modelId
stringlengths 5
139
| 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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| card
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|---|---|---|---|---|---|---|---|---|---|
pfunk/CartPole-v1-CP_DQPN_x10-seed888
|
pfunk
| 2023-03-20T20:46:41Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:40:00Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 20.10 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x10]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x10 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed888/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed888/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x10 --policy-network-frequency 1000 --seed 888
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x10',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 888,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
golightly/PPO_LunarLander-v2
|
golightly
| 2023-03-20T20:41:03Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-16T17:13:01Z |
---
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: 277.07 +/- 15.96
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
...
```
|
pfunk/CartPole-v1-CP_DQPN_x5-seed232
|
pfunk
| 2023-03-20T20:36:27Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T20:36:24Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 492.42 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x5.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x5]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x5 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed232/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed232/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed232/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x5 --policy-network-frequency 500 --seed 232
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x5',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 500,
'policy_tau': 1.0,
'save_model': True,
'seed': 232,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
keras-dreambooth/marvin_paranoid_android
|
keras-dreambooth
| 2023-03-20T20:36:26Z | 4 | 0 |
keras
|
[
"keras",
"tf-keras",
"keras-dreambooth",
"scifi",
"text-to-image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2023-03-12T20:28:20Z |
---
library_name: keras
license: apache-2.0
pipeline_tag: text-to-image
tags:
- keras-dreambooth
- scifi
---
## Model description
This model is a fine-tuned Stable Diffusion modeled, using the Dreambooth technique.
It was trained on 15 images of Marvin the Paranoid Android, scraped from the Internet, as instance images and 205 robot images generated
by Stable Diffusion as class images. You can find the full set here: [Marvin the Paranoid Android](https://huggingface.co/datasets/keras-dreambooth/marvin_paranoid_android)
It was created by [johko](https://huggingface.co/johko) for the [keras-dreambooth](https://huggingface.co/keras-dreambooth) sprint.
## Training procedure
This model was trained using the keras implementation of dreambooth.
You can find the notebook to train these models and how to attend this sprint [here](https://github.com/huggingface/community-events/tree/main/keras-dreambooth-sprint).
## Example Outputs
### 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>
|
pfunk/CartPole-v1-CP_DQPN_x50-seed888
|
pfunk
| 2023-03-20T20:35:51Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T20:35:48Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x50.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x50]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x50 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed888/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed888/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x50 --policy-network-frequency 5000 --seed 888
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x50',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 5000,
'policy_tau': 1.0,
'save_model': True,
'seed': 888,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x5-seed888
|
pfunk
| 2023-03-20T20:35:42Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T20:35:39Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 10.06 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x5.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x5]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x5 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed888/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed888/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x5 --policy-network-frequency 500 --seed 888
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x5',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 500,
'policy_tau': 1.0,
'save_model': True,
'seed': 888,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
Viswes/Reinforce-cart
|
Viswes
| 2023-03-20T20:21:49Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T20:21:40Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cart
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
|
Suchinthana/sinhala-gpt-neo-siwiki
|
Suchinthana
| 2023-03-20T20:07:11Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neo",
"text-generation",
"si",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-19T23:21:01Z |
---
license: mit
widget:
- text: බාහිර බැටරි
- text: පිරිසිදු පානීය ජලය
- text: පරික්ෂණය භාවිතා කරන විද්යාඥයන්
language:
- si
pipeline_tag: text-generation
datasets:
- wikipedia
---
### Fine tuned GPT Neo 125M with Sinhala Wikipedia Dataset
This model is fine tuned with acrticles from Sinhala Wikipedia [Dataset](https://dumps.wikimedia.org/siwiki/20230201/) for Sinhala text generation. Only the articles with word count in between 60 and 500 were used here.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='Suchinthana/sinhala-gpt-neo-siwiki')
>>> generator("නිර්මාණය කිරීම සඳහා ", do_sample=True, max_length=500)
```
|
pfunk/CartPole-v1-CP_DQPN_x10-seed410
|
pfunk
| 2023-03-20T19:43:22Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:43:18Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 495.31 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x10]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x10 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed410/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed410/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed410/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x10 --policy-network-frequency 1000 --seed 410
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x10',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 410,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x10-seed111
|
pfunk
| 2023-03-20T19:43:02Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:42:59Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x10]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x10 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed111/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed111/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed111/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x10 --policy-network-frequency 1000 --seed 111
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x10',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 111,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQN-seed111
|
pfunk
| 2023-03-20T19:43:00Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:42:56Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
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
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed111/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed111/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed111/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 111
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 111,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x100-seed828
|
pfunk
| 2023-03-20T19:42:58Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:42:55Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 496.83 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x100.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x100]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x100 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed828/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed828/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed828/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x100 --policy-network-frequency 10000 --seed 828
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x100',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 10000,
'policy_tau': 1.0,
'save_model': True,
'seed': 828,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQN-seed828
|
pfunk
| 2023-03-20T19:42:36Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:42:33Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
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
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed828/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed828/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed828/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 828
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 828,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x100-seed777
|
pfunk
| 2023-03-20T19:42:32Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:42:29Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 340.63 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x100.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x100]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x100 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed777/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed777/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed777/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x100 --policy-network-frequency 10000 --seed 777
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x100',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 10000,
'policy_tau': 1.0,
'save_model': True,
'seed': 777,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x10-seed555
|
pfunk
| 2023-03-20T19:42:03Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:42:00Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 83.65 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x10]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x10 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed555/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed555/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed555/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x10 --policy-network-frequency 1000 --seed 555
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x10',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 555,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x10-seed232
|
pfunk
| 2023-03-20T19:41:59Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:41:56Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 496.22 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x10]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x10 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed232/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed232/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x10-seed232/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x10 --policy-network-frequency 1000 --seed 232
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x10',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 232,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQN-seed929
|
pfunk
| 2023-03-20T19:40:58Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:40:55Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 497.87 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed929/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed929/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed929/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 929
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 929,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
zhh210/ppo-LunarLander-v2
|
zhh210
| 2023-03-20T19:39:59Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:39:35Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 275.48 +/- 19.59
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
...
```
|
pfunk/CartPole-v1-CP_DQN-seed888
|
pfunk
| 2023-03-20T19:39:49Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:39:46Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 491.27 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed888/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed888/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 888
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 888,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
MakiPan/ppo-SnowballTarget
|
MakiPan
| 2023-03-20T19:28:16Z | 16 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:28:09Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: MakiPan/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
domenicrosati/t5-small-finetuned-contradiction-local-test
|
domenicrosati
| 2023-03-20T19:23:49Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-04-24T00:22:27Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- snli
model-index:
- name: t5-small-finetuned-contradiction-local-test
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-contradiction-local-test
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the snli 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| No log | 1.0 | 405 | 2.5110 | 23.4004 | 8.9397 | 20.9541 | 21.5922 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Stokrotka/a2c-AntBulletEnv-v0
|
Stokrotka
| 2023-03-20T19:20:15Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:19:06Z |
---
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: 1631.72 +/- 383.20
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
...
```
|
pfunk/CartPole-v1-CP_DQPN_x100-seed355
|
pfunk
| 2023-03-20T19:09:48Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:09:44Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 495.12 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x100.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x100]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x100 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed355/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed355/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x100-seed355/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x100 --policy-network-frequency 10000 --seed 355
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x100',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 10000,
'policy_tau': 1.0,
'save_model': True,
'seed': 355,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQN-seed355
|
pfunk
| 2023-03-20T19:09:44Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:09:41Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 140.22 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed355/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed355/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed355/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 355
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 355,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQN-seed612
|
pfunk
| 2023-03-20T19:09:14Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T19:09:11Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 495.13 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed612/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed612/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed612/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 612
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 612,
'start_e': 1.0,
'target_network_frequency': 100,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
iotatouille/eva
|
iotatouille
| 2023-03-20T19:08:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-02-12T20:39:35Z |
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("iotatouille/DialoGPT-medium-eva0223.1")
model = AutoModelWithLMHead.from_pretrained("iotatouille/DialoGPT-medium-eva0223.1")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("eva0223.1: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
|
hdrwilkinson/distilbert-base-uncased-finetuned-emotion
|
hdrwilkinson
| 2023-03-20T19:02:55Z | 4 | 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
| 2023-03-20T12:18:39Z |
---
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: 0.921
- name: F1
type: f1
value: 0.9210026732533625
---
<!-- 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: 0.2174
- Accuracy: 0.921
- F1: 0.9210
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8079 | 1.0 | 250 | 0.3036 | 0.905 | 0.9028 |
| 0.2441 | 2.0 | 500 | 0.2174 | 0.921 | 0.9210 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
strateg17/q-FrozenLake-v1-4x4-noSlippery
|
strateg17
| 2023-03-20T18:56:33Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T18:56:29Z |
---
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="strateg17/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"])
```
|
prompthero-diffusion-models/openjourney-v4
|
prompthero-diffusion-models
| 2023-03-20T18:37:45Z | 0 | 10 | null |
[
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-20T14:23:17Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
pinned: true
---
# <u>Openjourney v4</u>
## Trained on +124k Midjourney v4 images, by [PromptHero](https://prompthero.com/?utm_source=huggingface&utm_medium=referral)
Trained on Stable Diffusion v1.5 using +124000 images, 12400 steps, 4 epochs +32 training hours.
So "mdjrny-v4 style" is not necessary anymore (yay!)
