modelId
stringlengths 5
139
| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 18:26:50
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-30 18:26:48
| card
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|---|---|---|---|---|---|---|---|---|---|
adielsa/vit-base-patch16-224-finetuned-chest
|
adielsa
| 2023-02-05T23:49:51Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-02-05T20:39:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-chest
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.989987217724755
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-chest
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0318
- Accuracy: 0.9900
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0947 | 0.98 | 36 | 0.0785 | 0.9732 |
| 0.048 | 1.98 | 72 | 0.0678 | 0.9732 |
| 0.0352 | 2.98 | 108 | 0.0329 | 0.9887 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
brutusxu/distilbert-base-cross-encoder-first-p
|
brutusxu
| 2023-02-05T23:41:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-05T23:30:03Z |
distilbert-base-uncased trained on MSMARCO Document Reranking task,
#### usage
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('brutusxu/distilbert-base-cross-encoder-first-p')
model = AutoModelForSequenceClassification.from_pretrained('brutusxu/distilbert-base-cross-encoder-first-p')
query = 'I love New York'
document = 'I like New York'
input = '<P>' + query + tokenizer.sep_token + '<Q>' + document
tokenized_input = tokenizer(input, return_tensors='pt')
ranking_score = model(**tokenized_input)
```
#### performance
on MSMARCO Document Reranking w. top-100 documents from BM25
```
MRR@10: 0.373
MRR@100: 0.381
nDCG@10: 0.442
nDCG@10: 0.475
```
|
pfunk/Pong-v4-DQPN_p2_pt0.1_tt0.1-seed1
|
pfunk
| 2023-02-05T23:26:37Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Pong-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T23:25:47Z |
---
tags:
- Pong-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: -3.00 +/- 5.33
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
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/DQPN_p2_pt0.1_tt0.1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p2_pt0.1_tt0.1]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p2_pt0.1_tt0.1 --env-id Pong-v4
```
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/Pong-v4-DQPN_p2_pt0.1_tt0.1-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p2_pt0.1_tt0.1-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p2_pt0.1_tt0.1-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p2_pt0.1_tt0.1 --start-policy-f 2000 --end-policy-f 2000 --evaluation-fraction 1.00 --target-tau 0.1 --policy-tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 2000,
'env_id': 'Pong-v4',
'evaluation_fraction': 1.0,
'exp_name': 'DQPN_p2_pt0.1_tt0.1',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 0.1,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 2000,
'target_network_frequency': 1000,
'target_tau': 0.1,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
hectorjelly/Reinforce-push1
|
hectorjelly
| 2023-02-05T23:25:35Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T23:25:22Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-push1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
agercas/poca-SoccerTwos
|
agercas
| 2023-02-05T23:20:05Z | 30 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-05T23:17:34Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: agercas/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
johnhudzinatr/a2c-AntBulletEnv-v0
|
johnhudzinatr
| 2023-02-05T22:44:50Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T22:43:42Z |
---
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: 1680.83 +/- 79.83
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
...
```
|
FBM/poca-SoccerTwos
|
FBM
| 2023-02-05T21:33:04Z | 41 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-05T21:32:49Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: FBM/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
SRobbins/dqn-SpaceInvadersNoFrameskip-v4
|
SRobbins
| 2023-02-05T21:02:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T21:01:35Z |
---
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: 678.50 +/- 248.40
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 SRobbins -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 SRobbins -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 SRobbins
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
MikkelGodsk/Reinforce-PixelCopter-PLE-v0
|
MikkelGodsk
| 2023-02-05T20:53:49Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T10:55:06Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 17.20 +/- 22.17
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
|
jrauch4/a2c-AntBulletEnv-v0
|
jrauch4
| 2023-02-05T20:44:08Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T20:38:39Z |
---
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: 2524.62 +/- 96.82
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
...
```
|
austinmw/ppo-Huggy
|
austinmw
| 2023-02-05T20:32:10Z | 24 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-02-05T20:32:02Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: austinmw/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
z4x/Reinforce-CartPole
|
z4x
| 2023-02-05T20:32:04Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T20:31:51Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mantury/taxi-v3
|
mantury
| 2023-02-05T20:25:55Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T20:25:52Z |
---
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: 7.52 +/- 2.74
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="mantury/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"])
```
|
Buseak/model_from_berturk_Feb_5_TrainTestSplit
|
Buseak
| 2023-02-05T20:16:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-05T19:50:28Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model_from_berturk_Feb_5_TrainTestSplit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_from_berturk_Feb_5_TrainTestSplit
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3125
- Precision: 0.9120
- Recall: 0.9126
- F1: 0.9123
- Accuracy: 0.9376
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 185 | 0.2333 | 0.9065 | 0.9066 | 0.9066 | 0.9343 |
| No log | 2.0 | 370 | 0.2115 | 0.9122 | 0.9143 | 0.9133 | 0.9389 |
| 0.3861 | 3.0 | 555 | 0.2049 | 0.9185 | 0.9175 | 0.9180 | 0.9423 |
| 0.3861 | 4.0 | 740 | 0.2073 | 0.9183 | 0.9185 | 0.9184 | 0.9420 |
| 0.3861 | 5.0 | 925 | 0.2174 | 0.9150 | 0.9155 | 0.9153 | 0.9397 |
| 0.1487 | 6.0 | 1110 | 0.2227 | 0.9177 | 0.9185 | 0.9181 | 0.9415 |
| 0.1487 | 7.0 | 1295 | 0.2399 | 0.9149 | 0.9160 | 0.9155 | 0.9396 |
| 0.1487 | 8.0 | 1480 | 0.2504 | 0.9158 | 0.9163 | 0.9160 | 0.9400 |
| 0.0942 | 9.0 | 1665 | 0.2692 | 0.9141 | 0.9152 | 0.9146 | 0.9392 |
| 0.0942 | 10.0 | 1850 | 0.2782 | 0.9130 | 0.9153 | 0.9141 | 0.9388 |
| 0.0589 | 11.0 | 2035 | 0.2908 | 0.9131 | 0.9144 | 0.9138 | 0.9388 |
| 0.0589 | 12.0 | 2220 | 0.2940 | 0.9121 | 0.9136 | 0.9128 | 0.9377 |
| 0.0589 | 13.0 | 2405 | 0.3068 | 0.9117 | 0.9130 | 0.9123 | 0.9376 |
| 0.0407 | 14.0 | 2590 | 0.3107 | 0.9132 | 0.9148 | 0.9140 | 0.9387 |
| 0.0407 | 15.0 | 2775 | 0.3125 | 0.9120 | 0.9126 | 0.9123 | 0.9376 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
bhuvanesh25/whis-tam-small
|
bhuvanesh25
| 2023-02-05T20:05:16Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"ta",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-05T17:53:13Z |
---
language:
- ta
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Tamil
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ta_in
split: test
metrics:
- type: wer
value: 15.8
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: ta
split: test
metrics:
- type: wer
value: 11.15
name: WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Ta - Bharat Ramanathan (Kudos to him for developing it)
# This is a copy of his model for academic purpose.
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1803
- Wer: 17.1456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3374 | 0.1 | 500 | 0.2579 | 23.3804 |
| 0.29 | 0.2 | 1000 | 0.2260 | 20.9937 |
| 0.2522 | 0.3 | 1500 | 0.2139 | 20.0682 |
| 0.2338 | 0.4 | 2000 | 0.2025 | 19.6785 |
| 0.223 | 0.5 | 2500 | 0.1979 | 18.3147 |
| 0.211 | 0.6 | 3000 | 0.1927 | 17.8276 |
| 0.2032 | 0.7 | 3500 | 0.1865 | 17.3892 |
| 0.1978 | 0.8 | 4000 | 0.1839 | 17.5353 |
| 0.1972 | 0.9 | 4500 | 0.1812 | 17.0969 |
| 0.1894 | 1.0 | 5000 | 0.1803 | 17.1456 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
sd99/poca-SoccerTwos
|
sd99
| 2023-02-05T19:56:49Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-05T19:56:34Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: sd99/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
huggingtweets/f3ralfluid
|
huggingtweets
| 2023-02-05T19:53:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-05T19:52:24Z |
---
language: en
thumbnail: http://www.huggingtweets.com/f3ralfluid/1675626824280/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590925174068711428/4PWe_NrY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">gross</div>
<div style="text-align: center; font-size: 14px;">@f3ralfluid</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from gross.
