modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 06:27:36
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 527
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e0_s6789_v3_l5_v20
|
KingKazma
| 2023-08-09T16:22:51Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T16:22:50Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v100
|
KingKazma
| 2023-08-09T16:20:40Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T16:20:39Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
kaoyer/pokemon-lora
|
kaoyer
| 2023-08-09T16:17:44Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-09T13:49:50Z |
---
license: creativeml-openrail-m
base_model: /root/autodl-fs/pre_trained_models/runwayml-stable-diffusion-v1-5/runwayml-stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - kaoyer/pokemon-lora
These are LoRA adaption weights for /root/autodl-fs/pre_trained_models/runwayml-stable-diffusion-v1-5/runwayml-stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
MarioNapoli/DynamicWav2Vec_TEST_9
|
MarioNapoli
| 2023-08-09T16:09:04Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_1_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-03T14:29:32Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_1_0
model-index:
- name: DynamicWav2Vec_TEST_9
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. -->
# DynamicWav2Vec_TEST_9
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_1_0 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Marco-Cheung/whisper-small-cantonese
|
Marco-Cheung
| 2023-08-09T16:07:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"zh",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-08T06:53:17Z |
---
language:
- zh
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Cantonese - Marco Cheung
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: zh-HK
split: test
args: zh-HK
metrics:
- name: Wer
type: wer
value: 57.700752823086574
---
<!-- 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. -->
# Whisper Small Cantonese - Marco Cheung
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2487
- Wer Ortho: 57.8423
- Wer: 57.7008
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 10
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.1621 | 1.14 | 1000 | 0.2587 | 61.0824 | 65.0094 |
| 0.0767 | 2.28 | 2000 | 0.2487 | 57.8423 | 57.7008 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e9_s6789_v3_l5_v20
|
KingKazma
| 2023-08-09T16:05:14Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T16:05:14Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
foilfoilfoil/cheesegulag3.5
|
foilfoilfoil
| 2023-08-09T16:04:54Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T16:04:02Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
marklim100/test-model-v4
|
marklim100
| 2023-08-09T16:03:36Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-08-09T16:02:51Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# marklim100/test-model-v4
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("marklim100/test-model-v4")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
tamiti1610001/bert-finetuned-ner
|
tamiti1610001
| 2023-08-09T16:02:50Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-09T14:13:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9457247828991316
- name: Recall
type: recall
value: 0.9530461124200605
- name: F1
type: f1
value: 0.949371332774518
- name: Accuracy
type: accuracy
value: 0.9913554768116506
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.9457
- Recall: 0.9530
- F1: 0.9494
- Accuracy: 0.9914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0136 | 1.0 | 878 | nan | 0.9401 | 0.9488 | 0.9445 | 0.9906 |
| 0.0063 | 2.0 | 1756 | nan | 0.9413 | 0.9507 | 0.9460 | 0.9907 |
| 0.0034 | 3.0 | 2634 | nan | 0.9457 | 0.9530 | 0.9494 | 0.9914 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e8_s6789_v3_l5_v20
|
KingKazma
| 2023-08-09T15:58:13Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:58:12Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
RogerB/marian-finetuned-Umuganda-Dataset-en-to-kin
|
RogerB
| 2023-08-09T15:53:16Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-en-rw",
"base_model:finetune:Helsinki-NLP/opus-mt-en-rw",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-08-08T18:52:54Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-rw
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-kin-Umuganda-Dataset
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. -->
# marian-finetuned-Umuganda-Dataset-en-to-kin-Umuganda-Dataset
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-rw](https://huggingface.co/Helsinki-NLP/opus-mt-en-rw) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8769
- Bleu: 32.8345
## Model Description
The model has been fine-tuned to perform machine translation from English to Kinyarwanda.
## Intended Uses & Limitations
The primary intended use of this model is for research purposes.
## Training and Evaluation Data
The model has been fine-tuned using the [Digital Umuganda](https://huggingface.co/datasets/DigitalUmuganda/kinyarwanda-english-machine-translation-dataset/tree/main) dataset.
The dataset was split with 90% used for training and 10% for testing.
The data used to train the model were cased and digits removed.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Shafaet02/bert-fine-tuned-cola
|
Shafaet02
| 2023-08-09T15:48:02Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T08:59:17Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: Shafaet02/bert-fine-tuned-cola
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. -->
# Shafaet02/bert-fine-tuned-cola
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: 0.2831
- Validation Loss: 0.4311
- Epoch: 1
## 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 |
|:----------:|:---------------:|:-----:|
| 0.4914 | 0.4282 | 0 |
| 0.2831 | 0.4311 | 1 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.11.0
- Datasets 2.14.3
- Tokenizers 0.13.3
|
mbueno/llama2-qlora-finetunined-french
|
mbueno
| 2023-08-09T15:40:36Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:40:28Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e5_s6789_v3_l5_v20
|
KingKazma
| 2023-08-09T15:37:07Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:37:06Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Ripo-2007/dreambooth_alfonso
|
Ripo-2007
| 2023-08-09T15:32:17Z | 4 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-08-09T13:35:48Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: alfonsoaraco
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Test enoder was not trained.
|
dkqjrm/20230809151609
|
dkqjrm
| 2023-08-09T15:30:37Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-09T06:16:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: '20230809151609'
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. -->
# 20230809151609
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the squad 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: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Meohong/Dialect-Polyglot-12.8b-QLoRA
|
Meohong
| 2023-08-09T15:26:17Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:26:09Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
felixshier/osc-01-bert-finetuned
|
felixshier
| 2023-08-09T15:24:55Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T13:35:56Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: osc-01-bert-finetuned
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. -->
# osc-01-bert-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3193
- Validation Loss: 0.7572
- Train Precision: 0.6026
- Epoch: 6
## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 110, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Epoch |
|:----------:|:---------------:|:---------------:|:-----:|
| 0.6873 | 0.6937 | 0.5147 | 0 |
| 0.6544 | 0.6854 | 0.5 | 1 |
| 0.6127 | 0.7071 | 0.5242 | 2 |
| 0.5651 | 0.6813 | 0.5591 | 3 |
| 0.5015 | 0.7012 | 0.5747 | 4 |
| 0.4006 | 0.7292 | 0.5882 | 5 |
| 0.3193 | 0.7572 | 0.6026 | 6 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.13.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3_l5_v20
|
KingKazma
| 2023-08-09T15:23:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:23:01Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
haris001/attestation2nd
|
haris001
| 2023-08-09T15:20:34Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:19:38Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
imvladikon/alephbertgimmel_parashoot
|
imvladikon
| 2023-08-09T15:10:27Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"he",
"dataset:imvladikon/parashoot",
"base_model:imvladikon/alephbertgimmel-base-512",
"base_model:finetune:imvladikon/alephbertgimmel-base-512",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-02T07:44:16Z |
---
base_model: imvladikon/alephbertgimmel-base-512
tags:
- generated_from_trainer
datasets:
- imvladikon/parashoot
model-index:
- name: alephbertgimmel_parashoot
results: []
language:
- he
metrics:
- f1
- exact_match
pipeline_tag: question-answering
---
<!-- 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. -->
# alephbertgimmel_parashoot
This model is a fine-tuned version of [imvladikon/alephbertgimmel-base-512](https://huggingface.co/imvladikon/alephbertgimmel-base-512) on the [imvladikon/parashoot](https://huggingface.co/datasets/imvladikon/parashoot) 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
```
***** predict metrics *****
predict_samples = 1102
test_exact_match = 27.7073
test_f1 = 51.787
test_runtime = 0:00:32.05
test_samples_per_second = 34.383
test_steps_per_second = 4.306
```
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e1_s6789_v3_l5_v20
|
KingKazma
| 2023-08-09T15:08:58Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:08:56Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v50
|
KingKazma
| 2023-08-09T15:03:33Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:03:32Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Cheetor1996/Efanatika_aku_no_onna_kanbu
|
Cheetor1996
| 2023-08-09T15:02:56Z | 0 | 0 | null |
[
"art",
"en",
"license:cc-by-2.0",
"region:us"
] | null | 2023-08-09T15:00:15Z |
---
license: cc-by-2.0
language:
- en
tags:
- art
---
**Efanatika** from **Aku no onna kanbu**
- Trained with Anime (final-full-pruned) model.
- Recommended LoRA weights: 0.7+
- Recommended LoRA weight blocks: ALL, MIDD, OUTD, and OUTALL
- **Activation ta**g: *efanatika*, use with pink hair, long hair, very long hair, colored skin, blue skin, yellow eyes, colored sclera, and black sclera.
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v20
|
KingKazma
| 2023-08-09T15:01:55Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T15:01:55Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
leonard-pak/q-FrozenLake-v1-4x4-noSlippery
|
leonard-pak
| 2023-08-09T14:59:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-09T14:58:08Z |
---
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
model = load_from_hub(repo_id="leonard-pak/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"])
|
LarryAIDraw/ToukaLora-15
|
LarryAIDraw
| 2023-08-09T14:58:48Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-09T14:39:49Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/125271/touka-kirishima-tokyo-ghoul-lora
|
LarryAIDraw/MiaChristoph-10
|
LarryAIDraw
| 2023-08-09T14:58:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-09T14:39:26Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/124748/mia-christoph-tenpuru
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e-1_s6789_v3_l5_v50
|
KingKazma
| 2023-08-09T14:56:04Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:56:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
liadraz/q-FrozenLake-v1-4x4-noSlippery
|
liadraz
| 2023-08-09T14:54:50Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-09T14:54:46Z |
---
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="liadraz/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"])
```
|
broAleks13/stablecode-completion-alpha-3b-4k
|
broAleks13
| 2023-08-09T14:49:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-09T14:42:38Z |
---
license: apache-2.0
--- stabilityai/stablecode-completion-alpha-3b-4k
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e8_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T14:43:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:43:41Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
zjoe/RLCourseppo-Huggy
|
zjoe
| 2023-08-09T14:43:19Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-09T14:43:10Z |
---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: zjoe/RLCourseppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
HG7/ReQLoRA_QK8
|
HG7
| 2023-08-09T14:42:05Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:41:34Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e7_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T14:36:45Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:36:44Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e7_s6789_v3_l5_r2
|
KingKazma
| 2023-08-09T14:36:15Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T16:35:18Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
dimonyara/Llama2-7b-lora-int4
|
dimonyara
| 2023-08-09T14:32:04Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:31:58Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e6_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T14:29:49Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:29:48Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
dinesh44/gptdatabot
|
dinesh44
| 2023-08-09T14:28:46Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-08T10:52:48Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
tolga-ozturk/mt5-base-nsp
|
tolga-ozturk
| 2023-08-09T14:27:30Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"nsp",
"next-sentence-prediction",
"t5",
"en",
"de",
"fr",
"es",
"tr",
"dataset:wikipedia",
"arxiv:2307.07331",
"endpoints_compatible",
"region:us"
] | null | 2023-08-03T18:56:52Z |
---
language:
- en
- de
- fr
- es
- tr
tags:
- nsp
- next-sentence-prediction
- t5
- mt5
datasets:
- wikipedia
metrics:
- accuracy
---
# mT5-base-nsp
mT5-base-nsp is fine-tuned for Next Sentence Prediction task on the [wikipedia dataset](https://huggingface.co/datasets/wikipedia) using [google/mt5-base](https://huggingface.co/google/mt5-base) model. It was introduced in this [paper](https://arxiv.org/abs/2307.07331) and first released on this page.
## Model description
mT5-base-nsp is a Transformer-based model which was fine-tuned for Next Sentence Prediction task on 2500 English, 2500 German, 2500 Turkish, 2500 Spanish and 2500 French Wikipedia articles.
## Intended uses
- Apply Next Sentence Prediction tasks. (compare the results with BERT models since BERT natively supports this task)
- See how to fine-tune an mT5 model using our [code](https://github.com/slds-lmu/stereotypes-multi/tree/main)
- Check our [paper](https://arxiv.org/abs/2307.07331) to see its results
## How to use
You can use this model directly with a pipeline for next sentence prediction. Here is how to use this model in PyTorch:
### Necessary Initialization
```python
import torch
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
from huggingface_hub import hf_hub_download
class ModelNSP(torch.nn.Module):
def __init__(self, pretrained_model, tokenizer, nsp_dim=300):
super(ModelNSP, self).__init__()
self.zero_token, self.one_token = (self.find_label_encoding(x, tokenizer).item() for x in ["0", "1"])
self.core_model = MT5ForConditionalGeneration.from_pretrained(pretrained_model)
self.nsp_head = torch.nn.Sequential(torch.nn.Linear(self.core_model.config.hidden_size, nsp_dim),
torch.nn.Linear(nsp_dim, nsp_dim), torch.nn.Linear(nsp_dim, 2))
def forward(self, input_ids, attention_mask=None):
outputs = self.core_model.generate(input_ids=input_ids, attention_mask=attention_mask, max_length=3,
output_scores=True, return_dict_in_generate=True)
logits = [torch.Tensor([score[self.zero_token], score[self.one_token]]) for score in outputs.scores[1]]
return torch.stack(logits).softmax(dim=-1)
@staticmethod
def find_label_encoding(input_str, tokenizer):
encoded_str = tokenizer.encode(input_str, add_special_tokens=False, return_tensors="pt")
return (torch.index_select(encoded_str, 1, torch.tensor([1])) if encoded_str.size(dim=1) == 2 else encoded_str)
tokenizer = MT5Tokenizer.from_pretrained("tolga-ozturk/mT5-base-nsp")
model = torch.nn.DataParallel(ModelNSP("google/mt5-base", tokenizer).eval())
model.load_state_dict(torch.load(hf_hub_download(repo_id="tolga-ozturk/mT5-base-nsp", filename="model_weights.bin")))
```
### Inference
```python
batch_texts = [("In Italy, pizza is presented unsliced.", "The sky is blue."),
("In Italy, pizza is presented unsliced.", "However, it is served sliced in Turkey.")]
encoded_dict = tokenizer.batch_encode_plus(batch_text_or_text_pairs=batch_texts, truncation="longest_first", padding=True, return_tensors="pt", return_attention_mask=True, max_length=256)
print(torch.argmax(model(encoded_dict.input_ids, attention_mask=encoded_dict.attention_mask), dim=-1))
```
### Training Metrics
<img src="https://huggingface.co/tolga-ozturk/mt5-base-nsp/resolve/main/metrics.png">
## BibTeX entry and citation info
```bibtex
@misc{title={How Different Is Stereotypical Bias Across Languages?},
author={Ibrahim Tolga Öztürk and Rostislav Nedelchev and Christian Heumann and Esteban Garces Arias and Marius Roger and Bernd Bischl and Matthias Aßenmacher},
year={2023},
eprint={2307.07331},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The work is done with Ludwig-Maximilians-Universität Statistics group, don't forget to check out [their huggingface page](https://huggingface.co/misoda) for other interesting works!
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e5_s6789_v3_l5_r2
|
KingKazma
| 2023-08-09T14:22:16Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T16:20:48Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Isaacgv/speecht5_finetuned_voxpopuli_nl
|
Isaacgv
| 2023-08-09T14:16:54Z | 85 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"text-to-speech",
"generated_from_trainer",
"nl",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-08-09T13:15:24Z |
---
language:
- nl
license: mit
base_model: microsoft/speecht5_tts
tags:
- text-to-speech
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch
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. -->
# SpeechT5 TTS Dutch
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4850
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5504 | 2.15 | 500 | 0.5040 |
| 0.5297 | 4.3 | 1000 | 0.4850 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e4_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T14:15:56Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:15:55Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e3_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T14:09:00Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:08:59Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e3_s6789_v3_l5_r2
|
KingKazma
| 2023-08-09T14:08:16Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T16:06:19Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Against61/llama2-qlora-finetunined-CHT
|
Against61
| 2023-08-09T14:06:35Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:06:18Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e2_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T14:02:03Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T14:02:02Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e2_s6789_v3_l5_r2
|
KingKazma
| 2023-08-09T14:01:16Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T15:59:05Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
nevoit/Song-Lyrics-Generator
|
nevoit
| 2023-08-09T13:58:24Z | 0 | 2 |
keras
|
[
"keras",
"music",
"text-generation",
"region:us"
] |
text-generation
| 2023-08-09T13:53:28Z |
---
library_name: keras
pipeline_tag: text-generation
tags:
- music
---
# The purpose
A Recurrent Neural Network that can learn song lyrics and their melodies and then given a melody and a few words to start with, predict the rest of the song. This is essentially done by generating new words for the song and attempting to be as “close” as possible to the original lyrics. However, this is entirely subjective leading the evaluation of generated words to use imaginative methods. For the training phase, however, we used Crossed Entropy loss.
## Table of Contents
* [Authors](#authors)
* [Introduction](#introduction)
* [Instructions](#instructions)
* [Dataset Analysis](#dataset-analysis)
* [Code Design](#code-design)
* [Melody Feature Integration](#melody-feature-integration)
* [Architecture](#architecture)
* [Results Evaluation](#results-evaluation)
* [Full Experimental Setup](#full-experimental-setup)
* [Analysis of how the Seed and Melody Effects the Generated Lyrics](#analysis-of-how-the-seed-and-melody-effects-the-generated-lyrics)
## Authors
* **Tomer Shahar** - [Tomer Shahar](https://github.com/Tomer-Shahar)
* **Nevo Itzhak** - [Nevo Itzhak](https://github.com/nevoit)
## Introduction
In this assignment, we were tasked with creating a Recurrent Neural Network that can learn song lyrics and their melodies and then given a melody and a few words to start with, predict the rest of the song. This is essentially done by generating new words for the song and attempting to be as “close” as possible to the original lyrics. However, this is quite subjective leading the evaluation of generated words to use imaginative methods. For the training phase, however, we used Crossed Entropy loss.
The melody files and lyrics for each song were given to us and the train / test sets were predefined. 20% of the training data was used as a validation set in order to track our progress between training iterations.
We implemented this using an LSTM network. LSTMs have proven in the past to be successful in similar tasks because of their ability to remember previous data, which in our case is relevant because each lyric depends on the words (and melody) that preceded it.
The network receives as input a sequence of lyrics and predicts the next word to appear. The length of this sequence greatly affects the network’s predicting abilities since 5 words in a row work much better than just a single word. We tried using different values to see how this changes the accuracy of the model. During the training phase, sequences from the actual lyrics are fed into the network to train. After fitting the model, we can generate the lyrics for a whole song by beginning with an initial “seed” which is a sequence of words, predicting a word and then using it to advance the sequence like a moving window.
## Instructions
1. Please download the following:
* A .zip file containing all the MIDI files of the participating songs
* the .csv file with all the lyrics of the of the participating songs (600 train and 5 test)
* [Pretty_Midi](https://nbviewer.jupyter.org/github/craffel/pretty-midi/blob/master/Tutorial.ipynb) , a python library for the analysis of MIDI files
2. Implement a recurrent neural net (LSTM or GRU) to carry out the task described in the introduction.
* During each step of the training phase, your architecture will receive as input one word of the lyrics. Words are to represented using the Word2Vec representation that can be found online (300 entries per term, as learned in class).
* The task of the network is to predict the next word of the song’s lyrics. Please see the figure 1 for an illustration. You may use any loss function
* In addition to this textual information, you need to include information extracted from the MIDI file. The method for implementing this requirement is entirely up to your consideration. Figure 1 shows one of the more simplistic options – inserting the entire melody representation at each step.
* Note that your mechanism for selecting the next word should not be deterministic (i.e., always select the word with the highest probability) but rather be sampling-based. The likelihood of a term to be selected by the sampling should be proportional to its probability.
* You may add whatever additions you want to the architecture (e.g., regularization, attention, teacher forcing)
* You may create a validation set. The manner of splitting (and all related decisions) are up to you.
3. The Pretty_Midi package offers multiple options for analyzing .mid files.
Figures 2-4 demonstrate the types of information that can be gathered.
4. You can add whatever other information you consider relevant to further improve the performance of your model.
5. You are to evaluate two approaches for integrating the melody information into your model. The two approaches don’t have to be completely different (one can build upon the other, for example), but please refrain from making only miniature changes.
6. Please include the following information in your report regarding the training phase:
* The chosen architecture of your model
* A clear description of your approach(s) for integrating the melody information together with the lyrics
* TensorBoard graphs showing the training and validation loss of your model.
7. Please include the following information in your report regarding the test phase:
* For each of the melodies in the test set, produce the outputs (lyrics) for each of the two architectural variants you developed. The input should be the melody and the initial word of the output lyrics. Include all generated lyrics in your submission.
* For each melody, repeat the process described above three times, with different words (the same words should be used for all melodies).
* Attempt to analyze the effect of the selection of the first word and/or melody on the generated lyrics.
## Dataset Analysis
- 600 song lyrics for the training
- 5 songs for the test set.
- Midi files for each song containing just the song's melody.
- Song lyrics features:
- The length of a song is the number of words in the lyrics that are also present in the word2vec data.
- For the training set:
- Minimal song length: 3 words (Perhaps a hip hop song with lots of slang)
- Maximal song length: 1338
- Average song length: 257.37
- For the test set:
- Minimal song length: 94 words
- Maximal song length: 389
- Average song length: 231.6
**Input Files:**
A screenshot of the input folder
You need to put files in two folders: input_files and midi_files, the other folders are generated automatically.
Inside input_files put the glove 6B 300d file and the training and testing set:
An example of the glove file:
An example for lyrics_train_set.csv (columns: artist, song name and lyrics):
Inside the folder midi_files put the midi files:
## Code Design
Our code consists of three scripts:
1. Experiment.py - the script that runs the experiments to find the optimal parameters for our LSTM network.
2. Data_loader.py - Loads the midi files, the lyrics, fixes irregularities and cleans the song file names, loads the word embeddings file, saves and loads the various .pkl files.
3. Prepare_data.py - Performs various helper functions on the data such as splitting it properly, creating a validation set and creating the word embeddings matrix.
4. Compute_score.py - Because of the nature of this task, it is difficult to judge the successfulness of our model based on classic loss functions such as MSE. So this script contains several different methods to automatically score the output of our model, such as measuring the cosine similarity or the subjectivity of the lyrics. Explained more later.
5. Extract_melodies_features - Extracts the features we want from the midi files and splits them into train / test / validation. Explained more later.
6. Lstm_lyrics.py - The first LSTM model. This one only takes into account the lyrics of the song. This is used for comparison to see the improvement of using melodies.
7. Lstm_melodies_lyrics.py - The second LSTM model. This one incorporates the features of the midi files of each song. More on this later.
## Melody Feature Integration
We devised two different methods to extract features from the melodies. One of them a more naive technique, and the other a more sophisticated way that expands the first method.
**Method #1**: Each midi file contains a list of all instruments used in the file. For each instrument, an Instrument object contains a list of all time periods this instrument was used, the pitch used (the note) and velocity (how strong the note was played) as you can see in figure 1.
Figure 1: The data available for each instrument of the midi file
The midi file contains the length of the melody, and we know the number of words in the lyrics, so we can easily approximate how many seconds lasts each word on average. Based on this, we assign each word a time span and can deduce what instruments were played during that word and how strong. If a word appears during times 15.2 - 15.8, we can search through the instrument objects for which ones appeared during that time frame.
Using this data, we can compute how many instruments were used, their average pitch and average velocity per word. This provides the network some information about the nature of the song during this lyric, i.e. a low or high pitch and how high the velocity is.
In addition, we can easily use the function get_beats() of pretty midi to find all the beat changes in the song and their times. We simply count the number of beat changes during the word’s time frame and thus add another feature for our network.
**Method #2**: With the first method we have the average pitch used for each word. Now, we want a more precise measurement of this. Each pretty midi object has a function getPianoRoll(fs) which returns a matrix that represents the notes used in the midi file on a near continuous time scale (See figure 1). Specifically, it returns an array of size 128\*S where the size of S equals the length of the song (i.e the time of the last note played) multiplied by how many times each second a sample is taken, denoted by the parameter fs. E.g, for fs=10 every 1/10ths of a second a sample will be made, meaning 10 samples per second so for a song of 120 seconds we will have 1200 samples. Thus getPianoRoll(fs=10) will return a matrix of size 128x1200. By this method, we can control the granularity of the data with ease.
Figure 2: Piano roll matrix. The value in each cell is the velocity summed across instruments.
The reason for the 128 is that musical pitch has a possible range of 0 to 127. So each column in this matrix represents the notes played during this sample (in our example, the notes played every 100 milliseconds).
After creating this matrix, we can calculate how many notes are played, on average, per word. For example, if there are 2000 columns and a song has 50 words, it means that each word in the lyrics can be connected to about 40 notes. This is not precise of course, but a useful approximation.
Figure 3: Notes played during a specific word in a song. Here each lyric received 40 notes representing it (columns 10-39 not shown). There are still 128 rows for each possible note.
We then iterate over every word in the song’s lyrics and find the notes that were played during that particular lyric. For example, in Figure 3 we can see that for a certain word, notes number 57, 64 and 69 were played.
