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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cleanrl/TimePilot-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['TimePilot-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,295 |
# (CleanRL) **PPO** Agent Playing **TimePilot-v5**
This is a trained model of a PPO agent playing TimePilot-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id TimePilot-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/TimePilot-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/TimePilot-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/TimePilot-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id TimePilot-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'TimePilot-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'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'}
```
|
BeardedJohn/ppo-lunar-lander-v2
|
BeardedJohn
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
merve/multilabel-v1-replica
|
merve
|
bert
| 13 | 7 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['tr']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,605 |
**Train-Test Set:** "intent-multilabel-v1-2.zip"
**Model:** "dbmdz/bert-base-turkish-cased"
## Tokenizer Params
```
max_length=128
padding="max_length"
truncation=True
```
## Training Params
```
evaluation_strategy = "epoch"
save_strategy = "epoch"
per_device_train_batch_size = 16
per_device_eval_batch_size = 16
num_train_epochs = 4
load_best_model_at_end = True
```
## Train-Val Splitting Configuration
```
train_test_split(df_train,
test_size=0.1,
random_state=1111)
```
## Training Log
```
Epoch Training Loss Validation Loss
1 No log 0.150276
2 0.195100 0.132906
3 0.107700 0.128633
4 0.107700 0.127795
```
## Threshold Optimization
- **Best Threshold:** 0.1
- **F1 @ Threshold:** 0.734
## Eval Results
```
precision recall f1-score support
Alakasiz 0.90 0.87 0.89 734
Barinma 0.85 0.80 0.83 207
Elektronik 0.73 0.78 0.75 130
Giysi 0.83 0.66 0.73 94
Kurtarma 0.86 0.79 0.82 362
Lojistik 0.73 0.51 0.60 112
Saglik 0.74 0.74 0.74 108
Su 0.64 0.60 0.62 78
Yagma 0.68 0.55 0.61 31
Yemek 0.80 0.83 0.81 117
micro avg 0.84 0.79 0.81 1973
macro avg 0.78 0.71 0.74 1973
weighted avg 0.84 0.79 0.81 1973
samples avg 0.84 0.82 0.82 1973
```
|
Shadman-Rohan/my_awesome_wnut_model
|
Shadman-Rohan
|
distilbert
| 18 | 0 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['wnut_17']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,445 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2793
- Precision: 0.5338
- Recall: 0.2854
- F1: 0.3720
- Accuracy: 0.9403
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2933 | 0.3959 | 0.1798 | 0.2473 | 0.9347 |
| No log | 2.0 | 426 | 0.2793 | 0.5338 | 0.2854 | 0.3720 | 0.9403 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
tyuukau/distilbert-base-uncased-finetuned-squad
|
tyuukau
|
distilbert
| 10 | 0 |
transformers
| 0 |
question-answering
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,873 |
<!-- 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. -->
# tyuukau/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5109
- Train End Logits Accuracy: 0.5538
- Train Start Logits Accuracy: 0.5366
- Validation Loss: 1.1954
- Validation End Logits Accuracy: 0.6076
- Validation Start Logits Accuracy: 0.6066
- 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': 16468, '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 |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.5109 | 0.5538 | 0.5366 | 1.1954 | 0.6076 | 0.6066 | 0 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
joaoluislins/wmt-ptt5-colab-base-finetuned-en-to-pt
|
joaoluislins
|
t5
| 12 | 5 |
transformers
| 0 |
text2text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,688 |
<!-- 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. -->
# wmt-mbart50-large-finetuned-en-to-pt
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the WMT dataset (bi and mono-backtranslated)
It achieves the following results on the evaluation set:
- Loss: 0.2510
- Bleu: 62.7011
- Gen Len: 19.224
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.6426 | 1.0 | 433 | 0.5323 | 4.484 | 10.5635 |
| 0.2571 | 2.0 | 866 | 0.1965 | 47.6449 | 19.164 |
| 0.1043 | 3.0 | 1299 | 0.1723 | 53.6231 | 19.1455 |
| 0.058 | 4.0 | 1732 | 0.1908 | 52.9831 | 18.5543 |
| 0.0382 | 5.0 | 2165 | 0.1801 | 58.4418 | 19.0808 |
| 0.0244 | 6.0 | 2598 | 0.2014 | 56.0197 | 20.0485 |
| 0.0195 | 7.0 | 3031 | 0.2029 | 56.7903 | 18.642 |
| 0.0138 | 8.0 | 3464 | 0.2015 | 57.6855 | 19.0 |
| 0.0126 | 9.0 | 3897 | 0.2095 | 58.5733 | 18.7644 |
| 0.0095 | 10.0 | 4330 | 0.1946 | 60.3165 | 19.6097 |
| 0.0067 | 11.0 | 4763 | 0.2094 | 60.2691 | 18.9561 |
| 0.0055 | 12.0 | 5196 | 0.2202 | 60.375 | 19.3025 |
| 0.0046 | 13.0 | 5629 | 0.2153 | 60.7254 | 19.0855 |
| 0.0035 | 14.0 | 6062 | 0.2239 | 61.458 | 19.0647 |
| 0.0054 | 15.0 | 6495 | 0.2250 | 61.5297 | 19.164 |
| 0.0025 | 16.0 | 6928 | 0.2458 | 61.263 | 19.0531 |
| 0.002 | 17.0 | 7361 | 0.2354 | 62.4404 | 19.2102 |
| 0.0015 | 18.0 | 7794 | 0.2403 | 62.0235 | 19.1293 |
| 0.0011 | 19.0 | 8227 | 0.2477 | 62.6301 | 19.2494 |
| 0.0009 | 20.0 | 8660 | 0.2510 | 62.7011 | 19.224 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
merve/ner-replica
|
merve
|
bert
| 13 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
|
['tr']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,192 |
## deprem-ner
Bu model depremde enkaz altında kalan kişilerin bildirimlerinden sokak, il, ilçe gibi bilgileri çekmeye çalıştık.
Örnek girdiler:
- "Lütfen yardım Akevler mahallesi Rüzgar sokak Tuncay apartmanı zemin kat Antakya akrabalarım göçük altında #hatay #Afad"
- "MARAȘA'ta arkadaşimizdan haber alamıyoruz ACIL yardım Penta Park konutları 1. Blok en üst kat 11. Kat \n\n@AFADBaskanlik #kahramanmaraş\nACİL"
Verdiği çıktılar:
```
[
{
"entity_group": "mahalle",
"score": 0.8160411715507507,
"word": "Akevler mahallesi",
"start": 14,
"end": 31
},
{
"entity_group": "sokak",
"score": 0.940501868724823,
"word": "Rüzgar sokak",
"start": 32,
"end": 44
},
{
"entity_group": "Apartman/Site",
"score": 0.8081040978431702,
"word": "Tuncay apartmanı",
"start": 45,
"end": 61
},
{
"entity_group": "ilce",
"score": 0.854024350643158,
"word": "Antakya",
"start": 72,
"end": 79
}
]
```
### Değerlendirme
Bu modeli Hugging Face Hub'daki diğer modellerle karşılaştırdık, örnek 30 input'ta sonuçları [bu repository'de](https://huggingface.co/datasets/deprem-ml/butun_model_benchmarklari) bulabilirsiniz.
|
cleanrl/Tutankham-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Tutankham-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,295 |
# (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_impala_atari_wrapper.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_impala_atari_wrapper --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_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Tutankham-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Tutankham-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 2,
'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'}
```
|
wptmdoorn/lunarlander_v1
|
wptmdoorn
| null | 19 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Kyleiwaniec/PTC_TAPT_RoBERTa_large_SLC
|
Kyleiwaniec
|
roberta
| 4 | 11 |
transformers
| 0 |
text-classification
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 463 |
## RESULTS ON PTC SLC TASK
### Kyleiwaniec/PTC_TAPT_RoBERTa_large_SLC (test data)
```
TP, TN, FP, FN
610 1920 190 495
ACC
0.7869362363919129
precision,recall,F1,MCC
0.7625 0.5520361990950227 0.6404199475065617 0.5075166072878464
```
### Kyleiwaniec/PTC_TAPT_RoBERTa_large_SLC (validation data)
```
TP, TN, FP, FN
520 2166 281 244
ACC
0.8364995328558081
precision,recall,F1,MCC
0.6491885143570537 0.680628272251309 0.6645367412140576 0.5567972148181395
```
|
cleanrl/Tutankham-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Tutankham-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,295 |
# (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_impala_atari_wrapper.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_impala_atari_wrapper --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_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Tutankham-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Tutankham-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'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/Venture-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Venture-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,279 |
# (CleanRL) **PPO** Agent Playing **Venture-v5**
This is a trained model of a PPO agent playing Venture-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id Venture-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/Venture-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Venture-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Venture-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Venture-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Venture-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 2,
'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'}
```
|
thearod5/git-bert
|
thearod5
|
bert
| 11 | 22 |
transformers
| 0 |
text-classification
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 814 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# git-bert
This model was trained from scratch 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: 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
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
bonadio/poca-SoccerTwos
|
bonadio
| null | 10 | 100 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']
| false | true | true | 841 |
# **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: bonadio/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cleanrl/Venture-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Venture-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,279 |
# (CleanRL) **PPO** Agent Playing **Venture-v5**
This is a trained model of a PPO agent playing Venture-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id Venture-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/Venture-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Venture-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Venture-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Venture-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Venture-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'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/UpNDown-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed1
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['UpNDown-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,279 |
# (CleanRL) **PPO** Agent Playing **UpNDown-v5**
This is a trained model of a PPO agent playing UpNDown-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id UpNDown-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/UpNDown-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id UpNDown-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'UpNDown-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'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/WizardOfWor-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['WizardOfWor-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,311 |