| Data | gross |
| --- | --- |
| Tweets downloaded | 236 |
| Retweets | 28 |
| Short tweets | 66 |
| Tweets kept | 142 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kjdh98mi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @f3ralfluid's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/d3ukvm2v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/d3ukvm2v/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/f3ralfluid')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
esoria3/clasificador-amazonreviews-en
|
esoria3
| 2023-02-05T19:44:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-05T19:44:01Z |
---
license: apache-2.0
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-amazonreviews-en
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. -->
# clasificador-amazonreviews-en
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2642
- Accuracy: 0.516
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2472 | 1.0 | 500 | 1.1511 | 0.463 |
| 0.9416 | 2.0 | 1000 | 1.1698 | 0.502 |
| 0.7039 | 3.0 | 1500 | 1.2642 | 0.516 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
mitra-mir/setfit-model-Ireland_3labels_balanced_data
|
mitra-mir
| 2023-02-05T19:41:01Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-02-05T19:38:13Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 53 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 53,
"warmup_steps": 6,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
cleanrl/DemonAttack-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T19:38:25Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"DemonAttack-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T19:38:23Z |
---
tags:
- DemonAttack-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: DemonAttack-v5
type: DemonAttack-v5
metrics:
- type: mean_reward
value: 761247.50 +/- 722977.50
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **DemonAttack-v5**
This is a trained model of a PPO agent playing DemonAttack-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id DemonAttack-v5
```
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/cleanrl/DemonAttack-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id DemonAttack-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'DemonAttack-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/VideoPinball-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:56:57Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"VideoPinball-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:56:56Z |
---
tags:
- VideoPinball-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: VideoPinball-v5
type: VideoPinball-v5
metrics:
- type: mean_reward
value: 788806.30 +/- 809677.37
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **VideoPinball-v5**
This is a trained model of a PPO agent playing VideoPinball-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id VideoPinball-v5
```
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/cleanrl/VideoPinball-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id VideoPinball-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'VideoPinball-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
pfunk/Pong-v4-DQN_baseline-seed1
|
pfunk
| 2023-02-05T18:55:01Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Pong-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:54:16Z |
---
tags:
- Pong-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: 5.60 +/- 6.89
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
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/DQN_baseline.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_baseline]"
python -m cleanrl_utils.enjoy --exp-name DQN_baseline --env-id Pong-v4
```
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/Pong-v4-DQN_baseline-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQN_baseline-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQN_baseline-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --exp-name DQN_baseline --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'Pong-v4',
'exp_name': 'DQN_baseline',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
cleanrl/Tennis-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:46:02Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Tennis-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:46:00Z |
---
tags:
- Tennis-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Tennis-v5
type: Tennis-v5
metrics:
- type: mean_reward
value: 23.70 +/- 8.93
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Tennis-v5**
This is a trained model of a PPO agent playing Tennis-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Tennis-v5
```
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/cleanrl/Tennis-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Tennis-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Tennis-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Tennis-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Tennis-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
apatidar0/t5-small-finetuned-amazon-en
|
apatidar0
| 2023-02-05T18:41:49Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-02-05T14:27:45Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: t5-small-finetuned-amazon-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-amazon-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.1622
- eval_rouge1: 14.7056
- eval_rouge2: 6.5373
- eval_rougeL: 13.8753
- eval_rougeLsum: 13.9924
- eval_runtime: 3.8484
- eval_samples_per_second: 35.08
- eval_steps_per_second: 4.417
- step: 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:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
cleanrl/Seaquest-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:30:43Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Seaquest-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:30:42Z |
---
tags:
- Seaquest-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Seaquest-v5
type: Seaquest-v5
metrics:
- type: mean_reward
value: 1844.00 +/- 8.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Seaquest-v5**
This is a trained model of a PPO agent playing Seaquest-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Seaquest-v5
```
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/cleanrl/Seaquest-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Seaquest-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Seaquest-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
sd-concepts-library/kamon-style
|
sd-concepts-library
| 2023-02-05T18:30:38Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2023-02-05T18:30:25Z |
---
license: mit
---
### kamon style on Stable Diffusion
This is the `<kamon-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
cleanrl/Pong-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:30:37Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Pong-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T15:49:42Z |
---
tags:
- Pong-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v5
type: Pong-v5
metrics:
- type: mean_reward
value: 17.90 +/- 1.97
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Pong-v5**
This is a trained model of a PPO agent playing Pong-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Pong-v5
```
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/cleanrl/Pong-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Pong-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Pong-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Pong-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Pong-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Phoenix-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:29:49Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Phoenix-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:29:48Z |
---
tags:
- Phoenix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Phoenix-v5
type: Phoenix-v5
metrics:
- type: mean_reward
value: 49392.00 +/- 15376.34
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Phoenix-v5**
This is a trained model of a PPO agent playing Phoenix-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Phoenix-v5
```
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/cleanrl/Phoenix-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Phoenix-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Phoenix-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Skiing-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:29:08Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Skiing-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:29:06Z |
---
tags:
- Skiing-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Skiing-v5
type: Skiing-v5
metrics:
- type: mean_reward
value: -16783.30 +/- 1601.13
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Skiing-v5**
This is a trained model of a PPO agent playing Skiing-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Skiing-v5
```
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/cleanrl/Skiing-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Skiing-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Skiing-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Skiing-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Skiing-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Breakout-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:28:55Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Breakout-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:28:54Z |
---
tags:
- Breakout-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Breakout-v5
type: Breakout-v5
metrics:
- type: mean_reward
value: 470.60 +/- 131.29
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Breakout-v5**
This is a trained model of a PPO agent playing Breakout-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Breakout-v5
```
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/cleanrl/Breakout-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Breakout-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Breakout-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Breakout-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Breakout-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/BeamRider-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:28:40Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"BeamRider-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:28:38Z |
---
tags:
- BeamRider-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BeamRider-v5
type: BeamRider-v5
metrics:
- type: mean_reward
value: 11643.20 +/- 4100.01
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **BeamRider-v5**
This is a trained model of a PPO agent playing BeamRider-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id BeamRider-v5
```
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/cleanrl/BeamRider-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/BeamRider-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/BeamRider-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id BeamRider-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'BeamRider-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
no3/kat-at3-beta1
|
no3
| 2023-02-05T18:27:57Z | 5 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-27T05:46:28Z |
---
license: creativeml-openrail-m
pipeline_tag: text-to-image
---
### kat from [Flipon](https://store.steampowered.com/app/1285020/Flipon/) on Anything V3.0 via Dreambooth
#### model by no3
This your Anything V3.0 model fine-tuned kat concept taught to Anything V3.0 with Dreambooth.
It can be used by modifying the `instance_prompt`: **sks_kat**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts).
### note
If you want to to use in UI like [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or any UI that's uses .ckpt files just download one file or more from here for your convenience.
[katFl-at3-beta1.ckpt](https://huggingface.co/no3/kat-at3-beta1/resolve/main/katFl-at3-beta1.ckpt) 4.27 GB
[katFl-at3-beta1-pruned.ckpt](https://huggingface.co/no3/kat-at3-beta1/resolve/main/katFl-at3-beta1-pruned.ckpt) 2.13 GB Uses less storage space
If you have issues or questions feel free to visit the Community Tab and start discussion about it.
