modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-02 12:29:30
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 548
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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|---|---|---|---|---|---|---|---|---|---|
Shilo1122/adirv2
|
Shilo1122
| 2023-10-16T14:10:10Z | 0 | 0 | null |
[
"he",
"license:artistic-2.0",
"region:us"
] | null | 2023-10-16T14:07:44Z |
---
license: artistic-2.0
language:
- he
---
|
agoel3705/Pyramids
|
agoel3705
| 2023-10-16T14:06:17Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-10-16T14:06:11Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: agoel3705/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
baayematar/bert-french
|
baayematar
| 2023-10-16T14:03:07Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-14T18:10:59Z |
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("baayematar/bert-french")
model = AutoModelForSequenceClassification.from_pretrained("baayematar/bert-french")
|
Francesco-A/bert-finetuned-squad-v1
|
Francesco-A
| 2023-10-16T13:56:55Z | 73 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-01T14:47:55Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad-v1
results: []
language:
- en
metrics:
- f1
- exact_match
---
## bert-finetuned-squad-v1
This model is a fine-tuned version of bert-base-cased on the Stanford Question Answering Dataset (SQuAD).
## Model description:
The bert-finetuned-squad-v1 model is built upon the BERT (Bidirectional Encoder Representations from Transformers) architecture and has been fine-tuned specifically for the task of question-answering on the SQuAD dataset. It takes a passage of text (context) and a question as input and predicts the start and end positions of the answer within the context.
## Intended uses & limitations:
### Intended Uses:
- This model is designed for question-answering tasks where a given context and question need to be answered with a span of text from the context.
- It can be used in applications such as chatbots, search engines, and any scenario where questions are answered based on a given passage.
### Limitations:
- The model's performance may vary depending on the complexity and length of the questions and contexts.
- It may not perform well on questions requiring common-sense reasoning or world knowledge beyond the training data.
- The model's output is limited to a single span within the context, which may not cover multi-sentence or complex answers.
- It is not suitable for tasks that involve generating lengthy or abstractive answers.
## Training and evaluation data:
The model was trained on the SQuAD dataset, which consists of two main splits:
- Training Set: It comprises 87,599 examples, each consisting of a context, a question, and the corresponding answer span(s).
- Validation Set: It consists of 10,570 examples, similar in structure to the training set, used for model evaluation during training.
## Training procedure:
The training process involved several key steps:
1. Preprocessing: The training data was preprocessed to convert text inputs into numerical IDs using a BERT tokenizer. Additionally, labels for start and end positions of answer spans were generated.
2. Sliding Window: To handle long contexts, a sliding window approach was employed. Long contexts were split into multiple input features with overlapping tokens.
3. Fine-tuning: The model was fine-tuned on the SQuAD training data, with a focus on minimizing the loss associated with predicting answer spans.
4. Post-processing: During inference, the model predicts start and end logits for answer spans, and these logits are used to determine the answer span with the highest score. The predictions are then converted back into text spans based on token offsets.
5. Evaluation: The model's performance was evaluated on the SQuAD validation set using metrics such as exact match (EM) and F1 score, which measure the accuracy of the predicted answers.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Validation results
- Number of Validation Examples: 10,570
- Exact Match (EM): 80.86%
- F1 Score: 88.28%
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
pipesanma/chasquilla-question-generator
|
pipesanma
| 2023-10-16T13:45:24Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-16T12:37:59Z |
---
license: apache-2.0
datasets:
- squad
language:
- en
---
# Question Generator
This model should be used to generate questions based on a given string.
### Out-of-Scope Use
English language support only.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
def question_parser(question: str) -> str:
return " ".join(question.split(":")[1].split())
def generate_questions_v2(context: str, answer: str, n_questions: int = 1):
model = T5ForConditionalGeneration.from_pretrained(
"pipesanma/chasquilla-question-generator"
)
tokenizer = T5Tokenizer.from_pretrained("pipesanma/chasquilla-question-generator")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
text = "context: " + context + " " + "answer: " + answer + " </s>"
encoding = tokenizer.encode_plus(
text, max_length=512, padding=True, return_tensors="pt"
)
input_ids, attention_mask = encoding["input_ids"].to(device), encoding[
"attention_mask"
].to(device)
model.eval()
beam_outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_length=72,
early_stopping=True,
num_beams=5,
num_return_sequences=n_questions,
)
questions = []
for beam_output in beam_outputs:
sent = tokenizer.decode(
beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
print(sent)
questions.append(question_parser(sent))
return questions
context = "President Donald Trump said and predicted that some states would reopen this month."
answer = "Donald Trump"
questions = generate_questions_v2(context, answer, 1)
print(questions)
```
## Training Details
### Dataset generation
The dataset is "squad" from datasets library.
Check the [utils/dataset_gen.py](utils/dataset_gen.py) file for the dataset generation.
### Training model
Check the [utils/t5_train_model.py](utils/t5_train_model.py) file for the training process
### Model and Tokenizer versions
(v1.0) Model and Tokenizer V1: trained with 1000 rows
(v1.1) Model and Tokenizer V2: trained with 3000 rows
(v1.2) Model and Tokenizer V3: trained with all rows from datasets (78664 rows-train, 9652 rows-validation)
|
Abhiram4/AnimeCharacterClassifierMark1
|
Abhiram4
| 2023-10-16T13:37:23Z | 88 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-10-16T12:45:22Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: AnimeCharacterClassifierMark1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8655030800821355
---
<!-- 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. -->
# AnimeCharacterClassifierMark1
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6720
- Accuracy: 0.8655
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 42
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0145 | 0.99 | 17 | 4.9303 | 0.0092 |
| 4.8416 | 1.97 | 34 | 4.7487 | 0.0287 |
| 4.4383 | 2.96 | 51 | 4.3597 | 0.1170 |
| 4.0762 | 4.0 | 69 | 3.6419 | 0.3224 |
| 3.108 | 4.99 | 86 | 2.8574 | 0.5246 |
| 2.1571 | 5.97 | 103 | 2.2129 | 0.6653 |
| 1.4685 | 6.96 | 120 | 1.7290 | 0.7495 |
| 1.1649 | 8.0 | 138 | 1.3862 | 0.7977 |
| 0.7905 | 8.99 | 155 | 1.1589 | 0.8214 |
| 0.5549 | 9.97 | 172 | 1.0263 | 0.8296 |
| 0.4577 | 10.96 | 189 | 0.8994 | 0.8368 |
| 0.2964 | 12.0 | 207 | 0.8086 | 0.8552 |
| 0.194 | 12.99 | 224 | 0.7446 | 0.8583 |
| 0.1358 | 13.97 | 241 | 0.7064 | 0.8573 |
| 0.1116 | 14.96 | 258 | 0.6720 | 0.8655 |
| 0.0811 | 16.0 | 276 | 0.6515 | 0.8645 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
cleanrl/Swimmer-v4-td3_continuous_action_jax-seed1
|
cleanrl
| 2023-10-16T13:37:11Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Swimmer-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T13:36:03Z |
---
tags:
- Swimmer-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Swimmer-v4
type: Swimmer-v4
metrics:
- type: mean_reward
value: 82.91 +/- 8.37
name: mean_reward
verified: false
---
# (CleanRL) **TD3** Agent Playing **Swimmer-v4**
This is a trained model of a TD3 agent playing Swimmer-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action_jax.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[td3_continuous_action_jax]"
python -m cleanrl_utils.enjoy --exp-name td3_continuous_action_jax --env-id Swimmer-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Swimmer-v4-td3_continuous_action_jax-seed1/raw/main/td3_continuous_action_jax.py
curl -OL https://huggingface.co/cleanrl/Swimmer-v4-td3_continuous_action_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Swimmer-v4-td3_continuous_action_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python td3_continuous_action_jax.py --track --capture-video --env-id Swimmer-v4 --seed 1 --save-model --upload-model --hf-entity cleanrl
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'env_id': 'Swimmer-v4',
'exp_name': 'td3_continuous_action_jax',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'policy_noise': 0.2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
anhuu/finetuning-ibm-claim-stance-classification
|
anhuu
| 2023-10-16T13:24:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-14T13:24:43Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-ibm-claim-stance-classification
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. -->
# finetuning-ibm-claim-stance-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8532
- Accuracy: 0.54
- F1: 0.5818
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
TheBloke/openbuddy-llama2-70B-v13-base-GPTQ
|
TheBloke
| 2023-10-16T13:23:43Z | 19 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"base_model:OpenBuddyEA/openbuddy-llama2-70b-v13-base",
"base_model:quantized:OpenBuddyEA/openbuddy-llama2-70b-v13-base",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-10-16T10:09:59Z |
---
base_model: OpenBuddy/openbuddy-llama2-70b-v13-base
inference: false
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
library_name: transformers
license: llama2
license_link: https://ai.meta.com/llama/license/
license_name: llama2
model_creator: OpenBuddy
model_name: OpenBuddy Llama2 70B v13 Base
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<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 style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# OpenBuddy Llama2 70B v13 Base - GPTQ
- Model creator: [OpenBuddy](https://huggingface.co/OpenBuddy)
- Original model: [OpenBuddy Llama2 70B v13 Base](https://huggingface.co/OpenBuddy/openbuddy-llama2-70b-v13-base)
<!-- description start -->
## Description
This repo contains GPTQ model files for [OpenBuddy's OpenBuddy Llama2 70B v13 Base](https://huggingface.co/OpenBuddy/openbuddy-llama2-70b-v13-base).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GGUF)
* [OpenBuddy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenBuddy/openbuddy-llama2-70b-v13-base)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.52 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.84 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.85 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.96 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.21 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 32.02 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/openbuddy-llama2-70B-v13-base-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/openbuddy-llama2-70B-v13-base-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `openbuddy-llama2-70B-v13-base-GPTQ`:
```shell
mkdir openbuddy-llama2-70B-v13-base-GPTQ
huggingface-cli download TheBloke/openbuddy-llama2-70B-v13-base-GPTQ --local-dir openbuddy-llama2-70B-v13-base-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir openbuddy-llama2-70B-v13-base-GPTQ
huggingface-cli download TheBloke/openbuddy-llama2-70B-v13-base-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir openbuddy-llama2-70B-v13-base-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir openbuddy-llama2-70B-v13-base-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/openbuddy-llama2-70B-v13-base-GPTQ --local-dir openbuddy-llama2-70B-v13-base-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/openbuddy-llama2-70B-v13-base-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/openbuddy-llama2-70B-v13-base-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/openbuddy-llama2-70B-v13-base-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `openbuddy-llama2-70B-v13-base-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/openbuddy-llama2-70B-v13-base-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/openbuddy-llama2-70B-v13-base-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## 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!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: OpenBuddy's OpenBuddy Llama2 70B v13 Base
# ⚠️ About Base-series Models ⚠️
This is a part of the Base-series models, trained utilizing approximately 50% of conversational data. It embodies cognitive and dialogue capabilities parallel to the fully-trained OpenBuddy models, yet **it hasn’t been extensively fine-tuned for generic conversational tasks**.
We released this model intending to empower the community, enabling further fine-tuning and deployment of specialized, domain-specific models.
For immediate use in generic conversations, consider referring to our versions that without the `-base` suffix.
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
This model is built upon Meta's LLaMA series of models and is subject to Meta's licensing agreement.
This model is intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.
If you have not obtained approval from Meta, you must visit the https://ai.meta.com/llama/ page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading the model from this page.
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
SvenN/sdxl-emoji
|
SvenN
| 2023-10-16T13:20:08Z | 1,152 | 61 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-10-16T13:19:53Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
pivotal_tuning: true
textual_embeddings: embeddings.pti
instance_prompt: <s0><s1>
inference: true
---
# sdxl-emoji LoRA by [fofr](https://replicate.com/fofr)
### An SDXL fine-tune based on Apple Emojis
>
## Inference with Replicate API
Grab your replicate token [here](https://replicate.com/account)
```bash
pip install replicate
export REPLICATE_API_TOKEN=r8_*************************************
```
```py
import replicate
output = replicate.run(
"sdxl-emoji@sha256:dee76b5afde21b0f01ed7925f0665b7e879c50ee718c5f78a9d38e04d523cc5e",
input={"prompt": "A TOK emoji of a man"}
)
print(output)
```
You may also do inference via the API with Node.js or curl, and locally with COG and Docker, [check out the Replicate API page for this model](https://replicate.com/fofr/sdxl-emoji/api)
## Inference with 🧨 diffusers
Replicate SDXL LoRAs are trained with Pivotal Tuning, which combines training a concept via Dreambooth LoRA with training a new token with Textual Inversion.
As `diffusers` doesn't yet support textual inversion for SDXL, we will use cog-sdxl `TokenEmbeddingsHandler` class.
The trigger tokens for your prompt will be `<s0><s1>`
```shell
pip install diffusers transformers accelerate safetensors huggingface_hub
git clone https://github.com/replicate/cog-sdxl cog_sdxl
```
```py
import torch
from huggingface_hub import hf_hub_download
from diffusers import DiffusionPipeline
from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler
from diffusers.models import AutoencoderKL
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
pipe.load_lora_weights("SvenN/sdxl-emoji", weight_name="lora.safetensors")
text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
tokenizers = [pipe.tokenizer, pipe.tokenizer_2]
embedding_path = hf_hub_download(repo_id="SvenN/sdxl-emoji", filename="embeddings.pti", repo_type="model")
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
embhandler.load_embeddings(embedding_path)
prompt="A <s0><s1> emoji of a man"
images = pipe(
prompt,
cross_attention_kwargs={"scale": 0.8},
).images
#your output image
images[0]
```
|
cimm-kzn/enrudr-bert
|
cimm-kzn
| 2023-10-16T13:14:51Z | 14 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bio",
"med",
"biomedical",
"ru",
"en",
"arxiv:2004.03659",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- ru
- en
tags:
- bio
- med
- biomedical
---
## EnRuDR-BERT
EnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \
link: https://yadi.sk/d/-PTn0xhk1PqvgQ
## Citing & Authors
If you find this repository helpful, feel free to cite our publication:
[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020.
preprint: https://arxiv.org/abs/2004.03659
```
@article{10.1093/bioinformatics/btaa675,
author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
journal = {Bioinformatics},
year = {2020},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa675},
url = {https://doi.org/10.1093/bioinformatics/btaa675},
note = {btaa675},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
}
```
[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.
[link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf)
```
@article{tutubalina2017using,
title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects},
author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE},
journal={Russian Chemical Bulletin},
volume={66},
number={11},
pages={2180--2189},
year={2017},
publisher={Springer}
}
```
|
cleanrl/Ant-v4-td3_continuous_action_jax-seed1
|
cleanrl
| 2023-10-16T13:10:52Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Ant-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T13:10:32Z |
---
tags:
- Ant-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Ant-v4
type: Ant-v4
metrics:
- type: mean_reward
value: 2392.75 +/- 30.84
name: mean_reward
verified: false
---
# (CleanRL) **TD3** Agent Playing **Ant-v4**
This is a trained model of a TD3 agent playing Ant-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action_jax.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[td3_continuous_action_jax]"
python -m cleanrl_utils.enjoy --exp-name td3_continuous_action_jax --env-id Ant-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Ant-v4-td3_continuous_action_jax-seed1/raw/main/td3_continuous_action_jax.py
curl -OL https://huggingface.co/cleanrl/Ant-v4-td3_continuous_action_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Ant-v4-td3_continuous_action_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python td3_continuous_action_jax.py --track --capture-video --env-id Ant-v4 --seed 1 --save-model --upload-model --hf-entity cleanrl
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'env_id': 'Ant-v4',
'exp_name': 'td3_continuous_action_jax',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'policy_noise': 0.2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
gambitvk/ppo-LunarLander-v2
|
gambitvk
| 2023-10-16T13:09:57Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T12:04:23Z |
---
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: 275.13 +/- 16.66
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
...
```
|
lakshinav/mt5-small-finetuned-amazon-en-es
|
lakshinav
| 2023-10-16T13:02:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-16T13:01:51Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5792
- Rouge1: 19.178
- Rouge2: 11.1294
- Rougel: 18.8056
- Rougelsum: 18.8857
## 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: 5.6e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 3.1482 | 1.0 | 1301 | 2.6708 | 17.2978 | 11.0922 | 17.1791 | 17.1414 |
| 2.867 | 2.0 | 2602 | 2.6532 | 17.7932 | 10.2988 | 17.6139 | 17.6418 |
| 2.74 | 3.0 | 3903 | 2.6575 | 19.2584 | 11.6796 | 18.98 | 19.0057 |
| 3.0353 | 4.0 | 5204 | 2.5845 | 19.1599 | 11.2723 | 18.8132 | 18.8559 |
| 2.9691 | 5.0 | 6505 | 2.5820 | 18.2435 | 9.5271 | 17.904 | 17.9735 |
| 2.9221 | 6.0 | 7806 | 2.5784 | 18.5969 | 10.5778 | 18.2837 | 18.2395 |
| 2.8944 | 7.0 | 9107 | 2.5738 | 18.6871 | 10.6402 | 18.4386 | 18.4199 |
| 2.8636 | 8.0 | 10408 | 2.5792 | 19.178 | 11.1294 | 18.8056 | 18.8857 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
marekk/videomae-base-finetuned-kinetics-finetuned-ucf101-subset
|
marekk
| 2023-10-16T12:59:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-10-16T12:32:33Z |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-kinetics-finetuned-ucf101-subset
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. -->
# videomae-base-finetuned-kinetics-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6776
- Accuracy: 0.85
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 119
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6226 | 0.15 | 18 | 0.5180 | 0.8 |
| 0.3479 | 1.15 | 36 | 0.8291 | 0.8 |
| 0.4482 | 2.15 | 54 | 0.7873 | 0.85 |
| 0.1432 | 3.15 | 72 | 0.8196 | 0.8 |
| 0.216 | 4.15 | 90 | 0.8239 | 0.85 |
| 0.1211 | 5.15 | 108 | 0.6926 | 0.85 |
| 0.02 | 6.09 | 119 | 0.6776 | 0.85 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
TheBloke/Airoboros-M-7B-3.1-GPTQ
|
TheBloke
| 2023-10-16T12:58:25Z | 14 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"dataset:jondurbin/airoboros-3.1",
"base_model:jondurbin/airoboros-m-7b-3.1",
"base_model:quantized:jondurbin/airoboros-m-7b-3.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-10-15T18:12:33Z |
---
base_model: jondurbin/airoboros-m-7b-3.1
datasets:
- jondurbin/airoboros-3.1
inference: false
license: apache-2.0
model_creator: Jon Durbin
model_name: Airoboros M 7B 3.1
model_type: mistral
prompt_template: '[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as
possible, while being safe. Your answers should not include any harmful, unethical,
racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses
are socially unbiased and positive in nature. If a question does not make any sense,
or is not factually coherent, explain why instead of answering something not correct.
If you don''t know the answer to a question, please don''t share false information.
<</SYS>>
{prompt}[/INST]
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<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 style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Airoboros M 7B 3.1 - GPTQ
- Model creator: [Jon Durbin](https://huggingface.co/jondurbin)
- Original model: [Airoboros M 7B 3.1](https://huggingface.co/jondurbin/airoboros-m-7b-3.1)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Jon Durbin's Airoboros M 7B 3.1](https://huggingface.co/jondurbin/airoboros-m-7b-3.1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GGUF)
* [Jon Durbin's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/airoboros-m-7b-3.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Llama-2-Chat
```
[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.29 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Airoboros-M-7B-3.1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Airoboros-M-7B-3.1-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Airoboros-M-7B-3.1-GPTQ`:
```shell
mkdir Airoboros-M-7B-3.1-GPTQ
huggingface-cli download TheBloke/Airoboros-M-7B-3.1-GPTQ --local-dir Airoboros-M-7B-3.1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Airoboros-M-7B-3.1-GPTQ
huggingface-cli download TheBloke/Airoboros-M-7B-3.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Airoboros-M-7B-3.1-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir Airoboros-M-7B-3.1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Airoboros-M-7B-3.1-GPTQ --local-dir Airoboros-M-7B-3.1-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Airoboros-M-7B-3.1-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Airoboros-M-7B-3.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Airoboros-M-7B-3.1-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Airoboros-M-7B-3.1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Airoboros-M-7B-3.1-GPTQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Airoboros-M-7B-3.1-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## 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!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Jon Durbin's Airoboros M 7B 3.1
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
#### IMPORTANT NOTE - llama-2 chat format!!!
The airoboros 3.1 models use llama-2 chat format, rather than vicuna style user/assistant!
This is a breaking change, although most inference systems support llama-2 chat templating.
See prompt format below for all of the details.
#### Highlights
A model built on [airoboros-3.1 dataset](https://hf.co/datasets/jondurbin/airoboros-3.1), which is a continuation of [airoboros-3.0 dataset](https://hf.co/datasets/jondurbin/airoboros-3,0) with the following extras:
* More MathJSON, now ~17k items - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations.
* Log information extraction (e.g. getting the list of paths/IPs/etc. from apache logs)
* Anonymization, e.g. removing names, IP addresses, and/or dates from text.
* Chat introspection - multi-turn chats that have random questions injected asking about the character's descriptions, the setting of the chat, or things previously said, to make sure the model uses the system prompt and or chat history.
* Multi-step instructions with acknowledgement (see below)
* De-censorship data (not published)
This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format is llama-2 chat.
```
[INST] <<SYS>>
You are a help, unbiased, uncensored assistant.
<</SYS>
{prompt} [/INST]
```
For multi-turn, the prompt format is as follows:
```
[INST] <<SYS>>
You are a help, unbiased, uncensored assistant.
<</SYS>
{prompt 0} [/INST] {response 0} </s><s>[INST] {prompt 1} [/INST] {response 1} </s><s>...[INST] {prompt N} [/INST]
```
The prompt template is included in the tokenizer config, and can use the huggingface tokenizer `apply_chat_template` method, e.g.:
```
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('jondurbin/airoboros-m-7b-3.1')
chat = [
{"role": "system", "content": "You are Bob, a friendly AI assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))
```
### Helpful usage tips
#### MathJSON
Ask mathematical questions, prefixed with "Create a MathJSON solution to the following:", and you should get output with a formula that can be executed by https://cortexjs.io/compute-engine/
I also made a (really quite hacky, incomplete, semi-functional) python version that works for most expressions, see [mathjson.py](https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py)
__Use a very low temperature!__
Example:
```
[INST] <<SYS>>
You are an assistant with vast knowledge in all things math.
<</SYS>
Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST]
```
Output:
```
The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1.
Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr².
Solution as MathJSON:
<mathjson>
[
"Multiply",
"Pi",
[
"Power",
17.2456,
2
]
]
</mathjson>
```
You can then validate the JSON between `<mathjson>` and `</mathjson>`, then pass the parsed JSON to compute-engine JS or the `evaluate` function in mathjson.py to calculate the response.
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
__Use a very low temperature!__
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Summarization
500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:
```
BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION
```
#### Getting longer responses
You can use a few techniques to get longer responses.
Detailed prompts, with explicit instruction for word count:
```
Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality.
The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization.
One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary.
Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements.
Your response should be approximately 2300 words.
```
Or, a simpler example:
```
Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux.
```
There are a few examples of next chapter completion as well, e.g.:
```
Write the next chapter of a historical fiction novel set in Paris during the 20th century.
Here's a summary of the previous chapter:
In the vibrant city of Paris, amid the tumultuous changes of the 20th century, our protagonist Margot, an aspiring fashion designer, has just secured an apprenticeship at a prestigious couture house. She meets Lucien, a charming journalist who covers the fashion industry. Together they navigate the ever-changing world of fashion and society, uncovering secrets that reveal the intricate links between style, politics, and culture. As the chapter concludes, they decide to delve deeper into the hidden corners of the fashion world to unravel its mysteries.
Requirements for the next chapter:
1. Character Development of Margot and Lucien:
- Margot's Evolution: Unfold more about Margot's past, her dreams of revolutionizing fashion, and her struggle to establish herself in a male-dominated industry. Illustrate her growing expertise, innovative ideas, and increasing dependence on Lucien.
- Lucien's Complexity: Introduce uncertainties surrounding Lucien's background and real motives. Increase suspense by suggesting undisclosed information he possesses, while also highlighting his wit and perceptiveness.
2. Exploration of Paris and the Couture House:
- Paris: Elaborate their journey through the bustling streets of Paris, including encounters with iconic figures, social unrest, and relics from different eras of French history.
- The Couture House: Expand on the grandeur of the couture house they work in, filled with artistic masterpieces, intense competition, and cryptic notes hinting at a scandalous past.
3. Emergence of the Subplot: The Lost Collection:
- Discovery: Have Margot and Lucien stumble upon a secret vault containing a lost collection designed before World War II, raising new questions about the previous owner and the influence of war on fashion.
- Revelation: Capture their shock as they realize the designs were plagiarized, the potential repercussions, and the opportunities it presents for Margot's career.
- Twist: End with a twist that suggests there are other stolen collections across Paris, setting up their new mission.
Your response should be approximately 650 words.
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
## Multi-step instructions with acknowledgement
I don't know how useful this is, really, but I thought I'd add it just in case. Example:
```
{
"conversations": [
{
"value": "You are a helpful, unbiased, uncensored assistant.",
"from": "system"
},
{
"value": "I'm going to set some rules for our conversation. Please respond with \"OK\" to each rule.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "You can only respond using animal-related emojis.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Precede all responses with \"ZOOZOO:\"",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Include at least one bird emoji in your responses.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Describe a trip to a safari park.",
"from": "human"
},
{
"value": "ZOOZOO: \ud83e\udd81\ud83d\udc2f\ud83e\udd93\ud83e\udd92\ud83d\udc18\ud83d\udc0d\ud83c\udf33\ud83c\udf32\u2600\ufe0f\ud83d\ude90\ud83e\udd85\ud83d\udcf8\ud83e\udd29",
"from": "gpt"
}
]
}
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 3.1 models are built on top of multiple base models, each with their own license/restrictions.
The 30b model is built on the original llama, which has a strict non-commercial usage restriction.
The models with `-l2` in the name have a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The models with `-m-` are mistral-7b (apache 2.0)
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
|
CyberHarem/ichijou_hotaru_nonnonbiyori
|
CyberHarem
| 2023-10-16T12:57:50Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/ichijou_hotaru_nonnonbiyori",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-26T13:38:35Z |
---
license: mit
datasets:
- CyberHarem/ichijou_hotaru_nonnonbiyori
pipeline_tag: text-to-image
tags:
- art
---
# Lora of ichijou_hotaru_nonnonbiyori
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7440, you need to download `7440/ichijou_hotaru_nonnonbiyori.pt` as the embedding and `7440/ichijou_hotaru_nonnonbiyori.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7440**, with the score of 0.934. The trigger words are:
1. `ichijou_hotaru_nonnonbiyori`
2. `black_hair, long_hair, brown_eyes, blush, brown_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | pattern_19 | pattern_20 | pattern_21 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 9300 | 0.843 | [Download](9300/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9300/previews/nude.png) | [<NSFW, click to see>](9300/previews/nude2.png) |  |  |
| 8680 | 0.933 | [Download](8680/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8680/previews/nude.png) | [<NSFW, click to see>](8680/previews/nude2.png) |  |  |
| 8060 | 0.896 | [Download](8060/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8060/previews/nude.png) | [<NSFW, click to see>](8060/previews/nude2.png) |  |  |
| **7440** | **0.934** | [**Download**](7440/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7440/previews/nude.png) | [<NSFW, click to see>](7440/previews/nude2.png) |  |  |
| 6820 | 0.927 | [Download](6820/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6820/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6820/previews/nude.png) | [<NSFW, click to see>](6820/previews/nude2.png) |  |  |
| 6200 | 0.932 | [Download](6200/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6200/previews/nude.png) | [<NSFW, click to see>](6200/previews/nude2.png) |  |  |
| 5580 | 0.905 | [Download](5580/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5580/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5580/previews/nude.png) | [<NSFW, click to see>](5580/previews/nude2.png) |  |  |
| 4960 | 0.904 | [Download](4960/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4960/previews/nude.png) | [<NSFW, click to see>](4960/previews/nude2.png) |  |  |
| 4340 | 0.868 | [Download](4340/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4340/previews/nude.png) | [<NSFW, click to see>](4340/previews/nude2.png) |  |  |
| 3720 | 0.899 | [Download](3720/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3720/previews/nude.png) | [<NSFW, click to see>](3720/previews/nude2.png) |  |  |
| 3100 | 0.911 | [Download](3100/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3100/previews/nude.png) | [<NSFW, click to see>](3100/previews/nude2.png) |  |  |
| 2480 | 0.902 | [Download](2480/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2480/previews/nude.png) | [<NSFW, click to see>](2480/previews/nude2.png) |  |  |
| 1860 | 0.855 | [Download](1860/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1860/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1860/previews/nude.png) | [<NSFW, click to see>](1860/previews/nude2.png) |  |  |
| 1240 | 0.819 | [Download](1240/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1240/previews/nude.png) | [<NSFW, click to see>](1240/previews/nude2.png) |  |  |
| 620 | 0.741 | [Download](620/ichijou_hotaru_nonnonbiyori.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](620/previews/bondage.png) |  |  |  | [<NSFW, click to see>](620/previews/nude.png) | [<NSFW, click to see>](620/previews/nude2.png) |  |  |
|
JanGr/ppo-Huggy
|
JanGr
| 2023-10-16T12:51:45Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-10-16T12:46:24Z |
---
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: JanGr/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
yejeekang/legal-llama-ko-7b-50step
|
yejeekang
| 2023-10-16T12:45:48Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:beomi/llama-2-ko-7b",
"base_model:adapter:beomi/llama-2-ko-7b",
"region:us"
] | null | 2023-10-16T12:45:43Z |
---
library_name: peft
base_model: beomi/llama-2-ko-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
tensor-diffusion/Ether-Blu-Mix-V5
|
tensor-diffusion
| 2023-10-16T12:43:49Z | 1 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-10-16T10:47:19Z |
---
pipeline_tag: text-to-image
---
|
theodullin/detr-resnet-50_finetuned_cppe5
|
theodullin
| 2023-10-16T12:37:27Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:blood-cell-object-detection",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-10-16T12:26:01Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- blood-cell-object-detection
model-index:
- name: detr-resnet-50_finetuned_cppe5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the blood-cell-object-detection dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
MananSantoki/Vadodara-BOT-Improved
|
MananSantoki
| 2023-10-16T12:35:44Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-16T12:09:43Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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:
- quant_method: bitsandbytes
- 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
- PEFT 0.5.0
|
t4ai/albert-finetuned-t3-qa
|
t4ai
| 2023-10-16T12:24:05Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"albert",
"question-answering",
"generated_from_keras_callback",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-16T06:03:32Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_keras_callback
model-index:
- name: t4ai/albert-finetuned-t3-qa
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. -->
# t4ai/albert-finetuned-t3-qa
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4886
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16617, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.9558 | 0 |
| 0.6737 | 1 |
| 0.4886 | 2 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
alexkoo300/shaky-wildbeast
|
alexkoo300
| 2023-10-16T12:12:42Z | 2 | 0 |
peft
|
[
"peft",
"pytorch",
"mistral",
"region:us"
] | null | 2023-10-16T11:11:57Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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
|
cleanrl/Ant-v4-td3_continuous_action-seed1
|
cleanrl
| 2023-10-16T11:54:42Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Ant-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T11:54:15Z |
---
tags:
- Ant-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Ant-v4
type: Ant-v4
metrics:
- type: mean_reward
value: 4666.31 +/- 933.53
name: mean_reward
verified: false
---
# (CleanRL) **TD3** Agent Playing **Ant-v4**
This is a trained model of a TD3 agent playing Ant-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[td3_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name td3_continuous_action --env-id Ant-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Ant-v4-td3_continuous_action-seed1/raw/main/td3_continuous_action.py
curl -OL https://huggingface.co/cleanrl/Ant-v4-td3_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Ant-v4-td3_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python td3_continuous_action.py --track --capture-video --env-id Ant-v4 --seed 1 --save-model --upload-model --hf-entity cleanrl
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'env_id': 'Ant-v4',
'exp_name': 'td3_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'policy_noise': 0.2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
yejeekang/legal-KoAlpaca-12.8B-50step
|
yejeekang
| 2023-10-16T11:44:51Z | 5 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:beomi/KoAlpaca-Polyglot-12.8B",
"base_model:adapter:beomi/KoAlpaca-Polyglot-12.8B",
"region:us"
] | null | 2023-10-16T11:44:49Z |
---
library_name: peft
base_model: beomi/KoAlpaca-Polyglot-12.8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
mnity/Reinforce-cartpole-v1
|
mnity
| 2023-10-16T11:43:30Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T11:43:19Z |
---
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: 448.60 +/- 79.78
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
|
Rehead/q-taxi-v1
|
Rehead
| 2023-10-16T11:41:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T11:41:03Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Rehead/q-taxi-v1", 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"])
```
|
Rehead/q-FrozenLake-v1-4x4-noSlippery
|
Rehead
| 2023-10-16T11:35:27Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T11:35:24Z |
---
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="Rehead/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"])
```
|
RayanRen/q-FrozenLake-v1-4x4-noSlippery
|
RayanRen
| 2023-10-16T11:23:55Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T11:23:50Z |
---
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="RayanRen/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"])
```
|
RadonDong/LUSC_black_formalin
|
RadonDong
| 2023-10-16T11:19:19Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-10-16T10:45:33Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - RadonDong/LUSC_black_formalin
These are LoRA adaption weights for RadonDong/LUSC_black_formalin. The weights were trained on LUSC black formalin using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
sekinat/q-FrozenLake-v1-4x4-Slippery
|
sekinat
| 2023-10-16T11:16:54Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T11:16:51Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.64 +/- 0.48
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="sekinat/q-FrozenLake-v1-4x4-Slippery", 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"])
```
|
takumi12/id2pg_pattern2_ja_e100_78_test
|
takumi12
| 2023-10-16T11:15:27Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"base_model:adapter:codellama/CodeLlama-7b-Instruct-hf",
"region:us"
] | null | 2023-10-16T11:15:21Z |
---
library_name: peft
base_model: codellama/CodeLlama-7b-Instruct-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
HCNMonsch/dqn-SpaceInvadersNoFrameskip-v4
|
HCNMonsch
| 2023-10-16T11:14:45Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T11:14:12Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 609.50 +/- 234.11
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HCNMonsch -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HCNMonsch -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga HCNMonsch
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
bh8648/KAPO-ft1-q-paged_adamw_8bit
|
bh8648
| 2023-10-16T11:10:50Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-16T11:10:39Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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
|
MananSantoki/ludwig-vadodara
|
MananSantoki
| 2023-10-16T11:06:51Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-16T11:06:49Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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:
- quant_method: bitsandbytes
- 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
- PEFT 0.5.0
|
IgnacioDM/Llama-2-7b-hf-bf16
|
IgnacioDM
| 2023-10-16T11:06:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-16T05:57:41Z |
---
license: llama2
---
`meta-llama/Llama-2-7b` converted to the HF format but maintaining the original precision it was traiend on (bf16) instead of converting to fp16 like `meta-llama/Llama-2-7b-hf` does.
When benchmarked, it performs almost identically to the fp16 version.
|
sekinat/taxi_v1_Q_learning_V0
|
sekinat
| 2023-10-16T11:04:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T11:04:00Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_v1_Q_learning_V0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.78
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sekinat/taxi_v1_Q_learning_V0", 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"])
```
|
yejeekang/legal-llama-ko-7b
|
yejeekang
| 2023-10-16T11:03:43Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-11T10:47:54Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
surathisin/nvso-model-test-5
|
surathisin
| 2023-10-16T11:00:28Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null | 2023-10-15T17:32:59Z |
---
base_model: meta-llama/Llama-2-13b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: nvso-model-test-5
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. -->
# nvso-model-test-5
This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 500
### Training results
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Vtamins/sd-class-butterflies-32
|
Vtamins
| 2023-10-16T10:51:43Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-10-16T10:51:24Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Vtamins/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Ingenieria/my_awesome_qa_model
|
Ingenieria
| 2023-10-16T10:46:46Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-12T16:50:54Z |
---
base_model: IIC/roberta-base-spanish-squades
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_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_qa_model
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface.co/IIC/roberta-base-spanish-squades) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7918
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 51 | 2.8486 |
| No log | 2.0 | 102 | 2.7668 |
| No log | 3.0 | 153 | 2.7918 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Akshitha2706/white-horse
|
Akshitha2706
| 2023-10-16T10:32:17Z | 5 | 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
| 2023-10-08T13:59:23Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### White-Horse- Dreambooth model trained by Akshitha2706 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: KMEC 165
Sample pictures of this concept:
|
Tngarg/outputs
|
Tngarg
| 2023-10-16T10:24:58Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-10-11T11:46:09Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
zeerakwyne/SD-Dog-Test6
|
zeerakwyne
| 2023-10-16T10:22:51Z | 1 | 0 |
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
| 2023-10-15T14:27:59Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - zeerakwyne/SD-Dog-Test6
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
Alex31y/bert-base-multilingual-uncased-finetuned-squad
|
Alex31y
| 2023-10-16T10:17:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-08T15:26:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-multilingual-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0349
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0032 | 1.0 | 5547 | 0.9935 |
| 0.7614 | 2.0 | 11094 | 0.9566 |
| 0.5752 | 3.0 | 16641 | 1.0349 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
cbellew09/a2c-PandaReachDense-v3
|
cbellew09
| 2023-10-16T10:14:00Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T10:08:26Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
pranjal0198/output1
|
pranjal0198
| 2023-10-16T10:12:22Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"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
| 2023-10-16T10:10:37Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - pranjal01/output1
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pranjal01/messi-ronaldo-dataset dataset. You can find some example images in the following.
|
BHARAT9983/ludwig-m
|
BHARAT9983
| 2023-10-16T09:54:57Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-16T09:54:56Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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:
- quant_method: bitsandbytes
- 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
- PEFT 0.5.0
|
pranjal0198/roberta_fine_tuned_qa_model
|
pranjal0198
| 2023-10-16T09:52:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-30T05:36:09Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta_fine_tuned_qa_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. -->
# roberta_fine_tuned_qa_model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0295
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2458 | 1.0 | 1000 | 1.1327 |
| 0.753 | 2.0 | 2000 | 1.2129 |
| 0.479 | 3.0 | 3000 | 1.4913 |
| 0.3606 | 4.0 | 4000 | 1.7434 |
| 0.231 | 5.0 | 5000 | 2.0295 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
MattStammers/appo-atari_tutankham
|
MattStammers
| 2023-10-16T09:42:40Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-27T11:46:44Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_tutankham
type: atari_tutankham
metrics:
- type: mean_reward
value: 262.50 +/- 30.92
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_tutankham** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-atari_tutankham
```
## About the Model
This model as with all the others in the benchmarks was trained initially asynchronously un-seeded to 10 million steps for the purposes of setting a sample factory async baseline for this model on this environment but only 3/57 made it.
The aim is to reach state-of-the-art (SOTA) performance on each atari environment. I will flag the models with SOTA when they reach at or near these levels.
The hyperparameters used in the model are the ones I have pushed to my fork of sample-factory: https://github.com/MattStammers/sample-factory. Given that https://huggingface.co/edbeeching has kindly shared his.
I saved time and energy by using many of his tuned hyperparameters to maximise performance. However, he used 2 billion training steps. I have started as explained above at 10 million then moved to 100m to see how performance goes:
```
hyperparameters = {
"device": "gpu",
"seed": 1234,
"num_policies": 2,
"async_rl": true,
"serial_mode": false,
"batched_sampling": true,
"num_batches_to_accumulate": 2,
"worker_num_splits": 1,
"policy_workers_per_policy": 1,
"max_policy_lag": 1000,
"num_workers": 16,
"num_envs_per_worker": 2,
"batch_size": 1024,
"num_batches_per_epoch": 8,
"num_epochs": 4,
"rollout": 128,
"recurrence": 1,
"shuffle_minibatches": false,
"gamma": 0.99,
"reward_scale": 1.0,
"reward_clip": 1000.0,
"value_bootstrap": false,
"normalize_returns": true,
"exploration_loss_coeff": 0.0004677351413,
"value_loss_coeff": 0.5,
"kl_loss_coeff": 0.0,
"exploration_loss": "entropy",
"gae_lambda": 0.95,
"ppo_clip_ratio": 0.1,
"ppo_clip_value": 1.0,
"with_vtrace": false,
"vtrace_rho": 1.0,
"vtrace_c": 1.0,
"optimizer": "adam",
"adam_eps": 1e-05,
"adam_beta1": 0.9,
"adam_beta2": 0.999,
"max_grad_norm": 0.0,
"learning_rate": 0.0003033891184,
"lr_schedule": "linear_decay",
"lr_schedule_kl_threshold": 0.008,
"lr_adaptive_min": 1e-06,
"lr_adaptive_max": 0.01,
"obs_subtract_mean": 0.0,
"obs_scale": 255.0,
"normalize_input": true,
"normalize_input_keys": [
"obs"
],
"decorrelate_experience_max_seconds": 0,
"decorrelate_envs_on_one_worker": true,
"actor_worker_gpus": [],
"set_workers_cpu_affinity": true,
"force_envs_single_thread": false,
"default_niceness": 0,
"log_to_file": true,
"experiment_summaries_interval": 3,
"flush_summaries_interval": 30,
"stats_avg": 100,
"summaries_use_frameskip": true,
"heartbeat_interval": 10,
"heartbeat_reporting_interval": 60,
"train_for_env_steps": 100000000,
"train_for_seconds": 10000000000,
"save_every_sec": 120,
"keep_checkpoints": 2,
"load_checkpoint_kind": "latest",
"save_milestones_sec": 1200,
"save_best_every_sec": 5,
"save_best_metric": "reward",
"save_best_after": 100000,
"benchmark": false,
"encoder_mlp_layers": [
512,
512
],
"encoder_conv_architecture": "convnet_atari",
"encoder_conv_mlp_layers": [
512
],
"use_rnn": false,
"rnn_size": 512,
"rnn_type": "gru",
"rnn_num_layers": 1,
"decoder_mlp_layers": [],
"nonlinearity": "relu",
"policy_initialization": "orthogonal",
"policy_init_gain": 1.0,
"actor_critic_share_weights": true,
"adaptive_stddev": false,
"continuous_tanh_scale": 0.0,
"initial_stddev": 1.0,
"use_env_info_cache": false,
"env_gpu_actions": false,
"env_gpu_observations": true,
"env_frameskip": 4,
"env_framestack": 4,
}
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m sf_examples.atari.enjoy_atari --algo=APPO --env=atari_tutankham --train_dir=./train_dir --experiment=APPO-atari_tutankham
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m sf_examples.atari.train_atari --algo=APPO --env=atari_tutankham --train_dir=./train_dir --experiment=APPO-atari_tutankham --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
leonorv/pt-neural-gender-neutralizer
|
leonorv
| 2023-10-16T09:36:18Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"m2m100",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-13T15:00:09Z |
---
language:
- pt
thumbnail: "PT Gender-Neutral Rewriter"
tags:
- m2m100
- pytorch
widget:
- text: "A Dani é simpática."
---
# pt-neural-gender-neutralizer
## Introduction
🌈 pt-neural-gender-neutralizer is a fine-tuned M2M100 model trained on the gender-neutral rewriting task for Portuguese 🌈
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("leonorv/pt-neural-gender-neutralizer")
model = AutoModelForSeq2SeqLM.from_pretrained("leonorv/pt-neural-gender-neutralizer")
```
## Additional Information
- This model is a neural version of the model hosted on https://github.com/leonorv/pt-gender-neutralizer
- The training data for pt-neural-gender-neutralizer can be found on https://github.com/leonorv/pt-gn-datasets
|
justtherightsize/robeczech-2stage-supportive-interactions-cs
|
justtherightsize
| 2023-10-16T09:22:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"cs",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-16T09:14:54Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for robeczech-2stage-supportive-interactions-cs
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for 2nd stage multi-label text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents - it expects inputs where at least one of the classes appears.
## Model Description
The model was fine-tuned on a dataset of Instant Messenger dialogs of Adolescents. The classification is 2stage and the model outputs probablities for labels {0,1,2,3,4}:
0. Informational Support
1. Emotional Support
2. Social Companionship
3. Appraisal
4. Instrumental Support
- **Developed by:** Anonymous
- **Language(s):** cs
- **Finetuned from:** ufal/robeczech
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Prepare input texts. This model is pretrained on multi-lingual data
# and fine-tuned on English
test_texts = ['Utterance1;Utterance2;Utterance3']
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
'justtherightsize/robeczech-2stage-supportive-interactions-cs', num_labels=5).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
'justtherightsize/robeczech-2stage-supportive-interactions-cs',
use_fast=False, truncation_side='left')
assert tokenizer.truncation_side == 'left'
# Define helper functions
def predict_one(text: str, tok, mod, threshold=0.5):
encoding = tok(text, return_tensors="pt", truncation=True, padding=True,
max_length=256)
encoding = {k: v.to(mod.device) for k, v in encoding.items()}
outputs = mod(**encoding)
logits = outputs.logits
sigmoid = torch.nn.Sigmoid()
probs = sigmoid(logits.squeeze().cpu())
predictions = np.zeros(probs.shape)
predictions[np.where(probs >= threshold)] = 1
return predictions, probs
def print_predictions(texts):
preds = [predict_one(tt, tokenizer, model) for tt in texts]
for c, p in preds:
print(f'{c}: {p.tolist():.4f}')
# Run the prediction
print_predictions(test_texts)
```
|
justtherightsize/small-e-czech-2stage-online-risks-cs
|
justtherightsize
| 2023-10-16T09:20:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"feature-extraction",
"cs",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-16T09:14:10Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for small-e-czech-2stage-online-risks-cs
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for 2nd stage multi-label text classification of Online Risks in Instant Messenger dialogs of Adolescents - it expects inputs where at least one of the classes appears.
## Model Description
The model was fine-tuned on a dataset of Instant Messenger dialogs of Adolescents. The classification is 2stage and the model outputs probablities for labels {0,1,2,3,4}:
0. Aggression, Harassing, Hate
1. Mental Health Problems
2. Alcohol, Drugs
3. Weight Loss, Diets
4. Sexual Content
- **Developed by:** Anonymous
- **Language(s):** cs
- **Finetuned from:** small-e-czech
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Prepare input texts. This model is pretrained on multi-lingual data
# and fine-tuned on English
test_texts = ['Utterance1;Utterance2;Utterance3']
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
'justtherightsize/small-e-czech-2stage-online-risks-cs', num_labels=5).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
'justtherightsize/small-e-czech-2stage-online-risks-cs',
use_fast=False, truncation_side='left')
assert tokenizer.truncation_side == 'left'
# Define helper functions
def predict_one(text: str, tok, mod, threshold=0.5):
encoding = tok(text, return_tensors="pt", truncation=True, padding=True,
max_length=256)
encoding = {k: v.to(mod.device) for k, v in encoding.items()}
outputs = mod(**encoding)
logits = outputs.logits
sigmoid = torch.nn.Sigmoid()
probs = sigmoid(logits.squeeze().cpu())
predictions = np.zeros(probs.shape)
predictions[np.where(probs >= threshold)] = 1
return predictions, probs
def print_predictions(texts):
preds = [predict_one(tt, tokenizer, model) for tt in texts]
for c, p in preds:
print(f'{c}: {p.tolist():.4f}')
# Run the prediction
print_predictions(test_texts)
```
|
khalidalt/bloom-7b1-ar-instruction
|
khalidalt
| 2023-10-16T09:11:36Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"ar",
"license:bigscience-bloom-rail-1.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-04T19:51:21Z |
---
license: bigscience-bloom-rail-1.0
language:
- ar
library_name: transformers
pipeline_tag: text-generation
---
This huggingface page hosts a low-rank adapter designed specifically for the fine-tuning of the bloom-7b model on Arabic instructions. Additional information regarding the datasets will be made available soon. The model was trained using the codebase found in the repository: https://github.com/KhalidAlt/alpaca-lora/tree/hf_models. This work is based on this repository: https://github.com/tloen/alpaca-lora, with certain modifications to adjust the requirements of bloom-7b.
|
yujiepan/llama-2-13b-w8a8-unstructured50
|
yujiepan
| 2023-10-16T09:00:51Z | 5 | 0 |
transformers
|
[
"transformers",
"openvino",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-16T08:22:21Z |
---
pipeline_tag: text-generation
inference: true
widget:
- text: 'Hello!'
example_title: Hello world
group: Python
library_name: transformers
---
# yujiepan/llama-2-13b-w8a8-unstructured50
This model is w8a8 quantized & unstructually sparsified by OpenVINO, exported from [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf).
**This model is not tuned for accuracy.**
- Quantization: 8-bit symmetric for weights & activations
- Unstructured sparsity in transformer block linear layers: 50%
Codes for export: https://gist.github.com/yujiepan-work/1e6dd9f9c2aac0e9ecaf2ed4d82d1158
|
flycutter/test
|
flycutter
| 2023-10-16T08:58:19Z | 0 | 0 |
pythae
|
[
"pythae",
"medical",
"object-detection",
"en",
"dataset:laion/dalle-3-dataset",
"license:mit",
"region:us"
] |
object-detection
| 2023-10-16T08:56:20Z |
---
license: mit
datasets:
- laion/dalle-3-dataset
language:
- en
metrics:
- cer
library_name: pythae
pipeline_tag: object-detection
tags:
- medical
---
|
mohit-99/t5-small-finetuned-en-to-de
|
mohit-99
| 2023-10-16T08:57:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-16T06:56:09Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-de
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. -->
# t5-small-finetuned-en-to-de
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0784
- Bleu: 41.5363
- Gen Len: 11.2069
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.1268 | 1.0 | 44307 | 0.0784 | 41.5363 | 11.2069 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Oscar10408/distilroberta-base-finetuned-wikitext2
|
Oscar10408
| 2023-10-16T08:52:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-16T08:07:53Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8269
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0834 | 1.0 | 2406 | 1.9288 |
| 1.9987 | 2.0 | 4812 | 1.8662 |
| 1.9406 | 3.0 | 7218 | 1.8571 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
jamesdborin/LeoLM-hessianai-7b
|
jamesdborin
| 2023-10-16T08:51:21Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"de",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:wikipedia",
"dataset:bjoernp/tagesschau-2018-2023",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-16T08:02:41Z |
---
datasets:
- oscar-corpus/OSCAR-2301
- wikipedia
- bjoernp/tagesschau-2018-2023
language:
- en
- de
library_name: transformers
pipeline_tag: text-generation
---
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## Model Details
- **Finetuned from:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected])
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
model="LeoLM/leo-hessianai-7b",
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True # True for flash-attn2 else False
)
```
## Training parameters
## Benchmarks
|
jamesdborin/LeoLM-hessianai-13b
|
jamesdborin
| 2023-10-16T08:50:41Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"de",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:wikipedia",
"dataset:bjoernp/tagesschau-2018-2023",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-16T08:19:11Z |
---
datasets:
- oscar-corpus/OSCAR-2301
- wikipedia
- bjoernp/tagesschau-2018-2023
language:
- en
- de
library_name: transformers
pipeline_tag: text-generation
---
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## Model Details
- **Finetuned from:** [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected])
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
model="LeoLM/leo-hessianai-13b",
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True # True for flash-attn2 else False
)
```
## Training parameters
## Benchmarks
|
Intel/whisper-base-onnx-int4-inc
|
Intel
| 2023-10-16T08:46:37Z | 4 | 9 |
transformers
|
[
"transformers",
"onnx",
"whisper",
"automatic-speech-recognition",
"ONNX",
"Intel® Neural Compressor",
"neural-compressor",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-08T05:16:50Z |
---
license: apache-2.0
datasets:
- librispeech_asr
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- automatic-speech-recognition
- ONNX
- Intel® Neural Compressor
- neural-compressor
library_name: transformers
---
## INT4 Whisper base ONNX Model
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. This is the repository of INT4 weight only quantization for the Whisper base model in ONNX format, powered by [Intel® Neural Compressor](https://github.com/intel/neural-compressor) and [Intel® Extension for Transformers](https://github.com/intel/intel-extension-for-transformers).
This INT4 ONNX model is generated by [Intel® Neural Compressor](https://github.com/intel/neural-compressor)'s weight-only quantization method.
| Model Detail | Description |
| ----------- | ----------- |
| Model Authors - Company | Intel |
| Date | October 8, 2023 |
| Version | 1 |
| Type | Speech Recognition |
| Paper or Other Resources | - |
| License | Apache 2.0 |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/whisper-base-onnx-int4/discussions)|
| Intended Use | Description |
| ----------- | ----------- |
| Primary intended uses | You can use the raw model for automatic speech recognition inference |
| Primary intended users | Anyone doing automatic speech recognition inference |
| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.|
### Export to ONNX Model
The FP32 model is exported with openai/whisper-base:
```shell
optimum-cli export onnx --model openai/whisper-base whisper-base-with-past/ --task automatic-speech-recognition-with-past --opset 13
```
### Install ONNX Runtime
Install `onnxruntime>=1.16.0` to support [`MatMulFpQ4`](https://github.com/microsoft/onnxruntime/blob/v1.16.0/docs/ContribOperators.md#com.microsoft.MatMulFpQ4) operator.
### Run Quantization
Build [Intel® Neural Compressor](https://github.com/intel/neural-compressor/tree/master) from master branch and run INT4 weight-only quantization.
The weight-only quantization cofiguration is as below:
| dtype | group_size | scheme | algorithm |
| :----- | :---------- | :------ | :--------- |
| INT4 | 32 | asym | RTN |
We provide the key code below. For the complete script, please refer to [whisper example](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/onnxruntime/speech-recognition/quantization).
```python
from neural_compressor import quantization, PostTrainingQuantConfig
from neural_compressor.utils.constant import FP32
model_list = ['encoder_model.onnx', 'decoder_model.onnx', 'decoder_with_past_model.onnx']
for model in model_list:
config = PostTrainingQuantConfig(
approach="weight_only",
calibration_sampling_size=[8],
op_type_dict={".*": {"weight": {"bits": 4,
"algorithm": ["RTN"],
"scheme": ["asym"],
"group_size": 32}}},)
q_model = quantization.fit(
os.path.join("/path/to/whisper-base-with-past", model), # FP32 model path
config,
calib_dataloader=dataloader)
q_model.save(os.path.join("/path/to/whisper-base-onnx-int4", model)) # INT4 model path
```
### Evaluation
**Operator Statistics**
Below shows the operator statistics in the INT4 ONNX model:
|Model| Op Type | Total | INT4 weight | FP32 weight |
|:-------:|:-------:|:-------:|:-------:|:-------:|
|encoder_model| MatMul | 48 | 36 | 12 |
|decoder_model| MatMul | 85 | 61 | 24 |
|decoder_with_past_model| MatMul | 73 | 49 | 24 |
**Evaluation of wer**
Evaluate the model on `librispeech_asr` dataset with below code:
```python
import os
from evaluate import load
from datasets import load_dataset
from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig
model_name = 'openai/whisper-base'
model_path = 'whisper-base-onnx-int4'
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
wer = load("wer")
librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")
from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import PretrainedConfig
model_config = PretrainedConfig.from_pretrained(model_name)
predictions = []
references = []
sessions = ORTModelForSpeechSeq2Seq.load_model(
os.path.join(model_path, 'encoder_model.onnx'),
os.path.join(model_path, 'decoder_model.onnx'),
os.path.join(model_path, 'decoder_with_past_model.onnx'))
model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2])
for idx, batch in enumerate(librispeech_test_clean):
audio = batch["audio"]
input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
reference = processor.tokenizer._normalize(batch['text'])
references.append(reference)
predicted_ids = model.generate(input_features)[0]
transcription = processor.decode(predicted_ids)
prediction = processor.tokenizer._normalize(transcription)
predictions.append(prediction)
wer_result = wer.compute(references=references, predictions=predictions)
print(f"Result wer: {wer_result * 100}")
```
## Metrics (Model Performance):
| Model | Model Size (MB) | wer |
|---|:---:|:---:|
| FP32 |465|5.04|
| INT4 |289|6.14|
|
justtherightsize/small-e-czech-multi-label-supportive-interactions-cs
|
justtherightsize
| 2023-10-16T08:40:54Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"feature-extraction",
"cs",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-16T08:37:21Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for small-e-czech-multi-label-supportive-interactions-cs
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for multi-label text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents.
## Model Description
The model was fine-tuned on a dataset of Instant Messenger dialogs of Adolescents. The classification is multi-label and the model outputs probablities for labels {0,1,2,3,4,5}:
0. None
1. Informational Support
2. Emotional Support
3. Social Companionship
4. Appraisal
5. Instrumental Support
- **Developed by:** Anonymous
- **Language(s):** cs
- **Finetuned from:** small-e-czech
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Prepare input texts. This model is pretrained on multi-lingual data
# and fine-tuned on English
test_texts = ['Utterance1;Utterance2;Utterance3']
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
'justtherightsize/small-e-czech-multi-label-supportive-interactions-cs', num_labels=6).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
'justtherightsize/small-e-czech-multi-label-supportive-interactions-cs',
use_fast=False, truncation_side='left')
assert tokenizer.truncation_side == 'left'
# Define helper functions
def predict_one(text: str, tok, mod, threshold=0.5):
encoding = tok(text, return_tensors="pt", truncation=True, padding=True,
max_length=256)
encoding = {k: v.to(mod.device) for k, v in encoding.items()}
outputs = mod(**encoding)
logits = outputs.logits
sigmoid = torch.nn.Sigmoid()
probs = sigmoid(logits.squeeze().cpu())
predictions = np.zeros(probs.shape)
predictions[np.where(probs >= threshold)] = 1
return predictions, probs
def print_predictions(texts):
preds = [predict_one(tt, tokenizer, model) for tt in texts]
for c, p in preds:
print(f'{c}: {p.tolist():.4f}')
# Run the prediction
print_predictions(test_texts)
```
|
justtherightsize/small-e-czech-multi-label-online-risks-cs
|
justtherightsize
| 2023-10-16T08:40:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"feature-extraction",
"cs",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-16T08:37:27Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for small-e-czech-multi-label-online-risks-cs
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for multi-label text classification of Online Risks in Instant Messenger dialogs of Adolescents.
## Model Description
The model was fine-tuned on a dataset of Instant Messenger dialogs of Adolescents. The classification is multi-label and the model outputs probablities for labels {0,1,2,3,4,5}:
0. None
1. Aggression, Harassing, Hate
2. Mental Health Problems
3. Alcohol, Drugs
4. Weight Loss, Diets
5. Sexual Content
- **Developed by:** Anonymous
- **Language(s):** cs
- **Finetuned from:** small-e-czech
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Prepare input texts. This model is pretrained on multi-lingual data
# and fine-tuned on English
test_texts = ['Utterance1;Utterance2;Utterance3']
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
'justtherightsize/small-e-czech-multi-label-online-risks-cs', num_labels=6).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
'justtherightsize/small-e-czech-multi-label-online-risks-cs',
use_fast=False, truncation_side='left')
assert tokenizer.truncation_side == 'left'
# Define helper functions
def predict_one(text: str, tok, mod, threshold=0.5):
encoding = tok(text, return_tensors="pt", truncation=True, padding=True,
max_length=256)
encoding = {k: v.to(mod.device) for k, v in encoding.items()}
outputs = mod(**encoding)
logits = outputs.logits
sigmoid = torch.nn.Sigmoid()
probs = sigmoid(logits.squeeze().cpu())
predictions = np.zeros(probs.shape)
predictions[np.where(probs >= threshold)] = 1
return predictions, probs
def print_predictions(texts):
preds = [predict_one(tt, tokenizer, model) for tt in texts]
for c, p in preds:
print(f'{c}: {p.tolist():.4f}')
# Run the prediction
print_predictions(test_texts)
```
|
marekk/videomae-base-finetuned-ucf101-subset
|
marekk
| 2023-10-16T08:37:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-10-16T08:34:49Z |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset
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. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0419
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 84
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6875 | 0.15 | 13 | 0.6332 | 0.6667 |
| 0.5825 | 1.15 | 26 | 0.5021 | 0.7778 |
| 0.4374 | 2.15 | 39 | 0.6035 | 0.7778 |
| 0.3854 | 3.15 | 52 | 0.1204 | 1.0 |
| 0.4212 | 4.15 | 65 | 0.3100 | 0.6667 |
| 0.2803 | 5.15 | 78 | 0.0453 | 1.0 |
| 0.2298 | 6.07 | 84 | 0.0419 | 1.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Intel/whisper-medium-onnx-int4-inc
|
Intel
| 2023-10-16T08:33:29Z | 5 | 1 |
transformers
|
[
"transformers",
"onnx",
"whisper",
"automatic-speech-recognition",
"ONNX",
"Intel® Neural Compressor",
"neural-compressor",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-08T05:42:03Z |
---
license: apache-2.0
datasets:
- librispeech_asr
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- automatic-speech-recognition
- ONNX
- Intel® Neural Compressor
- neural-compressor
library_name: transformers
---
## INT4 Whisper medium ONNX Model
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. This is the repository of INT4 weight only quantization for the Whisper medium model in ONNX format, powered by [Intel® Neural Compressor](https://github.com/intel/neural-compressor) and [Intel® Extension for Transformers](https://github.com/intel/intel-extension-for-transformers).
This INT4 ONNX model is generated by [Intel® Neural Compressor](https://github.com/intel/neural-compressor)'s weight-only quantization method.
| Model Detail | Description |
| ----------- | ----------- |
| Model Authors - Company | Intel |
| Date | October 8, 2023 |
| Version | 1 |
| Type | Speech Recognition |
| Paper or Other Resources | - |
| License | Apache 2.0 |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/whisper-medium-onnx-int4/discussions)|
| Intended Use | Description |
| ----------- | ----------- |
| Primary intended uses | You can use the raw model for automatic speech recognition inference |
| Primary intended users | Anyone doing automatic speech recognition inference |
| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.|
### Export to ONNX Model
The FP32 model is exported with openai/whisper-medium:
```shell
optimum-cli export onnx --model openai/whisper-medium whisper-medium-with-past/ --task automatic-speech-recognition-with-past --opset 13
```
### Install ONNX Runtime
Install `onnxruntime>=1.16.0` to support [`MatMulFpQ4`](https://github.com/microsoft/onnxruntime/blob/v1.16.0/docs/ContribOperators.md#com.microsoft.MatMulFpQ4) operator.
### Run Quantization
Build [Intel® Neural Compressor](https://github.com/intel/neural-compressor/tree/master) from master branch and run INT4 weight-only quantization.
The weight-only quantization cofiguration is as below:
| dtype | group_size | scheme | algorithm |
| :----- | :---------- | :------ | :--------- |
| INT4 | 32 | sym | RTN |
We provide the key code below. For the complete script, please refer to [whisper example](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/onnxruntime/speech-recognition/quantization).
```python
from neural_compressor import quantization, PostTrainingQuantConfig
from neural_compressor.utils.constant import FP32
model_list = ['encoder_model.onnx', 'decoder_model.onnx', 'decoder_with_past_model.onnx']
for model in model_list:
config = PostTrainingQuantConfig(
approach="weight_only",
calibration_sampling_size=[8],
op_type_dict={".*": {"weight": {"bits": 4,
"algorithm": ["RTN"],
"scheme": ["sym"],
"group_size": 32}}},)
q_model = quantization.fit(
os.path.join("/path/to/whisper-medium-with-past", model), # FP32 model path
config,
calib_dataloader=dataloader)
q_model.save(os.path.join("/path/to/whisper-medium-onnx-int4", model)) # INT4 model path
```
### Evaluation
**Operator Statistics**
Below shows the operator statistics in the INT4 ONNX model:
|Model| Op Type | Total | INT4 weight | FP32 weight |
|:-------:|:-------:|:-------:|:-------:|:-------:|
|encoder_model| MatMul | 192 | 144 | 48 |
|decoder_model| MatMul | 337 | 241 | 96 |
|decoder_with_past_model| MatMul | 289 | 193 | 96 |
**Evaluation of wer**
Evaluate the model on `librispeech_asr` dataset with below code:
```python
import os
from evaluate import load
from datasets import load_dataset
from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig
model_name = 'openai/whisper-medium'
model_path = 'whisper-medium-onnx-int4'
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
wer = load("wer")
librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")
from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import PretrainedConfig
model_config = PretrainedConfig.from_pretrained(model_name)
predictions = []
references = []
sessions = ORTModelForSpeechSeq2Seq.load_model(
os.path.join(model_path, 'encoder_model.onnx'),
os.path.join(model_path, 'decoder_model.onnx'),
os.path.join(model_path, 'decoder_with_past_model.onnx'))
model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2])
for idx, batch in enumerate(librispeech_test_clean):
audio = batch["audio"]
input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
reference = processor.tokenizer._normalize(batch['text'])
references.append(reference)
predicted_ids = model.generate(input_features)[0]
transcription = processor.decode(predicted_ids)
prediction = processor.tokenizer._normalize(transcription)
predictions.append(prediction)
wer_result = wer.compute(references=references, predictions=predictions)
print(f"Result wer: {wer_result * 100}")
```
## Metrics (Model Performance):
| Model | Model Size (GB) | wer |
|---|:---:|:---:|
| FP32 |4.9|2.88|
| INT4 |1.1|2.98|
|
Moses25/stable-diffusion-v2-webui
|
Moses25
| 2023-10-16T08:32:30Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-16T07:55:30Z |
---
license: apache-2.0
---
#### this is a stable diffusion webui repo,inlcuding v-1.5,v-2.1-512 and v-2.1-768,choose what you like .
fork from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
I just prepare the model and env
```
Step1
git clone git clone https://huggingface.co/Moses25/stable-diffusion-v2-webui
unzip SD.zip && cd SD
sh run.sh
```
### Note,if your nerwork is slow,you can download openai/clip-vit-large-patch14 to local,or modify the class FrozenCLIPEmbedder(version="openai/clip-vit-large-patch14") in SD/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/modules.py
for example
|
khouloudr/flowise
|
khouloudr
| 2023-10-16T08:11:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-10-16T08:11:10Z |
FROM node:18-alpine
USER root
# Arguments that can be passed at build time
ARG FLOWISE_PATH=/usr/local/lib/node_modules/flowise
ARG BASE_PATH=/root/.flowise
ARG DATABASE_PATH=$BASE_PATH
ARG APIKEY_PATH=$BASE_PATH
ARG SECRETKEY_PATH=$BASE_PATH
ARG LOG_PATH=$BASE_PATH/logs
# Install dependencies
RUN apk add --no-cache git python3 py3-pip make g++ build-base cairo-dev pango-dev chromium
ENV PUPPETEER_SKIP_DOWNLOAD=true
ENV PUPPETEER_EXECUTABLE_PATH=/usr/bin/chromium-browser
# Install Flowise globally
RUN npm install -g flowise
# Configure Flowise directories using the ARG
RUN mkdir -p $LOG_PATH $FLOWISE_PATH/uploads && chmod -R 777 $LOG_PATH $FLOWISE_PATH
WORKDIR /data
CMD ["npx", "flowise", "start"]
|
JiriG/Reinforce-Pixelcopter-PLE-v0
|
JiriG
| 2023-10-16T08:10:20Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-16T05:45:20Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 23.30 +/- 13.36
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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
|
justtherightsize/robeczech-binary-supportive-interactions-cs
|
justtherightsize
| 2023-10-16T08:08:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"cs",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-16T07:57:24Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for robeczech-binary-supportive-interactions-cs
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for binary text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents in Czech.
## Model Description
The model was fine-tuned on a dataset of Czech Instant Messenger dialogs of Adolescents. The classification is binary and the model outputs probablities for labels {0,1}: Supportive Interactions present or not.
- **Developed by:** Anonymous
- **Language(s):** cs
- **Finetuned from:** ufal/robeczech-base
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Prepare input texts. This model is fine-tuned for Czech
test_texts = ['Utterance1;Utterance2;Utterance3']
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
'justtherightsize/robeczech-binary-supportive-interactions-cs', num_labels=2).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
'justtherightsize/robeczech-binary-supportive-interactions-cs',
use_fast=False, truncation_side='left')
assert tokenizer.truncation_side == 'left'
# Define helper functions
def get_probs(text, tokenizer, model):
inputs = tokenizer(text, padding=True, truncation=True, max_length=256,
return_tensors="pt").to("cuda")
outputs = model(**inputs)
return outputs[0].softmax(1)
def preds2class(probs, threshold=0.5):
pclasses = np.zeros(probs.shape)
pclasses[np.where(probs >= threshold)] = 1
return pclasses.argmax(-1)
def print_predictions(texts):
probabilities = [get_probs(
texts[i], tokenizer, model).cpu().detach().numpy()[0]
for i in range(len(texts))]
predicted_classes = preds2class(np.array(probabilities))
for c, p in zip(predicted_classes, probabilities):
print(f'{c}: {p}')
# Run the prediction
print_predictions(test_texts)
```
|
justtherightsize/small-e-czech-binary-online-risks-cs
|
justtherightsize
| 2023-10-16T08:08:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"feature-extraction",
"cs",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-16T07:56:46Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for small-e-czech-binary-online-risks-cs
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for binary text classification of Online Risks in Instant Messenger dialogs of Adolescents in Czech.
## Model Description
The model was fine-tuned on a dataset of Czech Instant Messenger dialogs of Adolescents. The classification is binary and the model outputs probablities for labels {0,1}: Online Risks present or not.
- **Developed by:** Anonymous
- **Language(s):** cs
- **Finetuned from:** Seznam/small-e-czech
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Prepare input texts. This model is fine-tuned for Czech
test_texts = ['Utterance1;Utterance2;Utterance3']
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
'justtherightsize/small-e-czech-binary-online-risks-cs', num_labels=2).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
'justtherightsize/small-e-czech-binary-online-risks-cs',
use_fast=False, truncation_side='left')
assert tokenizer.truncation_side == 'left'
# Define helper functions
def get_probs(text, tokenizer, model):
inputs = tokenizer(text, padding=True, truncation=True, max_length=256,
return_tensors="pt").to("cuda")
outputs = model(**inputs)
return outputs[0].softmax(1)
def preds2class(probs, threshold=0.5):
pclasses = np.zeros(probs.shape)
pclasses[np.where(probs >= threshold)] = 1
return pclasses.argmax(-1)
def print_predictions(texts):
probabilities = [get_probs(
texts[i], tokenizer, model).cpu().detach().numpy()[0]
for i in range(len(texts))]
predicted_classes = preds2class(np.array(probabilities))
for c, p in zip(predicted_classes, probabilities):
print(f'{c}: {p}')
# Run the prediction
print_predictions(test_texts)
```
|
justtherightsize/small-e-czech-binary-supportive-interactions-cs
|
justtherightsize
| 2023-10-16T08:08:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"feature-extraction",
"cs",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-16T07:56:34Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for small-e-czech-binary-supportive-interactions-cs
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for binary text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents in Czech.
## Model Description
The model was fine-tuned on a dataset of Czech Instant Messenger dialogs of Adolescents. The classification is binary and the model outputs probablities for labels {0,1}: Supportive Interactions present or not.
- **Developed by:** Anonymous
- **Language(s):** cs
- **Finetuned from:** Seznam/small-e-czech
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Prepare input texts. This model is fine-tuned for Czech
test_texts = ['Utterance1;Utterance2;Utterance3']
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
'justtherightsize/small-e-czech-binary-supportive-interactions-cs', num_labels=2).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
'justtherightsize/small-e-czech-binary-supportive-interactions-cs',
use_fast=False, truncation_side='left')
assert tokenizer.truncation_side == 'left'
# Define helper functions
def get_probs(text, tokenizer, model):
inputs = tokenizer(text, padding=True, truncation=True, max_length=256,
return_tensors="pt").to("cuda")
outputs = model(**inputs)
return outputs[0].softmax(1)
def preds2class(probs, threshold=0.5):
pclasses = np.zeros(probs.shape)
pclasses[np.where(probs >= threshold)] = 1
return pclasses.argmax(-1)
def print_predictions(texts):
probabilities = [get_probs(
texts[i], tokenizer, model).cpu().detach().numpy()[0]
for i in range(len(texts))]
predicted_classes = preds2class(np.array(probabilities))
for c, p in zip(predicted_classes, probabilities):
print(f'{c}: {p}')
# Run the prediction
print_predictions(test_texts)
```
|
julietz/koalpaca-polyglot-12.8b-law_finance
|
julietz
| 2023-10-16T07:49:34Z | 3 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | null | 2023-10-16T07:49:32Z |
---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
IlluminatiPudding/huggy-ppo-staticSDK
|
IlluminatiPudding
| 2023-10-16T07:48:11Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-10-16T06:57:00Z |
---
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: IlluminatiPudding/huggy-ppo-staticSDK
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jamesdborin/LeoLM-hesseianai-13b-chat
|
jamesdborin
| 2023-10-16T07:43:11Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"de",
"dataset:LeoLM/OpenSchnabeltier",
"dataset:OpenAssistant/OASST-DE",
"dataset:FreedomIntelligence/alpaca-gpt4-deutsch",
"dataset:FreedomIntelligence/evol-instruct-deutsch",
"dataset:LeoLM/German_Poems",
"dataset:LeoLM/German_Songs",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-15T13:47:48Z |
---
datasets:
- LeoLM/OpenSchnabeltier
- OpenAssistant/OASST-DE
- FreedomIntelligence/alpaca-gpt4-deutsch
- FreedomIntelligence/evol-instruct-deutsch
- LeoLM/German_Poems
- LeoLM/German_Songs
language:
- en
- de
library_name: transformers
pipeline_tag: text-generation
---
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## LeoLM Chat
`LeoLM/leo-hessianai-13b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-13b` and finetuned on a selection of German instruction datasets.
The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores:
```
{
"first_turn": 6.525,
"second_turn": 5.15,
"categories": {
"writing": 6.925,
"roleplay": 6.7,
"reasoning": 4.55,
"math": 3.25,
"coding": 3.45,
"extraction": 5.4,
"stem": 7.55,
"humanities": 8.875
},
"average": 5.8375
}
```
## Model Details
- **Finetuned from:** [LeoLM/leo-hessianai-13b](https://huggingface.co/LeoLM/leo-hessianai-7b)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **Demo:** [Web Demo]()
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected])
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import pipeline
import torch
system_prompt = """<|im_start|>system
Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>
"""
prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."
generator = pipeline(model="LeoLM/leo-hessianai-13b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False
print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
```
"*Hamburg verfügt über ein umfangreiches Netzwerk an Radwegen, das ein effizientes und angenehmes Radfahren in der ganzen Stadt ermöglicht. Die Radwege variieren in Bezug auf ihre Qualität, wobei einige erstklassig mit eigens für Radfahrer reservierten Spuren sind, während andere einfache Fahrradsymbole auf dem Boden haben, die anzeigen, dass Radfahrer abwechselnd mit dem Autoverkehr auf der Straße fahren sollten. Einige Nebenstraßen haben auch spezielle Fahrradspuren, wobei einige mit Bordsteinabsenkungen zur Seite der Autospuren markiert sind. Zusätzlich haben viele Hauptstraßen, insbesondere in Nebenstraßen, fahrradfreundliche Abstände zwischen den geparkten Autos und dem Gehweg, was ein bequemes Fahren auf der Straße ermöglicht. Der Bau von Radschnellwegen, die schnelles und effizientes Radfahren in und aus der Stadt ermöglichen, ist im Gange und wird in den kommenden Jahren fortgesetzt. Insgesamt sind die Radwege in Hamburg weitläufig und gut ausgeschildert, was es zu einem angenehmen Ort macht, um mit dem Fahrrad zu fahren.*"
## Prompting / Prompt Template
Prompt dialogue template (ChatML format):
```
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
```
The model input can contain multiple conversation turns between user and assistant, e.g.
```
<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)
```
## Ethical Considerations and Limitations
LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios.
For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-13b-chat` 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 `LeoLM/leo-hessianai-13b-chat`, developers should
perform safety testing and tuning tailored to their specific applications of the model.
Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
## Finetuning Details
| Hyperparameter | Value |
|---|---|
| Num epochs | 3 |
| Examples per epoch | 131214 |
| Global batch size | 256 |
| Learning rate | 3e-5 |
| Warmup steps | 100 |
| LR scheduler | Cosine |
| Adam betas | (0.9, 0.95) |
## Dataset Details
```
## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%))
-----------------
Accepted: 3534/3534 (100.0%)
Accepted tokens: 2259302
Skipped: 0 (0.0%)
Min tokens per sample: 29
Max tokens per sample: 2484
Avg tokens per sample: 639.3044708545557
-----------------
## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
-----------------
Accepted: 57841/57841 (100.0%)
Accepted tokens: 42958192
Skipped: 0 (0.0%)
Min tokens per sample: 33
Max tokens per sample: 5507
Avg tokens per sample: 742.6944900675991
-----------------
## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
-----------------
Accepted: 48969/48969 (100.0%)
Accepted tokens: 13372005
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 1359
Avg tokens per sample: 273.07082031489307
-----------------
## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
-----------------
Accepted: 21314/21314 (100.0%)
Accepted tokens: 8134690
Skipped: 0 (0.0%)
Min tokens per sample: 25
Max tokens per sample: 1202
Avg tokens per sample: 381.65947264708643
-----------------
## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%))
-----------------
Accepted: 490/490 (100.0%)
Accepted tokens: 618642
Skipped: 0 (0.0%)
Min tokens per sample: 747
Max tokens per sample: 1678
Avg tokens per sample: 1262.534693877551
-----------------
## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%))
-----------------
Accepted: 392/392 (100.0%)
Accepted tokens: 187897
Skipped: 0 (0.0%)
Min tokens per sample: 231
Max tokens per sample: 826
Avg tokens per sample: 479.3290816326531
-----------------
## Stats for 'total' (132540 samples (100.0%))
-----------------
Accepted: 132540/132540 (100.0%)
Accepted tokens: 67530728
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 5507
Avg tokens per sample: 509.51205673758864
-----------------
```
|
jamesdborin/LeoLM-hessianai-7b-chat
|
jamesdborin
| 2023-10-16T07:38:20Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"de",
"dataset:LeoLM/OpenSchnabeltier",
"dataset:OpenAssistant/OASST-DE",
"dataset:FreedomIntelligence/alpaca-gpt4-deutsch",
"dataset:FreedomIntelligence/evol-instruct-deutsch",
"dataset:LeoLM/German_Poems",
"dataset:LeoLM/German_Songs",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-15T13:30:06Z |
---
datasets:
- LeoLM/OpenSchnabeltier
- OpenAssistant/OASST-DE
- FreedomIntelligence/alpaca-gpt4-deutsch
- FreedomIntelligence/evol-instruct-deutsch
- LeoLM/German_Poems
- LeoLM/German_Songs
language:
- en
- de
library_name: transformers
pipeline_tag: text-generation
---
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## LeoLM Chat
`LeoLM/leo-hessianai-7b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German instruction datasets.
The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores:
```
{
"first_turn": 5.75,
"second_turn": 4.45,
"categories": {
"writing": 5.875,
"roleplay": 6.3,
"reasoning": 3.5,
"math": 2.85,
"coding": 2.95,
"extraction": 4.3,
"stem": 7.4,
"humanities": 7.625
},
"average": 5.1
}
```
## Model Details
- **Finetuned from:** [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **Demo:** [Web Demo]()
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected])
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import pipeline
import torch
system_prompt = """<|im_start|>system
Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>
"""
prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."
generator = pipeline(model="LeoLM/leo-hessianai-7b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False
print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
```
"*Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen.*
*In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen.*"
## Prompting / Prompt Template
Prompt dialogue template (ChatML format):
```
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
```
The model input can contain multiple conversation turns between user and assistant, e.g.