Figure 4: The sum of the notes played during a specific word.
Finally, for each lyric-specific matrix, we sum each row to easily see what notes were played and how much. In figure 4, we can see the result of summing the matrix presented in figure 3. This is fed together with the array of word embeddings of each word in the sequence, thus attaching melody features to word features.
## Architecture
We used a fairly standard approach to a bidirectional LSTM network, with the addition of allowing it to receive as input both an embedding vector and the melody features. We also created an LSTM network that doesn’t receive melodies just to study the impact of melody on the results.
Number of layers: Both versions receive as input a sequence of lyrics. Then there is an embedding layer after the input that uses the word2vec dictionary to convert each word to the appropriate vector representing it. The difference between the networks is that the one using the melodies has a concatenating layer that appends the vectors of lyrics to the vector of melodies.
Additionally, we tried feeding the network various sequence lengths: 1, 5 and 10. We wanted to see how much the sequence length affects the results.
In addition to the piano roll matrix we keep the features extracted in method 1.
- Layers 3 & 4 are only for the model that uses the melody features.
- Since RNNs have to receive input of fixed length, we use masking to ensure that the input is the same size each time.
- We simply concatenate all of the features and feed it into the LSTM to utilize the melody features. However, the features entered vary greatly between our two approaches.
- We used a relatively high drop rate of 60% since we don’t want the network to converge too quickly and overfit on the training data. We tried lower values initially and found more success with 60%.
- The input of the final layer depends on the number of units in the Bidirectional LSTM.
- The final output is a probability for each word, and we sample one from there according to the distribution.
Tensorboard Graph:
**Stopping criteria:**
Here we also experimented with several parameters: We used the EarlyStopping function monitoring on the validation loss with a minimum delta of 0.1 (Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.) and patience of 0 (Number of epochs with no improvement after which training will be stopped). We experimented with several values and found the most success with these.
**Network Hyper-Parameters Tuning:**
NOTE: Here we explain the reasons behind the choices of the parameters.
After implementing our RNN, we optimized the different parameters used. Some parameters, like the number of units in an LSTM, it is very hard to predict what will work best so this method is the best way to find good values to use.
Each combination takes a long time to train (5-15 minutes):
- Learning Rate: We tried different values, ranging from 0.1 to 0.00001. After running numerous experiments, we found 0.00001 to work the best.
- Epochs: We tried epochs of 5, 10 and 150. We found 10 to work the best.
- Batch size: We tried 32 and 2048. 32 worked better.
- Units in LSTM: 64 and 256
- We tried all of the possible combinations of the parameters detailed above which led to a huge number of experiments but led to us finding the optimal settings which were used in the section below.
## Results Evaluation
In this assignment, we were asked to generate lyrics for the 5 songs in the test set. One way to evaluate the results is simply to see how many cases did our model predict the word that was actually used in the song. However, this is not actually a good method to evaluate the model since if it generated a word that was incredibly similar to it simple accuracy wouldn’t detect that. Note that we let our model predict the exact same number of words as in the original song. We devised a few methods to judge our models lyrical capabilities:
1. **Cosine Similarity**: this is a general method to compare the similarity of two vectors. So if our model predicted “happy”, and the original lyrics had the word “smile”, we take the vector of each word from the embedding matrix and calculate the cosine similarity, 1 being the best and 0 the worst. There are a few variations for this however:
2. Comparing each word predicted to the word in the song - the most straightforward method. If a song has 200 words we will perform 200 comparisons according to the index of each word.
3. Creating n-grams of the lyrics, calculating the average of each n-gram and then comparing the n-grams according to their order. This method is a bit better in our opinion, since if the model predicted words (“A”, “B”) and they appeared as (“B”, “A”) in the song, an n-gram style similarity will determine that this was a good prediction while a unigram style won’t. So we tried with 1, 2, 3 and 5-grams.
4. **Polarity**: Using the TextBlob package, we computed the polarity of the generated lyrics and the original ones. Polarity is a score ranging from -1 to 1, -1 representing a negative sentence and 1 representing a positive one. We checked if the lyrics carry the same feelings and themes more or less. We present in the results the absolute difference between them, meaning that a polarity difference of 0 means the lyrics have similar sentiments.
5. **Subjectivity**: Again drawing from TextBlob, subjectivity is a measure of how subjective a sentence is, 0 being very objective and 1 being very subjective. We calculate the absolute difference between the generated lyrics and the original lyrics.
Note: In the final section where we predict song lyrics, we tried with different seeds as requested. With a sequence length of S, we take the first S words (i.e, words #1, #2, ..#S) and predict the rest of the song. We then skip the first S words and take words S+1 until 2S. Then we skip the first 2S words and use words 2S+1 until 3S.
Example with Sequence Length of 3:
Seed 1, seed 2 and seed 3 -
## Full Experimental Setup
Validation Set: Empirically, we learned that using a validation set is better than not if there isn’t enough data. We used the fairly standard 80/20 ratio between training and validation which worked well.
- Batch sizes - 32
- Epochs - 5
- Learning rate: 0.01
- Min delta for improvement: 0.1
- 256 units in the LSTM layer
Additionally, we tried feeding the network various sequence lengths of 1 and 5 to study the effect on the quality of the results.
**Experimental Results:**
The best results are in bold -
**Analysis**: unlike our expectations, the model with simpler features worked better in almost all cases, perhaps due to Occam’s Razor. We theorize that the features about the instruments provided a good abstraction of the features of the entire piano roll.
However, it is clear that adding some melody features to the model improved it on all parameters (except subjectivity). Additionally, having a sequence length of 5 has mixed results and doesn’t seem to have much of an impact on the evaluation methods we chose. We will look into this manually in the next section.
An interesting point is that for all cosine similarity evaluations, an increased n gave a higher similarity. We are not sure why this happens, but we think that with greater values of n the “average” word is more similar. We tested the cosine similarity where n={length of song}, and indeed the similarity was over 0.9. We then tested with a random choice of words and all of the words in a song (i.e., the average vector of the whole song), and the cosine similarity was a staggering 0.75.
**Generated Lyrics:**
For Brevity’s sake we’ll only show both models with a sequence of 1 and the advanced model with a sequence of 5.
**Model with simple melody features - sequence length 1**
A screenshot from the TensorBoard framework:
1. **Lyrics for the bangles - eternal flame**
**Seed text: close **
**close** feelin dreams baby that cause like friends im have day cool let be their would your wit ignorance such forgiven oh may doll nothing down i now around suddenly ball have empty that beautiful how you lonely no goes gone you are called of for wanted me life of stress apart say i all way, required 55 words
**Seed text: your**
**your** gentle i were remember how swear she neither too girl out through with more love me me eyes said have i used heartache hmm anymore desire fighting she when stay be part lights spend by bite again say try ruining slide lover i eyes get always honey of maybe to it hope its white i, required 55 words
**Seed text: eyes **
**eyes** and walk the night woah not live you his world more when just wakes you you fans me to it son sleeping you up i that it da we me let the i longing my do maybe warm fought a believe guys the hear blind dont your through this a down what tell gonna oh, required 55 words
2. **Lyrics for billy joel - honesty**
**Seed text: if **
**if** do hell your as you hard so the be of mable we love do fat give about em with if show you me its of some can top tell if like over baby an the out that a right get as their leaves are oh come happy joy fight me thief give i goodbye sharing like hey all it last you open right i to tonight wake be shift i sister no i on got years wear to make show dont learn be you the live from outer jump drag the myself face shes raps, required 95 words
**Seed text: you **
**you** sherry really but take my girl you and its kick knew so or the a tuya love no how love have of the me there the like its if i winter see reason baa i have would want im high him dancin ever but worked wanna the i mean the you when ill say get well leave up just actor that shit now do the chaka over dead got better to no my the imitating me and my can here and itself footsteps to like leave looked are phone for will will keep my mind, required 95 words
**Seed text: search **
**search** it so class und any you and that friends cried day whoa fine the i three the in the you lovin its a and said hall way others let night hey beautiful dreams dishes save beer store evil back summer yeah forget when well both strong said you me way your the repeat jolly im what told the really to love huh the you baby go river get id and uranus what around with the down and you would always i heart dont with once go land mind come still so to them one else, required 95 words
3. **Lyrics for cardigans - lovefool**
**Seed text: dear **
**dear** to pick tears slide low live such ill yourself me deep out crazy never kick i the belongs get others shelter before her it i wasnt survive ring off baby im to want life ho hanging if i each high you out mine you won rang woman i the do you we you certain guy the jesus my my much flame to you just you world pretty me to dont fault to ear know see love guide, required 77 words
**Seed text: i **
i dumb look me kit i ive and clothes type meet all of didnt love baby the to you i the baby heart these and up look i out just family the what baby theyre all my love down sittin money be from something stars out no while now your got guide and time some was my you off would you is na man he and remember down hes best in hand be shotgun to leaves the that, required 77 words
**Seed text: fear **
**fear** at ive no i your be friend kill thats you years im so right your hurts a if love ill night ever feel what his like ride behind love but man a going can good and gone do see if have name all turn the is start the about you down breaking you at the lady did hard call you the about threatening ass thing together in fall love i they its a up drop youre out, required 77 words
4. **Lyrics for aqua - barbie girl**
**Seed text: hiya **
**hiya** put there to copa out kick when sad when it my cars girl the with in i me the some a around eyes stay cause be clock we never still cant missed anytime motion quiet ive go hot it on the a you had and sign live tennessee no fools got so i father hope for never for you the just it there me my believe other oh red your dont dream the drives the they chorus the happy crosses they to i i because if won this i want didnt ask the, required 93 words
**Seed text: barbie **
**barbie** me go to country smiling all now from love she my is this world not that in to though i beat your be bad new hard cant pretty to wont to round do things without try it walking of ill things in man love a hands were for well you to no chuckie gonna i wish done arms tell lets it beat waiting found we good man write i nigga at do never you it ooh try are attention yeah oh hurt that too without roll yourself with the you feeling switch dont, required 93 words
**Seed text: hi **
**hi** this feeling gotta that alone im do she sweet and you ever you the in had the the raise up skies it youre do me its inspired song with what that feel other mine time the easily what when you and three cause beat and its gets christmas your you sad a behind nothing a i number back or never and who your move beat you driving you i love and of do other like on go when oh yea heart plane after her that mine never soul like one you made you, required 93 words
5. **Lyrics for blink 182 - all the small things**
**Seed text: all **
**all** live ive fire love did my right so truck reading it life its sin heal well two home we confused mony its song you tried could disguise know find for amadeus where sailor you and the to wo insane yeah skin wind ride song me heart up bite a a new a i let money world didnt on, required 58 words
**Seed text: the **
**the** love want risk whoa breakin take need cebu me amadeus control weve lose and try cryin away know hopes away what theres makes in you right drunk live always ever one bop your lovely on steal bet i say somebody say gonna sad stay frosty a grease scene his hangin your dry touch mind i you you your, required 58 words
**Seed text: small **
**small** lost with sun find when casbah you time huh to please for you see make the life dont you me to she the waitin honey weed all fill fired wish on alone thats like im the to and yeah long sure the broadway the need somebody always achy dont well i as seen my that boy your that, required 58 words
**Model with advanced melody features - sequence length 5**
A screenshot from the TensorBoard framework:
1. **Lyrics for the bangles - eternal flame**
**Seed text: close your eyes give me **
**close your eyes give me** rest dreams want that ill what knows im yeah good trust gonna be those find your anyone temper boys dead oh forever somethin mine have i this would playin total yeah planetary that fast from you two like moon believe you will truth, required 43 words
**Seed text: hand darling do you feel **
**hand darling do you feel** of for isnt me more of rising midnight got i for cant whiskey i as rock need swear how watched mind thats got too when day love me me or every yeah i dreams forgotten cryin bag mony flesh how no own be, required 43 words
**Seed text: My heart beating understand the**
**My heart beating understand the** air comin lying through began girl never another thorns bought slim i or go some coming of maybe to it fly so fuck i and thing the ive safe here nothing you long eyes day no but danced you you abandoned me to, required 43 words
2. **Lyrics for billy joel - honesty**
**Seed text: If you search for tenderness **
**If you search for tenderness** my radio finger you now i that it ooh we me gonna the i gin my know maybe hair won a better bleed the live fantasy dont your too say a have baby think cant is do warm your tell you hard so the be of ow do love know believin really ever free when up said you me so of us can heartache more up what hot up before the got that a not are tell their di will oh down hands, required 83 words
**Seed text: it isnt hard to find**
**it isnt hard to find** tired whats me pryor wanna i goodbye sueno what them all it last you since not i to shes joy be america i law like i be they lover catch and right said we stayin be you the nothing life fingers front anyone the myself without try dream cat da but he my thats you and so goin knew so ever the a waters love no from is was of the me right the what its up i wit have watching lied dont, required 83 words
**Seed text: Can have the love need**
**Can have the love need** was over now im high story shoot give can wire wont the i mean the you no at got are only true want just handed that train youre know the yesterday hot women never alone to love my the believing it and my can need and clover tricks and if true needs will gun for let let much my then it dont note crowd wind you and that save nowhere good whoa alive the i breathe the it the you weve its a, required 83 words
3. **Lyrics for cardigans - lovefool**
**Seed text: Dear i fear were facing**
**Dear i fear were facing** and every distant cause quite gonna ive them fast dreams wintry song dre my here world summer get music when only follow along every you me cause your the making dub im if such the da to love trying the you up one taking last hes and boss baby would when the see and you been look i from we with shit one taught then, required 65 words
**Seed text: A problem you love me**
**A problem you love me** down wanna so to did man fool to wrote girls bought wake nothing boys at wait me though got crazy they goin the the drives go quite hiding keep again it i wasnt passed morning boy want oh to now night forgive sick up i fun has you got than you cares pushing wrong i the know you we you silk guy the rainy my, required 65 words
**Seed text: No longer know and maybe**
**No longer know and maybe** my hes alien to you just you around needed me to dont trouper to bleed know have is dying seemed long me leapin i by a turned probably beautiful all of friend love want the to you i the want as chance and want little i got but plays the if up theyre for my is see fake loves be life hold money got like, required 65 words
4. **Lyrics for aqua - barbie girl**
**Seed text: hiya barbie hi ken do**
**hiya barbie hi ken do** nobody youre of they dying and come little get my you face find you is rain look need and rock see them till in body be cute to jive the your feel by like i your love friend drag who you heard oh dont take your pieces a up love at ive give take baby really what ring else is but look a off with back and believe do have up yeah stop all gotta the im start the ever you, required 81 words
**Seed text: a ride sure jump in**
**a ride sure jump in** fly for they for you the just my feel me my better other all making your dont dream the stone the come ba the hands covered come to i i kiss baby cares say i now friend ask the me one to throw asked for this could love heart my is say around he that in to knows i beat your love other another hard there red to turn to sometimes know hey stay am it street of at hey in, required 81 words
**Seed text: you want to go for**
**you want to go for** have killing you not the dawn than hard id you the ever unopened dimension over old in fall love i come its a want desire this got should feel to mess say goin when alive when it my bar thats the when in i me the us a would or remember ill love limit do time wanna there rhymes sounding rendezvous quiet by one stay it on the a you try and town nothing moonlit like stormy they dont i middle, required 81 words
5. **Lyrics for blink 182 - all the small things**
**Seed text: All the small things true**
**All the small things true** his love a such away its only you to no bread ill i sing deep sun think walk it beat fuck best do back look bone i lot feel know they you it gotta am will shaking was all running that around stay bitch wait with, required 46 words
**Seed text: Care truth brings ill take**
**Care truth brings ill take** the you feeling push dont say goodbye an that hear im do how sweet and you his you the it new the the traveled want sayin it this do a its wannabe song with if that take other than come the horse if when you and, required 46 words
**Seed text: One lift your ride best**
**One lift your ride best ** breathe make beat and its reason black of you fine a sleep mine a i scene too ever time and why your room beat you sings you i is and in do matter what on one no oh anybody from tu touch think that than they, required 46 words
## Analysis of how the Seed and Melody Effects the Generated Lyrics
We see that the lyrics are mostly unintelligible, and tend to have words that are very common in the data set (the word “love” appears over 40 times in the generated lyrics and it is indeed a common lyric in popular songs). It doesn’t appear that more advanced melody features improved the subjective quality of the lyrics produced, like how our quantitative methods deemed that it doesn’t improve much. We did notice however a peculiar feature, where once a word appeared for the first time, it tended to appear many times after (or similar variations of it, e.g. if “i” appeared then “i”, “me” or “my” tend to appear after it a lot”). This is to be expected from a model that maintains a cell state and predicts words based on their embedding.
Also it’s apparent that the seed chosen wildly changes the words produced. We think this is because the melody plays a much smaller part in predicting the lyrics compared to the seed, so even with the same melody the dominating factor in producing the lyrics is the seed - see our evaluation table above; the results are slightly better with the melody attached, but not by much, meaning that the first word assists the model much more compared to, say, a baseline of a random word each time.
Personally, we don’t see much of an improvement in using 5 words as a seed versus just the first word. Occasionally it leads to better combinations but it’s a hit-or-miss usually. We think this is because of 2 main reasons:
Many songs contain slang that isn’t in the word embedding matrix so we cannot learn from them or predict them
Many song lyrics aren’t completely coherent and the words are fairly independent of each other (for a good example see the original lyrics of the last song in the test set, “All the Small Things” by Blink 182).
|
clibrain/Llama-2-ft-instruct-es
|
clibrain
| 2023-08-09T13:56:42Z | 1,483 | 18 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"es",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-21T08:40:47Z |
---
license: apache-2.0
language:
- es
pipeline_tag: text-generation
library_name: transformers
inference: false
---
# Llama-2-ft-instruct-es
# ⚠️ Please go to [clibrain/Llama-2-7b-ft-instruct-es](https://huggingface.co/clibrain/Llama-2-7b-ft-instruct-es) for the fixed and updated version.
[Llama 2 (7B)](https://huggingface.co/meta-llama/Llama-2-7b) fine-tuned on [Clibrain](https://huggingface.co/clibrain)'s Spanish instructions dataset.
## Model Details
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model. Links to other models can be found in the index at the bottom.
## Example of Usage
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer, GenerationConfig
model_id = "clibrain/Llama-2-ft-instruct-es"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)
def create_instruction(instruction, input_data=None, context=None):
sections = {
"Instrucción": instruction,
"Entrada": input_data,
"Contexto": context,
}
system_prompt = "A continuación hay una instrucción que describe una tarea, junto con una entrada que proporciona más contexto. Escriba una respuesta que complete adecuadamente la solicitud.\n\n"
prompt = system_prompt
for title, content in sections.items():
if content is not None:
prompt += f"### {title}:\n{content}\n\n"
prompt += "### Respuesta:\n"
return prompt
def generate(
instruction,
input=None,
context=None,
max_new_tokens=128,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
**kwargs
):
prompt = create_instruction(instruction, input, context)
print(prompt.replace("### Respuesta:\n", ""))
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to("cuda")
attention_mask = inputs["attention_mask"].to("cuda")
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
early_stopping=True
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
return output.split("### Respuesta:")[1].lstrip("\n")
instruction = "Dame una lista de lugares a visitar en España."
print(generate(instruction))
```
|
arminhaberl/faster-whisper-base
|
arminhaberl
| 2023-08-09T13:56:36Z | 9 | 1 |
ctranslate2
|
[
"ctranslate2",
"audio",
"automatic-speech-recognition",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2023-08-09T13:56:01Z |
---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
tags:
- audio
- automatic-speech-recognition
license: mit
library_name: ctranslate2
---
# Whisper base model for CTranslate2
This repository contains the conversion of [openai/whisper-base](https://huggingface.co/openai/whisper-base) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper).
## Example
```python
from faster_whisper import WhisperModel
model = WhisperModel("base")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Conversion details
The original model was converted with the following command:
```
ct2-transformers-converter --model openai/whisper-base --output_dir faster-whisper-base \
--copy_files tokenizer.json --quantization float16
```
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
## More information
**For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-base).**
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e1_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T13:55:07Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T13:55:06Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
clibrain/Llama-2-7b-ft-instruct-es
|
clibrain
| 2023-08-09T13:54:51Z | 2,400 | 25 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"es",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-09T13:24:07Z |
---
license: apache-2.0
language:
- es
pipeline_tag: text-generation
library_name: transformers
inference: false
---
# Llama-2-7B-ft-instruct-es
[Llama 2 (7B)](https://huggingface.co/meta-llama/Llama-2-7b) fine-tuned on [Clibrain](https://huggingface.co/clibrain)'s Spanish instructions dataset.
## Model Details
Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pre-trained model. Links to other models can be found in the index at the bottom.
## Example of Usage
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_id = "clibrain/Llama-2-7b-ft-instruct-es"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)
def create_instruction(instruction, input_data=None, context=None):
sections = {
"Instrucción": instruction,
"Entrada": input_data,
"Contexto": context,
}
system_prompt = "A continuación hay una instrucción que describe una tarea, junto con una entrada que proporciona más contexto. Escriba una respuesta que complete adecuadamente la solicitud.\n\n"
prompt = system_prompt
for title, content in sections.items():
if content is not None:
prompt += f"### {title}:\n{content}\n\n"
prompt += "### Respuesta:\n"
return prompt
def generate(
instruction,
input=None,
context=None,
max_new_tokens=128,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
**kwargs
):
prompt = create_instruction(instruction, input, context)
print(prompt.replace("### Respuesta:\n", ""))
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to("cuda")
attention_mask = inputs["attention_mask"].to("cuda")
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
early_stopping=True
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
return output.split("### Respuesta:")[1].lstrip("\n")
instruction = "Dame una lista de lugares a visitar en España."
print(generate(instruction))
```
## Example of Usage with `pipelines`
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "clibrain/Llama-2-7b-ft-instruct-es"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200, device=0)
prompt = """
A continuación hay una instrucción que describe una tarea. Escriba una respuesta que complete adecuadamente la solicitud.
### Instrucción:
Dame una lista de 5 lugares a visitar en España.
### Respuesta:
"""
result = pipe(prompt)
print(result[0]['generated_text'])
```
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e0_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T13:48:10Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T16:16:00Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Ilias7/ppo-LunarLander-v2
|
Ilias7
| 2023-08-09T13:47:59Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-09T13:47:36Z |
---
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: 262.64 +/- 21.10
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
...
```
|
KingKazma/xsum_gpt2_lora_500_10_3000_8_e-1_s6789_v3_l5_r4
|
KingKazma
| 2023-08-09T13:41:15Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T16:08:56Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
tolga-ozturk/t5-french-nsp
|
tolga-ozturk
| 2023-08-09T13:37:49Z | 4 | 0 |
transformers
|
[
"transformers",
"t5",
"text2text-generation",
"nsp",
"next-sentence-prediction",
"fr",
"dataset:wikipedia",
"arxiv:2307.07331",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-09T13:11:36Z |
---
language:
- fr
tags:
- nsp
- next-sentence-prediction
- t5
datasets:
- wikipedia
metrics:
- accuracy
---
# T5-french-nsp
T5-french-nsp is fine-tuned for Next Sentence Prediction task on the [wikipedia dataset](https://huggingface.co/datasets/wikipedia) using [plguillou/t5-base-fr-sum-cnndm](https://huggingface.co/plguillou/t5-base-fr-sum-cnndm) model. It was introduced in this [paper](https://arxiv.org/abs/2307.07331) and first released on this page.
## Model description
T5-french-nsp is a Transformer-based model which was fine-tuned for Next Sentence Prediction task on 14000 French Wikipedia articles.
