modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
distilbert-base-multilingual-cased
|
[
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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"DistilBertForMaskedLM"
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}
| 8,339,633 | 2023-03-08T05:53:12Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="proleetops/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"])
```
|
distilbert-base-uncased-finetuned-sst-2-english
|
[
"pytorch",
"tf",
"rust",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"transformers",
"license:apache-2.0",
"model-index",
"has_space"
] |
text-classification
|
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}
| 3,060,704 | 2023-03-08T05:58:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-text_summarization_data
results: []
language:
- en
pipeline_tag: summarization
---
# led-base-16384-text_summarization_data
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9531
- Rouge1: 43.3689
- Rouge2: 19.9885
- Rougel: 39.9887
- Rougelsum: 40.0679
- Gen Len: 14.0392
## Model description
This is a text summarization model.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Text%20Summarization/Text-Summarized%20Data%20-%20Comparison/LED%20-%20Text%20Summarization%20-%204%20Epochs.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/cuitengfeui/textsummarization-data
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.329 | 1.0 | 1197 | 0.9704 | 42.4111 | 19.8995 | 39.4717 | 39.5449 | 14.254 |
| 0.8367 | 2.0 | 2394 | 0.9425 | 43.1141 | 19.6089 | 39.7533 | 39.8298 | 14.1058 |
| 0.735 | 3.0 | 3591 | 0.9421 | 42.8101 | 19.8281 | 39.617 | 39.6751 | 13.7101 |
| 0.6737 | 4.0 | 4788 | 0.9531 | 43.3689 | 19.9885 | 39.9887 | 40.0679 | 14.0392 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1
|
gpt2-xl
|
[
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 50
},
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}
| 308,781 | 2023-03-08T06:23:43Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Isaac009/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ARTeLab/mbart-summarization-mlsum
|
[
"pytorch",
"mbart",
"text2text-generation",
"it",
"dataset:ARTeLab/mlsum-it",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
] |
summarization
|
{
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"MBartForConditionalGeneration"
],
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}
| 111 | 2023-03-08T11:52:46Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-Dani
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ASCCCCCCCC/distilbert-base-multilingual-cased-amazon_zh_20000
|
[
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
}
}
| 39 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -100.91 +/- 29.04
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'hub'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'michal512/LunarLander-v2-ppo'
'batch_size': 512
'minibatch_size': 128}
```
|
Pinwheel/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
}
| 4 | 2023-03-08T12:44:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jaiiiiii/my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jaiiiiii/my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 7.1466
- Validation Loss: 6.6334
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.1466 | 6.6334 | 0 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AdapterHub/roberta-base-pf-ud_pos
|
[
"roberta",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:pos/ud_ewt"
] |
token-classification
|
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| 8 | null |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetune_teacher_clean_mozilla_100_epochs_try2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune_teacher_clean_mozilla_100_epochs_try2
This model is a fine-tuned version of [finetune_teacher_clean_mozilla_200_epochs](https://huggingface.co/finetune_teacher_clean_mozilla_200_epochs) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 55.2160
- Wer: 0.2810
## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 42.7428 | 29.41 | 1000 | 48.0854 | 0.3160 |
| 45.9744 | 58.82 | 2000 | 50.4082 | 0.2936 |
| 30.0353 | 88.23 | 3000 | 55.2160 | 0.2810 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AdapterHub/roberta-base-pf-wic
|
[
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:wordsence/wic"
] |
text-classification
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 717.50 +/- 226.59
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zestyoreo -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 zestyoreo -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 zestyoreo
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AdapterHub/roberta-base-pf-wnut_17
|
[
"roberta",
"en",
"dataset:wnut_17",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification"
] |
token-classification
|
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}
| 4 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Agneev/ppo-Huggy2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Adarsh123/distilbert-base-uncased-finetuned-ner
|
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}
| 0 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1939.53 +/- 52.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Adharsh2608/DialoGPT-small-harrypotter
|
[] | null |
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}
}
| 0 | null |
---
license: cc-by-4.0
---
**pythia-1.4B-finetuned-oa-instructions**
This model is a fine-tuned version of pythia on the oa dataset. It achieves the following results on the evaluation set:
Loss: 0.1224
**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:
* seed: 42
* learning_rate: 5e-06
* train_batch_size: 32
* eval_batch_size: 8
* optimizer: Adam with betas : {'lr': 5e-06, 'betas': [0.9, 0.999], 'eps': 1e-08, 'weight_decay': 0.0}
* lr_scheduler_type: linear
* training_steps: 5000
* fp16
* warmup_steps 5
* Num examples = 53k
**Training results**
```
{
"epoch": 1.0,
"train_loss": 0.8031303182039198,
"train_runtime": 6338.6403,
"train_samples": 53455,
"train_samples_per_second": 8.433,
"train_steps_per_second": 0.264
}
```
**Framework versions**
* transformers 4.24.0
* torch 1.10.0+cu111
* datasets 2.10.0
* tokenizers 0.12.1
|
AdharshJolly/HarryPotterBot-Model
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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}
}
| 10 | null |
---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bert-large-uncased-whole-word-masking-squad2-finetuned-squad2-islamic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-squad2-finetuned-squad2-islamic
This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6799
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6646 | 1.0 | 1000 | 0.6799 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Adil617/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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}
| 4 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1333.59 +/- 87.55
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Adinda/Adinda
|
[
"license:artistic-2.0"
] | null |
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}
| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: victorivus/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AdrianGzz/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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}
}
}
| 9 | null |
# CentauriMix: First Model of Constellation Series
Anything-based 2D model designed for cute anime girls
<br> *WARNING* : Images for Alpha Centauri A & B are not updated yet, please keep that in mind.
<br>
<br>
## Recommended Settings
- Sampler: DPM++ 2M Karras (speed and detail balanced) or DPM++ SDE Karras (detailed)
- Prompts: (loli:1.4+) (strongly recommend to unlock the truth of Centauri Series)
- Negatives: (worst quality, low quality:1.4), EasyNegative (base, the more the better since it is not AOM-based)
<br>
- ## Alpha Centauri
<!-- HTML -->
- Alpha Centauri A
<div class="grid-image">
<img src="https://ac-p2.namu.la/20230308sac/7c460eddfc1e174b8750605446aab0a8787bba249182f428ecfadf7c8790e9be.png" height="300px" width="200px"/>
<img src="https://ac-p2.namu.la/20230308sac/99e08a25a680c69ab2978d3b426381c50d6b275a42d5b15580dac72338c3248a.png" height="300px" width="200px"/>
<img src="https://ac-p2.namu.la/20230308sac/609cef7a9a0cc92d8ef7da786f2264ca4ef0997f6ea7abf423457ca19265b288.png" height="300px" width="400px"/>
</div>
- Alpha Centauri B
<div class="grid-image">
<img src="https://cdn.discordapp.com/attachments/1047069007733329961/1083079815285964902/02918--1.0-20-5.5-DPM_2M_Karras.png" height="300px" width="400px"/>
<img src="https://cdn.discordapp.com/attachments/1047069007733329961/1083081033496408114/03055-794659742-28-5.png" height="300px" width="400px"/>
<img src="https://media.discordapp.net/attachments/1047069007733329961/1083083581687078983/03060-462301326-20-5.5-DPM_2M_Karras.png" height="300px" width="200px"/>
</div>
<style>
.grid-image {
display:flex;
flex-wrap:wrap;
align-items:flex-start;
margin:30px 0;
}
.grid-image img {
width:calc(33.333% - 10px);
margin:0 15px 15px 0;
}
.grid-image img:nth-of-type(3n),
.grid-image img:last-child {
margin-right:0;
}
@media screen and (max-width:640px){
.grid-image img {
width:calc(50% - 15px);
}
}
@media screen and (max-width:480px){
.grid-image img:nth-of-type(2n) {
margin-right:0;
}
.grid-image img:nth-of-type(3n) {
margin-right:15px;
}
}
</style>
designed for cute girls
<br> **Alpha-B** has specific face type(smooth which I like), try it!
<br><br> - *01_A1* = AOM3A1 x 0.3 + BACLA-MIX x 0.4 + EmiphaV4 x 0.3
<br> - *01_A2* = moontea_v2 x 0.55 + 7th-anime_v3a x 0.25 + Anything3.0+F222-SD1.4 x 0.2
<br> - *01_A3* = AlmondGrapeMix x 0.35 + BACLA-MIX x 0.4 + Coppermix_Gamma x 0.25
<br> **Alpha Centauri A v1.0** = *01_A1* x 0.15 + *01_A2* x 0.25 + *01_A3* x 0.6
<br> **Alpha Centauri B v1.0** = *01_A2* itself
<br>
<br> **Alpha Centauri B v1.1** = moontea_v2 x 0.5 + 7th-anime_v1.1 x 0.25 + Anything3.0+F222-SD1.4 x 0.25
<br> **Alpha Centauri A v1.1** = *01_A1* x 0.1 + **Alpha_B-v1.1** x 0.35 + *01_A3'* x 0.55
<br><br>
- ## Beta Centauri
<!-- HTML -->
<div class="grid-image">
<img src="https://ac-p2.namu.la/20230308sac/db9f25d3a1fb5521d53acceed24da64590f7fabeb20721405b86117b6af31bc3.png" height="300px" width="200px"/>
<img src="https://ac-p2.namu.la/20230308sac/6789643abefca668b87f9c399768d6ff2e3676fdab19bb31026c435d158560a7.png" height="300px" width="200px"/>
<img src="https://ac-p2.namu.la/20230308sac/3c8b082695508a673b6ea2d0229acda4f4df7a0886b452906392defbff559677.png" height="300px" width="200px"/>
</div>
<style>
/* CSS */
.grid-image {
display:flex;
flex-wrap:wrap;
align-items:flex-start;
margin:30px 0;
}
.grid-image img {
width:calc(33.333% - 10px);
margin:0 15px 15px 0;
}
.grid-image img:nth-of-type(3n),
.grid-image img:last-child {
margin-right:0;
}
@media screen and (max-width:640px){
.grid-image img {
width:calc(50% - 15px);
}
}
@media screen and (max-width:480px){
.grid-image img:nth-of-type(2n) {
margin-right:0;
}
.grid-image img:nth-of-type(3n) {
margin-right:15px;
}
}
</style>
designed for girls little bit older than **Alpha-A**
<br> has specific face type (kinda sharp, cute when strong loli tag is used)
<br> EasyNegative Recommended, but not a requirement
<br><br> **Beta Centauri** = 7pa x 0.3 + Counterfeit-v2.5 x 0.4 + *01_A1* x 0.3
<br><br>
- ## Theta Centauri
<!-- HTML -->
<div class="grid-image">
<img src="https://ac-p2.namu.la/20230308sac/1a35d497bd4657e3ba542313578a90846d6c1dab0f08a59f65eb84821f9c497d.png" height="300px" width="200px"/>
<img src="https://cdn.discordapp.com/attachments/1047069007733329961/1083073896447754260/03050-2411852086-28-5.5-DPM_2M_Karras.png" height="300px" width="200px"/>
<img src="https://ac-p2.namu.la/20230308sac/a7420f456bed6aaf536ccd08e539c1e41742810a6047e66444ed3fd6d250ca21.png" height="300px" width="200px"/>
</div>
<style>
/* CSS */
.grid-image {
display:flex;
flex-wrap:wrap;
align-items:flex-start;
margin:30px 0;
}
.grid-image img {
width:calc(33.333% - 10px);
margin:0 15px 15px 0;
}
.grid-image img:nth-of-type(3n),
.grid-image img:last-child {
margin-right:0;
}
@media screen and (max-width:640px){
.grid-image img {
width:calc(50% - 15px);
}
}
@media screen and (max-width:480px){
.grid-image img:nth-of-type(2n) {
margin-right:0;
}
.grid-image img:nth-of-type(3n) {
margin-right:15px;
}
}
</style>
designed for girls little bit older than **Alpha-A + (loli:1.5)**, different face type from **Beta**
<br> EasyNegative Recommended, but not a requirement
<br><br> **Theta Centauri** = CherryBlossomMix x 0.3 + Anything-v4.5 x 0.3 + reversalSigma x 0.4
<br><br>
|
Adrianaforididk/Jinx
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.01 +/- 22.57
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Advertisement/FischlUWU
|
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}
| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Rendel/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"])
```
|
Aeskybunnie/Me
|
[] | null |
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| 0 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 558.00 +/- 164.29
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yovchev -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 yovchev -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 yovchev
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AethiQs-Max/AethiQs_GemBERT_bertje_50k
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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}
| 11 | null |
---
license: openrail
---
Converted Canny SD 2.1-base model from https://huggingface.co/thibaud/controlnet-sd21/ to diffusers format.
