 0.7113 | 4.521659e-08 | 3984 |
| 0.0121 | 1.0 | 1.7198 | 0.7113 | 4.519858e-08 | 3985 |
| 0.0118 | 0.9976 | 1.7202 | 0.7113 | 4.518057e-08 | 3986 |
| 0.0088 | 1.0 | 1.7218 | 0.7042 | 4.5162565e-08 | 3987 |
| 0.0086 | 1.0 | 1.7235 | 0.6972 | 4.514456e-08 | 3988 |
| 0.0128 | 0.9976 | 1.7236 | 0.7042 | 4.512656e-08 | 3989 |
| 0.0115 | 1.0 | 1.7235 | 0.6972 | 4.510856e-08 | 3990 |
| 0.0105 | 1.0 | 1.7234 | 0.6972 | 4.5090566e-08 | 3991 |
| 0.0108 | 0.9976 | 1.7256 | 0.7042 | 4.507257e-08 | 3992 |
| 0.0138 | 0.9976 | 1.7277 | 0.7042 | 4.505458e-08 | 3993 |
| 0.0100 | 1.0 | 1.7270 | 0.7042 | 4.5036593e-08 | 3994 |
| 0.0129 | 0.9953 | 1.7259 | 0.6972 | 4.501861e-08 | 3995 |
| 0.0118 | 1.0 | 1.7244 | 0.6972 | 4.5000625e-08 | 3996 |
| 0.0078 | 1.0 | 1.7237 | 0.6972 | 4.4982645e-08 | 3997 |
| 0.0177 | 0.9953 | 1.7234 | 0.6972 | 4.496467e-08 | 3998 |
| 0.0102 | 1.0 | 1.7238 | 0.6972 | 4.4946695e-08 | 3999 |
### Framework versions
- Transformers 4.29.0.dev0
- TensorFlow 2.9.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 3 | null |
---
license: mit
---
# SRTK Scorer
This model is a trained scorer for [SRTK](https://github.com/happen2me/subgraph-retrieval-toolkit). It is used to compare the similarity between a query and the expansion path at the time of subgraph retrieval.
## Training Information
It is initialized with `roberta-base`. It is trained jointly on the following datasets:
- [WebQSP for Freebase](https://www.microsoft.com/en-us/download/details.aspx?id=52763)
- [SimpleQuestionsWikidata for Wikidata](https://github.com/askplatypus/wikidata-simplequestions)
- [SimpleDBpediaQA](https://github.com/castorini/SimpleDBpediaQA)
It achieves an answer coverage rate of 0.9728 on SimpleQuestionsWikidata (depth 1) 0.8501 on WebQSP test set (depth 2) with a beam width of only 2!
## Usage Example
First install the package:
```bash
pip install srtk
```
Then you can retrieve subgraphs with the help of this scorer:
```bash
srtk retrieve -i data/wikidata-simplequestions/intermediate/scores_test.jsonl \
-o artifacts/subgraphs/wikidata-simple-contrast \
-e http://localhost:1234/api/endpoint/sparql \
--scorer-model-path drt/srtk-scorer \
--scorer --beam-width 2 --max-depth 1 --evaluate
```
## Limitations
As both SimpleQuestionsWikidata and SimpleDBpediaQA contain only one-hop relations, the model tends to stop at one-hop when you retrieve subgraphs on Wikidata and DBpedia. We will release a updated version of the model that is trained on a more diverse dataset in the future.
## License
MIT
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 33 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: platzi-vit_model-Antoni-Sanchez
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value:
accuracy: 1.0
---
<!-- 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. -->
# platzi-vit_model-Antoni-Sanchez
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0073
- Accuracy: {'accuracy': 1.0}
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.0063 | 3.85 | 500 | 0.0073 | {'accuracy': 1.0} |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 8 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: ultmhxphxp
---
### ultmhxphxp-abstract20-v4 Dreambooth model trained by wimvanhenden with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
ultmhxphxp (use that on your prompt)
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: fnet-large-Financial_Sentiment_Analysis_v3
results: []
language:
- en
pipeline_tag: text-classification
---
# fnet-large-Financial_Sentiment_Analysis_v3
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large).
