modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 12:28:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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zdaniar/my_awesome_eli5_mlm_model
|
zdaniar
| 2023-03-09T14:07:11Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-09T10:26:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: zdaniar/my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# zdaniar/my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.0455
- Validation Loss: 1.8457
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.0455 | 1.8457 | 0 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
xixi1994/jiaxin-dreambooth-lora-xlt2
|
xixi1994
| 2023-03-09T13:54:31Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-09T13:41:33Z |
---
license: creativeml-openrail-m
base_model: /data/x/jiaxinchen/images_gen/diffusion_models/anything-v4.5/
instance_prompt: xlt
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - xixi1994/jiaxin-dreambooth-lora-xlt2
These are LoRA adaption weights for /data/x/jiaxinchen/images_gen/diffusion_models/anything-v4.5/. The weights were trained on xlt using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
egyee/poca-SoccerTwos-v2
|
egyee
| 2023-03-09T13:53:14Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-09T13:13:30Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: eryzml/poca-SoccerTwos-v2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
fathyshalab/reklambox3-2-10-xlm-lower
|
fathyshalab
| 2023-03-09T13:26:58Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-03-09T13:26:41Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/reklambox3-2-10-xlm-lower
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/reklambox3-2-10-xlm-lower")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
iblub/ppo-lunar-lander-week8
|
iblub
| 2023-03-09T13:24:58Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T13:24:47Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -164.53 +/- 111.38
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'iblub/ppo-lunar-lander-week8'
'batch_size': 512
'minibatch_size': 128}
```
|
Patil/rl_course_vizdoom_health_gathering_supreme
|
Patil
| 2023-03-09T13:22:56Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T13:22:45Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 4.38 +/- 0.87
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Patil/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
fathyshalab/reklambox3-2-8-xlm-lower
|
fathyshalab
| 2023-03-09T13:14:18Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-03-09T13:14:01Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/reklambox3-2-8-xlm-lower
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/reklambox3-2-8-xlm-lower")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Taratata/ppo-LunarLander-v1
|
Taratata
| 2023-03-09T13:13:55Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T13:13:26Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.80 +/- 17.78
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
uisikdag/weed_beit_imbalanced
|
uisikdag
| 2023-03-09T13:13:37Z | 203 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-09T11:14:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: weeds_hfclass14
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: 0.9625
---
<!-- 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. -->
# weeds_hfclass14
Model is trained on imbalanced dataset/ .8 .1 .1 split/ 224x224 resized
Dataset: https://www.kaggle.com/datasets/vbookshelf/v2-plant-seedlings-dataset
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1414
- Accuracy: 0.9625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5465 | 1.0 | 69 | 0.3265 | 0.9071 |
| 0.2812 | 2.0 | 138 | 0.2354 | 0.9304 |
| 0.2029 | 3.0 | 207 | 0.1909 | 0.9304 |
| 0.157 | 4.0 | 276 | 0.1910 | 0.9411 |
| 0.1556 | 5.0 | 345 | 0.2176 | 0.9321 |
| 0.1212 | 6.0 | 414 | 0.1597 | 0.9625 |
| 0.1256 | 7.0 | 483 | 0.1335 | 0.9661 |
| 0.0962 | 8.0 | 552 | 0.1714 | 0.9464 |
| 0.1005 | 9.0 | 621 | 0.1453 | 0.9571 |
| 0.0944 | 10.0 | 690 | 0.1414 | 0.9625 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
fathyshalab/reklambox3-2-14-xlm-lower
|
fathyshalab
| 2023-03-09T13:05:21Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-03-09T13:05:03Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/reklambox3-2-14-xlm-lower
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/reklambox3-2-14-xlm-lower")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
dreemer6/bert-finetuned-sst2
|
dreemer6
| 2023-03-09T13:04:58Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-08T10:33:02Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: dreemer6/bert-finetuned-sst2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dreemer6/bert-finetuned-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0990
- Train Accuracy: 0.9662
- Validation Loss: 0.4042
- Validation Accuracy: 0.875
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 84190, 'end_learning_rate': 0.0, 'power': 0.5, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2304 | 0.9112 | 0.3037 | 0.8956 | 0 |
| 0.1327 | 0.9544 | 0.3145 | 0.8911 | 1 |
| 0.0958 | 0.9688 | 0.2987 | 0.8888 | 2 |
| 0.0990 | 0.9662 | 0.4042 | 0.875 | 3 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
platzi/platzi_Santiago_Res_Esc
|
platzi
| 2023-03-09T13:04:03Z | 225 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-09T12:37:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: platzi_Santiago_Res_Esc
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi_Santiago_Res_Esc
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0250
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0059 | 3.85 | 500 | 0.0250 | 0.9925 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Ahmade/text2imagev2
|
Ahmade
| 2023-03-09T12:50:38Z | 30 | 1 |
diffusers
|
[
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-09T12:45:20Z |
---
{}
---
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
# DreamBooth model for the test concept trained by Ahmade on the maderix/flickr_bw_rgb dataset.
This is a Stable Diffusion model fine-tuned on the test concept with DreamBooth. It can be used by modifying the `instance_prompt`:
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
test
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Ahmade/text2imagev2')
image = pipeline().images[0]
image
```
|
sjiang1/codecse
|
sjiang1
| 2023-03-09T12:49:03Z | 62 | 3 |
transformers
|
[
"transformers",
"pytorch",
"roberta_for_cl",
"code",
"sentence embedding",
"feature-extraction",
"multilingual",
"dataset:CodeSearchNet",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-03-03T19:09:03Z |
---
language:
- multilingual
tags:
- code
- sentence embedding
license: mit
datasets:
- CodeSearchNet
pipeline_tag: feature-extraction
---
# Model Card for CodeCSE
A simple pre-trained model for code and comment sentence embeddings using contrastive learning. This model was pretrained using [CodeSearchNet](https://huggingface.co/datasets/code_search_net).
Please [**clone the CodeCSE repository**](https://github.com/emu-se/CodeCSE) to get `GraphCodeBERTForCL` and other dependencies to use this pretrained model. https://github.com/emu-se/CodeCSE
Detailed instructions are listed in the repository's README.md. Overall, you will need:
1. GraphCodeBERT (CodeCSE uses GraphCodeBERT's input format for code)
2. GraphCodeBERTForCL defined in [codecse/codecse](https://github.com/emu-se/CodeCSE/tree/main/codecse/codecse)
## Inference example
NL input example: example_nl.json
```json
{
"original_string": "",
"docstring_tokens": ["Save", "model", "to", "a", "pickle", "located", "at", "path"],
"url": "https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72"
}
```
Code snippet to get the embedding of an NL document ([link to complete code](https://github.com/emu-se/CodeCSE/blob/a04a025c7048204bdfd908fe259fafc55e2df169/inference.py#L105)):
```
nl_json = load_example("example_nl.json")
batch = prepare_inputs(nl_json, tokenizer, args)
nl_inputs = batch[3]
with torch.no_grad():
nl_vec = model(input_ids=nl_inputs, sent_emb="nl")[1]
```
|
Taratata/ppo-LunarLander-v2
|
Taratata
| 2023-03-09T12:39:37Z | 1 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-08T21:25:34Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -120.06 +/- 41.80
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
Rolo/doom_health_w128-epw64-r32_b4096-2b
|
Rolo
| 2023-03-09T12:38:20Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T10:15:29Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 19.77 +/- 2.36
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Rolo/doom_health_w128-epw64-r32_b4096-2b
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=doom_health_w128-epw64-r32_b4096-2b
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=doom_health_w128-epw64-r32_b4096-2b --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
johko/dreambooth_marvin_model
|
johko
| 2023-03-09T12:25:10Z | 4 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-03-08T21:28:40Z |
---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
MarkieMark1/dqn-SpaceInvadersNoFrameskip-v4
|
MarkieMark1
| 2023-03-09T12:09:13Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T12:08:37Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 497.00 +/- 140.66
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MarkieMark1 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MarkieMark1 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MarkieMark1
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
FBM/LunarLander-Manual
|
FBM
| 2023-03-09T12:03:54Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T11:04:01Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -86.93 +/- 79.19
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': True
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 2e-05
'num_envs': 4
'num_steps': 256
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'FBM/LunarLander-Manual'
'batch_size': 1024
'minibatch_size': 256}
```
|
SarahAdnan/bert-base-arabic-electra-xnli-finetuned
|
SarahAdnan
| 2023-03-09T11:59:31Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:xnli",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-09T08:00:35Z |
---
tags:
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
- f1
model-index:
- name: bert-base-arabic-electra-xnli-finetuned
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: xnli
type: xnli
config: ar
split: train
args: ar
metrics:
- name: Accuracy
type: accuracy
value: 0.7870259481037924
- name: F1
type: f1
value: 0.7875257009692987
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-arabic-electra-xnli-finetuned
This model is a fine-tuned version of [aubmindlab/araelectra-base-discriminator](https://huggingface.co/aubmindlab/araelectra-base-discriminator) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5453
- Accuracy: 0.7870
- F1: 0.7875
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5541 | 1.0 | 12271 | 0.5453 | 0.7870 | 0.7875 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
SyedB/Fake_News_model
|
SyedB
| 2023-03-09T11:46:42Z | 0 | 0 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2023-03-08T13:34:03Z |
---
license: artistic-2.0
---
**LSTM Model for Fake News Detection**
**Accuracy Obtained = 58%**
|
chandc/ppo-LunarLander-v2-10M-lro-5e-3
|
chandc
| 2023-03-09T11:34:30Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T03:28:39Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 283.20 +/- 16.26
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'CleanRL_ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 10000000
'learning_rate': 0.005
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'chandc/ppo-LunarLander-v2-10M-lro-5e-3'
'batch_size': 512
'minibatch_size': 128}
```
|
Mitsua/mitsua-diffusion-one
|
Mitsua
| 2023-03-09T11:31:33Z | 571 | 89 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"text-to-image",
"stable-diffusion-diffusers",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-03T09:55:55Z |
---
license: other
tags:
- stable-diffusion
- text-to-image
- stable-diffusion-diffusers
- diffusers
inference: true
---
# Mitsua Diffusion One Model Card
Mitsua Diffusion One is a latent text-to-image diffusion model, which is a successor of [Mitsua Diffusion CC0](https://huggingface.co/Mitsua/mitsua-diffusion-cc0).
This model is **trained from scratch using only public domain/CC0 or copyright images with permission for use**, with using a fixed pretrained text encoder ([OpenCLIP ViT-H/14](https://github.com/mlfoundations/open_clip), MIT License).
This will be used as a base model for [**AI VTuber Elan Mitsua🖌️**](https://elanmitsua.com/en/)’s activity.
❗❗ **Currently, the model is still of low quality and lacks diversity** ❗❗
## Further training will be done fully opt-in basis.
If you are interested in, [please click here to submit an opt-in application](https://forms.gle/Nk3M7UyqSgYAqdpA6).
We are active on [a Discord server for opt-in contributors only](https://discord.com/invite/7VTGRweTUg). Communication is currently in Japanese.
❗❗ **To train this model, images from opt-in contributors have not yet been used** ❗❗
You can check [here to all prompts to generate these images](https://huggingface.co/Mitsua/mitsua-diffusion-one/blob/main/mitsua-diffusion-one-prompts.csv).
## License
- Mitsua Open RAIL-M License (More restrictive variant of CreativeML Open RAIL-M)
This model is open access and available to all, with a Mitsua Open RAIL-M license further specifying rights and usage. The Mitsua Open RAIL-M License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You can't use the model to infringe any rights of other by feeding image sources or model weights to the model (e.g. using another person's copyrighted image for fine-tuning without permission, using another person's copyrighted image as a source for image2image without permission).
4. You can't misrepresent that a generated image as not AI-generated.
5. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the Mitsua Open RAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/Mitsua/mitsua-diffusion-one/blob/main/MODEL-LICENSE)
## Training Data Sources
All data was obtained ethically and in compliance with the site's terms and conditions.
No copyright images are used in the training of this model without the permission.
No AI generated images are in the dataset.
- The Metropolitan Museum of Art Open Access (CC0 / Public domain)
- Smithsonian Museum Open Access (CC0 / Public domain)
- Cleveland Museum of Art Open Access (CC0 / Public domain)
- National Gallery of Art Open Access (CC0 / Public domain)
- The Art Institute of Chicago Open Access (CC0 / Public domain)
- The Walters Art Museum Open Access (CC0 / Public domain)
- J. Paul Getty Museum Open Access (CC0 / Public domain)
- ArtBench-10 (public domain subset)
- Flickr (CC0 subset)
- Wikimedia Commons (CC0 subset)
- NFT arts *1 (goblintown.nft, mfer, tubby-cats, Timeless) (CC0)
- Full version of [VRoid Image Dataset](https://huggingface.co/datasets/Mitsua/vroid-image-dataset-lite) (CC0 or licensed)
- Open Clipart (Public domain)
- Open Duelyst (CC0)
- 3dicons (CC0)
- ambientCG (CC0)
- Wuffle comics made by Piti Yindee (CC0)
- 大崎一番太郎 made by 大崎駅西口商店会 (CC0)
- Traditional Generative Art (Non-AI) and Visual Artworks made by Rhizomatiks (licensed)
Approx 11M images in total with data augmentation.
1. Their work is released under a CC0 license, but if you are considering using this model to create a work inspired by their NFT and sell it as NFT, please consider paying them a royalty to help the CC0 NFT community grow.
## Training Notes
- Trained resolution : 256x256 --> 512x512 --> (512x512, 640x448, 448x640) --> (512x512, 768x512, 512x768)
- diffusers version and `mitsua-diffusion-one.ckpt` are fine-tuned with [Diffusion With Offset Noise](https://www.crosslabs.org/blog/diffusion-with-offset-noise) technique which is applied to last 12k steps with `p=0.02`.
- `mitsua-diffusion-one-base.ckpt` is non-fine-tuned version. For fine-tuning stuff, this version would be better choice.
