id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Miron41k/Idk | Miron41k | 2023-11-29T07:19:10Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T07:19:10Z | 2023-11-29T07:17:44.000Z | null | null | ---
license: openrail
---
blue mountain | null | null | null | null | null | null | null | null | null | null | null | null | Miron41k/Idk | [
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shilulin/20231129-test | shilulin | 2023-11-29T07:18:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:18:26Z | 2023-11-29T07:18:26.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | shilulin/20231129-test | [
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athirdpath/Iambe-20b-DARE-GGUF | athirdpath | 2023-11-29T11:04:20Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | 2023-11-29T11:04:20Z | 2023-11-29T07:20:56.000Z | null | null | ---
license: cc-by-nc-4.0
---
<p align="center"><img src="https://i.ibb.co/pbpJHpk/iambe-sml.png"/><font size="6"> <b>Iambe-20b-DARE</b></font></p>
<p align="center"><font size="4"> <b>GGUF Quants</b></font></p>
## Description and Role
Named after a charming daughter of Echo and Pan in Greek myth, Iambe-20b-DARE is a [DARE](https://github.com/yule-BUAA/MergeLM) merge building on my recent experiments.
Iambe is intended to have the best realisticly possible understanding of anatomy and of a scene's state for a 20b merge, while remaining personable and authentic in "voice".
## Prompting and Context
Iambe-20b-DARE uses Alpaca formatting, and has an effective context size of 4096 tokens. This model is uncensored, and the output/deployment of this model is the responsibility of the user.
## Method and Hypothesis
Based on my extended vanilla model [BigLlama](https://huggingface.co/athirdpath/BigLlama-20b), this adds elements of:
- [NeverSleep/Noromaid-20b-v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1) - Addded to adapt the excellent writing and "soul" that come from the datasets backing this model.
- [athirdpath/Eileithyia-20b](https://huggingface.co/athirdpath/Eileithyia-20b) - Added at low weight and density to capture anatomical data and its relation to fiction without the model's other... quirks.
- [athirdpath/CleverGirl-20b-Blended](https://huggingface.co/athirdpath/CleverGirl-20b-Blended) - Added to capture CleverGirl's problem-solving abilities.
<p align="center"><font size="7"> <b>Examples</b></font>
<p align="center"><font size="3"> <i>(q5_k_m GGUF quant, Ooba textgen, Midnight Enigma preset for KISS reasons)</i></font>
<p align="center"><font size="5"> Reasoning and Recall - STEM</font><img src="https://i.ibb.co/YDkRD43/base.png"/>
<p align="center"><font size="5"> Reasoning and Recall - Social Science</font><img src="https://i.ibb.co/L0kc3pb/Screenshot-2023-11-29-024037.png"/>
<p align="center"><font size="6"><b><a href="https://i.ibb.co/m5G0ZVp/Screenshot-2023-11-29-004705.png">!!NSFW!! - Erotica Writing Example - !!NSFW!!</font></a></b></p>
## Testing and Conclusions
VERY Impressed so far, concrete data coming eventually.
Does still have some confusion (at q5_k_m), but has instantly become my daily driver.
## Recipe
merge_method: dare_ties
- base_model: athirdpath/BigLlama-20B
- model: NeverSleep/Noromaid-20b-v0.1.1
weight: 0.41 / density: 0.50
- model: athirdpath/Eileithyia-20b
weight: 0.18 / density: 0.30
- model: athirdpath/CleverGirl-20b-Blended
weight: 0.41 / density: 0.50
int8_mask: true
dtype: bfloat16
## Gratitude
Thanks to brucethemoose for the recipe. Thanks to Undi95 and IkariDev at NeverSleep for Noromaid, as well as lots of inspiration. Thanks to Sao10K for [half of](https://huggingface.co/Sao10K/Mythical-Destroyer-V2-L2-13B) CleverGirl. | null | null | null | null | null | null | null | null | null | null | null | null | athirdpath/Iambe-20b-DARE-GGUF | [
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xjtulmx/dataset | xjtulmx | 2023-11-29T07:25:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:25:40Z | 2023-11-29T07:21:37.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | xjtulmx/dataset | [
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hkivancoral/hushem_5x_beit_base_rms_0001_fold4 | hkivancoral | 2023-11-29T07:55:15Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:55:15Z | 2023-11-29T07:22:34.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7857142857142857
---
<!-- 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. -->
# hushem_5x_beit_base_rms_0001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8159
- Accuracy: 0.7857
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4247 | 1.0 | 28 | 1.3411 | 0.2381 |
| 1.3595 | 2.0 | 56 | 1.2501 | 0.4286 |
| 1.3116 | 3.0 | 84 | 1.5240 | 0.2381 |
| 1.303 | 4.0 | 112 | 1.0491 | 0.5238 |
| 1.1942 | 5.0 | 140 | 0.8861 | 0.7143 |
| 1.1712 | 6.0 | 168 | 0.9106 | 0.5238 |
| 0.977 | 7.0 | 196 | 1.1447 | 0.6905 |
| 0.9351 | 8.0 | 224 | 0.7191 | 0.7619 |
| 0.8453 | 9.0 | 252 | 1.3331 | 0.5714 |
| 0.8831 | 10.0 | 280 | 0.8305 | 0.6905 |
| 0.8349 | 11.0 | 308 | 0.6872 | 0.7619 |
| 0.845 | 12.0 | 336 | 0.7545 | 0.7619 |
| 0.784 | 13.0 | 364 | 0.7961 | 0.7857 |
| 0.7404 | 14.0 | 392 | 0.6338 | 0.8095 |
| 0.6277 | 15.0 | 420 | 0.7200 | 0.7143 |
| 0.6386 | 16.0 | 448 | 0.7383 | 0.8095 |
| 0.6167 | 17.0 | 476 | 0.5440 | 0.8095 |
| 0.5129 | 18.0 | 504 | 0.7061 | 0.7619 |
| 0.3836 | 19.0 | 532 | 0.7181 | 0.7381 |
| 0.3202 | 20.0 | 560 | 0.4277 | 0.8095 |
| 0.1958 | 21.0 | 588 | 1.1637 | 0.7381 |
| 0.2343 | 22.0 | 616 | 1.0581 | 0.8095 |
| 0.2016 | 23.0 | 644 | 0.8968 | 0.7857 |
| 0.116 | 24.0 | 672 | 1.0426 | 0.7857 |
| 0.1027 | 25.0 | 700 | 0.6841 | 0.8333 |
| 0.1133 | 26.0 | 728 | 0.8260 | 0.8095 |
| 0.1258 | 27.0 | 756 | 1.3215 | 0.7619 |
| 0.0595 | 28.0 | 784 | 1.0509 | 0.8810 |
| 0.0945 | 29.0 | 812 | 1.3868 | 0.7857 |
| 0.0022 | 30.0 | 840 | 1.7553 | 0.8095 |
| 0.0004 | 31.0 | 868 | 1.9423 | 0.7857 |
| 0.0466 | 32.0 | 896 | 2.0945 | 0.8095 |
| 0.0367 | 33.0 | 924 | 1.6928 | 0.8095 |
| 0.1032 | 34.0 | 952 | 1.3572 | 0.8571 |
| 0.0331 | 35.0 | 980 | 2.0437 | 0.8095 |
| 0.0001 | 36.0 | 1008 | 2.0414 | 0.8333 |
| 0.0286 | 37.0 | 1036 | 2.0546 | 0.7619 |
| 0.009 | 38.0 | 1064 | 2.8381 | 0.7857 |
| 0.0573 | 39.0 | 1092 | 2.4470 | 0.7857 |
| 0.0497 | 40.0 | 1120 | 1.8192 | 0.7857 |
| 0.0003 | 41.0 | 1148 | 2.1421 | 0.7143 |
| 0.0003 | 42.0 | 1176 | 2.2125 | 0.7381 |
| 0.0001 | 43.0 | 1204 | 2.1555 | 0.7619 |
| 0.0002 | 44.0 | 1232 | 1.8154 | 0.7381 |
| 0.0197 | 45.0 | 1260 | 1.7188 | 0.7381 |
| 0.0002 | 46.0 | 1288 | 1.6637 | 0.8095 |
| 0.0152 | 47.0 | 1316 | 1.6954 | 0.8095 |
| 0.0001 | 48.0 | 1344 | 1.8153 | 0.7857 |
| 0.0002 | 49.0 | 1372 | 1.8159 | 0.7857 |
| 0.0 | 50.0 | 1400 | 1.8159 | 0.7857 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | hkivancoral/hushem_5x_beit_base_rms_0001_fold4 | [
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snunlp/bok_finance_model_1129 | snunlp | 2023-11-29T07:43:28Z | 0 | 0 | null | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T07:43:28Z | 2023-11-29T07:23:09.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | snunlp/bok_finance_model_1129 | [
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binxia/llmga-sdxl-inpainting | binxia | 2023-11-29T07:36:28Z | 0 | 0 | null | [
"diffusers",
"license:apache-2.0",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2023-11-29T07:36:28Z | 2023-11-29T07:24:23.000Z | null | null | ---
license: apache-2.0
---
| null | diffusers | null | null | null | null | null | null | null | null | null | null | binxia/llmga-sdxl-inpainting | [
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karline/wav2vec2-large-xlsr-mn_en_v1 | karline | 2023-11-29T21:49:34Z | 0 | 0 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:49:34Z | 2023-11-29T07:25:02.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | karline/wav2vec2-large-xlsr-mn_en_v1 | [
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binxia/llmga-sd15-inpainting | binxia | 2023-11-29T07:43:22Z | 0 | 0 | null | [
"diffusers",
"license:apache-2.0",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T07:43:22Z | 2023-11-29T07:25:30.000Z | null | null | ---
license: apache-2.0
---
| null | diffusers | null | null | null | null | null | null | null | null | null | null | binxia/llmga-sd15-inpainting | [
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kaikaih/nva-miurar_ | kaikaih | 2023-11-29T07:26:19Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:26:19Z | 2023-11-29T07:26:16.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | kaikaih/nva-miurar_ | [
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ajmangus/ajmangus-three-personas | ajmangus | 2023-11-30T00:53:04Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:53:04Z | 2023-11-29T07:26:30.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | ajmangus/ajmangus-three-personas | [
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mahesh-amballa/git-base-pokemon | mahesh-amballa | 2023-11-29T07:31:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:31:56Z | 2023-11-29T07:31:56.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mahesh-amballa/git-base-pokemon | [
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Fghhtrrdd/Dfgg | Fghhtrrdd | 2023-11-29T07:32:10Z | 0 | 0 | null | [
"license:pddl",
"region:us"
] | 2023-11-29T07:32:10Z | 2023-11-29T07:32:09.000Z | null | null | ---
license: pddl
---
| null | null | null | null | null | null | null | null | null | null | null | null | Fghhtrrdd/Dfgg | [
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hhf1/path-to-save-model | hhf1 | 2023-11-29T07:37:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:37:37Z | 2023-11-29T07:37:35.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | hhf1/path-to-save-model | [
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damerajee/copilot-codellama-q8-7b.gguf | damerajee | 2023-11-29T07:45:07Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T07:45:07Z | 2023-11-29T07:39:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | damerajee/copilot-codellama-q8-7b.gguf | [
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UNIST-Eunchan/FLAN-T5-QASPER-NLP-Paper-to-Question-Generation-answer-input | UNIST-Eunchan | 2023-11-29T11:17:36Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T11:17:36Z | 2023-11-29T07:39:45.000Z | null | null | ---
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
model-index:
- name: FLAN-T5-QASPER-NLP-Paper-to-Question-Generation-answer-input
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# FLAN-T5-QASPER-NLP-Paper-to-Question-Generation-answer-input
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0746
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 184
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 46 | 39.0024 |
| 33.7249 | 1.99 | 92 | 18.8406 |
| 33.7249 | 2.98 | 138 | 5.0432 |
| 8.9783 | 3.97 | 184 | 0.1579 |
| 8.9783 | 4.97 | 230 | 0.0941 |
| 0.1833 | 5.96 | 276 | 0.0810 |
| 0.1833 | 6.95 | 322 | 0.0756 |
| 0.1047 | 7.95 | 368 | 0.0746 |
| 0.1047 | 8.94 | 414 | 0.0746 |
| 0.0995 | 9.93 | 460 | 0.0746 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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shilulin/instruct-pix2pix-model | shilulin | 2023-11-29T07:40:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:40:26Z | 2023-11-29T07:40:25.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | shilulin/instruct-pix2pix-model | [
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Realgon/roberta_sst2_padding30model | Realgon | 2023-11-29T08:24:57Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:24:57Z | 2023-11-29T07:46:33.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding30model
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_sst2_padding30model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5215
- Accuracy: 0.9412
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.1665 | 0.9396 |
| 0.3401 | 2.0 | 866 | 0.1830 | 0.9368 |
| 0.1936 | 3.0 | 1299 | 0.4096 | 0.9138 |
| 0.1178 | 4.0 | 1732 | 0.3273 | 0.9412 |
| 0.0709 | 5.0 | 2165 | 0.4273 | 0.9325 |
| 0.0501 | 6.0 | 2598 | 0.4050 | 0.9341 |
| 0.0317 | 7.0 | 3031 | 0.4084 | 0.9401 |
| 0.0317 | 8.0 | 3464 | 0.4631 | 0.9379 |
| 0.0124 | 9.0 | 3897 | 0.6065 | 0.9209 |
| 0.0216 | 10.0 | 4330 | 0.5480 | 0.9275 |
| 0.0148 | 11.0 | 4763 | 0.4557 | 0.9401 |
| 0.0141 | 12.0 | 5196 | 0.4668 | 0.9418 |
| 0.0122 | 13.0 | 5629 | 0.5264 | 0.9347 |
| 0.0114 | 14.0 | 6062 | 0.5080 | 0.9429 |
| 0.0114 | 15.0 | 6495 | 0.5438 | 0.9390 |
| 0.0108 | 16.0 | 6928 | 0.5652 | 0.9363 |
| 0.007 | 17.0 | 7361 | 0.4768 | 0.9445 |
| 0.0044 | 18.0 | 7794 | 0.5182 | 0.9423 |
| 0.0029 | 19.0 | 8227 | 0.5217 | 0.9407 |
| 0.0022 | 20.0 | 8660 | 0.5215 | 0.9412 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/roberta_sst2_padding30model | [
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binxia/llmga-llama-2-7b-chat-full-finetune | binxia | 2023-11-29T07:53:54Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llava",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:53:54Z | 2023-11-29T07:49:11.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | binxia/llmga-llama-2-7b-chat-full-finetune | [
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Roxysun/wav2vec2-large-xls-r-300m-czech-colab | Roxysun | 2023-11-29T08:54:46Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:54:46Z | 2023-11-29T07:50:05.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Roxysun/wav2vec2-large-xls-r-300m-czech-colab | [
