modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-31 00:44:29
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
sricharan15/bert-finetuned-squad
|
sricharan15
| 2023-02-25T16:54:44Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-25T16:39:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-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-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
pnparam/xlsr_5ep2
|
pnparam
| 2023-02-25T16:45:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-25T15:30:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: xlsr_5ep2
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. -->
# xlsr_5ep2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- 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: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
LarryAIDraw/tyrcaVenusBloodFrontier_v3
|
LarryAIDraw
| 2023-02-25T16:31:45Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-25T16:29:52Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/11410/tyrca-venus-blood-frontier
|
LarryAIDraw/TokisakiKurumi_v1
|
LarryAIDraw
| 2023-02-25T16:26:45Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-25T16:26:04Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/9797/or-tokisaki-kurumi
|
dotunadegbite/poca-SoccerTwos
|
dotunadegbite
| 2023-02-25T16:15:51Z | 48 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-25T16:15:43Z |
---
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: dotunadegbite/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mlewand/a2c-AntBulletEnv-v0
|
mlewand
| 2023-02-25T16:14:13Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T16:13:00Z |
---
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: 1598.24 +/- 286.12
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
...
```
|
emre06c/ppo-Huggy
|
emre06c
| 2023-02-25T16:07:15Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-02-25T16:07:09Z |
---
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: emre06c/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DaniilSirota/a2c-AntBulletEnv-v0
|
DaniilSirota
| 2023-02-25T16:02:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T16:01:05Z |
---
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: 1977.96 +/- 64.02
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sr5434/sd-pokemon-model-lora
|
sr5434
| 2023-02-25T15:45:07Z | 4 | 3 |
diffusers
|
[
"diffusers",
"text-to-image",
"en",
"dataset:lambdalabs/pokemon-blip-captions",
"region:us"
] |
text-to-image
| 2023-01-30T23:02:07Z |
---
datasets:
- lambdalabs/pokemon-blip-captions
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
# Model Card for my pokemon generating AI
<!-- Provide a quick summary of what the model is/does. -->
I trained this model using LoRA and Stable Diffusion v1.4
# Model Details
## How to use
Sample code:
```
from diffusers import StableDiffusionPipeline
import torch
model_path = "sr5434/sd-pokemon-model-lora"
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.unet.load_attn_procs(model_path)
pipe.to("cuda")
prompt = input("Prompt:")
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("pokemon.png")
```
|
Yagorka/ddpm-pokemons-256_50_epochs_1000_steps_continue
|
Yagorka
| 2023-02-25T15:42:48Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2023-02-25T09:19:01Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-pokemons-256_50_epochs_1000_steps_continue
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 3
- eval_batch_size: 10
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/Yagorka/ddpm-pokemons-256_50_epochs_1000_steps_continue/tensorboard?#scalars)
|
pysentimiento/robertuito-ner
|
pysentimiento
| 2023-02-25T15:32:32Z | 3,551 | 2 |
pysentimiento
|
[
"pysentimiento",
"pytorch",
"roberta",
"twitter",
"named-entity-recognition",
"ner",
"es",
"dataset:lince",
"arxiv:2106.09462",
"region:us"
] | null | 2022-07-17T20:29:58Z |
---
language:
- es
library_name: pysentimiento
tags:
- twitter
- named-entity-recognition
- ner
datasets:
- lince
---
# Named Entity Recognition model for Spanish/English
## robertuito-ner
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with the Spanish/English split of the [LinCE NER corpus](https://ritual.uh.edu/lince/), a code-switched benchmark . Base model is [RoBERTuito](https://github.com/pysentimiento/robertuito), a RoBERTa model trained in Spanish tweets.