Want to learn how to train models like this one? 👉🏼 __[Join our course](https://prompthero.com/academy/dreambooth-stable-diffusion-train-fine-tune-course?utm_source=huggingface&utm_medium=referral)__ 🔥
<img src="https://s3.amazonaws.com/moonup/production/uploads/1679319414146-63265d019f9d19bfd4f45031.png" alt="openjourney-v4" width="50%">
# Openjourney Links
- [Lora version](https://huggingface.co/prompthero/openjourney-lora)
- [Openjourney Dreambooth](https://huggingface.co/prompthero/openjourney)
|
abhijitt/bert_st_qa_all-mpnet-base-v2-epochs-10
|
abhijitt
| 2023-03-20T18:34:22Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-03-20T16:22:19Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1369 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1369,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
joheras/bsc-bio-ehr-es-finetuned-clinais-augmented1
|
joheras
| 2023-03-20T18:33:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-20T16:48:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bsc-bio-ehr-es-finetuned-clinais-augmented1
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. -->
# bsc-bio-ehr-es-finetuned-clinais-augmented1
This model is a fine-tuned version of [joheras/bsc-bio-ehr-es-finetuned-clinais](https://huggingface.co/joheras/bsc-bio-ehr-es-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3581
- Precision: 0.5320
- Recall: 0.6494
- F1: 0.5849
- Accuracy: 0.8583
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 90 | 0.6246 | 0.2759 | 0.3485 | 0.3080 | 0.8286 |
| No log | 2.0 | 180 | 0.5890 | 0.3511 | 0.4520 | 0.3952 | 0.8402 |
| No log | 3.0 | 270 | 0.5973 | 0.3690 | 0.5322 | 0.4358 | 0.8539 |
| No log | 4.0 | 360 | 0.6676 | 0.3693 | 0.5713 | 0.4486 | 0.8554 |
| No log | 5.0 | 450 | 0.7173 | 0.4227 | 0.6061 | 0.4980 | 0.8544 |
| 0.3579 | 6.0 | 540 | 0.7854 | 0.4438 | 0.6082 | 0.5131 | 0.8552 |
| 0.3579 | 7.0 | 630 | 0.8437 | 0.4530 | 0.6103 | 0.5200 | 0.8522 |
| 0.3579 | 8.0 | 720 | 0.8716 | 0.4349 | 0.6103 | 0.5079 | 0.8513 |
| 0.3579 | 9.0 | 810 | 0.8868 | 0.4500 | 0.6030 | 0.5153 | 0.8544 |
| 0.3579 | 10.0 | 900 | 0.8917 | 0.4680 | 0.6251 | 0.5353 | 0.8574 |
| 0.3579 | 11.0 | 990 | 0.9175 | 0.4769 | 0.6336 | 0.5442 | 0.8548 |
| 0.0229 | 12.0 | 1080 | 0.9081 | 0.4767 | 0.6473 | 0.5490 | 0.8651 |
| 0.0229 | 13.0 | 1170 | 0.9692 | 0.4854 | 0.6336 | 0.5497 | 0.8532 |
| 0.0229 | 14.0 | 1260 | 0.9568 | 0.4947 | 0.6431 | 0.5592 | 0.8592 |
| 0.0229 | 15.0 | 1350 | 1.0028 | 0.4848 | 0.6241 | 0.5457 | 0.8505 |
| 0.0229 | 16.0 | 1440 | 1.0302 | 0.4821 | 0.6251 | 0.5444 | 0.8557 |
| 0.0076 | 17.0 | 1530 | 0.9892 | 0.4918 | 0.6325 | 0.5533 | 0.8584 |
| 0.0076 | 18.0 | 1620 | 1.0339 | 0.4755 | 0.6135 | 0.5357 | 0.8480 |
| 0.0076 | 19.0 | 1710 | 1.0066 | 0.4935 | 0.6399 | 0.5572 | 0.8570 |
| 0.0076 | 20.0 | 1800 | 1.0403 | 0.4959 | 0.6410 | 0.5592 | 0.8564 |
| 0.0076 | 21.0 | 1890 | 1.0374 | 0.4979 | 0.6336 | 0.5576 | 0.8561 |
| 0.0076 | 22.0 | 1980 | 1.0758 | 0.4821 | 0.6262 | 0.5448 | 0.8528 |
| 0.0044 | 23.0 | 2070 | 1.0818 | 0.4876 | 0.6230 | 0.5471 | 0.8524 |
| 0.0044 | 24.0 | 2160 | 1.0668 | 0.5096 | 0.6431 | 0.5686 | 0.8569 |
| 0.0044 | 25.0 | 2250 | 1.1033 | 0.4873 | 0.6294 | 0.5493 | 0.8541 |
| 0.0044 | 26.0 | 2340 | 1.0936 | 0.4880 | 0.6209 | 0.5465 | 0.8544 |
| 0.0044 | 27.0 | 2430 | 1.0802 | 0.4856 | 0.6399 | 0.5522 | 0.8583 |
| 0.0028 | 28.0 | 2520 | 1.1245 | 0.5034 | 0.6346 | 0.5614 | 0.8542 |
| 0.0028 | 29.0 | 2610 | 1.1293 | 0.4874 | 0.6336 | 0.5510 | 0.8521 |
| 0.0028 | 30.0 | 2700 | 1.0883 | 0.4984 | 0.6494 | 0.5640 | 0.8591 |
| 0.0028 | 31.0 | 2790 | 1.1434 | 0.5055 | 0.6315 | 0.5615 | 0.8565 |
| 0.0028 | 32.0 | 2880 | 1.1394 | 0.5041 | 0.6505 | 0.5680 | 0.8558 |
| 0.0028 | 33.0 | 2970 | 1.1473 | 0.5083 | 0.6452 | 0.5686 | 0.8550 |
| 0.0026 | 34.0 | 3060 | 1.2286 | 0.4996 | 0.6177 | 0.5524 | 0.8437 |
| 0.0026 | 35.0 | 3150 | 1.1982 | 0.4996 | 0.6251 | 0.5553 | 0.8521 |
| 0.0026 | 36.0 | 3240 | 1.1878 | 0.4987 | 0.6294 | 0.5565 | 0.8491 |
| 0.0026 | 37.0 | 3330 | 1.1633 | 0.4935 | 0.6399 | 0.5572 | 0.8511 |
| 0.0026 | 38.0 | 3420 | 1.1619 | 0.5097 | 0.6410 | 0.5678 | 0.8587 |
| 0.0021 | 39.0 | 3510 | 1.1438 | 0.5021 | 0.6420 | 0.5635 | 0.8575 |
| 0.0021 | 40.0 | 3600 | 1.1511 | 0.5087 | 0.6494 | 0.5705 | 0.8575 |
| 0.0021 | 41.0 | 3690 | 1.1631 | 0.5128 | 0.6558 | 0.5755 | 0.8576 |
| 0.0021 | 42.0 | 3780 | 1.1639 | 0.5137 | 0.6526 | 0.5749 | 0.8612 |
| 0.0021 | 43.0 | 3870 | 1.1946 | 0.5174 | 0.6452 | 0.5742 | 0.8568 |
| 0.0021 | 44.0 | 3960 | 1.1822 | 0.5132 | 0.6378 | 0.5687 | 0.8556 |
| 0.0012 | 45.0 | 4050 | 1.1533 | 0.5379 | 0.6441 | 0.5863 | 0.8617 |
| 0.0012 | 46.0 | 4140 | 1.1584 | 0.5242 | 0.6410 | 0.5767 | 0.8602 |
| 0.0012 | 47.0 | 4230 | 1.2217 | 0.5159 | 0.6357 | 0.5695 | 0.8567 |
| 0.0012 | 48.0 | 4320 | 1.2451 | 0.5265 | 0.6399 | 0.5777 | 0.8533 |
| 0.0012 | 49.0 | 4410 | 1.2191 | 0.5281 | 0.6357 | 0.5769 | 0.8563 |
| 0.0009 | 50.0 | 4500 | 1.2092 | 0.5320 | 0.6505 | 0.5853 | 0.8548 |
| 0.0009 | 51.0 | 4590 | 1.2168 | 0.5310 | 0.6431 | 0.5817 | 0.8607 |
| 0.0009 | 52.0 | 4680 | 1.2273 | 0.5068 | 0.6251 | 0.5598 | 0.8530 |
| 0.0009 | 53.0 | 4770 | 1.1903 | 0.5254 | 0.6441 | 0.5787 | 0.8618 |
| 0.0009 | 54.0 | 4860 | 1.1939 | 0.5354 | 0.6473 | 0.5860 | 0.8635 |
| 0.0009 | 55.0 | 4950 | 1.2311 | 0.5025 | 0.6357 | 0.5613 | 0.8581 |
| 0.001 | 56.0 | 5040 | 1.2224 | 0.5097 | 0.6389 | 0.5670 | 0.8606 |
| 0.001 | 57.0 | 5130 | 1.2298 | 0.5017 | 0.6410 | 0.5628 | 0.8586 |
| 0.001 | 58.0 | 5220 | 1.2278 | 0.5114 | 0.6389 | 0.5681 | 0.8584 |
| 0.001 | 59.0 | 5310 | 1.2703 | 0.5146 | 0.6505 | 0.5746 | 0.8586 |
| 0.001 | 60.0 | 5400 | 1.2709 | 0.5445 | 0.6336 | 0.5857 | 0.8549 |
| 0.001 | 61.0 | 5490 | 1.2691 | 0.5094 | 0.6283 | 0.5626 | 0.8554 |
| 0.0006 | 62.0 | 5580 | 1.2777 | 0.5076 | 0.6315 | 0.5628 | 0.8523 |
| 0.0006 | 63.0 | 5670 | 1.2472 | 0.5271 | 0.6357 | 0.5764 | 0.8563 |
| 0.0006 | 64.0 | 5760 | 1.2709 | 0.5220 | 0.6515 | 0.5796 | 0.8572 |
| 0.0006 | 65.0 | 5850 | 1.2792 | 0.5306 | 0.6410 | 0.5806 | 0.8613 |
| 0.0006 | 66.0 | 5940 | 1.2403 | 0.5058 | 0.6399 | 0.5650 | 0.8583 |
| 0.0005 | 67.0 | 6030 | 1.2778 | 0.5219 | 0.6410 | 0.5754 | 0.8564 |
| 0.0005 | 68.0 | 6120 | 1.3046 | 0.5431 | 0.6515 | 0.5924 | 0.8595 |
| 0.0005 | 69.0 | 6210 | 1.3002 | 0.5236 | 0.6452 | 0.5781 | 0.8547 |
| 0.0005 | 70.0 | 6300 | 1.3068 | 0.5179 | 0.6410 | 0.5729 | 0.8575 |
| 0.0005 | 71.0 | 6390 | 1.3123 | 0.5259 | 0.6431 | 0.5786 | 0.8572 |
| 0.0005 | 72.0 | 6480 | 1.3205 | 0.5395 | 0.6484 | 0.5890 | 0.8576 |
| 0.0004 | 73.0 | 6570 | 1.3281 | 0.5420 | 0.6473 | 0.5900 | 0.8578 |
| 0.0004 | 74.0 | 6660 | 1.3326 | 0.5381 | 0.6484 | 0.5881 | 0.8575 |
| 0.0004 | 75.0 | 6750 | 1.3532 | 0.5393 | 0.6452 | 0.5875 | 0.8553 |
| 0.0004 | 76.0 | 6840 | 1.3562 | 0.5215 | 0.6283 | 0.5699 | 0.8537 |
| 0.0004 | 77.0 | 6930 | 1.3385 | 0.5144 | 0.6420 | 0.5712 | 0.8569 |
| 0.0003 | 78.0 | 7020 | 1.3435 | 0.5303 | 0.6463 | 0.5826 | 0.8570 |
| 0.0003 | 79.0 | 7110 | 1.3402 | 0.5366 | 0.6505 | 0.5881 | 0.8568 |
| 0.0003 | 80.0 | 7200 | 1.3415 | 0.5469 | 0.6526 | 0.5951 | 0.8569 |
| 0.0003 | 81.0 | 7290 | 1.3335 | 0.5181 | 0.6505 | 0.5768 | 0.8578 |
| 0.0003 | 82.0 | 7380 | 1.3433 | 0.5258 | 0.6452 | 0.5794 | 0.8569 |
| 0.0003 | 83.0 | 7470 | 1.3351 | 0.5247 | 0.6515 | 0.5813 | 0.8566 |
| 0.0002 | 84.0 | 7560 | 1.3912 | 0.5187 | 0.6431 | 0.5743 | 0.8515 |
| 0.0002 | 85.0 | 7650 | 1.3507 | 0.5147 | 0.6463 | 0.5730 | 0.8566 |
| 0.0002 | 86.0 | 7740 | 1.3594 | 0.5221 | 0.6494 | 0.5788 | 0.8556 |
| 0.0002 | 87.0 | 7830 | 1.3647 | 0.5262 | 0.6463 | 0.5801 | 0.8547 |
| 0.0002 | 88.0 | 7920 | 1.3629 | 0.5263 | 0.6441 | 0.5793 | 0.8550 |
| 0.0002 | 89.0 | 8010 | 1.3769 | 0.5277 | 0.6441 | 0.5801 | 0.8535 |
| 0.0002 | 90.0 | 8100 | 1.3733 | 0.5268 | 0.6431 | 0.5792 | 0.8556 |
| 0.0002 | 91.0 | 8190 | 1.3648 | 0.5240 | 0.6452 | 0.5783 | 0.8562 |
| 0.0002 | 92.0 | 8280 | 1.3666 | 0.5228 | 0.6410 | 0.5759 | 0.8561 |
| 0.0002 | 93.0 | 8370 | 1.3577 | 0.5231 | 0.6452 | 0.5778 | 0.8580 |
| 0.0002 | 94.0 | 8460 | 1.3514 | 0.5340 | 0.6547 | 0.5882 | 0.8580 |
| 0.0002 | 95.0 | 8550 | 1.3564 | 0.5328 | 0.6526 | 0.5866 | 0.8582 |
| 0.0002 | 96.0 | 8640 | 1.3563 | 0.5342 | 0.6515 | 0.5871 | 0.8584 |
| 0.0002 | 97.0 | 8730 | 1.3567 | 0.5347 | 0.6505 | 0.5869 | 0.8584 |
| 0.0002 | 98.0 | 8820 | 1.3576 | 0.5347 | 0.6505 | 0.5869 | 0.8583 |
| 0.0002 | 99.0 | 8910 | 1.3583 | 0.5339 | 0.6494 | 0.5860 | 0.8582 |
| 0.0001 | 100.0 | 9000 | 1.3581 | 0.5320 | 0.6494 | 0.5849 | 0.8583 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1
|
gauthamk28/poca-SoccerTwos
|
gauthamk28
| 2023-03-20T18:26:03Z | 36 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-20T18:25:54Z |
---
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: gauthamk28/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Gokulapriyan/vit-base-patch16-224-finetuned-main-gpu-30e-final
|
Gokulapriyan
| 2023-03-20T18:23:24Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-20T08:18:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-main-gpu-30e-final
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9940476190476191
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-main-gpu-30e-final
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0231
- Accuracy: 0.9940
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5113 | 1.0 | 551 | 0.4745 | 0.7971 |
| 0.3409 | 2.0 | 1102 | 0.2697 | 0.8961 |
| 0.2675 | 3.0 | 1653 | 0.1611 | 0.9381 |
| 0.2092 | 4.0 | 2204 | 0.1176 | 0.9548 |
| 0.2008 | 5.0 | 2755 | 0.0889 | 0.9656 |
| 0.1555 | 6.0 | 3306 | 0.0666 | 0.9759 |
| 0.1614 | 7.0 | 3857 | 0.0576 | 0.9778 |
| 0.1518 | 8.0 | 4408 | 0.0517 | 0.9814 |
| 0.1231 | 9.0 | 4959 | 0.0528 | 0.9812 |
| 0.1076 | 10.0 | 5510 | 0.0426 | 0.9850 |
| 0.0953 | 11.0 | 6061 | 0.0634 | 0.9795 |
| 0.1097 | 12.0 | 6612 | 0.0398 | 0.9860 |
| 0.0763 | 13.0 | 7163 | 0.0348 | 0.9866 |
| 0.0895 | 14.0 | 7714 | 0.0341 | 0.9884 |
| 0.06 | 15.0 | 8265 | 0.0381 | 0.9883 |
| 0.0767 | 16.0 | 8816 | 0.0382 | 0.9875 |
| 0.0868 | 17.0 | 9367 | 0.0309 | 0.9898 |
| 0.091 | 18.0 | 9918 | 0.0339 | 0.9885 |
| 0.0817 | 19.0 | 10469 | 0.0243 | 0.9913 |
| 0.0641 | 20.0 | 11020 | 0.0286 | 0.9906 |
| 0.0703 | 21.0 | 11571 | 0.0314 | 0.9906 |
| 0.0642 | 22.0 | 12122 | 0.0261 | 0.9913 |
| 0.0695 | 23.0 | 12673 | 0.0260 | 0.9920 |
| 0.0664 | 24.0 | 13224 | 0.0241 | 0.9928 |
| 0.0552 | 25.0 | 13775 | 0.0258 | 0.9928 |
| 0.056 | 26.0 | 14326 | 0.0230 | 0.9939 |
| 0.0488 | 27.0 | 14877 | 0.0221 | 0.9936 |
| 0.0389 | 28.0 | 15428 | 0.0225 | 0.9930 |
| 0.0402 | 29.0 | 15979 | 0.0231 | 0.9940 |
| 0.0424 | 30.0 | 16530 | 0.0211 | 0.9939 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Raiden-1001/ppo-LunarLander-v2
|
Raiden-1001
| 2023-03-20T18:22:09Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T17:45:31Z |
---
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: 257.93 +/- 20.71
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
...