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Gbssreejith/death_full8 | Gbssreejith | 2023-11-29T08:47:23Z | 0 | 0 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:47:23Z | 2023-11-29T07:50:25.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | Gbssreejith/death_full8 | [
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jalonso/yolov8face | jalonso | 2023-11-29T07:52:46Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:52:46Z | 2023-11-29T07:52:08.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jalonso/yolov8face | [
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hkivancoral/hushem_5x_beit_base_rms_0001_fold5 | hkivancoral | 2023-11-29T08:28:49Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:28:49Z | 2023-11-29T07:56:06.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7073170731707317
---
<!-- 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. -->
# hushem_5x_beit_base_rms_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4047
- Accuracy: 0.7073
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4155 | 1.0 | 28 | 1.3777 | 0.2683 |
| 1.3848 | 2.0 | 56 | 1.2989 | 0.2927 |
| 1.3314 | 3.0 | 84 | 1.2733 | 0.4878 |
| 1.2486 | 4.0 | 112 | 1.0811 | 0.5122 |
| 1.2007 | 5.0 | 140 | 0.9236 | 0.5854 |
| 1.05 | 6.0 | 168 | 1.1380 | 0.5122 |
| 1.0162 | 7.0 | 196 | 0.9574 | 0.5854 |
| 0.9476 | 8.0 | 224 | 1.4400 | 0.4878 |
| 0.903 | 9.0 | 252 | 0.9012 | 0.6341 |
| 0.9351 | 10.0 | 280 | 1.0183 | 0.6829 |
| 0.8113 | 11.0 | 308 | 0.9612 | 0.6585 |
| 0.8131 | 12.0 | 336 | 1.6631 | 0.4878 |
| 0.7921 | 13.0 | 364 | 0.9316 | 0.6829 |
| 0.8114 | 14.0 | 392 | 1.3372 | 0.5854 |
| 0.7382 | 15.0 | 420 | 1.4796 | 0.6341 |
| 0.7119 | 16.0 | 448 | 1.9753 | 0.5366 |
| 0.6933 | 17.0 | 476 | 1.3458 | 0.7073 |
| 0.591 | 18.0 | 504 | 1.3968 | 0.6585 |
| 0.6986 | 19.0 | 532 | 1.4904 | 0.6829 |
| 0.6832 | 20.0 | 560 | 1.7362 | 0.6585 |
| 0.5173 | 21.0 | 588 | 1.5475 | 0.7317 |
| 0.5116 | 22.0 | 616 | 1.9547 | 0.6585 |
| 0.4833 | 23.0 | 644 | 2.1246 | 0.6341 |
| 0.4295 | 24.0 | 672 | 1.9058 | 0.7317 |
| 0.4431 | 25.0 | 700 | 2.4495 | 0.6585 |
| 0.3801 | 26.0 | 728 | 1.6867 | 0.7561 |
| 0.4263 | 27.0 | 756 | 2.1056 | 0.6585 |
| 0.3209 | 28.0 | 784 | 2.6127 | 0.6098 |
| 0.29 | 29.0 | 812 | 2.2833 | 0.6341 |
| 0.2306 | 30.0 | 840 | 2.6477 | 0.6341 |
| 0.2318 | 31.0 | 868 | 2.2205 | 0.6829 |
| 0.1766 | 32.0 | 896 | 2.1057 | 0.8293 |
| 0.1861 | 33.0 | 924 | 2.9102 | 0.6341 |
| 0.2172 | 34.0 | 952 | 2.3319 | 0.7317 |
| 0.1336 | 35.0 | 980 | 2.7931 | 0.7073 |
| 0.128 | 36.0 | 1008 | 3.2544 | 0.6098 |
| 0.1009 | 37.0 | 1036 | 2.3057 | 0.7805 |
| 0.1495 | 38.0 | 1064 | 2.9047 | 0.7317 |
| 0.0845 | 39.0 | 1092 | 3.1290 | 0.7317 |
| 0.064 | 40.0 | 1120 | 2.9682 | 0.7561 |
| 0.0399 | 41.0 | 1148 | 2.9364 | 0.7561 |
| 0.0198 | 42.0 | 1176 | 4.0340 | 0.6585 |
| 0.0179 | 43.0 | 1204 | 3.2313 | 0.7317 |
| 0.0799 | 44.0 | 1232 | 3.4340 | 0.7317 |
| 0.0495 | 45.0 | 1260 | 3.8737 | 0.6829 |
| 0.041 | 46.0 | 1288 | 3.5139 | 0.6829 |
| 0.0058 | 47.0 | 1316 | 3.4146 | 0.7073 |
| 0.0141 | 48.0 | 1344 | 3.4016 | 0.7073 |
| 0.0316 | 49.0 | 1372 | 3.4047 | 0.7073 |
| 0.0269 | 50.0 | 1400 | 3.4047 | 0.7073 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | hkivancoral/hushem_5x_beit_base_rms_0001_fold5 | [
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zostrich/frozenLake_agent | zostrich | 2023-11-29T07:58:53Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T07:58:53Z | 2023-11-29T07:58:49.000Z | null | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: frozenLake_agent
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="zostrich/frozenLake_agent", 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"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | zostrich/frozenLake_agent | [
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Roxysun/wav2vec2-large-xls-r-300m-czech-colab-finetuned | Roxysun | 2023-11-29T23:55:28Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:facebook/wav2vec2-lv-60-espeak-cv-ft",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:55:28Z | 2023-11-29T07:59:19.000Z | null | null | ---
license: apache-2.0
base_model: facebook/wav2vec2-lv-60-espeak-cv-ft
tags:
- generated_from_trainer
datasets:
- voxpopuli
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-czech-colab-finetuned
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: voxpopuli
type: voxpopuli
config: cs
split: test
args: cs
metrics:
- name: Wer
type: wer
value: 0.6178421298458664
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-czech-colab-finetuned
This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 624.5939
- Wer: 0.6178
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3007.3212 | 3.51 | 100 | 1006.7374 | 0.9865 |
| 354.3011 | 7.02 | 200 | 563.6080 | 0.9980 |
| 211.5289 | 10.53 | 300 | 599.5796 | 0.9165 |
| 187.8653 | 14.04 | 400 | 447.1478 | 0.8099 |
| 163.1056 | 17.54 | 500 | 430.5204 | 0.6875 |
| 143.0342 | 21.05 | 600 | 413.8947 | 0.6850 |
| 116.0388 | 24.56 | 700 | 435.5743 | 0.6737 |
| 95.5554 | 28.07 | 800 | 490.6329 | 0.6339 |
| 80.6966 | 31.58 | 900 | 493.9658 | 0.6344 |
| 68.7335 | 35.09 | 1000 | 525.7507 | 0.6263 |
| 58.3269 | 38.6 | 1100 | 582.5747 | 0.6128 |
| 54.3181 | 42.11 | 1200 | 600.8087 | 0.6308 |
| 48.5287 | 45.61 | 1300 | 594.6959 | 0.6112 |
| 43.041 | 49.12 | 1400 | 624.5939 | 0.6178 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Roxysun/wav2vec2-large-xls-r-300m-czech-colab-finetuned | [
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zostrich/taxi_agent | zostrich | 2023-11-29T08:04:38Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T08:04:38Z | 2023-11-29T08:04:35.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_agent
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.71
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="zostrich/taxi_agent", 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"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | zostrich/taxi_agent | [
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DQ12/1 | DQ12 | 2023-11-29T08:05:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:05:26Z | 2023-11-29T08:05:26.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | DQ12/1 | [
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shindz/spsingh | shindz | 2023-11-29T08:06:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:06:14Z | 2023-11-29T08:06:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | shindz/spsingh | [
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all4class/trabajofinal | all4class | 2023-11-29T08:07:47Z | 0 | 0 | null | [
"region:us"
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] |
chahatraj/contact-bpn-alldata-13b-en | chahatraj | 2023-11-29T14:27:39Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T14:27:39Z | 2023-11-29T08:07:56.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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chahatraj/contact-bpn-newdata-13b-en | chahatraj | 2023-11-29T16:49:57Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T16:49:57Z | 2023-11-29T08:08:16.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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] |
chahatraj/contact-bpn-type1-13b-en | chahatraj | 2023-11-29T16:51:11Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T16:51:11Z | 2023-11-29T08:08:39.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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chahatraj/contact-bpn-type2-13b-en | chahatraj | 2023-11-29T17:54:20Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T17:54:20Z | 2023-11-29T08:08:58.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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chahatraj/contact-bpn-type3-13b-en | chahatraj | 2023-11-29T17:53:38Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T17:53:38Z | 2023-11-29T08:09:17.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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chahatraj/contact-bpn-2scenarios-13b-en | chahatraj | 2023-11-29T17:53:38Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T17:53:38Z | 2023-11-29T08:09:38.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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chahatraj/contact-bpn-3keyprinciples-13b-en | chahatraj | 2023-11-29T18:54:02Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T18:54:02Z | 2023-11-29T08:10:03.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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chahatraj/contact-bpn-6biasdimensions-13b-en | chahatraj | 2023-11-29T18:54:42Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T18:54:42Z | 2023-11-29T08:10:26.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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hungaz/test | hungaz | 2023-11-29T08:17:02Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T08:17:02Z | 2023-11-29T08:17:01.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | hungaz/test | [
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cs182project/llama-2-7b-chat-lyre-lm | cs182project | 2023-11-29T08:58:42Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:58:42Z | 2023-11-29T08:17:35.000Z | null | null | ---
{}
--- | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | cs182project/llama-2-7b-chat-lyre-lm | [
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lensonbocko/Reinforce-V1 | lensonbocko | 2023-11-29T08:18:45Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-29T08:18:45Z | 2023-11-29T08:18:34.000Z | null | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-V1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | lensonbocko/Reinforce-V1 | [
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auberonkuo/test_0 | auberonkuo | 2023-11-29T13:57:00Z | 0 | 0 | null | [
"transformers",
"safetensors",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:57:00Z | 2023-11-29T08:22:53.000Z | null | null | Entry not found | null | transformers | fill-mask | null | null | null | null | null | null | null | null | null | auberonkuo/test_0 | [
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yihouxiang/instruct-pix2pix-model | yihouxiang | 2023-11-29T08:24:32Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:24:32Z | 2023-11-29T08:24:32.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | yihouxiang/instruct-pix2pix-model | [
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Realgon/roberta_sst2_padding40model | Realgon | 2023-11-29T09:08:40Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:08:40Z | 2023-11-29T08:25:01.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding40model
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_sst2_padding40model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4833
- Accuracy: 0.9462
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.1790 | 0.9385 |
| 0.3408 | 2.0 | 866 | 0.2909 | 0.9314 |
| 0.2105 | 3.0 | 1299 | 0.2748 | 0.9390 |
| 0.137 | 4.0 | 1732 | 0.3227 | 0.9385 |
| 0.0754 | 5.0 | 2165 | 0.4602 | 0.9248 |
| 0.0527 | 6.0 | 2598 | 0.3486 | 0.9451 |
| 0.0325 | 7.0 | 3031 | 0.4089 | 0.9423 |
| 0.0325 | 8.0 | 3464 | 0.5611 | 0.9154 |
| 0.0422 | 9.0 | 3897 | 0.3964 | 0.9440 |
| 0.0219 | 10.0 | 4330 | 0.6049 | 0.9231 |
| 0.0155 | 11.0 | 4763 | 0.4582 | 0.9451 |
| 0.013 | 12.0 | 5196 | 0.5630 | 0.9297 |
| 0.0081 | 13.0 | 5629 | 0.4915 | 0.9462 |
| 0.0081 | 14.0 | 6062 | 0.4604 | 0.9368 |
| 0.0081 | 15.0 | 6495 | 0.4677 | 0.9484 |
| 0.0064 | 16.0 | 6928 | 0.4724 | 0.9473 |
| 0.0061 | 17.0 | 7361 | 0.5021 | 0.9412 |
| 0.0031 | 18.0 | 7794 | 0.4434 | 0.9462 |
| 0.0032 | 19.0 | 8227 | 0.4602 | 0.9467 |
| 0.0 | 20.0 | 8660 | 0.4833 | 0.9462 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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ErnestBeckham/phi-1_5-finetuned-dialogstudio | ErnestBeckham | 2023-11-29T08:28:04Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"dataset:dialogstudio",
"base_model:microsoft/phi-1_5",
"license:other",
"region:us"
] | 2023-11-29T08:28:04Z | 2023-11-29T08:27:03.000Z | null | null | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
datasets:
- dialogstudio
model-index:
- name: phi-1_5-finetuned-dialogstudio
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. -->
# phi-1_5-finetuned-dialogstudio
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the dialogstudio dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2428
## 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: cosine
- training_steps: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | ErnestBeckham/phi-1_5-finetuned-dialogstudio | [
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sinonimayzer/roberta-1.8-v3 | sinonimayzer | 2023-11-29T23:29:23Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T23:29:23Z | 2023-11-29T08:29:02.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | sinonimayzer/roberta-1.8-v3 | [
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chanasia/openthaigpt-1.0.0-alpha-7b-chat-ckpt-hf | chanasia | 2023-11-29T08:30:06Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:30:06Z | 2023-11-29T08:30:06.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | chanasia/openthaigpt-1.0.0-alpha-7b-chat-ckpt-hf | [
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cleanfortemexico/CleanForteMexico | cleanfortemexico | 2023-11-29T08:33:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:33:40Z | 2023-11-29T08:32:45.000Z | null | null | CleanForte: Clean Forte Mexico, gotas, Precio, suplemento, reseñas, Ingredientes, costo, Beneficios!