## Usage
If you want to use this model, we suggest you use it directly from the `pysentimiento` library as it is not working properly with the pipeline due to tokenization issues
```python
from pysentimiento import create_analyzer
ner_analyzer = create_analyzer("ner", lang="es")
ner_analyzer.predict(
"rindanse ante el mejor, leonel andres messi cuccitini. serresiete no existis, segui en al-nassr"
)
# [{'type': 'PER',
# 'text': 'leonel andres messi cuccitini',
# 'start': 24,
# 'end': 53},
# {'type': 'PER', 'text': 'serresiete', 'start': 55, 'end': 65},
# {'type': 'LOC', 'text': 'al-nassr', 'start': 108, 'end': 116}]
```
## Results
Results are taken from the LinCE leaderboard
| Model | Sentiment | NER | POS |
|:-----------------------|:----------------|:-------------------|:--------|
| RoBERTuito | **60.6** | 68.5 | 97.2 |
| XLM Large | -- | **69.5** | **97.2** |
| XLM Base | -- | 64.9 | 97.0 |
| C2S mBERT | 59.1 | 64.6 | 96.9 |
| mBERT | 56.4 | 64.0 | 97.1 |
| BERT | 58.4 | 61.1 | 96.9 |
| BETO | 56.5 | -- | -- |
## Citation
If you use this model in your research, please cite pysentimiento, RoBERTuito and LinCE papers:
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{perez2022robertuito,
title={RoBERTuito: a pre-trained language model for social media text in Spanish},
author={P{\'e}rez, Juan Manuel and Furman, Dami{\'a}n Ariel and Alemany, Laura Alonso and Luque, Franco M},
booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},
pages={7235--7243},
year={2022}
}
@inproceedings{aguilar2020lince,
title={LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation},
author={Aguilar, Gustavo and Kar, Sudipta and Solorio, Thamar},
booktitle={Proceedings of the 12th Language Resources and Evaluation Conference},
pages={1803--1813},
year={2020}
}
```
|
Jojo78/Taxi-v3
|
Jojo78
| 2023-02-25T15:22:21Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T15:22:19Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="Jojo78/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"])
```
|
minhtoan/gpt2-finetune-vietnamese-news
|
minhtoan
| 2023-02-25T15:15:42Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"vi",
"vietnamese",
"lm",
"nlp",
"dataset:vietnews",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-18T16:11:47Z |
---
language: vi
tags:
- vi
- vietnamese
- gpt2
- text-generation
- lm
- nlp
datasets:
- vietnews
widget:
- text: "Tóm tắt văn bản: Hoa quả và rau thường rẻ hơn khi vào mùa. Kết quả tóm tắt văn bản là:"
---
inference:
parameters:
max_length: 120
do_sample: true
temperature: 0.8
# GPT-2
Pretrained gpt model on Vietnamese New for text summarization
# How to use the model
~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('minhtoan/gpt2-finetune-vietnamese-news')
model = GPT2LMHeadModel.from_pretrained('minhtoan/gpt2-finetune-vietnamese-news')
text = "Hoa quả và rau thường rẻ hơn khi vào mùa"
input_ids = tokenizer.encode(text, return_tensors='pt')
max_length = 80
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
do_sample=True,
max_length=max_length,
min_length=max_length,
num_return_sequences=3)
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
print('\n---')
~~~~
## Author
`
Phan Minh Toan
`
|
Theju/M12_SID_1
|
Theju
| 2023-02-25T15:08:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-25T11:17:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M12_SID_1
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. -->
# M12_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- 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: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Theju/M08_SID_1
|
Theju
| 2023-02-25T15:06:03Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-25T13:18:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M08_SID_1
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. -->
# M08_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- 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: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Jojo78/q-FrozenLake-v1-4x4-noSlippery
|
Jojo78
| 2023-02-25T15:02:02Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T15:02:00Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **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="Jojo78/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"])
```
|
AlmogM/segformer-b0-finetuned-enhanced-fish-almogm
|
AlmogM
| 2023-02-25T14:59:13Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"segformer",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-02-24T17:59:50Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-enhanced-fish-almogm
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. -->
# segformer-b0-finetuned-enhanced-fish-almogm
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-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
- num_epochs: 10
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
coreml-community/coreml-Ares-Mix
|
coreml-community
| 2023-02-25T14:55:56Z | 0 | 7 | null |
[
"coreml",
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-02-25T10:13:21Z |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model
This model was converted to Core ML for use on Apple Silicon devices by following Apple's instructions [here](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).<br>
Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br>
`split_einsum` version is compatible with all compute unit options including Neural Engine.<br>
`original` version is only compatible with CPU & GPU option.
# Ares Mix
Source: [CivitAI](https://civitai.com/models/6931/ares-mix)
Attention: You need to get your own VAE to use this model to the fullest. While it does work without a VAE, it works much better with one. I recommend you try [this one](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/tree/main) out.
Hey everyone. After GrapeLikeDreamFruit hit, I started missing having a more general purpose model for the mundane kind of pictures - nude photographs on different backgrounds and some light hardcore capabilities. This model here is my response to that need. It handles the female nude superbly, and while it's less of an artistic model than GrapeLike, it's still quite capable in that regard. It's quite good at hardcore, even if that is just a secondary goal for this model, and can be prompted for a variety of acts. Model has a good response to Danbooru tags.