## Cosine similarity (as a proof of full-scratch training)
- VAE
- 0.16694325 (vs Stable Diffusion v2.1 base)
- 0.20887965 (vs Stable Diffusion v.1.4)
- All fine-tuned variant would have over 0.90
- U-Net
- 0.07097270 (vs Stable Diffusion v2.1 base)
- 0.08351029 (vs Stable Diffusion v.1.4)
- All fine-tuned variant would have over 0.99
## Developed by
- Latent Diffusion Models (for algorithm and training scripts, MIT License) : Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser and Björn Ommer
- OpenCLIP : Ilharco Gabriel, Wortsman Mitchell, Wightman Ross, Gordon Cade, Carlini Nicholas, Taori Rohan, Dave Achal, Shankar Vaishaal, Namkoong Hongseok, Miller John, Hajishirzi Hannaneh, Farhadi Ali, Schmidt Ludwig
- Mitsua Diffusion One : Abstract Engine
- Special Thanks to Mitsua Contributors
|
VAZaytsev/poca-SoccerTwos
|
VAZaytsev
| 2023-03-09T11:23:26Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-09T11:24:43Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: VAZaytsev/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
FBM/CartPole-v0-Manual
|
FBM
| 2023-03-09T11:22:28Z | 0 | 0 | null |
[
"tensorboard",
"CartPole-v0",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T11:22:22Z |
---
tags:
- CartPole-v0
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v0
type: CartPole-v0
metrics:
- type: mean_reward
value: 80.20 +/- 34.74
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v0
This is a trained model of a PPO agent playing CartPole-v0.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': True
'env_id': 'CartPole-v0'
'total_timesteps': 500000
'learning_rate': 2e-05
'num_envs': 4
'num_steps': 256
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'FBM/CartPole-v0-Manual'
'batch_size': 1024
'minibatch_size': 256}
```
|
ivanlai/mt5-summarize-ch_trad-sweeps
|
ivanlai
| 2023-03-09T11:22:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-06T20:48:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5-summarize-ch_trad-sweeps
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-summarize-ch_trad-sweeps
This model is a fine-tuned version of [ivanlai/mt5-summarize-ch_trad-v2](https://huggingface.co/ivanlai/mt5-summarize-ch_trad-v2) 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: 3.9921804297917987e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
sayby/rl_course_vizdoom_health_gathering_supreme
|
sayby
| 2023-03-09T11:00:18Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T11:00:05Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.03 +/- 4.16
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r sayby/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Loges/loges-ppo-Huggy
|
Loges
| 2023-03-09T10:56:06Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-03-09T10:55:58Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Loges/loges-ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
FBM/CartPool-Manual
|
FBM
| 2023-03-09T10:52:58Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T10:37:48Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -111.18 +/- 71.79
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': True
'env_id': 'LunarLander-v2'
'total_timesteps': 500000
'learning_rate': 2e-05
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'FBM/CartPool-Manual'
'batch_size': 512
'minibatch_size': 128}
```
|
Sunoh/pegasus-samsum
|
Sunoh
| 2023-03-09T10:41:17Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-09T09:38:20Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4812
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6928 | 0.54 | 500 | 1.4812 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
aienthused/ppo-LunarLander-v2-test
|
aienthused
| 2023-03-09T10:39:38Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T10:39:11Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.33 +/- 18.32
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Mustafa21/layoutlm-funsd-tf
|
Mustafa21
| 2023-03-09T10:36:21Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"layoutlm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-01-31T16:05:46Z |
Full notebook:
https://github.com/MustafaAlahmid/hugging_face_models/blob/main/layoutlm_funsd.ipynb
---
tags:
- generated_from_keras_callback
model-index:
- name: layoutlm-funsd-tf
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd-tf
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0691
- Validation Loss: 0.7709
- Train Overall Precision: 0.7410
- Train Overall Recall: 0.7953
- Train Overall F1: 0.7672
- Train Overall Accuracy: 0.8057
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.1546 | 0.6939 | 0.6387 | 0.7381 | 0.6848 | 0.7761 | 0 |
| 0.6170 | 0.5872 | 0.7099 | 0.7832 | 0.7448 | 0.7949 | 1 |
| 0.4005 | 0.6761 | 0.6766 | 0.7777 | 0.7236 | 0.7729 | 2 |
| 0.2921 | 0.6447 | 0.7169 | 0.7852 | 0.7495 | 0.7934 | 3 |
| 0.2029 | 0.7472 | 0.7019 | 0.7953 | 0.7457 | 0.7852 | 4 |
| 0.1383 | 0.7195 | 0.7327 | 0.7938 | 0.7620 | 0.8048 | 5 |
| 0.0932 | 0.7851 | 0.7272 | 0.7998 | 0.7618 | 0.8063 | 6 |
| 0.0691 | 0.7709 | 0.7410 | 0.7953 | 0.7672 | 0.8057 | 7 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.10.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Mustafa21/detr-resnet-50_finetuned_cppe5
|
Mustafa21
| 2023-03-09T10:35:31Z | 194 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-02-08T16:03:16Z |
Full notebook:
https://github.com/MustafaAlahmid/hugging_face_models/blob/main/detr-resnet50-cppe5.ipynb
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Mustafa21/my_awesome_food_model
|
Mustafa21
| 2023-03-09T10:34:16Z | 210 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-02-09T08:47:37Z |
Full notebook :
https://github.com/MustafaAlahmid/hugging_face_models/blob/main/Vit-classifier_food_dataset.ipynb
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:1000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.985
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2335
- Accuracy: 0.985
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0523 | 1.0 | 50 | 1.9226 | 0.935 |
| 1.3718 | 2.0 | 100 | 1.3422 | 0.995 |
| 1.2298 | 3.0 | 150 | 1.2335 | 0.985 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
qgallouedec/sample-factory-peg-insert-side-v2
|
qgallouedec
| 2023-03-09T10:26:58Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T10:26:51Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: peg-insert-side-v2
type: peg-insert-side-v2
metrics:
- type: mean_reward
value: 4654.72 +/- 33.23
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **peg-insert-side-v2** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r qgallouedec/sample-factory-peg-insert-side-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=peg-insert-side-v2 --train_dir=./train_dir --experiment=sample-factory-peg-insert-side-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m train --algo=APPO --env=peg-insert-side-v2 --train_dir=./train_dir --experiment=sample-factory-peg-insert-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
annakotarba/model_fine_tuning
|
annakotarba
| 2023-03-09T10:20:21Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-07T20:41:01Z |
---
tags:
- generated_from_trainer
model-index:
- name: model_fine_tuning
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. -->
# model_fine_tuning
This model is a fine-tuned version of [henryk/bert-base-multilingual-cased-finetuned-polish-squad2](https://huggingface.co/henryk/bert-base-multilingual-cased-finetuned-polish-squad2) 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: 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: 10
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
ibadrehman/a2c-PandaReachDense-v2
|
ibadrehman
| 2023-03-09T09:54:54Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T09:52:28Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -7.02 +/- 2.46
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
vittai23/whisper-small-marathiV3
|
vittai23
| 2023-03-09T09:14:53Z | 87 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"mar",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-09T04:55:31Z |
---
language:
- mar
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper_marathi_small_V1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: mr
split: test
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 45.00676938946554
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper_marathi_small_V1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2754
- Wer: 45.0068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4794 | 0.41 | 100 | 0.4754 | 59.9317 |
| 0.3121 | 0.81 | 200 | 0.3161 | 52.8786 |
| 0.2051 | 1.22 | 300 | 0.2900 | 50.2547 |
| 0.1887 | 1.63 | 400 | 0.2779 | 48.1336 |
| 0.16 | 2.03 | 500 | 0.2679 | 46.2639 |
| 0.1131 | 2.44 | 600 | 0.2706 | 45.8449 |
| 0.1128 | 2.85 | 700 | 0.2658 | 45.1551 |
| 0.0678 | 3.25 | 800 | 0.2763 | 45.2195 |
| 0.075 | 3.66 | 900 | 0.2769 | 45.7611 |
| 0.0609 | 4.07 | 1000 | 0.2754 | 45.0068 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Mehtap/base_02
|
Mehtap
| 2023-03-09T09:04:29Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"tr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-24T10:11:17Z |
---
language:
- tr
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: base Turkish Whisper (bTW)
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. -->
# base Turkish Whisper (bTW)
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1975
- Wer: 1.6817
- Cer: 1.2800
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 1.5514 | 33.31 | 100 | 1.6389 | 0.8196 | 0.8754 |
| 0.1703 | 66.62 | 200 | 1.6896 | 1.0058 | 0.6987 |
| 0.0039 | 99.92 | 300 | 1.9380 | 1.7011 | 1.1631 |
| 0.0015 | 133.31 | 400 | 2.0324 | 1.6950 | 1.2498 |
| 0.0008 | 166.62 | 500 | 2.0957 | 1.4898 | 1.0992 |
| 0.0005 | 199.92 | 600 | 2.1417 | 1.7320 | 1.2528 |
| 0.0004 | 233.31 | 700 | 2.1681 | 1.6077 | 1.1845 |
| 0.0003 | 266.62 | 800 | 2.1847 | 1.625 | 1.2008 |
| 0.0003 | 299.92 | 900 | 2.1944 | 1.6515 | 1.2196 |
| 0.0003 | 333.31 | 1000 | 2.1975 | 1.6817 | 1.2800 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ThoDum/Reinforce-CartPole-v1
|
ThoDum
| 2023-03-09T08:59:51Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T08:37:01Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
luhehelen/t5-small-finetuned-xsum
|
luhehelen
| 2023-03-09T08:53:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-08T01:15:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.26.1
- Pytorch 1.8.0+cu101
- Datasets 2.10.1
- Tokenizers 0.13.2
|
charmquark/dqn-SpaceInvadersNoFrameskip-v4
|
charmquark
| 2023-03-09T08:51:59Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T07:30:20Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 722.00 +/- 268.96
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga charmquark -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga charmquark -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga charmquark
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
anjalyjayakrishnan/hi_do_model
|
anjalyjayakrishnan
| 2023-03-09T08:43:53Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-01T04:28:42Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
model-index:
- name: hi_do_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hi_do_model
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 398 | 2.5248 | 14.6378 | 39.5387 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.10.1+cu113
- Datasets 2.8.0
- Tokenizers 0.13.2
|
helenai/swin-base-food101-jpqd-ov
|
helenai
| 2023-03-09T08:37:40Z | 10 | 0 |
transformers
|
[
"transformers",
"openvino",
"swin",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-09T08:17:44Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-base-food101-jpqd-ov
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9060990099009901
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-food101-jpqd-ov
It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the
JPQD image classification example.
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3396
- Accuracy: 0.9061
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 128
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.2162 | 0.42 | 500 | 2.1111 | 0.7967 |
| 0.729 | 0.84 | 1000 | 0.5474 | 0.8773 |
| 0.7536 | 1.27 | 1500 | 0.3844 | 0.8984 |
| 0.4822 | 1.69 | 2000 | 0.3340 | 0.9043 |
| 12.2559 | 2.11 | 2500 | 12.0128 | 0.9033 |
| 48.7302 | 2.54 | 3000 | 48.3874 | 0.8681 |
| 75.1831 | 2.96 | 3500 | 75.3200 | 0.7183 |
| 93.5572 | 3.38 | 4000 | 93.4142 | 0.5939 |
| 103.798 | 3.8 | 4500 | 103.4427 | 0.5634 |
| 108.0993 | 4.23 | 5000 | 108.6461 | 0.5490 |
| 110.1265 | 4.65 | 5500 | 109.3663 | 0.5636 |
| 1.5584 | 5.07 | 6000 | 0.9255 | 0.8374 |
| 1.0883 | 5.49 | 6500 | 0.5841 | 0.8758 |
| 0.7024 | 5.92 | 7000 | 0.5055 | 0.8854 |
| 0.9033 | 6.34 | 7500 | 0.4639 | 0.8901 |
| 0.6901 | 6.76 | 8000 | 0.4360 | 0.8947 |
| 0.6114 | 7.19 | 8500 | 0.4080 | 0.8978 |
| 0.5102 | 7.61 | 9000 | 0.3911 | 0.9009 |
| 0.7154 | 8.03 | 9500 | 0.3747 | 0.9027 |
| 0.5621 | 8.45 | 10000 | 0.3622 | 0.9021 |
| 0.5262 | 8.88 | 10500 | 0.3554 | 0.9041 |
| 0.5442 | 9.3 | 11000 | 0.3462 | 0.9053 |
| 0.5615 | 9.72 | 11500 | 0.3416 | 0.9061 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
LarryAIDraw/chosenMix_chosenMix
|
LarryAIDraw
| 2023-03-09T08:21:27Z | 0 | 7 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-09T07:07:17Z |
---
license: creativeml-openrail-m
---
|
St0nedB/deepest-public
|
St0nedB
| 2023-03-09T08:18:39Z | 0 | 0 | null |
[
"parameter estimation",
"wireless channel",
"en",
"arxiv:2211.04846",
"region:us"
] | null | 2023-01-09T11:16:49Z |
---
language:
- en
tags:
- parameter estimation
- wireless channel
---
# deepest
`deepest` is a neural network trained to perform wireless channel parameter estimation.
For more information, please refer to the current paper at [Arxiv.org](https://arxiv.org/abs/2211.04846)
A demo with the model can be found in the EMS [Huggingface space](https://huggingface.co/spaces/EMS-TU-Ilmenau/deepest-demo).