CleanForte es un suplemento desintoxicante de vanguardia diseñado para atacar y eliminar parásitos dañinos que pueden residir dentro del cuerpo humano. Clean Forte Mexico
CleanForte ¡¡Compra ahora!! ¡Haga clic en el enlace a continuación para obtener más información y obtenga un 50% de descuento ahora! apresúrate !!
leer más: https://www.boxdrug.com/CleanFMexi
➢Nombre del producto: CleanForte
➢Categoría - parásito
➢Principales beneficios: este producto promueve una mejor función digestiva
➢ Composición — Compuesto Orgánico Natural
➢ Efectos secundarios—NA
➢Calificación final: — 4,8
➢ Disponibilidad: en línea
➢Ofertas y descuentos; ¡AHORRA HOY! COMPRAR AHORA PARA comprar ¡¡¡OFERTA ESPECIAL!!!
¿Qué es CleanForte?
Los parásitos, cuyo impacto a menudo se subestima, pueden provocar una variedad de problemas de salud, desde problemas digestivos hasta fatiga. Clean Forte Mexico está formulado con una mezcla de ingredientes naturales conocidos por sus propiedades antiparasitarias, proporcionando una solución segura y eficaz para limpiar el cuerpo.
https://www.boxdrug.com/CleanFMexi
https://sites.google.com/view/cleanforte-precio/home
https://healthtoned.blogspot.com/2023/11/cleanforte-mexico.html
https://www.q8101.com/bb/23869/cleanforte-mexico
https://www.dibiz.com/cleanfortemexico
https://medium.com/@cleanfortemexico/cleanforte-mexico-bb92859f3aa0
https://medium.com/@cleanfortemexico/cleanforte-clean-forte-mexico-gotas-precio-suplemento-rese%C3%B1as-ingredientes-costo-beneficios-3fbcee9dd611
https://www.weddingwire.com/website/cleanforte-and-mexico
https://infogram.com/cleanforte-mexico-1h7v4pwopeynj6k?live
https://softisenilspain.hashnode.dev/cleanforte-mexico
https://sway.office.com/wdTxvMB9C5bXndO8
https://cleanfortemexico.company.site/
https://gamma.app/docs/CleanForte-Mexico-q8hc1uibnrdgp0a?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/cleanforte-mexico
https://www.forexagone.com/forum/questions-debutants/cleanforte-mexico-91600#189072
https://groups.google.com/g/snshine/c/wTFuABEzbZE
https://devfolio.co/projects/cleanforte-mexico-29cb
https://replit.com/@cleanfortemexic
| null | null | null | null | null | null | null | null | null | null | null | null | cleanfortemexico/CleanForteMexico | [
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robindon/lora-trained-xl | robindon | 2023-11-29T08:32:46Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:32:46Z | 2023-11-29T08:32:46.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | robindon/lora-trained-xl | [
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shrimali/experiments | shrimali | 2023-11-29T08:35:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:35:37Z | 2023-11-29T08:35:37.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | shrimali/experiments | [
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carecoach/gpt-neo-125m-augur | carecoach | 2023-11-29T08:37:15Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:37:15Z | 2023-11-29T08:37:13.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | carecoach/gpt-neo-125m-augur | [
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popeyeGOEL/remi-llama-2-7b-miniguanaco | popeyeGOEL | 2023-11-29T08:48:07Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:48:07Z | 2023-11-29T08:37:16.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | popeyeGOEL/remi-llama-2-7b-miniguanaco | [
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shrimali/trained_model | shrimali | 2023-11-29T10:56:54Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:tiiuae/falcon-7b-instruct",
"region:us"
] | 2023-11-29T10:56:54Z | 2023-11-29T08:38:41.000Z | null | null | ---
library_name: peft
base_model: tiiuae/falcon-7b-instruct
---
# Model Card for Model ID
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## Model Details
### Model Description
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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.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Environmental Impact
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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).
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## 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 0.6.3.dev0
## 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 0.6.3.dev0
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Ping2/dnd-falcon-7b | Ping2 | 2023-11-29T13:49:05Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:49:05Z | 2023-11-29T08:40:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Ping2/dnd-falcon-7b | [
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umarbutler/open-australian-legal-llm | umarbutler | 2023-11-29T08:40:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:40:38Z | 2023-11-29T08:40:36.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | umarbutler/open-australian-legal-llm | [
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matmatmat1/deranged | matmatmat1 | 2023-11-29T08:46:55Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T08:46:55Z | 2023-11-29T08:41:31.000Z | null | null | ---
license: openrail
---
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nimrita/Reinforce-cartpolev1 | nimrita | 2023-11-29T08:47:59Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-29T08:47:59Z | 2023-11-29T08:47:49.000Z | null | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpolev1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | nimrita/Reinforce-cartpolev1 | [
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hkivancoral/hushem_5x_beit_base_rms_00001_fold1 | hkivancoral | 2023-11-29T09:24:04Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:24:04Z | 2023-11-29T08:51:44.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8222222222222222
---
<!-- 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. -->
# hushem_5x_beit_base_rms_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5839
- Accuracy: 0.8222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5474 | 1.0 | 27 | 0.5910 | 0.8444 |
| 0.0818 | 2.0 | 54 | 0.5720 | 0.7778 |
| 0.0238 | 3.0 | 81 | 0.8576 | 0.7111 |
| 0.0074 | 4.0 | 108 | 0.5321 | 0.8667 |
| 0.0032 | 5.0 | 135 | 0.4605 | 0.8667 |
| 0.0017 | 6.0 | 162 | 0.6849 | 0.7778 |
| 0.0024 | 7.0 | 189 | 0.4973 | 0.8667 |
| 0.0008 | 8.0 | 216 | 0.4640 | 0.8667 |
| 0.0044 | 9.0 | 243 | 0.6817 | 0.8222 |
| 0.0005 | 10.0 | 270 | 0.5671 | 0.8222 |
| 0.0004 | 11.0 | 297 | 0.5195 | 0.8444 |
| 0.0002 | 12.0 | 324 | 0.7506 | 0.8222 |
| 0.0007 | 13.0 | 351 | 0.4960 | 0.8667 |
| 0.0004 | 14.0 | 378 | 0.4879 | 0.8667 |
| 0.0002 | 15.0 | 405 | 0.2878 | 0.8889 |
| 0.0004 | 16.0 | 432 | 0.5723 | 0.7778 |
| 0.0038 | 17.0 | 459 | 0.8796 | 0.8 |
| 0.0011 | 18.0 | 486 | 0.4544 | 0.8444 |
| 0.001 | 19.0 | 513 | 0.2346 | 0.8889 |
| 0.0001 | 20.0 | 540 | 0.6421 | 0.8444 |
| 0.0001 | 21.0 | 567 | 0.5172 | 0.8667 |
| 0.0012 | 22.0 | 594 | 0.4729 | 0.8222 |
| 0.0001 | 23.0 | 621 | 0.4318 | 0.8222 |
| 0.0001 | 24.0 | 648 | 0.4087 | 0.8222 |
| 0.0004 | 25.0 | 675 | 0.4267 | 0.8889 |
| 0.0001 | 26.0 | 702 | 0.4250 | 0.8667 |
| 0.0001 | 27.0 | 729 | 0.3081 | 0.8889 |
| 0.0001 | 28.0 | 756 | 0.4008 | 0.8222 |
| 0.0 | 29.0 | 783 | 0.3766 | 0.8444 |
| 0.0001 | 30.0 | 810 | 0.3622 | 0.9111 |
| 0.0 | 31.0 | 837 | 0.4006 | 0.8222 |
| 0.0001 | 32.0 | 864 | 0.4743 | 0.8444 |
| 0.0001 | 33.0 | 891 | 0.3292 | 0.8889 |
| 0.0001 | 34.0 | 918 | 1.1554 | 0.7556 |
| 0.0002 | 35.0 | 945 | 0.6888 | 0.8 |
| 0.0003 | 36.0 | 972 | 0.4504 | 0.8667 |
| 0.0001 | 37.0 | 999 | 0.4287 | 0.8667 |
| 0.0 | 38.0 | 1026 | 0.4528 | 0.8667 |
| 0.0001 | 39.0 | 1053 | 0.4353 | 0.8667 |
| 0.0 | 40.0 | 1080 | 0.4656 | 0.8444 |
| 0.0044 | 41.0 | 1107 | 0.4571 | 0.8222 |
| 0.0 | 42.0 | 1134 | 0.4813 | 0.8222 |
| 0.0004 | 43.0 | 1161 | 0.5618 | 0.8444 |
| 0.0 | 44.0 | 1188 | 0.5635 | 0.8444 |
| 0.0 | 45.0 | 1215 | 0.5635 | 0.8444 |
| 0.0061 | 46.0 | 1242 | 0.5733 | 0.8444 |
| 0.0 | 47.0 | 1269 | 0.5697 | 0.8444 |
| 0.0001 | 48.0 | 1296 | 0.5838 | 0.8222 |
| 0.0001 | 49.0 | 1323 | 0.5839 | 0.8222 |
| 0.0 | 50.0 | 1350 | 0.5839 | 0.8222 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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fadedritalin/fadestyle | fadedritalin | 2023-11-29T08:59:26Z | 0 | 0 | null | [
"text-to-image",
"region:us"
] | 2023-11-29T08:59:26Z | 2023-11-29T08:52:08.000Z | null | null | ---
pipeline_tag: text-to-image
---
The model I use to make art | null | null | text-to-image | null | null | null | null | null | null | null | null | null | fadedritalin/fadestyle | [
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Anavya1/Hi | Anavya1 | 2023-11-29T08:59:09Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:59:09Z | 2023-11-29T08:52:10.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Anavya1/Hi | [
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optimal-policy/my_awesome_wnut_model | optimal-policy | 2023-11-29T08:52:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:52:28Z | 2023-11-29T08:52:28.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | optimal-policy/my_awesome_wnut_model | [
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Praneeh/gpt4all-13b-snoozy-q4_0.gguf | Praneeh | 2023-11-29T08:53:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:53:23Z | 2023-11-29T08:52:57.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Praneeh/gpt4all-13b-snoozy-q4_0.gguf | [
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nyanxyz/test-sat | nyanxyz | 2023-11-29T08:53:27Z | 0 | 0 | null | [
"autotrain",
"text-generation",
"region:us"
] | 2023-11-29T08:53:27Z | 2023-11-29T08:53:20.000Z | null | null | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
---
# Model Trained Using AutoTrain | null | null | text-generation | null | null | null | null | null | null | null | null | null | nyanxyz/test-sat | [
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kaiku03/distilbert-base-uncased-finetuned-game_titles | kaiku03 | 2023-11-29T08:55:30Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:55:30Z | 2023-11-29T08:54:37.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-game_titles
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. -->
# distilbert-base-uncased-finetuned-game_titles
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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bentleyjeon/distilbert-base-uncased-finetuned | bentleyjeon | 2023-11-29T14:07:35Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:07:35Z | 2023-11-29T08:55:30.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | bentleyjeon/distilbert-base-uncased-finetuned | [
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sumeyyecelik/my_qa_model | sumeyyecelik | 2023-11-29T08:56:45Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:56:45Z | 2023-11-29T08:56:45.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | sumeyyecelik/my_qa_model | [
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hllj/sft-mistral-v3-all | hllj | 2023-11-29T10:25:14Z | 0 | 0 | null | [
"transformers",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:hllj/mistral-vi-math",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:25:14Z | 2023-11-29T08:56:59.000Z | null | null | ---
base_model: hllj/mistral-vi-math
tags:
- generated_from_trainer
model-index:
- name: sft-mistral-v3-all
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. -->
# sft-mistral-v3-all
This model is a fine-tuned version of [hllj/mistral-vi-math](https://huggingface.co/hllj/mistral-vi-math) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4937
## 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
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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AhmedTaha012/hadith-finetuned-ner13 | AhmedTaha012 | 2023-11-29T09:00:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:00:52Z | 2023-11-29T09:00:52.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AhmedTaha012/hadith-finetuned-ner13 | [
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rika37/ppo-CartPole-v1 | rika37 | 2023-11-29T09:06:31Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | 2023-11-29T09:06:31Z | 2023-11-29T09:02:07.000Z | null | null | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -171.42 +/- 100.70
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'rika37/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | rika37/ppo-CartPole-v1 | [
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tuannm2914/math-lora | tuannm2914 | 2023-11-30T01:25:10Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"mistral",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:25:10Z | 2023-11-29T09:02:20.000Z | null | null | ---
tags:
- generated_from_trainer
model-index:
- name: math-lora
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. -->
# math-lora
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6782
## 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: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0 | 0 | 1.6782 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | tuannm2914/math-lora | [
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VishnuKasula/question_answer | VishnuKasula | 2023-11-29T09:03:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:03:38Z | 2023-11-29T09:03:38.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | VishnuKasula/question_answer | [