Saved only ControlNet weights
Usage:
```
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, DEISMultistepScheduler
import cv2
from PIL import Image
import numpy as np
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-base",
safety_checker=None,
# revision='fp16',
# torch_dtype=torch.float16,
controlnet=ControlNetModel.from_pretrained("thepowefuldeez/sd21-controlnet-canny")
).to('cuda')
pipe.scheduler = DEISMultistepScheduler.from_config(pipe.scheduler.config)
image = np.array(Image.open("10.png"))
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
im = pipe(
"beautiful woman", image=canny_image, num_inference_steps=30,
negative_prompt="ugly, blurry, bad, deformed, bad anatomy",
generator=torch.manual_seed(42)
).images[0]
```
|
AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_10
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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| 9 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Cart_Pole_V1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_30-epoch_30
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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| 8 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 562.00 +/- 92.42
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dineshresearch -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 dineshresearch -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 dineshresearch
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Aftabhussain/Tomato_Leaf_Classifier
|
[
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index",
"autotrain_compatible"
] |
image-classification
|
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| 50 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: mmhamdy/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ahda/M
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- M3ri4-style
---
### M3rii4-Style Dreambooth model trained by Anonim3327 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
### The model is based on Anything diffusion v4.5 (!!!VAE is not needed!!!)
### The images were taken from M3rii4 social media: https://vk.com/m3rii44, https://twitter.com/m3rii4
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
### Prompt: Young Woman sit in park, skirt, purple t-shirt
### Neg. Prompt: (bad quality:1.3), (bad anatomy:1.2), (bad finger anatomy:1.2),
### Sampler: K_Euler_a
### Guidance: 6
### Prompt: Young Woman sit street, skirt, purple t-shirt
### Neg. Prompt: (bad quality:1.3), (bad anatomy:1.2), (bad finger anatomy:1.2),
### Sampler: K_Euler_a
### Guidance: 6
|
Ahmad/parsT5
|
[
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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| 12 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: HXW/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ahmadvakili/A
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-multilingual-cased-finetuned-squad-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-squad-finetuned-squad
This model is a fine-tuned version of [JensH/bert-base-multilingual-cased-finetuned-squad](https://huggingface.co/JensH/bert-base-multilingual-cased-finetuned-squad) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Ahmed59/Demo-Team-5-SIAD
|
[
"tf",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"RobertaForSequenceClassification"
],
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| 14 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7326
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8712 | 1.0 | 1061 | 3.7513 |
| 3.7906 | 2.0 | 2122 | 3.7358 |
| 3.739 | 3.0 | 3183 | 3.7326 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AhmedHassan19/model
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-base-finetuned-es-to-pua
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-es-to-pua
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4986
- Bleu: 1.7461
- Gen Len: 15.8171
## 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 36 | 3.4870 | 0.0863 | 17.9878 |
| No log | 2.0 | 72 | 3.0772 | 0.2333 | 17.622 |
| No log | 3.0 | 108 | 2.7865 | 0.2752 | 16.6829 |
| No log | 4.0 | 144 | 2.5878 | 0.8782 | 17.9024 |
| No log | 5.0 | 180 | 2.4639 | 1.584 | 17.1463 |
| No log | 6.0 | 216 | 2.3559 | 0.9321 | 16.8049 |
| No log | 7.0 | 252 | 2.2704 | 1.0018 | 17.3902 |
| No log | 8.0 | 288 | 2.1956 | 1.2549 | 17.0732 |
| No log | 9.0 | 324 | 2.1307 | 0.9709 | 17.4268 |
| No log | 10.0 | 360 | 2.0866 | 0.7563 | 17.5 |
| No log | 11.0 | 396 | 2.0392 | 0.707 | 17.2439 |
| No log | 12.0 | 432 | 1.9920 | 0.8647 | 16.9756 |
| No log | 13.0 | 468 | 1.9630 | 0.8724 | 17.8171 |
| 2.7137 | 14.0 | 504 | 1.9244 | 1.0593 | 17.4146 |
| 2.7137 | 15.0 | 540 | 1.9010 | 1.6823 | 17.061 |
| 2.7137 | 16.0 | 576 | 1.8711 | 1.6452 | 16.5732 |
| 2.7137 | 17.0 | 612 | 1.8475 | 1.6622 | 16.8659 |
| 2.7137 | 18.0 | 648 | 1.8265 | 2.2968 | 16.7195 |
| 2.7137 | 19.0 | 684 | 1.8056 | 2.2125 | 16.6098 |
| 2.7137 | 20.0 | 720 | 1.7962 | 2.3889 | 16.3049 |
| 2.7137 | 21.0 | 756 | 1.7778 | 2.341 | 16.3537 |
| 2.7137 | 22.0 | 792 | 1.7626 | 2.3187 | 16.1341 |
| 2.7137 | 23.0 | 828 | 1.7450 | 2.5281 | 16.0732 |
| 2.7137 | 24.0 | 864 | 1.7357 | 2.6768 | 15.9268 |
| 2.7137 | 25.0 | 900 | 1.7177 | 2.3932 | 15.9146 |
| 2.7137 | 26.0 | 936 | 1.7126 | 2.611 | 15.8537 |
| 2.7137 | 27.0 | 972 | 1.7088 | 2.2829 | 15.622 |
| 1.9301 | 28.0 | 1008 | 1.6868 | 2.4441 | 15.8293 |
| 1.9301 | 29.0 | 1044 | 1.6707 | 2.5402 | 16.0976 |
| 1.9301 | 30.0 | 1080 | 1.6790 | 2.0723 | 15.561 |
| 1.9301 | 31.0 | 1116 | 1.6600 | 1.4278 | 15.9146 |
| 1.9301 | 32.0 | 1152 | 1.6661 | 1.4274 | 15.7317 |
| 1.9301 | 33.0 | 1188 | 1.6474 | 1.4484 | 15.6463 |
| 1.9301 | 34.0 | 1224 | 1.6484 | 1.5172 | 15.7805 |
| 1.9301 | 35.0 | 1260 | 1.6389 | 1.5497 | 15.7561 |
| 1.9301 | 36.0 | 1296 | 1.6384 | 1.52 | 15.6341 |
| 1.9301 | 37.0 | 1332 | 1.6304 | 1.4572 | 15.8293 |
| 1.9301 | 38.0 | 1368 | 1.6163 | 1.4786 | 16.1341 |
| 1.9301 | 39.0 | 1404 | 1.6116 | 1.5765 | 15.9634 |
| 1.9301 | 40.0 | 1440 | 1.6020 | 1.5902 | 16.0244 |
| 1.9301 | 41.0 | 1476 | 1.6064 | 1.6992 | 15.8659 |
| 1.6368 | 42.0 | 1512 | 1.5949 | 1.5409 | 16.0 |
| 1.6368 | 43.0 | 1548 | 1.5811 | 1.4916 | 16.2439 |
| 1.6368 | 44.0 | 1584 | 1.5849 | 1.6047 | 16.2683 |
| 1.6368 | 45.0 | 1620 | 1.5843 | 1.521 | 15.7073 |
| 1.6368 | 46.0 | 1656 | 1.5805 | 1.7424 | 15.9878 |
| 1.6368 | 47.0 | 1692 | 1.5791 | 1.6066 | 15.9268 |
| 1.6368 | 48.0 | 1728 | 1.5734 | 1.602 | 15.7195 |
| 1.6368 | 49.0 | 1764 | 1.5649 | 1.5817 | 15.939 |
| 1.6368 | 50.0 | 1800 | 1.5654 | 1.6469 | 15.8293 |
| 1.6368 | 51.0 | 1836 | 1.5587 | 1.7048 | 15.6463 |
| 1.6368 | 52.0 | 1872 | 1.5553 | 1.5203 | 15.8415 |
| 1.6368 | 53.0 | 1908 | 1.5500 | 1.5646 | 15.6951 |
| 1.6368 | 54.0 | 1944 | 1.5532 | 1.5003 | 15.7195 |
| 1.6368 | 55.0 | 1980 | 1.5344 | 1.5359 | 16.1098 |
| 1.4554 | 56.0 | 2016 | 1.5370 | 1.6052 | 15.6951 |
| 1.4554 | 57.0 | 2052 | 1.5394 | 1.5299 | 15.9146 |
| 1.4554 | 58.0 | 2088 | 1.5399 | 1.6024 | 15.6829 |
| 1.4554 | 59.0 | 2124 | 1.5403 | 1.6342 | 15.6829 |
| 1.4554 | 60.0 | 2160 | 1.5361 | 1.609 | 15.7195 |
| 1.4554 | 61.0 | 2196 | 1.5308 | 1.6753 | 15.878 |
| 1.4554 | 62.0 | 2232 | 1.5211 | 1.6381 | 16.0976 |
| 1.4554 | 63.0 | 2268 | 1.5242 | 1.7172 | 15.622 |
| 1.4554 | 64.0 | 2304 | 1.5215 | 1.6888 | 15.9024 |
| 1.4554 | 65.0 | 2340 | 1.5146 | 1.6619 | 16.0 |
| 1.4554 | 66.0 | 2376 | 1.5173 | 1.7203 | 15.8537 |
| 1.4554 | 67.0 | 2412 | 1.5235 | 1.7363 | 15.7317 |
| 1.4554 | 68.0 | 2448 | 1.5125 | 1.7295 | 16.0366 |
| 1.4554 | 69.0 | 2484 | 1.5141 | 1.7005 | 15.8902 |
| 1.3341 | 70.0 | 2520 | 1.5162 | 1.8302 | 15.7927 |
| 1.3341 | 71.0 | 2556 | 1.5129 | 1.8278 | 15.9024 |
| 1.3341 | 72.0 | 2592 | 1.5123 | 1.7764 | 15.6829 |
| 1.3341 | 73.0 | 2628 | 1.5046 | 1.7259 | 15.9634 |
| 1.3341 | 74.0 | 2664 | 1.5069 | 1.6517 | 15.9024 |
| 1.3341 | 75.0 | 2700 | 1.5026 | 1.7334 | 15.9024 |
| 1.3341 | 76.0 | 2736 | 1.4923 | 1.7531 | 15.9268 |
| 1.3341 | 77.0 | 2772 | 1.4956 | 1.7338 | 15.7561 |
| 1.3341 | 78.0 | 2808 | 1.4996 | 1.6956 | 15.7805 |
| 1.3341 | 79.0 | 2844 | 1.5010 | 1.7299 | 15.9268 |
| 1.3341 | 80.0 | 2880 | 1.5012 | 1.7097 | 15.9024 |
| 1.3341 | 81.0 | 2916 | 1.5032 | 1.7689 | 15.8902 |
| 1.3341 | 82.0 | 2952 | 1.5025 | 1.7353 | 15.939 |
| 1.3341 | 83.0 | 2988 | 1.5004 | 1.7472 | 15.9512 |
| 1.2568 | 84.0 | 3024 | 1.4989 | 1.7171 | 15.9756 |
| 1.2568 | 85.0 | 3060 | 1.5015 | 1.7704 | 15.9024 |
| 1.2568 | 86.0 | 3096 | 1.5017 | 1.7838 | 15.9024 |
| 1.2568 | 87.0 | 3132 | 1.5022 | 1.7562 | 16.0366 |
| 1.2568 | 88.0 | 3168 | 1.5004 | 1.7633 | 16.0366 |
| 1.2568 | 89.0 | 3204 | 1.4995 | 1.7633 | 15.9756 |
| 1.2568 | 90.0 | 3240 | 1.5038 | 1.766 | 15.8537 |
| 1.2568 | 91.0 | 3276 | 1.5001 | 1.7764 | 16.0 |
| 1.2568 | 92.0 | 3312 | 1.5010 | 1.7707 | 15.878 |
| 1.2568 | 93.0 | 3348 | 1.4996 | 1.7633 | 15.9268 |
| 1.2568 | 94.0 | 3384 | 1.5011 | 1.7453 | 15.8171 |
| 1.2568 | 95.0 | 3420 | 1.5014 | 1.7385 | 15.7927 |
| 1.2568 | 96.0 | 3456 | 1.4996 | 1.7253 | 15.7927 |
| 1.2568 | 97.0 | 3492 | 1.4988 | 1.7459 | 15.8049 |
| 1.2103 | 98.0 | 3528 | 1.4978 | 1.7461 | 15.8171 |
| 1.2103 | 99.0 | 3564 | 1.4986 | 1.7461 | 15.8293 |
| 1.2103 | 100.0 | 3600 | 1.4986 | 1.7461 | 15.8171 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AhmedSSoliman/MarianCG-CoNaLa
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] |
text2text-generation
|
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| 21 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: validate_bert_large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# validate_bert_large
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0445
- F1: 0.9828
- Roc Auc: 0.9871
- Accuracy: 0.9595
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
| 0.1224 | 1.0 | 4000 | 0.1122 | 0.9414 | 0.9558 | 0.8664 |
| 0.1063 | 2.0 | 8000 | 0.0881 | 0.9583 | 0.9681 | 0.9010 |
| 0.0922 | 3.0 | 12000 | 0.0806 | 0.9623 | 0.9714 | 0.9085 |
| 0.0673 | 4.0 | 16000 | 0.0610 | 0.9740 | 0.9814 | 0.9370 |
| 0.0468 | 5.0 | 20000 | 0.0462 | 0.9812 | 0.9855 | 0.9545 |
| 0.0369 | 6.0 | 24000 | 0.0445 | 0.9828 | 0.9871 | 0.9595 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ahmedahmed/Wewe
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.63 +/- 0.54
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ahren09/distilbert-base-uncased-finetuned-cola
|
[
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
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| 33 | null |
---
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
language:
- ja
pipeline_tag: text-generation
tags:
- gpt
- japanese
- language model
- reversed gpt-2
inference: false
---
# japanese-reversed-gpt2-medium-unidic
This is a medium-sized Japanese **reversed** GPT-2 model using BERT-like tokenizer.
Unlike most Language Models, this model generates sentences from right to left.
Not reversed version is published [here](https://huggingface.co/okazaki-lab/japanese-gpt2-medium-unidic/).
# How to use
The model depends on [PyTorch](https://pytorch.org/), [fugashi](https://github.com/polm/fugashi) with [unidic-lite](https://github.com/polm/unidic-lite), and [Hugging Face Transformers](https://github.com/huggingface/transformers).
```sh
pip install torch torchvision torchaudio
pip install fugashi[unidic-lite]
pip install transformers
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained('okazaki-lab/japanese-reversed-gpt2-medium-unidic')
model = AutoModelForCausalLM.from_pretrained('okazaki-lab/japanese-reversed-gpt2-medium-unidic')
text = 'ので、散歩に行きました。'
bos = tokenizer.convert_tokens_to_ids(['[BOS]']) # [32768]