## Intended uses
- Apply Next Sentence Prediction tasks. (compare the results with BERT models since BERT natively supports this task)
- See how to fine-tune a T5 model using our [code](https://github.com/slds-lmu/stereotypes-multi/tree/main)
- Check our [paper](https://arxiv.org/abs/2307.07331) to see its results
## How to use
You can use this model directly with a pipeline for next sentence prediction. Here is how to use this model in PyTorch:
### Necessary Initialization
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
from huggingface_hub import hf_hub_download
class ModelNSP(torch.nn.Module):
def __init__(self, pretrained_model, tokenizer, nsp_dim=300):
super(ModelNSP, self).__init__()
self.zero_token, self.one_token = (self.find_label_encoding(x, tokenizer).item() for x in ["0", "1"])
self.core_model = T5ForConditionalGeneration.from_pretrained(pretrained_model)
self.nsp_head = torch.nn.Sequential(torch.nn.Linear(self.core_model.config.hidden_size, nsp_dim),
torch.nn.Linear(nsp_dim, nsp_dim), torch.nn.Linear(nsp_dim, 2))
def forward(self, input_ids, attention_mask=None):
outputs = self.core_model.generate(input_ids=input_ids, attention_mask=attention_mask, max_length=3,
output_scores=True, return_dict_in_generate=True)
logits = [torch.Tensor([score[self.zero_token], score[self.one_token]]) for score in outputs.scores[1]]
return torch.stack(logits).softmax(dim=-1)
@staticmethod
def find_label_encoding(input_str, tokenizer):
encoded_str = tokenizer.encode(input_str, add_special_tokens=False, return_tensors="pt")
return (torch.index_select(encoded_str, 1, torch.tensor([1])) if encoded_str.size(dim=1) == 2 else encoded_str)
tokenizer = T5Tokenizer.from_pretrained("tolga-ozturk/t5-french-nsp")
model = torch.nn.DataParallel(ModelNSP("plguillou/t5-base-fr-sum-cnndm", tokenizer).eval())
model.load_state_dict(torch.load(hf_hub_download(repo_id="tolga-ozturk/t5-french-nsp", filename="model_weights.bin")))
```
### Inference
```python
batch_texts = [("classification binaire: En Italie, la pizza est présentée non tranchée.", "Le ciel est bleu."),
("classification binaire: En Italie, la pizza est présentée non tranchée.", "Cependant, il est servi en tranches en Turquie.")]
encoded_dict = tokenizer.batch_encode_plus(batch_text_or_text_pairs=batch_texts, truncation="longest_first", padding=True, return_tensors="pt", return_attention_mask=True, max_length=256)
print(torch.argmax(model(encoded_dict.input_ids, attention_mask=encoded_dict.attention_mask), dim=-1))
```
### Training Metrics
<img src="https://huggingface.co/tolga-ozturk/t5-french-nsp/resolve/main/metrics.png">
## BibTeX entry and citation info
```bibtex
@misc{title={How Different Is Stereotypical Bias Across Languages?},
author={Ibrahim Tolga Öztürk and Rostislav Nedelchev and Christian Heumann and Esteban Garces Arias and Marius Roger and Bernd Bischl and Matthias Aßenmacher},
year={2023},
eprint={2307.07331},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The work is done with Ludwig-Maximilians-Universität Statistics group, don't forget to check out [their huggingface page](https://huggingface.co/misoda) for other interesting works!
|
MemerOwO/Erkin_Koray
|
MemerOwO
| 2023-08-09T13:28:26Z | 0 | 0 | null |
[
"license:bigcode-openrail-m",
"region:us"
] | null | 2023-08-09T10:39:14Z |
---
license: bigcode-openrail-m
---
|
manuu01/SoccerTwos
|
manuu01
| 2023-08-09T13:28:00Z | 577 | 1 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-07-30T22:51:19Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: manuu01/SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Sivapriya2133/the-cat-csd
|
Sivapriya2133
| 2023-08-09T13:21:21Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-09T13:15:12Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### THE-CAT-CSD Dreambooth model trained by Sivapriya2133 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: CCIEK149
Sample pictures of this concept:
|
arminhaberl/faster-whisper-tiny
|
arminhaberl
| 2023-08-09T13:13:50Z | 12 | 0 |
ctranslate2
|
[
"ctranslate2",
"audio",
"automatic-speech-recognition",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2023-08-09T13:12:23Z |
---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
tags:
- audio
- automatic-speech-recognition
license: mit
library_name: ctranslate2
---
# Whisper tiny model for CTranslate2
This repository contains the conversion of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper).
## Example
```python
from faster_whisper import WhisperModel
model = WhisperModel("tiny")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Conversion details
The original model was converted with the following command:
```
ct2-transformers-converter --model openai/whisper-tiny --output_dir faster-whisper-tiny \
--copy_files tokenizer.json --quantization float16
```
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
## More information
**For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-tiny).**
|
amit0814/wav2vec2-large-xls-r-300m-hi-spot-colab
|
amit0814
| 2023-08-09T13:08:37Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-09T12:42:45Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hi-spot-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hi-spot-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
indolem/indobert-base-uncased
|
indolem
| 2023-08-09T13:07:37Z | 45,787 | 41 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"indobert",
"indolem",
"id",
"arxiv:2011.00677",
"license:mit",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: id
tags:
- indobert
- indolem
license: mit
inference: False
---
## About
[IndoBERT](https://arxiv.org/pdf/2011.00677.pdf) is the Indonesian version of BERT model. We train the model using over 220M words, aggregated from three main sources:
* Indonesian Wikipedia (74M words)
* news articles from Kompas, Tempo (Tala et al., 2003), and Liputan6 (55M words in total)
* an Indonesian Web Corpus (Medved and Suchomel, 2017) (90M words).
We trained the model for 2.4M steps (180 epochs) with the final perplexity over the development set being <b>3.97</b> (similar to English BERT-base).
This <b>IndoBERT</b> was used to examine IndoLEM - an Indonesian benchmark that comprises of seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse.
| Task | Metric | Bi-LSTM | mBERT | MalayBERT | IndoBERT |
| ---- | ---- | ---- | ---- | ---- | ---- |
| POS Tagging | Acc | 95.4 | <b>96.8</b> | <b>96.8</b> | <b>96.8</b> |
| NER UGM | F1| 70.9 | 71.6 | 73.2 | <b>74.9</b> |
| NER UI | F1 | 82.2 | 82.2 | 87.4 | <b>90.1</b> |
| Dep. Parsing (UD-Indo-GSD) | UAS/LAS | 85.25/80.35 | 86.85/81.78 | 86.99/81.87 | <b>87.12<b/>/<b>82.32</b> |
| Dep. Parsing (UD-Indo-PUD) | UAS/LAS | 84.04/79.01 | <b>90.58</b>/<b>85.44</b> | 88.91/83.56 | 89.23/83.95 |
| Sentiment Analysis | F1 | 71.62 | 76.58 | 82.02 | <b>84.13</b> |
| Summarization | R1/R2/RL | 67.96/61.65/67.24 | 68.40/61.66/67.67 | 68.44/61.38/67.71 | <b>69.93</b>/<b>62.86</b>/<b>69.21</b> |
| Next Tweet Prediction | Acc | 73.6 | 92.4 | 93.1 | <b>93.7</b> |
| Tweet Ordering | Spearman corr. | 0.45 | 0.53 | 0.51 | <b>0.59</b> |
The paper is published at the 28th COLING 2020. Please refer to https://indolem.github.io for more details about the benchmarks.
## How to use
### Load model and tokenizer (tested with transformers==3.5.1)
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("indolem/indobert-base-uncased")
model = AutoModel.from_pretrained("indolem/indobert-base-uncased")
```
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{koto2020indolem,
title={IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP},
author={Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin},
booktitle={Proceedings of the 28th COLING},
year={2020}
}
```
|
tolga-ozturk/t5-base-nsp
|
tolga-ozturk
| 2023-08-09T12:53:30Z | 3 | 0 |
transformers
|
[
"transformers",
"t5",
"text2text-generation",
"nsp",
"next-sentence-prediction",
"en",
"dataset:wikipedia",
"arxiv:2307.07331",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-09T11:34:07Z |
---
language:
- en
tags:
- nsp
- next-sentence-prediction
- t5
datasets:
- wikipedia
metrics:
- accuracy
---
# T5-base-nsp
T5-base-nsp is fine-tuned for Next Sentence Prediction task on the [wikipedia dataset](https://huggingface.co/datasets/wikipedia) using [t5-base](https://huggingface.co/t5-base) model. It was introduced in this [paper](https://arxiv.org/abs/2307.07331) and first released on this page.
## Model description
T5-base-nsp is a Transformer-based model which was fine-tuned for Next Sentence Prediction task on 22000 English Wikipedia articles.
## Intended uses
- Apply Next Sentence Prediction tasks. (compare the results with BERT models since BERT natively supports this task)
- See how to fine-tune a T5 model using our [code](https://github.com/slds-lmu/stereotypes-multi/tree/main)
- Check our [paper](https://arxiv.org/abs/2307.07331) to see its results
## How to use
You can use this model directly with a pipeline for next sentence prediction. Here is how to use this model in PyTorch:
### Necessary Initialization
```python
import torch
from transformers import T5ForConditionalGeneration, MT5Tokenizer
from huggingface_hub import hf_hub_download
class ModelNSP(torch.nn.Module):
def __init__(self, pretrained_model, tokenizer, nsp_dim=300):
super(ModelNSP, self).__init__()
self.zero_token, self.one_token = (self.find_label_encoding(x, tokenizer).item() for x in ["0", "1"])
self.core_model = T5ForConditionalGeneration.from_pretrained(pretrained_model)
self.nsp_head = torch.nn.Sequential(torch.nn.Linear(self.core_model.config.hidden_size, nsp_dim),
torch.nn.Linear(nsp_dim, nsp_dim), torch.nn.Linear(nsp_dim, 2))
def forward(self, input_ids, attention_mask=None):
outputs = self.core_model.generate(input_ids=input_ids, attention_mask=attention_mask, max_length=3,
output_scores=True, return_dict_in_generate=True)
logits = [torch.Tensor([score[self.zero_token], score[self.one_token]]) for score in outputs.scores[1]]
return torch.stack(logits).softmax(dim=-1)
@staticmethod
def find_label_encoding(input_str, tokenizer):
encoded_str = tokenizer.encode(input_str, add_special_tokens=False, return_tensors="pt")
return (torch.index_select(encoded_str, 1, torch.tensor([1])) if encoded_str.size(dim=1) == 2 else encoded_str)
tokenizer = MT5Tokenizer.from_pretrained("tolga-ozturk/t5-base-nsp")
model = torch.nn.DataParallel(ModelNSP("t5-base", tokenizer).eval())
model.load_state_dict(torch.load(hf_hub_download(repo_id="tolga-ozturk/t5-base-nsp", filename="model_weights.bin")))
```
### Inference
```python
batch_texts = [("binary classification: In Italy, pizza is presented unsliced.", "The sky is blue."),
("binary classification: In Italy, pizza is presented unsliced.", "However, it is served sliced in Turkey.")]
encoded_dict = tokenizer.batch_encode_plus(batch_text_or_text_pairs=batch_texts, truncation="longest_first", padding=True, return_tensors="pt", return_attention_mask=True, max_length=256)
print(torch.argmax(model(encoded_dict.input_ids, attention_mask=encoded_dict.attention_mask), dim=-1))
```
## BibTeX entry and citation info
```bibtex
@misc{title={How Different Is Stereotypical Bias Across Languages?},
author={Ibrahim Tolga Öztürk and Rostislav Nedelchev and Christian Heumann and Esteban Garces Arias and Marius Roger and Bernd Bischl and Matthias Aßenmacher},
year={2023},
eprint={2307.07331},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The work is done with Ludwig-Maximilians-Universität Statistics group, don't forget to check out [their huggingface page](https://huggingface.co/misoda) for other interesting works!
|
Phaaarus/QLoRA_replica_8rank_QKadap
|
Phaaarus
| 2023-08-09T12:51:49Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T12:48:23Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
KUN810/lora_of_Cecilia_Schariac_from_Honkai
|
KUN810
| 2023-08-09T12:49:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-09T12:34:51Z |
该模型的训练集已上传。
Images that this lora is trained from have been uploaded.
|
arywidanthi/Heart-failure-prediction
|
arywidanthi
| 2023-08-09T12:36:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-09T12:36:02Z |
---
title: Heart Failure Gc3
emoji: 📊
colorFrom: purple
colorTo: blue
sdk: streamlit
sdk_version: 1.21.0
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
TheFools/CelineV2
|
TheFools
| 2023-08-09T12:34:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-09T12:30:23Z |
---
license: creativeml-openrail-m
---
|
AIYIYA/my_wr1
|
AIYIYA
| 2023-08-09T12:27:37Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-chinese",
"base_model:finetune:google-bert/bert-base-chinese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T12:18:44Z |
---
base_model: bert-base-chinese
tags:
- generated_from_keras_callback
model-index:
- name: AIYIYA/my_wr1
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. -->
# AIYIYA/my_wr1
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.2336
- Validation Loss: 1.9643
- Train Accuracy: 0.5
- Epoch: 3
## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 30, '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 | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.9423 | 2.5705 | 0.1842 | 0 |
| 2.6021 | 2.2725 | 0.4474 | 1 |
| 2.3113 | 2.0867 | 0.4737 | 2 |
| 2.2336 | 1.9643 | 0.5 | 3 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
AIYIYA/my_wr
|
AIYIYA
| 2023-08-09T12:06:46Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-chinese",
"base_model:finetune:google-bert/bert-base-chinese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T09:11:05Z |
---
base_model: bert-base-chinese
tags:
- generated_from_keras_callback
model-index:
- name: AIYIYA/my_wr
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. -->
# AIYIYA/my_wr
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.3017
- Validation Loss: 1.2447
- Train Accuracy: 0.7895
- Epoch: 7
## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 120, '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 | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8885 | 2.6740 | 0.1316 | 0 |
| 2.5028 | 2.3158 | 0.4737 | 1 |
| 2.2462 | 2.0331 | 0.6579 | 2 |
| 1.9850 | 1.7608 | 0.7632 | 3 |
| 1.7761 | 1.6215 | 0.7632 | 4 |
| 1.6159 | 1.4274 | 0.7895 | 5 |
| 1.3905 | 1.3232 | 0.7895 | 6 |
| 1.3017 | 1.2447 | 0.7895 | 7 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
annaovesnaatatt/q-FrozenLake-v1-4x4-noSlippery
|
annaovesnaatatt
| 2023-08-09T12:02:45Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-09T12:02:41Z |
---
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="annaovesna/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"])
```
|
RIOLITE/products_matching_aumet_scratch_2023-08-09
|
RIOLITE
| 2023-08-09T12:02:09Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-08-09T07:52:17Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
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.
<!--- 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('{MODEL_NAME}')
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={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"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': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
JFuellem/whisper-tiny-en-US
|
JFuellem
| 2023-08-09T12:01:19Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-09T11:29:15Z |
---
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: Whisper Tiny en-US - JFuellem
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: MInDS-14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3530106257378985
---
<!-- 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. -->
# Whisper Tiny en-US - JFuellem
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the MInDS-14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6457
- Wer Ortho: 35.7187
- Wer: 0.3530
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0007 | 17.86 | 500 | 0.6457 | 35.7187 | 0.3530 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
hoang14/law_chatbot_1b7_2048_context_mixed_data
|
hoang14
| 2023-08-09T11:59:38Z | 0 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-08T00:48:33Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
Barkin07/ppo-LunarLander-v2
|
Barkin07
| 2023-08-09T11:59:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-09T11:58:56Z |
---
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: 237.22 +/- 24.77
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).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based
|
nrshoudi
| 2023-08-09T11:54:51Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-12T23:11:40Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-Arabic-phoneme-based
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-Arabic-phoneme-based
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7493
- Per: 0.1979
## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 6
- seed: 42
- 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
- lr_scheduler_warmup_steps: 250
- num_epochs: 30.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Per |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.9601 | 1.0 | 2187 | 1.7221 | 0.9190 |
| 1.307 | 2.0 | 4374 | 1.0964 | 0.4532 |
| 0.9363 | 3.0 | 6561 | 0.9163 | 0.3469 |
| 0.7942 | 4.0 | 8748 | 0.8432 | 0.3037 |
| 0.7 | 5.0 | 10935 | 0.7827 | 0.2881 |
| 0.6274 | 6.0 | 13122 | 0.7456 | 0.2713 |
| 0.5692 | 7.0 | 15309 | 0.6924 | 0.2572 |
| 0.5203 | 8.0 | 17496 | 0.6521 | 0.2491 |
| 0.4853 | 9.0 | 19683 | 0.6583 | 0.2420 |
| 0.4448 | 10.0 | 21870 | 0.6580 | 0.2312 |
| 0.4134 | 11.0 | 24057 | 0.6313 | 0.2380 |
| 0.389 | 12.0 | 26244 | 0.6099 | 0.2225 |
| 0.3644 | 13.0 | 28431 | 0.6238 | 0.2239 |
| 0.3432 | 14.0 | 30618 | 0.6369 | 0.2195 |
| 0.3191 | 15.0 | 32805 | 0.6391 | 0.2164 |
| 0.2992 | 16.0 | 34992 | 0.6314 | 0.2164 |
| 0.2827 | 17.0 | 37179 | 0.6385 | 0.2143 |
| 0.2666 | 18.0 | 39366 | 0.6330 | 0.2159 |
| 0.2479 | 19.0 | 41553 | 0.6653 | 0.2125 |
| 0.2341 | 20.0 | 43740 | 0.6692 | 0.2165 |
| 0.2209 | 21.0 | 45927 | 0.6656 | 0.2199 |
| 0.2075 | 22.0 | 48114 | 0.6669 | 0.2104 |
| 0.1955 | 23.0 | 50301 | 0.6830 | 0.2044 |
| 0.1825 | 24.0 | 52488 | 0.6973 | 0.2065 |
| 0.1758 | 25.0 | 54675 | 0.7265 | 0.2013 |
| 0.1644 | 26.0 | 56862 | 0.7416 | 0.2040 |
| 0.1571 | 27.0 | 59049 | 0.7202 | 0.2007 |
| 0.1489 | 28.0 | 61236 | 0.7224 | 0.2019 |
| 0.1432 | 29.0 | 63423 | 0.7357 | 0.1988 |
| 0.1373 | 30.0 | 65610 | 0.7493 | 0.1979 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
NickTheSickDick/john-cooper-V1-RVC-V2
|
NickTheSickDick
| 2023-08-09T11:42:50Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-08-09T11:19:27Z |
---
license: openrail
---
My RVC V2 model of John Cooper from Skillet.
630 epochs, trained on a trimmed ~25 minute long dataset
|
morell23/epinoiseoffset
|
morell23
| 2023-08-09T11:41:34Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-09T11:41:34Z |
---
license: creativeml-openrail-m
---
|
R-mokni/finetuning-sentiment-model-3000-samples
|
R-mokni
| 2023-08-09T11:37:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T11:28:33Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8692810457516339
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3141
- Accuracy: 0.8667
- F1: 0.8693
## 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
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
morell23/clthlift
|
morell23
| 2023-08-09T11:36:32Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-09T11:34:34Z |
---
license: creativeml-openrail-m
---
|
Rupak100/Error_correction
|
Rupak100
| 2023-08-09T11:32:01Z | 3 | 0 |
peft
|
[
"peft",
"pytorch",
"llama",
"8-bit",
"region:us"
] | null | 2023-08-09T11:10:08Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
caiAtSNU/q-FrozenLake-v1-4x4-noSlippery
|
caiAtSNU
| 2023-08-09T11:27:18Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-09T11:18:23Z |
---
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="caiAtSNU/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"])
```
|
simonycl/roberta-large-sst-2-32-13-30
|
simonycl
| 2023-08-09T11:25:51Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T11:20:12Z |
---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-sst-2-32-13-30
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-sst-2-32-13-30
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8494
- Accuracy: 0.6406
## 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: 1.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.7123 | 0.5 |
| No log | 2.0 | 4 | 0.7030 | 0.5 |
| No log | 3.0 | 6 | 0.6935 | 0.5 |
| No log | 4.0 | 8 | 0.6906 | 0.5312 |
| 0.718 | 5.0 | 10 | 0.6893 | 0.6094 |
| 0.718 | 6.0 | 12 | 0.6883 | 0.5625 |
| 0.718 | 7.0 | 14 | 0.6860 | 0.5469 |
| 0.718 | 8.0 | 16 | 0.6811 | 0.6094 |
| 0.718 | 9.0 | 18 | 0.6780 | 0.5781 |
| 0.6565 | 10.0 | 20 | 0.6859 | 0.5469 |
| 0.6565 | 11.0 | 22 | 0.6943 | 0.5469 |
| 0.6565 | 12.0 | 24 | 0.7061 | 0.5469 |
| 0.6565 | 13.0 | 26 | 0.6963 | 0.5469 |
| 0.6565 | 14.0 | 28 | 0.7058 | 0.5781 |
| 0.5726 | 15.0 | 30 | 0.7036 | 0.5938 |
| 0.5726 | 16.0 | 32 | 0.7185 | 0.6094 |
| 0.5726 | 17.0 | 34 | 0.7307 | 0.6094 |
| 0.5726 | 18.0 | 36 | 0.7743 | 0.6094 |
| 0.5726 | 19.0 | 38 | 0.7790 | 0.5938 |
| 0.4219 | 20.0 | 40 | 0.7805 | 0.6094 |
| 0.4219 | 21.0 | 42 | 0.7744 | 0.6094 |
| 0.4219 | 22.0 | 44 | 0.7960 | 0.5938 |
| 0.4219 | 23.0 | 46 | 0.8495 | 0.6094 |
| 0.4219 | 24.0 | 48 | 0.8893 | 0.5938 |
| 0.3261 | 25.0 | 50 | 0.8901 | 0.625 |
| 0.3261 | 26.0 | 52 | 0.8924 | 0.625 |
| 0.3261 | 27.0 | 54 | 0.8908 | 0.6094 |
| 0.3261 | 28.0 | 56 | 0.8769 | 0.6094 |
| 0.3261 | 29.0 | 58 | 0.8592 | 0.6094 |
| 0.2415 | 30.0 | 60 | 0.8494 | 0.6406 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
felixb85/reinforce-CartPole-v1
|
felixb85
| 2023-08-09T11:12:52Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-09T11:12:42Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-CartPole-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
|
openerotica/Llama-2-13B-GPTQ
|
openerotica
| 2023-08-09T11:11:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-09T09:50:30Z |
---
inference: false
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Meta's Llama 2 13B fp16
These files are fp16 format model files for [Meta's Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf).
They were produced by downloading the PTH files from Meta, and then converting to HF format using the latest Transformers 4.32.0.dev0, from Git, with the Llama 2 PR included: https://github.com/huggingface/transformers/pull/24891.