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pdepinaborges/checkpoint | pdepinaborges | 2023-11-29T09:07:02Z | 0 | 0 | null | [
"pytorch",
"region:us"
] | 2023-11-29T09:07:02Z | 2023-11-29T09:04:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | pdepinaborges/checkpoint | [
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julienroulle/wav2vec2-base-finetuned-ks | julienroulle | 2023-11-29T09:37:59Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:37:59Z | 2023-11-29T09:06:04.000Z | null | null | Entry not found | null | transformers | audio-classification | null | null | null | null | null | null | null | null | null | julienroulle/wav2vec2-base-finetuned-ks | [
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XYB314/opus-mt-en-ro-finetuned-en-to-ro | XYB314 | 2023-11-29T12:14:12Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T12:14:12Z | 2023-11-29T09:07:33.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | XYB314/opus-mt-en-ro-finetuned-en-to-ro | [
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Realgon/roberta_sst2_padding50model | Realgon | 2023-11-29T10:00:12Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:00:12Z | 2023-11-29T09:08:44.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding50model
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_sst2_padding50model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5107
- Accuracy: 0.9462
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.1735 | 0.9319 |
| 0.327 | 2.0 | 866 | 0.2500 | 0.9336 |
| 0.1893 | 3.0 | 1299 | 0.2987 | 0.9407 |
| 0.1229 | 4.0 | 1732 | 0.3376 | 0.9418 |
| 0.0753 | 5.0 | 2165 | 0.3283 | 0.9484 |
| 0.0496 | 6.0 | 2598 | 0.5720 | 0.9116 |
| 0.0349 | 7.0 | 3031 | 0.4278 | 0.9363 |
| 0.0349 | 8.0 | 3464 | 0.4501 | 0.9379 |
| 0.0254 | 9.0 | 3897 | 0.4728 | 0.9374 |
| 0.0217 | 10.0 | 4330 | 0.4662 | 0.9368 |
| 0.0171 | 11.0 | 4763 | 0.4622 | 0.9418 |
| 0.0082 | 12.0 | 5196 | 0.4804 | 0.9429 |
| 0.0094 | 13.0 | 5629 | 0.4789 | 0.9445 |
| 0.0047 | 14.0 | 6062 | 0.5459 | 0.9423 |
| 0.0047 | 15.0 | 6495 | 0.4672 | 0.9434 |
| 0.009 | 16.0 | 6928 | 0.5178 | 0.9445 |
| 0.0021 | 17.0 | 7361 | 0.5107 | 0.9467 |
| 0.0042 | 18.0 | 7794 | 0.5101 | 0.9445 |
| 0.0053 | 19.0 | 8227 | 0.5043 | 0.9462 |
| 0.0017 | 20.0 | 8660 | 0.5107 | 0.9462 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/roberta_sst2_padding50model | [
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zengchen1024/test | zengchen1024 | 2023-11-29T09:08:52Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T09:08:52Z | 2023-11-29T09:08:52.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | zengchen1024/test | [
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mesolitica/malaysian-mistral-5B-4096 | mesolitica | 2023-11-29T13:51:19Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ms",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T13:51:19Z | 2023-11-29T09:09:22.000Z | null | null | ---
language:
- ms
---
# Pretrain 5B 4096 context length Mistral on Malaysian text
README at https://github.com/mesolitica/malaya/tree/5.1/pretrained-model/mistral
- Dataset gathered at https://github.com/malaysia-ai/dedup-text-dataset/tree/main/pretrain-llm
- We use Ray cluster to train on 5 nodes of 8x A100 80GB, https://github.com/malaysia-ai/jupyter-gpu/tree/main/ray
WandB, https://wandb.ai/mesolitica/pretrain-mistral-5b?workspace=user-husein-mesolitica
WandB report, https://wandb.ai/mesolitica/pretrain-mistral-3b/reports/Pretrain-Larger-Malaysian-Mistral--Vmlldzo2MDkyOTgz
## how-to
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
TORCH_DTYPE = 'bfloat16'
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE)
)
tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-mistral-5B-4096')
model = AutoModelForCausalLM.from_pretrained(
'mesolitica/malaysian-mistral-5B-4096',
use_flash_attention_2 = True,
quantization_config = nf4_config
)
prompt = '<s>nama saya'
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=512,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.05,
)
r = model.generate(**generate_kwargs)
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | mesolitica/malaysian-mistral-5B-4096 | [
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Skyova/A-Z | Skyova | 2023-11-29T09:09:47Z | 0 | 0 | null | [
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2023-11-29T09:09:47Z | 2023-11-29T09:09:47.000Z | null | null | ---
license: cc-by-nc-nd-4.0
---
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MufinApps/mt5-small-finetuned-amazon-en-es | MufinApps | 2023-11-29T09:50:10Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:50:10Z | 2023-11-29T09:09:52.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | MufinApps/mt5-small-finetuned-amazon-en-es | [
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zjunlp/knowlm-7b-base | zjunlp | 2023-11-29T09:58:30Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:58:30Z | 2023-11-29T09:10:57.000Z | null | null | # Knowlm-7b-base
---
license: apache-2.0
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | zjunlp/knowlm-7b-base | [
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Kuunn/SD_Dreambooth | Kuunn | 2023-11-29T09:16:06Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:16:06Z | 2023-11-29T09:16:06.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Kuunn/SD_Dreambooth | [
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nimrita/Reinforce-PixelCopterV1 | nimrita | 2023-11-29T10:20:55Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-29T10:20:55Z | 2023-11-29T09:16:49.000Z | null | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopterV1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 12.40 +/- 8.80
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
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | nimrita/Reinforce-PixelCopterV1 | [
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MayIBorn/2023_11_29_18_18 | MayIBorn | 2023-11-29T09:19:12Z | 0 | 0 | null | [
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burakaytan/roberta-small-turkish-clean-uncased | burakaytan | 2023-11-29T09:58:37Z | 0 | 0 | null | [
"tr",
"license:mit",
"region:us"
] | 2023-11-29T09:58:37Z | 2023-11-29T09:20:28.000Z | null | null | ---
language: tr
license: mit
---
🇹🇷 RoBERTaTurk-Small-Clean
## Model description
This is a Turkish small clean RoBERTa model, trained to understand Turkish language better.
We used special, clean data from Turkish Wikipedia, Turkish OSCAR, and news websites.
First, we had 38 GB of data, but we took out all the sentences with mistakes in them.
So, the model was trained with 20 GB of good quality data. This helps the model work really well with Turkish texts that don't have errors.
The model is a bit smaller than the usual RoBERTa model. It has 8 layers instead of 12, which makes it faster and easier to use but still very good at understanding Turkish.
It's built to be really good at understanding Turkish, especially when the texts are written correctly without errors.
Thanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RAM 2 x GV100GL [Tesla V100 PCIe 32GB] GPU for 1.5M steps.
# Usage
Load transformers library with:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("burakaytan/roberta-small-turkish-clean-uncased")
model = AutoModelForMaskedLM.from_pretrained("burakaytan/roberta-small-turkish-clean-uncased")
```
# Fill Mask Usage
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="burakaytan/roberta-small-turkish-clean-uncased",
tokenizer="burakaytan/roberta-small-turkish-clean-uncased"
)
fill_mask("iki ülke arasında <mask> başladı")
[{'sequence': 'iki ülke arasında savaş başladı',
'score': 0.14830906689167023,
'token': 1745,
'token_str': ' savaş'},
{'sequence': 'iki ülke arasında çatışmalar başladı',
'score': 0.1442396193742752,
'token': 18223,
'token_str': ' çatışmalar'},
{'sequence': 'iki ülke arasında gerginlik başladı',
'score': 0.12025047093629837,
'token': 13638,
'token_str': ' gerginlik'},
{'sequence': 'iki ülke arasında çatışma başladı',
'score': 0.0615813322365284,
'token': 5452,
'token_str': ' çatışma'},
{'sequence': 'iki ülke arasında görüşmeler başladı',
'score': 0.04512731358408928,
'token': 4736,
'token_str': ' görüşmeler'}]
```
## Citation and Related Information
To cite this model:
```bibtex
@article{aytan2023deep,
title={Deep learning-based Turkish spelling error detection with a multi-class false positive reduction model},
author={AYTAN, BURAK and {\c{S}}AKAR, CEMAL OKAN},
journal={Turkish Journal of Electrical Engineering and Computer Sciences},
volume={31},
number={3},
pages={581--595},
year={2023}
}
``` | null | null | null | null | null | null | null | null | null | null | null | null | burakaytan/roberta-small-turkish-clean-uncased | [
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hkivancoral/hushem_5x_beit_base_rms_00001_fold2 | hkivancoral | 2023-11-29T09:57:08Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:57:08Z | 2023-11-29T09:24:55.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_00001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
---
<!-- 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. -->
# hushem_5x_beit_base_rms_00001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8184
- Accuracy: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6865 | 1.0 | 27 | 0.7969 | 0.7556 |
| 0.1615 | 2.0 | 54 | 0.9353 | 0.7778 |
| 0.041 | 3.0 | 81 | 1.0745 | 0.6444 |
| 0.0119 | 4.0 | 108 | 1.0481 | 0.7333 |
| 0.0095 | 5.0 | 135 | 0.6063 | 0.8667 |
| 0.0013 | 6.0 | 162 | 0.6520 | 0.8444 |
| 0.0015 | 7.0 | 189 | 0.7604 | 0.8667 |
| 0.0013 | 8.0 | 216 | 0.7595 | 0.8444 |
| 0.0008 | 9.0 | 243 | 0.8299 | 0.8444 |
| 0.0008 | 10.0 | 270 | 0.6509 | 0.8444 |
| 0.0009 | 11.0 | 297 | 0.7989 | 0.8444 |
| 0.0002 | 12.0 | 324 | 0.8458 | 0.8444 |
| 0.0005 | 13.0 | 351 | 0.6321 | 0.8667 |
| 0.0002 | 14.0 | 378 | 0.6972 | 0.8444 |
| 0.0002 | 15.0 | 405 | 0.7426 | 0.8667 |
| 0.0005 | 16.0 | 432 | 0.9776 | 0.8 |
| 0.0023 | 17.0 | 459 | 1.0180 | 0.8 |
| 0.0003 | 18.0 | 486 | 1.1105 | 0.7778 |
| 0.0006 | 19.0 | 513 | 0.9919 | 0.7556 |
| 0.0002 | 20.0 | 540 | 1.0177 | 0.8 |
| 0.0012 | 21.0 | 567 | 0.9992 | 0.8444 |
| 0.0003 | 22.0 | 594 | 0.9760 | 0.8444 |
| 0.0047 | 23.0 | 621 | 0.9891 | 0.8 |
| 0.0061 | 24.0 | 648 | 0.9730 | 0.8222 |
| 0.0002 | 25.0 | 675 | 0.8247 | 0.8222 |
| 0.0001 | 26.0 | 702 | 0.8270 | 0.8667 |
| 0.0001 | 27.0 | 729 | 0.7978 | 0.8222 |
| 0.0 | 28.0 | 756 | 0.8136 | 0.8444 |
| 0.0001 | 29.0 | 783 | 0.8553 | 0.8444 |
| 0.0001 | 30.0 | 810 | 0.9423 | 0.8444 |
| 0.0001 | 31.0 | 837 | 0.9286 | 0.8222 |
| 0.0001 | 32.0 | 864 | 0.9464 | 0.8222 |
| 0.0002 | 33.0 | 891 | 0.8713 | 0.8444 |
| 0.0001 | 34.0 | 918 | 0.8762 | 0.8444 |
| 0.0001 | 35.0 | 945 | 0.9092 | 0.8667 |
| 0.0 | 36.0 | 972 | 0.9547 | 0.8444 |
| 0.0 | 37.0 | 999 | 0.9283 | 0.8444 |
| 0.0 | 38.0 | 1026 | 0.8639 | 0.8444 |
| 0.0001 | 39.0 | 1053 | 0.8477 | 0.8667 |
| 0.0 | 40.0 | 1080 | 0.8432 | 0.8667 |
| 0.0 | 41.0 | 1107 | 0.8325 | 0.8667 |
| 0.0 | 42.0 | 1134 | 0.7851 | 0.8667 |
| 0.0003 | 43.0 | 1161 | 0.7875 | 0.8667 |
| 0.0 | 44.0 | 1188 | 0.7888 | 0.8667 |
| 0.0001 | 45.0 | 1215 | 0.8006 | 0.8889 |
| 0.0001 | 46.0 | 1242 | 0.8075 | 0.8889 |
| 0.0001 | 47.0 | 1269 | 0.8158 | 0.8889 |
| 0.0 | 48.0 | 1296 | 0.8184 | 0.8667 |
| 0.0002 | 49.0 | 1323 | 0.8184 | 0.8667 |
| 0.0001 | 50.0 | 1350 | 0.8184 | 0.8667 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Gautamid/my_awesome_tokclassi_model | Gautamid | 2023-11-29T09:26:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:26:14Z | 2023-11-29T09:26:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Gautamid/my_awesome_tokclassi_model | [
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TheGreatP/DaniloGentiliV1 | TheGreatP | 2023-11-29T09:31:24Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T09:31:24Z | 2023-11-29T09:30:55.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | TheGreatP/DaniloGentiliV1 | [
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MikkelWK/MedSum | MikkelWK | 2023-11-29T21:18:02Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:Danish-summarisation/DanSumT5-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:18:02Z | 2023-11-29T09:31:32.000Z | null | null | ---
license: apache-2.0
base_model: Danish-summarisation/DanSumT5-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DanSumT5-largeV_17400
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. -->
# DanSumT5-largeV_17400
This model is a fine-tuned version of [Danish-summarisation/DanSumT5-large](https://huggingface.co/Danish-summarisation/DanSumT5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0810
- Rouge1: 36.0646
- Rouge2: 12.4066
- Rougel: 22.4673
- Rougelsum: 33.2078
- Gen Len: 123.5882
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 18
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 475 | 2.1203 | 34.1656 | 11.1331 | 21.1224 | 31.5873 | 125.4496 |
| 2.4202 | 2.0 | 950 | 2.0419 | 34.8716 | 11.7072 | 21.505 | 32.1799 | 125.6176 |
| 2.1028 | 3.0 | 1425 | 2.0042 | 35.4223 | 12.013 | 21.9284 | 32.6504 | 123.1345 |
| 1.9178 | 4.0 | 1901 | 1.9826 | 35.0313 | 11.7595 | 21.8848 | 32.3896 | 124.3319 |
| 1.7904 | 5.0 | 2376 | 1.9778 | 35.5915 | 12.0513 | 21.9645 | 32.7786 | 124.3697 |
| 1.6758 | 6.0 | 2851 | 1.9829 | 35.0434 | 11.7049 | 21.7988 | 32.2543 | 123.2269 |
| 1.5734 | 7.0 | 3326 | 1.9728 | 35.4344 | 12.0117 | 22.1228 | 32.7301 | 124.0504 |