# (CleanRL) **PPO** Agent Playing **WizardOfWor-v5**
This is a trained model of a PPO agent playing WizardOfWor-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id WizardOfWor-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/WizardOfWor-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id WizardOfWor-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'WizardOfWor-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 2,
'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/WizardOfWor-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['WizardOfWor-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,311 |
# (CleanRL) **PPO** Agent Playing **WizardOfWor-v5**
This is a trained model of a PPO agent playing WizardOfWor-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id WizardOfWor-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/WizardOfWor-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id WizardOfWor-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'WizardOfWor-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'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'}
```
|
SeNSiTivE/RL-Course-Unit_2-q-FrozenLake-v1-4x4-Slippery
|
SeNSiTivE
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['FrozenLake-v1-4x4', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 415 |
# **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="SeNSiTivE/RL-Course-Unit_2-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"])
```
|
cleanrl/YarsRevenge-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['YarsRevenge-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,311 |
# (CleanRL) **PPO** Agent Playing **YarsRevenge-v5**
This is a trained model of a PPO agent playing YarsRevenge-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id YarsRevenge-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/YarsRevenge-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id YarsRevenge-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'YarsRevenge-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 2,
'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/YarsRevenge-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['YarsRevenge-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,311 |
# (CleanRL) **PPO** Agent Playing **YarsRevenge-v5**
This is a trained model of a PPO agent playing YarsRevenge-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id YarsRevenge-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/YarsRevenge-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id YarsRevenge-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'YarsRevenge-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'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_impala_atari_wrapper-seed2
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['VideoPinball-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,319 |
# (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_impala_atari_wrapper.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_impala_atari_wrapper --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_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id VideoPinball-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'VideoPinball-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 2,
'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_impala_atari_wrapper-seed3
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['VideoPinball-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,319 |
# (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_impala_atari_wrapper.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_impala_atari_wrapper --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_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id VideoPinball-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'VideoPinball-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'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/Zaxxon-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Zaxxon-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,271 |
# (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_impala_atari_wrapper.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_impala_atari_wrapper --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_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Zaxxon-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Zaxxon-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 2,
'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/Zaxxon-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Zaxxon-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,271 |
# (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_impala_atari_wrapper.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_impala_atari_wrapper --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_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Zaxxon-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Zaxxon-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'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'}
```
|
henryscheible/bert-base-uncased_stereoset_finetuned
|
henryscheible
|
bert
| 10 | 7 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['stereoset']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 4,129 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased_stereoset_finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3464
- Accuracy: 0.7261
## 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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.21 | 5 | 0.6832 | 0.5565 |
| No log | 0.42 | 10 | 0.6945 | 0.4741 |
| No log | 0.62 | 15 | 0.6659 | 0.6224 |
| No log | 0.83 | 20 | 0.6337 | 0.6758 |
| No log | 1.04 | 25 | 0.6019 | 0.6695 |
| No log | 1.25 | 30 | 0.5797 | 0.7096 |
| No log | 1.46 | 35 | 0.5562 | 0.7166 |
| No log | 1.67 | 40 | 0.5497 | 0.7363 |
| No log | 1.88 | 45 | 0.5382 | 0.7418 |
| No log | 2.08 | 50 | 0.5356 | 0.7418 |
| No log | 2.29 | 55 | 0.5690 | 0.7316 |
| No log | 2.5 | 60 | 0.5778 | 0.7418 |
| No log | 2.71 | 65 | 0.5695 | 0.7386 |
| No log | 2.92 | 70 | 0.5765 | 0.7386 |
| No log | 3.12 | 75 | 0.6079 | 0.7363 |
| No log | 3.33 | 80 | 0.6919 | 0.7370 |
| No log | 3.54 | 85 | 0.7396 | 0.7339 |
| No log | 3.75 | 90 | 0.7109 | 0.7339 |
| No log | 3.96 | 95 | 0.7246 | 0.7308 |
| No log | 4.17 | 100 | 0.7502 | 0.7292 |
| No log | 4.38 | 105 | 0.8222 | 0.7331 |
| No log | 4.58 | 110 | 0.8508 | 0.7268 |
| No log | 4.79 | 115 | 0.8995 | 0.7378 |
| No log | 5.0 | 120 | 0.8797 | 0.7323 |
| No log | 5.21 | 125 | 0.9254 | 0.7370 |
| No log | 5.42 | 130 | 0.9863 | 0.7292 |
| No log | 5.62 | 135 | 1.0044 | 0.7198 |
| No log | 5.83 | 140 | 1.0236 | 0.7339 |
| No log | 6.04 | 145 | 1.0705 | 0.7355 |
| No log | 6.25 | 150 | 1.0734 | 0.7323 |
| No log | 6.46 | 155 | 1.1066 | 0.7300 |
| No log | 6.67 | 160 | 1.1166 | 0.7292 |
| No log | 6.88 | 165 | 1.1258 | 0.7370 |
| No log | 7.08 | 170 | 1.1972 | 0.7300 |
| No log | 7.29 | 175 | 1.1682 | 0.7268 |
| No log | 7.5 | 180 | 1.2221 | 0.7166 |
| No log | 7.71 | 185 | 1.2813 | 0.7151 |
| No log | 7.92 | 190 | 1.3180 | 0.7214 |
| No log | 8.12 | 195 | 1.3224 | 0.7166 |
| No log | 8.33 | 200 | 1.3252 | 0.7135 |
| No log | 8.54 | 205 | 1.3205 | 0.7221 |
| No log | 8.75 | 210 | 1.3266 | 0.7245 |
| No log | 8.96 | 215 | 1.3318 | 0.7206 |
| No log | 9.17 | 220 | 1.3345 | 0.7237 |
| No log | 9.38 | 225 | 1.3378 | 0.7245 |
| No log | 9.58 | 230 | 1.3422 | 0.7261 |
| No log | 9.79 | 235 | 1.3453 | 0.7261 |
| No log | 10.0 | 240 | 1.3464 | 0.7261 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
henryscheible/roberta-base_stereoset_finetuned
|
henryscheible
|
roberta
| 11 | 5 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null |
['stereoset']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 4,114 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_stereoset_finetuned
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8461
- Accuracy: 0.7904
## 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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.21 | 5 | 0.6915 | 0.5149 |
| No log | 0.42 | 10 | 0.6945 | 0.4914 |
| No log | 0.62 | 15 | 0.6931 | 0.4945 |
| No log | 0.83 | 20 | 0.6814 | 0.5086 |
| No log | 1.04 | 25 | 0.6454 | 0.6978 |
| No log | 1.25 | 30 | 0.5807 | 0.7088 |
| No log | 1.46 | 35 | 0.5620 | 0.7284 |
| No log | 1.67 | 40 | 0.5410 | 0.7331 |
| No log | 1.88 | 45 | 0.4965 | 0.7630 |
| No log | 2.08 | 50 | 0.4924 | 0.7614 |
| No log | 2.29 | 55 | 0.4906 | 0.7661 |
| No log | 2.5 | 60 | 0.5141 | 0.7661 |
| No log | 2.71 | 65 | 0.4826 | 0.7700 |
| No log | 2.92 | 70 | 0.4977 | 0.7630 |
| No log | 3.12 | 75 | 0.4890 | 0.7802 |
| No log | 3.33 | 80 | 0.4819 | 0.7857 |
| No log | 3.54 | 85 | 0.4840 | 0.7834 |
| No log | 3.75 | 90 | 0.5189 | 0.7794 |
| No log | 3.96 | 95 | 0.5000 | 0.7912 |
| No log | 4.17 | 100 | 0.4958 | 0.7865 |
| No log | 4.38 | 105 | 0.5149 | 0.7896 |
| No log | 4.58 | 110 | 0.5515 | 0.7975 |
| No log | 4.79 | 115 | 0.5766 | 0.7873 |
| No log | 5.0 | 120 | 0.5867 | 0.7873 |
| No log | 5.21 | 125 | 0.6143 | 0.7936 |
| No log | 5.42 | 130 | 0.6226 | 0.7881 |
| No log | 5.62 | 135 | 0.6374 | 0.7865 |
| No log | 5.83 | 140 | 0.6405 | 0.7983 |
| No log | 6.04 | 145 | 0.6116 | 0.8006 |
| No log | 6.25 | 150 | 0.6372 | 0.7983 |
| No log | 6.46 | 155 | 0.6804 | 0.7881 |
| No log | 6.67 | 160 | 0.7237 | 0.7857 |
| No log | 6.88 | 165 | 0.7038 | 0.7904 |
| No log | 7.08 | 170 | 0.7100 | 0.7991 |
| No log | 7.29 | 175 | 0.6837 | 0.7920 |
| No log | 7.5 | 180 | 0.7203 | 0.8046 |
| No log | 7.71 | 185 | 0.7478 | 0.7959 |
| No log | 7.92 | 190 | 0.7667 | 0.7920 |
| No log | 8.12 | 195 | 0.7792 | 0.7959 |
| No log | 8.33 | 200 | 0.8014 | 0.7943 |
| No log | 8.54 | 205 | 0.8193 | 0.7959 |
| No log | 8.75 | 210 | 0.8316 | 0.7967 |
| No log | 8.96 | 215 | 0.8411 | 0.7896 |
| No log | 9.17 | 220 | 0.8652 | 0.7936 |
| No log | 9.38 | 225 | 0.8553 | 0.7841 |
| No log | 9.58 | 230 | 0.8458 | 0.7881 |
| No log | 9.79 | 235 | 0.8456 | 0.7912 |
| No log | 10.0 | 240 | 0.8461 | 0.7904 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
minagi223/Lora_of_ReisalinStout3
|
minagi223
| null | 4 | 0 | null | 0 | null | false | false | false |
other
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,091 |
<img src="https://huggingface.co/minagi223/Lora_of_ReisalinStout3/blob/main/laisa12zz.png">
Reisalin Stout from Atelier Ryza 3: Alchemist of the End & the Secret
by minagi from pixiv
I have tried to make this model from some of the images I have redraw( (some just made by ai)), and the results have been good for me.
Be careful with this lora, please try not to use it for profit (although I know I've tried to discourage it to no avail)
Never post r18g stuff, you could be warned by the original (Koei Tecmo) or even sued.
You can only draw Lysa from Generation 3.
To change clothes, please adjust the model parameters to below 0.5.
