modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
9.67k
| library_name
stringlengths 2
84
⌀ | tags
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stringlengths 5
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⌀ | createdAt
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| embedding
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predictwoo/distilbert-base-uncased-squad2-lora-merged
|
predictwoo
| 2023-12-13T10:46:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-13T10:45:55Z |
Entry not found
|
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godpow/guraa
|
godpow
| 2023-12-13T10:48:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:47:44Z |
Entry not found
|
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alinankali/test
|
alinankali
| 2023-12-13T10:51:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:51:38Z |
Entry not found
|
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] |
Realgon/N_bert_imdb_padding20model
|
Realgon
| 2023-12-13T13:16:05Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T10:52:15Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: N_bert_imdb_padding20model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.94048
---
<!-- 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. -->
# N_bert_imdb_padding20model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6296
- Accuracy: 0.9405
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2224 | 1.0 | 1563 | 0.2481 | 0.9156 |
| 0.1564 | 2.0 | 3126 | 0.2097 | 0.9386 |
| 0.0937 | 3.0 | 4689 | 0.2687 | 0.9347 |
| 0.056 | 4.0 | 6252 | 0.2934 | 0.9377 |
| 0.0382 | 5.0 | 7815 | 0.3589 | 0.9370 |
| 0.0304 | 6.0 | 9378 | 0.4149 | 0.9355 |
| 0.02 | 7.0 | 10941 | 0.4875 | 0.9348 |
| 0.0174 | 8.0 | 12504 | 0.4710 | 0.938 |
| 0.0135 | 9.0 | 14067 | 0.5213 | 0.9333 |
| 0.0122 | 10.0 | 15630 | 0.5069 | 0.9366 |
| 0.0132 | 11.0 | 17193 | 0.5394 | 0.9326 |
| 0.0067 | 12.0 | 18756 | 0.5691 | 0.9377 |
| 0.0023 | 13.0 | 20319 | 0.5857 | 0.9368 |
| 0.0043 | 14.0 | 21882 | 0.5734 | 0.9395 |
| 0.0048 | 15.0 | 23445 | 0.5936 | 0.9388 |
| 0.0046 | 16.0 | 25008 | 0.5803 | 0.9389 |
| 0.0032 | 17.0 | 26571 | 0.5693 | 0.9402 |
| 0.0006 | 18.0 | 28134 | 0.6308 | 0.9396 |
| 0.0016 | 19.0 | 29697 | 0.6221 | 0.9402 |
| 0.0008 | 20.0 | 31260 | 0.6296 | 0.9405 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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] |
Realgon/N_distilbert_agnews_padding40model
|
Realgon
| 2023-12-13T12:15:36Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T10:52:30Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_distilbert_agnews_padding40model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9448684210526316
---
<!-- 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. -->
# N_distilbert_agnews_padding40model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6441
- Accuracy: 0.9449
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.181 | 1.0 | 7500 | 0.1943 | 0.9399 |
| 0.1378 | 2.0 | 15000 | 0.2044 | 0.9443 |
| 0.1183 | 3.0 | 22500 | 0.2246 | 0.9459 |
| 0.088 | 4.0 | 30000 | 0.2517 | 0.9445 |
| 0.0614 | 5.0 | 37500 | 0.3074 | 0.9382 |
| 0.0464 | 6.0 | 45000 | 0.3765 | 0.9407 |
| 0.0368 | 7.0 | 52500 | 0.4057 | 0.9416 |
| 0.0245 | 8.0 | 60000 | 0.4436 | 0.9430 |
| 0.0202 | 9.0 | 67500 | 0.4608 | 0.9420 |
| 0.0119 | 10.0 | 75000 | 0.4479 | 0.9425 |
| 0.0125 | 11.0 | 82500 | 0.5133 | 0.9436 |
| 0.0147 | 12.0 | 90000 | 0.5036 | 0.9451 |
| 0.0103 | 13.0 | 97500 | 0.5727 | 0.9437 |
| 0.0051 | 14.0 | 105000 | 0.5684 | 0.9430 |
| 0.0056 | 15.0 | 112500 | 0.5746 | 0.9424 |
| 0.0031 | 16.0 | 120000 | 0.6067 | 0.9436 |
| 0.0009 | 17.0 | 127500 | 0.5994 | 0.9455 |
| 0.0025 | 18.0 | 135000 | 0.6187 | 0.9433 |
| 0.0024 | 19.0 | 142500 | 0.6413 | 0.9449 |
| 0.0011 | 20.0 | 150000 | 0.6441 | 0.9449 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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] |
sanghyo/FinalAssginment
|
sanghyo
| 2023-12-13T12:49:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-13T10:52:56Z |
Entry not found
|
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ll00292007/mylora
|
ll00292007
| 2023-12-14T04:05:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:54:42Z |
Entry not found
|
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] |
mokkaraveendra/test_model_000113122023
|
mokkaraveendra
| 2023-12-13T12:49:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:55:00Z |
Model Evaluation:
Use established benchmarks or datasets relevant to your task (e.g., sentiment analysis, named entity recognition).
Evaluate the model's performance metrics such as accuracy, precision, recall, F1-score, etc., using the test dataset.
Fine-tuning Evaluation (if applicable):
If you fine-tuned a pre-trained model on your specific dataset, evaluate its performance on a validation or test set from your domain.
Measure how well the fine-tuned model performs against your custom criteria.
Functional Testing:
Test the model's inference capabilities by providing input data and examining the output.
Verify if the model produces expected results for different types of inputs relevant to your application.
Comparative Testing:
Compare the performance of multiple models available in the Hugging Face model hub for your specific use case.
Evaluate which model suits your needs best based on criteria like accuracy, inference speed, model size, etc.
Adversarial Testing (if relevant):
Test the model's robustness by providing adversarial examples or edge cases to assess its resilience against manipulation or unexpected input.
Integration Testing (if using the model in an application):
Integrate the model into your application and
|
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AdnanRiaz107/santacoderPEFT-finetuned-the-stack-bash
|
AdnanRiaz107
| 2023-12-13T10:55:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:55:35Z |
Entry not found
|
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EmmaGthn/results_prefix_40
|
EmmaGthn
| 2023-12-13T10:56:55Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-13T10:55:48Z |
Entry not found
|
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mokkaraveendra/vsdfsfsfsfsf
|
mokkaraveendra
| 2023-12-13T10:56:25Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T10:56:25Z |
---
license: mit
---
|
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marcelittle/ddr_train_4_2
|
marcelittle
| 2023-12-13T10:56:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:56:54Z |
Entry not found
|
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mokkaraveendra/fdsfsdfsdfDF
|
mokkaraveendra
| 2023-12-13T10:57:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:57:17Z |
Entry not found
|
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] |
zebans/xlm-roberta-base-finetuned-panx-fr
|
zebans
| 2023-12-13T14:07:42Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-13T10:57:19Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8115649689023365
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3184
- F1: 0.8116
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7671 | 1.0 | 96 | 0.3643 | 0.7537 |
| 0.325 | 2.0 | 192 | 0.3360 | 0.7977 |
| 0.2209 | 3.0 | 288 | 0.3184 | 0.8116 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu118
- Datasets 1.16.1
- Tokenizers 0.15.0
|
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mokkaraveendra/SFDDSFSDF
|
mokkaraveendra
| 2023-12-13T10:57:33Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T10:57:33Z |
---
license: mit
---
|
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] |
zxrzxr/mistral-rdm
|
zxrzxr
| 2023-12-13T11:01:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T10:57:52Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: mistral-rdm
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. -->
# mistral-rdm
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3047
## 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: 5
- eval_batch_size: 5
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0 | 0 | 2.3047 |
| No log | 0 | 0 | 2.3047 |
| No log | 0 | 0 | 2.3047 |
| No log | 0 | 0 | 2.3047 |
| No log | 0 | 0 | 2.3047 |
| No log | 0 | 0 | 2.3047 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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shoryamishraa/shoryamishraa
|
shoryamishraa
| 2023-12-13T10:59:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:58:25Z |
Ben Carson CBD Gummies:- Listen to Your Body: Pay interest to how your body responds to Ben Carson CBD Gummies. If you notice any negative results or if you have concerns, seek advice from a healthcare professional.
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/VIVGzIidCdw
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/50r89WBDkTU
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/sv_UQCKAsSQ
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Uov5tKVP4Ko
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/pdzvxx-o1PA
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/KYg37DN5Sh8
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/3ed5MHZqo68
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/2J3JRVcY-w8
Facebook wibsite:- https://www.facebook.com/TryBenCarsonCBDGummies/
Facebook wibsite:- https://www.facebook.com/JungleBeastPro2024/
Official website:- https://www.outlookindia.com/health-spotlight/vigor-vita-cbd-gummies-exposed-2024-green-vibe-cbd-gummies-legit-price-alert-green-farms-cbd-gummies-beneficial-results--news-335523
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/3zb-qY6Hbxs
Google group:- https://groups.google.com/a/chromium.org/g/chromium-reviews/c/59Mbw7gtXeo
|
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] |
ArunIcfoss/flan-t5-base-samsum
|
ArunIcfoss
| 2023-12-13T10:59:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-13T10:58:27Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: test
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 47.2406
---
<!-- 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-base-samsum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3743
- Rouge1: 47.2406
- Rouge2: 23.5134
- Rougel: 39.8088
- Rougelsum: 43.4496
- Gen Len: 17.2466
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4528 | 1.0 | 1842 | 1.3867 | 46.678 | 22.7586 | 39.0448 | 42.8793 | 17.3993 |
| 1.3385 | 2.0 | 3684 | 1.3751 | 46.8872 | 23.2783 | 39.1489 | 43.0118 | 17.3431 |
| 1.2711 | 3.0 | 5526 | 1.3745 | 47.268 | 23.4954 | 39.641 | 43.4793 | 17.4493 |
| 1.2284 | 4.0 | 7368 | 1.3743 | 47.2406 | 23.5134 | 39.8088 | 43.4496 | 17.2466 |
| 1.2085 | 5.0 | 9210 | 1.3759 | 47.3976 | 23.7573 | 39.8619 | 43.663 | 17.2967 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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zebans/xlm-roberta-base-finetuned-panx
|
zebans
| 2023-12-13T10:59:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T10:59:11Z |
Entry not found
|
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] |
Norquinal/DeciLM-7B-claude-chat
|
Norquinal
| 2023-12-13T11:02:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deci_lm",
"text-generation",
"custom_code",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-12-13T10:59:28Z |
Entry not found
|
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] |
zebans/xlm-roberta-base-finetuned-panx-all
|
zebans
| 2023-12-13T14:43:17Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-13T10:59:38Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1828
- F1: 0.8519
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2947 | 1.0 | 739 | 0.1879 | 0.8175 |
| 0.152 | 2.0 | 1478 | 0.1853 | 0.8385 |
| 0.0974 | 3.0 | 2217 | 0.1828 | 0.8519 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu118
- Datasets 1.16.1
- Tokenizers 0.15.0
|
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Sabbasi-11/distilbert-base-uncased-finetuned-imdb
|
Sabbasi-11
| 2023-12-14T07:00:19Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-13T11:03:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4159
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6954 | 1.0 | 157 | 2.5243 |
| 2.563 | 2.0 | 314 | 2.4738 |
| 2.5258 | 3.0 | 471 | 2.4369 |
### Framework versions
- Transformers 4.17.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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shossain11/videomae-base-finetuned-inhard4
|
shossain11
| 2023-12-13T16:09:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:shossain11/videomae-base-finetuned-inhard-finetuned-inhard2-finetuned-inhard3",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-13T11:04:07Z |
---
license: cc-by-nc-4.0
base_model: shossain11/videomae-base-finetuned-inhard-finetuned-inhard2-finetuned-inhard3
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-inhard4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-inhard4
This model is a fine-tuned version of [shossain11/videomae-base-finetuned-inhard-finetuned-inhard2-finetuned-inhard3](https://huggingface.co/shossain11/videomae-base-finetuned-inhard-finetuned-inhard2-finetuned-inhard3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5601
- Accuracy: 0.6694
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 9580
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6133 | 0.2 | 1916 | 1.7373 | 0.5683 |
| 1.2931 | 1.2 | 3832 | 2.1067 | 0.5450 |
| 0.7879 | 2.2 | 5748 | 1.7517 | 0.5993 |
| 0.6454 | 3.2 | 7664 | 1.6389 | 0.6238 |
| 0.9419 | 4.2 | 9580 | 1.4057 | 0.6837 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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KeiHeityuu/wnli-deberta-v3-base
|
KeiHeityuu
| 2023-12-13T11:04:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T11:04:15Z |
Entry not found
|
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] |
hkivancoral/smids_3x_beit_base_adamax_00001_fold4
|
hkivancoral
| 2023-12-14T15:51:31Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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"
] |
image-classification
| 2023-12-13T11:05:51Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_adamax_00001_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: 0.8883333333333333
---
<!-- 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. -->
# smids_3x_beit_base_adamax_00001_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: 1.0796
- Accuracy: 0.8883
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2789 | 1.0 | 375 | 0.3658 | 0.8517 |
| 0.2051 | 2.0 | 750 | 0.3746 | 0.855 |
| 0.0849 | 3.0 | 1125 | 0.3986 | 0.87 |
| 0.1357 | 4.0 | 1500 | 0.4367 | 0.8633 |
| 0.0524 | 5.0 | 1875 | 0.4518 | 0.8867 |
| 0.0622 | 6.0 | 2250 | 0.5510 | 0.8867 |
| 0.0413 | 7.0 | 2625 | 0.6374 | 0.8767 |
| 0.0498 | 8.0 | 3000 | 0.6614 | 0.88 |
| 0.0181 | 9.0 | 3375 | 0.6937 | 0.8867 |
| 0.013 | 10.0 | 3750 | 0.7663 | 0.8817 |
| 0.0051 | 11.0 | 4125 | 0.8001 | 0.8783 |
| 0.0188 | 12.0 | 4500 | 0.8577 | 0.8817 |
| 0.0009 | 13.0 | 4875 | 0.8978 | 0.8783 |
| 0.0123 | 14.0 | 5250 | 0.9022 | 0.885 |
| 0.0631 | 15.0 | 5625 | 0.9459 | 0.875 |
| 0.0113 | 16.0 | 6000 | 0.9699 | 0.8717 |
| 0.0335 | 17.0 | 6375 | 0.9476 | 0.8867 |
| 0.014 | 18.0 | 6750 | 0.9219 | 0.8833 |
| 0.0214 | 19.0 | 7125 | 0.9440 | 0.8833 |
| 0.0134 | 20.0 | 7500 | 0.9965 | 0.8883 |
| 0.0053 | 21.0 | 7875 | 0.9823 | 0.8883 |
| 0.0026 | 22.0 | 8250 | 1.0367 | 0.8817 |
| 0.0174 | 23.0 | 8625 | 1.0171 | 0.8833 |
| 0.0002 | 24.0 | 9000 | 1.0087 | 0.8817 |
| 0.032 | 25.0 | 9375 | 1.0135 | 0.8917 |
| 0.0014 | 26.0 | 9750 | 1.0465 | 0.885 |
| 0.0249 | 27.0 | 10125 | 1.0322 | 0.8833 |
| 0.0001 | 28.0 | 10500 | 1.0170 | 0.8917 |
| 0.0002 | 29.0 | 10875 | 1.0011 | 0.8933 |
| 0.0298 | 30.0 | 11250 | 1.0259 | 0.89 |
| 0.006 | 31.0 | 11625 | 1.0094 | 0.8867 |
| 0.0305 | 32.0 | 12000 | 1.0462 | 0.885 |
| 0.0001 | 33.0 | 12375 | 1.0305 | 0.8917 |
| 0.0011 | 34.0 | 12750 | 1.0273 | 0.89 |
| 0.0001 | 35.0 | 13125 | 1.0726 | 0.89 |
| 0.0022 | 36.0 | 13500 | 1.0573 | 0.8917 |
| 0.0086 | 37.0 | 13875 | 1.0713 | 0.8833 |
| 0.0263 | 38.0 | 14250 | 1.0516 | 0.89 |
| 0.0001 | 39.0 | 14625 | 1.0815 | 0.8867 |
| 0.0002 | 40.0 | 15000 | 1.0626 | 0.8833 |
| 0.0099 | 41.0 | 15375 | 1.0610 | 0.8883 |
| 0.0004 | 42.0 | 15750 | 1.0801 | 0.885 |
| 0.0001 | 43.0 | 16125 | 1.0685 | 0.8867 |
| 0.007 | 44.0 | 16500 | 1.0616 | 0.8967 |
| 0.0 | 45.0 | 16875 | 1.0741 | 0.8917 |
| 0.0172 | 46.0 | 17250 | 1.0800 | 0.8883 |
| 0.004 | 47.0 | 17625 | 1.0888 | 0.89 |
| 0.0113 | 48.0 | 18000 | 1.0832 | 0.8867 |
| 0.0002 | 49.0 | 18375 | 1.0808 | 0.8867 |
| 0.0001 | 50.0 | 18750 | 1.0796 | 0.8883 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_3x_deit_tiny_rms_00001_fold2
|
hkivancoral
| 2023-12-13T11:44:35Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T11:10:56Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_tiny_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: 0.8752079866888519
---
<!-- 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. -->
# smids_3x_deit_tiny_rms_00001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0668
- Accuracy: 0.8752
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2993 | 1.0 | 225 | 0.3110 | 0.8719 |
| 0.214 | 2.0 | 450 | 0.3099 | 0.8719 |
| 0.1117 | 3.0 | 675 | 0.3255 | 0.8835 |
| 0.1732 | 4.0 | 900 | 0.3969 | 0.8636 |
| 0.1001 | 5.0 | 1125 | 0.4203 | 0.8735 |
| 0.0568 | 6.0 | 1350 | 0.5015 | 0.8735 |
| 0.0354 | 7.0 | 1575 | 0.5359 | 0.8769 |
| 0.0128 | 8.0 | 1800 | 0.6487 | 0.8769 |
| 0.012 | 9.0 | 2025 | 0.7872 | 0.8552 |
| 0.0123 | 10.0 | 2250 | 0.8404 | 0.8752 |
| 0.0004 | 11.0 | 2475 | 0.8481 | 0.8652 |
| 0.0319 | 12.0 | 2700 | 0.9253 | 0.8686 |
| 0.0001 | 13.0 | 2925 | 0.9570 | 0.8636 |
| 0.0029 | 14.0 | 3150 | 0.9176 | 0.8702 |
| 0.0009 | 15.0 | 3375 | 1.0326 | 0.8785 |