This model involves dreamlike photoreal, so here is the [license](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md) that you must abide by.
<img src="https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/09c059c5-8f95-48e0-c2e7-841011d3df00/width=512">
<img src="https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d34462fe-6a98-4133-8f71-132d7795cc00/width=512">
<img src="https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/3eb3c0e1-dd57-4ddd-2873-5a2f67381100/width=512">
<img src="https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9b891a1c-e95b-48af-0b30-608e0a295400/width=512">
Deeper explanation:
This model merge was done following a similar phylosophy to GrapeLike: a Block merge between a realistic anatomy + skin core and a posing anime core. The block merge is done in such a way as to emphasize the anime core in the center of the Unet, while rapidly decaying back to the realistic core at the edges. This has the effect of bringing a lot of posing and composition ideas from hentai models inside the photorealistic core we have available without touching textures and photorealism. Full recipe follows:
Anatomy core:
izumi, F222, dreamlike photoreal, realistic vision 1.2, sxd, all at the same intensity. ie, the merge chain was:
(((izumi + F222 0.5) + dreamlike photoreal 0.33) + realistic vision 0.25) + sxd 0.2
Anime core:
Anything v4.5 merged with Basil Mix using the same block merge coefficients used in mixing [Abyss Orange Mix](https://civitai.com/models/4451/abyssorangemix2-nsfw-hardcore), then merged 40% with grapefruit, the result was merged 30% with gape60 and finally 15% with RPG v4.
Bringing both together:
The block merge for both was done using a formula. I kept the bottleneck Model A was anatomy, model B was anime. I kept the center lalyer at 0.7, as well as base alpha, then followed 0.8/(n**1.1)as a merge rule, with n being distance from the center. Full numbers were "0.05199847612695355,0.05722134125067434,0.06354625877794251,0.07135480548979826,0.08122523963562354,0.09407671474206218,0.11146117361039158,0.13621438760332552,0.17411011265922482,0.23892225595753655,0.373213196614723,0.8,0.7,0.8,0.373213196614723,0.23892225595753655,0.17411011265922482,0.13621438760332552,0.11146117361039158,0.09407671474206218,0.08122523963562354,0.07135480548979826,0.06354625877794251,0.05722134125067434,0.05199847612695355".
|
chist/rl_course_vizdoom_health_gathering_supreme
|
chist
| 2023-02-25T14:54:16Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T14:32:13Z |
---
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: 9.50 +/- 4.38
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 chist/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 <path.to.enjoy.module> --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 <path.to.train.module> --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.
|
deprem-ml/adres_ner_v12
|
deprem-ml
| 2023-02-25T14:44:37Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-25T14:28:42Z |
---
language:
- tr
pipeline_tag: token-classification
---
{'eval_loss': 0.3053381145000458,
'eval_runtime': 0.8092,
'eval_samples_per_second': 160.649,
'eval_steps_per_second': 3.707,
'epoch': 3.0}
|
jinhu2659/ppo-PyramidsRDN
|
jinhu2659
| 2023-02-25T14:31:00Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-02-25T14:30:54Z |
---
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: jinhu2659/ppo-PyramidsRDN
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Theju/M14_SID_1
|
Theju
| 2023-02-25T14:29:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-25T13:17:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M14_SID_1
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. -->
# M14_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- 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: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
finiteautomata/beto-sentiment-analysis
|
finiteautomata
| 2023-02-25T14:23:57Z | 292,238 | 30 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment-analysis",
"es",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- es
tags:
- sentiment-analysis
---
# Sentiment Analysis in Spanish
## beto-sentiment-analysis
**NOTE: this model will be removed soon -- use [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) instead**
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/pysentimiento/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model trained in Spanish.
Uses `POS`, `NEG`, `NEU` labels.
## License
`pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)
2. [SEMEval 2017 Dataset license]()
## Citation
If you use this model in your work, please cite the following papers:
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{canete2020spanish,
title={Spanish pre-trained bert model and evaluation data},
author={Ca{\~n}ete, Jos{\'e} and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and P{\'e}rez, Jorge},
journal={Pml4dc at iclr},
volume={2020},
number={2020},
pages={1--10},
year={2020}
}
```
Enjoy! 🤗
|
jinhu2659/ppo-SnowballTarget
|
jinhu2659
| 2023-02-25T14:15:53Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-02-25T14:07:46Z |
---
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: jinhu2659/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
yizhangliu/rl_course_vizdoom_health_gathering_supreme
|
yizhangliu
| 2023-02-25T14:15:09Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T14:15:04Z |
---
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: 7.29 +/- 2.23
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 yizhangliu/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 <path.to.enjoy.module> --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 <path.to.train.module> --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.
|
Ibtisam/LunarLander-v2
|
Ibtisam
| 2023-02-25T14:10:04Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T14:09:44Z |
---
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: 297.88 +/- 15.51
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
...