```
|
matolszew/a2c-PandaReachDense-v2
|
matolszew
| 2023-03-20T18:20:15Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T10:03:17Z |
---
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: -0.37 +/- 0.13
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
...
```
|
Stokrotka/Pyramids
|
Stokrotka
| 2023-03-20T18:12:50Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-20T18:12:45Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: Find your model_id: Stokrotka/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
pfunk/CartPole-v1-CP_DQN-seed3
|
pfunk
| 2023-03-20T18:03:44Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:28:16Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 414.76 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed3/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed3/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 3
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 3,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQN-seed4
|
pfunk
| 2023-03-20T18:03:42Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:28:16Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 434.95 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed4/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed4/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 4
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 4,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQN-seed1
|
pfunk
| 2023-03-20T18:03:34Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:27:30Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 379.46 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed1/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQN --seed 1
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 1,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
gian-cr/cartpole-v1
|
gian-cr
| 2023-03-20T17:53:52Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T17:53:43Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: 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
|
mahesh006/ai_imagesVsoriginal_images
|
mahesh006
| 2023-03-20T17:51:46Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-03-20T17:51:22Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
bigscience/distill-bloom-1b3
|
bigscience
| 2023-03-20T17:49:35Z | 19 | 9 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zhs",
"zht",
"zu",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-18T07:59:47Z |
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
# <span style="color:red"><b>WARNING:</b> This is an <b>intermediary checkpoint</b> and WIP project. It is not fully trained yet. You might want to use [Bloom-1B3](https://huggingface.co/bigscience/bloom-1b3) if you want a model that has completed training. This model is a distilled version of [Bloom-1B3](https://huggingface.co/bigscience/bloom-1b3) </span>
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 18.Jul.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** bigscience-contact@googlegroups.com
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 288 million parameters:
* 12 layers, 8 attention heads
* Hidden layers are 1024-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
_In progress._
Current training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11-176B-ml-logs/)
- Checkpoint size:
- Bf16 weights: 329GB
- Full checkpoint with optimizer states: 2.3TB
- Training throughput: About 150 TFLOP per GPU per second
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Estimated end: 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
(More evaluation scores forthcoming at the end of model training.)
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay
|
inkasaras/Pyramids
|
inkasaras
| 2023-03-20T16:33:10Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-20T16:33:04Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: Find your model_id: inkasaras/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
marcatanante1/wav2vec2-base-finetuned-ks
|
marcatanante1
| 2023-03-20T16:28:54Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-03-19T13:07:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0953
- Accuracy: 0.9834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5959 | 1.0 | 399 | 0.4714 | 0.9434 |
| 0.2623 | 2.0 | 798 | 0.1542 | 0.9793 |
| 0.1809 | 3.0 | 1197 | 0.0953 | 0.9834 |
| 0.1643 | 4.0 | 1596 | 0.0844 | 0.9825 |
| 0.1208 | 5.0 | 1995 | 0.0824 | 0.9822 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu116
- Datasets 1.14.0
- Tokenizers 0.13.2
|
vocabtrimmer/mt5-small-trimmed-ko-10000-koquad-qa
|
vocabtrimmer
| 2023-03-20T16:26:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"ko",
"dataset:lmqg/qg_koquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T16:25:50Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ko
datasets:
- lmqg/qg_koquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: 매드 클라운이 참가해 큰 화제를 모았던 프로그램은?, context: 과거 소울 컴퍼니 소속으로 소울 컴퍼니 해체 후 현재의 소속사는 스타쉽 엑스이다. Mad Clown vs Crucial Star (매드 클라운 vs 크루셜 스타)라는 프로젝트 그룹으로 크루셜 스타와 함께 활동하기도 하였으며, 2013년부터는 MC인 저스디스와 팀을 이루어 랩 듀오 커먼콜드로 활동하고 있다. 또한 Mnet 《쇼미더머니 2》에서 참가자로 참가하여 큰 화제를 모았으며, 《쇼미더머니 5》에서는 길 & 매드 클라운 팀으로 프로듀서로 출연하였다., 재발매 물량도 완판되어 추가 제작에 들어갔다. 2016년 4월, 소속사와 자신의 SNS를 통해 2016년 5월 15일 현재 교제 중인 일반인 여자친구와의 결혼을 공식발표하였다."
example_title: "Question Answering Example 1"
- text: "question: 1913년 필라델피아 애슬레틱스의 개막전 상대는?, context: 1913년 시즌을 앞두고 스프링 트레이닝에서 잭 쿰스는 앨라배마 주 몽고메리에서 고열로 힘들어했는데, 당시에는 식중독 및 늑막염 진단을 받고 휴식을 취했다. 4월 10일, 보스턴 레드삭스를 상대로 치러진 개막전에서 잭 쿰스는 선발투수로 내정되었다. 그는 3이닝을 노히트로 막고 6회 치프 벤더와 교체되었으며, 경기는 10-5로 애슬레틱스가 승리했다. 이틀 뒤에 다시 선발 등판에 나섰으나 ⁄3이닝 동안 2피안타 1볼넷, 4실점만을 기록하고 강판되었다. 쿰스는 보스턴에서의 시리즈를 끝내고 팀 동료들과 함께 워싱턴으로 향했지만, 고통이 심해지자 구단은 그를 필라델피아로 돌려보냈다. 그곳에서 그는 장티푸스 진단을 받고 휴식을 취했으며, 8월에 다시 팀에 복귀하려고 했지만 정상적인 회복을 위해서 다시 병원에 들어갔다. 이 기간 몸무게가 25 kg 가량이나 감소했다. 이 해 필라델피아 애슬레틱스는 월드 시리즈에서 2년만에 다시 뉴욕 자이언츠와 맞붙었고, 우승을 차지했다. 쿰스의 공백기는 다음해인 1914년 시즌까지 길어졌다. 이 해 시즌에는 팀 순위가 정해진 시즌 막판에야 두 경기에 선발 출전해서, 도합 8이닝 8피안타 4실점, 4.50의 평균자책점을 기록했다. 시즌 후인 12월 9일, 애슬레틱스에서 방출되었다."
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ko-10000-koquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_koquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 42.29
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 79.14
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 57.58
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 97.68
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 93.32
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 83.74
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 77.68
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ko-10000-koquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ko-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-10000) for question answering task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-ko-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-10000)
- **Language:** ko
- **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ko", model="vocabtrimmer/mt5-small-trimmed-ko-10000-koquad-qa")
# model prediction
answers = model.answer_q(list_question="매드 클라운이 참가해 큰 화제를 모았던 프로그램은?", list_context=" 과거 소울 컴퍼니 소속으로 소울 컴퍼니 해체 후 현재의 소속사는 스타쉽 엑스이다. Mad Clown vs Crucial Star (매드 클라운 vs 크루셜 스타)라는 프로젝트 그룹으로 크루셜 스타와 함께 활동하기도 하였으며, 2013년부터는 MC인 저스디스와 팀을 이루어 랩 듀오 커먼콜드로 활동하고 있다. 또한 Mnet 《쇼미더머니 2》에서 참가자로 참가하여 큰 화제를 모았으며, 《쇼미더머니 5》에서는 길 & 매드 클라운 팀으로 프로듀서로 출연하였다., 재발매 물량도 완판되어 추가 제작에 들어갔다. 2016년 4월, 소속사와 자신의 SNS를 통해 2016년 5월 15일 현재 교제 중인 일반인 여자친구와의 결혼을 공식발표하였다.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ko-10000-koquad-qa")
output = pipe("question: 매드 클라운이 참가해 큰 화제를 모았던 프로그램은?, context: 과거 소울 컴퍼니 소속으로 소울 컴퍼니 해체 후 현재의 소속사는 스타쉽 엑스이다. Mad Clown vs Crucial Star (매드 클라운 vs 크루셜 스타)라는 프로젝트 그룹으로 크루셜 스타와 함께 활동하기도 하였으며, 2013년부터는 MC인 저스디스와 팀을 이루어 랩 듀오 커먼콜드로 활동하고 있다. 또한 Mnet 《쇼미더머니 2》에서 참가자로 참가하여 큰 화제를 모았으며, 《쇼미더머니 5》에서는 길 & 매드 클라운 팀으로 프로듀서로 출연하였다., 재발매 물량도 완판되어 추가 제작에 들어갔다. 2016년 4월, 소속사와 자신의 SNS를 통해 2016년 5월 15일 현재 교제 중인 일반인 여자친구와의 결혼을 공식발표하였다.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-10000-koquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_koquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 77.68 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| AnswerF1Score | 83.74 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| BERTScore | 97.68 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_1 | 74.3 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_2 | 66.31 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_3 | 56.53 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_4 | 42.29 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| METEOR | 57.58 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| MoverScore | 93.32 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| ROUGE_L | 79.14 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_koquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-ko-10000
- max_length: 512
- max_length_output: 32
- epoch: 17
- batch: 32
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-10000-koquad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
jamesimmanuel/Taxi-v3
|
jamesimmanuel
| 2023-03-20T16:24:08Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T16:24:05Z |
---
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="jamesimmanuel/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"])
```
|
alpindale/pygmalion-6b-int4
|
alpindale
| 2023-03-20T16:20:47Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-20T16:20:47Z |
---
license: creativeml-openrail-m
---
|
w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0
|
w11wo
| 2023-03-20T16:13:42Z | 25 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"indonesian-roberta-base-indolem-sentiment-classifier-fold-0",
"id",
"dataset:indolem",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: id
tags:
- indonesian-roberta-base-indolem-sentiment-classifier-fold-0
license: mit
datasets:
- indolem
widget:
- text: "Pelayanan hotel ini sangat baik."
---
## Indonesian RoBERTa Base IndoLEM Sentiment Classifier
Indonesian RoBERTa Base IndoLEM Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, which is then fine-tuned on [`indolem`](https://indolem.github.io/)'s [Sentiment Analysis](https://github.com/indolem/indolem/tree/main/sentiment) dataset consisting of Indonesian tweets and hotel reviews (Koto et al., 2020).