| 0.0207 | 16.0 | 3600 | 1.0323 | 0.8719 |
| 0.0113 | 17.0 | 3825 | 1.0675 | 0.8686 |
| 0.0006 | 18.0 | 4050 | 1.0013 | 0.8769 |
| 0.0 | 19.0 | 4275 | 1.1724 | 0.8669 |
| 0.0 | 20.0 | 4500 | 0.9929 | 0.8735 |
| 0.0002 | 21.0 | 4725 | 0.9953 | 0.8719 |
| 0.0 | 22.0 | 4950 | 1.1132 | 0.8735 |
| 0.0425 | 23.0 | 5175 | 1.0471 | 0.8686 |
| 0.0 | 24.0 | 5400 | 1.1403 | 0.8652 |
| 0.0141 | 25.0 | 5625 | 1.1287 | 0.8619 |
| 0.0 | 26.0 | 5850 | 0.9874 | 0.8785 |
| 0.0 | 27.0 | 6075 | 1.0105 | 0.8752 |
| 0.0 | 28.0 | 6300 | 1.0130 | 0.8802 |
| 0.0 | 29.0 | 6525 | 1.0721 | 0.8652 |
| 0.0053 | 30.0 | 6750 | 1.0713 | 0.8819 |
| 0.0 | 31.0 | 6975 | 1.0241 | 0.8835 |
| 0.0 | 32.0 | 7200 | 1.0643 | 0.8869 |
| 0.0 | 33.0 | 7425 | 1.0320 | 0.8669 |
| 0.0 | 34.0 | 7650 | 1.0424 | 0.8802 |
| 0.0 | 35.0 | 7875 | 1.0176 | 0.8802 |
| 0.0044 | 36.0 | 8100 | 0.9778 | 0.8785 |
| 0.0 | 37.0 | 8325 | 0.9990 | 0.8819 |
| 0.0 | 38.0 | 8550 | 1.0176 | 0.8752 |
| 0.0047 | 39.0 | 8775 | 1.0819 | 0.8719 |
| 0.0 | 40.0 | 9000 | 1.0393 | 0.8735 |
| 0.0 | 41.0 | 9225 | 1.0424 | 0.8735 |
| 0.0 | 42.0 | 9450 | 1.0459 | 0.8702 |
| 0.0 | 43.0 | 9675 | 1.0528 | 0.8752 |
| 0.0 | 44.0 | 9900 | 1.0545 | 0.8769 |
| 0.0 | 45.0 | 10125 | 1.0566 | 0.8785 |
| 0.0 | 46.0 | 10350 | 1.0564 | 0.8769 |
| 0.0 | 47.0 | 10575 | 1.0599 | 0.8785 |
| 0.0 | 48.0 | 10800 | 1.0618 | 0.8785 |
| 0.002 | 49.0 | 11025 | 1.0652 | 0.8752 |
| 0.002 | 50.0 | 11250 | 1.0668 | 0.8752 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Hhhuuu/Myyy
|
Hhhuuu
| 2023-12-13T11:14:17Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T11:13:04Z |
---
license: apache-2.0
---
|
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mokkaraveendra/test_new_model_12132023
|
mokkaraveendra
| 2023-12-13T11:19:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T11:13:15Z |
Artificial Intelligence Exam Questions - Free Practice Test
1983 Tests taken
Artificial Intelligence is the hottest buzzword in today’s world of technology. With the AI industry being very new and continuously expanding, making a career in AI can be a great option. If you are interested in making a career in AI, you might consider going for a certification. However, getting a certification is not that easy. This is where Simplilearn’s AI Practice test might help you. This test contains AI questions that you might expect to get in the final certification exam. These Artificial Intelligence questions and answers are curated by subject matter experts who are experienced in the domain. Take this Artificial Intelligence quiz today!
|
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] |
chenshao/StableVITON
|
chenshao
| 2023-12-13T11:15:51Z | 0 | 0 | null |
[
"af",
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T11:15:00Z |
---
license: apache-2.0
language:
- af
---
|
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] |
Ammad1Ali/m2m100_1.2B-2.0-v1
|
Ammad1Ali
| 2023-12-13T11:17:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-13T11:15:37Z |
Entry not found
|
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] |
sajjadsoltani2020/sajjad
|
sajjadsoltani2020
| 2023-12-13T11:16:03Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T11:16:03Z |
---
license: openrail
---
|
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tulayturanmaku/my_awesome_model
|
tulayturanmaku
| 2023-12-13T11:16:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T11:16:59Z |
Entry not found
|
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Parth673/CartPole-v1-take2
|
Parth673
| 2023-12-13T11:19:37Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T11:18:46Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1-take2
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
|
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] |
Teklia/pylaia-iam
|
Teklia
| 2023-12-13T12:48:30Z | 0 | 1 |
PyLaia
|
[
"PyLaia",
"PyTorch",
"Handwritten text recognition",
"en",
"arxiv:2304.13530",
"license:mit",
"region:us"
] | null | 2023-12-13T11:22:36Z |
---
library_name: PyLaia
license: mit
tags:
- PyLaia
- PyTorch
- Handwritten text recognition
metrics:
- CER
- WER
language:
- en
---
# English handwritten text recognition
This model performs Handwritten Text Recognition in English.
## Model description
The model has been trained using the PyLaia library on the [IAM](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database) dataset.
Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
## Evaluation results
The model achieves the following results:
| Split | CER (%) | WER (%) | Support |
| ----- | ------- | ------- | ------- |
| train | 0.32 | 1.26 | 6482 |
| val | 6.50 | 19.12 | 1926 |
| test | 7.68 | 19.82 | 1965 |
A similar model was trained on the RWTH split, corresponding to the results published in [Key-value information extraction from full handwritten pages](https://arxiv.org/pdf/2304.13530.pdf).
Results can be improved by combining PyLaia with a n-gram language model.
## How to use
Please refer to the [documentation](https://atr.pages.teklia.com/pylaia/).
|
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] |
kurepa/noL
|
kurepa
| 2023-12-13T11:29:56Z | 0 | 0 |
diffusers
|
[
"diffusers",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2023-12-13T11:23:14Z |
Entry not found
|
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LawrenceK/vit-base-patch16-224-in21k-finetuned-lora-food101
|
LawrenceK
| 2023-12-13T11:25:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T11:25:40Z |
Entry not found
|
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] |
Lilidiasaja/newmodel6
|
Lilidiasaja
| 2023-12-17T02:37:33Z | 0 | 0 | null |
[
"pytorch",
"license:afl-3.0",
"region:us"
] | null | 2023-12-13T11:26:52Z |
---
license: afl-3.0
---
|
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reza-alipour/u2
|
reza-alipour
| 2023-12-13T11:30:05Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"region:us"
] | null | 2023-12-13T11:27:22Z |
Entry not found
|
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reza-alipour/u3
|
reza-alipour
| 2023-12-13T11:31:22Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"region:us"
] | null | 2023-12-13T11:28:52Z |
Entry not found
|
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] |
Parth673/PixelCopter
|
Parth673
| 2023-12-13T11:30:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T11:29:56Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 83.00 +/- 54.60
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
This model didn't complete training but was still far better than the first one trained with fewer timesteps.
|
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] |
Tarun2001/WHISP-ZN-TW
|
Tarun2001
| 2023-12-13T11:30:54Z | 0 | 0 | null |
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kurepa/fim
|
kurepa
| 2023-12-13T11:38:42Z | 0 | 0 |
diffusers
|
[
"diffusers",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2023-12-13T11:31:04Z |
Entry not found
|
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isp-uv-es/cloudsen12
|
isp-uv-es
| 2023-12-14T13:48:07Z | 0 | 0 | null |
[
"remote sensing",
"sentinel2",
"clouds",
"image-segmentation",
"license:cc-by-nc-4.0",
"region:us"
] |
image-segmentation
| 2023-12-13T11:33:06Z |
---
license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- remote sensing
- sentinel2
- clouds
---
# CloudSEN12 trained models
This repository contains the trained models of the publications:
```
@inproceedings{aybar_lessons_2023,
title = {Lessons {Learned} {From} {Cloudsen12} {Dataset}: {Identifying} {Incorrect} {Annotations} in {Cloud} {Semantic} {Segmentation} {Datasets}},
url = {https://ieeexplore.ieee.org/document/10282381},
doi = {10.1109/IGARSS52108.2023.10282381},
urldate = {2023-10-23},
booktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
author = {Aybar, Cesar and Montero, David and Mateo-García, Gonzalo and Gómez-Chova, Luis},
month = jul,
year = {2023},
note = {ISSN: 2153-7003},
pages = {892--895},
}
@article{aybar_cloudsen12_2022,
title = {{CloudSEN12}, a global dataset for semantic understanding of cloud and cloud shadow in {Sentinel}-2},
volume = {9},
issn = {2052-4463},
url = {https://www.nature.com/articles/s41597-022-01878-2},
doi = {10.1038/s41597-022-01878-2},
number = {1},
urldate = {2023-01-02},
journal = {Scientific Data},
author = {Aybar, Cesar and Ysuhuaylas, Luis and Loja, Jhomira and Gonzales, Karen and Herrera, Fernando and Bautista, Lesly and Yali, Roy and Flores, Angie and Diaz, Lissette and Cuenca, Nicole and Espinoza, Wendy and Prudencio, Fernando and Llactayo, Valeria and Montero, David and Sudmanns, Martin and Tiede, Dirk and Mateo-García, Gonzalo and Gómez-Chova, Luis},
month = dec,
year = {2022},
pages = {782},
}
```
We include the trained models in L1C images:
* **Unet MobileNetV2 all S2 bands** - UNetMobV2_all.pt
* **Unet MobileNetV2 R-G-B-NIR bands** - UNetMobV2_RGBN.pt
* **ResNet10 all S2 bands** - ResNet10_all.pt
```python
import torch
import tempfile
import segmentation_models_pytorch as smp
from huggingface_hub import hf_hub_url, cached_download
# Load all S2 bands model
tmpfolder = tempfile.TemporaryDirectory()
model_path = hf_hub_url("isp-uv-es/cloudsen12", revision="main", filename="UNetMobV2_all.pt")
output_path = cached_download(model_path, cache_dir=tmpfolder.name)
model = smp.Unet(encoder_name="mobilenet_v2", encoder_weights=None, in_channels=13, classes=4)
weights = torch.load(output_path, map_location=torch.device('cpu'))
model.load_state_dict(weights)
model.eval()
# Load RGBN S2 model
tmpfolder = tempfile.TemporaryDirectory()
model_path = hf_hub_url("isp-uv-es/cloudsen12", revision="main", filename="UNetMobV2_RGBN.jit.pt")
output_path = cached_download(model_path, cache_dir=tmpfolder.name)
```
|
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] |
MNSTech/TestNik
|
MNSTech
| 2023-12-13T11:35:38Z | 0 | 0 | null |
[
"license:c-uda",
"region:us"
] | null | 2023-12-13T11:35:38Z |
---
license: c-uda
---
|
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umarigan/DeciLM-7B-hh-rlhf
|
umarigan
| 2023-12-13T11:37:38Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:Deci/DeciLM-7B",
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T11:35:49Z |
---
license: apache-2.0
base_model: Deci/DeciLM-7B
tags:
- generated_from_trainer
model-index:
- name: deciml-7B-rlhf
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. -->
# deciml-7B-rlhf
This model is a fine-tuned version of [Deci/DeciLM-7B](https://huggingface.co/Deci/DeciLM-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9043
- Rewards/chosen: -0.5041
- Rewards/rejected: -0.2949
- Rewards/accuracies: 0.375
- Rewards/margins: -0.2092
- Logps/rejected: -144.4697
- Logps/chosen: -168.8579
- Logits/rejected: -3.1080
- Logits/chosen: -3.2359
## 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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.692 | 0.0 | 10 | 0.7242 | -0.1287 | -0.0862 | 0.4375 | -0.0425 | -142.3823 | -165.1031 | -3.0799 | -3.2096 |
| 0.6635 | 0.0 | 20 | 0.7781 | -0.2928 | -0.2069 | 0.4375 | -0.0859 | -143.5897 | -166.7443 | -3.0979 | -3.2283 |
| 0.7188 | 0.0 | 30 | 0.8506 | -0.4155 | -0.2595 | 0.375 | -0.1559 | -144.1155 | -167.9709 | -3.1070 | -3.2367 |
| 0.9092 | 0.0 | 40 | 0.9032 | -0.5122 | -0.2980 | 0.375 | -0.2142 | -144.5009 | -168.9387 | -3.1100 | -3.2382 |
| 0.7197 | 0.0 | 50 | 0.9043 | -0.5041 | -0.2949 | 0.375 | -0.2092 | -144.4697 | -168.8579 | -3.1080 | -3.2359 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
-0.7339860200881958,
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] |
hkivancoral/smids_3x_beit_base_adamax_00001_fold5
|
hkivancoral
| 2023-12-14T17:52:55Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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"
] |
image-classification
| 2023-12-13T11:38:18Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_adamax_00001_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: 0.9083333333333333
---
<!-- 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. -->
# smids_3x_beit_base_adamax_00001_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: 0.7465
- Accuracy: 0.9083
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2292 | 1.0 | 375 | 0.3140 | 0.8683 |
| 0.2093 | 2.0 | 750 | 0.2336 | 0.9067 |
| 0.1226 | 3.0 | 1125 | 0.2477 | 0.9117 |
| 0.0876 | 4.0 | 1500 | 0.2419 | 0.92 |
| 0.0679 | 5.0 | 1875 | 0.3315 | 0.8983 |
| 0.0323 | 6.0 | 2250 | 0.3578 | 0.9033 |
| 0.0877 | 7.0 | 2625 | 0.4065 | 0.91 |
| 0.0394 | 8.0 | 3000 | 0.4499 | 0.91 |
| 0.0357 | 9.0 | 3375 | 0.4692 | 0.9167 |
| 0.0126 | 10.0 | 3750 | 0.5130 | 0.9083 |
| 0.0533 | 11.0 | 4125 | 0.4610 | 0.915 |
| 0.018 | 12.0 | 4500 | 0.5545 | 0.9167 |
| 0.0014 | 13.0 | 4875 | 0.6258 | 0.905 |
| 0.0294 | 14.0 | 5250 | 0.5991 | 0.9133 |
| 0.0147 | 15.0 | 5625 | 0.5948 | 0.91 |
| 0.0096 | 16.0 | 6000 | 0.6032 | 0.905 |
| 0.03 | 17.0 | 6375 | 0.6625 | 0.9017 |
| 0.0009 | 18.0 | 6750 | 0.6142 | 0.9067 |
| 0.0073 | 19.0 | 7125 | 0.7447 | 0.8917 |
| 0.0002 | 20.0 | 7500 | 0.6954 | 0.9067 |
| 0.0056 | 21.0 | 7875 | 0.7051 | 0.9133 |
| 0.0361 | 22.0 | 8250 | 0.6457 | 0.9067 |
| 0.0618 | 23.0 | 8625 | 0.6671 | 0.905 |
| 0.0003 | 24.0 | 9000 | 0.7426 | 0.905 |
| 0.0152 | 25.0 | 9375 | 0.6683 | 0.905 |
| 0.0005 | 26.0 | 9750 | 0.7087 | 0.9067 |
| 0.0002 | 27.0 | 10125 | 0.7480 | 0.905 |
| 0.004 | 28.0 | 10500 | 0.7511 | 0.905 |
| 0.0094 | 29.0 | 10875 | 0.7080 | 0.9017 |
| 0.0056 | 30.0 | 11250 | 0.7631 | 0.905 |
| 0.0441 | 31.0 | 11625 | 0.7580 | 0.9083 |
| 0.0024 | 32.0 | 12000 | 0.7682 | 0.9083 |
| 0.0002 | 33.0 | 12375 | 0.7496 | 0.9133 |
| 0.0005 | 34.0 | 12750 | 0.7614 | 0.9067 |
| 0.0057 | 35.0 | 13125 | 0.7635 | 0.9083 |
| 0.0004 | 36.0 | 13500 | 0.7425 | 0.9117 |
| 0.0153 | 37.0 | 13875 | 0.7300 | 0.91 |
| 0.0003 | 38.0 | 14250 | 0.7331 | 0.9083 |
| 0.0149 | 39.0 | 14625 | 0.7175 | 0.905 |
| 0.0093 | 40.0 | 15000 | 0.7444 | 0.9067 |
| 0.0001 | 41.0 | 15375 | 0.7317 | 0.9117 |
| 0.0 | 42.0 | 15750 | 0.7474 | 0.9033 |
| 0.0002 | 43.0 | 16125 | 0.7578 | 0.905 |
| 0.0004 | 44.0 | 16500 | 0.7636 | 0.905 |
| 0.0 | 45.0 | 16875 | 0.7676 | 0.91 |
| 0.0005 | 46.0 | 17250 | 0.7589 | 0.91 |
| 0.0158 | 47.0 | 17625 | 0.7484 | 0.9083 |
| 0.0001 | 48.0 | 18000 | 0.7568 | 0.91 |
| 0.0001 | 49.0 | 18375 | 0.7501 | 0.9083 |
| 0.0109 | 50.0 | 18750 | 0.7465 | 0.9083 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
athirdpath/Eta-17b
|
athirdpath
| 2023-12-13T11:53:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T11:40:16Z |
---
license: cc-by-nc-4.0
language:
- en
---
models:
- model: athirdpath/Zeta-17b
weight: 0.75
density: 0.60
- model: Undi95/Clover3-17B
weight: 0.25
density: 0.45
merge_method: dare_ties
base_model: athirdpath/Gamma-17b
int8_mask: true
dtype: bfloat16
|
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EleutherAI/pythia-14m-seed1
|
EleutherAI
| 2023-12-13T11:43:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T11:43:58Z |
Entry not found
|
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] |
hkivancoral/smids_3x_deit_tiny_rms_00001_fold3
|
hkivancoral
| 2023-12-13T12:18:56Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T11:45:12Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_tiny_rms_00001_fold3
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: 0.9033333333333333
---
<!-- 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. -->
# smids_3x_deit_tiny_rms_00001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9705
- Accuracy: 0.9033
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3413 | 1.0 | 225 | 0.2808 | 0.8933 |
| 0.2287 | 2.0 | 450 | 0.2742 | 0.9 |
| 0.1591 | 3.0 | 675 | 0.3006 | 0.89 |
| 0.0862 | 4.0 | 900 | 0.3381 | 0.8883 |
| 0.087 | 5.0 | 1125 | 0.4066 | 0.8783 |
| 0.0686 | 6.0 | 1350 | 0.4702 | 0.885 |
| 0.0747 | 7.0 | 1575 | 0.5496 | 0.8933 |
| 0.0256 | 8.0 | 1800 | 0.6539 | 0.885 |
| 0.0458 | 9.0 | 2025 | 0.6793 | 0.8883 |
| 0.0397 | 10.0 | 2250 | 0.7901 | 0.88 |
| 0.0252 | 11.0 | 2475 | 0.7599 | 0.8967 |
| 0.0212 | 12.0 | 2700 | 0.8640 | 0.89 |
| 0.033 | 13.0 | 2925 | 0.8965 | 0.88 |
| 0.0005 | 14.0 | 3150 | 0.8332 | 0.895 |
| 0.0068 | 15.0 | 3375 | 0.9483 | 0.8717 |
| 0.0008 | 16.0 | 3600 | 1.0157 | 0.8783 |
| 0.0016 | 17.0 | 3825 | 0.8948 | 0.8867 |
| 0.0002 | 18.0 | 4050 | 0.8418 | 0.895 |
| 0.0 | 19.0 | 4275 | 0.8994 | 0.8983 |
| 0.0044 | 20.0 | 4500 | 0.8798 | 0.9083 |
| 0.0 | 21.0 | 4725 | 1.0795 | 0.8717 |
| 0.0009 | 22.0 | 4950 | 1.0744 | 0.885 |
| 0.0066 | 23.0 | 5175 | 0.9462 | 0.8883 |
| 0.0 | 24.0 | 5400 | 0.8715 | 0.9017 |
| 0.0088 | 25.0 | 5625 | 0.9553 | 0.8983 |
| 0.0 | 26.0 | 5850 | 0.9300 | 0.9 |
| 0.0271 | 27.0 | 6075 | 0.9362 | 0.8967 |
| 0.0 | 28.0 | 6300 | 0.9453 | 0.8967 |
| 0.0056 | 29.0 | 6525 | 1.0241 | 0.8917 |
| 0.0134 | 30.0 | 6750 | 0.9852 | 0.9017 |
| 0.0062 | 31.0 | 6975 | 0.9946 | 0.9017 |
| 0.0 | 32.0 | 7200 | 1.0433 | 0.89 |
| 0.0 | 33.0 | 7425 | 0.9693 | 0.8983 |
| 0.0 | 34.0 | 7650 | 0.9977 | 0.8983 |
| 0.0 | 35.0 | 7875 | 0.9577 | 0.9033 |
| 0.0 | 36.0 | 8100 | 0.9692 | 0.8967 |
| 0.0 | 37.0 | 8325 | 0.9675 | 0.8967 |
| 0.0 | 38.0 | 8550 | 0.9775 | 0.9 |
| 0.0 | 39.0 | 8775 | 0.9600 | 0.9 |
| 0.0 | 40.0 | 9000 | 0.9503 | 0.8983 |
| 0.0 | 41.0 | 9225 | 0.9634 | 0.895 |
| 0.0 | 42.0 | 9450 | 0.9624 | 0.9 |
| 0.0 | 43.0 | 9675 | 0.9737 | 0.9 |
| 0.0 | 44.0 | 9900 | 0.9743 | 0.9017 |
| 0.0 | 45.0 | 10125 | 0.9706 | 0.9 |
| 0.0 | 46.0 | 10350 | 0.9706 | 0.9 |
| 0.0019 | 47.0 | 10575 | 0.9708 | 0.9017 |
| 0.0 | 48.0 | 10800 | 0.9707 | 0.9017 |
| 0.0 | 49.0 | 11025 | 0.9710 | 0.9017 |
| 0.0 | 50.0 | 11250 | 0.9705 | 0.9033 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
