modelId
stringlengths 5
139
| author
stringlengths 2
42
| 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
2025-08-29 12:28:30
| card
stringlengths 11
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|---|---|---|---|---|---|---|---|---|---|
pfunk/CartPole-v1-CP_DDQN-seed410
|
pfunk
| 2023-03-21T05:57:37Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T05:57:34Z |
---
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_DDQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DDQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DDQN --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_DDQN-seed410/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-seed410/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-seed410/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_DDQN --double-learning --seed 410
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'double_learning': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DDQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': inf,
'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_DDQN-seed111
|
pfunk
| 2023-03-21T05:57:35Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T05:57:32Z |
---
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_DDQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DDQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DDQN --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_DDQN-seed111/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-seed111/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-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_DDQN --double-learning --seed 111
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'double_learning': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DDQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': inf,
'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_DDQN-seed828
|
pfunk
| 2023-03-21T05:57:32Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T05:57:29Z |
---
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: 55.01 +/- 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_DDQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DDQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DDQN --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_DDQN-seed828/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-seed828/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-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_DDQN --double-learning --seed 828
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'double_learning': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DDQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': inf,
'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_DDQN-seed555
|
pfunk
| 2023-03-21T05:57:29Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T05:57:26Z |
---
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: 11.33 +/- 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_DDQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DDQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DDQN --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_DDQN-seed555/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-seed555/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-seed555/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_DDQN --double-learning --seed 555
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'double_learning': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DDQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': inf,
'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_DDQN-seed232
|
pfunk
| 2023-03-21T05:57:28Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T05:57:25Z |
---
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: 494.31 +/- 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_DDQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DDQN]"
python -m cleanrl_utils.enjoy --exp-name CP_DDQN --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_DDQN-seed232/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-seed232/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DDQN-seed232/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_DDQN --double-learning --seed 232
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'double_learning': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DDQN',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': inf,
'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'}
```
|
starboy1985/my_awesome_asr_mind_model
|
starboy1985
| 2023-03-21T05:53:00Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-21T04:54:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_asr_mind_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_asr_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2
|
cytsai/q-Taxi-v3
|
cytsai
| 2023-03-21T05:42:45Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T05:24:03Z |
---
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="cytsai/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"])
```
|
taoshiwu/lora
|
taoshiwu
| 2023-03-21T05:40:31Z | 0 | 2 | null |
[
"region:us"
] | null | 2023-03-21T03:17:55Z |
**1.povSquattingCowgirlLora_pscowgirl:**
一种sex position lora，在词条中移除penis和vaginal会有sex暗示效果，squatting会有更广的镜头。
激活词条：<lora:PSCowgirl:0.9>, 1boy, penis, squatting cowgirl position, vaginal, pov
|
velvrix/truefoundary_sentimental_RoBERTa
|
velvrix
| 2023-03-21T05:33:41Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-28T10:40:23Z |
# **roberta-base model is fine tuned on Airline Passenger Complaint Dataset.**
- model can be used to determine the sentiment of the statement
|
velvrix/roberta_Uniacco
|
velvrix
| 2023-03-21T05:28:15Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-11T17:56:15Z |
# **roberta-base model is fine tuned on news article abstracts and headlines.**
## Respective labels and thier categories
- 0: 'ARTS & CULTURE',
- 1: 'BUSINESS & FINANCES',
- 2: 'COMEDY',
- 3: 'CRIME',
- 4: 'DIVORCE',
- 5: 'EDUCATION',
- 6: 'ENTERTAINMENT',
- 7: 'ENVIRONMENT',
- 8: 'FOOD & DRINK',
- 9: 'GROUPS VOICES',
- 10: 'HOME & LIVING',
- 11: 'IMPACT',
- 12: 'MEDIA',
- 13: 'MISCELLANEOUS',
- 14: 'PARENTING',
- 15: 'POLITICS',
- 16: 'RELIGION',
- 17: 'SCIENCE & TECH',
- 18: 'SPORTS',
- 19: 'STYLE & BEAUTY',
- 20: 'TRAVEL',
- 21: 'U.S. NEWS',
- 22: 'WEDDINGS',
- 23: 'WEIRD NEWS',
- 24: 'WELLNESS',
- 25: 'WOMEN',
- 26: 'WORLD NEWS'
|
tiagoandros/a-historia-dos-games
|
tiagoandros
| 2023-03-21T05:14:14Z | 0 | 0 | null |
[
"pt",
"license:other",
"region:us"
] | null | 2023-03-21T05:13:02Z |
---
license: other
language:
- pt
---
|
Shailza/final_huggingface
|
Shailza
| 2023-03-21T05:04:46Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-20T07:06:46Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Shailza/final_huggingface
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Shailza/final_huggingface
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.6030
- Validation Loss: 5.0251
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 40, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.6030 | 5.0251 | 0 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
346368616E/Kawaii_LoRA
|
346368616E
| 2023-03-21T04:40:32Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-21T11:54:10Z |
---
license: creativeml-openrail-m
---
|
w11wo/sundanese-gpt2-base
|
w11wo
| 2023-03-21T04:34:22Z | 25 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"sundanese-gpt2-base",
"su",
"dataset:mc4",
"dataset:cc100",
"dataset:oscar",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: su
tags:
- sundanese-gpt2-base
license: mit
datasets:
- mc4
- cc100
- oscar
- wikipedia
widget:
- text: "Nami abdi Budi, ti Indonésia"
---
## Sundanese GPT-2 Base
Sundanese GPT-2 Base is a causal language model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. It was trained on four datasets: [OSCAR](https://hf.co/datasets/oscar)'s `unshuffled_deduplicated_su` subset, the Sundanese [mC4](https://hf.co/datasets/mc4) subset, the Sundanese [CC100](https://hf.co/datasets/cc100) subset, and Sundanese [Wikipedia](https://su.wikipedia.org/).
10% of the dataset is kept for evaluation purposes. The model was trained from scratch and achieved an evaluation loss of 3.61 and an evaluation perplexity of 36.97.
This model was trained using HuggingFace's Flax framework. All necessary scripts used for training could be found in the [Files and versions](https://hf.co/w11wo/sundanese-gpt2-base/tree/main) tab, as well as the [Training metrics](https://hf.co/w11wo/sundanese-gpt2-base/tensorboard) logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| --------------------- | ------- | ----- | ------------------------------------- |
| `sundanese-gpt2-base` | 124M | GPT-2 | OSCAR, mC4, CC100, Wikipedia (758 MB) |
## Evaluation Results
The model was trained for 50 epochs and the following is the final result once the training ended.
| train loss | valid loss | valid PPL | total time |
| ---------- | ---------- | --------- | ---------- |
| 2.436 | 3.61 | 36.97 | 7:1:54 |
## How to Use
### As Causal Language Model
```python
from transformers import pipeline
pretrained_name = "w11wo/sundanese-gpt2-base"
nlp = pipeline(
"text-generation",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("Nami abdi Budi, ti Indonésia")
```
### Feature Extraction in PyTorch
```python
from transformers import GPT2Model, GPT2TokenizerFast
pretrained_name = "w11wo/sundanese-gpt2-base"
model = GPT2Model.from_pretrained(pretrained_name)
tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name)
prompt = "Nami abdi Budi, ti Indonésia"
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)
```
## Disclaimer
Do consider the biases which came from all four datasets that may be carried over into the results of this model.
## Author
Sundanese GPT-2 Base was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/).