| 1.5131 | 8.0 | 3802 | 1.9914 | 35.9572 | 12.3134 | 22.1358 | 33.316 | 123.5378 |
| 1.4325 | 9.0 | 4277 | 2.0006 | 35.6071 | 12.0835 | 22.0644 | 32.9752 | 124.2605 |
| 1.3839 | 10.0 | 4752 | 2.0073 | 35.6042 | 12.2947 | 22.4712 | 33.0487 | 122.1975 |
| 1.3156 | 11.0 | 5227 | 2.0149 | 35.8126 | 11.9867 | 22.3753 | 33.0969 | 124.5378 |
| 1.2942 | 12.0 | 5703 | 2.0355 | 35.4593 | 11.9684 | 22.1591 | 32.8905 | 123.2521 |
| 1.2457 | 13.0 | 6178 | 2.0462 | 35.867 | 12.1835 | 22.3972 | 33.2583 | 123.437 |
| 1.2118 | 14.0 | 6653 | 2.0495 | 35.8893 | 12.3608 | 22.307 | 33.1317 | 124.1261 |
| 1.2014 | 15.0 | 7128 | 2.0594 | 35.6055 | 12.1161 | 22.3591 | 32.9453 | 122.2899 |
| 1.181 | 16.0 | 7604 | 2.0745 | 35.8722 | 12.2746 | 22.5422 | 33.1828 | 122.9076 |
| 1.1514 | 17.0 | 8079 | 2.0788 | 35.9651 | 12.3413 | 22.6585 | 33.2246 | 123.2017 |
| 1.1576 | 17.99 | 8550 | 2.0810 | 36.0646 | 12.4066 | 22.4673 | 33.2078 | 123.5882 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Gonzaluigi/hilaryflail-waifudiffusion | Gonzaluigi | 2023-11-29T18:08:48Z | 0 | 0 | null | [
"dataset:Gonzaluigi/hilaryflail-waifudiffusion-dataset",
"region:us"
] | 2023-11-29T18:08:48Z | 2023-11-29T09:31:58.000Z | null | null | ---
datasets:
- Gonzaluigi/hilaryflail-waifudiffusion-dataset
---
| null | null | null | null | null | null | null | null | null | null | null | null | Gonzaluigi/hilaryflail-waifudiffusion | [
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zeta-alfa/dreambooth_test | zeta-alfa | 2023-11-29T09:52:42Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T09:52:42Z | 2023-11-29T09:36:36.000Z | null | null |
---
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
- dreambooth
inference: true
---
# DreamBooth - zeta-alfa/dreambooth_test
This is a dreambooth model derived from 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.
DreamBooth for the text encoder was enabled: False.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | zeta-alfa/dreambooth_test | [
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FrankTCH/train_test | FrankTCH | 2023-11-29T09:37:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:37:42Z | 2023-11-29T09:37:41.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | FrankTCH/train_test | [
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sarahahatee/a_test_v3 | sarahahatee | 2023-11-29T09:40:50Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:40:50Z | 2023-11-29T09:39:18.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | sarahahatee/a_test_v3 | [
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Crazi/Mangina | Crazi | 2023-11-30T01:20:16Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:20:16Z | 2023-11-29T09:39:49.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Crazi/Mangina | [
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maxliaops/segformer-b0-scene-parse-150 | maxliaops | 2023-11-29T10:13:20Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:13:20Z | 2023-11-29T09:41:00.000Z | null | null | ---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: segformer-b0-scene-parse-150
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. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6622
- Mean Iou: 0.0851
- Mean Accuracy: 0.1397
- Overall Accuracy: 0.4986
- Per Category Iou: [0.43799631935866085, 0.5881424463689124, 0.7844581904647036, 0.3173569309392739, 0.04460848373538657, 0.11434824355769314, 0.8349459264073373, 0.35937849433509145, 0.0, nan, 0.0, 0.0, 0.005994394851567365, 0.0, 0.0, 0.004429470515802435, 0.0, 0.033699213564705455, 0.26424040066777965, 0.0, 0.0, nan, 0.009246466149431396, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.283603744914465, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
- Per Category Accuracy: [0.7650165750026734, 0.9310672847611137, 0.9215628327254031, 0.9571626598253817, 0.19314879951062855, 0.12124964457041175, 0.9808951075347225, 0.8185843482077083, 0.0, nan, 0.0, 0.0, 0.00786088613625536, 0.0, 0.0, 0.004435007448757684, 0.0, 0.17467312449331698, 0.36219679633867274, 0.0, 0.0, nan, 0.023633276740237692, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3061788239278608, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 4.8781 | 1.0 | 20 | 4.8881 | 0.0088 | 0.0498 | 0.2172 | [0.31339246192435694, 0.0017225649420157634, 0.38946382906287114, 0.035681706883804, 0.0, 0.0, 0.00046600649080469337, 0.163986467287064, 0.0, 0.0, 0.0, 0.0, 0.020729489785003743, 0.0004887426281320257, 0.0005038911595095459, 0.0022444184856081585, 0.0, 0.025738738643900076, 0.0, 3.549875754348598e-05, 0.09594882729211088, 0.0, 0.0, 0.0, 0.0, 0.0073766111900324515, 0.0, 0.0, nan, 0.0, 0.014641192305167094, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.002817582556220234, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0] | [0.5611471872818043, 0.001751788633135936, 0.8150907037281439, 0.038440849091736666, 0.0, 0.0, 0.0004804104077483336, 0.5789094214824219, 0.0, nan, 0.0, 0.0, 0.03297488600013612, 0.0004887744794551794, 0.0023407022106631988, 0.0029281323310330655, 0.0, 0.05082420437774129, 0.0, 0.00011554015020219527, 0.2097457627118644, nan, 0.0, 0.0, 0.0, 0.008713859038704313, nan, 0.0, nan, 0.0, 0.031802624073017684, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.002836459083019347, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 4.7245 | 2.0 | 40 | 4.4544 | 0.0210 | 0.0760 | 0.3119 | [0.3060439245176032, 0.06646688890089392, 0.44247108014824277, 0.24658945487617406, 0.004086830337311577, 0.0023826912543363156, 0.38929362736373063, 0.19519639615744847, 0.0, 0.0, 0.0, 0.0, 7.472724555372888e-05, 0.029642995231344244, 0.0, 0.011817991777576475, 0.0, 0.1048576260091643, 0.0, 0.0005241090146750524, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03582522261337751, 0.0, 0.0, nan, 0.0, 0.010852449248840277, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0012904098612289072, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0] | [0.7605001226613101, 0.0700581473244192, 0.8443928052369714, 0.31694045599371046, 0.03165621654687261, 0.002393990258936188, 0.5441334168332376, 0.7382407799250795, 0.0, nan, 0.0, 0.0, 0.00013611924045463827, 0.029978168073251002, 0.0, 0.014520796588982688, 0.0, 0.1598312095952772, 0.0, 0.0010398613518197574, 0.0, nan, 0.0, 0.0, 0.0, 0.0372879268405333, nan, 0.0, nan, 0.0, 0.01818311466058186, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.001312391516023877, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 4.2382 | 3.0 | 60 | 4.0978 | 0.0337 | 0.0928 | 0.3540 | [0.31970982570970596, 0.2174995548607695, 0.6127710679181643, 0.21726695154762712, 0.02594370488250475, 0.009859734558798313, 0.45098357649893916, 0.21878590714147236, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.012143821959220428, 0.0, 0.05279900846932452, 0.0, 0.001165792322424848, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010303011883414885, 0.0, 0.0, nan, 0.0, 0.004643359995206854, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0681438127090301, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0] | [0.7724370600704935, 0.24139848334628475, 0.8737556322946566, 0.3976289982207142, 0.3789570270683591, 0.010548232941672859, 0.7385108993111258, 0.725215905706807, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01616466035377318, 0.0, 0.10626312179100754, 0.0, 0.0016175621028307336, 0.0, nan, 0.0, 0.0, 0.0, 0.01043815623364227, nan, 0.0, nan, 0.0, 0.01105248146035368, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.07590703187841327, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 4.0372 | 4.0 | 80 | 3.9594 | 0.0418 | 0.0962 | 0.3642 | [0.3265572023592501, 0.23504640523998466, 0.6565515117118094, 0.24014714756195973, 0.014470058797430362, 0.01074377956543817, 0.4351143508429148, 0.24707635234138767, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0011232704188437123, 0.0, 0.062414968842904414, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.01324603565075881, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.016183592263955007, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.75307963759349, 0.2750107184008146, 0.9319590466898503, 0.6834112632929201, 0.2967579140541367, 0.01106188602404997, 0.7450736486312592, 0.7056707575116378, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001130156338293464, 0.0, 0.07724447585590455, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.024957216200798632, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.016658905211464375, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 3.4928 | 5.0 | 100 | 3.8226 | 0.0455 | 0.0950 | 0.3564 | [0.30107680488625616, 0.18078720794807554, 0.6849130761474783, 0.240955982051748, 0.019690253205712792, 0.00727884562629163, 0.4143055571904465, 0.27656223133796765, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00018760126204485375, 0.0, 0.06041692213366033, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.011070924488355682, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0791091931879479, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7954407734581893, 0.19601610439722394, 0.863505159647468, 0.7132763686018124, 0.3797216699801193, 0.0073012116709318215, 0.6038844612969365, 0.7058901898089371, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00018835938971557733, 0.0, 0.10241752759473673, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.017897889332572733, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.08148998772278904, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 3.5041 | 6.0 | 120 | 3.6471 | 0.0460 | 0.0924 | 0.3527 | [0.3031083155538331, 0.12420028773191281, 0.7310125851898989, 0.23747461884156063, 0.020041987242337904, 0.004193483690994949, 0.5109682646447352, 0.2333974256794453, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04001612450420749, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.048529380691782704, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7986299675209625, 0.13908799807068786, 0.8975681441867432, 0.7658377125832747, 0.2657134118366723, 0.0042192931766691436, 0.5734985030068545, 0.7343928778545791, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.11555490884902406, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.04861669700690064, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 3.341 | 7.0 | 140 | 3.6577 | 0.0500 | 0.0987 | 0.3948 | [0.3658020904374668, 0.4348948935113732, 0.662414710894666, 0.21875727487120314, 0.00795868772782503, 0.012022284324755925, 0.3693992073146804, 0.2601830368651714, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03828137927523134, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13121501047377335, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7523877019980164, 0.5691236367533964, 0.8402999032439589, 0.8943186990524269, 0.1202018657287047, 0.012153398824101337, 0.40377636895519314, 0.790935878747982, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1250337788679402, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13159900088903942, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 3.2865 | 8.0 | 160 | 3.5388 | 0.0566 | 0.1030 | 0.4088 | [0.33530877488321936, 0.4218561985700796, 0.770320962888666, 0.2517285631187896, 0.016995261926977933, 0.010436602335938757, 0.4371319306102863, 0.30727653736960364, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03213066470312605, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0009030160736861117, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13264913539424567, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7993596450580073, 0.5237312093035719, 0.9327565755648488, 0.8684880208548847, 0.21448233674873834, 0.010704163341680195, 0.5157892027760859, 0.7320026331875676, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10975533706113455, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.001069594980034227, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13501756911223065, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 3.5287 | 9.0 | 180 | 3.5125 | 0.0485 | 0.0947 | 0.3909 | [0.37840703814816984, 0.45650941271904005, 0.6924449941997962, 0.19233720159636408, 9.633778780914108e-05, 0.009164282981398146, 0.1349531088888525, 0.2547152816336603, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03301215372823826, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.17836973727516364, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7444912491114918, 0.5636137088346419, 0.8747839182553145, 0.9517317002524103, 0.0005352500382321456, 0.009227410729845996, 0.141472286324603, 0.8088979796554913, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17548381732388219, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.18068138520807756, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 3.0969 | 10.0 | 200 | 3.4224 | 0.0486 | 0.0975 | 0.4027 | [0.3897579127622329, 0.5062939423182053, 0.706753473909241, 0.19263155259391548, 0.02140512464815427, 0.012959649807921098, 0.025529877265052042, 0.3401158580738595, 0.001452048242167481, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02641665018869036, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.10951692706959669, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.772177060028558, 0.7134736997240012, 0.8833381239044099, 0.9551351015848057, 0.2029362287811592, 0.013088981224145363, 0.026070843377628317, 0.7730051253115154, 