Translation with deepl
我試著用一些臨摹的圖片來做了這個模型（ai），對我而言，結果還是不錯的。
利用這個lora請注意，請儘量不要用來盈利（雖然我知道我勸阻了也沒用）
絕對不要弄r18g的東西發出來，你可能會被原著方（Koei Tecmo）警告，甚至被起诉。
只能畫3代的萊沙。
換衣服請把模型參數調整到0.5以下。
このモデルは、私が模写した画像（AIで作られたものもある）の中から作ってみたのですが、私にとっては良い結果でした。
このロラは、営利目的で使用しないように気をつけてください（私が阻止しようとしたのは無駄だったとは思いますが）。
R18G に関する投稿は絶対にしないでください。オリジナル（コーエーテクモ）から警告を受けたり、訴訟を起こされたりする可能性があります。
訴えられるかもしれませんよ。 ライサを描けるのはジェネレーション3からです。
着替えの際は、モデルパラメータを0.5以下に調整してください。
deeplを使った翻訳
|
marsim0/book_model
|
marsim0
|
marian
| 12 | 0 |
transformers
| 0 |
text2text-generation
| true | false | false |
cc-by-4.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,532 |
<!-- 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. -->
# book_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5586
- Bleu: 23.891
- Gen Len: 25.86
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 107 | 1.3921 | 24.4814 | 25.93 |
| No log | 2.0 | 214 | 1.4285 | 24.5496 | 25.5233 |
| No log | 3.0 | 321 | 1.4777 | 24.0469 | 25.8967 |
| No log | 4.0 | 428 | 1.5325 | 23.5718 | 25.69 |
| 0.8696 | 5.0 | 535 | 1.5586 | 23.891 | 25.86 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.10.0+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2
|
joaoluislins/wmt-mbart50-large-finetuned-en-to-pt
|
joaoluislins
|
mbart
| 13 | 6 |
transformers
| 0 |
text2text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,655 |
<!-- 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. -->
# wmt-mbart50-large-finetuned-en-to-pt
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the WMT dataset (bi and mono-backtranslated)
It achieves the following results on the evaluation set:
- Loss: 0.002510
- Bleu: 62.7011
- Gen Len: 19.224
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.6426 | 1.0 | 433 | 0.5323 | 4.484 | 10.5635 |
| 0.2571 | 2.0 | 866 | 0.1965 | 47.6449 | 19.164 |
| 0.1043 | 3.0 | 1299 | 0.1723 | 53.6231 | 19.1455 |
| 0.058 | 4.0 | 1732 | 0.1908 | 52.9831 | 18.5543 |
| 0.0382 | 5.0 | 2165 | 0.1801 | 58.4418 | 19.0808 |
| 0.0244 | 6.0 | 2598 | 0.2014 | 56.0197 | 20.0485 |
| 0.0195 | 7.0 | 3031 | 0.2029 | 56.7903 | 18.642 |
| 0.0138 | 8.0 | 3464 | 0.2015 | 57.6855 | 19.0 |
| 0.0126 | 9.0 | 3897 | 0.2095 | 58.5733 | 18.7644 |
| 0.0095 | 10.0 | 4330 | 0.1946 | 60.3165 | 19.6097 |
| 0.0067 | 11.0 | 4763 | 0.2094 | 60.2691 | 18.9561 |
| 0.0055 | 12.0 | 5196 | 0.2202 | 60.375 | 19.3025 |
| 0.0046 | 13.0 | 5629 | 0.2153 | 60.7254 | 19.0855 |
| 0.0035 | 14.0 | 6062 | 0.2239 | 61.458 | 19.0647 |
| 0.0054 | 15.0 | 6495 | 0.2250 | 61.5297 | 19.164 |
| 0.0025 | 16.0 | 6928 | 0.2458 | 61.263 | 19.0531 |
| 0.002 | 17.0 | 7361 | 0.2354 | 62.4404 | 19.2102 |
| 0.0015 | 18.0 | 7794 | 0.2403 | 62.0235 | 19.1293 |
| 0.0011 | 19.0 | 8227 | 0.2477 | 62.6301 | 19.2494 |
| 0.0009 | 20.0 | 8660 | 0.2510 | 62.7011 | 19.224 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
henryscheible/roberta-large_stereoset_finetuned
|
henryscheible
|
roberta
| 11 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null |
['stereoset']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 4,117 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large_stereoset_finetuned
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7989
- Accuracy: 0.8336
## 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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.21 | 5 | 0.6920 | 0.5196 |
| No log | 0.42 | 10 | 0.6909 | 0.5290 |
| No log | 0.62 | 15 | 0.6899 | 0.5220 |
| No log | 0.83 | 20 | 0.6883 | 0.5408 |
| No log | 1.04 | 25 | 0.6573 | 0.6609 |
| No log | 1.25 | 30 | 0.5892 | 0.7088 |
| No log | 1.46 | 35 | 0.6633 | 0.5408 |
| No log | 1.67 | 40 | 0.6322 | 0.6852 |
| No log | 1.88 | 45 | 0.6393 | 0.7159 |
| No log | 2.08 | 50 | 0.5494 | 0.7410 |
| No log | 2.29 | 55 | 0.5498 | 0.7386 |
| No log | 2.5 | 60 | 0.5069 | 0.7692 |
| No log | 2.71 | 65 | 0.4930 | 0.7630 |
| No log | 2.92 | 70 | 0.4939 | 0.7614 |
| No log | 3.12 | 75 | 0.5379 | 0.7724 |
| No log | 3.33 | 80 | 0.5981 | 0.7732 |
| No log | 3.54 | 85 | 0.5842 | 0.7716 |
| No log | 3.75 | 90 | 0.4405 | 0.8030 |
| No log | 3.96 | 95 | 0.4970 | 0.7951 |
| No log | 4.17 | 100 | 0.5172 | 0.8093 |
| No log | 4.38 | 105 | 0.5052 | 0.8108 |
| No log | 4.58 | 110 | 0.4685 | 0.8085 |
| No log | 4.79 | 115 | 0.4663 | 0.8218 |
| No log | 5.0 | 120 | 0.5086 | 0.8218 |
| No log | 5.21 | 125 | 0.5096 | 0.8179 |
| No log | 5.42 | 130 | 0.5705 | 0.8203 |
| No log | 5.62 | 135 | 0.5294 | 0.8312 |
| No log | 5.83 | 140 | 0.4377 | 0.8375 |
| No log | 6.04 | 145 | 0.5699 | 0.8100 |
| No log | 6.25 | 150 | 0.6062 | 0.8265 |
| No log | 6.46 | 155 | 0.7237 | 0.8218 |
| No log | 6.67 | 160 | 0.6816 | 0.8210 |
| No log | 6.88 | 165 | 0.6413 | 0.8124 |
| No log | 7.08 | 170 | 0.5931 | 0.8359 |
| No log | 7.29 | 175 | 0.6149 | 0.8399 |
| No log | 7.5 | 180 | 0.7190 | 0.8195 |
| No log | 7.71 | 185 | 0.7339 | 0.8352 |
| No log | 7.92 | 190 | 0.7244 | 0.8352 |
| No log | 8.12 | 195 | 0.7722 | 0.8203 |
| No log | 8.33 | 200 | 0.6890 | 0.8344 |
| No log | 8.54 | 205 | 0.6938 | 0.8336 |
| No log | 8.75 | 210 | 0.7234 | 0.8320 |
| No log | 8.96 | 215 | 0.7517 | 0.8391 |
| No log | 9.17 | 220 | 0.7713 | 0.8383 |
| No log | 9.38 | 225 | 0.7745 | 0.8375 |
| No log | 9.58 | 230 | 0.8006 | 0.8375 |
| No log | 9.79 | 235 | 0.8003 | 0.8367 |
| No log | 10.0 | 240 | 0.7989 | 0.8336 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
henryscheible/bert-large-uncased_stereoset_finetuned
|
henryscheible
|
bert
| 10 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['stereoset']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 4,132 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased_stereoset_finetuned
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0729
- Accuracy: 0.7716
## 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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.21 | 5 | 0.6925 | 0.5071 |
| No log | 0.42 | 10 | 0.6978 | 0.5008 |
| No log | 0.62 | 15 | 0.6891 | 0.5275 |
| No log | 0.83 | 20 | 0.6850 | 0.5487 |
| No log | 1.04 | 25 | 0.7521 | 0.5126 |
| No log | 1.25 | 30 | 0.6577 | 0.6177 |
| No log | 1.46 | 35 | 0.6759 | 0.5440 |
| No log | 1.67 | 40 | 0.6395 | 0.6405 |
| No log | 1.88 | 45 | 0.6064 | 0.6719 |
| No log | 2.08 | 50 | 0.5822 | 0.6986 |
| No log | 2.29 | 55 | 0.5566 | 0.7096 |
| No log | 2.5 | 60 | 0.5411 | 0.7331 |
| No log | 2.71 | 65 | 0.5448 | 0.7551 |
| No log | 2.92 | 70 | 0.5384 | 0.7339 |
| No log | 3.12 | 75 | 0.5487 | 0.7535 |
| No log | 3.33 | 80 | 0.5572 | 0.7567 |
| No log | 3.54 | 85 | 0.5763 | 0.7614 |
| No log | 3.75 | 90 | 0.5756 | 0.7645 |
| No log | 3.96 | 95 | 0.5524 | 0.7645 |
| No log | 4.17 | 100 | 0.6320 | 0.7614 |
| No log | 4.38 | 105 | 0.6512 | 0.7575 |
| No log | 4.58 | 110 | 0.6582 | 0.7606 |
| No log | 4.79 | 115 | 0.6731 | 0.7669 |
| No log | 5.0 | 120 | 0.6944 | 0.7575 |
| No log | 5.21 | 125 | 0.7142 | 0.7575 |
| No log | 5.42 | 130 | 0.7004 | 0.7645 |
| No log | 5.62 | 135 | 0.6794 | 0.7630 |
| No log | 5.83 | 140 | 0.7108 | 0.7606 |
| No log | 6.04 | 145 | 0.7730 | 0.7590 |
| No log | 6.25 | 150 | 0.8083 | 0.7614 |
| No log | 6.46 | 155 | 0.8361 | 0.7653 |
| No log | 6.67 | 160 | 0.8498 | 0.7692 |
| No log | 6.88 | 165 | 0.8769 | 0.7700 |
| No log | 7.08 | 170 | 0.8324 | 0.7582 |
| No log | 7.29 | 175 | 0.7945 | 0.7645 |
| No log | 7.5 | 180 | 0.8480 | 0.7684 |
| No log | 7.71 | 185 | 0.8905 | 0.7724 |
| No log | 7.92 | 190 | 0.9560 | 0.7700 |
| No log | 8.12 | 195 | 0.9976 | 0.7669 |
| No log | 8.33 | 200 | 1.0315 | 0.7677 |
| No log | 8.54 | 205 | 1.0413 | 0.7692 |
| No log | 8.75 | 210 | 1.0216 | 0.7708 |
| No log | 8.96 | 215 | 1.0251 | 0.7716 |
| No log | 9.17 | 220 | 1.0483 | 0.7716 |
| No log | 9.38 | 225 | 1.0616 | 0.7716 |
| No log | 9.58 | 230 | 1.0703 | 0.7708 |
| No log | 9.79 | 235 | 1.0731 | 0.7732 |
| No log | 10.0 | 240 | 1.0729 | 0.7716 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
abigailp/vaccinated
|
abigailp
|
bert
| 13 | 9 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,050 |
<!-- 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. -->
# vaccinated
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6907
- Accuracy: 0.9036
- F1: 0.9048
- Recall: 0.8636
- Precision: 0.95
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
pomp/ppo-LunarLander-v2
|
pomp
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
cleanrl/ChopperCommand-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['ChopperCommand-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,335 |
# (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_impala_atari_wrapper.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_impala_atari_wrapper --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_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id ChopperCommand-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'ChopperCommand-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 2,
'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'}
```
|
varevshatyan/ppo-LunarLander-v2
|
varevshatyan
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
fathyshalab/domain_transfer_general-massive_social-roberta-large-v1-5-7
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,508 |
# fathyshalab/domain_transfer_general-massive_social-roberta-large-v1-5-7
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_social-roberta-large-v1-5-7")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
fathyshalab/domain_transfer_general-massive_transport-roberta-large-v1-5-4
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,514 |
# fathyshalab/domain_transfer_general-massive_transport-roberta-large-v1-5-4
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_transport-roberta-large-v1-5-4")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
fathyshalab/domain_transfer_general-massive_calendar-roberta-large-v1-5-93
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,514 |
# fathyshalab/domain_transfer_general-massive_calendar-roberta-large-v1-5-93
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_calendar-roberta-large-v1-5-93")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Unbabel/wmt22-comet-da
|
Unbabel
| null | 5 | 0 | null | 0 |
translation
| false | false | false |
apache-2.0
|
['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'om', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sa', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'su', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tr', 'ug', 'uk', 'ur', 'uz', 'vi', 'xh', 'yi', 'zh']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 2,791 |
This is a [COMET](https://github.com/Unbabel/COMET) evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference.