## Citation Information
```bib
@article{rs-907893,
author = {Wongso, Wilson
and Lucky, Henry
and Suhartono, Derwin},
journal = {Journal of Big Data},
year = {2022},
month = {Feb},
day = {26},
abstract = {The Sundanese language has over 32 million speakers worldwide, but the language has reaped little to no benefits from the recent advances in natural language understanding. Like other low-resource languages, the only alternative is to fine-tune existing multilingual models. In this paper, we pre-trained three monolingual Transformer-based language models on Sundanese data. When evaluated on a downstream text classification task, we found that most of our monolingual models outperformed larger multilingual models despite the smaller overall pre-training data. In the subsequent analyses, our models benefited strongly from the Sundanese pre-training corpus size and do not exhibit socially biased behavior. We released our models for other researchers and practitioners to use.},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-907893/v1},
url = {https://doi.org/10.21203/rs.3.rs-907893/v1}
}
```
|
rebolforces/Lunarlander
|
rebolforces
| 2023-03-21T04:29:01Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T04:10:31Z |
---
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: 96.36 +/- 78.76
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': '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': 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': 'rebolforces/Lunarlander'
'batch_size': 512
'minibatch_size': 128}
```
|
seongwoon/labor_space_distil_long_token
|
seongwoon
| 2023-03-21T04:21:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-21T04:05:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: labor_space_distil_long_token
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. -->
# labor_space_distil_long_token
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
Bwabwa22/olliegroggy1
|
Bwabwa22
| 2023-03-21T04:01:19Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-21T03:50:56Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### OLLIEGROGGY1 Dreambooth model trained by Bwabwa22 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Fred99774/grapeparailara
|
Fred99774
| 2023-03-21T03:49:13Z | 4 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-21T03:46:26Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### grapeparailara Dreambooth model trained by Fred99774 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
58AILab/wenet_efficient_conformer_librispeech_v2
|
58AILab
| 2023-03-21T03:44:13Z | 0 | 2 | null |
[
"automatic-speech-recognition",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2023-03-21T03:08:51Z |
---
license: apache-2.0
language:
- en
- zh
metrics:
- cer
pipeline_tag: automatic-speech-recognition
---
# Efficient Conformer v2 for non-streaming ASR
**Specification**: https://github.com/wenet-e2e/wenet/pull/1636
## Results
* Feature info:
* using fbank feature, cmvn, speed perturb, dither
* Training info:
* train_u2++_efficonformer_v2.yaml
* 8 gpu, batch size 16, acc_grad 1, 120 epochs
* lr 0.001, warmup_steps 35000
* Model info:
* Model Params: 50,341,278
* Downsample rate: 1/2 (conv2d2) * 1/4 (efficonformer block)
* encoder_dim 256, output_size 256, head 8, linear_units 2048
* num_blocks 12, cnn_module_kernel 15, group_size 3
* Decoding info:
* ctc_weight 0.5, reverse_weight 0.3, average_num 20
test clean
| decoding mode | full | 18 | 16 |
|------------------------|------|------|------|
| attention decoder | 3.49 | 3.71 | 3.72 |
| ctc_greedy_search | 3.49 | 3.74 | 3.77 |
| ctc prefix beam search | 3.47 | 3.72 | 3.74 |
| attention rescoring | 3.12 | 3.38 | 3.36 |
test other
| decoding mode | full | 18 | 16 |
|------------------------|------|------|------|
| attention decoder | 8.15 | 9.05 | 9.03 |
| ctc_greedy_search | 8.73 | 9.82 | 9.83 |
| ctc prefix beam search | 8.70 | 9.81 | 9.79 |
| attention rescoring | 8.05 | 9.08 | 9.10 |
## Start to Use
Install **WeNet** follow: https://wenet.org.cn/wenet/install.html#install-for-training
Decode
```sh
cd examples/librispeech/s0
cp exp/wenet_efficient_conformer_librispeech_v2/decode.sh ./
cp exp/wenet_efficient_conformer_librispeech_v2/wer.sh ./
dir=exp/wenet_efficient_conformer_librispeech_v2
decoding_chunk_size=-1
. ./decode.sh ${dir} 20 ${decoding_chunk_size}
# WER
. ./wer.sh test_clean wenet_efficient_conformer_librispeech_v2 ${decoding_chunk_size}
. ./wer.sh test_other wenet_efficient_conformer_librispeech_v2 ${decoding_chunk_size}
```
|
vocabtrimmer/mt5-small-trimmed-ja-60000-jaquad-qa
|
vocabtrimmer
| 2023-03-21T03:29:19Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"ja",
"dataset:lmqg/qg_jaquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-21T03:28:18Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ja
datasets:
- lmqg/qg_jaquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: 新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?, context: 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。"
example_title: "Question Answering Example 1"
- text: "question: 1968年に開催されたオリンピックの名前は何ですか?, context: オリンピックが世界的大イベントに成長するに従って政治に左右されるようになると、1968年のメキシコシティ大会では黒人差別を訴える場と化し、1972年のミュンヘン大会ではアラブのゲリラによるイスラエル選手に対するテロ事件まで起きた(ミュンヘンオリンピック事件)。1976年のモントリオール大会になると、ニュージーランドのラグビーチームの南アフリカ遠征に反対してアフリカの諸国22ヶ国がボイコットを行った。そして、1980年のモスクワ大会ではソ連のアフガニスタン侵攻に反発したアメリカ・西ドイツ・日本などの西側諸国が相次いでボイコットを行った。1984年ロサンゼルス大会ではソ連と東側諸国が報復ボイコットを行ない、参加したのはソ連と対立していた中国とルーマニアだけだった。中でも、イラン革命後のイラン・イスラム共和国はモスクワとロサンゼルス双方のオリンピックをボイコットしている。オリンピックが巨大化するに従って財政負担の増大が大きな問題となり、1976年の夏季大会では大幅な赤字を出し、その後夏季・冬季とも立候補都市が1〜2都市だけという状態が続いた。"
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ja-60000-jaquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_jaquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 0.0
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 62.58
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 48.53
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 96.15
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 88.59
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 64.56
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 64.56
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ja-60000-jaquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ja-60000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-60000) for question answering task on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-ja-60000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-60000)
- **Language:** ja
- **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (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="ja", model="vocabtrimmer/mt5-small-trimmed-ja-60000-jaquad-qa")
# model prediction
answers = model.answer_q(list_question="新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?", list_context=" 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ja-60000-jaquad-qa")
output = pipe("question: 新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?, context: 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-60000-jaquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_jaquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 64.56 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| AnswerF1Score | 64.56 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| BERTScore | 96.15 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_1 | 60.28 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_2 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_3 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_4 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| METEOR | 48.53 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| MoverScore | 88.59 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| ROUGE_L | 62.58 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_jaquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-ja-60000
- max_length: 512
- max_length_output: 32
- epoch: 18
- batch: 32
- lr: 0.001
- 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-ja-60000-jaquad-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",
}
```
|
AngelUrq/a2c-AntBulletEnv-v0
|
AngelUrq
| 2023-03-21T03:21:07Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T03:20:03Z |
---
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: 1724.14 +/- 312.33
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
...
```
|
santhosh-4000/wav2vec2-base-finetuned-mednames1
|
santhosh-4000
| 2023-03-21T03:20:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-03-19T03:42:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-mednames1