vinidiol/swpc-alpaca-lora-llama-v1-hf
|
vinidiol
| 2023-12-13T11:48:25Z | 0 | 0 | null |
[
"safetensors",
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-13T11:45:39Z |
---
license: cc-by-nc-nd-4.0
---
|
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tanatapanun/fine-tuned-biobart-large-30-epochs
|
tanatapanun
| 2023-12-13T11:50:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T11:50:34Z |
Entry not found
|
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h4g3n/results
|
h4g3n
| 2023-12-13T12:02:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:02:39Z |
Entry not found
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] |
sobikim/segformer-b0-scene-parse-150
|
sobikim
| 2023-12-13T12:32:50Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-12-13T12:02:43Z |
---
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
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# 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: 1.7293
- Mean Iou: 0.1237
- Mean Accuracy: 0.1909
- Overall Accuracy: 0.5750
- Per Category Iou: [0.5194973138518036, nan, nan, 0.7044669379850034, nan, 0.34772332290580465, nan, nan, 0.5564015598824595, nan, 0.4062946652059406, nan, 0.0, nan, 0.4247729319128107, 0.0, nan, nan, 0.046667038441519815, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13739290398405696, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19611110736285312, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1343402763679673, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.31478245182821646, 0.0, nan, nan, nan, 0.00016383370878558264, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.045484961930081565, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan]
- Per Category Accuracy: [0.6653449483980128, nan, nan, 0.8913306234632802, nan, 0.6243524932908944, nan, nan, 0.6636340558644579, nan, 0.573746977582886, nan, nan, nan, 0.5385355015418798, 0.0, nan, nan, 0.08630410120664168, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4259491999788773, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.33494273005922887, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2713282125577454, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3631361939570516, 0.0, nan, nan, nan, 0.00016383370878558264, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09710049805026832, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, 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 |
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| 3.9786 | 1.0 | 20 | 3.8216 | 0.0800 | 0.1800 | 0.4802 | [0.4198867018051586, nan, nan, 0.6407079944941267, nan, 0.30962178151724773, nan, nan, 0.4547464711940389, nan, 0.18428573788637226, nan, 0.0, nan, 0.3772855265627933, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.129621402176718, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0004865285646326169, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06718202320463006, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.2111141644989704, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.00453621148130768, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan] | [0.4610940999997382, nan, nan, 0.9421738014620052, nan, 0.9593441570599406, nan, nan, 0.4917579043048879, nan, 0.3428650465144871, nan, nan, nan, 0.5004386216935778, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5145799229022548, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0005185407112073933, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2703441489216303, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7294330346110324, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006717887340257133, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 3.7257 | 2.0 | 40 | 3.5413 | 0.0893 | 0.1773 | 0.5085 | [0.4796213122176483, nan, nan, 0.569710555487129, nan, 0.37797245678582847, nan, nan, 0.385562411285884, nan, 0.2071438459237031, nan, nan, nan, 0.3376899057647339, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12735592007042368, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.00018219816730078773, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09635303820293825, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.1857725674091442, 0.0, nan, 0.0, nan, 0.0, 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, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5426066529782728, nan, nan, 0.9573154073497636, nan, 0.9589786111036813, nan, nan, 0.43597533512942505, nan, 0.4752674070734806, nan, nan, nan, 0.46192802007316747, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.41713048529334107, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.00019589315756723746, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.37684170243009046, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5152764590351573, 0.0, nan, nan, nan, 0.0, 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, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 3.5889 | 3.0 | 60 | 3.3521 | 0.0943 | 0.1822 | 0.4964 | [0.44671302852086087, nan, nan, 0.6441959559067476, nan, 0.33197889313404605, nan, nan, 0.36668600855878725, nan, 0.22550499548694325, nan, nan, nan, 0.3607435243591464, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13298052198255864, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.01648341159851869, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09146424531039915, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.21127065071792822, 0.0, nan, nan, nan, 0.0, 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, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5012814326216009, nan, nan, 0.9242961098739165, nan, 0.9513288932873281, nan, nan, 0.41398822440774014, nan, 0.593848612761772, nan, nan, nan, 0.5148346070600854, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.3572054707715055, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.019900440183448182, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4221086296913867, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5846676118257612, 0.0, nan, nan, nan, 0.0, 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, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 3.2848 | 4.0 | 80 | 3.2736 | 0.0990 | 0.1883 | 0.5019 | [0.4459821396967382, nan, nan, 0.6207351493092033, nan, 0.3891790103084778, nan, nan, 0.35121012672244944, nan, 0.21693460283339402, nan, nan, nan, 0.3868444117143702, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1345446628133643, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.05449012125018115, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.12897355238553856, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.2425876195434741, 0.0, nan, nan, nan, 0.0, 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, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.4947560304123185, nan, nan, 0.9367554411265554, nan, 0.8936707055036955, nan, nan, 0.415552298205819, nan, 0.628642268759477, nan, nan, nan, 0.5714915052960218, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4176902360458362, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.07798852296559194, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5637317879889566, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.46058296372410024, 0.0, nan, nan, nan, 0.0, 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, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.9269 | 5.0 | 100 | 2.9183 | 0.1002 | 0.1773 | 0.5445 | [0.5279435318956285, nan, nan, 0.5951721583063695, nan, 0.3553062034603592, nan, nan, 0.31959348076265126, nan, 0.2231862410152919, nan, nan, nan, 0.3985381992705846, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12884859419861272, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.028723640779189683, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.17658325610132838, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.2532118069798349, 0.0, nan, nan, nan, 0.0, 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, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6319186235746953, nan, nan, 0.9509781195930228, nan, 0.9257139290840845, nan, nan, 0.33657066583686135, nan, 0.43170361870415147, nan, nan, nan, 0.5140531326016587, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.3635317104081956, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.038706183309902976, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5468660306699833, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.401454157616118, 0.0, nan, nan, nan, 0.0, 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, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.6513 | 6.0 | 120 | 3.0232 | 0.1023 | 0.1836 | 0.5146 | [0.4763877012699381, nan, nan, 0.6203459387996805, nan, 0.3693264502688378, nan, nan, 0.3772483182723677, nan, 0.2666202282286546, nan, nan, nan, 0.32110191304347824, 0.0, nan, nan, 0.009869430107096653, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13650858844410543, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0865154967469185, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.13516322402241895, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.269214884083382, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0007184179680114947, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5503249175598591, nan, nan, 0.9322932620267731, nan, 0.859835415162133, nan, nan, 0.4280075665140808, nan, 0.6156817343551494, nan, nan, nan, 0.4420540519833745, 0.0, nan, nan, 0.01271958472274743, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4914294766858531, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.14526053789956442, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3981630812125851, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.44934510880168566, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0007335624107177329, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.9523 | 7.0 | 140 | 2.9515 | 0.1075 | 0.1892 | 0.5202 | [0.45098753912967615, nan, nan, 0.6051129014622386, nan, 0.3367778830648513, nan, nan, 0.405201250970518, nan, 0.3054317548746518, nan, nan, nan, 0.429385138438927, 0.0, nan, nan, 0.033813166295247846, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13324643297516295, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.10452139823587063, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15470055825322895, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.2645047716112295, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0021850233316050662, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.532281019875513, nan, nan, 0.939214192875415, nan, 0.9030857428161304, nan, nan, 0.4572827698027302, nan, 0.5617085365353879, nan, nan, nan, 0.5752456473021893, 0.0, nan, nan, 0.04824847880642167, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.3994508105824576, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.18433546127077044, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.512068447092912, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3713174563947817, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.002277904328018223, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.4135 | 8.0 | 160 | 2.8940 | 0.1062 | 0.1912 | 0.5038 | [0.43023177565756143, nan, nan, 0.6386208132035783, nan, 0.3103156542654855, nan, nan, 0.39485066239825634, nan, 0.2766972953667327, nan, nan, nan, 0.4860929294408793, 0.0, nan, nan, 0.016076774296872112, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13267331129559162, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.08437947139656057, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1534104862475442, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.25979245091057074, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0026386218854838103, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.485367583986848, nan, nan, 0.9148667555239128, nan, 0.8960422963418657, nan, nan, 0.47470868101902275, nan, 0.6111839678701693, nan, nan, nan, 0.6102319523453811, 0.0, nan, nan, 0.02243115954484513, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5048423720758304, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.14406213270032955, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5464286690539322, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.33212803870816693, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0027025983552758583, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.5287 | 9.0 | 180 | 2.6942 | 0.1123 | 0.1883 | 0.5278 | [0.4804233762743274, nan, nan, 0.6496232588186611, nan, 0.3560819918109916, nan, nan, 0.4637348506322463, nan, 0.29151895670496447, nan, nan, nan, 0.3959881857677719, 0.0, nan, nan, 0.016928894047879003, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13040916836660252, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.1388164351014927, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15446056449485623, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.2838344433872502, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007207592949404124, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5646471195471486, nan, nan, 0.9032518440638116, nan, 0.7490036643753176, nan, nan, 0.5492765135075378, nan, 0.6293594524814556, nan, nan, nan, 0.4951062077419602, 0.0, nan, nan, 0.02531884904946887, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.500300998046153, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.24775875181489249, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.40015854358581854, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3880051506835061, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007798926682367476, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.4879 | 10.0 | 200 | 2.6745 | 0.1109 | 0.1887 | 0.5153 | [0.44361089949030885, nan, nan, 0.599364764999921, nan, 0.31801020664508584, nan, nan, 0.4812464012640769, nan, 0.29536251221820553, nan, nan, nan, 0.45776711963762323, 0.0, nan, nan, 0.021375606660634334, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13192104736850202, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.13731398385371707, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.13079699017199017, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3061083458625552, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004218202616715521, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5086244734869539, nan, nan, 0.9420247862044986, nan, 0.790033969026667, nan, nan, 0.5885994578969349, nan, 0.5851297077988608, nan, nan, nan, 0.572567948054933, 0.0, nan, nan, 0.03126611433875348, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4528172360986429, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.29144292595238647, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2793920673536889, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4263491278956336, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004555808656036446, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.599 | 11.0 | 220 | 2.5330 | 0.1129 | 0.1842 | 0.5335 | [0.4826929706868573, nan, nan, 0.6497580345699124, nan, 0.2973384829615617, nan, nan, 0.492684075918697, nan, 0.33117540064361284, nan, nan, nan, 0.43190662669032615, 0.0, nan, nan, 0.029673145160425803, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11828379186336013, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.15174636403702071, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.13072972061532076, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.2593405656557045, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010579029733959312, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5754404733752595, nan, nan, 0.8981273749306665, nan, 0.7062971977781939, nan, nan, 0.5994497080668539, nan, 0.630517191918364, nan, nan, nan, 0.5407994790170277, 0.0, nan, nan, 0.047578122314276876, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4199714843956276, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.31740453089350323, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2755104830112347, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3165329136480106, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.013049689201189143, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.0052 | 12.0 | 240 | 2.6565 | 0.1170 | 0.1912 | 0.4912 | [0.3924836802011318, nan, nan, 0.6556498223025284, nan, 0.3456201654020265, nan, nan, 0.4562117087538331, nan, 0.3620263864988075, nan, nan, nan, 0.5196880418009687, 0.0, nan, nan, 0.04022273405165947, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.10527998630972887, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.14196408150818457, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14500559415085995, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.338418554736287, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006696578048617156, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.44054754738944646, nan, nan, 0.9072048877004463, nan, 0.6684498176728096, nan, nan, 0.5859953978561544, nan, 0.6142883488381624, nan, nan, nan, 0.6682832461836082, 0.0, nan, nan, 0.07834576644092268, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.49380577705021916, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.30040791869281647, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.30467703578164723, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.47578788548833945, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007721709586502452, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.1036 | 13.0 | 260 | 2.4045 | 0.1251 | 0.1960 | 