input_ids = bos + tokenizer.encode(text)[1:-1][::-1] # [CLS] and [SEP] generated by BERT Tokenizer are removed then reversed
input_ids = torch.tensor(input_ids).unsqueeze(0)
output = model.generate(
input_ids,
do_sample=True,
max_new_tokens=30,
top_k=50,
top_p=0.95,
repetition_penalty=1.0,
num_return_sequences=1,
pad_token_id=0,
eos_token_id=32769,
)[0].flip(0)
print(tokenizer.decode(output))
```
# Model architecture
Transformer-based Language Model
- Layers: 24
- Heads: 16
- Dimensions of hidden states: 1024
# Training
We used a [codebase](https://github.com/rinnakk/japanese-pretrained-models) provided by rinna Co., Ltd. for training.
The model was trained on Japanese CC-100 and Japanese Wikipedia (2022/01/31).
We employed 8 A100 GPUs for 17 days.
The perplexity on the validation set is 9.79.
# Tokenization
Our tokenizer is based on [the one](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) provided by Tohoku NLP Group.
The texts are tokenized by MeCab and then WordPiece.
The vocabulary size is 32771 (32768 original tokens + 2 special tokens + 1 unused token).
# License
[Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
Copyright (c) 2021, Tohoku University
Copyright (c) 2023, Tokyo Institute of Technology
|
Aibox/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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| 10 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.62 +/- 0.18
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_no_lm
|
[] | null |
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}
| 0 | null |
---
language:
- fr
library_name: nemo
datasets:
- mozilla-foundation/common_voice_12_0
tags:
- automatic-speech-recognition
model-index:
- name: stt_fr_citrinet_512_gamma_0_25
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 12.0
type: mozilla-foundation/common_voice_12_0
config: clean
split: test
args:
language: fr
metrics:
- name: Test WER
type: wer
value: 14.90
license: bsd-3-clause
---
# NVIDIA Streaming Citrinet 512 (fr-FR)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [](#datasets) |
## Attribution
As initial checkpoint used [stt_en_citrinet_512_gamma_0_25](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_citrinet_512_gamma_0_25) by [NVIDIA](https://github.com/NVIDIA) licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
|
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ba",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"has_space"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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| 64 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.17 +/- 14.08
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AimB/mT5-en-kr-natural
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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}
}
| 78 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-cart-pole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 480.10 +/- 59.70
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AimB/mT5-en-kr-opus
|
[] | null |
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5470
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.956 | 1.0 | 16 | 2.6626 |
| 2.7754 | 2.0 | 32 | 2.5842 |
| 2.7703 | 3.0 | 48 | 2.5981 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Ajay191191/autonlp-Test-530014983
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Ajay191191/autonlp-data-Test",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
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| 34 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: mmhamdy/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AkaiSnow/Rick_bot
|
[] | null |
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}
| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.34 +/- 21.34
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Akame/Vi
|
[] | null |
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}
| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.73 +/- 18.65
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ankit-11/distilbert-base-uncased-finetuned-toxic
|
[] | null |
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| 0 | null |
Access to model mrm8488/bart-legal-base-es is restricted and you are not in the authorized list. Visit https://huggingface.co/mrm8488/bart-legal-base-es to ask for access.
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 6 | 2023-03-09T02:24:13Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 110.97 +/- 120.38
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'CleanRL_ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.001
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'chandc/ppo-LunarLander-v2-1M-lro-1e-3'
'batch_size': 512
'minibatch_size': 128}
```
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 6 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 246.08 +/- 67.20
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'CleanRL_ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.005
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'chandc/ppo-LunarLander-v2-1M-lro-5e-3'
'batch_size': 512
'minibatch_size': 128}
```
|
AnonymousSub/AR_rule_based_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"BertModel"
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| 5 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1941.87 +/- 122.89
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/SR_cline
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 6 | 2023-03-09T03:20:36Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt-expt-sp-v3-K-600-MA-kmeans-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-expt-sp-v3-K-600-MA-kmeans-v1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0165
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 0.1526 | 18.31 | 5000 | 0.0965 |
| 0.0728 | 36.63 | 10000 | 0.0381 |
| 0.0244 | 54.94 | 15000 | 0.0198 |
| 0.0204 | 73.26 | 20000 | 0.0183 |
| 0.023 | 91.57 | 25000 | 0.0173 |
| 0.0184 | 109.89 | 30000 | 0.0173 |
| 0.0182 | 128.2 | 35000 | 0.0172 |
| 0.0183 | 146.52 | 40000 | 0.0169 |
| 0.0175 | 164.83 | 45000 | 0.0170 |
| 0.0176 | 183.15 | 50000 | 0.0169 |
| 0.0174 | 201.46 | 55000 | 0.0170 |
| 0.0173 | 219.78 | 60000 | 0.0169 |
| 0.0172 | 238.1 | 65000 | 0.0168 |
| 0.0171 | 256.41 | 70000 | 0.0167 |
| 0.0171 | 274.72 | 75000 | 0.0167 |
| 0.017 | 293.04 | 80000 | 0.0167 |
| 0.0169 | 311.35 | 85000 | 0.0167 |
| 0.0169 | 329.67 | 90000 | 0.0166 |
| 0.0168 | 347.98 | 95000 | 0.0166 |
| 0.0168 | 366.3 | 100000 | 0.0166 |
| 0.0167 | 384.61 | 105000 | 0.0166 |
| 0.0167 | 402.93 | 110000 | 0.0166 |
| 0.0167 | 421.24 | 115000 | 0.0166 |
| 0.0166 | 439.56 | 120000 | 0.0165 |
| 0.0166 | 457.87 | 125000 | 0.0165 |
| 0.0166 | 476.19 | 130000 | 0.0165 |
| 0.0166 | 494.5 | 135000 | 0.0165 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_declutr
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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}
}
| 6 | 2023-03-09T03:23:12Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.00 +/- 25.45
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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},
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}
}
}
| 8 | 2023-03-09T04:11:58Z |
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- wofeishenling/autotrain-data-iemocap_text_4
co2_eq_emissions:
emissions: 0.438477125256298
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 39809103601
- CO2 Emissions (in grams): 0.4385
## Validation Metrics
- Loss: 0.875
- Accuracy: 0.694
- Macro F1: 0.697
- Micro F1: 0.694
- Weighted F1: 0.695
- Macro Precision: 0.708
- Micro Precision: 0.694
- Weighted Precision: 0.700
- Macro Recall: 0.690
- Micro Recall: 0.694
- Weighted Recall: 0.694
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/wofeishenling/autotrain-iemocap_text_4-39809103601
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("wofeishenling/autotrain-iemocap_text_4-39809103601", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("wofeishenling/autotrain-iemocap_text_4-39809103601", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 4 | 2023-03-09T04:12:31Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog in a bucket
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - davidhefan/lora_sks_dogs
本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of sks dog in a bucket 文本进行了训练。
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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},
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}
}
}
| 4 | 2023-03-09T04:39:33Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: handle-pull-v2
type: handle-pull-v2
metrics:
- type: mean_reward
value: 4642.87 +/- 14.75
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **handle-pull-v2** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r qgallouedec/sample-factory-handle-pull-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=handle-pull-v2 --train_dir=./train_dir --experiment=sample-factory-handle-pull-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m train --algo=APPO --env=handle-pull-v2 --train_dir=./train_dir --experiment=sample-factory-handle-pull-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AnonymousSub/SR_rule_based_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2 | 2023-03-09T04:55:31Z |
---
language:
- mar
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper_marathi_small_V1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: mr
split: test
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 45.00676938946554
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper_marathi_small_V1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2754
- Wer: 45.0068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4794 | 0.41 | 100 | 0.4754 | 59.9317 |
| 0.3121 | 0.81 | 200 | 0.3161 | 52.8786 |
| 0.2051 | 1.22 | 300 | 0.2900 | 50.2547 |
| 0.1887 | 1.63 | 400 | 0.2779 | 48.1336 |
| 0.16 | 2.03 | 500 | 0.2679 | 46.2639 |
| 0.1131 | 2.44 | 600 | 0.2706 | 45.8449 |
| 0.1128 | 2.85 | 700 | 0.2658 | 45.1551 |
| 0.0678 | 3.25 | 800 | 0.2763 | 45.2195 |
| 0.075 | 3.66 | 900 | 0.2769 | 45.7611 |
| 0.0609 | 4.07 | 1000 | 0.2754 | 45.0068 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/consert-s10-SR
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
}
| 28 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_xlsr_finetune-M01-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# torgo_xlsr_finetune-M01-2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5763
- Wer: 0.9555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 23.0716 | 0.9 | 500 | 3.3142 | 1.0 |
| 3.4092 | 1.8 | 1000 | 3.2440 | 1.0 |
| 2.9015 | 2.7 | 1500 | 2.8209 | 1.0 |
| 2.7211 | 3.6 | 2000 | 2.4913 | 1.2728 |
| 2.0884 | 4.5 | 2500 | 1.7817 | 1.4841 |
| 1.3426 | 5.41 | 3000 | 1.5117 | 1.4678 |
| 0.9866 | 6.31 | 3500 | 1.4760 | 1.3781 |
| 0.7874 | 7.21 | 4000 | 1.2179 | 1.2516 |
| 0.6424 | 8.11 | 4500 | 1.4501 | 1.2226 |
| 0.5505 | 9.01 | 5000 | 1.4132 | 1.3343 |
| 0.4709 | 9.91 | 5500 | 1.3289 | 1.1604 |
| 0.4358 | 10.81 | 6000 | 1.2615 | 1.1102 |
| 0.3892 | 11.71 | 6500 | 1.5597 | 1.1060 |
| 0.3602 | 12.61 | 7000 | 1.4205 | 1.1322 |
| 0.3298 | 13.51 | 7500 | 1.4411 | 1.1237 |
| 0.3184 | 14.41 | 8000 | 1.4017 | 1.1004 |
| 0.2954 | 15.32 | 8500 | 1.3428 | 1.0806 |
| 0.2745 | 16.22 | 9000 | 1.4793 | 1.0982 |
| 0.2533 | 17.12 | 9500 | 1.6004 | 1.1124 |
| 0.2378 | 18.02 | 10000 | 1.5802 | 1.0700 |
| 0.2234 | 18.92 | 10500 | 1.4462 | 1.0473 |
| 0.2147 | 19.82 | 11000 | 1.3814 | 1.0042 |
| 0.202 | 20.72 | 11500 | 1.5665 | 1.0226 |