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-mednames1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5263
- Accuracy: 0.9727
## 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: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2881 | 1.0 | 22 | 2.2690 | 0.2273 |
| 2.2234 | 2.0 | 44 | 2.1631 | 0.2273 |
| 1.9756 | 3.0 | 66 | 1.9277 | 0.5182 |
| 1.7191 | 4.0 | 88 | 1.6293 | 0.6818 |
| 1.4663 | 5.0 | 110 | 1.3919 | 0.7545 |
| 1.27 | 6.0 | 132 | 1.1689 | 0.8182 |
| 1.1112 | 7.0 | 154 | 1.0144 | 0.8364 |
| 0.9623 | 8.0 | 176 | 0.9137 | 0.8545 |
| 0.8764 | 9.0 | 198 | 0.7901 | 0.8909 |
| 0.7776 | 10.0 | 220 | 0.7229 | 0.8727 |
| 0.7266 | 11.0 | 242 | 0.6335 | 0.9 |
| 0.6379 | 12.0 | 264 | 0.5848 | 0.9636 |
| 0.6121 | 13.0 | 286 | 0.5509 | 0.9273 |
| 0.5732 | 14.0 | 308 | 0.5263 | 0.9727 |
| 0.5579 | 15.0 | 330 | 0.5221 | 0.9727 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.11.0+cu102
- Datasets 2.10.1
- Tokenizers 0.13.2
|
derek-thomas/t5-end2end-question-generation
|
derek-thomas
| 2023-03-21T03:20:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T13:41:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: t5-end2end-question-generation
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-end2end-question-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3875
- Rouge1: 29.8409
- Rouge2: 15.2583
- Rougel: 25.4802
- Rougelsum: 28.8023
- Gen Len: 18.9971
- Bleu: 1.8149
- Bleu 0: 71.9158
- Bleu 1: 46.3975
- Bleu 2: 31.3479
- Bleu 3: 20.236
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu | Bleu 0 | Bleu 1 | Bleu 2 | Bleu 3 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:------:|:-------:|:-------:|:-------:|:-------:|
| 1.4252 | 0.21 | 500 | 1.4638 | 29.5937 | 14.6438 | 25.1309 | 28.5076 | 18.9990 | 1.7595 | 70.9726 | 44.8789 | 29.8013 | 18.9402 |
| 1.3591 | 0.42 | 1000 | 1.4619 | 29.4017 | 14.7271 | 25.1139 | 28.3406 | 19.0 | 1.7286 | 70.9671 | 45.415 | 30.2413 | 19.1132 |
| 1.426 | 0.64 | 1500 | 1.4313 | 29.9163 | 15.0542 | 25.5098 | 28.852 | 19.0 | 1.8109 | 71.924 | 46.0312 | 30.8421 | 19.6842 |
| 1.5525 | 0.85 | 2000 | 1.4177 | 30.0353 | 15.2661 | 25.6495 | 28.9867 | 19.0 | 1.8387 | 72.1696 | 46.3888 | 31.1768 | 20.1203 |
| 1.5035 | 1.06 | 2500 | 1.4185 | 29.7649 | 15.1864 | 25.4353 | 28.738 | 19.0 | 1.7868 | 71.9618 | 46.6091 | 31.4797 | 20.209 |
| 1.4294 | 1.27 | 3000 | 1.4138 | 29.5473 | 14.877 | 25.1373 | 28.5195 | 18.9990 | 1.7516 | 71.3163 | 45.6707 | 30.7404 | 19.6335 |
| 1.4336 | 1.49 | 3500 | 1.4058 | 29.9003 | 15.213 | 25.4924 | 28.8375 | 19.0 | 1.799 | 71.8573 | 46.2609 | 31.2086 | 20.0675 |
| 1.4434 | 1.7 | 4000 | 1.3978 | 30.0046 | 15.2722 | 25.6091 | 28.9496 | 18.9990 | 1.839 | 72.2448 | 46.6283 | 31.463 | 20.2921 |
| 1.4285 | 1.91 | 4500 | 1.3984 | 30.0478 | 15.1083 | 25.4469 | 28.9337 | 18.9990 | 1.8247 | 71.6695 | 45.7508 | 30.7813 | 19.7828 |
| 1.3926 | 2.12 | 5000 | 1.3982 | 30.0837 | 15.4009 | 25.6203 | 29.0334 | 18.9990 | 1.8237 | 72.2626 | 46.662 | 31.5043 | 20.2789 |
| 1.369 | 2.33 | 5500 | 1.3980 | 29.9042 | 15.1828 | 25.4962 | 28.8323 | 18.9990 | 1.8064 | 71.8783 | 46.1411 | 31.0047 | 19.9691 |
| 1.3577 | 2.55 | 6000 | 1.3936 | 29.9335 | 15.2821 | 25.5855 | 28.9161 | 19.0 | 1.8099 | 71.8881 | 46.3101 | 31.3396 | 20.3185 |
| 1.3636 | 2.76 | 6500 | 1.3908 | 29.9512 | 15.2434 | 25.5476 | 28.9224 | 18.9995 | 1.8242 | 71.9772 | 46.3212 | 31.2688 | 20.1704 |
| 1.3799 | 2.97 | 7000 | 1.3900 | 29.9393 | 15.1658 | 25.4702 | 28.8729 | 18.9971 | 1.8055 | 71.9431 | 46.1286 | 30.9969 | 19.9389 |
| 1.3318 | 3.18 | 7500 | 1.3934 | 29.7982 | 15.132 | 25.3908 | 28.7333 | 18.9995 | 1.7908 | 71.7081 | 46.1832 | 31.1416 | 20.1409 |
| 1.3208 | 3.4 | 8000 | 1.3928 | 29.9378 | 15.1421 | 25.4586 | 28.8793 | 19.0 | 1.8258 | 71.7795 | 45.969 | 30.9173 | 19.9664 |
| 1.3135 | 3.61 | 8500 | 1.3888 | 29.9264 | 15.2179 | 25.5529 | 28.875 | 19.0 | 1.8363 | 71.9537 | 46.2706 | 31.2245 | 20.2624 |
| 1.323 | 3.82 | 9000 | 1.3868 | 29.8749 | 15.2251 | 25.4639 | 28.7949 | 18.9971 | 1.812 | 71.6918 | 46.1503 | 31.0437 | 19.9965 |
| 1.3325 | 4.03 | 9500 | 1.3868 | 29.8804 | 15.2658 | 25.4848 | 28.8238 | 18.9971 | 1.8105 | 71.9146 | 46.3617 | 31.2842 | 20.1447 |
| 1.296 | 4.24 | 10000 | 1.3882 | 29.941 | 15.28 | 25.5209 | 28.9109 | 18.9971 | 1.817 | 71.994 | 46.3801 | 31.216 | 20.0596 |
| 1.3027 | 4.46 | 10500 | 1.3883 | 29.8492 | 15.2017 | 25.4398 | 28.7911 | 18.9971 | 1.7994 | 71.8366 | 46.0939 | 30.9953 | 19.9115 |
| 1.3046 | 4.67 | 11000 | 1.3880 | 29.8538 | 15.2605 | 25.4897 | 28.8236 | 18.9971 | 1.8136 | 71.9285 | 46.3689 | 31.2969 | 20.1728 |
| 1.294 | 4.88 | 11500 | 1.3875 | 29.8409 | 15.2583 | 25.4802 | 28.8023 | 18.9971 | 1.8149 | 71.9158 | 46.3975 | 31.3479 | 20.236 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
luongphamit/NeverEnding-Dream2
|
luongphamit
| 2023-03-21T03:01:41Z | 75 | 12 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"text-to-image",
"art",
"artistic",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-21T03:01:16Z |
---
language:
- en
license: other
tags:
- stable-diffusion
- text-to-image
- art
- artistic
- diffusers
inference: true
duplicated_from: Lykon/NeverEnding-Dream
---
# NeverEnding Dream (NED)
## Official Repository
Read more about this model here: https://civitai.com/models/10028/neverending-dream-ned
Also please support by giving 5 stars and a heart, which will notify new updates.
Also consider supporting me on Patreon or ByuMeACoffee
- https://www.patreon.com/Lykon275
- https://www.buymeacoffee.com/lykon
You can run this model on:
- https://sinkin.ai/m/qGdxrYG
Some sample output:
|
Nobu90/distilbert-base-uncased-finetuned-emotion
|
Nobu90
| 2023-03-21T02:49:45Z | 3 | 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
| 2023-03-20T14:23:49Z |
---
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: 0.923
- name: F1
type: f1
value: 0.9228326728759837
---
<!-- 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: 0.2155
- Accuracy: 0.923
- F1: 0.9228
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3115 | 0.9075 | 0.9052 |
| No log | 2.0 | 500 | 0.2155 | 0.923 | 0.9228 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
yeongjoon/Kconvo-roberta
|
yeongjoon
| 2023-03-21T02:42:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ko",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-15T06:51:03Z |
---
license: mit
language:
- ko
---
# Kconvo-roberta: Korean conversation RoBERTa ([github](https://github.com/HeoTaksung/Domain-Robust-Retraining-of-Pretrained-Language-Model))
- There are many PLMs (Pretrained Language Models) for Korean, but most of them are trained with written language.
- Here, we introduce a retrained PLM for prediction of Korean conversation data where we use verbal data for training.
## Usage
```python
# Kconvo-roberta
from transformers import RobertaTokenizerFast, RobertaModel
tokenizer_roberta = RobertaTokenizerFast.from_pretrained("yeongjoon/Kconvo-roberta")
model_roberta = RobertaModel.from_pretrained("yeongjoon/Kconvo-roberta")
```
-----------------
## Domain Robust Retraining of Pretrained Language Model
- Kconvo-roberta uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the base model and retrained additionaly with the conversation dataset.
- The retrained dataset was collected through the [National Institute of the Korean Language](https://corpus.korean.go.kr/request/corpusRegist.do) and [AI-Hub](https://www.aihub.or.kr/aihubdata/data/list.do?pageIndex=1&currMenu=115&topMenu=100&dataSetSn=&srchdataClCode=DATACL001&srchOrder=&SrchdataClCode=DATACL002&searchKeyword=&srchDataRealmCode=REALM002&srchDataTy=DATA003), and the collected dataset is as follows.
```
- National Institute of the Korean Language
* 온라인 대화 말뭉치 2021
* 일상 대화 말뭉치 2020
* 구어 말뭉치
* 메신저 말뭉치
- AI-Hub
* 온라인 구어체 말뭉치 데이터
* 상담 음성
* 한국어 음성
* 자유대화 음성(일반남여)
* 일상생활 및 구어체 한-영 번역 병렬 말뭉치 데이터
* 한국인 대화음성
* 감성 대화 말뭉치
* 주제별 텍스트 일상 대화 데이터
* 용도별 목적대화 데이터
* 한국어 SNS
```
|
AndyPig/q-FrozenLake-v1-4x4-noSlippery
|
AndyPig
| 2023-03-21T02:11:10Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T02:11:07Z |
---
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="AndyPig/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"])
```
|
guialfaro/korean-paraphrasing
|
guialfaro
| 2023-03-21T02:10:34Z | 63 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-12-26T08:55:13Z |
---
license: mit
---
[Korean BART](https://huggingface.co/hyunwoongko/kobart) model for paraphrasing.
The dataset utilized can be found on the *Files and versions* tab under the name dataset.csv.