If you find it useful and use it in your work, please cite
```text
@misc{deepest2022,
doi = {10.48550/ARXIV.2211.04846},
url = {https://arxiv.org/abs/2211.04846},
author = {Schieler, Steffen and Semper, Sebastian and Faramarzahangari, Reza and Döbereiner, Michael and Schneider, Christian},
title = {Grid-free Harmonic Retrieval and Model Order Selection using Deep Convolutional Neural Networks},
publisher = {arXiv},
year = {2022},
}
```
|
Paperbag/a2c-AntBulletEnv-v0
|
Paperbag
| 2023-03-09T08:16:49Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T08:15:41Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1448.14 +/- 294.56
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mqy/mt5-small-finetuned
|
mqy
| 2023-03-09T08:15:46Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-03-05T10:08:43Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3994
- Rouge1: 20.69
- Rouge2: 6.09
- Rougel: 20.15
## 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: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 4.7204 | 1.45 | 500 | 2.6053 | 16.93 | 4.91 | 16.71 |
| 3.1289 | 2.9 | 1000 | 2.4878 | 18.05 | 5.26 | 17.79 |
| 2.8862 | 4.35 | 1500 | 2.4109 | 17.45 | 5.06 | 17.04 |
| 2.7669 | 5.8 | 2000 | 2.4006 | 18.61 | 5.28 | 18.12 |
| 2.6433 | 7.25 | 2500 | 2.4017 | 18.81 | 5.67 | 18.5 |
| 2.5514 | 8.7 | 3000 | 2.3917 | 19.5 | 5.88 | 19.09 |
| 2.4947 | 10.14 | 3500 | 2.3994 | 20.69 | 6.09 | 20.15 |
| 2.3995 | 11.59 | 4000 | 2.3608 | 20.2 | 6.51 | 19.67 |
| 2.3798 | 13.04 | 4500 | 2.3251 | 20.1 | 6.25 | 19.71 |
| 2.3029 | 14.49 | 5000 | 2.3387 | 19.75 | 6.11 | 19.37 |
| 2.2563 | 15.94 | 5500 | 2.3372 | 20.28 | 6.32 | 19.74 |
| 2.2109 | 17.39 | 6000 | 2.3410 | 20.67 | 6.38 | 20.13 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Falcon2006VN/GPasT-small-model
|
Falcon2006VN
| 2023-03-09T07:49:27Z | 170 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"en",
"dataset:Falcon2006VN/pascal-code-generation-2mb",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-08T14:46:02Z |
---
license: mit
datasets:
- Falcon2006VN/pascal-code-generation-2mb
language:
- en
widget:
- text: "writeln('Hello "
example_title: "Example 1"
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a small, imperfect model for Pascal code generation
## Model Details
Trained on 25 batch of data
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Falcon2006VN (aka buckdan)
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** MIT
- **Finetuned from model:** GPT-2
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
For Pascal code generation
**Note:** Do not use in production purpose. If there is anything that goes wrong I will not take resposible...
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
duykha0511/distilbert-base-uncased-finetuned-emotion
|
duykha0511
| 2023-03-09T07:34:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-09T07:08:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9265187798828386
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2182
- Accuracy: 0.9265
- F1: 0.9265
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8426 | 1.0 | 250 | 0.3237 | 0.903 | 0.8986 |
| 0.2521 | 2.0 | 500 | 0.2182 | 0.9265 | 0.9265 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
nitrosocke/Future-Diffusion
|
nitrosocke
| 2023-03-09T07:23:11Z | 85 | 401 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"text-to-image",
"en",
"license:openrail++",
"autotrain_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-11-24T23:43:44Z |
---
license: openrail++
language:
- en
tags:
- stable-diffusion
- text-to-image
- diffusers
thumbnail: "https://huggingface.co/nitrosocke/Future-Diffusion/resolve/main/images/future-diffusion-thumbnail-2.jpg"
inference: false
---
### Future Diffusion
This is the fine-tuned Stable Diffusion 2.0 model trained on high quality 3D images with a futuristic Sci-Fi theme.
Use the tokens
`future style`
in your prompts for the effect.
Trained on Stability.ai's [Stable Diffusion 2.0 Base](https://huggingface.co/stabilityai/stable-diffusion-2-base) with 512x512 resolution.
**If you enjoy my work and want to test new models before release, please consider supporting me**
[](https://patreon.com/user?u=79196446)
**Disclaimer: The SD 2.0 model is just over 24h old at this point and we still need to figure out how it works exactly. Please view this as an early prototype and experiment with the model.**
**Characters rendered with the model:**
**Cars and Animals rendered with the model:**
**Landscapes rendered with the model:**
#### Prompt and settings for the Characters:
**future style [subject] Negative Prompt: duplicate heads bad anatomy**
_Steps: 20, Sampler: Euler a, CFG scale: 7, Size: 512x704_
#### Prompt and settings for the Landscapes:
**future style city market street level at night Negative Prompt: blurry fog soft**
_Steps: 20, Sampler: Euler a, CFG scale: 7, Size: 1024x576_
This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ flag in 7.000 steps.
## License
This model is open access and available to all, with a CreativeML Open RAIL++-M License further specifying rights and usage.
[Please read the full license here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
|
finasgame/content
|
finasgame
| 2023-03-09T07:19:23Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-09T07:09:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: content
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. -->
# content
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6653
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.2798 |
| 2.7351 | 2.0 | 500 | 1.7146 |
| 2.7351 | 3.0 | 750 | 1.6653 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Bonges/aqd
|
Bonges
| 2023-03-09T07:05:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-03-09T07:04:55Z |
!pip install --upgrade diffusers transformers scipy &> /dev/null
|
LarryAIDraw/fateGrandOrderOkita_okitaSouji
|
LarryAIDraw
| 2023-03-09T06:58:45Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-09T06:57:31Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/17188/fate-grand-order-okita-souji
|
pianoweb/whisper-mine
|
pianoweb
| 2023-03-09T06:25:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-03-09T06:24:20Z |
# Imposta le credenziali per accedere alle tue API di Whisper
openai.api_key = "INSERISCI_QUI_LA_TUA_API_KEY"
model_id = "whisper-1"
def transcribe_audio(file):
# Trascrivi il file audio utilizzando le API di Whisper
response = openai.Audio.transcribe(api_key=openai.api_key, model=model_id, file=file)
return response.data['text']
# Definisci l'interfaccia utente con Gradio
audio_input = gr.inputs.Audio(label="Seleziona il file audio")
output_text = gr.outputs.Textbox(label="Trascrizione")
# Collega l'interfaccia utente alla funzione di trascrizione audio
app = gr.Interface(transcribe_audio, inputs=audio_input, outputs=output_text, title="Whisper: Trascrizione audio automatica")
# Esegui l'app Gradio
app.launch()
|
Tiezheng/bart-wikisum
|
Tiezheng
| 2023-03-09T06:22:59Z | 177 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-09T06:18:12Z |
---
license: cc
---
This model comes from the paper "Exploring Neural Models for Query-Focused Summarization".
This is the original release https://github.com/salesforce/query-focused-sum
|
yip-i/torgo_xlsr_finrtune-F03-no-lm
|
yip-i
| 2023-03-09T06:06:14Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-09T06:02:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_xlsr_finetune-F03-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# torgo_xlsr_finetune-F03-2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5454
- Wer: 0.8555
## 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: 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: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 25.1806 | 0.97 | 500 | 3.3512 | 1.0 |
| 3.3648 | 1.94 | 1000 | 3.1566 | 1.0 |
| 2.9865 | 2.91 | 1500 | 2.8249 | 1.0 |
| 2.8219 | 3.88 | 2000 | 2.7880 | 1.0 |
| 2.62 | 4.85 | 2500 | 2.4134 | 1.1793 |
| 2.0129 | 5.83 | 3000 | 1.7735 | 1.3777 |
| 1.3439 | 6.8 | 3500 | 1.4148 | 1.3656 |
| 0.9587 | 7.77 | 4000 | 1.3914 | 1.2437 |
| 0.7532 | 8.74 | 4500 | 1.2565 | 1.2957 |
| 0.6204 | 9.71 | 5000 | 1.2621 | 1.1074 |
| 0.5367 | 10.68 | 5500 | 1.3255 | 1.1199 |
| 0.4471 | 11.65 | 6000 | 1.2730 | 1.0789 |
| 0.3989 | 12.62 | 6500 | 1.2627 | 1.0258 |
| 0.3562 | 13.59 | 7000 | 1.3006 | 0.9754 |
| 0.3346 | 14.56 | 7500 | 1.2739 | 0.9598 |
| 0.2949 | 15.53 | 8000 | 1.3260 | 0.9238 |
| 0.2816 | 16.5 | 8500 | 1.3446 | 0.9152 |
| 0.2552 | 17.48 | 9000 | 1.3537 | 0.8848 |
| 0.2434 | 18.45 | 9500 | 1.3288 | 0.9258 |
| 0.2156 | 19.42 | 10000 | 1.3863 | 0.8812 |
| 0.2126 | 20.39 | 10500 | 1.3466 | 0.8867 |
| 0.1939 | 21.36 | 11000 | 1.4522 | 0.9113 |
| 0.1829 | 22.33 | 11500 | 1.5253 | 0.8922 |
| 0.179 | 23.3 | 12000 | 1.4589 | 0.8543 |
| 0.1684 | 24.27 | 12500 | 1.5436 | 0.8664 |
| 0.1516 | 25.24 | 13000 | 1.5324 | 0.8668 |
| 0.1472 | 26.21 | 13500 | 1.5561 | 0.8711 |
| 0.1399 | 27.18 | 14000 | 1.5400 | 0.8605 |
| 0.1405 | 28.16 | 14500 | 1.5626 | 0.8512 |
| 0.1349 | 29.13 | 15000 | 1.5454 | 0.8555 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
|
brand25/Reinforce-CartPole-v1
|
brand25
| 2023-03-09T05:35:44Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T04:58:52Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
yip-i/torgo_xlsr_finetune-F03-2
|
yip-i
| 2023-03-09T05:03:34Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-08T04:57:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_xlsr_finetune-F03-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# torgo_xlsr_finetune-F03-2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5454
- Wer: 0.8555
## 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: 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: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 25.1806 | 0.97 | 500 | 3.3512 | 1.0 |
| 3.3648 | 1.94 | 1000 | 3.1566 | 1.0 |
| 2.9865 | 2.91 | 1500 | 2.8249 | 1.0 |
| 2.8219 | 3.88 | 2000 | 2.7880 | 1.0 |
| 2.62 | 4.85 | 2500 | 2.4134 | 1.1793 |
| 2.0129 | 5.83 | 3000 | 1.7735 | 1.3777 |
| 1.3439 | 6.8 | 3500 | 1.4148 | 1.3656 |
| 0.9587 | 7.77 | 4000 | 1.3914 | 1.2437 |
| 0.7532 | 8.74 | 4500 | 1.2565 | 1.2957 |
| 0.6204 | 9.71 | 5000 | 1.2621 | 1.1074 |
| 0.5367 | 10.68 | 5500 | 1.3255 | 1.1199 |
| 0.4471 | 11.65 | 6000 | 1.2730 | 1.0789 |
| 0.3989 | 12.62 | 6500 | 1.2627 | 1.0258 |
| 0.3562 | 13.59 | 7000 | 1.3006 | 0.9754 |
| 0.3346 | 14.56 | 7500 | 1.2739 | 0.9598 |
| 0.2949 | 15.53 | 8000 | 1.3260 | 0.9238 |
| 0.2816 | 16.5 | 8500 | 1.3446 | 0.9152 |
| 0.2552 | 17.48 | 9000 | 1.3537 | 0.8848 |
| 0.2434 | 18.45 | 9500 | 1.3288 | 0.9258 |
| 0.2156 | 19.42 | 10000 | 1.3863 | 0.8812 |
| 0.2126 | 20.39 | 10500 | 1.3466 | 0.8867 |
| 0.1939 | 21.36 | 11000 | 1.4522 | 0.9113 |
| 0.1829 | 22.33 | 11500 | 1.5253 | 0.8922 |
| 0.179 | 23.3 | 12000 | 1.4589 | 0.8543 |
| 0.1684 | 24.27 | 12500 | 1.5436 | 0.8664 |
| 0.1516 | 25.24 | 13000 | 1.5324 | 0.8668 |
| 0.1472 | 26.21 | 13500 | 1.5561 | 0.8711 |
| 0.1399 | 27.18 | 14000 | 1.5400 | 0.8605 |
| 0.1405 | 28.16 | 14500 | 1.5626 | 0.8512 |
| 0.1349 | 29.13 | 15000 | 1.5454 | 0.8555 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
|
davidhefan/lora_sks_dogs
|
davidhefan
| 2023-03-09T04:12:54Z | 0 | 0 | null |
[
"paddlepaddle",
"stable-diffusion",
"stable-diffusion-ppdiffusers",
"text-to-image",
"ppdiffusers",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-09T04:12:31Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog in a bucket
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - davidhefan/lora_sks_dogs
本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of sks dog in a bucket 文本进行了训练。
|
huggingtweets/thatmosskid
|
huggingtweets
| 2023-03-09T04:06:01Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-09T04:00:40Z |
---
language: en
thumbnail: http://www.huggingtweets.com/thatmosskid/1678334692805/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1289352754888994817/9ZgPgVSM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">MOSO 🇦🇲🇺🇸</div>
<div style="text-align: center; font-size: 14px;">@thatmosskid</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from MOSO 🇦🇲🇺🇸.
| Data | MOSO 🇦🇲🇺🇸 |
| --- | --- |
| Tweets downloaded | 834 |
| Retweets | 475 |
| Short tweets | 50 |
| Tweets kept | 309 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8gj1evb3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thatmosskid's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rlu9kyup) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rlu9kyup/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/thatmosskid')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Rifky/IndoBERT-Lite-Large-QA
|
Rifky
| 2023-03-09T03:49:57Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-08T23:42:57Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Rifky/IndoBERT-Lite-Large-QA
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rifky/IndoBERT-Lite-Large-QA
This model is a fine-tuned version of [indobenchmark/indobert-lite-large-p2](https://huggingface.co/indobenchmark/indobert-lite-large-p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5500
- Validation Loss: 2.5090
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 41040, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.8848 | 1.8995 | 0 |
| 1.4465 | 1.8570 | 1 |
| 1.1241 | 1.8996 | 2 |
| 0.8182 | 2.1172 | 3 |
| 0.5500 | 2.5090 | 4 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
hunguyen3525/my_awesome_qa_model
|
hunguyen3525
| 2023-03-09T03:38:24Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-08T11:23:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 5.8140
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 5.8612 |
| No log | 2.0 | 2 | 5.8299 |
| No log | 3.0 | 3 | 5.8140 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
GanjinZero/coder_all
|
GanjinZero
| 2023-03-09T03:05:41Z | 209 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"biomedical",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:04Z |
---
language:
- en
license: apache-2.0
tags:
- bert
- biomedical
---
CODER: Knowledge infused cross-lingual medical term embedding for term normalization.
Multi lingual Version.