Here are images used for training this concept:
|
cleanrl/Zaxxon-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:27:35Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Zaxxon-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:27:34Z |
---
tags:
- Zaxxon-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Zaxxon-v5
type: Zaxxon-v5
metrics:
- type: mean_reward
value: 21370.00 +/- 4823.29
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Zaxxon-v5**
This is a trained model of a PPO agent playing Zaxxon-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Zaxxon-v5
```
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/cleanrl/Zaxxon-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Zaxxon-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Zaxxon-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
gudjonk93/IceBERT-finetuned-squad-10
|
gudjonk93
| 2023-02-05T18:27:26Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:icelandic-qa-n_qi_i",
"license:agpl-3.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-05T15:28:05Z |
---
license: agpl-3.0
tags:
- generated_from_trainer
datasets:
- icelandic-qa-n_qi_i
model-index:
- name: IceBERT-finetuned-squad-10
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. -->
# IceBERT-finetuned-squad-10
This model is a fine-tuned version of [mideind/IceBERT](https://huggingface.co/mideind/IceBERT) on the icelandic-qa-n_qi_i dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1511
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 293 | 1.7614 |
| 1.9509 | 2.0 | 586 | 1.5208 |
| 1.9509 | 3.0 | 879 | 1.5011 |
| 0.9529 | 4.0 | 1172 | 1.5694 |
| 0.9529 | 5.0 | 1465 | 1.7516 |
| 0.6647 | 6.0 | 1758 | 1.8629 |
| 0.4336 | 7.0 | 2051 | 1.8881 |
| 0.4336 | 8.0 | 2344 | 2.0768 |
| 0.335 | 9.0 | 2637 | 2.1238 |
| 0.335 | 10.0 | 2930 | 2.1511 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
cleanrl/Qbert-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:26:38Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Qbert-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:26:37Z |
---
tags:
- Qbert-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Qbert-v5
type: Qbert-v5
metrics:
- type: mean_reward
value: 20587.50 +/- 3425.81
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Qbert-v5**
This is a trained model of a PPO agent playing Qbert-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Qbert-v5
```
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/cleanrl/Qbert-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Qbert-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Qbert-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Qbert-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Qbert-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/CrazyClimber-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:26:20Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CrazyClimber-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:26:19Z |
---
tags:
- CrazyClimber-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CrazyClimber-v5
type: CrazyClimber-v5
metrics:
- type: mean_reward
value: 123530.00 +/- 16493.64
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **CrazyClimber-v5**
This is a trained model of a PPO agent playing CrazyClimber-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id CrazyClimber-v5
```
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/cleanrl/CrazyClimber-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id CrazyClimber-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'CrazyClimber-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Defender-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:25:35Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Defender-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:25:33Z |
---
tags:
- Defender-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Defender-v5
type: Defender-v5
metrics:
- type: mean_reward
value: 69060.00 +/- 11820.66
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Defender-v5**
This is a trained model of a PPO agent playing Defender-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Defender-v5
```
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/cleanrl/Defender-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Defender-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Defender-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Defender-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Defender-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Solaris-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:25:19Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Solaris-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:25:17Z |
---
tags:
- Solaris-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Solaris-v5
type: Solaris-v5
metrics:
- type: mean_reward
value: 1488.00 +/- 697.55
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Solaris-v5**
This is a trained model of a PPO agent playing Solaris-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Solaris-v5
```
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/cleanrl/Solaris-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Solaris-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Solaris-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Solaris-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Solaris-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Tutankham-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:24:39Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Tutankham-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:24:38Z |
---
tags:
- Tutankham-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Tutankham-v5
type: Tutankham-v5
metrics:
- type: mean_reward
value: 266.20 +/- 3.22
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Tutankham-v5**
This is a trained model of a PPO agent playing Tutankham-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Tutankham-v5
```
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/cleanrl/Tutankham-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Tutankham-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Tutankham-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/ChopperCommand-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:24:14Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"ChopperCommand-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:24:13Z |
---
tags:
- ChopperCommand-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ChopperCommand-v5
type: ChopperCommand-v5
metrics:
- type: mean_reward
value: 24610.00 +/- 14101.24
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **ChopperCommand-v5**
This is a trained model of a PPO agent playing ChopperCommand-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id ChopperCommand-v5
```
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/cleanrl/ChopperCommand-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id ChopperCommand-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'ChopperCommand-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Riverraid-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:23:54Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Riverraid-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:23:52Z |
---
tags:
- Riverraid-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Riverraid-v5
type: Riverraid-v5
metrics:
- type: mean_reward
value: 17370.00 +/- 2001.53
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Riverraid-v5**
This is a trained model of a PPO agent playing Riverraid-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Riverraid-v5
```
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/cleanrl/Riverraid-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Riverraid-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Riverraid-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/PrivateEye-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:23:53Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"PrivateEye-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:23:52Z |
---
tags:
- PrivateEye-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PrivateEye-v5
type: PrivateEye-v5
metrics:
- type: mean_reward
value: 100.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **PrivateEye-v5**
This is a trained model of a PPO agent playing PrivateEye-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id PrivateEye-v5
```
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/cleanrl/PrivateEye-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/PrivateEye-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/PrivateEye-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id PrivateEye-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'PrivateEye-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
Beegbrain/a2c-AntBulletEnv-v0
|
Beegbrain
| 2023-02-05T18:23:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:22:56Z |
---
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: 525.86 +/- 96.75
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
...
```
|
antoooooine/poca-SoccerTwos
|
antoooooine
| 2023-02-05T18:23:09Z | 36 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-05T08:01:44Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: antoooooine/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cleanrl/FishingDerby-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:22:55Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"FishingDerby-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:22:54Z |
---
tags:
- FishingDerby-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FishingDerby-v5
type: FishingDerby-v5
metrics:
- type: mean_reward
value: 62.20 +/- 8.82
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **FishingDerby-v5**
This is a trained model of a PPO agent playing FishingDerby-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id FishingDerby-v5
```
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/cleanrl/FishingDerby-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id FishingDerby-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'FishingDerby-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Boxing-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:22:42Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Boxing-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:22:41Z |
---
tags:
- Boxing-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Boxing-v5
type: Boxing-v5
metrics:
- type: mean_reward
value: 99.10 +/- 2.70
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Boxing-v5**
This is a trained model of a PPO agent playing Boxing-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Boxing-v5
```
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/cleanrl/Boxing-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Boxing-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Boxing-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Boxing-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Boxing-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/Frostbite-v5-sebulba_ppo_envpool-seed1
|
cleanrl
| 2023-02-05T18:22:36Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Frostbite-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:22:34Z |
---
tags:
- Frostbite-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Frostbite-v5
type: Frostbite-v5
metrics:
- type: mean_reward
value: 304.00 +/- 18.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Frostbite-v5**
This is a trained model of a PPO agent playing Frostbite-v5.
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/sebulba_ppo_envpool.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Frostbite-v5
```
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/cleanrl/Frostbite-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py
curl -OL https://huggingface.co/cleanrl/Frostbite-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Frostbite-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Frostbite-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 16,
'async_update': 4,
'batch_size': 8192,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Frostbite-v5',
'exp_name': 'sebulba_ppo_envpool',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 2048,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 64,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6103,
'params_queue_timeout': 0.02,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
splusminusx/dqn-SpaceInvadersNoFrameskip-v4
|
splusminusx
| 2023-02-05T18:19:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:19:10Z |
---
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: 560.00 +/- 85.56
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 splusminusx -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 splusminusx -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 splusminusx
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Yazdan/pft-clf-finetuned
|
Yazdan
| 2023-02-05T18:14:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-08T17:08:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: pft-clf-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pft-clf-finetuned
This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0987
- Matthews Correlation: 0.9737
## 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: 6
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1299 | 1.0 | 1268 | 0.0987 | 0.9737 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
mkuntz/Reinforce-PixelCopter
|
mkuntz
| 2023-02-05T18:14:31Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T18:14:28Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 11.40 +/- 15.09
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
|
wooihen/a2c-PandaReachDense-v2
|
wooihen
| 2023-02-05T17:38:49Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T17:36:37Z |
---
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: -2.75 +/- 0.70
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
...
```
|
khatkeashish/ppo-PyramidsRND
|
khatkeashish
| 2023-02-05T17:22:21Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-02-05T17:12:42Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: khatkeashish/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
tomekkorbak/silly_nobel
|
tomekkorbak
| 2023-02-05T17:22:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-02-05T12:40:08Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: silly_nobel
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. -->
# silly_nobel
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 2524
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.1,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0},
'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True,
'skip_tokens': 2969174016},
'generation': {'batch_size': 128,
'force_call_on': [503],
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'bad_words_ids': [[32769]],
'do_sample': True,
'eos_token_id': 0,
'max_length': 640,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_hits_threshold': 0,
'num_samples': 4096,
'prefix': '<|aligned|>',
'use_prompt_for_scoring': False}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [503],
'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>',
'should_insert_prefix': True},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '9cdfa11a07b00726ddfdabb554de05b29d777db3'},
'num_additional_tokens': 2,
'path_or_name': 'kejian/grainy-pep8'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 128,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'silly_nobel',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 10,
'num_tokens': 3300000000,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 100,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 2969174016,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/24pv07g1
|
stjiris/Word2vec-legal-portuguese
|
stjiris
| 2023-02-05T16:52:48Z | 0 | 2 | null |
[
"summarization",
"pt",
"license:mit",
"region:us"
] |
summarization
| 2023-01-24T16:55:16Z |
---
license: mit
language:
- pt
metrics:
- bleurt
thumbnail: Word2vec for Portuguese Legal Domain
pipeline_tag: summarization
---
[](https://www.inesc-id.pt/projects/PR07005/)
Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/).