```python
import torch
from transformers import BartForConditionalGeneration, AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BartForConditionalGeneration.from_pretrained('guialfaro/korean-paraphrasing').to(device)
tokenizer = AutoTokenizer.from_pretrained('guialfaro/korean-paraphrasing')
sentence = "7층 방문을 위해 방문록 작성이 필요합니다."
text = f"paraphrase: {sentence} "
encoding = tokenizer.batch_encode_plus(
[text],
max_length=256,
pad_to_max_length=True,
truncation=True,
padding="max_length",
return_tensors="pt",)
source_ids = encoding["input_ids"].to(device, dtype=torch.long)
source_mask = encoding["attention_mask"].to(device, dtype=torch.long)
generated_ids = model.generate(
input_ids=source_ids,
attention_mask=source_mask,
max_length=150,
num_beams=2,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True)
preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
print(f"Original Sentence :: {sentence}")
print(f"Paraphrased Sentence :: {preds[0]}")
```
|
kikijiki/ppo-LunarLander-v2
|
kikijiki
| 2023-03-21T02:05:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T01:02:36Z |
---
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: 261.34 +/- 18.84
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
luongphamit/stable-diffusion-2-base
|
luongphamit
| 2023-03-21T02:01:08Z | 7 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"text-to-image",
"arxiv:2112.10752",
"arxiv:2202.00512",
"arxiv:1910.09700",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-21T02:01:07Z |
---
license: openrail++
tags:
- stable-diffusion
- text-to-image
duplicated_from: stabilityai/stable-diffusion-2-base
---
# Stable Diffusion v2-base Model Card
This model card focuses on the model associated with the Stable Diffusion v2-base model, available [here](https://github.com/Stability-AI/stablediffusion).
The model is trained from scratch 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. Then it is further trained for 850k steps at resolution `512x512` on the same dataset on images with resolution `>= 512x512`.
- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt).
- Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-base#examples)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
```bash
pip install diffusers transformers accelerate scipy safetensors
```
Running the pipeline (if you don't swap the scheduler it will run with the default PNDM/PLMS scheduler, in this example we are swapping it to EulerDiscreteScheduler):
```python
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
import torch
model_id = "stabilityai/stable-diffusion-2-base"
# Use the Euler scheduler here instead
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
**Notes**:
- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a subset of the large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
**Training Procedure**
Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
We currently provide the following checkpoints:
- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized.
- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama).
- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 1
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 200000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
pfunk/CartPole-v1-CP_DQN_norm_40-seed111
|
pfunk
| 2023-03-21T02:00:10Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T02:00:07Z |
---
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: 302.65 +/- 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_norm_40.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN_norm_40]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN_norm_40 --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_norm_40-seed111/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_40-seed111/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_40-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_norm_40 --max-gradient-norm 40.0 --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_norm_40',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': 40.0,
'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'}
```
|
sanak/q-FrozenLake-v1-4x4-noSlippery
|
sanak
| 2023-03-21T01:59:51Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T01:59:49Z |
---
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="sanak/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"])
```
|
pfunk/CartPole-v1-CP_DQN_norm_40-seed888
|
pfunk
| 2023-03-21T01:59:18Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T01:59:15Z |
---
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: 496.06 +/- 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_norm_40.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN_norm_40]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN_norm_40 --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_norm_40-seed888/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_40-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_40-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_norm_40 --max-gradient-norm 40.0 --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_norm_40',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': 40.0,
'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'}
```
|
Isaac009/dqn-SpaceInvadersNoFrameskip-v4
|
Isaac009
| 2023-03-21T01:34:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T04:51:46Z |
---
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: 921.00 +/- 361.18
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 Isaac009 -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 Isaac009 -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 Isaac009
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Chattiori/DeepChillMix
|
Chattiori
| 2023-03-21T01:30:44Z | 0 | 1 | null |
[
"en",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-20T23:20:48Z |
---
license: creativeml-openrail-m
language:
- en
---
DeepChillMix is checkpoint merge model of ChillOutMix, LOFI and DDosMix.
|
Chattiori/ChillLOFIMix
|
Chattiori
| 2023-03-21T01:29:27Z | 0 | 1 | null |
[
"en",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-20T23:21:08Z |
---
license: creativeml-openrail-m
language:
- en
---
This model is checkpoint merge of ChillOutMix and LOFI.
|
rebolforces/rl_course_vizdoom_health_gathering_supreme
|
rebolforces
| 2023-03-21T00:54:08Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T00:38:24Z |
---
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: 12.20 +/- 4.65
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 rebolforces/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.
|
vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qa
|
vocabtrimmer
| 2023-03-21T00:33:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"es",
"dataset:lmqg/qg_esquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-21T00:32:57Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: es
datasets:
- lmqg/qg_esquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: ¿Cuál es la población de Nueva York a partir de 2014?, context: Situada en uno de los mayores puertos naturales del mundo, la ciudad de Nueva York consta de cinco municipios, cada uno de los cuales es un condado separado del estado de Nueva York. Los cinco distritos - Brooklyn, Queens, Manhattan, el Bronx y Staten Island - se consolidaron en una sola ciudad en 1898. Con una población censada estimada en 2014 de 8.491.079 habitantes distribuidos en una superficie de solo 790 km ², Nueva York es la ciudad más densamente poblada de los Estados Unidos. Hasta 800 idiomas se hablan en Nueva York, por lo que es la ciudad más lingüísticamente diversa del mundo. Según estimaciones del censo de 2014, la región metropolitana de la ciudad de Nueva York sigue siendo por un margen significativo la más poblada de los Estados Unidos, según lo definido tanto por el Área Estadística Metropolitana (20,1 millones de residentes). En 2013, el MSA produjo un producto metropolitano bruto (GMP) de casi US $1,39 billones, mientras que en 2012, el CSA generó un GMP de más de US $1,55 billones, ambos clasificados en primer lugar."
example_title: "Question Answering Example 1"
- text: "question: ¿Cómo se llama el ejército personal de Sassou?, context: El progreso democrático del Congo se descarriló en 1997, cuando Lissouba y Sassou comenzaron a luchar por el poder en la guerra civil. A medida que se acercaban las elecciones presidenciales de julio de 1997, las tensiones entre los campos de Lissouba y Sassou aumentaron. El 5 de junio, las fuerzas del gobierno del presidente Lissouba rodearon el complejo de Sassou en Brazzaville y Sassou ordenó a los miembros de su milicia privada (conocida como Cobras) resistir. Así comenzó un conflicto de cuatro meses que destruyó o dañó gran parte de Brazzaville y causó decenas de miles de muertes civiles. A principios de octubre, el régimen socialista angoleño comenzó una invasión del Congo para instalar a Sassou en el poder. A mediados de octubre, el gobierno de Lissouba cayó. Poco después, Sassou se declaró presidente."
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_esquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 14.81
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 35.33
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 30.92
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 90.62
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 74.78
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 58.12
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 37.52
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-es-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-10000) for question answering task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-es-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-10000)
- **Language:** es
- **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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="es", model="vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qa")
# model prediction
answers = model.answer_q(list_question="¿Cuál es la población de Nueva York a partir de 2014?", list_context=" Situada en uno de los mayores puertos naturales del mundo, la ciudad de Nueva York consta de cinco municipios, cada uno de los cuales es un condado separado del estado de Nueva York. Los cinco distritos - Brooklyn, Queens, Manhattan, el Bronx y Staten Island - se consolidaron en una sola ciudad en 1898. Con una población censada estimada en 2014 de 8.491.079 habitantes distribuidos en una superficie de solo 790 km ², Nueva York es la ciudad más densamente poblada de los Estados Unidos. Hasta 800 idiomas se hablan en Nueva York, por lo que es la ciudad más lingüísticamente diversa del mundo. Según estimaciones del censo de 2014, la región metropolitana de la ciudad de Nueva York sigue siendo por un margen significativo la más poblada de los Estados Unidos, según lo definido tanto por el Área Estadística Metropolitana (20,1 millones de residentes). En 2013, el MSA produjo un producto metropolitano bruto (GMP) de casi US $1,39 billones, mientras que en 2012, el CSA generó un GMP de más de US $1,55 billones, ambos clasificados en primer lugar.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qa")
output = pipe("question: ¿Cuál es la población de Nueva York a partir de 2014?, context: Situada en uno de los mayores puertos naturales del mundo, la ciudad de Nueva York consta de cinco municipios, cada uno de los cuales es un condado separado del estado de Nueva York. Los cinco distritos - Brooklyn, Queens, Manhattan, el Bronx y Staten Island - se consolidaron en una sola ciudad en 1898. Con una población censada estimada en 2014 de 8.491.079 habitantes distribuidos en una superficie de solo 790 km ², Nueva York es la ciudad más densamente poblada de los Estados Unidos. Hasta 800 idiomas se hablan en Nueva York, por lo que es la ciudad más lingüísticamente diversa del mundo. Según estimaciones del censo de 2014, la región metropolitana de la ciudad de Nueva York sigue siendo por un margen significativo la más poblada de los Estados Unidos, según lo definido tanto por el Área Estadística Metropolitana (20,1 millones de residentes). En 2013, el MSA produjo un producto metropolitano bruto (GMP) de casi US $1,39 billones, mientras que en 2012, el CSA generó un GMP de más de US $1,55 billones, ambos clasificados en primer lugar.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_esquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 37.52 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| AnswerF1Score | 58.12 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| BERTScore | 90.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_1 | 24.59 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_2 | 20.23 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_3 | 17.23 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_4 | 14.81 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| METEOR | 30.92 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| MoverScore | 74.78 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| ROUGE_L | 35.33 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_esquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-es-10000
- max_length: 512
- max_length_output: 32
- epoch: 13
- 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-es-10000-esquad-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",
}
```
|
MakiPan/a2c-PandaReachDense-v2
|
MakiPan
| 2023-03-21T00:08:17Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-21T00:05:42Z |
---
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: -1.52 +/- 0.26
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
...