| 0.1691 | 21.62 | 12000 | 1.4534 | 0.9958 |
| 0.1993 | 22.52 | 12500 | 1.4851 | 0.9894 |
| 0.1591 | 23.42 | 13000 | 1.3746 | 0.9746 |
| 0.1602 | 24.32 | 13500 | 1.4077 | 0.9710 |
| 0.1417 | 25.23 | 14000 | 1.5074 | 0.9668 |
| 0.1302 | 26.13 | 14500 | 1.5024 | 0.9456 |
| 0.1334 | 27.03 | 15000 | 1.4816 | 0.9541 |
| 0.1269 | 27.93 | 15500 | 1.5501 | 0.9541 |
| 0.1254 | 28.83 | 16000 | 1.5593 | 0.9527 |
| 0.12 | 29.73 | 16500 | 1.5763 | 0.9555 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
|
AnonymousSub/declutr-biomed-roberta-papers
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
| 7 | null |
---
license: cc
---
This model comes from the paper "Exploring Neural Models for Query-Focused Summarization".
This is the original release https://github.com/salesforce/query-focused-sum
|
AnonymousSub/hier_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
}
| 8 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.53 +/- 0.76
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/roberta-base_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 25 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 22.60 +/- 17.36
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/16956/alice-zuberg-or-sword-art-online-alicization
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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"num_beams": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 3 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/17188/fate-grand-order-okita-souji
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 31 | null |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 1 | null |
---
license: openrail
language:
- en
---
It generates tweets in the style of twitter users that consented to my Data Collection scheme (for the purpose of making a language model, of which I stated in the tweet)
I will update this model because I accidentally added <|startoftext|> and <|endoftext|> tokens to the dataset even though this is an OPT model.
Example code you can run in [Google Colab](https://colab.research.google.com/)
```python
%pip install -qq transformers accelerate
%pip install -qq git+https://github.com/huggingface/peft bitsandbytes
# from huggingface_hub import login; login(token="hf_...")
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
peft_model_id = "boopysaur/Mentallyill-2.7b"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,
load_in_8bit=True,
low_cpu_mem_usage=True,
device_map='auto')
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# just generate 32 examples and print them
prompt = "</s><|startoftext|>".strip()
inputs = tokenizer([prompt], return_tensors='pt')
for i in range(32):
with torch.autocast("cuda", dtype=torch.float16):
outputs = model.generate(
input_ids=inputs.input_ids.cuda(),
attention_mask=inputs.attention_mask.cuda(),
max_new_tokens=42,
top_p=0.95,
temperature=0.7,
do_sample=True,
repetition_penalty=1.1,
)
result = "\n".join(tokenizer.decode(outputs[0]).split("\n")[:prompt.count("\n")+1]).replace("</s>", "").replace("<|startoftext|>", "").split("<|endoftext|>")[0]
print(result)
```
Example (unfiltered, unconditional, but censored, so 18+) outputs:
```
do not go gentle into that good night.
i hate being a girl
if i could be as beautiful as her it would kill me
i wish people would let me live my life
my head hurts i can’t stop laughing
i need some bae to come over and play with me
i just saw a tweet by a guy who literally posted “i’m so tired of white girls in their 20s on tiktok saying they’re living in the past”
this is how i imagine most people in the world think
what if i told you, my favorite color is a shade of orange and im trans?
shes sucking my d*** and making me c**
i don't need to know your age, gender or ethnicity just because we have a mutual friend.
my life is a lie i keep telling myself that but i have no choice
you know what else is cute? the way my c*** is flicking out of me in your face
i’m so confused
i am a girl who wants to bang my boyfriend but only because i’m not gay
they are all so cute
the most beautiful thing about being a girl is that you can wear heels
it’s a good thing i don’t have any friends
i just want to know what the word for when you are in a relationship with someone and they don’t like you back
i have a good friend who is the only person in my life that can make me feel this way. shes the only one i ever love.
me and my friend are at the point where we have to start saying "no homo" every time we make out
why does it have to be a girl for me to want to marry someone i love
you guys arent funny, and i love u
i think im having a mental breakdown.
i need to get some sleep.
i am still in the stage where i’m just going through my normal cycle of being a miserable fucker and then it will be over
i am sooo f*cking sick of this sh*t
what about the time u were with ur bro nd all of a sudden btbam was playing and u realized they used to be ur favorite band?
i hate when people ask me how my day is going. its a little weird and i feel like they're trying to find out what the hell i do for a living so they can make fun of me <
im so h*rny rn
i’m gonna die alone and sad
a friend of mine got shot at in the face with a nerf gun
```
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9265187798828386
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2182
- Accuracy: 0.9265
- F1: 0.9265
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8426 | 1.0 | 250 | 0.3237 | 0.903 | 0.8986 |
| 0.2521 | 2.0 | 500 | 0.2182 | 0.9265 | 0.9265 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_only_classfn_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 2 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 722.00 +/- 268.96
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga charmquark -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga charmquark -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga charmquark
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AnonymousSub/rule_based_only_classfn_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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"prefix": null
}
}
}
| 32 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: imdb-sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.86
- name: F1
type: f1
value: 0.8618421052631579
---
<!-- 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. -->
# imdb-sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3236
- Accuracy: 0.86
- F1: 0.8618
## 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
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_only_classfn_twostage_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: dummy_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5650459791482846
---
<!-- 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. -->
# dummy_model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4429
- Matthews Correlation: 0.5650
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4577 | 1.0 | 1069 | 0.4429 | 0.5650 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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}
| 6 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8638300289723342
---
<!-- 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: 0.1358
- F1: 0.8638
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
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| 28 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: allways_pharma_v2.0
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
config: discharge
split: test
args: discharge
metrics:
- name: Precision
type: precision
value: 0.8775510204081632
- name: Recall
type: recall
value: 0.86
- name: F1
type: f1
value: 0.8686868686868686
- name: Accuracy
type: accuracy
value: 0.975609756097561
---
<!-- 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. -->
# allways_pharma_v2.0
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2375
- Precision: 0.8776
- Recall: 0.86
- F1: 0.8687
- Accuracy: 0.9756
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 8.33 | 100 | 0.1635 | 0.8542 | 0.82 | 0.8367 | 0.9695 |
| No log | 16.67 | 200 | 0.1860 | 0.8776 | 0.86 | 0.8687 | 0.9756 |
| No log | 25.0 | 300 | 0.2545 | 0.86 | 0.86 | 0.8600 | 0.9695 |
| No log | 33.33 | 400 | 0.2707 | 0.8542 | 0.82 | 0.8367 | 0.9695 |
| 0.1 | 41.67 | 500 | 0.2618 | 0.8542 | 0.82 | 0.8367 | 0.9695 |
| 0.1 | 50.0 | 600 | 0.2784 | 0.8542 | 0.82 | 0.8367 | 0.9695 |
| 0.1 | 58.33 | 700 | 0.2679 | 0.8542 | 0.82 | 0.8367 | 0.9695 |
| 0.1 | 66.67 | 800 | 0.2405 | 0.8542 | 0.82 | 0.8367 | 0.9695 |
| 0.1 | 75.0 | 900 | 0.2372 | 0.8776 | 0.86 | 0.8687 | 0.9756 |
| 0.0012 | 83.33 | 1000 | 0.2375 | 0.8776 | 0.86 | 0.8687 | 0.9756 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.2.2
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"RobertaForQuestionAnswering"
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| 4 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.83 +/- 3.78
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r css919/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"RobertaForQuestionAnswering"
],
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"task_specific_params": {
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}
| 4 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
library_name: ultralytics
library_version: 8.0.43
inference: false
model-index:
- name: eeshawn11/naruto_hand_seal_detection
results:
- task:
type: object-detection
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.995 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="eeshawn11/naruto_hand_seal_detection" src="https://huggingface.co/eeshawn11/naruto_hand_seal_detection/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['bird', 'boar', 'dog', 'dragon', 'hare', 'horse', 'monkey', 'ox', 'ram', 'rat', 'snake', 'tiger']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.28
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('eeshawn11/naruto_hand_seal_detection')
# set model parameters
model.overrides['conf'] = 0.50 # NMS confidence threshold
model.overrides['iou'] = 0.70 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 10 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
|
AnonymousSub/unsup-consert-base
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
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| 6 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: dgx1_whisper_tiny_finetune_teacher_no_noise_mozilla_100_epochs_batch_32_resume_training
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dgx1_whisper_tiny_finetune_teacher_no_noise_mozilla_100_epochs_batch_32_resume_training
This model is a fine-tuned version of [rohitp1/dgx1_whisper_tiny_finetune_teacher_no_noise_mozilla_40_epochs_batch_32](https://huggingface.co/rohitp1/dgx1_whisper_tiny_finetune_teacher_no_noise_mozilla_40_epochs_batch_32) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1796
- Wer: 37.3233
## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0059 | 7.35 | 500 | 0.9865 | 36.5295 |
| 0.0368 | 14.7 | 1000 | 1.0064 | 37.8595 |
| 0.0545 | 22.06 | 1500 | 1.0131 | 37.9918 |
| 0.0308 | 29.41 | 2000 | 1.0712 | 37.3895 |
| 0.0189 | 36.76 | 2500 | 1.1299 | 37.4278 |
| 0.0146 | 44.12 | 3000 | 1.1796 | 37.3233 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/unsup-consert-base_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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}
}
| 2 | null |
---
license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- stable-diffusion
- stable-diffusers
- art
- realism
---
Model files are in "Files and Versions" tab!