Github Link: https://github.com/GanjinZero/CODER
```
@article{YUAN2022103983,
title = {CODER: Knowledge-infused cross-lingual medical term embedding for term normalization},
journal = {Journal of Biomedical Informatics},
pages = {103983},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2021.103983},
url = {https://www.sciencedirect.com/science/article/pii/S1532046421003129},
author = {Zheng Yuan and Zhengyun Zhao and Haixia Sun and Jiao Li and Fei Wang and Sheng Yu},
keywords = {medical term normalization, cross-lingual, medical term representation, knowledge graph embedding, contrastive learning}
}
```
|
nergaldarski/LOFIV2
|
nergaldarski
| 2023-03-09T02:55:00Z | 0 | 2 | null |
[
"region:us"
] | null | 2023-03-09T01:57:27Z |
CivitAI: https://civitai.com/models/9052/lofi
|
zzen0008/q-Taxi-v3
|
zzen0008
| 2023-03-09T02:53:07Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T02:52:05Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.67
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="zzen0008/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
chandc/ppo-LunarLander-v2-1M-lro-5e-3
|
chandc
| 2023-03-09T02:52:17Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T02:32:50Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 246.08 +/- 67.20
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'CleanRL_ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.005
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'chandc/ppo-LunarLander-v2-1M-lro-5e-3'
'batch_size': 512
'minibatch_size': 128}
```
|
holaholax/rlsmengv10
|
holaholax
| 2023-03-09T02:50:46Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-09T02:41:33Z |
---
license: creativeml-openrail-m
---
|
wenjiewu/model_f
|
wenjiewu
| 2023-03-09T02:38:41Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2023-03-09T02:28:48Z |
---
license: mit
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Rishu115/mlm-bert-pretrained-main_epoch4
|
Rishu115
| 2023-03-09T02:34:37Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"tf",
"bert",
"generated_from_keras_callback",
"region:us"
] | null | 2023-03-08T19:00:45Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: Rishu115/mlm-bert-pretrained-main_epoch4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rishu115/mlm-bert-pretrained-main_epoch4
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2986
- Validation Loss: 1.1914
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 47396, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.6538 | 1.1918 | 0 |
| 1.2986 | 1.1914 | 1 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.10.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
chandc/ppo-LunarLander-v2-1M
|
chandc
| 2023-03-09T02:20:07Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-11T22:52:52Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -60.28 +/- 58.76
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'CleanRL_ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'chandc/ppo-LunarLander-v2-1M'
'batch_size': 512
'minibatch_size': 128}
```
|
nakayama/HonyararaMix
|
nakayama
| 2023-03-09T02:05:40Z | 0 | 26 | null |
[
"stable-diffusion",
"text-to-image",
"safetensors",
"dataset:nakayama/DeDeDeDataset",
"license:other",
"region:us"
] |
text-to-image
| 2023-03-01T16:43:48Z |
---
language: '-en'
license: other
tags:
- stable-diffusion
- text-to-image
- safetensors
datasets: nakayama/DeDeDeDataset
---
# ご利用の際は下記のライセンス内容を十分にご確認ください。
HonyararaMixはアニメ調の人物を出力しやすいように調整されたStable Diffusion 2.1 解像度768対応版モデルです。
ベースモデルの[Illuminati Diffusion 1.0](https://huggingface.co/IlluminatiAI/Illuminati_Diffusion_v1.0)へ[Waifu Diffusion 1.5 Beta](https://huggingface.co/waifu-diffusion/wd-1-5-beta) 、[Waifu Diffusion 1.5 Beta2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2)、[Replicant-V1.0](https://huggingface.co/gsdf/Replicant-V1.0)、[Stable Diffusion 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1)を用いマージしたものをベースに
[DeDeDeDataset](https://huggingface.co/datasets/nakayama/DeDeDeDataset)による学習などを行い調整したものです。
旧HonyararaMixにはライセンス上の問題があったため、それらを解決したモデルになります。
Prompt/Bad Promptについては、ベースモデルとなった二種のモデルが用意した文書の内容、およびTI Embeddingがこのモデルでも作用します。
WDについては[こちら](https://cafeai.notion.site/WD-1-5-232cfa9443fc4661b469e7d1df8b7dd8)を参照。Illuminati DiffusionについてはNfixerが割とよく効く印象です。
# 例
<img src="https://huggingface.co/nakayama/HonyararaMix/resolve/main/img/image01.png" style="max-width:1000px;" width="75%"/>
```
best quality, masterpiece, great nature and secret garden with anime 1girl with twintail green hair and green eye and beautiful iris and (((from behind)))
Negative prompt: nfixer,3d, flat shading, flat shadow, bad anatomy, bad hands, missing fingers, extra digit, fewer digits looking at viewer
Steps: 28, Sampler: DDIM, CFG scale: 9, Seed: 43662843, Size: 1024x768, Denoising strength: 0.35, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
<img src="https://huggingface.co/nakayama/HonyararaMix/resolve/main/img/image02.png" style="max-width:1000px;" width="75%"/>
```
newest, masterpiece, exceptional, anime game cg of cute cute cute 1girl sitting on a bench in harajuku park with detailed wavy pink hair pink and detailed yellow hair
Negative prompt: nfixer, 3d, flat shading, flat shadow, inaccurate limb, bad hands, missing fingers, extra digit, fewer digits
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 4179369813, Size: 768x1152, Model hash: 4dbf24eef6, Denoising strength: 0.35, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
<img src="https://huggingface.co/nakayama/HonyararaMix/resolve/main/img/image03.png" style="max-width:1000px;" width="75%"/>
```
newest, masterpiece, exceptional, anime game cg of kawaii samurai 1girl, fighting form and (((intricate hands))), fist and dynamic angle with detailed pink hair and detailed yellow eye and beautiful iris in japanese background
Negative prompt: nfixer, 3d, flat shading, flat shadow, ((bad hands, missing fingers, extra digit, fewer digits))
Steps: 16, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 2246223688, Size: 768x1152, Denoising strength: 0.35, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
<img src="https://huggingface.co/nakayama/HonyararaMix/resolve/main/img/image04.png" style="max-width:1000px;" width="75%"/>
```
((masterpiece, best quality)),best aesthetic, 1girl, solo, black skirt, blue eyes, guitar, headphones, instrument, long hair, music, one side up, pink hair, playing guiter, pleated skirt, black shirt, indoors
Negative prompt: nfixer,missing finger, extra digits, fewer digits,((mutated hands and fingers)), 3d, flat shadow
Steps: 16, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3689716537, Size: 768x1152, Denoising strength: 0.35, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
<img src="https://huggingface.co/nakayama/HonyararaMix/resolve/main/img/image05.png" style="max-width:1000px;" width="75%"/>
```
newest, masterpiece, exceptional, anime game cg of teenage 1boy wizard bust shot casting fire magic spell in New York City, detailed eye and beautiful iris,picturesque, golden hour, dynamic pose
Negative prompt: nfixer, 3d, flat shading, flat shadow, inaccurate limb, bad hands, missing fingers, extra digit, fewer digits
Steps: 25, Sampler: Euler a, CFG scale: 7.5, Seed: 1414427875, Size: 768x1152, Denoising strength: 0.35, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
<img src="https://huggingface.co/nakayama/HonyararaMix/resolve/main/img/image06.png" style="max-width:1000px;" width="75%"/>
```
newest, masterpiece, exceptional, anime game cg of samurai (((((old man))))), fighting pose, dynamic angle, fist with detailed pink hair and detailed yellow eye and beautiful iris in japanese background
nfixer, 3d, flat shading, flat shadow, inaccurate limb, bad hands, missing fingers, extra digit, fewer digits
Steps: 25, Sampler: DDIM, CFG scale: 9, Seed: 3970412380, Size: 768x1152, Denoising strength: 0.35, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
# マージ・学習手順について
1. Illuminati Diffusion 1.0にWaifu Diffusion 1.5 Beta1を差分マージする
| Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name |
| --- | --- | --- | --- | --- |
| Add Difference @ 0.6 | Illuminati Diffusion 1.0 | Waifu Diffusion 1.5 Beta1 | Stable Diffusion v2-1_768-ema-pruned | HonyararaMix_pre1 |
2. HonyararaMix_pre1に対し、[自前で用意した他Diffusion Modelの出力からなる素材画像](https://huggingface.co/datasets/nakayama/DeDeDeDataset)にて学習させる
用意の際に利用したサービス/モデルは、SD2.1、Novel AI、WD1.3/1.4、CoolJapan Diffusion 2.1、Dreamlike Photoreal 2.0。
使用したソフトは[kohya版Fine Tuning](https://github.com/kohya-ss/sd-scripts)。
総数は30000程、flipしたものと合わせてlearning rateは5e-6、以下の設定で学習させ、
試行錯誤の後にepoch 2相当のものをHonyararaMix_pre2とした。
```
accelerate launch --num_cpu_threads_per_process 1 fine_tune.py
--pretrained_model_name_or_path=Honyarara_pre1.safetensors
--in_json meta_lat.json --train_data_dir=train_data
--output_dir=fine_tuned
--shuffle_caption
--train_batch_size=4
--learning_rate=5e-6
--max_train_steps=60000
--v2
--v_parameterization
--use_8bit_adam
--xformers
--mixed_precision=bf16
--save_every_n_epochs=2
--save_precision=float
--max_token_length=150
--seed=42
```
3. Honyarara_pre1にHonyarara_pre2を加重平均でマージする
| Interpolation Method | Primary Model | Secondary Model | Merge Name |
| --- | --- | --- | --- |
| Weighted Sum @ 0.5 | HonyararaMix_pre1 | HonyararaMix_pre2 | HonyararaMix_pre3 |
4. HonyararaMix_pre3にIlluminati Diffusion 1.0を階層マージする
使用マージ用スクリプトは[Super Merger](https://github.com/hako-mikan/sd-webui-supermerger)。
| Model: A | Model: B | Weight | Merge Name |
| --- | --- | --- | --- |
| HonyararaMix_pre3 | Illuminati Diffusion 1.0 | 1.0,0.45,0.0,0.0,0.0,0.0,0.0,0.1,0.3,0.5,0.7,0.9,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.3,0.5,0.7,0.9,1.0 | HonyararaMix_pre4|
5. HonyararaMix_pre4にWaifu Diffusion 1.5 Beta2 Aestheticを差分マージする
| Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name |
| --- | --- | --- | --- | --- |
| Add Difference @ 0.5 | HonyararaMix_pre4 | WD 1.5 Beta 2 Aesthetic | WD 1.5 Beta 2 | HonyararaMix_pre5 |
6. HonyararaMix_pre5にReplicant-V1.0を加重平均でマージする
| Interpolation Method | Primary Model | Secondary Model | Merge Name |
| --- | --- | --- | --- |
| Weighted Sum @ 0.5 | HonyararaMix_pre5 | Replicant-V1.0 | HonyararaMix A |
7. HonyararaMix AにHonyararaMix_pre4を加重平均でマージする
| Interpolation Method | Primary Model | Secondary Model | Merge Name |
| --- | --- | --- | --- |
| Weighted Sum @ 0.5 | HonyararaMix A | HonyararaMix_pre4 | HonyararaMix B |
# ライセンスについて
Fair AI Public License 1.0-SD (https://freedevproject.org/faipl-1.0-sd/)
|
0xid/ppo-LunarLander-v2-unit1
|
0xid
| 2023-03-09T01:02:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T01:01:27Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.96 +/- 23.84
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Chooks22/chooksmix
|
Chooks22
| 2023-03-09T00:56:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-08T23:44:14Z |
---
license: creativeml-openrail-m
---
|
hfl/vle-base-for-vcr-qa2r
|
hfl
| 2023-03-09T00:47:29Z | 47 | 1 |
transformers
|
[
"transformers",
"pytorch",
"vle",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-03-08T10:29:24Z |
---
license: apache-2.0
language:
- en
---
**VLE** (**V**isual-**L**anguage **E**ncoder) is an image-text multimodal understanding model built on the pre-trained text and image encoders.
It can be used for multimodal discriminative tasks such as visual question answering and image-text retrieval.
Especially on the visual commonsense reasoning (VCR) task, which requires high-level language understanding and reasoning skills, VLE achieves significant improvements.
For more details see [https://github.com/iflytek/VLE](https://github.com/iflytek/VLE).
Online VLE demo on Visual Question Answering: [https://huggingface.co/spaces/hfl/VQA_VLE_LLM](https://huggingface.co/spaces/hfl/VQA_VLE_LLM)
|
hfl/vle-large-for-vqa
|
hfl
| 2023-03-09T00:46:40Z | 54 | 1 |
transformers
|
[
"transformers",
"pytorch",
"vle",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-03-08T08:53:24Z |
---
license: apache-2.0
language:
- en
---
**VLE** (**V**isual-**L**anguage **E**ncoder) is an image-text multimodal understanding model built on the pre-trained text and image encoders.
It can be used for multimodal discriminative tasks such as visual question answering and image-text retrieval.
Especially on the visual commonsense reasoning (VCR) task, which requires high-level language understanding and reasoning skills, VLE achieves significant improvements.
For more details see [https://github.com/iflytek/VLE](https://github.com/iflytek/VLE).