```
|
kostus/veka1-style
|
kostus
| 2023-03-20T23:59:46Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-20T23:58:42Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: veka1
---
### veka1-style Dreambooth model trained by kostus with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
veka1 (use that on your prompt)
|
pfunk/CartPole-v1-CP_DQN_norm_1-seed777
|
pfunk
| 2023-03-20T23:57:34Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:57:31Z |
---
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: 471.95 +/- 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_norm_1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN_norm_1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN_norm_1 --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_norm_1-seed777/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed777/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed777/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_norm_1 --max-gradient-norm 1.0 --seed 777
```
# 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_norm_1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': 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'}
```
|
jjpp3301/q-Taxi-v3
|
jjpp3301
| 2023-03-20T23:57:33Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:38:34Z |
---
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="jjpp3301/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"])
```
|
pfunk/CartPole-v1-CP_DQN_norm_1-seed555
|
pfunk
| 2023-03-20T23:57:31Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:57:27Z |
---
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: 269.02 +/- 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_norm_1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN_norm_1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN_norm_1 --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_norm_1-seed555/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed555/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed555/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_norm_1 --max-gradient-norm 1.0 --seed 555
```
# 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_norm_1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': 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_DQN_norm_1-seed232
|
pfunk
| 2023-03-20T23:57:29Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:57:26Z |
---
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: 467.31 +/- 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_norm_1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN_norm_1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN_norm_1 --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_norm_1-seed232/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed232/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed232/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_norm_1 --max-gradient-norm 1.0 --seed 232
```
# 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_norm_1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': 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_norm_1-seed888
|
pfunk
| 2023-03-20T23:57:01Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:56:58Z |
---
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: 496.89 +/- 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_norm_1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN_norm_1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN_norm_1 --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_norm_1-seed888/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-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_norm_1 --max-gradient-norm 1.0 --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_norm_1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': 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'}
```
|
SAL83/Taxi-v3
|
SAL83
| 2023-03-20T23:42:31Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:42:28Z |
---
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="SAL83/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"])
```
|
ashish-soni08/old-trafford-football-stadium
|
ashish-soni08
| 2023-03-20T23:38:36Z | 11 | 1 |
diffusers
|
[
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"landscape",
"dataset:Ashish08/old-trafford",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-07T15:46:31Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- landscape
datasets: Ashish08/old-trafford
widget:
- text: a photo of old-trafford football-stadium with a red sky
---
# DreamBooth model for the old-trafford concept trained by Ashish08 on the Ashish08/old-trafford dataset.
This is a Stable Diffusion model fine-tuned on the old-trafford concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of old-trafford football-stadium**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `football-stadium` images for the landscape theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Ashish08/old-trafford-football-stadium')
image = pipeline().images[0]
image
```
|
ashish-soni08/vada-sambhar-south-indian-dish
|
ashish-soni08
| 2023-03-20T23:38:13Z | 10 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"food",
"dataset:Ashish08/vada-sambhar",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-07T15:54:16Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- food
datasets: Ashish08/vada-sambhar
widget:
- text: a photo of vada sambhar south indian dish on a red table
---
# DreamBooth model for the vada-sambhar concept trained by Ashish08 on the Ashish08/vada-sambhar dataset.
This is a Stable Diffusion model fine-tuned on the vada-sambhar concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of vada-sambhar south-indian-dish**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `south-indian-dish` images for the food theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Ashish08/vada-sambhar-south-indian-dish')
image = pipeline().images[0]
image
```
|
pfunk/CartPole-v1-CP_DQPN_x1-seed410
|
pfunk
| 2023-03-20T23:37:36Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:37:33Z |
---
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: 234.63 +/- 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_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --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_x1-seed410/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed410/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-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_x1 --policy-network-frequency 100 --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_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'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_x1-seed111
|
pfunk
| 2023-03-20T23:37:04Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:37:01Z |
---
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_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --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_x1-seed111/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed111/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-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_x1 --policy-network-frequency 100 --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_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'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_DQPN_x1-seed828
|
pfunk
| 2023-03-20T23:36:37Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:36:34Z |
---
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_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --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_x1-seed828/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed828/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-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_x1 --policy-network-frequency 100 --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_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'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'}
```
|
SAL83/q-FrozenLake-v1-4x4-noSlippery
|
SAL83
| 2023-03-20T23:36:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:36:13Z |
---
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="SAL83/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"])
```
|
pfunk/CartPole-v1-CP_DQN_norm_1-seed612
|
pfunk
| 2023-03-20T23:28:05Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:28:02Z |
---
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_norm_1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN_norm_1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN_norm_1 --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_norm_1-seed612/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed612/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-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_norm_1 --max-gradient-norm 1.0 --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_norm_1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': 1.0,
'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'}
```
|
pfunk/CartPole-v1-CP_DQN_norm_1-seed355
|
pfunk
| 2023-03-20T23:27:36Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:27:33Z |
---
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: 496.52 +/- 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_norm_1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQN_norm_1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQN_norm_1 --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_norm_1-seed355/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-seed355/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQN_norm_1-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_norm_1 --max-gradient-norm 1.0 --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_norm_1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'max_gradient_norm': 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_DQPN_x1-seed355
|
pfunk
| 2023-03-20T23:22:55Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:22:53Z |
---
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_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --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_x1-seed355/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed355/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-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_x1 --policy-network-frequency 100 --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_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'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_DQPN_x20-seed612
|
pfunk
| 2023-03-20T23:22:25Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:22:22Z |
---
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_x20.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x20]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x20 --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_x20-seed612/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-seed612/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-seed612/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_x20 --policy-network-frequency 2000 --seed 612
```
# 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_x20',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 2000,
'policy_tau': 1.0,
'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'}
```
|
pfunk/CartPole-v1-CP_DQPN_x20-seed410
|
pfunk
| 2023-03-20T23:22:19Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:22:16Z |
---
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: 492.92 +/- 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_x20.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x20]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x20 --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_x20-seed410/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-seed410/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-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_x20 --policy-network-frequency 2000 --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_x20',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 2000,
'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_x2-seed410
|
pfunk
| 2023-03-20T23:22:16Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:22:13Z |
---
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: 482.67 +/- 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_x2.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x2]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x2 --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_x2-seed410/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-seed410/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-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_x2 --policy-network-frequency 200 --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_x2',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 200,
'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_x20-seed111
|
pfunk
| 2023-03-20T23:22:12Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:22:09Z |
---
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: 495.28 +/- 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_x20.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x20]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x20 --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_x20-seed111/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-seed111/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-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_x20 --policy-network-frequency 2000 --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_x20',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 2000,
'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_DQPN_x2-seed111
|
pfunk
| 2023-03-20T23:22:01Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:21:58Z |
---
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_x2.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x2]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x2 --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_x2-seed111/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-seed111/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-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_x2 --policy-network-frequency 200 --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_x2',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 200,
'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_DQPN_x20-seed828
|
pfunk
| 2023-03-20T23:21:37Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:21:34Z |
---
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: 14.78 +/- 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_x20.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x20]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x20 --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_x20-seed828/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-seed828/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-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_x20 --policy-network-frequency 2000 --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_x20',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 2000,
'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_DQPN_x2-seed828
|
pfunk
| 2023-03-20T23:20:59Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:20:56Z |
---