It achieves the following results on the evaluation set:
- Loss: 0.4741
- Accuracy: 0.8248
- Weighted f1: 0.8194
- Micro f1: 0.8248
- Macro f1: 0.7369
- Weighted recall: 0.8248
- Micro recall: 0.8248
- Macro recall: 0.7269
- Weighted precision: 0.8163
- Micro precision: 0.8248
- Macro precision: 0.7515
## Model description
This is a sentiment analysis (text classification) model concern comments about finances.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Financial%20Sentiment%20Analysis/Financial_Sentiment_Analysis_v3.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Sources:
- https://www.kaggle.com/datasets/sbhatti/financial-sentiment-analysis
- https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-for-financial-news
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.6757 | 1.0 | 134 | 0.5890 | 0.5855 | 0.4739 | 0.5855 | 0.3628 | 0.5855 | 0.5855 | 0.4298 | 0.5912 | 0.5855 | 0.5210 |
| 0.4815 | 2.0 | 268 | 0.3994 | 0.7827 | 0.7789 | 0.7827 | 0.7156 | 0.7827 | 0.7827 | 0.7039 | 0.7878 | 0.7827 | 0.7388 |
| 0.314 | 3.0 | 402 | 0.3560 | 0.7991 | 0.7977 | 0.7991 | 0.7368 | 0.7991 | 0.7991 | 0.7252 | 0.8101 | 0.7991 | 0.7612 |
| 0.235 | 4.0 | 536 | 0.3278 | 0.8201 | 0.8217 | 0.8201 | 0.7549 | 0.8201 | 0.8201 | 0.7509 | 0.8274 | 0.8201 | 0.7631 |
| 0.1986 | 5.0 | 670 | 0.3574 | 0.8618 | 0.8655 | 0.8618 | 0.8209 | 0.8618 | 0.8618 | 0.8401 | 0.8723 | 0.8618 | 0.8084 |
| 0.1605 | 6.0 | 804 | 0.3886 | 0.7995 | 0.7803 | 0.7995 | 0.6588 | 0.7995 | 0.7995 | 0.6469 | 0.7781 | 0.7995 | 0.6987 |
| 0.1436 | 7.0 | 938 | 0.4040 | 0.8230 | 0.8207 | 0.8230 | 0.7442 | 0.8230 | 0.8230 | 0.7336 | 0.8210 | 0.8230 | 0.7576 |
| 0.1373 | 8.0 | 1072 | 0.4517 | 0.8169 | 0.8076 | 0.8169 | 0.7123 | 0.8169 | 0.8169 | 0.7020 | 0.8030 | 0.8169 | 0.7323 |
| 0.1271 | 9.0 | 1206 | 0.4533 | 0.8070 | 0.7945 | 0.8070 | 0.6892 | 0.8070 | 0.8070 | 0.6768 | 0.7906 | 0.8070 | 0.7169 |
| 0.1199 | 10.0 | 1340 | 0.4741 | 0.8248 | 0.8194 | 0.8248 | 0.7369 | 0.8248 | 0.8248 | 0.7269 | 0.8163 | 0.8248 | 0.7515 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
| 5 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Dae314/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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| 27 | 2023-05-08T20:56:42Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: jovisaib/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Anthos23/distilbert-base-uncased-finetuned-sst2
|
[
"tf",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_keras_callback",
"license:apache-2.0"
] |
text-classification
|
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| 21 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
---
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** |
|
ArBert/bert-base-uncased-finetuned-ner-kmeans-twitter
|
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| 0 | null |
---
license: mit
tags:
- translation
- generated_from_trainer
model-index:
- name: m2m100_418M-en-kik-luo-mer-som-v3.0
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. -->