Command to convert was:
```
python3 /workspace/venv/pytorch2/lib/python3.10/site-packages/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir /workspace/git/llama/download --model_size 13B --output_dir /workspace/process/llama-2-13b/source
```
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ)
* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-13b-hf)
* [My fp16 conversion of the unquantised PTH model files](https://huggingface.co/TheBloke/Llama-2-13B-fp16)
## Prompt template: None
```
{prompt}
```
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
**Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Meta's Llama 2 13B
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
li-ping/video_0809_0
|
li-ping
| 2023-08-09T11:00:18Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T10:16:12Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
RebeccaKnudsen/falcon-7b-instruct-ft-adapters
|
RebeccaKnudsen
| 2023-08-09T10:52:31Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T10:52:29Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
univers1123/segformer-b0-scene-parse-150
|
univers1123
| 2023-08-09T10:49:56Z | 204 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2023-08-07T18:49:44Z |
---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: segformer-b0-scene-parse-150
results: []
widget:
- src: https://i.imgur.com/G0WTlvI.jpg
example_title: Test
---
<!-- 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. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3431
- Mean Iou: 0.0959
- Mean Accuracy: 0.1537
- Overall Accuracy: 0.5496
- Per Category Iou: [0.44824978876617866, 0.7548671615728508, 0.7119201505944329, 0.5304481563680256, 0.5684691275095736, 0.33051502835188457, 0.6982393617021276, 0.0, 0.3703529914609331, 0.6659141206351092, 0.028823893043720683, 0.17181416221210322, 0.052153820762502065, 0.0, 0.0, 0.0005543923800536699, 0.40565901784724534, 0.05230759173712194, 0.0, 0.07225859019823891, 0.29980315155352005, nan, 0.003601361102652032, 0.0, 0.0, nan, 0.0, 0.0, 0.38898705304076847, 0.05940808241958817, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0]
- Per Category Accuracy: [0.8427949438202247, 0.9402615186644498, 0.7846678763016725, 0.7286579984703183, 0.8303175022736334, 0.469325820621132, 0.9020126572710594, nan, 0.5974398752913491, 0.9683369330453564, 0.05725843345934362, 0.24220857754209693, 0.12377594986290638, 0.0, 0.0, 0.0005611873291065182, 0.9580213623749935, 0.08566177782535773, 0.0, 0.16335928996064641, 0.43531591571750716, nan, 0.0036190907034607555, 0.0, 0.0, nan, nan, 0.0, 0.45750991876062724, 0.24276243093922653, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0]
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 4.9918 | 0.5 | 20 | 4.8969 | 0.0108 | 0.0487 | 0.1875 | [0.18900717264720193, 0.17829851112253592, 0.40144144917749963, 0.1885612981412077, 0.11895876927062042, 0.09866217819019046, 0.0057814729592400894, 0.0, 0.0, 0.0, 0.009622579129617706, 0.022129523898301137, 0.0037298450062015365, 0.0, 0.0, 0.0, 0.06277911646586345, 0.0, 0.0, 0.003906402593851322, 0.012887091043266734, nan, 0.0019786836291242806, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015807537456273512, 0.016491354532320934, 0.0, 0.0, 0.0, nan, 0.001438298321545445, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025794247180438844, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.012904182735093445, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0] | [0.2067212858926342, 0.2463388525747603, 0.8113718838750394, 0.515406462102938, 0.1316758582686337, 0.11907251217424253, 0.007544887960475232, nan, 0.0, 0.0, 0.013315354795213214, 0.22085775420969392, 0.054576315445880666, 0.0, 0.0, 0.0, 0.07673176606105031, 0.0, 0.0, 0.004186552792430713, 0.013544374703761687, nan, 0.0021687933259709673, 0.0, 0.0, nan, nan, 0.0, 0.01809937653504629, 0.30082872928176796, 0.0, nan, 0.0, nan, 0.0019430975470621792, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.3248302818350134, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.05007914807886027, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 4.551 | 1.0 | 40 | 4.5955 | 0.0202 | 0.0640 | 0.3442 | [0.3519414971273263, 0.3937735618735424, 0.42161939446421154, 0.21975617697678057, 0.3809140886893701, 0.09030492572322127, 0.005777833411293457, 0.0, 0.0, 0.0, 0.0, 0.02885598249784122, 0.0, 0.0, 0.0, 0.0, 0.0573680633208358, 0.0, 0.0011308737583006134, 0.006298751950078003, 0.057306667023884476, nan, 0.0014234124996705063, 0.0, 0.0, 0.0, nan, 0.0, 0.017088433502956954, 0.023128390596745027, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0052120890103356235, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0] | [0.5003401997503121, 0.785197975265903, 0.952571789207952, 0.4632600048849975, 0.5973921536125012, 0.09887449654069073, 0.006026290292656446, nan, 0.0, 0.0, 0.0, 0.4399013861754582, 0.0, 0.0, 0.0, 0.0, 0.06849835069898948, 0.0, 0.0098046905639658, 0.0067612827597756005, 0.06144521207072375, nan, 0.0014369918969623589, 0.0, 0.0, nan, nan, 0.0, 0.018779520120914415, 0.07066298342541437, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.01695124459987657, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 4.3293 | 1.5 | 60 | 4.2491 | 0.0352 | 0.0776 | 0.4184 | [0.3871309742960387, 0.45006666311952553, 0.6112315905191344, 0.3032571607536305, 0.44533070206501846, 0.063346836376098, 0.022528980712202534, 0.0, 0.0, 0.0, 0.0, 0.029790687595074403, 0.0019532612486920127, 0.0, 0.0, 0.0, 0.24116048081196448, 0.0, 0.0, 0.0003640114056907116, 0.2558965972702686, nan, 0.0005833454863642993, 0.0, 0.0, nan, 0.0, 0.0, 0.002751498247333308, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7088701622971286, 0.8308114309791149, 0.8838434837488167, 0.5903983055833383, 0.8704499330230141, 0.06874412338436925, 0.02526644174013427, nan, 0.0, 0.0, 0.0, 0.3261233603125872, 0.0036558297427862646, 0.0, 0.0, 0.0, 0.305421749829834, 0.0, 0.0, 0.00037678975131876413, 0.350616893842552, nan, 0.0005854411432068869, 0.0, 0.0, nan, nan, 0.0, 0.0027583600982429624, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.9435 | 2.0 | 80 | 4.1371 | 0.0349 | 0.0760 | 0.4181 | [0.35782571228523724, 0.4339785275779836, 0.33734326770427064, 0.3463785302488506, 0.485148026657255, 0.060373176138162864, 0.25141442893216265, 0.0, 0.0, 0.0, 0.0, 0.03557946863062567, 0.0016987542468856172, 0.0, 0.0, 0.0, 0.07208336234305739, 0.0, 0.0, 0.0, 0.23636984914447706, nan, 0.0012355355980390855, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0013693940431359123, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.5806023720349563, 0.8385180609014508, 0.9781397917324077, 0.6769715154724274, 0.8375259283816657, 0.06956509535905245, 0.3441307230861203, nan, 0.0, 0.0, 0.0, 0.18348218438924552, 0.0019584802193497847, 0.0, 0.0, 0.0, 0.08131315775695062, 0.0, 0.0, 0.0, 0.26825905232466285, nan, 0.0012374096890509201, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0026519337016574587, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.951 | 2.5 | 100 | 3.7658 | 0.0401 | 0.0786 | 0.4477 | [0.405717317146636, 0.4086408628919567, 0.46930568969097103, 0.3717459827442089, 0.4797755033427323, 0.09497839561115337, 0.3326272962505519, 0.0, 0.0, 0.0, 0.0, 0.042534003647694614, 0.0014730878186968838, 0.0, 0.0, 0.0, 0.0077221054763652935, 0.0, 0.0, 0.0, 0.1928527248986324, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.6074812734082397, 0.8766682989045027, 0.9717970968759861, 0.7857572013599547, 0.917421184439835, 0.10295479742341103, 0.40897690494678035, nan, 0.0, 0.0, 0.0, 0.14699041771327565, 0.00169734952343648, 0.0, 0.0, 0.0, 0.007984711241426252, 0.0, 0.0, 0.0, 0.2015282306134467, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.7065 | 3.0 | 120 | 3.6117 | 0.0484 | 0.0779 | 0.4436 | [0.30003355195190606, 0.6100469285116588, 0.7166468505013384, 0.2808355790192315, 0.4914731888407471, 0.004109398150913026, 0.3171473942892336, 0.0, 0.0, 0.0, 0.0, 0.0061059528193994845, 0.0, 0.0, 0.0, 0.0, 0.29060069752832357, 0.0, 0.0, 0.0, 0.17904034375746, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8819553682896379, 0.8053072403033685, 0.8891882297254654, 0.6263173002009267, 0.8730573314039183, 0.005069677365030804, 0.3609246267067045, nan, 0.0, 0.0, 0.0, 0.007326262908177505, 0.0, 0.0, 0.0, 0.0, 0.351196397717158, 0.0, 0.0, 0.0, 0.1831291383594502, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.2097 | 3.5 | 140 | 3.5176 | 0.0525 | 0.0915 | 0.4632 | [0.33578357247667373, 0.5622071180819789, 0.5852978266414613, 0.30513441309066625, 0.47613907301517233, 0.11756483464192244, 0.4294152765583846, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.31985655288181825, 0.0, 0.0, 0.0, 0.2843118911299879, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7485081148564294, 0.7992375930944952, 0.9684679709687598, 0.6516123166120225, 0.9277790969083065, 0.15255694177414147, 0.8402174137113565, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4389758626106079, 0.0, 0.0, 0.0, 0.32737744710799593, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.2266 | 4.0 | 160 | 3.2810 | 0.0575 | 0.0941 | 0.4847 | [0.3992063720223229, 0.4874371519350326, 0.6386934837529401, 0.33463546895473684, 0.5312770971534357, 0.1190245013596048, 0.667785108591032, 0.0, 1.4805712043706462e-05, 0.0, 0.0, 0.0017248288238061224, 0.0, 0.0, 0.0, 0.0, 0.30151870601308806, 0.0, 0.0, 0.0, 0.2782594792704375, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.033659066232356136, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.6934503745318352, 0.9230902817318423, 0.9254102240454402, 0.8022610050614992, 0.8948483721680384, 0.14665927557994302, 0.8533694315222395, nan, 1.4989020542452654e-05, 0.0, 0.0, 0.0023025397711415015, 0.0, 0.0, 0.0, 0.0, 0.35161526781506885, 0.0, 0.0, 0.0, 0.32496445140255376, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.1061878453038674, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.9497 | 4.5 | 180 | 3.1974 | 0.0642 | 0.1033 | 0.5107 | [0.3818606056702946, 0.646717576231877, 0.6893883610101498, 0.35965565930008286, 0.515791148910165, 0.1709952263303293, 0.5905499827668784, 0.0, 0.0, 0.00023758099352051836, 0.0, 0.006708633919955889, 0.0, 0.0, 0.0, 0.0, 0.41776764937787264, 0.0, 0.0, 0.0, 0.3965531191844, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8420318352059926, 0.9034080556631061, 0.9232289365730514, 0.7692872717288791, 0.9074888782362877, 0.22193959891659767, 0.9065753217571756, nan, 0.0, 0.00023758099352051836, 0.0, 0.006791329425993115, 0.0, 0.0, 0.0, 0.0, 0.5687077857479449, 0.0, 0.0, 0.0, 0.563147235827241, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.6589 | 5.0 | 200 | 2.9663 | 0.0613 | 0.1024 | 0.5079 | [0.42472276030924794, 0.5137600415836002, 0.5653029156997235, 0.3946772310334331, 0.48347829428413946, 0.27808625336927223, 0.5484221892074007, 0.0, 0.0, 0.028530701280749877, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40734839451719446, 0.0, 0.0, 0.0, 0.3426947471859925, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7298252184769038, 0.931391919283941, 0.9522049542442411, 0.7744040392154765, 0.9018775956382078, 0.3619644385814727, 0.9283785362367638, nan, 0.0, 0.02853131749460043, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5053144143672443, 0.0, 0.0, 0.0, 0.4407882452637778, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.0452 | 5.5 | 220 | 2.9555 | 0.0726 | 0.1078 | 0.5230 | [0.41914107733008765, 0.6093066343751574, 0.8022820448088637, 0.37087502661273153, 0.5109690014151442, 0.285823996633528, 0.6652541677829964, 0.0, 0.08014894937494459, 0.02611231101511879, 0.0, 0.00470151136389698, 0.0, 0.0, 0.0, 0.0, 0.583424459802292, 0.0, 0.0, 0.009707533967511648, 0.3496014880350035, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0003960101972625795, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7738607990012485, 0.9296880337987109, 0.8900007888923951, 0.8633894394911045, 0.8759447339064831, 0.3812818389772233, 0.8360464237368497, nan, 0.08807548470745179, 0.02611231101511879, 0.0, 0.004767885384686948, 0.0, 0.0, 0.0, 0.0, 0.6983742604324834, 0.0, 0.0, 0.011471154651260152, 0.5210203524697299, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0008839779005524862, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.2101 | 6.0 | 240 | 2.9536 | 0.0660 | 0.1036 | 0.5149 | [0.4210730477967065, 0.6183098808624755, 0.6734301776779076, 0.35971565731735283, 0.4806664840057538, 0.27224325054511417, 0.7002499830387291, 0.0, 0.011102840450618944, 0.14168103448275862, 0.0, 0.04930317875757779, 0.00026014568158168577, 0.0, 0.0, 0.0, 0.34496024621697874, 0.0, 0.0, 0.0071056371387967785, 0.272962729746106, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.003966609436978272, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7978604868913858, 0.8565931969845355, 0.9539720732092143, 0.8333395732936828, 0.9431322829072044, 0.35000070168544845, 0.921994928845797, nan, 0.01330275573142673, 0.14198704103671705, 0.0, 0.05126058237975625, 0.0002611306959133046, 0.0, 0.0, 0.0, 0.4225352112676056, 0.0, 0.0, 0.007849786485807586, 0.3299915257888916, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.007403314917127072, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.1038 | 6.5 | 260 | 2.8714 | 0.0713 | 0.1055 | 0.5121 | [0.333925564423404, 0.752032134611127, 0.736398567000167, 0.33990449010114204, 0.5520605777794296, 0.21890770835546186, 0.7222780908403896, 0.0, 7.871071843208248e-05, 0.07598360127927194, 0.0, 0.060659762997758084, 0.0, 0.0, 0.0, 0.0, 0.5471373300677954, 0.0, 0.0, 0.0, 0.29813009355328596, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.926833645443196, 0.9138335003530189, 0.938908172925213, 0.6401967548640491, 0.8983786641339361, 0.281996856449191, 0.8340674367308234, nan, 8.993412325471592e-05, 0.07645788336933046, 0.0, 0.0660759140385152, 0.0, 0.0, 0.0, 0.0, 0.8070841405309178, 0.0, 0.0, 0.0, 0.3693750628384298, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.7936 | 7.0 | 280 | 2.7721 | 0.0696 | 0.1104 | 0.5252 | [0.4010650933145876, 0.6469693944006977, 0.6760729291497917, 0.3905446292113494, 0.5370746026129916, 0.3149111124917947, 0.4465377195337425, 0.0, 0.0015380273740651898, 0.29528359209671673, 0.0, 0.0016221666885878382, 0.0, 0.0, 0.0, 0.0, 0.4981196507305265, 0.0, 0.0, 0.0028347369074468746, 0.31121040158284957, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0025029983834802105, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8289122971285893, 0.9177295192110597, 0.9145945093089303, 0.7626461710711873, 0.9180707940020877, 0.4089984141908865, 0.9829174941077036, nan, 0.0016338032391273393, 0.3043844492440605, 0.0, 0.0017210903339845568, 0.0, 0.0, 0.0, 0.0, 0.603356196659511, 0.0, 0.0, 0.002867788662815038, 0.4111715955934102, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.005303867403314917, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7945 | 7.5 | 300 | 2.7396 | 0.0686 | 0.1095 | 0.5169 | [0.3938437827362631, 0.5819843893842082, 0.6473757929342038, 0.36969978588499103, 0.5214727294114572, 0.32975030466734545, 0.5638207004122257, 0.0, 0.01157430251082007, 0.20381216057927803, 0.0, 0.0004631023224581471, 0.0, 0.0, 0.0, 0.0, 0.5491695655139486, 0.0, 0.0, 0.0, 0.2884391553243545, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.836646379525593, 0.9250916710319539, 0.8823840328179237, 0.6836981927291982, 0.8937373158132887, 0.43858498112466143, 0.9389056476715981, nan, 0.01230598586535363, 0.20669546436285097, 0.0, 0.00046515954972555586, 0.0, 0.0, 0.0, 0.0, 0.8431200586418137, 0.0, 0.0, 0.0, 0.34882151013314566, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.7413 | 8.0 | 320 | 2.6350 | 0.0787 | 0.1150 | 0.5338 | [0.39834735692948187, 0.6935465113678552, 0.6795960676787267, 0.39310146783541766, 0.49596738777838023, 0.32174356359928624, 0.7683469195364054, 0.0, 0.007555978330533573, 0.47093784418445167, 0.0, 0.0837117604058215, 0.0, 0.0, 0.0, 0.0, 0.4067670846627588, 0.0, 0.0, 0.03581658980557869, 0.3532967032967033, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0038626609442060085, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8854556803995006, 0.8828632108775366, 0.9219233196591985, 0.785858823571361, 0.9486830846150055, 0.39855031786350814, 0.9520851514131204, nan, 0.008393851503773486, 0.4894600431965443, 0.0, 0.08789189692064378, 0.0, 0.0, 0.0, 0.0, 0.5023037855385099, 0.0, 0.0, 0.03690446286527673, 0.4525372362581331, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.007955801104972376, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.1279 | 8.5 | 340 | 2.7466 | 0.0803 | 0.1261 | 0.5379 | [0.4732868837154453, 0.5795366043613707, 0.6346097819782593, 0.44734531422398605, 0.5925445006034769, 0.33420482145366226, 0.5418888704389498, 0.0, 0.07466121037607358, 0.5396965892015397, 0.0, 0.0583367158255536, 0.0, 0.0, 0.0, 0.0, 0.5034441615738396, 0.0, 0.0, 0.034642576590730556, 0.38949905000231705, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.013078260869565218, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7515792759051186, 0.9406003029129751, 0.9365375512780056, 0.8283761305471019, 0.8555873142452657, 0.4673154918113308, 0.9642545472036501, nan, 0.08997159580607206, 0.5662850971922246, 0.0, 0.06016838775700065, 0.0, 0.0, 0.0, 0.0, 0.9499842923713283, 0.0, 0.0, 0.03692539562923888, 0.603608003102423, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.020773480662983426, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.92 | 9.0 | 360 | 2.6091 | 0.0828 | 0.1284 | 0.5504 | [0.40748999734012986, 0.7308994689832818, 0.6897258549098384, 0.4145929905545104, 0.5999534865692469, 0.34933757496681506, 0.6454950667228029, 0.0, 0.06552173666609987, 0.6525195767828322, 0.0, 0.09638426690376392, 0.0, 0.0, 0.0, 0.0, 0.3726748249924363, 0.0, 0.0, 0.02772521596051008, 0.4127414994395235, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9037031835205992, 0.9192924818366092, 0.9290785736825496, 0.8003123545866383, 0.8667919591776391, 0.4330416660819288, 0.9692295006493551, nan, 0.0779728848618387, 0.7073002159827214, 0.0, 0.10589357149502279, 0.0, 0.0, 0.0, 0.0, 0.9351798523482905, 0.0, 0.0, 0.028217365820983, 0.5394338077934016, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.3087 | 9.5 | 380 | 2.6150 | 0.0814 | 0.1198 | 0.5299 | [0.3815018185439071, 0.7057178358063663, 0.7535985879427576, 0.3887717278415141, 0.5256222584858059, 0.34564103730405565, 0.5961295113764614, 0.0, 0.07287089141198226, 0.5505775075987842, 0.0, 0.012498381038725553, 0.0, 0.0, 0.0, 0.0, 0.6003507871321013, 0.0, 0.0, 0.02191998411595354, 0.333831518774682, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0007605753528845938, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9088030586766542, 0.9184341335094632, 0.9228818239192175, 0.6333363641712174, 0.924981295724673, 0.42656160096551915, 0.8384170852544853, nan, 0.10986202606590673, 0.5868466522678186, 0.0, 0.013466368964554842, 0.0, 0.0, 0.0, 0.0, 0.9184904968846537, 0.0, 0.0, 0.023109771414217533, 0.4395242951323557, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0018784530386740331, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.1879 | 10.0 | 400 | 2.5502 | 0.0829 | 0.1264 | 0.5403 | [0.42541993453087873, 0.623683983411115, 0.7597304629844778, 0.4205088170462895, 0.5924400522095842, 0.34972647970735005, 0.542108695993839, 0.0, 0.10089223190785503, 0.6533185760423849, 0.0, 0.06435332551516167, 0.0, 0.0, 0.0, 0.0, 0.5496072567949156, 0.0, 0.0, 0.04006614926155156, 0.3497377708876208, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8211766541822721, 0.9566740496959483, 0.9156831808141369, 0.734648360405847, 0.8896447755710964, 0.4752831300784484, 0.9480653340571296, nan, 0.1252557501630056, 0.7776889848812095, 0.0, 0.07147176481533166, 0.0, 0.0, 0.0, 0.0, 0.8636839625111262, 0.0, 0.0, 0.05122247341538977, 0.4568748833000589, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.7904 | 10.5 | 420 | 2.5607 | 0.0847 | 0.1294 | 0.5527 | [0.4089931781192911, 0.6850029861356511, 0.7236868938816361, 0.44801602679367497, 0.6048018807233796, 0.3552823794275764, 0.7384593465045592, 0.0, 0.08126260723073171, 0.6861535314971366, 0.0, 0.07176723173949993, 0.0, 0.0, 0.0, 0.0, 0.5202545409432902, 0.0, 0.0, 0.03092656227494393, 0.31232833178217145, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0070865882197831175, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8964903245942571, 0.9436849477304302, 0.9098295992426633, 0.8017938994581931, 0.8528925545784034, 0.4667366013163619, 0.9616021548969621, nan, 0.10989949861726285, 0.8695032397408208, 0.0, 0.07463484975346543, 0.0, 0.0, 0.0, 0.0, 0.9331771296926541, 0.0, 0.0, 0.03146194423511681, 0.39524295132355686, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0356353591160221, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0716 | 11.0 | 440 | 2.5680 | 0.0847 | 0.1296 | 0.5415 | [0.41481850137628407, 0.7203849284695957, 0.6669945193799962, 0.4362842862741063, 0.5323843818628085, 0.3475532762707128, 0.7414915042691402, 0.0, 0.1676274467627447, 0.6863883910106163, 0.0, 0.07245906603508316, 0.0, 0.0, 0.0, 0.0, 0.42361977248072585, 0.0, 0.0, 0.03842720421848077, 0.3414301980308544, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8675733458177278, 0.9120399366843556, 0.944245029977911, 0.7105906389897683, 0.8862332053527828, 0.47125545560436166, 0.8980959121549656, nan, 0.23361887417466706, 0.860194384449244, 0.0, 0.0779142245790306, 0.0, 0.0, 0.0, 0.0, 0.9558615634326405, 0.0, 0.0, 0.038746546093946246, 0.43930884908722695, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0188 | 11.5 | 460 | 2.5824 | 0.0882 | 0.1300 | 0.5276 | [0.37841752646478294, 0.7626005273875548, 0.7449055407856049, 0.4290295891463663, 0.42924333297292483, 0.3342814382591885, 0.7440181926043109, 0.0, 0.2021076120669866, 0.6599823849517159, 0.0, 0.08257497897785668, 0.0, 0.0, 0.0, 0.0, 0.5392052554536042, 0.0, 0.0, 0.07886073041880408, 0.3480013762317715, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8847003745318353, 0.9254219145011046, 0.8344193751972231, 0.7922913312687979, 0.604714821402171, 0.46469820508862286, 0.879034419256643, nan, 0.2837346643583575, 0.906328293736501, 0.0, 0.10277700251186157, 0.0, 0.0, 0.0, 0.0, 0.9884418032357715, 0.0, 0.0, 0.08624298752407268, 0.566580009479626, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0354 | 12.0 | 480 | 2.5691 | 0.0858 | 0.1256 | 0.5228 | [0.388647954155662, 0.7558385896911918, 0.7917491167908167, 0.41462958598007127, 0.49312579910285853, 0.34302476815200184, 0.6070355141984659, 0.0, 0.17537265388996187, 0.683955223880597, 0.0, 0.06567059698554022, 0.0, 0.0, 0.0, 0.0, 0.5733892374517374, 0.0, 0.0, 0.0970983628377479, 0.2620320855614973, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.010986863532732602, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9077652933832709, 0.8871478351971211, 0.9043310192489744, 0.7432737684546827, 0.7205357262858909, 0.4256459014552956, 0.6939098049186073, nan, 0.2654030922349379, 0.910561555075594, 0.0, 0.06982044841380594, 0.0, 0.0, 0.0, 0.0, 0.9952877113985026, 0.0, 0.0, 0.15270451310391023, 0.3314852850351177, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.027955801104972377, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.1633 | 12.5 | 500 | 2.5259 | 0.0848 | 0.1259 | 0.5158 | [0.34497610745189206, 0.7463073107565349, 0.854543520288191, 0.42463992477652146, 0.3850483703078828, 