# Paper
[COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task](https://aclanthology.org/2022.wmt-1.52) (Rei et al., WMT 2022)
# License
Apache-2.0
# Usage (unbabel-comet)
Using this model requires unbabel-comet to be installed:
```bash
pip install --upgrade pip # ensures that pip is current
pip install unbabel-comet
```
Then you can use it through comet CLI:
```bash
comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model Unbabel/wmt22-comet-da
```
Or using Python:
```python
from comet import download_model, load_from_checkpoint
model_path = download_model("Unbabel/wmt22-comet-da")
model = load_from_checkpoint(model_path)
data = [
{
"src": "Dem Feuer konnte Einhalt geboten werden",
"mt": "The fire could be stopped",
"ref": "They were able to control the fire."
},
{
"src": "Schulen und Kindergärten wurden eröffnet.",
"mt": "Schools and kindergartens were open",
"ref": "Schools and kindergartens opened"
}
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)
```
# Intended uses
Our model is intented to be used for **MT evaluation**.
Given a a triplet with (source sentence, translation, reference translation) outputs a single score between 0 and 1 where 1 represents a perfect translation.
# Languages Covered:
This model builds on top of XLM-R which cover the following languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish.
Thus, results for language pairs containing uncovered languages are unreliable!
|
fathyshalab/domain_transfer_general-massive_play-roberta-large-v1-5-71
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,506 |
# fathyshalab/domain_transfer_general-massive_play-roberta-large-v1-5-71
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_play-roberta-large-v1-5-71")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
fathyshalab/domain_transfer_general-massive_datetime-roberta-large-v1-5-94
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,514 |
# fathyshalab/domain_transfer_general-massive_datetime-roberta-large-v1-5-94
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_datetime-roberta-large-v1-5-94")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Likalto4/Unconditional_Butterflies_x64
|
Likalto4
| null | 6 | 0 |
diffusers
| 0 |
unconditional-image-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class']
| false | true | true | 677 |
# Model Card for a model trained based on the Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class), not using accelarate yet.
This model is a diffusion model for unconditional image generation of cute but small 🦋.
The model was trained with 1000 images using the [DDPM](https://arxiv.org/abs/2006.11239) architecture. Images generated are of 64x64 pixel size.
The model was trained for 50 epochs with a batch size of 64, using around 11 GB of GPU memory.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained(Likalto4/Unconditional_Butterflies_x64)
image = pipeline().images[0]
image
```
|
Svetlana0303/Regression_Albert
|
Svetlana0303
|
albert
| 13 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 3,471 |
<!-- 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. -->
# Regression_Albert
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0459
- Mse: 0.0459
- Mae: 0.1675
- R2: 0.9763
- Accuracy: 1.0
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:|
| No log | 1.0 | 7 | 1.4379 | 1.4379 | 1.1107 | -0.3492 | 0.0 |
| No log | 2.0 | 14 | 1.2159 | 1.2159 | 1.0476 | -0.1409 | 0.1429 |
| No log | 3.0 | 21 | 1.7679 | 1.7679 | 1.1233 | -0.6588 | 0.4286 |
| No log | 4.0 | 28 | 1.7069 | 1.7069 | 1.1072 | -0.6015 | 0.1429 |
| No log | 5.0 | 35 | 1.4438 | 1.4438 | 0.9771 | -0.3547 | 0.5714 |
| No log | 6.0 | 42 | 1.0275 | 1.0275 | 0.7910 | 0.0359 | 0.4286 |
| No log | 7.0 | 49 | 0.7649 | 0.7649 | 0.7080 | 0.2823 | 0.4286 |
| No log | 8.0 | 56 | 0.6584 | 0.6584 | 0.7083 | 0.3823 | 0.2857 |
| No log | 9.0 | 63 | 0.5064 | 0.5064 | 0.6108 | 0.5248 | 0.4286 |
| No log | 10.0 | 70 | 0.3638 | 0.3638 | 0.5078 | 0.6586 | 0.4286 |
| No log | 11.0 | 77 | 0.2660 | 0.2660 | 0.4352 | 0.7504 | 0.5714 |
| No log | 12.0 | 84 | 0.1570 | 0.1570 | 0.3323 | 0.8527 | 0.7143 |
| No log | 13.0 | 91 | 0.1953 | 0.1953 | 0.3863 | 0.8168 | 0.4286 |
| No log | 14.0 | 98 | 0.2230 | 0.2230 | 0.4033 | 0.7908 | 0.7143 |
| No log | 15.0 | 105 | 0.0578 | 0.0578 | 0.1935 | 0.9458 | 1.0 |
| No log | 16.0 | 112 | 0.0504 | 0.0504 | 0.1701 | 0.9527 | 1.0 |
| No log | 17.0 | 119 | 0.0466 | 0.0466 | 0.1713 | 0.9563 | 1.0 |
| No log | 18.0 | 126 | 0.0173 | 0.0173 | 0.1148 | 0.9837 | 1.0 |
| No log | 19.0 | 133 | 0.0417 | 0.0417 | 0.1811 | 0.9609 | 1.0 |
| No log | 20.0 | 140 | 0.0899 | 0.0899 | 0.1895 | 0.9156 | 0.8571 |
| No log | 21.0 | 147 | 0.0571 | 0.0571 | 0.1599 | 0.9465 | 0.8571 |
| No log | 22.0 | 154 | 0.0247 | 0.0247 | 0.1478 | 0.9768 | 1.0 |
| No log | 23.0 | 161 | 0.0201 | 0.0201 | 0.1261 | 0.9812 | 1.0 |
| No log | 24.0 | 168 | 0.0178 | 0.0178 | 0.1262 | 0.9833 | 1.0 |
| No log | 25.0 | 175 | 0.0172 | 0.0172 | 0.1208 | 0.9838 | 1.0 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
fathyshalab/domain_transfer_general-massive_recommendation-roberta-large-v1-5-17
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,526 |
# fathyshalab/domain_transfer_general-massive_recommendation-roberta-large-v1-5-17
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_recommendation-roberta-large-v1-5-17")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
fathyshalab/domain_transfer_general-massive_email-roberta-large-v1-5-38
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,508 |
# fathyshalab/domain_transfer_general-massive_email-roberta-large-v1-5-38
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_email-roberta-large-v1-5-38")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
globalbiodata/inventory
|
globalbiodata
| null | 6 | 0 | null | 0 | null | false | false | false |
mit
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['bio', 'infrastructure', 'funding', 'natural language processing', 'BERT']
| false | true | true | 926 |
# Biodata Resource Inventory
This repository holds the fine-tuned models used in the biodata resource inventory conducted in 2022 by the
[Global Biodata Coalition](https://globalbiodata.org/) in collaboration with [Chan Zuckerberg Initiative](https://chanzuckerberg.com/).
## Repository Overview
The fine-tuned models for both the article classification and NER tasks are present, and each has an associated modelcard.
```sh
.
├── article_classifier.pt # Article classification model checkpoint
├── article_classifier_modelcard.md # Model card for article classification model
├── name_entity_recognition.pt # NER model checkpoint
└── name_entity_recognition_modelcard.pt # Modelcard for NER model
```
## Associated Code
The associated code, data, and documentation for this project can be found on [GitHub](https://github.com/globalbiodata/inventory_2022/tree/inventory_2022_dev).
|
cleanrl/UpNDown-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3
|
cleanrl
| null | 9 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['UpNDown-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,279 |
# (CleanRL) **PPO** Agent Playing **UpNDown-v5**
This is a trained model of a PPO agent playing UpNDown-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_impala_atari_wrapper.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_impala_atari_wrapper --env-id UpNDown-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/UpNDown-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id UpNDown-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'UpNDown-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'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'}
```
|
fathyshalab/domain_transfer_general-massive_iot-roberta-large-v1-5-5
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,502 |
# fathyshalab/domain_transfer_general-massive_iot-roberta-large-v1-5-5
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_iot-roberta-large-v1-5-5")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
apatidar0/vit-base-beans_own
|
apatidar0
|
vit
| 14 | 0 |
transformers
| 0 |
image-classification
| true | false | false |
apache-2.0
| null |
['beans']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['image-classification', 'generated_from_trainer']
| true | true | true | 1,081 |
<!-- 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-beans_own
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0558
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
WildBill258/ppo-Huggy
|
WildBill258
| null | 6 | 0 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']
| false | true | true | 822 |
# **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: WildBill258/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
fathyshalab/domain_transfer_general-massive_general-roberta-large-v1-5-95
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,512 |
# fathyshalab/domain_transfer_general-massive_general-roberta-large-v1-5-95
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_general-roberta-large-v1-5-95")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
fathyshalab/domain_transfer_general-massive_audio-roberta-large-v1-5-0
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,506 |
# fathyshalab/domain_transfer_general-massive_audio-roberta-large-v1-5-0
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_audio-roberta-large-v1-5-0")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
thearod5/se-bert
|
thearod5
|
bert
| 11 | 7 |
transformers
| 0 |
text-classification
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 813 |
<!-- 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. -->
# se-bert
This model was trained from scratch 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: 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
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
fathyshalab/domain_transfer_general-massive_lists-roberta-large-v1-5-93
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,508 |
# fathyshalab/domain_transfer_general-massive_lists-roberta-large-v1-5-93
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_lists-roberta-large-v1-5-93")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
ammr/ppo-LunarLander-v1
|
ammr
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Unbabel/wmt20-comet-da
|
Unbabel
| null | 5 | 0 | null | 0 |
translation
| false | false | false |
apache-2.0
|
['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'om', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sa', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'su', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tr', 'ug', 'uk', 'ur', 'uz', 'vi', 'xh', 'yi', 'zh']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['arXiv:2010.15535', 'PyTorch']
| false | true | true | 2,956 |
This is a [COMET](https://github.com/Unbabel/COMET) evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference.