Online VLE demo on Visual Question Answering: [https://huggingface.co/spaces/hfl/VQA_VLE_LLM](https://huggingface.co/spaces/hfl/VQA_VLE_LLM)
|
mcaoun/ppo-pyramids
|
mcaoun
| 2023-03-09T00:46:15Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-09T00:46:09Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: mcaoun/ppo-pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
rafiffks/segformer-b0-scene-parse-150
|
rafiffks
| 2023-03-09T00:44:24Z | 31 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-03-08T23:16:10Z |
---
license: other
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: segformer-b0-scene-parse-150
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0327
- Val Mean Iou: 0.0023
- Val Mean Accuracy: 0.0126
- Val Overall Accuracy: 0.0392
- Val Accuracy Wall: 0.0028
- Val Accuracy Building: 0.0
- Val Accuracy Sky: 0.3678
- Val Accuracy Floor: 0.0556
- Val Accuracy Tree: 0.0
- Val Accuracy Ceiling: 0.0921
- Val Accuracy Road: nan
- Val Accuracy Bed : 0.0
- Val Accuracy Windowpane: 0.0
- Val Accuracy Grass: 0.0
- Val Accuracy Cabinet: 0.0
- Val Accuracy Sidewalk: 0.0
- Val Accuracy Person: 0.0
- Val Accuracy Earth: 0.0
- Val Accuracy Door: 0.0
- Val Accuracy Table: nan
- Val Accuracy Mountain: nan
- Val Accuracy Plant: 0.0
- Val Accuracy Curtain: 0.0
- Val Accuracy Chair: 0.0
- Val Accuracy Car: nan
- Val Accuracy Water: 0.0
- Val Accuracy Painting: nan
- Val Accuracy Sofa: nan
- Val Accuracy Shelf: nan
- Val Accuracy House: nan
- Val Accuracy Sea: nan
- Val Accuracy Mirror: nan
- Val Accuracy Rug: 0.0
- Val Accuracy Field: nan
- Val Accuracy Armchair: 0.0
- Val Accuracy Seat: 0.0
- Val Accuracy Fence: nan
- Val Accuracy Desk: nan
- Val Accuracy Rock: nan
- Val Accuracy Wardrobe: 0.0
- Val Accuracy Lamp: 0.0
- Val Accuracy Bathtub: 0.0
- Val Accuracy Railing: nan
- Val Accuracy Cushion: nan
- Val Accuracy Base: nan
- Val Accuracy Box: nan
- Val Accuracy Column: 0.0
- Val Accuracy Signboard: nan
- Val Accuracy Chest of drawers: nan
- Val Accuracy Counter: nan
- Val Accuracy Sand: nan
- Val Accuracy Sink: nan
- Val Accuracy Skyscraper: nan
- Val Accuracy Fireplace: nan
- Val Accuracy Refrigerator: nan
- Val Accuracy Grandstand: 0.0
- Val Accuracy Path: 0.0
- Val Accuracy Stairs: 0.0
- Val Accuracy Runway: nan
- Val Accuracy Case: nan
- Val Accuracy Pool table: nan
- Val Accuracy Pillow: nan
- Val Accuracy Screen door: nan
- Val Accuracy Stairway: nan
- Val Accuracy River: nan
- Val Accuracy Bridge: nan
- Val Accuracy Bookcase: nan
- Val Accuracy Blind: nan
- Val Accuracy Coffee table: 0.0
- Val Accuracy Toilet: nan
- Val Accuracy Flower: nan
- Val Accuracy Book: nan
- Val Accuracy Hill: nan
- Val Accuracy Bench: 0.0
- Val Accuracy Countertop: nan
- Val Accuracy Stove: nan
- Val Accuracy Palm: 0.0
- Val Accuracy Kitchen island: 0.0
- Val Accuracy Computer: 0.0
- Val Accuracy Swivel chair: nan
- Val Accuracy Boat: nan
- Val Accuracy Bar: nan
- Val Accuracy Arcade machine: nan
- Val Accuracy Hovel: nan
- Val Accuracy Bus: nan
- Val Accuracy Towel: 0.0
- Val Accuracy Light: nan
- Val Accuracy Truck: nan
- Val Accuracy Tower: nan
- Val Accuracy Chandelier: nan
- Val Accuracy Awning: nan
- Val Accuracy Streetlight: nan
- Val Accuracy Booth: nan
- Val Accuracy Television receiver: 0.0
- Val Accuracy Airplane: nan
- Val Accuracy Dirt track: nan
- Val Accuracy Apparel: 0.0
- Val Accuracy Pole: nan
- Val Accuracy Land: nan
- Val Accuracy Bannister: nan
- Val Accuracy Escalator: nan
- Val Accuracy Ottoman: nan
- Val Accuracy Bottle: nan
- Val Accuracy Buffet: nan
- Val Accuracy Poster: 0.0
- Val Accuracy Stage: nan
- Val Accuracy Van: nan
- Val Accuracy Ship: nan
- Val Accuracy Fountain: nan
- Val Accuracy Conveyer belt: nan
- Val Accuracy Canopy: nan
- Val Accuracy Washer: nan
- Val Accuracy Plaything: nan
- Val Accuracy Swimming pool: nan
- Val Accuracy Stool: nan
- Val Accuracy Barrel: nan
- Val Accuracy Basket: nan
- Val Accuracy Waterfall: nan
- Val Accuracy Tent: nan
- Val Accuracy Bag: nan
- Val Accuracy Minibike: nan
- Val Accuracy Cradle: nan
- Val Accuracy Oven: nan
- Val Accuracy Ball: nan
- Val Accuracy Food: nan
- Val Accuracy Step: nan
- Val Accuracy Tank: nan
- Val Accuracy Trade name: nan
- Val Accuracy Microwave: nan
- Val Accuracy Pot: nan
- Val Accuracy Animal: nan
- Val Accuracy Bicycle: nan
- Val Accuracy Lake: 0.0
- Val Accuracy Dishwasher: nan
- Val Accuracy Screen: nan
- Val Accuracy Blanket: nan
- Val Accuracy Sculpture: nan
- Val Accuracy Hood: 0.0
- Val Accuracy Sconce: nan
- Val Accuracy Vase: nan
- Val Accuracy Traffic light: nan
- Val Accuracy Tray: nan
- Val Accuracy Ashcan: nan
- Val Accuracy Fan: nan
- Val Accuracy Pier: 0.0
- Val Accuracy Crt screen: nan
- Val Accuracy Plate: 0.0
- Val Accuracy Monitor: nan
- Val Accuracy Bulletin board: nan
- Val Accuracy Shower: nan
- Val Accuracy Radiator: nan
- Val Accuracy Glass: nan
- Val Accuracy Clock: nan
- Val Accuracy Flag: nan
- Val Iou Wall: 0.0019
- Val Iou Building: 0.0
- Val Iou Sky: 0.0280
- Val Iou Floor: 0.0193
- Val Iou Tree: 0.0
- Val Iou Ceiling: 0.0493
- Val Iou Road: 0.0
- Val Iou Bed : 0.0
- Val Iou Windowpane: 0.0
- Val Iou Grass: 0.0
- Val Iou Cabinet: 0.0
- Val Iou Sidewalk: 0.0
- Val Iou Person: 0.0
- Val Iou Earth: 0.0
- Val Iou Door: 0.0
- Val Iou Table: nan
- Val Iou Mountain: nan
- Val Iou Plant: 0.0
- Val Iou Curtain: 0.0
- Val Iou Chair: 0.0
- Val Iou Car: nan
- Val Iou Water: 0.0
- Val Iou Painting: nan
- Val Iou Sofa: nan
- Val Iou Shelf: nan
- Val Iou House: nan
- Val Iou Sea: nan
- Val Iou Mirror: nan
- Val Iou Rug: 0.0
- Val Iou Field: nan
- Val Iou Armchair: 0.0
- Val Iou Seat: 0.0
- Val Iou Fence: nan
- Val Iou Desk: nan
- Val Iou Rock: nan
- Val Iou Wardrobe: 0.0
- Val Iou Lamp: 0.0
- Val Iou Bathtub: 0.0
- Val Iou Railing: nan
- Val Iou Cushion: nan
- Val Iou Base: nan
- Val Iou Box: nan
- Val Iou Column: 0.0
- Val Iou Signboard: nan
- Val Iou Chest of drawers: nan
- Val Iou Counter: nan
- Val Iou Sand: nan
- Val Iou Sink: nan
- Val Iou Skyscraper: nan
- Val Iou Fireplace: nan
- Val Iou Refrigerator: nan
- Val Iou Grandstand: 0.0
- Val Iou Path: 0.0
- Val Iou Stairs: 0.0
- Val Iou Runway: nan
- Val Iou Case: nan
- Val Iou Pool table: nan
- Val Iou Pillow: nan
- Val Iou Screen door: nan
- Val Iou Stairway: nan
- Val Iou River: nan
- Val Iou Bridge: nan
- Val Iou Bookcase: nan
- Val Iou Blind: nan
- Val Iou Coffee table: 0.0
- Val Iou Toilet: nan
- Val Iou Flower: nan
- Val Iou Book: nan
- Val Iou Hill: nan
- Val Iou Bench: 0.0
- Val Iou Countertop: nan
- Val Iou Stove: nan
- Val Iou Palm: 0.0
- Val Iou Kitchen island: 0.0
- Val Iou Computer: 0.0
- Val Iou Swivel chair: nan
- Val Iou Boat: nan
- Val Iou Bar: nan
- Val Iou Arcade machine: nan
- Val Iou Hovel: nan
- Val Iou Bus: nan
- Val Iou Towel: 0.0
- Val Iou Light: nan
- Val Iou Truck: nan
- Val Iou Tower: nan
- Val Iou Chandelier: nan
- Val Iou Awning: nan
- Val Iou Streetlight: nan
- Val Iou Booth: nan
- Val Iou Television receiver: 0.0
- Val Iou Airplane: nan
- Val Iou Dirt track: nan
- Val Iou Apparel: 0.0
- Val Iou Pole: nan
- Val Iou Land: nan
- Val Iou Bannister: nan
- Val Iou Escalator: nan
- Val Iou Ottoman: nan
- Val Iou Bottle: nan
- Val Iou Buffet: nan
- Val Iou Poster: 0.0
- Val Iou Stage: nan
- Val Iou Van: nan
- Val Iou Ship: nan
- Val Iou Fountain: nan
- Val Iou Conveyer belt: nan
- Val Iou Canopy: nan
- Val Iou Washer: nan
- Val Iou Plaything: nan
- Val Iou Swimming pool: nan
- Val Iou Stool: nan
- Val Iou Barrel: nan
- Val Iou Basket: nan
- Val Iou Waterfall: nan
- Val Iou Tent: nan
- Val Iou Bag: nan
- Val Iou Minibike: nan
- Val Iou Cradle: nan
- Val Iou Oven: nan
- Val Iou Ball: nan
- Val Iou Food: nan
- Val Iou Step: nan
- Val Iou Tank: nan
- Val Iou Trade name: nan
- Val Iou Microwave: nan
- Val Iou Pot: nan
- Val Iou Animal: nan
- Val Iou Bicycle: nan
- Val Iou Lake: 0.0
- Val Iou Dishwasher: nan
- Val Iou Screen: nan
- Val Iou Blanket: nan
- Val Iou Sculpture: nan
- Val Iou Hood: 0.0
- Val Iou Sconce: nan
- Val Iou Vase: nan
- Val Iou Traffic light: nan
- Val Iou Tray: nan
- Val Iou Ashcan: nan
- Val Iou Fan: nan
- Val Iou Pier: 0.0
- Val Iou Crt screen: nan
- Val Iou Plate: 0.0
- Val Iou Monitor: nan
- Val Iou Bulletin board: nan
- Val Iou Shower: nan
- Val Iou Radiator: nan
- Val Iou Glass: nan
- Val Iou Clock: nan
- Val Iou Flag: 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 | Val Mean Iou | Val Mean Accuracy | Val Overall Accuracy | Val Accuracy Wall | Val Accuracy Building | Val Accuracy Sky | Val Accuracy Floor | Val Accuracy Tree | Val Accuracy Ceiling | Val Accuracy Road | Val Accuracy Bed | Val Accuracy Windowpane | Val Accuracy Grass | Val Accuracy Cabinet | Val Accuracy Sidewalk | Val Accuracy Person | Val Accuracy Earth | Val Accuracy Door | Val Accuracy Table | Val Accuracy Mountain | Val Accuracy Plant | Val Accuracy Curtain | Val Accuracy Chair | Val Accuracy Car | Val Accuracy Water | Val Accuracy Painting | Val Accuracy Sofa | Val Accuracy Shelf | Val Accuracy House | Val Accuracy Sea | Val Accuracy Mirror | Val Accuracy Rug | Val Accuracy Field | Val Accuracy Armchair | Val Accuracy Seat | Val Accuracy Fence | Val Accuracy Desk | Val Accuracy Rock | Val Accuracy Wardrobe | Val Accuracy Lamp | Val Accuracy Bathtub | Val Accuracy Railing | Val Accuracy Cushion | Val Accuracy Base | Val Accuracy Box | Val Accuracy Column | Val Accuracy Signboard | Val Accuracy Chest of drawers | Val Accuracy Counter | Val Accuracy Sand | Val Accuracy Sink | Val Accuracy Skyscraper | Val Accuracy Fireplace | Val Accuracy Refrigerator | Val Accuracy Grandstand | Val Accuracy Path | Val Accuracy Stairs | Val Accuracy Runway | Val Accuracy Case | Val Accuracy Pool table | Val Accuracy Pillow | Val Accuracy Screen door | Val Accuracy Stairway | Val Accuracy River | Val Accuracy Bridge | Val Accuracy Bookcase | Val Accuracy Blind | Val Accuracy Coffee table | Val Accuracy Toilet | Val Accuracy Flower | Val Accuracy Book | Val Accuracy Hill | Val Accuracy Bench | Val Accuracy Countertop | Val Accuracy Stove | Val Accuracy Palm | Val Accuracy Kitchen island | Val Accuracy Computer | Val Accuracy Swivel chair | Val Accuracy Boat | Val Accuracy Bar | Val Accuracy Arcade machine | Val Accuracy Hovel | Val Accuracy Bus | Val Accuracy Towel | Val Accuracy Light | Val Accuracy Truck | Val Accuracy Tower | Val Accuracy Chandelier | Val Accuracy Awning | Val Accuracy Streetlight | Val Accuracy Booth | Val Accuracy Television receiver | Val Accuracy Airplane | Val Accuracy Dirt track | Val Accuracy Apparel | Val Accuracy Pole | Val Accuracy Land | Val Accuracy Bannister | Val Accuracy Escalator | Val Accuracy Ottoman | Val Accuracy Bottle | Val Accuracy Buffet | Val Accuracy Poster | Val Accuracy Stage | Val Accuracy Van | Val Accuracy Ship | Val Accuracy Fountain | Val Accuracy Conveyer belt | Val Accuracy Canopy | Val Accuracy Washer | Val Accuracy Plaything | Val Accuracy Swimming pool | Val Accuracy Stool | Val Accuracy Barrel | Val Accuracy Basket | Val Accuracy Waterfall | Val Accuracy Tent | Val Accuracy Bag | Val Accuracy Minibike | Val Accuracy Cradle | Val Accuracy Oven | Val Accuracy Ball | Val Accuracy Food | Val Accuracy Step | Val Accuracy Tank | Val Accuracy Trade name | Val Accuracy Microwave | Val Accuracy Pot | Val Accuracy Animal | Val Accuracy Bicycle | Val Accuracy Lake | Val Accuracy Dishwasher | Val Accuracy Screen | Val Accuracy Blanket | Val Accuracy Sculpture | Val Accuracy Hood | Val Accuracy Sconce | Val Accuracy Vase | Val Accuracy Traffic light | Val Accuracy Tray | Val Accuracy Ashcan | Val Accuracy Fan | Val Accuracy Pier | Val Accuracy Crt screen | Val Accuracy Plate | Val Accuracy Monitor | Val Accuracy Bulletin board | Val Accuracy Shower | Val Accuracy Radiator | Val Accuracy Glass | Val Accuracy Clock | Val Accuracy Flag | Val Iou Wall | Val Iou Building | Val Iou Sky | Val Iou Floor | Val Iou Tree | Val Iou Ceiling | Val Iou Road | Val Iou Bed | Val Iou Windowpane | Val Iou Grass | Val Iou Cabinet | Val Iou Sidewalk | Val Iou Person | Val Iou Earth | Val Iou Door | Val Iou Table | Val Iou Mountain | Val Iou Plant | Val Iou Curtain | Val Iou Chair | Val Iou Car | Val Iou Water | Val Iou Painting | Val Iou Sofa | Val Iou Shelf | Val Iou House | Val Iou Sea | Val Iou Mirror | Val Iou Rug | Val Iou Field | Val Iou Armchair | Val Iou Seat | Val Iou Fence | Val Iou Desk | Val Iou Rock | Val Iou Wardrobe | Val Iou Lamp | Val Iou Bathtub | Val Iou Railing | Val Iou Cushion | Val Iou Base | Val Iou Box | Val Iou Column | Val Iou Signboard | Val Iou Chest of drawers | Val Iou Counter | Val Iou Sand | Val Iou Sink | Val Iou Skyscraper | Val Iou Fireplace | Val Iou Refrigerator | Val Iou Grandstand | Val Iou Path | Val Iou Stairs | Val Iou Runway | Val Iou Case | Val Iou Pool table | Val Iou Pillow | Val Iou Screen door | Val Iou Stairway | Val Iou River | Val Iou Bridge | Val Iou Bookcase | Val Iou Blind | Val Iou Coffee table | Val Iou Toilet | Val Iou Flower | Val Iou Book | Val Iou Hill | Val Iou Bench | Val Iou Countertop | Val Iou Stove | Val Iou Palm | Val Iou Kitchen island | Val Iou Computer | Val Iou Swivel chair | Val Iou Boat | Val Iou Bar | Val Iou Arcade machine | Val Iou Hovel | Val Iou Bus | Val Iou Towel | Val Iou Light | Val Iou Truck | Val Iou Tower | Val Iou Chandelier | Val Iou Awning | Val Iou Streetlight | Val Iou Booth | Val Iou Television receiver | Val Iou Airplane | Val Iou Dirt track | Val Iou Apparel | Val Iou Pole | Val Iou Land | Val Iou Bannister | Val Iou Escalator | Val Iou Ottoman | Val Iou Bottle | Val Iou Buffet | Val Iou Poster | Val Iou Stage | Val Iou Van | Val Iou Ship | Val Iou Fountain | Val Iou Conveyer belt | Val Iou Canopy | Val Iou Washer | Val Iou Plaything | Val Iou Swimming pool | Val Iou Stool | Val Iou Barrel | Val Iou Basket | Val Iou Waterfall | Val Iou Tent | Val Iou Bag | Val Iou Minibike | Val Iou Cradle | Val Iou Oven | Val Iou Ball | Val Iou Food | Val Iou Step | Val Iou Tank | Val Iou Trade name | Val Iou Microwave | Val Iou Pot | Val Iou Animal | Val Iou Bicycle | Val Iou Lake | Val Iou Dishwasher | Val Iou Screen | Val Iou Blanket | Val Iou Sculpture | Val Iou Hood | Val Iou Sconce | Val Iou Vase | Val Iou Traffic light | Val Iou Tray | Val Iou Ashcan | Val Iou Fan | Val Iou Pier | Val Iou Crt screen | Val Iou Plate | Val Iou Monitor | Val Iou Bulletin board | Val Iou Shower | Val Iou Radiator | Val Iou Glass | Val Iou Clock | Val Iou Flag |