```
<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)
```
## Ethical Considerations and Limitations
LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios.
For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` 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 `LeoLM/leo-hessianai-7b-chat`, developers should
perform safety testing and tuning tailored to their specific applications of the model.
Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
## Finetuning Details
| Hyperparameter | Value |
|---|---|
| Num epochs | 3 |
| Examples per epoch | 131214 |
| Global batch size | 256 |
| Learning rate | 3e-5 |
| Warmup steps | 100 |
| LR scheduler | Cosine |
| Adam betas | (0.9, 0.95) |
## Dataset Details
```
## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%))
-----------------
Accepted: 3534/3534 (100.0%)
Accepted tokens: 2259302
Skipped: 0 (0.0%)
Min tokens per sample: 29
Max tokens per sample: 2484
Avg tokens per sample: 639.3044708545557
-----------------
## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
-----------------
Accepted: 57841/57841 (100.0%)
Accepted tokens: 42958192
Skipped: 0 (0.0%)
Min tokens per sample: 33
Max tokens per sample: 5507
Avg tokens per sample: 742.6944900675991
-----------------
## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
-----------------
Accepted: 48969/48969 (100.0%)
Accepted tokens: 13372005
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 1359
Avg tokens per sample: 273.07082031489307
-----------------
## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
-----------------
Accepted: 21314/21314 (100.0%)
Accepted tokens: 8134690
Skipped: 0 (0.0%)
Min tokens per sample: 25
Max tokens per sample: 1202
Avg tokens per sample: 381.65947264708643
-----------------
## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%))
-----------------
Accepted: 490/490 (100.0%)
Accepted tokens: 618642
Skipped: 0 (0.0%)
Min tokens per sample: 747
Max tokens per sample: 1678
Avg tokens per sample: 1262.534693877551
-----------------
## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%))
-----------------
Accepted: 392/392 (100.0%)
Accepted tokens: 187897
Skipped: 0 (0.0%)
Min tokens per sample: 231
Max tokens per sample: 826
Avg tokens per sample: 479.3290816326531
-----------------
## Stats for 'total' (132540 samples (100.0%))
-----------------
Accepted: 132540/132540 (100.0%)
Accepted tokens: 67530728
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 5507
Avg tokens per sample: 509.51205673758864
-----------------
```
|
FENRlR/HitlerGPT-combination
|
FENRlR
| 2023-10-16T07:12:19Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-16T07:09:00Z |
---
library_name: peft
---
backup of https://github.com/FENRlR/HitlerGPT
## 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
|
nommis/lora_segformer
|
nommis
| 2023-10-16T06:51:37Z | 0 | 0 | null |
[
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/segformer-b0-finetuned-ade-512-512",
"base_model:finetune:nvidia/segformer-b0-finetuned-ade-512-512",
"license:other",
"region:us"
] | null | 2023-10-16T05:30:16Z |
---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: lora_segformer
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. -->
# lora_segformer
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4393
- Mean Iou: 0.3682
- Mean Accuracy: 0.5707
- Overall Accuracy: 0.8396
- Per Category Iou: [0.6438135421351039, 0.9050984072652993, 0.9749698684406963, 0.7369247110674255, 0.752654357505371, 0.8027859009457442, 0.5003933394519471, 0.6087506208515826, 0.7086873558198924, 0.0, 0.5266673119736714, 0.6564246536171983, 0.8532161335841957, 0.9411694700095208, 0.44680635049557726, 0.6368607368445408, 0.7191841188567591, 0.8702582894594784, 0.8936646895801497, 0.4566314362463284, 0.5366785367432826, nan, 0.9618790668147913, 0.0, nan, 0.9237170739775875, 0.0, nan, 0.0, 0.0, nan, 0.9728922153107986, 0.0, 0.0, nan, nan, 0.3453142950927294, nan, 0.0, 0.9417587233463799, nan, nan, 0.5209098524389458, 0.2924377427461732, 0.0, nan, nan, 0.1969524675915397, nan, nan, 0.857620930940067, nan, nan, nan, nan, nan, nan, 0.45423584504044273, nan, 0.0, nan, nan, nan, 0.43041626597060034, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4432651787107851, nan, nan, nan, nan, nan, 0.3351013020964255, nan, 0.0, nan, nan, 0.7692795267272343, nan, nan, nan, 0.8001664342584679, 0.0, nan, nan, nan, nan, nan, 0.12577983497685652, nan, 0.0, nan, 0.8418795086761552, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7059779664078021, nan, 0.42956026058631924, nan, 0.0, 0.0, 0.0, nan, nan, 0.017543859649122806, 0.32014218009478673, 0.0, 0.0, 0.0, nan, nan, nan, 0.6160586916649684, nan, 0.02803347280334728, nan, nan, 0.3776207302709069, nan, nan]
- Per Category Accuracy: [0.7197732663811959, 0.9539751388649803, 0.9812404259841538, 0.8428192733510058, 0.8449438987731134, 0.8896694090217593, 0.9736590343771925, 0.9186301631212439, 0.8542672739340398, nan, 0.7893896895250991, 0.751301657195561, 0.9366714807852123, 0.9824016563146998, 0.7211741330219792, 0.7214271985462424, 0.9486271036315324, 0.9383752542303241, 0.9309670282229777, 0.6021451981328831, 0.5816638012701308, nan, 0.9796657291565641, nan, nan, 0.9592377885131508, nan, nan, 0.0, 0.0, nan, 0.9877390687137204, 0.0, nan, nan, nan, 0.6032110091743119, nan, nan, 0.9756551961674547, nan, nan, 0.6600261267145656, 0.3362227475702653, nan, nan, nan, 0.1969524675915397, nan, nan, 0.9168331552292895, nan, nan, nan, nan, nan, nan, 0.5525634386328327, nan, nan, nan, nan, nan, 0.43041626597060034, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5317854133508099, nan, nan, nan, nan, nan, 0.8805268233398064, nan, nan, nan, nan, 0.965910598222576, nan, nan, nan, 0.9506212424849699, nan, nan, nan, nan, nan, nan, 0.12616067823980623, nan, 0.0, nan, 0.8465560057508822, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7475616752725186, nan, 0.503916698509744, nan, 0.0, nan, 0.0, nan, nan, 0.01847826086956522, 0.3420253164556962, nan, 0.0, nan, nan, nan, nan, 0.971401028277635, nan, 0.02803347280334728, nan, nan, 0.5647348951911221, nan, nan]
## 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: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.578 | 1.0 | 34 | 0.4335 | 0.4257 | 0.6028 | 0.8404 | [0.6418663414548549, 0.9074896632653997, 0.9748859857913768, 0.7452536974490729, 0.7725095836297798, 0.7895563784996465, 0.8048589812977329, 0.5835084591054788, 0.697607921158562, nan, 0.4623885262480463, 0.8163175164058556, 0.8420685621738349, 0.9439050269263879, 0.44604931475096343, 0.6465933007665364, 0.4895793655112369, 0.8706154885621441, 0.8845340235714567, 0.41549986836121683, 0.4928002043400985, nan, 0.9610218428320362, 0.0, nan, 0.9377188361788492, nan, 0.0, 0.0, 0.0, nan, 0.964612649417461, 0.0, nan, nan, nan, 0.348677327753699, nan, 0.0, 0.9272350843256967, nan, nan, 0.55854970518258, 0.4112942317014057, nan, nan, nan, 0.25881282692745056, nan, nan, 0.8393401015228427, nan, nan, nan, nan, nan, nan, 0.39275766016713093, nan, nan, nan, nan, nan, 0.40651188350048084, nan, nan, 0.0, nan, 0.25857991578704503, 0.0, nan, 0.5728976234003657, nan, nan, nan, nan, nan, 0.363462959372429, nan, 0.0, nan, nan, 0.7479575163398693, nan, nan, nan, 0.7846516007532957, 0.0, nan, nan, nan, nan, nan, 0.09929364278506558, nan, 0.0, nan, 0.9310013453776668, nan, nan, 0.4705128205128205, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.7383895756765787, nan, 0.438504855953644, nan, 0.05760041327650781, nan, 0.0, nan, nan, 0.003260869565217391, 0.5562637362637363, 0.0, 0.0, 0.0, nan, nan, nan, 0.759592795614722, nan, 0.14393305439330545, nan, nan, 0.3608919902912621, nan, nan] | [0.7211903514987216, 0.9599807007377846, 0.9831424027206697, 0.8425726812180201, 0.847612056811959, 0.8857605833502127, 0.8536258689967472, 0.7491993840367398, 0.8626896756917584, nan, 0.810748944127414, 0.9622593793698492, 0.9061166200046796, 0.9761697722567287, 0.6306765808394272, 0.7273680348063113, 0.49806244464127547, 0.9265145964157804, 0.908221358804166, 0.5747012281921409, 0.541548249769014, nan, 0.989946012349271, nan, nan, 0.9746537842190016, nan, nan, 0.0, 0.0, nan, 0.991783912024658, 0.0, nan, nan, nan, 0.6688646788990825, nan, nan, 0.9605942950183173, nan, nan, 0.8816949706074461, 0.4457578145521408, nan, nan, nan, 0.25881282692745056, nan, nan, 0.9218975049946568, nan, nan, nan, nan, nan, nan, 0.43811496633868463, nan, nan, nan, nan, nan, 0.40651188350048084, nan, nan, nan, nan, 0.8542301829268293, 0.0, nan, 0.9388634023031551, nan, nan, nan, nan, nan, 0.9162604525819363, nan, nan, nan, nan, 0.9714816288632445, nan, nan, nan, 0.8906346025384102, nan, nan, nan, nan, nan, nan, 0.09931368591037545, nan, 0.0, nan, 0.94967978042086, nan, nan, 0.4705128205128205, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8452859055268693, nan, 0.5132785632403516, nan, 0.058808016877637134, nan, 0.0, nan, nan, 0.003260869565217391, 0.640759493670886, nan, 0.0, nan, nan, nan, nan, 0.9350899742930592, nan, 0.14393305439330545, nan, nan, 0.4190593623392637, nan, nan] |
| 0.4993 | 2.0 | 68 | 0.4278 | 0.4096 | 0.5930 | 0.8430 | [0.6486897398675289, 0.9074343772576632, 0.9744601102660768, 0.7465532772015864, 0.7681879849069403, 0.7897608024986333, 0.7944699903194579, 0.6158370888921195, 0.6883525108587976, 0.0, 0.49035256796369914, 0.8199290959736643, 0.8454046065889448, 0.9456680469703335, 0.44078245406046235, 0.6456171665825928, 0.5266565939995683, 0.8698475329451425, 0.8893031409023717, 0.4024576802507837, 0.4831831409500525, nan, 0.9610213356899382, 0.0, nan, 0.9382585970022829, 0.0, 0.0, 0.0, 0.0, nan, 0.9632908109127779, 0.0, nan, nan, nan, 0.34785257386536705, nan, 0.0, 0.923812410031063, nan, nan, 0.5771766740528563, 0.3727795846885164, nan, nan, nan, 0.2733682055947237, nan, nan, 0.8165573770491803, nan, nan, nan, nan, nan, nan, 0.39990880072959417, nan, nan, nan, nan, nan, 0.38466822365709574, nan, nan, nan, nan, 0.24839483753810235, 0.0, nan, 0.5752045311516677, nan, nan, nan, nan, nan, 0.3594674325315685, nan, 0.0, nan, nan, 0.7540509856538342, nan, nan, nan, 0.7936648010961195, 0.0, nan, nan, nan, nan, nan, 0.0621719822365765, nan, 0.0, nan, 0.9265348060621629, nan, nan, 0.22948717948717948, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.74375, nan, 0.43235921851912656, nan, 0.06743400475415989, nan, 0.0, nan, nan, 0.0, 0.5388962765957447, 0.0, 0.0, 0.0, nan, nan, nan, 0.767913593256059, nan, 0.16778242677824268, nan, nan, 0.34077017114914426, nan, nan] | [0.7329041003049813, 0.9592775869133545, 0.9832450141823699, 0.865578276683626, 0.8444545422769059, 0.8903158520765906, 0.8374896358186109, 0.7504667420721985, 0.8269745725859977, nan, 0.7873907856981656, 0.9633453333730029, 0.9138945143253645, 0.9787370600414078, 0.6092709805608472, 0.72680016075204, 0.5403011514614703, 0.9329585219374233, 0.9171253575090389, 0.5312675869831496, 0.5223676362232904, nan, 0.9843532796689619, nan, nan, 0.9750939345142243, nan, nan, 0.0, 0.0, nan, 0.9930333407877722, 0.0, nan, nan, nan, 0.6548165137614679, nan, nan, 0.9544885242821806, nan, nan, 0.8719382756368387, 0.39138429209351194, nan, nan, nan, 0.2733682055947237, nan, nan, 0.9257073827997956, nan, nan, nan, nan, nan, nan, 0.45416882444329365, nan, nan, nan, nan, nan, 0.38466822365709574, nan, nan, nan, nan, 0.7298018292682927, 0.0, nan, 0.94129763130793, nan, nan, nan, nan, nan, 0.9126936947647921, nan, nan, nan, nan, 0.969094044302958, nan, nan, nan, 0.913186372745491, nan, nan, nan, nan, nan, nan, 0.0621719822365765, nan, 0.0, nan, 0.9428832832309502, nan, nan, 0.22948717948717948, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8420348058902276, nan, 0.5031524646541842, nan, 0.07107067510548523, nan, 0.0, nan, nan, 0.0, 0.6155696202531645, nan, 0.0, nan, nan, nan, nan, 0.9366966580976864, nan, 0.16778242677824268, nan, nan, 0.3928131055134754, nan, nan] |
| 0.5557 | 3.0 | 102 | 0.4295 | 0.4108 | 0.5965 | 0.8426 | [0.6488481958660854, 0.9080819839680278, 0.9758335149269993, 0.7487195591075382, 0.7730225898142805, 0.7979049791304281, 0.7225942715845869, 0.6050706012315568, 0.706028681486982, 0.0, 0.46621396588091624, 0.7937648646911358, 0.8453008164057253, 0.9442843996318969, 0.440574681330026, 0.6488939041305216, 0.5999384046812443, 0.8700610867687978, 0.8900720325712496, 0.42334788648541377, 0.44026873139800715, nan, 0.9611629148221406, 0.0, nan, 0.941291888263541, 0.0, 0.0, 0.0, 0.0, nan, 0.9647750436291921, 0.0, nan, nan, nan, 0.3419296116504854, nan, 0.0, 0.9283184373539918, nan, nan, 0.5892088342186189, 0.3618648248643315, nan, nan, nan, 0.23402319763475096, nan, nan, 0.8253955072042519, nan, nan, nan, nan, nan, nan, 0.4057246706042708, nan, nan, nan, nan, nan, 0.4295919769199066, nan, 0.0, nan, nan, 0.22031930333817126, 0.0, nan, 0.5898237132244561, nan, nan, nan, nan, nan, 0.36696012766096026, nan, 0.0, nan, nan, 0.7479600163198694, nan, nan, nan, 0.788396860986547, 0.0, nan, nan, nan, nan, nan, 0.07426841574167507, nan, 0.0, nan, 0.9270973963355834, nan, nan, 0.32051282051282054, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.7571858288770054, nan, 0.43562898740389333, nan, 0.040375466118040375, nan, 0.0, nan, nan, 0.008695652173913044, 0.5902710811100397, 0.0, 0.0, 0.0, nan, nan, nan, 0.7472052845528455, nan, 0.15523012552301255, nan, nan, 0.34555555555555556, nan, nan] | [0.7270681741166323, 0.9607613995359449, 0.9846522570856879, 0.8646172926359611, 0.8428699593368054, 0.8955218734781653, 0.8946361375087697, 0.7538872460173615, 0.8704369120911817, nan, 0.812360963342683, 0.9184939454345303, 0.9139187193906778, 0.9772463768115942, 0.6294065835023249, 0.7234365990459716, 0.6470327723649247, 0.931465667778318, 0.92018779342723, 0.5649683848114675, 0.477492017823936, nan, 0.9896873888727249, nan, nan, 0.9785292538915727, nan, nan, 0.0, 0.0, nan, 0.9917304345289605, 0.0, nan, nan, nan, 0.6462155963302753, nan, nan, 0.9642890691048001, nan, nan, 0.8603853690398432, 0.38534278959810875, nan, nan, nan, 0.23402319763475096, nan, nan, 0.9235701342749617, nan, nan, nan, nan, nan, nan, 0.4624546866908338, nan, nan, nan, nan, nan, 0.4295919769199066, nan, nan, nan, nan, 0.5785060975609756, 0.0, nan, 0.936616421683363, nan, nan, nan, nan, nan, 0.8931161574129116, nan, nan, nan, nan, 0.9726754211433877, nan, nan, nan, 0.9019639278557114, nan, nan, nan, nan, nan, nan, 0.0742834073475979, nan, 0.0, nan, 0.9424258266893216, nan, nan, 0.32051282051282054, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8665136737425894, nan, 0.5087886893389377, nan, 0.04140295358649789, nan, 0.0, nan, nan, 0.008695652173913044, 0.6973417721518987, nan, 0.0, nan, nan, nan, nan, 0.9450514138817481, nan, 0.15523012552301255, nan, nan, 0.3834771886559803, nan, nan] |
| 0.5656 | 4.0 | 136 | 0.4284 | 0.4146 | 0.5902 | 0.8437 | [0.6476246062769682, 0.9061586328384159, 0.9741115368551038, 0.748082221211202, 0.7698932724473, 0.7981297789598201, 0.8191450714458707, 0.6238647776566874, 0.7023130069089817, 0.0, 0.48409688902646647, 0.8236723580752691, 0.8456922768066265, 0.9445086705202312, 0.44901894547194776, 0.6451439125750347, 0.5809504110864057, 0.8704905119025509, 0.8907759081370645, 0.4316634060573042, 0.46811044937736873, nan, 0.962008183821215, 0.0, nan, 0.9375762715370136, nan, 0.0, 0.0, 0.0, nan, 0.9650466423205737, 0.0, nan, nan, nan, 0.33824658375556577, nan, 0.0, 0.930601134215501, nan, nan, 0.5895336317787726, 0.3320630081300813, nan, nan, nan, 0.23675233113486469, nan, nan, 0.8433471799922916, nan, nan, nan, nan, nan, nan, 0.4053431598341778, nan, nan, nan, nan, nan, 0.41379310344827586, nan, nan, 0.0, nan, 0.22594519597641347, 0.0, nan, 0.6024539877300613, nan, nan, nan, nan, nan, 0.3582138557513407, nan, 0.0, nan, nan, 0.7567399419328079, nan, nan, nan, 0.7907672879492996, 0.0, nan, nan, nan, nan, nan, 0.058336697618086394, nan, 0.0, nan, 0.9077321255257198, nan, nan, 0.10384615384615385, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.740983606557377, nan, 0.43253447143795143, nan, 0.020117570215545395, nan, 0.0, nan, nan, 0.002173913043478261, 0.5118289353958144, nan, 0.0, 0.0, nan, nan, nan, 0.7517875383043923, nan, 0.17196652719665273, nan, nan, 0.37844396859386153, nan, nan] | [0.7274008810572687, 0.9603346821804287, 0.9828968681516012, 0.8586374334110582, 0.8446875691798618, 0.889785768771629, 0.8628739077747305, 0.7957100611875009, 0.856155448241137, nan, 0.8067188960892414, 0.9630626878105382, 0.9176785728693492, 0.9810766045548655, 0.6534545975951986, 0.7323827995317224, 0.6219552701505757, 0.9307808722924898, 0.9220090658895904, 0.5770516767636641, 0.5055962948668468, nan, 0.9880386642097436, nan, nan, 0.9732474503488996, nan, nan, 0.0, 0.0, nan, 0.9917984967962119, 0.0, nan, nan, nan, 0.6315940366972477, nan, nan, 0.9633966872279801, nan, nan, 0.8494039843239712, 0.34331494615182556, nan, nan, nan, 0.23675233113486469, nan, nan, 0.9149746782511732, nan, nan, nan, nan, nan, nan, 0.4557224236147074, nan, nan, nan, nan, nan, 0.41379310344827586, nan, nan, nan, nan, 0.6206173780487805, 0.0, nan, 0.9193895702649565, nan, nan, nan, nan, nan, 0.9079776483176792, nan, nan, nan, nan, 0.9680328956094972, nan, nan, nan, 0.9035136940547762, nan, nan, nan, nan, nan, nan, 0.058336697618086394, nan, 0.0, nan, 0.9168082603581231, nan, nan, 0.10384615384615385, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8211895199847007, nan, 0.5034390523500191, nan, 0.02030590717299578, nan, 0.0, nan, nan, 0.002173913043478261, 0.569620253164557, nan, 0.0, nan, nan, nan, nan, 0.9460154241645244, nan, 0.17196652719665273, nan, nan, 0.46697199224942754, nan, nan] |
| 0.4441 | 5.0 | 170 | 0.4332 | 0.4010 | 0.5902 | 0.8418 | [0.6411026586407741, 0.9067119539060244, 0.9736276112624886, 0.7479702923159933, 0.7735621800662451, 0.7930973060674471, 0.7402617477328937, 0.6055771224162111, 0.702938938558113, 0.0, 0.48596321393998065, 0.7984633493293322, 0.8467093139972134, 0.9443313779480427, 0.446601250409445, 0.6449810279406692, 0.6021237349818037, 0.863799123014176, 0.8955260794293376, 0.4501448575567359, 0.4734163380764615, nan, 0.9619512195121951, 0.0, nan, 0.9427438050589871, 0.0, 0.0, 0.0, 0.0, nan, 0.9657218848940611, 0.0, nan, nan, nan, 0.3392695442197404, nan, 0.0, 0.9325021107224701, nan, nan, 0.5869140079342907, 0.3408141962421712, nan, nan, nan, 0.26313395496929726, nan, nan, 0.8257929883138564, nan, nan, nan, nan, nan, nan, 0.4342934293429343, nan, nan, nan, nan, nan, 0.4066492650089298, nan, nan, 0.0, nan, 0.1904292431366601, 0.0, nan, 0.6045194298940187, nan, nan, nan, nan, nan, 0.3545606807731519, nan, 0.0, nan, nan, 0.7599125819544177, nan, nan, nan, 0.7975458715596331, 0.0, nan, nan, nan, nan, nan, 0.04521598708114655, nan, 0.0, nan, 0.9056200168535684, nan, nan, 0.07307692307692308, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.7433367947386639, nan, 0.43603986276752166, nan, 0.04501731435167372, 0.0, 0.0, nan, nan, 0.0010869565217391304, 0.4933470979582473, nan, 0.0, 0.0, nan, nan, nan, 0.7463365336028297, nan, 0.1581589958158996, nan, nan, 0.38060802833530105, nan, nan] | [0.7214343365885216, 0.9591975774091953, 0.9821052940184847, 0.8545976740559692, 0.8381045591713563, 0.8826748951684846, 0.916321193953696, 0.8120630680966463, 0.8457533243311286, nan, 0.8092336460650611, 0.9183005563654756, 0.91199845087582, 0.9798757763975156, 0.6423523628095619, 0.7350998584683126, 0.6686226749335695, 0.9267611227906786, 0.9281339377259727, 0.6172410368457643, 0.5167421026162825, nan, 0.9881356480134484, nan, nan, 0.9822651637144391, nan, nan, 0.0, 0.0, nan, 0.9920464379126275, 0.0, nan, nan, nan, 0.6444954128440367, nan, nan, 0.9683282712840906, nan, nan, 0.8576094056172436, 0.34305227213028633, nan, nan, nan, 0.26313395496929726, nan, nan, 0.9192956372252938, nan, nan, nan, nan, nan, nan, 0.4997410668047644, nan, nan, nan, nan, nan, 0.4066492650089298, nan, nan, nan, nan, 0.47846798780487804, 0.0, nan, 0.9292201104765472, nan, nan, nan, nan, nan, 0.9004742466875388, nan, nan, nan, nan, 0.9685634699562277, nan, nan, nan, 0.9291382765531062, nan, nan, nan, nan, nan, nan, 0.04521598708114655, nan, 0.0, nan, 0.913017906156058, nan, nan, 0.07307692307692308, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8213807611397973, nan, 0.5099350401222774, nan, 0.04628164556962025, nan, 0.0, nan, nan, 0.0010869565217391304, 0.5444303797468354, nan, 0.0, nan, nan, nan, nan, 0.949228791773779, nan, 0.1581589958158996, nan, nan, 0.4542892372732077, nan, nan] |
| 0.5198 | 6.0 | 204 | 0.4301 | 0.3926 | 0.5859 | 0.8435 | [0.652800913341307, 0.9061414123853074, 0.9728513530705342, 0.7393825453992087, 0.7696589079509953, 0.7961328207247744, 0.6179423101359651, 0.6157318300391781, 0.7014382305752922, 0.0, 0.48796805976934254, 0.7472835291971754, 0.8451060474181862, 0.9441437843235148, 0.4503299578183101, 0.6415455715637165, 0.6245275705010176, 0.8629114158517658, 0.8959681821726585, 0.42245666917859837, 0.4894299491948678, nan, 0.9624405755123886, 0.0, nan, 0.938515835423052, 0.0, 0.0, 0.0, 0.0, nan, 0.9685952690951598, 0.0, 0.0, nan, nan, 0.3314408470154979, nan, 0.0, 0.9343609544206811, nan, nan, 0.5972301541976014, 0.34766649187205034, nan, nan, nan, 0.2378894700932454, nan, nan, 0.8412081339712919, nan, nan, nan, nan, nan, nan, 0.42112482853223593, nan, nan, nan, nan, nan, 0.4272564912762742, nan, nan, 0.0, nan, 0.1579031612645058, 0.0, nan, 0.6101548522425813, nan, nan, nan, nan, nan, 0.3613891278675209, nan, 0.0, nan, nan, 0.7672459189046866, nan, nan, nan, 0.7961464177319967, 0.0, nan, nan, nan, nan, nan, 0.04844570044408559, nan, 0.0, nan, 0.8878650227125243, nan, nan, 0.07948717948717948, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.7488781498101484, nan, 0.4331011880376895, nan, 0.039844760672703754, 0.0, 0.0, nan, nan, 0.0010869565217391304, 0.512683426231373, nan, 0.0, 0.0, nan, nan, nan, 0.7393590385578368, nan, 0.12217573221757322, nan, nan, 0.37487111503903375, nan, nan] | [0.7398182434305782, 0.9603977199715845, 0.9808409742225349, 0.8457348626880719, 0.8410406981486013, 0.879942595856731, 0.9304802602206773, 0.8588326678568021, 0.8405122101938526, nan, 0.7959667279233968, 0.8532474487518967, 0.9165651398649357, 0.9788405797101449, 0.6276244904647781, 0.7228163058482291, 0.7135186005314438, 0.9251587013538407, 0.9299821920025902, 0.5567583672658655, 0.5317942060512497, nan, 0.9882649597517215, nan, nan, 0.976328502415459, nan, nan, 0.0, 0.0, nan, 0.9909720264081597, 0.0, nan, nan, nan, 0.6192660550458715, nan, nan, 0.9698781977017252, nan, nan, 0.8538128674069235, 0.3483057525610717, nan, nan, nan, 0.2378894700932454, nan, nan, 0.9148817544022674, nan, nan, nan, nan, nan, nan, 0.476954945624029, nan, nan, nan, nan, nan, 0.4272564912762742, nan, nan, nan, nan, 0.37595274390243905, 0.0, nan, 0.9259432637393502, nan, nan, nan, nan, nan, 0.8934067821239382, nan, nan, nan, nan, 0.9663085289826236, nan, nan, nan, 0.9252104208416834, nan, nan, nan, nan, nan, nan, 0.04844570044408559, nan, 0.0, nan, 0.894131486080251, nan, nan, 0.07948717948717948, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8297953719640466, nan, 0.5049675200611388, nan, 0.04061181434599156, nan, 0.0, nan, nan, 0.0010869565217391304, 0.5705063291139241, nan, 0.0, nan, nan, nan, nan, 0.9489074550128535, nan, 0.12217573221757322, nan, nan, 0.4483001585344372, nan, nan] |
| 0.4888 | 7.0 | 238 | 0.4248 | 0.4050 | 0.5868 | 0.8454 | [0.6614425659163077, 0.9077871863634693, 0.9751633986928104, 0.743726602760517, 0.7713515421895561, 0.7992041569278913, 0.5623259052924791, 0.6078155455193724, 0.7192354450898284, 0.0, 0.49377944677417746, 0.717108427470199, 0.8480314665667728, 0.944087956021989, 0.4508868255382758, 0.6424524280122467, 0.6610892236384704, 0.8647866955892987, 0.9000296992626835, 0.43592486627690014, 0.4617764697004, nan, 0.962851817923366, 0.0, nan, 0.9401304518339629, 0.0, 0.0, 0.0, 0.0, nan, 0.9707229826021252, 0.0, nan, nan, nan, 0.33435394978567057, nan, 0.0, 0.9373384720018136, nan, nan, 0.5879341758972727, 0.37418218419728233, nan, nan, nan, 0.2478962929269957, nan, nan, 0.8418204286870925, nan, nan, nan, nan, nan, nan, 0.453125, nan, nan, nan, nan, nan, 0.45789256766039294, nan, nan, 0.0, nan, 0.10851373476441696, 0.0, nan, 0.6212696004046535, nan, nan, nan, nan, nan, 0.3672454440639466, nan, 0.0, nan, nan, 0.766902973230346, nan, nan, nan, 0.7984355102783336, 0.0, nan, nan, nan, nan, nan, 0.05248284214775938, nan, 0.0, nan, 0.8962607115035056, nan, nan, 0.029487179487179487, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.7264069264069264, nan, 0.4377549355522924, nan, 0.04131812420785805, 0.0, 0.0, nan, nan, 0.009782608695652175, 0.5330587966140471, nan, 0.0, 0.0, nan, nan, nan, 0.7354774370481176, nan, 0.14184100418410042, nan, nan, 0.4027961736571008, nan, nan] | [0.7487372539354918, 0.9585962938627861, 0.9841905055080367, 0.8494011074887855, 0.8400503338110384, 0.8915311650196734, 0.9270361630206008, 0.8711655605674493, 0.8530313780239398, nan, 0.7991101653931715, 0.8206390764928149, 0.9132651826272178, 0.9778053830227743, 0.6425776849177575, 0.7241355209589209, 0.7895814880425155, 0.9254874031870381, 0.9403297177702229, 0.5800642235243486, 0.500953182929254, nan, 0.9853877735751463, nan, nan, 0.9779173376274826, nan, nan, 0.0, 0.0, nan, 0.9868250896963451, 0.0, nan, nan, nan, 0.6261467889908257, nan, nan, 0.9709584494473494, nan, nan, 0.8298905943827564, 0.39059627002889413, nan, nan, nan, 0.2478962929269957, nan, nan, 0.9178553175672536, nan, nan, nan, nan, nan, nan, 0.5256343863283273, nan, nan, nan, nan, nan, 0.45789256766039294, nan, nan, nan, nan, 0.22732469512195122, 0.0, nan, 0.9199513154199045, nan, nan, nan, nan, nan, 0.845757539729719, nan, nan, nan, nan, 0.961400716275368, nan, nan, nan, 0.9381963927855711, nan, nan, nan, nan, nan, nan, 0.05248284214775938, nan, 0.0, nan, 0.9022350019605281, nan, nan, 0.029487179487179487, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8022566456301397, nan, 0.5126098586167367, nan, 0.04298523206751055, nan, 0.0, nan, nan, 0.009782608695652175, 0.589873417721519, nan, 0.0, nan, nan, nan, nan, 0.9479434447300771, nan, 0.14184100418410042, nan, nan, 0.48212083847102344, nan, nan] |
| 0.6308 | 8.0 | 272 | 0.4299 | 0.3974 | 0.5876 | 0.8435 | [0.6501148380964772, 0.9081126934584868, 0.9768032527769602, 0.7472636784498494, 0.7654742167240574, 0.7989495814497932, 0.6067420418039968, 0.6100529352854214, 0.7157272692557376, 0.0, 0.47944365865157945, 0.736477776631948, 0.8478686515409679, 0.9449087463847612, 0.44711118392311056, 0.6399813056902721, 0.6674291988836528, 0.8686029018027897, 0.8978952951373168, 0.4469841429144922, 0.47036786569198896, nan, 0.9623946196836223, 0.0, nan, 0.9371393834487306, 0.0, nan, 0.0, 0.0, nan, 0.9710279568244725, 0.0, nan, nan, nan, 0.3369698832206515, nan, 0.0, 0.9357948317542305, nan, nan, 0.5779072994726616, 0.3749405611031859, nan, nan, nan, 0.26392361900431915, nan, nan, 0.8242608225558282, nan, nan, nan, nan, nan, nan, 0.419124218051832, nan, nan, nan, nan, nan, 0.4191509822777854, nan, nan, 0.0, nan, 0.07875423409860745, 0.0, nan, 0.6250880902043693, nan, nan, nan, nan, nan, 0.363677855223048, nan, 0.0, nan, nan, 0.7581733264141152, nan, nan, nan, 0.8004446158383051, 0.0, nan, nan, nan, nan, nan, 0.08397254743641502, nan, 0.0, nan, 0.8966054390861297, nan, nan, 0.011538461538461539, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.7219130434782609, nan, 0.44080971659919027, nan, 0.024318064848172928, 0.0, 0.0, nan, nan, 0.004347826086956522, 0.4916870131380072, 0.0, 0.0, 0.0, nan, nan, nan, 0.6988036593947924, nan, 0.16276150627615063, nan, nan, 0.4055775764439411, nan, nan] | [0.7265432364991836, 0.9551486115926497, 0.9851689790892499, 0.8581152383059121, 0.8391881342701015, 0.8942117488870406, 0.9275463996428344, 0.8716288957632085, 0.8579291877417435, nan, 0.8196311700035465, 0.8499152063312606, 0.9153952283747913, 0.980807453416149, 0.6708453675822937, 0.7297705788820744, 0.8075730735163862, 0.9330749371700142, 0.9364038637958016, 0.5897639619955639, 0.512484942048817, nan, 0.985355445640578, nan, nan, 0.976435856146001, nan, nan, 0.0, 0.0, nan, 0.9857944325065389, 0.0, nan, nan, nan, 0.6287270642201835, nan, nan, 0.975717819457056, nan, nan, 0.8500163291966035, 0.41423693196742845, nan, nan, nan, 0.26404366613600183, nan, nan, 0.9209218045811457, nan, nan, nan, nan, nan, nan, 0.4857586742620404, nan, nan, nan, nan, nan, 0.4191509822777854, nan, nan, nan, nan, 0.15948932926829268, 0.0, nan, 0.913491246138002, nan, nan, nan, nan, nan, 0.852626851081256, nan, nan, nan, nan, 0.9689614007162753, nan, nan, nan, 0.9428456913827655, nan, nan, nan, nan, nan, nan, 0.08397254743641502, nan, 0.0, nan, 0.9027578094366749, nan, nan, 0.011538461538461539, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7938420348058902, nan, 0.5200611387084448, nan, 0.024920886075949368, nan, 0.0, nan, nan, 0.004347826086956522, 0.5353164556962026, nan, 0.0, nan, nan, nan, nan, 0.9572622107969152, nan, 0.16276150627615063, nan, nan, 0.5046679584287476, nan, nan] |
| 0.6601 | 9.0 | 306 | 0.4313 | 0.3928 | 0.5817 | 0.8431 | [0.6529024575649324, 0.9078050537515239, 0.9773431884352606, 0.7433321928665372, 0.7534248011152221, 0.7986265125870589, 0.540162980209546, 0.63103010250805, 0.6975687773855603, 0.0, 0.48656149576394975, 0.6899461100178775, 0.8491172224895436, 0.9434890409600317, 0.45727083361696913, 0.6366431724885756, 0.6939406153432756, 0.8695182063921132, 0.895149767490193, 0.4235203114083242, 0.4918293580364486, nan, 0.9619210002841716, 0.0, nan, 0.9264547525367571, 0.0, nan, 0.0, 0.0, nan, 0.9702219532072882, 0.0, 0.0, nan, nan, 0.33749037721324093, nan, 0.0, 0.9368730673504789, nan, nan, 0.5921162663283255, 0.3663272555055648, nan, nan, 0.0, 0.2219695246759154, nan, nan, 0.8240346926861813, nan, nan, nan, nan, nan, nan, 0.42211280214861235, nan, nan, nan, nan, nan, 0.3852177496908916, nan, nan, nan, nan, 0.03243183817062445, 0.0, nan, 0.6189717780467605, nan, nan, nan, nan, nan, 0.3629662744926999, nan, 0.0, nan, nan, 0.7690677966101694, nan, nan, nan, 0.8011790046479991, 0.0, nan, nan, nan, nan, nan, 0.0808957030461973, nan, 0.0, nan, 0.8801558947710295, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.7414574616457462, nan, 0.44018752020691887, nan, 0.011175337186897881, 0.0, 0.0, nan, nan, 0.004347826086956522, 0.4286557299657736, 0.0, 0.0, 0.0, nan, nan, nan, 0.7291002696739397, nan, 0.12719665271966527, nan, nan, 0.3842409892133649, nan, nan] | [0.7323964141585287, 0.953262326918834, 0.9865982101629324, 0.8587679821873447, 0.8375219919139665, 0.884571128129323, 0.9469991708654889, 0.8732914514656382, 0.843498958643261, nan, 0.8054454009091788, 0.7980125554134063, 0.9254645355451384, 0.9848033126293996, 0.6879903316331756, 0.7181160559836453, 0.8577834366696191, 0.936183908675674, 0.9297123738600184, 0.5618896282318668, 0.531525209641767, nan, 0.9848705266220541, nan, nan, 0.9605689747718733, nan, nan, 0.0, 0.0, nan, 0.9896788433303839, 0.0, nan, nan, nan, 0.6284403669724771, nan, nan, 0.9723831292857814, nan, nan, 0.838259307642064, 0.40635671132125034, nan, nan, nan, 0.2219695246759154, nan, nan, 0.918180551038424, nan, nan, nan, nan, nan, nan, 0.48834800621439667, nan, nan, nan, nan, nan, 0.3852177496908916, nan, nan, nan, nan, 0.05621189024390244, 0.0, nan, 0.9096526542458572, nan, nan, nan, nan, nan, 0.8794832164229383, nan, nan, nan, nan, 0.9629924393155591, nan, nan, nan, 0.9441816967267869, nan, nan, nan, nan, nan, nan, 0.08094469115865967, nan, 0.0, nan, 0.885505162723827, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8133486326257411, nan, 0.5202521971723347, nan, 0.011471518987341773, nan, 0.0, nan, nan, 0.004347826086956522, 0.45974683544303796, nan, 0.0, nan, nan, nan, nan, 0.955655526992288, nan, 0.12719665271966527, nan, nan, 0.5145323234102519, nan, nan] |
| 0.4561 | 10.0 | 340 | 0.4405 | 0.3932 | 0.5853 | 0.8391 | [0.6274744809543508, 0.9074326545720401, 0.9764665298934259, 0.7465249697926875, 0.7702707073320612, 0.7934881708466615, 0.5894461228600202, 0.6165330950452841, 0.7164166573379603, 0.0, 0.4614931824571268, 0.7282772020725389, 0.8475166599167925, 0.9443370083135628, 0.45465426629612304, 0.6392530790623758, 0.7060328100194038, 0.8652578309269962, 0.8955637947574477, 0.4510533129339767, 0.5113435993887386, nan, 0.9618000881778673, 0.0, nan, 0.9421786586685631, 0.0, nan, 0.0, 0.0, nan, 0.9678123916831665, 0.0, nan, nan, nan, 0.3384639303482587, nan, 0.0, 0.9357090319578498, nan, nan, 0.5660256751717893, 0.34209221616261776, nan, nan, 0.0, 0.2208323857175347, nan, nan, 0.8197966383067027, nan, nan, nan, nan, nan, nan, 0.42805755395683454, nan, nan, nan, nan, nan, 0.47135595548839127, nan, nan, 0.0, nan, 0.01487345567950102, 0.0, nan, 0.6165799739921977, nan, nan, nan, nan, nan, 0.3435445034655301, nan, 0.0, nan, nan, 0.7607790043740887, nan, nan, nan, 0.8003535971712227, 0.0, nan, nan, nan, nan, nan, 0.04440855874041179, nan, 0.0, nan, 0.8851276720161133, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7332063711911357, nan, 0.43534129972172203, nan, 0.006589147286821705, 0.0, 0.0, nan, nan, 0.002173913043478261, 0.4702325581395349, nan, 0.0, 0.0, nan, nan, nan, 0.6813211845102506, nan, 0.08786610878661087, nan, nan, 0.3983501118568233, nan, nan] | [0.6915769692862204, 0.9569524622318772, 0.985487807559533, 0.8536258109436138, 0.8324653081198224, 0.8851787846008645, 0.933286561642962, 0.9026314713617967, 0.878824983406953, nan, 0.8467775735886772, 0.8363779714974264, 0.9286434674562897, 0.9806832298136646, 0.6769290645035744, 0.730967482658, 0.8862931798051373, 0.9319998082572639, 0.9319788462576224, 0.6343893799450459, 0.5596294866846777, nan, 0.9873274496492419, nan, nan, 0.9813526570048309, nan, nan, 0.0, 0.0, nan, 0.990937995274534, 0.0, nan, nan, nan, 0.6241399082568807, nan, nan, 0.9759213451482607, nan, nan, 0.8675702155453952, 0.3624901497241923, nan, nan, nan, 0.2208323857175347, nan, nan, 0.9177623937183478, nan, nan, nan, nan, nan, nan, 0.49300880372863803, nan, nan, nan, nan, nan, 0.47135595548839127, nan, nan, nan, nan, 0.023628048780487805, 0.0, nan, 0.8878382173953749, nan, nan, nan, nan, nan, 0.8688886246846061, nan, nan, nan, nan, 0.9689614007162753, nan, nan, nan, 0.9434869739478958, nan, nan, nan, nan, nan, nan, 0.04440855874041179, nan, 0.0, nan, 0.8902757809436675, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8099062918340026, nan, 0.5081199847153229, nan, 0.006724683544303798, nan, 0.0, nan, nan, 0.002173913043478261, 0.5118987341772152, nan, 0.0, nan, nan, nan, nan, 0.9611182519280206, nan, 0.08786610878661087, nan, nan, 0.5018495684340321, nan, nan] |
| 0.4247 | 11.0 | 374 | 0.4333 | 0.3827 | 0.5830 | 0.8424 | [0.6433552114577914, 0.9077643526789826, 0.977406708290983, 0.7426200834759328, 0.76968345678483, 0.7988675672019634, 0.550401932294222, 0.6209928422996999, 0.712813708607402, 0.0, 0.47186755113730855, 0.702382326906054, 0.8479503511350646, 0.9438901747944768, 0.4546607753705815, 0.6413260602335586, 0.7090520064625999, 0.8673791673313128, 0.8967255710536861, 0.4267976463548419, 0.5123538448408296, nan, 0.9628773973036268, 0.0, nan, 0.9396454597136803, 0.0, 0.0, 0.0, 0.0, nan, 0.9709572690379565, 0.0, 0.0, nan, nan, 0.32683394598050186, nan, 0.0, 0.9396006580238156, nan, nan, 0.5845506799383149, 0.3173954829408938, nan, nan, 0.0, 0.23106663634296112, nan, nan, 0.8394819800630485, nan, nan, nan, nan, nan, nan, 0.44702276707530647, nan, nan, nan, nan, nan, 0.4771259788432477, nan, nan, nan, nan, 0.0019687461547926665, 0.0, nan, 0.6198083067092651, nan, nan, nan, nan, nan, 0.3536083031515217, nan, 0.0, nan, nan, 0.7669299631384939, nan, nan, nan, 0.7998195149464185, 0.0, nan, nan, nan, nan, nan, 0.05106984255147356, nan, 0.0, nan, 0.8644695787831513, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7234303753069099, nan, 0.43899052597190463, nan, 0.010881943565734532, 0.0, 0.0, nan, nan, 0.010869565217391304, 0.45952269536733736, 0.0, 0.0, 0.0, nan, nan, nan, 0.6908627087198516, nan, 0.1305439330543933, nan, nan, 0.3962473940236275, nan, nan] | [0.7162625920335172, 0.9559487066342426, 0.9867411332703006, 0.8529766934170779, 0.8366015356472905, 0.8907769814557036, 0.9301613623317814, 0.9162862321309331, 0.8668436591673724, nan, 0.8233226940065126, 0.814465502365297, 0.9215997934501093, 0.9793788819875776, 0.6534136299391631, 0.72928133354301, 0.8989149689991143, 0.9309109834347972, 0.9358372456964006, 0.5666898401032873, 0.5616878939920237, nan, 0.9835774092393237, nan, nan, 0.9759098228663446, nan, nan, 0.0, 0.0, nan, 0.9886870788647214, 0.0, nan, nan, nan, 0.586295871559633, nan, nan, 0.974684535178633, nan, nan, 0.8510777269758328, 0.3469923824533754, nan, nan, nan, 0.23106663634296112, nan, nan, 0.915578683269061, nan, nan, nan, nan, nan, nan, 0.5287415846711548, nan, nan, nan, nan, nan, 0.4771259788432477, nan, nan, nan, nan, 0.003048780487804878, 0.0, nan, 0.9081546671659957, nan, nan, nan, nan, nan, 0.8641329475950805, nan, nan, nan, nan, 0.965910598222576, nan, nan, nan, 0.9472812291249165, nan, nan, nan, nan, nan, nan, 0.05106984255147356, nan, 0.0, nan, 0.8691020781597177, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7888697647733792, nan, 0.5134696217042415, nan, 0.011339662447257384, nan, 0.0, nan, nan, 0.010869565217391304, 0.49721518987341773, nan, 0.0, nan, nan, nan, nan, 0.9572622107969152, nan, 0.1305439330543933, nan, nan, 0.5022018671833715, nan, nan] |
| 0.5702 | 12.0 | 408 | 0.4296 | 0.3952 | 0.5830 | 0.8440 | [0.6541033081452264, 0.9069054121008907, 0.9757985637669303, 0.7431414610512761, 0.755821369943224, 0.7966622886161593, 0.5421959410132866, 0.6312140900505335, 0.7117893623850613, 0.0, 0.5175081648399269, 0.6942855663966458, 0.8490399519419743, 0.9442230281774341, 0.4685685828116107, 0.6458767524667195, 0.6824868415133384, 0.8668665609900499, 0.8957087765613272, 0.4657354658784831, 0.4929765024270802, nan, 0.9628129252777162, 0.0, nan, 0.9298506537770341, 0.0, nan, 0.0, 0.0, nan, 0.9707514301690395, 0.0, 0.0, nan, nan, 0.3324488840330888, nan, 0.0, 0.9364879074658254, nan, nan, 0.5852104927905495, 0.36635333815377197, nan, nan, nan, 0.19308619513304526, nan, nan, 0.8526507276507277, nan, nan, nan, nan, nan, nan, 0.4246389891696751, nan, nan, nan, nan, nan, 0.43893391949443605, nan, nan, 0.0, nan, 0.030082231706047673, 0.0, nan, 0.620338305807139, nan, nan, nan, nan, nan, 0.35340863938241474, nan, 0.0, nan, nan, 0.7710434136503556, nan, nan, nan, 0.798716071629688, 0.0, nan, nan, nan, nan, nan, 0.053895841744045214, nan, 0.0, nan, 0.8870056497175142, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.7240225431489962, nan, 0.4360725026416321, nan, 0.021315919172005916, 0.0, 0.0, nan, nan, 0.010869565217391304, 0.46255049047893826, 0.0, 0.0, 0.0, nan, nan, nan, 0.6968706212050444, nan, 0.1707112970711297, nan, nan, 0.39098998887652947, nan, nan] | [0.7352515326083608, 0.9541448559950152, 0.9834905487514384, 0.8454773914903956, 0.8422291353536766, 0.8809294989204401, 0.947381848332164, 0.8698300650032025, 0.8628041104982491, nan, 0.789292968372183, 0.7981464401535212, 0.9236814290670561, 0.9810144927536232, 0.674552940453512, 0.7337282242141496, 0.8398472099202834, 0.9324860130522019, 0.9337731369057255, 0.6468368259012811, 0.5368700513432277, nan, 0.9834804254356189, nan, nan, 0.9665056360708535, nan, nan, 0.0, 0.0, nan, 0.9874911275973047, 0.0, nan, nan, nan, 0.6106651376146789, nan, nan, 0.9760152800826627, nan, nan, 0.8251143043762247, 0.39926451273969005, nan, nan, nan, 0.19308619513304526, nan, nan, 0.9146494447800028, nan, nan, nan, nan, nan, nan, 0.4873122734334542, nan, nan, nan, nan, nan, 0.43893391949443605, nan, nan, nan, nan, 0.05506859756097561, 0.0, nan, 0.8721093530568299, nan, nan, nan, nan, nan, 0.9047147254256992, nan, nan, nan, nan, 0.9635230136622894, nan, nan, nan, 0.9474682698730795, nan, nan, nan, nan, nan, nan, 0.053895841744045214, nan, 0.0, nan, 0.8926284145863286, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7861923886020271, nan, 0.5125143293847918, nan, 0.022811181434599155, nan, 0.0, nan, nan, 0.010869565217391304, 0.5073417721518987, nan, 0.0, nan, nan, nan, nan, 0.9588688946015425, nan, 0.1707112970711297, nan, nan, 0.49533204157125243, nan, nan] |
| 0.4984 | 13.0 | 442 | 0.4306 | 0.3887 | 0.5837 | 0.8422 | [0.6420434067914311, 0.9077086271920035, 0.9768249447867023, 0.7479206025180997, 0.7593343042276114, 0.8011442414045583, 0.5656530620110225, 0.6328884205596534, 0.7054054563622995, 0.0, 0.47444636938837, 0.7171723768596768, 0.8519204588568751, 0.9434318163767771, 0.4555360089459832, 0.6376792628572748, 0.6977503628447025, 0.8650242223355431, 0.8969534398527906, 0.4438105542713815, 0.5083884087442806, nan, 0.9627862896856737, 0.0, nan, 0.9315385728759589, 0.0, nan, 0.0, 0.0, nan, 0.9709098904460547, 0.0, 0.0, nan, nan, 0.34184706432019935, nan, 0.0, 0.936572705861174, nan, nan, 0.5826851538721677, 0.3251243781094527, nan, nan, 0.0, 0.21924039117580169, nan, nan, 0.8314924114671164, nan, nan, nan, nan, nan, nan, 0.43234905244601146, nan, 0.0, nan, nan, nan, 0.4276686358016211, nan, nan, nan, nan, 0.0743057027771889, 0.0, nan, 0.6105656848994857, nan, nan, nan, nan, nan, 0.3536831536673008, nan, 0.0, nan, nan, 0.7776342697233882, nan, nan, nan, 0.8005940234888678, 0.0, nan, nan, nan, nan, nan, 0.06257569640694388, nan, 0.0, nan, 0.8717498699947998, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7235530687183074, nan, 0.43292930936305213, nan, 0.006230133502860775, 0.0, 0.0, nan, nan, 0.006521739130434782, 0.4429159318048207, nan, 0.0, 0.0, nan, nan, nan, 0.6732539860768022, nan, 0.1694560669456067, nan, nan, 0.39932203389830506, nan, nan] | [0.720022180462709, 0.9564336127200564, 0.9854951369496544, 0.8556420642662614, 0.8383492374194601, 0.886510457293817, 0.9360928630652465, 0.8839617884738556, 0.8562927700089259, nan, 0.8122964825740723, 0.8167861711939544, 0.9239557531406072, 0.9837474120082815, 0.6842417911059219, 0.7237249043350632, 0.8516386182462357, 0.929301714043101, 0.9338001187199827, 0.5974774058992949, 0.549688315029882, nan, 0.9852584618368733, nan, nan, 0.9673000536768652, nan, nan, 0.0, 0.0, nan, 0.9875154355498945, 0.0, nan, nan, nan, 0.6293004587155964, nan, nan, 0.9774086482762939, nan, nan, 0.8440561724363161, 0.34331494615182556, nan, nan, nan, 0.21924039117580169, nan, nan, 0.9163685359847605, nan, nan, nan, nan, nan, nan, 0.5080269290523045, nan, nan, nan, nan, nan, 0.4276686358016211, nan, nan, nan, nan, 0.13357469512195122, 0.0, nan, 0.8559123677558281, nan, nan, nan, nan, nan, 0.884172842441776, nan, nan, nan, nan, 0.9583499137816687, nan, nan, nan, 0.9435136940547763, nan, nan, nan, nan, nan, nan, 0.06257569640694388, nan, 0.0, nan, 0.8764213828257744, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7913558997896347, nan, 0.5084065724111578, nan, 0.006460970464135021, nan, 0.0, nan, nan, 0.006521739130434782, 0.4768354430379747, nan, 0.0, nan, nan, nan, nan, 0.9633676092544987, nan, 0.1694560669456067, nan, nan, 0.5187599084023252, nan, nan] |
| 0.4527 | 14.0 | 476 | 0.4321 | 0.3866 | 0.5797 | 0.8430 | [0.6457992588030929, 0.9068993474800695, 0.9763020606271979, 0.7460977624763228, 0.7672935823908238, 0.7985010880446504, 0.514854933649124, 0.6503448897253897, 0.6999042756860242, 0.0, 0.49883484853030896, 0.6782421794074995, 0.8472896604062746, 0.9432876821402978, 0.461384479968199, 0.6418898769698962, 0.711262679258482, 0.8621045655000128, 0.8959168580436729, 0.4412998703181021, 0.5219282231910827, nan, 0.9635822408005573, 0.0, nan, 0.938978919426972, 0.0, nan, 0.0, 0.0, nan, 0.9710328237388611, 0.0, 0.0, nan, nan, 0.34385745545482965, nan, 0.0, 0.9375018797557821, nan, nan, 0.5858312927467703, 0.3085399449035813, nan, nan, 0.0, 0.20513986809188084, nan, nan, 0.8389971432226154, nan, nan, nan, nan, nan, nan, 0.44454110482818615, nan, 0.0, nan, nan, nan, 0.4298667399368045, nan, nan, nan, nan, 0.00298916427948686, 0.0, nan, 0.6218413978494624, nan, nan, nan, nan, nan, 0.3513753373468964, nan, 0.0, nan, nan, 0.7708774904620602, nan, nan, nan, 0.8018212371695885, 0.0, nan, nan, nan, nan, nan, 0.046427129592248685, nan, 0.0, nan, 0.8803457688808007, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7169945064681906, nan, 0.4342889814587928, nan, 0.015012106537530266, 0.0, 0.0, nan, nan, 0.010857763300760043, 0.441534144059869, nan, 0.0, 0.0, nan, nan, nan, 0.7024090694378838, nan, 0.08075313807531381, nan, nan, 0.3949986184028737, nan, nan] | [0.7210621977141801, 0.9573355380396702, 0.984824497753542, 0.8613136833249323, 0.8370792407983502, 0.8903201616969562, 0.942789718732062, 0.8852564015208296, 0.8534433433273064, nan, 0.8040268239997421, 0.7792389396328583, 0.9278204952356363, 0.9810973084886129, 0.665703926749831, 0.7334049728294106, 0.9005757307351638, 0.9263776373186148, 0.9315606281366359, 0.5745357036448505, 0.5684361952212204, nan, 0.9836743930430285, nan, nan, 0.9759634997316157, nan, nan, 0.0, 0.0, nan, 0.9890614213346038, 0.0, nan, nan, nan, 0.6307339449541285, nan, nan, 0.9760152800826627, nan, nan, 0.849689745264533, 0.32361439453638036, nan, nan, nan, 0.20513986809188084, nan, nan, 0.9142312874599265, nan, nan, nan, nan, nan, nan, 0.5292594510616261, nan, nan, nan, nan, nan, 0.4298667399368045, nan, nan, nan, nan, 0.004573170731707317, 0.0, nan, 0.8663046531223668, nan, nan, nan, nan, nan, 0.8943711277559809, nan, nan, nan, nan, 0.9648494495291152, nan, nan, nan, 0.9434602538410154, nan, nan, nan, nan, nan, nan, 0.046427129592248685, nan, 0.0, nan, 0.8851784080512352, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7737617135207496, nan, 0.5079289262514329, nan, 0.016350210970464137, nan, 0.0, nan, nan, 0.010869565217391304, 0.4779746835443038, nan, 0.0, nan, nan, nan, nan, 0.955655526992288, nan, 0.08075313807531381, nan, nan, 0.5036110621807293, nan, nan] |
| 0.4896 | 15.0 | 510 | 0.4364 | 0.3819 | 0.5813 | 0.8417 | [0.6450063091621345, 0.9057100236191108, 0.9760483827028205, 0.7399797305617034, 0.7659918248550088, 0.7971458330094673, 0.49983710171368995, 0.6477837837837838, 0.7105882133323377, 0.0, 0.5073625908052866, 0.6540073438777232, 0.8513304363354962, 0.9418600031781345, 0.4640083217753121, 0.6371695754826165, 0.7127035255703128, 0.8595045522990222, 0.895166002311178, 0.4459548400162009, 0.527059730580601, nan, 0.9637343477571723, 0.0, nan, 0.9393686423646539, 0.0, nan, 0.0, 0.0, nan, 0.9710977967679565, 0.0, 0.0, nan, nan, 0.3372410470531556, nan, 0.0, 0.9384443538770396, nan, nan, 0.5918794347746983, 0.32535527299925204, nan, nan, 0.0, 0.18603593359108483, nan, nan, 0.8323468916593209, nan, nan, nan, nan, nan, nan, 0.4490145672664953, nan, 0.0, nan, nan, nan, 0.42203599395521363, nan, nan, nan, nan, 0.003805774278215223, 0.0, nan, 0.6140219899552056, nan, nan, nan, nan, nan, 0.3449472759226713, nan, 0.0, nan, nan, 0.763758813006419, nan, nan, nan, 0.7969874402181201, 0.0, nan, nan, nan, nan, nan, 0.06721840936616875, nan, 0.0, nan, 0.8872141372141372, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7308228730822873, nan, 0.433751629726206, nan, 0.021440416962805024, 0.0, 0.0, nan, nan, 0.014084507042253521, 0.46385193753614806, 0.0, 0.0, 0.0, nan, nan, nan, 0.6693458361241349, nan, 0.14476987447698744, nan, nan, 0.4010014306151645, nan, nan] | [0.7231064970272019, 0.953895129360821, 0.984743874462206, 0.8499305553037253, 0.8340615424050707, 0.8839548524170506, 0.9785062822884113, 0.8573881522464943, 0.8712036252946697, nan, 0.7914853145049489, 0.7498289250542977, 0.9231166442097449, 0.9817184265010352, 0.6852864663348287, 0.7214708811658017, 0.8941541186891054, 0.9263913332283313, 0.9301036101667476, 0.5832091899228655, 0.5756523162929955, nan, 0.9828015388096855, nan, nan, 0.9784863123993559, nan, nan, 0.0, 0.0, nan, 0.9889009888475113, 0.0, nan, nan, nan, 0.6020642201834863, nan, nan, 0.9733381344522027, nan, nan, 0.796823971260614, 0.34278959810874704, nan, nan, nan, 0.18603593359108483, nan, nan, 0.921293499976769, nan, nan, nan, nan, nan, nan, 0.5427239772138788, nan, nan, nan, nan, nan, 0.42203599395521363, nan, nan, nan, nan, 0.005525914634146341, 0.0, nan, 0.8470180694691508, nan, nan, nan, nan, nan, 0.9334205207466413, nan, nan, nan, nan, 0.962727152142194, nan, nan, nan, 0.9528924515698063, nan, nan, nan, nan, nan, nan, 0.06721840936616875, nan, 0.0, nan, 0.8924323617827735, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8016829221648498, nan, 0.5085021016431028, nan, 0.02386603375527426, nan, 0.0, nan, nan, 0.014130434782608696, 0.5075949367088608, nan, 0.0, nan, nan, nan, nan, 0.9633676092544987, nan, 0.14476987447698744, nan, nan, 0.4937466971992249, nan, nan] |
| 0.4634 | 16.0 | 544 | 0.4347 | 0.3850 | 0.5801 | 0.8423 | [0.643432619488831, 0.9063441269375536, 0.9750563185814984, 0.7418543918397434, 0.7742568523999486, 0.8007648645698514, 0.532342060841145, 0.6313468950544668, 0.7073225984758557, 0.0, 0.5107069672131147, 0.6910409033418956, 0.8492950399318183, 0.9428770532603286, 0.45424930285892995, 0.6433945392596704, 0.7164652438501757, 0.8641709108591611, 0.8933169278590856, 0.45366304022357523, 0.5086918519957269, nan, 0.9625133993316098, 0.0, nan, 0.9410763469810912, 0.0, nan, 0.0, 0.0, nan, 0.9706223923609618, 0.0, 0.0, nan, nan, 0.32972456615188667, nan, 0.0, 0.9380016232302282, nan, nan, 0.589496151996152, 0.3035395976572447, nan, nan, 0.0, 0.19445076188310212, nan, nan, 0.8336432012169357, nan, nan, nan, nan, nan, nan, 0.446733888645331, nan, 0.0, nan, nan, nan, 0.43549938178321196, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6142025453560791, nan, nan, nan, nan, nan, 0.34666500798241795, nan, 0.0, nan, nan, 0.7494379726139383, nan, nan, nan, 0.8017235529619325, 0.0, nan, nan, nan, nan, nan, 0.06782398062171982, nan, 0.0, nan, 0.8795658390744833, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7061143984220908, nan, 0.4324830902126966, nan, 0.004800404244567963, 0.0, 0.0, nan, nan, 0.018240343347639486, 0.3965739762994251, nan, 0.0, 0.0, nan, nan, nan, 0.6569805549486564, nan, 0.07782426778242678, nan, nan, 0.3961859055316999, nan, nan] | [0.7149588737253936, 0.9586326618192221, 0.9834392430205883, 0.8539884464332986, 0.8412387710161138, 0.8852348096656165, 0.9453409018432298, 0.8995925375778471, 0.8932552125054356, nan, 0.803510977850856, 0.7884026062896076, 0.9326615082983032, 0.9804554865424431, 0.7140662446998095, 0.7291939683038913, 0.9254871567759079, 0.9249053270240842, 0.9305083373806055, 0.6018803588572186, 0.5513490754710361, nan, 0.9869395144344227, nan, nan, 0.9793558776167471, nan, nan, 0.0, 0.0, nan, 0.9873598646533199, 0.0, nan, nan, nan, 0.59375, nan, nan, 0.9770642201834863, nan, nan, 0.8004980404964076, 0.3131074336748096, nan, nan, nan, 0.19445076188310212, nan, nan, 0.9166473075314779, nan, nan, nan, nan, nan, nan, 0.5277058518902124, nan, nan, nan, nan, nan, 0.43549938178321196, nan, nan, nan, nan, 0.0, 0.0, nan, 0.8494522984739257, nan, nan, nan, nan, nan, 0.8834991215207598, nan, nan, nan, nan, 0.9728080647300703, nan, nan, nan, 0.9470941883767535, nan, nan, nan, nan, nan, nan, 0.06782398062171982, nan, 0.0, nan, 0.8843941968370148, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7531076687703194, nan, 0.5069736339319831, nan, 0.0050105485232067514, nan, 0.0, nan, nan, 0.01847826086956522, 0.4278481012658228, nan, 0.0, nan, nan, nan, nan, 0.9662596401028277, nan, 0.07782426778242678, nan, nan, 0.5525805883389114, nan, nan] |
| 0.5308 | 17.0 | 578 | 0.4315 | 0.3921 | 0.5797 | 0.8429 | [0.6511693161923856, 0.9042920935845441, 0.975291030898948, 0.7444846000323642, 0.7548122336740906, 0.8022636097598791, 0.5447636968951292, 0.6237536846579621, 0.7110831067188432, 0.0, 0.5113812430459513, 0.6933608718808049, 0.8540753530792049, 0.9423237007967654, 0.4672006262143386, 0.6396848797567493, 0.7218762422154499, 0.8649577953533729, 0.8970731958232602, 0.463625714217409, 0.4606488533008399, nan, 0.9636656354567839, 0.0, nan, 0.9306212193158123, 0.0, nan, 0.0, 0.0, nan, 0.9710997062530249, 0.0, 0.0, nan, nan, 0.32977196619358956, nan, 0.0, 0.9363594242754869, nan, nan, 0.5788855128131629, 0.32755589047977896, nan, nan, nan, 0.18194223334091425, nan, nan, 0.8257062265819054, nan, nan, nan, nan, nan, nan, 0.44052863436123346, nan, 0.0, nan, nan, nan, 0.4309657920043962, nan, nan, nan, nan, 0.015575130856632197, 0.0, nan, 0.582068676131198, nan, nan, nan, nan, nan, 0.3509840901397553, nan, 0.0, nan, nan, 0.7886501377410469, nan, nan, nan, 0.7982893799002139, 0.0, nan, nan, nan, nan, nan, 0.04925312878482035, nan, 0.0, nan, 0.8779647800376893, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7237962802016339, nan, 0.4328916348264369, nan, 0.008174386920980926, 0.0, 0.0, nan, nan, 0.01193058568329718, 0.4483565329278278, nan, 0.0, 0.0, nan, nan, nan, 0.6810872027180068, nan, 0.12510460251046024, nan, nan, 0.4045599888780759, nan, nan] | [0.7292061242722035, 0.9520136937478634, 0.9821712585295778, 0.8508661548671123, 0.8379297889941394, 0.8920052232598831, 0.9601377638880031, 0.919883893650945, 0.8587988922710732, nan, 0.7927588096850114, 0.794040641456666, 0.9338798299190744, 0.9843478260869565, 0.6846514676662775, 0.727839807097552, 0.9047829937998229, 0.9353963938669716, 0.94112568129081, 0.6420035091204026, 0.49519899886553687, nan, 0.9868748585652862, nan, nan, 0.9678260869565217, nan, nan, 0.0, 0.0, nan, 0.9852110416443843, 0.0, nan, nan, nan, 0.5928899082568807, nan, nan, 0.9808216175595704, nan, nan, 0.8244203135205748, 0.34252692408720775, nan, nan, nan, 0.18194223334091425, nan, nan, 0.9112577242949403, nan, nan, nan, nan, nan, nan, 0.5178663904712584, nan, nan, nan, nan, nan, 0.4309657920043962, nan, nan, nan, nan, 0.023246951219512195, 0.0, nan, 0.7792341541054209, nan, nan, nan, nan, nan, 0.862917607894424, nan, nan, nan, nan, 0.949330149887253, nan, nan, nan, 0.9576486305945224, nan, nan, nan, nan, nan, nan, 0.04925312878482035, nan, 0.0, nan, 0.8829564762776108, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7963281698221457, nan, 0.5086931601069927, nan, 0.00870253164556962, nan, 0.0, nan, nan, 0.011956521739130435, 0.4851898734177215, nan, 0.0, nan, nan, nan, nan, 0.9662596401028277, nan, 0.12510460251046024, nan, nan, 0.512594680288885, nan, nan] |
| 0.4481 | 18.0 | 612 | 0.4355 | 0.3799 | 0.5763 | 0.8415 | [0.6453072646510334, 0.9058641444539983, 0.9760780313562921, 0.739024937364342, 0.7690790962278178, 0.8018413674735755, 0.5190051941797668, 0.618741335244688, 0.709293876742189, 0.0, 0.5113924579110521, 0.6794861711888845, 0.857595382755065, 0.9425125468647716, 0.46219230975492037, 0.6393902682145103, 0.7121100039162787, 0.8547815895743894, 0.8958822994714903, 0.44971805699547374, 0.48512764801738184, nan, 0.9636093458534889, 0.0, nan, 0.941006022108563, 0.0, nan, 0.0, 0.0, nan, 0.9722032179200735, 0.0, 0.0, nan, nan, 0.3394745273950657, nan, 0.0, 0.9390637246036052, nan, nan, 0.5700390901535904, 0.3029110936270653, nan, nan, 0.0, 0.14418921992267456, nan, nan, 0.8494939884093071, nan, nan, nan, nan, nan, nan, 0.45446623093681915, nan, 0.0, nan, nan, nan, 0.39991757109493065, nan, nan, 0.0, nan, 0.017873215893017873, 0.0, nan, 0.6060792349726776, nan, nan, nan, nan, nan, 0.34469985725760655, nan, 0.0, nan, nan, 0.77066269545793, nan, nan, nan, 0.8002494048293844, 0.0, nan, nan, nan, nan, nan, 0.060557125555106985, nan, 0.0, nan, 0.8745612894839464, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7170706006322445, nan, 0.4285361435863659, nan, 0.013091001525165227, 0.0, 0.0, nan, nan, 0.013015184381778741, 0.440303738317757, nan, 0.0, 0.0, nan, nan, nan, 0.7070683111954459, nan, 0.10585774058577406, nan, nan, 0.40338840660879305, nan, nan] | [0.7257324173623733, 0.9578592366123491, 0.9853961901830148, 0.8482443002766908, 0.8316613653046244, 0.8899150573825951, 0.9623062695324957, 0.894059770240253, 0.860160666468313, nan, 0.7898249347132218, 0.7711463508970278, 0.9327260551391388, 0.9837060041407868, 0.7061594870849465, 0.7285212559626776, 0.9059455270150576, 0.9189886940265289, 0.9307511737089202, 0.5887708147118218, 0.5222740722547747, nan, 0.9852907897714415, nan, nan, 0.9796564680622651, nan, nan, 0.0, 0.0, nan, 0.9879092243818488, 0.0, nan, nan, nan, 0.6075114678899083, nan, nan, 0.9754360146538498, nan, nan, 0.756041802743305, 0.3033884948778566, nan, nan, nan, 0.14418921992267456, nan, nan, 0.9126051201040747, nan, nan, nan, nan, nan, nan, 0.5401346452615226, nan, nan, nan, nan, nan, 0.39991757109493065, nan, nan, nan, nan, 0.026486280487804877, 0.0, nan, 0.8307274599756577, nan, nan, nan, nan, nan, 0.89002496730472, nan, nan, nan, nan, 0.9610027855153204, nan, nan, nan, 0.9430861723446894, nan, nan, nan, nan, nan, nan, 0.060557125555106985, nan, 0.0, nan, 0.8793621748791007, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.780837636259323, nan, 0.4972296522735957, nan, 0.013581223628691984, nan, 0.0, nan, nan, 0.013043478260869565, 0.4770886075949367, nan, 0.0, nan, nan, nan, nan, 0.9579048843187661, nan, 0.10585774058577406, nan, nan, 0.5074863484234631, nan, nan] |
| 0.3462 | 19.0 | 646 | 0.4331 | 0.3865 | 0.5784 | 0.8430 | [0.6523621748775211, 0.9038421789954757, 0.973742160684582, 0.7408887221032648, 0.7661204398227072, 0.801833162009781, 0.530147575544624, 0.6237041345729859, 0.7076864401261383, 0.0, 0.5288795875434638, 0.6850310494181495, 0.8541437605614056, 0.9418885718814393, 0.462136964249782, 0.6384518540831537, 0.7239139784946237, 0.8625152485513876, 0.8944798466480157, 0.45874322912871335, 0.4777795847563289, nan, 0.9633530970102182, 0.0, nan, 0.9341684434968017, 0.0, nan, 0.0, 0.0, nan, 0.9712866877690676, 0.0, 0.0, nan, nan, 0.33153809599239664, nan, 0.0, 0.9369283051634055, nan, nan, 0.5675616266289841, 0.3348894348894349, nan, nan, 0.0, 0.1885376392995224, nan, nan, 0.8569121306036929, nan, nan, nan, nan, nan, nan, 0.4461674789543642, nan, 0.0, nan, nan, nan, 0.4313779365297431, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6076451747667456, nan, nan, nan, nan, nan, 0.34802339917630704, nan, 0.0, nan, nan, 0.7774669105778543, nan, nan, nan, 0.8003424503221737, 0.0, nan, nan, nan, nan, nan, 0.06802583770690351, nan, 0.0, nan, 0.8812524360140315, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.6942458899213724, nan, 0.43315639036341363, nan, 0.0035087719298245615, 0.0, 0.0, nan, nan, 0.010857763300760043, 0.45089968246501233, 0.0, 0.0, 0.0, nan, nan, nan, 0.6753480017961383, nan, 0.09288702928870293, nan, nan, 0.40857694429123, nan, nan] | [0.7316496719139891, 0.9541981956644547, 0.9803902167300659, 0.8519794458204447, 0.8377899728523658, 0.887480121876064, 0.9623062695324957, 0.9059565827666562, 0.8705856773396197, nan, 0.7821678434406938, 0.7811282020767011, 0.9339201716945966, 0.9842443064182195, 0.7056678752125197, 0.722169803078751, 0.9317426926483614, 0.9296304158762986, 0.9316955372079219, 0.6252193200251598, 0.5153971205688689, nan, 0.9874890893220832, nan, nan, 0.9783145464304884, nan, nan, 0.0, 0.0, nan, 0.9860618199850263, 0.0, nan, nan, nan, 0.6000573394495413, nan, nan, 0.9798039891035476, nan, nan, 0.7378347485303723, 0.35802469135802467, nan, nan, nan, 0.1885376392995224, nan, nan, 0.9120940389350927, nan, nan, nan, nan, nan, nan, 0.5214914552045572, nan, nan, nan, nan, nan, 0.4313779365297431, nan, nan, nan, nan, 0.0, 0.0, nan, 0.8170583278719221, nan, nan, nan, nan, nan, 0.8762995548157836, nan, nan, nan, nan, 0.9583499137816687, nan, nan, nan, 0.9492050768203073, nan, nan, nan, nan, nan, nan, 0.06802583770690351, nan, 0.0, nan, 0.8865507776761208, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7429718875502008, nan, 0.5066870462361482, nan, 0.003691983122362869, nan, 0.0, nan, nan, 0.010869565217391304, 0.4853164556962025, nan, 0.0, nan, nan, nan, nan, 0.9665809768637532, nan, 0.09288702928870293, nan, nan, 0.5219305971463801, nan, nan] |
| 0.4443 | 20.0 | 680 | 0.4329 | 0.3912 | 0.5735 | 0.8424 | [0.6486895112125557, 0.9051331512774459, 0.9722324270255525, 0.7463078128096483, 0.7609558808000169, 0.8028106450725264, 0.5522075591610901, 0.6723405614281608, 0.7038017324350336, 0.0, 0.522581621294485, 0.7099360867225744, 0.8590854740844714, 0.9439876057680848, 0.45753527048491077, 0.6401872769133303, 0.7200395576732895, 0.8627716617182505, 0.8952576573783411, 0.45585770395016634, 0.42453516038068384, nan, 0.9630920323142721, 0.0, nan, 0.9356446838253433, 0.0, nan, 0.0, 0.0, nan, 0.9710689595741013, 0.0, nan, nan, nan, 0.3494760241346459, nan, 0.0, 0.9358248734764771, nan, nan, 0.5733262652405751, 0.31355718252966425, 0.0, nan, nan, 0.1821696611325904, nan, nan, 0.8533683289588802, nan, nan, nan, nan, nan, nan, 0.4261992619926199, nan, 0.0, nan, nan, nan, 0.38013463387828, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5764247787610619, nan, nan, nan, nan, nan, 0.3472780370915808, nan, 0.0, nan, nan, 0.7887912087912088, nan, nan, nan, 0.8028945868945869, 0.0, nan, nan, nan, nan, nan, 0.04763827210335083, nan, 0.0, nan, 0.9028578834813039, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7312245979855098, nan, 0.42570281124497994, nan, 0.0006507027589796981, 0.0, 0.0, nan, nan, 0.005434782608695652, 0.45366479925303455, nan, 0.0, 0.0, nan, nan, nan, 0.6541386052574408, nan, 0.11338912133891213, nan, nan, 0.3922614164615805, nan, nan] | [0.7375336557715413, 0.9579004536296433, 0.9795400074759779, 0.8603599519870612, 0.8396075826954222, 0.880140838393546, 0.9420881433764908, 0.8606996361455963, 0.8368045224635525, nan, 0.7794918915433472, 0.8047216685013835, 0.9333069767066589, 0.9839751552795031, 0.7021651406214794, 0.723925844385036, 0.8867360496014172, 0.9332803758157626, 0.9349333549187847, 0.6032376601449995, 0.4507561138205677, nan, 0.9827692108751171, nan, nan, 0.9772088030059044, nan, nan, 0.0, 0.0, nan, 0.986090989528134, 0.0, nan, nan, nan, 0.6310206422018348, nan, nan, 0.9784889000219181, nan, nan, 0.7505715218811234, 0.3262411347517731, nan, nan, nan, 0.1821696611325904, nan, nan, 0.9063792222273847, nan, nan, nan, nan, nan, nan, 0.4785085447954428, nan, nan, nan, nan, nan, 0.38013463387828, nan, nan, nan, nan, 0.0, 0.0, nan, 0.7622881752644883, nan, nan, nan, nan, nan, 0.9189685464801385, nan, nan, nan, nan, 0.9521156652075872, nan, nan, nan, 0.9412692050768203, nan, nan, nan, nan, nan, nan, 0.04763827210335083, nan, 0.0, nan, 0.9104692197098418, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7913558997896347, nan, 0.496178830722201, nan, 0.0006592827004219409, nan, 0.0, nan, nan, 0.005434782608695652, 0.4920253164556962, nan, 0.0, nan, nan, nan, nan, 0.9675449871465296, nan, 0.11338912133891213, nan, nan, 0.5053725559274265, nan, nan] |
| 0.3263 | 21.0 | 714 | 0.4330 | 0.3713 | 0.5734 | 0.8420 | [0.646552245818145, 0.9032500811197051, 0.9766317105253596, 0.7402789498136858, 0.762963911202142, 0.80265153425157, 0.5560709209246653, 0.6028453815438046, 0.709452207034775, 0.0, 0.5032846049350176, 0.7027572293207801, 0.8567205754884021, 0.9427584015253834, 0.45608833315444886, 0.6395721597821014, 0.7207725321888412, 0.8581588204665812, 0.8974031502767135, 0.44739759095499665, 0.511676225516719, nan, 0.9630463744211127, 0.0, nan, 0.9324381247880986, 0.0, nan, 0.0, 0.0, nan, 0.9714475590340363, 0.0, nan, nan, nan, 0.3293041489709245, nan, 0.0, 0.9388364520340331, nan, nan, 0.5731998669368252, 0.29638916750250754, 0.0, nan, 0.0, 0.20695929042528996, nan, nan, 0.8374181958167586, nan, nan, nan, nan, nan, nan, 0.4484092863284609, nan, 0.0, nan, nan, nan, 0.3856298942162385, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.5716804174599817, nan, nan, nan, nan, nan, 0.34709090026483136, nan, 0.0, nan, nan, 0.7822095945798273, nan, nan, nan, 0.7960045332325948, 0.0, nan, nan, nan, nan, nan, 0.0502624142107388, nan, 0.0, nan, 0.8623894955798231, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6561271882815292, nan, 0.42971624826232724, nan, 0.008087594873709095, 0.0, 0.0, nan, nan, 0.014084507042253521, 0.3262686918638879, 0.0, 0.0, 0.0, nan, nan, nan, 0.6665926748057713, nan, 0.099581589958159, nan, nan, 0.3774074074074074, nan, nan] | [0.7241058500970395, 0.9516306179400704, 0.9849637561658494, 0.8493829757143012, 0.8333158563156117, 0.8918069807230681, 0.9481472032655144, 0.9319987462694703, 0.8547021261987046, nan, 0.8002869394203179, 0.8083663086489542, 0.9330326526331076, 0.9827329192546583, 0.6963477334644298, 0.730320979888522, 0.9296944198405669, 0.9254874031870381, 0.9384814634936053, 0.6012844704869732, 0.5530098359121902, nan, 0.981508421426955, nan, nan, 0.9743102522812668, nan, nan, 0.0, 0.0, nan, 0.9878119925714897, 0.0, nan, nan, nan, 0.5779816513761468, nan, nan, 0.979506528477941, nan, nan, 0.7737589810581319, 0.31048069345941687, nan, nan, nan, 0.20695929042528996, nan, nan, 0.9096315569390884, nan, nan, nan, nan, nan, nan, 0.5401346452615226, nan, nan, nan, nan, nan, 0.3856298942162385, nan, nan, nan, nan, 0.0, 0.0, nan, 0.7590113285272915, nan, nan, nan, nan, nan, 0.8500904899668423, nan, nan, nan, nan, 0.9494627934739356, nan, nan, nan, 0.9571409485637943, nan, nan, nan, nan, nan, nan, 0.0502624142107388, nan, 0.0, nan, 0.8670108482551301, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7024287626697265, nan, 0.5020061138708445, nan, 0.008570675105485231, nan, 0.0, nan, nan, 0.014130434782608696, 0.3507594936708861, nan, 0.0, nan, nan, nan, nan, 0.964974293059126, nan, 0.099581589958159, nan, nan, 0.5384886383653338, nan, nan] |
| 0.524 | 22.0 | 748 | 0.4333 | 0.3779 | 0.5752 | 0.8419 | [0.6451126174180877, 0.9050409025633497, 0.9746581320919406, 0.7397875045633647, 0.7678308234540211, 0.8030243204466111, 0.5168384879725086, 0.6274960873418438, 0.7079466686867921, 0.0, 0.5058497693694061, 0.6764111720776259, 0.8558324551378148, 0.9418883796599409, 0.4606955628816949, 0.640940188619979, 0.7179865828446057, 0.8561625506537197, 0.8964269971737496, 0.44285232198946084, 0.5156265223274695, nan, 0.9639282999746643, 0.0, nan, 0.9375109255622166, 0.0, nan, 0.0, 0.0, nan, 0.9722855210197316, 0.0, nan, nan, nan, 0.34417344173441733, nan, 0.0, 0.9385698840771217, nan, nan, 0.5742866816460956, 0.32191952011997, 0.0, nan, 0.0, 0.16761428246531726, nan, nan, 0.8433407994486561, nan, nan, nan, nan, nan, nan, 0.45478606087340095, nan, 0.0, nan, nan, nan, 0.4011540046709713, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.5776503080518377, nan, nan, nan, nan, nan, 0.34624209495890157, nan, 0.0, nan, nan, 0.7784243991809462, nan, nan, nan, 0.8, 0.0, nan, nan, nan, nan, nan, 0.06802583770690351, nan, 0.0, nan, 0.8756173641798805, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7060906515580736, nan, 0.4265359907758195, nan, 0.006668376506796614, 0.0, 0.0, nan, nan, 0.017204301075268817, 0.34223843709544544, nan, 0.0, 0.0, nan, nan, nan, 0.6694915254237288, nan, 0.09707112970711297, nan, nan, 0.38140161725067384, nan, nan] | [0.7265222882844028, 0.9565257448763611, 0.9821089587135454, 0.8524327401825507, 0.8376385053654445, 0.8852305000452511, 0.9592448497990943, 0.9069922732042355, 0.855537500286087, nan, 0.8008350259535094, 0.7720537919133617, 0.9304427106445808, 0.9828985507246377, 0.6984370839222434, 0.7308975904667051, 0.9420394154118689, 0.92018023817187, 0.9328287734067239, 0.5647697553547191, 0.5570681730465598, nan, 0.9839653444541429, nan, nan, 0.9787654320987654, nan, nan, 0.0, 0.0, nan, 0.9852985502737075, 0.0, nan, nan, nan, 0.6189793577981652, nan, nan, 0.9785671791339199, nan, nan, 0.7616753755715219, 0.33832413974257947, nan, nan, nan, 0.16761428246531726, nan, nan, 0.9096780188635414, nan, nan, nan, nan, nan, nan, 0.5339202485758674, nan, nan, nan, nan, nan, 0.4011540046709713, nan, nan, nan, nan, 0.0, 0.0, nan, 0.7636925381518584, nan, nan, nan, nan, nan, 0.8831028150966327, nan, nan, nan, nan, 0.9580846266083035, nan, nan, nan, 0.9454642618570475, nan, nan, nan, nan, nan, nan, 0.06802583770690351, nan, 0.0, nan, 0.8805384917004313, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7626697265251482, nan, 0.49474589224302634, nan, 0.006856540084388186, nan, 0.0, nan, nan, 0.017391304347826087, 0.3681012658227848, nan, 0.0, nan, nan, nan, nan, 0.9646529562982005, nan, 0.09707112970711297, nan, nan, 0.5483530033468381, nan, nan] |