# NOTICE!!
# It is version 1.0 but version 2.0 doesn't always produce better results than version 1.0!
# Differences of two version are just trained with different datasets!
# So I hope you try this too : https://huggingface.co/2jang/MTE_van_Kane-v2.0
<br />
# (English)Introduction of MTE van Kane v1.0
MTE van Kane, The full name is Mungtaeng-I van Kane and we call it MTE_VK in short which is same as the file name.<br />
This project aims to recreate MTE van Kane artworks with Stable diffusion AI.<br />
You can see original artworks by Sly rabbit at here : https://mungtaengi.wixsite.com/kane<br />
Original sources from Kane(kanetv8). But it's a fan made artwork, so it has **nothing to do** with Kane(kanetv8).<br />
-**Base model :** SD 1.5<br />
-**Model type :** LORA
<br />
# (한국어)MTE van Kane v1.0 소개
MTE 반 케인, 뭉탱이 반 케인을 뜻하는 약어입니다. 파일명은 MTE_VK입니다.<br />
이 프로젝트는 뭉탱이 반 케인 작품들을 Stable Diffusion AI로 재창작하는 것을 목표로 하고 있습니다.<br />
치졸한토끼님이 만드신 원작은 이 사이트에서 감상하실 수 있습니다. : https://mungtaengi.wixsite.com/kane<br />
원본 소스는 케인님(kanetv8)으로부터 가져왔습니다. 하지만 팬아트이기 때문에, 실제 케인님(kanetv8)과는 아무런 **관련이 없습니다**.<br />
-**베이스 모델 :** SD 1.5<br />
-**모델 타입 :** LORA
<br />
# Original MTE van Kane Exhibition tour video
The First MTE van Kane Exhibition
[](https://www.youtube.com/watch?v=Yk8UF0FOBzQ)
The Second MTE van Kane Exhibition
[](https://www.youtube.com/watch?v=rdDkXXMe3tE)
<br />
# Acknowledgements
Stable-diffusion-v1.5 : https://huggingface.co/runwayml/stable-diffusion-v1-5<br />
Sly rabbit(치졸한토끼) youtube : https://www.youtube.com/@user-vw4on4ee3l<br />
Kane(kanetv8) youtube : https://www.youtube.com/@kanetv8<br />
|
AnonymousSub/unsup-consert-emanuals
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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}
| 2 | null |
---
license: bigscience-bloom-rail-1.0
datasets:
- jslin09/Fraud_Case_Verdicts
language:
- zh
metrics:
- accuracy
pipeline_tag: text-generation
text-generation:
parameters:
max_length: 400
do_sample: true
temperature: 0.75
top_k: 50
top_p: 0.9
tags:
- legal
widget:
- text: 王大明意圖為自己不法所有，基於竊盜之犯意，
example_title: 生成竊盜罪之犯罪事實
- text: 騙人布意圖為自己不法所有，基於詐欺取財之犯意，
example_title: 生成詐欺罪之犯罪事實
- text: 梅友乾明知其無資力支付酒店消費，亦無付款意願，竟意圖為自己不法之所有，
example_title: 生成吃霸王餐之詐欺犯罪事實
- text: 闕很大明知金融帳戶之存摺、提款卡及密碼係供自己使用之重要理財工具，
example_title: 生成賣帳戶幫助詐欺犯罪事實
---
# 判決書草稿自動生成
本模型是以司法院公開之「詐欺」案件判決書做成之資料集，基於 [BLOOM 560m](https://huggingface.co/bigscience/bloomz-560m) 模型進行微調訓練，可以自動生成詐欺及竊盜案件之犯罪事實段落之草稿。資料集之資料範圍從100年1月1日至110年12月31日，所蒐集到的原始資料共有 74823 篇（判決以及裁定），我們只取判決書的「犯罪事實」欄位內容，並把這原始的資料分成三份，用於訓練的資料集有59858篇，約佔原始資料的80%，剩下的20%，則是各分配10%給驗證集（7482篇），10%給測試集（7483篇）。在本網頁進行測試時，請在模型載入完畢並生成第一小句後，持續按下Compute按鈕，就能持續生成文字。或是輸入自己想要測試的資料到文字框中進行測試。
# 使用範例
如果要在自己的程式中調用本模型，可以參考下列的 Python 程式碼，藉由呼叫 API 的方式來生成刑事判決書「犯罪事實」欄的內容。
<details>
<summary> 點擊後展開 </summary>
<pre>
<code>
import requests, json
from time import sleep
from tqdm.auto import tqdm, trange
API_URL = "https://api-inference.huggingface.co/models/jslin09/bloom-560m-finetuned-fraud"
API_TOKEN = 'XXXXXXXXXXXXXXX' # 調用模型的 API token
headers = {"Authorization": f"Bearer {API_TOKEN}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return json.loads(response.content.decode("utf-8"))
prompt = "森上梅前明知其無資力支付酒店消費，亦無付款意願，竟意圖為自己不法之所有，"
query_dict = {
"inputs": prompt,
}
text_len = 300
t = trange(text_len, desc= '生成例稿', leave=True)
for i in t:
response = query(query_dict)
try:
response_text = response[0]['generated_text']
query_dict["inputs"] = response_text
t.set_description(f"{i}: {response[0]['generated_text']}")
t.refresh()
except KeyError:
sleep(30) # 如果伺服器太忙無回應，等30秒後再試。
pass
print(response[0]['generated_text'])
</code>
</pre>
</details>
或是，你要使用 transformers 套件來實作你的程式，將本模型下載至你本地端的電腦中執行，可以參考下列程式碼:
<details>
<summary> 點擊後展開 </summary>
<pre>
<code>
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jslin09/bloom-560m-finetuned-fraud")
model = AutoModelForCausalLM.from_pretrained("jslin09/bloom-560m-finetuned-fraud")
</code>
</pre>
</details>
|
ArBert/roberta-base-finetuned-ner-kmeans-twitter
|
[
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] |
token-classification
|
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| 10 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Araf/Ummah
|
[] | null |
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}
| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Loges/loges-ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AriakimTaiyo/kumiko
|
[] | null |
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: VAZaytsev/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Arina/Erine
|
[] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog in a bucket
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - deerta/lora_sks_dogs
本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of sks dog in a bucket 文本进行了训练。
|
ArjunKadya/HuggingFace
|
[] | null |
{
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-T5_base_test_miniwob-T5_base_test_miniwob-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-T5_base_test_miniwob-T5_base_test_miniwob-2
This model is a fine-tuned version of [LucasThil/t5-small-T5_base_test_miniwob-T5_base_test_miniwob](https://huggingface.co/LucasThil/t5-small-T5_base_test_miniwob-T5_base_test_miniwob) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0562
- Rouge1: 84.4239
- Rouge2: 73.3153
- Rougel: 84.4783
- Rougelsum: 84.4683
- Gen Len: 10.0274
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 200 | 0.0772 | 80.07 | 64.9483 | 80.1152 | 80.1292 | 9.9726 |
| No log | 2.0 | 400 | 0.0770 | 80.182 | 65.1252 | 80.1982 | 80.2107 | 9.9972 |
| 0.0959 | 3.0 | 600 | 0.0760 | 80.4799 | 65.7983 | 80.5111 | 80.5247 | 9.9916 |
| 0.0959 | 4.0 | 800 | 0.0746 | 80.7629 | 66.2118 | 80.845 | 80.829 | 10.0084 |
| 0.0906 | 5.0 | 1000 | 0.0731 | 80.7116 | 65.9694 | 80.7465 | 80.7909 | 10.0056 |
| 0.0906 | 6.0 | 1200 | 0.0726 | 81.2349 | 67.1351 | 81.2811 | 81.2927 | 10.0084 |
| 0.0906 | 7.0 | 1400 | 0.0713 | 81.9427 | 68.2999 | 81.9585 | 81.9773 | 10.0014 |
| 0.088 | 8.0 | 1600 | 0.0700 | 82.0826 | 68.7335 | 82.1298 | 82.127 | 10.0162 |
| 0.088 | 9.0 | 1800 | 0.0691 | 81.772 | 67.9586 | 81.8152 | 81.8128 | 9.9958 |
| 0.0851 | 10.0 | 2000 | 0.0682 | 82.1352 | 68.82 | 82.1904 | 82.1951 | 10.0105 |
| 0.0851 | 11.0 | 2200 | 0.0676 | 82.0125 | 68.4698 | 82.0478 | 82.0485 | 10.0204 |
| 0.0851 | 12.0 | 2400 | 0.0665 | 82.2024 | 68.8334 | 82.2564 | 82.2301 | 9.9986 |
| 0.0829 | 13.0 | 2600 | 0.0661 | 82.3572 | 69.2544 | 82.4303 | 82.4034 | 10.007 |
| 0.0829 | 14.0 | 2800 | 0.0654 | 82.7308 | 69.9319 | 82.8031 | 82.7893 | 10.0148 |
| 0.0818 | 15.0 | 3000 | 0.0649 | 82.4837 | 69.4319 | 82.5495 | 82.5285 | 10.0127 |
| 0.0818 | 16.0 | 3200 | 0.0641 | 82.6841 | 69.8013 | 82.7367 | 82.7217 | 10.0253 |
| 0.0818 | 17.0 | 3400 | 0.0636 | 82.9736 | 70.4272 | 83.0124 | 83.0073 | 10.012 |
| 0.0796 | 18.0 | 3600 | 0.0632 | 82.7252 | 69.8741 | 82.8172 | 82.792 | 10.0077 |
| 0.0796 | 19.0 | 3800 | 0.0626 | 82.8893 | 70.1174 | 82.9326 | 82.9047 | 10.0098 |
| 0.0784 | 20.0 | 4000 | 0.0618 | 83.0279 | 70.5745 | 83.1153 | 83.097 | 10.0394 |
| 0.0784 | 21.0 | 4200 | 0.0611 | 82.9773 | 70.4364 | 83.044 | 83.0351 | 10.0408 |
| 0.0784 | 22.0 | 4400 | 0.0612 | 83.2734 | 70.9832 | 83.3207 | 83.2901 | 10.0253 |
| 0.0769 | 23.0 | 4600 | 0.0603 | 83.2998 | 71.1516 | 83.3714 | 83.3234 | 10.0338 |
| 0.0769 | 24.0 | 4800 | 0.0604 | 83.3777 | 71.1998 | 83.4301 | 83.4444 | 10.0274 |
| 0.0757 | 25.0 | 5000 | 0.0599 | 83.5509 | 71.5775 | 83.5965 | 83.5586 | 10.0204 |
| 0.0757 | 26.0 | 5200 | 0.0598 | 83.6255 | 71.6576 | 83.6737 | 83.6524 | 10.0246 |
| 0.0757 | 27.0 | 5400 | 0.0599 | 83.5788 | 71.6097 | 83.6267 | 83.6135 | 10.0197 |
| 0.0742 | 28.0 | 5600 | 0.0585 | 83.6857 | 71.8218 | 83.7284 | 83.7034 | 10.0084 |
| 0.0742 | 29.0 | 5800 | 0.0589 | 83.8396 | 72.1322 | 83.8789 | 83.861 | 10.0309 |
| 0.0727 | 30.0 | 6000 | 0.0582 | 83.781 | 71.9517 | 83.8336 | 83.7852 | 10.0274 |
| 0.0727 | 31.0 | 6200 | 0.0584 | 83.7964 | 72.0827 | 83.8786 | 83.847 | 10.0345 |
| 0.0727 | 32.0 | 6400 | 0.0576 | 83.9339 | 72.3444 | 83.992 | 83.9747 | 10.0281 |
| 0.0718 | 33.0 | 6600 | 0.0576 | 84.0992 | 72.6619 | 84.1725 | 84.1367 | 10.0253 |
| 0.0718 | 34.0 | 6800 | 0.0574 | 84.1596 | 72.7909 | 84.2312 | 84.2171 | 10.0267 |
| 0.0711 | 35.0 | 7000 | 0.0572 | 83.9027 | 72.2676 | 83.9513 | 83.9336 | 10.0204 |
| 0.0711 | 36.0 | 7200 | 0.0575 | 83.8542 | 72.1958 | 83.925 | 83.8919 | 10.0302 |
| 0.0711 | 37.0 | 7400 | 0.0570 | 84.0695 | 72.5868 | 84.1166 | 84.0999 | 10.0274 |