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: 10.83 +/- 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_x2.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x2]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x2 --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_x2-seed828/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-seed828/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-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_x2 --policy-network-frequency 200 --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_x2',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 200,
'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_DQPN_x1-seed612
|
pfunk
| 2023-03-20T23:13:51Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:13:48Z |
---
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: 20.32 +/- 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_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --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_x1-seed612/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed612/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed612/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_x1 --policy-network-frequency 100 --seed 612
```
# 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_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'policy_tau': 1.0,
'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'}
```
|
pfunk/CartPole-v1-CP_DQPN_x1-seed232
|
pfunk
| 2023-03-20T23:12:30Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:12:27Z |
---
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: 493.87 +/- 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_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --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_x1-seed232/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed232/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-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_x1 --policy-network-frequency 100 --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_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'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_DQPN_x1-seed888
|
pfunk
| 2023-03-20T23:12:27Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:12:24Z |
---
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: 421.95 +/- 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_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --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_x1-seed888/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-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_x1 --policy-network-frequency 100 --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_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'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'}
```
|
vocabtrimmer/mt5-small-trimmed-ja-30000-jaquad-qa
|
vocabtrimmer
| 2023-03-20T23:11:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"ja",
"dataset:lmqg/qg_jaquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T23:11:11Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ja
datasets:
- lmqg/qg_jaquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: 新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?, context: 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。"
example_title: "Question Answering Example 1"
- text: "question: 1968年に開催されたオリンピックの名前は何ですか?, context: オリンピックが世界的大イベントに成長するに従って政治に左右されるようになると、1968年のメキシコシティ大会では黒人差別を訴える場と化し、1972年のミュンヘン大会ではアラブのゲリラによるイスラエル選手に対するテロ事件まで起きた(ミュンヘンオリンピック事件)。1976年のモントリオール大会になると、ニュージーランドのラグビーチームの南アフリカ遠征に反対してアフリカの諸国22ヶ国がボイコットを行った。そして、1980年のモスクワ大会ではソ連のアフガニスタン侵攻に反発したアメリカ・西ドイツ・日本などの西側諸国が相次いでボイコットを行った。1984年ロサンゼルス大会ではソ連と東側諸国が報復ボイコットを行ない、参加したのはソ連と対立していた中国とルーマニアだけだった。中でも、イラン革命後のイラン・イスラム共和国はモスクワとロサンゼルス双方のオリンピックをボイコットしている。オリンピックが巨大化するに従って財政負担の増大が大きな問題となり、1976年の夏季大会では大幅な赤字を出し、その後夏季・冬季とも立候補都市が1〜2都市だけという状態が続いた。"
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ja-30000-jaquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_jaquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 0.0
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 58.21
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 46.65
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 95.64
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 86.95
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 60.45
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 60.45
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ja-30000-jaquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ja-30000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-30000) for question answering task on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-ja-30000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-30000)
- **Language:** ja
- **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (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="ja", model="vocabtrimmer/mt5-small-trimmed-ja-30000-jaquad-qa")
# model prediction
answers = model.answer_q(list_question="新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?", list_context=" 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ja-30000-jaquad-qa")
output = pipe("question: 新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?, context: 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-30000-jaquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_jaquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 60.45 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| AnswerF1Score | 60.45 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| BERTScore | 95.64 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_1 | 56.02 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_2 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_3 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_4 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| METEOR | 46.65 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| MoverScore | 86.95 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| ROUGE_L | 58.21 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_jaquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-ja-30000
- 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-ja-30000-jaquad-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",
}
```
|
kasseev/q-FrozenLake-v1-4x4-noSlippery
|
kasseev
| 2023-03-20T23:03:58Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T23:03:55Z |
---
tags:
- FrozenLake-v1-4x4
- 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
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.66 +/- 0.47
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="kasseev/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"])
```
|
jjpp3301/q-FrozenLake-v1-4x4-noSlippery
|
jjpp3301
| 2023-03-20T23:03:05Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T22:51:49Z |
---
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="jjpp3301/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"])
```
|
vocabtrimmer/mt5-small-trimmed-ja-10000-jaquad-qa
|
vocabtrimmer
| 2023-03-20T22:59:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"ja",
"dataset:lmqg/qg_jaquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T22:57:43Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ja
datasets:
- lmqg/qg_jaquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: 新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?, context: 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。"
example_title: "Question Answering Example 1"
- text: "question: 1968年に開催されたオリンピックの名前は何ですか?, context: オリンピックが世界的大イベントに成長するに従って政治に左右されるようになると、1968年のメキシコシティ大会では黒人差別を訴える場と化し、1972年のミュンヘン大会ではアラブのゲリラによるイスラエル選手に対するテロ事件まで起きた(ミュンヘンオリンピック事件)。1976年のモントリオール大会になると、ニュージーランドのラグビーチームの南アフリカ遠征に反対してアフリカの諸国22ヶ国がボイコットを行った。そして、1980年のモスクワ大会ではソ連のアフガニスタン侵攻に反発したアメリカ・西ドイツ・日本などの西側諸国が相次いでボイコットを行った。1984年ロサンゼルス大会ではソ連と東側諸国が報復ボイコットを行ない、参加したのはソ連と対立していた中国とルーマニアだけだった。中でも、イラン革命後のイラン・イスラム共和国はモスクワとロサンゼルス双方のオリンピックをボイコットしている。オリンピックが巨大化するに従って財政負担の増大が大きな問題となり、1976年の夏季大会では大幅な赤字を出し、その後夏季・冬季とも立候補都市が1〜2都市だけという状態が続いた。"
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ja-10000-jaquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_jaquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 0.0
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 49.22
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 41.36
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 94.41
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 83.61
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 52.19
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 52.17
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ja-10000-jaquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ja-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-10000) for question answering task on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-ja-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-10000)
- **Language:** ja
- **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (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="ja", model="vocabtrimmer/mt5-small-trimmed-ja-10000-jaquad-qa")
# model prediction
answers = model.answer_q(list_question="新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?", list_context=" 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ja-10000-jaquad-qa")
output = pipe("question: 新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?, context: 三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-10000-jaquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_jaquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 52.17 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| AnswerF1Score | 52.19 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| BERTScore | 94.41 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_1 | 46.85 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_2 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_3 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_4 | 0 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| METEOR | 41.36 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| MoverScore | 83.61 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| ROUGE_L | 49.22 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_jaquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-ja-10000
- max_length: 512
- max_length_output: 32
- epoch: 24
- batch: 32
- lr: 0.0005
- 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-ja-10000-jaquad-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",
}
```
|
dvesely/q-Taxi-v3
|
dvesely
| 2023-03-20T22:55:28Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T22:55:26Z |
---
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="dvesely/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"])
```
|
dvesely/ppo-LunarLander-v2
|
dvesely
| 2023-03-20T22:49:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T22:49:19Z |
---
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: 279.83 +/- 16.66
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
satyrical/slippyIce
|
satyrical
| 2023-03-20T22:43:45Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T22:43:36Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: slippyIce
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.73
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="satyrical/slippyIce", 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"])
```
|
satyrical/q-FrozenLake-v1-4x4-Slippery
|
satyrical
| 2023-03-20T22:42:35Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T22:40:38Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-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="satyrical/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
grosenthal/la_en_morphology
|
grosenthal
| 2023-03-20T22:38:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T21:34:40Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for la_en_morphology
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
satyrical/q-FrozenLake-v1-4x4-noSlippery
|
satyrical
| 2023-03-20T22:38:48Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T22:38:38Z |
---
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="satyrical/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"])
```
|
carlomax/bert-finetuned-ner
|
carlomax
| 2023-03-20T22:04:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-20T14:59:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9343150231634679
- name: Recall
type: recall
value: 0.9503534163581285
- name: F1
type: f1
value: 0.9422659769731353
- name: Accuracy
type: accuracy
value: 0.9859598516512628
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0611
- Precision: 0.9343
- Recall: 0.9504
- F1: 0.9423
- Accuracy: 0.9860
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.085 | 1.0 | 1756 | 0.0658 | 0.9169 | 0.9345 | 0.9257 | 0.9827 |
| 0.0331 | 2.0 | 3512 | 0.0641 | 0.9302 | 0.9493 | 0.9397 | 0.9858 |
| 0.018 | 3.0 | 5268 | 0.0611 | 0.9343 | 0.9504 | 0.9423 | 0.9860 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
pfunk/CartPole-v1-CP_DQPN_x20-seed555
|
pfunk
| 2023-03-20T21:56:17Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:56:13Z |
---
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: 498.52 +/- 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_x20.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x20]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x20 --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_x20-seed555/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-seed555/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-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_x20 --policy-network-frequency 2000 --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_x20',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 2000,
'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'}
```
|
morenolq/thext-cs-scibert
|
morenolq
| 2023-03-20T21:56:15Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"regression",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-13T07:58:56Z |
---
language: "en"
tags:
- bert
- regression
- pytorch
pipeline:
- text-classification
widget:
- text: "We propose a new approach, based on Transformer-based encoding, to highlight extraction. To the best of our knowledge, this is the first attempt to use transformer architectures to address automatic highlight generation. [SEP] Highlights are short sentences used to annotate scientific papers. They complement the abstract content by conveying the main result findings. To automate the process of paper annotation, highlights extraction aims at extracting from 3 to 5 paper sentences via supervised learning. Existing approaches rely on ad hoc linguistic features, which depend on the analyzed context, and apply recurrent neural networks, which are not effective in learning long-range text dependencies. This paper leverages the attention mechanism adopted in transformer models to improve the accuracy of sentence relevance estimation. Unlike existing approaches, it relies on the end-to-end training of a deep regression model. To attend patterns relevant to highlights content it also enriches sentence encodings with a section-level contextualization. The experimental results, achieved on three different benchmark datasets, show that the designed architecture is able to achieve significant performance improvements compared to the state-of-the-art."