| 0.45 | 23.0 | 782 | 0.4359 | 0.3880 | 0.5740 | 0.8416 | [0.6476777210632493, 0.9048594439091597, 0.9738261600183319, 0.7365632025390588, 0.7676740594569088, 0.8008836740447784, 0.5153564580791354, 0.629532575893744, 0.7106440199509808, 0.0, 0.5195737183619097, 0.6849302472217114, 0.8531898311375024, 0.9430286352396905, 0.45616755065959474, 0.6383271737016145, 0.7290646853146853, 0.8589163998351457, 0.8971910764744591, 0.44590354739132637, 0.4820325659741718, nan, 0.9630695291997344, 0.0, nan, 0.9404759454062622, 0.0, nan, 0.0, 0.0, nan, 0.9718706922959105, 0.0, nan, nan, nan, 0.33776, nan, 0.0, 0.936809531640082, nan, nan, 0.5698700021512646, 0.31084967320261436, 0.0, nan, nan, 0.1737548328405731, nan, nan, 0.8410921733231578, nan, nan, nan, nan, nan, nan, 0.44489067380633646, nan, 0.0, nan, nan, nan, 0.4253331501579887, nan, nan, nan, nan, 0.0012762336925694838, 0.0, nan, 0.5873422986274781, nan, nan, nan, nan, nan, 0.34728746857872833, nan, 0.0, nan, nan, 0.7794766878432217, nan, nan, nan, 0.8010340564235136, 0.0, nan, nan, nan, nan, nan, 0.04985870004037142, nan, 0.0, nan, 0.8873879724639564, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.712272565108812, nan, 0.42457231726283046, nan, 0.010583343832682373, 0.0, 0.0, nan, nan, 0.016198704103671708, 0.3721973094170404, nan, 0.0, 0.0, nan, nan, nan, 0.6322418136020151, nan, 0.043933054393305436, nan, nan, 0.38518336204774795, nan, nan] | [0.7275339638335233, 0.958591444801928, 0.9811671320829394, 0.8483168273746279, 0.8340149370244795, 0.8936601174802512, 0.9428534983098412, 0.9068559981466592, 0.8527224040464148, nan, 0.7796369732727214, 0.7756538038142274, 0.9274251458355185, 0.9791097308488613, 0.6870685593723755, 0.7167531582533941, 0.9234388839681134, 0.9276513569222552, 0.9380902271868761, 0.583407819379614, 0.5221103353098722, nan, 0.9846765590146446, nan, nan, 0.980901771336554, nan, nan, 0.0, 0.0, nan, 0.987150816261048, 0.0, nan, nan, nan, 0.6052178899082569, nan, nan, 0.9810877665403763, nan, nan, 0.7569807315480078, 0.3123194116101918, nan, nan, nan, 0.1737548328405731, nan, nan, 0.9217116572968452, nan, nan, nan, nan, nan, nan, 0.5163127912998446, nan, nan, nan, nan, nan, 0.4253331501579887, nan, nan, nan, nan, 0.001714939024390244, 0.0, nan, 0.7932777829791218, nan, nan, nan, nan, nan, 0.8906458473691858, nan, nan, nan, nan, 0.9602069239952248, nan, nan, nan, 0.9521442885771543, nan, nan, nan, nan, nan, nan, 0.04985870004037142, nan, 0.0, nan, 0.8929551692589204, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7636259323006311, nan, 0.4955101260985862, nan, 0.011075949367088608, nan, 0.0, nan, nan, 0.016304347826086956, 0.3992405063291139, nan, 0.0, nan, nan, nan, nan, 0.967866323907455, nan, 0.043933054393305436, nan, nan, 0.5513475427162233, nan, nan] |
| 0.4165 | 24.0 | 816 | 0.4353 | 0.3871 | 0.5761 | 0.8430 | [0.6495339143782631, 0.9074240704338437, 0.9769427497717194, 0.7407472312887065, 0.7532459406188405, 0.8037861512845459, 0.5053244307481596, 0.6620873402439532, 0.7052840720942334, 0.0, 0.5322575286025983, 0.666957201635541, 0.848981707768644, 0.9430499432689053, 0.4572884701174753, 0.6361102917203584, 0.7247859589041096, 0.8709630362533932, 0.8960236744504607, 0.4342626347399617, 0.5472132510472293, nan, 0.9616068028937682, 0.0, nan, 0.9250451566289931, 0.0, nan, 0.0, 0.0, nan, 0.9725129197419404, 0.0, nan, nan, nan, 0.34888960411972963, nan, 0.0, 0.9390385281320104, nan, nan, 0.5664082687338501, 0.3145796847635727, nan, nan, nan, 0.22560836934273368, nan, nan, 0.8245556014882183, nan, nan, nan, nan, nan, nan, 0.451380042462845, nan, 0.0, nan, nan, nan, 0.39813161148509413, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5404770380918477, nan, nan, nan, nan, nan, 0.3467163736914873, nan, 0.0, nan, nan, 0.7730691810573154, nan, nan, nan, 0.8020713980813732, 0.0, nan, nan, nan, nan, nan, 0.0502624142107388, nan, 0.0, nan, 0.8874967540898467, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7417739628040058, nan, 0.4345460451518597, nan, 0.00012896569512509673, 0.0, 0.0, nan, nan, 0.012958963282937365, 0.3444668964697492, 0.0, 0.0, 0.0, nan, nan, nan, 0.5707013574660633, nan, 0.033891213389121336, nan, nan, 0.370946668250386, nan, nan] | [0.7237361757185545, 0.953575091344184, 0.9841282056920043, 0.8583872149231757, 0.8415533573351044, 0.9008270161481475, 0.9412590088653613, 0.8669274062768292, 0.853054264985238, nan, 0.7746880742818454, 0.7740620630150844, 0.9249400924633495, 0.9808695652173913, 0.6929064503574428, 0.715914451957855, 0.93727856510186, 0.9403817050038006, 0.9354190275754142, 0.5481510908067666, 0.6004350724535982, nan, 0.9797303850257008, nan, nan, 0.9621363392377885, nan, nan, 0.0, 0.0, nan, 0.98714595467053, 0.0, nan, nan, nan, 0.6215596330275229, nan, nan, 0.9764536431098726, nan, nan, 0.7606139777922927, 0.37746256895193064, nan, nan, nan, 0.22560836934273368, nan, nan, 0.9267295451377596, nan, nan, nan, nan, nan, nan, 0.5504919730709477, nan, nan, nan, nan, nan, 0.39813161148509413, nan, nan, nan, nan, 0.0, 0.0, nan, 0.7107012452017601, nan, nan, nan, nan, nan, 0.9275816060978348, nan, nan, nan, nan, 0.9679002520228147, nan, nan, nan, 0.9539345357381429, nan, nan, nan, nan, nan, nan, 0.0502624142107388, nan, 0.0, nan, 0.8934126258005489, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7932683113406005, nan, 0.5111769201375621, nan, 0.00013185654008438817, nan, 0.0, nan, nan, 0.013043478260869565, 0.36683544303797466, nan, 0.0, nan, nan, nan, nan, 0.9726863753213367, nan, 0.033891213389121336, nan, nan, 0.5501144970935353, nan, nan] |
| 0.467 | 25.0 | 850 | 0.4319 | 0.3937 | 0.5721 | 0.8431 | [0.6508008784390278, 0.9052470497408184, 0.9750837269682656, 0.7421921874506584, 0.7601302268364847, 0.800613865453404, 0.5361426019757227, 0.6571226979436156, 0.70324411073537, 0.0, 0.54518300668612, 0.6953014563799297, 0.85700761777975, 0.9428276081424937, 0.4572209263578807, 0.6374167323534412, 0.7272096452424321, 0.8614464699697227, 0.8952138572207651, 0.4520004046330484, 0.4971677559912854, nan, 0.9611893881695862, 0.0, nan, 0.9338709345197636, 0.0, nan, 0.0, 0.0, nan, 0.971765167313984, 0.0, nan, nan, nan, 0.3512476007677543, nan, 0.0, 0.9389522602450359, nan, nan, 0.5567509752107713, 0.3027545499262174, nan, nan, nan, 0.18285194450761882, nan, nan, 0.8517324391711805, nan, nan, nan, nan, nan, nan, 0.4470486111111111, nan, 0.0, nan, nan, nan, 0.3437285341393049, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5169275573352748, nan, nan, nan, nan, nan, 0.34686266015346845, nan, 0.0, nan, nan, 0.775534188034188, nan, nan, nan, 0.799627318036909, 0.0, nan, nan, nan, nan, nan, 0.053492127573677835, nan, 0.0, nan, 0.8837888722976044, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7259100642398287, nan, 0.4275664621676892, nan, 0.0034285714285714284, 0.0, 0.0, nan, nan, 0.017130620985010708, 0.34849023090586145, nan, 0.0, 0.0, nan, nan, nan, 0.6351550960118169, nan, 0.11506276150627615, nan, nan, 0.3751819505094614, nan, nan] | [0.7361818797942146, 0.9552116493838055, 0.9826843158380791, 0.8523021914062642, 0.8351451175038158, 0.8903158520765906, 0.9380700299764015, 0.8757307749962524, 0.842434714942897, nan, 0.7649998387980784, 0.7975662729463568, 0.9349287160826523, 0.9819461697722567, 0.702656752493906, 0.7206147018224389, 0.9282550930026572, 0.9313218607262941, 0.9301171010738762, 0.5916840467441321, 0.5337824403822088, nan, 0.9791808101380403, nan, nan, 0.9734514224369297, nan, nan, 0.0, 0.0, nan, 0.9877050375800948, 0.0, nan, nan, nan, 0.6295871559633027, nan, nan, 0.9742618279738234, nan, nan, 0.7224853037230569, 0.32335172051484107, nan, nan, nan, 0.18285194450761882, nan, nan, 0.9205501091855225, nan, nan, nan, nan, nan, nan, 0.5334023821853962, nan, nan, nan, nan, nan, 0.3437285341393049, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6647317666885123, nan, nan, nan, nan, nan, 0.9201970963949325, nan, nan, nan, nan, 0.9628597957288765, nan, nan, nan, 0.9516900467601871, nan, nan, nan, nan, nan, nan, 0.053492127573677835, nan, 0.0, nan, 0.8896222715984838, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7779690189328744, nan, 0.49933129537638515, nan, 0.003560126582278481, nan, 0.0, nan, nan, 0.017391304347826087, 0.37253164556962026, nan, 0.0, nan, nan, nan, nan, 0.9672236503856041, nan, 0.11506276150627615, nan, nan, 0.5448300158534437, nan, nan] |
| 0.3942 | 26.0 | 884 | 0.4394 | 0.3804 | 0.5741 | 0.8403 | [0.6397114629316163, 0.9057167147033819, 0.9766901800023986, 0.7401317877683684, 0.7632936211930692, 0.8066048622865056, 0.5261001995588699, 0.6042265772002698, 0.7039493777200886, 0.0, 0.5048032134950766, 0.6808949149027181, 0.8581169527500241, 0.9429149877930172, 0.46183785574295416, 0.6355977432143947, 0.7236251649802024, 0.8601332547244569, 0.8962313190383365, 0.4566787003610108, 0.5354430792295408, nan, 0.962577502214349, 0.0, nan, 0.9353223579832279, 0.0, nan, 0.0, 0.0, nan, 0.9718722152486993, 0.0, 0.0, nan, nan, 0.3509156362498044, nan, 0.0, 0.9402195158622764, nan, nan, 0.5751388680378322, 0.3771483902202856, nan, nan, nan, 0.18489879463270412, nan, nan, 0.8112487692812602, nan, nan, nan, nan, nan, nan, 0.4572284003421728, nan, 0.0, nan, nan, nan, 0.3856298942162385, nan, nan, nan, nan, 0.001700198356474922, 0.0, nan, 0.5293264699485246, nan, nan, nan, nan, nan, 0.33566072551254594, nan, 0.0, nan, nan, 0.7865573770491803, nan, nan, nan, 0.7989656248606715, 0.0, nan, nan, nan, nan, nan, 0.048647557529269275, nan, 0.0, nan, 0.8506890275611024, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6682151152403073, nan, 0.4303724928366762, nan, 0.0017750729047800178, 0.0, 0.0, nan, nan, 0.0010869565217391304, 0.29688983855650525, 0.0, 0.0, 0.0, nan, nan, nan, 0.6245075679037944, nan, 0.100418410041841, nan, nan, 0.35866865375062096, nan, nan] | [0.7125054681001818, 0.9531314022756643, 0.9848831328745136, 0.8455934348470947, 0.8345742015915737, 0.9036627463486742, 0.9584157152879648, 0.9277197094615772, 0.8440368022337674, nan, 0.812360963342683, 0.7814257237214007, 0.9361873794789456, 0.9835403726708074, 0.6854298531309531, 0.7283203159127046, 0.9105403011514615, 0.929130515171644, 0.9304004101235767, 0.6281656569669282, 0.5764359145293148, nan, 0.9837067209775967, nan, nan, 0.9746430488459474, nan, nan, 0.0, 0.0, nan, 0.986197944519529, 0.0, nan, nan, nan, 0.6427752293577982, nan, nan, 0.97902119798353, nan, nan, 0.7819644023514043, 0.4092461255581823, nan, nan, nan, 0.18489879463270412, nan, nan, 0.9187845560563118, nan, nan, nan, nan, nan, nan, 0.5535991714137752, nan, nan, nan, nan, nan, 0.3856298942162385, nan, nan, nan, nan, 0.0022865853658536584, 0.0, nan, 0.6835502293792716, nan, nan, nan, nan, nan, 0.8675015522001611, nan, nan, nan, nan, 0.9546358933545563, nan, nan, nan, 0.9576486305945224, nan, nan, nan, nan, nan, nan, 0.048647557529269275, nan, 0.0, nan, 0.8552476800418246, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7152419200611971, nan, 0.5021971723347344, nan, 0.0018459915611814346, nan, 0.0, nan, nan, 0.0010869565217391304, 0.31658227848101267, nan, 0.0, nan, nan, nan, nan, 0.967866323907455, nan, 0.100418410041841, nan, nan, 0.5087193940461512, nan, nan] |
| 0.5931 | 27.0 | 918 | 0.4353 | 0.3760 | 0.5745 | 0.8420 | [0.6529822114853098, 0.9045389637117451, 0.97544128933231, 0.7378118256640717, 0.7671162711285641, 0.8013467751352614, 0.4879334434142004, 0.6118121703890653, 0.7058510439622008, 0.0, 0.5301473332894188, 0.6460179300805157, 0.855736438140385, 0.9415722309325818, 0.4643814263837235, 0.6369774944371299, 0.7165964616680708, 0.8588766132932821, 0.8939657409810537, 0.4436398685218595, 0.5201654935253345, nan, 0.9624007621467132, 0.0, nan, 0.9361653957374991, 0.0, nan, 0.0, 0.0, nan, 0.9719316054336428, 0.0, 0.0, nan, nan, 0.3414035661704564, nan, 0.0, 0.9403819423460446, nan, nan, 0.5527372319566773, 0.31591856757419673, 0.0, nan, 0.0, 0.19422333409142598, nan, nan, 0.8432549821154829, nan, nan, nan, nan, nan, nan, 0.4585934248855597, nan, 0.0, nan, nan, nan, 0.43453771122406926, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5372735801400015, nan, nan, nan, nan, nan, 0.3461325157482787, nan, 0.0, nan, nan, 0.7681511186154496, nan, nan, nan, 0.7969359766295743, 0.0, nan, nan, nan, nan, nan, 0.10900282599919257, nan, 0.0, nan, 0.8550168962828177, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6978814313690582, nan, 0.43413002691899827, nan, 0.001137081490840177, 0.0, 0.0, nan, nan, 0.01714898177920686, 0.31188645771732704, nan, 0.0, 0.0, nan, nan, nan, 0.6461241142366331, nan, 0.07615062761506276, nan, nan, 0.3709219858156028, nan, nan] | [0.7350888758818275, 0.9537254122307862, 0.9828015860800223, 0.8406579658324842, 0.8375918999848533, 0.8872387831355936, 0.980036992155112, 0.9216827244109511, 0.8650241457441695, nan, 0.7784279588612696, 0.7364404510428134, 0.9292405257340186, 0.9822567287784679, 0.6956922509678609, 0.7227726232286698, 0.941762621789194, 0.9241794438091064, 0.9293751011818034, 0.5942662296818618, 0.5661087915043916, nan, 0.9797303850257008, nan, nan, 0.9765968867418143, nan, nan, 0.0, 0.0, nan, 0.986829951286863, 0.0, nan, nan, nan, 0.5983371559633027, nan, nan, 0.9744496978426277, nan, nan, 0.7250163291966035, 0.33832413974257947, nan, nan, nan, 0.19422333409142598, nan, nan, 0.9200854899409934, nan, nan, nan, nan, nan, nan, 0.5706887622993267, nan, nan, nan, nan, nan, 0.43453771122406926, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6970321130980245, nan, nan, nan, nan, nan, 0.8739481366992959, nan, nan, nan, nan, 0.9655126674625282, nan, nan, nan, 0.9548964595858384, nan, nan, nan, nan, nan, nan, 0.10900282599919257, nan, 0.0, nan, 0.85982224545811, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.749665327978581, nan, 0.5084065724111578, nan, 0.0011867088607594937, nan, 0.0, nan, nan, 0.017391304347826087, 0.33379746835443036, nan, 0.0, nan, nan, nan, nan, 0.9669023136246787, nan, 0.07615062761506276, nan, nan, 0.5527567377135811, nan, nan] |
| 0.4198 | 28.0 | 952 | 0.4371 | 0.3865 | 0.5732 | 0.8403 | [0.6409973916297321, 0.905896349068283, 0.9762433150216466, 0.7451166438945398, 0.7542192878741905, 0.805839121179633, 0.5065944653001562, 0.6497773399823513, 0.7009259607784093, 0.0, 0.5108830424589736, 0.6627968955161789, 0.8581381646781558, 0.9418058610235986, 0.45618103310411, 0.637182117282756, 0.7249316706525453, 0.8657950687018056, 0.8943742377455433, 0.4557749591621024, 0.5097598007624332, nan, 0.96274727013768, 0.0, nan, 0.9273671495090701, 0.0, nan, 0.0, 0.0, nan, 0.9719607814962232, 0.0, nan, nan, nan, 0.3469520103761349, nan, 0.0, 0.9360382574908466, nan, nan, 0.5484302362605872, 0.3312574850299401, nan, nan, nan, 0.19217648396634068, nan, nan, 0.8428802451272449, nan, nan, nan, nan, nan, nan, 0.4470536109880372, nan, 0.0, nan, nan, nan, 0.36364885286440446, nan, nan, nan, nan, 0.0, 0.0, nan, 0.504547134935305, nan, nan, nan, nan, nan, 0.3393808671781171, nan, 0.0, nan, nan, 0.7806819404108852, nan, nan, nan, 0.8020856897253766, 0.0, nan, nan, nan, nan, nan, 0.066410981025434, nan, 0.0, nan, 0.8924284694738208, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7144390767579173, nan, 0.4299248611564848, nan, 0.0, 0.0, 0.0, nan, nan, 0.016146393972012917, 0.3477028463446321, 0.0, 0.0, 0.0, nan, nan, nan, 0.6203362033620337, nan, 0.09330543933054393, nan, nan, 0.37369172216936253, nan, nan] | [0.7198040725794029, 0.954486714785514, 0.9833842725946774, 0.8543256974387056, 0.8398639122886737, 0.8940393640724189, 0.9725747815549461, 0.862975429607119, 0.8523790996269426, nan, 0.7904052616307187, 0.7584273005861176, 0.9401086000597059, 0.9840993788819876, 0.7011614330486081, 0.7190858101378623, 0.9397143489813995, 0.9341911538119141, 0.9299687010954617, 0.628099447148012, 0.5458054103364794, nan, 0.9833511136973458, nan, nan, 0.9642726784755771, nan, nan, 0.0, 0.0, nan, 0.9865382558557858, 0.0, nan, nan, nan, 0.6135321100917431, nan, nan, 0.9806024360459655, nan, nan, 0.7031352057478772, 0.3632781717888101, nan, nan, nan, 0.19217648396634068, nan, nan, 0.9202248757143521, nan, nan, nan, nan, nan, nan, 0.5225271879854997, nan, nan, nan, nan, nan, 0.36364885286440446, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6388914895609026, nan, nan, nan, nan, nan, 0.9145695451723272, nan, nan, nan, nan, 0.962727152142194, nan, nan, nan, 0.9474148296593187, nan, nan, nan, nan, nan, nan, 0.066410981025434, nan, 0.0, nan, 0.8989021043000915, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7636259323006311, nan, 0.5028658769583493, nan, 0.0, nan, 0.0, nan, nan, 0.016304347826086956, 0.37265822784810126, nan, 0.0, nan, nan, nan, nan, 0.9723650385604113, nan, 0.09330543933054393, nan, nan, 0.55346133521226, nan, nan] |
| 0.2976 | 29.0 | 986 | 0.4369 | 0.3816 | 0.5754 | 0.8393 | [0.6428810383317772, 0.9047540283118995, 0.97386455400598, 0.7388601463334221, 0.7686440677966102, 0.798591417982923, 0.5028400696063302, 0.6192216926565175, 0.7059464106605531, 0.0, 0.5305249372476263, 0.6613972997771661, 0.859187742210076, 0.9421938917351761, 0.44990930026116377, 0.6404687825704294, 0.7264159100734976, 0.8535980306317966, 0.8966043001860657, 0.46692802573381587, 0.46262504181549385, nan, 0.963429693189374, 0.0, nan, 0.9389416414309911, 0.0, nan, 0.0, 0.0, nan, 0.9718957318709974, 0.0, nan, nan, nan, 0.3504725292327407, nan, 0.0, 0.9390139383824259, nan, nan, 0.5725519801201598, 0.33935546875, nan, nan, 0.0, 0.17125312713213556, nan, nan, 0.8131278213835454, nan, nan, nan, nan, nan, nan, 0.46157167530224524, nan, 0.0, nan, nan, nan, 0.4246462426157439, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5314920356568756, nan, nan, nan, nan, nan, 0.3406633186325745, nan, 0.0, nan, nan, 0.7548686244204018, nan, nan, nan, 0.7963127498889383, 0.0, nan, nan, nan, nan, nan, 0.06903512313282197, nan, 0.0, nan, 0.8923146825911983, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6865964661788327, nan, 0.42886228520237807, nan, 0.010994565904208265, 0.0, 0.0, nan, nan, 0.01810436634717785, 0.3292841136926524, 0.0, 0.0, 0.0, nan, nan, nan, 0.6417783450329717, nan, 0.11799163179916318, nan, nan, 0.3826876513317191, nan, nan] | [0.7246135362434922, 0.9548625170020196, 0.981145143912575, 0.8451909094535446, 0.8348538338751209, 0.8859502066462965, 0.976784233688373, 0.9118164102424333, 0.8456961069278832, nan, 0.7836347809265887, 0.7506173574127518, 0.9283852800929474, 0.9823395445134575, 0.7163399496097831, 0.7247645506805752, 0.9301372896368467, 0.9213169986783447, 0.9361340456532298, 0.6150892177309895, 0.5013976117797037, nan, 0.9836743930430285, nan, nan, 0.9819323671497584, nan, nan, 0.0, 0.0, nan, 0.9857020622866978, 0.0, nan, nan, nan, 0.6272935779816514, nan, nan, 0.977721764724301, nan, nan, 0.7430600914435009, 0.36511688993958497, nan, nan, nan, 0.17125312713213556, nan, nan, 0.9289597175114993, nan, nan, nan, nan, nan, nan, 0.5535991714137752, nan, nan, nan, nan, nan, 0.4246462426157439, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6810223761820055, nan, nan, nan, nan, nan, 0.8826536678159553, nan, nan, nan, nan, 0.9717469160366097, nan, nan, nan, 0.9579158316633266, nan, nan, nan, nan, nan, nan, 0.06903512313282197, nan, 0.0, nan, 0.8983792968239446, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7357047236565308, nan, 0.5030569354222392, nan, 0.011471518987341773, nan, 0.0, nan, nan, 0.01847826086956522, 0.3534177215189873, nan, 0.0, nan, nan, nan, nan, 0.9694730077120822, nan, 0.11799163179916318, nan, nan, 0.5568081733309846, nan, nan] |
| 0.5191 | 30.0 | 1020 | 0.4373 | 0.3801 | 0.5731 | 0.8413 | [0.645137383058362, 0.9055505296306899, 0.9740671933453793, 0.737604753050256, 0.7690127182582454, 0.801750995931758, 0.5041522721233058, 0.611301338840879, 0.7023536782651792, 0.0, 0.5241944292736209, 0.669414956668335, 0.8578705151691166, 0.9418288593932901, 0.4570235070308239, 0.6345099185441008, 0.7195272835871838, 0.859709924148656, 0.8959813023436993, 0.4400347696510617, 0.5437811912632057, nan, 0.9640030393212182, 0.0, nan, 0.9420548411380723, 0.0, nan, 0.0, 0.0, nan, 0.9720681344325638, 0.0, 0.0, nan, nan, 0.3509166934705693, nan, 0.0, 0.9395654005898995, nan, nan, 0.5544039838157485, 0.3022663610898905, nan, nan, 0.0, 0.18921992267455082, nan, nan, 0.8437273541071657, nan, nan, nan, nan, nan, nan, 0.4530526315789474, nan, 0.0, nan, nan, nan, 0.3945596922654211, nan, nan, nan, nan, 0.0, 0.0, nan, 0.508241957737274, nan, nan, nan, nan, nan, 0.34034226823134095, nan, 0.0, nan, nan, 0.7613801704427354, nan, nan, nan, 0.799928241467462, 0.0, nan, nan, nan, nan, nan, 0.11606782398062172, nan, 0.0, nan, 0.8676499252810084, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6996415770609319, nan, 0.4282190429205723, nan, 0.00026065424214779094, 0.0, 0.0, nan, nan, 0.01798941798941799, 0.3096283984328624, nan, 0.0, 0.0, nan, nan, nan, 0.6018315747561218, nan, 0.06694560669456066, nan, nan, 0.36482007575757575, nan, nan] | [0.7229672530113058, 0.958625388227935, 0.9818597594494162, 0.8400850017587821, 0.8313118249501905, 0.8994306991497119, 0.967982651954844, 0.9196385985473079, 0.8595083880713158, nan, 0.7736080214076152, 0.7560917556752254, 0.9294745080320475, 0.9828364389233955, 0.7037014277228129, 0.7206933305376457, 0.9336248892825509, 0.9275075498702312, 0.930926555501592, 0.5865527857781309, 0.588751271885197, nan, 0.9843532796689619, nan, nan, 0.9806977992485238, nan, nan, 0.0, 0.0, nan, 0.9882398125370696, 0.0, nan, nan, nan, 0.6255733944954128, nan, nan, 0.9774869273882957, nan, nan, 0.7271799477465709, 0.3117940635671132, nan, nan, nan, 0.18921992267455082, nan, nan, 0.9196208706964643, nan, nan, nan, nan, nan, nan, 0.5572242361470741, nan, nan, nan, nan, nan, 0.3945596922654211, nan, nan, nan, nan, 0.0, 0.0, nan, 0.655275723246887, nan, nan, nan, nan, nan, 0.8777394681567788, nan, nan, nan, nan, 0.9717469160366097, nan, nan, nan, 0.9531596526386106, nan, nan, nan, nan, nan, nan, 0.11606782398062172, nan, 0.0, nan, 0.8726963795582277, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7466054694970358, nan, 0.4975162399694306, nan, 0.00026371308016877635, nan, 0.0, nan, nan, 0.01847826086956522, 0.330126582278481, nan, 0.0, nan, nan, nan, nan, 0.971401028277635, nan, 0.06694560669456066, nan, nan, 0.5428923727320768, nan, nan] |
| 0.4119 | 31.0 | 1054 | 0.4311 | 0.3893 | 0.5765 | 0.8427 | [0.6483202560025112, 0.9054059637765994, 0.9749826419813225, 0.7450374832621411, 0.7703225390549455, 0.8040824017566326, 0.5093624161073825, 0.6364800406615319, 0.7062322434746254, 0.0, 0.5282432047659723, 0.6696560856966789, 0.8597884388399994, 0.9417469939381894, 0.4624426527675978, 0.6394479053287325, 0.7162551964028167, 0.8549824157514166, 0.8960911336100443, 0.45105839963969374, 0.4900561712463944, nan, 0.9628948369909933, 0.0, nan, 0.9391753212101309, 0.0, nan, 0.0, 0.0, nan, 0.9722849458499331, 0.0, nan, nan, nan, 0.3524471006299467, nan, 0.0, 0.9371479928100659, nan, nan, 0.5606199840715554, 0.34751063297473106, nan, nan, 0.0, 0.17762110529906755, nan, nan, 0.8481170647729718, nan, nan, nan, nan, nan, nan, 0.4550150667240637, nan, 0.0, nan, nan, nan, 0.39689517790905343, nan, nan, nan, nan, 0.0, 0.0, nan, 0.536725188104622, nan, nan, nan, nan, nan, 0.34706449898615704, nan, 0.0, nan, nan, 0.7837514934289128, nan, nan, nan, 0.8003010086034549, 0.0, nan, nan, nan, nan, nan, 0.057529269277351636, nan, 0.0, nan, 0.9053826199740597, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7167344053719739, nan, 0.42750716332378225, nan, 0.0021780909673286353, 0.0, 0.0, nan, nan, 0.015151515151515152, 0.4188074475606882, nan, 0.0, 0.0, nan, nan, nan, 0.56912701386287, nan, 0.10711297071129706, nan, nan, 0.40139455334824364, nan, nan] | [0.7329755706848218, 0.9562881408943122, 0.9828968681516012, 0.8494555028122383, 0.8353664930616239, 0.9011243799533699, 0.9681102111104024, 0.8873822924190186, 0.8505481427230906, nan, 0.7775735886771771, 0.7597810240695011, 0.9286111940358719, 0.9810351966873706, 0.6978840205657634, 0.7236986947633276, 0.9347320637732507, 0.9206390511473749, 0.9349333549187847, 0.5967822028006753, 0.5285428581453282, nan, 0.9815407493615232, nan, nan, 0.9824691358024691, nan, nan, 0.0, 0.0, nan, 0.9859597265841493, 0.0, nan, nan, nan, 0.6255733944954128, nan, nan, 0.9794908726555406, nan, nan, 0.7471423905943828, 0.36485421591804573, nan, nan, nan, 0.17762110529906755, nan, nan, 0.915578683269061, nan, nan, nan, nan, nan, nan, 0.5473847747281202, nan, nan, nan, nan, nan, 0.39689517790905343, nan, nan, nan, nan, 0.0, 0.0, nan, 0.7012452017601348, nan, nan, nan, nan, nan, 0.888611474392, nan, nan, nan, nan, 0.9571561215015254, nan, nan, nan, 0.9519572478289913, nan, nan, nan, nan, nan, nan, 0.057529269277351636, nan, 0.0, nan, 0.9123643968108744, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7756741250717154, nan, 0.4988536492166603, nan, 0.002241561181434599, nan, 0.0, nan, nan, 0.015217391304347827, 0.449873417721519, nan, 0.0, nan, nan, nan, nan, 0.9762210796915167, nan, 0.10711297071129706, nan, nan, 0.5374317421173155, nan, nan] |
| 0.4903 | 32.0 | 1088 | 0.4366 | 0.3902 | 0.5716 | 0.8402 | [0.6399856685343, 0.9049629369457473, 0.9747363364412516, 0.7430659937028306, 0.7629117914647822, 0.8022063399417879, 0.5045534864754655, 0.6367029911829608, 0.7069323252707274, 0.0, 0.5196253915489356, 0.6705136202952797, 0.8576650995862315, 0.9423226191660862, 0.4569595013930887, 0.6351618025684905, 0.7226249360395702, 0.8660459089696523, 0.8957901379248174, 0.44769512315758236, 0.5169997715230708, nan, 0.9618959992385545, 0.0, nan, 0.9345825182744775, 0.0, nan, 0.0, 0.0, nan, 0.9731339129767183, 0.0, nan, nan, nan, 0.3399039257909558, nan, 0.0, 0.9392867358152558, nan, nan, 0.5622253871094816, 0.29777886258237735, nan, nan, nan, 0.17011598817375484, nan, nan, 0.8319983224994758, nan, nan, nan, nan, nan, nan, 0.4580152671755725, nan, 0.0, nan, nan, nan, 0.3860420387415854, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4850762768074287, nan, nan, nan, nan, nan, 0.33688834365539483, nan, 0.0, nan, nan, 0.7705577309768229, nan, nan, nan, 0.8002339076064955, 0.0, nan, nan, nan, nan, nan, 0.05732741219216794, nan, 0.0, nan, 0.8782958825821535, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6894443445423485, nan, 0.42647781234648763, nan, 0.00038407374215849446, 0.0, 0.0, nan, nan, 0.010869565217391304, 0.3432198362795112, nan, 0.0, 0.0, nan, nan, nan, 0.58688906128782, nan, 0.099163179916318, nan, nan, 0.37616250611845325, nan, nan] | [0.7175552201102862, 0.9557741404433496, 0.9815519250643154, 0.851540656877926, 0.8402367553334033, 0.8944099914238555, 0.9469991708654889, 0.8925607446069146, 0.8538667521113221, nan, 0.7861978914788664, 0.767486834667222, 0.9332101564454055, 0.9802691511387164, 0.7088633523832933, 0.7198633607660184, 0.9381643046944198, 0.9337323408364092, 0.9384140089579623, 0.5992981759194889, 0.5557582774873396, nan, 0.9801183202405198, nan, nan, 0.9745142243692968, nan, nan, 0.0, 0.0, nan, 0.9859548649936313, 0.0, nan, nan, nan, 0.588302752293578, nan, nan, 0.976829382847481, nan, nan, 0.7574297844546048, 0.3204623062779091, nan, nan, nan, 0.17011598817375484, nan, nan, 0.9217581192212981, nan, nan, nan, nan, nan, nan, 0.559295701708959, nan, nan, nan, nan, nan, 0.3860420387415854, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6162344349779983, nan, nan, nan, nan, nan, 0.899668423625147, nan, nan, nan, nan, 0.9657779546358933, nan, nan, nan, 0.9507014028056112, nan, nan, nan, nan, nan, nan, 0.05732741219216794, nan, 0.0, nan, 0.8838060384263495, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7332185886402753, nan, 0.4976117692013756, nan, 0.00039556962025316455, nan, 0.0, nan, nan, 0.010869565217391304, 0.3662025316455696, nan, 0.0, nan, nan, nan, nan, 0.9723650385604113, nan, 0.099163179916318, nan, nan, 0.541483177734719, nan, nan] |
| 0.4019 | 33.0 | 1122 | 0.4406 | 0.3734 | 0.5736 | 0.8387 | [0.6343020830436354, 0.9058862982265536, 0.9744276171536382, 0.7402147907413653, 0.757705380954871, 0.8017694403074335, 0.5124623761371707, 0.6242593627724986, 0.69749034024834, 0.0, 0.5209418869190526, 0.6693062085672237, 0.8533998660513862, 0.9422376926902039, 0.4385284625673371, 0.6371322024999807, 0.7232921741329176, 0.8698575244879786, 0.8931335440485946, 0.4605959205767291, 0.5442393037183851, nan, 0.9614653916788044, 0.0, nan, 0.9284203357650677, 0.0, nan, 0.0, 0.0, nan, 0.9734224201930215, 0.0, 0.0, nan, nan, 0.35004042037186744, nan, 0.0, 0.9407102140268022, nan, nan, 0.5635576424672989, 0.2994713858883015, 0.0, nan, nan, 0.2071867182169661, nan, nan, 0.8266923109085588, nan, nan, nan, nan, nan, nan, 0.44075207695671187, nan, 0.0, nan, nan, nan, 0.39978018958648165, nan, nan, nan, nan, 0.0, 0.0, nan, 0.483055285404354, nan, nan, nan, nan, nan, 0.33153664162187313, nan, 0.0, nan, nan, 0.7718554625410617, nan, nan, nan, 0.8024442227462362, 0.0, nan, nan, nan, nan, nan, 0.06277755349212757, nan, 0.0, nan, 0.8798208024931827, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6987410071942446, nan, 0.4294443539188529, nan, 0.00039072675175827037, 0.0, 0.0, nan, nan, 0.014054054054054054, 0.31516587677725116, 0.0, 0.0, 0.0, nan, nan, nan, 0.5740318906605922, nan, 0.0602510460251046, nan, nan, 0.36728871983834543, nan, nan] | [0.702615446227781, 0.9562178295118693, 0.9804268636806731, 0.8515587886524103, 0.8441166532676198, 0.895543421579993, 0.9664519420881433, 0.9131519058066802, 0.851074542832948, nan, 0.8009962278750362, 0.7644818660557555, 0.9355580477807989, 0.9807660455486542, 0.7320305618714025, 0.7209554262550016, 0.9501217891939769, 0.9365194584637299, 0.9263531379849981, 0.6197239050551197, 0.5909149386571232, nan, 0.9816377331652281, nan, nan, 0.965925925925926, nan, nan, 0.0, 0.0, nan, 0.9880502105068695, 0.0, nan, nan, nan, 0.6206995412844036, nan, nan, 0.9791934120299339, nan, nan, 0.7545313520574788, 0.3422642500656685, nan, nan, nan, 0.2071867182169661, nan, nan, 0.9186451702829531, nan, nan, nan, nan, nan, nan, 0.5220093215950284, nan, nan, nan, nan, nan, 0.39978018958648165, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6045314109165808, nan, nan, nan, nan, nan, 0.8848597735769297, nan, nan, nan, nan, 0.9661758853959411, nan, nan, nan, 0.9456513026052105, nan, nan, nan, nan, nan, nan, 0.06277755349212757, nan, 0.0, nan, 0.8855705136583453, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7429718875502008, nan, 0.5035345815819641, nan, 0.00039556962025316455, nan, 0.0, nan, nan, 0.014130434782608696, 0.3367088607594937, nan, 0.0, nan, nan, nan, nan, 0.9717223650385605, nan, 0.0602510460251046, nan, nan, 0.5443015677294346, nan, nan] |
| 0.4135 | 34.0 | 1156 | 0.4361 | 0.3860 | 0.5727 | 0.8394 | [0.6360205632268718, 0.9048471218499878, 0.9728305950492601, 0.7413948539554205, 0.772220435295379, 0.8050050606896445, 0.5170862682195411, 0.6297883765306025, 0.7021621929458218, 0.0, 0.5103439745188789, 0.6842804350399138, 0.8603242763011771, 0.9429151086005251, 0.44356584297779167, 0.6395962996710476, 0.7202333150175408, 0.8561120813839348, 0.8949210546783853, 0.45351556789942993, 0.5125625401946785, nan, 0.9625641350478241, 0.0, nan, 0.9401352979334631, 0.0, nan, 0.0, 0.0, nan, 0.9727516656542805, 0.0, nan, nan, nan, 0.3578336557059961, nan, 0.0, 0.9413701941234233, nan, nan, 0.5561756310337086, 0.34453125, 0.0, nan, nan, 0.1682965658403457, nan, nan, 0.8530149666926204, nan, nan, nan, nan, nan, nan, 0.46147332768839966, nan, 0.0, nan, nan, nan, 0.420799560379173, nan, nan, nan, nan, 0.0, 0.0, nan, 0.48394149577804585, nan, nan, nan, nan, nan, 0.33466141337229033, nan, 0.0, nan, nan, 0.7705196182396606, nan, nan, nan, 0.8012147859393061, 0.0, nan, nan, nan, nan, nan, 0.10254339927331449, nan, 0.0, nan, 0.8809925945173445, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6747095621090259, nan, 0.42336187026671057, nan, 0.0005191434133679429, 0.0, 0.0, nan, nan, 0.018162393162393164, 0.3103896103896104, nan, 0.0, 0.0, nan, nan, nan, 0.6025513254933227, nan, 0.06903765690376569, nan, nan, 0.36880177342200443, nan, nan] | [0.7137882381935245, 0.9598837195206218, 0.9799394592375968, 0.8439688278533067, 0.8391881342701015, 0.8980473110123729, 0.9457873588876842, 0.8950545781605592, 0.8465543679765638, nan, 0.7981107134797046, 0.7740323108506144, 0.9375751365569102, 0.9814906832298137, 0.7261926708863352, 0.7236375390959445, 0.9433126660761736, 0.9197625129255148, 0.9313582645297069, 0.616247889562022, 0.5499573114393647, nan, 0.9825105873985711, nan, nan, 0.9801932367149758, nan, nan, 0.0, 0.0, nan, 0.9887502795414548, 0.0, nan, nan, nan, 0.6364678899082569, nan, nan, 0.9755769170554529, nan, nan, 0.7079114957544089, 0.3475177304964539, nan, nan, nan, 0.1682965658403457, nan, nan, 0.9162291502114017, nan, nan, nan, nan, nan, nan, 0.5644743656136717, nan, nan, nan, nan, nan, 0.420799560379173, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6009736916019099, nan, nan, nan, nan, nan, 0.8987833392779297, nan, nan, nan, nan, 0.9637883008356546, nan, nan, nan, 0.9446092184368737, nan, nan, nan, nan, nan, nan, 0.10254339927331449, nan, 0.0, nan, 0.8862893739380473, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7219353604895774, nan, 0.4913068398930073, nan, 0.0005274261603375527, nan, 0.0, nan, nan, 0.01847826086956522, 0.3327848101265823, nan, 0.0, nan, nan, nan, nan, 0.971401028277635, nan, 0.06903765690376569, nan, nan, 0.5568081733309846, nan, nan] |
| 0.5802 | 35.0 | 1190 | 0.4332 | 0.3780 | 0.5750 | 0.8416 | [0.6476432545026136, 0.9050442912818052, 0.9757726405892886, 0.7397889128632806, 0.7557274247491639, 0.8036901276406083, 0.49657913598748943, 0.6236323240961952, 0.7083249653325683, 0.0, 0.5351457503647873, 0.6531194480826839, 0.8595055413469735, 0.9417951618344546, 0.4583922693563525, 0.6361379459226078, 0.7239456998170135, 0.8640638412869996, 0.8971295577967416, 0.4532690327280034, 0.528361675113132, nan, 0.9621154335113146, 0.0, nan, 0.925914869524937, 0.0, nan, 0.0, 0.0, nan, 0.97322379212109, 0.0, nan, nan, nan, 0.3504854368932039, nan, 0.0, 0.9408417970813274, nan, nan, 0.5210041147012855, 0.35854214123006833, 0.0, nan, 0.0, 0.2008187400500341, nan, nan, 0.8553562898953494, nan, nan, nan, nan, nan, nan, 0.4686031610422896, nan, 0.0, nan, nan, nan, 0.41722764115949995, nan, nan, nan, nan, 0.0, 0.0, nan, 0.45868087735557617, nan, nan, nan, nan, nan, 0.340610729135805, nan, 0.0, nan, nan, 0.7797214123744736, nan, nan, nan, 0.7985260720488044, 0.0, nan, nan, nan, nan, nan, 0.10476382721033509, nan, 0.0, nan, 0.8770300116928673, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.687196983300413, nan, 0.4325118328708993, nan, 0.001420637995608937, 0.0, 0.0, nan, nan, 0.016146393972012917, 0.4359367626238792, 0.0, 0.0, 0.0, nan, nan, nan, 0.6062374245472837, nan, 0.07866108786610879, nan, nan, 0.4172859895403248, nan, nan] | [0.7271840054218909, 0.9509711456633636, 0.9825597162060145, 0.847954191884943, 0.8424854649469281, 0.90144329186042, 0.9721283245104917, 0.9142148512557746, 0.8475728377543313, nan, 0.7863107328239353, 0.7492190056826634, 0.93546929587465, 0.982567287784679, 0.710297220344538, 0.7208418514441474, 0.941762621789194, 0.9305480418273083, 0.9360531002104582, 0.5974111960803787, 0.5694303123866998, nan, 0.9786312352503799, nan, nan, 0.965625335480408, nan, nan, 0.0, 0.0, nan, 0.9865285326747498, 0.0, nan, nan, nan, 0.6209862385321101, nan, nan, 0.9760309359050631, nan, nan, 0.6668027433050294, 0.41344890990281064, nan, nan, nan, 0.2008187400500341, nan, nan, 0.9152069878734377, nan, nan, nan, nan, nan, nan, 0.5680994303469705, nan, nan, nan, nan, nan, 0.41722764115949995, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5560340792060668, nan, nan, nan, nan, nan, 0.8808570786932456, nan, nan, nan, nan, 0.9578193394349384, nan, nan, nan, 0.958316633266533, nan, nan, nan, nan, nan, nan, 0.10476382721033509, nan, 0.0, nan, 0.8823029669324272, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7318799005545994, nan, 0.5063049293083683, nan, 0.0014504219409282701, nan, 0.0, nan, nan, 0.016304347826086956, 0.46772151898734177, nan, 0.0, nan, nan, nan, nan, 0.9681876606683805, nan, 0.07866108786610879, nan, nan, 0.5340849039985908, nan, nan] |
| 0.5707 | 36.0 | 1224 | 0.4393 | 0.3807 | 0.5724 | 0.8408 | [0.642292372890665, 0.9066473030573635, 0.9754871287416872, 0.7370651676167317, 0.7587218472020693, 0.804106050618552, 0.505804367576298, 0.6405935864004618, 0.7095176136089827, 0.0, 0.5315678903140203, 0.6662552295818933, 0.8556391088523815, 0.9415780282584537, 0.4574969851266247, 0.6360032274582215, 0.7262827134986226, 0.8642247262339311, 0.8964226505025386, 0.45502806006370394, 0.5419882863669333, nan, 0.9616642845799587, 0.0, nan, 0.9310476623349793, 0.0, nan, 0.0, 0.0, nan, 0.9732741555908109, 0.0, nan, nan, nan, 0.3552948738550538, nan, 0.0, 0.9403903586835198, nan, nan, 0.5412276247302272, 0.33285951196398955, 0.0, nan, nan, 0.17989538321582899, nan, nan, 0.8500064264598775, nan, nan, nan, nan, nan, nan, 0.46595744680851064, nan, 0.0, nan, nan, nan, 0.38755323533452396, nan, nan, nan, nan, 0.0, 0.0, nan, 0.47309605817068506, nan, nan, nan, nan, nan, 0.3367078394836429, nan, 0.0, nan, nan, 0.7763888888888889, nan, nan, nan, 0.8000312765291989, 0.0, nan, nan, nan, nan, nan, 0.058336697618086394, nan, 0.0, nan, 0.8793013894299442, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7137726701382654, nan, 0.42946682946682946, nan, 0.0, 0.0, 0.0, nan, nan, 0.015135135135135135, 0.3366559867093865, 0.0, 0.0, 0.0, nan, nan, nan, 0.5858996707340693, nan, 0.08326359832635984, nan, nan, 0.3716159809183065, nan, nan] | [0.7205150796340224, 0.9550976964536393, 0.9826440041924112, 0.8463477166656392, 0.8407494145199064, 0.9042316162369257, 0.9587346131768608, 0.8771071530777722, 0.8424232714622479, nan, 0.7749782377405938, 0.7628306209276725, 0.9345817768131611, 0.9823602484472049, 0.6993793400110613, 0.7161940207230347, 0.9340677590788308, 0.9322600305418787, 0.9337191732772112, 0.5958883702453074, 0.5855233149714045, nan, 0.9788252028577894, nan, nan, 0.967589908749329, nan, nan, 0.0, 0.0, nan, 0.9871994321662275, 0.0, nan, nan, nan, 0.6338876146788991, nan, nan, 0.9760779033722642, nan, nan, 0.6961544741998694, 0.369057000262674, nan, nan, nan, 0.17989538321582899, nan, nan, 0.921804581145751, nan, nan, nan, nan, nan, nan, 0.5670636975660279, nan, nan, nan, nan, nan, 0.38755323533452396, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5786911337889711, nan, nan, nan, nan, nan, 0.9261945336133899, nan, nan, nan, nan, 0.9639209444223372, nan, nan, nan, 0.95687374749499, nan, nan, nan, nan, nan, nan, 0.058336697618086394, nan, 0.0, nan, 0.8850477061821984, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7601835915088927, nan, 0.504012227741689, nan, 0.0, nan, 0.0, nan, nan, 0.015217391304347827, 0.3591139240506329, nan, 0.0, nan, nan, nan, nan, 0.9720437017994858, nan, 0.08326359832635984, nan, nan, 0.5488814514708473, nan, nan] |
| 0.3664 | 37.0 | 1258 | 0.4409 | 0.3813 | 0.5746 | 0.8392 | [0.6363549419020085, 0.9059447807362262, 0.9742738710017869, 0.7395510725505051, 0.7633672701846574, 0.8045218102676505, 0.5050787624471309, 0.6456100295998465, 0.7028708407054391, 0.0, 0.5312819498037664, 0.6665661149437347, 0.858696537376639, 0.9419863707706674, 0.4453864204072364, 0.6373922288357222, 0.7249367251512162, 0.8601626635199777, 0.8947463838493713, 0.46419630685338953, 0.5146434427564639, nan, 0.9619159174370014, 0.0, nan, 0.9361132836619602, 0.0, nan, 0.0, 0.0, nan, 0.9725428823033923, 0.0, nan, nan, nan, 0.35445288508162276, nan, 0.0, 0.9401584528256416, nan, nan, 0.5587831084457772, 0.34423076923076923, 0.0, nan, nan, 0.1603365931316807, nan, nan, 0.8256884538818845, nan, nan, nan, nan, nan, nan, 0.4589247311827957, nan, 0.0, nan, nan, nan, 0.4000549526033796, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4801091157081155, nan, nan, nan, nan, nan, 0.33458022794940356, nan, 0.0, nan, nan, 0.77472, nan, nan, nan, 0.8005030655557303, 0.0, nan, nan, nan, nan, nan, 0.07119806373537717, nan, 0.0, nan, 0.8906696080975863, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7174223491610139, nan, 0.42814029363784667, nan, 0.0014300572022880914, 0.0, 0.0, nan, nan, 0.010857763300760043, 0.3860063822243234, 0.0, 0.0, 0.0, nan, nan, nan, 0.568858909499719, nan, 0.0895397489539749, nan, nan, 0.38440928808369484, nan, nan] | [0.7133150549890638, 0.9557765649737787, 0.9810755147064213, 0.8460322237896134, 0.8395376746245354, 0.8984998211507549, 0.9672810765992729, 0.870893010452297, 0.8455587851600943, nan, 0.7790405261630718, 0.7560471274285204, 0.9358081667890367, 0.9816356107660456, 0.7231610643397038, 0.7175743915011096, 0.9355070859167405, 0.9284731115052489, 0.9321542280502941, 0.6199887443307842, 0.554284644983217, nan, 0.9822842918565933, nan, nan, 0.976532474503489, nan, nan, 0.0, 0.0, nan, 0.9879092243818488, 0.0, nan, nan, nan, 0.6287270642201835, nan, nan, 0.9772207784074898, nan, nan, 0.7303233180927499, 0.3761491988442343, nan, nan, nan, 0.1603365931316807, nan, nan, 0.9250104539330019, nan, nan, nan, nan, nan, nan, 0.5525634386328327, nan, nan, nan, nan, nan, 0.4000549526033796, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5932028836251287, nan, nan, nan, nan, nan, 0.932258021902535, nan, nan, nan, nan, 0.9633903700756068, nan, nan, nan, 0.9524114896459586, nan, nan, nan, nan, nan, nan, 0.07125555106984255, nan, 0.0, nan, 0.8970722781335773, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7685982023331421, nan, 0.5014329384791746, nan, 0.0014504219409282701, nan, 0.0, nan, nan, 0.010869565217391304, 0.41341772151898737, nan, 0.0, nan, nan, nan, nan, 0.9755784061696658, nan, 0.0895397489539749, nan, nan, 0.5307380658798662, nan, nan] |
| 0.4709 | 38.0 | 1292 | 0.4338 | 0.3744 | 0.5721 | 0.8433 | [0.6531547713288965, 0.9080252043769238, 0.9764992721979622, 0.7349318658280922, 0.7595943903258314, 0.8023024268823895, 0.5068544030253916, 0.6113283564403366, 0.7026441589483119, 0.0, 0.5246468470637152, 0.6673220912819845, 0.854083983649201, 0.941639970551366, 0.45939624694044057, 0.6400272273015578, 0.7165067258936428, 0.8626748463134315, 0.8937950599932328, 0.45630078712076827, 0.5766699431599641, nan, 0.9623712157235695, 0.0, nan, 0.9303699419577386, 0.0, nan, 0.0, 0.0, nan, 0.9721732464545803, 0.0, 0.0, nan, nan, 0.3563218390804598, nan, 0.0, 0.9383183183183184, nan, nan, 0.5295667823627715, 0.3382776178879787, 0.0, nan, nan, 0.17511939959062997, nan, nan, 0.8531438228539239, nan, nan, nan, nan, nan, nan, 0.451277609354699, nan, 0.0, nan, nan, nan, 0.4125566698722352, nan, nan, nan, nan, 0.0, 0.0, nan, 0.47240618101545256, nan, nan, nan, nan, nan, 0.3475454957749269, nan, 0.0, nan, nan, 0.7652601821798765, nan, nan, nan, 0.8018991338866835, 0.0, nan, nan, nan, nan, nan, 0.07413376309427881, nan, 0.0, nan, 0.8644662465076993, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7262780269058295, nan, 0.42743052725939207, nan, 0.0, 0.0, 0.0, nan, nan, 0.01702127659574468, 0.314380478559583, 0.0, 0.0, 0.0, nan, nan, nan, 0.599960372498514, nan, 0.007112970711297071, nan, nan, 0.3725241824044219, nan, nan] | [0.7371085302362835, 0.956278442772596, 0.9833916019847988, 0.8339492092733147, 0.8431146375849092, 0.889022965966928, 0.9573952420434977, 0.9139559286463799, 0.8611905797267296, nan, 0.7729309733372022, 0.762354586296153, 0.9271911635374896, 0.9797929606625259, 0.6920256457526782, 0.7228949345634359, 0.9465234720992028, 0.9283019126337919, 0.9265689924990557, 0.593008243122455, 0.6312527045834649, nan, 0.9814114376232502, nan, nan, 0.967085346215781, nan, nan, 0.0, 0.0, nan, 0.9864750551790524, 0.0, nan, nan, nan, 0.648795871559633, nan, nan, 0.9783636534427154, nan, nan, 0.6726812540822992, 0.4013659049120042, nan, nan, nan, 0.17511939959062997, nan, nan, 0.9147423686289086, nan, nan, nan, nan, nan, nan, 0.5396167788710513, nan, nan, nan, nan, nan, 0.4125566698722352, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5810317386012546, nan, nan, nan, nan, nan, 0.8807249765518699, nan, nan, nan, nan, 0.9694919750630057, nan, nan, nan, 0.9499799599198396, nan, nan, nan, nan, nan, nan, 0.0742834073475979, nan, 0.0, nan, 0.8694941837668279, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7743354369860393, nan, 0.5010508215513947, nan, 0.0, nan, 0.0, nan, nan, 0.017391304347826087, 0.3359493670886076, nan, 0.0, nan, nan, nan, nan, 0.9730077120822622, nan, 0.007112970711297071, nan, nan, 0.569843227056544, nan, nan] |
| 0.3278 | 39.0 | 1326 | 0.4365 | 0.3740 | 0.5740 | 0.8396 | [0.640509910013284, 0.905774550484094, 0.9767012184033461, 0.7395959861418696, 0.7579605841248588, 0.807629166150415, 0.5099759493242099, 0.5981940913474183, 0.7064548033141748, 0.0, 0.5068553018060595, 0.6711004734625546, 0.8601043985911403, 0.9417949381708118, 0.44637212007789384, 0.6398284238361724, 0.7154027240625526, 0.8624114469191173, 0.8950663596616045, 0.46646123505485027, 0.5233582387251889, nan, 0.962344954477683, 0.0, nan, 0.9261360710939028, 0.0, nan, 0.0, 0.0, nan, 0.9723302903688495, 0.0, nan, nan, nan, 0.3478814909206754, nan, 0.0, 0.9403991451190512, nan, nan, 0.5569509528272415, 0.3178628028073353, 0.0, nan, 0.0, 0.17329997725722082, nan, nan, 0.8421344008176476, nan, nan, nan, nan, nan, nan, 0.46379897785349233, nan, 0.0, nan, nan, nan, 0.4493749141365572, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4522239263803681, nan, nan, nan, nan, nan, 0.34021414303670056, nan, 0.0, nan, nan, 0.7647676004616515, nan, nan, nan, 0.7985254154229964, 0.0, nan, nan, nan, nan, nan, 0.0747983870967742, nan, 0.0, nan, 0.8684125024368055, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6796721895599501, nan, 0.42854815056216394, nan, 0.0, 0.0, 0.0, nan, nan, 0.010857763300760043, 0.3366792631081719, 0.0, 0.0, 0.0, nan, nan, nan, 0.584070796460177, nan, 0.08284518828451883, nan, nan, 0.3793230316409124, nan, nan] | [0.715356889806229, 0.9526586188419958, 0.9841318703870651, 0.8368466668358966, 0.8442331667190978, 0.8989350928076746, 0.9602015434657822, 0.929886482877039, 0.8664431373446547, nan, 0.8039139826546733, 0.763306655559192, 0.9319434246940076, 0.9792132505175983, 0.7230791290276327, 0.7271758312802502, 0.9420947741364039, 0.9270144971204349, 0.9334898278560251, 0.6264110967656503, 0.5677227699612879, nan, 0.9807002230627485, nan, nan, 0.9655179817498658, nan, nan, 0.0, 0.0, nan, 0.9854006436745846, 0.0, nan, nan, nan, 0.6261467889908257, nan, nan, 0.9782070952187119, nan, nan, 0.7277922926192031, 0.36879432624113473, nan, nan, nan, 0.17329997725722082, nan, nan, 0.9187845560563118, nan, nan, nan, nan, nan, nan, 0.5639564992232005, nan, nan, nan, nan, nan, 0.4493749141365572, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5521018631214306, nan, nan, nan, nan, nan, 0.8617418988361801, nan, nan, nan, nan, 0.9668391033293541, nan, nan, nan, 0.9578891115564462, nan, nan, nan, nan, nan, nan, 0.07488897860314897, nan, 0.0, nan, 0.8733498889034114, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7295850066934404, nan, 0.5024837600305694, nan, 0.0, nan, 0.0, nan, nan, 0.010869565217391304, 0.3608860759493671, nan, 0.0, nan, nan, nan, nan, 0.9755784061696658, nan, 0.08284518828451883, nan, nan, 0.5448300158534437, nan, nan] |
| 0.4907 | 40.0 | 1360 | 0.4359 | 0.3807 | 0.5716 | 0.8410 | [0.6439168096450548, 0.905352623479932, 0.9767972751429496, 0.7398828674844159, 0.7442532293343238, 0.8103928180153535, 0.5081620314389359, 0.6331391248055499, 0.7087368579718883, 0.0, 0.5226763476537252, 0.6633393595041323, 0.8599091278489897, 0.9427118509395733, 0.44788005061802566, 0.634708154306721, 0.7138552938680246, 0.8658478620453638, 0.8942748438534754, 0.4285898228265348, 0.5427441237045997, nan, 0.9616155191604389, 0.0, nan, 0.9194718379088754, 0.0, nan, 0.0, 0.0, nan, 0.9732995835714869, 0.0, nan, nan, nan, 0.3596392333709132, nan, 0.0, 0.9399526259410691, nan, nan, 0.5315441363520733, 0.31126436781609196, 0.0, nan, nan, 0.12963384125540142, nan, nan, 0.8458850104419725, nan, nan, nan, nan, nan, nan, 0.4505446623093682, nan, 0.0, nan, nan, nan, 0.3941475477400742, nan, nan, nan, nan, 0.0, 0.0, nan, 0.43560666137985726, nan, nan, nan, nan, nan, 0.34031636595032544, nan, 0.0, nan, nan, 0.77780177067588, nan, nan, nan, 0.7982008592910849, 0.0, nan, nan, nan, nan, nan, 0.07769929364278506, nan, 0.0, nan, 0.8674503182231459, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7196029776674938, nan, 0.42746837522589126, nan, 0.0, 0.0, 0.0, nan, nan, 0.01193058568329718, 0.44036697247706424, 0.0, 0.0, 0.0, nan, nan, nan, 0.6111111111111112, nan, 0.1087866108786611, nan, nan, 0.40894017774240615, nan, nan] | [0.7223659160223037, 0.9514487781578903, 0.9847548685473881, 0.8539485565294334, 0.840481433581507, 0.9021543792207345, 0.9649212322214427, 0.8929831972854009, 0.8609044927105028, nan, 0.7821356030563884, 0.7641694683288209, 0.9391000556716502, 0.982567287784679, 0.7177328499149921, 0.7149796438992854, 0.9312444641275465, 0.9355881366030036, 0.9291052830392316, 0.5509650081107028, 0.5825058769867724, nan, 0.977532085475059, nan, nan, 0.9538915727321524, nan, nan, 0.0, 0.0, nan, 0.9851527025581689, 0.0, nan, nan, nan, 0.6401949541284404, nan, nan, 0.9753733913642484, nan, nan, 0.6823971260613978, 0.35566062516417124, nan, nan, nan, 0.12963384125540142, nan, nan, 0.9221298146169215, nan, nan, nan, nan, nan, nan, 0.5354738477472812, nan, nan, nan, nan, nan, 0.3941475477400742, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5142776893549293, nan, nan, nan, nan, nan, 0.8972377442238338, nan, nan, nan, nan, 0.9555643984613343, nan, nan, nan, 0.9531062124248497, nan, nan, nan, nan, nan, nan, 0.07771497779572063, nan, 0.0, nan, 0.8728924323617828, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7764390896921017, nan, 0.4971341230416507, nan, 0.0, nan, 0.0, nan, nan, 0.011956521739130435, 0.4739240506329114, nan, 0.0, nan, nan, nan, nan, 0.9720437017994858, nan, 0.1087866108786611, nan, nan, 0.5430685221067465, nan, nan] |
| 0.3889 | 41.0 | 1394 | 0.4326 | 0.3784 | 0.5677 | 0.8422 | [0.6538767743273876, 0.9052118040119709, 0.9750029996400432, 0.7339493215503222, 0.7642344867272669, 0.803186412948211, 0.5165823496346262, 0.5960715913473338, 0.7010110104477332, 0.0, 0.5492640899844442, 0.6855871187328184, 0.8605749242739206, 0.9420817178645988, 0.4471592506453344, 0.641272917554944, 0.7098544698544699, 0.8593397801807064, 0.8942841575424213, 0.4508213141025641, 0.5065681534198946, nan, 0.963317283324886, 0.0, nan, 0.9376148683531234, 0.0, nan, 0.0, 0.0, nan, 0.9727979274611399, 0.0, 0.0, nan, nan, 0.34386587135257135, nan, 0.0, 0.9406538037985186, nan, nan, 0.5240587109125718, 0.26220570012391575, 0.0, nan, nan, 0.14305208096429384, nan, nan, 0.8603607567091949, nan, nan, nan, nan, nan, nan, 0.46669477234401346, nan, 0.0, nan, nan, nan, 0.43261437010578374, nan, nan, nan, nan, 0.0, 0.0, nan, 0.45827244058149375, nan, nan, nan, nan, nan, 0.3464270635423264, nan, 0.0, nan, nan, 0.7663512092534175, nan, nan, nan, 0.8006423646205332, 0.0, nan, nan, nan, nan, nan, 0.0621719822365765, nan, 0.0, nan, 0.845908884122961, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7155913978494624, nan, 0.4246755380318712, nan, 0.0, 0.0, 0.0, nan, nan, 0.016881827209533268, 0.3495271867612293, nan, 0.0, 0.0, nan, nan, nan, 0.6247674178209635, nan, 0.03138075313807531, nan, nan, 0.37915532946882763, nan, nan] | [0.7418440590246758, 0.9577598308647572, 0.9827172980936256, 0.8330498732588963, 0.8355762172742843, 0.8886006231711049, 0.9378149116652847, 0.9267657840585438, 0.8553544045957018, nan, 0.7399490601927975, 0.7790901788105085, 0.93525145028683, 0.9809937888198758, 0.7167701099981565, 0.722693994513463, 0.9450841452612931, 0.9268022105198283, 0.9299956829097189, 0.5960207898831397, 0.545267417517514, nan, 0.9830924902207998, nan, nan, 0.9748577563070316, nan, nan, 0.0, 0.0, nan, 0.9857847093255029, 0.0, nan, nan, nan, 0.6115252293577982, nan, nan, 0.9762188057738673, nan, nan, 0.670476812540823, 0.2779091147885474, nan, nan, nan, 0.14305208096429384, nan, nan, 0.9086093946011243, nan, nan, nan, nan, nan, nan, 0.5732780942516831, nan, nan, nan, nan, nan, 0.43261437010578374, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5578129388634023, nan, nan, nan, nan, nan, 0.8671316662043091, nan, nan, nan, nan, 0.9667064597426714, nan, nan, nan, 0.9524916499665999, nan, nan, nan, nan, nan, nan, 0.0621719822365765, nan, 0.0, nan, 0.8506077636910208, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7636259323006311, nan, 0.4938861291555216, nan, 0.0, nan, 0.0, nan, nan, 0.01847826086956522, 0.3743037974683544, nan, 0.0, nan, nan, nan, nan, 0.9710796915167095, nan, 0.03138075313807531, nan, nan, 0.5645587458164524, nan, nan] |
| 0.3488 | 42.0 | 1428 | 0.4356 | 0.3791 | 0.5698 | 0.8410 | [0.6430394863704093, 0.9057000304814464, 0.9756934982832852, 0.7387444011669063, 0.7708197787898503, 0.8015891120691322, 0.5200862304954832, 0.6157185559898827, 0.7083925495769372, 0.0, 0.5342958675100016, 0.6790433821206697, 0.8585350079738956, 0.9416661699548986, 0.4520408031339289, 0.6391104460112004, 0.710382968106782, 0.8540188113223104, 0.8942564848233064, 0.45812564773232, 0.5313313793216166, nan, 0.9637151624434532, 0.0, nan, 0.9432533509164152, 0.0, nan, 0.0, 0.0, nan, 0.9725961123937806, 0.0, nan, nan, nan, 0.3444572939038493, nan, 0.0, 0.9381922275677053, nan, nan, 0.5227073811144608, 0.31934976402726795, 0.0, nan, 0.0, 0.14509893108937913, nan, nan, 0.8481824048182405, nan, nan, nan, nan, nan, nan, 0.4670353041259039, nan, 0.0, nan, nan, nan, 0.41654073361725513, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4585200061795149, nan, nan, nan, nan, nan, 0.3386915678932688, nan, 0.0, nan, nan, 0.7712306872669152, nan, nan, nan, 0.800407605652729, 0.0, nan, nan, nan, nan, nan, 0.061566410981025436, nan, 0.0, nan, 0.8730674288683903, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7040255364426317, nan, 0.42437423061140744, nan, 0.0006508721687060661, 0.0, 0.0, nan, nan, 0.017130620985010708, 0.3303792981212336, nan, 0.0, 0.0, nan, nan, nan, 0.5921438342778972, nan, 0.05564853556485356, nan, nan, 0.3772484200291687, nan, nan] | [0.7232247928283171, 0.958138057611692, 0.9841355350821258, 0.8439253115945445, 0.828235869831172, 0.8934661845638018, 0.9693858026659864, 0.9122797454381925, 0.8613050145332204, nan, 0.7707386272044363, 0.7674719585849871, 0.9425452433012482, 0.9812629399585922, 0.7161965628136586, 0.7238472156698292, 0.945748449955713, 0.9221045134870471, 0.9292671739247746, 0.6146257489985765, 0.5703191700875992, nan, 0.9848381986874859, nan, nan, 0.9828770799785292, nan, nan, 0.0, 0.0, nan, 0.9871216467179402, 0.0, nan, nan, nan, 0.5977637614678899, nan, nan, 0.9800388264395529, nan, nan, 0.6582707380796865, 0.3199369582348306, nan, nan, nan, 0.14509893108937913, nan, nan, 0.9160433025135901, nan, nan, nan, nan, nan, nan, 0.5686172967374418, nan, nan, nan, nan, nan, 0.41654073361725513, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5557532066285928, nan, nan, nan, nan, nan, 0.8806589254811821, nan, nan, nan, nan, 0.9600742804085423, nan, nan, nan, 0.9549498997995992, nan, nan, nan, nan, nan, nan, 0.061566410981025436, nan, 0.0, nan, 0.8783165599268069, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7592273857334099, nan, 0.49398165838746655, nan, 0.0006592827004219409, nan, 0.0, nan, nan, 0.017391304347826087, 0.3539240506329114, nan, 0.0, nan, nan, nan, nan, 0.9736503856041131, nan, 0.05564853556485356, nan, nan, 0.5467676589748106, nan, nan] |
| 0.4009 | 43.0 | 1462 | 0.4337 | 0.3848 | 0.5699 | 0.8423 | [0.6560146665401557, 0.9053595306757009, 0.9764455674113391, 0.7345822804171938, 0.7599919300033979, 0.8058954845648801, 0.512341921958477, 0.5889672049218467, 0.7115341187097746, 0.0, 0.5565020694965829, 0.6719112007015983, 0.860596525438752, 0.9415021331481298, 0.4503845263509864, 0.638432804077308, 0.7128857381150959, 0.8639533778861685, 0.8953594880858695, 0.44755489021956085, 0.4815785164565253, nan, 0.9636744009128947, 0.0, nan, 0.9350275490620937, 0.0, nan, 0.0, 0.0, nan, 0.971812795867221, 0.0, 0.0, nan, nan, 0.3371409921671018, nan, 0.0, 0.9379286539702549, nan, nan, 0.5071838154758069, 0.28002826189354685, nan, nan, nan, 0.15419604275642484, nan, nan, 0.8627545858443673, nan, nan, nan, nan, nan, nan, 0.46814752724224645, nan, 0.0, nan, nan, nan, 0.42107432339607087, nan, nan, nan, nan, 0.0, 0.0, nan, 0.47078045444182687, nan, nan, nan, nan, nan, 0.34758781346616285, nan, 0.0, nan, nan, 0.7755541209382397, nan, nan, nan, 0.7981633016098906, 0.0, nan, nan, nan, nan, nan, 0.07186112232539363, nan, 0.0, nan, 0.8400493955544001, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7288468404141378, nan, 0.43042520199134904, nan, 0.0, 0.0, 0.0, nan, nan, 0.01825168107588857, 0.3876537369914853, nan, 0.0, 0.0, nan, nan, nan, 0.607815631262525, nan, 0.0497907949790795, nan, nan, 0.3990300920169112, nan, nan] | [0.7412106835895382, 0.953752082065506, 0.9842857875796155, 0.8359473308214782, 0.8339217262632971, 0.8976896125220329, 0.9663881625103642, 0.9314672735449231, 0.8653445632023437, nan, 0.7455911274462391, 0.7636190532861266, 0.9400521215739747, 0.9823395445134575, 0.717364141010672, 0.7212000489245339, 0.9463573959255979, 0.9339788672113073, 0.9362959365387729, 0.5938358658589069, 0.5159468088838988, nan, 0.9828338667442537, nan, nan, 0.9728502415458937, nan, nan, 0.0, 0.0, nan, 0.9840588446916293, 0.0, nan, nan, nan, 0.5923165137614679, nan, nan, 0.9784419325547171, nan, nan, 0.6427171783148269, 0.3123194116101918, nan, nan, nan, 0.15419604275642484, nan, nan, 0.9112577242949403, nan, nan, nan, nan, nan, nan, 0.5784567581563956, nan, nan, nan, nan, nan, 0.42107432339607087, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5800018724838498, nan, nan, nan, nan, nan, 0.8554538369066963, nan, nan, nan, nan, 0.9560949728080648, nan, nan, nan, 0.9591182364729459, nan, nan, nan, nan, nan, nan, 0.07186112232539363, nan, 0.0, nan, 0.8446608286498497, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.780837636259323, nan, 0.503821169277799, nan, 0.0, nan, 0.0, nan, nan, 0.020652173913043477, 0.4149367088607595, nan, 0.0, nan, nan, nan, nan, 0.9746143958868895, nan, 0.0497907949790795, nan, nan, 0.5652633433151313, nan, nan] |
| 0.508 | 44.0 | 1496 | 0.4355 | 0.3812 | 0.5695 | 0.8407 | [0.648469008501776, 0.9045975240386274, 0.9737308715962091, 0.7323261730864167, 0.7539777392099603, 0.7996750394539419, 0.49936628643852976, 0.6149690928328786, 0.7004161820820638, 0.0, 0.55764149130393, 0.6536630417815703, 0.8617985477564699, 0.9418113282412459, 0.4513157726406284, 0.6381435655034988, 0.7259602990461459, 0.8627429791886497, 0.8944258726580113, 0.44763456713483873, 0.5234929174168637, nan, 0.9616265021936796, 0.0, nan, 0.9263801782472355, 0.0, nan, 0.0, 0.0, nan, 0.9730472070030204, 0.0, 0.0, nan, nan, 0.3454866688082236, nan, 0.0, 0.9411160458019879, nan, nan, 0.5134199974130126, 0.3223374175306315, nan, nan, nan, 0.16397543779849896, nan, nan, 0.8615613123061465, nan, nan, nan, nan, nan, nan, 0.4655322862129145, nan, 0.0, nan, nan, nan, 0.41090809177084764, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4705074399084319, nan, nan, nan, nan, nan, 0.33948229249129935, nan, 0.0, nan, nan, 0.7792320173066523, nan, nan, nan, 0.7982293366719578, 0.0, nan, nan, nan, nan, nan, 0.07223567393058919, nan, 0.0, nan, 0.8492625560392437, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7002887044388307, nan, 0.42776412776412776, nan, 0.0005116398055768739, 0.0, 0.0, nan, nan, 0.016042780748663103, 0.39616159222840897, 0.0, 0.0, 0.0, nan, nan, nan, 0.6721973094170404, nan, 0.06820083682008368, nan, nan, 0.4066035253880558, nan, nan] | [0.7374387726810634, 0.9536599499092013, 0.9800933764301473, 0.839203797518848, 0.8453050904726951, 0.8866009593214934, 0.980036992155112, 0.9029312764884643, 0.8339093218593367, nan, 0.7484121610729599, 0.7475380083901104, 0.93365391597615, 0.9828571428571429, 0.7226284848112415, 0.7218378151701001, 0.9353410097431355, 0.9294386731402666, 0.9312773190869355, 0.572913563081405, 0.564483117551431, nan, 0.9778230368861733, nan, nan, 0.9641116478797638, nan, nan, 0.0, 0.0, nan, 0.9851527025581689, 0.0, nan, nan, nan, 0.6166857798165137, nan, nan, 0.975357735541848, nan, nan, 0.6481466361854997, 0.35933806146572106, nan, nan, nan, 0.16397543779849896, nan, nan, 0.9163220740603075, nan, nan, nan, nan, nan, nan, 0.5525634386328327, nan, nan, nan, nan, nan, 0.41090809177084764, nan, nan, nan, nan, 0.0, 0.0, nan, 0.577286770901601, nan, nan, nan, nan, nan, 0.9007252407561527, nan, nan, nan, nan, 0.9555643984613343, nan, nan, nan, 0.9540146960587842, nan, nan, nan, nan, nan, nan, 0.072264836495761, nan, 0.0, nan, 0.8542020650895308, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7422069229298145, nan, 0.49894917844860526, nan, 0.0005274261603375527, nan, 0.0, nan, nan, 0.016304347826086956, 0.4232911392405063, nan, 0.0, nan, nan, nan, nan, 0.9633676092544987, nan, 0.06820083682008368, nan, nan, 0.5444777171041043, nan, nan] |
| 0.5719 | 45.0 | 1530 | 0.4395 | 0.3799 | 0.5700 | 0.8400 | [0.6414370240841469, 0.9043700099428001, 0.973426644722469, 0.7405897094574748, 0.7536704900938478, 0.804312945006287, 0.4960754546335476, 0.6365588277963151, 0.7037455494896748, 0.0, 0.5426602715665744, 0.6548818464920315, 0.8550905043641263, 0.9410902464147056, 0.43856059399389835, 0.6356925312678756, 0.7196804037005887, 0.8703939734053684, 0.8937119149487878, 0.4478634608197334, 0.5344280466865179, nan, 0.9615934432478795, 0.0, nan, 0.927006244516695, 0.0, nan, 0.0, 0.0, nan, 0.9740371925614877, 0.0, nan, nan, nan, 0.3424408014571949, nan, 0.0, 0.9408860300779127, nan, nan, 0.5203239021892203, 0.2780983152550196, 0.0, nan, nan, 0.14669092563111213, nan, nan, 0.8619544799266087, nan, nan, nan, nan, nan, nan, 0.4619068350021768, nan, 0.0, nan, nan, nan, 0.41873883775243853, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4487229862475442, nan, nan, nan, nan, nan, 0.33335628464213174, nan, 0.0, nan, nan, 0.7682217285517825, nan, nan, nan, 0.8001753439438899, 0.0, nan, nan, nan, nan, nan, 0.13951612903225807, nan, 0.0, nan, 0.8724522913150721, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6800654783557657, nan, 0.4300229432972796, nan, 0.0, 0.0, 0.0, nan, nan, 0.013001083423618635, 0.40168107020243876, 0.0, 0.0, 0.0, nan, nan, nan, 0.6416382252559727, nan, 0.024686192468619247, nan, nan, 0.39916296102537274, nan, nan] | [0.7150857952620067, 0.9548891868367394, 0.979712248143832, 0.8594642423275396, 0.8421242732473464, 0.8986894444468386, 0.9795267555328784, 0.8969215464493534, 0.8482136726706795, nan, 0.7769610213753748, 0.7445479158608789, 0.9422063723868614, 0.9836438923395445, 0.733198140068416, 0.7184655169401198, 0.9474092116917626, 0.9399639797574454, 0.929955210188333, 0.5950607475088555, 0.5751494099622235, nan, 0.9785665793812434, nan, nan, 0.9641760601180891, nan, nan, 0.0, 0.0, nan, 0.986732719476504, 0.0, nan, nan, nan, 0.5928899082568807, nan, nan, 0.9755612612330525, nan, nan, 0.6636593729588505, 0.31652219595482006, nan, nan, nan, 0.14669092563111213, nan, nan, 0.9167402313803837, nan, nan, nan, nan, nan, nan, 0.5494562402900052, nan, nan, nan, nan, nan, 0.41873883775243853, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5345941391255501, nan, nan, nan, nan, nan, 0.8954015244587115, nan, nan, nan, nan, 0.9632577264889243, nan, nan, nan, 0.9511022044088177, nan, nan, nan, nan, nan, nan, 0.13968510294711345, nan, 0.0, nan, 0.8783819108613253, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7150506789061006, nan, 0.5013374092472297, nan, 0.0, nan, 0.0, nan, nan, 0.013043478260869565, 0.42949367088607593, nan, 0.0, nan, nan, nan, nan, 0.9665809768637532, nan, 0.024686192468619247, nan, nan, 0.5376078914919852, nan, nan] |
| 0.5135 | 46.0 | 1564 | 0.4395 | 0.3743 | 0.5705 | 0.8402 | [0.6420677357932264, 0.9066535356975214, 0.9757722790643538, 0.7364802831174689, 0.7556677422712605, 0.8044408285290534, 0.5061810669421212, 0.6173092849912579, 0.7135310620490349, 0.0, 0.5187822141371055, 0.662158829865933, 0.8563279413595389, 0.9415793234873749, 0.4588028546898476, 0.6401749010792751, 0.7190131858783496, 0.8659171258400484, 0.8942357580159801, 0.4444416916482917, 0.5483737356121381, nan, 0.9614272551075526, 0.0, nan, 0.9248294637034583, 0.0, nan, 0.0, 0.0, nan, 0.9723422686135719, 0.0, 0.0, nan, nan, 0.35584499115613444, nan, 0.0, 0.9412403638722525, nan, nan, 0.5245030568803816, 0.3055364116701126, 0.0, nan, nan, 0.18626336138276098, nan, nan, 0.8559996462994075, nan, nan, nan, nan, nan, nan, 0.4585708022797019, nan, 0.0, nan, nan, nan, 0.439208682511334, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4373285255154033, nan, nan, nan, nan, nan, 0.3324376809722712, nan, 0.0, nan, nan, 0.7821150519031141, nan, nan, nan, 0.801143268668137, 0.0, nan, nan, nan, nan, nan, 0.09453739165490828, nan, 0.0, nan, 0.8784810126582279, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.6686583378598587, nan, 0.42724585317786173, nan, 0.0, 0.0, 0.0, nan, nan, 0.015135135135135135, 0.3627450980392157, 0.0, 0.0, 0.0, nan, nan, nan, 0.6044791041791642, nan, 0.022594142259414227, nan, nan, 0.3881668523332096, nan, nan] | [0.7192606512430301, 0.9553328759052591, 0.9831350733305482, 0.8406978557363495, 0.8458527036946415, 0.8905916677799852, 0.9688755660437528, 0.9141739687385018, 0.8702309294394983, nan, 0.7999000548086533, 0.758977715628812, 0.943521514268886, 0.9820496894409938, 0.7005674020360925, 0.7265293285107721, 0.9357838795394154, 0.930883591615364, 0.930090119259619, 0.593868970768365, 0.588400407003263, nan, 0.9782109721009925, nan, nan, 0.9591519055287171, nan, nan, 0.0, 0.0, nan, 0.9877196223516486, 0.0, nan, nan, nan, 0.6344610091743119, nan, nan, 0.9767980712026803, nan, nan, 0.6689255388634879, 0.3493564486472288, nan, nan, nan, 0.18626336138276098, nan, nan, 0.8995493193328068, nan, nan, nan, nan, nan, nan, 0.5416882444329363, nan, nan, nan, nan, nan, 0.439208682511334, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5223293699091845, nan, nan, nan, nan, nan, 0.8842124730841887, nan, nan, nan, nan, 0.9594110624751293, nan, nan, nan, 0.9511823647294589, nan, nan, nan, nan, nan, nan, 0.09467097295115058, nan, 0.0, nan, 0.8843941968370148, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7062535857716581, nan, 0.4970385938097058, nan, 0.0, nan, 0.0, nan, nan, 0.015217391304347827, 0.3887341772151899, nan, 0.0, nan, nan, nan, nan, 0.971401028277635, nan, 0.022594142259414227, nan, nan, 0.5524044389642416, nan, nan] |