```
|
Sanyam/xlm-roberta-base-finetuned-panx-de
|
Sanyam
| 2023-02-25T13:34:22Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-25T13:18:28Z |
---
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: 0.8503382026942534
---
<!-- 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: 0.8503
## 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: 96
- eval_batch_size: 96
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 132 | 0.1692 | 0.8089 |
| No log | 2.0 | 264 | 0.1397 | 0.8394 |
| No log | 3.0 | 396 | 0.1358 | 0.8503 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.11.0
|
hectorjelly/The_Twits2
|
hectorjelly
| 2023-02-25T13:29:40Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-25T13:29:31Z |
---
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: hectorjelly/The_Twits2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Theju/M01_SID_1
|
Theju
| 2023-02-25T13:20:29Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-25T12:16:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M01_SID_1
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. -->
# M01_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- 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: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Zarmonela/ppo-HuggyTheDog
|
Zarmonela
| 2023-02-25T13:18:40Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-02-25T13:18:33Z |
---
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: Zarmonela/ppo-HuggyTheDog
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Constien/IM_seg
|
Constien
| 2023-02-25T13:12:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-25T13:11:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: IM_seg
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. -->
# IM_seg
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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
- num_epochs: 3
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
rdesarz/a2c-AntBulletEnv-v0
|
rdesarz
| 2023-02-25T13:03:26Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T13:02:17Z |
---
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: 1148.33 +/- 221.61
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
...
```
|
Theju/M09_SID_1
|
Theju
| 2023-02-25T12:57:22Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-25T11:27:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M09_SID_1
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. -->
# M09_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- 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: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
rwcuffney/autotrain-pick_a_card-3726099222
|
rwcuffney
| 2023-02-25T12:47:19Z | 21 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain",
"vision",
"dataset:rwcuffney/autotrain-data-pick_a_card",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-02-25T12:34:12Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- rwcuffney/autotrain-data-pick_a_card
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.08500926102855322
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3726099222
- CO2 Emissions (in grams): 0.0850
## Validation Metrics
- Loss: 0.314
- Accuracy: 0.909
- Macro F1: 0.904
- Micro F1: 0.909
- Weighted F1: 0.904
- Macro Precision: 0.926
- Micro Precision: 0.909
- Weighted Precision: 0.926
- Macro Recall: 0.909
- Micro Recall: 0.909
- Weighted Recall: 0.909
|
LarryAIDraw/guilingao_v61
|
LarryAIDraw
| 2023-02-25T12:46:15Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-25T10:51:09Z |
---
license: creativeml-openrail-m
---
|
rwcuffney/autotrain-pick_a_card-3726099221
|
rwcuffney
| 2023-02-25T12:44:44Z | 35 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"autotrain",
"vision",
"dataset:rwcuffney/autotrain-data-pick_a_card",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-02-25T12:34:08Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- rwcuffney/autotrain-data-pick_a_card
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.06604434070314698
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3726099221
- CO2 Emissions (in grams): 0.0660
## Validation Metrics
- Loss: 0.061
- Accuracy: 0.981
- Macro F1: 0.980
- Micro F1: 0.981
- Weighted F1: 0.980
- Macro Precision: 0.984
- Micro Precision: 0.981
- Weighted Precision: 0.984
- Macro Recall: 0.981
- Micro Recall: 0.981
- Weighted Recall: 0.981
|
rwcuffney/autotrain-pick_a_card-3726099225