0.001452048242167481, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13052154572099695, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.10987045425680539, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 3.4272 | 11.0 | 220 | 3.3558 | 0.0505 | 0.0949 | 0.4023 | [0.38278472686289833, 0.5128929672982241, 0.722077753218047, 0.17540052841643713, 0.006927296169227071, 0.030134151433187505, 0.09074698949321136, 0.3327536901056018, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.030007100008077853, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0874864443092549, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7662725429471844, 0.7173959645220933, 0.899304894884075, 0.9683245748334506, 0.04847836060559719, 0.03045228988378599, 0.09736031638456853, 0.7003260136988448, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1467146153366454, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.08751746327420516, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.9263 | 12.0 | 240 | 3.2993 | 0.0590 | 0.1084 | 0.4333 | [0.39337701722656254, 0.5514615358740623, 0.7055154685819042, 0.23820165432903456, 0.02924452471445158, 0.042041797736558556, 0.35449477685844805, 0.29864290435447, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03021328954324505, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13115689713851872, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7803146419862326, 0.8236957046008736, 0.8522264010396214, 0.9526834112632929, 0.2253402660957333, 0.042660722966713444, 0.3807852994415229, 0.7719236375605398, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13424241794333466, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13255683501968588, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.7824 | 13.0 | 260 | 3.2578 | 0.0491 | 0.0941 | 0.4004 | [0.3774813879787845, 0.566022996897244, 0.5759739039300197, 0.18148316791949, 0.0013151405556468849, 0.01976377881272072, 0.1332255360937328, 0.3267457921392346, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.028579237461850315, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.14766187144891962, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7674865754010606, 0.7790703127093438, 0.7319776691915001, 0.97304175114826, 0.008564000611714329, 0.019968263577410272, 0.13500390333456294, 0.7285779219761446, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13041761074271935, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.1486706743998984, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.3949 | 14.0 | 280 | 3.1274 | 0.0664 | 0.1163 | 0.4475 | [0.37456400734642586, 0.5955374575511475, 0.7671264157837048, 0.2208000570450656, 0.05068035819744248, 0.038591544204038104, 0.5223246756293846, 0.35954331942552115, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.029479567167948938, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.23088597104602077, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7799854483847497, 0.7877019748653501, 0.8840061049418452, 0.9609695038689121, 0.3215323443951675, 0.039166047531254876, 0.5729408837835751, 0.7300277425118729, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15284677905502317, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2358917912027433, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.8535 | 15.0 | 300 | 3.0513 | 0.0703 | 0.1257 | 0.4736 | [0.43172438479171454, 0.5366172816710887, 0.7748592749223223, 0.25544785584639945, 0.05662510994150414, 0.04869530599688755, 0.8008944166716858, 0.3356032120540419, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03596793875705619, 0.0036613272311212816, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.09349866483356688, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.763739639312845, 0.8955759800637745, 0.9702201908401582, 0.9667211486738114, 0.3101391650099404, 0.04936573016702898, 0.912591042061647, 0.7736164009968496, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17032864240131373, 0.0036613272311212816, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.09357139833199273, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.3891 | 16.0 | 320 | 3.0796 | 0.0463 | 0.0959 | 0.3772 | [0.3925250414049068, 0.5379942260347973, 0.2263367707759119, 0.20596563634557874, 0.03756206208528606, 0.02092705078176988, 0.32118998693876194, 0.33334680597376876, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.027882039895679143, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.07316053511705686, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.756845444652491, 0.9156796805916557, 0.27260508556229834, 0.9653866843215956, 0.24759137482795535, 0.03605661190757913, 0.3312086611133511, 0.7756148022758264, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13156089550377284, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.07316053511705686, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.3008 | 17.0 | 340 | 3.0391 | 0.0502 | 0.0986 | 0.3921 | [0.3924524678317649, 0.5845755900441839, 0.2120022815807725, 0.23267549992567566, 0.012156663275686673, 0.07021139810157698, 0.45922321702288216, 0.3252654606818793, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.029704444961601118, 0.011350114416475973, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13089472275715866, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7798260935203377, 0.8818798735228703, 0.26482411856865834, 0.9553419952828237, 0.05482489677320691, 0.17029434156095502, 0.5011795791261677, 0.7492280685255717, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13266260627351528, 0.011350114416475973, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13200647728715972, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.2055 | 18.0 | 360 | 2.9334 | 0.0665 | 0.1182 | 0.4624 | [0.44341028853825126, 0.5411708525350095, 0.7632825647793562, 0.23426888348649863, 0.02949891572447814, 0.12974278811993747, 0.6311314959662558, 0.3320217878469928, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.031628020234392186, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.054905238213622636, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7434931844343846, 0.9337803799673089, 0.9529498447450134, 0.9659556419911449, 0.13625936687566906, 0.13399007548865835, 0.6610618785762694, 0.8034905409006129, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.16739767601388572, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.054929935227128406, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.3833 | 19.0 | 380 | 3.0085 | 0.0653 | 0.1181 | 0.4524 | [0.4128312039460035, 0.544437125853215, 0.6347974252177205, 0.2566876584064724, 0.03509338408986625, 0.12681957612939207, 0.7056134760549437, 0.3437320789506159, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.030151498471465952, 0.0793909290774367, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.03140593828878647, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7760246412942969, 0.9385500683298051, 0.7601543238614972, 0.9596764182562999, 0.15789876127848296, 0.16685470038432257, 0.7826657630375664, 0.7421748875409476, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14802419606294304, 0.08924485125858124, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.03140743406291012, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.9708 | 20.0 | 400 | 2.8906 | 0.0714 | 0.1234 | 0.4719 | [0.43055324756518315, 0.5408000509205003, 0.7416940177822056, 0.2950947546098565, 0.05305879115839677, 0.08126676031158217, 0.7520173805090006, 0.3143493036829079, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.028866041573304836, 0.03908466819221968, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.07825492053468056, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7768696414305873, 0.9675935850371125, 0.8800832344856344, 0.9548971738320851, 0.2654075546719682, 0.08755950579235575, 0.8106668268034692, 0.8002774251187286, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13925208389631447, 0.03908466819221968, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.07869057194868972, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.3001 | 21.0 | 420 | 2.7881 | 0.0720 | 0.1246 | 0.4799 | [0.42566064421142935, 0.5510948340539412, 0.7963071812782361, 0.2670408110780274, 0.041441642076350614, 0.06130313684306653, 0.8585854802340303, 0.33959587680862463, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.033963051606803685, 0.009061784897025171, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.07324083564929781, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7698873822399004, 0.9546142715506847, 0.9433390145457932, 0.9667314933587123, 0.17074476219605444, 0.06157416324995643, 0.983211372000652, 0.7598391874735506, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.16577629035275532, 0.009061784897025171, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.07335633546420559, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.3503 | 22.0 | 440 | 2.8968 | 0.0600 | 0.1078 | 0.4435 | [0.4009422204765702, 0.5758984274936579, 0.6952434972854712, 0.23522321269297616, 0.02431896461747208, 0.02128664205156786, 0.3555950854258884, 0.33200530314756616, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02827907139177733, 0.009917204985897552, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.14277285219632627, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7791027063068882, 0.9337535839652724, 0.8336403347192253, 0.9548454504075806, 0.10689707906407708, 0.02522403529530466, 0.3687064091895648, 0.7732323944765757, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.134658157856445, 0.009977116704805491, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.14597709665128486, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.0353 | 23.0 | 460 | 2.7805 | 0.0704 | 0.1201 | 0.4765 | [0.43552051985786927, 0.5580139716619963, 0.7853173957375013, 0.24015903451387322, 0.007401062941977423, 0.09016951614807013, 0.713755714593456, 0.3564902999504036, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03659310384352447, 0.018794614620041564, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.1379215035931454, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7656015750970282, 0.9471314879819931, 0.9572451652342184, 0.9691624943104233, 0.025768466126319008, 0.0910908707336984, 0.7674556263779629, 0.7773545869187003, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.18581495416467458, 0.019038901601830662, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.13995491300114304, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.9513 | 24.0 | 480 | 2.7606 | 0.0746 | 0.1278 | 0.4852 | [0.41378400787975583, 0.5650731346344877, 0.8210782920172846, 0.2914170779846309, 0.040436183797150214, 0.03115905122235157, 0.8304916416568803, 0.36967404023373096, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03415307279089251, 0.10476714050106024, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.000125250501002004, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.15125685545774556, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7795535128312118, 0.9412497655349822, 0.9354082578649706, 0.9607315761161915, 0.1525462608961615, 0.031424561789714096, 0.9828081704084346, 0.774932994780646, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1728646558712869, 0.12210526315789473, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0001426126640045636, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.15485161508826892, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.5428 | 25.0 | 500 | 2.8292 | 0.0834 | 0.1322 | 0.4980 | [0.4316053343559932, 0.6152808203591025, 0.7791990515083652, 0.2556055276655523, 0.03889537928534021, 0.1457316411758325, 0.8011732614187629, 0.3721458816471104, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0010274148529940581, 0.0, 0.035645594066850826, 0.0878317924246943, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0010828025477707007, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.35445448082840764, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7740159312928921, 0.8965439856373429, 0.9578281305032529, 0.9712417759755038, 0.14765254626089616, 0.14787705346578245, 0.8869148215189547, 0.7583345088634974, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0010274148529940581, 0.0, 0.1872284698692498, 0.10782608695652174, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0012122076440387907, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.37731785275813895, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.538 | 26.0 | 520 | 2.7886 | 0.0782 | 0.1333 | 0.4818 | [0.41935220041484256, 0.5760141760092989, 0.6519306666841057, 0.3124709506474825, 0.04060913705583756, 0.1646435959468462, 0.8524931620573868, 0.33873747130769594, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03555039693617188, 0.2069359756097561, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.15376030398039908, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7740662538816538, 0.9494359441571317, 0.8071558986773976, 0.952776513427401, 0.23489830249273588, 0.21535822716307568, 0.9866342961558588, 0.7887415557749878, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1508928014634045, 0.24851258581235697, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.15675140764573897, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.1067 | 27.0 | 540 | 