| 0.4754 | 47.0 | 1598 | 0.4424 | 0.3861 | 0.5717 | 0.8392 | [0.635800731775964, 0.9059691183658296, 0.9745545592174217, 0.7378417284436017, 0.7612581170548046, 0.8026475527784566, 0.5057853483085066, 0.6390832521859596, 0.7045740063956144, 0.0, 0.5404117939224274, 0.6736021829821845, 0.8624582746087086, 0.9417129262490087, 0.44633872615159575, 0.6386271087841768, 0.7192878839014513, 0.8640161793421753, 0.8943704717748705, 0.452955622255848, 0.525642843895574, nan, 0.9625987374298132, 0.0, nan, 0.9345442154392631, 0.0, nan, 0.0, 0.0, nan, 0.9733701170565359, 0.0, nan, nan, nan, 0.34633666612030817, nan, 0.0, 0.9415209988649262, nan, nan, 0.546814717229698, 0.2978826965198345, 0.0, nan, nan, 0.15214919263133955, nan, nan, 0.859492952829777, nan, nan, nan, nan, nan, nan, 0.4690677966101695, nan, 0.0, nan, nan, nan, 0.42945459541145764, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4575310559006211, nan, nan, nan, nan, nan, 0.33117652800825537, nan, 0.0, nan, nan, 0.7806932296728215, nan, nan, nan, 0.801031554764747, 0.0, nan, nan, nan, nan, nan, 0.07706273956021788, nan, 0.0, nan, 0.8530959651744526, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7205169628432956, nan, 0.426376858963883, nan, 0.0, 0.0, 0.0, nan, nan, 0.016789087093389297, 0.37205725777830356, nan, 0.0, 0.0, nan, nan, nan, 0.5942598781206998, nan, 0.07280334728033473, nan, nan, 0.3854625550660793, nan, nan] | [0.7153975539878623, 0.9572409813529364, 0.9813760197014006, 0.8438600372064012, 0.8386638237384506, 0.8918371480656269, 0.9506983863766821, 0.8675406440359221, 0.8471723159316138, nan, 0.771738079117903, 0.7638273184374164, 0.9380915112835946, 0.9834368530020704, 0.7250455765173396, 0.7193216962834827, 0.9438662533215235, 0.9332598319511878, 0.9342723004694836, 0.6044956467044063, 0.5635123913780803, nan, 0.9809588465392947, nan, nan, 0.971604938271605, nan, nan, 0.0, 0.0, nan, 0.9880113177827258, 0.0, nan, nan, nan, 0.6057912844036697, nan, nan, 0.9739643673482168, nan, nan, 0.7116672109732202, 0.3215130023640662, nan, nan, nan, 0.15214919263133955, nan, nan, 0.9151605259489848, nan, nan, nan, nan, nan, nan, 0.5732780942516831, nan, nan, nan, nan, nan, 0.42945459541145764, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5517273663514652, nan, nan, nan, nan, nan, 0.9411881266595331, nan, nan, nan, nan, 0.9590131317150816, nan, nan, nan, 0.9503006012024048, nan, nan, nan, nan, nan, nan, 0.07710940654016955, nan, 0.0, nan, 0.8580577702261142, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7676419965576592, nan, 0.4984715322888804, nan, 0.0, nan, 0.0, nan, nan, 0.017391304347826087, 0.3981012658227848, nan, 0.0, nan, nan, nan, nan, 0.971401028277635, nan, 0.07280334728033473, nan, nan, 0.5548705302096177, nan, nan] |
| 0.3732 | 48.0 | 1632 | 0.4324 | 0.3705 | 0.5732 | 0.8420 | [0.6522859304068301, 0.906379682399159, 0.97747071629239, 0.7375585034645583, 0.7646863342052228, 0.8031975093572922, 0.5037176563894121, 0.5817909968660512, 0.7074276155185326, 0.0, 0.5285925793990939, 0.6627830867914648, 0.8622244451577745, 0.9421804526217489, 0.45635228885972196, 0.6414667479982396, 0.7028606456885983, 0.8522384082430272, 0.8979623480159704, 0.4521165338645418, 0.5075188781014024, nan, 0.9622389625250309, 0.0, nan, 0.9386656213896682, 0.0, nan, 0.0, 0.0, nan, 0.9719177522669062, 0.0, nan, nan, nan, 0.351994851994852, nan, 0.0, 0.9381446390021588, nan, nan, 0.5363382899628253, 0.2963134709019244, 0.0, nan, 0.0, 0.16283829884011827, nan, nan, 0.8550414666084679, nan, nan, nan, nan, nan, nan, 0.47127393838467946, nan, 0.0, nan, nan, nan, 0.42890506937766176, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.46982824137406903, nan, nan, nan, nan, nan, 0.34779278349395365, nan, 0.0, nan, nan, 0.7848697175388658, nan, nan, nan, 0.7974646947625931, 0.0, nan, nan, nan, nan, nan, 0.08215583366976181, nan, 0.0, nan, 0.8502437439064023, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7120948065528059, nan, 0.4296574838551459, nan, 0.0005219206680584551, 0.0, 0.0, nan, nan, 0.01810436634717785, 0.44147000117412233, 0.0, 0.0, 0.0, nan, nan, nan, 0.5951679434295816, nan, 0.045188284518828455, nan, nan, 0.41456323509977533, nan, nan] | [0.7406339915591017, 0.9531895910059619, 0.985634395361962, 0.8349210723856701, 0.8311254034278257, 0.8961381491904378, 0.972192104088271, 0.9385672040446437, 0.8323644519717117, nan, 0.7654512041783538, 0.7531760435571688, 0.9319030829184854, 0.98175983436853, 0.6979659558778345, 0.7258041970260872, 0.9521147032772365, 0.9198378404289559, 0.9375640818088609, 0.6010858410302248, 0.5502380033449119, nan, 0.9786635631849481, nan, nan, 0.9768974771873322, nan, nan, 0.0, 0.0, nan, 0.9833004365708286, 0.0, nan, nan, nan, 0.6272935779816514, nan, nan, 0.9797100541691455, nan, nan, 0.706768451992162, 0.33569739952718675, nan, nan, nan, 0.16283829884011827, nan, nan, 0.9101426381080704, nan, nan, nan, nan, nan, nan, 0.5862247540134645, nan, nan, nan, nan, nan, 0.42890506937766176, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5787847579814624, nan, nan, nan, nan, nan, 0.8457311193014438, nan, nan, nan, nan, 0.950921872927444, nan, nan, nan, 0.95812959251837, nan, nan, nan, nan, nan, nan, 0.08215583366976181, nan, 0.0, nan, 0.8548555744347144, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7814113597246127, nan, 0.5021016431027895, nan, 0.0005274261603375527, nan, 0.0, nan, nan, 0.01847826086956522, 0.4759493670886076, nan, 0.0, nan, nan, nan, nan, 0.9736503856041131, nan, 0.045188284518828455, nan, nan, 0.5525805883389114, nan, nan] |
| 0.4609 | 49.0 | 1666 | 0.4434 | 0.3791 | 0.5715 | 0.8385 | [0.6357552863322999, 0.9060669008495456, 0.9762225001090544, 0.7366142738483163, 0.7605642712550608, 0.803235443805143, 0.5075830805639812, 0.6249329039184112, 0.7119418181818182, nan, 0.5075042549899428, 0.664340771849282, 0.8607069691213171, 0.9428531618861841, 0.45614852275751927, 0.6396386699980781, 0.7216139322355375, 0.8595621958811348, 0.8960687897433257, 0.4543855240010053, 0.5258929152299785, nan, 0.9625035688227643, 0.0, nan, 0.9326485784116574, 0.0, nan, 0.0, 0.0, nan, 0.9725947746024729, 0.0, 0.0, nan, nan, 0.3520026050146532, nan, 0.0, 0.9379856589961297, nan, nan, 0.5427974947807933, 0.3154963680387409, 0.0, nan, nan, 0.16306572663179442, nan, nan, 0.825908070817056, nan, nan, nan, nan, nan, nan, 0.46275010642826736, nan, 0.0, nan, nan, nan, 0.41763978568484683, nan, nan, nan, nan, 0.0, 0.0, nan, 0.45021210952564594, nan, nan, nan, nan, nan, 0.33362747792710035, nan, 0.0, nan, nan, 0.7672069038097242, nan, nan, nan, 0.7980623269934816, 0.0, nan, nan, nan, nan, nan, 0.05954784012918853, nan, 0.0, nan, 0.8970569168935564, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7075353499194559, nan, 0.42392015706806285, nan, 0.0, 0.0, 0.0, nan, nan, 0.010857763300760043, 0.3621160004744396, 0.0, 0.0, 0.0, nan, nan, nan, 0.5835416266102672, nan, 0.06276150627615062, nan, nan, 0.38576966362385895, nan, nan] | [0.7169304704106466, 0.9549498000974661, 0.9841575232524902, 0.8361467803408048, 0.8405047362718026, 0.8910226298165395, 0.971235410421583, 0.8884861203853859, 0.8401689057743803, nan, 0.7931134539123706, 0.7577430008033085, 0.9321935437022454, 0.980703933747412, 0.7020832053094083, 0.7268875259911587, 0.9267050487156776, 0.926624163693513, 0.9391290270357778, 0.5985367630019532, 0.5672432546226448, nan, 0.9808618627355898, nan, nan, 0.9708856682769726, nan, nan, 0.0, 0.0, nan, 0.9839664744717882, 0.0, nan, nan, nan, 0.6198394495412844, nan, nan, 0.9789272630491279, nan, nan, 0.7111365120836055, 0.3422642500656685, nan, nan, nan, 0.16306572663179442, nan, nan, 0.9233378246526971, nan, nan, nan, nan, nan, nan, 0.5629207664422579, nan, nan, nan, nan, nan, 0.41763978568484683, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5464844115719502, nan, nan, nan, nan, nan, 0.9139882957502741, nan, nan, nan, nan, 0.9669717469160366, nan, nan, nan, 0.9552438209752839, nan, nan, nan, nan, nan, nan, 0.05954784012918853, nan, 0.0, nan, 0.9043262318651156, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.755976286096768, nan, 0.4950324799388613, nan, 0.0, nan, 0.0, nan, nan, 0.010869565217391304, 0.3864556962025317, nan, 0.0, nan, nan, nan, nan, 0.9752570694087404, nan, 0.06276150627615062, nan, nan, 0.543420820856086, nan, nan] |
| 0.5577 | 50.0 | 1700 | 0.4393 | 0.3682 | 0.5707 | 0.8396 | [0.6438135421351039, 0.9050984072652993, 0.9749698684406963, 0.7369247110674255, 0.752654357505371, 0.8027859009457442, 0.5003933394519471, 0.6087506208515826, 0.7086873558198924, 0.0, 0.5266673119736714, 0.6564246536171983, 0.8532161335841957, 0.9411694700095208, 0.44680635049557726, 0.6368607368445408, 0.7191841188567591, 0.8702582894594784, 0.8936646895801497, 0.4566314362463284, 0.5366785367432826, nan, 0.9618790668147913, 0.0, nan, 0.9237170739775875, 0.0, nan, 0.0, 0.0, nan, 0.9728922153107986, 0.0, 0.0, nan, nan, 0.3453142950927294, nan, 0.0, 0.9417587233463799, nan, nan, 0.5209098524389458, 0.2924377427461732, 0.0, nan, nan, 0.1969524675915397, nan, nan, 0.857620930940067, nan, nan, nan, nan, nan, nan, 0.45423584504044273, nan, 0.0, nan, nan, nan, 0.43041626597060034, nan, nan, nan, nan, 0.0, 0.0, nan, 0.4432651787107851, nan, nan, nan, nan, nan, 0.3351013020964255, nan, 0.0, nan, nan, 0.7692795267272343, nan, nan, nan, 0.8001664342584679, 0.0, nan, nan, nan, nan, nan, 0.12577983497685652, nan, 0.0, nan, 0.8418795086761552, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.7059779664078021, nan, 0.42956026058631924, nan, 0.0, 0.0, 0.0, nan, nan, 0.017543859649122806, 0.32014218009478673, 0.0, 0.0, 0.0, nan, nan, nan, 0.6160586916649684, nan, 0.02803347280334728, nan, nan, 0.3776207302709069, nan, nan] | [0.7197732663811959, 0.9539751388649803, 0.9812404259841538, 0.8428192733510058, 0.8449438987731134, 0.8896694090217593, 0.9736590343771925, 0.9186301631212439, 0.8542672739340398, nan, 0.7893896895250991, 0.751301657195561, 0.9366714807852123, 0.9824016563146998, 0.7211741330219792, 0.7214271985462424, 0.9486271036315324, 0.9383752542303241, 0.9309670282229777, 0.6021451981328831, 0.5816638012701308, nan, 0.9796657291565641, nan, nan, 0.9592377885131508, nan, nan, 0.0, 0.0, nan, 0.9877390687137204, 0.0, nan, nan, nan, 0.6032110091743119, nan, nan, 0.9756551961674547, nan, nan, 0.6600261267145656, 0.3362227475702653, nan, nan, nan, 0.1969524675915397, nan, nan, 0.9168331552292895, nan, nan, nan, nan, nan, nan, 0.5525634386328327, nan, nan, nan, nan, nan, 0.43041626597060034, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5317854133508099, nan, nan, nan, nan, nan, 0.8805268233398064, nan, nan, nan, nan, 0.965910598222576, nan, nan, nan, 0.9506212424849699, nan, nan, nan, nan, nan, nan, 0.12616067823980623, nan, 0.0, nan, 0.8465560057508822, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7475616752725186, nan, 0.503916698509744, nan, 0.0, nan, 0.0, nan, nan, 0.01847826086956522, 0.3420253164556962, nan, 0.0, nan, nan, nan, nan, 0.971401028277635, nan, 0.02803347280334728, nan, nan, 0.5647348951911221, nan, nan] |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
vgarg/my-fw9-identification-model-e5_large_v5_16_10_23
|
vgarg
| 2023-10-16T06:43:59Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-10-16T06:41:35Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# vgarg/my-fw9-identification-model-e5_large_v5_16_10_23
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("vgarg/my-fw9-identification-model-e5_large_v5_16_10_23")
# 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}
}
```
|
iamplus/gpt-neoxt-20b-v12
|
iamplus
| 2023-10-16T06:43:16Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"dataset:iamplus/Instruction_Tuning",
"dataset:iamplus/Conversational_Data",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T18:16:48Z |
---
license: bigscience-openrail-m
datasets:
- iamplus/Instruction_Tuning
- iamplus/Conversational_Data
---
Instruction Tuned GPT-NeoXT-20B model on Instruction Tuning dataset as listed below (~5.2M data) using ***Colossal AI***
**Base Model:** togethercomputer/GPT-NeoXT-Chat-Base-20B (GPT-NeoXT-Chat-Base-20B-v0.16 - fine-tuned on feedback data)
**Training Details :**
* Epochs: 4
* Batch Size : 5 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 40
* Block Size : 2020
* Weight Decay : 0
* Learning Rate : 1e-6
* Learning Rate Scheduler Type : Cosine
* Number of warmup steps : 600
* Machine : 8xA100 80GB
**Training Data Specifics :**
* Labels and Input ids are exactly the same.
* Block Size is 2020, Multiple instructions are clubbed together in each data.
* "###" is the EOS Token used in the data.
|
WenYee/Bert-VITS2
|
WenYee
| 2023-10-16T06:40:34Z | 11 | 1 |
transformers
|
[
"transformers",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-10-15T23:38:13Z |
---
license: apache-2.0
language:
- zh
---
女声，长阅读版本，
推荐给有小说阅读需要的。
基于Bert-VITS2语言模型
真人录音
版本为v1.0.1
---
license: apache-2.0
---
|
nanocu/KPDIS-1E10
|
nanocu
| 2023-10-16T06:39:27Z | 0 | 0 |
keras
|
[
"keras",
"agriculture",
"plant disease",
"plant",
"disease",
"image-classification",
"en",
"license:cc-by-nc-nd-4.0",
"region:us"
] |
image-classification
| 2023-10-16T00:53:30Z |
---
license: cc-by-nc-nd-4.0
language:
- en
library_name: keras
pipeline_tag: image-classification
tags:
- agriculture
- plant disease
- plant
- disease
---
# KPDIS-1E10
K Plant Disease Detection 1 Epoch 10
38 diffrent diseased/healthy types of plant photos used
Device used: 8XLarge - 32 cores - 256 GB RAM - 100Gi Disk
Provider: Saturn Cloud
5 Epoch first training (KPDIS-1E5) + 5 Epoch second training
## Accuracy Last 5 Epoch 1E10
## Loss Last 5 Epoch 1E10
## Evaluation
|
WhitePeak/GPT2-finetuned-study
|
WhitePeak
| 2023-10-16T06:24:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-16T05:53:06Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: GPT2-finetuned-study
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GPT2-finetuned-study
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 5.0
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.14.1
|
IlluminatiPudding/ppo-Huggy
|
IlluminatiPudding
| 2023-10-16T06:19:38Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-10-16T06:19:27Z |
---
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: IlluminatiPudding/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kwwww/bert-base-uncased-test_16_535
|
kwwww
| 2023-10-16T06:12:29Z | 0 | 0 | null |
[
"pytorch",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"region:us"
] | null | 2023-10-15T21:10:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- f1
- accuracy
model-index:
- name: bert-base-uncased-test_16_535
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-test_16_535
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4602
- F1: {'f1': 0.8391959798994975}
- Accuracy: {'accuracy': 0.7545541706615532}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------------------------:|:--------------------------------:|
| No log | 1.0 | 34 | 0.6290 | {'f1': 0.8167895632444696} | {'accuracy': 0.6903163950143816} |
| No log | 2.0 | 68 | 0.6274 | {'f1': 0.8167895632444696} | {'accuracy': 0.6903163950143816} |
| No log | 3.0 | 102 | 0.6252 | {'f1': 0.8167895632444696} | {'accuracy': 0.6903163950143816} |
| No log | 4.0 | 136 | 0.6251 | {'f1': 0.8179239931934204} | {'accuracy': 0.6922339405560882} |
| No log | 5.0 | 170 | 0.6246 | {'f1': 0.8179239931934204} | {'accuracy': 0.6922339405560882} |
| No log | 6.0 | 204 | 0.6136 | {'f1': 0.8179239931934204} | {'accuracy': 0.6922339405560882} |
| No log | 7.0 | 238 | 0.6178 | {'f1': 0.81838819523269} | {'accuracy': 0.6931927133269415} |
| No log | 8.0 | 272 | 0.6112 | {'f1': 0.8179749715585892} | {'accuracy': 0.6931927133269415} |
| No log | 9.0 | 306 | 0.6028 | {'f1': 0.8173515981735159} | {'accuracy': 0.6931927133269415} |
| No log | 10.0 | 340 | 0.5813 | {'f1': 0.8187683284457478} | {'accuracy': 0.7037392138063279} |
| No log | 11.0 | 374 | 0.5890 | {'f1': 0.8234610917537746} | {'accuracy': 0.7085330776605945} |
| No log | 12.0 | 408 | 0.5631 | {'f1': 0.8330319469559975} | {'accuracy': 0.7344199424736337} |
| No log | 13.0 | 442 | 0.6507 | {'f1': 0.8307873090481785} | {'accuracy': 0.7238734419942474} |
| No log | 14.0 | 476 | 0.6269 | {'f1': 0.8347205707491082} | {'accuracy': 0.7334611697027804} |
| 0.5155 | 15.0 | 510 | 0.5856 | {'f1': 0.8366093366093366} | {'accuracy': 0.7449664429530202} |
| 0.5155 | 16.0 | 544 | 0.6232 | {'f1': 0.8405620036652413} | {'accuracy': 0.7497603068072867} |
| 0.5155 | 17.0 | 578 | 0.6568 | {'f1': 0.8360556563823351} | {'accuracy': 0.7401725790987536} |
| 0.5155 | 18.0 | 612 | 0.6778 | {'f1': 0.8416565164433618} | {'accuracy': 0.75071907957814} |
| 0.5155 | 19.0 | 646 | 0.7373 | {'f1': 0.8395657418576598} | {'accuracy': 0.7449664429530202} |
| 0.5155 | 20.0 | 680 | 0.6615 | {'f1': 0.8373531230674086} | {'accuracy': 0.74784276126558} |
| 0.5155 | 21.0 | 714 | 0.8051 | {'f1': 0.8383594692400483} | {'accuracy': 0.7430488974113135} |
| 0.5155 | 22.0 | 748 | 0.7765 | {'f1': 0.8386308068459658} | {'accuracy': 0.7468839884947267} |
| 0.5155 | 23.0 | 782 | 0.7695 | {'f1': 0.839073262366938} | {'accuracy': 0.7535953978907} |
| 0.5155 | 24.0 | 816 | 0.7644 | {'f1': 0.843297671491504} | {'accuracy': 0.7612655800575263} |
| 0.5155 | 25.0 | 850 | 0.8090 | {'f1': 0.844002486016159} | {'accuracy': 0.7593480345158198} |
| 0.5155 | 26.0 | 884 | 0.8430 | {'f1': 0.8419737663960025} | {'accuracy': 0.7574304889741131} |
| 0.5155 | 27.0 | 918 | 0.8578 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.5155 | 28.0 | 952 | 0.8818 | {'f1': 0.8409805153991201} | {'accuracy': 0.7574304889741131} |
| 0.5155 | 29.0 | 986 | 0.9517 | {'f1': 0.8421052631578948} | {'accuracy': 0.7555129434324065} |
| 0.2477 | 30.0 | 1020 | 1.1130 | {'f1': 0.8364741641337385} | {'accuracy': 0.7420901246404602} |
| 0.2477 | 31.0 | 1054 | 1.0485 | {'f1': 0.837954405422058} | {'accuracy': 0.74784276126558} |
| 0.2477 | 32.0 | 1088 | 1.0609 | {'f1': 0.8381070983810709} | {'accuracy': 0.75071907957814} |
| 0.2477 | 33.0 | 1122 | 0.9798 | {'f1': 0.8361290322580646} | {'accuracy': 0.7564717162032598} |
| 0.2477 | 34.0 | 1156 | 1.0213 | {'f1': 0.843650287172942} | {'accuracy': 0.7651006711409396} |
| 0.2477 | 35.0 | 1190 | 1.1001 | {'f1': 0.8452830188679246} | {'accuracy': 0.7641418983700863} |
| 0.2477 | 36.0 | 1224 | 1.1814 | {'f1': 0.8402734617775015} | {'accuracy': 0.7535953978907} |
| 0.2477 | 37.0 | 1258 | 1.1825 | {'f1': 0.8431618569636137} | {'accuracy': 0.7603068072866731} |
| 0.2477 | 38.0 | 1292 | 1.2292 | {'f1': 0.8449466415568111} | {'accuracy': 0.763183125599233} |
| 0.2477 | 39.0 | 1326 | 1.1645 | {'f1': 0.8372395833333335} | {'accuracy': 0.7603068072866731} |
| 0.2477 | 40.0 | 1360 | 1.3783 | {'f1': 0.8405438813349814} | {'accuracy': 0.7526366251198466} |
| 0.2477 | 41.0 | 1394 | 1.4388 | {'f1': 0.8418461538461539} | {'accuracy': 0.7535953978907} |
| 0.2477 | 42.0 | 1428 | 1.4159 | {'f1': 0.8412600370599136} | {'accuracy': 0.7535953978907} |
| 0.2477 | 43.0 | 1462 | 1.4079 | {'f1': 0.8421052631578948} | {'accuracy': 0.7583892617449665} |
| 0.2477 | 44.0 | 1496 | 1.3515 | {'f1': 0.839821314613912} | {'accuracy': 0.7593480345158198} |
| 0.0896 | 45.0 | 1530 | 1.5365 | {'f1': 0.8408113091579594} | {'accuracy': 0.7516778523489933} |
| 0.0896 | 46.0 | 1564 | 1.5318 | {'f1': 0.8394296342219467} | {'accuracy': 0.7516778523489933} |
| 0.0896 | 47.0 | 1598 | 1.4766 | {'f1': 0.843297671491504} | {'accuracy': 0.7612655800575263} |
| 0.0896 | 48.0 | 1632 | 1.4258 | {'f1': 0.8437900128040973} | {'accuracy': 0.7660594439117929} |
| 0.0896 | 49.0 | 1666 | 1.4192 | {'f1': 0.845518118245391} | {'accuracy': 0.7670182166826462} |
| 0.0896 | 50.0 | 1700 | 1.4329 | {'f1': 0.8419023136246786} | {'accuracy': 0.7641418983700863} |
| 0.0896 | 51.0 | 1734 | 1.6575 | {'f1': 0.8423645320197044} | {'accuracy': 0.7545541706615532} |
| 0.0896 | 52.0 | 1768 | 1.5449 | {'f1': 0.8377358490566037} | {'accuracy': 0.7526366251198466} |
| 0.0896 | 53.0 | 1802 | 1.5137 | {'f1': 0.8401534526854221} | {'accuracy': 0.7603068072866731} |
| 0.0896 | 54.0 | 1836 | 1.5939 | {'f1': 0.8385876418663304} | {'accuracy': 0.7545541706615532} |
| 0.0896 | 55.0 | 1870 | 1.6615 | {'f1': 0.8384279475982533} | {'accuracy': 0.7516778523489933} |
| 0.0896 | 56.0 | 1904 | 1.6269 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0896 | 57.0 | 1938 | 1.7063 | {'f1': 0.8371517027863778} | {'accuracy': 0.74784276126558} |
| 0.0896 | 58.0 | 1972 | 1.6755 | {'f1': 0.837468982630273} | {'accuracy': 0.7488015340364333} |
| 0.0471 | 59.0 | 2006 | 1.8673 | {'f1': 0.833029751062538} | {'accuracy': 0.7363374880153404} |
| 0.0471 | 60.0 | 2040 | 1.6474 | {'f1': 0.8434343434343433} | {'accuracy': 0.7622243528283796} |
| 0.0471 | 61.0 | 2074 | 1.6060 | {'f1': 0.8400770712909442} | {'accuracy': 0.7612655800575263} |
| 0.0471 | 62.0 | 2108 | 1.6977 | {'f1': 0.8417721518987342} | {'accuracy': 0.7603068072866731} |
| 0.0471 | 63.0 | 2142 | 1.8631 | {'f1': 0.8386699507389164} | {'accuracy': 0.7488015340364333} |
| 0.0471 | 64.0 | 2176 | 1.7244 | {'f1': 0.8383005707038681} | {'accuracy': 0.7555129434324065} |
| 0.0471 | 65.0 | 2210 | 1.7629 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0471 | 66.0 | 2244 | 1.8566 | {'f1': 0.8362282878411912} | {'accuracy': 0.7468839884947267} |
| 0.0471 | 67.0 | 2278 | 1.9643 | {'f1': 0.8364972443355787} | {'accuracy': 0.7440076701821668} |
| 0.0471 | 68.0 | 2312 | 1.9447 | {'f1': 0.8346650276582667} | {'accuracy': 0.7420901246404602} |
| 0.0471 | 69.0 | 2346 | 1.7232 | {'f1': 0.8421725239616613} | {'accuracy': 0.763183125599233} |
| 0.0471 | 70.0 | 2380 | 1.7151 | {'f1': 0.8384615384615385} | {'accuracy': 0.7583892617449665} |
| 0.0471 | 71.0 | 2414 | 1.7411 | {'f1': 0.8368253968253969} | {'accuracy': 0.7535953978907} |
| 0.0471 | 72.0 | 2448 | 1.7967 | {'f1': 0.8404522613065327} | {'accuracy': 0.7564717162032598} |
| 0.0471 | 73.0 | 2482 | 1.8218 | {'f1': 0.8423028785982478} | {'accuracy': 0.7583892617449665} |
| 0.0278 | 74.0 | 2516 | 1.7536 | {'f1': 0.8346153846153845} | {'accuracy': 0.7526366251198466} |
| 0.0278 | 75.0 | 2550 | 1.8290 | {'f1': 0.8415779586725111} | {'accuracy': 0.7574304889741131} |
| 0.0278 | 76.0 | 2584 | 1.9225 | {'f1': 0.8405438813349814} | {'accuracy': 0.7526366251198466} |
| 0.0278 | 77.0 | 2618 | 2.0916 | {'f1': 0.8350578210590383} | {'accuracy': 0.7401725790987536} |
| 0.0278 | 78.0 | 2652 | 1.7630 | {'f1': 0.8355899419729207} | {'accuracy': 0.7555129434324065} |
| 0.0278 | 79.0 | 2686 | 1.9106 | {'f1': 0.8421052631578948} | {'accuracy': 0.7583892617449665} |
| 0.0278 | 80.0 | 2720 | 1.9050 | {'f1': 0.8446115288220551} | {'accuracy': 0.7622243528283796} |
| 0.0278 | 81.0 | 2754 | 1.9276 | {'f1': 0.8450000000000001} | {'accuracy': 0.7622243528283796} |
| 0.0278 | 82.0 | 2788 | 1.9414 | {'f1': 0.8442776735459662} | {'accuracy': 0.7612655800575263} |
| 0.0278 | 83.0 | 2822 | 2.0313 | {'f1': 0.8419094854308741} | {'accuracy': 0.7555129434324065} |
| 0.0278 | 84.0 | 2856 | 2.0073 | {'f1': 0.8432835820895522} | {'accuracy': 0.7583892617449665} |
| 0.0278 | 85.0 | 2890 | 1.9976 | {'f1': 0.8434782608695651} | {'accuracy': 0.7583892617449665} |
| 0.0278 | 86.0 | 2924 | 1.8581 | {'f1': 0.8375080282594733} | {'accuracy': 0.7574304889741131} |
| 0.0278 | 87.0 | 2958 | 1.9573 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0278 | 88.0 | 2992 | 1.9758 | {'f1': 0.8456712672521958} | {'accuracy': 0.7641418983700863} |
| 0.0146 | 89.0 | 3026 | 2.1224 | {'f1': 0.8405438813349814} | {'accuracy': 0.7526366251198466} |
| 0.0146 | 90.0 | 3060 | 2.0026 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0146 | 91.0 | 3094 | 2.1244 | {'f1': 0.8402220851326342} | {'accuracy': 0.7516778523489933} |
| 0.0146 | 92.0 | 3128 | 2.0497 | {'f1': 0.8402734617775015} | {'accuracy': 0.7535953978907} |
| 0.0146 | 93.0 | 3162 | 1.8572 | {'f1': 0.8289473684210527} | {'accuracy': 0.75071907957814} |
| 0.0146 | 94.0 | 3196 | 2.0854 | {'f1': 0.8418430884184309} | {'accuracy': 0.7564717162032598} |
| 0.0146 | 95.0 | 3230 | 2.0927 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0146 | 96.0 | 3264 | 2.0346 | {'f1': 0.8404522613065327} | {'accuracy': 0.7564717162032598} |
| 0.0146 | 97.0 | 3298 | 1.9551 | {'f1': 0.8385449904275686} | {'accuracy': 0.7574304889741131} |
| 0.0146 | 98.0 | 3332 | 1.9431 | {'f1': 0.8416347381864623} | {'accuracy': 0.7622243528283796} |
| 0.0146 | 99.0 | 3366 | 2.1328 | {'f1': 0.8427594779366064} | {'accuracy': 0.7574304889741131} |
| 0.0146 | 100.0 | 3400 | 1.9871 | {'f1': 0.8400254939451881} | {'accuracy': 0.7593480345158198} |
| 0.0146 | 101.0 | 3434 | 2.0223 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0146 | 102.0 | 3468 | 2.0622 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0203 | 103.0 | 3502 | 2.0059 | {'f1': 0.8420382165605096} | {'accuracy': 0.7622243528283796} |
| 0.0203 | 104.0 | 3536 | 2.1729 | {'f1': 0.8399503722084367} | {'accuracy': 0.7526366251198466} |
| 0.0203 | 105.0 | 3570 | 1.9968 | {'f1': 0.8386683738796414} | {'accuracy': 0.7583892617449665} |
| 0.0203 | 106.0 | 3604 | 2.1000 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0203 | 107.0 | 3638 | 1.9776 | {'f1': 0.8380462724935733} | {'accuracy': 0.7583892617449665} |
| 0.0203 | 108.0 | 3672 | 2.1636 | {'f1': 0.842170929507174} | {'accuracy': 0.7574304889741131} |
| 0.0203 | 109.0 | 3706 | 2.0026 | {'f1': 0.8353619474695709} | {'accuracy': 0.7535953978907} |
| 0.0203 | 110.0 | 3740 | 2.0159 | {'f1': 0.8389560789306174} | {'accuracy': 0.7574304889741131} |
| 0.0203 | 111.0 | 3774 | 2.0359 | {'f1': 0.8349267049075845} | {'accuracy': 0.7516778523489933} |
| 0.0203 | 112.0 | 3808 | 2.1721 | {'f1': 0.8409937888198759} | {'accuracy': 0.7545541706615532} |
| 0.0203 | 113.0 | 3842 | 2.1287 | {'f1': 0.840251572327044} | {'accuracy': 0.7564717162032598} |
| 0.0203 | 114.0 | 3876 | 2.1114 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0203 | 115.0 | 3910 | 2.1644 | {'f1': 0.841708542713568} | {'accuracy': 0.7583892617449665} |
| 0.0203 | 116.0 | 3944 | 2.2183 | {'f1': 0.839030453697949} | {'accuracy': 0.7516778523489933} |
| 0.0203 | 117.0 | 3978 | 2.0891 | {'f1': 0.8385053831538949} | {'accuracy': 0.7555129434324065} |
| 0.0155 | 118.0 | 4012 | 2.0074 | {'f1': 0.8385012919896642} | {'accuracy': 0.7603068072866731} |
| 0.0155 | 119.0 | 4046 | 2.1334 | {'f1': 0.8401253918495297} | {'accuracy': 0.7555129434324065} |
| 0.0155 | 120.0 | 4080 | 2.0441 | {'f1': 0.8395378690629013} | {'accuracy': 0.7603068072866731} |
| 0.0155 | 121.0 | 4114 | 2.0961 | {'f1': 0.8376830044557607} | {'accuracy': 0.7555129434324065} |
| 0.0155 | 122.0 | 4148 | 2.1073 | {'f1': 0.8384223918575064} | {'accuracy': 0.7564717162032598} |
| 0.0155 | 123.0 | 4182 | 2.1261 | {'f1': 0.8384223918575064} | {'accuracy': 0.7564717162032598} |
| 0.0155 | 124.0 | 4216 | 2.2520 | {'f1': 0.8381897086174829} | {'accuracy': 0.7497603068072867} |
| 0.0155 | 125.0 | 4250 | 2.0553 | {'f1': 0.8349641226353556} | {'accuracy': 0.7574304889741131} |
| 0.0155 | 126.0 | 4284 | 2.2367 | {'f1': 0.8381070983810709} | {'accuracy': 0.75071907957814} |
| 0.0155 | 127.0 | 4318 | 2.3076 | {'f1': 0.8393846153846154} | {'accuracy': 0.7497603068072867} |
| 0.0155 | 128.0 | 4352 | 2.1961 | {'f1': 0.835538752362949} | {'accuracy': 0.7497603068072867} |
| 0.0155 | 129.0 | 4386 | 2.0988 | {'f1': 0.8360337005832793} | {'accuracy': 0.7574304889741131} |
| 0.0155 | 130.0 | 4420 | 2.1998 | {'f1': 0.842039018250472} | {'accuracy': 0.7593480345158198} |
| 0.0155 | 131.0 | 4454 | 2.1090 | {'f1': 0.8377504848093084} | {'accuracy': 0.7593480345158198} |
| 0.0155 | 132.0 | 4488 | 2.1998 | {'f1': 0.8417721518987342} | {'accuracy': 0.7603068072866731} |
| 0.0106 | 133.0 | 4522 | 2.1535 | {'f1': 0.839821314613912} | {'accuracy': 0.7593480345158198} |
| 0.0106 | 134.0 | 4556 | 2.2231 | {'f1': 0.8437499999999999} | {'accuracy': 0.7603068072866731} |
| 0.0106 | 135.0 | 4590 | 2.1219 | {'f1': 0.834190231362468} | {'accuracy': 0.7526366251198466} |
| 0.0106 | 136.0 | 4624 | 2.1088 | {'f1': 0.8332251784555484} | {'accuracy': 0.7535953978907} |
| 0.0106 | 137.0 | 4658 | 2.2496 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0106 | 138.0 | 4692 | 2.1308 | {'f1': 0.8385449904275686} | {'accuracy': 0.7574304889741131} |
| 0.0106 | 139.0 | 4726 | 2.2872 | {'f1': 0.8399503722084367} | {'accuracy': 0.7526366251198466} |
| 0.0106 | 140.0 | 4760 | 2.1879 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0106 | 141.0 | 4794 | 2.1646 | {'f1': 0.841300191204589} | {'accuracy': 0.7612655800575263} |
| 0.0106 | 142.0 | 4828 | 2.1105 | {'f1': 0.8339869281045752} | {'accuracy': 0.7564717162032598} |
| 0.0106 | 143.0 | 4862 | 2.2799 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0106 | 144.0 | 4896 | 2.1721 | {'f1': 0.8330084470435347} | {'accuracy': 0.7535953978907} |
| 0.0106 | 145.0 | 4930 | 2.2596 | {'f1': 0.8400503778337531} | {'accuracy': 0.7564717162032598} |
| 0.0106 | 146.0 | 4964 | 2.2096 | {'f1': 0.8394113883557262} | {'accuracy': 0.7593480345158198} |
| 0.0106 | 147.0 | 4998 | 2.1835 | {'f1': 0.8390433096315449} | {'accuracy': 0.7612655800575263} |
| 0.0098 | 148.0 | 5032 | 2.2462 | {'f1': 0.844106463878327} | {'accuracy': 0.7641418983700863} |
| 0.0098 | 149.0 | 5066 | 2.1821 | {'f1': 0.8371187540558079} | {'accuracy': 0.7593480345158198} |
| 0.0098 | 150.0 | 5100 | 2.2533 | {'f1': 0.846641318124208} | {'accuracy': 0.7679769894534996} |
| 0.0098 | 151.0 | 5134 | 2.3029 | {'f1': 0.8427672955974843} | {'accuracy': 0.7603068072866731} |
| 0.0098 | 152.0 | 5168 | 2.2849 | {'f1': 0.8412397216951296} | {'accuracy': 0.7593480345158198} |
| 0.0098 | 153.0 | 5202 | 2.3098 | {'f1': 0.8446115288220551} | {'accuracy': 0.7622243528283796} |
| 0.0098 | 154.0 | 5236 | 2.3253 | {'f1': 0.8446115288220551} | {'accuracy': 0.7622243528283796} |
| 0.0098 | 155.0 | 5270 | 2.3003 | {'f1': 0.8444165621079048} | {'accuracy': 0.7622243528283796} |
| 0.0098 | 156.0 | 5304 | 2.3194 | {'f1': 0.8455790784557908} | {'accuracy': 0.7622243528283796} |
| 0.0098 | 157.0 | 5338 | 2.3352 | {'f1': 0.8471615720524018} | {'accuracy': 0.7651006711409396} |
| 0.0098 | 158.0 | 5372 | 2.2277 | {'f1': 0.8406169665809768} | {'accuracy': 0.7622243528283796} |
| 0.0098 | 159.0 | 5406 | 2.3006 | {'f1': 0.8426395939086295} | {'accuracy': 0.7622243528283796} |
| 0.0098 | 160.0 | 5440 | 2.2040 | {'f1': 0.8339869281045752} | {'accuracy': 0.7564717162032598} |
| 0.0098 | 161.0 | 5474 | 2.1621 | {'f1': 0.8347485303723058} | {'accuracy': 0.7574304889741131} |
| 0.0109 | 162.0 | 5508 | 2.2833 | {'f1': 0.8419047619047619} | {'accuracy': 0.7612655800575263} |
| 0.0109 | 163.0 | 5542 | 2.3822 | {'f1': 0.8446662507797879} | {'accuracy': 0.7612655800575263} |
| 0.0109 | 164.0 | 5576 | 2.4010 | {'f1': 0.8452454940957116} | {'accuracy': 0.7612655800575263} |
| 0.0109 | 165.0 | 5610 | 2.2678 | {'f1': 0.8393316195372751} | {'accuracy': 0.7603068072866731} |
| 0.0109 | 166.0 | 5644 | 2.3334 | {'f1': 0.8429752066115702} | {'accuracy': 0.763183125599233} |
| 0.0109 | 167.0 | 5678 | 2.3240 | {'f1': 0.8369704749679076} | {'accuracy': 0.7564717162032598} |
| 0.0109 | 168.0 | 5712 | 2.3419 | {'f1': 0.8363171355498722} | {'accuracy': 0.7545541706615532} |
| 0.0109 | 169.0 | 5746 | 2.4900 | {'f1': 0.8412600370599136} | {'accuracy': 0.7535953978907} |
| 0.0109 | 170.0 | 5780 | 2.3639 | {'f1': 0.8438871473354232} | {'accuracy': 0.7612655800575263} |
| 0.0109 | 171.0 | 5814 | 2.2685 | {'f1': 0.8378378378378379} | {'accuracy': 0.7583892617449665} |
| 0.0109 | 172.0 | 5848 | 2.5680 | {'f1': 0.8385922330097086} | {'accuracy': 0.7449664429530202} |
| 0.0109 | 173.0 | 5882 | 2.1783 | {'f1': 0.8419047619047619} | {'accuracy': 0.7612655800575263} |
| 0.0109 | 174.0 | 5916 | 2.0917 | {'f1': 0.8380462724935733} | {'accuracy': 0.7583892617449665} |
| 0.0109 | 175.0 | 5950 | 2.1091 | {'f1': 0.8384166125892277} | {'accuracy': 0.7612655800575263} |
| 0.0109 | 176.0 | 5984 | 2.1659 | {'f1': 0.8412291933418694} | {'accuracy': 0.7622243528283796} |
| 0.0104 | 177.0 | 6018 | 2.3873 | {'f1': 0.8437306979617049} | {'accuracy': 0.7574304889741131} |
| 0.0104 | 178.0 | 6052 | 2.1769 | {'f1': 0.843236409608091} | {'accuracy': 0.7622243528283796} |
| 0.0104 | 179.0 | 6086 | 2.1418 | {'f1': 0.8401534526854221} | {'accuracy': 0.7603068072866731} |
| 0.0104 | 180.0 | 6120 | 2.3104 | {'f1': 0.8432229856339788} | {'accuracy': 0.7593480345158198} |
| 0.0104 | 181.0 | 6154 | 2.1719 | {'f1': 0.8381689232753062} | {'accuracy': 0.7593480345158198} |
| 0.0104 | 182.0 | 6188 | 2.4002 | {'f1': 0.8381897086174829} | {'accuracy': 0.7497603068072867} |
| 0.0104 | 183.0 | 6222 | 2.3276 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0104 | 184.0 | 6256 | 2.3433 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0104 | 185.0 | 6290 | 2.7125 | {'f1': 0.8338368580060423} | {'accuracy': 0.7363374880153404} |
| 0.0104 | 186.0 | 6324 | 2.2872 | {'f1': 0.842911877394636} | {'accuracy': 0.7641418983700863} |
| 0.0104 | 187.0 | 6358 | 2.2043 | {'f1': 0.842443729903537} | {'accuracy': 0.7651006711409396} |
| 0.0104 | 188.0 | 6392 | 2.3637 | {'f1': 0.8448598130841122} | {'accuracy': 0.7612655800575263} |
| 0.0104 | 189.0 | 6426 | 2.2187 | {'f1': 0.8384166125892277} | {'accuracy': 0.7612655800575263} |
| 0.0104 | 190.0 | 6460 | 2.6381 | {'f1': 0.8382798304058148} | {'accuracy': 0.7440076701821668} |
| 0.0104 | 191.0 | 6494 | 2.1810 | {'f1': 0.8346354166666669} | {'accuracy': 0.7564717162032598} |
| 0.0086 | 192.0 | 6528 | 2.2396 | {'f1': 0.8451242829827915} | {'accuracy': 0.7670182166826462} |
| 0.0086 | 193.0 | 6562 | 2.1954 | {'f1': 0.8289473684210527} | {'accuracy': 0.75071907957814} |
| 0.0086 | 194.0 | 6596 | 2.2838 | {'f1': 0.8456632653061223} | {'accuracy': 0.7679769894534996} |
| 0.0086 | 195.0 | 6630 | 2.2866 | {'f1': 0.8449266113592852} | {'accuracy': 0.7670182166826462} |
| 0.0086 | 196.0 | 6664 | 2.2259 | {'f1': 0.8323471400394478} | {'accuracy': 0.7555129434324065} |
| 0.0086 | 197.0 | 6698 | 2.3507 | {'f1': 0.8457142857142858} | {'accuracy': 0.7670182166826462} |
| 0.0086 | 198.0 | 6732 | 2.2460 | {'f1': 0.8349769888231428} | {'accuracy': 0.7593480345158198} |
| 0.0086 | 199.0 | 6766 | 2.3341 | {'f1': 0.8504435994930292} | {'accuracy': 0.7737296260786194} |
| 0.0086 | 200.0 | 6800 | 2.3180 | {'f1': 0.8500948766603417} | {'accuracy': 0.7727708533077661} |
| 0.0086 | 201.0 | 6834 | 2.1842 | {'f1': 0.8381201044386423} | {'accuracy': 0.7622243528283796} |
| 0.0086 | 202.0 | 6868 | 2.1754 | {'f1': 0.8361730013106159} | {'accuracy': 0.7603068072866731} |
| 0.0086 | 203.0 | 6902 | 2.2473 | {'f1': 0.8460559796437659} | {'accuracy': 0.7679769894534996} |
| 0.0086 | 204.0 | 6936 | 2.9134 | {'f1': 0.8379821958456972} | {'accuracy': 0.738255033557047} |
| 0.0086 | 205.0 | 6970 | 2.5587 | {'f1': 0.8396111786148239} | {'accuracy': 0.7468839884947267} |
| 0.0113 | 206.0 | 7004 | 2.1203 | {'f1': 0.84107259646828} | {'accuracy': 0.7670182166826462} |
| 0.0113 | 207.0 | 7038 | 2.3156 | {'f1': 0.8475420037336653} | {'accuracy': 0.7651006711409396} |
| 0.0113 | 208.0 | 7072 | 2.1289 | {'f1': 0.8389610389610391} | {'accuracy': 0.7622243528283796} |
| 0.0113 | 209.0 | 7106 | 2.2731 | {'f1': 0.8497800125707102} | {'accuracy': 0.7708533077660594} |
| 0.0113 | 210.0 | 7140 | 2.1861 | {'f1': 0.8420373952288847} | {'accuracy': 0.7651006711409396} |
| 0.0113 | 211.0 | 7174 | 2.4715 | {'f1': 0.8478126925446704} | {'accuracy': 0.763183125599233} |
| 0.0113 | 212.0 | 7208 | 2.1161 | {'f1': 0.8236914600550963} | {'accuracy': 0.7545541706615532} |
| 0.0113 | 213.0 | 7242 | 2.1706 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0113 | 214.0 | 7276 | 2.2738 | {'f1': 0.851010101010101} | {'accuracy': 0.7737296260786194} |
| 0.0113 | 215.0 | 7310 | 2.2877 | {'f1': 0.8459099556119215} | {'accuracy': 0.7670182166826462} |
| 0.0113 | 216.0 | 7344 | 2.3012 | {'f1': 0.8459099556119215} | {'accuracy': 0.7670182166826462} |
| 0.0113 | 217.0 | 7378 | 2.3190 | {'f1': 0.846641318124208} | {'accuracy': 0.7679769894534996} |
| 0.0113 | 218.0 | 7412 | 2.5086 | {'f1': 0.8463911165946946} | {'accuracy': 0.7612655800575263} |
| 0.0113 | 219.0 | 7446 | 2.3970 | {'f1': 0.8473091364205256} | {'accuracy': 0.7660594439117929} |
| 0.0113 | 220.0 | 7480 | 2.2260 | {'f1': 0.8412903225806452} | {'accuracy': 0.7641418983700863} |
| 0.0095 | 221.0 | 7514 | 2.2546 | {'f1': 0.8417721518987342} | {'accuracy': 0.7603068072866731} |
| 0.0095 | 222.0 | 7548 | 2.2881 | {'f1': 0.846641318124208} | {'accuracy': 0.7679769894534996} |
| 0.0095 | 223.0 | 7582 | 2.2982 | {'f1': 0.8481411468178953} | {'accuracy': 0.7689357622243528} |
| 0.0095 | 224.0 | 7616 | 2.3669 | {'f1': 0.8473520249221185} | {'accuracy': 0.7651006711409396} |
| 0.0095 | 225.0 | 7650 | 2.1390 | {'f1': 0.8414239482200647} | {'accuracy': 0.7651006711409396} |
| 0.0095 | 226.0 | 7684 | 2.4351 | {'f1': 0.8467692307692308} | {'accuracy': 0.7612655800575263} |
| 0.0095 | 227.0 | 7718 | 2.1890 | {'f1': 0.8382541720154044} | {'accuracy': 0.7583892617449665} |
| 0.0095 | 228.0 | 7752 | 2.3498 | {'f1': 0.8444722048719551} | {'accuracy': 0.7612655800575263} |
| 0.0095 | 229.0 | 7786 | 2.2806 | {'f1': 0.845620667926906} | {'accuracy': 0.7651006711409396} |
| 0.0095 | 230.0 | 7820 | 2.3795 | {'f1': 0.8437499999999999} | {'accuracy': 0.7603068072866731} |
| 0.0095 | 231.0 | 7854 | 2.3482 | {'f1': 0.8428390367553865} | {'accuracy': 0.7622243528283796} |
| 0.0095 | 232.0 | 7888 | 2.3239 | {'f1': 0.844106463878327} | {'accuracy': 0.7641418983700863} |
| 0.0095 | 233.0 | 7922 | 2.4025 | {'f1': 0.8441396508728181} | {'accuracy': 0.7603068072866731} |
| 0.0095 | 234.0 | 7956 | 2.2347 | {'f1': 0.8421733505821475} | {'accuracy': 0.7660594439117929} |
| 0.0095 | 235.0 | 7990 | 2.2863 | {'f1': 0.843650287172942} | {'accuracy': 0.7651006711409396} |
| 0.0065 | 236.0 | 8024 | 2.5848 | {'f1': 0.8375229920294298} | {'accuracy': 0.7459252157238735} |
| 0.0065 | 237.0 | 8058 | 2.6884 | {'f1': 0.8398058252427185} | {'accuracy': 0.7468839884947267} |
| 0.0065 | 238.0 | 8092 | 2.4341 | {'f1': 0.8436137071651092} | {'accuracy': 0.7593480345158198} |
| 0.0065 | 239.0 | 8126 | 2.2822 | {'f1': 0.8380462724935733} | {'accuracy': 0.7583892617449665} |
| 0.0065 | 240.0 | 8160 | 2.3062 | {'f1': 0.8412291933418694} | {'accuracy': 0.7622243528283796} |
| 0.0065 | 241.0 | 8194 | 2.3330 | {'f1': 0.8396166134185302} | {'accuracy': 0.7593480345158198} |
| 0.0065 | 242.0 | 8228 | 2.3629 | {'f1': 0.8406349206349206} | {'accuracy': 0.7593480345158198} |
| 0.0065 | 243.0 | 8262 | 2.3821 | {'f1': 0.8425047438330171} | {'accuracy': 0.7612655800575263} |
| 0.0065 | 244.0 | 8296 | 2.4227 | {'f1': 0.8454773869346733} | {'accuracy': 0.7641418983700863} |
| 0.0065 | 245.0 | 8330 | 2.4613 | {'f1': 0.8465873512836568} | {'accuracy': 0.7651006711409396} |
| 0.0065 | 246.0 | 8364 | 2.4779 | {'f1': 0.8473091364205256} | {'accuracy': 0.7660594439117929} |
| 0.0065 | 247.0 | 8398 | 2.2809 | {'f1': 0.8364820846905537} | {'accuracy': 0.7593480345158198} |
| 0.0065 | 248.0 | 8432 | 2.3375 | {'f1': 0.8447517284726588} | {'accuracy': 0.763183125599233} |
| 0.0065 | 249.0 | 8466 | 2.2943 | {'f1': 0.8474148802017654} | {'accuracy': 0.7679769894534996} |
| 0.0048 | 250.0 | 8500 | 2.2874 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0048 | 251.0 | 8534 | 2.3495 | {'f1': 0.8454773869346733} | {'accuracy': 0.7641418983700863} |
| 0.0048 | 252.0 | 8568 | 2.2195 | {'f1': 0.8428479794740218} | {'accuracy': 0.7651006711409396} |
| 0.0048 | 253.0 | 8602 | 2.2957 | {'f1': 0.8468354430379746} | {'accuracy': 0.7679769894534996} |
| 0.0048 | 254.0 | 8636 | 2.3333 | {'f1': 0.8468809073724007} | {'accuracy': 0.7670182166826462} |
| 0.0048 | 255.0 | 8670 | 2.3021 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0048 | 256.0 | 8704 | 2.2203 | {'f1': 0.8369636963696371} | {'accuracy': 0.763183125599233} |
| 0.0048 | 257.0 | 8738 | 2.2789 | {'f1': 0.8407479045776919} | {'accuracy': 0.763183125599233} |
| 0.0048 | 258.0 | 8772 | 2.6917 | {'f1': 0.8439587128111719} | {'accuracy': 0.7535953978907} |
| 0.0048 | 259.0 | 8806 | 2.3821 | {'f1': 0.8429022082018928} | {'accuracy': 0.7612655800575263} |
| 0.0048 | 260.0 | 8840 | 2.3406 | {'f1': 0.8346055979643766} | {'accuracy': 0.75071907957814} |
| 0.0048 | 261.0 | 8874 | 2.4332 | {'f1': 0.8415779586725111} | {'accuracy': 0.7574304889741131} |
| 0.0048 | 262.0 | 8908 | 2.3776 | {'f1': 0.8344958782498415} | {'accuracy': 0.7497603068072867} |
| 0.0048 | 263.0 | 8942 | 2.2821 | {'f1': 0.8360548661005879} | {'accuracy': 0.7593480345158198} |
| 0.0048 | 264.0 | 8976 | 2.5451 | {'f1': 0.8416512630930376} | {'accuracy': 0.7535953978907} |
| 0.0062 | 265.0 | 9010 | 2.5588 | {'f1': 0.8449660284126004} | {'accuracy': 0.7593480345158198} |
| 0.0062 | 266.0 | 9044 | 2.3861 | {'f1': 0.8500948766603417} | {'accuracy': 0.7727708533077661} |
| 0.0062 | 267.0 | 9078 | 2.5880 | {'f1': 0.8452088452088452} | {'accuracy': 0.7583892617449665} |
| 0.0062 | 268.0 | 9112 | 2.3555 | {'f1': 0.8389610389610391} | {'accuracy': 0.7622243528283796} |
| 0.0062 | 269.0 | 9146 | 2.4106 | {'f1': 0.8491761723700887} | {'accuracy': 0.7718120805369127} |
| 0.0062 | 270.0 | 9180 | 2.3277 | {'f1': 0.8436692506459949} | {'accuracy': 0.7679769894534996} |
| 0.0062 | 271.0 | 9214 | 2.3585 | {'f1': 0.8479495268138802} | {'accuracy': 0.7689357622243528} |
| 0.0062 | 272.0 | 9248 | 2.5677 | {'f1': 0.8446362515413072} | {'accuracy': 0.7583892617449665} |
| 0.0062 | 273.0 | 9282 | 2.3107 | {'f1': 0.8386675375571522} | {'accuracy': 0.763183125599233} |
| 0.0062 | 274.0 | 9316 | 2.4515 | {'f1': 0.849502487562189} | {'accuracy': 0.7679769894534996} |
| 0.0062 | 275.0 | 9350 | 2.2714 | {'f1': 0.8425806451612904} | {'accuracy': 0.7660594439117929} |
| 0.0062 | 276.0 | 9384 | 2.5433 | {'f1': 0.847247990105133} | {'accuracy': 0.763183125599233} |
| 0.0062 | 277.0 | 9418 | 2.2435 | {'f1': 0.8381578947368422} | {'accuracy': 0.7641418983700863} |
| 0.0062 | 278.0 | 9452 | 2.3457 | {'f1': 0.8488228004956629} | {'accuracy': 0.7660594439117929} |
| 0.0062 | 279.0 | 9486 | 2.1632 | {'f1': 0.8431245965138799} | {'accuracy': 0.7670182166826462} |
| 0.007 | 280.0 | 9520 | 2.2274 | {'f1': 0.8504137492043286} | {'accuracy': 0.7746883988494727} |
| 0.007 | 281.0 | 9554 | 2.3106 | {'f1': 0.8518987341772152} | {'accuracy': 0.775647171620326} |
| 0.007 | 282.0 | 9588 | 2.3713 | {'f1': 0.8490920475892298} | {'accuracy': 0.7689357622243528} |
| 0.007 | 283.0 | 9622 | 2.3418 | {'f1': 0.8506616257088847} | {'accuracy': 0.7727708533077661} |
| 0.007 | 284.0 | 9656 | 2.4010 | {'f1': 0.8497800125707102} | {'accuracy': 0.7708533077660594} |
| 0.007 | 285.0 | 9690 | 2.4244 | {'f1': 0.8517587939698492} | {'accuracy': 0.7737296260786194} |
| 0.007 | 286.0 | 9724 | 2.5561 | {'f1': 0.8483353884093712} | {'accuracy': 0.7641418983700863} |
| 0.007 | 287.0 | 9758 | 2.3890 | {'f1': 0.849714648065948} | {'accuracy': 0.7727708533077661} |
| 0.007 | 288.0 | 9792 | 2.5052 | {'f1': 0.8466334164588529} | {'accuracy': 0.7641418983700863} |
| 0.007 | 289.0 | 9826 | 2.6953 | {'f1': 0.8410194174757281} | {'accuracy': 0.7488015340364333} |
| 0.007 | 290.0 | 9860 | 2.4344 | {'f1': 0.8463949843260189} | {'accuracy': 0.7651006711409396} |
| 0.007 | 291.0 | 9894 | 2.2203 | {'f1': 0.8442408376963352} | {'accuracy': 0.7718120805369127} |
| 0.007 | 292.0 | 9928 | 2.2713 | {'f1': 0.8463508322663252} | {'accuracy': 0.7698945349952061} |
| 0.007 | 293.0 | 9962 | 2.4526 | {'f1': 0.8478396994364433} | {'accuracy': 0.7670182166826462} |
| 0.007 | 294.0 | 9996 | 2.2525 | {'f1': 0.8435286542176433} | {'accuracy': 0.7670182166826462} |
| 0.0049 | 295.0 | 10030 | 2.3821 | {'f1': 0.8495238095238095} | {'accuracy': 0.7727708533077661} |
| 0.0049 | 296.0 | 10064 | 2.4593 | {'f1': 0.8505025125628141} | {'accuracy': 0.7718120805369127} |
| 0.0049 | 297.0 | 10098 | 2.2676 | {'f1': 0.8447937131630648} | {'accuracy': 0.7727708533077661} |
| 0.0049 | 298.0 | 10132 | 2.3211 | {'f1': 0.8460543337645536} | {'accuracy': 0.7718120805369127} |
| 0.0049 | 299.0 | 10166 | 2.4604 | {'f1': 0.8467336683417086} | {'accuracy': 0.7660594439117929} |
| 0.0049 | 300.0 | 10200 | 2.3786 | {'f1': 0.8467432950191572} | {'accuracy': 0.7698945349952061} |
| 0.0049 | 301.0 | 10234 | 2.3910 | {'f1': 0.8462029355456286} | {'accuracy': 0.7689357622243528} |
| 0.0049 | 302.0 | 10268 | 2.4143 | {'f1': 0.8500635324015249} | {'accuracy': 0.7737296260786194} |
| 0.0049 | 303.0 | 10302 | 2.4846 | {'f1': 0.846641318124208} | {'accuracy': 0.7679769894534996} |
| 0.0049 | 304.0 | 10336 | 2.4818 | {'f1': 0.8449809402795427} | {'accuracy': 0.7660594439117929} |
| 0.0049 | 305.0 | 10370 | 3.0166 | {'f1': 0.8330341113105926} | {'accuracy': 0.7325023969319271} |
| 0.0049 | 306.0 | 10404 | 2.2982 | {'f1': 0.8373612018288701} | {'accuracy': 0.7612655800575263} |
| 0.0049 | 307.0 | 10438 | 2.7863 | {'f1': 0.8360755636806826} | {'accuracy': 0.7420901246404602} |
| 0.0049 | 308.0 | 10472 | 2.6580 | {'f1': 0.8407407407407408} | {'accuracy': 0.7526366251198466} |
| 0.0055 | 309.0 | 10506 | 2.4721 | {'f1': 0.8448060075093867} | {'accuracy': 0.7622243528283796} |
| 0.0055 | 310.0 | 10540 | 2.4401 | {'f1': 0.8460087994971716} | {'accuracy': 0.7651006711409396} |
| 0.0055 | 311.0 | 10574 | 2.4511 | {'f1': 0.8460087994971716} | {'accuracy': 0.7651006711409396} |
| 0.0055 | 312.0 | 10608 | 2.3430 | {'f1': 0.8420373952288847} | {'accuracy': 0.7651006711409396} |
| 0.0055 | 313.0 | 10642 | 2.4703 | {'f1': 0.8425692695214106} | {'accuracy': 0.7603068072866731} |
| 0.0055 | 314.0 | 10676 | 2.4643 | {'f1': 0.840378548895899} | {'accuracy': 0.7574304889741131} |
| 0.0055 | 315.0 | 10710 | 2.3307 | {'f1': 0.8431245965138799} | {'accuracy': 0.7670182166826462} |
| 0.0055 | 316.0 | 10744 | 2.7334 | {'f1': 0.8372943327239487} | {'accuracy': 0.7440076701821668} |
| 0.0055 | 317.0 | 10778 | 2.3630 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0055 | 318.0 | 10812 | 2.6220 | {'f1': 0.8451053283767039} | {'accuracy': 0.7603068072866731} |
| 0.0055 | 319.0 | 10846 | 2.4094 | {'f1': 0.8461538461538461} | {'accuracy': 0.7698945349952061} |
| 0.0055 | 320.0 | 10880 | 2.5369 | {'f1': 0.8477987421383647} | {'accuracy': 0.7679769894534996} |
| 0.0055 | 321.0 | 10914 | 2.7735 | {'f1': 0.8426829268292683} | {'accuracy': 0.7526366251198466} |
| 0.0055 | 322.0 | 10948 | 2.5698 | {'f1': 0.844838505383154} | {'accuracy': 0.7651006711409396} |
| 0.0055 | 323.0 | 10982 | 2.5724 | {'f1': 0.844838505383154} | {'accuracy': 0.7651006711409396} |
| 0.0038 | 324.0 | 11016 | 2.7663 | {'f1': 0.842883548983364} | {'accuracy': 0.7555129434324065} |
| 0.0038 | 325.0 | 11050 | 2.7974 | {'f1': 0.8430769230769232} | {'accuracy': 0.7555129434324065} |
| 0.0038 | 326.0 | 11084 | 2.6639 | {'f1': 0.8451053283767039} | {'accuracy': 0.7603068072866731} |
| 0.0038 | 327.0 | 11118 | 2.4709 | {'f1': 0.8434343434343433} | {'accuracy': 0.7622243528283796} |
| 0.0038 | 328.0 | 11152 | 2.3253 | {'f1': 0.8425806451612904} | {'accuracy': 0.7660594439117929} |
| 0.0038 | 329.0 | 11186 | 2.5298 | {'f1': 0.8418461538461539} | {'accuracy': 0.7535953978907} |
| 0.0038 | 330.0 | 11220 | 2.2083 | {'f1': 0.8373612018288701} | {'accuracy': 0.7612655800575263} |
| 0.0038 | 331.0 | 11254 | 2.8618 | {'f1': 0.8344370860927152} | {'accuracy': 0.7363374880153404} |
| 0.0038 | 332.0 | 11288 | 2.2406 | {'f1': 0.832214765100671} | {'accuracy': 0.7603068072866731} |
| 0.0038 | 333.0 | 11322 | 2.5084 | {'f1': 0.8424953675108092} | {'accuracy': 0.7555129434324065} |
| 0.0038 | 334.0 | 11356 | 2.3157 | {'f1': 0.8424396442185516} | {'accuracy': 0.7622243528283796} |
| 0.0038 | 335.0 | 11390 | 2.3621 | {'f1': 0.8405063291139242} | {'accuracy': 0.7583892617449665} |
| 0.0038 | 336.0 | 11424 | 2.3904 | {'f1': 0.8385053831538949} | {'accuracy': 0.7555129434324065} |
| 0.0038 | 337.0 | 11458 | 2.3480 | {'f1': 0.8378033205619413} | {'accuracy': 0.7564717162032598} |
| 0.0038 | 338.0 | 11492 | 2.5572 | {'f1': 0.8428217821782179} | {'accuracy': 0.7564717162032598} |
| 0.009 | 339.0 | 11526 | 2.2762 | {'f1': 0.8376408217362492} | {'accuracy': 0.7651006711409396} |
| 0.009 | 340.0 | 11560 | 2.5688 | {'f1': 0.838909541511772} | {'accuracy': 0.75071907957814} |
| 0.009 | 341.0 | 11594 | 2.2469 | {'f1': 0.8365758754863812} | {'accuracy': 0.7583892617449665} |
| 0.009 | 342.0 | 11628 | 2.3048 | {'f1': 0.8356867779204109} | {'accuracy': 0.7545541706615532} |
| 0.009 | 343.0 | 11662 | 2.4827 | {'f1': 0.8448275862068966} | {'accuracy': 0.7583892617449665} |
| 0.009 | 344.0 | 11696 | 2.5491 | {'f1': 0.8424279583077867} | {'accuracy': 0.7535953978907} |
| 0.009 | 345.0 | 11730 | 2.1947 | {'f1': 0.8423772609819122} | {'accuracy': 0.7660594439117929} |
| 0.009 | 346.0 | 11764 | 2.2454 | {'f1': 0.8456632653061223} | {'accuracy': 0.7679769894534996} |
| 0.009 | 347.0 | 11798 | 2.2735 | {'f1': 0.8457142857142858} | {'accuracy': 0.7670182166826462} |
| 0.009 | 348.0 | 11832 | 2.1648 | {'f1': 0.83672131147541} | {'accuracy': 0.7612655800575263} |
| 0.009 | 349.0 | 11866 | 2.1912 | {'f1': 0.8443579766536965} | {'accuracy': 0.7698945349952061} |
| 0.009 | 350.0 | 11900 | 2.3321 | {'f1': 0.8447517284726588} | {'accuracy': 0.763183125599233} |
| 0.009 | 351.0 | 11934 | 2.2339 | {'f1': 0.845360824742268} | {'accuracy': 0.7698945349952061} |
| 0.009 | 352.0 | 11968 | 2.3889 | {'f1': 0.8446115288220551} | {'accuracy': 0.7622243528283796} |
| 0.0049 | 353.0 | 12002 | 2.4719 | {'f1': 0.8441961514587213} | {'accuracy': 0.7593480345158198} |
| 0.0049 | 354.0 | 12036 | 2.2481 | {'f1': 0.8432786885245902} | {'accuracy': 0.7708533077660594} |
| 0.0049 | 355.0 | 12070 | 2.3296 | {'f1': 0.8458093410108765} | {'accuracy': 0.7689357622243528} |
| 0.0049 | 356.0 | 12104 | 2.2860 | {'f1': 0.8391793514228987} | {'accuracy': 0.7670182166826462} |
| 0.0049 | 357.0 | 12138 | 2.3120 | {'f1': 0.8407310704960835} | {'accuracy': 0.7660594439117929} |
| 0.0049 | 358.0 | 12172 | 2.3255 | {'f1': 0.8426527958387516} | {'accuracy': 0.7679769894534996} |
| 0.0049 | 359.0 | 12206 | 2.6370 | {'f1': 0.8448804414469651} | {'accuracy': 0.7574304889741131} |
| 0.0049 | 360.0 | 12240 | 2.2975 | {'f1': 0.8418334409296322} | {'accuracy': 0.7651006711409396} |
| 0.0049 | 361.0 | 12274 | 2.2947 | {'f1': 0.8423772609819122} | {'accuracy': 0.7660594439117929} |
| 0.0049 | 362.0 | 12308 | 2.3139 | {'f1': 0.8442728442728443} | {'accuracy': 0.7679769894534996} |
| 0.0049 | 363.0 | 12342 | 2.6460 | {'f1': 0.8407565588773642} | {'accuracy': 0.7497603068072867} |
| 0.0049 | 364.0 | 12376 | 2.2519 | {'f1': 0.8281998631074606} | {'accuracy': 0.7593480345158198} |
| 0.0049 | 365.0 | 12410 | 2.2843 | {'f1': 0.8444730077120822} | {'accuracy': 0.7679769894534996} |
| 0.0049 | 366.0 | 12444 | 2.4303 | {'f1': 0.8458646616541354} | {'accuracy': 0.7641418983700863} |
| 0.0049 | 367.0 | 12478 | 2.7581 | {'f1': 0.8407026044821321} | {'accuracy': 0.74784276126558} |
| 0.0042 | 368.0 | 12512 | 2.4722 | {'f1': 0.8457711442786069} | {'accuracy': 0.7622243528283796} |
| 0.0042 | 369.0 | 12546 | 2.4394 | {'f1': 0.8479495268138802} | {'accuracy': 0.7689357622243528} |
| 0.0042 | 370.0 | 12580 | 2.3956 | {'f1': 0.8472843450479234} | {'accuracy': 0.7708533077660594} |
| 0.0042 | 371.0 | 12614 | 2.3798 | {'f1': 0.8351351351351352} | {'accuracy': 0.7660594439117929} |
| 0.0042 | 372.0 | 12648 | 2.8006 | {'f1': 0.8391013964784457} | {'accuracy': 0.7459252157238735} |
| 0.0042 | 373.0 | 12682 | 2.5319 | {'f1': 0.8478396994364433} | {'accuracy': 0.7670182166826462} |
| 0.0042 | 374.0 | 12716 | 2.6566 | {'f1': 0.8453481207640172} | {'accuracy': 0.7593480345158198} |
| 0.0042 | 375.0 | 12750 | 2.6527 | {'f1': 0.844774273345702} | {'accuracy': 0.7593480345158198} |
| 0.0042 | 376.0 | 12784 | 2.5238 | {'f1': 0.843297671491504} | {'accuracy': 0.7612655800575263} |
| 0.0042 | 377.0 | 12818 | 2.4145 | {'f1': 0.8384565075212557} | {'accuracy': 0.763183125599233} |
| 0.0042 | 378.0 | 12852 | 2.5525 | {'f1': 0.8475420037336653} | {'accuracy': 0.7651006711409396} |
| 0.0042 | 379.0 | 12886 | 2.4333 | {'f1': 0.8394584139264991} | {'accuracy': 0.7612655800575263} |
| 0.0042 | 380.0 | 12920 | 2.4523 | {'f1': 0.8447284345047924} | {'accuracy': 0.7670182166826462} |
| 0.0042 | 381.0 | 12954 | 2.4587 | {'f1': 0.8441890166028098} | {'accuracy': 0.7660594439117929} |
| 0.0042 | 382.0 | 12988 | 2.4679 | {'f1': 0.8449266113592852} | {'accuracy': 0.7670182166826462} |
| 0.0016 | 383.0 | 13022 | 2.4727 | {'f1': 0.8449266113592852} | {'accuracy': 0.7670182166826462} |
| 0.0016 | 384.0 | 13056 | 2.4799 | {'f1': 0.8449266113592852} | {'accuracy': 0.7670182166826462} |
| 0.0016 | 385.0 | 13090 | 2.4846 | {'f1': 0.8449266113592852} | {'accuracy': 0.7670182166826462} |
| 0.0016 | 386.0 | 13124 | 2.4887 | {'f1': 0.8449266113592852} | {'accuracy': 0.7670182166826462} |
| 0.0016 | 387.0 | 13158 | 2.4999 | {'f1': 0.8433121019108281} | {'accuracy': 0.7641418983700863} |
| 0.0016 | 388.0 | 13192 | 2.6596 | {'f1': 0.8477722772277226} | {'accuracy': 0.7641418983700863} |
| 0.0016 | 389.0 | 13226 | 2.5240 | {'f1': 0.8425047438330171} | {'accuracy': 0.7612655800575263} |
| 0.0016 | 390.0 | 13260 | 2.5159 | {'f1': 0.8402291534054743} | {'accuracy': 0.7593480345158198} |
| 0.0016 | 391.0 | 13294 | 2.4972 | {'f1': 0.8409805153991201} | {'accuracy': 0.7574304889741131} |
| 0.0016 | 392.0 | 13328 | 2.4648 | {'f1': 0.8417037507946599} | {'accuracy': 0.7612655800575263} |
| 0.0016 | 393.0 | 13362 | 2.4204 | {'f1': 0.8349514563106797} | {'accuracy': 0.7555129434324065} |
| 0.0016 | 394.0 | 13396 | 2.6356 | {'f1': 0.8443627450980392} | {'accuracy': 0.7564717162032598} |
| 0.0016 | 395.0 | 13430 | 2.4198 | {'f1': 0.8402291534054743} | {'accuracy': 0.7593480345158198} |
| 0.0016 | 396.0 | 13464 | 2.5434 | {'f1': 0.8426966292134831} | {'accuracy': 0.7583892617449665} |
| 0.0016 | 397.0 | 13498 | 2.5672 | {'f1': 0.8423676012461059} | {'accuracy': 0.7574304889741131} |
| 0.0029 | 398.0 | 13532 | 2.5736 | {'f1': 0.8423676012461059} | {'accuracy': 0.7574304889741131} |
| 0.0029 | 399.0 | 13566 | 2.5827 | {'f1': 0.842039800995025} | {'accuracy': 0.7564717162032598} |
| 0.0029 | 400.0 | 13600 | 2.5551 | {'f1': 0.8413793103448275} | {'accuracy': 0.7574304889741131} |
| 0.0029 | 401.0 | 13634 | 2.5580 | {'f1': 0.8413793103448275} | {'accuracy': 0.7574304889741131} |
| 0.0029 | 402.0 | 13668 | 2.4031 | {'f1': 0.8451776649746193} | {'accuracy': 0.7660594439117929} |
| 0.0029 | 403.0 | 13702 | 2.8069 | {'f1': 0.8391777509068924} | {'accuracy': 0.7449664429530202} |
| 0.0029 | 404.0 | 13736 | 2.3951 | {'f1': 0.8297455968688846} | {'accuracy': 0.7497603068072867} |
| 0.0029 | 405.0 | 13770 | 3.0938 | {'f1': 0.837181764357608} | {'accuracy': 0.7363374880153404} |
| 0.0029 | 406.0 | 13804 | 2.5174 | {'f1': 0.8362944162436549} | {'accuracy': 0.7526366251198466} |
| 0.0029 | 407.0 | 13838 | 2.6228 | {'f1': 0.8413192283758557} | {'accuracy': 0.7555129434324065} |
| 0.0029 | 408.0 | 13872 | 2.4119 | {'f1': 0.8357615894039736} | {'accuracy': 0.7622243528283796} |
| 0.0029 | 409.0 | 13906 | 2.5100 | {'f1': 0.8370607028753995} | {'accuracy': 0.7555129434324065} |
| 0.0029 | 410.0 | 13940 | 2.6617 | {'f1': 0.8407960199004975} | {'accuracy': 0.7545541706615532} |
| 0.0029 | 411.0 | 13974 | 2.4633 | {'f1': 0.8365508365508365} | {'accuracy': 0.7564717162032598} |
| 0.0059 | 412.0 | 14008 | 2.4974 | {'f1': 0.8351231838281743} | {'accuracy': 0.7497603068072867} |
| 0.0059 | 413.0 | 14042 | 2.4125 | {'f1': 0.8383311603650586} | {'accuracy': 0.7622243528283796} |
| 0.0059 | 414.0 | 14076 | 2.5143 | {'f1': 0.8359721342621914} | {'accuracy': 0.7516778523489933} |
| 0.0059 | 415.0 | 14110 | 2.5524 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0059 | 416.0 | 14144 | 2.5503 | {'f1': 0.8373266078184111} | {'accuracy': 0.7526366251198466} |
| 0.0059 | 417.0 | 14178 | 2.5565 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0059 | 418.0 | 14212 | 2.5622 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0059 | 419.0 | 14246 | 2.5504 | {'f1': 0.837238758708043} | {'accuracy': 0.7535953978907} |
| 0.0059 | 420.0 | 14280 | 2.5631 | {'f1': 0.8367088607594937} | {'accuracy': 0.7526366251198466} |
| 0.0059 | 421.0 | 14314 | 2.5915 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0059 | 422.0 | 14348 | 2.5984 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0059 | 423.0 | 14382 | 2.6025 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0059 | 424.0 | 14416 | 2.6443 | {'f1': 0.8419071518193225} | {'accuracy': 0.7583892617449665} |
| 0.0059 | 425.0 | 14450 | 2.6552 | {'f1': 0.8406524466750315} | {'accuracy': 0.7564717162032598} |
| 0.0059 | 426.0 | 14484 | 2.6820 | {'f1': 0.8406524466750315} | {'accuracy': 0.7564717162032598} |
| 0.0007 | 427.0 | 14518 | 2.6997 | {'f1': 0.8400503778337531} | {'accuracy': 0.7564717162032598} |
| 0.0007 | 428.0 | 14552 | 2.7150 | {'f1': 0.8400503778337531} | {'accuracy': 0.7564717162032598} |
| 0.0007 | 429.0 | 14586 | 2.5110 | {'f1': 0.8286099865047233} | {'accuracy': 0.7564717162032598} |
| 0.0007 | 430.0 | 14620 | 2.7481 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0007 | 431.0 | 14654 | 2.8417 | {'f1': 0.840937114673243} | {'accuracy': 0.7526366251198466} |
| 0.0007 | 432.0 | 14688 | 2.5962 | {'f1': 0.8384615384615385} | {'accuracy': 0.7583892617449665} |
| 0.0007 | 433.0 | 14722 | 2.5856 | {'f1': 0.8375404530744337} | {'accuracy': 0.7593480345158198} |
| 0.0007 | 434.0 | 14756 | 2.6317 | {'f1': 0.8425748884639899} | {'accuracy': 0.763183125599233} |
| 0.0007 | 435.0 | 14790 | 2.6556 | {'f1': 0.8417037507946599} | {'accuracy': 0.7612655800575263} |
| 0.0007 | 436.0 | 14824 | 2.6632 | {'f1': 0.8411689961880559} | {'accuracy': 0.7603068072866731} |
| 0.0007 | 437.0 | 14858 | 2.6848 | {'f1': 0.8436317780580076} | {'accuracy': 0.7622243528283796} |
| 0.0007 | 438.0 | 14892 | 2.9743 | {'f1': 0.8418491484184915} | {'accuracy': 0.75071907957814} |
| 0.0007 | 439.0 | 14926 | 2.6257 | {'f1': 0.8433734939759037} | {'accuracy': 0.763183125599233} |
| 0.0007 | 440.0 | 14960 | 2.7683 | {'f1': 0.8419094854308741} | {'accuracy': 0.7555129434324065} |
| 0.0007 | 441.0 | 14994 | 2.5979 | {'f1': 0.8404868673926971} | {'accuracy': 0.7612655800575263} |
| 0.0042 | 442.0 | 15028 | 2.4658 | {'f1': 0.8387942332896462} | {'accuracy': 0.7641418983700863} |
| 0.0042 | 443.0 | 15062 | 2.8625 | {'f1': 0.8411330049261084} | {'accuracy': 0.7526366251198466} |
| 0.0042 | 444.0 | 15096 | 2.8880 | {'f1': 0.8413284132841329} | {'accuracy': 0.7526366251198466} |
| 0.0042 | 445.0 | 15130 | 2.7701 | {'f1': 0.8407960199004975} | {'accuracy': 0.7545541706615532} |
| 0.0042 | 446.0 | 15164 | 2.6883 | {'f1': 0.8433583959899749} | {'accuracy': 0.7603068072866731} |
| 0.0042 | 447.0 | 15198 | 3.2037 | {'f1': 0.8344291691572026} | {'accuracy': 0.7344199424736337} |
| 0.0042 | 448.0 | 15232 | 2.5410 | {'f1': 0.8372093023255814} | {'accuracy': 0.7583892617449665} |
| 0.0042 | 449.0 | 15266 | 2.6753 | {'f1': 0.8415094339622642} | {'accuracy': 0.7583892617449665} |
| 0.0042 | 450.0 | 15300 | 2.6834 | {'f1': 0.8409805153991201} | {'accuracy': 0.7574304889741131} |
| 0.0042 | 451.0 | 15334 | 2.6860 | {'f1': 0.8409805153991201} | {'accuracy': 0.7574304889741131} |
| 0.0042 | 452.0 | 15368 | 2.4472 | {'f1': 0.8311345646437995} | {'accuracy': 0.7545541706615532} |
| 0.0042 | 453.0 | 15402 | 2.7076 | {'f1': 0.8426270136307311} | {'accuracy': 0.7564717162032598} |
| 0.0042 | 454.0 | 15436 | 2.9443 | {'f1': 0.8379351740696279} | {'accuracy': 0.7411313518696069} |
| 0.0042 | 455.0 | 15470 | 2.6437 | {'f1': 0.840024706609018} | {'accuracy': 0.7516778523489933} |
| 0.0059 | 456.0 | 15504 | 2.5004 | {'f1': 0.8296488946684005} | {'accuracy': 0.7488015340364333} |
| 0.0059 | 457.0 | 15538 | 2.4383 | {'f1': 0.8282828282828282} | {'accuracy': 0.7555129434324065} |
| 0.0059 | 458.0 | 15572 | 2.8096 | {'f1': 0.8390243902439024} | {'accuracy': 0.7468839884947267} |
| 0.0059 | 459.0 | 15606 | 2.6592 | {'f1': 0.8366583541147131} | {'accuracy': 0.7488015340364333} |
| 0.0059 | 460.0 | 15640 | 2.5181 | {'f1': 0.8345230369889682} | {'accuracy': 0.7555129434324065} |
| 0.0059 | 461.0 | 15674 | 2.5745 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0059 | 462.0 | 15708 | 2.6229 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0059 | 463.0 | 15742 | 2.6767 | {'f1': 0.8409232688708672} | {'accuracy': 0.7555129434324065} |
| 0.0059 | 464.0 | 15776 | 2.6814 | {'f1': 0.8409232688708672} | {'accuracy': 0.7555129434324065} |
| 0.0059 | 465.0 | 15810 | 2.6861 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0059 | 466.0 | 15844 | 2.6917 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0059 | 467.0 | 15878 | 2.6872 | {'f1': 0.84} | {'accuracy': 0.7545541706615532} |
| 0.0059 | 468.0 | 15912 | 2.6880 | {'f1': 0.84} | {'accuracy': 0.7545541706615532} |
| 0.0059 | 469.0 | 15946 | 2.6887 | {'f1': 0.8405253283302063} | {'accuracy': 0.7555129434324065} |
| 0.0059 | 470.0 | 15980 | 2.5945 | {'f1': 0.832258064516129} | {'accuracy': 0.75071907957814} |
| 0.0021 | 471.0 | 16014 | 2.6036 | {'f1': 0.832258064516129} | {'accuracy': 0.75071907957814} |
| 0.0021 | 472.0 | 16048 | 2.8426 | {'f1': 0.840418977202711} | {'accuracy': 0.7516778523489933} |
| 0.0021 | 473.0 | 16082 | 2.8052 | {'f1': 0.8402734617775015} | {'accuracy': 0.7535953978907} |
| 0.0021 | 474.0 | 16116 | 3.2189 | {'f1': 0.8371257485029939} | {'accuracy': 0.7392138063279002} |
| 0.0021 | 475.0 | 16150 | 2.5583 | {'f1': 0.8308295231874592} | {'accuracy': 0.7516778523489933} |
| 0.0021 | 476.0 | 16184 | 2.7091 | {'f1': 0.8415779586725111} | {'accuracy': 0.7574304889741131} |
| 0.0021 | 477.0 | 16218 | 2.7367 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0021 | 478.0 | 16252 | 2.6907 | {'f1': 0.8377693282636249} | {'accuracy': 0.7545541706615532} |
| 0.0021 | 479.0 | 16286 | 2.7290 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0021 | 480.0 | 16320 | 2.6727 | {'f1': 0.8385449904275686} | {'accuracy': 0.7574304889741131} |
| 0.0021 | 481.0 | 16354 | 2.6050 | {'f1': 0.8335517693315858} | {'accuracy': 0.7564717162032598} |
| 0.0021 | 482.0 | 16388 | 2.8131 | {'f1': 0.8391521197007481} | {'accuracy': 0.7526366251198466} |
| 0.0021 | 483.0 | 16422 | 2.6203 | {'f1': 0.837299035369775} | {'accuracy': 0.7574304889741131} |
| 0.0021 | 484.0 | 16456 | 2.7171 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0021 | 485.0 | 16490 | 3.0218 | {'f1': 0.8403157255616271} | {'accuracy': 0.74784276126558} |
| 0.0019 | 486.0 | 16524 | 2.7476 | {'f1': 0.8414403032217308} | {'accuracy': 0.7593480345158198} |
| 0.0019 | 487.0 | 16558 | 2.7457 | {'f1': 0.8407079646017699} | {'accuracy': 0.7583892617449665} |
| 0.0019 | 488.0 | 16592 | 2.7488 | {'f1': 0.8407079646017699} | {'accuracy': 0.7583892617449665} |
| 0.0019 | 489.0 | 16626 | 2.7570 | {'f1': 0.8414403032217308} | {'accuracy': 0.7593480345158198} |
| 0.0019 | 490.0 | 16660 | 2.7705 | {'f1': 0.8421717171717172} | {'accuracy': 0.7603068072866731} |
| 0.0019 | 491.0 | 16694 | 2.7697 | {'f1': 0.8421717171717172} | {'accuracy': 0.7603068072866731} |
| 0.0019 | 492.0 | 16728 | 2.7588 | {'f1': 0.8403041825095058} | {'accuracy': 0.7583892617449665} |
| 0.0019 | 493.0 | 16762 | 2.7615 | {'f1': 0.8403041825095058} | {'accuracy': 0.7583892617449665} |
| 0.0019 | 494.0 | 16796 | 3.1328 | {'f1': 0.8414043583535109} | {'accuracy': 0.7488015340364333} |
| 0.0019 | 495.0 | 16830 | 3.2243 | {'f1': 0.8373493975903615} | {'accuracy': 0.7411313518696069} |
| 0.0019 | 496.0 | 16864 | 2.9653 | {'f1': 0.8386699507389164} | {'accuracy': 0.7488015340364333} |
| 0.0019 | 497.0 | 16898 | 2.5668 | {'f1': 0.8325487558843309} | {'accuracy': 0.7612655800575263} |
| 0.0019 | 498.0 | 16932 | 2.8481 | {'f1': 0.840418977202711} | {'accuracy': 0.7516778523489933} |
| 0.0019 | 499.0 | 16966 | 2.6740 | {'f1': 0.8421717171717172} | {'accuracy': 0.7603068072866731} |
| 0.0018 | 500.0 | 17000 | 2.5600 | {'f1': 0.8356435643564357} | {'accuracy': 0.7612655800575263} |
| 0.0018 | 501.0 | 17034 | 2.5829 | {'f1': 0.8376068376068375} | {'accuracy': 0.763183125599233} |
| 0.0018 | 502.0 | 17068 | 2.9520 | {'f1': 0.8411442483262326} | {'accuracy': 0.7497603068072867} |
| 0.0018 | 503.0 | 17102 | 2.5230 | {'f1': 0.8441558441558442} | {'accuracy': 0.7698945349952061} |
| 0.0018 | 504.0 | 17136 | 2.6823 | {'f1': 0.8436911487758946} | {'accuracy': 0.7612655800575263} |
| 0.0018 | 505.0 | 17170 | 2.7025 | {'f1': 0.8435544430538173} | {'accuracy': 0.7603068072866731} |
| 0.0018 | 506.0 | 17204 | 2.7052 | {'f1': 0.844082654978084} | {'accuracy': 0.7612655800575263} |
| 0.0018 | 507.0 | 17238 | 2.7081 | {'f1': 0.844082654978084} | {'accuracy': 0.7612655800575263} |
| 0.0018 | 508.0 | 17272 | 2.7151 | {'f1': 0.8430268918073796} | {'accuracy': 0.7593480345158198} |
| 0.0018 | 509.0 | 17306 | 2.7236 | {'f1': 0.8432229856339788} | {'accuracy': 0.7593480345158198} |
| 0.0018 | 510.0 | 17340 | 2.7288 | {'f1': 0.8432229856339788} | {'accuracy': 0.7593480345158198} |
| 0.0018 | 511.0 | 17374 | 2.7628 | {'f1': 0.8425637834474176} | {'accuracy': 0.7574304889741131} |
| 0.0018 | 512.0 | 17408 | 2.7730 | {'f1': 0.8425637834474176} | {'accuracy': 0.7574304889741131} |
| 0.0018 | 513.0 | 17442 | 2.8088 | {'f1': 0.8426270136307311} | {'accuracy': 0.7564717162032598} |
| 0.0018 | 514.0 | 17476 | 2.7555 | {'f1': 0.8426332288401255} | {'accuracy': 0.7593480345158198} |
| 0.0023 | 515.0 | 17510 | 2.8072 | {'f1': 0.8405977584059775} | {'accuracy': 0.7545541706615532} |
| 0.0023 | 516.0 | 17544 | 2.7877 | {'f1': 0.8395989974937343} | {'accuracy': 0.7545541706615532} |
| 0.0023 | 517.0 | 17578 | 2.7925 | {'f1': 0.8395989974937343} | {'accuracy': 0.7545541706615532} |
| 0.0023 | 518.0 | 17612 | 2.8634 | {'f1': 0.8430160692212608} | {'accuracy': 0.7564717162032598} |
| 0.0023 | 519.0 | 17646 | 2.6918 | {'f1': 0.8405612244897959} | {'accuracy': 0.7603068072866731} |
| 0.0023 | 520.0 | 17680 | 2.6507 | {'f1': 0.839425587467363} | {'accuracy': 0.7641418983700863} |
| 0.0023 | 521.0 | 17714 | 2.6582 | {'f1': 0.839425587467363} | {'accuracy': 0.7641418983700863} |
| 0.0023 | 522.0 | 17748 | 2.6777 | {'f1': 0.838206627680312} | {'accuracy': 0.7612655800575263} |
| 0.0023 | 523.0 | 17782 | 2.6893 | {'f1': 0.8393782383419689} | {'accuracy': 0.7622243528283796} |
| 0.0023 | 524.0 | 17816 | 2.7195 | {'f1': 0.8381317978246962} | {'accuracy': 0.7574304889741131} |
| 0.0023 | 525.0 | 17850 | 2.7556 | {'f1': 0.8407643312101911} | {'accuracy': 0.7603068072866731} |
| 0.0023 | 526.0 | 17884 | 2.7625 | {'f1': 0.841502227880331} | {'accuracy': 0.7612655800575263} |
| 0.0023 | 527.0 | 17918 | 2.7651 | {'f1': 0.8409669211195929} | {'accuracy': 0.7603068072866731} |
| 0.0023 | 528.0 | 17952 | 2.7865 | {'f1': 0.8406349206349206} | {'accuracy': 0.7593480345158198} |
| 0.0023 | 529.0 | 17986 | 2.7938 | {'f1': 0.8413705583756345} | {'accuracy': 0.7603068072866731} |
| 0.0 | 530.0 | 18020 | 2.8313 | {'f1': 0.8436317780580076} | {'accuracy': 0.7622243528283796} |
| 0.0 | 531.0 | 18054 | 3.0190 | {'f1': 0.8442521631644005} | {'accuracy': 0.7583892617449665} |
| 0.0 | 532.0 | 18088 | 3.0035 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 533.0 | 18122 | 3.1189 | {'f1': 0.8403465346534654} | {'accuracy': 0.7526366251198466} |
| 0.0 | 534.0 | 18156 | 3.1287 | {'f1': 0.8403465346534654} | {'accuracy': 0.7526366251198466} |
| 0.0 | 535.0 | 18190 | 2.9201 | {'f1': 0.8435718809373022} | {'accuracy': 0.763183125599233} |
| 0.0 | 536.0 | 18224 | 3.1018 | {'f1': 0.8413878562577447} | {'accuracy': 0.7545541706615532} |
| 0.0 | 537.0 | 18258 | 2.9163 | {'f1': 0.8406735751295337} | {'accuracy': 0.7641418983700863} |