|
rwcuffney
| 2023-02-25T12:43:26Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"autotrain",
"vision",
"dataset:rwcuffney/autotrain-data-pick_a_card",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-02-25T12:34:13Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- rwcuffney/autotrain-data-pick_a_card
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 14.334546926203066
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3726099225
- CO2 Emissions (in grams): 14.3345
## Validation Metrics
- Loss: 0.085
- Accuracy: 0.974
- Macro F1: 0.973
- Micro F1: 0.974
- Weighted F1: 0.973
- Macro Precision: 0.979
- Micro Precision: 0.974
- Weighted Precision: 0.979
- Macro Recall: 0.974
- Micro Recall: 0.974
- Weighted Recall: 0.974
|
afaji/fine-tuned-IndoNLI-Basic-with-indobert-large-p2
|
afaji
| 2023-02-25T12:32:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-25T08:56:59Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine-tuned-IndoNLI-Basic-with-indobert-large-p2
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. -->
# fine-tuned-IndoNLI-Basic-with-indobert-large-p2
This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2497
- Accuracy: 0.7751
## 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
- 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: 0.06
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.978 | 1.0 | 161 | 0.8505 | 0.6236 |
| 0.6752 | 2.0 | 322 | 0.6163 | 0.7542 |
| 0.5579 | 3.0 | 483 | 0.6259 | 0.7551 |
| 0.4328 | 4.0 | 644 | 0.6153 | 0.7706 |
| 0.3217 | 5.0 | 805 | 0.6348 | 0.7711 |
| 0.229 | 6.0 | 966 | 0.7245 | 0.7720 |
| 0.1688 | 7.0 | 1127 | 0.8032 | 0.7774 |
| 0.1258 | 8.0 | 1288 | 0.8898 | 0.7742 |
| 0.0942 | 9.0 | 1449 | 0.9629 | 0.7651 |
| 0.0718 | 10.0 | 1610 | 0.9848 | 0.7783 |
| 0.0635 | 11.0 | 1771 | 1.0794 | 0.7674 |
| 0.0407 | 12.0 | 1932 | 1.1378 | 0.7679 |
| 0.0394 | 13.0 | 2093 | 1.2195 | 0.7651 |
| 0.0323 | 14.0 | 2254 | 1.2151 | 0.7756 |
| 0.0235 | 15.0 | 2415 | 1.2509 | 0.7711 |
| 0.0277 | 16.0 | 2576 | 1.2497 | 0.7751 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
mqy/mt5-small-finetuned-25feb-3
|
mqy
| 2023-02-25T12:26:46Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-02-25T09:32:31Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-25feb-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-25feb-3
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3532
- Rouge1: 20.83
- Rouge2: 6.54
- Rougel: 20.4
## 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: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 5.3088 | 1.45 | 500 | 2.6216 | 17.35 | 5.18 | 17.14 |
| 3.2982 | 2.9 | 1000 | 2.5396 | 17.76 | 5.55 | 17.53 |
| 2.9211 | 4.35 | 1500 | 2.4802 | 18.72 | 5.62 | 18.47 |
| 2.8162 | 5.8 | 2000 | 2.4158 | 18.16 | 5.26 | 17.7 |
| 2.6661 | 7.25 | 2500 | 2.4387 | 18.59 | 5.38 | 18.21 |
| 2.6102 | 8.7 | 3000 | 2.4044 | 19.54 | 5.63 | 19.16 |
| 2.5043 | 10.14 | 3500 | 2.3738 | 19.65 | 5.79 | 19.16 |
| 2.4598 | 11.59 | 4000 | 2.3805 | 19.86 | 6.29 | 19.43 |
| 2.3807 | 13.04 | 4500 | 2.3590 | 20.13 | 5.91 | 19.62 |
| 2.3461 | 14.49 | 5000 | 2.3611 | 20.73 | 6.28 | 20.31 |
| 2.3024 | 15.94 | 5500 | 2.3571 | 20.64 | 6.12 | 20.25 |
| 2.2704 | 17.39 | 6000 | 2.3723 | 19.71 | 5.95 | 19.35 |
| 2.2356 | 18.84 | 6500 | 2.3532 | 20.83 | 6.54 | 20.4 |
| 2.2019 | 20.29 | 7000 | 2.3597 | 19.67 | 5.91 | 19.28 |
| 2.1646 | 21.74 | 7500 | 2.3733 | 20.63 | 6.53 | 20.24 |
| 2.1511 | 23.19 | 8000 | 2.3534 | 20.63 | 6.22 | 20.22 |
| 2.128 | 24.64 | 8500 | 2.3552 | 20.12 | 5.92 | 19.77 |
| 2.0933 | 26.09 | 9000 | 2.3587 | 20.53 | 5.88 | 20.06 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
chist/ppo-LunarLander-v2-CleanRL
|
chist
| 2023-02-25T12:09:00Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T09:51:32Z |
---
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: -103.68 +/- 49.54
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': 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': 'chist/ppo-LunarLander-v2-CleanRL'
'batch_size': 512
'minibatch_size': 128}
```
|
Zarmonela/ppo-BipedalWalker-v3
|
Zarmonela
| 2023-02-25T12:02:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T12:01:57Z |
---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
metrics:
- type: mean_reward
value: 208.46 +/- 3.32
name: mean_reward
verified: false
---
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
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
...