0.3376058072128456, 0.7316751525159396, 0.0, 0.1667267825954894, 0.7156769039332023, 0.0, 0.1001707892767362, 0.0, 0.0, 0.0, 0.0, 0.45019734202157285, 0.0, 0.0, 0.042506510283171685, 0.28340423893583844, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.013840120972909245, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9170614856429463, 0.9196654292026328, 0.9188466393183969, 0.714963959771867, 0.5245171608926084, 0.4617756851958404, 0.8768699022188018, nan, 0.2632821458281809, 0.8571274298056155, 0.0, 0.12140664247837007, 0.0, 0.0, 0.0, 0.0, 0.9839389496832295, 0.0, 0.0, 0.043393619693544334, 0.42290622351809026, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.02983425414364641, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.5483 | 13.0 | 520 | 2.4923 | 0.0896 | 0.1335 | 0.5398 | [0.4049279962132016, 0.7282255784140709, 0.8169033163371633, 0.46268063074208626, 0.566813817579484, 0.32839844004056473, 0.7444598645497946, 0.0, 0.19716698176235625, 0.6840045841519319, 0.0, 0.0826003003647286, 0.0, 0.0, 0.0, 0.0, 0.4191962626156142, 0.0, 0.0, 0.06262263410511064, 0.3241681763952101, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.002826652155511216, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8330243445692884, 0.9274802423303801, 0.9277098453770906, 0.7134342780633303, 0.8171125078961162, 0.47830739436125574, 0.9215139250596102, nan, 0.41581041886817904, 0.902354211663067, 0.0, 0.09849753465438646, 0.0, 0.0, 0.0, 0.0, 0.9889653908581602, 0.0, 0.0, 0.08685003767897513, 0.42148427962024043, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.010331491712707183, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.9456 | 13.5 | 540 | 2.4341 | 0.0888 | 0.1350 | 0.5530 | [0.4220915846224215, 0.7247157964309319, 0.830505564319672, 0.4851139449459602, 0.5575769305206115, 0.3589983865973501, 0.6960131553803293, 0.0, 0.17180970380326335, 0.5458583575361728, 0.0, 0.059761036448993575, 0.0, 0.0, 0.0, 0.0, 0.5318637215410267, 0.0005402141790333344, 0.0, 0.0806004398126016, 0.39301167361406775, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8784550561797753, 0.9364921880337987, 0.9157739034395709, 0.7188452151092617, 0.909948434440955, 0.4637123370335546, 0.9510475575314886, nan, 0.2806993876985108, 0.8971058315334773, 0.0, 0.06898316122429994, 0.0, 0.0, 0.0, 0.0, 0.9436619718309859, 0.0006066445419833708, 0.0, 0.10587792012057272, 0.5701133246197377, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.1989 | 14.0 | 560 | 2.4261 | 0.0929 | 0.1401 | 0.5574 | [0.43310794022368454, 0.7737883098855011, 0.8060680007048083, 0.489362410594968, 0.610805991884693, 0.3470086891880301, 0.6952947225347309, 0.0, 0.24357075198103878, 0.6835668749605504, 0.0, 0.1061102667862963, 0.0, 0.0, 0.0, 0.0, 0.45462912605112327, 0.0, 0.0, 0.1259496932719323, 0.33998677576659225, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.023312883435582823, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8650249687890137, 0.895028127633407, 0.9563663616282739, 0.7860460223818464, 0.8794078248831823, 0.4683925789746972, 0.9385277161253084, nan, 0.4374620590417519, 0.9356155507559395, 0.0, 0.13477997953297982, 0.0, 0.0, 0.0, 0.0, 0.9957196711869731, 0.0, 0.0, 0.1585866197772754, 0.47265989687315973, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.03988950276243094, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.759 | 14.5 | 580 | 2.4448 | 0.0892 | 0.1338 | 0.5493 | [0.4089707662271852, 0.7381139623935532, 0.8059721309871557, 0.4820201034873249, 0.609475169473329, 0.34888232677538816, 0.7307581018518519, 0.0, 0.18989091820207427, 0.6821615899138637, 0.0, 0.10031948881789138, 0.0, 0.0, 0.0, 0.0, 0.4281578977124959, 0.0, 0.0, 0.035161336998987124, 0.3180350017944225, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.008382156737629792, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8979978152309613, 0.9191857220944268, 0.9098532660145157, 0.7118261511389711, 0.8901667032538719, 0.4567902100846233, 0.9371121906973868, nan, 0.3180745104211165, 0.9185313174946005, 0.0, 0.11684807889105964, 0.0, 0.0, 0.0, 0.0, 0.9912822660872297, 0.0, 0.0, 0.045779954785229844, 0.4327592893153125, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.01596685082872928, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.072 | 15.0 | 600 | 2.5027 | 0.0906 | 0.1397 | 0.5482 | [0.44100427698407574, 0.7311103048313435, 0.7543839224188112, 0.4799902126596585, 0.6074534907993699, 0.31991640494013746, 0.6473536180005114, 0.0, 0.2340784880166992, 0.580419398818412, 0.0, 0.10936251189343482, 0.0, 0.0, 0.0, 0.0, 0.6328263382425094, 0.0, 0.0, 0.0914391513824814, 0.31230421658155033, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.03962762162933371, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.756437265917603, 0.9035276265743504, 0.9456847585989271, 0.8358712143497694, 0.8604459457643217, 0.47530418064190183, 0.9568470888963712, nan, 0.3874362029813162, 0.9654643628509719, 0.0, 0.13366359661363847, 0.0, 0.0, 0.0, 0.0, 0.9960992722132049, 0.0, 0.0, 0.1871179770576907, 0.47254499231575775, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.06138121546961326, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.2554 | 15.5 | 620 | 2.4405 | 0.0887 | 0.1312 | 0.5334 | [0.3809029194882498, 0.7608027733964248, 0.7083251720737159, 0.42916048469757884, 0.5873085910806192, 0.3468435260718393, 0.7091692465883562, 0.0, 0.18719278445710874, 0.6750140822402833, 0.0, 0.10760567447568856, 0.0, 0.0, 0.0, 0.0, 0.5381403956788087, 0.0, 0.0, 0.06071958043072803, 0.3247804716408019, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.03655427552761218, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9219366416978777, 0.9227984717698773, 0.8130680025244557, 0.6539050563290135, 0.8259001572503147, 0.45392733345495884, 0.8720117639783136, nan, 0.30082214777675353, 0.9058747300215982, 0.0, 0.1386640617731882, 0.0, 0.0, 0.0, 0.0, 0.981988585789832, 0.0, 0.0, 0.07973289793184292, 0.42233170073108023, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.10535911602209945, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0588 | 16.0 | 640 | 2.4574 | 0.0902 | 0.1345 | 0.5342 | [0.41010385424628426, 0.7977529216153805, 0.8340696293159445, 0.48971245825550463, 0.4919749346259328, 0.3272816966190114, 0.6597098182842653, 0.0, 0.2774115424895272, 0.5641758131609281, 0.0, 0.051266510616953684, 0.0, 0.0, 0.0, 0.0, 0.5074328777519622, 0.0, 0.004271625102079277, 0.1204259768046877, 0.3244459964513572, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.00036442367467684196, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8264341448189763, 0.9063033798710912, 0.9167363521615651, 0.802086286171713, 0.69115545380828, 0.48181582160349157, 0.8045269327762852, nan, 0.4446867669432141, 0.9725269978401728, 0.0, 0.05705181877383943, 0.0, 0.0, 0.0, 0.0, 0.9977223938426095, 0.0, 0.005333751666797396, 0.2073599598090932, 0.49375924622610345, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0009392265193370166, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.6455 | 16.5 | 660 | 2.4313 | 0.0867 | 0.1369 | 0.5498 | [0.4264700319086269, 0.6718440123427368, 0.7997752877640454, 0.49715012632283856, 0.5883438717881697, 0.36612418224363996, 0.6399541890021792, 0.0, 0.2783565401956925, 0.6650282147881061, 0.0, 0.0636950146627566, 0.0, 0.0, 0.0, 0.0, 0.33662366696414886, 0.0016348511804587201, 0.0, 0.046494402214828666, 0.42791272943155395, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8620318352059925, 0.9229707108205981, 0.929366519406753, 0.6988416850745497, 0.8974938511094883, 0.4622037133193932, 0.8293604711088511, nan, 0.5140409649931426, 0.8883369330453563, 0.0, 0.07577449065029306, 0.0, 0.0, 0.0, 0.0, 0.9775904497617677, 0.0018199336259501124, 0.0, 0.080151553211086, 0.6211596742455797, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1235 | 17.0 | 680 | 2.4456 | 0.0871 | 0.1344 | 0.5283 | [0.3931975460054607, 0.7312132393646948, 0.752251080518649, 0.48870145905866136, 0.4924137639251856, 0.32257473443500867, 0.6552130418265605, 0.0, 0.23244626064529095, 0.6940257824525398, 0.0, 0.0777659332785768, 0.0, 0.0, 0.0, 0.0, 0.48322164719046834, 0.0, 0.0, 0.08978965642537043, 0.3441371440991089, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07899262429777965, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8845521223470661, 0.9212796365044298, 0.8897404544020195, 0.6964580202210372, 0.6778451778810184, 0.46323519092861054, 0.8177545368964262, nan, 0.416072726727672, 0.9348812095032397, 0.0, 0.10349799981393618, 0.0, 0.0, 0.0, 0.0, 0.9948688413005916, 0.0, 0.0, 0.13966340115548856, 0.545811010729213, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.11419889502762431, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.8286 | 17.5 | 700 | 2.4301 | 0.0920 | 0.1407 | 0.5505 | [0.4526525403911307, 0.7425194076825603, 0.7298432909571285, 0.5114968994416101, 0.5950013338508995, 0.35561215385364114, 0.6371370923132846, 0.0, 0.2790519814464187, 0.6854922770776083, 0.0, 0.11547506230037556, 0.0, 0.0, 0.0, 0.0, 0.5099499129213863, 0.0, 0.0, 0.05601597473277561, 0.38623015388905346, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.10698106201262532, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8729900124843946, 0.924192042271164, 0.8564373619438309, 0.7564632617877308, 0.834356281724467, 0.46144940146231245, 0.8310508558431653, nan, 0.5058869378180483, 0.937451403887689, 0.0, 0.15303749185970789, 0.0, 0.0, 0.0, 0.0, 0.9888606733336824, 0.0, 0.0, 0.08984342292556309, 0.6366143372161498, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.15917127071823203, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0479 | 18.0 | 720 | 2.4075 | 0.0898 | 0.1414 | 0.5436 | [0.4374213467259567, 0.6982741654601179, 0.8058703485110762, 0.49490116233697407, 0.5458747562993242, 0.34662800424368545, 0.6675288926019943, 0.0, 0.25922904558504306, 0.6564580958665787, 0.0, 0.06828609986504723, 0.0, 0.0, 0.0, 0.0, 0.5524867235515245, 0.004030176361849884, 0.0, 0.06495693639766788, 0.3537035899505954, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.05760272180057577, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8255727215980025, 0.9343384881681737, 0.9236707163142948, 0.75750800943482, 0.7457316171694047, 0.4768549054829701, 0.8564959561324547, nan, 0.5549010349918685, 0.9453563714902807, 0.0, 0.08237975625639594, 0.0, 0.0, 0.0, 0.0, 0.9695926488297817, 0.004317881739999287, 0.0, 0.14855982583940383, 0.5789322494003418, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.24320441988950275, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.5913 | 18.5 | 740 | 2.3742 | 0.0890 | 0.1390 | 0.5469 | [0.454543608042716, 0.7561710883076548, 0.6476174668581449, 0.5068105753496513, 0.5979298898988566, 0.31709395674098895, 0.63015072260012, 0.0, 0.27305328355515024, 0.6493838093129658, 0.0, 0.09270533141210374, 0.0, 0.0, 0.0, 0.0, 0.5510527590433066, 0.0022979287796301723, 0.0, 0.10461312981465264, 0.3512229542899809, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.030816640986132512, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8730727215980025, 0.9087019154121211, 0.70795992426633, 0.7923769078678768, 0.8732051735801551, 0.4568919544746481, 0.8377299369885041, nan, 0.43083691196198787, 0.9503023758099352, 0.0, 0.1197088101218718, 0.0, 0.0, 0.0, 0.0, 0.9828655950573328, 0.0026585304928094778, 0.0, 0.2450389349409696, 0.6148255605187941, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.10276243093922652, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.1711 | 19.0 | 760 | 2.4791 | 0.0828 | 0.1303 | 0.5109 | [0.3990185372974579, 0.7262255396409119, 0.6844075233528706, 0.4626738323314395, 0.4562280397881922, 0.3094077256824913, 0.6116278156029165, 0.0, 0.28992189510205096, 0.7007724513972282, 0.0, 0.07657237409565386, 0.0, 0.0, 0.0, 0.0, 0.43612176002746467, 0.0, 0.0, 0.06802583124299515, 0.29350917328309334, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.03117790351476407, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8633583021223471, 0.9223785501172934, 0.7493846639318397, 0.6603001242643532, 0.6401767834022517, 0.46511570793044893, 0.7487648509918985, nan, 0.5235589930376, 0.9326781857451404, 0.0, 0.1011722020653084, 0.0, 0.0, 0.0, 0.0, 0.9977093041520498, 0.0, 0.0, 0.13340450473080465, 0.46990218749551155, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.13364640883977902, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.24 | 19.5 | 780 | 2.3946 | 0.0901 | 0.1394 | 0.5429 | [0.4548092562154161, 0.6318103867369477, 0.72163513476081, 0.49794478543154436, 0.5991916108852994, 0.3383029823036611, 0.6064419856631512, 0.0, 0.28341717835243485, 0.7149914761855061, 0.0, 0.1054460128055879, 0.0, 0.0, 0.0, 0.0, 0.5981121440422982, 0.0010851791288836034, 0.0, 0.05393735796858018, 0.3585824286192549, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.007273887382357633, 0.06396948848339815, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8247534332084894, 0.939783235019473, 0.822081098138214, 0.7006298794261376, 0.8882873155892854, 0.46158272169751746, 0.8684935648564891, nan, 0.5053997946504186, 0.8877321814254859, 0.0, 0.13482649548795236, 0.0, 0.0, 0.0, 0.0, 0.9861772867689408, 0.001302501516611355, 0.0, 0.11427195846939629, 0.5384427559858093, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.00861515208766295, 0.23629834254143647, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.232 | 20.0 | 800 | 2.3526 | 0.0948 | 0.1466 | 0.5590 | [0.4425789938260126, 0.6937296397977262, 0.8186584931612056, 0.5324322624613841, 0.599114348804341, 0.35870491493331946, 0.6951078135868305, 0.0, 0.2920867935913111, 0.6704938805785635, 0.0, 0.12268269987831938, 0.0, 0.0, 0.0, 0.0, 0.6003949350977511, 0.02812804473375789, 0.0, 0.07798367783487177, 0.3614614438784472, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.05487663750405036, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8505259051186017, 0.9330459835561528, 0.9250078889239508, 0.7396011060775431, 0.8982756226171649, 0.45928470185385295, 0.9270317256354403, nan, 0.5514385712465619, 0.9371274298056156, 0.0, 0.15945669364592055, 0.0, 0.0, 0.0, 0.0, 0.8397560081679669, 0.03554223316561396, 0.0, 0.1404169806581261, 0.5911264955546299, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.39298342541436465, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.6883 | 20.5 | 820 | 2.3970 | 0.0945 | 0.1446 | 0.5412 | [0.4141798017429065, 0.7802581769922772, 0.7493453021254498, 0.5208791649277187, 0.5111936316209107, 0.33498169560972163, 0.676565287832489, 0.0, 0.27079508232840294, 0.6661949111273435, 0.0, 0.17192469151507442, 0.0, 0.0, 0.0, 0.0, 0.6279362521715048, 0.00806402808036795, 0.00468899769436025, 0.11971994860328412, 0.3208761133473644, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.061126428514345084, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8716338951310861, 0.9071987382421938, 0.7968444304196908, 0.7876915890683026, 0.7295921795968837, 0.4658173933788961, 0.9067608517889905, nan, 0.4145063740809857, 0.9455075593952483, 0.0, 0.2514652525816355, 0.0, 0.0, 0.0, 0.0, 0.979409916749568, 0.009510045319915783, 0.02839438387324496, 0.2476973959641631, 0.5759160047685391, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.3380662983425414, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.5655 | 21.0 | 840 | 2.3849 | 0.0913 | 0.1451 | 0.5354 | [0.39695922222126684, 0.7761112626124897, 0.7398181021041216, 0.4805221133856404, 0.5447557296135301, 0.31805360331269233, 0.6925109462912826, 0.0, 0.2604956962542102, 0.6627857938970388, 0.0, 0.10330072264341897, 0.0, 0.0, 0.0, 0.0, 0.6444934523198961, 0.025419071173399286, 0.017420612885313224, 0.10974960558281793, 0.27499042512447336, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0692949397844124, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8645162297128589, 0.9115146787528184, 0.8852516566740296, 0.6803517911360472, 0.7708670271626399, 0.46996786280646113, 0.8770485607679569, nan, 0.4590687321536974, 0.9504319654427645, 0.0, 0.1353149130151642, 0.0, 0.0, 0.0, 0.0, 0.9869364888214043, 0.029707740070656248, 0.10581222056631892, 0.23735661056685925, 0.41250736107320857, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.508950276243094, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7399 | 21.5 | 860 | 2.3781 | 0.0906 | 0.1404 | 0.5386 | [0.39331511754852533, 0.7754545465515764, 0.627463766072637, 0.49043222556163374, 0.5995606151388057, 0.3503685192626312, 0.6888485650165912, 0.0, 0.2431310643278037, 0.672429906542056, 0.0, 0.19986591689583932, 0.0, 0.0, 0.0, 0.0, 0.47157075191286174, 0.03745141914968791, 0.006777572396796057, 0.09541783908677053, 0.26727518593644356, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.059640638046389575, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9091011235955057, 0.9147260117976632, 0.7021891763963395, 0.6874546488596027, 0.8809198471401499, 0.44664033007283493, 0.8373451339595545, nan, 0.40169076151718863, 0.9323974082073434, 0.0, 0.339752535119546, 0.0, 0.0, 0.0, 0.0, 0.9656657416618671, 0.045391285729579275, 0.04486626402070751, 0.17462111697228502, 0.3974405009838703, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.30386740331491713, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0019 | 22.0 | 880 | 2.3395 | 0.0911 | 0.1433 | 0.5513 | [0.4387708086453384, 0.7685412462908012, 0.6613413573765997, 0.5320871602231129, 0.6142786316311354, 0.3506061584145633, 0.6823298195461854, 0.0, 0.2801467728991371, 0.5419867613663816, 0.0, 0.20394669475285615, 0.0, 0.0, 0.0, 0.0, 0.5595260358614912, 0.05133376806539179, 0.008706221182936335, 0.09404473644106541, 0.30720067771084336, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.010004950903539896, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8470755305867665, 0.9216867469879518, 0.7230277690123067, 0.7916833808461743, 0.8742512689786793, 0.476377759378026, 0.8922345374461448, nan, 0.507535730077718, 0.9691144708423326, 0.0, 0.324262722113685, 0.0, 0.0, 0.0, 0.0, 0.9790826744855752, 0.06039681690040324, 0.0516903286532277, 0.23252114209160177, 0.4687675049911667, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.05359116022099448, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.8521 | 22.5 | 900 | 2.3802 | 0.0876 | 0.1388 | 0.5362 | [0.40506840771806973, 0.762763967794697, 0.6071606249102668, 0.49263320443279435, 0.6002160001991933, 0.34321369615548336, 0.6813770136393861, 0.0, 0.24928234799638715, 0.678501833581391, 0.0, 0.09633167687878934, 0.0, 0.0, 0.0, 0.0, 0.5182393876130829, 0.03320218227559274, 0.0051270619705751225, 0.07954303070635713, 0.25899725617360936, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0592231628370207, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8929666042446941, 0.9253792106042317, 0.7172767434521932, 0.6768271049614816, 0.8639672775983263, 0.463926351095331, 0.8379360814682985, nan, 0.41782644213113895, 0.9550755939524838, 0.0, 0.13931528514280397, 0.0, 0.0, 0.0, 0.0, 0.9748023456725483, 0.04050244442065446, 0.03968938740293356, 0.19515615841915768, 0.3728365626301653, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.3679558011049724, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.5347 | 23.0 | 920 | 2.3594 | 0.0896 | 0.1371 | 0.5433 | [0.39949160354707997, 0.75373807284121, 0.7333642959538925, 0.4879978829522525, 0.6027853892837028, 0.350799823813156, 0.6891289925125581, 0.0, 0.24400705662596422, 0.6809165526675787, 0.0, 0.12095083841463415, 0.0, 0.0, 0.0, 0.0, 0.5479324171283401, 0.022911594700760235, 0.0011939628340799852, 0.04572868845080565, 0.2617659886602434, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0573588139827495, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.906206304619226, 0.9375654793085385, 0.791010571158094, 0.7101413618446036, 0.8534772031844309, 0.4582497158173934, 0.8493633571315683, nan, 0.3700639281726136, 0.9460475161987041, 0.0, 0.17715601451297794, 0.0, 0.0, 0.0, 0.0, 0.9373003822189644, 0.027370374335367376, 0.00917719036787199, 0.07443690864941807, 0.3792999439840283, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.34756906077348065, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3155 | 23.5 | 940 | 2.3369 | 0.0918 | 0.1460 | 0.5511 | [0.4381093224801547, 0.8019171553204106, 0.6937287734535938, 0.5437472809894092, 0.6018169036088111, 0.32174778667344434, 0.6733454960577836, 0.0, 0.30267400925831817, 0.5778369520688242, 0.0, 0.20820499281293087, 0.0, 0.0, 0.0, 0.0, 0.5123551354359162, 0.039373532158116634, 0.02494658298933897, 0.09779724088519773, 0.29932684073211047, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.01698146624022003, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8335440074906367, 0.9182462363632222, 0.7863718838750394, 0.7955147165007729, 0.8629122220679089, 0.47966866413124326, 0.8673460272523003, nan, 0.4910028404193928, 0.9748596112311015, 0.0, 0.3301469904177133, 0.0, 0.0, 0.0, 0.0, 0.9762553013246766, 0.04965564000999179, 0.1749156796611499, 0.28001758352172823, 0.43173951136836963, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.09005524861878453, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.3405 | 24.0 | 960 | 2.3595 | 0.0879 | 0.1398 | 0.5410 | [0.43989517732610944, 0.7150986100442155, 0.6360758191098184, 0.5255540722395773, 0.5869049715503682, 0.3234512702432299, 0.7026329783033322, 0.0, 0.30250286746010635, 0.5443713495235167, 0.0, 0.17120688775076143, 0.0, 0.0, 0.0, 0.0, 0.5297883536014967, 0.047709467851342315, 0.0, 0.07997174381054897, 0.25611794662468174, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.008733939656416919, 0.01860040062401344, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8427184769038701, 0.9395156239324026, 0.7314570842537078, 0.697656092608143, 0.8659945074391495, 0.4741148238067839, 0.9170405898480715, nan, 0.5198417159430717, 0.9561771058315335, 0.0, 0.23402176946692715, 0.0, 0.0, 0.0, 0.0, 0.948897848054872, 0.07128073368304606, 0.0, 0.2488277652181194, 0.37850997515188944, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.009144152654449273, 0.11132596685082873, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.333 | 24.5 | 980 | 2.4020 | 0.0880 | 0.1418 | 0.5423 | [0.4585738127457231, 0.6275648067447941, 0.6314083697244316, 0.5392521405984272, 0.5951768537916903, 0.3282610533434779, 0.6981764940769333, 0.0, 0.3272000900292595, 0.7247379174063033, 0.0, 0.14323702298385843, 0.0, 0.0, 0.0, 0.0, 0.3870775652021896, 0.04730719166937846, 0.0, 0.06134763660744217, 0.3309438242948247, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.08457718344245922, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8159987515605494, 0.9603864417974355, 0.7277335121489429, 0.715686012326596, 0.8718297933345579, 0.4631895813744615, 0.9010918785946443, nan, 0.5229669267261731, 0.9347084233261339, 0.0, 0.21422923062610474, 0.0, 0.0, 0.0, 0.0, 0.9274438452274988, 0.062252435499411195, 0.0, 0.12258226576237126, 0.4986426899156888, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.3368508287292818, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2894 | 25.0 | 1000 | 2.3864 | 0.0895 | 0.1462 | 0.5330 | [0.39820238483984177, 0.796022219535693, 0.6640978857302814, 0.5145788754543814, 0.5063150858146147, 0.31625115083674027, 0.6810959828963655, 0.0, 0.26113527257733576, 0.6726899445979198, 0.0, 0.22906033835073447, 0.0, 0.0, 0.0, 0.0, 