| 0.0 | 538.0 | 18292 | 3.1607 | {'f1': 0.8391521197007481} | {'accuracy': 0.7526366251198466} |
| 0.0 | 539.0 | 18326 | 2.9552 | {'f1': 0.8309764309764309} | {'accuracy': 0.7593480345158198} |
| 0.0 | 540.0 | 18360 | 3.2303 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0 | 541.0 | 18394 | 3.1611 | {'f1': 0.8395989974937343} | {'accuracy': 0.7545541706615532} |
| 0.0 | 542.0 | 18428 | 3.1450 | {'f1': 0.8406524466750315} | {'accuracy': 0.7564717162032598} |
| 0.0 | 543.0 | 18462 | 3.1537 | {'f1': 0.8406524466750315} | {'accuracy': 0.7564717162032598} |
| 0.0 | 544.0 | 18496 | 2.9722 | {'f1': 0.8428479794740218} | {'accuracy': 0.7651006711409396} |
| 0.0011 | 545.0 | 18530 | 3.0524 | {'f1': 0.8448928121059269} | {'accuracy': 0.7641418983700863} |
| 0.0011 | 546.0 | 18564 | 3.2339 | {'f1': 0.837954405422058} | {'accuracy': 0.74784276126558} |
| 0.0011 | 547.0 | 18598 | 3.2356 | {'f1': 0.837954405422058} | {'accuracy': 0.74784276126558} |
| 0.0011 | 548.0 | 18632 | 3.1960 | {'f1': 0.8403465346534654} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 549.0 | 18666 | 3.1065 | {'f1': 0.844082654978084} | {'accuracy': 0.7612655800575263} |
| 0.0011 | 550.0 | 18700 | 3.0559 | {'f1': 0.8452305748578648} | {'accuracy': 0.7651006711409396} |
| 0.0011 | 551.0 | 18734 | 3.0519 | {'f1': 0.8452305748578648} | {'accuracy': 0.7651006711409396} |
| 0.0011 | 552.0 | 18768 | 3.0572 | {'f1': 0.8452305748578648} | {'accuracy': 0.7651006711409396} |
| 0.0011 | 553.0 | 18802 | 3.0586 | {'f1': 0.8452305748578648} | {'accuracy': 0.7651006711409396} |
| 0.0011 | 554.0 | 18836 | 3.0616 | {'f1': 0.8452305748578648} | {'accuracy': 0.7651006711409396} |
| 0.0011 | 555.0 | 18870 | 2.9069 | {'f1': 0.8338892595063376} | {'accuracy': 0.7612655800575263} |
| 0.0011 | 556.0 | 18904 | 3.0913 | {'f1': 0.8419737663960025} | {'accuracy': 0.7574304889741131} |
| 0.0011 | 557.0 | 18938 | 2.9118 | {'f1': 0.839124839124839} | {'accuracy': 0.7603068072866731} |
| 0.0011 | 558.0 | 18972 | 2.8145 | {'f1': 0.8312375909993381} | {'accuracy': 0.7555129434324065} |
| 0.0017 | 559.0 | 19006 | 2.8932 | {'f1': 0.841300191204589} | {'accuracy': 0.7612655800575263} |
| 0.0017 | 560.0 | 19040 | 3.3450 | {'f1': 0.8364741641337385} | {'accuracy': 0.7420901246404602} |
| 0.0017 | 561.0 | 19074 | 2.7462 | {'f1': 0.8367875647668395} | {'accuracy': 0.7583892617449665} |
| 0.0017 | 562.0 | 19108 | 2.8912 | {'f1': 0.8385053831538949} | {'accuracy': 0.7555129434324065} |
| 0.0017 | 563.0 | 19142 | 2.9074 | {'f1': 0.8394437420986094} | {'accuracy': 0.7564717162032598} |
| 0.0017 | 564.0 | 19176 | 2.9109 | {'f1': 0.8401768793430195} | {'accuracy': 0.7574304889741131} |
| 0.0017 | 565.0 | 19210 | 2.9144 | {'f1': 0.8401768793430195} | {'accuracy': 0.7574304889741131} |
| 0.0017 | 566.0 | 19244 | 2.9008 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0017 | 567.0 | 19278 | 2.9005 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0017 | 568.0 | 19312 | 2.9041 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0017 | 569.0 | 19346 | 2.8724 | {'f1': 0.8392857142857142} | {'accuracy': 0.7583892617449665} |
| 0.0017 | 570.0 | 19380 | 2.8106 | {'f1': 0.8376068376068375} | {'accuracy': 0.763183125599233} |
| 0.0017 | 571.0 | 19414 | 2.9517 | {'f1': 0.8393524283935242} | {'accuracy': 0.7526366251198466} |
| 0.0017 | 572.0 | 19448 | 3.1770 | {'f1': 0.838749233599019} | {'accuracy': 0.74784276126558} |
| 0.0017 | 573.0 | 19482 | 2.8087 | {'f1': 0.8463022508038586} | {'accuracy': 0.7708533077660594} |
| 0.0035 | 574.0 | 19516 | 2.8659 | {'f1': 0.8447837150127226} | {'accuracy': 0.7660594439117929} |
| 0.0035 | 575.0 | 19550 | 2.8199 | {'f1': 0.8437900128040973} | {'accuracy': 0.7660594439117929} |
| 0.0035 | 576.0 | 19584 | 2.8267 | {'f1': 0.8439897698209718} | {'accuracy': 0.7660594439117929} |
| 0.0035 | 577.0 | 19618 | 2.8513 | {'f1': 0.8451242829827915} | {'accuracy': 0.7670182166826462} |
| 0.0035 | 578.0 | 19652 | 2.6924 | {'f1': 0.8369926813040585} | {'accuracy': 0.7651006711409396} |
| 0.0035 | 579.0 | 19686 | 2.6959 | {'f1': 0.83994708994709} | {'accuracy': 0.7679769894534996} |
| 0.0035 | 580.0 | 19720 | 2.7683 | {'f1': 0.8435114503816794} | {'accuracy': 0.7641418983700863} |
| 0.0035 | 581.0 | 19754 | 3.0111 | {'f1': 0.8404907975460122} | {'accuracy': 0.75071907957814} |
| 0.0035 | 582.0 | 19788 | 3.0117 | {'f1': 0.8410067526089625} | {'accuracy': 0.7516778523489933} |
| 0.0035 | 583.0 | 19822 | 2.6870 | {'f1': 0.8396946564885497} | {'accuracy': 0.7583892617449665} |
| 0.0035 | 584.0 | 19856 | 2.7800 | {'f1': 0.8408521303258145} | {'accuracy': 0.7564717162032598} |
| 0.0035 | 585.0 | 19890 | 2.8259 | {'f1': 0.8428927680798005} | {'accuracy': 0.7583892617449665} |
| 0.0035 | 586.0 | 19924 | 2.8269 | {'f1': 0.842170929507174} | {'accuracy': 0.7574304889741131} |
| 0.0035 | 587.0 | 19958 | 2.8064 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0035 | 588.0 | 19992 | 2.8121 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0027 | 589.0 | 20026 | 2.8147 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0027 | 590.0 | 20060 | 2.8251 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0027 | 591.0 | 20094 | 2.8277 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0027 | 592.0 | 20128 | 2.8308 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0027 | 593.0 | 20162 | 2.8341 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0027 | 594.0 | 20196 | 2.8349 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0027 | 595.0 | 20230 | 2.8360 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0027 | 596.0 | 20264 | 2.9174 | {'f1': 0.8430160692212608} | {'accuracy': 0.7564717162032598} |
| 0.0027 | 597.0 | 20298 | 2.7736 | {'f1': 0.8398983481575605} | {'accuracy': 0.7583892617449665} |
| 0.0027 | 598.0 | 20332 | 2.8574 | {'f1': 0.8456712672521958} | {'accuracy': 0.7641418983700863} |
| 0.0027 | 599.0 | 20366 | 2.8608 | {'f1': 0.8456712672521958} | {'accuracy': 0.7641418983700863} |
| 0.0027 | 600.0 | 20400 | 2.8844 | {'f1': 0.8453350031308704} | {'accuracy': 0.763183125599233} |
| 0.0027 | 601.0 | 20434 | 2.8882 | {'f1': 0.8453350031308704} | {'accuracy': 0.763183125599233} |
| 0.0027 | 602.0 | 20468 | 2.8884 | {'f1': 0.8453350031308704} | {'accuracy': 0.763183125599233} |
| 0.0015 | 603.0 | 20502 | 2.8916 | {'f1': 0.8453350031308704} | {'accuracy': 0.763183125599233} |
| 0.0015 | 604.0 | 20536 | 2.8952 | {'f1': 0.8453350031308704} | {'accuracy': 0.763183125599233} |
| 0.0015 | 605.0 | 20570 | 2.7938 | {'f1': 0.8369152970922884} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 606.0 | 20604 | 2.9111 | {'f1': 0.8391521197007481} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 607.0 | 20638 | 2.9166 | {'f1': 0.8386292834890965} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 608.0 | 20672 | 2.9669 | {'f1': 0.8424953675108092} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 609.0 | 20706 | 2.9137 | {'f1': 0.8389513108614233} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 610.0 | 20740 | 2.8178 | {'f1': 0.8385053831538949} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 611.0 | 20774 | 2.8212 | {'f1': 0.8385053831538949} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 612.0 | 20808 | 2.8262 | {'f1': 0.8385053831538949} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 613.0 | 20842 | 2.9007 | {'f1': 0.8397234443746072} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 614.0 | 20876 | 2.9044 | {'f1': 0.8397234443746072} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 615.0 | 20910 | 2.8261 | {'f1': 0.8378893833439287} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 616.0 | 20944 | 3.2320 | {'f1': 0.8419139915202907} | {'accuracy': 0.7497603068072867} |
| 0.0015 | 617.0 | 20978 | 2.9549 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0015 | 618.0 | 21012 | 2.9439 | {'f1': 0.8375314861460957} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 619.0 | 21046 | 3.1955 | {'f1': 0.8440925700365408} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 620.0 | 21080 | 3.0307 | {'f1': 0.8360450563204005} | {'accuracy': 0.7488015340364333} |
| 0.0015 | 621.0 | 21114 | 3.0285 | {'f1': 0.8360450563204005} | {'accuracy': 0.7488015340364333} |
| 0.0015 | 622.0 | 21148 | 2.8398 | {'f1': 0.8365019011406846} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 623.0 | 21182 | 2.8117 | {'f1': 0.8368522072936659} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 624.0 | 21216 | 2.8077 | {'f1': 0.8382541720154044} | {'accuracy': 0.7583892617449665} |
| 0.0015 | 625.0 | 21250 | 2.8137 | {'f1': 0.837924407431134} | {'accuracy': 0.7574304889741131} |
| 0.0015 | 626.0 | 21284 | 2.8642 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0015 | 627.0 | 21318 | 2.8839 | {'f1': 0.8404522613065327} | {'accuracy': 0.7564717162032598} |
| 0.0015 | 628.0 | 21352 | 2.8897 | {'f1': 0.8404522613065327} | {'accuracy': 0.7564717162032598} |
| 0.0015 | 629.0 | 21386 | 2.7354 | {'f1': 0.8275401069518716} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 630.0 | 21420 | 2.8438 | {'f1': 0.8375634517766497} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 631.0 | 21454 | 2.7435 | {'f1': 0.8371187540558079} | {'accuracy': 0.7593480345158198} |
| 0.0015 | 632.0 | 21488 | 3.0514 | {'f1': 0.8437884378843787} | {'accuracy': 0.7564717162032598} |
| 0.0024 | 633.0 | 21522 | 3.1385 | {'f1': 0.8419117647058822} | {'accuracy': 0.7526366251198466} |
| 0.0024 | 634.0 | 21556 | 2.7682 | {'f1': 0.8380829015544042} | {'accuracy': 0.7603068072866731} |
| 0.0024 | 635.0 | 21590 | 3.0654 | {'f1': 0.8427518427518428} | {'accuracy': 0.7545541706615532} |
| 0.0024 | 636.0 | 21624 | 2.8195 | {'f1': 0.8378033205619413} | {'accuracy': 0.7564717162032598} |
| 0.0024 | 637.0 | 21658 | 2.9246 | {'f1': 0.844082654978084} | {'accuracy': 0.7612655800575263} |
| 0.0024 | 638.0 | 21692 | 3.1259 | {'f1': 0.8419117647058822} | {'accuracy': 0.7526366251198466} |
| 0.0024 | 639.0 | 21726 | 3.0291 | {'f1': 0.8456292622442653} | {'accuracy': 0.7612655800575263} |
| 0.0024 | 640.0 | 21760 | 2.8685 | {'f1': 0.8328981723237598} | {'accuracy': 0.7545541706615532} |
| 0.0024 | 641.0 | 21794 | 2.8966 | {'f1': 0.8401534526854221} | {'accuracy': 0.7603068072866731} |
| 0.0024 | 642.0 | 21828 | 3.0719 | {'f1': 0.8453481207640172} | {'accuracy': 0.7593480345158198} |
| 0.0024 | 643.0 | 21862 | 3.0737 | {'f1': 0.8453481207640172} | {'accuracy': 0.7593480345158198} |
| 0.0024 | 644.0 | 21896 | 3.0739 | {'f1': 0.8453481207640172} | {'accuracy': 0.7593480345158198} |
| 0.0024 | 645.0 | 21930 | 3.0741 | {'f1': 0.8458692971639952} | {'accuracy': 0.7603068072866731} |
| 0.0024 | 646.0 | 21964 | 3.0746 | {'f1': 0.8458692971639952} | {'accuracy': 0.7603068072866731} |
| 0.0024 | 647.0 | 21998 | 3.0600 | {'f1': 0.845679012345679} | {'accuracy': 0.7603068072866731} |
| 0.0015 | 648.0 | 22032 | 2.9669 | {'f1': 0.8430268918073796} | {'accuracy': 0.7593480345158198} |
| 0.0015 | 649.0 | 22066 | 2.8459 | {'f1': 0.835193696651346} | {'accuracy': 0.7593480345158198} |
| 0.0015 | 650.0 | 22100 | 3.3386 | {'f1': 0.8432563791008505} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 651.0 | 22134 | 3.3767 | {'f1': 0.8429351121892056} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 652.0 | 22168 | 3.3389 | {'f1': 0.8432563791008505} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 653.0 | 22202 | 3.3229 | {'f1': 0.8440925700365408} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 654.0 | 22236 | 3.3206 | {'f1': 0.8446069469835467} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 655.0 | 22270 | 2.8669 | {'f1': 0.8390433096315449} | {'accuracy': 0.7612655800575263} |
| 0.0015 | 656.0 | 22304 | 2.8560 | {'f1': 0.8359375000000001} | {'accuracy': 0.7583892617449665} |
| 0.0015 | 657.0 | 22338 | 2.8596 | {'f1': 0.8374512353706112} | {'accuracy': 0.7603068072866731} |
| 0.0015 | 658.0 | 22372 | 2.8640 | {'f1': 0.8386260531432276} | {'accuracy': 0.7612655800575263} |
| 0.0015 | 659.0 | 22406 | 2.8739 | {'f1': 0.84} | {'accuracy': 0.7622243528283796} |
| 0.0015 | 660.0 | 22440 | 2.8786 | {'f1': 0.8422408242112042} | {'accuracy': 0.7651006711409396} |
| 0.0015 | 661.0 | 22474 | 3.0281 | {'f1': 0.8462500000000001} | {'accuracy': 0.7641418983700863} |
| 0.0009 | 662.0 | 22508 | 3.0632 | {'f1': 0.8453865336658354} | {'accuracy': 0.7622243528283796} |
| 0.0009 | 663.0 | 22542 | 3.0650 | {'f1': 0.8453865336658354} | {'accuracy': 0.7622243528283796} |
| 0.0009 | 664.0 | 22576 | 3.0647 | {'f1': 0.8453865336658354} | {'accuracy': 0.7622243528283796} |
| 0.0009 | 665.0 | 22610 | 3.0650 | {'f1': 0.8459139114160948} | {'accuracy': 0.763183125599233} |
| 0.0009 | 666.0 | 22644 | 2.8335 | {'f1': 0.8356435643564357} | {'accuracy': 0.7612655800575263} |
| 0.0009 | 667.0 | 22678 | 3.6002 | {'f1': 0.8363201911589008} | {'accuracy': 0.7372962607861937} |
| 0.0009 | 668.0 | 22712 | 2.8852 | {'f1': 0.8420373952288847} | {'accuracy': 0.7651006711409396} |
| 0.0009 | 669.0 | 22746 | 2.9744 | {'f1': 0.8397720075997466} | {'accuracy': 0.7574304889741131} |
| 0.0009 | 670.0 | 22780 | 2.9828 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0009 | 671.0 | 22814 | 2.9874 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0009 | 672.0 | 22848 | 2.9887 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0009 | 673.0 | 22882 | 2.9903 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0009 | 674.0 | 22916 | 2.9922 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0009 | 675.0 | 22950 | 2.9965 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0009 | 676.0 | 22984 | 2.9995 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0014 | 677.0 | 23018 | 3.0019 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0014 | 678.0 | 23052 | 3.0081 | {'f1': 0.840378548895899} | {'accuracy': 0.7574304889741131} |
| 0.0014 | 679.0 | 23086 | 3.2019 | {'f1': 0.8422344996930632} | {'accuracy': 0.7535953978907} |
| 0.0014 | 680.0 | 23120 | 2.9295 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0014 | 681.0 | 23154 | 2.9268 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0014 | 682.0 | 23188 | 2.9346 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0014 | 683.0 | 23222 | 2.9397 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0014 | 684.0 | 23256 | 2.9429 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0014 | 685.0 | 23290 | 3.1432 | {'f1': 0.8413878562577447} | {'accuracy': 0.7545541706615532} |
| 0.0014 | 686.0 | 23324 | 3.5300 | {'f1': 0.8385542168674699} | {'accuracy': 0.7430488974113135} |
| 0.0014 | 687.0 | 23358 | 3.1182 | {'f1': 0.8438661710037174} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 688.0 | 23392 | 3.1548 | {'f1': 0.8424953675108092} | {'accuracy': 0.7555129434324065} |
| 0.0014 | 689.0 | 23426 | 3.1529 | {'f1': 0.8424953675108092} | {'accuracy': 0.7555129434324065} |
| 0.0014 | 690.0 | 23460 | 3.1593 | {'f1': 0.8424953675108092} | {'accuracy': 0.7555129434324065} |
| 0.0014 | 691.0 | 23494 | 3.1600 | {'f1': 0.8424953675108092} | {'accuracy': 0.7555129434324065} |
| 0.0011 | 692.0 | 23528 | 3.1630 | {'f1': 0.8424953675108092} | {'accuracy': 0.7555129434324065} |
| 0.0011 | 693.0 | 23562 | 3.0266 | {'f1': 0.8395217117684078} | {'accuracy': 0.7555129434324065} |
| 0.0011 | 694.0 | 23596 | 2.9661 | {'f1': 0.8388746803069054} | {'accuracy': 0.7583892617449665} |
| 0.0011 | 695.0 | 23630 | 2.9710 | {'f1': 0.8393650793650793} | {'accuracy': 0.7574304889741131} |
| 0.0011 | 696.0 | 23664 | 2.9480 | {'f1': 0.8354922279792747} | {'accuracy': 0.7564717162032598} |
| 0.0011 | 697.0 | 23698 | 3.0036 | {'f1': 0.837238758708043} | {'accuracy': 0.7535953978907} |
| 0.0011 | 698.0 | 23732 | 3.5675 | {'f1': 0.8378541289933695} | {'accuracy': 0.7420901246404602} |
| 0.0011 | 699.0 | 23766 | 2.9796 | {'f1': 0.8388324873096447} | {'accuracy': 0.7564717162032598} |
| 0.0011 | 700.0 | 23800 | 3.5724 | {'f1': 0.8373493975903615} | {'accuracy': 0.7411313518696069} |
| 0.0011 | 701.0 | 23834 | 3.1092 | {'f1': 0.836341008089608} | {'accuracy': 0.74784276126558} |
| 0.0011 | 702.0 | 23868 | 3.0981 | {'f1': 0.8368617683686177} | {'accuracy': 0.7488015340364333} |
| 0.0011 | 703.0 | 23902 | 3.0988 | {'f1': 0.8368617683686177} | {'accuracy': 0.7488015340364333} |
| 0.0011 | 704.0 | 23936 | 3.1014 | {'f1': 0.8368617683686177} | {'accuracy': 0.7488015340364333} |
| 0.0011 | 705.0 | 23970 | 2.9096 | {'f1': 0.835820895522388} | {'accuracy': 0.7574304889741131} |
| 0.0011 | 706.0 | 24004 | 2.8964 | {'f1': 0.835742444152431} | {'accuracy': 0.7603068072866731} |
| 0.0011 | 707.0 | 24038 | 3.1746 | {'f1': 0.8408521303258145} | {'accuracy': 0.7564717162032598} |
| 0.0011 | 708.0 | 24072 | 3.2400 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 709.0 | 24106 | 3.2435 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 710.0 | 24140 | 3.2399 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 711.0 | 24174 | 3.2439 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 712.0 | 24208 | 3.2452 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 713.0 | 24242 | 3.2460 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 714.0 | 24276 | 3.2482 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 715.0 | 24310 | 3.2496 | {'f1': 0.8395522388059702} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 716.0 | 24344 | 2.8230 | {'f1': 0.8267558528428093} | {'accuracy': 0.7516778523489933} |
| 0.0011 | 717.0 | 24378 | 3.0923 | {'f1': 0.8367473618870268} | {'accuracy': 0.74784276126558} |
| 0.0011 | 718.0 | 24412 | 3.1070 | {'f1': 0.8366583541147131} | {'accuracy': 0.7488015340364333} |
| 0.0011 | 719.0 | 24446 | 3.1593 | {'f1': 0.8410636982065552} | {'accuracy': 0.7535953978907} |
| 0.0011 | 720.0 | 24480 | 3.4845 | {'f1': 0.8378216636744464} | {'accuracy': 0.7401725790987536} |
| 0.0043 | 721.0 | 24514 | 2.9314 | {'f1': 0.8373266078184111} | {'accuracy': 0.7526366251198466} |
| 0.0043 | 722.0 | 24548 | 2.9134 | {'f1': 0.837650031585597} | {'accuracy': 0.7535953978907} |
| 0.0043 | 723.0 | 24582 | 2.9149 | {'f1': 0.837650031585597} | {'accuracy': 0.7535953978907} |
| 0.0043 | 724.0 | 24616 | 2.9433 | {'f1': 0.8373266078184111} | {'accuracy': 0.7526366251198466} |
| 0.0043 | 725.0 | 24650 | 2.9492 | {'f1': 0.8373266078184111} | {'accuracy': 0.7526366251198466} |
| 0.0043 | 726.0 | 24684 | 2.9308 | {'f1': 0.8371212121212122} | {'accuracy': 0.7526366251198466} |
| 0.0043 | 727.0 | 24718 | 2.9115 | {'f1': 0.8359721342621914} | {'accuracy': 0.7516778523489933} |
| 0.0043 | 728.0 | 24752 | 3.3925 | {'f1': 0.8399518652226233} | {'accuracy': 0.7449664429530202} |
| 0.0043 | 729.0 | 24786 | 2.8874 | {'f1': 0.8383005707038681} | {'accuracy': 0.7555129434324065} |
| 0.0043 | 730.0 | 24820 | 2.8933 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0043 | 731.0 | 24854 | 2.7986 | {'f1': 0.8317089018843405} | {'accuracy': 0.7516778523489933} |
| 0.0043 | 732.0 | 24888 | 2.7958 | {'f1': 0.8307291666666667} | {'accuracy': 0.75071907957814} |
| 0.0043 | 733.0 | 24922 | 2.8255 | {'f1': 0.8336569579288027} | {'accuracy': 0.7535953978907} |
| 0.0043 | 734.0 | 24956 | 2.8411 | {'f1': 0.832258064516129} | {'accuracy': 0.75071907957814} |
| 0.0043 | 735.0 | 24990 | 3.2118 | {'f1': 0.8390243902439024} | {'accuracy': 0.7468839884947267} |
| 0.0023 | 736.0 | 25024 | 2.8077 | {'f1': 0.8343004513217279} | {'accuracy': 0.7535953978907} |
| 0.0023 | 737.0 | 25058 | 2.8435 | {'f1': 0.834293026231606} | {'accuracy': 0.7516778523489933} |
| 0.0023 | 738.0 | 25092 | 2.8521 | {'f1': 0.8337595907928389} | {'accuracy': 0.75071907957814} |
| 0.0023 | 739.0 | 25126 | 2.8913 | {'f1': 0.832908163265306} | {'accuracy': 0.7488015340364333} |
| 0.0023 | 740.0 | 25160 | 2.8955 | {'f1': 0.832908163265306} | {'accuracy': 0.7488015340364333} |
| 0.0023 | 741.0 | 25194 | 2.8995 | {'f1': 0.832908163265306} | {'accuracy': 0.7488015340364333} |
| 0.0023 | 742.0 | 25228 | 2.8991 | {'f1': 0.832908163265306} | {'accuracy': 0.7488015340364333} |
| 0.0023 | 743.0 | 25262 | 2.9040 | {'f1': 0.832908163265306} | {'accuracy': 0.7488015340364333} |
| 0.0023 | 744.0 | 25296 | 2.9465 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0023 | 745.0 | 25330 | 2.9596 | {'f1': 0.8394437420986094} | {'accuracy': 0.7564717162032598} |
| 0.0023 | 746.0 | 25364 | 2.9590 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0023 | 747.0 | 25398 | 2.9597 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0023 | 748.0 | 25432 | 2.9618 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0023 | 749.0 | 25466 | 2.9805 | {'f1': 0.840378548895899} | {'accuracy': 0.7574304889741131} |
| 0.0 | 750.0 | 25500 | 2.9831 | {'f1': 0.8398486759142497} | {'accuracy': 0.7564717162032598} |
| 0.0 | 751.0 | 25534 | 2.9550 | {'f1': 0.8386277001270648} | {'accuracy': 0.7564717162032598} |
| 0.0 | 752.0 | 25568 | 2.9528 | {'f1': 0.8391608391608393} | {'accuracy': 0.7574304889741131} |
| 0.0 | 753.0 | 25602 | 2.9878 | {'f1': 0.8389134554643083} | {'accuracy': 0.7555129434324065} |
| 0.0 | 754.0 | 25636 | 2.9908 | {'f1': 0.8389134554643083} | {'accuracy': 0.7555129434324065} |
| 0.0 | 755.0 | 25670 | 2.9924 | {'f1': 0.8389134554643083} | {'accuracy': 0.7555129434324065} |
| 0.0 | 756.0 | 25704 | 2.9938 | {'f1': 0.8389134554643083} | {'accuracy': 0.7555129434324065} |
| 0.0 | 757.0 | 25738 | 3.0004 | {'f1': 0.8389134554643083} | {'accuracy': 0.7555129434324065} |
| 0.0 | 758.0 | 25772 | 3.0155 | {'f1': 0.840378548895899} | {'accuracy': 0.7574304889741131} |
| 0.0 | 759.0 | 25806 | 3.0170 | {'f1': 0.840378548895899} | {'accuracy': 0.7574304889741131} |
| 0.0 | 760.0 | 25840 | 3.0184 | {'f1': 0.840378548895899} | {'accuracy': 0.7574304889741131} |
| 0.0 | 761.0 | 25874 | 3.0192 | {'f1': 0.840378548895899} | {'accuracy': 0.7574304889741131} |
| 0.0 | 762.0 | 25908 | 3.1057 | {'f1': 0.8402734617775015} | {'accuracy': 0.7535953978907} |
| 0.0 | 763.0 | 25942 | 2.6765 | {'f1': 0.8327911515940144} | {'accuracy': 0.7535953978907} |
| 0.0 | 764.0 | 25976 | 2.7808 | {'f1': 0.8396756082345601} | {'accuracy': 0.7535953978907} |
| 0.0029 | 765.0 | 26010 | 2.9076 | {'f1': 0.8375540457072268} | {'accuracy': 0.74784276126558} |
| 0.0029 | 766.0 | 26044 | 2.6470 | {'f1': 0.8363171355498722} | {'accuracy': 0.7545541706615532} |
| 0.0029 | 767.0 | 26078 | 2.6168 | {'f1': 0.8330084470435347} | {'accuracy': 0.7535953978907} |
| 0.0029 | 768.0 | 26112 | 2.5902 | {'f1': 0.8344283837056505} | {'accuracy': 0.7583892617449665} |
| 0.0029 | 769.0 | 26146 | 2.5932 | {'f1': 0.8364820846905537} | {'accuracy': 0.7593480345158198} |
| 0.0029 | 770.0 | 26180 | 2.6266 | {'f1': 0.8343004513217279} | {'accuracy': 0.7535953978907} |
| 0.0029 | 771.0 | 26214 | 2.6331 | {'f1': 0.834726688102894} | {'accuracy': 0.7535953978907} |
| 0.0029 | 772.0 | 26248 | 2.6463 | {'f1': 0.8371794871794872} | {'accuracy': 0.7564717162032598} |
| 0.0029 | 773.0 | 26282 | 3.1462 | {'f1': 0.8367593712212817} | {'accuracy': 0.7411313518696069} |
| 0.0029 | 774.0 | 26316 | 2.9040 | {'f1': 0.8391089108910891} | {'accuracy': 0.75071907957814} |
| 0.0029 | 775.0 | 26350 | 3.3436 | {'f1': 0.8363201911589008} | {'accuracy': 0.7372962607861937} |
| 0.0029 | 776.0 | 26384 | 2.7757 | {'f1': 0.8389134554643083} | {'accuracy': 0.7555129434324065} |
| 0.0029 | 777.0 | 26418 | 2.7650 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0029 | 778.0 | 26452 | 2.7741 | {'f1': 0.8394437420986094} | {'accuracy': 0.7564717162032598} |
| 0.0029 | 779.0 | 26486 | 2.7977 | {'f1': 0.8391167192429022} | {'accuracy': 0.7555129434324065} |
| 0.0003 | 780.0 | 26520 | 2.8005 | {'f1': 0.8391167192429022} | {'accuracy': 0.7555129434324065} |
| 0.0003 | 781.0 | 26554 | 2.6762 | {'f1': 0.8367875647668395} | {'accuracy': 0.7583892617449665} |
| 0.0003 | 782.0 | 26588 | 2.6492 | {'f1': 0.8342105263157895} | {'accuracy': 0.7583892617449665} |
| 0.0003 | 783.0 | 26622 | 2.7796 | {'f1': 0.8380952380952381} | {'accuracy': 0.7555129434324065} |
| 0.0003 | 784.0 | 26656 | 2.8384 | {'f1': 0.8397234443746072} | {'accuracy': 0.7555129434324065} |
| 0.0003 | 785.0 | 26690 | 2.8411 | {'f1': 0.8397234443746072} | {'accuracy': 0.7555129434324065} |
| 0.0003 | 786.0 | 26724 | 2.8711 | {'f1': 0.837238758708043} | {'accuracy': 0.7535953978907} |
| 0.0003 | 787.0 | 26758 | 2.8324 | {'f1': 0.8354591836734695} | {'accuracy': 0.7526366251198466} |
| 0.0003 | 788.0 | 26792 | 3.2309 | {'f1': 0.8357487922705313} | {'accuracy': 0.7392138063279002} |
| 0.0003 | 789.0 | 26826 | 3.0963 | {'f1': 0.8378378378378378} | {'accuracy': 0.7468839884947267} |
| 0.0003 | 790.0 | 26860 | 2.8388 | {'f1': 0.8398983481575605} | {'accuracy': 0.7583892617449665} |
| 0.0003 | 791.0 | 26894 | 2.8356 | {'f1': 0.8402291534054743} | {'accuracy': 0.7593480345158198} |
| 0.0003 | 792.0 | 26928 | 2.8395 | {'f1': 0.8402291534054743} | {'accuracy': 0.7593480345158198} |
| 0.0003 | 793.0 | 26962 | 2.8449 | {'f1': 0.8398983481575605} | {'accuracy': 0.7583892617449665} |
| 0.0003 | 794.0 | 26996 | 2.8514 | {'f1': 0.8413705583756345} | {'accuracy': 0.7603068072866731} |
| 0.0005 | 795.0 | 27030 | 2.8518 | {'f1': 0.8406349206349206} | {'accuracy': 0.7593480345158198} |
| 0.0005 | 796.0 | 27064 | 2.8494 | {'f1': 0.8404322949777496} | {'accuracy': 0.7593480345158198} |
| 0.0005 | 797.0 | 27098 | 2.8529 | {'f1': 0.8411689961880559} | {'accuracy': 0.7603068072866731} |
| 0.0005 | 798.0 | 27132 | 2.8607 | {'f1': 0.8413705583756345} | {'accuracy': 0.7603068072866731} |
| 0.0005 | 799.0 | 27166 | 2.8993 | {'f1': 0.8405063291139242} | {'accuracy': 0.7583892617449665} |
| 0.0005 | 800.0 | 27200 | 2.9058 | {'f1': 0.8407079646017699} | {'accuracy': 0.7583892617449665} |
| 0.0005 | 801.0 | 27234 | 2.9114 | {'f1': 0.8407079646017699} | {'accuracy': 0.7583892617449665} |
| 0.0005 | 802.0 | 27268 | 2.9156 | {'f1': 0.8401768793430195} | {'accuracy': 0.7574304889741131} |
| 0.0005 | 803.0 | 27302 | 2.9184 | {'f1': 0.8401768793430195} | {'accuracy': 0.7574304889741131} |
| 0.0005 | 804.0 | 27336 | 2.7580 | {'f1': 0.833112582781457} | {'accuracy': 0.7583892617449665} |
| 0.0005 | 805.0 | 27370 | 2.9621 | {'f1': 0.8415779586725111} | {'accuracy': 0.7574304889741131} |
| 0.0005 | 806.0 | 27404 | 2.8754 | {'f1': 0.8441396508728181} | {'accuracy': 0.7603068072866731} |
| 0.0005 | 807.0 | 27438 | 3.1519 | {'f1': 0.8371810449574727} | {'accuracy': 0.7430488974113135} |
| 0.0005 | 808.0 | 27472 | 3.1315 | {'f1': 0.836275106512477} | {'accuracy': 0.7420901246404602} |
| 0.0018 | 809.0 | 27506 | 2.7069 | {'f1': 0.8364576599870717} | {'accuracy': 0.7574304889741131} |
| 0.0018 | 810.0 | 27540 | 2.6744 | {'f1': 0.8370273794002607} | {'accuracy': 0.7603068072866731} |
| 0.0018 | 811.0 | 27574 | 2.7519 | {'f1': 0.8385852090032154} | {'accuracy': 0.7593480345158198} |
| 0.0018 | 812.0 | 27608 | 2.6985 | {'f1': 0.837393021724819} | {'accuracy': 0.763183125599233} |
| 0.0018 | 813.0 | 27642 | 2.9175 | {'f1': 0.8382630585273756} | {'accuracy': 0.7535953978907} |
| 0.0018 | 814.0 | 27676 | 2.9293 | {'f1': 0.8372093023255814} | {'accuracy': 0.7516778523489933} |
| 0.0018 | 815.0 | 27710 | 2.8987 | {'f1': 0.8367729831144466} | {'accuracy': 0.7497603068072867} |
| 0.0018 | 816.0 | 27744 | 2.8777 | {'f1': 0.8374136848713121} | {'accuracy': 0.7516778523489933} |
| 0.0018 | 817.0 | 27778 | 2.9923 | {'f1': 0.8349875930521091} | {'accuracy': 0.7449664429530202} |
| 0.0018 | 818.0 | 27812 | 2.8838 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.0018 | 819.0 | 27846 | 2.8828 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.0018 | 820.0 | 27880 | 2.8843 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.0018 | 821.0 | 27914 | 2.8861 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.0018 | 822.0 | 27948 | 2.8891 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.0018 | 823.0 | 27982 | 2.8908 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.0014 | 824.0 | 28016 | 2.7491 | {'f1': 0.8410852713178294} | {'accuracy': 0.7641418983700863} |
| 0.0014 | 825.0 | 28050 | 2.7360 | {'f1': 0.8429967426710098} | {'accuracy': 0.7689357622243528} |
| 0.0014 | 826.0 | 28084 | 2.7455 | {'f1': 0.8426527958387516} | {'accuracy': 0.7679769894534996} |
| 0.0014 | 827.0 | 28118 | 3.2188 | {'f1': 0.8390243902439024} | {'accuracy': 0.7468839884947267} |
| 0.0014 | 828.0 | 28152 | 2.9161 | {'f1': 0.8409805153991201} | {'accuracy': 0.7574304889741131} |
| 0.0014 | 829.0 | 28186 | 2.8024 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0014 | 830.0 | 28220 | 2.8061 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0014 | 831.0 | 28254 | 2.9948 | {'f1': 0.8423676012461059} | {'accuracy': 0.7574304889741131} |
| 0.0014 | 832.0 | 28288 | 2.8699 | {'f1': 0.8430379746835445} | {'accuracy': 0.7622243528283796} |
| 0.0014 | 833.0 | 28322 | 2.8678 | {'f1': 0.8430379746835445} | {'accuracy': 0.7622243528283796} |
| 0.0014 | 834.0 | 28356 | 3.1026 | {'f1': 0.8439803439803439} | {'accuracy': 0.7564717162032598} |
| 0.0014 | 835.0 | 28390 | 3.3516 | {'f1': 0.8371531966224366} | {'accuracy': 0.7411313518696069} |
| 0.0014 | 836.0 | 28424 | 3.1450 | {'f1': 0.8433292533659731} | {'accuracy': 0.7545541706615532} |
| 0.0014 | 837.0 | 28458 | 3.1280 | {'f1': 0.842040565457898} | {'accuracy': 0.7535953978907} |
| 0.0014 | 838.0 | 28492 | 2.9122 | {'f1': 0.8435718809373022} | {'accuracy': 0.763183125599233} |
| 0.0009 | 839.0 | 28526 | 2.8641 | {'f1': 0.8431122448979593} | {'accuracy': 0.7641418983700863} |
| 0.0009 | 840.0 | 28560 | 2.8744 | {'f1': 0.8420382165605096} | {'accuracy': 0.7622243528283796} |
| 0.0009 | 841.0 | 28594 | 2.8800 | {'f1': 0.8420382165605096} | {'accuracy': 0.7622243528283796} |
| 0.0009 | 842.0 | 28628 | 2.8838 | {'f1': 0.841502227880331} | {'accuracy': 0.7612655800575263} |
| 0.0009 | 843.0 | 28662 | 2.8855 | {'f1': 0.841502227880331} | {'accuracy': 0.7612655800575263} |
| 0.0009 | 844.0 | 28696 | 2.8870 | {'f1': 0.8422391857506362} | {'accuracy': 0.7622243528283796} |
| 0.0009 | 845.0 | 28730 | 2.9000 | {'f1': 0.8422391857506362} | {'accuracy': 0.7622243528283796} |
| 0.0009 | 846.0 | 28764 | 3.3231 | {'f1': 0.8415300546448088} | {'accuracy': 0.7497603068072867} |
| 0.0009 | 847.0 | 28798 | 2.9730 | {'f1': 0.843236409608091} | {'accuracy': 0.7622243528283796} |
| 0.0009 | 848.0 | 28832 | 2.9669 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0009 | 849.0 | 28866 | 2.9737 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0009 | 850.0 | 28900 | 2.9732 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0009 | 851.0 | 28934 | 2.9738 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0009 | 852.0 | 28968 | 2.9759 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 853.0 | 29002 | 2.9783 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 854.0 | 29036 | 2.9797 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 855.0 | 29070 | 2.9807 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 856.0 | 29104 | 2.9821 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 857.0 | 29138 | 2.9829 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 858.0 | 29172 | 2.9849 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 859.0 | 29206 | 2.9873 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 860.0 | 29240 | 2.9890 | {'f1': 0.8439671509791535} | {'accuracy': 0.763183125599233} |
| 0.0002 | 861.0 | 29274 | 2.9899 | {'f1': 0.8439671509791535} | {'accuracy': 0.763183125599233} |
| 0.0002 | 862.0 | 29308 | 2.9899 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 863.0 | 29342 | 2.9914 | {'f1': 0.8437697659709045} | {'accuracy': 0.763183125599233} |
| 0.0002 | 864.0 | 29376 | 2.9946 | {'f1': 0.8439671509791535} | {'accuracy': 0.763183125599233} |
| 0.0002 | 865.0 | 29410 | 2.9973 | {'f1': 0.8446969696969698} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 866.0 | 29444 | 2.8587 | {'f1': 0.8432786885245902} | {'accuracy': 0.7708533077660594} |
| 0.0002 | 867.0 | 29478 | 2.8662 | {'f1': 0.8415516107823802} | {'accuracy': 0.7689357622243528} |
| 0.0011 | 868.0 | 29512 | 3.2207 | {'f1': 0.8417177914110429} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 869.0 | 29546 | 2.7637 | {'f1': 0.8442146089204914} | {'accuracy': 0.7689357622243528} |
| 0.0011 | 870.0 | 29580 | 2.7202 | {'f1': 0.8416230366492147} | {'accuracy': 0.7679769894534996} |
| 0.0011 | 871.0 | 29614 | 2.7220 | {'f1': 0.8416230366492147} | {'accuracy': 0.7679769894534996} |
| 0.0011 | 872.0 | 29648 | 3.0772 | {'f1': 0.8411330049261084} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 873.0 | 29682 | 3.0933 | {'f1': 0.8402948402948403} | {'accuracy': 0.75071907957814} |
| 0.0011 | 874.0 | 29716 | 3.0004 | {'f1': 0.8413192283758557} | {'accuracy': 0.7555129434324065} |
| 0.0011 | 875.0 | 29750 | 2.7432 | {'f1': 0.8403141361256544} | {'accuracy': 0.7660594439117929} |
| 0.0011 | 876.0 | 29784 | 2.7075 | {'f1': 0.8397350993377483} | {'accuracy': 0.7679769894534996} |
| 0.0011 | 877.0 | 29818 | 2.9783 | {'f1': 0.8419071518193225} | {'accuracy': 0.7583892617449665} |
| 0.0011 | 878.0 | 29852 | 2.8295 | {'f1': 0.8420382165605096} | {'accuracy': 0.7622243528283796} |
| 0.0011 | 879.0 | 29886 | 2.9081 | {'f1': 0.8440251572327044} | {'accuracy': 0.7622243528283796} |
| 0.0011 | 880.0 | 29920 | 3.0216 | {'f1': 0.8425637834474176} | {'accuracy': 0.7574304889741131} |
| 0.0011 | 881.0 | 29954 | 3.0263 | {'f1': 0.842039800995025} | {'accuracy': 0.7564717162032598} |
| 0.0011 | 882.0 | 29988 | 3.0273 | {'f1': 0.842039800995025} | {'accuracy': 0.7564717162032598} |
| 0.0028 | 883.0 | 30022 | 3.0274 | {'f1': 0.8425637834474176} | {'accuracy': 0.7574304889741131} |
| 0.0028 | 884.0 | 30056 | 3.0276 | {'f1': 0.8425637834474176} | {'accuracy': 0.7574304889741131} |
| 0.0028 | 885.0 | 30090 | 3.0270 | {'f1': 0.8425637834474176} | {'accuracy': 0.7574304889741131} |
| 0.0028 | 886.0 | 30124 | 2.7614 | {'f1': 0.8427184466019418} | {'accuracy': 0.7670182166826462} |
| 0.0028 | 887.0 | 30158 | 2.7108 | {'f1': 0.8393919365499009} | {'accuracy': 0.7670182166826462} |
| 0.0028 | 888.0 | 30192 | 2.8418 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0028 | 889.0 | 30226 | 2.8564 | {'f1': 0.8401253918495297} | {'accuracy': 0.7555129434324065} |
| 0.0028 | 890.0 | 30260 | 2.8748 | {'f1': 0.8401253918495297} | {'accuracy': 0.7555129434324065} |
| 0.0028 | 891.0 | 30294 | 2.8871 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0028 | 892.0 | 30328 | 2.8915 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0028 | 893.0 | 30362 | 2.8922 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0028 | 894.0 | 30396 | 2.8906 | {'f1': 0.8395989974937343} | {'accuracy': 0.7545541706615532} |
| 0.0028 | 895.0 | 30430 | 2.8917 | {'f1': 0.8395989974937343} | {'accuracy': 0.7545541706615532} |
| 0.0028 | 896.0 | 30464 | 2.9596 | {'f1': 0.8392302917442583} | {'accuracy': 0.7516778523489933} |
| 0.0028 | 897.0 | 30498 | 2.7362 | {'f1': 0.8395860284605433} | {'accuracy': 0.7622243528283796} |
| 0.0015 | 898.0 | 30532 | 2.7578 | {'f1': 0.838709677419355} | {'accuracy': 0.7603068072866731} |
| 0.0015 | 899.0 | 30566 | 2.9297 | {'f1': 0.8405063291139242} | {'accuracy': 0.7583892617449665} |
| 0.0015 | 900.0 | 30600 | 2.9516 | {'f1': 0.8370044052863437} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 901.0 | 30634 | 2.9536 | {'f1': 0.8370044052863437} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 902.0 | 30668 | 2.9556 | {'f1': 0.8370044052863437} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 903.0 | 30702 | 2.9577 | {'f1': 0.8370044052863437} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 904.0 | 30736 | 2.9551 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0015 | 905.0 | 30770 | 2.9679 | {'f1': 0.8370044052863437} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 906.0 | 30804 | 2.9534 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0015 | 907.0 | 30838 | 2.8899 | {'f1': 0.8385449904275686} | {'accuracy': 0.7574304889741131} |
| 0.0015 | 908.0 | 30872 | 2.9045 | {'f1': 0.8369426751592357} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 909.0 | 30906 | 2.9126 | {'f1': 0.8371501272264632} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 910.0 | 30940 | 2.9177 | {'f1': 0.8371501272264632} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 911.0 | 30974 | 2.9081 | {'f1': 0.8367346938775511} | {'accuracy': 0.7545541706615532} |
| 0.0 | 912.0 | 31008 | 2.9141 | {'f1': 0.8367346938775511} | {'accuracy': 0.7545541706615532} |
| 0.0 | 913.0 | 31042 | 2.9189 | {'f1': 0.8367346938775511} | {'accuracy': 0.7545541706615532} |
| 0.0 | 914.0 | 31076 | 2.9624 | {'f1': 0.8401015228426397} | {'accuracy': 0.7583892617449665} |
| 0.0 | 915.0 | 31110 | 2.9685 | {'f1': 0.8401015228426397} | {'accuracy': 0.7583892617449665} |
| 0.0 | 916.0 | 31144 | 2.9741 | {'f1': 0.8403041825095058} | {'accuracy': 0.7583892617449665} |
| 0.0 | 917.0 | 31178 | 3.4838 | {'f1': 0.8393285371702637} | {'accuracy': 0.7430488974113135} |
| 0.0 | 918.0 | 31212 | 3.3321 | {'f1': 0.8401937046004843} | {'accuracy': 0.7468839884947267} |
| 0.0 | 919.0 | 31246 | 2.8801 | {'f1': 0.8407643312101911} | {'accuracy': 0.7603068072866731} |
| 0.0 | 920.0 | 31280 | 2.8712 | {'f1': 0.839821314613912} | {'accuracy': 0.7593480345158198} |
| 0.0 | 921.0 | 31314 | 2.8728 | {'f1': 0.839821314613912} | {'accuracy': 0.7593480345158198} |
| 0.0 | 922.0 | 31348 | 3.3941 | {'f1': 0.8387484957882071} | {'accuracy': 0.7430488974113135} |
| 0.0 | 923.0 | 31382 | 2.8636 | {'f1': 0.8399746995572422} | {'accuracy': 0.7574304889741131} |
| 0.0 | 924.0 | 31416 | 2.9903 | {'f1': 0.838909541511772} | {'accuracy': 0.75071907957814} |
| 0.0 | 925.0 | 31450 | 3.1526 | {'f1': 0.8437118437118436} | {'accuracy': 0.7545541706615532} |
| 0.0 | 926.0 | 31484 | 2.8688 | {'f1': 0.8398983481575605} | {'accuracy': 0.7583892617449665} |
| 0.0011 | 927.0 | 31518 | 2.8078 | {'f1': 0.8382541720154044} | {'accuracy': 0.7583892617449665} |
| 0.0011 | 928.0 | 31552 | 2.9345 | {'f1': 0.8371212121212122} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 929.0 | 31586 | 2.9553 | {'f1': 0.8373266078184111} | {'accuracy': 0.7526366251198466} |
| 0.0011 | 930.0 | 31620 | 2.9571 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0011 | 931.0 | 31654 | 2.9587 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0011 | 932.0 | 31688 | 3.4801 | {'f1': 0.8402652200120554} | {'accuracy': 0.7459252157238735} |
| 0.0011 | 933.0 | 31722 | 3.0138 | {'f1': 0.8383005707038681} | {'accuracy': 0.7555129434324065} |
| 0.0011 | 934.0 | 31756 | 2.9768 | {'f1': 0.8378033205619413} | {'accuracy': 0.7564717162032598} |
| 0.0011 | 935.0 | 31790 | 2.8962 | {'f1': 0.8374193548387098} | {'accuracy': 0.7583892617449665} |
| 0.0011 | 936.0 | 31824 | 2.9182 | {'f1': 0.8393316195372751} | {'accuracy': 0.7603068072866731} |
| 0.0011 | 937.0 | 31858 | 2.9293 | {'f1': 0.8389993585631816} | {'accuracy': 0.7593480345158198} |
| 0.0011 | 938.0 | 31892 | 2.9323 | {'f1': 0.8389993585631816} | {'accuracy': 0.7593480345158198} |
| 0.0011 | 939.0 | 31926 | 2.9567 | {'f1': 0.8381317978246962} | {'accuracy': 0.7574304889741131} |
| 0.0011 | 940.0 | 31960 | 3.4603 | {'f1': 0.8407026044821321} | {'accuracy': 0.74784276126558} |
| 0.0011 | 941.0 | 31994 | 3.4616 | {'f1': 0.8407026044821321} | {'accuracy': 0.74784276126558} |
| 0.0002 | 942.0 | 32028 | 3.0111 | {'f1': 0.8389993585631816} | {'accuracy': 0.7593480345158198} |
| 0.0002 | 943.0 | 32062 | 3.0014 | {'f1': 0.839871382636656} | {'accuracy': 0.7612655800575263} |
| 0.0002 | 944.0 | 32096 | 3.0133 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0002 | 945.0 | 32130 | 3.0148 | {'f1': 0.8382541720154044} | {'accuracy': 0.7583892617449665} |
| 0.0002 | 946.0 | 32164 | 3.0166 | {'f1': 0.8382541720154044} | {'accuracy': 0.7583892617449665} |
| 0.0002 | 947.0 | 32198 | 3.0195 | {'f1': 0.8382541720154044} | {'accuracy': 0.7583892617449665} |
| 0.0002 | 948.0 | 32232 | 3.0212 | {'f1': 0.8382541720154044} | {'accuracy': 0.7583892617449665} |
| 0.0002 | 949.0 | 32266 | 3.0254 | {'f1': 0.8397435897435898} | {'accuracy': 0.7603068072866731} |
| 0.0002 | 950.0 | 32300 | 3.0303 | {'f1': 0.8397435897435898} | {'accuracy': 0.7603068072866731} |
| 0.0002 | 951.0 | 32334 | 3.0326 | {'f1': 0.8397435897435898} | {'accuracy': 0.7603068072866731} |
| 0.0002 | 952.0 | 32368 | 3.0352 | {'f1': 0.8397435897435898} | {'accuracy': 0.7603068072866731} |
| 0.0002 | 953.0 | 32402 | 3.0372 | {'f1': 0.8397435897435898} | {'accuracy': 0.7603068072866731} |
| 0.0002 | 954.0 | 32436 | 3.0389 | {'f1': 0.8397435897435898} | {'accuracy': 0.7603068072866731} |
| 0.0002 | 955.0 | 32470 | 3.0450 | {'f1': 0.8397435897435898} | {'accuracy': 0.7603068072866731} |
| 0.0 | 956.0 | 32504 | 3.0479 | {'f1': 0.8397435897435898} | {'accuracy': 0.7603068072866731} |
| 0.0 | 957.0 | 32538 | 3.0549 | {'f1': 0.8381317978246962} | {'accuracy': 0.7574304889741131} |
| 0.0 | 958.0 | 32572 | 3.0569 | {'f1': 0.8381317978246962} | {'accuracy': 0.7574304889741131} |
| 0.0 | 959.0 | 32606 | 3.0581 | {'f1': 0.8381317978246962} | {'accuracy': 0.7574304889741131} |
| 0.0 | 960.0 | 32640 | 3.0593 | {'f1': 0.8375959079283888} | {'accuracy': 0.7564717162032598} |
| 0.0 | 961.0 | 32674 | 3.0681 | {'f1': 0.8372686662412252} | {'accuracy': 0.7555129434324065} |
| 0.0 | 962.0 | 32708 | 3.0729 | {'f1': 0.8380102040816327} | {'accuracy': 0.7564717162032598} |
| 0.0 | 963.0 | 32742 | 3.0794 | {'f1': 0.8374760994263862} | {'accuracy': 0.7555129434324065} |
| 0.0 | 964.0 | 32776 | 3.0859 | {'f1': 0.8369426751592357} | {'accuracy': 0.7545541706615532} |
| 0.0 | 965.0 | 32810 | 3.0894 | {'f1': 0.8369426751592357} | {'accuracy': 0.7545541706615532} |
| 0.0 | 966.0 | 32844 | 3.0944 | {'f1': 0.8364099299809039} | {'accuracy': 0.7535953978907} |
| 0.0 | 967.0 | 32878 | 3.0978 | {'f1': 0.8364099299809039} | {'accuracy': 0.7535953978907} |
| 0.0 | 968.0 | 32912 | 3.1684 | {'f1': 0.8367088607594937} | {'accuracy': 0.7526366251198466} |
| 0.0 | 969.0 | 32946 | 3.2303 | {'f1': 0.8397234443746072} | {'accuracy': 0.7555129434324065} |
| 0.0 | 970.0 | 32980 | 3.2326 | {'f1': 0.8397234443746072} | {'accuracy': 0.7555129434324065} |
| 0.0 | 971.0 | 33014 | 3.2334 | {'f1': 0.8397234443746072} | {'accuracy': 0.7555129434324065} |
| 0.0 | 972.0 | 33048 | 3.2207 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 973.0 | 33082 | 3.2192 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 974.0 | 33116 | 3.2198 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 975.0 | 33150 | 3.2214 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 976.0 | 33184 | 3.2224 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 977.0 | 33218 | 3.2231 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 978.0 | 33252 | 3.2246 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 979.0 | 33286 | 2.9749 | {'f1': 0.8339869281045752} | {'accuracy': 0.7564717162032598} |
| 0.0 | 980.0 | 33320 | 3.2905 | {'f1': 0.8409232688708672} | {'accuracy': 0.7555129434324065} |
| 0.0 | 981.0 | 33354 | 3.0802 | {'f1': 0.8366688185926405} | {'accuracy': 0.7574304889741131} |
| 0.0 | 982.0 | 33388 | 3.2283 | {'f1': 0.8383838383838385} | {'accuracy': 0.7545541706615532} |
| 0.0 | 983.0 | 33422 | 3.2954 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 984.0 | 33456 | 3.3092 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0 | 985.0 | 33490 | 3.3124 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0002 | 986.0 | 33524 | 3.3133 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0002 | 987.0 | 33558 | 3.3138 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0002 | 988.0 | 33592 | 3.1806 | {'f1': 0.8356687898089172} | {'accuracy': 0.7526366251198466} |
| 0.0002 | 989.0 | 33626 | 3.0940 | {'f1': 0.8359173126614987} | {'accuracy': 0.7564717162032598} |
| 0.0002 | 990.0 | 33660 | 3.3259 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0002 | 991.0 | 33694 | 3.4057 | {'f1': 0.8362282878411912} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 992.0 | 33728 | 3.3716 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 993.0 | 33762 | 3.3704 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 994.0 | 33796 | 3.3698 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 995.0 | 33830 | 3.3698 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 996.0 | 33864 | 3.3700 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 997.0 | 33898 | 3.3723 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 998.0 | 33932 | 3.3734 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 999.0 | 33966 | 3.3725 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 1000.0 | 34000 | 3.3716 | {'f1': 0.8360248447204969} | {'accuracy': 0.7468839884947267} |
| 0.0002 | 1001.0 | 34034 | 3.2074 | {'f1': 0.8397720075997466} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1002.0 | 34068 | 3.5080 | {'f1': 0.8362068965517242} | {'accuracy': 0.7449664429530202} |
| 0.0002 | 1003.0 | 34102 | 3.9168 | {'f1': 0.835928143712575} | {'accuracy': 0.7372962607861937} |
| 0.0002 | 1004.0 | 34136 | 3.0892 | {'f1': 0.8336620644312952} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1005.0 | 34170 | 3.0851 | {'f1': 0.837730870712401} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1006.0 | 34204 | 3.0825 | {'f1': 0.837730870712401} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1007.0 | 34238 | 3.1966 | {'f1': 0.8356687898089172} | {'accuracy': 0.7526366251198466} |
| 0.0002 | 1008.0 | 34272 | 3.3321 | {'f1': 0.8373831775700934} | {'accuracy': 0.7497603068072867} |
| 0.0002 | 1009.0 | 34306 | 3.0045 | {'f1': 0.8387942332896462} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1010.0 | 34340 | 3.0188 | {'f1': 0.8392156862745098} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1011.0 | 34374 | 3.0502 | {'f1': 0.8416938110749186} | {'accuracy': 0.7670182166826462} |
| 0.0002 | 1012.0 | 34408 | 3.0786 | {'f1': 0.8382923673997412} | {'accuracy': 0.7603068072866731} |
| 0.0002 | 1013.0 | 34442 | 3.0865 | {'f1': 0.8385012919896642} | {'accuracy': 0.7603068072866731} |
| 0.0002 | 1014.0 | 34476 | 3.3899 | {'f1': 0.8369497830130194} | {'accuracy': 0.74784276126558} |
| 0.0035 | 1015.0 | 34510 | 3.0160 | {'f1': 0.8393782383419689} | {'accuracy': 0.7622243528283796} |
| 0.0035 | 1016.0 | 34544 | 3.0109 | {'f1': 0.8391699092088196} | {'accuracy': 0.7622243528283796} |
| 0.0035 | 1017.0 | 34578 | 3.0121 | {'f1': 0.8391699092088196} | {'accuracy': 0.7622243528283796} |
| 0.0035 | 1018.0 | 34612 | 3.0183 | {'f1': 0.8393782383419689} | {'accuracy': 0.7622243528283796} |
| 0.0035 | 1019.0 | 34646 | 3.0028 | {'f1': 0.8395061728395061} | {'accuracy': 0.763183125599233} |
| 0.0035 | 1020.0 | 34680 | 3.0492 | {'f1': 0.8380829015544042} | {'accuracy': 0.7603068072866731} |
| 0.0035 | 1021.0 | 34714 | 3.0977 | {'f1': 0.8389993585631816} | {'accuracy': 0.7593480345158198} |
| 0.0035 | 1022.0 | 34748 | 3.0795 | {'f1': 0.8384615384615385} | {'accuracy': 0.7583892617449665} |
| 0.0035 | 1023.0 | 34782 | 3.0618 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0035 | 1024.0 | 34816 | 3.0620 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0035 | 1025.0 | 34850 | 3.0627 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0035 | 1026.0 | 34884 | 3.0639 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0035 | 1027.0 | 34918 | 3.0649 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0035 | 1028.0 | 34952 | 3.0655 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0035 | 1029.0 | 34986 | 3.0662 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0005 | 1030.0 | 35020 | 3.0774 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0005 | 1031.0 | 35054 | 3.0823 | {'f1': 0.8382541720154044} | {'accuracy': 0.7583892617449665} |
| 0.0005 | 1032.0 | 35088 | 3.0838 | {'f1': 0.8377164849262349} | {'accuracy': 0.7574304889741131} |
| 0.0005 | 1033.0 | 35122 | 3.0843 | {'f1': 0.8377164849262349} | {'accuracy': 0.7574304889741131} |
| 0.0005 | 1034.0 | 35156 | 3.0851 | {'f1': 0.8377164849262349} | {'accuracy': 0.7574304889741131} |
| 0.0005 | 1035.0 | 35190 | 2.8143 | {'f1': 0.8436460412508316} | {'accuracy': 0.7746883988494727} |
| 0.0005 | 1036.0 | 35224 | 3.0323 | {'f1': 0.8405612244897959} | {'accuracy': 0.7603068072866731} |
| 0.0005 | 1037.0 | 35258 | 3.0444 | {'f1': 0.8407643312101911} | {'accuracy': 0.7603068072866731} |
| 0.0005 | 1038.0 | 35292 | 3.0491 | {'f1': 0.841502227880331} | {'accuracy': 0.7612655800575263} |
| 0.0005 | 1039.0 | 35326 | 3.0463 | {'f1': 0.8407643312101911} | {'accuracy': 0.7603068072866731} |
| 0.0005 | 1040.0 | 35360 | 3.0357 | {'f1': 0.8405612244897959} | {'accuracy': 0.7603068072866731} |
| 0.0005 | 1041.0 | 35394 | 3.0096 | {'f1': 0.8385852090032154} | {'accuracy': 0.7593480345158198} |
| 0.0005 | 1042.0 | 35428 | 3.0116 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0005 | 1043.0 | 35462 | 3.0128 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0005 | 1044.0 | 35496 | 3.0931 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.001 | 1045.0 | 35530 | 3.3652 | {'f1': 0.838749233599019} | {'accuracy': 0.74784276126558} |
| 0.001 | 1046.0 | 35564 | 3.3300 | {'f1': 0.837954405422058} | {'accuracy': 0.74784276126558} |
| 0.001 | 1047.0 | 35598 | 3.3274 | {'f1': 0.8377544725478101} | {'accuracy': 0.74784276126558} |
| 0.001 | 1048.0 | 35632 | 3.2568 | {'f1': 0.8404717566728741} | {'accuracy': 0.7535953978907} |
| 0.001 | 1049.0 | 35666 | 3.2510 | {'f1': 0.8409937888198759} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1050.0 | 35700 | 3.2511 | {'f1': 0.8409937888198759} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1051.0 | 35734 | 3.2509 | {'f1': 0.8409937888198759} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1052.0 | 35768 | 3.2512 | {'f1': 0.8409937888198759} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1053.0 | 35802 | 3.2516 | {'f1': 0.8409937888198759} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1054.0 | 35836 | 3.2526 | {'f1': 0.8409937888198759} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1055.0 | 35870 | 3.2527 | {'f1': 0.8409937888198759} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1056.0 | 35904 | 3.2526 | {'f1': 0.8415164698570541} | {'accuracy': 0.7555129434324065} |
| 0.001 | 1057.0 | 35938 | 3.2494 | {'f1': 0.8407960199004975} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1058.0 | 35972 | 3.2428 | {'f1': 0.8405977584059775} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1059.0 | 36006 | 3.2393 | {'f1': 0.8411214953271028} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1060.0 | 36040 | 3.2392 | {'f1': 0.8411214953271028} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1061.0 | 36074 | 3.2396 | {'f1': 0.8411214953271028} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1062.0 | 36108 | 3.1980 | {'f1': 0.8379396984924623} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1063.0 | 36142 | 3.1939 | {'f1': 0.8375314861460957} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1064.0 | 36176 | 3.1937 | {'f1': 0.8375314861460957} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1065.0 | 36210 | 3.1932 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0 | 1066.0 | 36244 | 3.1905 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1067.0 | 36278 | 3.1906 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1068.0 | 36312 | 3.1909 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1069.0 | 36346 | 3.1910 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1070.0 | 36380 | 3.1941 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1071.0 | 36414 | 3.1951 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1072.0 | 36448 | 3.1958 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1073.0 | 36482 | 3.1964 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1074.0 | 36516 | 3.1977 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1075.0 | 36550 | 3.2002 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1076.0 | 36584 | 3.2008 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1077.0 | 36618 | 2.8517 | {'f1': 0.8384512683578104} | {'accuracy': 0.7679769894534996} |
| 0.0 | 1078.0 | 36652 | 3.3185 | {'f1': 0.8424279583077867} | {'accuracy': 0.7535953978907} |
| 0.0 | 1079.0 | 36686 | 3.0491 | {'f1': 0.8397720075997466} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1080.0 | 36720 | 3.0037 | {'f1': 0.8433121019108281} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1081.0 | 36754 | 3.0040 | {'f1': 0.8433121019108281} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1082.0 | 36788 | 3.0057 | {'f1': 0.8433121019108281} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1083.0 | 36822 | 3.0094 | {'f1': 0.8422391857506362} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1084.0 | 36856 | 3.0202 | {'f1': 0.8424396442185516} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1085.0 | 36890 | 3.0208 | {'f1': 0.8424396442185516} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1086.0 | 36924 | 3.0250 | {'f1': 0.8419047619047619} | {'accuracy': 0.7612655800575263} |
| 0.0 | 1087.0 | 36958 | 3.5543 | {'f1': 0.8391777509068924} | {'accuracy': 0.7449664429530202} |
| 0.0 | 1088.0 | 36992 | 3.6408 | {'f1': 0.8392534617700181} | {'accuracy': 0.7440076701821668} |
| 0.0014 | 1089.0 | 37026 | 3.3823 | {'f1': 0.8382716049382717} | {'accuracy': 0.7488015340364333} |
| 0.0014 | 1090.0 | 37060 | 3.0723 | {'f1': 0.8378378378378379} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 1091.0 | 37094 | 3.1840 | {'f1': 0.8414403032217308} | {'accuracy': 0.7593480345158198} |
| 0.0014 | 1092.0 | 37128 | 3.1862 | {'f1': 0.8414403032217308} | {'accuracy': 0.7593480345158198} |
| 0.0014 | 1093.0 | 37162 | 3.1828 | {'f1': 0.8407079646017699} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 1094.0 | 37196 | 3.1838 | {'f1': 0.8407079646017699} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 1095.0 | 37230 | 3.1897 | {'f1': 0.8407079646017699} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 1096.0 | 37264 | 3.2495 | {'f1': 0.8419071518193225} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 1097.0 | 37298 | 3.2523 | {'f1': 0.8419071518193225} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 1098.0 | 37332 | 3.2532 | {'f1': 0.8419071518193225} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 1099.0 | 37366 | 2.9331 | {'f1': 0.8345323741007195} | {'accuracy': 0.7574304889741131} |
| 0.0014 | 1100.0 | 37400 | 2.9004 | {'f1': 0.835294117647059} | {'accuracy': 0.7583892617449665} |
| 0.0014 | 1101.0 | 37434 | 3.2158 | {'f1': 0.8400746733042936} | {'accuracy': 0.7535953978907} |
| 0.0014 | 1102.0 | 37468 | 3.2373 | {'f1': 0.8387096774193548} | {'accuracy': 0.75071907957814} |
| 0.0015 | 1103.0 | 37502 | 3.2362 | {'f1': 0.8387096774193548} | {'accuracy': 0.75071907957814} |
| 0.0015 | 1104.0 | 37536 | 3.1432 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 1105.0 | 37570 | 3.1413 | {'f1': 0.8384663733500944} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1106.0 | 37604 | 3.1483 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 1107.0 | 37638 | 3.1493 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 1108.0 | 37672 | 3.1466 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 1109.0 | 37706 | 3.1439 | {'f1': 0.8384663733500944} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1110.0 | 37740 | 3.1489 | {'f1': 0.8384663733500944} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1111.0 | 37774 | 3.1559 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 1112.0 | 37808 | 3.2079 | {'f1': 0.8394753279200501} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1113.0 | 37842 | 3.2164 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 1114.0 | 37876 | 3.2097 | {'f1': 0.8394753279200501} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1115.0 | 37910 | 3.1312 | {'f1': 0.8369152970922884} | {'accuracy': 0.7526366251198466} |
| 0.0015 | 1116.0 | 37944 | 3.1161 | {'f1': 0.8361796331435801} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 1117.0 | 37978 | 3.1189 | {'f1': 0.8361796331435801} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1118.0 | 38012 | 3.1477 | {'f1': 0.8369152970922884} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1119.0 | 38046 | 3.1505 | {'f1': 0.8369152970922884} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1120.0 | 38080 | 3.1519 | {'f1': 0.8369152970922884} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1121.0 | 38114 | 3.1540 | {'f1': 0.8369152970922884} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1122.0 | 38148 | 3.1563 | {'f1': 0.837650031585597} | {'accuracy': 0.7535953978907} |
| 0.0 | 1123.0 | 38182 | 3.1566 | {'f1': 0.837650031585597} | {'accuracy': 0.7535953978907} |
| 0.0 | 1124.0 | 38216 | 3.0363 | {'f1': 0.8349514563106797} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1125.0 | 38250 | 3.1563 | {'f1': 0.8357641090678504} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1126.0 | 38284 | 3.2192 | {'f1': 0.840251572327044} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1127.0 | 38318 | 3.2219 | {'f1': 0.840251572327044} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1128.0 | 38352 | 3.2218 | {'f1': 0.840251572327044} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1129.0 | 38386 | 3.2225 | {'f1': 0.840251572327044} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1130.0 | 38420 | 3.2176 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1131.0 | 38454 | 3.2204 | {'f1': 0.8395217117684078} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1132.0 | 38488 | 3.2214 | {'f1': 0.8395217117684078} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1133.0 | 38522 | 3.2216 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1134.0 | 38556 | 3.2217 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1135.0 | 38590 | 3.2225 | {'f1': 0.8387909319899244} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1136.0 | 38624 | 3.2210 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0 | 1137.0 | 38658 | 3.2216 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0 | 1138.0 | 38692 | 3.2232 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0 | 1139.0 | 38726 | 3.1040 | {'f1': 0.8395378690629013} | {'accuracy': 0.7603068072866731} |
| 0.0 | 1140.0 | 38760 | 3.1561 | {'f1': 0.8404322949777496} | {'accuracy': 0.7593480345158198} |
| 0.0 | 1141.0 | 38794 | 3.1639 | {'f1': 0.8404322949777496} | {'accuracy': 0.7593480345158198} |
| 0.0 | 1142.0 | 38828 | 3.1654 | {'f1': 0.8404322949777496} | {'accuracy': 0.7593480345158198} |
| 0.0 | 1143.0 | 38862 | 3.1672 | {'f1': 0.8404322949777496} | {'accuracy': 0.7593480345158198} |
| 0.0 | 1144.0 | 38896 | 3.1690 | {'f1': 0.8411689961880559} | {'accuracy': 0.7603068072866731} |
| 0.0 | 1145.0 | 38930 | 3.1702 | {'f1': 0.8406349206349206} | {'accuracy': 0.7593480345158198} |
| 0.0 | 1146.0 | 38964 | 3.2433 | {'f1': 0.8384663733500944} | {'accuracy': 0.7535953978907} |
| 0.0 | 1147.0 | 38998 | 3.1837 | {'f1': 0.8375634517766497} | {'accuracy': 0.7545541706615532} |
| 0.0006 | 1148.0 | 39032 | 3.9121 | {'f1': 0.8363201911589008} | {'accuracy': 0.7372962607861937} |
| 0.0006 | 1149.0 | 39066 | 3.1272 | {'f1': 0.8369070825211176} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1150.0 | 39100 | 3.0978 | {'f1': 0.8385826771653543} | {'accuracy': 0.7641418983700863} |
| 0.0006 | 1151.0 | 39134 | 3.1093 | {'f1': 0.8384565075212557} | {'accuracy': 0.763183125599233} |
| 0.0006 | 1152.0 | 39168 | 3.2410 | {'f1': 0.8367473618870268} | {'accuracy': 0.74784276126558} |
| 0.0006 | 1153.0 | 39202 | 3.2573 | {'f1': 0.8357098574085554} | {'accuracy': 0.7459252157238735} |
| 0.0006 | 1154.0 | 39236 | 3.2450 | {'f1': 0.8357098574085554} | {'accuracy': 0.7459252157238735} |
| 0.0006 | 1155.0 | 39270 | 3.0282 | {'f1': 0.8377850162866449} | {'accuracy': 0.7612655800575263} |
| 0.0006 | 1156.0 | 39304 | 2.9980 | {'f1': 0.8393442622950821} | {'accuracy': 0.7651006711409396} |
| 0.0006 | 1157.0 | 39338 | 3.1611 | {'f1': 0.8385053831538949} | {'accuracy': 0.7555129434324065} |
| 0.0006 | 1158.0 | 39372 | 3.3974 | {'f1': 0.8411330049261084} | {'accuracy': 0.7526366251198466} |
| 0.0006 | 1159.0 | 39406 | 3.4052 | {'f1': 0.8411330049261084} | {'accuracy': 0.7526366251198466} |
| 0.0006 | 1160.0 | 39440 | 3.4009 | {'f1': 0.8411330049261084} | {'accuracy': 0.7526366251198466} |
| 0.0006 | 1161.0 | 39474 | 3.1150 | {'f1': 0.8410123296560675} | {'accuracy': 0.7651006711409396} |
| 0.0015 | 1162.0 | 39508 | 3.1018 | {'f1': 0.8405985686402083} | {'accuracy': 0.7651006711409396} |
| 0.0015 | 1163.0 | 39542 | 3.3718 | {'f1': 0.840024706609018} | {'accuracy': 0.7516778523489933} |
| 0.0015 | 1164.0 | 39576 | 3.4885 | {'f1': 0.838749233599019} | {'accuracy': 0.74784276126558} |
| 0.0015 | 1165.0 | 39610 | 3.4889 | {'f1': 0.838749233599019} | {'accuracy': 0.74784276126558} |
| 0.0015 | 1166.0 | 39644 | 3.4886 | {'f1': 0.838749233599019} | {'accuracy': 0.74784276126558} |
| 0.0015 | 1167.0 | 39678 | 3.4881 | {'f1': 0.838749233599019} | {'accuracy': 0.74784276126558} |
| 0.0015 | 1168.0 | 39712 | 3.4828 | {'f1': 0.8385512584407613} | {'accuracy': 0.74784276126558} |
| 0.0015 | 1169.0 | 39746 | 3.4809 | {'f1': 0.8385512584407613} | {'accuracy': 0.74784276126558} |
| 0.0015 | 1170.0 | 39780 | 3.2265 | {'f1': 0.8387096774193549} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 1171.0 | 39814 | 3.1164 | {'f1': 0.8408057179987004} | {'accuracy': 0.7651006711409396} |
| 0.0015 | 1172.0 | 39848 | 3.1195 | {'f1': 0.8402597402597403} | {'accuracy': 0.7641418983700863} |
| 0.0015 | 1173.0 | 39882 | 3.1201 | {'f1': 0.8402597402597403} | {'accuracy': 0.7641418983700863} |
| 0.0015 | 1174.0 | 39916 | 3.0571 | {'f1': 0.8317631224764469} | {'accuracy': 0.7603068072866731} |
| 0.0015 | 1175.0 | 39950 | 3.1408 | {'f1': 0.8410852713178294} | {'accuracy': 0.7641418983700863} |
| 0.0015 | 1176.0 | 39984 | 3.1916 | {'f1': 0.8417680973734787} | {'accuracy': 0.763183125599233} |
| 0.0002 | 1177.0 | 40018 | 3.1935 | {'f1': 0.8417680973734787} | {'accuracy': 0.763183125599233} |
| 0.0002 | 1178.0 | 40052 | 3.1942 | {'f1': 0.8417680973734787} | {'accuracy': 0.763183125599233} |
| 0.0002 | 1179.0 | 40086 | 3.1949 | {'f1': 0.8417680973734787} | {'accuracy': 0.763183125599233} |
| 0.0002 | 1180.0 | 40120 | 3.1953 | {'f1': 0.8417680973734787} | {'accuracy': 0.763183125599233} |
| 0.0002 | 1181.0 | 40154 | 3.1956 | {'f1': 0.8417680973734787} | {'accuracy': 0.763183125599233} |
| 0.0002 | 1182.0 | 40188 | 3.1975 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1183.0 | 40222 | 3.1991 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0002 | 1184.0 | 40256 | 3.1996 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0002 | 1185.0 | 40290 | 3.1998 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0002 | 1186.0 | 40324 | 3.2002 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0002 | 1187.0 | 40358 | 3.1993 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1188.0 | 40392 | 3.1996 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1189.0 | 40426 | 3.2006 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1190.0 | 40460 | 3.2010 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1191.0 | 40494 | 3.2014 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1192.0 | 40528 | 3.2026 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1193.0 | 40562 | 3.1400 | {'f1': 0.8405985686402083} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1194.0 | 40596 | 3.1234 | {'f1': 0.84107259646828} | {'accuracy': 0.7670182166826462} |
| 0.0 | 1195.0 | 40630 | 3.1383 | {'f1': 0.8405228758169935} | {'accuracy': 0.7660594439117929} |
| 0.0 | 1196.0 | 40664 | 3.1422 | {'f1': 0.8405228758169935} | {'accuracy': 0.7660594439117929} |
| 0.0 | 1197.0 | 40698 | 3.1468 | {'f1': 0.8405228758169935} | {'accuracy': 0.7660594439117929} |
| 0.0 | 1198.0 | 40732 | 3.1486 | {'f1': 0.8399738732854344} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1199.0 | 40766 | 3.5103 | {'f1': 0.838390092879257} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1200.0 | 40800 | 3.5735 | {'f1': 0.8374384236453202} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1201.0 | 40834 | 3.5755 | {'f1': 0.8374384236453202} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1202.0 | 40868 | 3.5755 | {'f1': 0.8374384236453202} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1203.0 | 40902 | 3.5754 | {'f1': 0.8374384236453202} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1204.0 | 40936 | 3.5755 | {'f1': 0.8374384236453202} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1205.0 | 40970 | 3.5741 | {'f1': 0.8374384236453202} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1206.0 | 41004 | 3.5775 | {'f1': 0.8374384236453202} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1207.0 | 41038 | 3.5288 | {'f1': 0.8378712871287128} | {'accuracy': 0.7488015340364333} |
| 0.0 | 1208.0 | 41072 | 3.4448 | {'f1': 0.8389513108614233} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1209.0 | 41106 | 3.4430 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1210.0 | 41140 | 3.4432 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1211.0 | 41174 | 3.4431 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1212.0 | 41208 | 3.4433 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1213.0 | 41242 | 3.4435 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1214.0 | 41276 | 3.4440 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1215.0 | 41310 | 3.4444 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1216.0 | 41344 | 3.4445 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1217.0 | 41378 | 3.4447 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1218.0 | 41412 | 3.4447 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1219.0 | 41446 | 3.4446 | {'f1': 0.83875} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1220.0 | 41480 | 4.0574 | {'f1': 0.8362327534493101} | {'accuracy': 0.738255033557047} |
| 0.0 | 1221.0 | 41514 | 3.5882 | {'f1': 0.840937114673243} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1222.0 | 41548 | 3.5761 | {'f1': 0.840937114673243} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1223.0 | 41582 | 3.5726 | {'f1': 0.840937114673243} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1224.0 | 41616 | 3.5726 | {'f1': 0.840937114673243} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1225.0 | 41650 | 3.5726 | {'f1': 0.840937114673243} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1226.0 | 41684 | 3.4831 | {'f1': 0.8400746733042936} | {'accuracy': 0.7535953978907} |
| 0.0 | 1227.0 | 41718 | 3.4732 | {'f1': 0.8393524283935242} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1228.0 | 41752 | 3.4731 | {'f1': 0.8393524283935242} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1229.0 | 41786 | 3.4732 | {'f1': 0.8393524283935242} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1230.0 | 41820 | 3.4732 | {'f1': 0.8393524283935242} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1231.0 | 41854 | 3.4734 | {'f1': 0.8393524283935242} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1232.0 | 41888 | 3.4727 | {'f1': 0.8398753894080997} | {'accuracy': 0.7535953978907} |
| 0.0 | 1233.0 | 41922 | 3.4723 | {'f1': 0.8398753894080997} | {'accuracy': 0.7535953978907} |
| 0.0 | 1234.0 | 41956 | 3.4728 | {'f1': 0.8398753894080997} | {'accuracy': 0.7535953978907} |
| 0.0 | 1235.0 | 41990 | 3.4730 | {'f1': 0.8398753894080997} | {'accuracy': 0.7535953978907} |
| 0.0 | 1236.0 | 42024 | 3.4730 | {'f1': 0.8398753894080997} | {'accuracy': 0.7535953978907} |