# m2m100_418M-en-kik-luo-mer-som-v3.0
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2721
- Bleu En-kik: 1.9349
- Bleu Kik-en: 60.2829
- Bleu En-luo: 3.7739
- Bleu Luo-en: 55.8705
- Bleu En-mer: 1.2501
- Bleu Mer-en: 44.5639
- Bleu En-som: 42.3143
- Bleu Som-en: 66.5379
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu En-kik | Bleu Kik-en | Bleu En-luo | Bleu Luo-en | Bleu En-mer | Bleu Mer-en | Bleu En-som | Bleu Som-en |
|:-------------:|:-----:|:------:|:---------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| 0.3848 | 1.0 | 68844 | 0.4257 | 5.2935 | 20.3130 | 3.0719 | 20.2845 | 1.6189 | 15.1713 | 20.9910 | 32.8460 |
| 0.2997 | 2.0 | 137688 | 0.3580 | 3.7897 | 27.5164 | 3.5093 | 25.9468 | 1.4533 | 20.8950 | 28.4691 | 41.7619 |
| 0.2462 | 3.0 | 206532 | 0.3187 | 3.9265 | 35.9890 | 3.3539 | 32.6297 | 1.3470 | 26.3997 | 32.3476 | 49.1208 |
| 0.2025 | 4.0 | 275376 | 0.2942 | 2.9946 | 42.9766 | 3.3316 | 39.2179 | 1.2677 | 31.3102 | 37.0367 | 54.3808 |
| 0.164 | 5.0 | 344220 | 0.2792 | 2.5606 | 48.7531 | 3.2041 | 44.4729 | 1.2543 | 35.2164 | 39.7524 | 58.4996 |
| 0.137 | 6.0 | 413064 | 0.2714 | 2.4539 | 52.5136 | 3.2301 | 48.0269 | 1.2164 | 37.7580 | 41.3162 | 61.4504 |
| 0.1086 | 7.0 | 481908 | 0.2681 | 2.1823 | 55.9011 | 3.5149 | 51.3594 | 1.1864 | 40.8313 | 43.2016 | 63.7481 |
| 0.0873 | 8.0 | 550752 | 0.2683 | 2.0400 | 58.0083 | 3.3212 | 53.7066 | 1.2227 | 42.9244 | 43.4146 | 65.2219 |
| 0.0663 | 9.0 | 619596 | 0.2708 | 1.8615 | 59.6493 | 3.6441 | 55.2954 | 1.1786 | 43.6710 | 43.4339 | 65.9029 |
| 0.0554 | 10.0 | 688440 | 0.2721 | 1.9349 | 60.2829 | 3.7739 | 55.8705 | 1.2501 | 44.5639 | 42.3143 | 66.5379 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ArBert/bert-base-uncased-finetuned-ner-kmeans
|
[
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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| 6 | null |
GGML conversions of the RedPajama 3B Base model, not fine-tuned nor filtered.
I use it with KoboldCpp, version 1.20 brought support for RedPajama models.
https://github.com/LostRuins/koboldcpp
---
license: apache-2.0
---
|
Araby/Arabic-TTS
|
[] | null |
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}
| 0 | 2023-05-08T23:21:02Z |
---
license: openrail++
tags:
- stable-diffusion
- image-to-image
pinned: true
duplicated_from: stabilityai/stable-diffusion-2-1-unclip-small
---
# Stable Diffusion v2-1-unclip (small) Model Card
This model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available [here](https://github.com/Stability-AI/stablediffusion).
This `stable-diffusion-2-1-unclip-small` is a finetuned version of Stable Diffusion 2.1, modified to accept (noisy) CLIP image embedding in addition to the text prompt, and can be used to create image variations (Examples) or can be chained with text-to-image CLIP priors. The amount of noise added to the image embedding can be specified via the noise_level (0 means no noise, 1000 full noise).
- Use it with 🧨 [`diffusers`](#examples)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion UnCLIP 2-1-small in a simple and efficient manner.