| 0.0702 | 38.0 | 7600 | 0.0568 | 84.0717 | 72.5816 | 84.1382 | 84.1049 | 10.0246 |
| 0.0702 | 39.0 | 7800 | 0.0567 | 84.294 | 73.0424 | 84.3486 | 84.2989 | 10.0267 |
| 0.0711 | 40.0 | 8000 | 0.0567 | 84.2451 | 72.8972 | 84.2691 | 84.2299 | 10.0323 |
| 0.0711 | 41.0 | 8200 | 0.0564 | 84.3601 | 73.1693 | 84.4026 | 84.394 | 10.0281 |
| 0.0711 | 42.0 | 8400 | 0.0566 | 84.1651 | 72.7519 | 84.2129 | 84.1615 | 10.0309 |
| 0.0697 | 43.0 | 8600 | 0.0565 | 84.3877 | 73.2265 | 84.4256 | 84.4025 | 10.0309 |
| 0.0697 | 44.0 | 8800 | 0.0564 | 84.3716 | 73.1688 | 84.4227 | 84.3819 | 10.0295 |
| 0.0696 | 45.0 | 9000 | 0.0563 | 84.2959 | 73.0299 | 84.3343 | 84.3229 | 10.0295 |
| 0.0696 | 46.0 | 9200 | 0.0562 | 84.2854 | 73.0477 | 84.3241 | 84.3009 | 10.0302 |
| 0.0696 | 47.0 | 9400 | 0.0563 | 84.3664 | 73.1641 | 84.3985 | 84.3866 | 10.0288 |
| 0.0686 | 48.0 | 9600 | 0.0563 | 84.4239 | 73.3153 | 84.4783 | 84.4683 | 10.026 |
| 0.0686 | 49.0 | 9800 | 0.0562 | 84.4239 | 73.3153 | 84.4783 | 84.4683 | 10.0267 |
| 0.0692 | 50.0 | 10000 | 0.0562 | 84.4239 | 73.3153 | 84.4783 | 84.4683 | 10.0274 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Arnold/wav2vec2-hausa2-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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| 9 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of nature, face the sea, with spring blossoms
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - Abner94/lora_nature
本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of nature, face the sea, with spring blossoms 文本进行了训练。
|
ArpanZS/search_model
|
[
"joblib"
] | null |
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| 0 | null |
---
license: afl-3.0
language:
- en
metrics:
- accuracy
library_name: transformers
---
# Model Description
The fake news detection model is a deep learning model designed to classify news as either "fake" or "real."
The intended use of the fake news detection model is to provide a tool for identifying fake news articles.
This model uses a pre-trained model of [`bert-base-uncased`](https://huggingface.co/bert-base-uncased), and fine-tune on
a [Fake News dataset](https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english ).
|
ArvinZhuang/BiTAG-t5-large
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
"model_type": "t5",
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}
| 4 | null |
---
license: mit
language:
- gl
metrics:
- bleu (Gold1): 82.6
- bleu (Gold2): 49.9
- bleu (Flores): 23.8
- bleu (Test-suite): 77.2
---
license: mit
---
**English text [here](https://huggingface.co/proxectonos/Nos_MT-OpenNMT-gl-es/blob/main/README_English.md)**
**Descrición do Modelo**
Modelo feito con OpenNMT para o par galego-inglés utilizando unha arquitectura transformer.
**Como traducir**
+ Abrir terminal bash
+ Instalar [Python 3.9](https://www.python.org/downloads/release/python-390/)
+ Instalar [Open NMT toolkit v.2.2](https://github.com/OpenNMT/OpenNMT-py)
+ Traducir un input_text utilizando o modelo NOS-MT-gl-es co seguinte comando:
```bash
onmt_translate -src input_text -model NOS-MT-gl-es.pt --output ./output_file.txt --replace_unk -gpu 0
```
+ O resultado da tradución estará no PATH indicado no flag -output.
**Adestramento**
No adestramento, utilizamos córpora auténticos e sintéticos do [ProxectoNós](https://github.com/proxectonos/corpora). Os primeiros son córpora de traducións feitas directamente por tradutores humanos. Os segundos son córpora de traducións inglés-portugués, que convertemos en inglés-galego a través da tradución automática portugués-galego con Opentrad/Apertium e transliteración para palabras fóra de vocabulario.
**Procedemento de adestramento / Training process**
+ Tokenización dos datasets feita co tokenizador (tokenizer.pl) de [linguakit](https://github.com/citiususc/Linguakit) que foi modificado para evitar o salto de liña por token do ficheiro orixinal.
+ O vocabulario BPE para os modelos foi xerado a través do script [learn_bpe.py](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT
+ Utilizando o .yaml deste repositorio pode replicar o proceso de adestramento. É preciso modificar os paths do ficheiro .yaml para a Open NMT saber onde ir buscar os textos. Após facer isto, pode do seguinte xeito comezar o proceso:
```bash
onmt_build_vocab -config bpe-gl-es_emb.yaml -n_sample 100000
onmt_train -config bpe-gl-es_emb.yaml
```
**Hiperparámetros**
Os parámetros usados para o desenvolvemento do modelo poden ser consultados directamente no mesmo ficheiro .yaml bpe-gl-es_emb.yaml
**Avaliación**
A avalación BLEU dos modelos é feita cunha mistura de tests desenvolvidos internamente (gold1, gold2, test-suite) con outros datasets disponíbeis en galego (Flores).
| GOLD 1 | GOLD 2 | FLORES | TEST-SUITE|
| ------------- |:-------------:| -------:|----------:|
| 82.6 | 49.9 | 23.8 | 77.2 |
**Licenzas do Modelo**
MIT License
Copyright (c) 2023 Proxecto Nós
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
**Financiamento**
Esta investigación foi financiada polo proxecto "Nós: o galego na sociedade e economía da intelixencia artificial", resultado dun acordo entre a Xunta de Galicia e a Universidade de Santiago de Compostela, o que resultou no subsidio ED431G2019/04 da Consellaría de Educación, Universidade e Formación Profesional da Galiza, e polo Fondo Europeo de Desenvolvemento Rexional (programa ERDF/FEDER), e Grupos de Referencia: ED431C 2020/21.
**Citar este traballo**
Se utilizar este modelo no seu traballo, cite por favor así:
Gamallo, Pablo; Bardanca, Daniel; Pichel, José Ramom; García, Marcos; Rodríguez-Rey, Sandra; de-Dios-Flores, Iria. 2023.
NOS-MT-OpenNMT-gl-es. Url: https://huggingface.co/proxectonos/NOS-MT-OpenNMT-gl-es
|
Ateeb/FullEmotionDetector
|
[
"pytorch",
"funnel",
"text-classification",
"transformers"
] |
text-classification
|
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| 31 | null |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
tags:
- code
---
# MagicPrompt_SD_V1
This is a Prompt Generator likes [Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion)!
But I wash the origin prompts data, and trains a powerful model to generate prompt for [魔导绪论](https://magic-tag.netlify.app/)
It's using Paddle to handle the training and other things. Not PyTorch or Tensorsflow.
There's the result I get form this model:
- You can use CPU to run the model! But GPU 10x faster then CPU 🚀.
- CPU (about 300ms/per ) | GPU (about 90ms/per 🚀 ) V2-10 Model
- You can add some change easier passing some params.
## 📕 Using example is here
[飞桨仓库](https://aistudio.baidu.com/aistudio/projectdetail/5116158?contributionType=1)
You can wrapper a FastAPI or Flask to easily deploy it to your server
|
Augustvember/WokkaBot3
|
[
"conversational"
] |
conversational
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| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.80 +/- 17.78
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Augustvember/WokkaBot4
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: eryzml/poca-SoccerTwos-v2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Augustvember/WokkaBot5
|
[] | null |
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| 0 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 12.50 +/- 12.39
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Augustvember/wokka4
|
[
"conversational"
] |
conversational
|
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| 0 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: peg-unplug-side-v2
type: peg-unplug-side-v2
metrics:
- type: mean_reward
value: 4312.88 +/- 44.21
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **peg-unplug-side-v2** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r qgallouedec/sample-factory-peg-unplug-side-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=peg-unplug-side-v2 --train_dir=./train_dir --experiment=sample-factory-peg-unplug-side-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m train --algo=APPO --env=peg-unplug-side-v2 --train_dir=./train_dir --experiment=sample-factory-peg-unplug-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Ayah/GPT2-DBpedia
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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| 6 | null |
---
license: mit
language:
- ru
pipeline_tag: text-generation
tags:
- gpt
- gpt2
- gpt3
- ai dungeon
- ai
- dungeon
- medium
- ru
- rus
- text
- generation
- text generation
---
Medium model from https://github.com/A1exRey/Clover-Edition-ru
Working good only with Russian language.