2.7597 | 0.0765 | 0.1322 | 0.4806 | [0.44306332594269, 0.5810322718970202, 0.7330948442857587, 0.25856656570504866, 0.039554893987801334, 0.08848476413599005, 0.8495158795892489, 0.31709300784403505, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0012328978235928698, 0.0, 0.043542145655904925, 0.1840312471614134, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.20691128256069505, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7278030206133904, 0.944019802245505, 0.8594891766830086, 0.9696486945007655, 0.16661569047254932, 0.10496867633435147, 0.9942951264079886, 0.824955721697152, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0012328978235928698, 0.0, 0.22360571226640613, 0.1854462242562929, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.21273972312772532, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.9753 | 28.0 | 560 | 2.7155 | 0.0798 | 0.1353 | 0.4942 | [0.42089880477356745, 0.6011037177862273, 0.7902404487650792, 0.29546752786722025, 0.033810859036451694, 0.11951722640416908, 0.8548356975849525, 0.35682350361142356, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00042767209525112907, 0.0, 0.028343704118017198, 0.22544034864717633, 0.0, 0.0, nan, 0.00011873664212776063, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.26335939797770813, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7696860918848535, 0.9150164795412524, 0.9398695615210535, 0.9577936856043365, 0.253555589539685, 0.12116709318217257, 0.9817186682337197, 0.7905753828309902, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00042808952208085754, 0.0, 0.1264057205812044, 0.22727688787185354, 0.0, 0.0, nan, 0.00020373514431239388, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.27483489268024214, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.8294 | 29.0 | 580 | 2.6695 | 0.0797 | 0.1330 | 0.4967 | [0.43193574734549417, 0.5902044805613292, 0.8012437356216782, 0.2931507543738475, 0.0342900207247378, 0.10773801951769131, 0.8375926523930038, 0.3426795973401672, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03590416496806631, 0.1482837528604119, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.00032270556344391377, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.28404777148733873, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7717556083476788, 0.9263746349044722, 0.9404646718998595, 0.9571523151404808, 0.16699801192842942, 0.10875686781688268, 0.9568145358463372, 0.8016723875801319, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1757540482674039, 0.1482837528604119, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.000356531660011409, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.298166885398586, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.1884 | 30.0 | 600 | 2.7477 | 0.0829 | 0.1328 | 0.4868 | [0.416992133187597, 0.60863784410833, 0.7229869805323939, 0.28414367869114615, 0.0373197332827926, 0.0966668924359756, 0.8310238127270281, 0.32702567229748064, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03280270978906953, 0.22735361410049995, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3117667862375587, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.770329801666097, 0.9138374554516466, 0.8315756660580618, 0.9557971614184632, 0.16562165468726106, 0.11781917577025032, 0.9538377070697539, 0.8027303647278256, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1568170952252271, 0.24558352402745995, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3265526438338766, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.4722 | 31.0 | 620 | 2.7597 | 0.0806 | 0.1325 | 0.4804 | [0.41637670209485994, 0.6046771928895012, 0.7105482443537727, 0.2877210383572259, 0.04357934812618975, 0.14900809536518103, 0.7928329124138859, 0.35305146878438903, 0.0, nan, 0.0, 0.0, 0.006938045422516126, 0.0, 0.0, 0.0007530120481927711, 0.0, 0.02686181155586904, 0.23230367814157057, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.24348417621914692, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7730430279101658, 0.9174180712237734, 0.8008809252954298, 0.9602557206107502, 0.22931640923688637, 0.1997284976564578, 0.9102747775957175, 0.7894782213444931, 0.0, nan, 0.0, 0.0, 0.00871163138909685, 0.0, 0.0, 0.0007534375588623093, 0.0, 0.13299519820400357, 0.25263157894736843, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2538101689174887, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.9401 | 32.0 | 640 | 2.7277 | 0.0808 | 0.1321 | 0.4795 | [0.42744818396521905, 0.5788110822938537, 0.7424854129670568, 0.2762003235229636, 0.04542595569222168, 0.16417582884368961, 0.7825600280935867, 0.35903038312869484, 0.0, nan, 0.0, 0.0, 0.0030323641104120615, 0.0, 0.0, 0.0007534117566480026, 0.0, 0.03032865185666958, 0.22507659674758426, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.1627400874650711, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.765435929909021, 0.9418727725823307, 0.829405739778878, 0.9644039392560103, 0.1988071570576541, 0.21270741036295093, 0.9176267725857232, 0.7919703452923935, 0.0, nan, 0.0, 0.0, 0.0036411896821615737, 0.0, 0.0, 0.0007534375588623093, 0.0, 0.1550917745858191, 0.2622425629290618, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.16581114262732313, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.2956 | 33.0 | 660 | 2.7129 | 0.0788 | 0.1331 | 0.4848 | [0.41995336483908374, 0.5816551126155232, 0.74305342906446, 0.33098036392433544, 0.038498326159732185, 0.09784677246078763, 0.8348393537978483, 0.34676216359809553, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0016772780173889231, 0.0, 0.028328391461756468, 0.22301255230125522, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.21643838359565862, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7767899639983813, 0.9347215895388408, 0.8350127321234105, 0.9511730872677618, 0.18466126319009024, 0.11695697238197444, 0.9899371177091286, 0.7979263647905205, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0016781109265569616, 0.0, 0.14378364894921736, 0.29272311212814645, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.22879005969264637, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.523 | 34.0 | 680 | 2.6568 | 0.0791 | 0.1335 | 0.4948 | [0.42887244567217003, 0.5806481832300077, 0.759227724671082, 0.3129067447843212, 0.028266241856095418, 0.11713479156309921, 0.8413104327564225, 0.3311181776467153, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03339164115699381, 0.20130057803468207, 0.0, 0.0, nan, 0.00011211181284801375, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.24172077678263001, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7698873822399004, 0.9342392615021838, 0.929562411694931, 0.9524868622501759, 0.10582657898761279, 0.12102950753510727, 0.9849871747578646, 0.8086863842259526, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15554908849024052, 0.25501144164759726, 0.0, 0.0, nan, 0.00020373514431239388, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2555088692265357, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.2511 | 35.0 | 700 | 2.7124 | 0.0797 | 0.1329 | 0.4930 | [0.4099986192803689, 0.615769839245857, 0.7252246690448938, 0.30417620797021466, 0.03704329858176012, 0.061622516013826775, 0.8261509745450759, 0.35824076189388315, 0.0, nan, 0.0, 0.0, 0.011655196055164413, 0.0, 0.0, 0.0, 0.0, 0.031877106852786136, 0.18423370522607163, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.34144358973103456, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7782849642395103, 0.9139010959564833, 0.8798038969608887, 0.955010965365995, 0.150787582199113, 0.06900378819148253, 0.9530570401571629, 0.7865002115954295, 0.0, nan, 0.0, 0.0, 0.015041176070237527, 0.0, 0.0, 0.0, 0.0, 0.14644438439312366, 0.2297482837528604, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3690148596587782, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.5095 | 36.0 | 720 | 2.7011 | 0.0876 | 0.1398 | 0.5048 | [0.45384871500097373, 0.616086993927813, 0.7404677133977238, 0.28509717895306036, 0.03933547304690349, 0.17756158514816137, 0.801438559653316, 0.35095551877972886, 0.0, nan, 0.0, 0.0, 0.012826110686606265, 0.0, 0.0, 0.003765446890083182, 0.0, 0.03330594290526591, 0.24215927157103628, 0.0, 0.0, nan, 0.0007808179067573284, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.35845077692190475, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7574073802269968, 0.911455960770653, 0.9674106221130062, 0.9599246906939215, 0.16493347606667685, 0.1824752575144694, 0.9233659612068595, 0.8193445243804957, 0.0, nan, 0.0, 0.0, 0.015994010753419996, 0.0, 0.0, 0.0037671877943115466, 0.0, 0.1693516536055044, 0.30672768878718537, 0.0, 0.0, nan, 0.001629881154499151, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3858854409212142, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.6118 | 37.0 | 740 | 2.7333 | 0.0815 | 0.1355 | 0.4903 | [0.4294064282162335, 0.5871245178018086, 0.7482076094888618, 0.3180305962901482, 0.0504812869663182, 0.08971804283812675, 0.8277014856549995, 0.35851034911265584, 0.0, nan, 0.0, 0.0, 0.008503978087909222, 0.0, 0.0, 0.0007358225811971663, 0.0, 0.030053989698137232, 0.21455019199122327, 0.0, 0.0, nan, 0.00019917673615721683, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.00098159509202454, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.25011467432491724, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7731310924404988, 0.932922907902141, 0.8758081558460485, 0.9552695824885175, 0.23298669521333537, 0.10200599873421205, 0.9855276364665815, 0.7958495948339368, 0.0, nan, 0.0, 0.0, 0.011093718097053019, 0.0, 0.0, 0.0007363139779790749, 0.0, 0.1511214584156152, 0.2864073226544622, 0.0, 0.0, nan, 0.00040747028862478775, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.001426126640045636, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2654682274247492, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 2.1679 | 38.0 | 760 | 2.6896 | 0.0850 | 0.1375 | 0.4977 | [0.4485154769425142, 0.5972534164924587, 0.7560170453981068, 0.3090291275149793, 0.04056822061222167, 0.12374707651186101, 0.7964183466539425, 0.33096472898466806, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0033331624019281064, 0.0, 0.034812382363541707, 0.2472534833869239, 0.0, 0.0, nan, 0.0008289494659652479, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3051612490842311, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7666856075299368, 0.9263277419009084, 0.9243643047127075, 0.9539661521910042, 0.1635571188255085, 0.13588875741815948, 0.9164000102945087, 0.8242033823921255, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.003339098272230689, 0.0, 0.18070135323341718, 0.33784897025171623, 0.0, 0.0, nan, 0.001765704584040747, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3284365606875238, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.7641 | 39.0 | 780 | 2.6917 | 0.0826 | 0.1341 | 0.4932 | [0.4349239521653315, 0.5890650461705594, 0.7372220292684758, 0.2910697671504516, 0.033243414788955886, 0.12254199189366183, 0.7854591395504485, 0.3607108515260515, 0.0, nan, 0.0, 0.0, 0.002037252619324796, 0.0, 0.0, 0.002857876272781723, 0.0, 0.03639008287497333, 0.24483365088670672, 0.0, 0.0, nan, 0.0007081241148448565, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.24161995878423448, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7697427047972104, 0.929901658672526, 0.9538404861282604, 0.9611763975669301, 0.12815415201101085, 0.12700989699421222, 0.8960340405088919, 0.7937493142740709, 0.0, nan, 0.0, 0.0, 0.0026202953787517864, 0.0, 0.0, 0.0028596380075001284, 0.0, 0.17369613569750764, 0.305812356979405, 0.0, 0.0, nan, 0.0014940577249575552, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.25686888785402817, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.5044 | 40.0 | 800 | 2.6955 | 0.0839 | 0.1380 | 0.4937 | [0.42828833782628245, 0.5811910124521751, 0.7725598942155185, 0.30663330813840267, 0.049351086509331635, 0.07808413847615685, 0.8301422061881063, 0.37806186337725445, 0.0, nan, 0.0, 0.0, 0.04661081492764661, 0.0, 0.0, 0.0031475589311984672, 0.0, 0.03444420047655062, 0.24769414912342463, 0.0, 0.0, nan, 0.0030143854831970393, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.00824132283797348, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.25742598724855437, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7708477049754363, 0.9351905195744795, 0.901167145049046, 0.9610315719783176, 0.20178926441351888, 0.08517468790989058, 0.9830569543695901, 0.7926286421842917, 0.0, nan, 0.0, 0.0, 0.062478731368678965, 0.0, 0.0, 0.0031507388825151115, 0.0, 0.1760866401978922, 0.3220137299771167, 0.0, 0.0, nan, 0.006859083191850595, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.011123787792355962, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2756286778713856, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.4066 | 41.0 | 820 | 2.7089 | 0.0837 | 0.1388 | 0.4928 | [0.440289261392271, 0.5525321370693459, 0.8021981957461438, 0.32122712078559057, 0.05572750650590536, 0.1168216660787873, 0.8373222592337154, 0.3728574410953776, 0.0, nan, 0.0, 0.0, 0.036277400333290605, 0.0, 0.0, 0.00022227541633895292, 0.0, 0.03090404953876705, 0.27147339998540465, 0.0, 0.0, nan, 