Word2Vec trained for Portuguese Legal Domain
## Citing & Authors
### Contributions
[@MartimZanatti](https://github.com/MartimZanatti)
|
eldraco/poca-SoccerTwos
|
eldraco
| 2023-02-05T16:50:10Z | 15 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-05T13:39:09Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: eldraco/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CLARA-MeD/pegasus-xsum
|
CLARA-MeD
| 2023-02-05T16:48:50Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"simplification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-03T11:34:15Z |
---
tags:
- simplification
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-xsum-clara-med
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-xsum-clara-med
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9013
- Rouge1: 43.7595
- Rouge2: 25.7022
- Rougel: 39.6153
- Rougelsum: 39.7151
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 1.0 | 190 | 2.5468 | 41.6125 | 24.1264 | 37.7704 | 37.8615 |
| No log | 2.0 | 380 | 2.3603 | 41.9598 | 24.315 | 38.1087 | 38.217 |
| 2.7787 | 3.0 | 570 | 2.2604 | 42.0463 | 24.5067 | 38.1632 | 38.2716 |
| 2.7787 | 4.0 | 760 | 2.1846 | 42.1471 | 24.639 | 38.3677 | 38.471 |
| 2.2691 | 5.0 | 950 | 2.1361 | 42.4562 | 24.8962 | 38.6107 | 38.7065 |
| 2.2691 | 6.0 | 1140 | 2.0887 | 42.6005 | 24.947 | 38.7049 | 38.805 |
| 2.2691 | 7.0 | 1330 | 2.0617 | 42.7946 | 24.9509 | 38.9123 | 39.0003 |
| 2.0313 | 8.0 | 1520 | 2.0222 | 43.0201 | 25.3552 | 39.151 | 39.266 |
| 2.0313 | 9.0 | 1710 | 2.0049 | 43.2293 | 25.4719 | 39.4239 | 39.4944 |
| 1.872 | 10.0 | 1900 | 1.9899 | 43.2629 | 25.5285 | 39.4124 | 39.4591 |
| 1.872 | 11.0 | 2090 | 1.9772 | 43.4294 | 25.8006 | 39.5863 | 39.6726 |
| 1.872 | 12.0 | 2280 | 1.9630 | 43.63 | 25.7259 | 39.5521 | 39.6888 |
| 1.7497 | 13.0 | 2470 | 1.9513 | 43.4053 | 25.5567 | 39.4567 | 39.5918 |
| 1.7497 | 14.0 | 2660 | 1.9336 | 43.2584 | 25.4554 | 39.2917 | 39.3944 |
| 1.6609 | 15.0 | 2850 | 1.9345 | 43.2644 | 25.5958 | 39.3474 | 39.4645 |
| 1.6609 | 16.0 | 3040 | 1.9152 | 43.4404 | 25.6127 | 39.4472 | 39.5418 |
| 1.6609 | 17.0 | 3230 | 1.9106 | 43.2751 | 25.3213 | 39.2723 | 39.3871 |
| 1.5809 | 18.0 | 3420 | 1.9125 | 43.2335 | 25.341 | 39.2705 | 39.3577 |
| 1.5809 | 19.0 | 3610 | 1.9086 | 43.1679 | 25.3275 | 39.1858 | 39.303 |
| 1.5221 | 20.0 | 3800 | 1.9030 | 43.2794 | 25.4126 | 39.2902 | 39.4092 |
| 1.5221 | 21.0 | 3990 | 1.8996 | 43.1731 | 25.3819 | 39.1873 | 39.3172 |
| 1.5221 | 22.0 | 4180 | 1.9006 | 43.4949 | 25.4485 | 39.3092 | 39.4516 |
| 1.4714 | 23.0 | 4370 | 1.8977 | 43.5657 | 25.5974 | 39.4489 | 39.5257 |
| 1.4714 | 24.0 | 4560 | 1.9035 | 43.6444 | 25.6794 | 39.5809 | 39.683 |
| 1.4421 | 25.0 | 4750 | 1.9000 | 43.4825 | 25.5898 | 39.4319 | 39.4973 |
| 1.4421 | 26.0 | 4940 | 1.9030 | 43.4623 | 25.5726 | 39.461 | 39.6009 |
| 1.4421 | 27.0 | 5130 | 1.8993 | 43.3357 | 25.5518 | 39.3897 | 39.4672 |
| 1.4139 | 28.0 | 5320 | 1.9009 | 43.5834 | 25.7211 | 39.584 | 39.6725 |
| 1.4139 | 29.0 | 5510 | 1.9002 | 43.7115 | 25.6997 | 39.6603 | 39.7621 |
| 1.4016 | 30.0 | 5700 | 1.9013 | 43.7595 | 25.7022 | 39.6153 | 39.7151 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1
|
gabriellabollici/clasificador-rottentomatoes
|
gabriellabollici
| 2023-02-05T16:31:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-05T16:30:28Z |
---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-rottentomatoes
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. -->
# clasificador-rottentomatoes
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0103
- Accuracy: 0.4783
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6937 | 1.0 | 853 | 0.8311 | 0.0 |
| 0.6578 | 2.0 | 1706 | 0.7352 | 0.6190 |
| 0.5328 | 3.0 | 2559 | 1.0103 | 0.4783 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
seongwoon/labor_space_v4
|
seongwoon
| 2023-02-05T16:27:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-02-05T12:47:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-labor_space_v3-finetuned-labor_space_v4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-labor_space_v3-finetuned-labor_space_v4
This model is a fine-tuned version of [seongwoon/distilbert-base-uncased-finetuned-labor_space_v3](https://huggingface.co/seongwoon/distilbert-base-uncased-finetuned-labor_space_v3) 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: 64
- 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 4.26.0
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
pfunk/Pong-v4-DQPN_p2_e0.10-seed1
|
pfunk
| 2023-02-05T16:17:17Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Pong-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T16:16:20Z |
---
tags:
- Pong-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: 5.90 +/- 4.46
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
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/DQPN_p2_e0.10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p2_e0.10]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p2_e0.10 --env-id Pong-v4
```
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/Pong-v4-DQPN_p2_e0.10-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p2_e0.10-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p2_e0.10-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p2_e0.10 --start-policy-f 2000 --end-policy-f 1000 --evaluation-fraction 0.10 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 1000,
'env_id': 'Pong-v4',
'evaluation_fraction': 0.1,
'exp_name': 'DQPN_p2_e0.10',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 1.0,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 2000,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pietrocagnasso/bart-paper-titles
|
pietrocagnasso
| 2023-02-05T16:17:11Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-25T11:27:10Z |
---
language:
- en
---
BART model used to generate scientific papers' title given the highlights and the abstract of the paper.
This model is the result of a fine-tuning process done on [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6).
We performed the fine-tuning for one epoch on CSPubSumm (Ed Collins, et al. "A supervised approach to extractive summarisation of scientific papers."),
BIOPubSumm, and AIPubSumm (L. Cagliero, M. La Quatra "Extracting highlights of scientific articles: A supervised summarization approach.").
You can find more details in the [GitHub repo](https://github.com/nicolovergaro/DNLP_project).
# Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the
[BART docs](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartForConditionalGeneration) for more information.
# Metrics
We have tested the model on all three the test sets, with the following results:
| Dataset | Rouge-1 F1 | Rouge-2 F1 | Rouge-L F1 | BERTScore F1 |
|:----------:|:----------:|:----------:|:----------:|:------------:|
| AIPubSumm | 0.42713 | 0.21781 | 0.35251 | 0.90391 |
| BIOPubSumm | 0.45758 | 0.25219 | 0.39350 | 0.90205 |
| CSPubSumm | 0.51502 | 0.33377 | 0.45760 | 0.91703 |
|
pietrocagnasso/bart-paper-titles-cs
|
pietrocagnasso
| 2023-02-05T16:13:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-12-18T17:07:41Z |
---
language:
- en
---
BART model used to generate scientific papers' title given the highlights and the abstract of the paper. This model is specifically tuned for computer
science papers.
This model is the result of a fine-tuning process done on [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6).