A 5-fold cross-validation experiment was performed, with splits provided by the original dataset authors. This model was trained on fold 0. You can find models trained on [fold 0](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0), [fold 1](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-1), [fold 2](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-2), [fold 3](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-3), and [fold 4](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-4), in their respective links.
On **fold 0**, the model achieved an F1 of 86.42% on dev/validation and 83.12% on test. On all **5 folds**, the models achieved an average F1 of 84.14% on dev/validation and 84.64% on test.
Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------------------------------------- | ------- | ------------ | ------------------------------- |
| `indonesian-roberta-base-indolem-sentiment-classifier-fold-0` | 124M | RoBERTa Base | `IndoLEM`'s Sentiment Analysis |
## Evaluation Results
The model was trained for 10 epochs and the best model was loaded at the end.
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- |
| 1 | 0.563500 | 0.420457 | 0.796992 | 0.626728 | 0.680000 | 0.581197 |
| 2 | 0.293600 | 0.281360 | 0.884712 | 0.811475 | 0.779528 | 0.846154 |
| 3 | 0.163000 | 0.267922 | 0.904762 | 0.844262 | 0.811024 | 0.880342 |
| 4 | 0.090200 | 0.335411 | 0.899749 | 0.838710 | 0.793893 | 0.888889 |
| 5 | 0.065200 | 0.462526 | 0.897243 | 0.835341 | 0.787879 | 0.888889 |
| 6 | 0.039200 | 0.423001 | 0.912281 | 0.859438 | 0.810606 | 0.914530 |
| 7 | 0.025300 | 0.452100 | 0.912281 | 0.859438 | 0.810606 | 0.914530 |
| 8 | 0.010400 | 0.525200 | 0.914787 | 0.855932 | 0.848739 | 0.863248 |
| 9 | 0.007100 | 0.513585 | 0.909774 | 0.850000 | 0.829268 | 0.871795 |
| 10 | 0.007200 | 0.537254 | 0.917293 | 0.864198 | 0.833333 | 0.897436 |
## How to Use
### As Text Classifier
```python
from transformers import pipeline
pretrained_name = "w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0"
nlp = pipeline(
"sentiment-analysis",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("Pelayanan hotel ini sangat baik.")
```
## Disclaimer
Do consider the biases which come from both the pre-trained RoBERTa model and `IndoLEM`'s Sentiment Analysis dataset that may be carried over into the results of this model.
## Author
Indonesian RoBERTa Base IndoLEM Sentiment Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.
|
cleth/q-Taxi-v3
|
cleth
| 2023-03-20T16:05:06Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T15:23:36Z |
---
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: 7.56 +/- 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="cleth/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"])
```
|
juansebashr/ppoCustom-LunarLander-v2
|
juansebashr
| 2023-03-20T15:57:06Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T18:09:35Z |
---
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: 96.60 +/- 51.50
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': 500000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 1024
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.98
'num_minibatches': 64
'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': 'juansebashr/ppoCustom-LunarLander-v2'
'batch_size': 4096
'minibatch_size': 64}
```
|
jaimin/git-base-image_cap
|
jaimin
| 2023-03-20T15:53:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"git",
"image-text-to-text",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-03-19T12:56:41Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: git-base-image_cap
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. -->
# git-base-image_cap
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
avojarot/rl_course_vizdoom_health_gathering_supreme
|
avojarot
| 2023-03-20T15:39:47Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T15:39:39Z |
---
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: 10.83 +/- 3.06
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 avojarot/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.
|
J3/ppo-Pyramids
|
J3
| 2023-03-20T15:39:45Z | 6 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-20T15:39:40Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: Find your model_id: J3/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
wendys-llc/autotrain-amber-mines-42327108538
|
wendys-llc
| 2023-03-20T15:38:20Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"autotrain",
"vision",
"dataset:wendys-llc/autotrain-data-amber-mines",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-20T15:37:03Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- wendys-llc/autotrain-data-amber-mines
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.5194842286637519
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 42327108538
- CO2 Emissions (in grams): 0.5195
## Validation Metrics
- Loss: 0.073
- Accuracy: 0.950
- Precision: 0.979
- Recall: 0.920
- AUC: 0.999
- F1: 0.948
|
EK12317/Ekmix-Diffusion
|
EK12317
| 2023-03-20T15:28:39Z | 5,163 | 61 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-12-18T06:45:54Z |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
## Example:
”Negative prompt: (worst quality, low quality:1.4)” is really useful in anywhere
I think all models are great with correct Hires.fix
## Ekmix-Pastel
pastel but lines(with Hires.fix) (Merging the loras into the model.)
~~~
python networks\merge_lora.py --sd_model .\models\model.safetensors --save_to .\lora\2.safetensors --models .\lora\MagicLORA.pt .\lora\Jordan_3.safetensors .\lora\sttabi_v1.4-04.safetensors .\lora\xlimo768.pt .\lora\dpep2.pt --ratios 0.3 1 0.5 0.6 0.35
~~~
~~~
masterpiece,best quality,best quality,Amazing,beautiful detailed eyes,1girl,finely detail,Depth offield,extremely detailed CG unity 8k wallpaper,masterpiece,upper body,(vtuber minato aqua),pink hair,blue streaked hair, palace,holy,white long split mop dress ,mature female,standing,medium_breasts,silver-tiara,smile,black high heels,very long hair, body towards aside,jewelry,hair blue flower,grey eyes,close-up,
Negative prompt: (worst quality, low quality:1.3)
Steps: 30, Sampler: Euler a, CFG scale: 6, Seed: 191289851, Size: 512x768, Model hash: 0526445f65, Denoising strength: 0.5, Eta: 0.5, Clip skip: 2, ENSD: 31337, Hires resize: 856x1280, Hires steps: 30, Hires upscaler: Latent
~~~
pastel but lines(without hires fix) (better!)
~~~
{masterpiece},{best quality},{1girl,{{loli},black hair,blue eyes,very long hair,hair flower,hanfu,happy}},Amazing,beautiful detailed eyes,finely detail,Depth of field,extremely detailed CG,original,outdoors,beautiful detailed hand,beautiful detailed fingers,{{soaked},{wet through}},{body under water},standing,{beautiful detailed water,beautiful detailed sky,fluttered detailed splashs}
Negative prompt: (worst quality, low quality:1.3)
Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 6, Seed: 2035526620, Size: 768x512, Model hash: ca485b96f8, Eta: 0.5, Clip skip: 2, ENSD: 31337
~~~
## Ekmix-gen4
balance between anime and reality(Merging by block weighted merge.)
~~~
masterpiece,best quality,best quality,Amazing,beautiful detailed eyes,1girl,finely detail,Depth offield,extremely detailed CG unity 8k wallpaper,masterpiece,upper body,(vtuber minato aqua),pink hair,blue streaked hair, palace,holy,white long split mop dress ,mature female,standing,medium_breasts,silver-tiara,smile,black high heels,very long hair, body towards aside,jewelry,hair blue flower,grey eyes,close-up,
~~~
~~~
{masterpiece},{best quality},{1girl,{{loli},black hair,blue eyes,very long hair,hair flower,hanfu,happy}},Amazing,beautiful detailed eyes,finely detail,Depth of field,extremely detailed CG,original,outdoors,beautiful detailed hand,beautiful detailed fingers,{{soaked},{wet through}},{body under water},standing,{beautiful detailed water,beautiful detailed sky,fluttered detailed splashs},by Paul Hedley,
~~~
# Great hypernetworks
style1 and 2 are my favourite.
3,4 may need retrain.
|
joe-hug/ppo-LunarLander-v2
|
joe-hug
| 2023-03-20T15:24:04Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T15:23:40Z |
---
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: 240.96 +/- 81.96
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
...
```
|
apparition/cleanrl_ppo-LunarLander
|
apparition
| 2023-03-20T15:17:08Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T15:17:02Z |
---
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: -132.79 +/- 73.34
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': 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': 'apparition/cleanrl_ppo-LunarLander'
'batch_size': 512
'minibatch_size': 128}
```
|
cleth/q-FrozenLake-v1-4x4-noSlippery
|
cleth
| 2023-03-20T15:17:07Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T15:17:03Z |
---
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="cleth/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"])
```
|
Mendel192/san
|
Mendel192
| 2023-03-20T14:57:06Z | 0 | 1 |
open_clip
|
[
"open_clip",
"image-segmentation",
"en",
"arxiv:2302.12242",
"license:mit",
"region:us"
] |
image-segmentation
| 2023-02-22T04:27:21Z |
---
license: mit
language:
- en
library_name: open_clip
pipeline_tag: image-segmentation
---
<h1 align="center"> Side Adapter Network for Open-Vocabulary Semantic Segmentation</h1>
<div align="center"><a href="https://arxiv.org/abs/2302.12242">Paper</a> | <a href="https://github.com/MendelXu/SAN">Code</a> | <a href="https://huggingface.co/spaces/Mendel192/SAN-Demo">Demo</a></div>
## Model description
SAN models the semantic segmentation task as a region recognition problem. A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias which is applied in the CLIP model to recognize the class of masks. This decoupled design has the benefit CLIP in recognizing the class of mask proposals. Since the attached side network can reuse CLIP features, it can be very light. In addition, the entire network can be trained end-to-end, allowing the side network to be adapted to the frozen CLIP model, which makes the predicted mask proposals CLIP-aware.
Our approach is fast, accurate, and only adds a few additional trainable parameters. We evaluate our approach on multiple semantic segmentation benchmarks. Our method significantly outperforms other counterparts, with up to 18 times fewer trainable parameters and 19 times faster inference speed.
| Model| Pretrained Weights|
|-----------|-------------------|
| SAN-ViT-B/16| [san_vit_b_16.pth](https://huggingface.co/Mendel192/san/blob/main/san_vit_b_16.pth)|
| SAN-ViT-L/14| [san_vit_large_14.pth](https://huggingface.co/Mendel192/san/blob/main/san_vit_large_14.pth)|
## Citation
If you find it useful in your research, please cite the following paper:
```
@proceedings{xu2023side,
title={Side Adapter Network for Open-Vocabulary Semantic Segmentation},
author={Mengde Xu, Zheng Zhang, Fangyun Wei, Han Hu, Xiang Bai},
journal={CVPR},
year={2023}
eprint={2302.12242},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
finiteautomata/bertweet-base-emotion-analysis
|
finiteautomata
| 2023-03-20T14:47:04Z | 37,498 | 15 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"emotion-analysis",
"en",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- emotion-analysis
---
# Emotion Analysis in English
## bertweet-base-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with EmoEvent corpus for Emotion detection in English. Base model is [BerTweet](https://huggingface.co/vinai/bertweet-base).
## License
`pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)
2. [SEMEval 2017 Dataset license]()
## Citation
If you use `pysentimiento` in your work, please cite [this paper](https://arxiv.org/abs/2106.09462)
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
and also the dataset related paper
```
@inproceedings{del2020emoevent,
title={EmoEvent: A multilingual emotion corpus based on different events},
author={del Arco, Flor Miriam Plaza and Strapparava, Carlo and Lopez, L Alfonso Urena and Mart{\'\i}n-Valdivia, M Teresa},
booktitle={Proceedings of the 12th Language Resources and Evaluation Conference},
pages={1492--1498},
year={2020}
}
```
Enjoy! 🤗
|
vesteinn/FoBERT
|
vesteinn
| 2023-03-20T14:45:49Z | 301 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"fo",
"dataset:vesteinn/FC3",
"dataset:vesteinn/IC3",
"dataset:mideind/icelandic-common-crawl-corpus-IC3",
"dataset:DDSC/partial-danish-gigaword-no-twitter",
"dataset:NbAiLab/NCC",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-20T14:23:44Z |
---
license: agpl-3.0
datasets:
- vesteinn/FC3
- vesteinn/IC3
- mideind/icelandic-common-crawl-corpus-IC3
- DDSC/partial-danish-gigaword-no-twitter
- NbAiLab/NCC
widget:
- text: Býir vaksa <mask> enn nakað annað búøki á jørðini.
language:
- fo
---
This is a Faroese language model, it was trained by adapting the [ScandiBERT-no-faroese](https://huggingface.co/vesteinn/ScandiBERT-no-faroese) model on the [FC3 corpus](https://huggingface.co/datasets/vesteinn/FC3) for 50 epochs.