```bash
pip install diffusers transformers accelerate scipy safetensors
```
Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to DPMSolverMultistepScheduler):
```python
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
import torch
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-unclip-small", torch_dtype=torch.float16)
pipe.to("cuda")
# get image
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
image = load_image(url)
# run image variation
image = pipe(image).images[0]
```
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a subset of the large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 200000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
Aravinth/test
|
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| 0 | null |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for beitv2_base_patch16_224.in1k_ft_in1k
A BEiT-v2 image classification model. Trained on ImageNet-1k with self-supervised masked image modelling (MIM) using a VQ-KD encoder as a visual tokenizer (via OpenAI CLIP B/16 teacher). Fine-tuned on ImageNet-1k.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 86.5
- GMACs: 17.6
- Activations (M): 23.9
- Image size: 224 x 224
- **Papers:**
- BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers: https://arxiv.org/abs/2208.06366
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/microsoft/unilm/tree/master/beit2
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('beitv2_base_patch16_224.in1k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'beitv2_base_patch16_224.in1k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{peng2022beit,
title={Beit v2: Masked image modeling with vector-quantized visual tokenizers},
author={Peng, Zhiliang and Dong, Li and Bao, Hangbo and Ye, Qixiang and Wei, Furu},
journal={arXiv preprint arXiv:2208.06366},
year={2022}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
ArcQ/gpt-experiments
|
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| 0 | 2023-05-08T23:36:02Z |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for beitv2_large_patch16_224.in1k_ft_in1k
A BEiT-v2 image classification model. Trained on ImageNet-1k with self-supervised masked image modelling (MIM) using a VQ-KD encoder as a visual tokenizer (via OpenAI CLIP B/16 teacher). Fine-tuned on ImageNet-1k.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 304.4
- GMACs: 61.6
- Activations (M): 63.5
- Image size: 224 x 224
- **Papers:**
- BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers: https://arxiv.org/abs/2208.06366
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/microsoft/unilm/tree/master/beit2
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('beitv2_large_patch16_224.in1k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'beitv2_large_patch16_224.in1k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 1024) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{peng2022beit,
title={Beit v2: Masked image modeling with vector-quantized visual tokenizers},
author={Peng, Zhiliang and Dong, Li and Bao, Hangbo and Ye, Qixiang and Wei, Furu},
journal={arXiv preprint arXiv:2208.06366},
year={2022}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
ArenaGrenade/char-cnn
|
[] | null |
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| 0 | null |
Access to model leadmaister/langchain-prompt-master is restricted and you are not in the authorized list. Visit https://huggingface.co/leadmaister/langchain-prompt-master to ask for access.
|
AriakimTaiyo/DialoGPT-small-Kumiko
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 11 | 2023-05-09T00:05:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.0375
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5758
- Accuracy: 0.0375
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.7057 | 0.98 | 11 | 4.6533 | 0.0125 |
| 4.5745 | 1.96 | 22 | 4.5758 | 0.0375 |
| 4.4531 | 2.93 | 33 | 4.5287 | 0.0375 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AriakimTaiyo/DialoGPT-small-Rikka
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 8 | 2023-05-09T00:07:26Z |
---
license: apache-2.0
---
This checkpoint is a small testing version of the UniDiffuser-v1 model for 32 x 32 images, consisting of small random models for each of the components.
Please reference the [model card]() for the full UniDiffuser-v1 checkpoint for information about the UniDiffuser model.
|
Aries/T5_question_generation
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"prefix": "translate English to German: "
},
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 13 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jasonshahmf/my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jasonshahmf/my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8495
- Validation Loss: 3.7266
- Epoch: 0
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.8495 | 3.7266 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ArjunKadya/HuggingFace
|
[] | null |
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}
| 0 | 2023-05-09T00:25:34Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: mattjmattj/HF_RL_unit7_poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Arkadiusz/Test-model
|
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| 0 | 2023-05-09T00:26:20Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: NathanS-HuggingFace/SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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