We performed a first fine-tuning epoch on CSPubSumm (Ed Collins, et al. "A supervised approach to extractive summarisation of scientific papers."),
BIOPubSumm, and AIPubSumm (L. Cagliero, M. La Quatra "Extracting highlights of scientific articles: A supervised summarization approach.").
A second fine-tuning epoch was performed only on CSPubSumm to let the model better understand how computer science titles are composed.
You can find more details in the [GitHub repo](https://github.com/nicolovergaro/DNLP_project).
# Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the
[BART docs](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartForConditionalGeneration) for more information.
# Metrics
We have tested the model on all three the test sets, with the following results:
| Dataset | Rouge-1 F1 | Rouge-2 F1 | Rouge-L F1 | BERTScore F1 |
|:----------:|:----------:|:----------:|:----------:|:------------:|
| CSPubSumm | 0.55842 | 0.38177 | 0.50117 | 0.92329 |
| AIPubSumm | 0.44824 | 0.25147 | 0.37326 | 0.90774 |
| BIOPubSumm | 0.45350 | 0.24498 | 0.38614 | 0.90123 |
|
pietrocagnasso/bart-paper-titles-ai
|
pietrocagnasso
| 2023-02-05T16:10:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-12-18T17:09:22Z |
---
language:
- en
---
BART model used to generate scientific papers' title given the highlights and the abstract of the paper. This model is specifically tuned for artificial
intelligence papers.
This model is the result of a fine-tuning process done on [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6).
We performed a first fine-tuning epoch on CSPubSumm (Ed Collins, et al. "A supervised approach to extractive summarisation of scientific papers."),
BIOPubSumm, and AIPubSumm (L. Cagliero, M. La Quatra "Extracting highlights of scientific articles: A supervised summarization approach.").
A second fine-tuning epoch was performed only on AIPubSumm to let the model better understand how artifical intelligence titles are composed.
You can find more details in the [GitHub repo](https://github.com/nicolovergaro/DNLP_project).
# Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the
[BART docs](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartForConditionalGeneration) for more information.
# Metrics
We have tested the model on all three the test sets, with the following results:
| Dataset | Rouge-1 F1 | Rouge-2 F1 | Rouge-L F1 | BERTScore F1 |
|:----------:|:----------:|:----------:|:----------:|:------------:|
| AIPubSumm | 0.43325 | 0.22397 | 0.36069 | 0.90644 |
| BIOPubSumm | 0.45472 | 0.24739 | 0.39121 | 0.90131 |
| CSPubSumm | 0.52281 | 0.33140 | 0.46635 | 0.91861 |
|
pietrocagnasso/bart-paper-titles-bio
|
pietrocagnasso
| 2023-02-05T16:09:22Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-12-18T17:10:17Z |
---
language:
- en
---
BART model used to generate scientific papers' title given the highlights and the abstract of the paper. This model is specifically tuned for biology and
medicine papers.
This model is the result of a fine-tuning process done on [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6).
We performed a first fine-tuning epoch on CSPubSumm (Ed Collins, et al. "A supervised approach to extractive summarisation of scientific papers."),
BIOPubSumm, and AIPubSumm (L. Cagliero, M. La Quatra "Extracting highlights of scientific articles: A supervised summarization approach.").
A second fine-tuning epoch was performed only on BIOPubSumm to let the model better understand how biology and medicine titles are composed.
You can find more details in the [GitHub repo](https://github.com/nicolovergaro/DNLP_project).
# Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the
[BART docs](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartForConditionalGeneration) for more information.
# Metrics
We have tested the model on all three the test sets, with the following results:
| Dataset | Rouge-1 F1 | Rouge-2 F1 | Rouge-L F1 | BERTScore F1 |
|:----------:|:----------:|:----------:|:----------:|:------------:|
| BIOPubSumm | 0.45979 | 0.25406 | 0.39607 | 0.90272 |
| AIPubSumm | 0.44455 | 0.23214 | 0.35779 | 0.90721 |
| CSPubSumm | 0.49769 | 0.30773 | 0.43376 | 0.91561 |
|
wooihen/a2c-AntBulletEnv-v0
|
wooihen
| 2023-02-05T16:00:31Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T15:59:30Z |
---
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: 1161.19 +/- 177.02
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
...
```
|
khatkeashish/ppo-SnowballTarget
|
khatkeashish
| 2023-02-05T15:55:21Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-02-05T15:52:53Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: khatkeashish/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
eldraco/tqc-PandaReachDense-v2
|
eldraco
| 2023-02-05T15:43:36Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T11:59:17Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.24 +/- 0.19
name: mean_reward
verified: false
---
# **TQC** Agent playing **PandaReachDense-v2**
This is a trained model of a **TQC** 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
...
```
|
bond005/wav2vec2-mbart50-ru
|
bond005
| 2023-02-05T15:42:54Z | 32 | 6 |
transformers
|
[
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"audio",
"speech",
"common_voice",
"SberDevices/Golos",
"sova_rudevices",
"rulibrispeech",
"ru",
"dataset:bond005/sberdevices_golos_10h_crowd",
"dataset:bond005/sberdevices_golos_100h_farfield",
"dataset:common_voice",
"dataset:bond005/sova_rudevices",
"dataset:bond005/rulibrispeech",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-06T13:32:44Z |
---
language: ru
datasets:
- bond005/sberdevices_golos_10h_crowd
- bond005/sberdevices_golos_100h_farfield
- common_voice
- bond005/sova_rudevices
- bond005/rulibrispeech
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- common_voice
- SberDevices/Golos
- sova_rudevices
- rulibrispeech
license: apache-2.0
widget:
- example_title: test sound with Russian speech
src: https://huggingface.co/bond005/wav2vec2-mbart50-ru/resolve/main/test_sound.wav
model-index:
- name: Wav2Vec2-mBART-50 for speech-to-text in Russian by Ivan Bondarenko
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Sberdevices Golos (crowd)
type: SberDevices/Golos
args: ru
metrics:
- name: Test WER
type: wer
value: 13.204
- name: Test CER
type: cer
value: 4.157
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Sberdevices Golos (farfield)
type: SberDevices/Golos
args: ru
metrics:
- name: Test WER
type: wer
value: 17.681
- name: Test CER
type: cer
value: 6.773
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ru
type: common_voice
args: ru
metrics:
- name: Test WER
type: wer
value: 14.693
- name: Test CER
type: cer
value: 5.765
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Sova RuDevices
type: sova_rudevices
args: ru
metrics:
- name: Test WER
type: wer
value: 22.727
- name: Test CER
type: cer
value: 9.183
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Russian Librispeech
type: rulibrispeech
args: ru
metrics:
- name: Test WER
type: wer
value: 32.540
- name: Test CER
type: cer
value: 10.369
---
# Wav2Vec2-mBART-50-Ru
Wav2Vec2-mBART-50-Ru is a speech-sequence-to-text-sequence model, which can convert an input audio with Russian speech into a text with punctuation, capitalization and so on.
Wav2Vec2-mBART-50-Ru is the [SpeechEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/speech-encoder-decoder), which was initialized with [Wav2Vec2-Large-Ru-Golos](https://huggingface.co/bond005/wav2vec2-large-ru-golos) as the encoder and [mBART-large-50](https://huggingface.co/facebook/mbart-large-50) as the decoder. After its initialization the model was fine-tuned using the training parts of several annotated speech corpora:
- [the 10 hours crowd subset of SberDevices Golos](https://huggingface.co/datasets/bond005/sberdevices_golos_10h_crowd)
- [the 100 hours farfield subset of SberDevices Golos](https://huggingface.co/datasets/bond005/sberdevices_golos_100h_farfield)
- [the Russian subset of Common Voice 6.0](https://huggingface.co/datasets/common_voice)
- [Sova RuDevices](https://huggingface.co/datasets/bond005/sova_rudevices)
- 15% part of the training subset of [Russian Librispeech](https://huggingface.co/datasets/bond005/rulibrispeech)
CommonVoice 6.0 contains "rich" text annotations with punctuation and capitalization, but other speech corpora includes plain texts only. Therefore, text annotations of these corpora were riched automatically using the [Silero text enhancement model](https://github.com/snakers4/silero-models#text-enhancement).
## Usage
When using this model, make sure that your speech input is sampled at 16kHz.