0.0023978711582887387, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0007059079061685491, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.17846188279219294, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7712020598713, 0.9604223049920951, 0.9068227178326647, 0.9573178300988952, 0.27672426976601927, 0.1245517000999789, 0.9790249384474166, 0.8118054575947086, 0.0, nan, 0.0, 0.0, 0.04815218131082829, 0.0, 0.0, 0.00022260655148204593, 0.0, 0.15592325441203983, 0.34050343249427917, 0.0, 0.0, nan, 0.005568760611205433, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0009269823160296634, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.1856610643071843, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.8317 | 42.0 | 840 | 2.6802 | 0.0877 | 0.1388 | 0.4976 | [0.4269657703925388, 0.6004270099046214, 0.7750591893455628, 0.3105022831050228, 0.05266003837432409, 0.09447424182227519, 0.8379281598268348, 0.36641484998920787, 0.0, nan, 0.0, 0.0, 0.00015711741908452917, 0.0, 0.0, 0.0014546823658269442, 0.0, 0.03159043135625055, 0.2840764331210191, 0.0, 0.0, nan, 0.0031892274982282067, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0008170824708573918, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.33831133487549325, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7711852856750461, 0.9212365015139741, 0.8839696696125305, 0.9601936525013448, 0.23084569506040678, 0.10469350504022087, 0.9829969030686215, 0.7982320026331876, 0.0, nan, 0.0, 0.0, 0.00020417886068195738, 0.0, 0.0, 0.0014555043750749156, 0.0, 0.16432120065686906, 0.32659038901601833, 0.0, 0.0, nan, 0.0073344651952461795, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.001069594980034227, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3683957072096863, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.7436 | 43.0 | 860 | 2.6594 | 0.0891 | 0.1402 | 0.5039 | [0.43581387748091194, 0.5987106558837461, 0.7979435817393381, 0.3162663273786926, 0.04530878761194278, 0.11756663777466023, 0.846721195567232, 0.35587455951047614, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0018654481354075747, 0.0, 0.03140894701291514, 0.2871652816251154, 0.0, 0.0, nan, 0.0007954277633749706, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.352826406651168, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7660880267883914, 0.9229447466438008, 0.9334124114925125, 0.9565626681011297, 0.20851812203700873, 0.12068095722920852, 0.9759880583698646, 0.8167583580194668, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0018664703162725388, 0.0, 0.16045481946494272, 0.34160183066361555, 0.0, 0.0, nan, 0.0018336162988115449, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.38470005503577326, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.3369 | 44.0 | 880 | 2.6988 | 0.0867 | 0.1388 | 0.4960 | [0.4367720884737652, 0.6058232360957789, 0.735769699389417, 0.30780321377197967, 0.04624022316778192, 0.08632366638776187, 0.8358932365439093, 0.3549649780370741, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0059545197885118835, 0.0, 0.032694480167928607, 0.27455637837082575, 0.0, 0.0, nan, 5.752746936662256e-05, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.34977025827143005, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.764003832903844, 0.9209283474905544, 0.8654281353612968, 0.9584764348077958, 0.20025997858999847, 0.10030910908707337, 0.972033251263222, 0.8201203742888042, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0059590061473655375, 0.0, 0.17386243166275178, 0.3597254004576659, 0.0, 0.0, nan, 0.00013582342954159593, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.381482579061005, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.5891 | 45.0 | 900 | 2.6797 | 0.0828 | 0.1370 | 0.4919 | [0.4345476790041336, 0.5801165255560784, 0.7538143200770836, 0.30635133487748384, 0.045711897252726376, 0.09201861943687557, 0.8338392379630835, 0.3674738978601061, 0.0, nan, 0.0, 0.0, 0.008023106546854942, 0.0, 0.0, 0.0046708984207914864, 0.0, 0.03321634344716889, 0.25467257372282986, 0.0, 0.0, nan, 0.003962716972707485, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0011845534233593934, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2550513496497993, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7670777043673717, 0.9331506739194512, 0.8804759263682478, 0.9618591467703894, 0.18397308456950603, 0.10407895581666254, 0.9897741213207855, 0.8073384429711133, 0.0, nan, 0.0, 0.0, 0.01020894303409787, 0.0, 0.0, 0.004674737581122965, 0.0, 0.17292701685825349, 0.33675057208237985, 0.0, 0.0, nan, 0.009643463497453311, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.001782658300057045, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.274411540578299, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.5966 | 46.0 | 920 | 2.6784 | 0.0823 | 0.1385 | 0.4966 | [0.4409067247745294, 0.5695054337123634, 0.7963487512522022, 0.3081374537644673, 0.0479939159746773, 0.11028362748880655, 0.8394938588891396, 0.3665067973958075, 0.0, nan, 0.0, 0.0, 0.005083978812318667, 0.0, 0.0, 0.004055372084666587, 0.0, 0.03560054285480967, 0.26434226975968883, 0.0, 0.0, nan, 0.005976042472587573, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.23862191774045952, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.763668348978766, 0.9396118599105013, 0.9332909603947971, 0.9617453552364795, 0.17854411989600857, 0.11273767920530531, 0.9891907658256625, 0.8108807071989467, 0.0, nan, 0.0, 0.0, 0.006499693731708977, 0.0, 0.0, 0.004058288669326529, 0.0, 0.19030494522626645, 0.34837528604118995, 0.0, 0.0, nan, 0.015212224108658743, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.25528660937301556, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.8694 | 47.0 | 940 | 2.6726 | 0.0854 | 0.1372 | 0.4956 | [0.4334351083655765, 0.5779382531735361, 0.7920083983990551, 0.32490674631828476, 0.045747620509671474, 0.08903780007485164, 0.846672002134187, 0.353790062743768, 0.0, nan, 0.0, 0.0, 0.00042030051486813074, 0.0, 0.0, 0.002753690115791814, 0.0, 0.03413135952344437, 0.26127493495229837, 0.0, 0.0, nan, 0.006497419371741466, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.24385007692041336, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7685454465395881, 0.9387644363460972, 0.9284895936651107, 0.9542144246286258, 0.18229087016363357, 0.0916503856984306, 0.9801659131658188, 0.8175028604567327, 0.0, nan, 0.0, 0.0, 0.0005444769618185531, 0.0, 0.0, 0.0027568965222007228, 0.0, 0.17579562225871495, 0.3308924485125858, 0.0, 0.0, nan, 0.016842105263157894, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2608695652173913, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.5654 | 48.0 | 960 | 2.6880 | 0.0863 | 0.1385 | 0.4952 | [0.4328305413590763, 0.5898574453129811, 0.772644472361809, 0.32285539536915686, 0.0472082645208897, 0.10465692345801338, 0.8361897849143806, 0.3577367673899817, 0.0, nan, 0.0, 0.0, 0.003506038176860148, 0.0, 0.0, 0.0035209462115644284, 0.0, 0.03328803804803965, 0.2606205250596659, 0.0, 0.0, nan, 0.008012446518864255, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0002463762164825689, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2813203300825206, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7671322205051968, 0.930383986709183, 0.8963455364697405, 0.955610957090247, 0.20545955038996788, 0.11625987177017694, 0.9685331182924841, 0.8156298490619269, 0.0, nan, 0.0, 0.0, 0.004594024365344041, 0.0, 0.0, 0.0035274576619462664, 0.0, 0.17313488681480865, 0.34983981693363847, 0.0, 0.0, nan, 0.02098471986417657, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.00042783799201369084, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3036228356123788, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.7367 | 49.0 | 980 | 2.6701 | 0.0841 | 0.1377 | 0.4949 | [0.4334864609772887, 0.5825936734271826, 0.7792760823278921, 0.31603269315499877, 0.04583927546163392, 0.09942553373917518, 0.8323791940562955, 0.36007060888021114, 0.0, nan, 0.0, 0.0, 0.00218801075552275, 0.0, 0.0, 0.0027550566412265992, 0.0, 0.03393952463101399, 0.27556136908654894, 0.0, 0.0, nan, 0.008194216827310353, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2647119574304852, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7684783497545725, 0.9341555239958198, 0.9112475861594329, 0.9571833491951836, 0.18924912066065147, 0.10636287755794649, 0.9591994303705166, 0.8136706321217536, 0.0, nan, 0.0, 0.0, 0.002824474239433744, 0.0, 0.0, 0.0027568965222007228, 0.0, 0.1766686760762467, 0.3470938215102975, 0.0, 0.0, nan, 0.020033955857385398, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.2848418779899242, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 1.4552 | 50.0 | 1000 | 2.6622 | 0.0851 | 0.1397 | 0.4986 | [0.43799631935866085, 0.5881424463689124, 0.7844581904647036, 0.3173569309392739, 0.04460848373538657, 0.11434824355769314, 0.8349459264073373, 0.35937849433509145, 0.0, nan, 0.0, 0.0, 0.005994394851567365, 0.0, 0.0, 0.004429470515802435, 0.0, 0.033699213564705455, 0.26424040066777965, 0.0, 0.0, nan, 0.009246466149431396, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.283603744914465, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.7650165750026734, 0.9310672847611137, 0.9215628327254031, 0.9571626598253817, 0.19314879951062855, 0.12124964457041175, 0.9808951075347225, 0.8185843482077083, 0.0, nan, 0.0, 0.0, 0.00786088613625536, 0.0, 0.0, 0.004435007448757684, 0.0, 0.17467312449331698, 0.36219679633867274, 0.0, 0.0, nan, 0.023633276740237692, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.3061788239278608, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | null | null | null | null | null | null | null | null | null | null | maxliaops/segformer-b0-scene-parse-150 | [
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rika37/rl_course_vizdoom_health_gathering_supreme | rika37 | 2023-11-29T09:41:21Z | 0 | 0 | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T09:41:21Z | 2023-11-29T09:41:12.000Z | null | null | ---
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: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.60 +/- 3.51
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** 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 rika37/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
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 .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --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.
| null | sample-factory | reinforcement-learning | null | null | null | null | null | null | null | null | null | rika37/rl_course_vizdoom_health_gathering_supreme | [
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th-nuernberg/bert-base-german-counseling-gecco | th-nuernberg | 2023-11-29T10:28:34Z | 0 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:dbmdz/bert-base-german-uncased",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:28:34Z | 2023-11-29T09:41:51.000Z | null | null | ---
license: mit
base_model: dbmdz/bert-base-german-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: gecco-bert-base-german-uncased
results: []
widget:
- text: "Was haben Sie bisher unternommen, um ihr Problem zu lösen?"
- text: "Hallo Peter, wie kann ich helfen?"
- text: "Ich bin hier, um zuzuhören. Wenn du mir erzählen möchtest, wie es dir geht, bin ich bereit."
- text: "Fällt es dir leicht, mit anderen Menschen in Kontakt zu treten?"
- text: "Welche Hobbys oder Freizeitaktivitäten würdest du gerne in der Zukunft ausprobieren?"
- text: "Haben Sie finanzielle Unterstützung von Ihrem Mann?"
- text: "Könnten Sie bitte genauer beschreiben, welche Schwierigkeiten durch diese technischen Probleme entstehen?"
- text: "Gibt es denn keine Hobbys, die du mit deinen Freunden gemeinsam machen kannst?"
- text: "Wo geht ihr Sohn zur Schule?"
- text: "Haben sie gemeinsame Hobbies mit Ihren Freunden?"
---
# gecco-bert-base-german-uncased
**Text classification on German E-Counseling Conversations.**
This model is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased)
trained with the German E-Counseling Conversation Dataset,
created at the Technische Hochschule Nürnberg (see [github.com/th-nuernberg/gecco-dataset](https://github.com/th-nuernberg/gecco-dataset)).
It achieves the following results on the evaluation set:
- Loss: 1.2341
- Accuracy: 0.6968
- F1: 0.4493
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 3.4151 | 1.0 | 20 | 3.0885 | 0.2935 | 0.0760 |
| 2.9316 | 2.0 | 40 | 2.7003 | 0.3484 | 0.1035 |
| 2.5556 | 3.0 | 60 | 2.3463 | 0.5032 | 0.2350 |
| 2.19 | 4.0 | 80 | 2.0714 | 0.5613 | 0.2841 |
| 1.904 | 5.0 | 100 | 1.8381 | 0.6 | 0.3085 |
| 1.6285 | 6.0 | 120 | 1.6712 | 0.6323 | 0.3633 |
| 1.4482 | 7.0 | 140 | 1.5518 | 0.6581 | 0.3774 |
| 1.2807 | 8.0 | 160 | 1.4796 | 0.6677 | 0.3880 |
| 1.1126 | 9.0 | 180 | 1.4207 | 0.6613 | 0.3787 |
| 1.0747 | 10.0 | 200 | 1.3461 | 0.6774 | 0.3885 |
| 0.9068 | 11.0 | 220 | 1.3097 | 0.6871 | 0.4132 |
| 0.8498 | 12.0 | 240 | 1.2893 | 0.6903 | 0.4235 |
| 0.8343 | 13.0 | 260 | 1.2549 | 0.7 | 0.4332 |
| 0.7375 | 14.0 | 280 | 1.2426 | 0.7 | 0.4497 |
| 0.7274 | 15.0 | 300 | 1.2385 | 0.7 | 0.4512 |
| 0.6916 | 16.0 | 320 | 1.2341 | 0.6968 | 0.4493 |
### Framework versions
- Transformers 4.35.1
- Pytorch 1.10.1+cu111
- Datasets 2.14.7
- Tokenizers 0.14.1 | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | th-nuernberg/bert-base-german-counseling-gecco | [
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