- text: "We design a context-aware sentence-level regressor, in which the semantic similarity between candidate sentences and highlights is estimated by also attending the contextual knowledge provided by the other paper sections. [SEP] Highlights are short sentences used to annotate scientific papers. They complement the abstract content by conveying the main result findings. To automate the process of paper annotation, highlights extraction aims at extracting from 3 to 5 paper sentences via supervised learning. Existing approaches rely on ad hoc linguistic features, which depend on the analyzed context, and apply recurrent neural networks, which are not effective in learning long-range text dependencies. This paper leverages the attention mechanism adopted in transformer models to improve the accuracy of sentence relevance estimation. Unlike existing approaches, it relies on the end-to-end training of a deep regression model. To attend patterns relevant to highlights content it also enriches sentence encodings with a section-level contextualization. The experimental results, achieved on three different benchmark datasets, show that the designed architecture is able to achieve significant performance improvements compared to the state-of-the-art."
- text: "Fig. 2, Fig. 3, Fig. 4 show the effect of varying the number K of selected highlights on the extraction performance. As expected, recall values increase while increasing the number of selected highlights, whereas precision values show an opposite trend. [SEP] Highlights are short sentences used to annotate scientific papers. They complement the abstract content by conveying the main result findings. To automate the process of paper annotation, highlights extraction aims at extracting from 3 to 5 paper sentences via supervised learning. Existing approaches rely on ad hoc linguistic features, which depend on the analyzed context, and apply recurrent neural networks, which are not effective in learning long-range text dependencies. This paper leverages the attention mechanism adopted in transformer models to improve the accuracy of sentence relevance estimation. Unlike existing approaches, it relies on the end-to-end training of a deep regression model. To attend patterns relevant to highlights content it also enriches sentence encodings with a section-level contextualization. The experimental results, achieved on three different benchmark datasets, show that the designed architecture is able to achieve significant performance improvements compared to the state-of-the-art."
---
# General Information
This model is trained on journal publications of belonging to the domain: **Computer Science**.
This is an `allenai/scibert_scivocab_cased` model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the provided context (e.g., the abstract of the scientific paper).
The model is used in the paper 'Transformer-based highlights extraction from scientific papers' published in Knowledge-Based Systems scientific journal.
The model is able to achieve state-of-the-art performance in the task of highlights extraction from scientific papers.
Access to the full paper: [here](https://doi.org/10.1016/j.knosys.2022.109382).
# Usage:
For detailed usage please use the official repository https://github.com/MorenoLaQuatra/THExt .
# References:
If you find it useful, please cite the following paper:
```bibtex
@article{thext,
title={Transformer-based highlights extraction from scientific papers},
author={La Quatra, Moreno and Cagliero, Luca},
journal={Knowledge-Based Systems},
pages={109382},
year={2022},
publisher={Elsevier}
}
```
|
pfunk/CartPole-v1-CP_DQPN_x20-seed888
|
pfunk
| 2023-03-20T21:55:13Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:55:11Z |
---
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: 10.08 +/- 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_x20.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x20]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x20 --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_x20-seed888/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-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_x20 --policy-network-frequency 2000 --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_x20',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 2000,
'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_x2-seed888
|
pfunk
| 2023-03-20T21:54:48Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:54:45Z |
---
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: 102.39 +/- 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_x2.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x2]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x2 --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_x2-seed888/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-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_x2 --policy-network-frequency 200 --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_x2',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 200,
'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'}
```
|
nolanaatama/nypstl
|
nolanaatama
| 2023-03-20T21:54:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-20T21:50:11Z |
---
license: creativeml-openrail-m
---
|
aihijo/gpt2-zh-21k
|
aihijo
| 2023-03-20T21:42:11Z | 60 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-27T15:04:14Z |
---
license: cc-by-nc-sa-4.0
---
|
cloudqi/cqi_brain_memory_summarizer_oneline_pt_v0
|
cloudqi
| 2023-03-20T21:37:31Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:arxiv",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T02:26:53Z |
---
datasets:
- arxiv
widget:
- text: "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks.
In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,
Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing.
In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors
1.7-2.9 times versus production systems."
license: mit
---
# T5 One Line Summary
A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers. It is trained using [simpleT5](https://github.com/Shivanandroy/simpleT5) library - A python package built on top of pytorch lightning⚡️ & transformers🤗 to quickly train T5 models
## Usage:[](https://colab.research.google.com/drive/1HrfT8IKLXvZzPFpl1EhZ3s_iiXG3O2VY?usp=sharing)
```python
abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a
set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks.
In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,
Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time,
Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems.
"""
```
### Using Transformers🤗
```python
model_name = "snrspeaks/t5-one-line-summary"
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_ids = tokenizer.encode("summarize: " + abstract, return_tensors="pt", add_special_tokens=True)
generated_ids = model.generate(input_ids=input_ids,num_beams=5,max_length=50,repetition_penalty=2.5,length_penalty=1,early_stopping=True,num_return_sequences=3)
preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
print(preds)
# output
["Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers",
"Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems",
"Overton: Building, Monitoring, and Improving Production Machine Learning Systems"]
```
### Using simpleT5⚡️
```python
# pip install --upgrade simplet5
from simplet5 import SimpleT5
model = SimpleT5()
model.load_model("t5","snrspeaks/t5-one-line-summary")
model.predict(abstract)
# output
"Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers"
```
|
dvesely/q-FrozenLake-v1-4x4-noSlippery
|
dvesely
| 2023-03-20T21:35:02Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:35:01Z |
---
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="dvesely/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"])
```
|
MakiPan/Pyramids
|
MakiPan
| 2023-03-20T21:25:09Z | 11 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:25:03Z |
---
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: MakiPan/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
liuyanchen1015/roberta-base-mnli_MULTI
|
liuyanchen1015
| 2023-03-20T21:13:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-18T19:07:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-mnli_MULTI
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-mnli_MULTI
This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6448
- Acc: 0.8397
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4594 | 0.33 | 2000 | 0.4535 | 0.8292 |
| 0.4231 | 0.67 | 4000 | 0.4440 | 0.8323 |
| 0.4128 | 1.0 | 6000 | 0.4484 | 0.8352 |
| 0.3137 | 1.33 | 8000 | 0.4905 | 0.8338 |
| 0.3168 | 1.66 | 10000 | 0.4740 | 0.8372 |
| 0.3176 | 2.0 | 12000 | 0.4624 | 0.8415 |
| 0.2347 | 2.33 | 14000 | 0.5300 | 0.8361 |
| 0.2361 | 2.66 | 16000 | 0.5097 | 0.8378 |
| 0.238 | 3.0 | 18000 | 0.5016 | 0.8407 |
| 0.18 | 3.33 | 20000 | 0.5873 | 0.8382 |
| 0.179 | 3.66 | 22000 | 0.5917 | 0.8374 |
| 0.1786 | 4.0 | 24000 | 0.5868 | 0.8398 |
| 0.1409 | 4.33 | 26000 | 0.6468 | 0.8371 |
| 0.1395 | 4.66 | 28000 | 0.6423 | 0.8371 |
| 0.1401 | 4.99 | 30000 | 0.6448 | 0.8397 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qa
|
vocabtrimmer
| 2023-03-20T21:12:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"ru",
"dataset:lmqg/qg_ruquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-20T21:11:45Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ru
datasets:
- lmqg/qg_ruquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: чем соответствует абсолютная погрешность скорости света ?, context: Наивысшая точность измерений была достигнута в начале 1970-х. В 1975 году XV Генеральная конференция по мерам и весам зафиксировала это положение и рекомендовала считать скорость света, равной 299 792 458 м/с с относительной погрешностью 4•10−9, что соответствует абсолютной погрешности 1,1 м/с. Впоследствии это значение скорости света было положено в основу определения метра в Международной системе единиц (СИ), а сама скорость света стала рассматриваться как фундаментальная физическая постоянная, по определению равная указанному значению точно."
example_title: "Question Answering Example 1"
- text: "question: Какие начинания предпринял Lloyds в начале 1970-х годов?, context: В начале 1970-х Lloyds начал расширять деятельность на международной арене, для чего был создан Lloyds Bank International. География его деятельности включала ФРГ, Швейцарию, Ближний Восток, Австралию, Канаду и США; к 1978 году Lloyds был представлен в 43 странах. В 1972 году было создано подразделение страхования, а в 1973 году была основана лизинговая компания Lloyds Leasing. В 1979 году банк начал предоставлять услуги ипотечного кредитования (при покупке недвижимости стоимостью от £25 000 до £150 000). В 1982 году начало работу агентство недвижимости Blackhorse Agencies, к 1989 году у него было 563 отделения. В 1986 году сфера деятельности Lloyds Bank PLC ещё больше расширилась с учреждением брокерской конторы и торгового банка Lloyds Merchant Bank. В 1988 году была поглощена страховая компания Abbey Life Group PLC; после объединения с ней всей своей страховой деятельности была образована дочерняя компания Lloyds Abbey Life. В 1995 году Lloyds Bank Plc объединился с TSB Group plc (группой, образованной в 1986 году из четырёх сберегательных банков Trustee Savings Banks) под названием Lloyds TSB Bank plc. В 2000 году за £7 млрд была поглощена шотландская взаимная страховая компания Scottish Widows."