You can use this model by writing your own inference script:
```python
import os
import warnings
import torch
from datasets import load_dataset
from datasets.features import Audio
from transformers import SpeechEncoderDecoderModel, Wav2Vec2Processor
LANG_ID = "ru"
MODEL_ID = "bond005/wav2vec2-mbart50-ru"
SAMPLES = 30
num_processes = max(1, os.cpu_count())
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = SpeechEncoderDecoderModel.from_pretrained(MODEL_ID)
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
if test_dataset.features['audio'].sampling_rate != 16_000:
test_dataset = test_dataset.cast_column(
'audio',
Audio(sampling_rate=16_000)
)
audio_data = [test_dataset[i]['audio']['array'] for i in range(SAMPLES)]
processed = processor(audio_data, sampling_rate=16_000,
return_tensors="pt", padding='longest')
with torch.no_grad():
predicted_ids = model.generate(**processed)
predicted_sentences = processor.batch_decode(
predicted_ids,
num_processes=num_processes,
skip_special_tokens=True
)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference: ", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
```
```text
----------------------------------------------------------------------------------------------------
Reference: Я беру маленький кусочек бумажки.
Prediction: Я беру маленькие кусочек бумажки.
----------------------------------------------------------------------------------------------------
Reference: О потерях пока не сообщается.
Prediction: А потеря их пока не сообщается.
----------------------------------------------------------------------------------------------------
Reference: Ваша воля.
Prediction: Ваша воля.
----------------------------------------------------------------------------------------------------
Reference: Мы высоко ценим ее роль в этом отношении.
Prediction: Мы высоко ценим ее роль в этом отношении.
----------------------------------------------------------------------------------------------------
Reference: Вот это вызывало у нас жуткое отторжение.
Prediction: Вот это вызвало у нас жуткое отвержение.
----------------------------------------------------------------------------------------------------
Reference: Он положил ей букет на книгу.
Prediction: Он положил ее букет на книгу.
----------------------------------------------------------------------------------------------------
Reference: Ну и положу, – обиделась Женя.
Prediction: – Ну и положи, – обиделась Женя.
----------------------------------------------------------------------------------------------------
Reference: Благодарю представителя Австралии за ее заявление.
Prediction: Благодарю представителя Австралии за ее заявление.
----------------------------------------------------------------------------------------------------
Reference: Для меня это не было неожиданностью.
Prediction: Для меня это не было неожиданностью.
----------------------------------------------------------------------------------------------------
Reference: Поздняя ночь.
Prediction: Поздняя ночь.
----------------------------------------------------------------------------------------------------
Reference: Тем не менее нужно вновь вычленить некоторые элементы наших политических установок.
Prediction: Тем не менее нужно назвать нищие нынешние элементы наших политических устоков.
----------------------------------------------------------------------------------------------------
Reference: Мы не можем позволить себе упустить эту возможность.
Prediction: Мы не можем позволить себе упустить эту возможность.
----------------------------------------------------------------------------------------------------
Reference: В предстоящие месяцы Суд примет решение по ордеру на арест министра обороны Хусейна.
Prediction: В предстоящие месяцы Суд примет решение по оратору на орифлейм министра иностранных дел Кубы.
----------------------------------------------------------------------------------------------------
Reference: Валерия живет в старом панельном доме советских времён.
Prediction: Валерия живет в старом Баньяном, да не советских временах.
----------------------------------------------------------------------------------------------------
Reference: Я вернусь скоро.
Prediction: Я вернусь скоро...
----------------------------------------------------------------------------------------------------
Reference: Слово предоставляется Его Превосходительству принцу Зайду.
Prediction: Слово предоставляется Его Превосходительству Пан Ги Муну.
----------------------------------------------------------------------------------------------------
Reference: Ну конечно, тебе бы этого хотелось.
Prediction: Ну, конечно, тебе бы этого хотелось.
----------------------------------------------------------------------------------------------------
Reference: Общественные объединения равны перед законом.
Prediction: Общественные объединения равны перед законом.
----------------------------------------------------------------------------------------------------
Reference: Ну, что же, нету этики, эстетики.
Prediction: Ну что же, не туда зайти? Не туда зайти?
----------------------------------------------------------------------------------------------------
Reference: Сразу же она легла в постель.
Prediction: Сразу же она легла в постель.
----------------------------------------------------------------------------------------------------
Reference: Сейчас я сделаю заявление в своем национальном качестве.
Prediction: Сейчас я сделаю заявление в своем национальном качестве.
----------------------------------------------------------------------------------------------------
Reference: Что там сейчас происходит в Твиттере?
Prediction: Что там сейчас происходит в Твиттере?
----------------------------------------------------------------------------------------------------
Reference: Ну хорошо, что револьвер был заряжен холостыми.
Prediction: Ну хорошо, что Револьвер был заряжен холостыми.
----------------------------------------------------------------------------------------------------
Reference: А потом дальше может проходить работа такая.
Prediction: А потом дальше может проходить работа такая.
----------------------------------------------------------------------------------------------------
Reference: Из Microsoft написали что на текущий момент у них нет открытых вакансий.
Prediction: Из моих красотов написали, что на текущий момент у них нет открытых вакансий.
----------------------------------------------------------------------------------------------------
Reference: Мы добились многого, но сейчас не время терять набранную динамику.
Prediction: Мы добились многого, но сейчас не время терять набранную динамику.
----------------------------------------------------------------------------------------------------
Reference: Мы внимательно проанализировали документ и содержащиеся в нем выводы и рекомендации.
Prediction: Мы внимательно проанализировали документ, содержащийся в нем, выводы рекомендаций.
----------------------------------------------------------------------------------------------------
Reference: А сейчас слово имеет представитель Соединенных Штатов Америки.
Prediction: А сейчас слово имеет представитель Соединенных Штатов Америки.
----------------------------------------------------------------------------------------------------
Reference: Обстоятельства изменились, и мы должны учитывать это.
Prediction: Обстоятельно изменились и мы должны учитывать это.
----------------------------------------------------------------------------------------------------
Reference: На этом принципе основывается и наша позиция по Фолклендским островам.
Prediction: На этом принципе основывается и наша позиция по Фолклендским островам.
```
The Google Colab version of [this script](https://colab.research.google.com/drive/1VlTrsc9d9wyzLPAWagpXLzoDLn2PRvZA?usp=sharing) is available too.
## Evaluation
This model was evaluated on the test subsets of [SberDevices Golos](https://huggingface.co/datasets/SberDevices/Golos), [Common Voice 6.0](https://huggingface.co/datasets/common_voice) (Russian part), and [Sova RuDevices](https://huggingface.co/datasets/bond005/sova_rudevices).
The evaluation script [wav2vec2_mbart50_ru_eval](https://www.kaggle.com/code/bond005/wav2vec2-mbart50-ru-eval) is available for checking and reproducibility.
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{bondarenko2023-wav2vec2-mbart50-ru,
title={Wav2Vec2-mBART-50 for speech-to-text in Russian by Ivan Bondarenko},
author={Bondarenko, Ivan},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/bond005/wav2vec2-mbart50-ru}},
year={2023}
}
```
|
PeterDerLustige/poca-SoccerTwos
|
PeterDerLustige
| 2023-02-05T15:39:45Z | 30 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-05T15:39:37Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: PeterDerLustige/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Beegbrain/ppo-Snowball
|
Beegbrain
| 2023-02-05T15:33:02Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-02-05T15:32:55Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: Beegbrain/ppo-Snowball
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
raquelsmv/clasificador-rotten_tomatoes
|
raquelsmv
| 2023-02-05T15:30:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-05T15:29:29Z |
---
license: apache-2.0
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-rotten_tomatoes
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. -->
# clasificador-rotten_tomatoes
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4363
- Accuracy: 0.9138
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4625 | 1.0 | 853 | 0.3543 | 0.9027 |
| 0.2407 | 2.0 | 1706 | 0.3710 | 0.9115 |
| 0.0962 | 3.0 | 2559 | 0.4363 | 0.9138 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Beegbrain/Reinforce-model-pixelcopter
|
Beegbrain
| 2023-02-05T15:09:15Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T15:09:05Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-model-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 74.00 +/- 69.99
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
|
hr1588/xlm-roberta-base-finetuned-panx-de
|
hr1588
| 2023-02-05T15:03:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-05T14:50:25Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8654677896653767
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1405
- F1: 0.8655
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2495 | 1.0 | 787 | 0.1764 | 0.8184 |
| 0.1299 | 2.0 | 1574 | 0.1427 | 0.8562 |
| 0.0771 | 3.0 | 2361 | 0.1405 | 0.8655 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Akriel/MLP-Lunar-Lander
|
Akriel
| 2023-02-05T14:38:19Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T14:08:36Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-MLP
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.78 +/- 15.02
name: mean_reward
verified: false
---
# **PPO-MLP** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-MLP** 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
...