0.5501 | [0.4750214274712341, nan, nan, 0.6738366975027412, nan, 0.31889668734061094, nan, nan, 0.49240484506394744, nan, 0.3833054086601393, nan, nan, nan, 0.5310855761397358, 0.0, nan, nan, 0.0433843889781771, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11661668246292801, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.16773605852714282, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15084003529979853, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3953460644238221, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004984051036682616, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5624498792739508, nan, nan, 0.9030241818648431, nan, 0.6477474344914899, nan, nan, 0.6315009376253921, nan, 0.6000368837342732, nan, nan, nan, 0.691742802965006, 0.0, nan, nan, 0.0770222420846368, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.45872102233722345, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.33949436519093823, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24762867998797256, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.517539638151477, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005791282189876839, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.794 | 14.0 | 280 | 2.3167 | 0.1274 | 0.2011 | 0.5611 | [0.48759849200932825, nan, nan, 0.6989665993595102, nan, 0.35030947985666194, nan, nan, 0.4807849206920548, nan, 0.4232590215243425, nan, nan, nan, 0.48824090281632015, 0.0, nan, nan, 0.055335442325214736, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12160636541059901, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.17626745287783105, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1622038810483871, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.368734846377042, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008758051757260707, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5829292406606381, nan, nan, 0.8998327717665758, nan, 0.6615311917689749, nan, nan, 0.6562804523530712, nan, 0.6539690996270645, nan, nan, nan, 0.657131912122431, 0.0, nan, nan, 0.09290453436006738, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4277446269208428, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.42231107833421677, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2814968701309351, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.48265546349648164, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0119686498590788, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.8382 | 15.0 | 300 | 2.2015 | 0.1260 | 0.1888 | 0.5892 | [0.5428404035076511, nan, nan, 0.6668123832611373, nan, 0.33136602876081833, nan, nan, 0.5084115362139493, nan, 0.38357612620652726, nan, nan, nan, 0.4594509125642151, 0.0, nan, nan, 0.049335576840107, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12215850796718364, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.18333519851780009, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15875710128862408, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.36907323665471176, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0035252263906856403, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6969387853048431, nan, nan, 0.9028958631708791, nan, 0.6707768297358262, nan, nan, 0.6055749813703252, nan, 0.5724560468833244, nan, nan, nan, 0.5454308644103507, 0.0, nan, nan, 0.07862078448898209, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.3458097903575012, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.33979396649074695, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.25055353579531475, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4617795871649129, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004208331724643836, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 2.0824 | 16.0 | 320 | 2.1058 | 0.1245 | 0.1927 | 0.5583 | [0.49940918071362245, nan, nan, 0.6721999347487222, nan, 0.33369263707520164, nan, nan, 0.48797201486710184, nan, 0.39894524802059145, nan, nan, nan, 0.5046095902825352, 0.0, nan, nan, 0.04578177150192555, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11020357517607486, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.200689131704973, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.16109440769693326, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3145279722153737, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004608740492458425, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5993038291143935, nan, nan, 0.9210674459612725, nan, 0.6016975597578481, nan, nan, 0.6396898056781611, nan, 0.5843817876316545, nan, nan, nan, 0.6292363385623169, 0.0, nan, nan, 0.07662690364055141, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.43148333949411205, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.4234979604065359, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.292922942350272, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3816448369600562, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005521022354349253, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.7332 | 17.0 | 340 | 2.2257 | 0.1262 | 0.2031 | 0.5543 | [0.472017760120135, nan, nan, 0.688271044992743, nan, 0.31055586423080433, nan, nan, 0.45982389723084777, nan, 0.3978133209704837, nan, nan, nan, 0.5248825550413687, 0.0, nan, nan, 0.07655870407368845, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11334890085822778, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.17907910927258539, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1327995675821763, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.42242321351910395, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008087037468000931, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5573450915065931, nan, nan, 0.8793969849246231, nan, 0.6677722202904753, nan, nan, 0.7081733092034688, nan, 0.632986353018319, nan, nan, nan, 0.6882376601735333, 0.0, nan, nan, 0.1402935817663034, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.39628241009663623, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.43099951602866954, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2149085640871443, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5631283898911332, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010733176325238408, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.4288 | 18.0 | 360 | 2.1115 | 0.1258 | 0.1952 | 0.5677 | [0.5090348710575205, nan, nan, 0.6516802046084259, nan, 0.297545971892537, nan, nan, 0.5009740904353005, nan, 0.43029384865642256, nan, nan, nan, 0.47768960582802134, 0.0, nan, nan, 0.055080392765845615, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11943414972637953, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.18384099605649626, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1577759659901581, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3791353001017294, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010844010814623252, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6240389255337994, nan, nan, 0.9112614141547938, nan, 0.6046130116528918, nan, nan, 0.6612101509208382, nan, 0.6314700217204213, nan, nan, nan, 0.5907946905705913, 0.0, nan, nan, 0.07925676372511946, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.3999155093203781, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3620681708188334, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.296230489571659, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4847495545179038, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.014246554187097023, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.3557 | 19.0 | 380 | 2.1644 | 0.1235 | 0.1952 | 0.5340 | [0.45051944557883883, nan, nan, 0.680187130824496, nan, 0.3233930982051045, nan, nan, 0.48768868560777917, nan, 0.3719932174873157, nan, nan, nan, 0.5244535379948155, 0.0, nan, nan, 0.06326307980974823, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1235196044613085, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.18615950618388388, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1338282685299521, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.34819411901293984, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010321412403802625, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5304206083336315, nan, nan, 0.8594082438551903, nan, 0.5916316723281712, nan, nan, 0.6513671315214098, nan, 0.5799045121101594, nan, nan, nan, 0.7099274071520236, 0.0, nan, nan, 0.12460036439891368, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5445846755029836, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3836625106588924, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24292704261542247, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.42909355774358443, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.013204123392919192, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.2805 | 20.0 | 400 | 2.1733 | 0.1242 | 0.2025 | 0.5293 | [0.4359919702458697, nan, nan, 0.6776664423104268, nan, 0.3320810974499282, nan, nan, 0.47790932220872395, nan, 0.38331230110830683, nan, nan, nan, 0.5442070071960895, 0.0, nan, nan, 0.056890719745731864, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13689397253995042, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.18726600714350605, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15429938763958215, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3277648057737211, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010367824795410199, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5045057387413251, nan, nan, 0.8876963069052015, nan, 0.6990665204482842, nan, nan, 0.6829434067328873, nan, 0.5726199745912053, nan, nan, nan, 0.6666053745522803, 0.0, nan, nan, 0.11014472824779126, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5551460104557217, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.4513032656541679, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3278572014323593, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4016752728171377, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.013744643063974363, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.372 | 21.0 | 420 | 2.0209 | 0.1268 | 0.1973 | 0.5657 | [0.5010337907030282, nan, nan, 0.6861817147192716, nan, 0.35235541766238193, nan, nan, 0.5041431987328845, nan, 0.4331889706943843, nan, nan, nan, 0.4880826848684898, 0.0, nan, nan, 0.04398303192676937, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11698496863904399, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.18097382756655236, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1360123411534702, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3331465034060533, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.027245614921902642, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6058301039369693, nan, nan, 0.8984502413219309, nan, 0.7006802721088435, nan, nan, 0.6750739045341763, nan, 0.6148723412974878, nan, nan, nan, 0.6259189028711525, 0.0, nan, nan, 0.07110935405135962, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.3949516818925912, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.4463598442073241, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24341907443348004, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4020134490069326, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.041620014671248214, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.4077 | 22.0 | 440 | 1.9685 | 0.1282 | 0.2006 | 0.5808 | [0.5194961664841183, nan, nan, 0.6680392748105989, nan, 0.3328232729860771, nan, nan, 0.5312333681575369, nan, 0.3734029500413433, nan, 0.0, nan, 0.5304863989031876, 0.0, nan, nan, 0.09759663791971282, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12179886174657742, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.1982319419727467, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1419518156424581, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.4269761725661373, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03260925916325367, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6373759471126536, nan, nan, 0.8803697234111248, nan, 0.6670946229081409, nan, nan, 0.662069982066379, nan, 0.5737162411376583, nan, nan, nan, 0.6521825738857285, 0.0, nan, nan, 0.19160163635738595, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4029994191265776, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.35554608098453594, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.22226170625700462, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5241861009585994, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04856955329910042, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.3165 | 23.0 | 460 | 2.0245 | 0.1248 | 0.1986 | 0.5627 | [0.493390646293021, nan, nan, 0.6321674469843018, nan, 0.3501202780810329, nan, nan, 0.5275281009857569, nan, 0.3936452693993297, nan, 0.0, nan, 0.5131925959168117, 0.0, nan, nan, 0.06250061227088824, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12599091688812025, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.1824444300434191, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14252971590000532, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.4256623004675062, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.019257880037238995, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5939547096248373, nan, nan, 0.902630947157534, nan, 0.6605058799404427, nan, nan, 0.6560265974434354, nan, 0.6257735338715626, nan, nan, nan, 0.6395793828650232, 0.0, nan, nan, 0.10966344666368731, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4731794898875218, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.32441059205826095, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.21928218024765603, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.525811948024921, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.027952588703138874, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.2786 | 24.0 | 480 | 2.0101 | 0.1284 | 0.1961 | 0.5490 | [0.47740550792458475, nan, nan, 0.7147584658141862, nan, 0.3338715073235298, nan, nan, 0.5181145719308721, nan, 0.3598918229651998, nan, nan, nan, 0.504395131823882, 0.0, nan, nan, 0.07240644415546886, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11426079165254328, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.1745514812112544, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14023398023329509, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.4139434247830999, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.028146861354993193, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.5789989903864193, nan, nan, 0.8484432044903264, nan, 0.6139567229250809, nan, nan, 0.6395342171851585, nan, 0.5262796606696447, nan, nan, nan, 0.6425137428412726, 0.0, nan, nan, 0.1378184193337688, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5090985900617838, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.4162729598303796, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.22379247191318372, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5051441801178413, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0462916489710822, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.3317 | 25.0 | 500 | 1.8858 | 0.1314 | 0.1935 | 0.5843 | [0.5301516817532359, nan, nan, 0.6962216459311296, nan, 0.31580737791029245, nan, nan, 0.5299626090017334, nan, 0.36406788477794394, nan, nan, nan, 0.5453687925056984, 0.0, nan, nan, 0.045733134930958055, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12598183022617582, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.18737449004839146, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14208431901120464, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.43213266336021183, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.027452631578947367, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6575935245112711, nan, nan, 0.8779068323495567, nan, 0.5628337835789624, nan, nan, 0.6534471040068132, nan, 0.5162800704889143, nan, nan, nan, 0.6755118849240552, 0.0, nan, nan, 0.07554402007631751, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4499128689866399, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3752391048835012, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2117923625727797, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.517903827894333, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03775915987799699, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.3294 | 26.0 | 520 | 1.9586 | 0.1267 | 0.2008 | 0.5607 | [0.49048977024276574, nan, nan, 0.6999728275277026, nan, 0.3381723096343718, nan, nan, 0.5145174424285854, nan, 0.37485694220465066, nan, 0.0, nan, 0.47685307286787865, 0.0, nan, nan, 0.09269540554819215, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13914883292591618, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.17943198526976725, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14833319408471884, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.43227071188110683, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04113598052069676, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6007942526198032, nan, nan, 0.8743677199837739, nan, 0.6018937063685238, nan, nan, 0.6467649876757536, nan, 0.5906212860128683, nan, nan, nan, 0.6025780995613783, 0.0, nan, nan, 0.17190346866513115, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5633099223741881, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.31217303127376644, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2426536916053905, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.547806407138119, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06783521871742404, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.2465 | 27.0 | 540 | 1.8813 | 0.1278 | 0.2017 | 0.5720 | [0.4965603283844338, nan, nan, 0.6888599667784658, nan, 0.3322820711616885, nan, nan, 0.53191389390451, nan, 0.4056226508308465, nan, 0.0, nan, 0.5143450684915175, 0.0, nan, nan, 0.07467337287592177, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1390831697727977, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.1952248248851397, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15533038147138964, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.40376103734347546, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.025654813918576776, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6081338030319822, nan, nan, 0.8960659972018246, nan, 0.6763402608749922, nan, nan, 0.6489186599736319, nan, 0.6224847342322036, nan, nan, nan, 0.6610775919860561, 0.0, nan, nan, 0.1281068445116711, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4834662301314886, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3584153395865502, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24932345625017086, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4722370354954932, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.044206787382726535, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.9738 | 28.0 | 560 | 1.8231 | 0.1274 | 0.2013 | 0.5786 | [0.515238353544077, nan, nan, 0.7113293510852016, nan, 0.36264016677981115, nan, nan, 0.5151446050805606, nan, 0.3962353450257603, nan, 0.0, nan, 0.502814892316302, 0.0, nan, nan, 0.045927383938114436, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14831834812278671, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19892386002357898, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15337510009892127, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3819992241044873, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.019000534473543558, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6299264474254853, nan, nan, 0.8808540229980214, nan, 0.6715346689134369, nan, nan, 0.6474774191963445, nan, 0.6004262120404902, nan, nan, nan, 0.6332969411404165, 0.0, nan, nan, 0.07459864553611331, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5334635897977504, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.4102463644534581, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.26700926659924007, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4610642144557314, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.027450677580016215, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.2255 | 29.0 | 580 | 1.8577 | 0.1298 | 0.2061 | 0.5790 | [0.5085307554134902, nan, nan, 0.7026949830303437, nan, 0.33532129520990445, nan, nan, 0.5287269996244837, nan, 0.39869289963433935, nan, 0.0, nan, 0.5066058916775193, 0.0, nan, nan, 0.12668441396817023, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14676321951186924, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.20393755372542496, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.13928000946577532, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3793473382303036, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.048396745632926536, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6274901111098506, nan, nan, 0.865592377041716, nan, 0.6140815434955109, nan, nan, 0.6572057944430342, nan, 0.5987664439981968, nan, nan, nan, 0.658656553467793, 0.0, nan, nan, 0.2642064010450686, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4640544964883561, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.423739946071766, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2574146461471175, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.45072382711392633, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09370294583220726, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.7742 | 30.0 | 600 | 1.8306 | 0.1234 | 0.1977 | 0.5704 | [0.5079305819206281, nan, nan, 0.6844006547349558, nan, 0.3324820597494262, nan, nan, 0.5305967040691468, nan, 0.39659824125843546, nan, 0.0, nan, 0.5222298496570746, 0.0, nan, nan, 0.07746855216640632, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14712055362902615, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19870039539561493, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.12016351482319028, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.26438011110727716, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04411700021992523, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6173424911189782, nan, nan, 0.8947952282003097, nan, 0.653498096486301, nan, nan, 0.6675810902658925, nan, 0.6039301667964427, nan, nan, nan, 0.6469612518914364, 0.0, nan, nan, 0.12956787789198665, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5130379679991551, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.36539835449747643, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2643577618019299, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.29897376533173786, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0774487471526196, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.0007 | 31.0 | 620 | 1.7903 | 0.1260 | 0.1938 | 0.5843 | [0.5307466542323287, nan, nan, 0.7115300248155578, nan, 0.345585489194618, nan, nan, 0.5441693162922404, nan, 0.39158243555143946, nan, 0.0, nan, 0.4622300127938188, 0.0, nan, nan, 0.08470204534069471, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13946529250544007, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.20064555581473054, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.13681045121207394, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3222098462909334, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03489059727971614, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6783844088437612, nan, nan, 0.8794590746152509, nan, 0.6105063257281943, nan, nan, 0.6404841258792797, nan, 0.5544957173886316, nan, nan, nan, 0.5646804190848321, 0.0, nan, nan, 0.13659802674550517, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.47991762158736867, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3667465603466157, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2636470491758467, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3762600314763992, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0683371298405467, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.3284 | 32.0 | 640 | 1.8145 | 0.1287 | 0.2010 | 0.5842 | [0.5192500726510545, nan, nan, 0.7049099120975183, nan, 0.3423862558546578, nan, nan, 0.5278006416280502, nan, 0.43408617766363095, nan, 0.0, nan, 0.4964408197899848, 0.0, nan, nan, 0.08980982224355175, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1356179918582326, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19908685955059652, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14308645131963976, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.35317639542184864, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04361614410452652, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6455008494890413, nan, nan, 0.9012194415239294, nan, 0.6243792405559865, nan, nan, 0.6668768476133544, nan, 0.6216650956927995, nan, nan, nan, 0.6454595950889693, 0.0, nan, nan, 0.1640482656674344, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4155251623805249, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3713212417321564, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.27534647240521554, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.41701026234668265, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07964943438477279, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.1653 | 33.0 | 660 | 1.7478 | 0.1258 | 0.1887 | 0.5999 | [0.5494416329120891, nan, nan, 0.7113240497585706, nan, 0.3481276076638407, nan, nan, 0.5525964081859948, nan, 0.4048768986210516, nan, 0.0, nan, 0.39329058482277807, 0.0, nan, nan, 0.07192039677362573, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14560752807957955, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.2091592878225269, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.13843012163129026, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.32389194859690534, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.05226497574098093, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.7403835833515855, nan, nan, 0.9036988898363316, nan, 0.6055491659311169, nan, nan, 0.650081479237125, nan, 0.5453874841195033, nan, nan, nan, 0.4685015993411098, 0.0, nan, nan, 0.11939221011378871, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.3520092939747584, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.37971007812680047, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.25386108301670174, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.36945748735090983, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.08401220030114667, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.2007 | 34.0 | 680 | 1.8022 | 0.1277 | 0.2003 | 0.5683 | [0.4969052889471535, nan, nan, 0.728289191708223, nan, 0.35597857806539573, nan, nan, 0.5314453621772132, nan, 0.4149301334401236, nan, 0.0, nan, 0.522438044206296, 0.0, nan, nan, 0.07230648411849862, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14546070287176247, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.20042934120252656, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1317073860823065, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.317305968811615, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04144064442530208, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6052838480151974, nan, nan, 0.8862309902063862, nan, 0.6797460792967253, nan, nan, 0.650187934521811, nan, 0.5944735871480678, nan, nan, nan, 0.6573579267942309, 0.0, nan, nan, 0.13135549520437279, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5398215134392987, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.37332626581549166, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2544897903397753, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3684039384519335, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06872321531987181, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.8721 | 35.0 | 700 | 1.7645 | 0.1265 | 0.1927 | 0.5854 | [0.5319156930808397, nan, nan, 0.7282370480292943, nan, 0.35369395441437557, nan, nan, 0.5433595869011405, nan, 0.38299615691392935, nan, 0.0, nan, 0.46762841782902786, 0.0, nan, nan, 0.053679359689546444, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13526314046302124, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.20808023029526787, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.13224326682239046, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.33657964741422064, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04662379421221865, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6794079842371135, nan, nan, 0.8775342942057901, nan, 0.6145629942671695, nan, nan, 0.6479851290156162, nan, 0.5411561001598295, nan, nan, nan, 0.5884617594667586, 0.0, nan, nan, 0.0951046787445426, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.431324919469821, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3856444884883962, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.25689527922805677, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3883823472028927, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.08285394386317131, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.7034 | 36.0 | 720 | 1.7507 | 0.1260 | 0.1928 | 0.5827 | [0.5270254123341084, nan, nan, 0.7163941322010208, nan, 0.3544803691759605, nan, nan, 0.5618863030683066, nan, 0.4062475733295683, nan, 0.0, nan, 0.4644592533020649, 0.0, nan, nan, 0.051604593961226346, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13542116931829273, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.20654294187788547, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1293585816939569, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.30601174881992455, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04623215889809362, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6697010339594915, nan, nan, 0.8953043636634573, nan, 0.6458305471598862, nan, nan, 0.6457250014330519, nan, 0.5895967378386132, nan, nan, nan, 0.579639525752265, 0.0, nan, nan, 0.09159819863178521, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.41028674024396683, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3892166578322694, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24013886231309625, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3448746797081279, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.08941739701169839, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.0078 | 37.0 | 740 | 1.7266 | 0.1249 | 0.1874 | 0.5876 | [0.5365675905382735, nan, nan, 0.7197175896141601, nan, 0.3642732592734404, nan, nan, 0.5644186131520269, nan, 0.39627388680277903, nan, 0.0, nan, 0.43232420453498976, 0.0, nan, nan, 0.019497188704933537, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13324231803732986, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.197425975215553, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1394884153688931, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.33302817617739805, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03599159587101489, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6957825726908689, nan, nan, 0.902991067363175, nan, 0.6276869856723817, nan, nan, 0.6322133691459829, nan, 0.5341276996844392, nan, nan, nan, 0.5574977494301748, 0.0, nan, nan, 0.03224586613496511, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4143634155357237, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3427438869811712, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2499794986742476, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.38325767724984716, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06084707154163932, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.9811 | 38.0 | 760 | 1.7541 | 0.1269 | 0.1947 | 0.5788 | [0.5184820500063916, nan, nan, 0.7195566101740684, nan, 0.3572767695010748, nan, nan, 0.5443114422594085, nan, 0.3899310736872024, nan, 0.0, nan, 0.487109019976382, 0.0, nan, nan, 0.056860483651673274, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13798662231613926, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.18913854060006569, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14271030878584767, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.35382126520138346, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.036275294163560796, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6547697478932933, nan, nan, 0.8824104045764242, nan, 0.6490491347259743, nan, nan, 0.6568536731167651, nan, 0.5622208106225155, nan, nan, nan, 0.6036162347487981, 0.0, nan, nan, 0.10423184021451408, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.44206579711675553, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.338434237514692, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2694694256895279, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4124839041140434, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0717732906065403, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.5625 | 39.0 | 780 | 1.7657 | 0.1280 | 0.1952 | 0.5789 | [0.5102751986027965, nan, nan, 0.7240462381344197, nan, 0.37437802263548886, nan, nan, 0.5616050885500588, nan, 0.3866788541729619, nan, 0.0, nan, 0.46431302362693383, 0.0, nan, nan, 0.06373117033603708, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13281328771451043, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19335343445472958, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15072251988959245, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.37061265000451143, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03587553884814187, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6455357540207839, nan, nan, 0.8960411613255735, nan, 0.6694751295013418, nan, nan, 0.6536108813678685, nan, 0.5244354739559854, nan, nan, nan, 0.6420004213832864, 0.0, nan, nan, 0.12668018838736292, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4173522733273486, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.33769675739208593, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.25375174261268896, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.42740267679460997, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06779661016949153, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.7157 | 40.0 | 800 | 1.8065 | 0.1214 | 0.1889 | 0.5592 | [0.4990243270558229, nan, nan, 0.7043645418069693, nan, 0.3753328351768631, nan, nan, 0.5436199895024725, nan, 0.3907085586565362, nan, 0.0, nan, 0.4225510349706832, 0.0, nan, nan, 0.02772833353111032, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13252358085616647, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19464198552990095, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14146572895735654, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.301612939408215, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.031228437242638057, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6286314892978343, nan, nan, 0.9039596665369682, nan, 0.6359251433207621, nan, nan, 0.6445785599056643, nan, 0.5337076349329946, nan, nan, nan, 0.5330881648757877, 