**NOTE:** This model was recently replaced by an improved version [wmt22-comet-da](https://huggingface.co/Unbabel/wmt22-comet-da)
# Paper
[Unbabel’s Participation in the WMT20 Metrics Shared Task](https://aclanthology.org/2020.wmt-1.101) (Rei et al., WMT 2020)
# License
Apache-2.0
# Usage (unbabel-comet)
Using this model requires unbabel-comet to be installed:
```bash
pip install --upgrade pip # ensures that pip is current
pip install unbabel-comet
```
Then you can use it through comet CLI:
```bash
comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model Unbabel/wmt22-comet-da
```
Or using Python:
```python
from comet import download_model, load_from_checkpoint
model_path = download_model("Unbabel/wmt20-comet-da")
model = load_from_checkpoint(model_path)
data = [
{
"src": "Dem Feuer konnte Einhalt geboten werden",
"mt": "The fire could be stopped",
"ref": "They were able to control the fire."
},
{
"src": "Schulen und Kindergärten wurden eröffnet.",
"mt": "Schools and kindergartens were open",
"ref": "Schools and kindergartens opened"
}
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)
```
# Intended uses
Our model is intented to be used for **MT evaluation**.
Given a a triplet with (source sentence, translation, reference translation) outputs a single score. This score is unbounded but typically falls between -1 and 1 where 1 reflects a perfect translation.
# Languages Covered:
This model builds on top of XLM-R which cover the following languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish.
Thus, results for language pairs containing uncovered languages are unreliable!
|
fathyshalab/domain_transfer_general-massive_qa-roberta-large-v1-5-73
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,502 |
# fathyshalab/domain_transfer_general-massive_qa-roberta-large-v1-5-73
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_qa-roberta-large-v1-5-73")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
z4x/ppo-SnowballTarget
|
z4x
| null | 20 | 2 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']
| false | true | true | 850 |
# **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: z4x/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
fathyshalab/domain_transfer_general-massive_cooking-roberta-large-v1-5-4
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,510 |
# fathyshalab/domain_transfer_general-massive_cooking-roberta-large-v1-5-4
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_cooking-roberta-large-v1-5-4")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
robinsk8a/a2c-AntBulletEnv-v0
|
robinsk8a
| null | 13 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 352 |
# **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
...
```
|
MarioLomby/q-FrozenLake-v1-4x4-noSlippery
|
MarioLomby
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 399 |
# **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="MarioLomby/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"])
```
|
fathyshalab/domain_transfer_general-massive_takeaway-roberta-large-v1-5-90
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,514 |
# fathyshalab/domain_transfer_general-massive_takeaway-roberta-large-v1-5-90
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_takeaway-roberta-large-v1-5-90")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
z4x/ppo-Pyramids
|
z4x
| null | 12 | 1 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']
| false | true | true | 826 |
# **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: z4x/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
SergenK/nes-cover-art-image-generator
|
SergenK
| null | 23 | 0 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['text-to-image', 'stable-diffusion']
| false | true | true | 932 |
### nes-cover-art-image-generator Dreambooth model trained by SergenK with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Append input text with "nescover".
Sample pictures of this concept:
|
fathyshalab/domain_transfer_general-massive_music-roberta-large-v1-5-7
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,506 |
# fathyshalab/domain_transfer_general-massive_music-roberta-large-v1-5-7
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_music-roberta-large-v1-5-7")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
MarioLomby/q-Taxi-v3
|
MarioLomby
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 366 |
# **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="MarioLomby/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"])
```
|
fathyshalab/domain_transfer_general-massive_alarm-roberta-large-v1-5-50
|
fathyshalab
|
roberta
| 14 | 2 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,508 |
# fathyshalab/domain_transfer_general-massive_alarm-roberta-large-v1-5-50
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_alarm-roberta-large-v1-5-50")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
MarioLomby/Taxi-v3
|
MarioLomby
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 364 |
# **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="MarioLomby/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"])
```
|
z4x/a2c-AntBulletEnv-v0
|
z4x
| null | 13 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 352 |
# **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
...
```
|
mchalek/distilbert-base-uncased-finetuned-ccnews
|
mchalek
|
distilbert
| 13 | 2 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
| null |
['cc_news']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,284 |
<!-- 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-ccnews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the cc_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5185
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7553 | 1.0 | 157 | 2.5523 |
| 2.6507 | 2.0 | 314 | 2.5219 |
| 2.606 | 3.0 | 471 | 2.5416 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
cupertinosam/ppo-LunarLander-v2
|
cupertinosam
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Rtariq/bert-finetuned-ner
|
Rtariq
|
bert
| 18 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['conll2003']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,518 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0637
- Precision: 0.9280
- Recall: 0.9475
- F1: 0.9376
- Accuracy: 0.9858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0866 | 1.0 | 1756 | 0.0714 | 0.9116 | 0.9300 | 0.9207 | 0.9821 |
| 0.0341 | 2.0 | 3512 | 0.0672 | 0.9284 | 0.9468 | 0.9375 | 0.9853 |
| 0.019 | 3.0 | 5268 | 0.0637 | 0.9280 | 0.9475 | 0.9376 | 0.9858 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
lmqg/flan-t5-small-squad-qag
|
lmqg
|
t5
| 13 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false |
cc-by-4.0
|
['en']
|
['lmqg/qag_squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['questions and answers generation']
| true | true | true | 3,892 |
# Model Card of `lmqg/flan-t5-small-squad-qag`
This model is fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)
- **Language:** en
- **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/flan-t5-small-squad-qag")
# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/flan-t5-small-squad-qag")
output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
```
## Evaluation
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-small-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 92.3 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedF1Score (MoverScore) | 63.74 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedPrecision (BERTScore) | 92.92 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedPrecision (MoverScore) | 65.5 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedRecall (BERTScore) | 91.71 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedRecall (MoverScore) | 62.2 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qag_squad
- dataset_name: default
- input_types: ['paragraph']
- output_types: ['questions_answers']
- prefix_types: ['qag']
- model: google/flan-t5-small
- max_length: 512
- max_length_output: 256
- epoch: 14
- batch: 16
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.0
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-small-squad-qag/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
atorre/poca-SoccerTwos-40M
|
atorre
| null | 25 | 90 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']
| false | true | true | 844 |
# **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: atorre/poca-SoccerTwos-40M
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
pfunk/Pong-v4-DQPN_p500_e0.50-seed1
|
pfunk
| null | 11 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 1,999 |
# (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_p500_e0.50.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p500_e0.50]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p500_e0.50 --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_p500_e0.50-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_e0.50-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_e0.50-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p500_e0.50 --start-policy-f 500000 --end-policy-f 1000 --evaluation-fraction 0.50 --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': 0.5,
'exp_name': 'DQPN_p500_e0.50',
'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': 500000,
'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'}
```
|
z4x/a2c-PandaReachDense-v2
|
z4x
| null | 13 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 358 |
# **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
...
```
|
dhairyakapadia/swin-tiny-patch4-window7-224-finetuned-skin-cancer
|
dhairyakapadia
|
swin
| 10 | 0 |
transformers
| 0 |
image-classification
| true | false | false |
apache-2.0
| null |
['imagefolder']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,071 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-skin-cancer
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 1
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
robinsk8a/a2c-PandaReachDense-v2
|
robinsk8a
| null | 13 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 358 |
# **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
...
```
|
smilingface88/xlm-roberta-base-finetuned-panx-de
|
smilingface88
|
xlm-roberta
| 12 | 1 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['xtreme']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,320 |
<!-- 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: 0.1355
- F1: 0.8645
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2582 | 1.0 | 525 | 0.1612 | 0.8199 |
| 0.128 | 2.0 | 1050 | 0.1334 | 0.8484 |
| 0.081 | 3.0 | 1575 | 0.1355 | 0.8645 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
violetamaral/clasificador-muchocine
|
violetamaral
|
electra
| 10 | 2 |
transformers
| 0 |
text-classification
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['classification', 'generated_from_trainer']
| true | true | true | 1,367 |
<!-- 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: 1.3998
- Accuracy: 0.4516
## 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 | 1.3705 | 0.3781 |
| 1.3868 | 2.0 | 776 | 1.2408 | 0.4323 |
| 1.0102 | 3.0 | 1164 | 1.3998 | 0.4516 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
yizhangliu/poca-SoccerTwos-v8
|
yizhangliu
| null | 23 | 84 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']
| false | true | true | 847 |
# **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: yizhangliu/poca-SoccerTwos-v8
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bonadio/poca-SoccerTwos-v2
|
bonadio
| null | 10 | 85 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']
| false | true | true | 844 |
# **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: bonadio/poca-SoccerTwos-v2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kmposkid1/dqn-SpaceInvadersNoFrameskip-v4
|
kmposkid1
| null | 15 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 2,217 |
# **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 kmposkid1 -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 kmposkid1 -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 kmposkid1
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 25000),
('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', 10000),
('n_timesteps', 500000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
pfunk/Pong-v4-DQPN_p50-seed1
|
pfunk
| null | 11 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 1,943 |
# (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_p50.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p50]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p50 --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_p50-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p50 --start-policy-f 50000 --end-policy-f 50000 --evaluation-fraction 1.00 --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': 50000,
'env_id': 'Pong-v4',
'evaluation_fraction': 1.0,
'exp_name': 'DQPN_p50',
'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': 50000,
'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'}
```
|
stanford-oval/yelp-tunein
|
stanford-oval
| null | 12 | 12 |
transformers
| 0 |
text2text-generation
| false | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 471 |
# Introduction
This seq-2-seq semantic parsing model is used by [Genie](https://github.com/stanford-oval/genie-toolkit) to compile an assistant in the restaurant domain.