```
|
Mehtap/whisper-tiny
|
Mehtap
| 2023-02-05T14:32:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"tr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-01-13T11:45:37Z |
---
language:
- tr
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Tiny Turkish Whisper (TTW)
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. -->
# Tiny Turkish Whisper (TTW)
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Ermetal Meetings 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 0
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.5.2
- Tokenizers 0.13.1
|
dawokim/xlm-roberta-base-finetuned-panx-de
|
dawokim
| 2023-02-05T14:06:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-05T13:28:25Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8616659101225601
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1344
- F1: 0.8617
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2564 | 1.0 | 525 | 0.1610 | 0.8285 |
| 0.1307 | 2.0 | 1050 | 0.1378 | 0.8491 |
| 0.0813 | 3.0 | 1575 | 0.1344 | 0.8617 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.10.1+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2
|
nolanaatama/at80slora
|
nolanaatama
| 2023-02-05T13:59:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-05T13:57:58Z |
---
license: creativeml-openrail-m
---
|
nolanaatama/vpm
|
nolanaatama
| 2023-02-05T13:28:39Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-05T13:24:39Z |
---
license: creativeml-openrail-m
---
|
xuancaiqisehua/icefall_asr_tal-csasr_conv_emformer_transducer_stateless2
|
xuancaiqisehua
| 2023-02-05T13:23:17Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-02-05T08:48:14Z |
Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/874.
### Pre-trained conv_emformer_transducer_stateless2 models for the TAL_CSASR dataset with icefall.
The model was trained on the far data of TAL_CSASR with the scripts in icefall based on the latest version k2.
You can use the trained model to export it to ncnn and run it with sherpa-ncnn.
### Training procedure
- Install k2 : https://k2.readthedocs.io/en/latest/installation/index.html
- Install lhotse : https://lhotse.readthedocs.io/en/latest/getting-started.html#installation
- Clone icefall : https://github.com/k2-fsa/icefall
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
- Preparing data.
```
cd egs/tal_csasr_conv_emformer/ASR
bash ./prepare.sh
```
- Training
```
bash run.sh
```
- Evaluation results
The decoding results (CER%) on TAL_CSASR(dev and test) are listed below:
|decoding-method|epoch(iter) |avg| dev|test|
|----|---|---|---|---|
|fast_beam_search | 6 | 3 | 11.36 | 11.37|
- Export model to ncnn
reference : https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
```
./conv_emformer_transducer_stateless2/export-for-ncnn.py \
--exp-dir exp_conv_emformer \
--lang_dir data/lang_char \
--epoch 5 \
--iter 8000 \
--avg 3 \
--use-averaged-model 1 \
--num-encoder-layers 12 \
--chunk-length 32 \
--cnn-module-kernel 31 \
--left-context-length 32 \
--right-context-length 8 \
--memory-size 32
```
- Export torchscript model via pnnx
```
pnnx ./encoder_jit_trace-pnnx.pt
pnnx ./decoder_jit_trace-pnnx.pt
pnnx ./joiner_jit_trace-pnnx.pt
```
- Modify the following two lines in your encoder_jit_trace-pnnx.ncnn.param file.
- Then you can use the following code to test the converted models.
```
model/tokens.txt \
model/encoder_jit_trace-pnnx.ncnn.param \
model/encoder_jit_trace-pnnx.ncnn.bin \
model/decoder_jit_trace-pnnx.ncnn.param \
model/decoder_jit_trace-pnnx.ncnn.bin \
model/joiner_jit_trace-pnnx.ncnn.param \
model/joiner_jit_trace-pnnx.ncnn.bin \
test_wavs/0.wav
```
|
ovillan/distilbert-finetuning-fakenews
|
ovillan
| 2023-02-05T13:07:15Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-25T12:13:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-finetuning-fakenews
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-finetuning-fakenews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an external dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2804
- Accuracy: 0.8833
- F1: 0.9014
## Model description
More information needed
## Intended uses & limitations
A DistilBERT model is trained on an external dataset (Spanish Fake and Real News) to detect fake news in spanish.
## Training and evaluation data
Dataset obtained from: https://www.kaggle.com/datasets/zulanac/fake-and-real-news, under a CC BY-SA 4.0 license. Author: Fabricio A. Zules.
<p>For compatibility reasons with the model, it was necessary to change 'texto' and 'clase' headers to 'text' and 'label'; and 'fake' and 'true' values (from class/label), were replaced by '0' and '1' values.</p>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
apatidar0/distilbert-base-uncased-finetuned-imdb
|
apatidar0
| 2023-02-05T13:04:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-02-05T12:46:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
IMDB dataset for getting intuition on how to train an MLM model
## Training procedure
You need to create the dataset in the exact format in which the model was trained by the author.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
pfunk/Pong-v4-DQN_tt0.1-seed1
|
pfunk
| 2023-02-05T12:49:45Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Pong-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T12:49:25Z |
---
tags:
- Pong-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: 2.90 +/- 9.04
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
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/DQN_tt0.1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_tt0.1]"
python -m cleanrl_utils.enjoy --exp-name DQN_tt0.1 --env-id Pong-v4
```
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/Pong-v4-DQN_tt0.1-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQN_tt0.1-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQN_tt0.1-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --exp-name DQN_tt0.1 --tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'Pong-v4',
'exp_name': 'DQN_tt0.1',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 0.1,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
AinhoaC/clasificador-muchocine
|
AinhoaC
| 2023-02-05T12:48:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-05T12:47:19Z |
---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine
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. -->
# clasificador-muchocine
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4463
- Accuracy: 0.4503
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.3448 | 0.3871 |
| 1.3815 | 2.0 | 776 | 1.3046 | 0.4284 |
| 1.0077 | 3.0 | 1164 | 1.4463 | 0.4503 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
mrigendraagrawal/taxi-RL
|
mrigendraagrawal
| 2023-02-05T12:41:15Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T12:41:13Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-RL
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="mrigendraagrawal/taxi-RL", 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"])
```
|
Svengali75/ProtogenX53Photorealism
|
Svengali75
| 2023-02-05T12:36:13Z | 0 | 4 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-02-05T12:28:42Z |
---
# 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 Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- 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]
|
khatkeashish/Reinforce-Pixelcopter-PLE-v0
|
khatkeashish
| 2023-02-05T12:35:01Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T01:42:24Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 57.20 +/- 27.11
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
|
sheldon297/distilbert-base-uncased_trivia-qa
|
sheldon297
| 2023-02-05T12:06:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-05T11:59:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: result
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. -->
# result
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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
KKHyun/distilbert-base-uncased-finetuned-squad
|
KKHyun
| 2023-02-05T11:38:48Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-05T10:22:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1664
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2096 | 1.0 | 5533 | 1.1505 |
| 0.952 | 2.0 | 11066 | 1.1238 |
| 0.7347 | 3.0 | 16599 | 1.1664 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
lora-library/a-photo-of-simbatheog
|
lora-library
| 2023-02-05T11:13:54Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-02-05T11:13:52Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: simbatheog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - a-photo-of-simbatheog
These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "simbatheog" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
Test prompt: A photo of simbatheog in a bucket