| 0.0 | 1237.0 | 42058 | 3.5325 | {'f1': 0.8406695598264106} | {'accuracy': 0.7535953978907} |
| 0.0 | 1238.0 | 42092 | 3.5571 | {'f1': 0.8403465346534654} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1239.0 | 42126 | 3.1688 | {'f1': 0.8385382059800663} | {'accuracy': 0.7670182166826462} |
| 0.0 | 1240.0 | 42160 | 3.1687 | {'f1': 0.8345612860013396} | {'accuracy': 0.763183125599233} |
| 0.0 | 1241.0 | 42194 | 3.2048 | {'f1': 0.837730870712401} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1242.0 | 42228 | 3.2593 | {'f1': 0.8399738732854344} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1243.0 | 42262 | 3.2657 | {'f1': 0.8399738732854344} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1244.0 | 42296 | 3.2672 | {'f1': 0.8399738732854344} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1245.0 | 42330 | 3.2699 | {'f1': 0.8399738732854344} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1246.0 | 42364 | 3.2720 | {'f1': 0.8399738732854344} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1247.0 | 42398 | 3.2756 | {'f1': 0.8399738732854344} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1248.0 | 42432 | 3.7098 | {'f1': 0.837954405422058} | {'accuracy': 0.74784276126558} |
| 0.0 | 1249.0 | 42466 | 3.7278 | {'f1': 0.8366093366093366} | {'accuracy': 0.7449664429530202} |
| 0.0002 | 1250.0 | 42500 | 3.6318 | {'f1': 0.8372670807453417} | {'accuracy': 0.7488015340364333} |
| 0.0002 | 1251.0 | 42534 | 3.5856 | {'f1': 0.8359326263256395} | {'accuracy': 0.74784276126558} |
| 0.0002 | 1252.0 | 42568 | 3.5843 | {'f1': 0.8359326263256395} | {'accuracy': 0.74784276126558} |
| 0.0002 | 1253.0 | 42602 | 3.5852 | {'f1': 0.8359326263256395} | {'accuracy': 0.74784276126558} |
| 0.0002 | 1254.0 | 42636 | 3.5853 | {'f1': 0.8359326263256395} | {'accuracy': 0.74784276126558} |
| 0.0002 | 1255.0 | 42670 | 3.5857 | {'f1': 0.8359326263256395} | {'accuracy': 0.74784276126558} |
| 0.0002 | 1256.0 | 42704 | 3.5859 | {'f1': 0.8359326263256395} | {'accuracy': 0.74784276126558} |
| 0.0002 | 1257.0 | 42738 | 3.5858 | {'f1': 0.8359326263256395} | {'accuracy': 0.74784276126558} |
| 0.0002 | 1258.0 | 42772 | 3.5470 | {'f1': 0.8383084577114429} | {'accuracy': 0.75071907957814} |
| 0.0002 | 1259.0 | 42806 | 3.2429 | {'f1': 0.8412184057031757} | {'accuracy': 0.7651006711409396} |
| 0.0002 | 1260.0 | 42840 | 3.3302 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0002 | 1261.0 | 42874 | 3.4607 | {'f1': 0.8379396984924623} | {'accuracy': 0.7526366251198466} |
| 0.0002 | 1262.0 | 42908 | 3.4639 | {'f1': 0.8379396984924623} | {'accuracy': 0.7526366251198466} |
| 0.0002 | 1263.0 | 42942 | 3.2307 | {'f1': 0.8427835051546391} | {'accuracy': 0.7660594439117929} |
| 0.0002 | 1264.0 | 42976 | 3.1433 | {'f1': 0.8409387222946545} | {'accuracy': 0.7660594439117929} |
| 0.0034 | 1265.0 | 43010 | 3.1429 | {'f1': 0.8409387222946545} | {'accuracy': 0.7660594439117929} |
| 0.0034 | 1266.0 | 43044 | 3.1814 | {'f1': 0.8405422853453841} | {'accuracy': 0.763183125599233} |
| 0.0034 | 1267.0 | 43078 | 3.1860 | {'f1': 0.8405422853453841} | {'accuracy': 0.763183125599233} |
| 0.0034 | 1268.0 | 43112 | 3.1868 | {'f1': 0.8405422853453841} | {'accuracy': 0.763183125599233} |
| 0.0034 | 1269.0 | 43146 | 3.1928 | {'f1': 0.8407479045776919} | {'accuracy': 0.763183125599233} |
| 0.0034 | 1270.0 | 43180 | 3.1940 | {'f1': 0.8407479045776919} | {'accuracy': 0.763183125599233} |
| 0.0034 | 1271.0 | 43214 | 3.1958 | {'f1': 0.8402061855670104} | {'accuracy': 0.7622243528283796} |
| 0.0034 | 1272.0 | 43248 | 3.1996 | {'f1': 0.8402061855670104} | {'accuracy': 0.7622243528283796} |
| 0.0034 | 1273.0 | 43282 | 3.2036 | {'f1': 0.840952994204765} | {'accuracy': 0.763183125599233} |
| 0.0034 | 1274.0 | 43316 | 3.2040 | {'f1': 0.840952994204765} | {'accuracy': 0.763183125599233} |
| 0.0034 | 1275.0 | 43350 | 3.2090 | {'f1': 0.840952994204765} | {'accuracy': 0.763183125599233} |
| 0.0034 | 1276.0 | 43384 | 3.2103 | {'f1': 0.8416988416988417} | {'accuracy': 0.7641418983700863} |
| 0.0034 | 1277.0 | 43418 | 3.2108 | {'f1': 0.8416988416988417} | {'accuracy': 0.7641418983700863} |
| 0.0034 | 1278.0 | 43452 | 3.2112 | {'f1': 0.8416988416988417} | {'accuracy': 0.7641418983700863} |
| 0.0034 | 1279.0 | 43486 | 3.2141 | {'f1': 0.8416988416988417} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1280.0 | 43520 | 3.4151 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1281.0 | 43554 | 3.6340 | {'f1': 0.8379204892966362} | {'accuracy': 0.7459252157238735} |
| 0.0 | 1282.0 | 43588 | 3.4145 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1283.0 | 43622 | 3.3389 | {'f1': 0.8410386320455985} | {'accuracy': 0.7593480345158198} |
| 0.0 | 1284.0 | 43656 | 3.3696 | {'f1': 0.8421717171717172} | {'accuracy': 0.7603068072866731} |
| 0.0 | 1285.0 | 43690 | 3.3808 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0 | 1286.0 | 43724 | 3.3817 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1287.0 | 43758 | 3.3820 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1288.0 | 43792 | 3.3856 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1289.0 | 43826 | 3.3874 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1290.0 | 43860 | 3.3880 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1291.0 | 43894 | 3.3882 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1292.0 | 43928 | 3.3884 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1293.0 | 43962 | 3.3882 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1294.0 | 43996 | 3.3883 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1295.0 | 44030 | 3.3888 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1296.0 | 44064 | 3.3892 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1297.0 | 44098 | 3.3896 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1298.0 | 44132 | 3.3898 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1299.0 | 44166 | 3.3897 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1300.0 | 44200 | 3.3902 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1301.0 | 44234 | 3.3908 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0001 | 1302.0 | 44268 | 3.3910 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0001 | 1303.0 | 44302 | 3.3910 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1304.0 | 44336 | 3.3911 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1305.0 | 44370 | 3.3913 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1306.0 | 44404 | 3.3915 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1307.0 | 44438 | 3.3917 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0001 | 1308.0 | 44472 | 3.3919 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1309.0 | 44506 | 3.3922 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1310.0 | 44540 | 3.3924 | {'f1': 0.8413098236775819} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1311.0 | 44574 | 3.3941 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1312.0 | 44608 | 3.3953 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1313.0 | 44642 | 3.3957 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1314.0 | 44676 | 3.3959 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1315.0 | 44710 | 3.3962 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1316.0 | 44744 | 3.3965 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1317.0 | 44778 | 3.3969 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1318.0 | 44812 | 3.3973 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1319.0 | 44846 | 3.3976 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1320.0 | 44880 | 3.3979 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1321.0 | 44914 | 3.3984 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1322.0 | 44948 | 3.0822 | {'f1': 0.8387096774193548} | {'accuracy': 0.7698945349952061} |
| 0.0 | 1323.0 | 44982 | 3.2877 | {'f1': 0.839871382636656} | {'accuracy': 0.7612655800575263} |
| 0.0 | 1324.0 | 45016 | 3.3477 | {'f1': 0.8422391857506362} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1325.0 | 45050 | 3.3501 | {'f1': 0.8422391857506362} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1326.0 | 45084 | 3.3505 | {'f1': 0.8422391857506362} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1327.0 | 45118 | 3.7730 | {'f1': 0.8389057750759878} | {'accuracy': 0.7459252157238735} |
| 0.0 | 1328.0 | 45152 | 3.7891 | {'f1': 0.8365617433414043} | {'accuracy': 0.7411313518696069} |
| 0.0 | 1329.0 | 45186 | 3.7873 | {'f1': 0.8375757575757576} | {'accuracy': 0.7430488974113135} |
| 0.0 | 1330.0 | 45220 | 3.7863 | {'f1': 0.8380836870830807} | {'accuracy': 0.7440076701821668} |
| 0.0 | 1331.0 | 45254 | 3.7860 | {'f1': 0.8380836870830807} | {'accuracy': 0.7440076701821668} |
| 0.0 | 1332.0 | 45288 | 3.7577 | {'f1': 0.8389057750759878} | {'accuracy': 0.7459252157238735} |
| 0.0 | 1333.0 | 45322 | 3.7510 | {'f1': 0.8394160583941607} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1334.0 | 45356 | 3.3854 | {'f1': 0.840251572327044} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1335.0 | 45390 | 3.2651 | {'f1': 0.8421725239616613} | {'accuracy': 0.763183125599233} |
| 0.0 | 1336.0 | 45424 | 3.2774 | {'f1': 0.8416347381864623} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1337.0 | 45458 | 3.2789 | {'f1': 0.8423739629865986} | {'accuracy': 0.763183125599233} |
| 0.0 | 1338.0 | 45492 | 3.2792 | {'f1': 0.8423739629865986} | {'accuracy': 0.763183125599233} |
| 0.0006 | 1339.0 | 45526 | 3.3656 | {'f1': 0.8411097099621689} | {'accuracy': 0.7583892617449665} |
| 0.0006 | 1340.0 | 45560 | 3.3723 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1341.0 | 45594 | 3.3726 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1342.0 | 45628 | 3.3729 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1343.0 | 45662 | 3.3712 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1344.0 | 45696 | 3.3716 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1345.0 | 45730 | 3.3713 | {'f1': 0.8418399495904222} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1346.0 | 45764 | 3.3704 | {'f1': 0.8411097099621689} | {'accuracy': 0.7583892617449665} |
| 0.0006 | 1347.0 | 45798 | 3.3706 | {'f1': 0.8411097099621689} | {'accuracy': 0.7583892617449665} |
| 0.0006 | 1348.0 | 45832 | 3.7868 | {'f1': 0.8362537764350454} | {'accuracy': 0.7401725790987536} |
| 0.0006 | 1349.0 | 45866 | 3.8531 | {'f1': 0.8377403846153847} | {'accuracy': 0.7411313518696069} |
| 0.0006 | 1350.0 | 45900 | 3.8530 | {'f1': 0.8377403846153847} | {'accuracy': 0.7411313518696069} |
| 0.0006 | 1351.0 | 45934 | 3.1111 | {'f1': 0.8415584415584416} | {'accuracy': 0.7660594439117929} |
| 0.0006 | 1352.0 | 45968 | 3.2749 | {'f1': 0.842039018250472} | {'accuracy': 0.7593480345158198} |
| 0.0019 | 1353.0 | 46002 | 3.1427 | {'f1': 0.8408057179987004} | {'accuracy': 0.7651006711409396} |
| 0.0019 | 1354.0 | 46036 | 3.1345 | {'f1': 0.83984375} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1355.0 | 46070 | 3.1395 | {'f1': 0.83984375} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1356.0 | 46104 | 3.1872 | {'f1': 0.8422408242112042} | {'accuracy': 0.7651006711409396} |
| 0.0019 | 1357.0 | 46138 | 3.1972 | {'f1': 0.8421052631578947} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1358.0 | 46172 | 3.2015 | {'f1': 0.8421052631578947} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1359.0 | 46206 | 3.2024 | {'f1': 0.8421052631578947} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1360.0 | 46240 | 3.2040 | {'f1': 0.8421052631578947} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1361.0 | 46274 | 3.2048 | {'f1': 0.8421052631578947} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1362.0 | 46308 | 3.2051 | {'f1': 0.8421052631578947} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1363.0 | 46342 | 3.2058 | {'f1': 0.8421052631578947} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1364.0 | 46376 | 3.2061 | {'f1': 0.8421052631578947} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1365.0 | 46410 | 3.2106 | {'f1': 0.8423076923076922} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1366.0 | 46444 | 3.2117 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0019 | 1367.0 | 46478 | 3.2120 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1368.0 | 46512 | 3.2123 | {'f1': 0.8425096030729833} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1369.0 | 46546 | 3.2148 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1370.0 | 46580 | 3.2165 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1371.0 | 46614 | 3.2183 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1372.0 | 46648 | 3.2189 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1373.0 | 46682 | 3.2191 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1374.0 | 46716 | 3.2195 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1375.0 | 46750 | 3.2199 | {'f1': 0.8432501599488165} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1376.0 | 46784 | 3.2209 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1377.0 | 46818 | 3.2212 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1378.0 | 46852 | 3.2214 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1379.0 | 46886 | 3.1193 | {'f1': 0.8378200919238344} | {'accuracy': 0.763183125599233} |
| 0.0 | 1380.0 | 46920 | 3.1839 | {'f1': 0.8384166125892277} | {'accuracy': 0.7612655800575263} |
| 0.0 | 1381.0 | 46954 | 3.1916 | {'f1': 0.8391699092088196} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1382.0 | 46988 | 3.3572 | {'f1': 0.8357641090678504} | {'accuracy': 0.7516778523489933} |
| 0.0004 | 1383.0 | 47022 | 3.4433 | {'f1': 0.8360450563204005} | {'accuracy': 0.7488015340364333} |
| 0.0004 | 1384.0 | 47056 | 3.4482 | {'f1': 0.83625} | {'accuracy': 0.7488015340364333} |
| 0.0004 | 1385.0 | 47090 | 3.4485 | {'f1': 0.83625} | {'accuracy': 0.7488015340364333} |
| 0.0004 | 1386.0 | 47124 | 3.4495 | {'f1': 0.83625} | {'accuracy': 0.7488015340364333} |
| 0.0004 | 1387.0 | 47158 | 3.4496 | {'f1': 0.83625} | {'accuracy': 0.7488015340364333} |
| 0.0004 | 1388.0 | 47192 | 3.4498 | {'f1': 0.83625} | {'accuracy': 0.7488015340364333} |
| 0.0004 | 1389.0 | 47226 | 3.4501 | {'f1': 0.83625} | {'accuracy': 0.7488015340364333} |
| 0.0004 | 1390.0 | 47260 | 3.1782 | {'f1': 0.84} | {'accuracy': 0.7622243528283796} |
| 0.0004 | 1391.0 | 47294 | 3.1475 | {'f1': 0.8395860284605433} | {'accuracy': 0.7622243528283796} |
| 0.0004 | 1392.0 | 47328 | 3.1939 | {'f1': 0.8402061855670104} | {'accuracy': 0.7622243528283796} |
| 0.0004 | 1393.0 | 47362 | 3.2117 | {'f1': 0.8380462724935733} | {'accuracy': 0.7583892617449665} |
| 0.0004 | 1394.0 | 47396 | 3.2137 | {'f1': 0.8380462724935733} | {'accuracy': 0.7583892617449665} |
| 0.0004 | 1395.0 | 47430 | 3.2156 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0004 | 1396.0 | 47464 | 3.2167 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0004 | 1397.0 | 47498 | 3.2173 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1398.0 | 47532 | 3.2179 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1399.0 | 47566 | 3.2188 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1400.0 | 47600 | 3.2193 | {'f1': 0.8387925497752088} | {'accuracy': 0.7593480345158198} |
| 0.0006 | 1401.0 | 47634 | 3.2565 | {'f1': 0.8367346938775511} | {'accuracy': 0.7545541706615532} |
| 0.0006 | 1402.0 | 47668 | 3.2680 | {'f1': 0.8367346938775511} | {'accuracy': 0.7545541706615532} |
| 0.0006 | 1403.0 | 47702 | 3.2696 | {'f1': 0.8367346938775511} | {'accuracy': 0.7545541706615532} |
| 0.0006 | 1404.0 | 47736 | 3.2711 | {'f1': 0.8362014021669855} | {'accuracy': 0.7535953978907} |
| 0.0006 | 1405.0 | 47770 | 3.2875 | {'f1': 0.835346471710108} | {'accuracy': 0.7516778523489933} |
| 0.0006 | 1406.0 | 47804 | 3.2885 | {'f1': 0.8342857142857142} | {'accuracy': 0.7497603068072867} |
| 0.0006 | 1407.0 | 47838 | 3.2888 | {'f1': 0.8342857142857142} | {'accuracy': 0.7497603068072867} |
| 0.0006 | 1408.0 | 47872 | 3.2893 | {'f1': 0.8342857142857142} | {'accuracy': 0.7497603068072867} |
| 0.0006 | 1409.0 | 47906 | 3.2896 | {'f1': 0.8342857142857142} | {'accuracy': 0.7497603068072867} |
| 0.0006 | 1410.0 | 47940 | 3.7820 | {'f1': 0.8348457350272231} | {'accuracy': 0.738255033557047} |
| 0.0006 | 1411.0 | 47974 | 3.4282 | {'f1': 0.8407407407407408} | {'accuracy': 0.7526366251198466} |
| 0.0013 | 1412.0 | 48008 | 3.2529 | {'f1': 0.8377693282636249} | {'accuracy': 0.7545541706615532} |
| 0.0013 | 1413.0 | 48042 | 3.2523 | {'f1': 0.8377693282636249} | {'accuracy': 0.7545541706615532} |
| 0.0013 | 1414.0 | 48076 | 3.2875 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0013 | 1415.0 | 48110 | 3.2892 | {'f1': 0.841640378548896} | {'accuracy': 0.7593480345158198} |
| 0.0013 | 1416.0 | 48144 | 3.2895 | {'f1': 0.841640378548896} | {'accuracy': 0.7593480345158198} |
| 0.0013 | 1417.0 | 48178 | 3.2896 | {'f1': 0.841640378548896} | {'accuracy': 0.7593480345158198} |
| 0.0013 | 1418.0 | 48212 | 3.2898 | {'f1': 0.841640378548896} | {'accuracy': 0.7593480345158198} |
| 0.0013 | 1419.0 | 48246 | 3.2899 | {'f1': 0.841640378548896} | {'accuracy': 0.7593480345158198} |
| 0.0013 | 1420.0 | 48280 | 3.2901 | {'f1': 0.841640378548896} | {'accuracy': 0.7593480345158198} |
| 0.0013 | 1421.0 | 48314 | 3.2901 | {'f1': 0.841640378548896} | {'accuracy': 0.7593480345158198} |
| 0.0013 | 1422.0 | 48348 | 3.2901 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0013 | 1423.0 | 48382 | 3.2902 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0013 | 1424.0 | 48416 | 3.2903 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0013 | 1425.0 | 48450 | 3.2905 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0013 | 1426.0 | 48484 | 3.2906 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1427.0 | 48518 | 3.2908 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1428.0 | 48552 | 3.2909 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1429.0 | 48586 | 3.2911 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1430.0 | 48620 | 3.2912 | {'f1': 0.8409090909090908} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1431.0 | 48654 | 3.3021 | {'f1': 0.8411097099621689} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1432.0 | 48688 | 3.3061 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1433.0 | 48722 | 3.3061 | {'f1': 0.8411097099621689} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1434.0 | 48756 | 3.3061 | {'f1': 0.8411097099621689} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1435.0 | 48790 | 3.3103 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1436.0 | 48824 | 3.3113 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1437.0 | 48858 | 3.3112 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1438.0 | 48892 | 3.3113 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1439.0 | 48926 | 3.3113 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1440.0 | 48960 | 3.3115 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1441.0 | 48994 | 3.3115 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1442.0 | 49028 | 3.3116 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1443.0 | 49062 | 3.3130 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1444.0 | 49096 | 3.3500 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1445.0 | 49130 | 3.3590 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1446.0 | 49164 | 3.3541 | {'f1': 0.83875} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1447.0 | 49198 | 3.3539 | {'f1': 0.83875} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1448.0 | 49232 | 3.3541 | {'f1': 0.83875} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1449.0 | 49266 | 3.3543 | {'f1': 0.83875} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1450.0 | 49300 | 3.0960 | {'f1': 0.8415516107823802} | {'accuracy': 0.7689357622243528} |
| 0.0 | 1451.0 | 49334 | 3.0652 | {'f1': 0.8354430379746836} | {'accuracy': 0.763183125599233} |
| 0.0 | 1452.0 | 49368 | 3.0665 | {'f1': 0.8354430379746836} | {'accuracy': 0.763183125599233} |
| 0.0 | 1453.0 | 49402 | 3.2554 | {'f1': 0.8383838383838385} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1454.0 | 49436 | 3.2680 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0 | 1455.0 | 49470 | 3.2685 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1456.0 | 49504 | 3.2689 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1457.0 | 49538 | 3.2691 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1458.0 | 49572 | 3.2232 | {'f1': 0.8387507966857872} | {'accuracy': 0.7574304889741131} |
| 0.0015 | 1459.0 | 49606 | 3.2149 | {'f1': 0.839821314613912} | {'accuracy': 0.7593480345158198} |
| 0.0015 | 1460.0 | 49640 | 3.2635 | {'f1': 0.8385876418663304} | {'accuracy': 0.7545541706615532} |
| 0.0015 | 1461.0 | 49674 | 3.6601 | {'f1': 0.8380024360535933} | {'accuracy': 0.7449664429530202} |
| 0.0015 | 1462.0 | 49708 | 3.6704 | {'f1': 0.8369829683698297} | {'accuracy': 0.7430488974113135} |
| 0.0015 | 1463.0 | 49742 | 3.6704 | {'f1': 0.8369829683698297} | {'accuracy': 0.7430488974113135} |
| 0.0015 | 1464.0 | 49776 | 3.6667 | {'f1': 0.8380024360535933} | {'accuracy': 0.7449664429530202} |
| 0.0015 | 1465.0 | 49810 | 3.2441 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.0015 | 1466.0 | 49844 | 3.2329 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1467.0 | 49878 | 3.2329 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1468.0 | 49912 | 3.2341 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1469.0 | 49946 | 3.2346 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0015 | 1470.0 | 49980 | 3.2343 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1471.0 | 50014 | 3.2345 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1472.0 | 50048 | 3.2347 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1473.0 | 50082 | 3.2350 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1474.0 | 50116 | 3.2351 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1475.0 | 50150 | 3.2353 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1476.0 | 50184 | 3.2355 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1477.0 | 50218 | 3.2357 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1478.0 | 50252 | 3.2371 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1479.0 | 50286 | 3.2385 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1480.0 | 50320 | 3.2398 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1481.0 | 50354 | 3.2400 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1482.0 | 50388 | 3.2403 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1483.0 | 50422 | 3.2405 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1484.0 | 50456 | 3.2408 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0008 | 1485.0 | 50490 | 3.2412 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1486.0 | 50524 | 3.2414 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1487.0 | 50558 | 3.2417 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1488.0 | 50592 | 3.2419 | {'f1': 0.8378548895899053} | {'accuracy': 0.7535953978907} |
| 0.0 | 1489.0 | 50626 | 3.5483 | {'f1': 0.8384332925336598} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1490.0 | 50660 | 3.5694 | {'f1': 0.8386308068459658} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1491.0 | 50694 | 3.5701 | {'f1': 0.8386308068459658} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1492.0 | 50728 | 3.5701 | {'f1': 0.8386308068459658} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1493.0 | 50762 | 3.5686 | {'f1': 0.8386308068459658} | {'accuracy': 0.7468839884947267} |
| 0.0 | 1494.0 | 50796 | 3.5683 | {'f1': 0.8391437308868501} | {'accuracy': 0.74784276126558} |
| 0.0 | 1495.0 | 50830 | 3.5680 | {'f1': 0.8391437308868501} | {'accuracy': 0.74784276126558} |
| 0.0 | 1496.0 | 50864 | 3.5678 | {'f1': 0.8391437308868501} | {'accuracy': 0.74784276126558} |
| 0.0 | 1497.0 | 50898 | 3.1547 | {'f1': 0.840952994204765} | {'accuracy': 0.763183125599233} |
| 0.0 | 1498.0 | 50932 | 3.1486 | {'f1': 0.8402061855670104} | {'accuracy': 0.7622243528283796} |
| 0.0 | 1499.0 | 50966 | 3.3102 | {'f1': 0.8411910669975187} | {'accuracy': 0.7545541706615532} |
| 0.0016 | 1500.0 | 51000 | 3.3585 | {'f1': 0.8402948402948403} | {'accuracy': 0.75071907957814} |
| 0.0016 | 1501.0 | 51034 | 3.3615 | {'f1': 0.8402948402948403} | {'accuracy': 0.75071907957814} |
| 0.0016 | 1502.0 | 51068 | 3.3616 | {'f1': 0.8402948402948403} | {'accuracy': 0.75071907957814} |
| 0.0016 | 1503.0 | 51102 | 3.3617 | {'f1': 0.8402948402948403} | {'accuracy': 0.75071907957814} |
| 0.0016 | 1504.0 | 51136 | 3.3629 | {'f1': 0.8402948402948403} | {'accuracy': 0.75071907957814} |
| 0.0016 | 1505.0 | 51170 | 3.3640 | {'f1': 0.8402948402948403} | {'accuracy': 0.75071907957814} |
| 0.0016 | 1506.0 | 51204 | 3.3591 | {'f1': 0.8402948402948403} | {'accuracy': 0.75071907957814} |
| 0.0016 | 1507.0 | 51238 | 3.3488 | {'f1': 0.8386699507389164} | {'accuracy': 0.7488015340364333} |
| 0.0016 | 1508.0 | 51272 | 3.3076 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0016 | 1509.0 | 51306 | 3.3130 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0016 | 1510.0 | 51340 | 3.3085 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0016 | 1511.0 | 51374 | 3.3059 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0016 | 1512.0 | 51408 | 3.3057 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0016 | 1513.0 | 51442 | 3.3078 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0016 | 1514.0 | 51476 | 3.3077 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1515.0 | 51510 | 3.3075 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1516.0 | 51544 | 3.3076 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1517.0 | 51578 | 3.3078 | {'f1': 0.8417132216014898} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1518.0 | 51612 | 3.8592 | {'f1': 0.830732292917167} | {'accuracy': 0.7296260786193672} |
| 0.0 | 1519.0 | 51646 | 3.3649 | {'f1': 0.839030453697949} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1520.0 | 51680 | 3.3407 | {'f1': 0.8396756082345601} | {'accuracy': 0.7535953978907} |
| 0.0 | 1521.0 | 51714 | 3.3402 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1522.0 | 51748 | 3.3402 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1523.0 | 51782 | 3.3403 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1524.0 | 51816 | 3.3407 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1525.0 | 51850 | 3.3403 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1526.0 | 51884 | 3.3403 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1527.0 | 51918 | 3.3405 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1528.0 | 51952 | 3.3407 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1529.0 | 51986 | 3.3410 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1530.0 | 52020 | 3.3411 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1531.0 | 52054 | 3.3412 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1532.0 | 52088 | 3.3410 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1533.0 | 52122 | 3.3424 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1534.0 | 52156 | 3.3426 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1535.0 | 52190 | 3.3423 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1536.0 | 52224 | 3.3424 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1537.0 | 52258 | 3.3426 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1538.0 | 52292 | 3.3426 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1539.0 | 52326 | 3.3427 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1540.0 | 52360 | 3.3428 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1541.0 | 52394 | 3.3429 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1542.0 | 52428 | 3.3429 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1543.0 | 52462 | 3.3431 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0005 | 1544.0 | 52496 | 3.3432 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1545.0 | 52530 | 3.3433 | {'f1': 0.8401997503121098} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1546.0 | 52564 | 3.1951 | {'f1': 0.843650287172942} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1547.0 | 52598 | 3.1803 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1548.0 | 52632 | 3.1809 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1549.0 | 52666 | 3.1814 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1550.0 | 52700 | 3.1818 | {'f1': 0.8427109974424553} | {'accuracy': 0.7641418983700863} |
| 0.0 | 1551.0 | 52734 | 3.1857 | {'f1': 0.8434504792332268} | {'accuracy': 0.7651006711409396} |
| 0.0 | 1552.0 | 52768 | 3.5203 | {'f1': 0.8395820528580209} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1553.0 | 52802 | 3.6158 | {'f1': 0.8366972477064221} | {'accuracy': 0.7440076701821668} |
| 0.0 | 1554.0 | 52836 | 3.4598 | {'f1': 0.8384710234278668} | {'accuracy': 0.7488015340364333} |
| 0.0 | 1555.0 | 52870 | 3.1748 | {'f1': 0.8405063291139242} | {'accuracy': 0.7583892617449665} |
| 0.0 | 1556.0 | 52904 | 3.0995 | {'f1': 0.8396651641983257} | {'accuracy': 0.7612655800575263} |
| 0.0 | 1557.0 | 52938 | 3.0600 | {'f1': 0.8412802090137166} | {'accuracy': 0.7670182166826462} |
| 0.0 | 1558.0 | 52972 | 3.1169 | {'f1': 0.839871382636656} | {'accuracy': 0.7612655800575263} |
| 0.0012 | 1559.0 | 53006 | 3.1407 | {'f1': 0.8402822322001283} | {'accuracy': 0.7612655800575263} |
| 0.0012 | 1560.0 | 53040 | 3.1418 | {'f1': 0.8402822322001283} | {'accuracy': 0.7612655800575263} |
| 0.0012 | 1561.0 | 53074 | 3.1431 | {'f1': 0.8402822322001283} | {'accuracy': 0.7612655800575263} |
| 0.0012 | 1562.0 | 53108 | 3.1438 | {'f1': 0.8412291933418694} | {'accuracy': 0.7622243528283796} |
| 0.0012 | 1563.0 | 53142 | 3.1443 | {'f1': 0.8412291933418694} | {'accuracy': 0.7622243528283796} |
| 0.0012 | 1564.0 | 53176 | 3.1461 | {'f1': 0.8406909788867563} | {'accuracy': 0.7612655800575263} |
| 0.0012 | 1565.0 | 53210 | 3.1464 | {'f1': 0.8406909788867563} | {'accuracy': 0.7612655800575263} |
| 0.0012 | 1566.0 | 53244 | 3.1469 | {'f1': 0.8406909788867563} | {'accuracy': 0.7612655800575263} |
| 0.0012 | 1567.0 | 53278 | 3.1273 | {'f1': 0.8400770712909442} | {'accuracy': 0.7612655800575263} |
| 0.0012 | 1568.0 | 53312 | 2.9980 | {'f1': 0.8336646785950961} | {'accuracy': 0.7593480345158198} |
| 0.0012 | 1569.0 | 53346 | 3.0979 | {'f1': 0.841025641025641} | {'accuracy': 0.7622243528283796} |
| 0.0012 | 1570.0 | 53380 | 3.1300 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0012 | 1571.0 | 53414 | 3.1323 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0012 | 1572.0 | 53448 | 3.1325 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0012 | 1573.0 | 53482 | 3.1326 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1574.0 | 53516 | 3.1328 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1575.0 | 53550 | 3.1334 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1576.0 | 53584 | 3.1338 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1577.0 | 53618 | 3.1342 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1578.0 | 53652 | 3.1342 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1579.0 | 53686 | 3.1350 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1580.0 | 53720 | 3.1354 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1581.0 | 53754 | 3.1357 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1582.0 | 53788 | 3.1360 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1583.0 | 53822 | 3.1362 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1584.0 | 53856 | 3.1366 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1585.0 | 53890 | 3.1372 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1586.0 | 53924 | 3.1374 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1587.0 | 53958 | 3.1378 | {'f1': 0.839568801521877} | {'accuracy': 0.7574304889741131} |
| 0.0024 | 1588.0 | 53992 | 3.1384 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1589.0 | 54026 | 3.1388 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1590.0 | 54060 | 3.1392 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1591.0 | 54094 | 3.1401 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1592.0 | 54128 | 3.1417 | {'f1': 0.8390367553865652} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1593.0 | 54162 | 3.2202 | {'f1': 0.8404522613065327} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1594.0 | 54196 | 3.2302 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1595.0 | 54230 | 3.2304 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1596.0 | 54264 | 3.2310 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1597.0 | 54298 | 3.2315 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1598.0 | 54332 | 3.2326 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1599.0 | 54366 | 3.2334 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1600.0 | 54400 | 3.2338 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1601.0 | 54434 | 3.2342 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1602.0 | 54468 | 3.2349 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1603.0 | 54502 | 3.2352 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1604.0 | 54536 | 3.2374 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1605.0 | 54570 | 3.2378 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1606.0 | 54604 | 3.2380 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1607.0 | 54638 | 3.2382 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1608.0 | 54672 | 3.2382 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1609.0 | 54706 | 3.2384 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1610.0 | 54740 | 3.2387 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1611.0 | 54774 | 3.2390 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1612.0 | 54808 | 3.2393 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1613.0 | 54842 | 3.2396 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1614.0 | 54876 | 3.2399 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1615.0 | 54910 | 3.2400 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1616.0 | 54944 | 3.2408 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1617.0 | 54978 | 3.2421 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1618.0 | 55012 | 3.2423 | {'f1': 0.8411801632140615} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1619.0 | 55046 | 3.2770 | {'f1': 0.8408521303258145} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1620.0 | 55080 | 3.2857 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1621.0 | 55114 | 3.2874 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1622.0 | 55148 | 3.2880 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1623.0 | 55182 | 3.2884 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1624.0 | 55216 | 3.2886 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1625.0 | 55250 | 3.2892 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1626.0 | 55284 | 3.2913 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1627.0 | 55318 | 3.2924 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1628.0 | 55352 | 3.2927 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1629.0 | 55386 | 3.2930 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1630.0 | 55420 | 3.2934 | {'f1': 0.8407245471580262} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1631.0 | 55454 | 3.2925 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1632.0 | 55488 | 3.2925 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1633.0 | 55522 | 3.2927 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1634.0 | 55556 | 3.2951 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1635.0 | 55590 | 3.2958 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1636.0 | 55624 | 3.2946 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1637.0 | 55658 | 3.2949 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1638.0 | 55692 | 3.2957 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1639.0 | 55726 | 3.2971 | {'f1': 0.84125} | {'accuracy': 0.7564717162032598} |
| 0.0 | 1640.0 | 55760 | 3.2154 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1641.0 | 55794 | 3.2130 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1642.0 | 55828 | 3.2133 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1643.0 | 55862 | 3.2135 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1644.0 | 55896 | 3.2145 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1645.0 | 55930 | 3.2152 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1646.0 | 55964 | 3.2158 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1647.0 | 55998 | 3.2162 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1648.0 | 56032 | 3.2222 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1649.0 | 56066 | 3.2236 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1650.0 | 56100 | 3.2241 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1651.0 | 56134 | 3.2254 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1652.0 | 56168 | 3.2259 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1653.0 | 56202 | 3.2264 | {'f1': 0.8405797101449275} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1654.0 | 56236 | 3.3787 | {'f1': 0.8404717566728741} | {'accuracy': 0.7535953978907} |
| 0.0 | 1655.0 | 56270 | 3.5560 | {'f1': 0.8366972477064221} | {'accuracy': 0.7440076701821668} |
| 0.0 | 1656.0 | 56304 | 3.2787 | {'f1': 0.8409805153991201} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1657.0 | 56338 | 3.2719 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1658.0 | 56372 | 3.2716 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1659.0 | 56406 | 3.2721 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1660.0 | 56440 | 3.2724 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0 | 1661.0 | 56474 | 3.2726 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1662.0 | 56508 | 3.2729 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1663.0 | 56542 | 3.2732 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1664.0 | 56576 | 3.2736 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1665.0 | 56610 | 3.2741 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1666.0 | 56644 | 3.2740 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1667.0 | 56678 | 3.2746 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1668.0 | 56712 | 3.2748 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1669.0 | 56746 | 3.2750 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1670.0 | 56780 | 3.2758 | {'f1': 0.84078036500944} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1671.0 | 56814 | 3.2817 | {'f1': 0.8409805153991201} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1672.0 | 56848 | 3.2835 | {'f1': 0.8409805153991201} | {'accuracy': 0.7574304889741131} |
| 0.0002 | 1673.0 | 56882 | 3.5116 | {'f1': 0.8350705088902514} | {'accuracy': 0.7420901246404602} |
| 0.0002 | 1674.0 | 56916 | 3.6489 | {'f1': 0.8350578210590383} | {'accuracy': 0.7401725790987536} |
| 0.0002 | 1675.0 | 56950 | 3.6522 | {'f1': 0.8350578210590383} | {'accuracy': 0.7401725790987536} |
| 0.0002 | 1676.0 | 56984 | 3.6524 | {'f1': 0.8350578210590383} | {'accuracy': 0.7401725790987536} |
| 0.001 | 1677.0 | 57018 | 3.2075 | {'f1': 0.8366179275270186} | {'accuracy': 0.7535953978907} |
| 0.001 | 1678.0 | 57052 | 3.1894 | {'f1': 0.8372686662412252} | {'accuracy': 0.7555129434324065} |
| 0.001 | 1679.0 | 57086 | 3.1911 | {'f1': 0.8372686662412252} | {'accuracy': 0.7555129434324065} |
| 0.001 | 1680.0 | 57120 | 3.1913 | {'f1': 0.8372686662412252} | {'accuracy': 0.7555129434324065} |
| 0.001 | 1681.0 | 57154 | 3.1916 | {'f1': 0.8372686662412252} | {'accuracy': 0.7555129434324065} |
| 0.001 | 1682.0 | 57188 | 3.1919 | {'f1': 0.8372686662412252} | {'accuracy': 0.7555129434324065} |
| 0.001 | 1683.0 | 57222 | 3.3888 | {'f1': 0.8342644320297952} | {'accuracy': 0.7440076701821668} |
| 0.001 | 1684.0 | 57256 | 3.4412 | {'f1': 0.8348794063079776} | {'accuracy': 0.7440076701821668} |
| 0.001 | 1685.0 | 57290 | 3.4428 | {'f1': 0.8348794063079776} | {'accuracy': 0.7440076701821668} |
| 0.001 | 1686.0 | 57324 | 3.3052 | {'f1': 0.8374136848713121} | {'accuracy': 0.7516778523489933} |
| 0.001 | 1687.0 | 57358 | 3.2888 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1688.0 | 57392 | 3.2888 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1689.0 | 57426 | 3.2895 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1690.0 | 57460 | 3.2896 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.001 | 1691.0 | 57494 | 3.2899 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1692.0 | 57528 | 3.2901 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1693.0 | 57562 | 3.2902 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1694.0 | 57596 | 3.2904 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1695.0 | 57630 | 3.2905 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1696.0 | 57664 | 3.2902 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1697.0 | 57698 | 3.2904 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1698.0 | 57732 | 3.2906 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1699.0 | 57766 | 3.2909 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1700.0 | 57800 | 3.2914 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1701.0 | 57834 | 3.2920 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1702.0 | 57868 | 3.2922 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1703.0 | 57902 | 3.2924 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1704.0 | 57936 | 3.2926 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1705.0 | 57970 | 3.2928 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1706.0 | 58004 | 3.2931 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1707.0 | 58038 | 3.2933 | {'f1': 0.8389937106918239} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1708.0 | 58072 | 3.4850 | {'f1': 0.8358024691358025} | {'accuracy': 0.7449664429530202} |
| 0.0 | 1709.0 | 58106 | 3.5404 | {'f1': 0.8390663390663391} | {'accuracy': 0.7488015340364333} |
| 0.0 | 1710.0 | 58140 | 3.5419 | {'f1': 0.8390663390663391} | {'accuracy': 0.7488015340364333} |
| 0.0 | 1711.0 | 58174 | 3.5419 | {'f1': 0.8390663390663391} | {'accuracy': 0.7488015340364333} |
| 0.0 | 1712.0 | 58208 | 3.5420 | {'f1': 0.8390663390663391} | {'accuracy': 0.7488015340364333} |
| 0.0 | 1713.0 | 58242 | 3.5419 | {'f1': 0.8390663390663391} | {'accuracy': 0.7488015340364333} |
| 0.0 | 1714.0 | 58276 | 3.5354 | {'f1': 0.8383527965580824} | {'accuracy': 0.74784276126558} |
| 0.0 | 1715.0 | 58310 | 3.5339 | {'f1': 0.8383527965580824} | {'accuracy': 0.74784276126558} |
| 0.0 | 1716.0 | 58344 | 3.5338 | {'f1': 0.8383527965580824} | {'accuracy': 0.74784276126558} |
| 0.0 | 1717.0 | 58378 | 3.5339 | {'f1': 0.8383527965580824} | {'accuracy': 0.74784276126558} |
| 0.0 | 1718.0 | 58412 | 3.5344 | {'f1': 0.8383527965580824} | {'accuracy': 0.74784276126558} |
| 0.0 | 1719.0 | 58446 | 3.5343 | {'f1': 0.8383527965580824} | {'accuracy': 0.74784276126558} |
| 0.0 | 1720.0 | 58480 | 3.5345 | {'f1': 0.8383527965580824} | {'accuracy': 0.74784276126558} |
| 0.0 | 1721.0 | 58514 | 3.5346 | {'f1': 0.8383527965580824} | {'accuracy': 0.74784276126558} |
| 0.0 | 1722.0 | 58548 | 3.2877 | {'f1': 0.8380592312539382} | {'accuracy': 0.7535953978907} |
| 0.0 | 1723.0 | 58582 | 3.2489 | {'f1': 0.8366179275270186} | {'accuracy': 0.7535953978907} |
| 0.0 | 1724.0 | 58616 | 3.2485 | {'f1': 0.8366179275270186} | {'accuracy': 0.7535953978907} |
| 0.0 | 1725.0 | 58650 | 3.2488 | {'f1': 0.8366179275270186} | {'accuracy': 0.7535953978907} |
| 0.0 | 1726.0 | 58684 | 3.2495 | {'f1': 0.8366179275270186} | {'accuracy': 0.7535953978907} |
| 0.0 | 1727.0 | 58718 | 3.2502 | {'f1': 0.8366179275270186} | {'accuracy': 0.7535953978907} |
| 0.0 | 1728.0 | 58752 | 3.2505 | {'f1': 0.8366179275270186} | {'accuracy': 0.7535953978907} |
| 0.0 | 1729.0 | 58786 | 3.2512 | {'f1': 0.8366179275270186} | {'accuracy': 0.7535953978907} |
| 0.0 | 1730.0 | 58820 | 3.2878 | {'f1': 0.8385876418663304} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1731.0 | 58854 | 3.3391 | {'f1': 0.8365685660613651} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1732.0 | 58888 | 3.3829 | {'f1': 0.8346849656893326} | {'accuracy': 0.7459252157238735} |
| 0.0 | 1733.0 | 58922 | 3.3845 | {'f1': 0.8346849656893326} | {'accuracy': 0.7459252157238735} |
| 0.0 | 1734.0 | 58956 | 3.3850 | {'f1': 0.8346849656893326} | {'accuracy': 0.7459252157238735} |
| 0.0 | 1735.0 | 58990 | 3.3997 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1736.0 | 59024 | 3.4004 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1737.0 | 59058 | 3.4007 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1738.0 | 59092 | 3.4010 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1739.0 | 59126 | 3.4012 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1740.0 | 59160 | 3.4014 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1741.0 | 59194 | 3.4016 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1742.0 | 59228 | 3.4018 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1743.0 | 59262 | 3.4020 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1744.0 | 59296 | 3.4018 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1745.0 | 59330 | 3.4020 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1746.0 | 59364 | 3.4022 | {'f1': 0.8348909657320873} | {'accuracy': 0.7459252157238735} |
| 0.0005 | 1747.0 | 59398 | 3.4678 | {'f1': 0.8371517027863778} | {'accuracy': 0.74784276126558} |
| 0.0005 | 1748.0 | 59432 | 3.4849 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0005 | 1749.0 | 59466 | 3.4854 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1750.0 | 59500 | 3.4857 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1751.0 | 59534 | 3.4858 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1752.0 | 59568 | 3.4860 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1753.0 | 59602 | 3.4860 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1754.0 | 59636 | 3.4866 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1755.0 | 59670 | 3.4867 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1756.0 | 59704 | 3.4869 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1757.0 | 59738 | 3.4871 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1758.0 | 59772 | 3.4873 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1759.0 | 59806 | 3.4874 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1760.0 | 59840 | 3.4877 | {'f1': 0.8387893761581224} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1761.0 | 59874 | 3.7082 | {'f1': 0.835965978128797} | {'accuracy': 0.7411313518696069} |
| 0.0 | 1762.0 | 59908 | 3.8132 | {'f1': 0.8304164152082076} | {'accuracy': 0.7305848513902206} |
| 0.0 | 1763.0 | 59942 | 3.8065 | {'f1': 0.8304164152082076} | {'accuracy': 0.7305848513902206} |
| 0.0 | 1764.0 | 59976 | 3.8057 | {'f1': 0.8309178743961353} | {'accuracy': 0.7315436241610739} |
| 0.0 | 1765.0 | 60010 | 3.8056 | {'f1': 0.8309178743961353} | {'accuracy': 0.7315436241610739} |
| 0.0 | 1766.0 | 60044 | 3.8055 | {'f1': 0.8309178743961353} | {'accuracy': 0.7315436241610739} |
| 0.0 | 1767.0 | 60078 | 3.6801 | {'f1': 0.8374923919659158} | {'accuracy': 0.7440076701821668} |
| 0.0 | 1768.0 | 60112 | 3.6579 | {'f1': 0.8367844092570037} | {'accuracy': 0.7430488974113135} |
| 0.0 | 1769.0 | 60146 | 3.6572 | {'f1': 0.8372943327239487} | {'accuracy': 0.7440076701821668} |
| 0.0 | 1770.0 | 60180 | 3.4614 | {'f1': 0.8387096774193548} | {'accuracy': 0.75071907957814} |
| 0.0 | 1771.0 | 60214 | 3.4396 | {'f1': 0.836341008089608} | {'accuracy': 0.74784276126558} |
| 0.0 | 1772.0 | 60248 | 3.4393 | {'f1': 0.836341008089608} | {'accuracy': 0.74784276126558} |
| 0.0 | 1773.0 | 60282 | 3.3123 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1774.0 | 60316 | 3.3044 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1775.0 | 60350 | 3.3044 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1776.0 | 60384 | 3.3047 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1777.0 | 60418 | 3.3048 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1778.0 | 60452 | 3.3049 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1779.0 | 60486 | 3.3052 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1780.0 | 60520 | 3.3054 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1781.0 | 60554 | 3.3056 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1782.0 | 60588 | 3.3062 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1783.0 | 60622 | 3.3064 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1784.0 | 60656 | 3.3066 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1785.0 | 60690 | 3.3067 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1786.0 | 60724 | 3.3069 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1787.0 | 60758 | 3.3072 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1788.0 | 60792 | 3.3079 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1789.0 | 60826 | 3.3086 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1790.0 | 60860 | 3.3093 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1791.0 | 60894 | 3.3095 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1792.0 | 60928 | 3.3098 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1793.0 | 60962 | 3.3100 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1794.0 | 60996 | 3.3104 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1795.0 | 61030 | 3.3126 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1796.0 | 61064 | 3.3128 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1797.0 | 61098 | 3.3133 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1798.0 | 61132 | 3.3143 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1799.0 | 61166 | 3.3151 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1800.0 | 61200 | 3.3154 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1801.0 | 61234 | 3.3171 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1802.0 | 61268 | 3.3175 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1803.0 | 61302 | 3.3175 | {'f1': 0.8355555555555555} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1804.0 | 61336 | 3.3776 | {'f1': 0.8393194706994328} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1805.0 | 61370 | 3.5318 | {'f1': 0.8391089108910891} | {'accuracy': 0.75071907957814} |
| 0.0 | 1806.0 | 61404 | 3.5371 | {'f1': 0.8391089108910891} | {'accuracy': 0.75071907957814} |
| 0.0 | 1807.0 | 61438 | 3.5401 | {'f1': 0.8385899814471244} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1808.0 | 61472 | 3.5405 | {'f1': 0.8385899814471244} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1809.0 | 61506 | 3.5389 | {'f1': 0.8391089108910891} | {'accuracy': 0.75071907957814} |
| 0.0 | 1810.0 | 61540 | 3.5389 | {'f1': 0.8391089108910891} | {'accuracy': 0.75071907957814} |
| 0.0 | 1811.0 | 61574 | 3.5391 | {'f1': 0.8391089108910891} | {'accuracy': 0.75071907957814} |
| 0.0 | 1812.0 | 61608 | 3.4608 | {'f1': 0.8371802869619464} | {'accuracy': 0.7497603068072867} |
| 0.0 | 1813.0 | 61642 | 3.4160 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1814.0 | 61676 | 3.4151 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1815.0 | 61710 | 3.4151 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1816.0 | 61744 | 3.4154 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1817.0 | 61778 | 3.4155 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1818.0 | 61812 | 3.4156 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1819.0 | 61846 | 3.4113 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1820.0 | 61880 | 3.4103 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1821.0 | 61914 | 3.4104 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1822.0 | 61948 | 3.4108 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1823.0 | 61982 | 3.4110 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1824.0 | 62016 | 3.4112 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1825.0 | 62050 | 3.4113 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1826.0 | 62084 | 3.4116 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1827.0 | 62118 | 3.4117 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1828.0 | 62152 | 3.4123 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1829.0 | 62186 | 3.4214 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1830.0 | 62220 | 3.4287 | {'f1': 0.8381430363864492} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1831.0 | 62254 | 3.4340 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1832.0 | 62288 | 3.4342 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1833.0 | 62322 | 3.4344 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1834.0 | 62356 | 3.4379 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1835.0 | 62390 | 3.4397 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1836.0 | 62424 | 3.4400 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1837.0 | 62458 | 3.4401 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1838.0 | 62492 | 3.4403 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1839.0 | 62526 | 3.4404 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1840.0 | 62560 | 3.4406 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1841.0 | 62594 | 3.4408 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1842.0 | 62628 | 3.4409 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1843.0 | 62662 | 3.4411 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1844.0 | 62696 | 3.4414 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1845.0 | 62730 | 3.4415 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1846.0 | 62764 | 3.4417 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1847.0 | 62798 | 3.4418 | {'f1': 0.8383458646616541} | {'accuracy': 0.7526366251198466} |
| 0.0 | 1848.0 | 62832 | 3.4457 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1849.0 | 62866 | 3.4466 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1850.0 | 62900 | 3.4468 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1851.0 | 62934 | 3.4470 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1852.0 | 62968 | 3.4479 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1853.0 | 63002 | 3.4482 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1854.0 | 63036 | 3.4515 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1855.0 | 63070 | 3.4533 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1856.0 | 63104 | 3.4535 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1857.0 | 63138 | 3.4537 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1858.0 | 63172 | 3.4539 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1859.0 | 63206 | 3.4541 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1860.0 | 63240 | 3.4544 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1861.0 | 63274 | 3.4546 | {'f1': 0.8378209142141516} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1862.0 | 63308 | 3.4841 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1863.0 | 63342 | 3.4893 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1864.0 | 63376 | 3.4897 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1865.0 | 63410 | 3.4900 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1866.0 | 63444 | 3.4905 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1867.0 | 63478 | 3.4906 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1868.0 | 63512 | 3.4908 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1869.0 | 63546 | 3.4910 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1870.0 | 63580 | 3.4914 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1871.0 | 63614 | 3.4916 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1872.0 | 63648 | 3.4918 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1873.0 | 63682 | 3.4920 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1874.0 | 63716 | 3.4922 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1875.0 | 63750 | 3.4926 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1876.0 | 63784 | 3.4936 | {'f1': 0.8380237648530332} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1877.0 | 63818 | 3.5059 | {'f1': 0.8375} | {'accuracy': 0.75071907957814} |
| 0.0 | 1878.0 | 63852 | 3.5066 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1879.0 | 63886 | 3.5068 | {'f1': 0.8382261086820737} | {'accuracy': 0.7516778523489933} |
| 0.0 | 1880.0 | 63920 | 3.5058 | {'f1': 0.8375} | {'accuracy': 0.75071907957814} |
| 0.0 | 1881.0 | 63954 | 3.5058 | {'f1': 0.8375} | {'accuracy': 0.75071907957814} |
| 0.0 | 1882.0 | 63988 | 3.4003 | {'f1': 0.8397720075997466} | {'accuracy': 0.7574304889741131} |
| 0.0001 | 1883.0 | 64022 | 3.3884 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1884.0 | 64056 | 3.3882 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1885.0 | 64090 | 3.3883 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1886.0 | 64124 | 3.3884 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1887.0 | 64158 | 3.3885 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1888.0 | 64192 | 3.3885 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1889.0 | 64226 | 3.3886 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1890.0 | 64260 | 3.3891 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1891.0 | 64294 | 3.3892 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1892.0 | 64328 | 3.3897 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1893.0 | 64362 | 3.3898 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1894.0 | 64396 | 3.3897 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1895.0 | 64430 | 3.3899 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1896.0 | 64464 | 3.3900 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1897.0 | 64498 | 3.3901 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1898.0 | 64532 | 3.3902 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1899.0 | 64566 | 3.3904 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1900.0 | 64600 | 3.3906 | {'f1': 0.8373570520965693} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1901.0 | 64634 | 3.4909 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1902.0 | 64668 | 3.4949 | {'f1': 0.8392745465916197} | {'accuracy': 0.7535953978907} |
| 0.0 | 1903.0 | 64702 | 3.4952 | {'f1': 0.8392745465916197} | {'accuracy': 0.7535953978907} |
| 0.0 | 1904.0 | 64736 | 3.4926 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1905.0 | 64770 | 3.4924 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1906.0 | 64804 | 3.4926 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1907.0 | 64838 | 3.4927 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1908.0 | 64872 | 3.4928 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1909.0 | 64906 | 3.4928 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1910.0 | 64940 | 3.4929 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1911.0 | 64974 | 3.4930 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1912.0 | 65008 | 3.4932 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1913.0 | 65042 | 3.4933 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1914.0 | 65076 | 3.4934 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1915.0 | 65110 | 3.4935 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1916.0 | 65144 | 3.4935 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1917.0 | 65178 | 3.4935 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1918.0 | 65212 | 3.4936 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1919.0 | 65246 | 3.4938 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1920.0 | 65280 | 3.4939 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1921.0 | 65314 | 3.4940 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1922.0 | 65348 | 3.4941 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1923.0 | 65382 | 3.4942 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1924.0 | 65416 | 3.4942 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1925.0 | 65450 | 3.4943 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1926.0 | 65484 | 3.4944 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1927.0 | 65518 | 3.4945 | {'f1': 0.8397997496871088} | {'accuracy': 0.7545541706615532} |
| 0.0 | 1928.0 | 65552 | 3.4926 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1929.0 | 65586 | 3.4925 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1930.0 | 65620 | 3.4926 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1931.0 | 65654 | 3.4928 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1932.0 | 65688 | 3.4929 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1933.0 | 65722 | 3.4930 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1934.0 | 65756 | 3.4931 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1935.0 | 65790 | 3.4932 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1936.0 | 65824 | 3.4932 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1937.0 | 65858 | 3.4933 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1938.0 | 65892 | 3.4934 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1939.0 | 65926 | 3.4935 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1940.0 | 65960 | 3.4936 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1941.0 | 65994 | 3.4937 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1942.0 | 66028 | 3.4937 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1943.0 | 66062 | 3.4938 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1944.0 | 66096 | 3.4939 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1945.0 | 66130 | 3.4939 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1946.0 | 66164 | 3.4940 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1947.0 | 66198 | 3.4941 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1948.0 | 66232 | 3.4941 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1949.0 | 66266 | 3.4942 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1950.0 | 66300 | 3.4943 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1951.0 | 66334 | 3.4948 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1952.0 | 66368 | 3.4948 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1953.0 | 66402 | 3.4949 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1954.0 | 66436 | 3.4950 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1955.0 | 66470 | 3.4952 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1956.0 | 66504 | 3.4953 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1957.0 | 66538 | 3.4953 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1958.0 | 66572 | 3.4954 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1959.0 | 66606 | 3.4955 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1960.0 | 66640 | 3.4956 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1961.0 | 66674 | 3.4956 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1962.0 | 66708 | 3.4957 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1963.0 | 66742 | 3.4958 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1964.0 | 66776 | 3.4959 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1965.0 | 66810 | 3.4960 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1966.0 | 66844 | 3.4961 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1967.0 | 66878 | 3.4962 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1968.0 | 66912 | 3.4962 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1969.0 | 66946 | 3.4974 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0 | 1970.0 | 66980 | 3.4976 | {'f1': 0.8403256105197245} | {'accuracy': 0.7555129434324065} |
| 0.0011 | 1971.0 | 67014 | 3.4573 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1972.0 | 67048 | 3.4534 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1973.0 | 67082 | 3.4533 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1974.0 | 67116 | 3.4534 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1975.0 | 67150 | 3.4532 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1976.0 | 67184 | 3.4532 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1977.0 | 67218 | 3.4533 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1978.0 | 67252 | 3.4533 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1979.0 | 67286 | 3.4491 | {'f1': 0.8395217117684078} | {'accuracy': 0.7555129434324065} |
| 0.0011 | 1980.0 | 67320 | 3.4555 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1981.0 | 67354 | 3.4592 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1982.0 | 67388 | 3.4601 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1983.0 | 67422 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1984.0 | 67456 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0011 | 1985.0 | 67490 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1986.0 | 67524 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1987.0 | 67558 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1988.0 | 67592 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1989.0 | 67626 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1990.0 | 67660 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1991.0 | 67694 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1992.0 | 67728 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1993.0 | 67762 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1994.0 | 67796 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1995.0 | 67830 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1996.0 | 67864 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1997.0 | 67898 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1998.0 | 67932 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0001 | 1999.0 | 67966 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
| 0.0 | 2000.0 | 68000 | 3.4602 | {'f1': 0.8391959798994975} | {'accuracy': 0.7545541706615532} |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
juanreno2012/my_swag_model
|
juanreno2012
| 2023-10-16T06:03:48Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-10-04T15:41:45Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- swag
model-index:
- name: my_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_swag_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
IDEA-CCNL/Ziya2-13B-Chat
|
IDEA-CCNL
| 2023-10-16T05:48:15Z | 10 | 11 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"transformer",
"封神榜",
"en",
"zh",
"cn",
"arxiv:2210.08590",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-13T06:00:56Z |
---
tasks:
- text-generation
model_type:
- gpt
- llama
domain:
- nlp
license: apache-2.0
language:
- en
- zh
- cn
tags:
- transformer
- 封神榜
---
# Ziya2-13B-Chat
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
# 姜子牙系列模型
- [Ziya-LLaMA-13B-v1.1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1)
- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward)
- [Ziya-LLaMA-13B-Pretrain-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1)
- [Ziya-Writing-LLaMa-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-Writing-LLaMa-13B-v1)
- [Ziya-BLIP2-14B-Visual-v1](https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1)
- [Ziya-Coding-15B-v1](https://huggingface.co/IDEA-CCNL/Ziya-Coding-15B-v1)
- [Ziya-Coding-34B-v1.0](https://huggingface.co/IDEA-CCNL/Ziya-Coding-34B-v1.0)
- [Ziya2-13B-Base](https://huggingface.co/IDEA-CCNL/Ziya2-13B-Base)
- [Ziya-Reader-13B-v1.0](https://huggingface.co/IDEA-CCNL/Ziya-Reader-13B-v1.0)
## 简介 Brief Introduction
Ziya2-13B-Chat是基于Ziya2-13B-Base的对话模型，在30万高质量的通用指令微调数据以及40万知识增强的指令微调数据上进行了有监督训练，并且在数万条高质量人类偏好数据训练的奖励模型上进行了全参数的人类反馈强化学习训练。
Ziya2-13B-Chat is a chat version of Ziya2-13B-Base.