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_ruquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 30.64
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 56.86
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 43.58
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 95.54
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 85.12
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 76.94
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 56.39
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ru-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-10000) for question answering task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-ru-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-10000)
- **Language:** ru
- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (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="ru", model="vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qa")
# model prediction
answers = model.answer_q(list_question="чем соответствует абсолютная погрешность скорости света ?", list_context=" Наивысшая точность измерений была достигнута в начале 1970-х. В 1975 году XV Генеральная конференция по мерам и весам зафиксировала это положение и рекомендовала считать скорость света, равной 299 792 458 м/с с относительной погрешностью 4•10−9, что соответствует абсолютной погрешности 1,1 м/с. Впоследствии это значение скорости света было положено в основу определения метра в Международной системе единиц (СИ), а сама скорость света стала рассматриваться как фундаментальная физическая постоянная, по определению равная указанному значению точно.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qa")
output = pipe("question: чем соответствует абсолютная погрешность скорости света ?, context: Наивысшая точность измерений была достигнута в начале 1970-х. В 1975 году XV Генеральная конференция по мерам и весам зафиксировала это положение и рекомендовала считать скорость света, равной 299 792 458 м/с с относительной погрешностью 4•10−9, что соответствует абсолютной погрешности 1,1 м/с. Впоследствии это значение скорости света было положено в основу определения метра в Международной системе единиц (СИ), а сама скорость света стала рассматриваться как фундаментальная физическая постоянная, по определению равная указанному значению точно.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 56.39 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| AnswerF1Score | 76.94 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| BERTScore | 95.54 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 47.61 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 41.64 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 36.04 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 30.64 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 43.58 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 85.12 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 56.86 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_ruquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-ru-10000
- max_length: 512
- max_length_output: 32
- epoch: 10
- batch: 32
- lr: 0.001
- 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-ru-10000-ruquad-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",
}
```
|
pfunk/CartPole-v1-CP_DQPN_x20-seed355
|
pfunk
| 2023-03-20T21:08:55Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:08:52Z |
---
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: 74.53 +/- 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_x20.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x20]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x20 --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_x20-seed355/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-seed355/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x20-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_x20 --policy-network-frequency 2000 --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_x20',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 2000,
'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_DQPN_DQN-seed777
|
pfunk
| 2023-03-20T21:07:00Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:06:57Z |
---
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: 387.57 +/- 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-seed777/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-seed777/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-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_DQN --policy-network-frequency 1 --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_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': 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_DQN-seed355
|
pfunk
| 2023-03-20T21:06:35Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:06:31Z |
---
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: 140.22 +/- 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_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-seed355/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-seed355/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_DQN-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_DQN --policy-network-frequency 1 --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_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': 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_DQPN_x5-seed555
|
pfunk
| 2023-03-20T21:06:24Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:06:21Z |
---
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_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-seed555/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed555/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-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_x5 --policy-network-frequency 500 --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_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': 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_x50-seed555
|
pfunk
| 2023-03-20T21:05:30Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:05:26Z |
---
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: 9.67 +/- 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-seed555/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed555/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-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_x50 --policy-network-frequency 5000 --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_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': 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_x5-seed612
|
pfunk
| 2023-03-20T21:05:29Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:05:26Z |
---
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: 338.79 +/- 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-seed612/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed612/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed612/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 612
```
# 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': 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'}
```
|
pfunk/CartPole-v1-CP_DQPN_x5-seed355
|
pfunk
| 2023-03-20T21:05:19Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:05:16Z |
---
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: 496.53 +/- 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-seed355/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed355/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-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_x5 --policy-network-frequency 500 --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_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': 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'}
```
|
adasfgadfssda/asdasda
|
adasfgadfssda
| 2023-03-20T21:04:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-03-20T21:04:23Z |
logický rámec pro organizace oslava silvestru
napiš hierarchie cílů pro aktivity
|
galsenai/hubert-large-ls960-ft-waxal-keyword-spotting
|
galsenai
| 2023-03-20T21:02:24Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-03-20T20:55:53Z |
---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- precision
- f1
model-index:
- name: hubert-large-ls960-ft
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. -->
# hubert-large-ls960-ft
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the galsenai/waxal_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3272
- Accuracy: 0.9413
- Precision: 0.9865
- F1: 0.9628
## 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: 12
- eval_batch_size: 12
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|
| 4.7142 | 1.01 | 500 | 5.2765 | 0.0 | 0.0 | 0.0 |
| 4.396 | 2.02 | 1000 | 5.4145 | 0.0 | 0.0 | 0.0 |
| 3.8883 | 3.04 | 1500 | 4.4336 | 0.0474 | 0.0408 | 0.0104 |
| 2.7848 | 4.05 | 2000 | 3.9772 | 0.1300 | 0.1281 | 0.0964 |
| 1.8649 | 5.06 | 2500 | 3.4482 | 0.1576 | 0.3339 | 0.1547 |
| 1.3084 | 6.07 | 3000 | 2.9703 | 0.3081 | 0.5296 | 0.3402 |
| 0.9868 | 7.08 | 3500 | 2.3985 | 0.4687 | 0.8032 | 0.5353 |
| 0.7679 | 8.1 | 4000 | 1.7937 | 0.6521 | 0.8389 | 0.7095 |
| 0.6232 | 9.11 | 4500 | 1.4768 | 0.7389 | 0.8698 | 0.7847 |
| 0.5126 | 10.12 | 5000 | 1.0542 | 0.8287 | 0.9443 | 0.8763 |
| 0.4453 | 11.13 | 5500 | 0.9050 | 0.8518 | 0.9511 | 0.8960 |
| 0.3775 | 12.15 | 6000 | 0.6996 | 0.8928 | 0.9662 | 0.9266 |
| 0.3568 | 13.16 | 6500 | 0.6157 | 0.8958 | 0.9743 | 0.9285 |
| 0.3165 | 14.17 | 7000 | 0.4925 | 0.9151 | 0.9764 | 0.9436 |
| 0.2951 | 15.18 | 7500 | 0.4992 | 0.9038 | 0.9773 | 0.9369 |
| 0.2763 | 16.19 | 8000 | 0.5212 | 0.9072 | 0.9821 | 0.9404 |
| 0.2634 | 17.21 | 8500 | 0.5201 | 0.9087 | 0.9817 | 0.9418 |
| 0.2422 | 18.22 | 9000 | 0.4504 | 0.9235 | 0.9840 | 0.9514 |
| 0.236 | 19.23 | 9500 | 0.3829 | 0.9257 | 0.9825 | 0.9518 |
| 0.2272 | 20.24 | 10000 | 0.4632 | 0.9155 | 0.9822 | 0.9451 |
| 0.226 | 21.25 | 10500 | 0.4731 | 0.9159 | 0.9837 | 0.9470 |
| 0.2129 | 22.27 | 11000 | 0.3814 | 0.9299 | 0.9832 | 0.9549 |
| 0.2009 | 23.28 | 11500 | 0.4119 | 0.9257 | 0.9814 | 0.9515 |
| 0.1973 | 24.29 | 12000 | 0.4310 | 0.9216 | 0.9843 | 0.9493 |
| 0.1965 | 25.3 | 12500 | 0.3272 | 0.9413 | 0.9865 | 0.9628 |
| 0.1989 | 26.32 | 13000 | 0.4231 | 0.9242 | 0.9878 | 0.9528 |
| 0.1916 | 27.33 | 13500 | 0.3978 | 0.9284 | 0.9876 | 0.9559 |
| 0.1849 | 28.34 | 14000 | 0.4529 | 0.9216 | 0.9865 | 0.9507 |
| 0.1844 | 29.35 | 14500 | 0.3854 | 0.9314 | 0.9864 | 0.9566 |
| 0.1831 | 30.36 | 15000 | 0.4178 | 0.9257 | 0.9853 | 0.9528 |
| 0.1778 | 31.38 | 15500 | 0.3737 | 0.9360 | 0.9884 | 0.9606 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
sherpaNet/ppo-LunarLander-v2-Baseline
|
sherpaNet
| 2023-03-20T21:02:18Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T21:01:50Z |
---
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: 254.90 +/- 16.78
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
aaronrmm/q-FrozenLake-v1-4x4-noSlippery
|
aaronrmm
| 2023-03-20T20:59:10Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T20:59:06Z |
---
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="aaronrmm/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"])
```
|
dugongo/Reinforce-PixelCopterV1
|
dugongo
| 2023-03-20T20:58:40Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T20:58:31Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopterV1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 95.10 +/- 67.74
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
|
manpreets7/dreamshaper-3.2
|
manpreets7
| 2023-03-20T20:51:10Z | 5 | 0 |
diffusers
|
[
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-20T20:45:45Z |
This is DreamShaper 3.32 with baked-in vae and clip fix
You can try it out here: https://sinkin.ai/m/4zdwGOB
Read more about it here: https://civitai.com/models/4384/dreamshaper
|
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