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| 4.8668 | 1.0 | 20 | 4.9878 | 0.0007 | 0.0030 | 0.0066 | 0.0005 | 0.0 | 0.0420 | 0.0184 | 0.0 | 0.0022 | nan | 0.0 | 0.0048 | 0.0 | 0.0003 | 0.0 | 0.0187 | 0.0100 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0001 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0012 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0257 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0005 | 0.0 | 0.0214 | 0.0146 | 0.0 | 0.0011 | 0.0 | 0.0 | 0.0018 | 0.0 | 0.0003 | 0.0 | 0.0162 | 0.0096 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0008 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0199 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.7406 | 2.0 | 40 | 4.8589 | 0.0012 | 0.0157 | 0.0448 | 0.0227 | 0.0 | 0.4851 | 0.0168 | 0.0 | 0.1029 | nan | 0.0 | 0.0154 | 0.0 | 0.0 | 0.0 | 0.0018 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0003 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0109 | 0.0 | 0.0589 | 0.0113 | 0.0 | 0.0288 | 0.0 | 0.0 | 0.0037 | 0.0 | 0.0 | 0.0 | 0.0016 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | nan | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan |
| 4.311 | 3.0 | 60 | 4.6453 | 0.0017 | 0.0177 | 0.0521 | 0.0330 | 0.0 | 0.5743 | 0.0656 | 0.0 | 0.0412 | nan | 0.0 | 0.0130 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0122 | 0.0 | 0.0471 | 0.0326 | 0.0 | 0.0229 | 0.0 | 0.0 | 0.0048 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | nan | nan |
| 4.2761 | 4.0 | 80 | 4.6194 | 0.0023 | 0.0164 | 0.0542 | 0.0506 | 0.0 | 0.4301 | 0.1581 | 0.0 | 0.0321 | nan | 0.0 | 0.0021 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0137 | 0.0 | 0.0419 | 0.0682 | 0.0 | 0.0147 | 0.0 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | 0.0 | nan | nan | nan |
| 3.9233 | 5.0 | 100 | 4.3733 | 0.0029 | 0.0182 | 0.0599 | 0.0753 | 0.0 | 0.4805 | 0.1828 | 0.0 | 0.0046 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0001 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0202 | 0.0 | 0.0422 | 0.0721 | 0.0 | 0.0028 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0001 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.7373 | 6.0 | 120 | 4.3308 | 0.0022 | 0.0137 | 0.0425 | 0.1201 | 0.0 | 0.3164 | 0.0886 | 0.0 | 0.0330 | nan | 0.0 | 0.0031 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0268 | 0.0 | 0.0361 | 0.0301 | 0.0 | 0.0140 | 0.0 | 0.0 | 0.0018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.9523 | 7.0 | 140 | 4.2634 | 0.0016 | 0.0107 | 0.0329 | 0.1250 | 0.0 | 0.2345 | 0.0722 | 0.0 | 0.0051 | nan | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0291 | 0.0 | 0.0245 | 0.0231 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.4878 | 8.0 | 160 | 4.2745 | 0.0018 | 0.0097 | 0.0335 | 0.0545 | 0.0 | 0.2071 | 0.1212 | 0.0 | 0.0133 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0243 | 0.0 | 0.0232 | 0.0314 | 0.0 | 0.0043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.8144 | 9.0 | 180 | 4.2319 | 0.0014 | 0.0110 | 0.0331 | 0.0372 | 0.0 | 0.3469 | 0.0550 | 0.0 | 0.0107 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0148 | 0.0 | 0.0293 | 0.0175 | 0.0 | 0.0054 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.5547 | 10.0 | 200 | 4.1808 | 0.0017 | 0.0112 | 0.0362 | 0.0457 | 0.0 | 0.3084 | 0.0964 | 0.0 | 0.0099 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0152 | 0.0 | 0.0294 | 0.0285 | 0.0 | 0.0043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.8354 | 11.0 | 220 | 4.1384 | 0.0019 | 0.0119 | 0.0381 | 0.0492 | 0.0 | 0.3289 | 0.0967 | 0.0 | 0.0131 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0178 | 0.0 | 0.0301 | 0.0291 | 0.0 | 0.0057 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.7114 | 12.0 | 240 | 4.1094 | 0.0016 | 0.0104 | 0.0329 | 0.0355 | 0.0 | 0.2914 | 0.0693 | 0.0 | 0.0314 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0165 | 0.0 | 0.0276 | 0.0207 | 0.0 | 0.0106 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.4283 | 13.0 | 260 | 4.0419 | 0.0018 | 0.0093 | 0.0300 | 0.1163 | 0.0 | 0.1697 | 0.0841 | 0.0 | 0.0109 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0269 | 0.0 | 0.0267 | 0.0211 | 0.0 | 0.0028 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.2474 | 14.0 | 280 | 4.0800 | 0.0015 | 0.0084 | 0.0287 | 0.0615 | 0.0 | 0.1770 | 0.1034 | 0.0 | 0.0019 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0156 | 0.0 | 0.0213 | 0.0267 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.3058 | 15.0 | 300 | 4.1441 | 0.0012 | 0.0085 | 0.0285 | 0.0007 | 0.0 | 0.2548 | 0.0908 | 0.0 | 0.0026 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0004 | 0.0 | 0.0217 | 0.0250 | 0.0 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.2503 | 16.0 | 320 | 4.1082 | 0.0017 | 0.0087 | 0.0295 | 0.1247 | 0.0 | 0.1154 | 0.0996 | 0.0 | 0.0178 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0266 | 0.0 | 0.0183 | 0.0240 | 0.0 | 0.0053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.6224 | 17.0 | 340 | 4.1608 | 0.0010 | 0.0076 | 0.0243 | 0.0067 | 0.0 | 0.2368 | 0.0635 | 0.0 | 0.0033 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0037 | 0.0 | 0.0202 | 0.0183 | 0.0 | 0.0016 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.8128 | 18.0 | 360 | 4.1084 | 0.0012 | 0.0087 | 0.0272 | 0.0026 | 0.0 | 0.2744 | 0.0567 | 0.0 | 0.0213 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0012 | 0.0 | 0.0242 | 0.0180 | 0.0 | 0.0078 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.9326 | 19.0 | 380 | 4.1652 | 0.0010 | 0.0078 | 0.0245 | 0.0004 | 0.0 | 0.2480 | 0.0505 | 0.0 | 0.0200 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0003 | 0.0 | 0.0218 | 0.0150 | 0.0 | 0.0071 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.1999 | 20.0 | 400 | 4.1714 | 0.0011 | 0.0081 | 0.0268 | 0.0014 | 0.0 | 0.2498 | 0.0776 | 0.0 | 0.0052 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0008 | 0.0 | 0.0213 | 0.0224 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.0094 | 21.0 | 420 | 4.1315 | 0.0016 | 0.0094 | 0.0312 | 0.0057 | 0.0 | 0.2601 | 0.0861 | 0.0 | 0.0332 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0022 | 0.0 | 0.0229 | 0.0263 | 0.0 | 0.0148 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.6791 | 22.0 | 440 | 4.1714 | 0.0014 | 0.0111 | 0.0330 | 0.0007 | 0.0 | 0.3900 | 0.0426 | 0.0 | 0.0217 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0006 | 0.0 | 0.0269 | 0.0158 | 0.0 | 0.0133 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.9848 | 23.0 | 460 | 4.0862 | 0.0013 | 0.0107 | 0.0335 | 0.0 | 0.0 | 0.3614 | 0.0752 | 0.0 | 0.0014 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0265 | 0.0251 | 0.0 | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.0619 | 24.0 | 480 | 4.1014 | 0.0013 | 0.0089 | 0.0293 | 0.0035 | 0.0 | 0.2635 | 0.0834 | 0.0 | 0.0134 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0020 | 0.0 | 0.0227 | 0.0233 | 0.0 | 0.0065 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.7187 | 25.0 | 500 | 4.0968 | 0.0013 | 0.0093 | 0.0299 | 0.0082 | 0.0 | 0.2912 | 0.0738 | 0.0 | 0.0096 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0040 | 0.0 | 0.0242 | 0.0221 | 0.0 | 0.0054 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.4236 | 26.0 | 520 | 4.0949 | 0.0011 | 0.0094 | 0.0286 | 0.0004 | 0.0 | 0.3252 | 0.0453 | 0.0 | 0.0162 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0003 | 0.0 | 0.0257 | 0.0151 | 0.0 | 0.0067 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.6204 | 27.0 | 540 | 4.1028 | 0.0013 | 0.0099 | 0.0308 | 0.0068 | 0.0 | 0.3238 | 0.0628 | 0.0 | 0.0132 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0046 | 0.0 | 0.0264 | 0.0195 | 0.0 | 0.0061 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.1183 | 28.0 | 560 | 4.0692 | 0.0013 | 0.0113 | 0.0335 | 0.0027 | 0.0 | 0.4026 | 0.0482 | 0.0 | 0.0083 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0019 | 0.0 | 0.0297 | 0.0172 | 0.0 | 0.0047 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.6252 | 29.0 | 580 | 4.0671 | 0.0012 | 0.0112 | 0.0335 | 0.0011 | 0.0 | 0.4003 | 0.0525 | 0.0 | 0.0045 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0008 | 0.0 | 0.0289 | 0.0191 | 0.0 | 0.0027 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.414 | 30.0 | 600 | 4.0899 | 0.0013 | 0.0121 | 0.0369 | 0.0 | 0.0 | 0.4248 | 0.0685 | 0.0 | 0.0027 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0301 | 0.0250 | 0.0 | 0.0015 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.1755 | 31.0 | 620 | 4.0466 | 0.0022 | 0.0133 | 0.0414 | 0.0024 | 0.0 | 0.3823 | 0.0585 | 0.0 | 0.1026 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0017 | 0.0 | 0.0312 | 0.0200 | 0.0 | 0.0418 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.3822 | 32.0 | 640 | 4.0491 | 0.0018 | 0.0126 | 0.0381 | 0.0049 | 0.0 | 0.3984 | 0.0473 | 0.0 | 0.0648 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0034 | 0.0 | 0.0319 | 0.0163 | 0.0 | 0.0277 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.7267 | 33.0 | 660 | 4.0467 | 0.0028 | 0.0152 | 0.0469 | 0.0111 | 0.0 | 0.4019 | 0.0502 | 0.0 | 0.1581 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0071 | 0.0 | 0.0344 | 0.0175 | 0.0 | 0.0578 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.7925 | 34.0 | 680 | 4.0938 | 0.0016 | 0.0124 | 0.0372 | 0.0045 | 0.0 | 0.4278 | 0.0495 | 0.0 | 0.0281 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0035 | 0.0 | 0.0309 | 0.0184 | 0.0 | 0.0148 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.6708 | 35.0 | 700 | 4.0915 | 0.0024 | 0.0134 | 0.0429 | 0.0111 | 0.0 | 0.3789 | 0.0870 | 0.0 | 0.0734 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0067 | 0.0 | 0.0300 | 0.0288 | 0.0 | 0.0356 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.9814 | 36.0 | 720 | 4.0398 | 0.0027 | 0.0140 | 0.0438 | 0.0084 | 0.0 | 0.3781 | 0.0588 | 0.0 | 0.1290 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0064 | 0.0 | 0.0299 | 0.0207 | 0.0 | 0.0591 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.806 | 37.0 | 740 | 4.0684 | 0.0022 | 0.0133 | 0.0409 | 0.0033 | 0.0 | 0.4026 | 0.0509 | 0.0 | 0.0904 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0021 | 0.0 | 0.0313 | 0.0182 | 0.0 | 0.0419 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.5626 | 38.0 | 760 | 4.0667 | 0.0025 | 0.0127 | 0.0419 | 0.0078 | 0.0 | 0.2997 | 0.0908 | 0.0 | 0.1205 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0043 | 0.0 | 0.0264 | 0.0268 | 0.0 | 0.0480 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.9812 | 39.0 | 780 | 4.0141 | 0.0023 | 0.0115 | 0.0388 | 0.0109 | 0.0 | 0.2528 | 0.0928 | 0.0 | 0.1159 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0057 | 0.0 | 0.0243 | 0.0263 | 0.0 | 0.0411 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.5248 | 40.0 | 800 | 4.0718 | 0.0017 | 0.0117 | 0.0360 | 0.0 | 0.0 | 0.3762 | 0.0609 | 0.0 | 0.0418 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0282 | 0.0210 | 0.0 | 0.0215 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.1112 | 41.0 | 820 | 4.0534 | 0.0014 | 0.0107 | 0.0339 | 0.0024 | 0.0 | 0.3486 | 0.0768 | 0.0 | 0.0128 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0015 | 0.0 | 0.0269 | 0.0250 | 0.0 | 0.0073 