|
Aybars/ModelOnWhole
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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| 4 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 456.70 +/- 129.90
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Ayham/albert_gpt2_summarization_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
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}
| 7 | null |
---
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6178
- Answer: {'precision': 0.6652719665271967, 'recall': 0.7861557478368356, 'f1': 0.7206798866855525, 'number': 809}
- Header: {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119}
- Question: {'precision': 0.7537248028045574, 'recall': 0.8075117370892019, 'f1': 0.7796917497733454, 'number': 1065}
- Overall Precision: 0.6893
- Overall Recall: 0.7692
- Overall F1: 0.7271
- Overall Accuracy: 0.8014
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.5284 | 1.0 | 38 | 1.0167 | {'precision': 0.3938144329896907, 'recall': 0.4721878862793572, 'f1': 0.4294547498594716, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5845528455284553, 'recall': 0.6751173708920187, 'f1': 0.6265795206971678, 'number': 1065} | 0.4959 | 0.5524 | 0.5227 | 0.6689 |
| 0.8661 | 2.0 | 76 | 0.7179 | {'precision': 0.630346232179226, 'recall': 0.765142150803461, 'f1': 0.6912339475153545, 'number': 809} | {'precision': 0.2087912087912088, 'recall': 0.15966386554621848, 'f1': 0.18095238095238092, 'number': 119} | {'precision': 0.7058823529411765, 'recall': 0.7436619718309859, 'f1': 0.7242798353909465, 'number': 1065} | 0.6515 | 0.7175 | 0.6829 | 0.7596 |
| 0.6265 | 3.0 | 114 | 0.6470 | {'precision': 0.6458546571136131, 'recall': 0.7799752781211372, 'f1': 0.7066069428891377, 'number': 809} | {'precision': 0.2972972972972973, 'recall': 0.2773109243697479, 'f1': 0.28695652173913044, 'number': 119} | {'precision': 0.7359649122807017, 'recall': 0.787793427230047, 'f1': 0.7609977324263038, 'number': 1065} | 0.6746 | 0.7541 | 0.7122 | 0.7879 |
| 0.5076 | 4.0 | 152 | 0.6207 | {'precision': 0.6680851063829787, 'recall': 0.7762669962917181, 'f1': 0.7181246426529445, 'number': 809} | {'precision': 0.28, 'recall': 0.29411764705882354, 'f1': 0.28688524590163933, 'number': 119} | {'precision': 0.7368421052631579, 'recall': 0.828169014084507, 'f1': 0.7798408488063661, 'number': 1065} | 0.6830 | 0.7752 | 0.7262 | 0.8003 |
| 0.4471 | 5.0 | 190 | 0.6178 | {'precision': 0.6652719665271967, 'recall': 0.7861557478368356, 'f1': 0.7206798866855525, 'number': 809} | {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119} | {'precision': 0.7537248028045574, 'recall': 0.8075117370892019, 'f1': 0.7796917497733454, 'number': 1065} | 0.6893 | 0.7692 | 0.7271 | 0.8014 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Ayham/roberta_distilgpt2_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
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"EncoderDecoderModel"
],
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},
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}
| 4 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.72 +/- 15.94
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ayham/robertagpt2_xsum4
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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}
}
}
| 8 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.20 +/- 2.14
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r AntiSquid/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Ayham/xlmroberta_large_gpt2_summarization_cnndm
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
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"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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}
}
| 12 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.19 +/- 0.53
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ayham/xlnet_gpt_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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},
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}
}
}
| 11 | 2023-03-09T15:12:00Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: chatgpt-prompt-generator-v12
results: []
datasets:
- fka/awesome-chatgpt-prompts
---
# ChatGPT Prompt Generator v12
This model is a fine-tuned version of [BART-large](https://huggingface.co/facebook/bart-large) on a ChatGPT prompts dataset.
It achieves the following results on the evaluation set:
It achieves the following results on the evaluation set:
- Train Loss: 2.4800
- Validation Loss: 2.7320
- Epoch: 4
## Intended uses & limitations
You can use this to generate ChatGPT personas. Simply input a persona like below:
```
from transformers import BartForConditionalGeneration, BartTokenizer
example_english_phrase = "photographer"
batch = tokenizer(example_english_phrase, return_tensors="pt")
generated_ids = model.generate(batch["input_ids"], max_new_tokens=150)
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.3808 | 3.3133 | 0 |
| 3.2642 | 3.0104 | 1 |
| 2.8886 | 2.8600 | 2 |
| 2.6594 | 2.7949 | 3 |
| 2.4800 | 2.7320 | 4 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Ayoola/cdial-yoruba-test
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"has_space"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 25 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="propet/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"])
```
|
Ayran/DialoGPT-small-gandalf
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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}
| 11 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -205.58 +/- 128.61
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 10000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'jackoyoungblood/ppo-CartPole-v1-23-1'
'batch_size': 512
'minibatch_size': 128}
```
|
AyushPJ/ai-club-inductions-21-nlp-XLNet
|
[
"pytorch",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"XLNetForQuestionAnsweringSimple"
],
"model_type": "xlnet",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
"do_sample": true,
"max_length": 250
},
"translation_en_to_de": {
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},
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}
| 9 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: weeds_hfclass18
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7766666666666666
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# weeds_hfclass18
Model is trained on balanced dataset/250 per class/ .8 .1 .1 split/ 224x224 resized
Dataset: https://www.kaggle.com/datasets/vbookshelf/v2-plant-seedlings-dataset
This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2397
- Accuracy: 0.7767
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4803 | 0.99 | 37 | 2.4724 | 0.1133 |
| 2.4464 | 1.99 | 74 | 2.4305 | 0.2967 |
| 2.3843 | 2.99 | 111 | 2.3658 | 0.4233 |
| 2.3018 | 3.99 | 148 | 2.2287 | 0.5067 |
| 2.1075 | 4.99 | 185 | 2.0144 | 0.5967 |
| 1.8743 | 5.99 | 222 | 1.7228 | 0.65 |
| 1.7114 | 6.99 | 259 | 1.5487 | 0.6833 |
| 1.5345 | 7.99 | 296 | 1.3920 | 0.7267 |
| 1.4471 | 8.99 | 333 | 1.2914 | 0.7333 |
| 1.3994 | 9.99 | 370 | 1.2397 | 0.7767 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AyushPJ/ai-club-inductions-21-nlp-distilBERT
|
[
"pytorch",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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}
| 8 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: aces-roberta-10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# aces-roberta-10
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6188
- Precision: 0.8040
- Recall: 0.8198
- F1: 0.8097
- Accuracy: 0.8198
- F1 Who: 0.7939
- F1 What: 0.7929
- F1 Where: 0.7769
- F1 How: 0.8905
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 | Precision | Recall | F1 | Accuracy | F1 Who | F1 What | F1 Where | F1 How |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
| 1.6596 | 0.15 | 20 | 1.2172 | 0.5510 | 0.6640 | 0.5906 | 0.6640 | 0.0 | 0.6409 | 0.3258 | 0.7719 |
| 1.0566 | 0.31 | 40 | 0.9097 | 0.6534 | 0.7087 | 0.6590 | 0.7087 | 0.3855 | 0.7020 | 0.5620 | 0.8086 |
| 0.8056 | 0.46 | 60 | 0.7640 | 0.7092 | 0.7570 | 0.7196 | 0.7570 | 0.6857 | 0.7709 | 0.6696 | 0.8114 |
| 0.6996 | 0.61 | 80 | 0.6706 | 0.7601 | 0.7931 | 0.7687 | 0.7931 | 0.8103 | 0.7743 | 0.7471 | 0.8499 |
| 0.6346 | 0.76 | 100 | 0.6471 | 0.7763 | 0.8032 | 0.7852 | 0.8032 | 0.7874 | 0.7813 | 0.7490 | 0.8665 |
| 0.523 | 0.92 | 120 | 0.6635 | 0.7872 | 0.8061 | 0.7865 | 0.8061 | 0.8244 | 0.7718 | 0.7692 | 0.8771 |
| 0.5324 | 1.07 | 140 | 0.6162 | 0.8045 | 0.8212 | 0.8110 | 0.8212 | 0.8197 | 0.8008 | 0.8033 | 0.8852 |
| 0.4734 | 1.22 | 160 | 0.6147 | 0.7935 | 0.8097 | 0.7978 | 0.8097 | 0.7939 | 0.7861 | 0.7698 | 0.8911 |
| 0.5111 | 1.37 | 180 | 0.6142 | 0.8022 | 0.8154 | 0.8051 | 0.8154 | 0.8244 | 0.8047 | 0.768 | 0.8909 |
| 0.4416 | 1.53 | 200 | 0.6204 | 0.8006 | 0.8190 | 0.8079 | 0.8190 | 0.8271 | 0.7984 | 0.7773 | 0.8886 |
| 0.5249 | 1.68 | 220 | 0.6239 | 0.7907 | 0.8133 | 0.8006 | 0.8133 | 0.8182 | 0.7969 | 0.7739 | 0.8776 |
| 0.4599 | 1.83 | 240 | 0.6458 | 0.7989 | 0.8082 | 0.7967 | 0.8082 | 0.8244 | 0.7953 | 0.7751 | 0.8853 |
| 0.4979 | 1.98 | 260 | 0.6390 | 0.8071 | 0.8183 | 0.8051 | 0.8183 | 0.7869 | 0.8000 | 0.7583 | 0.8871 |
| 0.393 | 2.14 | 280 | 0.6348 | 0.7994 | 0.8125 | 0.8021 | 0.8125 | 0.8271 | 0.7904 | 0.7653 | 0.8812 |
| 0.4079 | 2.29 | 300 | 0.6227 | 0.8002 | 0.8140 | 0.8040 | 0.8140 | 0.8182 | 0.7908 | 0.7668 | 0.8784 |
| 0.3731 | 2.44 | 320 | 0.6319 | 0.7887 | 0.8075 | 0.7965 | 0.8075 | 0.8030 | 0.7814 | 0.7692 | 0.8702 |
| 0.3987 | 2.6 | 340 | 0.6171 | 0.7922 | 0.8140 | 0.8015 | 0.8140 | 0.7907 | 0.7813 | 0.7968 | 0.8759 |
| 0.3865 | 2.75 | 360 | 0.6161 | 0.7968 | 0.8118 | 0.8032 | 0.8118 | 0.7846 | 0.7824 | 0.7692 | 0.8851 |
| 0.4222 | 2.9 | 380 | 0.6137 | 0.7955 | 0.8140 | 0.8033 | 0.8140 | 0.8060 | 0.7897 | 0.7874 | 0.8746 |
| 0.4164 | 3.05 | 400 | 0.6016 | 0.8017 | 0.8176 | 0.8079 | 0.8176 | 0.7846 | 0.7954 | 0.7843 | 0.8832 |
| 0.3505 | 3.21 | 420 | 0.6239 | 0.7912 | 0.8075 | 0.7949 | 0.8075 | 0.7846 | 0.7930 | 0.7786 | 0.8556 |
| 0.3834 | 3.36 | 440 | 0.6038 | 0.8022 | 0.8169 | 0.8082 | 0.8169 | 0.7907 | 0.7976 | 0.7757 | 0.8835 |
| 0.3139 | 3.51 | 460 | 0.6068 | 0.7978 | 0.8161 | 0.8052 | 0.8161 | 0.7970 | 0.7904 | 0.7846 | 0.8870 |
| 0.3679 | 3.66 | 480 | 0.6070 | 0.8026 | 0.8183 | 0.8063 | 0.8183 | 0.7907 | 0.7953 | 0.7799 | 0.8835 |
| 0.3387 | 3.82 | 500 | 0.6059 | 0.8025 | 0.8205 | 0.8094 | 0.8205 | 0.7879 | 0.7977 | 0.7937 | 0.8879 |
| 0.3208 | 3.97 | 520 | 0.6064 | 0.8015 | 0.8183 | 0.8082 | 0.8183 | 0.7970 | 0.7900 | 0.7782 | 0.8854 |
| 0.3008 | 4.12 | 540 | 0.6088 | 0.8020 | 0.8205 | 0.8107 | 0.8205 | 0.7970 | 0.7946 | 0.7813 | 0.8883 |
| 0.3014 | 4.27 | 560 | 0.6093 | 0.8032 | 0.8212 | 0.8114 | 0.8212 | 0.8120 | 0.7961 | 0.7813 | 0.8867 |
| 0.3486 | 4.43 | 580 | 0.6112 | 0.8042 | 0.8205 | 0.8107 | 0.8205 | 0.7939 | 0.7961 | 0.7829 | 0.8873 |
| 0.2793 | 4.58 | 600 | 0.6156 | 0.8047 | 0.8183 | 0.8088 | 0.8183 | 0.7846 | 0.7945 | 0.7769 | 0.8905 |
| 0.2943 | 4.73 | 620 | 0.6170 | 0.8044 | 0.8212 | 0.8107 | 0.8212 | 0.7846 | 0.7992 | 0.7843 | 0.8895 |
| 0.3314 | 4.89 | 640 | 0.6188 | 0.8040 | 0.8198 | 0.8097 | 0.8198 | 0.7939 | 0.7929 | 0.7769 | 0.8905 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AyushPJ/test-squad-trained-finetuned-squad
|
[
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | 2023-03-09T15:44:35Z |
---
tags:
- espnet
- audio
- text-to-speech
language: jp
datasets:
- amadeus
license: cc-by-4.0
---
## 原项目链接如下：
[**mio/amadeus**](https://huggingface.co/mio/amadeus)
## ESPnet2 TTS model
### `mio/amadeus`
This model was trained by mio using [amadeus recipe](https://github.com/mio2333/espnet/tree/master/egs2/amadeus/tts1) 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 d5b5ec7b2e77bd3e10707141818b7e6c57ac6b3f
pip install -e .