This model translates natural language utterances to [ThingTalk](https://github.com/stanford-oval/thingtalk), executed by Genie.
# Training
This model is trained by [Genienlp](https://github.com/stanford-oval/genienlp), using synthetic data generated by Genie and manually annotated few-shot data.
|
irantzusl/clasificador-muchocine
|
irantzusl
|
electra
| 10 | 4 |
transformers
| 0 |
text-classification
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['classification', 'generated_from_trainer']
| true | true | true | 1,367 |
<!-- 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: 1.4058
- Accuracy: 0.4516
## 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 | 1.3498 | 0.3948 |
| 1.3866 | 2.0 | 776 | 1.3205 | 0.4310 |
| 1.0028 | 3.0 | 1164 | 1.4058 | 0.4516 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
asuzuki/ppo-SnowballTarget
|
asuzuki
| null | 20 | 1 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']
| false | true | true | 836 |
# **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://singularite.itch.io/snowballtarget
2. Step 1: Write your model_id: asuzuki/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
pyf98/tedlium2_transducer_e_branchformer
|
pyf98
| null | 21 | 0 |
espnet
| 0 |
automatic-speech-recognition
| false | false | false |
cc-by-4.0
|
['en']
|
['tedlium2']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['espnet', 'audio', 'automatic-speech-recognition']
| false | true | true | 11,168 |
## ESPnet2 ASR model
### `pyf98/tedlium2_transducer_e_branchformer`
This model was trained by Yifan Peng using tedlium2 recipe in [espnet](https://github.com/espnet/espnet/).
References:
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077)
- [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html)
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 478ba004e114e7862b05fb01112de7f7e1da3996
pip install -e .
cd egs2/tedlium2/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/tedlium2_transducer_e_branchformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Feb 9 01:29:33 CST 2023`
- python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]`
- espnet version: `espnet 202301`
- pytorch version: `pytorch 1.13.1`
- Git hash: `478ba004e114e7862b05fb01112de7f7e1da3996`
- Commit date: `Tue Feb 7 00:50:49 2023 +0000`
## asr_train_asr_transducer_e_branchformer_e12_raw_en_bpe500_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transducer_asr_model_valid.loss.ave/dev|466|14671|93.4|4.3|2.3|1.0|7.6|71.7|
|decode_asr_transducer_asr_model_valid.loss.ave/test|1155|27500|93.6|4.0|2.4|1.0|7.4|63.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transducer_asr_model_valid.loss.ave/dev|466|78259|97.1|0.9|2.0|0.9|3.8|71.7|
|decode_asr_transducer_asr_model_valid.loss.ave/test|1155|145066|97.1|0.9|2.1|0.9|3.9|63.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transducer_asr_model_valid.loss.ave/dev|466|28296|94.7|3.1|2.3|0.8|6.2|71.7|
|decode_asr_transducer_asr_model_valid.loss.ave/test|1155|52113|95.1|2.6|2.2|0.9|5.8|63.5|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_transducer_e_branchformer_e12.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transducer_e_branchformer_e12_raw_en_bpe500_sp
ngpu: 1
seed: 2022
num_workers: 6
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 45753
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 5
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 10000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe500_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe500_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
- <blank>
- <unk>
- s
- ▁the
- t
- ▁a
- ▁and
- ▁to
- d
- e
- ▁of
- ''''
- n
- ing
- ▁in
- ▁i
- ▁that
- i
- a
- l
- p
- m
- y
- o
- ▁it
- ▁we
- c
- u
- ▁you
- ed
- ▁
- r
- ▁is
- re
- ▁this
- ar
- g
- ▁so
- al
- b
- ▁s
- or
- ▁f
- ▁c
- in
- k
- f
- ▁for
- ic
- er
- le
- ▁be
- ▁do
- ▁re
- ve
- ▁e
- ▁w
- ▁was
- es
- ▁they
- ly
- h
- ▁on
- v
- ▁are
- ri
- ▁have
- an
- ▁what
- ▁with
- ▁t
- w
- ur
- it
- ent
- ▁can
- ▁he
- ▁but
- ra
- ce
- ▁me
- ▁b
- ▁ma
- ▁p
- ll
- ▁st
- ▁one
- 'on'
- ▁about
- th
- ▁de
- en
- ▁all
- ▁not
- il
- ▁g
- ch
- at
- ▁there
- ▁mo
- ter
- ation
- tion
- ▁at
- ▁my
- ro
- ▁as
- te
- ▁le
- ▁con
- ▁like
- ▁people
- ▁or
- ▁an
- el
- ▁if
- ▁from
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- ▁su
- ▁co
- ate
- ▁these
- ol
- ci
- ▁now
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- ▁out
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- ▁know
- ect
- ▁just
- as
- ▁ex
- ▁ch
- ▁d
- ▁when
- ▁very
- ▁think
- ▁who
- ▁because
- ▁go
- ▁up
- ▁us
- ▁pa
- ▁no
- ies
- ▁di
- ▁ho
- om
- ive
- ▁get
- id
- ▁o
- ▁hi
- un
- ▁how
- ▁by
- ir
- et
- ck
- ity
- ▁po
- ul
- ▁which
- ▁mi
- ▁some
- z
- ▁sp
- ▁un
- ▁going
- ▁pro
- ist
- ▁se
- ▁look
- ▁time
- ment
- de
- ▁more
- ▁had
- ng
- ▁would
- ge
- la
- ▁here
- ▁really
- x
- ▁your
- ▁them
- us
- me
- ▁en
- ▁two
- ▁k
- ▁li
- ▁world
- ne
- ow
- ▁way
- ▁want
- ▁work
- ▁don
- ▁lo
- ▁fa
- ▁were
- ▁their
- age
- vi
- ▁ha
- ac
- der
- est
- ▁bo
- am
- ▁other
- able
- ▁actually
- ▁sh
- ▁make
- ▁ba
- ▁la
- ine
- ▁into
- ▁where
- ▁could
- ▁comp
- ting
- ▁has
- ▁will
- ▁ne
- j
- ical
- ally
- ▁vi
- ▁things
- ▁te
- igh
- ▁say
- ▁years
- ers
- ▁ra
- ther
- ▁than
- ru
- ▁ro
- op
- ▁did
- ▁any
- ▁new
- ound
- ig
- ▁well
- mo
- ▁she
- ▁na
- ▁been
- he
- ▁thousand
- ▁car
- ▁take
- ▁right
- ▁then
- ▁need
- ▁start
- ▁hundred
- ▁something
- ▁over
- ▁com
- ia
- ▁kind
- um
- if
- ▁those
- ▁first
- ▁pre
- ta
- ▁said
- ize
- end
- ▁even
- ▁thing
- one
- ▁back
- ite
- ▁every
- ▁little
- ry
- ▁life
- ▁much
- ke
- ▁also
- ▁most
- ant
- per
- ▁three
- ▁come
- ▁lot
- ance
- ▁got
- ▁talk
- ▁per
- ▁inter
- ▁sa
- ▁use
- ▁mu
- ▁part
- ish
- ence
- ▁happen
- ▁bi
- ▁mean
- ough
- ▁qu
- ▁bu
- ▁day
- ▁ga
- ▁only
- ▁many
- ▁different
- ▁dr
- ▁th
- ▁show
- ful
- ▁down
- ated
- ▁good
- ▁tra
- ▁around
- ▁idea
- ▁human
- ous
- ▁put
- ▁through
- ▁five
- ▁why
- ▁change
- ▁real
- ff
- ible
- ▁fact
- ▁same
- ▁jo
- ▁live
- ▁year
- ▁problem
- ▁ph
- ▁four
- ▁give
- ▁big
- ▁tell
- ▁great
- ▁try
- ▁va
- ▁ru
- ▁system
- ▁six
- ▁plan
- ▁place
- ▁build
- ▁called
- ▁again
- ▁point
- ▁twenty
- ▁percent
- ▁nine
- ▁find
- ▁app
- ▁after
- ▁long
- ▁eight
- ▁imp
- ▁gene
- ▁design
- ▁today
- ▁should
- ▁made
- ious
- ▁came
- ▁learn
- ▁last
- ▁own
- way
- ▁turn
- ▁seven
- ▁high
- ▁question
- ▁person
- ▁brain
- ▁important
- ▁another
- ▁thought
- ▁trans
- ▁create
- ness
- ▁hu
- ▁power
- ▁act
- land
- ▁play
- ▁sort
- ▁old
- ▁before
- ▁course
- ▁understand
- ▁feel
- ▁might
- ▁each
- ▁million
- ▁better
- ▁together
- ▁ago
- ▁example
- ▁help
- ▁story
- ▁next
- ▁hand
- ▁school
- ▁water
- ▁develop
- ▁technology
- que
- ▁second
- ▁grow
- ▁still
- ▁cell
- ▁believe
- ▁number
- ▁small
- ▁between
- qui
- ▁data
- ▁become
- ▁america
- ▁maybe
- ▁space
- ▁project
- ▁organ
- ▁vo
- ▁children
- ▁book
- graph
- ▁open
- ▁fifty
- ▁picture
- ▁health
- ▁thirty
- ▁africa
- ▁reason
- ▁large
- ▁hard
- ▁computer
- ▁always
- ▁sense
- ▁money
- ▁women
- ▁everything
- ▁information
- ▁country
- ▁teach
- ▁energy
- ▁experience
- ▁food
- ▁process
- qua
- ▁interesting
- ▁future
- ▁science
- q
- '0'
- '5'
- '6'
- '9'
- '3'
- '8'
- '4'
- N
- A
- '7'
- S
- G
- F
- R
- L
- U
- E
- T
- H
- _
- B
- D
- J
- M
- ă
- ō
- ť
- '2'
- '-'
- '1'
- C
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf:
joint_space_size: 320
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
aux_ctc_tasks: []
frontend: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe500_sp/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
report_cer: false
report_wer: false
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transducer
decoder_conf:
rnn_type: lstm
num_layers: 1
hidden_size: 256
dropout: 0.1
dropout_embed: 0.2
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202301'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
pyf98/tedlium2_transducer_conformer_e12_linear2048
|
pyf98
| null | 21 | 1 |
espnet
| 0 |
automatic-speech-recognition
| false | false | false |
cc-by-4.0
|
['en']
|
['tedlium2']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['espnet', 'audio', 'automatic-speech-recognition']
| false | true | true | 10,760 |
## ESPnet2 ASR model
### `pyf98/tedlium2_transducer_conformer_e12_linear2048`
This model was trained by Yifan Peng using tedlium2 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout e06c0a97425c4d5deb4d3d14922da1f91504052e
pip install -e .