Ziya2-13B-Chat was fine-tuned on 300,000 high-quality general instruction data as well as 400,000 knowledge-enhanced instruction data, and then trained with full-parameter RLHF on a feedback model trained on tens of thousands of high-quality human preference data.S
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
|:----------:|:-------:|:---------:|:--------:|:------------:|:---------------:|
| 通用 General | AGI模型 | 姜子牙 Ziya | LLaMA2 | 13B | English&Chinese |
## 模型信息 Model Information
### 继续预训练 Continual Pretraining
Meta在2023年7月份发布了Llama2系列大模型，相比于LLaMA1的1.4万亿Token数据，Llama2预训练的Token达到了2万亿，并在各个榜单中明显超过LLaMA1。
Meta released the Llama2 series of large models in July 2023, with pre-trained tokens reaching 200 billion compared to Llama1's 140 billion tokens, significantly outperforming Llama1 in various rankings.
Ziya2-13B-Base沿用了Ziya-LLaMA-13B高效的中文编解码方式，但采取了更优化的初始化算法使得初始训练loss更低。同时，我们对Fengshen-PT继续训练框架进行了优化，效率方面，整合了FlashAttention2、Apex RMS norm等技术来帮助提升效率，对比Ziya-LLaMA-13B训练速度提升38%(163 TFLOPS/per gpu/per sec)。稳定性方面，我们采取BF16进行训练，修复了底层分布式框架的bug，确保模型能够持续稳定训练，解决了Ziya-LLaMA-13B遇到的训练后期不稳定的问题，并在7.25号进行了直播，最终完成了全部数据的继续训练。我们也发现，模型效果还有进一步提升的趋势，后续也会对Ziya2-13B-Base进行继续优化。
Ziya2-13B-Base retained the efficient Chinese encoding and decoding techniques of Ziya-LLaMA-13B, but employed a more optimized initialization algorithm to achieve lower initial training loss. Additionally, we optimized the Fengshen-PT fine-tuning framework. In terms of efficiency, we integrated technologies such as FlashAttention2 and Apex RMS norm to boost efficiency, resulting in a 38% increase in training speed compared to Ziya-LLaMA-13B (163 TFLOPS per GPU per second). For stability, we used BF16 for training, fixed underlying distributed framework bugs to ensure consistent model training, and resolved the late-stage instability issues encountered in the training of Ziya-LLaMA-13B. We also conducted a live broadcast on July 25th to complete the continued training of all data. We have observed a trend towards further improvements in model performance and plan to continue optimizing Ziya2-13B-Base in the future.
### 指令微调 Supervised Fine-tuning
依托于Ziya2-13B-Base强大的基础能力，我们优化了SFT阶段的训练策略。
我们发现高质量和多样的任务指令数据能够最大程度地激发预训练阶段所学到的知识。因此，我们利用Evol-Instruct的方法，对我们收集到的指令数据集进行了数据增强，并利用奖励模型筛选出了高质量的样本。最终，我们从2000万的指令数据集中，构造得到了30万高质量的通用指令微调数据，涵盖了问答、推理、代码、常识、对话、写作、自然语言理解、安全性等广泛的任务。
此外，我们发现在有监督微调阶段，引入知识增强训练，可以进一步提升模型的效果。我们利用检索模块，显式地将与指令有关的知识拼到上下文后进行训练。在这一部分，我们构造了约10万条知识增强的指令样本。
最终，我们在经过了300B token预训练的Ziya2-13B-Base模型的基础上，使用约40万的指令样本，使用8k的上下文窗口，经过两个epoch的训练得到SFT阶段的模型。
Based on the strong capability of Ziya2-13B-Base, we optimized the training strategy for the supervised fine-tuning phase (SFT).
We found that high-quality and varied task instruction data maximizes the stimulation of the knowledge learned in the pre-training phase. Therefore, we utilized the Evol-Instruct approach to augment our collected instruction dataset with data and filtered out high-quality samples using a reward model. We eventually constructed 300,000 high-quality general-purpose instruction fine-tuning data from a 20 million instruction dataset, covering a wide range of tasks such as QA, reasoning, coding, common sense, dialog, writing, natural language understanding, security, etc.
In addition, we find that the introduction of knowledge-enhanced training can further improve the model. We used the retrieval module to obtain knowledge related to the questions and concatenated their text into the context of the training data. In this section, we constructed about 100,000 samples of knowledge-enhanced instructions.
Finally, we obtained the SFT model after two epochs of training using about 400,000 instruction samples with a context window of 8k, based on the Ziya2-13B-Base model that had been pre-trained with 300B tokens.
### 人类反馈学习 Reinforcement learning from Human Feedback
基于SFT阶段的模型，Ziya2-13B-Chat针对多种问答、写作以及模型安全性的任务上进行了人类偏好的对齐。我们自行采集了数万条高质量人类偏好数据，使用Ziya2-13B-Base训练了人类偏好反馈模型，在各任务的偏好数据上达到了72%以上的准确率。
Based on SFT model, Ziya2-13B-Chat was aligned for human preferences on a variety of Q&A, writing, and safety tasks.
We collected tens of thousands of high-quality human preference data on our own and trained a human preference feedback model using Ziya2-13B-Base, achieving over 72% accuracy on preference data across tasks.
| 任务类型 task | 偏好识别准确率 Acc|
|:----------:|:-------:|
| 日常问答 Daily QA | 76.8% |
| 知识问答 Knowledge Quizzing | 76.7% |
| 日常写作 Daily Writing | 82.3% |
| 任务型写作 Task-based Writing | 72.7% |
| 故事写作 Story Writing | 75.1% |
| 角色扮演 Role-playinh | 77.6% |
| 安全类 Safety & Harmlessness| 72.0% |
基于 Fengshen-RLHF 框架，Ziya2-13B-Chat使用以上人类偏好反馈模型进行了人类反馈强化学习，使模型输出更贴合人类偏好的同时具有更高的安全性。
Using Fengshen-RLHF Framework, Ziya2-13B-Chat used the above feedback model for reinforcement learning, making itself more closely match human preferences with higher security.
### 效果评估 Performance
我们在涵盖了常识问答、写作、数学推理、自然语言理解、安全等多种任务的通用能力测试集上进行了人工评估。最终，Ziya2-13B-Chat模型与Ziya-LlaMA-13B-v1.1模型在side-by-side评测下取得了66.5%的胜率，并对人类反馈强化学习前的版本取得了58.4%的胜率。
We conducted human evaluations of Ziya2-13B-Chat on a variety of tasks covering knowledge quizzing, writing, mathematical reasoning, natural language understanding, security, etc.
Ziya2-13B-Chat achieved a 66.5% win rate against Ziya-LlaMA-13B-v1.1 under side-by-side comparison, and a 58.4% win rate against the version before performing RLHF.
| | Better | Worse | Same | Win Rate|
|:----------:|:-------:|:---------:|:--------:|:------------:|
| v.s. Ziya-LlaMA-13B-v1.1 | 53.2% | 20.3% | 26.5% | 66.5% |
| v.s. w/o RLHF | 37.5% | 20.8% | 41.7% | 58.4% |
## 使用 Usage
Ziya2-13B-Chat采用"\<human\>:"和"\<bot\>:"作为用户和模型的角色识别Prompt，使用"\n"分隔不同角色对话内容。
在推理时，需要将"\<human\>:"和"\<bot\>:"作为前缀分别拼接至用户问题和模型回复的前面，并使用"\n"串连各对话内容。
Ziya2-13B-Chat adopts "\<human\>:" and "\<bot\>:" as the role recognition prompts for users and models, and uses "\n" to separate the contents of different roles.
When doing inference, "\<human\>:" and "\<bot\>:" need to be concatenated as prefixes in front of the user's question and the model's reply respectively, and "\n" is used to join the contents of each role.
以下为具体使用方法：
Following are the details of how to use it:
```python3
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = torch.device("cuda")
messages = [{"role": "user", "content": "手机如果贴膜贴了一张防指纹的钢化膜，那屏幕指纹解锁还有效吗？"}]
user_prefix = "<human>:"
assistant_prefix = "<bot>:"
separator = "\n"
prompt = []
for item in messages:
prefix = user_prefix if item["role"] == "user" else assistant_prefix
prompt.append(f"{prefix}{item['content']}")
prompt.append(assistant_prefix)
prompt = separator.join(prompt)
model_path="IDEA-CCNL/Ziya2-13B-Chat"
model = AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.bfloat16).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
generate_ids = model.generate(
input_ids,
max_new_tokens=512,
do_sample = True,
top_p = 0.9,
temperature = 0.85,
repetition_penalty=1.05,
eos_token_id=tokenizer.encode("</s>"),
)
output = tokenizer.batch_decode(generate_ids)[0]
print(output)
```
上面是简单的问答示例，其他更多prompt和玩法，感兴趣的朋友可以下载下来自行发掘。
The above is a simple example of question answering. For more prompts and creative ways to use the model, interested individuals can download it and explore further on their own.
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
欢迎引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
Chat-Error/Kimiko-v2-13B
|
Chat-Error
| 2023-10-16T05:29:43Z | 0 | 21 | null |
[
"text-generation",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
text-generation
| 2023-08-30T15:02:34Z |
---
license: creativeml-openrail-m
language:
- en
pipeline_tag: text-generation
---
For llama-anon it is llama2 license
## Model Details
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** nRuaif
- **Model type:** large language model
- **License:**
- **Finetuned from model [optional]:** Llama-13B
### Model Sources [optional]
## 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. -->
The model uses Fastchat/ShareGPT format.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is finetuned for normal and erotic roleplay while can still an assistant. (Might not be a helpfull one through)
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Do anything you want. I don't care
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Model might have bias to NSFW due to the large % of NSFW data in the training set.
## 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. -->
3000 convos with 4090 cut off len.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Training Hyperparameters
- **Training regime:** BF16, QLoRA, constant LR 5e-5 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
### Compute Infrastructure
The model is trained on 1 A100 for 2 hours on runpod.
|
xuhongru/sd-class-butterflies-32
|
xuhongru
| 2023-10-16T05:24:36Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-10-16T01:44:38Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('xuhongru/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
nommis/final_segformer
|
nommis
| 2023-10-16T05:19:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/segformer-b0-finetuned-ade-512-512",
"base_model:finetune:nvidia/segformer-b0-finetuned-ade-512-512",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-10-16T04:53:05Z |
---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: final_segformer
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. -->
# final_segformer
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6049
- Mean Iou: 0.3447
- Mean Accuracy: 0.5676
- Overall Accuracy: 0.8226
- Per Category Iou: [0.7326548261190029, 0.31403251023310264, 0.9683257053860633, 0.7656613170152077, 0.757276930666942, 0.7763142928638049, 0.8294222929302973, 0.9085291734197731, 0.40465645381465193, 0.7791935365674924, 0.0, 0.7775910859568447, 0.933100826663359, nan, 0.3629440157229978, 0.6522244125080773, 0.0, 0.4452428449293653, 0.34196252465483234, 0.35037349788892497, 0.06929248723559446, nan, 0.6822634757324983, 0.0, 0.0, nan, 0.942328838963983, 0.6631026269580487, nan, nan, nan, 0.9594628211313473, 0.0, nan, 0.584703235853954, nan, 0.3723730141768184, 0.0, nan, 0.8259911894273128, nan, 0.0, 0.10308946232402307, 0.09477124183006536, 0.8886679238813646, nan, nan, 0.8582377791188895, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9396076304131634, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8530927835051546, 0.15884194053208137, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6692727724000993, nan, nan, 0.0, 0.5950668036998972, nan, nan, 0.7356296621325142, 0.0, 0.20084121976866456, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5774251140646697, 0.05, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3490271438866202, 0.0, nan, nan, 0.0, nan, nan, 0.5098799492953545, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan]
- Per Category Accuracy: [0.8157732759163365, 0.702416307881557, 0.9901226124761858, 0.9007108337125891, 0.9465649402333303, 0.8498462228088307, 0.8763460629300835, 0.9500476665681743, 0.48168342751909293, 0.7791935365674924, 0.0, 0.8805107022155464, 0.979024405181125, nan, 0.7747478175031619, 0.7461936534376433, nan, 0.582611938310737, 0.45757958981690217, 0.6249806890159123, 0.08987701040681173, nan, 0.990646921278254, nan, 0.0, nan, 0.9717725052337753, 0.8103726681681044, nan, nan, nan, 0.9714692424236642, nan, nan, 0.7224201824401368, nan, 0.4742763814535886, nan, nan, 0.8401639344262295, nan, 0.0, 0.10760642948416167, 0.1152099886492622, 0.9185568471789056, nan, nan, 0.9834716459197786, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.963325712167089, nan, nan, nan, nan, nan, nan, nan, nan, 0.8560049653460226, 0.6351192804067266, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6780236359064622, nan, nan, nan, 0.6447661469933185, nan, nan, 0.8501521298174443, 0.0, 0.24869791666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6417390537501654, 0.05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.39835503769705277, 0.0, nan, nan, 0.0, nan, nan, 0.5098799492953545, nan, nan, nan, nan, nan, nan, nan, nan]
## 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: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 34 | 0.4099 | 0.3924 | 0.6115 | 0.8582 | [0.7895737886426407, 0.6517723052079128, 0.9790681253204105, 0.8023465851372054, 0.7544404336273537, 0.7761905309388581, 0.7886807031099092, 0.8705055048163957, 0.4732123295751386, 0.9010210155212673, 0.00604441945459657, 0.7159981857059547, 0.925089463220676, nan, 0.5452695829094608, 0.5559540223316022, 0.0, 0.45108934375823595, 0.24360162727225912, 0.39154358528815525, 0.3748902546093064, nan, 0.6488236289776574, nan, 0.0, nan, 0.9523320935402505, 0.5932424471934696, nan, nan, nan, 0.9675527258852488, 0.0, 0.0, 0.700149387511204, 0.0, 0.41872612458096203, 0.0, nan, 0.7851718714895529, nan, 0.0, 0.20415078356628547, 0.0, 0.7057254258157193, 0.0, nan, 0.8861777807326641, nan, nan, nan, nan, nan, 0.017542738456622464, nan, nan, 0.9361249325580001, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8991798359671934, 0.1630449185106665, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.7601681295586599, nan, nan, 0.0, 0.7805825242718447, nan, nan, 0.7402959468153549, 0.0, 0.162227602905569, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.8398942295389602, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.35974329054842474, 0.0, 0.0, nan, 0.0, nan, nan, 0.9907799836419064, nan, 0.0, nan, nan, nan, 0.0, nan, nan] | [0.9044820696231601, 0.709269835691064, 0.9889013726735382, 0.8872273873249102, 0.9426569816218386, 0.8406258171358842, 0.8579082838682566, 0.9516978775833329, 0.5069601169949627, 0.9013263960134839, 0.00661258698243051, 0.8298911002628614, 0.9795985546743424, nan, 0.7936576487645371, 0.7282638193541142, nan, 0.5470793490131316, 0.5202067410787925, 0.7310366136258304, 0.40397350993377484, nan, 0.9959080280592362, nan, 0.0, nan, 0.9715631542219121, 0.8986742021841669, nan, nan, nan, 0.9870970450286796, nan, nan, 0.8350199543899658, nan, 0.4873423464295436, nan, nan, 0.9548633879781421, nan, 0.0, 0.2107068397336741, 0.0, 0.7312872129288347, nan, nan, 0.9217842323651452, nan, nan, nan, nan, nan, 0.01998689384010485, nan, nan, 0.9649323364023976, nan, nan, nan, nan, nan, nan, nan, nan, 0.9299679321402711, 0.9624560031286664, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7730701533819462, nan, nan, nan, 0.9512806236080178, nan, nan, 0.8752535496957403, 0.0, 0.17447916666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.8683363463997531, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4226182316655243, 0.0, nan, nan, 0.0, nan, nan, 0.9935873536649019, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 2.0 | 68 | 0.4758 | 0.3795 | 0.6006 | 0.8343 | [0.751878165329026, 0.3370218551918578, 0.9763034087301206, 0.8144820643735031, 0.7545900490539593, 0.7180163301181968, 0.7164431280115141, 0.8768919319964387, 0.6099629347718172, 0.8524264375921533, 0.01029310851877267, 0.5943792325056434, 0.9223843752268932, nan, 0.5528213739800641, 0.60410600563661, nan, 0.4545798806023345, 0.33462883793237463, 0.3646815682812312, 0.35784313725490197, nan, 0.6735760474798477, nan, 0.0, nan, 0.9508956724787374, 0.6269341355182901, nan, nan, nan, 0.9674200384923577, 0.0, 0.0, 0.6799495889095601, nan, 0.42194950363480027, 0.0, nan, 0.8840855582828232, nan, 0.0, 0.0, 0.0, 0.7557161272813624, 0.0, nan, 0.8896939897780941, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9071624087591241, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.8975727666431664, 0.19378710254533627, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.781856383636139, nan, nan, 0.0, 0.7931396569828492, nan, nan, 0.7513249947000212, 0.0, 0.18270401948842874, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7585528566541225, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3395642338373616, 0.0, 0.0, nan, 0.0, nan, nan, 0.9850346213982578, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8334891550422118, 0.7404797469466655, 0.9914122417077817, 0.8942598732924459, 0.9265518898027377, 0.7730447883851527, 0.9021116772600725, 0.9224791075480429, 0.6655442612887501, 0.8526216473691924, 0.012110261043404713, 0.659181374389786, 0.9725326882444805, nan, 0.7921152481722553, 0.7261302065299782, nan, 0.5435633806568468, 0.5207565843734535, 0.7019542715896802, 0.41438032166508987, nan, 0.9877890361132762, nan, 0.0, nan, 0.973307745987439, 0.801470275782943, nan, nan, nan, 0.9860017631367042, nan, nan, 0.8074401368301026, nan, 0.48979221486253516, nan, nan, 0.9644262295081967, nan, 0.0, 0.0, 0.0, 0.7895165863339949, nan, nan, 0.9510373443983402, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9830068590496199, nan, nan, nan, nan, nan, nan, nan, nan, 0.9218992448536257, 0.9319515056707078, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.795323107870254, nan, nan, nan, 0.9462694877505567, nan, nan, 0.8985801217038539, 0.0, 0.1953125, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7821332510251775, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3909527073337903, 0.0, nan, nan, 0.0, nan, nan, 0.9865036164342704, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 3.0 | 102 | 0.4677 | 0.4126 | 0.6091 | 0.8363 | [0.7577584081630948, 0.38862957211951193, 0.9783660688869912, 0.8124463292510536, 0.7521159775065529, 0.7484274921518347, 0.8284574468085106, 0.8781548122207463, 0.41747180580387844, 0.8041184229810934, 0.02012000143714296, 0.7527752441547463, 0.9206270698034493, nan, 0.4385770618340465, 0.5490514510432218, 0.0, 0.4381432200835379, 0.3710471647303224, 0.32195986035393054, 0.3497942386831276, nan, 0.6754184007792438, nan, 0.0, nan, 0.9492431292528622, 0.5837156604934818, nan, nan, nan, 0.9684890782606091, 0.0, 0.0, 0.6789179653043222, 0.0, 0.4239012268985867, 0.0, nan, 0.8695887712386112, nan, 0.0, 0.17288684671581106, 0.0, 0.7540519429798868, nan, nan, 0.8900675024108003, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9395383693045564, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8826556351528605, 0.1836797774515107, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8488555165314279, nan, nan, 0.0, 0.8045785143674735, nan, nan, 0.7361287590004235, 0.0, 0.1375, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.8128547383254394, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3348751156336725, 0.0, 0.0, nan, 0.0, nan, nan, 0.9949049797314887, nan, nan, nan, nan, nan, 0.0, nan, nan] | [0.8456222648627099, 0.7279061593884545, 0.990586683601192, 0.8884915732975027, 0.9341168372066342, 0.8104182490567917, 0.8835951042706309, 0.9324584241599813, 0.43843320634806, 0.8041184229810934, 0.021529352966052823, 0.8471147828263863, 0.9831965581651714, nan, 0.8062744856094025, 0.705523998465296, nan, 0.5420451215939056, 0.5303238577005553, 0.7230418662135022, 0.402081362346263, nan, 0.9908417770849571, nan, 0.0, nan, 0.9698360083740405, 0.886648536098245, nan, nan, nan, 0.987150473413654, nan, nan, 0.8227622576966933, nan, 0.4953271028037383, nan, nan, 0.9648633879781421, nan, 0.0, 0.17586925818817675, 0.0, 0.8211298554011908, nan, nan, 0.9574688796680498, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9683927578322932, nan, nan, nan, nan, nan, nan, nan, nan, 0.90493431260991, 0.9296050058662495, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.881191853155645, nan, nan, nan, 0.9393095768374164, nan, nan, 0.8813387423935092, 0.0, 0.14322916666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.9156488381321928, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3969842357779301, 0.0, nan, nan, 0.0, nan, nan, 0.9973902020729252, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 4.0 | 136 | 0.4451 | 0.3986 | 0.6062 | 0.8542 | [0.7925532079736293, 0.5682686702506639, 0.9788751169430482, 0.8088236245431316, 0.7537738619676946, 0.7688211677477181, 0.8148221100578604, 0.8707500338228101, 0.409519217756124, 0.8272570716693537, 0.011077941229363737, 0.7516643886953838, 0.9292796680859089, nan, 0.40605872300761225, 0.561599262751214, nan, 0.4612188997945674, 0.36782456821269166, 0.36812411847672777, 0.40465918895599656, nan, 0.6718405503130788, nan, 0.0, nan, 0.949401537319672, 0.6557168363973019, nan, nan, nan, 0.9682746778541355, 0.0, 0.0, 0.6679300291545189, nan, 0.4199254382485921, 0.0, nan, 0.8730630229219268, nan, 0.0, 0.12699151837685016, 0.0, 0.7356488833327702, nan, nan, 0.8929720684772815, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9400503778337531, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.8715549679650157, 0.17343470483005366, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8327832783278328, nan, nan, 0.0, 0.812972838893712, nan, nan, 0.7442528735632183, 0.0, 0.15432098765432098, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.787654615598775, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.39997611369879377, 0.0, 0.0, nan, 0.0, nan, nan, 0.9790332191270669, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8943901853306878, 0.7708900799578244, 0.9864637780274534, 0.9032211458581656, 0.9201987680967166, 0.9025040156392026, 0.869202080159689, 0.9329211522486912, 0.4370610432050842, 0.8272570716693537, 0.011841144131329053, 0.8535986982100388, 0.9807774749670821, nan, 0.8063053336212481, 0.741355592779405, nan, 0.5382095197506859, 0.5210864903502502, 0.6653020237911323, 0.44370860927152317, nan, 0.9896076903091712, nan, 0.0, nan, 0.9707431960921145, 0.842710236688905, nan, nan, nan, 0.9856201318154584, nan, nan, 0.81634834663626, nan, 0.48035568460212325, nan, nan, 0.9636612021857923, nan, 0.0, 0.1283879211782904, 0.0, 0.7716543804933371, nan, nan, 0.9595435684647303, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9801025767781004, nan, nan, nan, nan, nan, nan, nan, nan, 0.8865211544429502, 0.75831052014079, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8565501634397787, nan, nan, nan, 0.9124443207126949, nan, nan, 0.9193711967545639, 0.0, 0.16276041666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.8732307420962123, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4590815627141878, 0.0, nan, nan, 0.0, nan, nan, 0.9801282529267019, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 5.0 | 170 | 0.4378 | 0.4268 | 0.6127 | 0.8487 | [0.7838558484357825, 0.4423435115747403, 0.9801142004755932, 0.7856299293622976, 0.7518859109348649, 0.7593764282842967, 0.8082851887354595, 0.894111093426707, 0.421081495151378, 0.8360508573941081, 0.0, 0.722557221113423, 0.9283495412777605, nan, 0.4246381868634, 0.5963426655386264, nan, 0.4259776825455976, 0.3547547010838344, 0.33567197996581466, 0.516, nan, 0.6751829673985362, nan, 0.0, nan, 0.9513513971641424, 0.6682878907445283, nan, nan, nan, 0.9672510676556529, 0.0, nan, 0.6656495204882301, nan, 0.41609232248757816, 0.0, nan, 0.8927173857803526, nan, 0.0, 0.2301095604538933, 0.0, 0.7929898621638044, nan, nan, 0.9028758169934641, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9203354297693921, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.9205940594059406, 0.18680037313432835, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7829687306884192, nan, nan, 0.0, 0.8207759699624531, nan, nan, 0.7372057870870222, 0.0, 0.17105263157894737, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.8041413669624786, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3432063939821345, 0.0, 0.0, nan, 0.0, nan, nan, 0.994308882606755, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8786887195185925, 0.715736754239522, 0.9885838503248497, 0.8962392273295335, 0.9462637764504263, 0.8585498873130705, 0.8194305825497715, 0.9395665966070323, 0.4366818928629462, 0.8360508573941081, 0.0, 0.8519464263362123, 0.9814587990323668, nan, 0.7883517907270876, 0.7189807319789259, nan, 0.5480648855978478, 0.5093198438445043, 0.6522864205159895, 0.6102175969725638, nan, 0.9886983632112237, nan, 0.0, nan, 0.9791434054431263, 0.8816555475290019, nan, nan, nan, 0.9853644388302236, nan, nan, 0.7617588369441277, nan, 0.4711006260774884, nan, nan, 0.9598907103825136, nan, 0.0, 0.2373058040217903, 0.0, 0.8787921746526793, nan, nan, 0.9553250345781467, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9765803621083854, nan, nan, nan, nan, nan, nan, nan, nan, 0.9618289024516395, 0.9397731716855691, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7964546140306764, nan, nan, nan, 0.9128619153674833, nan, nan, 0.8656186612576064, 0.0, 0.18619791666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.9109749107103488, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4002741603838245, 0.0, nan, nan, 0.0, nan, nan, 0.9966072626948028, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 6.0 | 204 | 0.4332 | 0.4301 | 0.6187 | 0.8598 | [0.8020267501440416, 0.6496986232308479, 0.97362599394396, 0.8007705423306446, 0.7657066971570133, 0.7457782418308313, 0.8083589398886115, 0.8933475926724024, 0.3873581847649919, 0.887087912087912, 0.009961545504486358, 0.7117151351230525, 0.931148994302988, nan, 0.3748600183248738, 0.5613468699998584, 0.0, 0.4537041196819264, 0.4125902711541082, 0.33652433953249694, 0.5442729488220959, nan, 0.6867792608735132, nan, 0.0, nan, 0.9446213201089136, 0.6353188039486861, nan, nan, nan, 0.9674941164125856, 0.0, nan, 0.6604531115572125, nan, 0.39172749391727496, 0.0, nan, 0.8966317635528963, nan, 0.0, 0.4409673257523478, 0.08582089552238806, 0.8048948876385101, nan, nan, 0.8721590909090909, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9375586144304946, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.8469211195928753, 0.18401053804843448, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8107041558761869, nan, nan, 0.0, 0.810031746031746, nan, nan, 0.7505385609651013, 0.0, 0.17464114832535885, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.8312394298139647, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4637181224590366, 0.0, 0.0, nan, 0.0, nan, nan, 0.9889021301951437, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.9103504472976786, 0.7699850628240049, 0.9911142591959358, 0.8821092401158717, 0.9219842390953613, 0.8715867066777901, 0.8844093081893155, 0.9360598981998205, 0.401303554985827, 0.8873479407885094, 0.010457114297797086, 0.7970960070096382, 0.9809612028049116, nan, 0.795107505321282, 0.7419732175442865, nan, 0.5379964307593958, 0.499477648870072, 0.7345125907616252, 0.6338694418164617, nan, 0.982527929332294, nan, 0.0, nan, 0.965361130495464, 0.8491480049292482, nan, nan, nan, 0.9852575820602748, nan, nan, 0.8206242873432155, nan, 0.4528627166318846, nan, nan, 0.9498907103825137, nan, 0.0, 0.4942161544152263, 0.09137343927355278, 0.8263751063226538, nan, nan, 0.976625172890733, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9575171476240499, nan, nan, nan, nan, nan, nan, nan, nan, 0.8607634219509672, 0.7102072741493938, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8265023887352275, nan, nan, nan, 0.887945434298441, nan, nan, 0.8833671399594321, 0.0, 0.19010416666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.9102253185766568, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5159698423577793, 0.0, nan, nan, 0.0, nan, nan, 0.9900082022220565, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 7.0 | 238 | 0.4407 | 0.4100 | 0.6186 | 0.8573 | [0.7985606971248648, 0.5902534535673878, 0.9777806709195851, 0.791430642266332, 0.7691987389209446, 0.7650941115887046, 0.8405137215114696, 0.896849841179716, 0.35266890239642285, 0.8721465786413457, 0.0, 0.7469846984698469, 0.9267212367284018, nan, 0.503359664985248, 0.5803722128453407, 0.0, 0.45141739560274236, 0.26908073270013566, 0.3358689162594255, 0.3441441441441441, nan, 0.665099473248879, nan, 0.0, nan, 0.9435159485887528, 0.5968662594896968, nan, nan, nan, 0.9648158721096203, 0.0, nan, 0.6548817739563298, 0.0, 0.39984917043740575, 0.0, nan, 0.8928515807506618, nan, 0.0, 0.5263525915566821, 0.015129331381161543, 0.8071182548794489, nan, nan, 0.8533719038148617, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9389579508172049, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8063228974831185, 0.18941214159859043, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7215632413534774, nan, nan, 0.0, 0.8108779336893084, nan, nan, 0.73989898989899, 0.0, 0.17427884615384615, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7944415933046465, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.528141592920354, 0.0, nan, nan, 0.0, nan, nan, 0.9950450786081514, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.885191264347449, 0.765503910025481, 0.9905671437643496, 0.9189620671969024, 0.9272259230311418, 0.9064885258557358, 0.8921048484530125, 0.933439630709535, 0.3645440265766335, 0.8724901069910597, 0.0, 0.8310426836900738, 0.9802339467801696, nan, 0.8157448252460129, 0.7336539991203526, nan, 0.5594651466318621, 0.5233958321878265, 0.5866290746176425, 0.36140018921475875, nan, 0.9923356716030137, nan, 0.0, nan, 0.9637212142358688, 0.8886669782858114, nan, nan, nan, 0.9802620280651674, nan, nan, 0.8250427594070695, nan, 0.48108157154523185, nan, nan, 0.9398360655737705, nan, 0.0, 0.6075055484565203, 0.017593643586833144, 0.8720229656932237, nan, nan, 0.979253112033195, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9638200580856454, nan, nan, nan, nan, nan, nan, nan, nan, 0.8152477500775835, 0.9249120062573328, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.726552677897913, nan, nan, nan, 0.9089643652561247, nan, nan, 0.8914807302231237, 0.0, 0.18880208333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.8873407116715905, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6135709389993146, 0.0, nan, nan, 0.0, nan, nan, 0.9957870404891507, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 8.0 | 272 | 0.4366 | 0.3956 | 0.6145 | 0.8601 | [0.8040508902005498, 0.64098621502119, 0.9771342639006709, 0.801379973116591, 0.7609859228757637, 0.7531138222582802, 0.8179943284351637, 0.894888041668904, 0.38038173471853104, 0.8979565667390249, 0.0, 0.7687082096088527, 0.9372560549818075, nan, 0.4376169113873761, 0.5719367844591535, 0.0, 0.4503125814014066, 0.29478063774955754, 0.3543482622556123, 0.28957528957528955, nan, 0.6797694628303841, nan, 0.0, nan, 0.936840119046598, 0.6929483038566964, nan, nan, nan, 0.9657043506995817, 0.0, nan, 0.6427735206470838, nan, 0.3915108795586883, 0.0, nan, 0.8874772221097388, 0.0, 0.0, 0.5438860117530254, 0.05283842794759825, 0.820763389969015, nan, nan, 0.8730609974661641, nan, nan, nan, nan, nan, 0.001383427988932576, nan, nan, 0.9388341295277257, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.803835560542691, 0.19108172261783654, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6745033945184813, nan, nan, 0.0, 0.8064438276819115, nan, nan, 0.7329557830538009, 0.0, 0.18364928909952608, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7161537549875558, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5280362389813908, 0.0, nan, nan, 0.0, nan, nan, 0.8155245693833421, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8927814524759331, 0.73623583164924, 0.9903180108446095, 0.9216602012584069, 0.9368990169153658, 0.9189027654991222, 0.8333771077375637, 0.9291078267947439, 0.4263816419015292, 0.8980470467536275, 0.0, 0.9008386531480786, 0.9761230364087332, nan, 0.8154980411512478, 0.744715097182321, nan, 0.5525663905388488, 0.5220212239511739, 0.6138961841495443, 0.3547776726584674, nan, 0.9805793712652637, nan, 0.0, nan, 0.9528000697836706, 0.8432838992053712, nan, nan, nan, 0.9814374525346045, nan, nan, 0.8608181299885975, nan, 0.46366028491062516, nan, nan, 0.958087431693989, nan, 0.0, 0.6815858497545229, 0.06867196367763904, 0.8543025233909839, nan, nan, 0.9769709543568464, nan, nan, nan, nan, nan, 0.001383427988932576, nan, nan, 0.9593709448186368, nan, nan, nan, nan, nan, nan, nan, nan, 0.8151443053687804, 0.8815017598748534, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6745033945184813, nan, nan, nan, 0.9302616926503341, nan, nan, 0.8531947261663286, 0.0, 0.20182291666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7993297764451696, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5912268677176148, 0.0, nan, nan, 0.0, nan, nan, 0.8155245693833421, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 9.0 | 306 | 0.4803 | 0.4011 | 0.6079 | 0.8393 | [0.7630492904931592, 0.40970070234406114, 0.9766849237201998, 0.7758249553582349, 0.7558808918500336, 0.7361635093915556, 0.8171638117739712, 0.9022714846134654, 0.3252123418270064, 0.8356569690751868, 0.0, 0.7366005428186854, 0.9270084040881637, nan, 0.42356922664522184, 0.6258951452590233, 0.0, 0.4491813141948515, 0.29086135450605766, 0.3306038213168231, 0.4217171717171717, nan, 0.6749433660551681, nan, 0.0, nan, 0.9399611167010591, 0.5863554177017949, nan, nan, nan, 0.967671195967413, 0.0, nan, 0.6405529953917051, nan, 0.3939961013645224, 0.0, nan, 0.8461702127659575, nan, 0.0, 0.3068192717158477, 0.08118219229330341, 0.8316311254532752, nan, nan, 0.8644379132990448, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9401168889554923, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8085717215215831, 0.1933958418263351, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7653769841269841, nan, nan, 0.0, 0.7993540051679586, nan, nan, 0.7535181236673774, 0.0, 0.17341040462427745, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7861542059717783, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4837986041874377, 0.0, 0.0, nan, 0.0, nan, nan, 0.9266273954216688, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8397422297037367, 0.7411475265793867, 0.990430364906453, 0.9164698148509344, 0.9274051871876322, 0.9135610314900823, 0.8720649261963545, 0.9475835001198633, 0.33873472114395076, 0.8356569690751868, 0.0, 0.8356990862435849, 0.9845898276020455, nan, 0.7798994354813832, 0.7336539991203526, nan, 0.5568015342407373, 0.5055809094408094, 0.5753900818785725, 0.4739829706717124, nan, 0.9869446609508964, nan, 0.0, nan, 0.9615579204466155, 0.8474907576594569, nan, nan, nan, 0.9868375357302325, nan, nan, 0.8122862029646523, nan, 0.4584883404409763, nan, nan, 0.8692896174863388, nan, 0.0, 0.32271840742484365, 0.123155505107832, 0.8859512333427843, nan, nan, 0.9763485477178423, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9691342767101279, nan, nan, nan, nan, nan, nan, nan, nan, 0.8157649736215993, 0.9276495893625342, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7759617802363591, nan, nan, nan, 0.8612193763919822, nan, nan, 0.8960446247464503, 0.0, 0.1953125, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.889280832488205, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.532145305003427, 0.0, nan, nan, 0.0, nan, nan, 0.9266273954216688, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 10.0 | 340 | 0.4947 | 0.4086 | 0.5987 | 0.8349 | [0.7615373264895288, 0.36224429627613736, 0.9743266337706101, 0.7792383452083965, 0.7567759169251884, 0.7537972912929715, 0.7791016696715529, 0.895747264810857, 0.3524421489283826, 0.8332173212192263, 0.0, 0.7469956243965571, 0.9355237732755592, nan, 0.3688836138075255, 0.6050574607459602, 0.0, 0.4322746958135698, 0.3933588761174968, 0.3242607852641784, 0.296137339055794, nan, 0.6749966709574327, nan, 0.0, nan, 0.941720826296846, 0.5719388677880267, nan, nan, nan, 0.9631851824022242, 0.0, nan, 0.650875979714154, nan, 0.3905894832550386, nan, nan, 0.8851965314917701, nan, 0.0, 0.2353908188585608, 0.0, 0.8365381307832548, nan, nan, 0.852287899016871, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9404900468558809, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8390945243193637, 0.17492625368731562, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7766009852216749, nan, nan, 0.0, 0.8041040020392557, nan, nan, 0.7605724049551473, 0.0, 0.17001180637544275, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.8044234784626092, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5152389313934529, 0.0, 0.0, nan, 0.0, nan, nan, 0.7829020952949072, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8422987160446621, 0.7017309551006062, 0.988676664549851, 0.909350641845278, 0.9568188499845833, 0.8555366014618172, 0.7831328465619583, 0.9416405104504073, 0.3911657970281835, 0.8333394401289755, 0.0, 0.9103266992114157, 0.9784655663410601, nan, 0.8053490452540334, 0.7424879048483544, nan, 0.5583197933036784, 0.4911200307912245, 0.5167233122199907, 0.32639545884578997, nan, 0.9877240841777085, nan, 0.0, nan, 0.9631280530355897, 0.8062507967534951, nan, nan, nan, 0.9783920345910629, nan, nan, 0.8048745724059293, nan, 0.4677433989656111, nan, nan, 0.9315846994535519, nan, 0.0, 0.25519537292353217, 0.0, 0.8618868726963425, nan, nan, 0.9712309820193638, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.973645183216956, nan, nan, nan, nan, nan, nan, nan, nan, 0.8512465087410779, 0.9276495893625342, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7928086497359819, nan, nan, nan, 0.8782015590200446, nan, nan, 0.9028904665314401, 0.0, 0.1875, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.9173243970192689, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5631254283755998, 0.0, nan, nan, 0.0, nan, nan, 0.7829020952949072, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 11.0 | 374 | 0.4762 | 0.3957 | 0.5988 | 0.8475 | [0.7904149742473096, 0.5711637268923142, 0.9751564145598992, 0.7672420995305977, 0.7594172479054767, 0.7653626647296876, 0.743812883836404, 0.8938581897822516, 0.3256919414040541, 0.8440660266744834, 0.0, 0.7288643722268857, 0.9312437904752235, nan, 0.3920841821495857, 0.6057092225903382, 0.0, 0.4492490654307763, 0.35121969929426206, 0.35328002025615596, 0.2694099378881988, nan, 0.6637353983462396, nan, 0.0, nan, 0.9398623098706188, 0.564578654498119, nan, nan, nan, 0.962515055889714, 0.0, nan, 0.6052212858742413, nan, 0.3854692230070636, 0.0, nan, 0.8722604552673372, nan, 0.0, 0.49171125339882465, 0.08373732921992066, 0.7904254573491274, nan, nan, 0.8339718276654683, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9375616757872073, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.7824982213639597, 0.17203495630461924, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7044359322871617, nan, nan, 0.0, 0.8069425229530877, nan, nan, 0.7278132569918521, 0.0, 0.16346153846153846, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6421164270563281, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5393140457302846, 0.0, 0.0, nan, 0.0, nan, nan, 0.7409961971515919, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8801820432772453, 0.7579650294350233, 0.9905476039275072, 0.8956035223833156, 0.9235976165037753, 0.9184047017220555, 0.7475442559226769, 0.9465465431981759, 0.35565204831458647, 0.8440660266744834, 0.0, 0.9129553135561397, 0.9830357963070705, nan, 0.7896474072246044, 0.735675316532692, nan, 0.5473723463761554, 0.5034915049210975, 0.6466476131623667, 0.3282876064333018, nan, 0.985385814497272, nan, 0.0, nan, 0.9669661549197488, 0.8385671185144266, nan, nan, nan, 0.9789492163200818, nan, nan, 0.7318985176738882, nan, 0.48525542146810635, nan, nan, 0.9003825136612021, nan, 0.0, 0.5655390409576972, 0.10783200908059024, 0.7993337113694358, nan, nan, 0.9785615491009682, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.968701724031391, nan, nan, nan, nan, nan, nan, nan, nan, 0.7964208130754112, 0.8083691826359014, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7167462911742519, nan, nan, nan, 0.8930957683741648, nan, nan, 0.8379817444219066, 0.0, 0.17708333333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7207989770272057, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6337217272104181, 0.0, nan, nan, 0.0, nan, nan, 0.7409961971515919, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 12.0 | 408 | 0.4745 | 0.3824 | 0.5877 | 0.8467 | [0.7846357187284617, 0.4698568097492151, 0.9799695409384291, 0.779785423237681, 0.7571621304551853, 0.7936978462574176, 0.7660720296196567, 0.907214110771236, 0.38671345336806723, 0.8657848453759344, 0.0, 0.7437658408960838, 0.923573147516253, nan, 0.3732520360112786, 0.6061325912686741, 0.0, 0.444213112364931, 0.3907279103583953, 0.35513911507672546, 0.14430379746835442, nan, 0.6774725764737358, nan, 0.0, nan, 0.9406386091453457, 0.5661557269136731, nan, nan, nan, 0.9687166779304648, 0.0, nan, 0.5900341685649203, nan, 0.3711238844350325, 0.0, nan, 0.8310889619340472, nan, 0.0, 0.09334323826749782, 0.008108108108108109, 0.7827798277982779, nan, nan, 0.8139907338557456, nan, nan, nan, nan, nan, 0.0009451452251990258, nan, nan, 0.9445495779640143, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.8239754098360655, 0.1757566895744992, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7537128712871287, nan, nan, 0.0, 0.7601615424700364, nan, nan, 0.7525795356835769, 0.0, 0.17452830188679244, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7111128706599628, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.45601229350749134, 0.0, 0.0, nan, 0.0, nan, nan, 0.8030348221609127, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8705925965647725, 0.7666373780862842, 0.9901421523130282, 0.911738147353517, 0.9255838633576894, 0.9101617462116024, 0.777118243420707, 0.9623183234748092, 0.4248108761983859, 0.8657848453759344, 0.0, 0.9109525597696834, 0.9842147165998102, nan, 0.8044544529105099, 0.7521453102628648, nan, 0.5683083397703966, 0.5023368340023093, 0.597983933261239, 0.16177861873226113, nan, 0.986814757079761, nan, 0.0, nan, 0.9646632937892533, 0.7674967067522203, nan, nan, nan, 0.9846393393198567, nan, nan, 0.7383836944127709, nan, 0.4452409037292442, nan, nan, 0.8566120218579235, nan, 0.0, 0.09718205662788351, 0.01021566401816118, 0.7894102636801814, nan, nan, 0.9841632088520055, nan, nan, nan, nan, nan, 0.0009465559924275521, nan, nan, 0.9715442130630909, nan, nan, nan, nan, nan, nan, nan, nan, 0.8319023481948898, 0.9401642549863121, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7656525018858436, nan, nan, nan, 0.8122216035634744, nan, nan, 0.8876774847870182, 0.0, 0.19270833333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7920102297279421, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4881425633995888, 0.0, nan, nan, 0.0, nan, nan, 0.8030348221609127, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 13.0 | 442 | 0.4674 | 0.3920 | 0.6032 | 0.8477 | [0.7693530697718926, 0.46130850037160703, 0.9763910195181723, 0.7787162005616121, 0.7540597261405078, 0.7872044432689764, 0.7894546207182035, 0.9014465973285465, 0.38962729625046205, 0.8815495383262495, 0.0, 0.7462589732320378, 0.9389519695642895, nan, 0.38779138978044997, 0.6268788752418941, 0.0, 0.4447732577525842, 0.33920303328593826, 0.33735590778097985, 0.12478920741989882, nan, 0.6692521263937243, nan, 0.0, nan, 0.9415588830157894, 0.6485414235705951, nan, nan, nan, 0.9676401124648547, 0.0, nan, 0.58353104565708, nan, 0.3931527464258841, 0.0, nan, 0.8775563575500811, nan, 0.0, 0.510979067382518, 0.04894046417759838, 0.8499808515823556, nan, nan, 0.8416106584190804, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9467205750224618, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.7536008230452675, 0.1746948322491046, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7210460772104608, nan, nan, 0.0, 0.8000251224720513, nan, nan, 0.7425805093664491, 0.0, 0.18729096989966554, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7020408163265306, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.56, 0.0, nan, nan, 0.0, nan, nan, 0.733539631645664, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8529245131251617, 0.7308057288463228, 0.9887157442235357, 0.9104992451003764, 0.9429653159710022, 0.9265293670854553, 0.8014393024110942, 0.9518985789230143, 0.4377019878310795, 0.8815495383262495, 0.0, 0.8926523970459381, 0.9767967051474415, nan, 0.7938735848474566, 0.7427031377209646, nan, 0.5684681565138641, 0.5115742013526146, 0.5787115711416654, 0.1400189214758751, nan, 0.9863600935307872, nan, 0.0, nan, 0.9664776692254012, 0.8266264394679811, nan, nan, nan, 0.9850591337732271, nan, nan, 0.7186431014823261, nan, 0.47409490971781143, nan, nan, 0.9168306010928962, nan, 0.0, 0.5696751630909946, 0.055051078320090804, 0.865253756733768, nan, nan, 0.9785615491009682, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9766730519681147, nan, nan, nan, nan, nan, nan, nan, nan, 0.757732491983035, 0.9346890887759093, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7279356298717626, nan, nan, nan, 0.8865534521158129, nan, nan, 0.8945233265720081, 0.0, 0.21875, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.788747299263636, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6640164496230295, 0.0, nan, nan, 0.0, nan, nan, 0.733539631645664, nan, nan, nan, nan, nan, nan, nan, nan] |
| No log | 14.0 | 476 | 0.5126 | 0.3872 | 0.5987 | 0.8371 | [0.7524063684673707, 0.36591968649354745, 0.9774688167264084, 0.7777208054185831, 0.7457458081406221, 0.7985558511086992, 0.8126733186471427, 0.8998117905490064, 0.3738844758262234, 0.8673457236811973, 0.0, 0.7651825297499099, 0.9314810505802623, nan, 0.45173683081981275, 0.6362460786896238, 0.0, 0.44884777263824643, 0.23632574217161448, 0.36497329241959314, 0.19344262295081968, nan, 0.663055822486241, nan, 0.0, nan, 0.9416944211391622, 0.5971728320373021, nan, nan, nan, 0.9684101657061058, 0.0, nan, 0.56781365210591, nan, 0.3913010301411675, 0.0, nan, 0.887879424430207, nan, 0.0, 0.3965392707973495, 0.0945273631840796, 0.8676901082442644, nan, nan, 0.8426612855362337, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9483981487046944, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.8085933503836317, 0.1831457255856404, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6468382904273932, nan, nan, 0.0, 0.7963943519919314, nan, nan, 0.7404980340760158, 0.0, 0.19815668202764977, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6798288652510107, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5085597988746559, 0.0, 0.0, nan, 0.0, nan, nan, 0.6542763403176497, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8292330074677845, 0.7137861347860469, 0.9884128767524791, 0.9149058362048414, 0.9436752020307043, 0.8840632042933098, 0.8235803960708095, 0.9408767303521751, 0.4130030512575153, 0.8673695588450828, 0.0, 0.9030667167355113, 0.9806549897418624, nan, 0.7798685874695376, 0.7458941990061856, nan, 0.5737953812961137, 0.5112442953758178, 0.6201529429939749, 0.22327341532639547, nan, 0.9859703819173812, nan, 0.0, nan, 0.9692864619678995, 0.8598776186631538, nan, nan, nan, 0.986242190869089, nan, nan, 0.7244156214367161, nan, 0.46529353053261957, nan, nan, 0.923879781420765, nan, 0.0, 0.42460824534265923, 0.10783200908059024, 0.9034235894527928, nan, nan, 0.9818810511756569, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9750046344929865, nan, nan, nan, nan, nan, nan, nan, nan, 0.8176269783800558, 0.9417285881892843, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6507417651496102, nan, nan, nan, 0.879315144766147, nan, nan, 0.8595334685598377, 0.0, 0.22395833333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7637021032673398, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5823166552433173, 0.0, nan, nan, 0.0, nan, nan, 0.6542763403176497, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 15.0 | 510 | 0.4473 | 0.3912 | 0.6012 | 0.8589 | [0.8077559570568211, 0.6635697231820713, 0.9754716981132076, 0.7723805970605684, 0.7576262436643514, 0.7766527787076473, 0.8211780849766213, 0.9038144204086613, 0.3935100011743062, 0.7937307635937271, 0.0, 0.7620273040519912, 0.9314828416222466, 0.0, 0.4011163282440374, 0.622989960820761, 0.0, 0.4452362772546025, 0.31786261718889247, 0.3609837432263443, 0.18691588785046728, nan, 0.6712383136355619, nan, 0.0, nan, 0.9422601510379627, 0.7577724568820382, nan, nan, nan, 0.9680692598026588, 0.0, nan, 0.5806489126642633, nan, 0.38950215659039406, 0.0, nan, 0.8323749126672757, nan, 0.0, 0.4953889278206027, 0.02835538752362949, 0.8388125953669024, nan, nan, 0.8755836814942246, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9391111381322957, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.8417760459392944, 0.17538667175864045, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7284086732818816, nan, nan, 0.0, 0.7959028831562974, nan, nan, 0.74375400384369, 0.0, 0.1965909090909091, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6424249757986448, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5735844642021526, 0.0, nan, nan, 0.0, nan, nan, 0.7197077026321677, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8990310298921217, 0.7643616553905632, 0.9900053734551316, 0.9122727174219275, 0.9260642912970838, 0.9388813487567083, 0.8487419236224195, 0.9496574139632382, 0.45376171303735535, 0.7937307635937271, 0.0, 0.8747527850794843, 0.9805784364761001, nan, 0.7958478576055773, 0.7276368366382497, nan, 0.5591455131449271, 0.49029526584923294, 0.6689324887996292, 0.20813623462630085, nan, 0.988633411275656, nan, 0.0, nan, 0.9708304256803908, 0.8606849955381805, nan, nan, nan, 0.989600546496052, nan, nan, 0.7116590649942988, nan, 0.4834407041103348, nan, nan, 0.8463387978142076, nan, 0.0, 0.5942901338354967, 0.04256526674233825, 0.8572441168131557, nan, nan, 0.9855463347164592, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9544892788728913, nan, nan, nan, nan, nan, nan, nan, nan, 0.849177614565015, 0.718420023464998, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7475484033190848, nan, nan, nan, 0.8761135857461024, nan, nan, 0.8831135902636917, 0.0, 0.22526041666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.702279641959522, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6721041809458533, 0.0, nan, nan, 0.0, nan, nan, 0.7197077026321677, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 16.0 | 544 | 0.4741 | 0.3740 | 0.5894 | 0.8478 | [0.7751407758498613, 0.5170321978534764, 0.9748176517003136, 0.771798966708823, 0.7580857715241377, 0.790371118701325, 0.7561613730312385, 0.9021232632324513, 0.3987471005259891, 0.815990033709512, 0.0, 0.743168296863971, 0.9289126877447256, nan, 0.4126473906257244, 0.6527727553980537, 0.0, 0.45891647855530476, 0.3140868846246598, 0.35244414741264085, 0.07021791767554479, nan, 0.6750720141812542, nan, 0.0, nan, 0.9437515885791747, 0.678239497371291, nan, nan, nan, 0.9673391547223077, 0.0, nan, 0.6387371706380169, nan, 0.38997321086873327, 0.0, nan, 0.6663043478260869, nan, 0.0, 0.4554274765047599, 0.09509844993715962, 0.9002200033846675, nan, nan, 0.7610945167484532, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9353596020262687, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.7181352459016394, 0.1750530883574924, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7133233532934131, nan, nan, 0.0, 0.74955391282182, nan, nan, 0.7546157148990983, 0.0, 0.19605695509309967, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.5930214178455703, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4457686288672204, 0.0, nan, nan, 0.0, nan, nan, 0.8400193870703154, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8678286070005525, 0.7691064054125296, 0.9897415856577597, 0.9059517875589652, 0.9198474103499953, 0.9205775049495087, 0.7591006986394915, 0.944400153871027, 0.44072616317908536, 0.815990033709512, 0.0, 0.9225184628864689, 0.9734283614538997, nan, 0.8237036123021871, 0.7601744322063241, nan, 0.5415123991156807, 0.5012921317424534, 0.5702919820794068, 0.08230842005676443, nan, 0.9894128345024682, nan, 0.0, nan, 0.971659106769016, 0.8853312369863596, nan, nan, nan, 0.9833646907068957, nan, nan, 0.7584093500570125, nan, 0.4622992468922965, nan, nan, 0.6699453551912569, nan, 0.0, 0.5035308359674491, 0.12883087400681045, 0.9426212078253473, nan, nan, 0.9867911479944675, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9527281715380337, nan, nan, nan, nan, nan, nan, nan, nan, 0.7250439640012414, 0.7414939382088385, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7188835805883832, nan, nan, nan, 0.8186247216035635, nan, nan, 0.8912271805273834, 0.0, 0.23307291666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.66171347943031, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.507607950651131, 0.0, nan, nan, 0.0, nan, nan, 0.8400193870703154, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 17.0 | 578 | 0.4993 | 0.3622 | 0.5800 | 0.8383 | [0.7530321261730883, 0.4105640358249962, 0.9789888378200919, 0.7712483105995198, 0.7572948684788297, 0.7680838323353293, 0.8340382730695827, 0.8993471250734683, 0.39019249125039773, 0.8188938150373736, 0.0, 0.7857406322549849, 0.9283282137475368, nan, 0.34466263463384167, 0.6382548980004039, 0.0, 0.46237681916073875, 0.3445310089478556, 0.355347126019122, 0.0885640584694755, nan, 0.6701531400326581, 0.0, 0.0, nan, 0.941136838514349, 0.7591195792547119, nan, nan, 0.0, 0.9683007257632866, 0.0, nan, 0.6055464742686433, nan, 0.3941236068895643, 0.0, nan, 0.7713297671151457, nan, 0.0, 0.28569154355578197, 0.0, 0.8761772853185595, nan, nan, 0.8188339263169976, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9480730957119595, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.818638507279065, 0.17679368348781324, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7056847866650081, nan, nan, 0.0, 0.6851607801792303, nan, nan, 0.750984682713348, 0.0, 0.17061611374407584, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6365786040443575, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5552407932011332, 0.0, nan, nan, 0.0, nan, nan, 0.7624338229811349, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8433572156331529, 0.7983217643440822, 0.9905524888867178, 0.9130998562439951, 0.9027599509533268, 0.8784413094096699, 0.8493722750433367, 0.9469089206170451, 0.44283857222814016, 0.8188938150373736, 0.0, 0.9065965702841406, 0.9809458921517592, nan, 0.6979054199956812, 0.7392781276611673, nan, 0.5661508137335854, 0.4911750151206906, 0.6043179360420207, 0.09744560075685904, nan, 0.9862951415952196, nan, 0.0, nan, 0.9626046755059317, 0.8617473335316365, nan, nan, nan, 0.9872725954364526, nan, nan, 0.7656071835803877, nan, 0.4588512839125306, nan, nan, 0.7800546448087432, nan, 0.0, 0.30173515367543213, 0.0, 0.896796144031755, nan, nan, 0.9867911479944675, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.971389729963542, nan, nan, nan, nan, nan, nan, nan, nan, 0.8260059997931106, 0.604223699648025, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.713226049786271, nan, nan, nan, 0.7236915367483296, nan, nan, 0.8701825557809331, 0.0, 0.1875, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6884783279686053, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6179575051405072, 0.0, nan, nan, 0.0, nan, nan, 0.7624338229811349, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 18.0 | 612 | 0.5067 | 0.3827 | 0.5821 | 0.8379 | [0.7593016098708265, 0.3906373937677054, 0.9779664951445042, 0.7766439770741425, 0.7559788262476363, 0.7868856142873997, 0.7683524924098918, 0.9022476014482632, 0.4465250212786267, 0.7765004396892863, 0.0, 0.742724881212146, 0.9326948500249713, nan, 0.39245574919233744, 0.6239000007822216, 0.0, 0.4618491057883379, 0.34530892448512585, 0.3540823323821532, 0.05660377358490566, nan, 0.6761972519898617, nan, 0.0, nan, 0.9418335966834871, 0.725503416856492, nan, nan, nan, 0.9689102672166091, 0.0, nan, 0.624985344120061, nan, 0.37569967908052837, nan, nan, 0.8240526117190448, nan, 0.0, 0.3568273599192327, 0.03777472527472527, 0.8119302902245661, nan, nan, 0.8332455370207784, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.937623792546546, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.7812918512413722, 0.16908665105386417, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7167466469792051, nan, nan, 0.0, 0.7494910941475827, nan, nan, 0.735351775212372, 0.0, 0.18213058419243985, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.5294117647058824, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4672451723295038, 0.0, nan, nan, 0.0, nan, nan, 0.7950935798970994, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.844153421821412, 0.7269747825322906, 0.9878364515656294, 0.9181891077736601, 0.9431589212600119, 0.8720349640771501, 0.7776960655565478, 0.9363665252465561, 0.5067434596565982, 0.7765004396892863, 0.0, 0.9117786957065965, 0.9721652325688214, nan, 0.7981923065058457, 0.7463901704082874, nan, 0.5757398183416349, 0.497828118986089, 0.6182218445852, 0.065279091769158, nan, 0.9877240841777085, nan, 0.0, nan, 0.9670882763433356, 0.8458760039094038, nan, nan, nan, 0.9870436166437052, nan, nan, 0.7597633979475484, nan, 0.45676435895109335, nan, nan, 0.8353551912568306, nan, 0.0, 0.38032147420808393, 0.06242905788876277, 0.8239296852849447, nan, nan, 0.984508990318119, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9506889946239881, nan, nan, nan, nan, nan, nan, nan, nan, 0.7845246715630495, 0.7059053578412202, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7323359316067387, nan, nan, nan, 0.8200167037861915, nan, nan, 0.8559837728194726, 0.0, 0.20703125, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.5793906256889634, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5240575736806031, 0.0, nan, nan, 0.0, nan, nan, 0.7950935798970994, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 19.0 | 646 | 0.5727 | 0.3798 | 0.5737 | 0.8223 | [0.7248719396071935, 0.31170156027962614, 0.9783915359217218, 0.766424560546875, 0.7570508392520826, 0.7645828871580419, 0.7883266859570919, 0.9008971228368192, 0.4183237931376766, 0.8031474424739851, 0.0, 0.762329790767187, 0.9305475901821428, nan, 0.38283134759120846, 0.6511938660261203, 0.0, 0.4455692985428668, 0.3409671150402012, 0.3315998324008577, 0.056768558951965066, nan, 0.66782158677251, nan, 0.0, nan, 0.9413619755681962, 0.5865037227859634, nan, nan, nan, 0.9643655915597961, 0.0, nan, 0.5781888334089877, nan, 0.36363636363636365, nan, nan, 0.68, nan, 0.0, 0.0743210436021105, 0.0022263450834879408, 0.8669115863168475, nan, nan, 0.804162885662432, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9428259947913512, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.7560724578015644, 0.18179154049037805, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.677860696517413, nan, nan, 0.0, 0.6967941290073387, nan, nan, 0.7547983610092732, 0.0, 0.19217491369390102, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7333903178180122, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5299777942264989, 0.0, nan, nan, 0.0, nan, nan, 0.8143315189023935, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.80090415449431, 0.7651436604867762, 0.9915587904840995, 0.9071148386537503, 0.919460199771976, 0.8446103273524175, 0.7942690549981615, 0.9461451405188129, 0.4565331214906025, 0.8031474424739851, 0.0, 0.9194141945174615, 0.9812750711945372, nan, 0.797914674399235, 0.7661541628844948, nan, 0.5603707748448445, 0.49199978006268213, 0.5196199598331531, 0.06149479659413434, nan, 0.9890231228890621, nan, 0.0, nan, 0.9686322400558269, 0.793311519993201, nan, nan, nan, 0.9774417726011609, nan, nan, 0.7261972633979475, nan, 0.4692859087197169, nan, nan, 0.6837158469945355, nan, 0.0, 0.077207613154886, 0.00340522133938706, 0.8864828466118514, nan, nan, 0.980567081604426, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9619353642711488, nan, nan, nan, nan, nan, nan, nan, nan, 0.7599048308679011, 0.7278060226828315, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6851898415891375, nan, nan, nan, 0.7533407572383074, nan, nan, 0.8874239350912779, 0.0, 0.21744791666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.8323118303276159, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5888965044551062, 0.0, nan, nan, 0.0, nan, nan, 0.8143315189023935, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 20.0 | 680 | 0.5063 | 0.3773 | 0.5803 | 0.8465 | [0.7757400170030017, 0.4836524519889278, 0.9776040209537223, 0.7701109051090878, 0.7544775379519091, 0.7786744873057327, 0.8094432442281163, 0.9011138700145334, 0.43385535112605245, 0.827394474571303, 0.0, 0.7677682113615262, 0.9281252035933779, nan, 0.3772225716082557, 0.6352977945890499, 0.0, 0.44441166554281863, 0.37451062057476053, 0.3496804636140825, 0.060443764345830146, nan, 0.6680843600648924, nan, 0.0, nan, 0.9421437168381618, 0.6900463093162346, nan, nan, nan, 0.9678139848105054, 0.0, nan, 0.6253615915933645, nan, 0.354863813229572, 0.0, nan, 0.8626999839477768, nan, 0.0, 0.4099790330912516, 0.018964633521271142, 0.8714581893572909, nan, nan, 0.7943752105085887, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9374962017623822, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.8192189105858171, 0.18199608610567514, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6603254067584481, nan, nan, 0.0, 0.5836702752054258, nan, nan, 0.7253228277522433, 0.0, 0.1961231470923603, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.5857453969510988, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5150791656195024, 0.0, nan, nan, 0.0, nan, nan, 0.6653493400939527, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8658643208699282, 0.7415253492663211, 0.9900395681696058, 0.9106906789762261, 0.9276418158741996, 0.9058534945399759, 0.8167778536534118, 0.9367623528887056, 0.5149403289580587, 0.827394474571303, 0.0, 0.9108273876580298, 0.9815200416449765, nan, 0.8141407286300398, 0.7418515641815068, nan, 0.5617558532882295, 0.49441909055919064, 0.5896029661671559, 0.07473982970671712, nan, 0.9896726422447389, nan, 0.0, nan, 0.969609211444522, 0.7819869969829601, nan, nan, nan, 0.9872306159911156, nan, nan, 0.7549173318129989, nan, 0.455131113329099, nan, nan, 0.8810382513661202, nan, 0.0, 0.4536956083125967, 0.020998864926220204, 0.8938191097249787, nan, nan, 0.9786307053941908, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9532534140765, nan, nan, nan, nan, nan, nan, nan, nan, 0.8245577738698665, 0.6546734454438795, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6633140558209706, nan, nan, nan, 0.6229120267260579, nan, nan, 0.84026369168357, 0.0, 0.22395833333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6522774372767759, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5618917066483893, 0.0, nan, nan, 0.0, nan, nan, 0.6653493400939527, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 21.0 | 714 | 0.5269 | 0.3594 | 0.5821 | 0.8395 | [0.7589432757949085, 0.4249754539118579, 0.9738167799494314, 0.7766570162336265, 0.7580013762077958, 0.7789715629677143, 0.8284005201560468, 0.897375835231382, 0.35670240319463714, 0.819516708192877, 0.0, 0.7851145611887737, 0.9308284199609831, nan, 0.3928883005926416, 0.6526911545429767, 0.0, 0.44676967484506325, 0.30597901741758055, 0.36139408957415564, 0.025954198473282442, nan, 0.6650496044753289, 0.0, 0.0, nan, 0.9426396928579298, 0.5969832818139151, nan, nan, nan, 0.9692282400765184, 0.0, nan, 0.6315281630683874, nan, 0.3691125789775991, 0.0, nan, 0.8266466423514267, nan, 0.0, 0.37401377582968065, 0.030381622823267878, 0.873219227221482, nan, nan, 0.8236315241291517, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9409427323465178, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.8364979985630709, 0.17000826787928897, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6548286604361371, nan, nan, 0.0, 0.6974592833876222, nan, nan, 0.7401727861771058, 0.0, 0.1858108108108108, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6719063150862578, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4889103182256509, 0.0, nan, nan, 0.0, nan, nan, 0.6768324509730818, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8471913622703184, 0.7340040418240927, 0.9896292315959162, 0.9127567200514343, 0.9399751898407417, 0.9072854278990424, 0.8365813941272259, 0.9269279872443149, 0.40157437665878276, 0.819516708192877, 0.0, 0.915308549255226, 0.9825688213859203, nan, 0.8057500694080266, 0.7619056531381888, nan, 0.5606904083317795, 0.49551877714851267, 0.6083346207322725, 0.03216650898770104, nan, 0.9883736035333853, nan, 0.0, nan, 0.9701936496859734, 0.7837292312922279, nan, nan, nan, 0.9899974430701477, nan, nan, 0.7462941847206386, nan, 0.466473096815171, nan, nan, 0.8421857923497268, nan, 0.0, 0.40170825206806104, 0.046538024971623154, 0.9058690104905018, nan, nan, 0.9843706777316735, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9584440462213434, nan, nan, nan, nan, nan, nan, nan, nan, 0.8430743767456295, 0.6433320297223308, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.660673874779985, nan, nan, nan, 0.7451280623608018, nan, nan, 0.8689148073022313, 0.0, 0.21484375, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7539133118744212, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5559972583961618, 0.0, nan, nan, 0.0, nan, nan, 0.6768324509730818, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 22.0 | 748 | 0.5473 | 0.3773 | 0.5766 | 0.8328 | [0.7473139940205169, 0.35683189253063, 0.9768844948155293, 0.7648903206013192, 0.7554404790907701, 0.7979772268322897, 0.8497101083942525, 0.8968153135984416, 0.4135912345239508, 0.7732394108163564, 0.0, 0.78125, 0.9284959773108757, nan, 0.3891674127126231, 0.6498798428214204, 0.0, 0.47185592185592184, 0.33464933517076983, 0.36563731208592876, 0.0734375, nan, 0.6719355269602386, nan, 0.0, nan, 0.9432825959901405, 0.6465927633017646, nan, nan, nan, 0.967568440543393, 0.0, nan, 0.6171772559303851, nan, 0.3764842166232262, nan, nan, 0.695769625587552, nan, 0.0, 0.18572927597061908, 0.025213867627194957, 0.8690606226105954, nan, nan, 0.8061183949145808, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9408615088437011, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.8257257154579957, 0.19798319327731093, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6848341232227488, nan, nan, 0.0, 0.6002094514988873, nan, nan, 0.7457111834961998, 0.0, 0.2006651884700665, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7120563511830635, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3993153197212373, 0.0, nan, nan, 0.0, nan, nan, 0.7065095816866751, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8325006003604494, 0.7057815657675073, 0.9894826828195985, 0.9005193998367395, 0.9560946227923619, 0.8782856644793365, 0.8853285706781531, 0.9444782043920144, 0.47195190207088306, 0.7732394108163564, 0.0, 0.8724496182250595, 0.9824310255075481, nan, 0.8045778449578924, 0.7490759023404235, nan, 0.6176118051301175, 0.49403420025292794, 0.6415881353313765, 0.08893093661305582, nan, 0.9801896596518577, nan, 0.0, nan, 0.9714148639218423, 0.816682955849233, nan, nan, nan, 0.9831509771669981, nan, nan, 0.7379561003420753, nan, 0.471826513020597, nan, nan, 0.7037158469945355, nan, 0.0, 0.19641536081780886, 0.03178206583427923, 0.9023249220300539, nan, nan, 0.9822268326417704, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9663844775381574, nan, nan, nan, nan, nan, nan, nan, nan, 0.832729905865315, 0.691044192412984, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6903444807643953, nan, nan, nan, 0.6382238307349666, nan, nan, 0.8706896551724138, 0.0, 0.23567708333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.8067816041271662, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4477039067854695, 0.0, nan, nan, 0.0, nan, nan, 0.7065095816866751, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 23.0 | 782 | 0.5210 | 0.3677 | 0.5764 | 0.8398 | [0.7645540256237217, 0.444285995789299, 0.9764792585884665, 0.7639629688396333, 0.7622751809160125, 0.7950064685913951, 0.8044631598981753, 0.8923875250237067, 0.4247083857540034, 0.7966253847281255, 0.0, 0.7440755106158778, 0.932058138437983, nan, 0.37510042507194086, 0.6496730849488147, 0.0, 0.44380617263015093, 0.33035413784076884, 0.3448307986119198, 0.11672862453531599, nan, 0.6690663272024933, 0.0, 0.0, nan, 0.9434983409181097, 0.5804347113398682, nan, nan, nan, 0.9674860418930565, 0.0, nan, 0.627877002393666, nan, 0.3748114088655794, 0.0, nan, 0.7288318412253263, nan, 0.0, 0.3020661157024793, 0.09464450600184672, 0.88502027050046, nan, nan, 0.8176830676830676, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9425552987784748, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.8349095394736842, 0.14896415652745806, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.699328525242477, nan, nan, 0.0, 0.6518208016767094, nan, nan, 0.756802009208874, 0.0, 0.17684887459807075, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6281322957198444, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.38088235294117645, 0.0, nan, nan, 0.0, nan, nan, 0.6930131981209455, 0.0, nan, nan, nan, nan, 0.0, nan, nan] | [0.8544866160420975, 0.7528073104296634, 0.9902838161301353, 0.9043227936342818, 0.9275270868140457, 0.8952447360884561, 0.8881126227872038, 0.9443722786849602, 0.4936898550201311, 0.7966253847281255, 0.0, 0.8308424083114282, 0.9832960774106624, nan, 0.7921460961841009, 0.742946444446524, nan, 0.5576805263298085, 0.4857315665035465, 0.5718368608064267, 0.14853358561967833, nan, 0.9899974019225773, nan, 0.0, nan, 0.9747644801116538, 0.7523477669655377, nan, nan, nan, 0.9853453572641614, nan, nan, 0.7290478905359179, nan, 0.47336902277470283, nan, nan, 0.7384699453551913, nan, 0.0, 0.3195574685587464, 0.11634506242905789, 0.9206833002551744, nan, nan, 0.9791839557399723, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9703083482666996, nan, nan, nan, nan, nan, nan, nan, nan, 0.8401779248991414, 0.5314822057098162, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7070656273573045, nan, nan, nan, 0.6926503340757239, nan, nan, 0.9168356997971603, 0.0, 0.21484375, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7118038714229022, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.42604523646333103, 0.0, nan, nan, 0.0, nan, nan, 0.6930131981209455, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 24.0 | 816 | 0.5798 | 0.3706 | 0.5732 | 0.8269 | [0.7301437017399903, 0.326717478546389, 0.9764583293132393, 0.7640735951904529, 0.756272853360232, 0.7751990634904858, 0.7999854287587731, 0.8967911497498704, 0.38311354157866034, 0.8484812399237872, 0.0, 0.7406585194302796, 0.9367348134262785, nan, 0.3782360176542942, 0.641366392430102, 0.0, 0.5169385975837588, 0.3375432324252905, 0.36555527236497604, 0.06175771971496437, nan, 0.6742710043939462, 0.0, 0.0, nan, 0.9435144329549665, 0.6270374093487471, nan, nan, nan, 0.9682299602641092, 0.0, nan, 0.5969538257780989, nan, 0.36208490364787027, 0.0, nan, 0.8135967273118743, nan, 0.0, 0.07421259842519685, 0.09460547504025765, 0.837611919418019, nan, nan, 0.8413177194227585, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9420640440264885, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.8870189840944074, 0.22518976665729548, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6990049751243781, nan, nan, 0.0, 0.5976417594064652, nan, nan, 0.7592553416543262, 0.0, 0.18383167220376523, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6292103700771818, 0.08846153846153847, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3950191098508199, 0.0, nan, nan, 0.0, nan, nan, 0.612631421967042, nan, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8090993660659953, 0.7239170547403567, 0.9887743637340628, 0.9199445202956028, 0.9461992413540897, 0.8740209933882034, 0.8651835898513421, 0.945442685829928, 0.45029519562352177, 0.8484812399237872, 0.0, 0.843584929277757, 0.9793306182441743, nan, 0.8063053336212481, 0.7433862681427275, nan, 0.6633193937618198, 0.4775938857425634, 0.6093387919048354, 0.07379375591296121, nan, 0.9867498051441933, nan, 0.0, nan, 0.9773900907187718, 0.7421705689882293, nan, nan, nan, 0.9894211797750665, nan, nan, 0.7066704675028507, nan, 0.4620270392886308, nan, nan, 0.8259562841530055, nan, 0.0, 0.07606429484161678, 0.1333711691259932, 0.848773745392685, nan, nan, 0.9837482710926694, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9625841932892542, nan, nan, nan, nan, nan, nan, nan, nan, 0.894176062894383, 0.6265154477903794, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7065627357304501, nan, nan, nan, 0.6279231625835189, nan, nan, 0.9099898580121704, 0.0, 0.21614583333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7009568323118304, 0.08846153846153847, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4392049348869088, 0.0, nan, nan, 0.0, nan, nan, 0.612631421967042, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 25.0 | 850 | 0.5358 | 0.3695 | 0.5795 | 0.8372 | [0.7533446421209327, 0.37114531412608864, 0.9760813244599397, 0.7677558195926285, 0.7551882177249086, 0.7953345237642947, 0.8152322659301834, 0.9034853828503442, 0.428107476635514, 0.7995383262494504, 0.0, 0.7476143683808276, 0.9339172090449773, nan, 0.37406309388065373, 0.6553864752056328, 0.0, 0.4985895480285016, 0.329821176819767, 0.36046735726331003, 0.1287758346581876, nan, 0.6789297658862876, nan, 0.0, nan, 0.941762871829443, 0.721043406921241, nan, nan, nan, 0.9695998620731012, 0.0, nan, 0.6118179593834108, nan, 0.3705562230546328, 0.0, nan, 0.747618532149816, nan, 0.0, 0.14469442906912383, 0.10771543086172344, 0.8735750938457829, nan, nan, 0.837568539590826, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9427348624129721, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.7962275819418676, 0.20830929024812464, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7246734039930983, nan, nan, 0.0, 0.5644397685428721, nan, nan, 0.7636594663278272, 0.0, 0.19811320754716982, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6417367942791672, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.47146461612399376, 0.0, nan, nan, 0.0, nan, nan, 0.6786965923495638, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan] | [0.8371461079156651, 0.7144539144187682, 0.9901568071906599, 0.9149022242449197, 0.9464071877756186, 0.9123158720474157, 0.8605610127646163, 0.9483194050320286, 0.4962265513568166, 0.7995383262494504, 0.0, 0.8571035173363375, 0.9820329485255841, nan, 0.7589844834500417, 0.7508539130272036, nan, 0.635564552646299, 0.4888106889536482, 0.5779005098099799, 0.1532639545884579, nan, 0.9888932190179267, nan, 0.0, nan, 0.9748691556175855, 0.821633450898738, nan, nan, nan, 0.9872687791232402, nan, nan, 0.7127993158494869, nan, 0.47572815533980584, nan, nan, 0.7548087431693989, nan, 0.0, 0.14943842894612952, 0.12202043132803632, 0.8989934788772328, nan, nan, 0.9824343015214384, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9664153741580671, nan, nan, nan, nan, nan, nan, nan, nan, 0.799110375504293, 0.7059053578412202, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7392506914759869, nan, nan, nan, 0.5974387527839644, nan, nan, 0.9143002028397565, 0.0, 0.24609375, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7162132369152079, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5379026730637423, 0.0, nan, nan, 0.0, nan, nan, 0.6786965923495638, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 26.0 | 884 | 0.5314 | 0.3653 | 0.5797 | 0.8391 | [0.756887546060873, 0.3949414217181142, 0.9787283528283054, 0.7750152518003256, 0.7585221605510508, 0.798722081117097, 0.7957645087824926, 0.9048001531963786, 0.43912106850495475, 0.8104023156969076, 0.0, 0.772327147993553, 0.9351867395572554, nan, 0.3680406852248394, 0.643487664210189, 0.0, 0.44875522635142306, 0.3453853853853854, 0.3673037040340744, 0.04372019077901431, nan, 0.6725448598956952, 0.0, 0.0, nan, 0.9417955145784612, 0.699579907364188, nan, nan, nan, 0.9662647439383427, 0.0, nan, 0.5978764949963388, nan, 0.3670201559098251, 0.0, nan, 0.8540450821866467, nan, 0.0, 0.21688699631371552, 0.04339250493096647, 0.861687749090279, nan, nan, 0.8425865447419987, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9406541632690043, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.839497270011332, 0.17036184898556786, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6988086373790022, nan, nan, 0.0, 0.6829873351612482, nan, nan, 0.7529082292115468, 0.0, 0.17524644030668127, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6417129389206248, 0.019230769230769232, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4377756001959824, 0.0, nan, nan, 0.0, nan, nan, 