If you find this model useful, please cite
```
@inproceedings{snaebjarnarson-etal-2023-transfer,
title = "{T}ransfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese",
author = "Snæbjarnarson, Vésteinn and
Simonsen, Annika and
Glavaš, Goran and
Vulić, Ivan",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = "may 22--24",
year = "2023",
address = "Tórshavn, Faroe Islands",
publisher = {Link{\"o}ping University Electronic Press, Sweden},
}
```
|
prompthero-diffusion-models/anyataylorjoy
|
prompthero-diffusion-models
| 2023-03-20T14:41:35Z | 0 | 1 | null |
[
"stable-diffusion",
"text-to-image",
"en",
"dataset:prompthero-diffusion-models/anyataylorjoy",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-02-28T09:10:40Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
datasets:
- prompthero-diffusion-models/anyataylorjoy
---
# [PromptHero Academy](https://prompthero.com/academy/courses)
All the Dreambooth models featuring the Anya Taylor-Joy character.
Find prompt inspiration at this [link](https://prompthero.com/search?q=anyataylorjoy)
### anyataylorjoy-2400-shivam.ckpt
<img src="https://s3.amazonaws.com/moonup/production/uploads/1677581716897-63265d019f9d19bfd4f45031.png" width="20%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1677581721925-63265d019f9d19bfd4f45031.png" width="20%"/>
|
joshnielsen876/LKD_Experience
|
joshnielsen876
| 2023-03-20T14:38:14Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-14T14:32:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: LKD_Experience
results: []
widget:
- text: Health A father donated a kidney to save his daughter. Because of his donation the physically active 53-yr-old man has been unable to obtain private health insurance
example_title: Example 1
- text: lastweektonight A year since John Oliver discussed kidney disease and I'm getting ready to donate a kidney
example_title: Example 2
- text: AskReddit [Serious] If you somehow found out that you were a match for a total stranger who needed a kidney would you donate one of yours? What are your reasons?
example_title: Example 3
---
<!-- 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. -->
# LKD_Experience
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an dataset of Reddit comments and posts related to Living Kidney Donation (LKD).
This model identifies documents as either describing a personal experience with LKD, or simply sharing news like headlines and noise/nonsense. The first token/word in each document is
the name of the subreddit where the post was written.
It achieves the following results on the evaluation set:
- Loss: 0.1651
- Accuracy: 0.9574
## 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: 4
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 63 | 0.3311 | 0.8723 |
| No log | 2.0 | 126 | 0.1708 | 0.9574 |
| No log | 3.0 | 189 | 0.1651 | 0.9574 |
### Framework versions
- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
pfunk/CartPole-v1-CP_DQPN_DQN-seed3
|
pfunk
| 2023-03-20T14:29:20Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T14:29:17Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 494.56 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-seed3/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-seed3/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_DQN --policy-network-frequency 1 --seed 3
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1,
'policy_tau': 1.0,
'save_model': True,
'seed': 3,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_DQN-seed2
|
pfunk
| 2023-03-20T14:29:16Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T14:29:13Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_DQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_DQN --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-seed2/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_DQN --policy-network-frequency 1 --seed 2
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_DQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1,
'policy_tau': 1.0,
'save_model': True,
'seed': 2,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
LarryAIDraw/reinesElMelloiArchisorte_v10
|
LarryAIDraw
| 2023-03-20T14:27:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-19T15:55:09Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/21513/reines-el-melloi-archisorte-lora
|
LarryAIDraw/SaegusaMayumiTheIrregularAt_mayumi
|
LarryAIDraw
| 2023-03-20T14:26:10Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-19T15:55:31Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/21440/saegusa-mayumi-the-irregular-at-magic-high-school-mahouka
|
joheras/clinico-bsc-bio-ehr-es-finetuned
|
joheras
| 2023-03-20T14:24:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-20T13:45:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: clinico-bsc-bio-ehr-es-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. -->
# clinico-bsc-bio-ehr-es-finetuned
This model is a fine-tuned version of [joheras/bsc-bio-ehr-es-finetuned-clinais](https://huggingface.co/joheras/bsc-bio-ehr-es-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9890
- Precision: 0.4656
- Recall: 0.6294
- F1: 0.5352
- Accuracy: 0.8600
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 25 | 1.1489 | 0.0399 | 0.0623 | 0.0486 | 0.6528 |
| No log | 2.0 | 50 | 0.7106 | 0.1739 | 0.2070 | 0.1890 | 0.8063 |
| No log | 3.0 | 75 | 0.6518 | 0.2380 | 0.2724 | 0.2541 | 0.8202 |
| No log | 4.0 | 100 | 0.6281 | 0.3515 | 0.4562 | 0.3971 | 0.8288 |
| No log | 5.0 | 125 | 0.6029 | 0.3424 | 0.4372 | 0.3840 | 0.8357 |
| No log | 6.0 | 150 | 0.5911 | 0.3479 | 0.4446 | 0.3904 | 0.8344 |
| No log | 7.0 | 175 | 0.5809 | 0.3363 | 0.4382 | 0.3806 | 0.8429 |
| No log | 8.0 | 200 | 0.5792 | 0.3338 | 0.4593 | 0.3867 | 0.8442 |
| No log | 9.0 | 225 | 0.5980 | 0.3465 | 0.4984 | 0.4088 | 0.8531 |
| No log | 10.0 | 250 | 0.6144 | 0.3700 | 0.5333 | 0.4369 | 0.8511 |
| No log | 11.0 | 275 | 0.6376 | 0.3634 | 0.5280 | 0.4305 | 0.8445 |
| No log | 12.0 | 300 | 0.6668 | 0.3802 | 0.5396 | 0.4461 | 0.8454 |
| No log | 13.0 | 325 | 0.6618 | 0.3957 | 0.5692 | 0.4669 | 0.8506 |
| No log | 14.0 | 350 | 0.6622 | 0.3906 | 0.5713 | 0.4640 | 0.8563 |
| No log | 15.0 | 375 | 0.6637 | 0.4241 | 0.5808 | 0.4902 | 0.8565 |
| No log | 16.0 | 400 | 0.6884 | 0.4251 | 0.5903 | 0.4943 | 0.8594 |
| No log | 17.0 | 425 | 0.7183 | 0.4213 | 0.6051 | 0.4967 | 0.8517 |
| No log | 18.0 | 450 | 0.7387 | 0.4104 | 0.5977 | 0.4867 | 0.8545 |
| No log | 19.0 | 475 | 0.7256 | 0.4261 | 0.5998 | 0.4982 | 0.8563 |
| 0.346 | 20.0 | 500 | 0.7427 | 0.4178 | 0.6040 | 0.4940 | 0.8534 |
| 0.346 | 21.0 | 525 | 0.7562 | 0.4240 | 0.6008 | 0.4972 | 0.8569 |
| 0.346 | 22.0 | 550 | 0.7590 | 0.4038 | 0.6051 | 0.4844 | 0.8517 |
| 0.346 | 23.0 | 575 | 0.7677 | 0.4163 | 0.6146 | 0.4964 | 0.8573 |
| 0.346 | 24.0 | 600 | 0.8148 | 0.4127 | 0.6040 | 0.4904 | 0.8483 |
| 0.346 | 25.0 | 625 | 0.7992 | 0.4230 | 0.6030 | 0.4972 | 0.8533 |
| 0.346 | 26.0 | 650 | 0.8156 | 0.4203 | 0.6072 | 0.4968 | 0.8537 |
| 0.346 | 27.0 | 675 | 0.7999 | 0.4356 | 0.6103 | 0.5084 | 0.8562 |
| 0.346 | 28.0 | 700 | 0.8326 | 0.4379 | 0.6146 | 0.5114 | 0.8508 |
| 0.346 | 29.0 | 725 | 0.8394 | 0.4441 | 0.6209 | 0.5178 | 0.8542 |
| 0.346 | 30.0 | 750 | 0.8414 | 0.4373 | 0.6072 | 0.5084 | 0.8535 |
| 0.346 | 31.0 | 775 | 0.8363 | 0.4394 | 0.6082 | 0.5102 | 0.8566 |
| 0.346 | 32.0 | 800 | 0.8442 | 0.4536 | 0.6188 | 0.5234 | 0.8574 |
| 0.346 | 33.0 | 825 | 0.8470 | 0.4655 | 0.6199 | 0.5317 | 0.8608 |
| 0.346 | 34.0 | 850 | 0.8323 | 0.4647 | 0.6177 | 0.5304 | 0.8587 |
| 0.346 | 35.0 | 875 | 0.8590 | 0.4495 | 0.6199 | 0.5211 | 0.8573 |
| 0.346 | 36.0 | 900 | 0.8457 | 0.4542 | 0.6230 | 0.5254 | 0.8589 |
| 0.346 | 37.0 | 925 | 0.8720 | 0.4543 | 0.6251 | 0.5262 | 0.8552 |
| 0.346 | 38.0 | 950 | 0.8736 | 0.4562 | 0.6167 | 0.5245 | 0.8562 |
| 0.346 | 39.0 | 975 | 0.8710 | 0.4384 | 0.6199 | 0.5136 | 0.8543 |
| 0.0257 | 40.0 | 1000 | 0.8805 | 0.4416 | 0.6230 | 0.5169 | 0.8569 |
| 0.0257 | 41.0 | 1025 | 0.8963 | 0.4634 | 0.6209 | 0.5307 | 0.8536 |
| 0.0257 | 42.0 | 1050 | 0.8973 | 0.4619 | 0.6146 | 0.5274 | 0.8546 |
| 0.0257 | 43.0 | 1075 | 0.9123 | 0.4733 | 0.6188 | 0.5364 | 0.8571 |
| 0.0257 | 44.0 | 1100 | 0.9169 | 0.4570 | 0.6230 | 0.5273 | 0.8532 |
| 0.0257 | 45.0 | 1125 | 0.9094 | 0.4847 | 0.6357 | 0.5500 | 0.8592 |
| 0.0257 | 46.0 | 1150 | 0.9096 | 0.4761 | 0.6304 | 0.5425 | 0.8611 |
| 0.0257 | 47.0 | 1175 | 0.9074 | 0.4622 | 0.6262 | 0.5318 | 0.8590 |
| 0.0257 | 48.0 | 1200 | 0.9087 | 0.4536 | 0.6199 | 0.5239 | 0.8582 |
| 0.0257 | 49.0 | 1225 | 0.9412 | 0.4426 | 0.6103 | 0.5131 | 0.8580 |
| 0.0257 | 50.0 | 1250 | 0.9221 | 0.4435 | 0.6262 | 0.5193 | 0.8587 |
| 0.0257 | 51.0 | 1275 | 0.9232 | 0.4608 | 0.6199 | 0.5286 | 0.8578 |
| 0.0257 | 52.0 | 1300 | 0.9313 | 0.4696 | 0.6199 | 0.5344 | 0.8592 |
| 0.0257 | 53.0 | 1325 | 0.9340 | 0.4529 | 0.6241 | 0.5249 | 0.8603 |
| 0.0257 | 54.0 | 1350 | 0.9418 | 0.4599 | 0.6241 | 0.5296 | 0.8561 |
| 0.0257 | 55.0 | 1375 | 0.9428 | 0.4608 | 0.6146 | 0.5267 | 0.8579 |
| 0.0257 | 56.0 | 1400 | 0.9386 | 0.4728 | 0.6230 | 0.5376 | 0.8608 |
| 0.0257 | 57.0 | 1425 | 0.9467 | 0.4641 | 0.6209 | 0.5312 | 0.8579 |
| 0.0257 | 58.0 | 1450 | 0.9402 | 0.4639 | 0.6167 | 0.5295 | 0.8614 |
| 0.0257 | 59.0 | 1475 | 0.9389 | 0.4667 | 0.6220 | 0.5333 | 0.8601 |
| 0.0095 | 60.0 | 1500 | 0.9363 | 0.4633 | 0.6262 | 0.5326 | 0.8597 |
| 0.0095 | 61.0 | 1525 | 0.9302 | 0.4706 | 0.6251 | 0.5370 | 0.8604 |
| 0.0095 | 62.0 | 1550 | 0.9456 | 0.4707 | 0.6272 | 0.5378 | 0.8609 |
| 0.0095 | 63.0 | 1575 | 0.9470 | 0.4700 | 0.6283 | 0.5377 | 0.8602 |
| 0.0095 | 64.0 | 1600 | 0.9706 | 0.4609 | 0.6230 | 0.5299 | 0.8562 |
| 0.0095 | 65.0 | 1625 | 0.9710 | 0.4785 | 0.6230 | 0.5413 | 0.8567 |
| 0.0095 | 66.0 | 1650 | 0.9715 | 0.4806 | 0.6283 | 0.5446 | 0.8568 |
| 0.0095 | 67.0 | 1675 | 0.9638 | 0.4621 | 0.6177 | 0.5287 | 0.8586 |
| 0.0095 | 68.0 | 1700 | 0.9750 | 0.4754 | 0.6230 | 0.5393 | 0.8568 |
| 0.0095 | 69.0 | 1725 | 0.9856 | 0.4643 | 0.6251 | 0.5329 | 0.8554 |