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.3833 | 42.0 | 840 | 4.0497 | 0.0026 | 0.0134 | 0.0424 | 0.0042 | 0.0 | 0.3587 | 0.0678 | 0.0 | 0.1169 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0025 | 0.0 | 0.0288 | 0.0224 | 0.0 | 0.0548 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.7442 | 43.0 | 860 | 4.0550 | 0.0011 | 0.0112 | 0.0334 | 0.0 | 0.0 | 0.4068 | 0.0486 | 0.0 | 0.0043 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0287 | 0.0178 | 0.0 | 0.0028 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.2235 | 44.0 | 880 | 4.0411 | 0.0022 | 0.0127 | 0.0391 | 0.0015 | 0.0 | 0.3884 | 0.0555 | 0.0 | 0.0752 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0010 | 0.0 | 0.0288 | 0.0196 | 0.0 | 0.0441 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.6543 | 45.0 | 900 | 4.0411 | 0.0015 | 0.0114 | 0.0344 | 0.0001 | 0.0 | 0.3861 | 0.0532 | 0.0 | 0.0262 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0001 | 0.0 | 0.0283 | 0.0189 | 0.0 | 0.0165 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.4057 | 46.0 | 920 | 4.0483 | 0.0025 | 0.0127 | 0.0399 | 0.0072 | 0.0 | 0.3478 | 0.0612 | 0.0 | 0.1044 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0045 | 0.0 | 0.0271 | 0.0205 | 0.0 | 0.0570 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 3.3863 | 47.0 | 940 | 4.0374 | 0.0025 | 0.0129 | 0.0405 | 0.0047 | 0.0 | 0.3576 | 0.0624 | 0.0 | 0.1031 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0032 | 0.0 | 0.0276 | 0.0214 | 0.0 | 0.0555 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.1049 | 48.0 | 960 | 4.0540 | 0.0021 | 0.0116 | 0.0370 | 0.0139 | 0.0 | 0.3286 | 0.0743 | 0.0 | 0.0599 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0078 | 0.0 | 0.0263 | 0.0237 | 0.0 | 0.0334 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 1.8716 | 49.0 | 980 | 4.0432 | 0.0026 | 0.0124 | 0.0402 | 0.0098 | 0.0 | 0.3138 | 0.0749 | 0.0 | 0.1119 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0059 | 0.0 | 0.0259 | 0.0236 | 0.0 | 0.0530 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
| 2.6473 | 50.0 | 1000 | 4.0327 | 0.0023 | 0.0126 | 0.0392 | 0.0028 | 0.0 | 0.3678 | 0.0556 | 0.0 | 0.0921 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0019 | 0.0 | 0.0280 | 0.0193 | 0.0 | 0.0493 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
nolanaatama/epnsffst
|
nolanaatama
| 2023-03-09T00:25:21Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-09T00:22:23Z |
---
license: creativeml-openrail-m
---
|
hasarinduperera/a2c-AntBulletEnv-v0
|
hasarinduperera
| 2023-03-09T00:09:22Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T00:08:05Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1277.87 +/- 145.47
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
chandc/ppo-CartPole-v5
|
chandc
| 2023-03-08T23:57:01Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-08T19:18:06Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -156.23 +/- 77.00
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'CleanRL_ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 16
'num_steps': 1024
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'chandc/ppo-CartPole-v5'
'batch_size': 16384
'minibatch_size': 4096}
```
|
Rifky/XLM-ROBERTA-QA
|
Rifky
| 2023-03-08T23:35:59Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"albert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-08T23:11:31Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Rifky/XLM-ROBERTA-QA
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rifky/XLM-ROBERTA-QA
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.3902
- Validation Loss: 4.2857
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 41275, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.3902 | 4.2857 | 0 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Mahmoud22/my-awesome-model
|
Mahmoud22
| 2023-03-08T23:16:15Z | 104 | 1 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-08T23:14:30Z |
---
tags:
- generated_from_trainer
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [salti/AraElectra-base-finetuned-ARCD](https://huggingface.co/salti/AraElectra-base-finetuned-ARCD) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0151
## 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: 8e-05
- train_batch_size: 48
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 172 | 0.0317 |
| No log | 2.0 | 344 | 0.0156 |
| 1.1794 | 3.0 | 516 | 0.0151 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
qgallouedec/sample-factory-handle-press-v2
|
qgallouedec
| 2023-03-08T22:48:57Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-08T22:48:52Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: handle-press-v2
type: handle-press-v2
metrics:
- type: mean_reward
value: 4839.48 +/- 52.67
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **handle-press-v2** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r qgallouedec/sample-factory-handle-press-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=handle-press-v2 --train_dir=./train_dir --experiment=sample-factory-handle-press-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m train --algo=APPO --env=handle-press-v2 --train_dir=./train_dir --experiment=sample-factory-handle-press-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
BridgeTower/bridgetower-large-itm-mlm-itc
|
BridgeTower
| 2023-03-08T22:33:21Z | 895,974 | 11 |
transformers
|
[
"transformers",
"pytorch",
"bridgetower",
"gaudi",
"en",
"dataset:conceptual_captions",
"dataset:conceptual_12m",
"dataset:sbu_captions",
"dataset:visual_genome",
"dataset:mscoco_captions",
"arxiv:2206.08657",
"arxiv:1504.00325",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-02-11T00:25:58Z |
---
language: en
tags:
- bridgetower
- gaudi
license: mit
datasets:
- conceptual_captions
- conceptual_12m
- sbu_captions
- visual_genome
- mscoco_captions
---
# BridgeTower large-itm-mlm-itc model
The BridgeTower model was proposed in "BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning" by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
The model was pretrained on English language using masked language modeling (MLM) and image text matching (ITM)objectives. It was introduced in
[this paper](https://arxiv.org/pdf/2206.08657.pdf) and first released in
[this repository](https://github.com/microsoft/BridgeTower).
BridgeTower got accepted to [AAAI'23](https://aaai.org/Conferences/AAAI-23/).
## Model description
The abstract from the paper is the following:
Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder. Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BridgeTower, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the cross-modal encoder. This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BridgeTower achieves state-of-the-art performance on various downstream vision-language tasks. In particular, on the VQAv2 test-std set, BridgeTower achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs. Notably, when further scaling the model, BridgeTower achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets.
## Intended uses & limitations
### How to use
Here is how to use this model to perform contrastive learning between image and text pairs:
```python
from transformers import BridgeTowerProcessor, BridgeTowerForContrastiveLearning
import requests
from PIL import Image
import torch
image_urls = [
"https://farm4.staticflickr.com/3395/3428278415_81c3e27f15_z.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg"]
texts = [
"two dogs in a car",
"two cats sleeping on a couch"]
images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]
processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm")
model = BridgeTowerForContrastiveLearning.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
inputs = processor(images, texts, padding=True, return_tensors="pt")
outputs = model(**inputs)
inputs = processor(images, texts[::-1], padding=True, return_tensors="pt")
outputs_swapped = model(**inputs)
print('Loss', outputs.loss.item())
# Loss 0.00191505195107311
print('Loss with swapped images', outputs_swapped.loss.item())
# Loss with swapped images 2.1259872913360596
```
Here is how to use this model to perform image and text matching
```python
from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval
import requests
from PIL import Image
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]
processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-gaudi")
model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-gaudi")
# forward pass
scores = dict()
for text in texts:
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
outputs = model(**encoding)
scores[text] = outputs.logits[0,1].item()
```
Here is how to use this model to perform masked language modeling:
```python
from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000360943.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
text = "a <mask> looking out of the window"
processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-gaudi")
model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-gaudi")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
outputs = model(**encoding)
results = processor.decode(outputs.logits.argmax(dim=-1).squeeze(0).tolist())
print(results)
#.a cat looking out of the window.
```
## Training data
The BridgeTower model was pretrained on four public image-caption datasets:
- [Conceptual Captions (CC3M)](https://ai.google.com/research/ConceptualCaptions/)
- [Conceptual 12M (CC12M)](https://github.com/google-research-datasets/conceptual-12m)
- [SBU Captions](https://www.cs.rice.edu/~vo9/sbucaptions/)
- [MSCOCO Captions](https://arxiv.org/pdf/1504.00325.pdf)
- [Visual Genome](https://visualgenome.org/)
The total number of unique images in the combined data is around 14M.
## Training procedure
### Pretraining
The model was pre-trained for 10 epochs on an Intel AI supercomputing cluster using 512 Gaudis and 128 Xeons with a batch size of 2048.
The optimizer used was AdamW with a learning rate of 1e-7. No data augmentation was used except for center-crop. The image resolution in pre-training is set to 294 x 294.
## Evaluation results
Please refer to [Table 5](https://arxiv.org/pdf/2206.08657.pdf) for BridgeTower's performance on Image Retrieval and other downstream tasks.
### BibTeX entry and citation info
```bibtex
@article{xu2022bridge,
title={BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning},
author={Xu, Xiao and Wu, Chenfei and Rosenman, Shachar and Lal, Vasudev and Che, Wanxiang and Duan, Nan},
journal={arXiv preprint arXiv:2206.08657},
year={2022}
}
```
|
hmatzner/Taxi-v3
|
hmatzner
| 2023-03-08T22:12:00Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-08T19:03:56Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hmatzner/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
hmatzner/q-FrozenLake-v1-4x4-noSlippery
|
hmatzner
| 2023-03-08T21:57:23Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-08T21:46:43Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="hmatzner/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
jackoyoungblood/poca-SoccerTwos
|
jackoyoungblood
| 2023-03-08T21:43:54Z | 39 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-08T19:42:22Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: jackoyoungblood/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
rodrfons/a2c-AntBulletEnv-v0
|
rodrfons
| 2023-03-08T21:18:21Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-08T21:15:35Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1886.52 +/- 24.26
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hugginglearners/ml-news-classify-fastai
|
hugginglearners
| 2023-03-08T20:56:58Z | 0 | 1 |
fastai
|
[
"fastai",
"text-classification",
"dataset:rajeshradhakrishnan/malayalam_news",
"arxiv:2005.00085",
"region:us"
] |
text-classification
| 2022-06-20T05:46:49Z |
---
tags:
- fastai
- text-classification
datasets: rajeshradhakrishnan/malayalam_news
widget:
- text: ഓഹരി വിപണി തകരുമ്പോള് നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം
example_title: Malayalam News Classifier
---
# Malayalam (മലയാളം) Classifier using fastai (Working in Progress)
🥳 This model is my attempt to use machine learning using Malayalam Language. Huge inspiration from [Malayalam Text Classifier](https://kurianbenoy.com/2022-05-30-malayalamtext-0/). Courtesy to @waydegilliam for [blurr](https://ohmeow.github.io/blurr/text-examples-multilabel.html)
🌈 മലയാളത്തിൽ മെഷീൻ ലീർണിങ് പഠിക്കാനും പിന്നേ പരിചയപ്പെടാനും, to be continued...
# How its built ? & How to use ?
Please find the [notebook](https://nbviewer.org/github/rajeshradhakrishnanmvk/kitchen2.0/blob/feature101-frontend/ml/fastai_X_Hugging_Face_Group_2022.ipynb) used for training the model
Usage:
First, install the utilities to load the model as well as `blurr`, which was used to train this model.