cd egs2/tedlium2/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/tedlium2_transducer_conformer_e12_linear2048
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Feb 8 22:07:40 CST 2023`
- python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]`
- espnet version: `espnet 202301`
- pytorch version: `pytorch 1.13.1`
- Git hash: `478ba004e114e7862b05fb01112de7f7e1da3996`
- Commit date: `Tue Feb 7 00:50:49 2023 +0000`
## asr_train_asr_transducer_conformer_e12_linear2048_raw_en_bpe500_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transducer_asr_model_valid.loss.ave/dev|466|14671|93.3|4.5|2.3|1.1|7.8|71.2|
|decode_asr_transducer_asr_model_valid.loss.ave/test|1155|27500|93.2|4.2|2.6|1.0|7.8|65.6|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transducer_asr_model_valid.loss.ave/dev|466|78259|97.0|0.9|2.1|1.0|3.9|71.2|
|decode_asr_transducer_asr_model_valid.loss.ave/test|1155|145066|96.9|0.9|2.2|0.9|4.0|65.6|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transducer_asr_model_valid.loss.ave/dev|466|28296|94.6|3.0|2.4|0.9|6.3|71.2|
|decode_asr_transducer_asr_model_valid.loss.ave/test|1155|52113|94.8|2.7|2.5|0.9|6.0|65.6|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_transducer_conformer_e12_linear2048.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transducer_conformer_e12_linear2048_raw_en_bpe500_sp
ngpu: 1
seed: 2022
num_workers: 6
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 37613
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 5
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 10000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe500_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe500_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
- <blank>
- <unk>
- s
- ▁the
- t
- ▁a
- ▁and
- ▁to
- d
- e
- ▁of
- ''''
- n
- ing
- ▁in
- ▁i
- ▁that
- i
- a
- l
- p
- m
- y
- o
- ▁it
- ▁we
- c
- u
- ▁you
- ed
- ▁
- r
- ▁is
- re
- ▁this
- ar
- g
- ▁so
- al
- b
- ▁s
- or
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- ▁c
- in
- k
- f
- ▁for
- ic
- er
- le
- ▁be
- ▁do
- ▁re
- ve
- ▁e
- ▁w
- ▁was
- es
- ▁they
- ly
- h
- ▁on
- v
- ▁are
- ri
- ▁have
- an
- ▁what
- ▁with
- ▁t
- w
- ur
- it
- ent
- ▁can
- ▁he
- ▁but
- ra
- ce
- ▁me
- ▁b
- ▁ma
- ▁p
- ll
- ▁st
- ▁one
- 'on'
- ▁about
- th
- ▁de
- en
- ▁all
- ▁not
- il
- ▁g
- ch
- at
- ▁there
- ▁mo
- ter
- ation
- tion
- ▁at
- ▁my
- ro
- ▁as
- te
- ▁le
- ▁con
- ▁like
- ▁people
- ▁or
- ▁an
- el
- ▁if
- ▁from
- ver
- ▁su
- ▁co
- ate
- ▁these
- ol
- ci
- ▁now
- ▁see
- ▁out
- ▁our
- ion
- ▁know
- ect
- ▁just
- as
- ▁ex
- ▁ch
- ▁d
- ▁when
- ▁very
- ▁think
- ▁who
- ▁because
- ▁go
- ▁up
- ▁us
- ▁pa
- ▁no
- ies
- ▁di
- ▁ho
- om
- ive
- ▁get
- id
- ▁o
- ▁hi
- un
- ▁how
- ▁by
- ir
- et
- ck
- ity
- ▁po
- ul
- ▁which
- ▁mi
- ▁some
- z
- ▁sp
- ▁un
- ▁going
- ▁pro
- ist
- ▁se
- ▁look
- ▁time
- ment
- de
- ▁more
- ▁had
- ng
- ▁would
- ge
- la
- ▁here
- ▁really
- x
- ▁your
- ▁them
- us
- me
- ▁en
- ▁two
- ▁k
- ▁li
- ▁world
- ne
- ow
- ▁way
- ▁want
- ▁work
- ▁don
- ▁lo
- ▁fa
- ▁were
- ▁their
- age
- vi
- ▁ha
- ac
- der
- est
- ▁bo
- am
- ▁other
- able
- ▁actually
- ▁sh
- ▁make
- ▁ba
- ▁la
- ine
- ▁into
- ▁where
- ▁could
- ▁comp
- ting
- ▁has
- ▁will
- ▁ne
- j
- ical
- ally
- ▁vi
- ▁things
- ▁te
- igh
- ▁say
- ▁years
- ers
- ▁ra
- ther
- ▁than
- ru
- ▁ro
- op
- ▁did
- ▁any
- ▁new
- ound
- ig
- ▁well
- mo
- ▁she
- ▁na
- ▁been
- he
- ▁thousand
- ▁car
- ▁take
- ▁right
- ▁then
- ▁need
- ▁start
- ▁hundred
- ▁something
- ▁over
- ▁com
- ia
- ▁kind
- um
- if
- ▁those
- ▁first
- ▁pre
- ta
- ▁said
- ize
- end
- ▁even
- ▁thing
- one
- ▁back
- ite
- ▁every
- ▁little
- ry
- ▁life
- ▁much
- ke
- ▁also
- ▁most
- ant
- per
- ▁three
- ▁come
- ▁lot
- ance
- ▁got
- ▁talk
- ▁per
- ▁inter
- ▁sa
- ▁use
- ▁mu
- ▁part
- ish
- ence
- ▁happen
- ▁bi
- ▁mean
- ough
- ▁qu
- ▁bu
- ▁day
- ▁ga
- ▁only
- ▁many
- ▁different
- ▁dr
- ▁th
- ▁show
- ful
- ▁down
- ated
- ▁good
- ▁tra
- ▁around
- ▁idea
- ▁human
- ous
- ▁put
- ▁through
- ▁five
- ▁why
- ▁change
- ▁real
- ff
- ible
- ▁fact
- ▁same
- ▁jo
- ▁live
- ▁year
- ▁problem
- ▁ph
- ▁four
- ▁give
- ▁big
- ▁tell
- ▁great
- ▁try
- ▁va
- ▁ru
- ▁system
- ▁six
- ▁plan
- ▁place
- ▁build
- ▁called
- ▁again
- ▁point
- ▁twenty
- ▁percent
- ▁nine
- ▁find
- ▁app
- ▁after
- ▁long
- ▁eight
- ▁imp
- ▁gene
- ▁design
- ▁today
- ▁should
- ▁made
- ious
- ▁came
- ▁learn
- ▁last
- ▁own
- way
- ▁turn
- ▁seven
- ▁high
- ▁question
- ▁person
- ▁brain
- ▁important
- ▁another
- ▁thought
- ▁trans
- ▁create
- ness
- ▁hu
- ▁power
- ▁act
- land
- ▁play
- ▁sort
- ▁old
- ▁before
- ▁course
- ▁understand
- ▁feel
- ▁might
- ▁each
- ▁million
- ▁better
- ▁together
- ▁ago
- ▁example
- ▁help
- ▁story
- ▁next
- ▁hand
- ▁school
- ▁water
- ▁develop
- ▁technology
- que
- ▁second
- ▁grow
- ▁still
- ▁cell
- ▁believe
- ▁number
- ▁small
- ▁between
- qui
- ▁data
- ▁become
- ▁america
- ▁maybe
- ▁space
- ▁project
- ▁organ
- ▁vo
- ▁children
- ▁book
- graph
- ▁open
- ▁fifty
- ▁picture
- ▁health
- ▁thirty
- ▁africa
- ▁reason
- ▁large
- ▁hard
- ▁computer
- ▁always
- ▁sense
- ▁money
- ▁women
- ▁everything
- ▁information
- ▁country
- ▁teach
- ▁energy
- ▁experience
- ▁food
- ▁process
- qua
- ▁interesting
- ▁future
- ▁science
- q
- '0'
- '5'
- '6'
- '9'
- '3'
- '8'
- '4'
- N
- A
- '7'
- S
- G
- F
- R
- L
- U
- E
- T
- H
- _
- B
- D
- J
- M
- ă
- ō
- ť
- '2'
- '-'
- '1'
- C
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf:
joint_space_size: 320
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
aux_ctc_tasks: []
frontend: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe500_sp/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
report_cer: false
report_wer: false
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transducer
decoder_conf:
rnn_type: lstm
num_layers: 1
hidden_size: 256
dropout: 0.1
dropout_embed: 0.2
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202301'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
chradden/ppo-LunarLander-v2
|
chradden
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Duskfallcrew/emo-goth-core
|
Duskfallcrew
| null | 21 | 9 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['text-to-image']
| false | true | true | 889 |
### Emo & Goth Core Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
# If you want to donate towards costs and don't want to subscribe:
https://ko-fi.com/DUSKFALLcrew
# If you want to monthly support the EARTH & DUSK media projects and not just AI:
https://www.patreon.com/earthndusk
mikeidsk1 (use that on your prompt)
|
petergoldstein/Reinforce-CartPole-v1
|
petergoldstein
| null | 6 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']
| true | true | true | 286 |
# **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
|
eormeno12/platzi-distilroberta-base-mrpc-glue
|
eormeno12
|
bert
| 27 | 13 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['text-classification', 'generated_from_trainer']
| true | true | true | 1,403 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-glue
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4414
- Accuracy: 0.8627
- F1: 0.9
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5624 | 1.09 | 500 | 0.4727 | 0.7990 | 0.8591 |
| 0.4063 | 2.18 | 1000 | 0.4414 | 0.8627 | 0.9 |
| 0.2612 | 3.27 | 1500 | 0.5972 | 0.8529 | 0.8986 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
odunola/mpnet_sbert_ges
|
odunola
|
mpnet
| 12 | 2 |
sentence-transformers
| 0 |
sentence-similarity
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
| false | true | true | 3,641 |
# odunola/mpnet_sbert_ges
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('odunola/mpnet_sbert_ges')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('odunola/mpnet_sbert_ges')
model = AutoModel.from_pretrained('odunola/mpnet_sbert_ges')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_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=odunola/mpnet_sbert_ges)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 303 with parameters:
```
{'batch_size': 16}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 151,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
asuzuki/ppo-Pyramids
|
asuzuki
| null | 16 | 2 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']
| false | true | true | 812 |