0.0, nan, nan, 0.04819691292241053, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.5054021228283255, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3630130672259224, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2771232539704234, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.34975222090709257, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0541677927493147, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.6014 | 41.0 | 820 | 1.7393 | 0.1245 | 0.1906 | 0.5820 | [0.5309857943249103, nan, nan, 0.7042225354565893, nan, 0.35210798026682505, nan, nan, 0.5475814013977015, nan, 0.38517370005842433, nan, 0.0, nan, 0.42020517175596006, 0.0, nan, nan, 0.04394958222289665, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13832667829206985, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.21264794369855933, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14545374072380166, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.33506991126646946, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0439161653405783, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.684009274134084, nan, nan, 0.9037278650252912, nan, 0.6026247982810424, nan, nan, 0.6551094442215253, nan, 0.5268534076472275, nan, nan, nan, 0.523147350073742, 0.0, nan, nan, 0.07965210216920486, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.43647885092675714, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.36976561959853427, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.26949676079053114, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3889936656998296, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.08737114397127524, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.5889 | 42.0 | 840 | 1.7220 | 0.1293 | 0.1970 | 0.5915 | [0.535421579074004, nan, nan, 0.7182238827227351, nan, 0.3593407923808755, nan, nan, 0.562842617938782, nan, 0.4156914834410549, nan, 0.0, nan, 0.4659394106174996, 0.0, nan, nan, 0.041294653747450796, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1497515005024307, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.2026740421118417, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15121819652971322, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3579053106820766, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.047138501910238106, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.679635736306734, nan, nan, 0.8975851249658506, nan, 0.635113809612967, nan, nan, 0.648238983925252, nan, 0.5739416417359944, nan, nan, nan, 0.5967208718803272, 0.0, nan, nan, 0.07274227371171234, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4658816074351798, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3512710008988039, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.27586583932427633, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.42171871545074985, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09432068259912745, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.7813 | 43.0 | 860 | 1.7547 | 0.1257 | 0.1926 | 0.5784 | [0.5230326164004733, nan, nan, 0.7186647930214541, nan, 0.35623270715013833, nan, nan, 0.5590593944181519, nan, 0.4172766991695842, nan, 0.0, nan, 0.4340370031895127, 0.0, nan, nan, 0.022453083109919572, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14335914802300878, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.20532109502872364, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14500102889731606, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.33509112058937573, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03595105049169821, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6657646753922178, nan, nan, 0.8869926237447534, nan, 0.6106846408288086, nan, nan, 0.6490824373346872, nan, 0.5910208598008279, nan, nan, nan, 0.5494493286598097, 0.0, nan, nan, 0.038004056516208876, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.49247504884617416, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.36860178378004654, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2696607713965503, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.39341596972022425, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0693023435388595, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 1.0398 | 44.0 | 880 | 1.7927 | 0.1249 | 0.1938 | 0.5710 | [0.5108075145706407, nan, nan, 0.7075978561413164, nan, 0.35776407618447337, nan, nan, 0.5546690592863551, nan, 0.3954948573789603, nan, 0.0, nan, 0.4463746987593347, 0.0, nan, nan, 0.03904293710413905, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1374491630341874, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.20271195063014397, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14251316342959922, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3423995735276152, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03541601623477989, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6410444133988026, nan, nan, 0.8940501519127764, nan, 0.6275265020818288, nan, nan, 0.6581065699288388, nan, 0.54918855784599, nan, nan, nan, 0.5747323258442031, 0.0, nan, nan, 0.07289697136374575, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.47722448117442046, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3857021041229748, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2685673673564224, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.400998920437548, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07007451449750975, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.8604 | 45.0 | 900 | 1.7486 | 0.1250 | 0.1902 | 0.5792 | [0.5248087666056819, nan, nan, 0.7091471585739102, nan, 0.35479033189721415, nan, nan, 0.5640257536930426, nan, 0.3881567826195603, nan, 0.0, nan, 0.42684331456990804, 0.0, nan, nan, 0.044603190865147295, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1352500578799944, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.21044953640121172, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1444317923024368, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3323488964537728, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03917852722772277, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6748389810320048, nan, nan, 0.8871457783149685, nan, 0.5969097993063542, nan, nan, 0.6506547000008189, nan, 0.5410946272693742, nan, nan, nan, 0.553659330766726, 0.0, nan, nan, 0.08231633950978033, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4380524898347151, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.36903966260284393, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.26003881584342453, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.38349179922739746, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07822091811126984, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.7519 | 46.0 | 920 | 1.7624 | 0.1253 | 0.1937 | 0.5741 | [0.5150589229086175, nan, nan, 0.702421661682486, nan, 0.35270264043131655, nan, nan, 0.560850404377936, nan, 0.3893207264751432, nan, 0.0, nan, 0.4497428911769215, 0.0, nan, nan, 0.060121390340771554, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13518387486615613, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.20632370467430025, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1393133022833125, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3347716246730309, 0.0, nan, nan, nan, 4.095842719639566e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.039160296887116426, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6491187042041636, nan, nan, 0.8944765011217537, nan, 0.6141707010458181, nan, nan, 0.6638469664338299, nan, 0.5382976107536577, nan, nan, nan, 0.5843168802313777, 0.0, nan, nan, 0.11526693939289766, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4560173205893225, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3610771819040815, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2681846759423776, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3895269435375831, 0.0, nan, nan, nan, 4.095842719639566e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0818887301648585, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.6697 | 47.0 | 940 | 1.7463 | 0.1226 | 0.1895 | 0.5716 | [0.5166182569073502, nan, nan, 0.7011657967904005, nan, 0.3507737807946272, nan, nan, 0.5480504054828933, nan, 0.3922175175328902, nan, 0.0, nan, 0.4264940769333156, 0.0, nan, nan, 0.057636360133325626, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1336658749010293, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19118476820323058, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1357729377369312, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.30480280873981663, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04078393608673943, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6603998488633775, nan, nan, 0.8925144668979162, nan, 0.6090887206783107, nan, nan, 0.6574432716165645, nan, 0.5494959222982665, nan, nan, nan, 0.5400869581872857, 0.0, nan, nan, 0.10283956134621335, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4457411416803084, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.33578391832407645, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2653144903370418, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.34891978720913597, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.08829774912165553, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.5782 | 48.0 | 960 | 1.7423 | 0.1239 | 0.1913 | 0.5720 | [0.5154453328597143, nan, nan, 0.7053634276466835, nan, 0.34938426332320216, nan, nan, 0.553065890667221, nan, 0.40569057157447297, nan, 0.0, nan, 0.42533648984929856, 0.0, nan, nan, 0.055352769515158905, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13695884427384206, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.1881598973511968, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.13673384870237437, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3268801694800658, 0.0, nan, nan, nan, 8.191685439279132e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04198142414860681, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6584417046326168, nan, nan, 0.8876590530908248, nan, 0.6139121441499273, nan, nan, 0.6536682034442379, nan, 0.5779066431703619, nan, nan, nan, 0.5382137179413511, 0.0, nan, nan, 0.10139571659390148, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4589744943760891, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3176810859394805, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.27075417543667823, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.377300573598845, 0.0, nan, nan, nan, 8.191685439279132e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0916180842438516, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.9024 | 49.0 | 980 | 1.7273 | 0.1243 | 0.1924 | 0.5733 | [0.5155469511609068, nan, nan, 0.7075002228965991, nan, 0.34951576733017126, nan, nan, 0.5555047280920357, nan, 0.40696828996148726, nan, 0.0, nan, 0.4339558533372278, 0.0, nan, nan, 0.04773232568140022, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1381459950476216, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19573223930895312, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1354403360187289, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3201033720037918, 0.0, nan, nan, nan, 4.095842719639566e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04693866843881709, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6552941884827261, nan, nan, 0.8868601657380808, nan, 0.6280881946487639, nan, nan, 0.6662626825093967, nan, 0.5792180648334084, nan, nan, nan, 0.5577582409163171, 0.0, nan, nan, 0.08967307229536939, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.43719702170354335, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.33865317692609065, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.26884071836645435, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.36893721628968695, 0.0, nan, nan, nan, 4.095842719639566e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1036253426508629, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
| 0.9524 | 50.0 | 1000 | 1.7293 | 0.1237 | 0.1909 | 0.5750 | [0.5194973138518036, nan, nan, 0.7044669379850034, nan, 0.34772332290580465, nan, nan, 0.5564015598824595, nan, 0.4062946652059406, nan, 0.0, nan, 0.4247729319128107, 0.0, nan, nan, 0.046667038441519815, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13739290398405696, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19611110736285312, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1343402763679673, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.31478245182821646, 0.0, nan, nan, nan, 0.00016383370878558264, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.045484961930081565, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] | [0.6653449483980128, nan, nan, 0.8913306234632802, nan, 0.6243524932908944, nan, nan, 0.6636340558644579, nan, 0.573746977582886, nan, nan, nan, 0.5385355015418798, 0.0, nan, nan, 0.08630410120664168, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.4259491999788773, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.33494273005922887, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2713282125577454, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3631361939570516, 0.0, nan, nan, nan, 0.00016383370878558264, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09710049805026832, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, 0.0, nan, nan] |
### Framework versions
- Transformers 4.35.2
- Pytorch 1.12.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Robinsh2023/distilbert-base-uncased-distilled-clinc
|
Robinsh2023
| 2023-12-13T12:18:23Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T12:03:32Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9487096774193549
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3062
- Accuracy: 0.9487
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 1.9733 | 0.7374 |
| 2.3454 | 2.0 | 636 | 0.9990 | 0.8616 |
| 2.3454 | 3.0 | 954 | 0.5629 | 0.92 |
| 0.8888 | 4.0 | 1272 | 0.4126 | 0.9358 |
| 0.4016 | 5.0 | 1590 | 0.3554 | 0.9419 |
| 0.4016 | 6.0 | 1908 | 0.3299 | 0.9471 |
| 0.2814 | 7.0 | 2226 | 0.3176 | 0.9471 |
| 0.2423 | 8.0 | 2544 | 0.3119 | 0.9487 |
| 0.2423 | 9.0 | 2862 | 0.3081 | 0.9477 |
| 0.2274 | 10.0 | 3180 | 0.3062 | 0.9487 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.14.1
|
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Numeronvr/teo-rvc
|
Numeronvr
| 2023-12-13T12:26:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:08:58Z |
Entry not found
|
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] |
zinngu/zin
|
zinngu
| 2023-12-13T12:27:39Z | 0 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-12-13T12:10:26Z |
Entry not found
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] |
JeukHwang/distilbert-base-uncased-squad2-lora-test
|
JeukHwang
| 2023-12-13T16:35:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-13T12:12:12Z |
Entry not found
|
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Arekku21/vivit-b-16x2-kinetics400-finetuned-MSL_40_classes_2
|
Arekku21
| 2023-12-13T12:14:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:14:22Z |
Entry not found
|
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aaa12963337/output
|
aaa12963337
| 2023-12-13T12:14:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:14:43Z |
Entry not found
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deymhr/1
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deymhr
| 2023-12-13T12:15:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:15:18Z |
Entry not found
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xreborn/roberta-base_ag_news
|
xreborn
| 2023-12-13T12:30:06Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T12:15:45Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base_ag_news
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-base_ag_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3108
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3947 | 1.0 | 100 | 0.3689 |
| 0.3548 | 2.0 | 200 | 0.3108 |
| 0.0662 | 3.0 | 300 | 0.4454 |
| 0.435 | 4.0 | 400 | 0.4059 |
| 0.4893 | 5.0 | 500 | 0.4149 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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] |
Realgon/N_distilbert_agnews_padding50model
|
Realgon
| 2023-12-13T13:42:21Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T12:15:54Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_distilbert_agnews_padding50model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9418421052631579
---
<!-- 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. -->
# N_distilbert_agnews_padding50model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6732
- Accuracy: 0.9418
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1757 | 1.0 | 7500 | 0.1847 | 0.9428 |
| 0.1399 | 2.0 | 15000 | 0.1983 | 0.9439 |
| 0.1196 | 3.0 | 22500 | 0.2251 | 0.9418 |
| 0.0894 | 4.0 | 30000 | 0.2583 | 0.9436 |
| 0.0587 | 5.0 | 37500 | 0.3116 | 0.9425 |
| 0.0404 | 6.0 | 45000 | 0.3567 | 0.9432 |
| 0.0318 | 7.0 | 52500 | 0.4279 | 0.9392 |
| 0.0257 | 8.0 | 60000 | 0.4443 | 0.9407 |
| 0.0212 | 9.0 | 67500 | 0.4974 | 0.9378 |
| 0.0106 | 10.0 | 75000 | 0.4965 | 0.9417 |
| 0.0145 | 11.0 | 82500 | 0.4986 | 0.9433 |
| 0.011 | 12.0 | 90000 | 0.5389 | 0.9392 |
| 0.0121 | 13.0 | 97500 | 0.5671 | 0.9441 |
| 0.0046 | 14.0 | 105000 | 0.6063 | 0.9396 |
| 0.0011 | 15.0 | 112500 | 0.6245 | 0.9414 |
| 0.002 | 16.0 | 120000 | 0.6103 | 0.9426 |
| 0.001 | 17.0 | 127500 | 0.6181 | 0.9426 |