```
|
mafwalter/distilroberta-base-finetuned-question-v-statement
|
mafwalter
| 2023-02-25T11:55:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-25T11:03:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilroberta-base-finetuned-question-v-statement
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. -->
# distilroberta-base-finetuned-question-v-statement
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0048
- Accuracy: 0.9992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0069 | 1.0 | 7932 | 0.0088 | 0.9987 |
| 0.0011 | 2.0 | 15864 | 0.0048 | 0.9992 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
domadapter/joint_dt_travel_telephone
|
domadapter
| 2023-02-25T11:50:35Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"bert",
"adapterhub:nli/multinli",
"dataset:multi_nli",
"region:us"
] | null | 2023-02-25T11:50:26Z |
---
tags:
- bert
- adapter-transformers
- adapterhub:nli/multinli
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_travel_telephone` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_travel_telephone", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
domadapter/joint_dt_fiction_slate
|
domadapter
| 2023-02-25T11:50:23Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"bert",
"adapterhub:nli/multinli",
"dataset:multi_nli",
"region:us"
] | null | 2023-02-25T11:50:15Z |
---
tags:
- bert
- adapter-transformers
- adapterhub:nli/multinli
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_fiction_slate` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_fiction_slate", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Ibtisam/Taxi
|
Ibtisam
| 2023-02-25T11:38:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T11:01:06Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 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="Ibtisam/Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
zipbomb/poca-SoccerTwos
|
zipbomb
| 2023-02-25T11:31:12Z | 31 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-25T11:31:06Z |
---
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: zipbomb/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mdapri/Reinforce-CartPole8
|
mdapri
| 2023-02-25T10:49:52Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T10:49:39Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
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
|
wang-sy/gpt2-stinfo
|
wang-sy
| 2023-02-25T10:26:36Z | 0 | 0 | null |
[
"text-generation",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-23T08:56:58Z |
---
pipeline_tag: text-generation
widget:
- text: "My name is Julien and I like to"
example_title: "Julien"
- text: "My name is Merve and my favorite"
example_title: "Merve"
---
|
OliP/ppo-LunarLander-v2-unit8-v0
|
OliP
| 2023-02-25T10:22:39Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T10:21:55Z |
---
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: 125.90 +/- 43.30
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': 2000000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 2048
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.98
'num_minibatches': 64
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'OliP/ppo-LunarLander-v2-unit8-v0'
'batch_size': 8192
'minibatch_size': 128}
```
|
JessicaHsu/dqn-SpaceInvadersNoFrameskip-v4
|
JessicaHsu
| 2023-02-25T09:47:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T09:47:10Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 528.50 +/- 158.78
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 JessicaHsu -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 JessicaHsu -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 JessicaHsu
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
ritheshwar/autotrain-codet5_base_cpsl-3727399186
|
ritheshwar
| 2023-02-25T09:19:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"translation",
"unk",
"dataset:ritheshwar/autotrain-data-codet5_base_cpsl",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-02-25T09:17:08Z |
---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- ritheshwar/autotrain-data-codet5_base_cpsl
co2_eq_emissions:
emissions: 3.846331276578152
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 3727399186
- CO2 Emissions (in grams): 3.8463
## Validation Metrics
- Loss: 0.223
- SacreBLEU: 2.566
- Gen len: 19.000
|
swl-models/DanMix-v2
|
swl-models
| 2023-02-25T09:15:28Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-11T16:02:41Z |
---
license: creativeml-openrail-m
---
|
ThePioneer/SnowMix
|
ThePioneer
| 2023-02-25T08:49:46Z | 0 | 0 | null |
[
"art",
"en",
"ja",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-25T07:03:44Z |
---
license: creativeml-openrail-m
language:
- en
- ja
tags:
- art
---
## Download
<div align="center">
[](https://huggingface.co/ThePioneer/SnowMix/resolve/main/SnowMix.safetensors)
[](https://huggingface.co/ThePioneer/SnowMix/resolve/main/SnowMix_fp16.safetensors)
</div>
## About
<div align="center">
[](https://twitter.com/ThePioneerJPnew/status/1629399166664478720)
</div>
Introducing SnowMix.