| 0.0095 | 70.0 | 1750 | 0.9855 | 0.4512 | 0.6199 | 0.5222 | 0.8570 |
| 0.0095 | 71.0 | 1775 | 0.9811 | 0.4756 | 0.6272 | 0.5410 | 0.8563 |
| 0.0095 | 72.0 | 1800 | 0.9858 | 0.4679 | 0.6167 | 0.5321 | 0.8569 |
| 0.0095 | 73.0 | 1825 | 0.9794 | 0.4676 | 0.6241 | 0.5346 | 0.8580 |
| 0.0095 | 74.0 | 1850 | 0.9774 | 0.4772 | 0.6199 | 0.5393 | 0.8572 |
| 0.0095 | 75.0 | 1875 | 0.9772 | 0.4810 | 0.6272 | 0.5445 | 0.8580 |
| 0.0095 | 76.0 | 1900 | 0.9805 | 0.4757 | 0.6294 | 0.5418 | 0.8584 |
| 0.0095 | 77.0 | 1925 | 0.9782 | 0.4782 | 0.6251 | 0.5419 | 0.8585 |
| 0.0095 | 78.0 | 1950 | 0.9921 | 0.4731 | 0.6315 | 0.5409 | 0.8572 |
| 0.0095 | 79.0 | 1975 | 0.9797 | 0.4684 | 0.6188 | 0.5332 | 0.8599 |
| 0.0057 | 80.0 | 2000 | 0.9844 | 0.4747 | 0.6251 | 0.5397 | 0.8585 |
| 0.0057 | 81.0 | 2025 | 0.9824 | 0.4636 | 0.6251 | 0.5324 | 0.8579 |
| 0.0057 | 82.0 | 2050 | 0.9803 | 0.4765 | 0.6220 | 0.5396 | 0.8591 |
| 0.0057 | 83.0 | 2075 | 0.9834 | 0.4742 | 0.6304 | 0.5413 | 0.8607 |
| 0.0057 | 84.0 | 2100 | 0.9897 | 0.4727 | 0.6315 | 0.5407 | 0.8585 |
| 0.0057 | 85.0 | 2125 | 0.9835 | 0.4723 | 0.6220 | 0.5369 | 0.8573 |
| 0.0057 | 86.0 | 2150 | 0.9838 | 0.4773 | 0.6230 | 0.5405 | 0.8580 |
| 0.0057 | 87.0 | 2175 | 0.9879 | 0.4663 | 0.6220 | 0.5330 | 0.8579 |
| 0.0057 | 88.0 | 2200 | 0.9844 | 0.4806 | 0.6262 | 0.5438 | 0.8595 |
| 0.0057 | 89.0 | 2225 | 0.9903 | 0.4767 | 0.6262 | 0.5413 | 0.8588 |
| 0.0057 | 90.0 | 2250 | 0.9929 | 0.4806 | 0.6283 | 0.5446 | 0.8581 |
| 0.0057 | 91.0 | 2275 | 0.9947 | 0.4873 | 0.6294 | 0.5493 | 0.8576 |
| 0.0057 | 92.0 | 2300 | 0.9888 | 0.4713 | 0.6251 | 0.5374 | 0.8581 |
| 0.0057 | 93.0 | 2325 | 0.9869 | 0.4780 | 0.6315 | 0.5441 | 0.8586 |
| 0.0057 | 94.0 | 2350 | 0.9866 | 0.4807 | 0.6315 | 0.5459 | 0.8587 |
| 0.0057 | 95.0 | 2375 | 0.9892 | 0.4850 | 0.6315 | 0.5486 | 0.8587 |
| 0.0057 | 96.0 | 2400 | 0.9891 | 0.4735 | 0.6325 | 0.5416 | 0.8589 |
| 0.0057 | 97.0 | 2425 | 0.9883 | 0.4635 | 0.6294 | 0.5338 | 0.8593 |
| 0.0057 | 98.0 | 2450 | 0.9883 | 0.4670 | 0.6283 | 0.5358 | 0.8601 |
| 0.0057 | 99.0 | 2475 | 0.9889 | 0.4671 | 0.6304 | 0.5366 | 0.8601 |
| 0.0045 | 100.0 | 2500 | 0.9890 | 0.4656 | 0.6294 | 0.5352 | 0.8600 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1
|
elliotb/taxi
|
elliotb
| 2023-03-20T14:07:27Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T14:07:25Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.81
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="elliotb/taxi", 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"])
```
|
israel-avihail/dqn-SpaceInvadersNoFrameskip-v4
|
israel-avihail
| 2023-03-20T14:06:48Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T14:06:06Z |
---
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: 667.00 +/- 256.27
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 israel-avihail -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 israel-avihail -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 israel-avihail
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 150000),
('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)])
```
|
awsgcptest/test_model_2
|
awsgcptest
| 2023-03-20T14:04:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-20T13:38:41Z |
---
tags:
- generated_from_trainer
model-index:
- name: test_model_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. -->
# test_model_2
This model is a fine-tuned version of [uer/gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 246 | 5.3086 |
| No log | 2.0 | 492 | 5.2396 |
| 5.4029 | 3.0 | 738 | 5.2228 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
harshasurampudi/autotrain-intent-classification-roberta-3647697496
|
harshasurampudi
| 2023-03-20T14:02:22Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:harshasurampudi/autotrain-data-intent-classification-roberta",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-22T02:56:23Z |
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- harshasurampudi/autotrain-data-intent-classification-roberta
co2_eq_emissions:
emissions: 1.1197250732252992
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3647697496
- CO2 Emissions (in grams): 1.1197
## Validation Metrics
- Loss: 1.379
- Accuracy: 0.875
- Macro F1: 0.867
- Micro F1: 0.875
- Weighted F1: 0.867
- Macro Precision: 0.917
- Micro Precision: 0.875
- Weighted Precision: 0.917
- Macro Recall: 0.875
- Micro Recall: 0.875
- Weighted Recall: 0.875
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/harshasurampudi/autotrain-intent-classification-roberta-3647697496
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("harshasurampudi/autotrain-intent-classification-roberta-3647697496", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("harshasurampudi/autotrain-intent-classification-roberta-3647697496", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
butchland/a2c-AntBulletEnv-v0
|
butchland
| 2023-03-20T13:55:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T13:54:11Z |
---
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: 757.21 +/- 90.16
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
...
```
|
elliotb/ppo-LunarLander-v2
|
elliotb
| 2023-03-20T13:51:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T13:51:17Z |
---
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: 264.33 +/- 15.48
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
...
```
|
eyechen/Pixelcopter-PLE-v0
|
eyechen
| 2023-03-20T13:50:33Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T13:50:30Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.60 +/- 24.60
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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
|
lambdarw/t5_pegasus_ch_ans
|
lambdarw
| 2023-03-20T13:46:31Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"summarization",
"zh",
"dataset:lambdarw/autotrain-data-t5-pegasus_ch_ansmrc",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-03-20T12:34:38Z |
---
tags:
- autotrain
- summarization
language:
- zh
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lambdarw/autotrain-data-t5-pegasus_ch_ansmrc
co2_eq_emissions:
emissions: 4.429613533710655
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 42285108445
- CO2 Emissions (in grams): 4.4296
## Validation Metrics
- Loss: 3.292
- Rouge1: 6.468
- Rouge2: 1.995
- RougeL: 6.485
- RougeLsum: 6.428
- Gen Len: 19.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/lambdarw/autotrain-t5-pegasus_ch_ansmrc-42285108445
```
|
hoanglongvn/unit422
|
hoanglongvn
| 2023-03-20T13:37:09Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T13:36:59Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: unit422
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 49.90 +/- 38.96
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
inkasaras/ppo-SnowballTarget
|
inkasaras
| 2023-03-20T13:36:04Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-10T12:00:01Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: inkasaras/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
hoanglongvn/unit42
|
hoanglongvn
| 2023-03-20T13:35:47Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-15T03:53:11Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: unit42
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 48.50 +/- 45.35
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
aalindt/NASDAQ_news_binary_headlines_10min
|
aalindt
| 2023-03-20T13:33:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"Economics",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T13:31:47Z |
---
language:
- en
pipeline_tag: text-classification
tags:
- Economics
---
# Pretrained model used for the NASDAQ_news dataset
Model for Binary classification on headlines from the NASDAQ_news dataset.
Label_0 = Downward movement
Label_1 = Upward movement
The target_variable is the return 10-minutes after an article has been published
## Model Info:
This model is a fine-tuned DistilBertForSequenceClassification on random samples of the NASDAQ_news dataset.
It uses the DistilBertTokenizerFast as a tokenizer.
### Model Data:
The model is finetuned on headlines
|
aalindt/NASDAQ_news_binary_headlines_20min
|
aalindt
| 2023-03-20T13:30:05Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"Economics",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T11:52:23Z |
---
language:
- en
pipeline_tag: text-classification
tags:
- Economics
---
# Pretrained model used for the NASDAQ_news dataset
Model for Binary classification on headlines from the NASDAQ_news dataset.
Label_0 = Downward movement
Label_1 = Upward movement
The target_variable is the return 20-minutes after an article has been published
## Model Info:
This model is a fine-tuned DistilBertForSequenceClassification on random samples of the NASDAQ_news dataset.
It uses the DistilBertTokenizerFast as a tokenizer.
### Model Data:
The model is finetuned on headlines
|
vocabtrimmer/mt5-small-trimmed-it-5000-itquad-qa
|
vocabtrimmer
| 2023-03-20T13:26:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"it",
"dataset:lmqg/qg_itquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T13:25:41Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: it
datasets:
- lmqg/qg_itquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: Quale batterio ha il nome del paese che colpisce di più nel suo nome?, context: Il complesso M. tubercolosi (MTBC) comprende altri quattro micobatteri causa di tubercolosi: M. bovis, M. africanum, M. canetti e M. microti. M. africanum non è molto diffuso, ma è una causa significativa di tubercolosi in alcune parti dell' Africa. M. bovis era una volta una causa comune della tubercolosi, ma l' introduzione del latte pastorizzato ha quasi completamente eliminato questo problema di salute pubblica nei paesi sviluppati. M. canetti è raro e sembra essere limitato al Corno d' Africa, anche se alcuni casi sono stati osservati negli emigranti africani. M. microti è anche raro ed è visto quasi solo in persone immunodeficienti, anche se la sua prevalenza può essere significativamente sottovalutata."