| 0.0017 | 18.0 | 135000 | 0.6401 | 0.9408 |
| 0.0002 | 19.0 | 142500 | 0.6667 | 0.9422 |
| 0.0004 | 20.0 | 150000 | 0.6732 | 0.9418 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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deymhr/taeil
|
deymhr
| 2023-12-13T12:20:55Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T12:16:37Z |
---
license: openrail
---
|
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] |
hkivancoral/smids_3x_deit_tiny_rms_00001_fold4
|
hkivancoral
| 2023-12-13T12:53:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T12:19:34Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_tiny_rms_00001_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: 0.8783333333333333
---
<!-- 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. -->
# smids_3x_deit_tiny_rms_00001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3270
- Accuracy: 0.8783
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3199 | 1.0 | 225 | 0.3628 | 0.8533 |
| 0.1542 | 2.0 | 450 | 0.3942 | 0.8633 |
| 0.1186 | 3.0 | 675 | 0.4019 | 0.8783 |
| 0.1068 | 4.0 | 900 | 0.4521 | 0.8667 |
| 0.0629 | 5.0 | 1125 | 0.5068 | 0.8733 |
| 0.0347 | 6.0 | 1350 | 0.6175 | 0.8733 |
| 0.0294 | 7.0 | 1575 | 0.7043 | 0.8683 |
| 0.0692 | 8.0 | 1800 | 0.7322 | 0.8683 |
| 0.0709 | 9.0 | 2025 | 0.8957 | 0.8683 |
| 0.0668 | 10.0 | 2250 | 0.9618 | 0.8633 |
| 0.0043 | 11.0 | 2475 | 1.0931 | 0.855 |
| 0.0053 | 12.0 | 2700 | 1.0669 | 0.8617 |
| 0.0043 | 13.0 | 2925 | 1.2308 | 0.8533 |
| 0.0002 | 14.0 | 3150 | 1.1863 | 0.86 |
| 0.0021 | 15.0 | 3375 | 1.1504 | 0.8633 |
| 0.0 | 16.0 | 3600 | 1.1831 | 0.8683 |
| 0.0542 | 17.0 | 3825 | 1.2418 | 0.8633 |
| 0.0002 | 18.0 | 4050 | 1.2334 | 0.8567 |
| 0.0 | 19.0 | 4275 | 1.2760 | 0.8717 |
| 0.0018 | 20.0 | 4500 | 1.3796 | 0.8567 |
| 0.0 | 21.0 | 4725 | 1.2762 | 0.8683 |
| 0.0 | 22.0 | 4950 | 1.3847 | 0.8583 |
| 0.0001 | 23.0 | 5175 | 1.2718 | 0.8733 |
| 0.0 | 24.0 | 5400 | 1.2359 | 0.8717 |
| 0.0 | 25.0 | 5625 | 1.1935 | 0.8783 |
| 0.0 | 26.0 | 5850 | 1.2054 | 0.8783 |
| 0.0 | 27.0 | 6075 | 1.3706 | 0.86 |
| 0.0 | 28.0 | 6300 | 1.3639 | 0.8683 |
| 0.0 | 29.0 | 6525 | 1.2958 | 0.865 |
| 0.0 | 30.0 | 6750 | 1.3600 | 0.8617 |
| 0.0001 | 31.0 | 6975 | 1.4078 | 0.865 |
| 0.0 | 32.0 | 7200 | 1.3542 | 0.8667 |
| 0.0 | 33.0 | 7425 | 1.3120 | 0.865 |
| 0.0 | 34.0 | 7650 | 1.2757 | 0.8717 |
| 0.0 | 35.0 | 7875 | 1.2928 | 0.8667 |
| 0.0 | 36.0 | 8100 | 1.3460 | 0.8667 |
| 0.0002 | 37.0 | 8325 | 1.3775 | 0.8633 |
| 0.0 | 38.0 | 8550 | 1.3704 | 0.8617 |
| 0.0 | 39.0 | 8775 | 1.3338 | 0.87 |
| 0.0 | 40.0 | 9000 | 1.3261 | 0.8717 |
| 0.0 | 41.0 | 9225 | 1.3217 | 0.8717 |
| 0.0068 | 42.0 | 9450 | 1.3066 | 0.8733 |
| 0.0 | 43.0 | 9675 | 1.3141 | 0.875 |
| 0.0059 | 44.0 | 9900 | 1.3176 | 0.8767 |
| 0.0 | 45.0 | 10125 | 1.3464 | 0.865 |
| 0.0 | 46.0 | 10350 | 1.3263 | 0.8733 |
| 0.0 | 47.0 | 10575 | 1.3244 | 0.875 |
| 0.0 | 48.0 | 10800 | 1.3245 | 0.8767 |
| 0.0 | 49.0 | 11025 | 1.3277 | 0.8783 |
| 0.0 | 50.0 | 11250 | 1.3270 | 0.8783 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
riteshctx/OccupancyModel
|
riteshctx
| 2023-12-13T13:32:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T12:21:16Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: OccupancyModel
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. -->
# OccupancyModel
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5828
- eval_Accuracy: 0.9059
- eval_F1: 0.8446
- eval_Precision: 0.8555
- eval_Recall: 0.8476
- eval_runtime: 24.9685
- eval_samples_per_second: 57.432
- eval_steps_per_second: 14.378
- epoch: 0.03
- step: 18
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cpu
- Tokenizers 0.14.1
|
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] |
businessradar/ovrx-pvtn-nf39-0
|
businessradar
| 2023-12-13T12:21:42Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-13T12:21:38Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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Gonzaluigi/Calico-Electronico-RVCv2
|
Gonzaluigi
| 2023-12-13T12:24:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:23:56Z |
Entry not found
|
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ytjhyjy/gfhdt
|
ytjhyjy
| 2023-12-13T12:24:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:24:20Z |
Entry not found
|
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xjyplayer/mecha_dragon
|
xjyplayer
| 2023-12-17T08:28:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:24:23Z |
Entry not found
|
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] |
hkivancoral/smids_3x_beit_base_sgd_001_fold1
|
hkivancoral
| 2023-12-13T22:25:30Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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"
] |
image-classification
| 2023-12-13T12:24:37Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_sgd_001_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: 0.8681135225375626
---
<!-- 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. -->
# smids_3x_beit_base_sgd_001_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: 0.3433
- Accuracy: 0.8681
## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8537 | 1.0 | 226 | 0.8519 | 0.6194 |
| 0.627 | 2.0 | 452 | 0.6595 | 0.7162 |
| 0.5599 | 3.0 | 678 | 0.5836 | 0.7563 |
| 0.5294 | 4.0 | 904 | 0.5378 | 0.7830 |
| 0.541 | 5.0 | 1130 | 0.5094 | 0.7930 |
| 0.4706 | 6.0 | 1356 | 0.4828 | 0.8030 |
| 0.4252 | 7.0 | 1582 | 0.4633 | 0.8264 |
| 0.4132 | 8.0 | 1808 | 0.4456 | 0.8214 |
| 0.3872 | 9.0 | 2034 | 0.4395 | 0.8197 |
| 0.4235 | 10.0 | 2260 | 0.4220 | 0.8347 |
| 0.363 | 11.0 | 2486 | 0.4107 | 0.8414 |
| 0.3912 | 12.0 | 2712 | 0.4033 | 0.8381 |
| 0.304 | 13.0 | 2938 | 0.4009 | 0.8381 |
| 0.3002 | 14.0 | 3164 | 0.4007 | 0.8414 |
| 0.3207 | 15.0 | 3390 | 0.3907 | 0.8431 |
| 0.3295 | 16.0 | 3616 | 0.3840 | 0.8481 |
| 0.2579 | 17.0 | 3842 | 0.3879 | 0.8447 |
| 0.2728 | 18.0 | 4068 | 0.3774 | 0.8514 |
| 0.2754 | 19.0 | 4294 | 0.3738 | 0.8514 |
| 0.2647 | 20.0 | 4520 | 0.3691 | 0.8531 |
| 0.2981 | 21.0 | 4746 | 0.3671 | 0.8548 |
| 0.2768 | 22.0 | 4972 | 0.3598 | 0.8614 |
| 0.3183 | 23.0 | 5198 | 0.3674 | 0.8548 |
| 0.2945 | 24.0 | 5424 | 0.3555 | 0.8598 |
| 0.2478 | 25.0 | 5650 | 0.3546 | 0.8648 |
| 0.325 | 26.0 | 5876 | 0.3592 | 0.8564 |
| 0.2291 | 27.0 | 6102 | 0.3605 | 0.8548 |
| 0.2815 | 28.0 | 6328 | 0.3547 | 0.8564 |
| 0.2072 | 29.0 | 6554 | 0.3519 | 0.8614 |
| 0.3343 | 30.0 | 6780 | 0.3526 | 0.8631 |
| 0.2535 | 31.0 | 7006 | 0.3589 | 0.8614 |
| 0.25 | 32.0 | 7232 | 0.3529 | 0.8648 |
| 0.21 | 33.0 | 7458 | 0.3549 | 0.8581 |
| 0.2119 | 34.0 | 7684 | 0.3504 | 0.8598 |
| 0.2671 | 35.0 | 7910 | 0.3485 | 0.8648 |
| 0.2299 | 36.0 | 8136 | 0.3541 | 0.8614 |
| 0.2465 | 37.0 | 8362 | 0.3470 | 0.8631 |
| 0.2631 | 38.0 | 8588 | 0.3446 | 0.8648 |
| 0.239 | 39.0 | 8814 | 0.3471 | 0.8598 |
| 0.1883 | 40.0 | 9040 | 0.3479 | 0.8648 |
| 0.226 | 41.0 | 9266 | 0.3450 | 0.8681 |
| 0.258 | 42.0 | 9492 | 0.3451 | 0.8698 |
| 0.2562 | 43.0 | 9718 | 0.3456 | 0.8681 |
| 0.1938 | 44.0 | 9944 | 0.3435 | 0.8681 |
| 0.2807 | 45.0 | 10170 | 0.3449 | 0.8664 |
| 0.253 | 46.0 | 10396 | 0.3419 | 0.8698 |
| 0.2465 | 47.0 | 10622 | 0.3435 | 0.8664 |
| 0.2602 | 48.0 | 10848 | 0.3432 | 0.8664 |
| 0.2179 | 49.0 | 11074 | 0.3434 | 0.8681 |
| 0.2786 | 50.0 | 11300 | 0.3433 | 0.8681 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_3x_beit_base_rms_0001_fold1
|
hkivancoral
| 2023-12-13T13:13:45Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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"
] |
image-classification
| 2023-12-13T12:25:09Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_rms_0001_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: 0.8497495826377296
---
<!-- 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. -->
# smids_3x_beit_base_rms_0001_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: 1.4390
- Accuracy: 0.8497
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8068 | 1.0 | 226 | 1.0605 | 0.3272 |
| 0.7543 | 2.0 | 452 | 0.8522 | 0.5943 |
| 0.6302 | 3.0 | 678 | 0.8188 | 0.6227 |
| 0.591 | 4.0 | 904 | 0.7284 | 0.6795 |
| 0.5365 | 5.0 | 1130 | 0.5240 | 0.7830 |
| 0.4875 | 6.0 | 1356 | 0.4715 | 0.8030 |
| 0.2956 | 7.0 | 1582 | 0.5230 | 0.8130 |
| 0.3385 | 8.0 | 1808 | 0.4637 | 0.8047 |
| 0.2498 | 9.0 | 2034 | 0.5733 | 0.8230 |
| 0.2375 | 10.0 | 2260 | 0.5001 | 0.8381 |
| 0.2383 | 11.0 | 2486 | 0.5213 | 0.8164 |
| 0.1638 | 12.0 | 2712 | 0.7500 | 0.8097 |
| 0.1669 | 13.0 | 2938 | 0.6347 | 0.8347 |
| 0.091 | 14.0 | 3164 | 0.8704 | 0.8164 |
| 0.0933 | 15.0 | 3390 | 0.6698 | 0.8280 |
| 0.1167 | 16.0 | 3616 | 0.7435 | 0.8481 |
| 0.0442 | 17.0 | 3842 | 0.8758 | 0.8164 |
| 0.0649 | 18.0 | 4068 | 0.8054 | 0.8247 |
| 0.0996 | 19.0 | 4294 | 0.8135 | 0.8164 |
| 0.0421 | 20.0 | 4520 | 0.8460 | 0.8464 |
| 0.0255 | 21.0 | 4746 | 1.2147 | 0.8097 |
| 0.0814 | 22.0 | 4972 | 0.8708 | 0.8331 |
| 0.07 | 23.0 | 5198 | 1.0564 | 0.8364 |
| 0.029 | 24.0 | 5424 | 1.0607 | 0.8364 |
| 0.0335 | 25.0 | 5650 | 1.0179 | 0.8464 |
| 0.0974 | 26.0 | 5876 | 0.8966 | 0.8364 |
| 0.0251 | 27.0 | 6102 | 1.0900 | 0.8297 |
| 0.0304 | 28.0 | 6328 | 0.9348 | 0.8347 |
| 0.0116 | 29.0 | 6554 | 1.0392 | 0.8447 |
| 0.036 | 30.0 | 6780 | 1.0080 | 0.8414 |
| 0.0176 | 31.0 | 7006 | 1.0131 | 0.8364 |
| 0.0187 | 32.0 | 7232 | 0.9626 | 0.8397 |
| 0.0495 | 33.0 | 7458 | 0.9911 | 0.8414 |
| 0.0106 | 34.0 | 7684 | 1.2195 | 0.8331 |
| 0.0005 | 35.0 | 7910 | 1.2232 | 0.8464 |
| 0.0148 | 36.0 | 8136 | 1.1060 | 0.8364 |
| 0.0093 | 37.0 | 8362 | 1.0552 | 0.8364 |
| 0.0212 | 38.0 | 8588 | 1.1910 | 0.8364 |
| 0.0009 | 39.0 | 8814 | 1.1001 | 0.8431 |
| 0.0083 | 40.0 | 9040 | 1.2874 | 0.8481 |
| 0.0296 | 41.0 | 9266 | 1.3495 | 0.8381 |
| 0.0225 | 42.0 | 9492 | 1.3683 | 0.8414 |
| 0.0158 | 43.0 | 9718 | 1.2852 | 0.8481 |
| 0.0056 | 44.0 | 9944 | 1.3620 | 0.8447 |
| 0.0126 | 45.0 | 10170 | 1.3137 | 0.8431 |
| 0.0 | 46.0 | 10396 | 1.4527 | 0.8497 |
| 0.013 | 47.0 | 10622 | 1.4028 | 0.8531 |
| 0.0375 | 48.0 | 10848 | 1.3979 | 0.8481 |
| 0.0006 | 49.0 | 11074 | 1.4369 | 0.8497 |
| 0.0135 | 50.0 | 11300 | 1.4390 | 0.8497 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
soniarocca31/prova_modello
|
soniarocca31
| 2023-12-13T16:04:50Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:swiss_law_area_prediction",
"base_model:distilbert-base-multilingual-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T12:25:20Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- swiss_law_area_prediction
metrics:
- precision
- recall
- f1
model-index:
- name: prova_modello
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: swiss_law_area_prediction
type: swiss_law_area_prediction
config: main
split: validation
args: main
metrics:
- name: Precision
type: precision
value: 0.9875458681646828
- name: Recall
type: recall
value: 0.9851501871487068
- name: F1
type: f1
value: 0.9862914035976681
---
<!-- 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. -->
# prova_modello
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the swiss_law_area_prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0821
- Precision: 0.9875
- Recall: 0.9852
- F1: 0.9863
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.089 | 0.38 | 500 | 0.1169 | 0.9765 | 0.9777 | 0.9770 |
| 0.0011 | 0.76 | 1000 | 0.0962 | 0.9855 | 0.9802 | 0.9826 |
| 0.0001 | 1.15 | 1500 | 0.0993 | 0.9834 | 0.9801 | 0.9817 |
| 0.0002 | 1.53 | 2000 | 0.1114 | 0.9868 | 0.9782 | 0.9822 |
| 0.2093 | 1.91 | 2500 | 0.0821 | 0.9875 | 0.9852 | 0.9863 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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kjhgf74258/jhgfd
|
kjhgf74258
| 2023-12-13T12:26:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:26:43Z |
Entry not found
|
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testecoveria/ivetesangalo.zip
|
testecoveria
| 2023-12-13T12:27:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:27:23Z |
Entry not found
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] |
Arekku21/vivit-b-16x2-kinetics400-finetuned-MSL_40_classes_3
|
Arekku21
| 2023-12-13T15:41:44Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vivit",
"video-classification",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-13T12:29:39Z |
Entry not found
|
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yyyyc/t5-small-fr-finetune-sum
|
yyyyc
| 2023-12-13T12:31:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:31:40Z |
Entry not found
|
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] |
ADISH007/Aws_donut_10k_incremental_1_Epoch_10
|
ADISH007
| 2023-12-13T12:34:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-13T12:33:45Z |
Entry not found
|
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misterytoon/yellow-diamond
|
misterytoon
| 2023-12-13T12:34:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:34:00Z |
Entry not found
|
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] |
alpcaferoglu/t5-table-tag
|
alpcaferoglu
| 2023-12-13T17:40:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-13T12:34:25Z |
---
license: apache-2.0
---
|
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Denm/output
|
Denm
| 2023-12-13T12:39:27Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/rugpt3small_based_on_gpt2",
"region:us"
] | null | 2023-12-13T12:39:24Z |
---
base_model: ai-forever/rugpt3small_based_on_gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [ai-forever/rugpt3small_based_on_gpt2](https://huggingface.co/ai-forever/rugpt3small_based_on_gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7083
- Accuracy: 0.5209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7676 | 0.13 | 50 | 0.7502 | 0.4717 |
| 0.7482 | 0.25 | 100 | 0.7369 | 0.4830 |
| 0.7414 | 0.38 | 150 | 0.7276 | 0.4915 |
| 0.7261 | 0.5 | 200 | 0.7205 | 0.5062 |
| 0.7272 | 0.63 | 250 | 0.7143 | 0.5132 |
| 0.7209 | 0.76 | 300 | 0.7106 | 0.5148 |
| 0.7218 | 0.88 | 350 | 0.7083 | 0.5209 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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citiusLTL/pyplexity
|
citiusLTL
| 2023-12-13T12:56:39Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-12-13T12:45:53Z |
---
license: gpl-3.0
---
In this card, we provide a text preprocessing tool named "Pyplexity". This tool is available as a Python package through PyPi to preprocess text for different downstream tasks:
```bash
pip install pyplexity
```
You can also check the documentation [here](https://github.com/citiususc/pyplexity). We have also created a web [demo](https://tec.citius.usc.es/pyplexity/).
Please cite our research:
Fernández-Pichel, M., Prada-Corral, M., Losada, D. E., Pichel, J. C., & Gamallo, P. (2023). An unsupervised perplexity-based method for boilerplate removal. Natural Language Engineering, 1-18.
|
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cancl/falcon7binstruct_mentalhealthmodel_oct23
|
cancl
| 2023-12-13T22:25:46Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T12:51:13Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
model-index:
- name: falcon7binstruct_mentalhealthmodel_oct23
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. -->
# falcon7binstruct_mentalhealthmodel_oct23
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Thekingbalxd/fav_models
|
Thekingbalxd
| 2023-12-13T12:53:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:53:14Z |
Entry not found
|
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camenduru/EdgeSAM
|
camenduru
| 2023-12-13T12:53:24Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2023-12-13T12:53:18Z |
Entry not found
|
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] |
hkivancoral/smids_3x_deit_tiny_rms_00001_fold5
|
hkivancoral
| 2023-12-13T13:27:26Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T12:53:46Z |
Entry not found
|
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mrtechnoo/roberta-base-squad2-finetuned-squad
|
mrtechnoo
| 2023-12-13T12:56:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:56:19Z |
Entry not found
|
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ArtrosilForteBelarus/ArtrosilForteBelarus
|
ArtrosilForteBelarus
| 2023-12-13T12:57:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T12:56:31Z |
Artrosil Forte - Артросил форте Belarus, гель, креме, капсула, планшет, Цена, Жалоба!
Artrosil Forte — это натуральные ингредиенты, которые облегчают боль в суставах, а также помогают пользователям восстанавливать поврежденный хрящ в организме. Читать далее. Артросил форте Belarus
Artrosil Forte Купить сейчас!! Нажмите на ссылку ниже, чтобы получить дополнительную информацию и получить скидку 50% прямо сейчас! торопиться !!
читать далее: https://www.nutritioncrawler.com/АртрфорBela
➢Название продукта — Artrosil Forte
➢Категория – Здоровье суставов
➢Основные преимущества — дайте вам больше комфорта для суставов и сделайте вашу жизнь счастливее и активнее.
➢ Состав — натуральное органическое соединение.
➢ Побочные эффекты — нет данных
➢Итоговый рейтинг: — 4,8
➢ Доступность — Онлайн
➢Предложения и скидки; СОХРАНИТЕ СЕГОДНЯ! КУПИТЬ СЕЙЧАС, чтобы купить СПЕЦИАЛЬНОЕ ПРЕДЛОЖЕНИЕ!!!
Что такое Artrosil Forte?
Artrosil Forte — это новая революционная формула, которая помогает решить основные проблемы, вызывающие хроническую боль в суставах. Artrosil Forte компонента снимают скованность суставов, боли в плечах и коленях, отеки в суставах. Люди использовали эту формулу для снятия токсинов и боли в организме. Облегчение боли и увеличение доставки питательных веществ — лишь два из многих преимуществ приема этого продукта.
Artrosil Forte Купить сейчас!! Нажмите на ссылку ниже, чтобы получить дополнительную информацию и получить скидку 50% прямо сейчас! торопиться !!