0.7147118037431959, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.843101916723594, 0.7624198225112029, 0.9867031410287724, 0.9131143040836819, 0.9270179766096128, 0.87319296235883, 0.7984188685191994, 0.9482971048831751, 0.5060393233069131, 0.8104023156969076, 0.0, 0.9356865690324195, 0.9808693388859969, nan, 0.7634882931795046, 0.7517522763215766, nan, 0.5631942039794369, 0.47429482597459727, 0.6361424378186312, 0.05203405865657521, nan, 0.9883736035333853, nan, 0.0, nan, 0.9765788555478018, 0.8279437385798666, nan, nan, nan, 0.9832349360576721, nan, nan, 0.6982611174458381, nan, 0.47418564558570003, nan, nan, 0.8716393442622951, nan, 0.0, 0.2295043378841886, 0.06242905788876277, 0.8812021548057839, nan, nan, 0.9813278008298755, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9623061237100661, nan, nan, nan, nan, nan, nan, nan, nan, 0.8429709320368263, 0.6370746969104419, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7079456877042998, nan, nan, nan, 0.7281458797327395, nan, nan, 0.8861561866125761, 0.0, 0.20833333333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7083204726839808, 0.019230769230769232, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.48992460589444825, 0.0, nan, nan, 0.0, nan, nan, 0.7147118037431959, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 27.0 | 918 | 0.5512 | 0.3625 | 0.5775 | 0.8334 | [0.744959773180659, 0.3650637669716267, 0.9777352860226268, 0.7675059367959569, 0.7572316275602612, 0.7792693859255939, 0.8185323223236088, 0.9021861224143778, 0.39713878312101414, 0.8107229224681225, 0.0, 0.7657166088928832, 0.933374199103872, nan, 0.3857845997337043, 0.6577769428062666, 0.0, 0.439374640479908, 0.3264279155188246, 0.3580170631431355, 0.0468384074941452, nan, 0.6862104929034598, 0.0, 0.0, nan, 0.9401164660674082, 0.662052565262915, nan, nan, nan, 0.9658095623743963, 0.0, nan, 0.614549276039234, nan, 0.3644472538277848, 0.0, nan, 0.8216018109302576, nan, 0.0, 0.27518396743385, 0.07496251874062969, 0.8826256963908786, nan, nan, 0.8224103932258439, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9411764705882353, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.803155289750464, 0.17476417486560503, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7401584550631345, nan, nan, 0.0, 0.5228526398739165, nan, nan, 0.7465156794425087, 0.0, 0.19094922737306844, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6435878194241479, 0.019230769230769232, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.48518017464026564, 0.0, nan, nan, 0.0, nan, nan, 0.6776899560062635, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8319118973954849, 0.7475002196643529, 0.9891456206340677, 0.9105570364591234, 0.9323528779067684, 0.8659087796192303, 0.8363975416294585, 0.9492894615071555, 0.45759835340422844, 0.8107229224681225, 0.0, 0.8958568031042684, 0.9791468904063447, nan, 0.7954776814634297, 0.753586434714255, nan, 0.5696667820898703, 0.4886457359652499, 0.5818399505638807, 0.05676442762535478, nan, 0.9829176409457002, nan, 0.0, nan, 0.9688677599441731, 0.7926316236773892, nan, nan, nan, 0.9830555693366866, nan, nan, 0.7501425313568986, nan, 0.47082841847382273, nan, nan, 0.8330054644808743, nan, 0.0, 0.295514156970879, 0.0851305334846765, 0.9039906436064644, nan, nan, 0.9806362378976486, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9634801952666379, nan, nan, nan, nan, nan, nan, nan, nan, 0.8057308368676942, 0.6738365271802894, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7516972592406337, nan, nan, nan, 0.5541481069042317, nan, nan, 0.8691683569979716, 0.0, 0.22526041666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7185061069712069, 0.019230769230769232, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5407813570939, 0.0, nan, nan, 0.0, nan, nan, 0.6776899560062635, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 28.0 | 952 | 0.5983 | 0.3546 | 0.5669 | 0.8185 | [0.7133293615623796, 0.2995819754645191, 0.9783503413669151, 0.7667038861388398, 0.7560280423467762, 0.7491560201981992, 0.794157444546465, 0.9017132551848512, 0.4385003204113619, 0.7827934925985637, 0.0, 0.7417620837339394, 0.9349885944902615, nan, 0.39395382442773164, 0.6527583855570348, 0.0, 0.48613638587222385, 0.2658502744766846, 0.3614669445680429, 0.02511773940345369, nan, 0.6767613788189186, 0.0, 0.0, nan, 0.9412580904437582, 0.6835592114799246, nan, nan, nan, 0.9657426925634927, 0.0, nan, 0.5602128352698447, nan, 0.3694390715667311, 0.0, nan, 0.798361009273237, nan, 0.0, 0.05245274304753736, 0.04808510638297872, 0.8598006644518272, nan, nan, 0.8700467634752646, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9414430746918057, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.7959141560049525, 0.2102496714848883, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7012089810017271, nan, nan, 0.0, 0.5968166272033676, nan, nan, 0.7282489590181898, 0.0, 0.20021186440677965, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6276001426363961, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4288494283629287, 0.0, nan, nan, 0.0, nan, nan, 0.6591603907240325, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.7900160640135786, 0.7713821281082506, 0.9905134092130331, 0.9102175122264843, 0.9186570963508989, 0.813749050565925, 0.8632400063035142, 0.9477563262734778, 0.5127105638507231, 0.7827934925985637, 0.0, 0.8498185004381024, 0.9789937838748201, nan, 0.7400746521886664, 0.7524915544492378, nan, 0.6617478624510561, 0.48463187991422446, 0.5961300787888151, 0.030274361400189215, nan, 0.987009612886464, nan, 0.0, nan, 0.9704466154919749, 0.8015765095822887, nan, nan, nan, 0.9808535566130984, nan, nan, 0.7353192702394526, nan, 0.4679248707013883, nan, nan, 0.8091803278688524, nan, 0.0, 0.05346694464994283, 0.0641316685584563, 0.8713495888857385, nan, nan, 0.9778699861687413, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9626768831489835, nan, nan, nan, nan, nan, nan, nan, nan, 0.7979724837074583, 0.6257332811888933, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7146090017601207, nan, nan, nan, 0.6315423162583519, nan, nan, 0.8425456389452333, 0.0, 0.24609375, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6984434939812161, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.48334475668265936, 0.0, nan, nan, 0.0, nan, nan, 0.6591603907240325, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.3613 | 29.0 | 986 | 0.5557 | 0.3665 | 0.5752 | 0.8321 | [0.7445816882065228, 0.3509452061853627, 0.9797139267420508, 0.7710544579651136, 0.755619737556615, 0.7673416922244272, 0.8439727381509807, 0.9046401130064422, 0.4478948061301139, 0.7970139683993588, 0.0, 0.7993612016315256, 0.9423594172568035, nan, 0.3720868133335311, 0.6581388512354853, 0.0, 0.45455455455455457, 0.31718242562245696, 0.36743259738312956, 0.08009331259720062, nan, 0.6806083650190115, 0.0, 0.0, nan, 0.9424475678608365, 0.686343208291296, nan, nan, nan, 0.9651833384646576, 0.0, nan, 0.6069020230067433, nan, 0.3770288958439479, 0.0, nan, 0.822762395363812, nan, 0.0, 0.06598134524435102, 0.051366480079025355, 0.8724319487938332, nan, nan, 0.8717444717444718, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9400496307953032, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.7788778877887789, 0.16674142184346266, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7202461538461539, nan, nan, 0.0, 0.5859548361832659, nan, nan, 0.7335814360770578, 0.0, 0.16988062442607896, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7188326848249027, 0.016666666666666666, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.36924210784915856, 0.0, nan, nan, 0.0, nan, nan, 0.6927149355007084, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8285766910929824, 0.7779105526755118, 0.9903864002735577, 0.9028888455453699, 0.9079801231903284, 0.8473808071123508, 0.8521300625098492, 0.9425715416650406, 0.5221261306804846, 0.7970650740143632, 0.0, 0.9272750031293028, 0.9789708178950914, nan, 0.773729833112256, 0.7542227753811026, nan, 0.6047731934048958, 0.46720184747347004, 0.6626757299551985, 0.09744560075685904, nan, 0.9882436996622499, nan, 0.0, nan, 0.9697575017445917, 0.7949687672629924, nan, nan, nan, 0.9810405559605088, nan, nan, 0.7632554161915621, nan, 0.4700117956628255, nan, nan, 0.8378688524590164, nan, 0.0, 0.06755666151052525, 0.08853575482406356, 0.89849730649277, nan, nan, 0.9814661134163208, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9597417042575542, nan, nan, nan, nan, nan, nan, nan, nan, 0.781214440881349, 0.5815408682049277, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7357304500880061, nan, nan, nan, 0.6248608017817372, nan, nan, 0.8496450304259635, 0.0, 0.24088541666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.8145861810485471, 0.016666666666666666, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.41206305688827966, 0.0, nan, nan, 0.0, nan, nan, 0.6927149355007084, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 30.0 | 1020 | 0.5677 | 0.3714 | 0.5738 | 0.8328 | [0.7435101509500087, 0.35156474789702624, 0.9794402203110102, 0.7739771613801774, 0.7535075542910897, 0.7956999570786365, 0.8278462112728631, 0.9109300380433316, 0.39728682170542634, 0.8509443640427208, 0.0, 0.7722293387517116, 0.9387464912216075, nan, 0.3714189859385028, 0.6526396514729997, 0.0, 0.4498443266871482, 0.36750693000693, 0.3636402405219839, 0.043510324483775814, nan, 0.6846295713193289, 0.0, 0.0, nan, 0.9438291339446137, 0.6806404154045868, nan, nan, nan, 0.962458724153234, 0.0, nan, 0.5881020159182013, nan, 0.36589558117882975, 0.0, nan, 0.8813128639491795, nan, 0.0, 0.1664305036712611, 0.15637065637065636, 0.8687887929539859, nan, nan, 0.8240756863427942, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9379548872180451, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.7910309278350516, 0.15754312493768072, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6978739276389407, nan, nan, 0.0, 0.5606160326444649, nan, nan, 0.7344434706397897, 0.0, 0.20270270270270271, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.5907323777734145, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.38959764474975467, 0.0, nan, nan, 0.0, nan, nan, 0.6129296845872791, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan] | [0.8309734698968779, 0.7300412968983393, 0.9885643104880074, 0.907270152930383, 0.9419614366946558, 0.87717124677815, 0.8737458633188002, 0.9411164569523501, 0.45895246176900717, 0.8509911329327275, 0.0, 0.8753285767930905, 0.9754187463637198, nan, 0.7854829256254434, 0.7485986468402879, nan, 0.5811203153717072, 0.46654203551987683, 0.6586590452649467, 0.05581835383159887, nan, 0.9885684593400883, nan, 0.0, nan, 0.9760118632240056, 0.8355075850932733, nan, nan, nan, 0.9766403468265448, nan, nan, 0.7214224629418472, nan, 0.4635695490427366, nan, nan, 0.9097267759562842, nan, 0.0, 0.1737843836169211, 0.18388195232690124, 0.8879713637652396, nan, nan, 0.9818810511756569, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9635728851263672, nan, nan, nan, nan, nan, nan, nan, nan, 0.7937312506465294, 0.617911615174032, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7056826753834549, nan, nan, nan, 0.5928452115812918, nan, nan, 0.8498985801217038, 0.0, 0.234375, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.66330085100754, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4353666895133653, 0.0, nan, nan, 0.0, nan, nan, 0.6129296845872791, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 31.0 | 1054 | 0.5328 | 0.3716 | 0.5848 | 0.8424 | [0.7586327718479858, 0.44315120898009663, 0.9770444555829735, 0.7679931824247277, 0.7552970745535845, 0.8123256571861001, 0.7862095531587057, 0.9086916419095014, 0.42923641853479794, 0.8307104646050124, 0.0, 0.738391133667339, 0.9374413076652189, nan, 0.37263473053892215, 0.6455832403221533, 0.0, 0.4470311920557089, 0.3396118503831929, 0.34912762377114515, 0.03736263736263736, nan, 0.6655608904408555, 0.0, 0.0, nan, 0.9449749273102692, 0.6642222681264202, nan, nan, nan, 0.9614393759895898, 0.0, nan, 0.5921313842266739, nan, 0.3683577442864219, 0.0, nan, 0.8754246284501062, nan, 0.0, 0.46444306454951356, 0.16063879755753874, 0.8642223454394233, nan, nan, 0.8217775978719714, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9297310468764277, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.774615939787607, 0.1606467473524962, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.650629440358968, nan, nan, 0.0, 0.5701334024090143, nan, nan, 0.744165757906216, 0.0, 0.20504731861198738, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6126864187711275, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.463248265318123, 0.0, nan, nan, 0.0, nan, nan, 0.6972261576317947, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8455464912137539, 0.743019066865829, 0.9903033559669777, 0.9146746707698531, 0.9265805720677762, 0.915571963989989, 0.8576981667279509, 0.9446956308433359, 0.4927961434993771, 0.8307104646050124, 0.0, 0.8522969082488422, 0.9781746639311633, nan, 0.7678687108615849, 0.7523699010864581, nan, 0.5779506166262686, 0.48974542255457193, 0.608991194191256, 0.048249763481551564, nan, 0.9903871135359834, nan, 0.0, nan, 0.9780704815073273, 0.8198274763098627, nan, nan, nan, 0.9756023096327562, nan, nan, 0.7014680729760547, nan, 0.4723709282279285, nan, nan, 0.9012568306010929, nan, 0.0, 0.5281794337211648, 0.19409761634506242, 0.8838247802665155, nan, nan, 0.9827800829875518, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9430884261261818, nan, nan, nan, nan, nan, nan, nan, nan, 0.7771800972380263, 0.664450527962456, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6562735730450088, nan, nan, nan, 0.612750556792873, nan, nan, 0.8651115618661258, 0.0, 0.25390625, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6793068477446096, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5399588759424263, 0.0, nan, nan, 0.0, nan, nan, 0.6972261576317947, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 32.0 | 1088 | 0.5631 | 0.3700 | 0.5815 | 0.8368 | [0.7439442226515882, 0.37374682097969114, 0.9699066992964209, 0.7719869305221626, 0.7544786165475821, 0.7984140210691286, 0.8360605519107549, 0.9107948109679211, 0.41924565567112115, 0.810228272021105, 0.0, 0.7737618014939431, 0.9304021066529319, nan, 0.3350183956661549, 0.6586711050192546, 0.0, 0.4359504946592566, 0.3875546343441626, 0.3662605353945587, 0.023718439173680182, nan, 0.6730420954989178, 0.0, 0.0, nan, 0.9449272424772184, 0.7014508928571429, nan, nan, nan, 0.9627400002257498, 0.0, nan, 0.5783832571783155, nan, 0.3705258663630542, 0.0, nan, 0.8481263383297645, nan, 0.0, 0.35036268134067033, 0.10863661053775123, 0.8820926243567753, nan, nan, 0.8161484221477285, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9393445098927654, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.7713490099009901, 0.1752778319360499, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6409521435692921, nan, nan, 0.0, 0.5466129241971135, nan, nan, 0.7388437019125083, 0.0, 0.20962566844919786, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6996247284218843, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.47431810131066243, 0.0, nan, nan, 0.0, nan, nan, 0.6879800164044441, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.833521795998685, 0.7127756787628504, 0.9912559230130429, 0.9046081384680955, 0.9404054238163188, 0.9007670182166826, 0.8601933077690813, 0.9503654436893366, 0.4752288443136476, 0.810228272021105, 0.0, 0.8842908999874828, 0.9818339100346021, nan, 0.7134836659777277, 0.7554860987638147, nan, 0.5892176970407267, 0.4729202177379447, 0.6478448941758072, 0.0293282876064333, nan, 0.9896726422447389, nan, 0.0, nan, 0.975976971388695, 0.8546296689754812, nan, nan, nan, 0.9765105921773212, nan, nan, 0.7306870011402509, nan, 0.4724616640958171, nan, nan, 0.8657377049180328, nan, 0.0, 0.3768242652498487, 0.11350737797956867, 0.9112914658349872, nan, nan, 0.9765560165975103, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9607921893344868, nan, nan, nan, nan, nan, nan, nan, nan, 0.7736629771387193, 0.7031677747360188, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6465929092280613, nan, nan, nan, 0.5851893095768375, nan, nan, 0.8521805273833671, 0.0, 0.2552083333333333, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7809427223422549, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5506511309115832, 0.0, nan, nan, 0.0, nan, nan, 0.6879800164044441, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 33.0 | 1122 | 0.5596 | 0.3661 | 0.5853 | 0.8369 | [0.7480882694833144, 0.40236570818803713, 0.9691074092491297, 0.7707188910774803, 0.7550448034996119, 0.793406087699691, 0.7647780230561687, 0.9124060534105499, 0.40209828736934894, 0.8317089256925106, 0.0, 0.6895718543943636, 0.9353101304160616, 0.0, 0.37284277853006426, 0.6540447917750963, 0.0, 0.45335692724055376, 0.39416651616398773, 0.35773992409181277, 0.18698060941828254, nan, 0.675119702074836, 0.0, 0.0, nan, 0.9437820719088184, 0.7034876548649173, nan, nan, nan, 0.9602194373902625, 0.0, 0.0, 0.60484102799977, nan, 0.36822060794132166, 0.0, nan, 0.8568220701454234, nan, 0.0, 0.42320025889205964, 0.11738918655625913, 0.8872873830179657, nan, nan, 0.8174235403151066, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.936238269108905, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.7618654560462237, 0.15983813859382903, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6969133506879881, nan, nan, 0.0, 0.6056084383843581, nan, nan, 0.7469618055555556, 0.0, 0.19458896982310095, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5915273844190035, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.39724356628857177, 0.0, nan, nan, 0.0, nan, nan, 0.6893221981955111, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan] | [0.8327465732824445, 0.7421404094543538, 0.9900981876801329, 0.9101597208677372, 0.9208082662287841, 0.901632404029336, 0.9008247097757, 0.9468531702449113, 0.46120028165453986, 0.8317089256925106, 0.0, 0.76447615471273, 0.9810989986832839, nan, 0.7984082425887652, 0.7643855101486978, nan, 0.5800548704152572, 0.48001319623907185, 0.6115788660590143, 0.2554399243140965, nan, 0.9890880748246298, nan, 0.0, nan, 0.9722173761339846, 0.8481281604555305, nan, nan, nan, 0.9725797895684895, nan, nan, 0.749714937286203, nan, 0.47373196624625713, nan, nan, 0.8757377049180328, nan, 0.0, 0.48372452754052053, 0.13677639046538026, 0.9206833002551744, nan, nan, 0.9759336099585062, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9585985293208923, nan, nan, nan, nan, nan, nan, nan, nan, 0.7638357298024206, 0.617911615174032, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7068141815438773, nan, nan, nan, 0.6553452115812918, nan, nan, 0.8727180527383367, 0.0, 0.24348958333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6538648088540059, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4464701850582591, 0.0, nan, nan, 0.0, nan, nan, 0.6893221981955111, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 34.0 | 1156 | 0.6001 | 0.3665 | 0.5761 | 0.8241 | [0.722570590450061, 0.3123139037045792, 0.9703797310816568, 0.7726955676927963, 0.7573571840738603, 0.7913282948312177, 0.81723031476011, 0.9070026094024041, 0.42527480974709814, 0.8148992882855651, 0.0, 0.7690172365561317, 0.9334694175111176, nan, 0.37332155221113317, 0.6558654300663396, 0.0, 0.4538564609329202, 0.3346474776085126, 0.37090508869610256, 0.020537124802527645, nan, 0.6754370396663413, 0.0, 0.0, nan, 0.9422635135135136, 0.6488124850293885, nan, nan, nan, 0.9594961393522544, 0.0, nan, 0.576103543774374, nan, 0.37270378874856486, 0.0, nan, 0.8662787594085304, nan, 0.0, 0.16211240310077518, 0.04574881312041433, 0.8776873019658614, nan, nan, 0.8732672047316863, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9358478011009619, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8910962429233145, 0.15966468466712155, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6442727159416677, nan, nan, 0.0, 0.5686451612903226, nan, nan, 0.7518486298390604, 0.0, 0.2045929018789144, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.6266092709952569, 0.16153846153846155, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4083323328130628, 0.0, nan, nan, 0.0, nan, nan, 0.635821340690478, nan, nan, nan, nan, 0.0, nan, 0.0, nan] | [0.8031610434847485, 0.719787364906423, 0.9902935860485564, 0.8962897947684373, 0.9411368215747997, 0.8748054438370834, 0.8428586436938593, 0.9456601122812495, 0.4994312744867929, 0.8149366114612341, 0.0, 0.8946551508323946, 0.9802186361270172, nan, 0.7950458092975907, 0.7530904633121531, nan, 0.5774178941480436, 0.4807279925221312, 0.6637957670322879, 0.02459791863765374, nan, 0.9887633151467914, nan, 0.0, nan, 0.9731681786461968, 0.7481621552713211, nan, nan, nan, 0.9726637484591636, nan, nan, 0.7264110604332953, nan, 0.4712820978132656, nan, nan, 0.886775956284153, nan, 0.0, 0.16877395924406483, 0.060158910329171394, 0.9129926282960023, nan, nan, 0.9802213001383125, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9559723166285609, nan, nan, nan, nan, nan, nan, nan, nan, 0.8955208441088238, 0.6405944466171295, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6498617048026151, nan, nan, nan, 0.6134465478841871, nan, nan, 0.8765212981744422, 0.0, 0.2552083333333333, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6931963490453724, 0.16153846153846155, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.46620973269362576, 0.0, nan, nan, 0.0, nan, nan, 0.635821340690478, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 35.0 | 1190 | 0.5857 | 0.3646 | 0.5730 | 0.8275 | [0.7302123470915852, 0.3294754946874347, 0.970552593891148, 0.7629772123719821, 0.756425899047608, 0.7841679642372796, 0.8228240901643414, 0.9047783418654545, 0.4230434917579859, 0.8264967756119009, 0.0, 0.7807849334913737, 0.9311266045481201, nan, 0.37111396099588734, 0.6562265749763713, 0.0, 0.4443785530244162, 0.33229299117635724, 0.35496854182087345, 0.055841293166789124, nan, 0.6763321492007105, 0.0, 0.0, nan, 0.9425807431919097, 0.6596527289446639, nan, nan, nan, 0.96366677087767, 0.0, nan, 0.5970411087070931, nan, 0.36722210504113056, 0.0, nan, 0.8276883312691832, nan, 0.0, 0.2153509899717151, 0.0649895178197065, 0.8782494469026548, nan, nan, 0.8604354946321344, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9385793074349779, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8099412189336909, 0.16118935837245696, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6658394341729743, nan, nan, 0.0, 0.5202508492291612, nan, nan, 0.7471088806458651, 0.0, 0.1882229232386961, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6036389582590082, 0.028205128205128206, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4755490838490474, 0.0, nan, nan, 0.0, nan, nan, 0.6328759973156364, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan] | [0.8094444276058558, 0.7203936385203409, 0.9898441698011822, 0.9111313380866726, 0.9379889429868277, 0.8747743148510166, 0.870541576929138, 0.9498581153029196, 0.4920829797605936, 0.8264967756119009, 0.0, 0.8904994367254976, 0.9845515509691644, nan, 0.7766295462257458, 0.7536893721750686, nan, 0.5788296087153397, 0.474184857315665, 0.5926927236211957, 0.07190160832544938, nan, 0.9892829306313328, nan, 0.0, nan, 0.9740055826936497, 0.7773127098117537, nan, nan, nan, 0.9763693885884602, nan, nan, 0.7276225769669328, nan, 0.4739134379820343, nan, nan, 0.8399453551912568, nan, 0.0, 0.22530096173246353, 0.08796821793416572, 0.900411114261412, nan, nan, 0.9810511756569847, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9622134338503368, nan, nan, nan, nan, nan, nan, nan, nan, 0.8124547429398986, 0.6445052796245601, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6746291174251948, nan, nan, nan, 0.5542873051224945, nan, nan, 0.8681541582150102, 0.0, 0.23307291666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6714581771683055, 0.028205128205128206, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5372172721041809, 0.0, nan, nan, 0.0, nan, nan, 0.6328759973156364, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 36.0 | 1224 | 0.5466 | 0.3595 | 0.5800 | 0.8407 | [0.7564610642901737, 0.42503227194492255, 0.9681310121712868, 0.769081930990704, 0.7535924913077982, 0.791968647661996, 0.8372650340747397, 0.9095841643612345, 0.40550199532237774, 0.8238451603447802, 0.0, 0.7879358886253297, 0.9330271216097987, nan, 0.372102121247247, 0.6600450078520893, 0.0, 0.43787303309820946, 0.3551527212944536, 0.36080001918511234, 0.07773851590106007, nan, 0.6819037375638612, 0.0, 0.0, nan, 0.9434531585823255, 0.6737480241101982, nan, nan, nan, 0.9605874193353838, 0.0, nan, 0.590622607056606, nan, 0.36841339155749636, 0.0, nan, 0.8649502194625843, nan, 0.0, 0.4531300395836388, 0.11244315832988838, 0.8789135392908978, nan, nan, 0.8779105964387997, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9391741407331607, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8499381188118812, 0.16294050776809396, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.662031288800596, nan, nan, 0.0, 0.5072008379156847, nan, nan, 0.7455102934734997, 0.0, 0.18775933609958506, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5604024176872913, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.44452554744525546, 0.0, nan, nan, 0.0, nan, nan, 0.5564462008798747, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8489073439820568, 0.6943326596959846, 0.9896634263103903, 0.910221124186406, 0.9464932345707341, 0.9184296049109089, 0.8809161107317329, 0.945816213323224, 0.4742448589019084, 0.8238678000879378, 0.0, 0.8897984729002378, 0.9796827632666809, nan, 0.7922077922077922, 0.7630286072561552, nan, 0.5803745039021921, 0.4513663605872326, 0.5810675112003708, 0.10406811731315042, nan, 0.9883086515978177, nan, 0.0, nan, 0.9764131193300768, 0.8240980750435558, nan, nan, nan, 0.9725492590627898, nan, nan, 0.7145809578107184, nan, 0.4593049632519735, nan, nan, 0.8830054644808744, nan, 0.0, 0.5196718003900733, 0.1543700340522134, 0.9014034590303374, nan, nan, 0.9751728907330567, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9641290242847432, nan, nan, nan, nan, nan, nan, nan, nan, 0.8524878452467156, 0.6726632772780602, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6703545385969324, nan, nan, nan, 0.5392538975501113, nan, nan, 0.8630831643002028, 0.0, 0.23567708333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6214118788306363, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5008910212474298, 0.0, nan, nan, 0.0, nan, nan, 0.5564462008798747, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 37.0 | 1258 | 0.5698 | 0.3531 | 0.5793 | 0.8314 | [0.7357710224714739, 0.342209322957593, 0.9705206077909138, 0.7695889208027661, 0.753841628019658, 0.8007517540928833, 0.8573690932311622, 0.9039680426098535, 0.4127483738923474, 0.813370090855803, 0.0, 0.8029823668665236, 0.9342382776865535, nan, 0.3788405373831776, 0.6586318180007171, 0.0, 0.4428378518749351, 0.3467282215977519, 0.3476260762607626, 0.0640176600441501, nan, 0.67876523440975, 0.0, 0.0, nan, 0.9434496473502819, 0.6672242550967068, nan, nan, nan, 0.9644917419592761, 0.0, 0.0, 0.5902297832122394, nan, 0.3767569917403275, 0.0, nan, 0.7799762136447184, nan, 0.0, 0.2437205151090144, 0.11412213740458016, 0.8791603369700317, nan, nan, 0.8430791127980074, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9385399083674946, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8800741809190191, 0.15886854596532016, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7023956532477155, nan, nan, 0.0, 0.5402627813191102, nan, nan, 0.7346582223192837, 0.0, 0.18881856540084388, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.7021050560692504, 0.04230769230769231, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4555036531321116, 0.0, nan, nan, 0.0, nan, nan, 0.6048020281858176, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8173225556003721, 0.7117388630173096, 0.989702505984075, 0.9116550722753181, 0.9371141339031543, 0.8952634134800961, 0.8815989914377266, 0.9461953158537333, 0.480618195605467, 0.8134893008940349, 0.0, 0.9085993240705971, 0.9799889763297303, nan, 0.8002899713113489, 0.7734814385042251, nan, 0.5677756172921716, 0.4748996535987244, 0.5457670322879654, 0.08230842005676443, nan, 0.9911665367627955, nan, 0.0, nan, 0.9766399162595952, 0.8135809289083414, nan, nan, nan, 0.9781401579190407, nan, nan, 0.7120866590649944, nan, 0.471826513020597, nan, nan, 0.788415300546448, nan, 0.0, 0.2571121124487188, 0.16969353007945517, 0.9024666855684718, nan, nan, 0.9831258644536652, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9620280541308781, nan, nan, nan, nan, nan, nan, nan, nan, 0.8836247025964622, 0.6413766132186156, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7151118933869751, nan, nan, nan, 0.5780902004454342, nan, nan, 0.8529411764705882, 0.0, 0.23307291666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7868071784470215, 0.04230769230769231, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5213159698423577, 0.0, nan, nan, 0.0, nan, nan, 0.6048020281858176, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 38.0 | 1292 | 0.5808 | 0.3603 | 0.5734 | 0.8286 | [0.7321720298387965, 0.33804522436864465, 0.9732281800541777, 0.7681409210441725, 0.7567339822751707, 0.7896551916562966, 0.8592632747360541, 0.9053684828432681, 0.4014001258885119, 0.8287933466449284, 0.0, 0.8037588855388543, 0.9341470732439098, nan, 0.37209643359924915, 0.6573128424602017, 0.0, 0.4522593073411784, 0.33147156046962273, 0.3485141227343822, 0.04240555127216654, nan, 0.6885253310435215, 0.0, 0.0, nan, 0.9424933856861192, 0.6635297104208598, nan, nan, nan, 0.9627977592007263, 0.0, nan, 0.6011192009146158, nan, 0.3693700063635721, 0.0, nan, 0.7912058268141354, nan, 0.0, 0.22227509199340184, 0.08123370981754996, 0.8783625327631397, nan, nan, 0.8539766894136119, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9384545728946574, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8763778716390234, 0.16168958742632614, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.7025191405285256, nan, nan, 0.0, 0.5256360078277886, nan, nan, 0.7347969264544456, 0.0, 0.18873239436619718, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.6371842584527675, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4484348459111866, 0.0, nan, nan, 0.0, nan, nan, 0.5776974125717694, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8166056203064053, 0.7285036464282576, 0.9898392848419716, 0.9062913117916044, 0.9337152854960956, 0.8809938862671365, 0.8742711561695645, 0.9458440885092908, 0.47206023074006537, 0.828869265718892, 0.0, 0.9143071723619978, 0.9772330587622868, nan, 0.7827374525711818, 0.7588736770196798, nan, 0.575313640359055, 0.47968329026227524, 0.585200061795149, 0.05203405865657521, nan, 0.9895427383736035, nan, 0.0, nan, 0.9757240055826937, 0.7774826838907066, nan, nan, nan, 0.9753199024550343, nan, nan, 0.7119441277080958, nan, 0.4740041738499229, nan, nan, 0.8013661202185792, nan, 0.0, 0.2356244535611003, 0.1061293984108967, 0.9026438899914941, nan, nan, 0.977939142461964, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9606068096150281, nan, nan, nan, nan, nan, nan, nan, nan, 0.8800041377883522, 0.643723113023074, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7152376162936888, nan, nan, nan, 0.5608296213808464, nan, nan, 0.848630831643002, 0.0, 0.26171875, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7096432823316725, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.506648389307745, 0.0, nan, nan, 0.0, nan, nan, 0.5776974125717694, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 39.0 | 1326 | 0.5811 | 0.3505 | 0.5712 | 0.8282 | [0.7367668233298156, 0.3456847148077599, 0.9749309110133852, 0.7699124813708594, 0.7569719012142899, 0.7918225995643188, 0.840156427899609, 0.9085011495231607, 0.407621535165593, 0.834462862899533, 0.0, 0.7837614658423907, 0.9361188377503614, nan, 0.375778678890456, 0.6487177432280814, 0.0, 0.45469018132197575, 0.3417524771711677, 0.35617040930784966, 0.08082975679542204, nan, 0.6747190514113796, 0.0, 0.0, nan, 0.9420876101869174, 0.6792206186295465, nan, nan, nan, 0.9667105213697703, 0.0, nan, 0.588477366255144, nan, 0.3677270824612809, 0.0, nan, 0.7909877937301716, nan, 0.0, 0.18977914740626606, 0.0777736822705492, 0.8738120013874436, nan, nan, 0.8350691379817593, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9384624621018821, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8311875579359357, 0.16485347446829363, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.671774592189817, nan, nan, 0.0, 0.5998195643768527, nan, nan, 0.7245522062035824, 0.0, 0.1702127659574468, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.5600352677140109, 0.00641025641025641, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4419578745811393, 0.0, nan, nan, 0.0, nan, nan, 0.3905376183729774, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan] | [0.813034932817917, 0.7430717863105175, 0.990909090909091, 0.9068547775393884, 0.9252970407073047, 0.8825690129621098, 0.8915270263171718, 0.9517201777321863, 0.5015436835358478, 0.8346310274072989, 0.0, 0.8791588434096883, 0.9764292494717824, nan, 0.788999598975846, 0.757498058225171, nan, 0.5857816370561756, 0.4835871776543685, 0.6254441526340182, 0.10690633869441817, nan, 0.9905170174071187, nan, 0.0, nan, 0.9751395673412422, 0.8169379169676624, nan, nan, nan, 0.9814565341006667, nan, nan, 0.7133694412770809, nan, 0.47826875964068594, nan, nan, 0.8038251366120218, nan, 0.0, 0.19880287847198871, 0.11975028376844495, 0.8928267649560533, nan, nan, 0.9814661134163208, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9659210282395106, nan, nan, nan, nan, nan, nan, nan, nan, 0.8347988000413779, 0.6578021118498241, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6834297208951471, nan, nan, nan, 0.6478285077951003, nan, nan, 0.841024340770791, 0.0, 0.23958333333333334, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6161647338947925, 0.00641025641025641, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5062371487320082, 0.0, nan, nan, 0.0, nan, nan, 0.3905376183729774, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 40.0 | 1360 | 0.5604 | 0.3479 | 0.5732 | 0.8332 | [0.7488854499219946, 0.36746124058165985, 0.968911867535859, 0.7679598117471946, 0.751838329235539, 0.7907115751606747, 0.8374456091772152, 0.9123553791746012, 0.4095230908542107, 0.818426645170746, 0.0, 0.7835086074083998, 0.9328303262256242, nan, 0.34014359223448337, 0.656110062918546, 0.0, 0.4495555646581739, 0.30137359828215005, 0.35500429650400256, 0.06607929515418502, nan, 0.6778786260900516, 0.0, 0.0, nan, 0.9420699072005934, 0.6467024844048607, nan, nan, nan, 0.9651978965265605, 0.0, nan, 0.5946506938678686, nan, 0.36105668535909735, 0.0, nan, 0.7991918103448276, nan, 0.0, 0.2999266906958275, 0.1133428981348637, 0.8769657083477659, nan, nan, 0.8505176231224941, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9400655579947674, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8329554822753503, 0.16386475255005667, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6762421403032918, nan, nan, 0.0, 0.5401895857680821, nan, nan, 0.7396193771626297, 0.0, 0.19452590420332355, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.6072153864532314, 0.03974358974358974, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.36548163792091987, 0.0, nan, nan, 0.0, nan, nan, 0.5183431511445828, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8332210328997526, 0.6911958527370179, 0.9896096917590738, 0.9094228810437119, 0.9494331667371773, 0.8977039259877228, 0.8896622366969585, 0.9535655150498129, 0.48991640637694767, 0.818426645170746, 0.0, 0.8784829140067593, 0.9795832440211899, nan, 0.6912730974488694, 0.7582279784018491, nan, 0.5846629198519031, 0.4861714411392753, 0.6063262783871466, 0.08514664143803216, nan, 0.9896076903091712, nan, 0.0, nan, 0.9749651081646895, 0.7621212765053329, nan, nan, nan, 0.9792430724374411, nan, nan, 0.7145809578107184, nan, 0.48489247799655205, nan, nan, 0.8105464480874317, nan, 0.0, 0.33018360347030734, 0.1345062429057889, 0.8972923164162178, nan, nan, 0.9829183955739972, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9658283383797812, nan, nan, nan, nan, nan, nan, nan, nan, 0.8361435812558188, 0.6785295267892061, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6895901433241136, nan, nan, nan, 0.579064587973274, nan, nan, 0.8671399594320487, 0.0, 0.2591145833333333, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6723841439216897, 0.03974358974358974, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4270047978067169, 0.0, nan, nan, 0.0, nan, nan, 0.5183431511445828, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 41.0 | 1394 | 0.5768 | 0.3477 | 0.5656 | 0.8280 | [0.7407581790364577, 0.34156864866214265, 0.9678680542295207, 0.7706719125520799, 0.7569323007744192, 0.7947884032590958, 0.8511547899117149, 0.9117709971016352, 0.4006113375324194, 0.8272709785565764, 0.0, 0.7900239744272776, 0.9361745827984596, nan, 0.3694168415799439, 0.6591748718366018, 0.0, 0.45410191163172176, 0.338175570134333, 0.35557454079453227, 0.07872823618470856, nan, 0.679495183731716, 0.0, 0.0, nan, 0.9436883379711611, 0.6777748668238581, nan, nan, nan, 0.9619474358008886, 0.0, nan, 0.6040381043676233, nan, 0.3805309734513274, 0.0, nan, 0.7983428386957925, nan, 0.0, 0.17270124885966845, 0.07078313253012049, 0.8908632038470721, nan, nan, 0.8374088878438749, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9375451698385932, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8132989690721649, 0.16208393632416787, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6567738730907736, nan, nan, 0.0, 0.5449612403100775, nan, nan, 0.7471812662619254, 0.0, 0.1998001998001998, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.6382927802153969, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.34184226369656834, 0.0, nan, nan, 0.0, nan, nan, 0.24263664156289613, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8234928621222684, 0.7213338019506195, 0.9904205949880318, 0.8999523221290336, 0.9342530779655669, 0.8702730634657768, 0.883726427483322, 0.9505605699918047, 0.5075649520645639, 0.8272937124432068, 0.0, 0.8909500563274503, 0.9770033989649999, nan, 0.7763827621309807, 0.7580501773331711, nan, 0.5751538236155874, 0.4761642931764447, 0.6429785261856944, 0.09839167455061495, nan, 0.9896726422447389, nan, 0.0, nan, 0.9762037683182135, 0.8245017634810692, nan, nan, nan, 0.9749688016394882, nan, nan, 0.7185005701254276, nan, 0.47990200526268034, nan, nan, 0.8108196721311476, nan, 0.0, 0.1846122805837649, 0.08002270147559591, 0.9257513467536149, nan, nan, 0.9852005532503458, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9619353642711488, nan, nan, nan, nan, nan, nan, nan, nan, 0.8160753077480087, 0.6570199452483378, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6649484536082474, nan, nan, nan, 0.5871380846325167, nan, nan, 0.8737322515212982, 0.0, 0.2604166666666667, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7055866660787513, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.38917066483893076, 0.0, nan, nan, 0.0, nan, nan, 0.24263664156289613, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 42.0 | 1428 | 0.6262 | 0.3327 | 0.5654 | 0.8180 | [0.7233558876962749, 0.30030863528272295, 0.9679533380746228, 0.7692959322798958, 0.7562252421738977, 0.7546728163735947, 0.8546624948707428, 0.9117119439700084, 0.4014735986191254, 0.8230450036182433, 0.0, 0.8032913288685231, 0.9351320405736682, nan, 0.3669293140185045, 0.6314478027312148, 0.0, 0.454761161411177, 0.33677063441544663, 0.3351529036756649, 0.056327724945135334, nan, 0.6856062311467291, 0.0, 0.0, nan, 0.9432705169231811, 0.6681721284821331, nan, nan, nan, 0.9599073917217235, 0.0, 0.0, 0.6178261888029983, nan, 0.37990914419695193, 0.0, nan, 0.8288027451611174, nan, 0.0, 0.07807504078303426, 0.0605705353653771, 0.8799530985964066, nan, nan, 0.8452487074344803, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9397543322221221, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8379185986604842, 0.16487730061349692, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6898898651157035, nan, nan, 0.0, 0.6121794871794872, nan, nan, 0.7310887625710538, 0.0, 0.18515037593984962, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.64452131043257, 0.09358974358974359, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.31375900378464167, 0.0, nan, nan, 0.0, nan, nan, 0.281261650883603, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8034571435898991, 0.7301291626394869, 0.9906355331932979, 0.8987567633949534, 0.9281150732473343, 0.8149444036308849, 0.8752692125860168, 0.9463514168957078, 0.5081246501886725, 0.8230525428697054, 0.0, 0.9128301414444862, 0.9753498484245338, nan, 0.7915291359471882, 0.7304067901292333, nan, 0.581972671336867, 0.43635563864298677, 0.6497373706164066, 0.0728476821192053, nan, 0.9890880748246298, nan, 0.0, nan, 0.971920795533845, 0.7858326605192708, nan, nan, nan, 0.9731026244785962, nan, nan, 0.7518529076396807, nan, 0.4704654750022684, nan, nan, 0.8446994535519126, nan, 0.0, 0.08046943304862465, 0.08796821793416572, 0.9043096115679047, nan, nan, 0.983609958506224, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9667861335969845, nan, nan, nan, nan, nan, nan, nan, nan, 0.8412123719871728, 0.6726632772780602, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.700905204928338, nan, nan, nan, 0.6646714922048997, nan, nan, 0.847870182555781, 0.0, 0.2565104166666667, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7148022399576701, 0.09358974358974359, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3522960932145305, 0.0, nan, nan, 0.0, nan, nan, 0.281261650883603, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 43.0 | 1462 | 0.5845 | 0.3338 | 0.5650 | 0.8273 | [0.7450309438338649, 0.33854373026929613, 0.9645151000080939, 0.7702613303137625, 0.7549893842887474, 0.7839827923703737, 0.8258910792512943, 0.9114825628027848, 0.3980513614659956, 0.8121610728418585, 0.0, 0.7763036506611607, 0.9356477064220183, nan, 0.37141088930786836, 0.6395473828455145, 0.0, 0.45280103564195184, 0.33984686552879245, 0.3511148928742855, 0.05738880918220947, nan, 0.6908785318433724, 0.0, 0.0, nan, 0.9433042390854323, 0.6620647059892096, nan, nan, nan, 0.9633627985706226, 0.0, 0.0, 0.6091532639545885, nan, 0.3741762618582084, 0.0, nan, 0.8051514171785753, nan, 0.0, 0.17198374956697005, 0.10397700047915669, 0.879547570605883, nan, nan, 0.8400543189466848, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9330668734713357, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8637487126673532, 0.1586227786752827, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6996785361028685, nan, nan, 0.0, 0.5647741935483871, nan, nan, 0.7316593886462882, 0.0, 0.18942307692307692, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5860202213199586, 0.07948717948717948, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3288980283053103, 0.0, nan, nan, 0.0, nan, nan, 0.2892028931474163, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8327652252575721, 0.6982163254547052, 0.989595036881442, 0.9041747032774924, 0.9434457439103966, 0.8577529852697638, 0.8714871040605138, 0.9503208433916296, 0.5012096701392024, 0.8121610728418585, 0.0, 0.8847665540117662, 0.975916342591175, nan, 0.7960637936884968, 0.7293680575701145, nan, 0.5776309831393336, 0.46857645571012263, 0.6215433338482929, 0.07568590350047304, nan, 0.9878539880488438, nan, 0.0, nan, 0.9738398464759246, 0.774359410189946, nan, nan, nan, 0.9773959768426115, nan, nan, 0.7341790193842646, nan, 0.468832229380274, nan, nan, 0.8165027322404371, nan, 0.0, 0.18363709731656466, 0.123155505107832, 0.9039552027218599, nan, nan, 0.9839557399723374, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9665080640177964, nan, nan, nan, nan, nan, nan, nan, nan, 0.8675907727319747, 0.6143918654673446, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7114659290922806, nan, nan, nan, 0.6092706013363028, nan, nan, 0.8496450304259635, 0.0, 0.2565104166666667, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6491467877772389, 0.07948717948717948, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.37272104180945853, 0.0, nan, nan, 0.0, nan, nan, 0.2892028931474163, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2219 | 44.0 | 1496 | 0.5938 | 0.3523 | 0.5707 | 0.8257 | [0.7382453149834671, 0.3287607879094982, 0.9700375786878576, 0.768301160201282, 0.7586459486895534, 0.7796681536780772, 0.8368380855397148, 0.9094805984154573, 0.40568235766716576, 0.7865674923054375, 0.0, 0.7889003128212981, 0.9382093501911203, nan, 0.373183887712786, 0.6523822629474332, 0.0, 0.4452049180327869, 0.32824634979807393, 0.3625075239093118, 0.05522827687776141, nan, 0.682558868296177, 0.0, 0.0, nan, 0.9430427876166013, 0.6480473751600512, nan, nan, nan, 0.9589970101594331, 0.0, 0.0, 0.5845831880013952, nan, 0.3719300728997098, 0.0, nan, 0.7588012958963283, nan, 0.0, 0.13563812807252762, 0.11767554479418886, 0.8768403268627384, nan, nan, 0.8789907631268985, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9383615477629987, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8818322182192486, 0.15617782909930716, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6840148698884758, nan, nan, nan, 0.5685384515627027, nan, nan, 0.7476655808903366, 0.0, 0.20568927789934355, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.6899096326111834, 0.13333333333333333, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.36867239732569246, 0.0, nan, nan, 0.0, nan, nan, 0.4014987696666915, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8219377536960054, 0.718293647306915, 0.9898685945972351, 0.9022206329598567, 0.9431517506937522, 0.8530711857653372, 0.863345064873667, 0.9510121480060879, 0.5014173000884684, 0.7865674923054375, 0.0, 0.9028163725122043, 0.9770799522307622, nan, 0.7939352808711478, 0.7541947015281534, nan, 0.5786964280957835, 0.4647825369769616, 0.6280318245017766, 0.07095553453169347, nan, 0.9903221616004156, nan, 0.0, nan, 0.9735781577110956, 0.7742531763906004, nan, nan, nan, 0.9719272000091591, nan, nan, 0.7166476624857469, nan, 0.47681698575446874, nan, nan, 0.7679234972677595, nan, 0.0, 0.14288116214943844, 0.13791146424517595, 0.901297136376524, nan, nan, 0.980567081604426, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.959061978619539, nan, nan, nan, nan, nan, nan, nan, nan, 0.8862108203165409, 0.6347281971059836, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6939904450590898, nan, nan, nan, 0.6102449888641426, nan, nan, 0.8729716024340771, 0.0, 0.24479166666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.7708893690197981, 0.13333333333333333, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4233036326250857, 0.0, nan, nan, 0.0, nan, nan, 0.4014987696666915, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.1853 | 45.0 | 1530 | 0.5913 | 0.3422 | 0.5622 | 0.8255 | [0.7388017981371966, 0.325757885025188, 0.9718305123820868, 0.7658085852966707, 0.7560574691723165, 0.7813376211675575, 0.8261413696435723, 0.912060729218755, 0.40830462726852473, 0.790204089110362, 0.0, 0.7782451528097273, 0.9374898761577994, nan, 0.3775393879161673, 0.6518813117825419, 0.0, 0.44455599721345734, 0.32447418738049716, 0.3515283353143011, 0.043510324483775814, nan, 0.6896926350097324, 0.0, 0.0, nan, 0.9430608292670551, 0.6469379872182035, nan, nan, nan, 0.9625751208711761, 0.0, 0.0, 0.6134578772013327, nan, 0.37154402381965124, 0.0, nan, 0.7612254822499595, nan, 0.0, 0.12407799371432236, 0.12047569803516028, 0.8841945916190739, nan, nan, 0.8436980773795395, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9396572531981656, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8227286789728782, 0.15786259541984732, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6766385302879842, nan, nan, 0.0, 0.570707724155777, nan, nan, 0.7271528998242531, 0.0, 0.20088300220750552, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5682971301728238, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.34614460725438384, 0.0, nan, nan, 0.0, nan, nan, 0.45552158675713966, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.825747419615816, 0.7011422546349179, 0.9884177617116897, 0.9049584985804997, 0.9531618611921784, 0.8501014804945773, 0.8668907916163261, 0.9558457052700827, 0.4943488544243234, 0.790204089110362, 0.0, 0.8892977844536237, 0.9747374222984353, nan, 0.7968658419964834, 0.7423849673875408, nan, 0.5779239805023573, 0.46654203551987683, 0.6053993511509347, 0.05581835383159887, nan, 0.9896076903091712, nan, 0.0, nan, 0.9727494766224704, 0.7828793608974631, nan, nan, nan, 0.975575595440269, nan, nan, 0.7348204104903079, nan, 0.4755466836040287, nan, nan, 0.7698360655737705, nan, 0.0, 0.13010289864819424, 0.1322360953461975, 0.910830734335129, nan, nan, 0.9832641770401106, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9622443304702466, nan, nan, nan, nan, nan, nan, nan, nan, 0.8252818868314886, 0.6065701994524834, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6853155644958512, nan, nan, nan, 0.6140033407572383, nan, nan, 0.8392494929006086, 0.0, 0.23697916666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.632170730631862, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3950651130911583, 0.0, nan, nan, 0.0, nan, nan, 0.45552158675713966, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.1853 | 46.0 | 1564 | 0.5893 | 0.3466 | 0.5696 | 0.8286 | [0.7448710978705039, 0.34925767101756305, 0.9713872028689507, 0.7661046220692517, 0.7591134542679755, 0.7912380396208616, 0.8165654496231799, 0.9079819197022069, 0.41308629534628066, 0.7769309687820607, 0.0, 0.7699273361750999, 0.9345702695974344, nan, 0.37505096988407993, 0.6450669476917547, 0.0, 0.4478521229259367, 0.32960329486731166, 0.35151501757022563, 0.06177325581395349, nan, 0.6826328524061296, 0.0, 0.0, nan, 0.9432385482021183, 0.6426900888727197, nan, nan, nan, 0.9638803510765168, 0.0, nan, 0.5971623563218391, nan, 0.37462213905282854, 0.0, nan, 0.7851128144229731, nan, 0.0, 0.12604073267580376, 0.10691537761601456, 0.8841066410801178, nan, nan, 0.87268262737876, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9396463274790271, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8608390887537367, 0.15951755665791265, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6812887236679058, nan, nan, 0.0, 0.5443940375891121, nan, nan, 0.7385692408663312, 0.0, 0.1953781512605042, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5824840764331211, 0.10128205128205128, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3619850409592782, 0.0, nan, nan, 0.0, nan, nan, 0.5562225039146969, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8289439018533069, 0.7184869519374396, 0.9897415856577597, 0.9068800612588402, 0.9416100789479345, 0.8772770853307766, 0.87934023217944, 0.9519153040346544, 0.4884810515102822, 0.7769309687820607, 0.0, 0.8726999624483665, 0.9771105735370671, nan, 0.7944596970725237, 0.7389225255238113, nan, 0.5737154729243801, 0.4664320668609446, 0.6142824038312993, 0.0804162724692526, nan, 0.9895427383736035, nan, 0.0, nan, 0.9756891137473831, 0.7882122976246122, nan, nan, nan, 0.9777776081638572, nan, nan, 0.7108751425313569, nan, 0.47228019236003993, nan, nan, 0.7948087431693989, nan, 0.0, 0.13235590826551885, 0.1333711691259932, 0.914374822795577, nan, nan, 0.9831258644536652, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9620589507507878, nan, nan, nan, nan, nan, nan, nan, nan, 0.8638667632150615, 0.6413766132186156, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6912245411113905, nan, nan, nan, 0.5846325167037862, nan, nan, 0.8559837728194726, 0.0, 0.2421875, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6451783588341637, 0.10128205128205128, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4179575051405072, 0.0, nan, nan, 0.0, nan, nan, 0.5562225039146969, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.1853 | 47.0 | 1598 | 0.5806 | 0.3528 | 0.5702 | 0.8318 | [0.7477094022789365, 0.36590569464560774, 0.9677766048406743, 0.7650843606272518, 0.7554227762325012, 0.7826952526799388, 0.8411169956668272, 0.9117651790166817, 0.401527807307961, 0.7903781327861644, 0.0, 0.7931125362382501, 0.9348052214424895, nan, 0.36241960716148097, 0.650876033327621, 0.0, 0.4490809429607649, 0.32310689067445825, 0.35395927601809957, 0.08243727598566308, nan, 0.6884838067667812, 0.0, 0.0, nan, 0.9421938892134204, 0.6584828509993302, nan, nan, nan, 0.9642882670047362, 0.0, 0.0, 0.6129221732745962, nan, 0.3685587050258005, 0.0, nan, 0.8513267708110421, nan, 0.0, 0.18234338892011742, 0.13274745605920443, 0.8816988950276243, nan, nan, 0.8547465881079781, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9401268115942029, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8445430162987415, 0.15580592773041008, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6837861307530083, nan, nan, nan, 0.5279906481361216, nan, nan, 0.7425569176882661, 0.0, 0.19291338582677164, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.6012660731948566, 0.035897435897435895, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.33033896275002994, 0.0, nan, nan, 0.0, nan, nan, 0.5181940198344642, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8371659256392382, 0.6992267814779017, 0.9897171608617068, 0.9096793301981522, 0.9464573817394359, 0.8909613876056829, 0.8718022797709724, 0.9479570276131594, 0.47735028075180097, 0.7903781327861644, 0.0, 0.9040430592064088, 0.9769268456992375, nan, 0.7718172563778265, 0.7463808124573044, nan, 0.5804810483978371, 0.4377852312091054, 0.6042406921056697, 0.10879848628192999, nan, 0.988633411275656, nan, 0.0, nan, 0.9707868108862526, 0.7938001954701908, nan, nan, nan, 0.9782355657493522, nan, nan, 0.743657354618016, nan, 0.47309681517103713, nan, nan, 0.8695628415300547, nan, 0.0, 0.19634810679938125, 0.16288308740068105, 0.9049475474907853, nan, nan, 0.9831950207468879, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9620280541308781, nan, nan, nan, nan, nan, nan, nan, nan, 0.8469018309713459, 0.6003128666405945, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.692984661805381, nan, nan, nan, 0.5658407572383074, nan, nan, 0.8600405679513184, 0.0, 0.2552083333333333, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6700912738656907, 0.035897435897435895, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.378067169294037, 0.0, nan, nan, 0.0, nan, nan, 0.5181940198344642, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.1853 | 48.0 | 1632 | 0.5929 | 0.3386 | 0.5648 | 0.8261 | [0.7395597607241505, 0.33295338727458035, 0.9673606504965094, 0.7669795219228495, 0.7561759573881899, 0.7831479559606533, 0.8201576190709285, 0.9111712321046105, 0.3953664593477331, 0.7906071376227466, 0.0, 0.7696542270958613, 0.9338659012528282, nan, 0.3572659474332054, 0.656169139563067, 0.0, 0.448602372356885, 0.32762733855104004, 0.35267847022284693, 0.075, nan, 0.686429215509468, 0.0, 0.0, nan, 0.9420642978003384, 0.6648914947872396, nan, nan, nan, 0.9613899613899614, 0.0, 0.0, 0.5993414476392074, nan, 0.3704974271012007, 0.0, nan, 0.8361324247464721, nan, 0.0, 0.12977293716760613, 0.1390032007315958, 0.8870159296896457, nan, nan, 0.8440323347598668, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9396346066737128, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8316658071170706, 0.16131291904333034, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.6676598386095592, nan, nan, 0.0, 0.5683229813664596, nan, nan, 0.7343612334801762, 0.0, 0.19242579324462641, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5728597449908925, 0.016666666666666666, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.33018296377075, 0.0, nan, nan, 0.0, nan, nan, 0.440272910297517, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8290068522693625, 0.7032510324224585, 0.9902935860485564, 0.9040844042794501, 0.9446790813070508, 0.8644457172740969, 0.8801281714555865, 0.946323541709641, 0.47572535071406646, 0.7906071376227466, 0.0, 0.8715233445988234, 0.9763526962060202, nan, 0.7610513002436993, 0.7498900440759492, nan, 0.5792291505740085, 0.4304173310606477, 0.6008419589062258, 0.09933774834437085, nan, 0.9888932190179267, nan, 0.0, nan, 0.9713189113747384, 0.8143670590234989, nan, nan, nan, 0.9740185396495861, nan, nan, 0.7264110604332953, nan, 0.4703747391343798, nan, nan, 0.8515300546448088, nan, 0.0, 0.13703006254623715, 0.17253121452894438, 0.9156861355259427, nan, nan, 0.9820193637621023, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9613792251127726, nan, nan, nan, nan, nan, nan, nan, nan, 0.8340746870797558, 0.6304262807978099, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6761377923057581, nan, nan, nan, 0.611358574610245, nan, nan, 0.8453346855983773, 0.0, 0.24479166666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6379029057718594, 0.016666666666666666, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.37354352296093213, 0.0, nan, nan, 0.0, nan, nan, 0.440272910297517, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.1853 | 49.0 | 1666 | 0.5798 | 0.3463 | 0.5709 | 0.8303 | [0.7466320614775621, 0.3605835686555385, 0.9676787650393807, 0.7650954885221484, 0.7561258609356691, 0.786043968062999, 0.8193691714271764, 0.9107801728971214, 0.4060517840846526, 0.7875934339733255, 0.0, 0.7650291386913072, 0.934667894521429, nan, 0.357606334115863, 0.6499922816310945, 0.0, 0.4429050369592438, 0.3352452769644218, 0.3533907665120389, 0.0945945945945946, nan, 0.6834625902183171, 0.0, 0.0, nan, 0.9432840861122836, 0.6608441998159499, nan, nan, nan, 0.9635221972511774, 0.0, nan, 0.5853143159006867, nan, 0.36705423658010167, 0.0, nan, 0.8522209719766383, nan, 0.0, 0.18813969356739657, 0.14964028776978416, 0.8824339681118496, nan, nan, 0.8555101058710298, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.938736612815393, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8410848716097762, 0.1611578142911731, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6703869047619048, nan, nan, 0.0, 0.5172278778386844, nan, nan, 0.7414581066376497, 0.0, 0.203913491246138, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5749930470022647, 0.028205128205128206, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3653250773993808, 0.0, nan, nan, 0.0, nan, nan, 0.48482588919543657, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8320238092462504, 0.6944996046041648, 0.9896927360656539, 0.9054063816107897, 0.9485798693522828, 0.8948711882556561, 0.8815201975101119, 0.9509340974851007, 0.4860165742863849, 0.7875934339733255, 0.0, 0.8643134309675804, 0.9790014392013964, nan, 0.7565474905142363, 0.7486641524971692, nan, 0.5841301973736782, 0.45955902567768186, 0.6178742468716206, 0.12582781456953643, nan, 0.990257209664848, nan, 0.0, nan, 0.9746336357292393, 0.8086516806187056, nan, nan, nan, 0.9767891830418306, nan, nan, 0.7106613454960091, nan, 0.47835949550857454, nan, nan, 0.8691256830601093, nan, 0.0, 0.20398143789091397, 0.17707150964812712, 0.908172667989793, nan, nan, 0.9835408022130013, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9586912191806216, nan, nan, nan, nan, nan, nan, nan, nan, 0.8436950449984484, 0.6597575283535393, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.679658033693739, nan, nan, nan, 0.5516425389755011, nan, nan, 0.8638438133874239, 0.0, 0.2578125, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6381233740464747, 0.028205128205128206, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4205620287868403, 0.0, nan, nan, 0.0, nan, nan, 0.48482588919543657, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.1853 | 50.0 | 1700 | 0.6049 | 0.3447 | 0.5676 | 0.8226 | [0.7326548261190029, 0.31403251023310264, 0.9683257053860633, 0.7656613170152077, 0.757276930666942, 0.7763142928638049, 0.8294222929302973, 0.9085291734197731, 0.40465645381465193, 0.7791935365674924, 0.0, 0.7775910859568447, 0.933100826663359, nan, 0.3629440157229978, 0.6522244125080773, 0.0, 0.4452428449293653, 0.34196252465483234, 0.35037349788892497, 0.06929248723559446, nan, 0.6822634757324983, 0.0, 0.0, nan, 0.942328838963983, 0.6631026269580487, nan, nan, nan, 0.9594628211313473, 0.0, nan, 0.584703235853954, nan, 0.3723730141768184, 0.0, nan, 0.8259911894273128, nan, 0.0, 0.10308946232402307, 0.09477124183006536, 0.8886679238813646, nan, nan, 0.8582377791188895, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.9396076304131634, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.8530927835051546, 0.15884194053208137, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6692727724000993, nan, nan, 0.0, 0.5950668036998972, nan, nan, 0.7356296621325142, 0.0, 0.20084121976866456, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.5774251140646697, 0.05, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3490271438866202, 0.0, nan, nan, 0.0, nan, nan, 0.5098799492953545, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] | [0.8157732759163365, 0.702416307881557, 0.9901226124761858, 0.9007108337125891, 0.9465649402333303, 0.8498462228088307, 0.8763460629300835, 0.9500476665681743, 0.48168342751909293, 0.7791935365674924, 0.0, 0.8805107022155464, 0.979024405181125, nan, 0.7747478175031619, 0.7461936534376433, nan, 0.582611938310737, 0.45757958981690217, 0.6249806890159123, 0.08987701040681173, nan, 0.990646921278254, nan, 0.0, nan, 0.9717725052337753, 0.8103726681681044, nan, nan, nan, 0.9714692424236642, nan, nan, 0.7224201824401368, nan, 0.4742763814535886, nan, nan, 0.8401639344262295, nan, 0.0, 0.10760642948416167, 0.1152099886492622, 0.9185568471789056, nan, nan, 0.9834716459197786, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.963325712167089, nan, nan, nan, nan, nan, nan, nan, nan, 0.8560049653460226, 0.6351192804067266, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6780236359064622, nan, nan, nan, 0.6447661469933185, nan, nan, 0.8501521298174443, 0.0, 0.24869791666666666, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.6417390537501654, 0.05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.39835503769705277, 0.0, nan, nan, 0.0, nan, nan, 0.5098799492953545, nan, nan, nan, nan, nan, nan, nan, nan] |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
IHaBiS/Mistral-11B-SynthIAirOmniMix-4.125bpw-h8-exl2
|
IHaBiS
| 2023-10-16T05:05:45Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-15T21:26:25Z |
---
license: cc-by-nc-4.0
---
exl2 version of [NeverSleep/Mistral-11B-SynthIAirOmniMix](https://huggingface.co/NeverSleep/Mistral-11B-SynthIAirOmniMix)
used dataset : [wikitext](https://huggingface.co/datasets/wikitext)
quantized by IHaBiS
command : python convert.py -i models/NeverSleep_Mistral-11B-SynthIAirOmniMix -o NeverSleep_Mistral-11B-SynthIAirOmniMix-temp3 -cf NeverSleep_Mistral-11B-SynthIAirOmniMix-4.125bpw-h8-exl2 -c 0000.parquet -l 4096 -b 4.125 -hb 8 -ss 4096 -m NeverSleep_Mistral-11B-SynthIAirOmniMix-temp/measurement.json
Below this sentence is original model card
Replaced Zephyr by Airoboros 2.2 and OpenOrca by SynthIA in the mix, the reason why is to see if using merged Mistral models using all the same prompt format would be a better step or not.
## Description
This repo contains fp16 files of Mistral-11B-SynthIAirOmniMix.
## Model used
- [SynthIA-7B-v1.5](https://huggingface.co/migtissera/SynthIA-7B-v1.5)
- [Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus)
- [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B)
- [airoboros-mistral2.2-7b](https://huggingface.co/teknium/airoboros-mistral2.2-7b)
## Prompt template
3 out of 4 models use the same prompting format in this merge.
The best one should be this one, since Zephyr and OpenOrca is out of the merge:
```
(SYSTEM: {context}) - Not mandatory
USER: {prompt}
ASSISTANT:
```
But this one (maybe) work too:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## The secret sauce
Mistral-11B-SynthIAOpenPlatypus :
```
slices:
- sources:
- model: "/content/drive/MyDrive/SynthIA-7B-v1.5-bf16"
layer_range: [0, 24]
- sources:
- model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
Mistral-11B-CC-Airo :
```
slices:
- sources:
- model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
layer_range: [0, 24]
- sources:
- model: "/content/drive/MyDrive/Mistral-7B-Airoboros-2.2-bf16"
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
Mistral-11B-SynthIAirOmniMix :
```
slices:
- sources:
- model: Mistral-11B-SynthIAOpenPlatypus
layer_range: [0, 48]
- model: Mistral-11B-CC-Airo
layer_range: [0, 48]
merge_method: slerp
base_model: Mistral-11B-OpenOrcaPlatypus
parameters:
t:
- filter: lm_head
value: [0.75]
- filter: embed_tokens
value: [0.75]
- filter: self_attn
value: [0.75, 0.25]
- filter: mlp
value: [0.25, 0.75]
- filter: layernorm
value: [0.5, 0.5]
- filter: modelnorm
value: [0.75]
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
```
I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here.
## Some scoring I done myself
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5410|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8228|± |0.0078|
| | |acc_norm|0.8068|± |0.0081|
|hellaswag | 0|acc |0.6274|± |0.0048|
| | |acc_norm|0.8167|± |0.0039|
|piqa | 0|acc |0.8052|± |0.0092|
| | |acc_norm|0.8232|± |0.0089|
|truthfulqa_mc| 1|mc1 |0.3905|± |0.0171|
| | |mc2 |0.5592|± |0.0155|
|winogrande | 0|acc |0.7364|± |0.0124|
## Others
Special thanks to Sushi, [Henky](https://github.com/KoboldAI/KoboldAI-Client) for the machine he give me for big task, and [Charles Goddard](https://github.com/cg123) for his amazing tool.
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
IHaBiS/Mistral-11B-SynthIAirOmniMix-6.125bpw-h8-exl2
|
IHaBiS
| 2023-10-16T05:05:23Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-15T21:25:32Z |
---
license: cc-by-nc-4.0
---
exl2 version of [NeverSleep/Mistral-11B-SynthIAirOmniMix](https://huggingface.co/NeverSleep/Mistral-11B-SynthIAirOmniMix)
used dataset : [wikitext](https://huggingface.co/datasets/wikitext)
quantized by IHaBiS
command : python convert.py -i models/NeverSleep_Mistral-11B-SynthIAirOmniMix -o NeverSleep_Mistral-11B-SynthIAirOmniMix-temp2 -cf NeverSleep_Mistral-11B-SynthIAirOmniMix-6.125bpw-h8-exl2 -c 0000.parquet -l 4096 -b 6.125 -hb 8 -ss 4096 -m NeverSleep_Mistral-11B-SynthIAirOmniMix-temp/measurement.json
Below this sentence is original model card
Replaced Zephyr by Airoboros 2.2 and OpenOrca by SynthIA in the mix, the reason why is to see if using merged Mistral models using all the same prompt format would be a better step or not.
## Description
This repo contains fp16 files of Mistral-11B-SynthIAirOmniMix.
## Model used
- [SynthIA-7B-v1.5](https://huggingface.co/migtissera/SynthIA-7B-v1.5)
- [Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus)
- [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B)
- [airoboros-mistral2.2-7b](https://huggingface.co/teknium/airoboros-mistral2.2-7b)
## Prompt template
3 out of 4 models use the same prompting format in this merge.
The best one should be this one, since Zephyr and OpenOrca is out of the merge:
```
(SYSTEM: {context}) - Not mandatory
USER: {prompt}
ASSISTANT:
```
But this one (maybe) work too:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## The secret sauce
Mistral-11B-SynthIAOpenPlatypus :
```
slices:
- sources:
- model: "/content/drive/MyDrive/SynthIA-7B-v1.5-bf16"
layer_range: [0, 24]
- sources:
- model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
Mistral-11B-CC-Airo :
```
slices:
- sources:
- model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
layer_range: [0, 24]
- sources:
- model: "/content/drive/MyDrive/Mistral-7B-Airoboros-2.2-bf16"
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
Mistral-11B-SynthIAirOmniMix :
```
slices:
- sources:
- model: Mistral-11B-SynthIAOpenPlatypus
layer_range: [0, 48]
- model: Mistral-11B-CC-Airo
layer_range: [0, 48]
merge_method: slerp
base_model: Mistral-11B-OpenOrcaPlatypus
parameters:
t:
- filter: lm_head
value: [0.75]
- filter: embed_tokens
value: [0.75]
- filter: self_attn
value: [0.75, 0.25]
- filter: mlp
value: [0.25, 0.75]
- filter: layernorm
value: [0.5, 0.5]
- filter: modelnorm
value: [0.75]
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
```
I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here.
## Some scoring I done myself
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5410|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8228|± |0.0078|
| | |acc_norm|0.8068|± |0.0081|
|hellaswag | 0|acc |0.6274|± |0.0048|
| | |acc_norm|0.8167|± |0.0039|
|piqa | 0|acc |0.8052|± |0.0092|
| | |acc_norm|0.8232|± |0.0089|
|truthfulqa_mc| 1|mc1 |0.3905|± |0.0171|
| | |mc2 |0.5592|± |0.0155|
|winogrande | 0|acc |0.7364|± |0.0124|
## Others
Special thanks to Sushi, [Henky](https://github.com/KoboldAI/KoboldAI-Client) for the machine he give me for big task, and [Charles Goddard](https://github.com/cg123) for his amazing tool.
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
IHaBiS/Synatra-11B-Testbench-6.125bpw-h8-exl2
|
IHaBiS
| 2023-10-16T05:03:03Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-15T15:13:17Z |
---
language:
- ko
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-4.0
---
exl2 version of [maywell/Synatra-11B-Testbench](https://huggingface.co/maywell/Synatra-11B-Testbench)
used dataset : [beomi/KoAlpaca](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a)
quantized by IHaBiS
command : python convert.py -i models/maywell_Synatra-11B-Testbench -o maywell_Synatra-11B-Testbench-temp2 -cf maywell_Synatra-11B-Testbench-6.125bpw-h8-exl2 -c train-00000-of-00001-21df739eb88d711e.parquet -l 4096 -b 6.125 -hb 8 -ss 4096 -m maywell_Synatra-11B-Testbench-temp/measurement.json
Below this sentence is original model card
# **Synatra-11B-Testbench**
Made by StableFluffy
**Contact (Do not Contact for personal things.)**
Discord : is.maywell
Telegram : AlzarTakkarsen
## License
This model is strictly [*non-commercial*](https://creativecommons.org/licenses/by-nc/4.0/) (**cc-by-nc-4.0**) use only which takes priority over the **MISTRAL APACHE 2.0**.
The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included **cc-by-nc-4.0** license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences.
The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.
## Model Details
**Base Model**
[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
**Trained On**
A100 80GB * 4
# **Model Benchmark**
X
```
> Readme format: [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b)
---
|
IHaBiS/Synatra-11B-Testbench-8bpw-h8-exl2
|
IHaBiS
| 2023-10-16T05:02:22Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-15T14:23:56Z |
---
language:
- ko
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-4.0
---
exl2 version of [maywell/Synatra-11B-Testbench](https://huggingface.co/maywell/Synatra-11B-Testbench)
used dataset : [beomi/KoAlpaca](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a)
quantized by IHaBiS
command : python convert.py -i models/maywell_Synatra-11B-Testbench -o maywell_Synatra-11B-Testbench-temp -cf maywell_Synatra-11B-Testbench-8bpw-h8-exl2 -c train-00000-of-00001-21df739eb88d711e.parquet -l 4096 -b 8 -hb 8 -ss 4096
Below this sentence is original model card
# **Synatra-11B-Testbench**
Made by StableFluffy
**Contact (Do not Contact for personal things.)**
Discord : is.maywell
Telegram : AlzarTakkarsen
## License
This model is strictly [*non-commercial*](https://creativecommons.org/licenses/by-nc/4.0/) (**cc-by-nc-4.0**) use only which takes priority over the **MISTRAL APACHE 2.0**.
The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included **cc-by-nc-4.0** license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences.
The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.
## Model Details
**Base Model**
[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
**Trained On**
A100 80GB * 4
# **Model Benchmark**
X
```
> Readme format: [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b)
---
|
dqduong2003/roberta-fintuned-squadv2-covidqa
|
dqduong2003
| 2023-10-16T04:52:09Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"squad_v2",
"question_answering",
"legacy107/cpgQA",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-13T23:24:07Z |
---
tags:
- squad_v2
- roberta
- question_answering
- legacy107/cpgQA
---
|
if001/llama2_ja_ss
|
if001
| 2023-10-16T04:49:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-2",
"ja",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-10-16T04:12:34Z |
---
license: cc-by-4.0
language:
- ja
- en
pipeline_tag: text-generation
inference: false
tags:
- llama-2
---
日本語でtrainingしたllama2
`model size: 130.78M`
trainingは以下のscript参照
https://github.com/Lightning-AI/lit-gpt/tree/main
## use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("if001/sentencepiece_ja", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("if001/llama2_ja_ss")
import torch
from transformers import GenerationConfig
prompt="あのイーハトーヴォのすきとおった風、"
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"]
generation_config = GenerationConfig(
temperature=0.8,
top_p=0.95,
top_k=50,
num_beams=1,
do_sample=True,
repetition_penalty=1.2,
pad_token_id= tokenizer.pad_token_id,
# pad_token_id=tokenizer.unk_token_id,
eos_token_id=tokenizer.eos_token_id
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=64,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
print(output)
> あの イ ー ハ トー ヴォ の すき と おった 風 、 その な ま めかし い こと 。...... 彼は それを 見ると 、 ひどく て ら っている ことが わかった 。
```
## dataset
英語と日本語のデータセットを使用
```
total tokens: 2.27B
wikipedia_ja: 844.65M
aozorabunko: 92.97M
open-text-books: 60.17M
wikipedia_en: 1.28B
```
https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720
https://huggingface.co/datasets/izumi-lab/wikipedia-en-20230720
https://huggingface.co/datasets/izumi-lab/open-text-books
https://huggingface.co/datasets/if001/aozorabunko-clean-sin
|
hyyoka/vp-tapt-lora-kobert
|
hyyoka
| 2023-10-16T04:47:05Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-12T07:22:41Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
sandeep12345/roberta_finetune_model
|
sandeep12345
| 2023-10-16T04:35:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:data_loader",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-16T03:45:05Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- data_loader
model-index:
- name: roberta_finetune_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. -->
# roberta_finetune_model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the data_loader dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0282
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0006 | 1.0 | 720 | 0.0326 |
| 0.1065 | 2.0 | 1440 | 0.0324 |
| 0.0001 | 3.0 | 2160 | 0.0282 |
| 0.0 | 4.0 | 2880 | 0.0309 |
| 0.0 | 5.0 | 3600 | 0.0317 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
dalietng/fine-tune-simcse
|
dalietng
| 2023-10-16T04:30:26Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-10-14T06:42:17Z |
---
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 843 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss`
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": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 216, 'do_lower_case': False}) with Transformer model: RobertaModel
(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 -->
|
tt1717/output
|
tt1717
| 2023-10-16T04:27:26Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"dataset:decision_transformer_gym_replay",
"endpoints_compatible",
"region:us"
] | null | 2023-10-15T16:08:18Z |
---
tags:
- generated_from_trainer
datasets:
- decision_transformer_gym_replay
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [](https://huggingface.co/) on the decision_transformer_gym_replay 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 120
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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