```bash
!pip install huggingface_hub[fastai]
!git clone https://github.com/ohmeow/blurr.git && cd blurr && pip install -e ".[dev]"
```
```python
from huggingface_hub import from_pretrained_fastai
learner = from_pretrained_fastai("rajeshradhakrishnan/ml-news-classify-fastai")
sentences = ["ഓഹരി വിപണി തകരുമ്പോള് നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം",
"വാര്ണറുടെ ഒറ്റക്കയ്യന് ക്യാച്ചില് അമ്പരന്ന് ക്രിക്കറ്റ് ലോകം"]
probs = learner.predict(sentences)
# 'business', 'entertainment', 'sports', 'technology'
for idx in range(len(sentences)):
print(f"Probability that sentence '{sentences[idx]}' is business is: {100*probs[idx]['probs'][0]:.2f}%")
print(f"Probability that sentence '{sentences[idx]}' is entertainment is: {100*probs[idx]['probs'][1]:.2f}%")
print(f"Probability that sentence '{sentences[idx]}' is sports is: {100*probs[idx]['probs'][2]:.2f}%")
print(f"Probability that sentence '{sentences[idx]}' is technology is: {100*probs[idx]['probs'][3]:.2f}%")
```
---
# Model card
## Model description
The is a Malayalam classifier model for labels 'business', 'entertainment', 'sports', 'technology'.
## Intended uses & limitations
The model can be used to categorize malayalam new sfeed.
## Training and evaluation data
Data is from the [AI4Bharat-IndicNLP Dataset](https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset) and wrapper to extract only Malayalam data( [HF dataset](https://huggingface.co/datasets/rajeshradhakrishnan/malayalam_news))!.
## Citation
```
@article{kunchukuttan2020indicnlpcorpus,
title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},
author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}
```
|
uisikdag/weed_vit_imbalanced
|
uisikdag
| 2023-03-08T20:52:18Z | 205 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-08T17:40:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: weeds_hfclass10
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: 0.952212389380531
---
<!-- 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. -->
# weeds_hfclass10
Model is trained on imbalanced dataset/ .8 .1 .1 split/ 224x224 resized
Dataset: https://www.kaggle.com/datasets/vbookshelf/v2-plant-seedlings-dataset
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2434
- Accuracy: 0.9522
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8538 | 1.0 | 69 | 1.4703 | 0.7717 |
| 0.6867 | 2.0 | 138 | 0.6294 | 0.9204 |
| 0.4748 | 3.0 | 207 | 0.4252 | 0.9434 |
| 0.3641 | 4.0 | 276 | 0.4082 | 0.9257 |
| 0.3193 | 5.0 | 345 | 0.3724 | 0.9292 |
| 0.2881 | 6.0 | 414 | 0.3108 | 0.9487 |
| 0.2584 | 7.0 | 483 | 0.2891 | 0.9434 |
| 0.2306 | 8.0 | 552 | 0.2798 | 0.9469 |
| 0.2171 | 9.0 | 621 | 0.2558 | 0.9451 |
| 0.1928 | 10.0 | 690 | 0.2434 | 0.9522 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
uisikdag/weed_beit_balanced
|
uisikdag
| 2023-03-08T20:51:15Z | 202 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-08T19:55:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: weeds_hfclass12
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: 0.96
---
<!-- 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. -->
# weeds_hfclass12
Model is trained on balanced dataset/ 250 image per class/ .8 .1 .1 split/ 224x224 resized
Dataset: https://www.kaggle.com/datasets/vbookshelf/v2-plant-seedlings-dataset
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1257
- Accuracy: 0.96
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6013 | 0.99 | 37 | 0.7579 | 0.8067 |
| 0.3887 | 1.99 | 74 | 0.2834 | 0.9033 |
| 0.2846 | 2.99 | 111 | 0.2767 | 0.9 |
| 0.2086 | 3.99 | 148 | 0.2642 | 0.9067 |
| 0.1664 | 4.99 | 185 | 0.2016 | 0.9333 |
| 0.168 | 5.99 | 222 | 0.1498 | 0.9533 |
| 0.1159 | 6.99 | 259 | 0.1607 | 0.9533 |
| 0.1195 | 7.99 | 296 | 0.1719 | 0.9467 |
| 0.1013 | 8.99 | 333 | 0.1442 | 0.9533 |
| 0.0939 | 9.99 | 370 | 0.1257 | 0.96 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AntiSquid/poca-SoccerTwos
|
AntiSquid
| 2023-03-08T20:39:24Z | 31 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-08T20:34:32Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: AntiSquid/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
research-backup/relbert-roberta-large-nce-e-semeval2012-nell-t-rex
|
research-backup
| 2023-03-08T20:10:24Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-02-20T11:17:21Z |
---
datasets:
- relbert/relational_similarity
model-index:
- name: relbert/relbert-roberta-large-nce-e-semeval2012-nell-t-rex
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.806984126984127
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6016042780748663
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6053412462908012
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7787659811006115
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6008771929824561
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5879629629629629
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.36577181208053694
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6939890710382514
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.845
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9231580533373512
- name: F1 (macro)
type: f1_macro
value: 0.9209337542310293
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8511737089201878
- name: F1 (macro)
type: f1_macro
value: 0.6840382588713123
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6776814734561214
- name: F1 (macro)
type: f1_macro
value: 0.6741581804439568
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9545106767754051
- name: F1 (macro)
type: f1_macro
value: 0.8794140203700929
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8937637104356001
- name: F1 (macro)
type: f1_macro
value: 0.8889590618516027
---
# relbert/relbert-roberta-large-nce-e-semeval2012-nell-t-rex
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/relational_similarity](https://huggingface.co/datasets/relbert/relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-e-semeval2012-nell-t-rex/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6016042780748663
- Accuracy on SAT: 0.6053412462908012
- Accuracy on BATS: 0.7787659811006115
- Accuracy on U2: 0.6008771929824561
- Accuracy on U4: 0.5879629629629629
- Accuracy on Google: 0.926
- Accuracy on ConceptNet Analogy: 0.36577181208053694
- Accuracy on T-Rex Analogy: 0.6939890710382514
- Accuracy on NELL-ONE Analogy: 0.845
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-e-semeval2012-nell-t-rex/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9231580533373512
- Micro F1 score on CogALexV: 0.8511737089201878
- Micro F1 score on EVALution: 0.6776814734561214
- Micro F1 score on K&H+N: 0.9545106767754051
- Micro F1 score on ROOT09: 0.8937637104356001
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-e-semeval2012-nell-t-rex/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.806984126984127
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-nce-e-semeval2012-nell-t-rex")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 20
- batch: 64
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/relational_similarity
- data_name: nell_relational_similarity.semeval2012_relational_similarity.t_rex_relational_similarity
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 10}
- augment_negative_by_positive: False
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-nce-e-semeval2012-nell-t-rex/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
research-backup/relbert-roberta-large-nce-d-semeval2012-nell-t-rex
|
research-backup
| 2023-03-08T20:06:48Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-02-17T14:15:46Z |
---
datasets:
- relbert/relational_similarity
model-index:
- name: relbert/relbert-roberta-large-nce-d-semeval2012-nell-t-rex
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8230555555555555
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7005347593582888
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7002967359050445
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8271261812117843
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.968
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6403508771929824
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6319444444444444
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44966442953020136
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6939890710382514
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8133333333333334
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9207473255989151
- name: F1 (macro)
type: f1_macro
value: 0.915821178267561
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8539906103286385
- name: F1 (macro)
type: f1_macro
value: 0.6948529487301902
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6912242686890574
- name: F1 (macro)
type: f1_macro
value: 0.6744871084088281
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9540237879947138
- name: F1 (macro)
type: f1_macro
value: 0.8703542419341167
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9069257286117205
- name: F1 (macro)
type: f1_macro
value: 0.9061597394839002
---
# relbert/relbert-roberta-large-nce-d-semeval2012-nell-t-rex
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/relational_similarity](https://huggingface.co/datasets/relbert/relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012-nell-t-rex/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.7005347593582888
- Accuracy on SAT: 0.7002967359050445
- Accuracy on BATS: 0.8271261812117843
- Accuracy on U2: 0.6403508771929824
- Accuracy on U4: 0.6319444444444444
- Accuracy on Google: 0.968
- Accuracy on ConceptNet Analogy: 0.44966442953020136
- Accuracy on T-Rex Analogy: 0.6939890710382514
- Accuracy on NELL-ONE Analogy: 0.8133333333333334
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012-nell-t-rex/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9207473255989151
- Micro F1 score on CogALexV: 0.8539906103286385
- Micro F1 score on EVALution: 0.6912242686890574
- Micro F1 score on K&H+N: 0.9540237879947138
- Micro F1 score on ROOT09: 0.9069257286117205
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012-nell-t-rex/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8230555555555555
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-nce-d-semeval2012-nell-t-rex")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 20
- batch: 64
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/relational_similarity
- data_name: nell_relational_similarity.semeval2012_relational_similarity.t_rex_relational_similarity
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 10}
- augment_negative_by_positive: False
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012-nell-t-rex/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
research-backup/relbert-roberta-large-nce-b-semeval2012-nell-t-rex
|
research-backup
| 2023-03-08T20:02:07Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-02-17T11:06:21Z |
---
datasets:
- relbert/relational_similarity
model-index:
- name: relbert/relbert-roberta-large-nce-b-semeval2012-nell-t-rex
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8159126984126984
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6310160427807486
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6409495548961425
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7943301834352418
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.914
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6008771929824561
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5671296296296297
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.40939597315436244
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6612021857923497
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.825
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9106524031942143
- name: F1 (macro)
type: f1_macro
value: 0.9075677789228916
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8514084507042253
- name: F1 (macro)
type: f1_macro
value: 0.6853569151292189
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6776814734561214
- name: F1 (macro)
type: f1_macro
value: 0.6611858882704498
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.950963344230368
- name: F1 (macro)
type: f1_macro
value: 0.8553801117340581
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8887496082732684
- name: F1 (macro)
type: f1_macro
value: 0.8843965682691514
---
# relbert/relbert-roberta-large-nce-b-semeval2012-nell-t-rex
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/relational_similarity](https://huggingface.co/datasets/relbert/relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012-nell-t-rex/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6310160427807486
- Accuracy on SAT: 0.6409495548961425
- Accuracy on BATS: 0.7943301834352418
- Accuracy on U2: 0.6008771929824561
- Accuracy on U4: 0.5671296296296297
- Accuracy on Google: 0.914
- Accuracy on ConceptNet Analogy: 0.40939597315436244
- Accuracy on T-Rex Analogy: 0.6612021857923497
- Accuracy on NELL-ONE Analogy: 0.825
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012-nell-t-rex/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9106524031942143
- Micro F1 score on CogALexV: 0.8514084507042253
- Micro F1 score on EVALution: 0.6776814734561214
- Micro F1 score on K&H+N: 0.950963344230368
- Micro F1 score on ROOT09: 0.8887496082732684
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012-nell-t-rex/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8159126984126984
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-nce-b-semeval2012-nell-t-rex")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 20
- batch: 64
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/relational_similarity
- data_name: nell_relational_similarity.semeval2012_relational_similarity.t_rex_relational_similarity
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 10}
- augment_negative_by_positive: False
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012-nell-t-rex/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
wvangils/GPT2-Beatles-Lyrics-finetuned-newlyrics
|
wvangils
| 2023-03-08T20:01:52Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:cmotions/Beatles_lyrics",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-03T13:55:45Z |
---
license: mit
tags:
- generated_from_trainer
datasets: cmotions/Beatles_lyrics
model-index:
- name: GPT2-Beatles-Lyrics-finetuned-newlyrics
results: []
---
# GPT2-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text.
## 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: 8
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9259 | 1.0 | 35 | 1.6643 |
| 1.9188 | 2.0 | 70 | 1.6643 |
| 1.9725 | 3.0 | 105 | 1.6643 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics
|
wvangils
| 2023-03-08T20:01:40Z | 95 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"ctrl",
"text-generation",
"generated_from_trainer",
"dataset:cmotions/Beatles_lyrics",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T12:53:32Z |
---
tags:
- generated_from_trainer
datasets: cmotions/Beatles_lyrics
model-index:
- name: CTRL-Beatles-Lyrics-finetuned-newlyrics
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. -->
# CTRL-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [sshleifer/tiny-ctrl](https://huggingface.co/sshleifer/tiny-ctrl) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text.
## 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: 8
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 12.361 | 1.0 | 35 | 12.3685 |
| 12.3529 | 2.0 | 70 | 12.3583 |
| 12.3374 | 3.0 | 105 | 12.3401 |
| 12.3158 | 4.0 | 140 | 12.3237 |
| 12.301 | 5.0 | 175 | 12.3180 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics
|
wvangils
| 2023-03-08T20:01:32Z | 107 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"dataset:cmotions/Beatles_lyrics",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T13:51:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets: cmotions/Beatles_lyrics
model-index:
- name: GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics
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. -->
# GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text.
## 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: 16
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4438 | 1.0 | 18 | 1.8004 |
| 2.1981 | 2.0 | 36 | 1.6985 |
| 1.9766 | 3.0 | 54 | 1.6487 |
| 1.8233 | 4.0 | 72 | 1.6384 |
| 1.6137 | 5.0 | 90 | 1.6574 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
wvangils/BLOOM-350m-Beatles-Lyrics-finetuned-newlyrics
|
wvangils
| 2023-03-08T20:01:00Z | 111 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bloom",
"text-generation",
"generated_from_trainer",
"dataset:cmotions/Beatles_lyrics",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-05T07:14:38Z |
---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
datasets: cmotions/Beatles_lyrics
widget:
- text: Last night I couldn't sleep
example_title: Sleep
- text: It hasn't rained in weeks
example_title: Rain
model-index:
- name: BLOOM-350m-Beatles-Lyrics-finetuned-newlyrics
results: []
---
# BLOOM-350m-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [bigscience/bloom-350m](https://huggingface.co/bigscience/bloom-350m) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1135 | 1.0 | 138 | 3.4852 |
| 2.0717 | 2.0 | 276 | 3.8820 |
| 1.3296 | 3.0 | 414 | 3.6281 |
| 0.8146 | 4.0 | 552 | 3.8985 |
| 0.477 | 5.0 | 690 | 4.0317 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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