|
Beegbrain/Reinforce-model-cartpole1
|
Beegbrain
| 2023-02-05T11:13:20Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T11:13:08Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-model-cartpole1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
brand25/q-Taxi-v3
|
brand25
| 2023-02-05T11:11:02Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T11:10:58Z |
---
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="brand25/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"])
```
|
gabriellabollici/modelo-muchocine
|
gabriellabollici
| 2023-02-05T10:40:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-05T10:39:06Z |
---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: modelo-muchocine
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. -->
# modelo-muchocine
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3273
- Accuracy: 0.4181
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.5467 | 0.3355 |
| 1.5099 | 2.0 | 776 | 1.2819 | 0.4065 |
| 1.2196 | 3.0 | 1164 | 1.3273 | 0.4181 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
LowGI/STT_Model_3
|
LowGI
| 2023-02-05T10:24:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-05T10:18:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: STT_Model_3
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. -->
# STT_Model_3
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BachNgoH/Reinforce-CartPol-v1
|
BachNgoH
| 2023-02-05T09:10:31Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T09:10:21Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPol-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
evanarlian/wav2vec2-xls-r-113m-id
|
evanarlian
| 2023-02-05T08:48:16Z | 29 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:evanarlian/common_voice_11_0_id_filtered",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-20T16:52:02Z |
---
tags:
- generated_from_trainer
datasets:
- evanarlian/common_voice_11_0_id_filtered
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-113m-id
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: evanarlian/common_voice_11_0_id_filtered
type: evanarlian/common_voice_11_0_id_filtered
metrics:
- name: Wer
type: wer
value: 0.4274974633336408
---
<!-- 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-xls-r-113m-id
This model is a fine-tuned version of [evanarlian/distil-wav2vec2-xls-r-113m-id](https://huggingface.co/evanarlian/distil-wav2vec2-xls-r-113m-id) on the evanarlian/common_voice_11_0_id_filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4804
- Wer: 0.4275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.2694 | 0.92 | 1000 | 2.9168 | 1.0000 |
| 2.2449 | 1.84 | 2000 | 1.5711 | 0.9901 |
| 1.2118 | 2.75 | 3000 | 1.0133 | 0.9261 |
| 0.971 | 3.67 | 4000 | 0.8860 | 0.8743 |
| 0.8472 | 4.59 | 5000 | 0.7562 | 0.8180 |
| 0.7436 | 5.51 | 6000 | 0.6800 | 0.7505 |
| 0.6603 | 6.43 | 7000 | 0.6275 | 0.7023 |
| 0.5961 | 7.35 | 8000 | 0.5913 | 0.6589 |
| 0.5458 | 8.26 | 9000 | 0.5605 | 0.6358 |
| 0.5113 | 9.18 | 10000 | 0.5346 | 0.6039 |
| 0.463 | 10.1 | 11000 | 0.5052 | 0.5689 |
| 0.4326 | 11.02 | 12000 | 0.4880 | 0.5497 |
| 0.3981 | 11.94 | 13000 | 0.4778 | 0.5357 |
| 0.3602 | 12.86 | 14000 | 0.4656 | 0.5198 |
| 0.3501 | 13.77 | 15000 | 0.4510 | 0.5085 |
| 0.3199 | 14.69 | 16000 | 0.4617 | 0.5010 |
| 0.3058 | 15.61 | 17000 | 0.4385 | 0.4880 |
| 0.2844 | 16.53 | 18000 | 0.4638 | 0.4930 |
| 0.2729 | 17.45 | 19000 | 0.4594 | 0.4783 |
| 0.2648 | 18.37 | 20000 | 0.4521 | 0.4703 |
| 0.2515 | 19.28 | 21000 | 0.4727 | 0.4627 |
| 0.2428 | 20.2 | 22000 | 0.4566 | 0.4587 |
| 0.2343 | 21.12 | 23000 | 0.4554 | 0.4545 |
| 0.2228 | 22.04 | 24000 | 0.4670 | 0.4506 |
| 0.2135 | 22.96 | 25000 | 0.4458 | 0.4446 |
| 0.2067 | 23.88 | 26000 | 0.4571 | 0.4402 |
| 0.2065 | 24.79 | 27000 | 0.4680 | 0.4359 |
| 0.1968 | 25.71 | 28000 | 0.4702 | 0.4346 |
| 0.1914 | 26.63 | 29000 | 0.4687 | 0.4320 |
| 0.182 | 27.55 | 30000 | 0.4807 | 0.4332 |
| 0.1771 | 28.47 | 31000 | 0.4824 | 0.4308 |
| 0.1728 | 29.38 | 32000 | 0.4804 | 0.4275 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.1
|
neithangurthang/q-FrozenLake-v1-4x4-noSlippery
|
neithangurthang
| 2023-02-05T08:35:28Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T08:35:24Z |
---
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="neithangurthang/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"])
```
|
Zekunli/flan-t5-large-da-multiwoz_1000
|
Zekunli
| 2023-02-05T08:34:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-05T06:31:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flan-t5-large-da-multiwoz_1000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-large-da-multiwoz_1000
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3538
- Accuracy: 41.3747
- Num: 3689
- Gen Len: 15.5115
## 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: 24
- seed: 1799
- 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 | Accuracy | Num | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:-------:|
| 1.3315 | 0.24 | 200 | 0.5697 | 25.9543 | 3689 | 14.556 |
| 0.6418 | 0.48 | 400 | 0.4645 | 30.0503 | 3689 | 14.9314 |
| 0.5433 | 0.72 | 600 | 0.4307 | 31.9506 | 3689 | 16.1515 |
| 0.4909 | 0.95 | 800 | 0.4177 | 34.7593 | 3689 | 15.418 |
| 0.4769 | 1.19 | 1000 | 0.3996 | 35.0943 | 3689 | 14.9607 |
| 0.4491 | 1.43 | 1200 | 0.3881 | 36.2741 | 3689 | 15.543 |
| 0.4531 | 1.67 | 1400 | 0.3820 | 35.7704 | 3689 | 14.1583 |
| 0.4322 | 1.91 | 1600 | 0.3726 | 37.4853 | 3689 | 15.961 |
| 0.4188 | 2.15 | 1800 | 0.3699 | 38.4117 | 3689 | 15.0773 |
| 0.4085 | 2.38 | 2000 | 0.3674 | 38.5353 | 3689 | 15.4012 |
| 0.4063 | 2.62 | 2200 | 0.3606 | 40.0046 | 3689 | 15.3546 |
| 0.3977 | 2.86 | 2400 | 0.3570 | 40.6543 | 3689 | 15.704 |
| 0.3992 | 3.1 | 2600 | 0.3549 | 40.4284 | 3689 | 15.7446 |
| 0.3828 | 3.34 | 2800 | 0.3538 | 41.3747 | 3689 | 15.5115 |
| 0.3792 | 3.58 | 3000 | 0.3539 | 39.8513 | 3689 | 14.7951 |
| 0.3914 | 3.81 | 3200 | 0.3498 | 41.0388 | 3689 | 15.4153 |
| 0.3707 | 4.05 | 3400 | 0.3498 | 40.9596 | 3689 | 16.3136 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
nandysoham16/Adult_contemporary_music-clustered
|
nandysoham16
| 2023-02-05T08:17:03Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-05T08:05:26Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham16/Adult_contemporary_music-clustered
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. -->
# nandysoham16/Adult_contemporary_music-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3351
- Train End Logits Accuracy: 0.8993
- Train Start Logits Accuracy: 0.8854
- Validation Loss: 0.1132
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3351 | 0.8993 | 0.8854 | 0.1132 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ishaankul67/Adult_contemporary_music-clustered
|
ishaankul67
| 2023-02-05T08:13:24Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-05T08:02:24Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ishaankul67/Adult_contemporary_music-clustered
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. -->
# ishaankul67/Adult_contemporary_music-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3734
- Train End Logits Accuracy: 0.9167
- Train Start Logits Accuracy: 0.8889
- Validation Loss: 0.1582
- Validation End Logits Accuracy: 0.8571
- Validation Start Logits Accuracy: 1.0
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3734 | 0.9167 | 0.8889 | 0.1582 | 0.8571 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Deep98/Pub-clustered
|
Deep98
| 2023-02-05T07:59:35Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-05T07:45:55Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Deep98/Pub-clustered
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. -->
# Deep98/Pub-clustered
This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3841
- Train End Logits Accuracy: 0.8993
- Train Start Logits Accuracy: 0.8576
- Validation Loss: 0.2110
- Validation End Logits Accuracy: 0.9231
- Validation Start Logits Accuracy: 0.8462
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3841 | 0.8993 | 0.8576 | 0.2110 | 0.9231 | 0.8462 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
nandysoham16/Web_browser-clustered
|
nandysoham16
| 2023-02-05T07:40:13Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-05T07:28:46Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham16/Web_browser-clustered
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. -->
# nandysoham16/Web_browser-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1876
- Train End Logits Accuracy: 0.9792
- Train Start Logits Accuracy: 0.9375
- Validation Loss: 0.0125
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1876 | 0.9792 | 0.9375 | 0.0125 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
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Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.