From NAI leak free anime images, holara-like semi-real images, midjourney-like fantasy or cyberpunk digital art, to ChilloutMix free AI cosplay.
SnowMix is a merged model of 5 anime models and 1 realistic model.
It's a merged model of [Untitled](https://huggingface.co/alfredplpl/untitled), [Replicant v1.0](https://huggingface.co/gsdf/Replicant-V1.0), [Aikimi Diffusion v3](https://huggingface.co/Aikimi/Aikimi_diffusion_base_wd-1-5_beta1), [Subtly](https://huggingface.co/ddPn08/subtly), [RuminationDiffusion](https://huggingface.co/JosephusCheung/RuminationDiffusion), and [Illuminati Diffusion v1.0](https://huggingface.co/IlluminatiAI/Illuminati_Diffusion_v1.0).
Its potential should exceed the previous powerful merge, [Quattro4Merge+i](https://huggingface.co/ThePioneer/quattro-4merge-plus-i), but yet unknown.
Now is your turn to download this model, and discover the true power.
## Samples
See the [civtai](https://civitai.com/models/12863/snowmix) page for sample prompts.
|
swl-models/DanMix-v2.2
|
swl-models
| 2023-02-25T08:21:00Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-11T16:05:22Z |
---
license: creativeml-openrail-m
---
|
Akuxcw/toy_dog
|
Akuxcw
| 2023-02-25T08:16:53Z | 2 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-02-25T07:59:02Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Akuxcw/toy_dog
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
nbtpj/bart-base-rmqa-fine-tune
|
nbtpj
| 2023-02-25T08:10:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-03T01:47:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-rmqa-fine-tune
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. -->
# bart-base-rmqa-fine-tune
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.8
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
nbtpj/bart-base-mqa-fine-tune
|
nbtpj
| 2023-02-25T08:09:50Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-01T01:43:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-mqa-fine-tune
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. -->
# bart-base-mqa-fine-tune
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.8
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Yuta5566/ll
|
Yuta5566
| 2023-02-25T07:31:58Z | 0 | 0 |
flair
|
[
"flair",
"music",
"feature-extraction",
"ae",
"dataset:poloclub/diffusiondb",
"license:openrail",
"region:us"
] |
feature-extraction
| 2023-02-25T07:30:43Z |
---
license: openrail
datasets:
- poloclub/diffusiondb
language:
- ae
metrics:
- bleurt
library_name: flair
pipeline_tag: feature-extraction
tags:
- music
---
|
sd99/ppo-LunarLander-v2-unit8
|
sd99
| 2023-02-25T07:25:36Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T07:25:29Z |
---
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: -139.25 +/- 99.91
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': 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': 'sd99/ppo-LunarLander-v2-unit8'
'batch_size': 512
'minibatch_size': 128}
```
|
Ibtisam/q-FrozenLake-v1-4x4-noSlippery
|
Ibtisam
| 2023-02-25T06:44:52Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T06:44:47Z |
---
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="Ibtisam/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"])
```
|
lllyasviel/ControlNet
|
lllyasviel
| 2023-02-25T05:57:36Z | 0 | 3,662 | null |
[
"license:openrail",
"region:us"
] | null | 2023-02-08T18:51:21Z |
---
license: openrail
---
This is the pretrained weights and some other detector weights of ControlNet.
See also: https://github.com/lllyasviel/ControlNet
# Description of Files
ControlNet/models/control_sd15_canny.pth
- The ControlNet+SD1.5 model to control SD using canny edge detection.
ControlNet/models/control_sd15_depth.pth
- The ControlNet+SD1.5 model to control SD using Midas depth estimation.
ControlNet/models/control_sd15_hed.pth
- The ControlNet+SD1.5 model to control SD using HED edge detection (soft edge).
ControlNet/models/control_sd15_mlsd.pth
- The ControlNet+SD1.5 model to control SD using M-LSD line detection (will also work with traditional Hough transform).
ControlNet/models/control_sd15_normal.pth
- The ControlNet+SD1.5 model to control SD using normal map. Best to use the normal map generated by that Gradio app. Other normal maps may also work as long as the direction is correct (left looks red, right looks blue, up looks green, down looks purple).
ControlNet/models/control_sd15_openpose.pth
- The ControlNet+SD1.5 model to control SD using OpenPose pose detection. Directly manipulating pose skeleton should also work.