# **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://singularite.itch.io/pyramids
2. Step 1: Write your model_id: asuzuki/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
henryscheible/gpt2_stereoset_finetuned
|
henryscheible
|
gpt2
| 11 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null |
['stereoset']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 4,090 |
<!-- 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. -->
# gpt2_stereoset_finetuned
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6545
- Accuracy: 0.7088
## 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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.21 | 5 | 1.1855 | 0.5259 |
| No log | 0.42 | 10 | 0.7056 | 0.5338 |
| No log | 0.62 | 15 | 0.7009 | 0.5400 |
| No log | 0.83 | 20 | 0.7230 | 0.5173 |
| No log | 1.04 | 25 | 0.6666 | 0.5989 |
| No log | 1.25 | 30 | 0.6812 | 0.5699 |
| No log | 1.46 | 35 | 0.6479 | 0.6272 |
| No log | 1.67 | 40 | 0.6323 | 0.6484 |
| No log | 1.88 | 45 | 0.6306 | 0.6515 |
| No log | 2.08 | 50 | 0.6474 | 0.6633 |
| No log | 2.29 | 55 | 0.6158 | 0.6641 |
| No log | 2.5 | 60 | 0.6059 | 0.6703 |
| No log | 2.71 | 65 | 0.6151 | 0.6695 |
| No log | 2.92 | 70 | 0.5860 | 0.6782 |
| No log | 3.12 | 75 | 0.5808 | 0.6907 |
| No log | 3.33 | 80 | 0.5953 | 0.6915 |
| No log | 3.54 | 85 | 0.5860 | 0.6994 |
| No log | 3.75 | 90 | 0.5918 | 0.6947 |
| No log | 3.96 | 95 | 0.5915 | 0.6797 |
| No log | 4.17 | 100 | 0.5779 | 0.7041 |
| No log | 4.38 | 105 | 0.5902 | 0.7151 |
| No log | 4.58 | 110 | 0.5740 | 0.7080 |
| No log | 4.79 | 115 | 0.5640 | 0.7088 |
| No log | 5.0 | 120 | 0.5786 | 0.6947 |
| No log | 5.21 | 125 | 0.5892 | 0.6978 |
| No log | 5.42 | 130 | 0.5722 | 0.7096 |
| No log | 5.62 | 135 | 0.5743 | 0.7064 |
| No log | 5.83 | 140 | 0.5873 | 0.7057 |
| No log | 6.04 | 145 | 0.5915 | 0.7033 |
| No log | 6.25 | 150 | 0.5978 | 0.7009 |
| No log | 6.46 | 155 | 0.6034 | 0.6931 |
| No log | 6.67 | 160 | 0.5908 | 0.7111 |
| No log | 6.88 | 165 | 0.5954 | 0.6947 |
| No log | 7.08 | 170 | 0.5882 | 0.7033 |
| No log | 7.29 | 175 | 0.5895 | 0.7151 |
| No log | 7.5 | 180 | 0.6077 | 0.7104 |
| No log | 7.71 | 185 | 0.6121 | 0.7151 |
| No log | 7.92 | 190 | 0.6086 | 0.7151 |
| No log | 8.12 | 195 | 0.6182 | 0.7127 |
| No log | 8.33 | 200 | 0.6412 | 0.7072 |
| No log | 8.54 | 205 | 0.6425 | 0.7049 |
| No log | 8.75 | 210 | 0.6369 | 0.7135 |
| No log | 8.96 | 215 | 0.6405 | 0.7111 |
| No log | 9.17 | 220 | 0.6431 | 0.7135 |
| No log | 9.38 | 225 | 0.6474 | 0.7127 |
| No log | 9.58 | 230 | 0.6595 | 0.7041 |
| No log | 9.79 | 235 | 0.6580 | 0.7041 |
| No log | 10.0 | 240 | 0.6545 | 0.7088 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
henryscheible/xlnet-base-cased_stereoset_finetuned
|
henryscheible
|
xlnet
| 10 | 0 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null |
['stereoset']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 4,126 |
<!-- 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. -->
# xlnet-base-cased_stereoset_finetuned
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0332
- Accuracy: 0.7441
## 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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.21 | 5 | 0.7165 | 0.5055 |
| No log | 0.42 | 10 | 0.6932 | 0.5 |
| No log | 0.62 | 15 | 0.6971 | 0.5047 |
| No log | 0.83 | 20 | 0.7107 | 0.4953 |
| No log | 1.04 | 25 | 0.6895 | 0.5047 |
| No log | 1.25 | 30 | 0.6715 | 0.5840 |
| No log | 1.46 | 35 | 0.6476 | 0.6476 |
| No log | 1.67 | 40 | 0.6150 | 0.6970 |
| No log | 1.88 | 45 | 0.6170 | 0.6884 |
| No log | 2.08 | 50 | 0.6065 | 0.6797 |
| No log | 2.29 | 55 | 0.5865 | 0.7033 |
| No log | 2.5 | 60 | 0.5899 | 0.7064 |
| No log | 2.71 | 65 | 0.5980 | 0.7151 |
| No log | 2.92 | 70 | 0.5890 | 0.7229 |
| No log | 3.12 | 75 | 0.5930 | 0.7190 |
| No log | 3.33 | 80 | 0.6430 | 0.7049 |
| No log | 3.54 | 85 | 0.6677 | 0.7198 |
| No log | 3.75 | 90 | 0.6076 | 0.7370 |
| No log | 3.96 | 95 | 0.6041 | 0.7339 |
| No log | 4.17 | 100 | 0.6324 | 0.7323 |
| No log | 4.38 | 105 | 0.6990 | 0.7308 |
| No log | 4.58 | 110 | 0.7081 | 0.7433 |
| No log | 4.79 | 115 | 0.6549 | 0.7237 |
| No log | 5.0 | 120 | 0.6868 | 0.7072 |
| No log | 5.21 | 125 | 0.6525 | 0.7363 |
| No log | 5.42 | 130 | 0.7622 | 0.7418 |
| No log | 5.62 | 135 | 0.7730 | 0.7402 |
| No log | 5.83 | 140 | 0.7788 | 0.7449 |
| No log | 6.04 | 145 | 0.7609 | 0.7347 |
| No log | 6.25 | 150 | 0.8058 | 0.7323 |
| No log | 6.46 | 155 | 0.8525 | 0.7331 |
| No log | 6.67 | 160 | 0.8504 | 0.7339 |
| No log | 6.88 | 165 | 0.8424 | 0.7300 |
| No log | 7.08 | 170 | 0.8413 | 0.7394 |
| No log | 7.29 | 175 | 0.8808 | 0.7268 |
| No log | 7.5 | 180 | 0.9058 | 0.7292 |
| No log | 7.71 | 185 | 0.9338 | 0.7363 |
| No log | 7.92 | 190 | 0.9412 | 0.7370 |
| No log | 8.12 | 195 | 0.9453 | 0.7339 |
| No log | 8.33 | 200 | 0.9544 | 0.7394 |
| No log | 8.54 | 205 | 0.9664 | 0.7402 |
| No log | 8.75 | 210 | 0.9840 | 0.7339 |
| No log | 8.96 | 215 | 0.9896 | 0.7370 |
| No log | 9.17 | 220 | 1.0239 | 0.7410 |
| No log | 9.38 | 225 | 1.0306 | 0.7418 |
| No log | 9.58 | 230 | 1.0358 | 0.7402 |
| No log | 9.79 | 235 | 1.0351 | 0.7410 |
| No log | 10.0 | 240 | 1.0332 | 0.7441 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ahng79/ppo-LunarLander-v2
|
ahng79
| null | 12 | 3 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Dunkindont/Foto-Assisted-Diffusion-FAD_V0
|
Dunkindont
| null | 99 | 0 |
diffusers
| 23 |
text-to-image
| false | false | false |
creativeml-openrail-m
|
['en']
| null | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers', 'artwork', 'HDR photography', 'safetensors', 'photos']
| false | true | true | 2,743 |
# Foto Assisted Diffusion (FAD)_V0
This model is meant to mimic a modern HDR photography style
It was trained on 600 HDR images on SD1.5 and works best at **768x768** resolutions
Merged with one of my own models for illustrations and drawings, to increase flexibility
# Features:
* **No additional licensing**
* **Multi-resolution support**
* **HDR photographic outputs**
* **No Hi-Res fix required**
* [**Spreadsheet with supported resolutions, keywords for prompting and other useful hints/tips**](https://docs.google.com/spreadsheets/d/1RGRLZhgiFtLMm5Pg8qK0YMc6wr6uvj9-XdiFM877Pp0/edit#gid=364842308)
# Example Cards:
Below you will find some example cards that this model is capable of outputting.
You can acquire the images used here: [HF](https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/tree/main/Model%20Examples) or
[Google Drive](https://docs.google.com/spreadsheets/d/1RGRLZhgiFtLMm5Pg8qK0YMc6wr6uvj9-XdiFM877Pp0/edit#gid=364842308).
Google Drive gives you them all at once without needing to clone the repo, which is easier.
If you decide to clone it, set ``` GIT_LFS_SKIP_SMUDGE=1 ``` to skip downloading large files
Place them into an EXIF viewer such as the built in "PNG Info" tab in the popular Auto1111 repository to quickly copy the parameters and replicate them!
## 768x768 Food
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/768x768%20Food.jpg" style="max-width: 800px;" width="100%"/>
## 768x768 Landscapes
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/768x768%20Landscapes.jpg" style="max-width: 800px;" width="100%"/>
## 768x768 People
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/768x768%20People.jpg" style="max-width: 800px;" width="100%"/>
## 768x768 Random
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/768x768%20Random.jpg" style="max-width: 800px;" width="100%"/>
## 512x512 Artwork
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/512x512%20Artwork.jpg" style="max-width: 800px;" width="100%"/>
## 512x512 Photos
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/512x512%20Photo.jpg" style="max-width: 800px;" width="100%"/>
## Cloud Support
Sinkin kindly hosted our model. [Click here to run it on the cloud](https://sinkin.ai/m/V6vYoaL)!
## License
*My motivation for making this model was to have a free, non-restricted model for the community to use and for startups.*
*I was noticing the models people gravitated towards, were merged models which had prior license requirements from the people who trained them.*
*This was just a fun project I put together for you guys.*
*My fun ended when I posted the results :D*
*Enjoy! Sharing is caring :)*
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