example_title: "Question Answering Example 1"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-it-5000-itquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_itquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 15.31
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 33.11
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 29.32
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 91.55
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 77.25
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 57.63
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 43.69
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-it-5000-itquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-it-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-5000) for question answering task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-it-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-5000)
- **Language:** it
- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="it", model="vocabtrimmer/mt5-small-trimmed-it-5000-itquad-qa")
# model prediction
answers = model.answer_q(list_question="Quale batterio ha il nome del paese che colpisce di più nel suo nome?", list_context=" Il complesso M. tubercolosi (MTBC) comprende altri quattro micobatteri causa di tubercolosi: M. bovis, M. africanum, M. canetti e M. microti. M. africanum non è molto diffuso, ma è una causa significativa di tubercolosi in alcune parti dell' Africa. M. bovis era una volta una causa comune della tubercolosi, ma l' introduzione del latte pastorizzato ha quasi completamente eliminato questo problema di salute pubblica nei paesi sviluppati. M. canetti è raro e sembra essere limitato al Corno d' Africa, anche se alcuni casi sono stati osservati negli emigranti africani. M. microti è anche raro ed è visto quasi solo in persone immunodeficienti, anche se la sua prevalenza può essere significativamente sottovalutata.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-it-5000-itquad-qa")
output = pipe("question: Quale batterio ha il nome del paese che colpisce di più nel suo nome?, context: Il complesso M. tubercolosi (MTBC) comprende altri quattro micobatteri causa di tubercolosi: M. bovis, M. africanum, M. canetti e M. microti. M. africanum non è molto diffuso, ma è una causa significativa di tubercolosi in alcune parti dell' Africa. M. bovis era una volta una causa comune della tubercolosi, ma l' introduzione del latte pastorizzato ha quasi completamente eliminato questo problema di salute pubblica nei paesi sviluppati. M. canetti è raro e sembra essere limitato al Corno d' Africa, anche se alcuni casi sono stati osservati negli emigranti africani. M. microti è anche raro ed è visto quasi solo in persone immunodeficienti, anche se la sua prevalenza può essere significativamente sottovalutata.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-5000-itquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_itquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 43.69 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| AnswerF1Score | 57.63 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| BERTScore | 91.55 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1 | 27.26 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2 | 21.84 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3 | 18.34 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4 | 15.31 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR | 29.32 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore | 77.25 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L | 33.11 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_itquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-it-5000
- max_length: 512
- max_length_output: 32
- epoch: 21
- batch: 32
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-5000-itquad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
ldaquan1996/Taxi_v1
|
ldaquan1996
| 2023-03-20T13:20:35Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T13:20:32Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi_v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ldaquan1996/Taxi_v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Fer14/Pyramids_Training
|
Fer14
| 2023-03-20T13:17:07Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-20T13:17:02Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: Find your model_id: Fer14/Pyramids_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
butchland/round2-ppo-SnowballTarget
|
butchland
| 2023-03-20T13:16:02Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-20T13:15:56Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: butchland/round2-ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
microsoft/lts-gpt2-sm
|
microsoft
| 2023-03-20T13:14:26Z | 0 | 5 |
archai
|
[
"archai",
"gpt2",
"text-generation",
"lts",
"pareto-frontier",
"memory",
"latency",
"en",
"dataset:the_pile",
"license:mit",
"region:us"
] |
text-generation
| 2023-03-02T13:35:21Z |
---
license: mit
datasets:
- the_pile
language:
- en
metrics:
- perplexity
library_name: archai
tags:
- gpt2
- text-generation
- lts
- pareto-frontier
- memory
- latency
---
# Lightweight Transformer Search GPT-2-Small
The Lightweight Transformer Search (LTS) method is designed to identify the most optimal transformer architectures that exist on the Pareto Frontier, where trade-offs are made between several objectives, such as latency and memory usage. It is based on a combination of architecture search and performance prediction, and it is capable of finding high-performance models that are also highly efficient.
LTS GPT-2-Small is a variant of the GPT-2 language model, which is known for its ability to generate coherent and fluent text. The LTS method was used to optimize the GPT-2 architecture for both memory and latency, resulting in a model that is smaller and faster than its counterparts, while still maintaining a high level of language generation quality.
For additional information, please refer to the [GitHub repository](https://github.com/microsoft/archai/tree/main/tasks/text_generation).
## Model Description
LTS was applied to the base GPT-2 architecture to find the best performing models while adhering to a specific set of constraints. The table below displays the outcome of the search:
| Model | Non-Embedding Parameters (M) | Latency (s) | Memory (MB) |
| - | - | - | - |
gpt2_a9e3147996070fda25af4b39ed95b6a18d6d0402 | 1.06 | 0.008 | 29.06
gpt2_80fabe4acddff0dc796e287588e40d86e79df4b2 | 2.08 | 0.013 | 45.46
gpt2_90682823835acabd965294775983a1d5a2c2fa43 | 3.13 | 0.021 | 74.50
gpt2_c76bdddb5cf59275711672daa5b8c70e6c78bf4e | 3.95 | 0.024 | 77.62
gpt2_8f5159304179c77ecdc69c953b71a3f8fa528564 | 5.13 | 0.030 | 94.64
gpt2_131845381012a68c3a358514fdffc12b09db1ed8 | 6.44 | 0.036 | 112.16
gpt2_917c2f9601a1c29d1f280bb172015e5fb210b6b3 | 7.41 | 0.042 | 90.76
gpt2_538d4b101df48595a935d90dbf4a7fb2ac09ac01 | 8.23 | 0.047 | 93.88
gpt2_c679fa01f00dd6f584614c6d9784eb233b047283 | 9.46 | 0.053 | 148.71
gpt2_39563367097004cfd771d76d8822e51ad79b56d6 | 10.65 | 0.051 | 190.77
gpt2_ddf63c1125f1fed5a7dd3537f640834187719996 | 13.32 | 0.069 | 125.78
gpt2_0e1b5a3c867d6473da270799061f3089a1df5afd | 16.04 | 0.084 | 173.74
gpt2_3b30c85ac08c6b12b0ea46cb832270ba52b7fcd8 | 18.97 | 0.096 | 209.94
gpt2_1e9d92f0fed7288facc68cb448863e8120ccca9c | 20.96 | 0.105 | 217.50
gpt2_0e8c86e6babd924ff8b511c94cc1647bf61f81a2 | 24.83 | 0.121 | 244.77
gpt2_5fea22df661ad91676709da7a334505f15765659 | 26.89 | 0.131 | 252.65
gpt2_46e7c68a025417e20a7e13bd4c1ee71438d28069 | 30.07 | 0.146 | 252.23
gpt2_98b0196b5a865ba76f31723646f33e0461dc910d | 33.24 | 0.160 | 314.39
gpt2_4352a56f3fa9e7ba6d291867d356a08022753658 | 40.34 | 0.195 | 328.88
gpt2_6c6e63116ff74ba444ff5a08cef54380073ebea3 | 49.85 | 0.230 | 377.68
## How to Get Started with the Model
Use the code below to get started with the *gpt2_0e1b5a3c867d6473da270799061f3089a1df5afd* model. For other models, please change the `subfolder` argument to the proper identifier.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/lts-gpt2-sm")
model = AutoModelForCausalLM.from_pretrained("microsoft/lts-gpt2-sm", subfolder="gpt2_0e1b5a3c867d6473da270799061f3089a1df5afd")
text = "# Halo Infinite Review"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
## Training Details
### Training Data
The models have been trained for only 7.8B tokens from [The Pile](https://huggingface.co/datasets/the_pile) dataset, which is roughy 10-15x times more than "Training Compute-Optimal Large Language Models" suggestion. However, since we are dealing with small-sized models (100M parameters less), there might be a chance of producing repetitive text when generating a large number of tokens given a short context.
### Training Procedure
Please refer to the [training script](https://github.com/microsoft/archai/blob/main/tasks/text_generation/train.py).
## Limitations
Comparing smaller-sized transformers to large language models can be misleading and inaccurate, as they are fundamentally different in terms of the number of parameters, computational power, and capabilities. While smaller models may perform well on certain tasks, they lack the complexity and depth of larger models, which can lead to significant differences in their overall performance.
It is important to note that smaller models have their advantages. They require less computational resources, have a smaller memory footprint, and faster inference latency, which can be beneficial for real-time applications and devices with limited computing power. Additionally, research with smaller-sized transformers may lead to the discovery of more efficient architectures, which better utilizes computational resources and provide insights for training/deploying larger models.
## Bias and Risks
Pre-training a language model using The Pile dataset may have several limitations and potential biases that need to be considered. The following are some of the technical and sociotechnical limitations associated with using this dataset for pre-training:
* **Domain-Specific Bias**: Large and diverse dataset that contains text from various sources, including academic papers, news articles, and social media. However, the dataset may still have a bias towards certain domains, such as technology or politics.
* **Quality Control**: Collection of various smaller datasets, each with its own quality control measures. The quality of the data may vary across different datasets, and some datasets may contain low-quality or biased text.
* **Societal Biases**: May also contain biases related to race, gender, and other societal factors. These biases may be present in the text itself or may be introduced through the quality control measures used in the dataset creation process.
* **Data Privacy**: Contains a large amount of user-generated content, which may raise privacy concerns. While the dataset is anonymized, it may still be possible to identify individuals based on the content of their text.
## BibTeX entry and citation info
```bibtex
@misc{Archai:22,
title=Archai: Platform for Neural Architecture Search,
url=https://www.microsoft.com/en-us/research/project/archai-platform-for-neural-architecture-search,
journal=Microsoft Research,
year=2022,
month=Jul
}
```
|
aubmindlab/aragpt2-mega-detector-long
|
aubmindlab
| 2023-03-20T13:07:22Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"ar",
"arxiv:2012.15520",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: ar
widget:
- text: "وإذا كان هناك من لا يزال يعتقد أن لبنان هو سويسرا الشرق ، فهو مخطئ إلى حد بعيد . فلبنان ليس سويسرا ، ولا يمكن أن يكون كذلك . لقد عاش اللبنانيون في هذا البلد منذ ما يزيد عن ألف وخمسمئة عام ، أي منذ تأسيس الإمارة الشهابية التي أسسها الأمير فخر الدين المعني الثاني ( 1697 - 1742 )"
---
# AraGPT2 Detector
Machine generated detector model from the [AraGPT2: Pre-Trained Transformer for Arabic Language Generation paper](https://arxiv.org/abs/2012.15520)
This model is trained on the long text passages, and achieves a 99.4% F1-Score.
# How to use it:
```python
from transformers import pipeline
from arabert.preprocess import ArabertPreprocessor
processor = ArabertPreprocessor(model="aubmindlab/araelectra-base-discriminator")
pipe = pipeline("sentiment-analysis", model = "aubmindlab/aragpt2-mega-detector-long")
text = " "
text_prep = processor.preprocess(text)
result = pipe(text_prep)
# [{'label': 'machine-generated', 'score': 0.9977743625640869}]
```
# If you used this model please cite us as :
```
@misc{antoun2020aragpt2,
title={AraGPT2: Pre-Trained Transformer for Arabic Language Generation},
author={Wissam Antoun and Fady Baly and Hazem Hajj},
year={2020},
eprint={2012.15520},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# Contacts
**Wissam Antoun**: [Linkedin](https://www.linkedin.com/in/wissam-antoun-622142b4/) | [Twitter](https://twitter.com/wissam_antoun) | [Github](https://github.com/WissamAntoun) | <wfa07@mail.aub.edu> | <wissam.antoun@gmail.com>
**Fady Baly**: [Linkedin](https://www.linkedin.com/in/fadybaly/) | [Twitter](https://twitter.com/fadybaly) | [Github](https://github.com/fadybaly) | <fgb06@mail.aub.edu> | <baly.fady@gmail.com>
|
mehmetyigitrl/ppo-SnowballTarget1
|
mehmetyigitrl
| 2023-03-20T13:07:20Z | 16 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-20T11:56:53Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: mehmetyigitrl/ppo-SnowballTarget1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
aubmindlab/bert-large-arabertv2
|
aubmindlab
| 2023-03-20T13:07:11Z | 1,452 | 11 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"ar",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: ar
datasets:
- wikipedia
- Osian
- 1.5B-Arabic-Corpus
- oscar-arabic-unshuffled
- Assafir(private)
widget:
- text: " عاصم +ة لبنان هي [MASK] ."
---
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT** is an Arabic pretrained language model based on [Google's BERT architechture](https://github.com/google-research/bert). AraBERT uses the same BERT-Base config. More details are available in the [AraBERT Paper](https://arxiv.org/abs/2003.00104) and in the [AraBERT Meetup](https://github.com/WissamAntoun/pydata_khobar_meetup)
There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the [Farasa Segmenter](http://alt.qcri.org/farasa/segmenter.html).