читать далее: https://www.nutritioncrawler.com/АртрфорBela
Артросил форте Артросил форте гель Артросил форте креме Артросил форте капсула Артросил форте планшет Артросил форте таблетки Артросил форте Цена Артросил форте Отзывы Артросил форте Ингредиенты Артросил форте Преимущества Артросил форте Побочные эффекты Артросил форте Цена крема Артросил форте Отзывы о креме Артросил форте Сочинение Артросил форте Жалоба Артросил форте Где купить Артросил форте Как использовать Артросил форте Стоимость Артросил форте работает Артросил форте Форум Артросил форте оригинальный Артросил форте аптека
https://www.nutritioncrawler.com/АртрфорBela
https://sites.google.com/view/artrosil-forte/home
https://healthtoned.blogspot.com/2023/12/artrosil-forte-belarus.html
https://www.dibiz.com/artrosilfortebelarus
https://www.weddingwire.com/website/artrosil-forte-and-belarus
https://medium.com/@healthytalk24x7/artrosil-forte-belarus-1298a076b976
https://medium.com/@healthytalk24x7/artrosil-forte-a2d530c6fc25
https://infogram.com/artrosil-forte-belarus-1hnp27mwvywny2g?live
https://softisenilspain.hashnode.dev/artrosil-forte-belarus
https://sway.office.com/5UPJRJ4BLc8EzySZ
https://artrosilfortebelarus.company.site/?lang=en&from_admin&vertical
https://gamma.app/docs/Artrosil-Forte-Belarus-i90ggql6pco2csg?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/artrosil-forte-belarus
https://groups.google.com/g/snshine/c/c6XzRMBj0vU
https://devfolio.co/projects/artrosil-forte-belarus-5ec4
https://nepal.tribe.so/post/artrosil-forte-belarus-6579a88bb12aee264539693d
https://forum.teknofest.az/d/12308-artrosil-forte-belarus
https://soundcloud.com/artrosilfortebelarus/artrosil-forte-belarus?si=ac4b20efcd514387a10677186a88efad&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
https://www.deviantart.com/artrosilforte/art/Artrosil-Forte-Belarus-1001632987
https://replit.com/@artrosilfortebe
|
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mahmoud-mohey/acceptance_model
|
mahmoud-mohey
| 2023-12-13T13:00:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:00:11Z |
Entry not found
|
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] |
cwiz/skeleton-kris
|
cwiz
| 2023-12-13T13:02:53Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T13:02:53Z |
---
license: apache-2.0
---
|
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EmmaGthn/results_lora_40
|
EmmaGthn
| 2023-12-13T14:38:41Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-13T13:04:38Z |
Entry not found
|
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0.31015992164611816,
-1.0262157917022705
] |
zebans/xlm-roberta-base-finetuned-panx-it
|
zebans
| 2023-12-13T14:02:34Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-13T13:06:04Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8218390804597702
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2503
- F1: 0.8218
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8253 | 1.0 | 70 | 0.3503 | 0.7160 |
| 0.2781 | 2.0 | 140 | 0.2643 | 0.8148 |
| 0.1871 | 3.0 | 210 | 0.2503 | 0.8218 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu118
- Datasets 1.16.1
- Tokenizers 0.15.0
|
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mantagen/text_to_image__nona_2
|
mantagen
| 2023-12-13T13:08:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:08:16Z |
Entry not found
|
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] |
MichaelAdams/bert-finetuned-ner
|
MichaelAdams
| 2023-12-14T08:12:30Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-13T13:09:25Z |
Entry not found
|
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EthanRhys/Shiki-Misaki
|
EthanRhys
| 2023-12-13T13:13:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:11:53Z |
Entry not found
|
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] |
hkivancoral/smids_3x_beit_base_sgd_001_fold2
|
hkivancoral
| 2023-12-13T23:14:15Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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"
] |
image-classification
| 2023-12-13T13:13:14Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_sgd_001_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: 0.8685524126455907
---
<!-- 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. -->
# smids_3x_beit_base_sgd_001_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: 0.3078
- Accuracy: 0.8686
## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8275 | 1.0 | 225 | 0.8216 | 0.6506 |
| 0.6191 | 2.0 | 450 | 0.6029 | 0.7704 |
| 0.5649 | 3.0 | 675 | 0.5207 | 0.7953 |
| 0.5387 | 4.0 | 900 | 0.4780 | 0.8070 |
| 0.4277 | 5.0 | 1125 | 0.4562 | 0.8020 |
| 0.4656 | 6.0 | 1350 | 0.4304 | 0.8170 |
| 0.4051 | 7.0 | 1575 | 0.4146 | 0.8220 |
| 0.4601 | 8.0 | 1800 | 0.4030 | 0.8353 |
| 0.3696 | 9.0 | 2025 | 0.3828 | 0.8369 |
| 0.3307 | 10.0 | 2250 | 0.3745 | 0.8403 |
| 0.3955 | 11.0 | 2475 | 0.3651 | 0.8386 |
| 0.3579 | 12.0 | 2700 | 0.3632 | 0.8419 |
| 0.3293 | 13.0 | 2925 | 0.3578 | 0.8536 |
| 0.3333 | 14.0 | 3150 | 0.3518 | 0.8502 |
| 0.3685 | 15.0 | 3375 | 0.3389 | 0.8619 |
| 0.3757 | 16.0 | 3600 | 0.3549 | 0.8519 |
| 0.2934 | 17.0 | 3825 | 0.3349 | 0.8619 |
| 0.3154 | 18.0 | 4050 | 0.3310 | 0.8586 |
| 0.2645 | 19.0 | 4275 | 0.3332 | 0.8602 |
| 0.3308 | 20.0 | 4500 | 0.3318 | 0.8536 |
| 0.3463 | 21.0 | 4725 | 0.3245 | 0.8669 |
| 0.3576 | 22.0 | 4950 | 0.3244 | 0.8602 |
| 0.3535 | 23.0 | 5175 | 0.3229 | 0.8652 |
| 0.3392 | 24.0 | 5400 | 0.3212 | 0.8636 |
| 0.3203 | 25.0 | 5625 | 0.3258 | 0.8569 |
| 0.3082 | 26.0 | 5850 | 0.3176 | 0.8652 |
| 0.273 | 27.0 | 6075 | 0.3142 | 0.8652 |
| 0.2914 | 28.0 | 6300 | 0.3128 | 0.8652 |
| 0.2752 | 29.0 | 6525 | 0.3158 | 0.8602 |
| 0.2968 | 30.0 | 6750 | 0.3177 | 0.8602 |
| 0.286 | 31.0 | 6975 | 0.3206 | 0.8586 |
| 0.2639 | 32.0 | 7200 | 0.3171 | 0.8619 |
| 0.3029 | 33.0 | 7425 | 0.3130 | 0.8652 |
| 0.2671 | 34.0 | 7650 | 0.3120 | 0.8636 |
| 0.2568 | 35.0 | 7875 | 0.3117 | 0.8652 |
| 0.291 | 36.0 | 8100 | 0.3108 | 0.8669 |
| 0.2853 | 37.0 | 8325 | 0.3128 | 0.8669 |
| 0.2438 | 38.0 | 8550 | 0.3121 | 0.8652 |
| 0.2575 | 39.0 | 8775 | 0.3073 | 0.8702 |
| 0.2608 | 40.0 | 9000 | 0.3102 | 0.8652 |
| 0.2156 | 41.0 | 9225 | 0.3132 | 0.8686 |
| 0.2705 | 42.0 | 9450 | 0.3087 | 0.8669 |
| 0.2268 | 43.0 | 9675 | 0.3074 | 0.8702 |
| 0.2401 | 44.0 | 9900 | 0.3088 | 0.8686 |
| 0.2744 | 45.0 | 10125 | 0.3055 | 0.8702 |
| 0.2462 | 46.0 | 10350 | 0.3088 | 0.8719 |
| 0.2575 | 47.0 | 10575 | 0.3071 | 0.8702 |
| 0.254 | 48.0 | 10800 | 0.3073 | 0.8686 |
| 0.2358 | 49.0 | 11025 | 0.3080 | 0.8686 |
| 0.216 | 50.0 | 11250 | 0.3078 | 0.8686 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_3x_beit_base_rms_0001_fold2
|
hkivancoral
| 2023-12-13T14:03:41Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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"
] |
image-classification
| 2023-12-13T13:14:40Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_rms_0001_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: 0.9001663893510815
---
<!-- 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. -->
# smids_3x_beit_base_rms_0001_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: 1.0800
- Accuracy: 0.9002
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4112 | 1.0 | 225 | 0.4131 | 0.8602 |
| 0.2912 | 2.0 | 450 | 0.4363 | 0.8369 |
| 0.1579 | 3.0 | 675 | 0.3616 | 0.8752 |
| 0.187 | 4.0 | 900 | 0.2854 | 0.8852 |
| 0.1222 | 5.0 | 1125 | 0.4884 | 0.8835 |
| 0.0818 | 6.0 | 1350 | 0.4361 | 0.8885 |
| 0.0981 | 7.0 | 1575 | 0.4218 | 0.8769 |
| 0.1165 | 8.0 | 1800 | 0.5449 | 0.8702 |
| 0.0593 | 9.0 | 2025 | 0.5250 | 0.9002 |
| 0.0725 | 10.0 | 2250 | 0.5111 | 0.8985 |
| 0.0275 | 11.0 | 2475 | 0.5486 | 0.8702 |
| 0.0408 | 12.0 | 2700 | 0.6442 | 0.8852 |
| 0.0654 | 13.0 | 2925 | 0.6277 | 0.8968 |
| 0.0139 | 14.0 | 3150 | 0.6248 | 0.8918 |
| 0.0393 | 15.0 | 3375 | 0.5753 | 0.8935 |
| 0.0368 | 16.0 | 3600 | 0.6499 | 0.8902 |
| 0.0316 | 17.0 | 3825 | 0.6023 | 0.8918 |
| 0.0193 | 18.0 | 4050 | 0.7084 | 0.8952 |
| 0.001 | 19.0 | 4275 | 0.7253 | 0.9002 |
| 0.0578 | 20.0 | 4500 | 0.7248 | 0.8785 |
| 0.08 | 21.0 | 4725 | 0.6832 | 0.8902 |
| 0.0213 | 22.0 | 4950 | 0.8468 | 0.8902 |
| 0.008 | 23.0 | 5175 | 0.8669 | 0.8935 |
| 0.0041 | 24.0 | 5400 | 0.8402 | 0.8802 |
| 0.0205 | 25.0 | 5625 | 0.8106 | 0.8869 |
| 0.0196 | 26.0 | 5850 | 0.8576 | 0.8902 |
| 0.0001 | 27.0 | 6075 | 0.7352 | 0.8985 |
| 0.0003 | 28.0 | 6300 | 0.7339 | 0.9018 |
| 0.0078 | 29.0 | 6525 | 0.8497 | 0.8985 |
| 0.007 | 30.0 | 6750 | 1.0378 | 0.8802 |
| 0.0063 | 31.0 | 6975 | 0.9737 | 0.8902 |
| 0.0147 | 32.0 | 7200 | 0.9357 | 0.8902 |
| 0.0358 | 33.0 | 7425 | 0.9702 | 0.8885 |
| 0.0003 | 34.0 | 7650 | 0.7989 | 0.8902 |
| 0.0001 | 35.0 | 7875 | 0.9353 | 0.8885 |
| 0.0032 | 36.0 | 8100 | 0.8664 | 0.8952 |
| 0.0015 | 37.0 | 8325 | 0.7955 | 0.8968 |
| 0.0 | 38.0 | 8550 | 0.8664 | 0.8952 |
| 0.0222 | 39.0 | 8775 | 0.9521 | 0.8985 |
| 0.0042 | 40.0 | 9000 | 0.9427 | 0.8985 |
| 0.0027 | 41.0 | 9225 | 0.9502 | 0.9002 |
| 0.0 | 42.0 | 9450 | 1.0516 | 0.8935 |
| 0.0 | 43.0 | 9675 | 0.9695 | 0.8952 |
| 0.0 | 44.0 | 9900 | 1.0122 | 0.8985 |
| 0.0 | 45.0 | 10125 | 0.9974 | 0.8985 |
| 0.0 | 46.0 | 10350 | 1.0109 | 0.9002 |
| 0.0 | 47.0 | 10575 | 1.0770 | 0.8952 |
| 0.0 | 48.0 | 10800 | 1.0946 | 0.8985 |
| 0.0025 | 49.0 | 11025 | 1.0859 | 0.9002 |
| 0.002 | 50.0 | 11250 | 1.0800 | 0.9002 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Vinnyyw/Moranguinhovoices
|
Vinnyyw
| 2023-12-13T13:16:13Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T13:14:59Z |
---
license: openrail
---
|
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] |
Realgon/N_bert_imdb_padding30model
|
Realgon
| 2023-12-13T15:40:44Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T13:16:38Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: N_bert_imdb_padding30model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93944
---
<!-- 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. -->
# N_bert_imdb_padding30model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6907
- Accuracy: 0.9394
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2178 | 1.0 | 1563 | 0.2389 | 0.9234 |
| 0.1617 | 2.0 | 3126 | 0.2474 | 0.9303 |
| 0.0872 | 3.0 | 4689 | 0.3029 | 0.9283 |
| 0.065 | 4.0 | 6252 | 0.3493 | 0.9316 |
| 0.0348 | 5.0 | 7815 | 0.3685 | 0.9365 |
| 0.0311 | 6.0 | 9378 | 0.4913 | 0.9310 |
| 0.0205 | 7.0 | 10941 | 0.4485 | 0.9362 |
| 0.0177 | 8.0 | 12504 | 0.4903 | 0.9354 |
| 0.0147 | 9.0 | 14067 | 0.5786 | 0.9322 |
| 0.0119 | 10.0 | 15630 | 0.5245 | 0.9356 |
| 0.01 | 11.0 | 17193 | 0.5730 | 0.9364 |
| 0.0091 | 12.0 | 18756 | 0.5730 | 0.9383 |
| 0.006 | 13.0 | 20319 | 0.5596 | 0.9386 |
| 0.004 | 14.0 | 21882 | 0.6760 | 0.9354 |
| 0.0018 | 15.0 | 23445 | 0.5813 | 0.9402 |
| 0.0018 | 16.0 | 25008 | 0.6526 | 0.9378 |
| 0.0035 | 17.0 | 26571 | 0.6453 | 0.9384 |
| 0.0002 | 18.0 | 28134 | 0.6714 | 0.9392 |
| 0.0001 | 19.0 | 29697 | 0.6893 | 0.9397 |
| 0.0 | 20.0 | 31260 | 0.6907 | 0.9394 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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bartowski/SciPhi-Self-RAG-Mistral-7B-32k-exl2
|
bartowski
| 2023-12-13T14:50:14Z | 0 | 0 | null |
[
"text-generation",
"license:mit",
"region:us"
] |
text-generation
| 2023-12-13T13:23:02Z |
---
license: mit
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of SciPhi-Self-RAG-Mistral-7B-32k
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.10">turboderp's ExLlamaV2 v0.0.10</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using VMWareOpenInstruct.parquet as calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/SciPhi/SciPhi-Self-RAG-Mistral-7B-32k
<a href="https://huggingface.co/bartowski/SciPhi-Self-RAG-Mistral-7B-32k-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/SciPhi-Self-RAG-Mistral-7B-32k-exl2/tree/6_0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/SciPhi-Self-RAG-Mistral-7B-32k-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/SciPhi-Self-RAG-Mistral-7B-32k-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `SciPhi-Self-RAG-Mistral-7B-32k-exl2`:
```shell
mkdir SciPhi-Self-RAG-Mistral-7B-32k-exl2
huggingface-cli download bartowski/SciPhi-Self-RAG-Mistral-7B-32k-exl2 --local-dir SciPhi-Self-RAG-Mistral-7B-32k-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir SciPhi-Self-RAG-Mistral-7B-32k-exl2
huggingface-cli download bartowski/SciPhi-Self-RAG-Mistral-7B-32k-exl2 --revision 4_0 --local-dir SciPhi-Self-RAG-Mistral-7B-32k-exl2 --local-dir-use-symlinks False
```
|
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] |
ethompson93/Reinforce-Pixelcopter-PLE-v0
|
ethompson93
| 2023-12-13T14:42:40Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T13:29:03Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.90 +/- 28.70
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
|
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