cd egs2/amadeus/tts1
./run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus
```
## TTS config
<details><summary>expand</summary>
```
config: conf/tuning/finetune_vits.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_amadeus_vits_finetune_from_jsut_32_sentence
ngpu: 1
seed: 777
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 2000
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- total_count
- max
keep_nbest_models: 3
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: true
wandb_project: amadeus
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- downloads/f3698edf589206588f58f5ec837fa516/exp/tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause/train.total_count.ave_10best.pth:tts:tts
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 5000000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/text_shape.phn
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/text_shape.phn
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
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/22k/raw/train/text
- text
- text
- - dump/22k/raw/train/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/22k/raw/dev/text
- text
- text
- - dump/22k/raw/dev/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adamw
optim_conf:
lr: 0.0001
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler: exponentiallr
scheduler_conf:
gamma: 0.999875
optim2: adamw
optim2_conf:
lr: 0.0001
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler2: exponentiallr
scheduler2_conf:
gamma: 0.999875
generator_first: false
token_list:
- <blank>
- <unk>
- '1'
- '2'
- '0'
- '3'
- '4'
- '-1'
- '5'
- a
- o
- '-2'
- i
- '-3'
- u
- e
- k
- n
- t
- '6'
- r
- '-4'
- s
- N
- m
- pau
- '7'
- sh
- d
- g
- w
- '8'
- U
- '-5'
- I
- cl
- h
- y
- b
- '9'
- j
- ts
- ch
- '-6'
- z
- p
- '-7'
- f
- ky
- ry
- '-8'
- gy
- '-9'
- hy
- ny
- '-10'
- by
- my
- '-11'
- '-12'
- '-13'
- py
- '-14'
- '-15'
- v
- '10'
- '-16'
- '-17'
- '11'
- '-21'
- '-20'
- '12'
- '-19'
- '13'
- '-18'
- '14'
- dy
- '15'
- ty
- '-22'
- '16'
- '18'
- '19'
- '17'
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: jaconv
g2p: pyopenjtalk_accent_with_pause
feats_extract: linear_spectrogram
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
normalize: null
normalize_conf: {}
tts: vits
tts_conf:
generator_type: vits_generator
generator_params:
hidden_channels: 192
spks: -1
global_channels: -1
segment_size: 32
text_encoder_attention_heads: 2
text_encoder_ffn_expand: 4
text_encoder_blocks: 6
text_encoder_positionwise_layer_type: conv1d
text_encoder_positionwise_conv_kernel_size: 3
text_encoder_positional_encoding_layer_type: rel_pos
text_encoder_self_attention_layer_type: rel_selfattn
text_encoder_activation_type: swish
text_encoder_normalize_before: true
text_encoder_dropout_rate: 0.1
text_encoder_positional_dropout_rate: 0.0
text_encoder_attention_dropout_rate: 0.1
use_macaron_style_in_text_encoder: true
use_conformer_conv_in_text_encoder: false
text_encoder_conformer_kernel_size: -1
decoder_kernel_size: 7
decoder_channels: 512
decoder_upsample_scales:
- 8
- 8
- 2
- 2
decoder_upsample_kernel_sizes:
- 16
- 16
- 4
- 4
decoder_resblock_kernel_sizes:
- 3
- 7
- 11
decoder_resblock_dilations:
- - 1
- 3
- 5
- - 1
- 3
- 5
- - 1
- 3
- 5
use_weight_norm_in_decoder: true
posterior_encoder_kernel_size: 5
posterior_encoder_layers: 16
posterior_encoder_stacks: 1
posterior_encoder_base_dilation: 1
posterior_encoder_dropout_rate: 0.0
use_weight_norm_in_posterior_encoder: true
flow_flows: 4
flow_kernel_size: 5
flow_base_dilation: 1
flow_layers: 4
flow_dropout_rate: 0.0
use_weight_norm_in_flow: true
use_only_mean_in_flow: true
stochastic_duration_predictor_kernel_size: 3
stochastic_duration_predictor_dropout_rate: 0.5
stochastic_duration_predictor_flows: 4
stochastic_duration_predictor_dds_conv_layers: 3
vocabs: 85
aux_channels: 513
discriminator_type: hifigan_multi_scale_multi_period_discriminator
discriminator_params:
scales: 1
scale_downsample_pooling: AvgPool1d
scale_downsample_pooling_params:
kernel_size: 4
stride: 2
padding: 2
scale_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 15
- 41
- 5
- 3
channels: 128
max_downsample_channels: 1024
max_groups: 16
bias: true
downsample_scales:
- 2
- 2
- 4
- 4
- 1
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
follow_official_norm: false
periods:
- 2
- 3
- 5
- 7
- 11
period_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 5
- 3
channels: 32
downsample_scales:
- 3
- 3
- 3
- 3
- 1
max_downsample_channels: 1024
bias: true
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
generator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
discriminator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
feat_match_loss_params:
average_by_discriminators: false
average_by_layers: false
include_final_outputs: true
mel_loss_params:
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
window: hann
n_mels: 80
fmin: 0
fmax: null
log_base: null
lambda_adv: 1.0
lambda_mel: 45.0
lambda_feat_match: 2.0
lambda_dur: 1.0
lambda_kl: 1.0
sampling_rate: 22050
cache_generator_outputs: true
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: '202207'
distributed: false
```
</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}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
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}
}
```
|
Azaghast/DistilBART-SCP-ParaSummarization
|
[
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 142,
"min_length": 56,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="propet/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"])
```
|
Azaghast/DistilBERT-SCP-Class-Classification
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 42 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: aces-roberta-13
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# aces-roberta-13
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4600
- Precision: 0.8364
- Recall: 0.8452
- F1: 0.8383
- Accuracy: 0.8452
- F1 Who: 0.9189
- F1 What: 0.8621
- F1 Where: 0.9231
- F1 How: 0.9141
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 | Precision | Recall | F1 | Accuracy | F1 Who | F1 What | F1 Where | F1 How |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
| 1.9849 | 0.35 | 20 | 1.4123 | 0.5426 | 0.6351 | 0.5494 | 0.6351 | 0.1026 | 0.6222 | 0.3232 | 0.7857 |
| 1.2159 | 0.7 | 40 | 0.9450 | 0.6559 | 0.7188 | 0.6592 | 0.7188 | 0.6780 | 0.7539 | 0.7071 | 0.7882 |
| 0.8634 | 1.05 | 60 | 0.6885 | 0.7652 | 0.7994 | 0.7725 | 0.7994 | 0.9067 | 0.8152 | 0.8070 | 0.8940 |
| 0.6777 | 1.4 | 80 | 0.6144 | 0.7650 | 0.7946 | 0.7711 | 0.7946 | 0.9189 | 0.7876 | 0.8039 | 0.9085 |
| 0.6051 | 1.75 | 100 | 0.5485 | 0.8126 | 0.8278 | 0.8150 | 0.8278 | 0.9315 | 0.8362 | 0.8148 | 0.9241 |
| 0.5511 | 2.11 | 120 | 0.5264 | 0.8113 | 0.8167 | 0.8036 | 0.8167 | 0.9315 | 0.8444 | 0.8257 | 0.9199 |
| 0.486 | 2.46 | 140 | 0.4867 | 0.8230 | 0.8357 | 0.8248 | 0.8357 | 0.9315 | 0.8539 | 0.9091 | 0.9048 |
| 0.4813 | 2.81 | 160 | 0.4767 | 0.8285 | 0.8278 | 0.8213 | 0.8278 | 0.9189 | 0.8701 | 0.9076 | 0.9135 |
| 0.4494 | 3.16 | 180 | 0.5042 | 0.8152 | 0.8199 | 0.8126 | 0.8199 | 0.9315 | 0.8427 | 0.8333 | 0.8956 |
| 0.4018 | 3.51 | 200 | 0.4802 | 0.8248 | 0.8357 | 0.8249 | 0.8357 | 0.9189 | 0.8736 | 0.8780 | 0.9357 |
| 0.4205 | 3.86 | 220 | 0.4723 | 0.8340 | 0.8389 | 0.8346 | 0.8389 | 0.9189 | 0.8636 | 0.9138 | 0.8986 |
| 0.3535 | 4.21 | 240 | 0.4669 | 0.8324 | 0.8452 | 0.8364 | 0.8452 | 0.9189 | 0.8571 | 0.9138 | 0.9167 |
| 0.3808 | 4.56 | 260 | 0.4585 | 0.8349 | 0.8452 | 0.8383 | 0.8452 | 0.9189 | 0.8621 | 0.9231 | 0.9141 |
| 0.3491 | 4.91 | 280 | 0.4600 | 0.8364 | 0.8452 | 0.8383 | 0.8452 | 0.9189 | 0.8621 | 0.9231 | 0.9141 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Azaghast/GPT2-SCP-ContainmentProcedures
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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| 5 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 512.50 +/- 195.85
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Yureeh -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 Yureeh -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 Yureeh
```
## Hyperparameters
```python
OrderedDict([('batch_size', 16),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Azaghast/GPT2-SCP-Descriptions
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9141935483870968
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7816
- Accuracy: 0.9142
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2905 | 1.0 | 318 | 3.2789 | 0.7274 |
| 2.6269 | 2.0 | 636 | 1.8737 | 0.8297 |
| 1.5487 | 3.0 | 954 | 1.1620 | 0.8910 |
| 1.0178 | 4.0 | 1272 | 0.8663 | 0.9061 |
| 0.8036 | 5.0 | 1590 | 0.7816 | 0.9142 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Azizun/Geotrend-10-epochs
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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"BertForTokenClassification"
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}
| 6 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery-1-always
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Emperor/q-FrozenLake-v1-4x4-noSlippery-1-always", 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"])
```
|
Azura/data
|
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}
| 0 | null |
---
license: apache-2.0
---
ComBERT is a pre-trained NLP model to analyse sentiment of commodity specific news.
It is built by further training the BERT language model in the commodity news domain, we use a large open source commodity news corpus and re-tune for commodity specific sentiment classification.
For more details, please see the paper ComBERT (Paper Pending)
The model will give softmax outputs for three labels: positive, negative or neutral.
|
Azuris/DialoGPT-medium-envy
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 12 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.52 +/- 13.10
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BME-TMIT/foszt2oszt
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
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}
}
| 15 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: qxakshat/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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