0.526772925150962, 0.03492519015909281, 0.018926486228376706, 0.08876722490545956, 0.2864895938888456, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.002796029637914162, 0.08532477102852933, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8886953807740324, 0.9091574236454324, 0.7767079520353424, 0.7472976514572091, 0.7041050844268427, 0.4567516173849587, 0.8318479478317037, nan, 0.39870045191896936, 0.9624406047516199, 0.0, 0.4112708158898502, 0.0, 0.0, 0.0, 0.0, 0.9820278548615111, 0.044731113728009135, 0.13679504274845086, 0.23044879845934857, 0.422317337661405, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0030228603816361234, 0.44779005524861876, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1434 | 25.5 | 1020 | 2.3808 | 0.0870 | 0.1430 | 0.5415 | [0.4312894090280853, 0.7514860907423327, 0.6238930671133326, 0.5171519380411355, 0.6101057211497077, 0.3008154566626281, 0.6915124668035739, 0.0, 0.2912826758689131, 0.5666113040414716, 0.0, 0.16547638988290111, 0.00046409096182851837, 0.0, 0.0, 0.0, 0.4371475202267487, 0.05034426295515858, 0.002705062738472636, 0.06979845880260818, 0.38125408011248557, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.025401436479253397, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8541401373283396, 0.9307228915662651, 0.7375433890817292, 0.6814250643161628, 0.8732186137779948, 0.4590636709375921, 0.8838856860144714, nan, 0.4839430117438976, 0.9726133909287257, 0.0, 0.25274444134338075, 0.0005222613918266092, 0.0, 0.0, 0.0, 0.9851955599769622, 0.06692716696998893, 0.022354694485842028, 0.16760864104496356, 0.6542809129167085, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.12270718232044199, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.4152 | 26.0 | 1040 | 2.3638 | 0.0936 | 0.1532 | 0.5505 | [0.4390581433407791, 0.7964640058941477, 0.7072400355967855, 0.5218684081011842, 0.5850293135240993, 0.3303222917660343, 0.6725471140032113, 0.0, 0.27670817596368846, 0.6731058554492387, 0.0, 0.20708020499863622, 0.008931439953736426, 0.0, 0.0, 0.0, 0.5095680959455151, 0.03638896025746778, 0.02076316003579855, 0.08472208194458472, 0.2849621516471434, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.12568393434230313, 0.08349886634964586, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8352512484394506, 0.917123123875464, 0.8275757336699274, 0.7732077496741849, 0.8478457602895915, 0.47623391386109437, 0.8749596300393736, nan, 0.4952672167637206, 0.9663066954643629, 0.0, 0.3354963252395572, 0.01814858336597467, 0.0, 0.0, 0.0, 0.9832844651552437, 0.048014131249330905, 0.2001725625539258, 0.21070920204303775, 0.3871421800267153, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.14842244473833366, 0.4598342541436464, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1933 | 26.5 | 1060 | 2.3583 | 0.0945 | 0.1501 | 0.5469 | [0.42529111600331887, 0.6990144112716127, 0.7540950160303118, 0.5036250399233472, 0.6048482256574182, 0.3278464408631659, 0.6501386244117096, 0.0, 0.3309863153710749, 0.6701461061127885, 0.0, 0.16068823815424188, 0.0759493670886076, 0.0, 0.0, 0.0, 0.6276421330982812, 0.03285733530917419, 0.0, 0.06674753570299997, 0.41239939395531733, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.005582040914851246, 0.0798621109529647, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8582896379525593, 0.9401120550253946, 0.8164405175134112, 0.6747073012884627, 0.868055337774572, 0.45685687020222576, 0.9055720852888428, nan, 0.4805405040807608, 0.9539956803455724, 0.0, 0.238277979346916, 0.07833920877399138, 0.0, 0.0, 0.0, 0.9320645059950783, 0.04351782464404239, 0.0, 0.13706773842418152, 0.676342587937894, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.006990364632533535, 0.5362983425414365, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7676 | 27.0 | 1080 | 2.3567 | 0.0926 | 0.1509 | 0.5459 | [0.41386442762131637, 0.7960055724879699, 0.7218561977621083, 0.5168035299390555, 0.5880580526892997, 0.3300573993396712, 0.6781909579446623, 0.0, 0.2845297119281992, 0.7012563536971325, 0.0, 0.1888364952297401, 0.039780521262002745, 0.0, 0.0, 0.0, 0.4978975535168196, 0.04567537266951792, 0.02838107928047968, 0.10232412915208916, 0.279087459984982, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.00678570204599198, 0.07854484909880542, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8624313358302123, 0.9312766187623841, 0.836683496371095, 0.7154007569963327, 0.8227730712196083, 0.46541743267328123, 0.8862632190147668, nan, 0.4713822125293223, 0.9475593952483802, 0.0, 0.28035166061959255, 0.06058232145188667, 0.0, 0.0, 0.0, 0.9547620294256244, 0.05888020554544481, 0.2506078908149659, 0.2675416562002847, 0.40571362911681486, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.007481579444549405, 0.43265193370165744, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.5575 | 27.5 | 1100 | 2.3697 | 0.0901 | 0.1457 | 0.5435 | [0.4330449058471925, 0.7415496484649239, 0.6582991510224409, 0.5107206399916884, 0.5919085961931496, 0.32152780787548696, 0.68747208399237, 0.0, 0.2695065829717953, 0.6896950020291575, 0.0034094166276947215, 0.17989194664865776, 0.023761959835979393, 0.0, 0.0, 0.0, 0.43100931316176777, 0.05545048793403434, 0.00019565120725688115, 0.08220297718419588, 0.35493738896832616, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.023562421824819987, 0.06554660197672603, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8504338327091137, 0.9337022001047669, 0.7716708740927738, 0.7011219448708418, 0.8615346017893384, 0.4684978317919643, 0.8989823334180816, nan, 0.42924807578448787, 0.954341252699784, 0.0054417182345717656, 0.2478137501162899, 0.05901553727640684, 0.0, 0.0, 0.0, 0.9468427666369967, 0.0784712557542019, 0.001725625539257981, 0.1978983504981998, 0.5882826077589303, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.026336671075004724, 0.30154696132596687, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2554 | 28.0 | 1120 | 2.3600 | 0.0908 | 0.1478 | 0.5396 | [0.4245870411963722, 0.7870399701396511, 0.6743924693680554, 0.5037828604923488, 0.6021610759004483, 0.3176424244260717, 0.6686784469885515, 0.0, 0.3011351900484342, 0.664582242501309, 0.0020511317852172, 0.1970743537790876, 0.032291965224037454, 0.0, 0.0, 0.0, 0.46678139857369255, 0.06349432185632616, 0.0, 0.06986375791100168, 0.31471513785167443, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.010739894166640574, 0.07464363872952032, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8389887640449438, 0.9124555879472521, 0.7550844114862733, 0.6772710335692038, 0.8725779643476352, 0.4781074140084483, 0.8861738897401893, nan, 0.5353928247558664, 0.9594816414686825, 0.0033709758975223325, 0.2986091729463206, 0.04413108760934848, 0.0, 0.0, 0.0, 0.9869888475836431, 0.0875887663704814, 0.0, 0.20727622875324458, 0.48120592332993406, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.012960513886264877, 0.4183425414364641, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.2107 | 28.5 | 1140 | 2.3688 | 0.0905 | 0.1456 | 0.5420 | [0.41859370389806955, 0.7667516378963897, 0.6755595944797275, 0.5229827746052027, 0.5762823201665176, 0.3283558940003052, 0.7027730340733512, 0.0, 0.28120109546165883, 0.6846517626827171, 0.0, 0.1786280502754033, 0.03361994840928633, 0.0, 0.0, 0.0, 0.4284454994324958, 0.06547736706859063, 0.012462958100453881, 0.06991453779704264, 0.266741758305561, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.06730802868637398, 0.07145529791938318, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8700889513108614, 0.9342488099847405, 0.7711857052698012, 0.7180785914091792, 0.8155400047488699, 0.46050212610690877, 0.890750297191625, nan, 0.4740277746550652, 0.9630669546436285, 0.0, 0.25418643594753, 0.05105105105105105, 0.0, 0.0, 0.0, 0.9734017487826587, 0.09456517860329015, 0.1042434700760844, 0.19042535376371095, 0.3299340735101906, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.07269979217834877, 0.3481767955801105, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7361 | 29.0 | 1160 | 2.3508 | 0.0918 | 0.1483 | 0.5436 | [0.41654288665276806, 0.7925411643174287, 0.6930136805331298, 0.5061774346683199, 0.5923356779044385, 0.3230293544867808, 0.6895139627440876, 0.0, 0.2363788767812238, 0.6377222182874142, 0.0, 0.2168449899419842, 0.033075299085151305, 0.0, 0.0, 0.0, 0.5181226765799256, 0.05391543657743308, 0.012268226822682268, 0.07735822505746048, 0.28361562718329275, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.07860554988996833, 0.08121365560346533, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8848845193508115, 0.9195430113649304, 0.744907699589776, 0.7394870039454378, 0.847944321740416, 0.4446124591268226, 0.8642744745033636, nan, 0.35505991860961844, 0.9723542116630669, 0.0, 0.3459856730858685, 0.03681942812377595, 0.0, 0.0, 0.0, 0.9778653332635217, 0.08459122863362238, 0.10691034590948309, 0.1726115716319183, 0.43146661304453987, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.11067447572265256, 0.45370165745856356, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0032 | 29.5 | 1180 | 2.3543 | 0.0922 | 0.1512 | 0.5443 | [0.42172693105885056, 0.7906304092636438, 0.6995716766885557, 0.5235794280056563, 0.5966555557905998, 0.31724619307417273, 0.6576375643470949, 0.0, 0.2830730969646176, 0.6118519315903495, 0.0, 0.18571861492575403, 0.05163853028798411, 0.0, 0.0, 0.0, 0.5885383948663714, 0.04839595441856208, 0.016447623896516314, 0.08342743868257928, 0.26942911340520653, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.047753564702717244, 0.0753557388848901, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8540121722846442, 0.9050464618397978, 0.7975702114231619, 0.7089807292195949, 0.8845330203260592, 0.46881709867100774, 0.88135698039566, nan, 0.4657613298259025, 0.9820950323974083, 0.0, 0.27081588985021865, 0.06110458284371328, 0.0, 0.0, 0.0, 0.9688203570867585, 0.0754737180173429, 0.14950192171935053, 0.21495855312735493, 0.4059721643709694, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.05365577177404118, 0.526353591160221, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.4753 | 30.0 | 1200 | 2.3802 | 0.0906 | 0.1452 | 0.5353 | [0.4182968446268334, 0.7245614420751814, 0.7399438662308484, 0.5222310167082108, 0.5399183829320097, 0.320465614650302, 0.7037323784638214, 0.0, 0.28915533720845393, 0.6621481788079471, 0.007442458767859604, 0.15757803044508464, 0.04727904284060208, 0.0, 0.0, 0.0, 0.480756707265963, 0.045882830155878775, 0.0017206593322928166, 0.06188961172578628, 0.2852105120765736, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.07911695693843154, 0.07258344037351928, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8311267166042446, 0.9383555014006878, 0.8340012622278321, 0.7093176870784684, 0.7601843099130419, 0.461694991369269, 0.8994083653429901, nan, 0.4455186575833202, 0.967451403887689, 0.015602802725674797, 0.21499674388315193, 0.06397702049875963, 0.0, 0.0, 0.0, 0.9759935075134824, 0.07063840416800485, 0.01772688053965017, 0.16369421418404087, 0.43910776611177343, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.09045909692046099, 0.39337016574585637, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1907 | 30.5 | 1220 | 2.3697 | 0.0901 | 0.1445 | 0.5424 | [0.4251599503270035, 0.765785046340057, 0.6581471380414344, 0.5261900558188807, 0.5930662858860665, 0.3183352326985647, 0.6964540996606768, 0.0, 0.30089709108924906, 0.6814073297137216, 0.0, 0.18263983917017215, 0.05210697720319747, 0.0, 0.0, 0.0, 0.3354583969276623, 0.055768695462742206, 0.0005001955309802923, 0.05114433011928644, 0.34335973445510715, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.08411629143768835, 0.05435565940905418, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8500405742821473, 0.916817079281208, 0.7662590722625434, 0.6907439993867011, 0.8838430901702873, 0.4596320361508343, 0.8871152828645837, nan, 0.5097915776693572, 0.95414686825054, 0.0, 0.25144199460414923, 0.06893850372111242, 0.0, 0.0, 0.0, 0.9752997539138175, 0.08221817792527567, 0.004314063848144953, 0.09926316670853219, 0.6002470447984143, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.10757604383147554, 0.13751381215469613, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0148 | 31.0 | 1240 | 2.3644 | 0.0922 | 0.1475 | 0.5320 | [0.39348806932490793, 0.7835452714344651, 0.7081997736482787, 0.4780721893372995, 0.5336730686426202, 0.29837125871030734, 0.6930428493344302, 0.0, 0.29924119164509083, 0.6571159339203156, 0.0, 0.2044692058961942, 0.06489203701588027, 0.0, 0.0, 0.0, 0.4866554648118479, 0.044145581642732315, 0.0, 0.06382143649425778, 0.2935269694364677, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.19617771013814095, 0.07329523564915885, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8640839575530587, 0.9192028036531761, 0.7651585673714105, 0.6830973736898311, 0.75793979687381, 0.4706590229731816, 0.8625497323557504, nan, 0.4755416657298529, 0.9639308855291576, 0.0, 0.3132616987626756, 0.07416111763937851, 0.0, 0.0, 0.0, 0.9781140373841563, 0.06219890803982443, 0.0, 0.15657707443690866, 0.46099708429685593, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.25327791422633666, 0.3783977900552486, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.4776 | 31.5 | 1260 | 2.3296 | 0.0927 | 0.1495 | 0.5486 | [0.4542888767818597, 0.7347226902082351, 0.7218922155688623, 0.5378129096513363, 0.583355831137352, 0.3442385557337257, 0.6746452615225272, 0.0, 0.29534572862973707, 0.6409269162210338, 0.0010316812105059536, 0.19597793646577183, 0.035507354033575994, 0.0, 0.0, 0.0, 0.47966749487016436, 0.05985256395442886, 0.0005085775011386064, 0.05393716324508912, 0.2532068763509282, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1537234330214699, 0.08031746031746032, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8223876404494382, 0.9311627417040563, 0.8321749763332281, 0.7307118368482138, 0.8906035096836625, 0.46621384565726876, 0.8951755320245449, nan, 0.49512482106856726, 0.9707343412526998, 0.0017336447472971997, 0.29170155363289607, 0.0624102363232798, 0.0, 0.0, 0.0, 0.9577464788732394, 0.09242408021981943, 0.00525531414228567, 0.1401867202545424, 0.3600677936888672, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.214812015870017, 0.40535911602209945, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.6071 | 32.0 | 1280 | 2.3263 | 0.0926 | 0.1478 | 0.5431 | [0.44611914667788144, 0.6906991054003323, 0.6866730381225443, 0.5074964861193935, 0.5781552048719472, 0.32377948368416076, 0.6837998986219906, 0.0, 0.3208871482320843, 0.6827765565497383, 0.0025829619013119556, 0.16798124067363035, 0.04280860702151756, 0.0, 0.0, 0.0, 0.5491364944705348, 0.05461611175296713, 0.0, 0.04500268780538684, 0.2887424974381496, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1499665551839465, 0.07299177849774834, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8343305243445693, 0.9491154599494386, 0.7651940675291891, 0.7267646162156958, 0.8200178306624674, 0.4566568898494183, 0.8991609919672368, nan, 0.5464996889778237, 0.9526133909287257, 0.003948857479954732, 0.2382547213694297, 0.07403055229142186, 0.0, 0.0, 0.0, 0.9489632965076705, 0.0835028369553581, 0.0, 0.11741187306371934, 0.4249457794119759, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.1694313243907047, 0.4468508287292818, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.9074 | 32.5 | 1300 | 2.3673 | 0.0920 | 0.1471 | 0.5351 | [0.3967544010551064, 0.7805696445736764, 0.7306059291494311, 0.50022581423016, 0.5067946749880464, 0.3351793597162349, 0.7093531658204227, 0.0, 0.3347792611677796, 0.6612333363023664, 0.0, 0.1795758966804837, 0.06716556373306945, 0.0, 0.0, 0.0, 0.4484492323879669, 0.045980321574274056, 0.0, 0.09171652606934483, 0.2733365472780724, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.13645581028558554, 0.05806729939603106, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8853901373283396, 0.9281820097023253, 0.8436809719154308, 0.6694193806037072, 0.7217744645201177, 0.45419397392536875, 0.8985494300105134, nan, 0.4985872848138738, 0.9620302375809935, 0.0, 0.2438366359661364, 0.1417939678809244, 0.0, 0.0, 0.0, 0.9818969579559139, 0.06837240837883167, 0.0, 0.25831030729297494, 0.4153225227295578, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.1830719818628377, 0.26027624309392267, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2064 | 33.0 | 1320 | 2.3523 | 0.0919 | 0.1456 | 0.5390 | [0.4014502191538961, 0.7878413260529862, 0.6706802808459629, 0.5061977421770346, 0.5603734397105256, 0.32183266872206184, 0.7139795096759863, 0.0, 0.32093588479813073, 0.6589957542162991, 0.0, 0.18433973921243457, 0.06545315821025668, 0.0, 0.0, 0.0, 0.45807238916454507, 0.04980389715495238, 0.0036028209321340965, 0.0826925852860511, 0.29005297723748164, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.11148026315789474, 0.05935588082356976, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8927543695380774, 0.9379996355934134, 0.7415154622909436, 0.6966131278068679, 0.7923332631456335, 0.4493663780400522, 0.8835146259508414, nan, 0.46116719502964076, 0.9654643628509719, 0.0, 0.2636989487394176, 0.15680898289593942, 0.0, 0.0, 0.0, 0.9679564375098173, 0.07136994611569068, 0.03317907286846027, 0.17987524072678557, 0.40027002570989473, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.12805592291706028, 0.2964088397790055, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.903 | 33.5 | 1340 | 2.3609 | 0.0927 | 0.1487 | 0.5370 | [0.4075733622834502, 0.7542732883284411, 0.6648988920602986, 0.5248570716781542, 0.509697676245945, 0.3181806033068896, 0.6797000421349064, 0.0, 0.3447921257529014, 0.6636860554928405, 0.0, 0.16469173965309977, 0.06162012462497115, 0.0, 0.0, 0.0, 0.4308227696583617, 0.05094559030540425, 0.004221586775181469, 0.08378457218910099, 0.31144272714711513, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.25169195240080827, 0.07541807044410413, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8694319600499376, 0.9455311795385701, 0.7378431681918586, 0.7324358487504925, 0.7119922405257806, 0.4594355642252691, 0.8756948786839737, nan, 0.5100988525904775, 0.9650539956803456, 0.0, 0.24534840450274445, 0.1045828437132785, 0.0, 0.0, 0.0, 0.965809728258024, 0.07426042893337616, 0.03914032473135148, 0.1923930335761534, 0.4532410266722204, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.2965048176837332, 0.3401104972375691, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2941 | 34.0 | 1360 | 2.3475 | 0.0927 | 0.1528 | 0.5455 | [0.4208584637279458, 0.8046875292830021, 0.6522785851757649, 0.5324975634251897, 0.5918016998899351, 0.3248033646493336, 0.676478485049397, 0.0, 0.33967654387607726, 0.5946243841546818, 0.00020294561057636554, 0.20067689467574787, 0.07388928486576384, 0.0, 0.0, 0.0, 0.38386995936230073, 0.049376613390454224, 0.01695264241592313, 0.09430783002474856, 0.3106020836698433, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1503295656087037, 0.08701964014014713, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8805149812734082, 0.916788610016626, 0.7566503628905017, 0.7032809711517719, 0.8671906850468839, 0.45898297711102065, 0.8483395062152561, nan, 0.4811925264743575, 0.9671058315334773, 0.0003370975897522333, 0.3337287189506001, 0.1218174696435566, 0.0, 0.0, 0.0, 0.9805225404471438, 0.0802019769475074, 0.15499254843517138, 0.22494348153730218, 0.44190856469844736, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.2145853013413943, 0.3444198895027624, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.14 | 34.5 | 1380 | 2.3476 | 0.0930 | 0.1484 | 0.5484 | [0.4507045961653645, 0.6939744427313351, 0.640811892188368, 0.5383862308542555, 0.5913912741715294, 0.3223392970447658, 0.6843672644334188, 0.0, 0.37972545527132023, 0.6784252016621853, 0.0, 0.16304582959918415, 0.06193293885601578, 0.0, 0.0, 0.0, 0.38587078842822076, 0.05217989804916869, 0.0, 0.060556068189655976, 0.33883223066477913, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2243663294044018, 0.056978303099948016, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8548813982521848, 0.945048625503906, 0.7189215840959293, 0.7294549305492413, 0.8665455555505777, 0.46162482282442424, 0.8700877488335658, nan, 0.5560177170222812, 0.9591144708423326, 0.0, 0.24541817843520328, 0.12299255777516648, 0.0, 0.0, 0.0, 0.9649850777527619, 0.0865717446383328, 0.0, 0.1360001674621117, 0.505350243454031, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.24383147553372378, 0.23011049723756907, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3208 | 35.0 | 1400 | 2.3157 | 0.0941 | 0.1493 | 0.5541 | [0.4444540497720132, 0.7645915345192558, 0.6473698095654649, 0.5409062259439483, 0.5856129155450979, 0.33085967470669236, 0.703044209235194, 0.0, 0.3400476510847957, 0.6569651741293532, 0.0012611017199280904, 0.16309550359403682, 0.04457278246705168, 0.0, 0.0, 0.0, 0.36734724425949766, 0.05026343639405077, 0.0007944532717030189, 0.07843816285009307, 0.32638752925162456, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.30151397646424116, 0.049744167895238924, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8825561797752809, 0.9378074680574852, 0.7356145471757652, 0.7604140481118772, 0.8518621394106921, 0.45690598818361705, 0.9137560211366806, nan, 0.5038034639626474, 0.9696976241900648, 0.0022633695311935664, 0.22111359196204297, 0.07860033946990469, 0.0, 0.0, 0.0, 0.9637022880779098, 0.07881026299825143, 0.006902502157031924, 0.18965084149711128, 0.5028223431911868, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.33788021915737765, 0.15845303867403315, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.3582 | 35.5 | 1420 | 2.3832 | 0.0909 | 0.1445 | 0.5306 | [0.4051867678695122, 0.7980607418502008, 0.7580956363529654, 0.5205549496763452, 0.4407461812951589, 0.33508630761833824, 0.6726644846774493, 0.0, 0.34557420044197285, 0.6784928828883866, 0.0034325347676846312, 0.17634282975345256, 0.0457234409662312, 0.0, 0.0, 0.0, 0.3998636834450521, 0.04623497669855286, 0.005790456783834268, 0.08466625590470322, 0.2743658403155682, nan, 0.00023949864949372648, 0.0, 0.0, nan, 0.0, 0.0, 0.14829105358633995, 0.04160713176368267, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8635549313358302, 0.9268468011934315, 0.8151980119911644, 0.7514195909795133, 0.6122861328518756, 0.4719115314986598, 0.8713452301603117, nan, 0.5332493948182956, 0.9595032397408207, 0.007006814186992848, 0.27381616894594846, 0.09687948818383602, 0.0, 0.0, 0.0, 0.975273574532698, 0.06726617421403847, 0.05349439171699741, 0.21573306539395462, 0.4105970728064002, nan, 0.00023949864949372648, 0.0, 0.0, nan, nan, 0.0, 0.19459663706782543, 0.1480110497237569, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.386 | 36.0 | 1440 | 2.3824 | 0.0934 | 0.1488 | 0.5302 | [0.39236390712040153, 0.8145851925312122, 0.7558239703806888, 0.4900571399155771, 0.48914028430650736, 0.33919614966813755, 0.6652323001411955, 0.0, 0.3456263625131779, 0.6560158432230317, 0.001601036396079294, 0.18811622781579862, 0.05869991594284113, 0.0, 0.0, 0.0, 0.4797751879268533, 0.046657224133211134, 0.00930281474682576, 0.07758739097589451, 0.3090916412471183, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1549231307719232, 0.07425068119891008, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8881335830212235, 0.9108228756234769, 0.8132888923950773, 0.6788969889517045, 