We evaluate AraBERT models on different downstream tasks and compare them to [mBERT]((https://github.com/google-research/bert/blob/master/multilingual.md)), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets ([HARD](https://github.com/elnagara/HARD-Arabic-Dataset), [ASTD-Balanced](https://www.aclweb.org/anthology/D15-1299), [ArsenTD-Lev](https://staff.aub.edu.lb/~we07/Publications/ArSentD-LEV_Sentiment_Corpus.pdf), [LABR](https://github.com/mohamedadaly/LABR)), Named Entity Recognition with the [ANERcorp](http://curtis.ml.cmu.edu/w/courses/index.php/ANERcorp), and Arabic Question Answering on [Arabic-SQuAD and ARCD](https://github.com/husseinmozannar/SOQAL)
# AraBERTv2
## What's New!
AraBERT now comes in 4 new variants to replace the old v1 versions:
More Detail in the AraBERT folder and in the [README](https://github.com/aub-mind/arabert/blob/master/AraBERT/README.md) and in the [AraBERT Paper](https://arxiv.org/abs/2003.00104v2)
Model | HuggingFace Model Name | Size (MB/Params)| Pre-Segmentation | DataSet (Sentences/Size/nWords) |
---|:---:|:---:|:---:|:---:
AraBERTv0.2-base | [bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) | 543MB / 136M | No | 200M / 77GB / 8.6B |
AraBERTv0.2-large| [bert-large-arabertv02](https://huggingface.co/aubmindlab/bert-large-arabertv02) | 1.38G 371M | No | 200M / 77GB / 8.6B |
AraBERTv2-base| [bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) | 543MB 136M | Yes | 200M / 77GB / 8.6B |
AraBERTv2-large| [bert-large-arabertv2](https://huggingface.co/aubmindlab/bert-large-arabertv2) | 1.38G 371M | Yes | 200M / 77GB / 8.6B |
AraBERTv0.2-Twitter-base| [bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) | 543MB / 136M | No | Same as v02 + 60M Multi-Dialect Tweets|
AraBERTv0.2-Twitter-large| [bert-large-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-large-arabertv02-twitter) | 1.38G / 371M | No | Same as v02 + 60M Multi-Dialect Tweets|
AraBERTv0.1-base| [bert-base-arabertv01](https://huggingface.co/aubmindlab/bert-base-arabertv01) | 543MB 136M | No | 77M / 23GB / 2.7B |
AraBERTv1-base| [bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) | 543MB 136M | Yes | 77M / 23GB / 2.7B |
All models are available in the `HuggingFace` model page under the [aubmindlab](https://huggingface.co/aubmindlab/) name. Checkpoints are available in PyTorch, TF2 and TF1 formats.
## Better Pre-Processing and New Vocab
We identified an issue with AraBERTv1's wordpiece vocabulary. The issue came from punctuations and numbers that were still attached to words when learned the wordpiece vocab. We now insert a space between numbers and characters and around punctuation characters.
The new vocabulary was learned using the `BertWordpieceTokenizer` from the `tokenizers` library, and should now support the Fast tokenizer implementation from the `transformers` library.
**P.S.**: All the old BERT codes should work with the new BERT, just change the model name and check the new preprocessing function
**Please read the section on how to use the [preprocessing function](#Preprocessing)**
## Bigger Dataset and More Compute
We used ~3.5 times more data, and trained for longer.
For Dataset Sources see the [Dataset Section](#Dataset)
Model | Hardware | num of examples with seq len (128 / 512) |128 (Batch Size/ Num of Steps) | 512 (Batch Size/ Num of Steps) | Total Steps | Total Time (in Days) |
---|:---:|:---:|:---:|:---:|:---:|:---:
AraBERTv0.2-base | TPUv3-8 | 420M / 207M | 2560 / 1M | 384/ 2M | 3M | -
AraBERTv0.2-large | TPUv3-128 | 420M / 207M | 13440 / 250K | 2056 / 300K | 550K | 7
AraBERTv2-base | TPUv3-8 | 420M / 207M | 2560 / 1M | 384/ 2M | 3M | -
AraBERTv2-large | TPUv3-128 | 520M / 245M | 13440 / 250K | 2056 / 300K | 550K | 7
AraBERT-base (v1/v0.1) | TPUv2-8 | - |512 / 900K | 128 / 300K| 1.2M | 4
# Dataset
The pretraining data used for the new AraBERT model is also used for Arabic **GPT2 and ELECTRA**.
The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation)
For the new dataset we added the unshuffled OSCAR corpus, after we thoroughly filter it, to the previous dataset used in AraBERTv1 but with out the websites that we previously crawled:
- OSCAR unshuffled and filtered.
- [Arabic Wikipedia dump](https://archive.org/details/arwiki-20190201) from 2020/09/01
- [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4)
- [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619)
- Assafir news articles. Huge thank you for Assafir for providing us the data
# Preprocessing
It is recommended to apply our preprocessing function before training/testing on any dataset.
**Install the arabert python package to segment text for AraBERT v1 & v2 or to clean your data `pip install arabert`**
```python
from arabert.preprocess import ArabertPreprocessor
model_name="aubmindlab/bert-large-arabertv2"
arabert_prep = ArabertPreprocessor(model_name=model_name)
text = "ولن نبالغ إذا قلنا إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري"
arabert_prep.preprocess(text)
>>>"و+ لن نبالغ إذا قل +نا إن هاتف أو كمبيوتر ال+ مكتب في زمن +نا هذا ضروري"
```
# TensorFlow 1.x models
The TF1.x model are available in the HuggingFace models repo.
You can download them as follows:
- via git-lfs: clone all the models in a repo
```bash
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/aubmindlab/MODEL_NAME
tar -C ./MODEL_NAME -zxvf /content/MODEL_NAME/tf1_model.tar.gz
```
where `MODEL_NAME` is any model under the `aubmindlab` name
- via `wget`:
- Go to the tf1_model.tar.gz file on huggingface.co/models/aubmindlab/MODEL_NAME.
- copy the `oid sha256`
- then run `wget https://cdn-lfs.huggingface.co/aubmindlab/aragpt2-base/INSERT_THE_SHA_HERE` (ex: for `aragpt2-base`: `wget https://cdn-lfs.huggingface.co/aubmindlab/aragpt2-base/3766fc03d7c2593ff2fb991d275e96b81b0ecb2098b71ff315611d052ce65248`)
# If you used this model please cite us as :
Google Scholar has our Bibtex wrong (missing name), use this instead
```
@inproceedings{antoun2020arabert,
title={AraBERT: Transformer-based Model for Arabic Language Understanding},
author={Antoun, Wissam and Baly, Fady and Hajj, Hazem},
booktitle={LREC 2020 Workshop Language Resources and Evaluation Conference 11--16 May 2020},
pages={9}
}
```
# Acknowledgments
Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn't have done it without this program, and to the [AUB MIND Lab](https://sites.aub.edu.lb/mindlab/) Members for the continuous support. Also thanks to [Yakshof](https://www.yakshof.com/#/) and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT.
# Contacts
**Wissam Antoun**: [Linkedin](https://www.linkedin.com/in/wissam-antoun-622142b4/) | [Twitter](https://twitter.com/wissam_antoun) | [Github](https://github.com/WissamAntoun) | <wfa07@mail.aub.edu> | <wissam.antoun@gmail.com>
**Fady Baly**: [Linkedin](https://www.linkedin.com/in/fadybaly/) | [Twitter](https://twitter.com/fadybaly) | [Github](https://github.com/fadybaly) | <fgb06@mail.aub.edu> | <baly.fady@gmail.com>
|
Srini138/PPOLunarLanderV2
|
Srini138
| 2023-03-20T12:59:40Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T12:59:14Z |
---
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: 254.88 +/- 23.22
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
...
```
|
ldaquan1996/Reinforce_LunaLander2
|
ldaquan1996
| 2023-03-20T12:52:53Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T12:52:25Z |
---
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: 271.64 +/- 11.36
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
...
```
|
Cornchips1234/model_test
|
Cornchips1234
| 2023-03-20T12:37:23Z | 0 | 0 |
open_clip
|
[
"open_clip",
"art",
"feature-extraction",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"license:creativeml-openrail-m",
"region:us"
] |
feature-extraction
| 2023-03-20T12:35:56Z |
---
license: creativeml-openrail-m
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
library_name: open_clip
pipeline_tag: feature-extraction
tags:
- art
---
|
yonathanstwn/opus-mt-en-id-jakarta
|
yonathanstwn
| 2023-03-20T12:24:16Z | 29 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:inglish",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T11:29:35Z |
---
tags:
- generated_from_trainer
datasets:
- inglish
metrics:
- bleu
model-index:
- name: opus-mt-en-id-jakarta
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: inglish
type: inglish
config: default
split: validation
args: default
metrics:
- name: Bleu
type: bleu
value: 81.3279
---
<!-- 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. -->
# opus-mt-en-id-jakarta
This model was trained from scratch on the inglish dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3122
- Bleu: 81.3279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.9934 | 1.0 | 272 | 0.7998 | 57.6098 |
| 0.8752 | 2.0 | 544 | 0.6945 | 60.2893 |
| 0.762 | 3.0 | 816 | 0.6118 | 63.4407 |
| 0.6825 | 4.0 | 1088 | 0.5591 | 65.9376 |
| 0.6178 | 5.0 | 1360 | 0.5200 | 67.9008 |
| 0.5655 | 6.0 | 1632 | 0.4866 | 69.3854 |
| 0.516 | 7.0 | 1904 | 0.4580 | 70.8301 |
| 0.4685 | 8.0 | 2176 | 0.4334 | 72.0389 |
| 0.428 | 9.0 | 2448 | 0.4102 | 73.1174 |
| 0.3871 | 10.0 | 2720 | 0.3908 | 74.4526 |
| 0.3507 | 11.0 | 2992 | 0.3750 | 75.4508 |
| 0.3154 | 12.0 | 3264 | 0.3619 | 76.2748 |
| 0.2845 | 13.0 | 3536 | 0.3491 | 77.0737 |
| 0.2549 | 14.0 | 3808 | 0.3390 | 77.958 |
| 0.2269 | 15.0 | 4080 | 0.3312 | 78.6107 |
| 0.2045 | 16.0 | 4352 | 0.3240 | 79.3878 |
| 0.183 | 17.0 | 4624 | 0.3200 | 79.7554 |
| 0.1662 | 18.0 | 4896 | 0.3176 | 80.1064 |
| 0.1539 | 19.0 | 5168 | 0.3154 | 80.3286 |
| 0.1439 | 20.0 | 5440 | 0.3127 | 80.7742 |
| 0.133 | 21.0 | 5712 | 0.3126 | 80.8623 |
| 0.1262 | 22.0 | 5984 | 0.3133 | 81.0054 |
| 0.1213 | 23.0 | 6256 | 0.3132 | 81.2101 |
| 0.1169 | 24.0 | 6528 | 0.3123 | 81.2647 |
| 0.1149 | 25.0 | 6800 | 0.3122 | 81.3279 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.11.0
|
prompthero-diffusion-models/sixnfive
|
prompthero-diffusion-models
| 2023-03-20T12:23:40Z | 0 | 3 | null |
[
"stable-diffusion",
"text-to-image",
"en",
"dataset:prompthero-diffusion-models/anyataylorjoy",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-07T15:18:20Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
datasets:
- prompthero-diffusion-models/anyataylorjoy
---
# [PromptHero Academy](https://prompthero.com/academy/courses)
All the Dreambooth models featuring the [Six N Five](https://www.instagram.com/sixnfive/?hl=en) interior design style.
Find prompt inspiration at this [link](https://prompthero.com/search?q=sxnfv+style)
# snfv-7000-noreg
<img src="https://s3.amazonaws.com/moonup/production/uploads/1678280010147-63265d019f9d19bfd4f45031.png" width="20%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1678280010152-63265d019f9d19bfd4f45031.png" width="20%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1678280010150-63265d019f9d19bfd4f45031.png" width="20%"/>
|
OedoSoldier/Face-Landmark-ControlNet-diff
|
OedoSoldier
| 2023-03-20T12:22:11Z | 0 | 14 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-20T11:41:33Z |
---
license: apache-2.0
---
A differenced model extracted from https://huggingface.co/georgefen/Face-Landmark-ControlNet.
|
tomhosking/bert-base-uncased-debiased-nli
|
tomhosking
| 2023-03-20T12:14:09Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-06T14:36:20Z |
---
license: apache-2.0
widget:
- text: "[CLS] Rover is a dog. [SEP] Rover is a cat. [SEP]"
---
`bert-base-uncased`, fine tuned on the debiased NLI dataset from "Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets", Wu et al., 2022.
Tuned using the code at https://github.com/jimmycode/gen-debiased-nli
|
butchland/round2-Pixelcopter-PLE-v0
|
butchland
| 2023-03-20T11:58:13Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T13:31:29Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: round2-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 14.50 +/- 6.34
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
niks-salodkar/poca-SoccerTwos
|
niks-salodkar
| 2023-03-20T11:56:35Z | 15 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-20T11:56:29Z |
---
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: niks-salodkar/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DoctorRobotnik/pixelcopter
|
DoctorRobotnik
| 2023-03-20T11:53:19Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T11:10:46Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 14.90 +/- 14.64
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Alesteba/deep_model_05
|
Alesteba
| 2023-03-20T11:48:05Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-03-20T11:47:54Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
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