0.696477772152806, 0.45792694051110766, 0.8158373932343381, nan, 0.5430072471914322, 0.9587041036717062, 0.0031542703041101826, 0.3017489999069681, 0.10941376158767463, 0.0, 0.0, 0.0, 0.9866616053196502, 0.06994254719337686, 0.0763197113499098, 0.1891903206899439, 0.4409893282392313, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.21741923294917817, 0.36734806629834255, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.851 | 36.5 | 1460 | 2.3500 | 0.0967 | 0.1573 | 0.5450 | [0.41524299726375297, 0.8009912665364631, 0.6891513755788327, 0.5282791330311297, 0.5382617997822409, 0.3209485427756586, 0.6855615317723598, 0.0, 0.34495694031815277, 0.6450787627917672, 0.00034193392126971464, 0.18070362473347548, 0.05615745756590827, 0.0, 0.0, 0.0, 0.5498109654736161, 0.04774509910281306, 0.01089028271073159, 0.08318980206500814, 0.3353840629016406, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.36103354423668305, 0.07984250543084721, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.860379213483146, 0.9282332543785728, 0.7684206374250552, 0.7450690941895272, 0.7596713423621596, 0.4702380117041133, 0.877598279380742, nan, 0.5499621527231303, 0.9693736501079914, 0.0005778815824323999, 0.2838403572425342, 0.1218174696435566, 0.0, 0.0, 0.0, 0.9784412796481491, 0.07852478321378867, 0.10173346929170915, 0.18906472410617098, 0.5097309797049826, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.385943699225392, 0.48734806629834254, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3517 | 37.0 | 1480 | 2.3574 | 0.0943 | 0.1502 | 0.5440 | [0.4228595354346009, 0.7748181297538302, 0.6949422948852119, 0.5342279860915328, 0.5451333645297145, 0.3215454815990477, 0.6760519655457277, 0.0, 0.3373484782973615, 0.6933557004891509, 0.00421865306403805, 0.17283593452607537, 0.04307984578420965, 0.0, 0.0, 0.0, 0.40725727575039666, 0.04887517311320241, 0.0043186259216079085, 0.07464663207256432, 0.27873249635393826, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.29052538571331477, 0.08985310669244755, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8557943196004993, 0.9320908397294281, 0.7491243294414642, 0.7601234442441714, 0.7828579236686364, 0.4776478100397154, 0.882868706580819, nan, 0.4970284266774588, 0.9551835853131749, 0.007367990176013099, 0.26400130244673925, 0.10066588327457893, 0.0, 0.0, 0.0, 0.9810723074506519, 0.0799700246226314, 0.04125813789316809, 0.18759943062882023, 0.3925426942246097, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.3230304175325902, 0.3440331491712707, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.351 | 37.5 | 1500 | 2.3734 | 0.0904 | 0.1459 | 0.5398 | [0.4358926457706038, 0.767827928071386, 0.6770891494509585, 0.5237361027822186, 0.5741180617280167, 0.3277300764927676, 0.6693217782875592, 0.0, 0.33305621947417646, 0.6817848186433421, 0.019828058775553863, 0.16795366795366795, 0.06148850895439095, 0.0, 0.0, 0.0, 0.3150709636290431, 0.05170151342839647, 0.0038583458742915693, 0.09165746801200231, 0.29477336063242904, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.09638852753515456, 0.05374195633649693, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8666073657927591, 0.9266076593709431, 0.731847585989271, 0.6897794798012483, 0.8391947529467634, 0.45651304433248663, 0.8661091603735338, nan, 0.5284379192241683, 0.9629805615550756, 0.03570826611446871, 0.2569773932458833, 0.1582452017234626, 0.0, 0.0, 0.0, 0.9833368239174826, 0.08435927630874639, 0.03780688681465213, 0.24681821987775265, 0.4466627407609554, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.11758926884564519, 0.10104972375690607, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.9016 | 38.0 | 1520 | 2.3797 | 0.0922 | 0.1468 | 0.5361 | [0.3979803210771621, 0.8215521095152025, 0.7240312356453835, 0.4978367086289089, 0.5282189976939653, 0.32252930323024087, 0.6956390892056101, 0.0, 0.3261938294162919, 0.6670155189426786, 0.0, 0.2158865285041332, 0.04337220080987462, 0.0, 0.0, 0.0, 0.3376964639840277, 0.041506735010132315, 0.013210224784063633, 0.08663169738888706, 0.27028534152603007, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.23038631423964104, 0.046741210839125615, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8922659176029962, 0.9077510419750837, 0.7771655096244873, 0.7171782542730357, 0.7511793773604347, 0.4642596516833434, 0.841818469171093, nan, 0.4669379679384851, 0.963585313174946, 0.0, 0.3869197134617174, 0.1160725943334639, 0.0, 0.0, 0.0, 0.9874469867532332, 0.06212753809370874, 0.11828378696368343, 0.21918697144770996, 0.3689728968875228, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.24832798035140752, 0.10254143646408839, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.6249 | 38.5 | 1540 | 2.3389 | 0.0948 | 0.1506 | 0.5476 | [0.44896937443748053, 0.7327870092041515, 0.702833401443945, 0.5179074282523701, 0.5741124663766068, 0.3256582936711242, 0.6919663902831313, 0.0, 0.3850746129757246, 0.6641039748985085, 0.018652642790573826, 0.17898514779259433, 0.05360252503265128, 0.0, 0.0, 0.0, 0.3991114932645457, 0.048388596939961864, 0.0, 0.06818984365021526, 0.2746326684658415, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.28833227198754685, 0.0698983248825294, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8578636079900125, 0.9518485093493065, 0.7326522562322499, 0.7243221174503165, 0.8299837373606139, 0.46163183967890875, 0.8573205340516323, nan, 0.6202156920056059, 0.9645572354211663, 0.03373383737449134, 0.28532886780165595, 0.1286068677373025, 0.0, 0.0, 0.0, 0.9478244934289753, 0.07426042893337616, 0.0, 0.18998576572050574, 0.38148313057466643, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.30795390137918005, 0.290939226519337, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0949 | 39.0 | 1560 | 2.3474 | 0.0965 | 0.1556 | 0.5483 | [0.4252860384788539, 0.800874486291488, 0.7321616393418507, 0.5220628125928689, 0.563952966520061, 0.32862220717670954, 0.6963647914920424, 0.0, 0.3485466160598535, 0.6593487333215776, 0.019300024474668716, 0.18426230540383132, 0.06387929740279237, 0.0, 0.0, 0.0, 0.5084594403754829, 0.049878946007568814, 0.011522930116515343, 0.08760321624528901, 0.26784043386101936, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.3098973395026027, 0.07873112577800342, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8682943196004994, 0.928452467715854, 0.7852832123698328, 0.7360104546078542, 0.8162993759268137, 0.4631895813744615, 0.8962749692501151, nan, 0.5017199901072464, 0.9691144708423326, 0.039873829187835594, 0.2722578844543678, 0.1111111111111111, 0.0, 0.0, 0.0, 0.9614246819205194, 0.07572351282874781, 0.10542003294376029, 0.23695888805157833, 0.4029128305301409, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.323937275647081, 0.46265193370165747, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2577 | 39.5 | 1580 | 2.3334 | 0.0944 | 0.1513 | 0.5470 | [0.4402755503484393, 0.7402331985677358, 0.6773202538216256, 0.5072853858237666, 0.5930683129436178, 0.33015401415690654, 0.7049174176186468, 0.0, 0.357822011528309, 0.6655650925621318, 0.011452232625806693, 0.1390162139974003, 0.07664071894183913, 0.0, 0.0, 0.0, 0.44731726767513147, 0.04801383619727739, 0.0, 0.05874611150993061, 0.31305863769456005, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2304008152173913, 0.07994399179722385, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8585455680399501, 0.9481503518801102, 0.756591195960871, 0.6730278605315376, 0.8680822181702514, 0.47095723928877165, 0.903572483834837, nan, 0.5973649301886368, 0.9613174946004319, 0.022176205725843345, 0.1890408410084659, 0.15811463637550593, 0.0, 0.0, 0.0, 0.9398136028064297, 0.07677621953395425, 0.0, 0.13361383237042618, 0.4668428536546831, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.2563007746079728, 0.4006077348066298, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1701 | 40.0 | 1600 | 2.3302 | 0.0953 | 0.1541 | 0.5490 | [0.4259301389453817, 0.8066144094268355, 0.7156322044500044, 0.5189784024862559, 0.5884532221551915, 0.32656141740077205, 0.7052324253246934, 0.0, 0.3676639641819942, 0.683115567087869, 0.010315416018662519, 0.17511329024589556, 0.06753026120195371, 0.0, 0.0, 0.0, 0.44391109193439166, 0.0498715279315997, 0.006932507840821148, 0.07890224309460545, 0.27097698845413726, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.25831137078080846, 0.07708268849376941, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.867730961298377, 0.9171031953902566, 0.789744398863995, 0.7061905755204573, 0.8827365138814843, 0.4630071431578652, 0.8697441747005752, nan, 0.546507183488095, 0.9609503239740821, 0.020442560978546145, 0.2741185226532701, 0.16607912260086174, 0.0, 0.0, 0.0, 0.9696188282109011, 0.08034471683973879, 0.06675033335947918, 0.21382818387339864, 0.38833431480976116, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.3073871150576233, 0.37149171270718234, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1454 | 40.5 | 1620 | 2.3452 | 0.0937 | 0.1484 | 0.5472 | [0.41590111896925575, 0.7956456544034374, 0.7433866866772062, 0.5075708130433674, 0.5719918487860337, 0.3325156545780296, 0.7216679076693969, 0.0, 0.34159443418760627, 0.692085970298891, 0.0007803923124743727, 0.18136915775078433, 0.05085877283486071, 0.0, 0.0, 0.0, 0.4747165401851638, 0.044398764313752816, 8.663109016563865e-06, 0.0787716253878109, 0.2466963662712683, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1938508839354343, 0.07160791623266269, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.887791822721598, 0.9320481358325552, 0.804970022088987, 0.7098739349724817, 0.8305863062304277, 0.46035828058997713, 0.899078534175319, nan, 0.49087543374478193, 0.9542116630669546, 0.001420625556812983, 0.26218718020280957, 0.10941376158767463, 0.0, 0.0, 0.0, 0.9678386302947798, 0.06821182600007138, 7.84375245117264e-05, 0.18814368249183622, 0.3300777042069431, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.23823918382769696, 0.36082872928176796, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3269 | 41.0 | 1640 | 2.3383 | 0.0968 | 0.1571 | 0.5504 | [0.44117825990446985, 0.7865425366699565, 0.6838256139003621, 0.5341472651761701, 0.5823088792515934, 0.3153129970992795, 0.6957088082357267, 0.0, 0.35667411972151025, 0.655019170442663, 0.02082355913241185, 0.18702265053922051, 0.04257827852744661, 0.0, 0.0, 0.0, 0.4492152762756267, 0.0543782136318288, 0.005244184722801958, 0.08332128659197686, 0.2751865862386259, nan, 0.0033214205051216306, 0.0, 0.0, nan, 0.0, 0.0, 0.42900848740048686, 0.07639193083573487, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8374469413233459, 0.9335982872890427, 0.7516606184916378, 0.741781526508243, 0.847881600817164, 0.4729009079809703, 0.910169107188258, nan, 0.588199144126927, 0.9741252699784018, 0.041679709132936844, 0.2653967810959159, 0.127301214257736, 0.0, 0.0, 0.0, 0.9786114456254255, 0.08849873318345644, 0.05420032943760295, 0.19304194925898016, 0.3865963833790558, nan, 0.003326370131857312, 0.0, 0.0, nan, nan, 0.0, 0.4927640279614585, 0.36613259668508286, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.8185 | 41.5 | 1660 | 2.3574 | 0.0927 | 0.1490 | 0.5449 | [0.4211759415401911, 0.7995945186741249, 0.6978932238632433, 0.5148802388613652, 0.5681032954662072, 0.3273415115720924, 0.709560116667298, 0.0, 0.34431855998331107, 0.6651933866095212, 0.0010200958890135673, 0.17970467088875502, 0.05918037206794284, 0.0, 0.0, 0.0, 0.3746546582488056, 0.048634381455129674, 0.00826594929383612, 0.06770428015564202, 0.28484075132707226, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.26019523473123113, 0.062167810304256964, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8769569288389513, 0.93192998838454, 0.77183654149574, 0.7083478189555733, 0.8184229271854881, 0.4641614157205608, 0.901030035250706, nan, 0.5195344410219515, 0.9654211663066955, 0.001926271941441333, 0.25672155549353426, 0.11463637550594072, 0.0, 0.0, 0.0, 0.9834022723702812, 0.07561645790957428, 0.08290846340889481, 0.14751318764129615, 0.4007727331485285, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.30617797090496884, 0.2087292817679558, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.4443 | 42.0 | 1680 | 2.3533 | 0.0946 | 0.1519 | 0.5481 | [0.4556085209645064, 0.7171990698571566, 0.6706899591548882, 0.5366851043897921, 0.5814569413194934, 0.32244517492484903, 0.7128303140195261, 0.0, 0.3762961100986615, 0.6560768647576836, 0.0015664352847226488, 0.1634929226328134, 0.07514845403044161, 0.0, 0.0, 0.0, 0.4917144503278554, 0.0537456789043014, 0.0, 0.06913935598983319, 0.28430204378382745, nan, 0.0016475121238291371, 0.0, 0.0, nan, 0.0, 0.0, 0.2811459027315123, 0.07812873178887031, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.86375, 0.952699740360307, 0.7202350899337331, 0.7272228075899312, 0.8399698043555202, 0.4587514209130331, 0.899559537961506, nan, 0.538525530049239, 0.9719006479481641, 0.0024559967253376994, 0.23183551958321705, 0.14375244810027418, 0.0, 0.0, 0.0, 0.9383999162259804, 0.08766013631659708, 0.0, 0.17196265594909152, 0.4043922267066917, nan, 0.0016498795854012268, 0.0, 0.0, nan, nan, 0.0, 0.3348573587757415, 0.43375690607734807, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.8899 | 42.5 | 1700 | 2.3360 | 0.0951 | 0.1534 | 0.5492 | [0.42846571344838863, 0.8007632484952961, 0.6826133744392173, 0.5345972402179221, 0.5726169929331992, 0.32029962113543686, 0.7003049806266755, 0.0, 0.36421663255308623, 0.6963148209314396, 0.003372704496021585, 0.18627909013928226, 0.06906686260102865, 0.0, 0.0, 0.0, 0.4428928219783387, 0.0483309094598708, 0.004074604060051903, 0.09843533548822506, 0.27695480393257854, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.25219468979032994, 0.08084125389588646, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8647097378277153, 0.9310033138223973, 0.7406161249605554, 0.7436873886835644, 0.8338477942395313, 0.46626998049314455, 0.8867510942836135, nan, 0.5627777652869274, 0.9561771058315335, 0.005899207820664082, 0.29674853474741836, 0.12273142707925316, 0.0, 0.0, 0.0, 0.965626472590188, 0.07649073974949148, 0.03953251235391011, 0.23203968852047224, 0.389641354150209, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.3549593803136218, 0.3467955801104972, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.8087 | 43.0 | 1720 | 2.3284 | 0.0972 | 0.1525 | 0.5505 | [0.43836696729880237, 0.7567624699912359, 0.6673363858428416, 0.5294658081291947, 0.5873144378689118, 0.3192053517516581, 0.7007850338821053, 0.0, 0.3705301979871153, 0.6521739130434783, 0.004574081111895325, 0.17451251689297895, 0.06082198753022013, 0.0, 0.0, 0.0, 0.38038830433546184, 0.04935740581531204, 0.0, 0.06753617165955196, 0.28932680972698915, nan, 0.0011563613163910894, 0.0, 0.0, nan, 0.0, 0.0, 0.5113006396588486, 0.04920763681664107, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8605243445692884, 0.9427440385359965, 0.7467813190280845, 0.7202411833817376, 0.8547674621770432, 0.46924512679456054, 0.896797201932261, nan, 0.5888061994588963, 0.9732181425485961, 0.008114420553321615, 0.2522792817936552, 0.1248204726465596, 0.0, 0.0, 0.0, 0.9629954447876852, 0.07989865467651572, 0.0, 0.14969019509336012, 0.3983453743734111, nan, 0.0011575768058863446, 0.0, 0.0, nan, nan, 0.0, 0.5436614396372568, 0.1876795580110497, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.8746 | 43.5 | 1740 | 2.3485 | 0.0936 | 0.1477 | 0.5446 | [0.4272191745573527, 0.7504909199535571, 0.6849456177073291, 0.5154349225261836, 0.5841193162988642, 0.32693051310406657, 0.7123769775246045, 0.0, 0.36178099163105865, 0.6689666526971203, 0.0011928429423459245, 0.16990881915548814, 0.07231482641935079, 0.0, 0.0, 0.0, 0.3994246685770762, 0.04632826297499003, 0.0, 0.06954148268216795, 0.2777953386437392, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.22221839120091025, 0.07084158351774922, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.00014685048892574548, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8757553058676654, 0.947696267110028, 0.7370108867150521, 0.6910399517918492, 0.8488716953913562, 0.4657086321343868, 0.8679232317957246, nan, 0.5361872428446163, 0.9653347732181425, 0.0019503503407093496, 0.2630709833472881, 0.15067241154197675, 0.0, 0.0, 0.0, 0.946921304780355, 0.07254755022659957, 0.0, 0.15626308297747635, 0.37868233198799245, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.24352918949556018, 0.3024309392265193, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.00014685048892574548, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.831 | 44.0 | 1760 | 2.3582 | 0.0946 | 0.1529 | 0.5471 | [0.44862804953591057, 0.7731316248333944, 0.7137798359999303, 0.5272975648440227, 0.5739832784018088, 0.33877220463425345, 0.6904242373379212, 0.0, 0.35824641385185696, 0.6585803739545475, 0.030590522028878193, 0.1564589009798778, 0.08286701777485188, 0.0, 0.0, 0.00017225505627988476, 0.47857741082439215, 0.049684259435257346, 0.0, 0.0840623961777595, 0.2905621537623233, nan, 0.005873482022382729, 0.0, 0.0, nan, 0.0, 0.0, 0.19286701322629465, 0.07456105895753957, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8476591760299625, 0.9429447468512994, 0.808591038182392, 0.6923022066282641, 0.8336529113708554, 0.4681645312039519, 0.9044795195459324, nan, 0.5830354265500521, 0.9745140388768898, 0.06366328766463605, 0.2150200018606382, 0.13147930539234887, 0.0, 0.0, 0.00017313226110733006, 0.9499319336090895, 0.08394889911858117, 0.0, 0.22245248262580591, 0.4135845912988524, nan, 0.0059076333541785864, 0.0, 0.0, nan, nan, 0.0, 0.22150009446438693, 0.4195027624309392, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7194 | 44.5 | 1780 | 2.3640 | 0.0941 | 0.1514 | 0.5428 | [0.43350567785320054, 0.7731674676783076, 0.7072758223454915, 0.5135186808138796, 0.547413130716231, 0.3328523221188296, 0.7025313371416195, 0.0, 0.3597874653923118, 0.656186049231129, 0.018551556209166084, 0.17703048675137825, 0.05200519330640508, 0.0, 0.0, 0.0002494209869944771, 0.41845855107227503, 0.04923929580525973, 0.0, 0.0882416325448005, 0.28095806107692145, nan, 0.004210693572733773, 0.0, 0.0, nan, 0.0, 0.0, 0.3001928924052702, 0.0747682335502349, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8531023720349563, 0.9457575101919967, 0.7604331019248974, 0.7134948948210112, 0.7740008332922661, 0.47098881513395174, 0.8915542606628232, nan, 0.5902001783693445, 0.9695680345572354, 0.037056656473477646, 0.262140664247837, 0.09413761587674631, 0.0, 0.0, 0.00025074327470716765, 0.9590816273103304, 0.08084430646254862, 0.0, 0.22285020514108683, 0.3822874624764805, nan, 0.004231142807722501, 0.0, 0.0, nan, nan, 0.0, 0.34694880030228603, 0.32883977900552486, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.5284 | 45.0 | 1800 | 2.3472 | 0.0964 | 0.1553 | 0.5461 | [0.4203439092672654, 0.8073401576234241, 0.7243798955613577, 0.5223530028573675, 0.549703015370608, 0.3335615466389159, 0.7053615999830061, 0.0, 0.36481394845791265, 0.6520617809846707, 0.01039811419591409, 0.16680997420464316, 0.05976206359795419, 0.0, 0.0, 0.0005878964589630457, 0.401571427796257, 0.050430558139025965, 0.008387202676400928, 0.09611780899155901, 0.2984873714963755, nan, 0.0018627672738400947, 0.0, 0.0, nan, 0.0, 0.0, 0.4031013001083424, 0.07301291288745726, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.00019868007325247918, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8653043071161048, 0.9231756895255882, 0.8054354686020827, 0.7105068452365034, 0.8006281052457093, 0.4700660987692437, 0.9126771983590899, nan, 0.5435093793796044, 0.9720086393088553, 0.01911824901880523, 0.23011442924923248, 0.14035774905340123, 0.0, 0.0, 0.0005910377189526096, 0.9687156395622808, 0.08223602041180458, 0.08416346380108244, 0.2426525998492841, 0.4299584907286385, nan, 0.0018627672738400947, 0.0, 0.0, nan, nan, 0.0, 0.449877196296996, 0.28552486187845305, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.00019868007325247918, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.6914 | 45.5 | 1820 | 2.3321 | 0.0985 | 0.1570 | 0.5504 | [0.44062770914279387, 0.761767946650595, 0.7469356134927128, 0.5301679169197479, 0.5513342308861576, 0.33925065755065226, 0.6870311731837669, 0.0, 0.3704766251548543, 0.6613854686143843, 0.023482004567768357, 0.17349297705785757, 0.048038547649177456, 0.0, 0.0, 0.0005811919178740236, 0.4371920436798586, 0.05251944968520128, 0.0, 0.07658178836605475, 0.28944202056980234, nan, 0.00536928767450185, 0.0, 0.0, nan, 0.0, 0.0, 0.5335586990055466, 0.0678812913945492, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8510736579275905, 0.9427739312638076, 0.8138687283054591, 0.7329546568824088, 0.7962286804861768, 0.47108003424224987, 0.8930522438826626, nan, 0.5961733030555119, 0.971036717062635, 0.043076256290481806, 0.25309331100567495, 0.12886799843321584, 0.0, 0.0, 0.0005850676409833912, 0.945402900675428, 0.08840952075081183, 0.0, 0.17596081386586285, 0.39733995949614354, nan, 0.005388719613608845, 0.0, 0.0, nan, nan, 0.0, 0.6142830153032307, 0.32502762430939225, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.9363 | 46.0 | 1840 | 2.3603 | 0.0957 | 0.1533 | 0.5448 | [0.4307641160941553, 0.7948637865598526, 0.7199057146569164, 0.5296008038661645, 0.5420462362516328, 0.3320313857919698, 0.7084345558217591, 0.0, 0.37007814061503613, 0.6535473021947754, 0.030037394170060253, 0.18495110006140575, 0.0516514213726242, 0.0, 0.0, 0.0002496077592354871, 0.39646599039893343, 0.0522977819099482, 0.0039492332436262875, 0.08375527426160338, 0.2983843452082288, nan, 0.0018980117331634414, 0.0, 0.0, nan, 0.0, 0.0, 0.3670740429766458, 0.05197259285971872, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8532927590511861, 0.9310360534766666, 0.7649850110444936, 0.7348105993749342, 0.785460841983594, 0.4691433824045357, 0.8716681898453229, nan, 0.5828180857521865, 0.9737149028077754, 0.0651802268185211, 0.28721276397804446, 0.12312312312312312, 0.0, 0.0, 0.00025074327470716765, 0.9653777684695534, 0.08430574884915962, 0.0389050121578163, 0.2077576823243741, 0.42707151372391305, nan, 0.0019026837154223824, 0.0, 0.0, nan, nan, 0.0, 0.44602304931041, 0.19906077348066298, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1896 | 46.5 | 1860 | 2.3470 | 0.0961 | 0.1539 | 0.5448 | [0.4302888708712368, 0.7840775863614363, 0.7197212972507954, 0.5239124080169796, 0.5361995966189897, 0.33269042907597124, 0.686007758583737, 0.0, 0.36299630377477604, 0.6463129034100645, 0.020041912745284815, 0.18784904637894959, 0.04363136176066025, 0.0, 0.0, 0.00018989520158562494, 0.4620590700154913, 0.051365775665998606, 6.591794863343853e-05, 0.08219591952797327, 0.2808638539463431, nan, 0.0025166562909784496, 0.0, 0.0, nan, 0.0, 0.0, 0.4082182774490467, 0.06904881516081106, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0002159566013613904, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8596551186017478, 0.9338957911039242, 0.765592458188703, 0.7349425299651811, 0.7801295635071748, 0.46598930631376567, 0.8797627964185832, nan, 0.564523986180123, 0.9775377969762419, 0.037995714044930295, 0.2902363010512606, 0.10353832092962528, 0.0, 0.0, 0.00019104249501498489, 0.9526414995549505, 0.08139742354494522, 0.0006275001960938112, 0.19362806664992047, 0.39861827269724087, nan, 0.002528041300211557, 0.0, 0.0, nan, nan, 0.0, 0.4692235027394672, 0.3569060773480663, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0002159566013613904, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0699 | 47.0 | 1880 | 2.3703 | 0.0966 | 0.1545 | 0.5362 | [0.39480193878006337, 0.8176080694917801, 0.7219032652986643, 0.505227403004498, 0.5027757960037987, 0.3315813337318605, 0.7109051055395131, 0.0, 0.361499711633177, 0.6554905325530573, 0.0046006010462657215, 0.19404779798587105, 0.05704644062349349, 0.0, 0.0, 0.0005837952588748673, 0.45511712954766076, 0.04679955428995914, 0.011199338690575546, 0.08728850172372749, 0.28654787628263345, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.4469559109919229, 0.07303409090909091, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8836860174781523, 0.9216653950395154, 0.7791811296939097, 0.6976899666786117, 0.7151058863586472, 0.45926715971764176, 0.8847514928296079, nan, 0.5308286680006895, 0.9652699784017279, 0.008957164527702198, 0.3002604893478463, 0.09270139704922313, 0.0, 0.0, 0.0005910377189526096, 0.9757709827739672, 0.07418905898726047, 0.11051847203702252, 0.20723436322532027, 0.3942806256553151, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.5290005667863216, 0.3550828729281768, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2962 | 47.5 | 1900 | 2.3289 | 0.1004 | 0.1610 | 0.5506 | [0.43813563391067495, 0.7919837442756195, 0.7229490864037844, 0.530693124964866, 0.5619734040268292, 0.32844023814193124, 0.6849341364586738, 0.0, 0.37461864366255104, 0.6442053497825407, 0.03035054317392765, 0.19145526513998173, 0.035979926516712966, 0.0, 0.0, 0.00031904334262891713, 0.5248448757220687, 0.053110580747812254, 0.005331374546833164, 0.09117176661865672, 0.2788984917106411, nan, 0.003947699603905308, 0.0, 0.0, nan, 0.0, 0.0, 0.5592105263157895, 0.07566214768571534, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.840165418227216, 0.929310816043, 0.7848611549384664, 0.7405620599713675, 0.8205464784441627, 0.4759392059727465, 0.9071662692659195, nan, 0.5871274291581416, 0.9789416846652268, 0.05872721581469264, 0.2778630570285608, 0.1048439744091918, 0.0, 0.0, 0.000322384210337787, 0.9621446149013038, 0.08933733005031581, 0.052945329045415324, 0.21598425856150047, 0.38563405771081394, nan, 0.003965033197173916, 0.0, 0.0, nan, nan, 0.0, 0.632722463631211, 0.45265193370165746, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.6174 | 48.0 | 1920 | 2.3525 | 0.0971 | 0.1552 | 0.5450 | [0.4086238986289587, 0.8169527171190324, 0.7147347588187357, 0.5193104998447364, 0.5504920947069307, 0.3318776638002968, 0.7144779541250872, 0.0, 0.36262716401927536, 0.646572972972973, 0.007966049455890372, 0.1963503761004562, 0.055534229046705054, 0.0, 0.0, 0.0007429201478764866, 0.4022438568103258, 0.04821108340987622, 0.008825570478017717, 0.0758365001390305, 0.2995559310508258, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.46843680853833136, 0.0690767784745311, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8733270911360799, 0.9145737012321498, 0.7991401072893657, 0.7125892091474253, 0.8032534238903997, 0.46606649171309483, 0.886551821286479, nan, 0.5329571089177177, 0.9688120950323974, 0.016180684308107197, 0.29930691227090894, 0.1133307220263742, 0.0, 0.0, 0.000752229824121503, 0.9794360961306875, 0.07866752310602006, 0.08479096399717626, 0.17127187473834046, 0.4175918877382474, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.5439637256754204, 0.2720441988950276, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.5171 | 48.5 | 1940 | 2.3309 | 0.0949 | 0.1514 | 0.5467 | [0.4272610254871079, 0.7765229274757001, 0.7065223827054288, 0.521725648381944, 0.5604860198244929, 0.33056051974210365, 0.6943983809185744, 0.0, 0.3654088019294603, 0.6576679731653968, 0.011089910230634277, 0.17749015194147438, 0.05580952380952381, 0.0, 0.0, 0.0008791054223222449, 0.37348308478564973, 0.04977036796996346, 0.0, 0.06882807742697396, 0.28323033982427753, nan, 0.0009555788551634438, 0.0, 0.0, nan, 0.0, 0.0, 0.43712612163509473, 0.05106490021801107, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8619444444444444, 0.9342046826246384, 0.8001577784790155, 0.7139192121247778, 0.8127914842906487, 0.46984155942574063, 0.900624617773777, nan, 0.5552457824643449, 0.9676241900647948, 0.020851893766102428, 0.25674481347102057, 0.11476694085389738, 0.0, 0.0, 0.0008955116953827417, 0.968061155034295, 0.07947043499982158, 0.0, 0.15615841915766557, 0.3745744940608707, nan, 0.0009579945979749059, 0.0, 0.0, nan, nan, 0.0, 0.5301341394294351, 0.16823204419889504, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3054 | 49.0 | 1960 | 2.3445 | 0.0965 | 0.1510 | 0.5448 | [0.42060555530518123, 0.775225963597999, 0.7057189441614043, 0.5143286771089474, 0.5549782193785344, 0.3272389694170118, 0.7080437975404985, 0.0, 0.3697319477914729, 0.6593277211985636, 0.006849495508311599, 0.1797058129473767, 0.06041899257897156, 0.0, 0.0, 0.0004667182616812491, 0.398030812628838, 0.0480704205053159, 0.0, 0.06379430807210397, 0.3090374985213517, nan, 0.0003989096469649624, 0.0, 0.0, nan, 0.0, 0.0, 0.3985289200936142, 0.05904316085708713, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.873859238451935, 0.9350786890473045, 0.7665785736825497, 0.7055487510273649, 0.8096352778312896, 0.46129503066365407, 0.887802431130565, nan, 0.541575795729628, 0.9676025917926566, 0.012520767619368664, 0.2656758768257512, 0.12012012012012012, 0.0, 0.0, 0.000471636159568244, 0.960429865437981, 0.0773650215894087, 0.0, 0.1433475676128276, 0.4502822343191187, nan, 0.00039916441582287746, 0.0, 0.0, nan, nan, 0.0, 0.45040619686378236, 0.23414364640883978, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0223 | 49.5 | 1980 | 2.3584 | 0.0957 | 0.1510 | 0.5439 | [0.4080709894226452, 0.8048750591212661, 0.7407096572019292, 0.507544480008525, 0.5479745676250415, 0.3399907521254348, 0.7024147379086071, 0.0, 0.36409692514711495, 0.6423189649009936, 0.001532833552309555, 0.19033508092090268, 0.06216861081654295, 0.0, 0.0, 0.0008764396550709975, 0.39762865306209805, 0.04659518329310054, 0.0, 0.058694683342657476, 0.28469598076262453, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.4455286771981303, 0.05693710068296982, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8836360799001248, 0.9253464709499625, 0.811143105080467, 0.6963029126352066, 0.7988741594276267, 0.4540255694177414, 0.8860776889829518, nan, 0.543464412317977, 0.9703455723542117, 0.0028653295128939827, 0.29130616801562936, 0.12247029638333987, 0.0, 0.0, 0.0008895416174135234, 0.9727734436357924, 0.07404631909502908, 0.0, 0.12957380892573056, 0.3809086077876564, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.4934063857925562, 0.22845303867403316, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.5046 | 50.0 | 2000 | 2.3431 | 0.0959 | 0.1537 | 0.5496 | [0.44824978876617866, 0.7548671615728508, 0.7119201505944329, 0.5304481563680256, 0.5684691275095736, 0.33051502835188457, 0.6982393617021276, 0.0, 0.3703529914609331, 0.6659141206351092, 0.028823893043720683, 0.17181416221210322, 0.052153820762502065, 0.0, 0.0, 0.0005543923800536699, 0.40565901784724534, 0.05230759173712194, 0.0, 0.07225859019823891, 0.29980315155352005, nan, 0.003601361102652032, 0.0, 0.0, nan, 0.0, 0.0, 0.38898705304076847, 0.05940808241958817, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8427949438202247, 0.9402615186644498, 0.7846678763016725, 0.7286579984703183, 0.8303175022736334, 0.469325820621132, 0.9020126572710594, nan, 0.5974398752913491, 0.9683369330453564, 0.05725843345934362, 0.24220857754209693, 0.12377594986290638, 0.0, 0.0, 0.0005611873291065182, 0.9580213623749935, 0.08566177782535773, 0.0, 0.16335928996064641, 0.43531591571750716, nan, 0.0036190907034607555, 0.0, 0.0, nan, nan, 0.0, 0.45750991876062724, 0.24276243093922653, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
jordyvl/vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_simkd_rand
|
jordyvl
| 2023-08-09T10:48:04Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-08-08T21:40:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_simkd_rand
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. -->
# vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_simkd_rand
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0667
- Accuracy: 0.5865
- Brier Loss: 0.5908
- Nll: 3.0393
- F1 Micro: 0.5865
- F1 Macro: 0.5890
- Ece: 0.1479
- Aurc: 0.2054
## 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: 0.0001
- 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_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0807 | 1.0 | 1000 | 0.0798 | 0.095 | 0.9362 | 7.0778 | 0.095 | 0.0517 | 0.0524 | 0.8510 |
| 0.0785 | 2.0 | 2000 | 0.0782 | 0.142 | 0.9268 | 6.5000 | 0.142 | 0.0892 | 0.0843 | 0.7446 |
| 0.0768 | 3.0 | 3000 | 0.0761 | 0.253 | 0.8945 | 4.3268 | 0.253 | 0.1827 | 0.1545 | 0.5697 |
| 0.0753 | 4.0 | 4000 | 0.0747 | 0.327 | 0.8672 | 3.7313 | 0.327 | 0.2733 | 0.2052 | 0.4558 |
| 0.074 | 5.0 | 5000 | 0.0739 | 0.359 | 0.8410 | 3.6965 | 0.359 | 0.2941 | 0.2102 | 0.4159 |
| 0.0729 | 6.0 | 6000 | 0.0725 | 0.3795 | 0.8104 | 3.2323 | 0.3795 | 0.3340 | 0.2147 | 0.3672 |
| 0.0718 | 7.0 | 7000 | 0.0717 | 0.4165 | 0.7806 | 3.1185 | 0.4165 | 0.3770 | 0.2186 | 0.3378 |
| 0.071 | 8.0 | 8000 | 0.0714 | 0.4175 | 0.7785 | 3.1984 | 0.4175 | 0.3999 | 0.2170 | 0.3408 |
| 0.0703 | 9.0 | 9000 | 0.0707 | 0.457 | 0.7563 | 2.8932 | 0.457 | 0.4310 | 0.2437 | 0.2965 |
| 0.0696 | 10.0 | 10000 | 0.0699 | 0.4665 | 0.7452 | 2.7889 | 0.4665 | 0.4529 | 0.2456 | 0.2828 |
| 0.0691 | 11.0 | 11000 | 0.0693 | 0.499 | 0.7219 | 2.7292 | 0.499 | 0.4756 | 0.2543 | 0.2579 |
| 0.0685 | 12.0 | 12000 | 0.0691 | 0.4955 | 0.7144 | 2.8807 | 0.4955 | 0.4734 | 0.2443 | 0.2515 |
| 0.068 | 13.0 | 13000 | 0.0688 | 0.5072 | 0.7096 | 2.6737 | 0.5072 | 0.4944 | 0.2525 | 0.2468 |
| 0.0675 | 14.0 | 14000 | 0.0685 | 0.513 | 0.6952 | 2.7492 | 0.513 | 0.5001 | 0.2404 | 0.2453 |
| 0.0669 | 15.0 | 15000 | 0.0682 | 0.5232 | 0.6855 | 2.7789 | 0.5232 | 0.5048 | 0.2441 | 0.2379 |
| 0.0664 | 16.0 | 16000 | 0.0680 | 0.529 | 0.6790 | 2.8249 | 0.529 | 0.5182 | 0.2366 | 0.2340 |
| 0.0658 | 17.0 | 17000 | 0.0678 | 0.5347 | 0.6668 | 2.7035 | 0.5347 | 0.5237 | 0.2338 | 0.2228 |
| 0.0652 | 18.0 | 18000 | 0.0676 | 0.5335 | 0.6673 | 2.8630 | 0.5335 | 0.5249 | 0.2319 | 0.2252 |
| 0.0651 | 19.0 | 19000 | 0.0675 | 0.5385 | 0.6524 | 2.7522 | 0.5385 | 0.5286 | 0.2172 | 0.2256 |
| 0.0645 | 20.0 | 20000 | 0.0671 | 0.5593 | 0.6454 | 2.7445 | 0.5593 | 0.5563 | 0.2324 | 0.2122 |
| 0.0639 | 21.0 | 21000 | 0.0672 | 0.5453 | 0.6541 | 2.9011 | 0.5453 | 0.5451 | 0.2236 | 0.2204 |
| 0.0634 | 22.0 | 22000 | 0.0668 | 0.5617 | 0.6398 | 2.8668 | 0.5617 | 0.5604 | 0.2264 | 0.2108 |
| 0.0629 | 23.0 | 23000 | 0.0670 | 0.5577 | 0.6295 | 2.8351 | 0.5577 | 0.5521 | 0.1984 | 0.2180 |
| 0.0625 | 24.0 | 24000 | 0.0666 | 0.5765 | 0.6201 | 2.7133 | 0.5765 | 0.5754 | 0.2138 | 0.2035 |
| 0.0618 | 25.0 | 25000 | 0.0666 | 0.565 | 0.6219 | 2.8775 | 0.565 | 0.5614 | 0.2010 | 0.2078 |
| 0.0613 | 26.0 | 26000 | 0.0664 | 0.5795 | 0.6121 | 2.8665 | 0.5795 | 0.5805 | 0.1996 | 0.2024 |
| 0.0606 | 27.0 | 27000 | 0.0667 | 0.5723 | 0.6101 | 2.9450 | 0.5723 | 0.5711 | 0.1804 | 0.2113 |
| 0.0603 | 28.0 | 28000 | 0.0664 | 0.583 | 0.6106 | 2.9126 | 0.583 | 0.5845 | 0.2004 | 0.2006 |
| 0.0597 | 29.0 | 29000 | 0.0665 | 0.5857 | 0.6050 | 2.9881 | 0.5857 | 0.5862 | 0.1912 | 0.2006 |
| 0.0594 | 30.0 | 30000 | 0.0665 | 0.5775 | 0.6043 | 2.9735 | 0.5775 | 0.5797 | 0.1823 | 0.2029 |
| 0.0589 | 31.0 | 31000 | 0.0666 | 0.5733 | 0.6080 | 2.9942 | 0.5733 | 0.5739 | 0.1721 | 0.2129 |
| 0.0585 | 32.0 | 32000 | 0.0667 | 0.5803 | 0.6066 | 3.0341 | 0.5803 | 0.5826 | 0.1748 | 0.2114 |
| 0.0583 | 33.0 | 33000 | 0.0665 | 0.5827 | 0.6033 | 3.0209 | 0.5827 | 0.5880 | 0.1799 | 0.2029 |
| 0.0578 | 34.0 | 34000 | 0.0667 | 0.577 | 0.6020 | 3.0483 | 0.577 | 0.5816 | 0.1636 | 0.2081 |
| 0.0576 | 35.0 | 35000 | 0.0667 | 0.577 | 0.6029 | 3.0263 | 0.577 | 0.5840 | 0.1573 | 0.2117 |
| 0.0574 | 36.0 | 36000 | 0.0667 | 0.5803 | 0.6006 | 3.0578 | 0.5803 | 0.5851 | 0.1627 | 0.2082 |
| 0.057 | 37.0 | 37000 | 0.0666 | 0.582 | 0.5997 | 3.1133 | 0.582 | 0.5867 | 0.1612 | 0.2094 |
| 0.0567 | 38.0 | 38000 | 0.0667 | 0.5817 | 0.5951 | 3.0727 | 0.5817 | 0.5836 | 0.1552 | 0.2091 |
| 0.0566 | 39.0 | 39000 | 0.0666 | 0.5815 | 0.5951 | 3.0308 | 0.5815 | 0.5853 | 0.1559 | 0.2049 |
| 0.0564 | 40.0 | 40000 | 0.0666 | 0.5853 | 0.5940 | 3.0629 | 0.5853 | 0.5880 | 0.1564 | 0.2057 |
| 0.0562 | 41.0 | 41000 | 0.0666 | 0.5845 | 0.5949 | 3.0956 | 0.5845 | 0.5881 | 0.1585 | 0.2055 |
| 0.0561 | 42.0 | 42000 | 0.0666 | 0.5827 | 0.5960 | 3.0679 | 0.5827 | 0.5876 | 0.1540 | 0.2098 |
| 0.0559 | 43.0 | 43000 | 0.0666 | 0.5833 | 0.5909 | 2.9904 | 0.5833 | 0.5854 | 0.1491 | 0.2049 |
| 0.0559 | 44.0 | 44000 | 0.0665 | 0.585 | 0.5915 | 3.0150 | 0.585 | 0.5876 | 0.1543 | 0.2032 |
| 0.0557 | 45.0 | 45000 | 0.0667 | 0.583 | 0.5923 | 3.0501 | 0.583 | 0.5851 | 0.1501 | 0.2056 |
| 0.0557 | 46.0 | 46000 | 0.0666 | 0.5905 | 0.5914 | 3.0110 | 0.5905 | 0.5940 | 0.1550 | 0.2045 |
| 0.0555 | 47.0 | 47000 | 0.0667 | 0.584 | 0.5922 | 3.0464 | 0.584 | 0.5872 | 0.1497 | 0.2069 |
| 0.0555 | 48.0 | 48000 | 0.0667 | 0.588 | 0.5917 | 3.0408 | 0.588 | 0.5919 | 0.1489 | 0.2051 |
| 0.0554 | 49.0 | 49000 | 0.0667 | 0.589 | 0.5908 | 3.0433 | 0.589 | 0.5923 | 0.1496 | 0.2044 |
| 0.0554 | 50.0 | 50000 | 0.0667 | 0.5865 | 0.5908 | 3.0393 | 0.5865 | 0.5890 | 0.1479 | 0.2054 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Hekenye/cartoon
|
Hekenye
| 2023-08-09T10:42:22Z | 2 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-09T10:28:16Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: A woman walking a dog in flat cartoon illustration style
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Hekenye/cartoon
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on A woman walking a dog in flat cartoon illustration style using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
DragosGorduza/fiqa_1400_gpl_trained
|
DragosGorduza
| 2023-08-09T10:31:44Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-08-09T09:58:31Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- 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('{MODEL_NAME}')
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
#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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- 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={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2800 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"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": 1400,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, '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})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Evan-Lin/Bart-abs-amazon-entailment-50
|
Evan-Lin
| 2023-08-09T10:25:26Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-08-09T10:17:54Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpg0xs53u8/Evan-Lin/Bart-abs-amazon-entailment-50")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpg0xs53u8/Evan-Lin/Bart-abs-amazon-entailment-50")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpg0xs53u8/Evan-Lin/Bart-abs-amazon-entailment-50")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
ganlongnz/finetuning-sentiment-model-3000-samples
|
ganlongnz
| 2023-08-09T10:20:11Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T05:55:33Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8633333333333333
- name: F1
type: f1
value: 0.8628762541806019
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3263
- Accuracy: 0.8633
- F1: 0.8629
## 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
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Daris1/my-pet-dog
|
Daris1
| 2023-08-09T10:19:40Z | 9 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-09T10:07:09Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### my-pet-dog Dreambooth model trained by Daris1 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: VJCET4
Sample pictures of this concept:
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
|
Rupesh153/mec-falcon-7b-pretrained
|
Rupesh153
| 2023-08-09T10:12:01Z | 0 | 0 |
transformers
|
[
"transformers",
"text-classification",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T09:38:53Z |
---
license: mit
library_name: transformers
pipeline_tag: text-classification
---
|
Nabil000/trial
|
Nabil000
| 2023-08-09T09:59:55Z | 0 | 0 | null |
[
"tabular-classification",
"arxiv:1910.09700",
"region:us"
] |
tabular-classification
| 2023-08-09T09:58:08Z |
---
metrics:
- accuracy
- brier_score
pipeline_tag: tabular-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bjfxs/llama2-qlora-finetunined-french-a
|
bjfxs
| 2023-08-09T09:58:37Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-09T09:58:28Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
justinmeans/stablecode-completion-alpha-3b-4k-coreml
|
justinmeans
| 2023-08-09T09:58:19Z | 0 | 1 | null |
[
"causal-lm",
"code",
"dataset:bigcode/starcoderdata",
"arxiv:2104.09864",
"arxiv:1910.02054",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2023-08-09T09:09:38Z |
---
datasets:
- bigcode/starcoderdata
language:
- code
tags:
- causal-lm
model-index:
- name: stabilityai/stablecode-completion-alpha-3b
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.2018
verified: false
- name: pass@10
type: pass@10
value: 0.3375
verified: false
license: apache-2.0
---
# `StableCode-Completion-Alpha-3B`
# Does not work, uploaded for debugging purposes
### Track here: https://github.com/huggingface/swift-transformers/issues/13
## Model Description
`StableCode-Completion-Alpha-3B` is a 3 billion parameter decoder-only code completion model pre-trained on diverse set of programming languages that were the top used languages based on the 2023 stackoverflow developer survey.
## Usage
The model is intended to do single/multiline code completion from a long context window upto 16k tokens.
Get started generating code with `StableCode-Completion-Alpha-3B` by using the following code snippet:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablecode-completion-alpha-3b")
model = AutoModelForCausalLM.from_pretrained(
"stabilityai/stablecode-completion-alpha-3b",
trust_remote_code=True,
torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("import torch\nimport torch.nn as nn", return_tensors="pt").to("cuda")
tokens = model.generate(
**inputs,
max_new_tokens=48,
temperature=0.2,
do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
```
## Model Details
* **Developed by**: [Stability AI](https://stability.ai/)
* **Model type**: `StableCode-Completion-Alpha-3B` models are auto-regressive language models based on the transformer decoder architecture.
* **Language(s)**: Code
* **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
* **License**: Model checkpoints are licensed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license.
* **Contact**: For questions and comments about the model, please email `lm@stability.ai`
### Model Architecture
| Parameters | Hidden Size | Layers | Heads | Sequence Length |
|----------------|-------------|--------|-------|-----------------|
| 2,796,431,360 | 2560 | 32 | 32 | 16384 |
* **Decoder Layer**: Parallel Attention and MLP residuals with a single input LayerNorm ([Wang & Komatsuzaki, 2021](https://github.com/kingoflolz/mesh-transformer-jax/tree/master))
* **Position Embeddings**: Rotary Position Embeddings ([Su et al., 2021](https://arxiv.org/abs/2104.09864))
* **Bias**: LayerNorm bias terms only
## Training
`StableCode-Completion-Alpha-3B` is pre-trained using a multi-stage context length extension schedule following similar work ([Nijkamp et al. 2023](https://blog.salesforceairesearch.com/xgen/)); first pre-training at a context length of 4096 for 300 billion tokens, then fine-tuning at a context length of 16384 for another 200B tokens.
### Training Dataset
The first pre-training stage relies on 300B tokens sourced from various top programming languages occuring in the stackoverflow developer survey in the `starcoder-data` dataset. We then finetune it on a longer context augmentation of `starcoder-data` dataset which increased the average token per sample to 20k.
### Training Procedure
The model is pre-trained on the dataset mixes mentioned above in mixed-precision BF16), optimized with AdamW, and trained using the StarCoder tokenizer with a vocabulary size of 49k.
* **Software**: We use a fork of gpt-neox ([EleutherAI, 2021](https://github.com/EleutherAI/gpt-neox)) and train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 ([Rajbhandari et al., 2019](https://arxiv.org/abs/1910.02054v3)) and rely on flash-attention as well as rotary embedding kernels from FlashAttention-2 ([Dao et al., 2023](https://tridao.me/publications/flash2/flash2.pdf))
## Use and Limitations
### Intended Use
StableCode-Completion-Alpha-3B independently generates new code completions, but we recommend that you use StableCode-Completion-Alpha-3B together with the tool developed by BigCode and HuggingFace [(huggingface/huggingface-vscode: Code completion VSCode extension for OSS models (github.com))](https://github.com/huggingface/huggingface-vscode), to identify and, if necessary, attribute any outputs that match training code.
### Limitations and bias
This model is intended to be used responsibly. It is not intended to be used to create unlawful content of any kind, to further any unlawful activity, or to engage in activities with a high risk of physical or economic harm.
## How to cite
```bibtex
@misc{StableCodeCompleteAlpha,
url={[https://huggingface.co/stabilityai/stablecode-complete-alpha-3b](https://huggingface.co/stabilityai/stablecode-complete-alpha-3b)},
title={Stable Code Complete Alpha},
author={Adithyan, Reshinth and Phung, Duy and Cooper, Nathan and Pinnaparaju, Nikhil and Laforte, Christian}
}
```
|
seven-wind-c/my_awesome_swag_model
|
seven-wind-c
| 2023-08-09T09:50:33Z | 158 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"multiple-choice",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-08-08T16:00:56Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_swag_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. -->
# my_awesome_swag_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6955
- Accuracy: 0.9304
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 347 | 1.9247 | 0.5565 |
| 2.1957 | 2.0 | 694 | 1.8741 | 0.5739 |
| 1.669 | 3.0 | 1041 | 1.3841 | 0.6522 |
| 1.669 | 4.0 | 1388 | 1.3004 | 0.7391 |
| 1.5076 | 5.0 | 1735 | 1.1071 | 0.8087 |
| 0.9475 | 6.0 | 2082 | 0.8683 | 0.8435 |
| 0.9475 | 7.0 | 2429 | 0.7487 | 0.8609 |
| 0.4713 | 8.0 | 2776 | 0.7383 | 0.8870 |
| 0.2741 | 9.0 | 3123 | 0.7910 | 0.8957 |
| 0.2741 | 10.0 | 3470 | 0.7189 | 0.9043 |
| 0.1831 | 11.0 | 3817 | 0.6801 | 0.9304 |
| 0.0457 | 12.0 | 4164 | 0.6955 | 0.9304 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
jayavibhav/roberta-classification-10ksamples
|
jayavibhav
| 2023-08-09T09:43:45Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-09T08:25:40Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-classification-10ksamples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-classification-10ksamples
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: 0.0123
- Accuracy: 0.9983
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.066 | 1.0 | 1250 | 0.0775 | 0.9877 |
| 0.0174 | 2.0 | 2500 | 0.0123 | 0.9983 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
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