ControlNet/models/control_sd15_scribble.pth
- The ControlNet+SD1.5 model to control SD using human scribbles. The model is trained with boundary edges with very strong data augmentation to simulate boundary lines similar to that drawn by human.
ControlNet/models/control_sd15_seg.pth
- The ControlNet+SD1.5 model to control SD using semantic segmentation. The protocol is ADE20k.
ControlNet/annotator/ckpts/body_pose_model.pth
- Third-party model: Openpose’s pose detection model.
ControlNet/annotator/ckpts/hand_pose_model.pth
- Third-party model: Openpose’s hand detection model.
ControlNet/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt
- Third-party model: Midas depth estimation model.
ControlNet/annotator/ckpts/mlsd_large_512_fp32.pth
- Third-party model: M-LSD detection model.
ControlNet/annotator/ckpts/mlsd_tiny_512_fp32.pth
- Third-party model: M-LSD’s another smaller detection model (we do not use this one).
ControlNet/annotator/ckpts/network-bsds500.pth
- Third-party model: HED boundary detection.
ControlNet/annotator/ckpts/upernet_global_small.pth
- Third-party model: Uniformer semantic segmentation.
ControlNet/training/fill50k.zip
- The data for our training tutorial.
# Related Resources
Special Thank to the great project - [Mikubill' A1111 Webui Plugin](https://github.com/Mikubill/sd-webui-controlnet) !
We also thank Hysts for making [Gradio](https://github.com/gradio-app/gradio) demo in [Hugging Face Space](https://huggingface.co/spaces/hysts/ControlNet) as well as more than 65 models in that amazing [Colab list](https://github.com/camenduru/controlnet-colab)!
Thank haofanwang for making [ControlNet-for-Diffusers](https://github.com/haofanwang/ControlNet-for-Diffusers)!
We also thank all authors for making Controlnet DEMOs, including but not limited to [fffiloni](https://huggingface.co/spaces/fffiloni/ControlNet-Video), [other-model](https://huggingface.co/spaces/hysts/ControlNet-with-other-models), [ThereforeGames](https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/7784), [RamAnanth1](https://huggingface.co/spaces/RamAnanth1/ControlNet), etc!
# Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
|
HighCWu/ControlLoRA
|
HighCWu
| 2023-02-25T05:47:04Z | 0 | 17 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"controlnet",
"control-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-02-18T02:36:12Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- controlnet
- control-lora
inference: true
---
# ControlLoRA text2image fine-tuning - Official Model Repository
These are ControlLoRA adaption weights for [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). You can find the code repository in [HighCWu/ControlLoRA](https://github.com/HighCWu/ControlLoRA).
With ControlLoRA, a simple and small (~7M parameters, ~25M storage space) network, you could control the spatial information of stable diffusion.
Each of the weights is fine-tuned on the [diffusiondb_2m_first_5k_canny](https://huggingface.co/datasets/HighCWu/diffusiondb_2m_first_5k_canny) and [mpii_100_openpose](https://huggingface.co/datasets/HighCWu/mpii_100_openpose) datasets with 30k steps on RTX4080 in 3 hours.
You can find some example images in the following.
*boys are playing with a frisbee in a field,
2009 cinematography, trending on artforum, running pose,
bruce springsteen, connected to heart machines, with tattoos,
beautiful - n 9, by Eric Dinyer, young child, midlands*
*portrait of a dancing eagle woman, "
"beautiful blonde haired lakota sioux goddess, "
"intricate, highly detailed art by james jean, "
"ray tracing, digital painting, artstation, "
"concept art, smooth, sharp focus, illustration, "
"artgerm and greg rutkowski and alphonse mucha, "
"vladimir kush, giger, roger dean, 8 k*
I also uploaded a lora model fine-tuned on my selfies with 2k steps which could be use in the experiment of mixing LoRA and ControlLoRA.
*portrait of female HighCWu as a cute pink hair girl*
|
mafwalter/distilbert-base-sst-2-english-finetuned-question-v-statement
|
mafwalter
| 2023-02-25T05:46:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-25T04:52:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english-finetuned-question-v-statement
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-sst-2-english-finetuned-question-v-statement
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0077
- Accuracy: 0.9988
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0067 | 1.0 | 7932 | 0.0077 | 0.9985 |
| 0.001 | 2.0 | 15864 | 0.0077 | 0.9988 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.0.dev0
|
JamesFlare/pastel-mix
|
JamesFlare
| 2023-02-25T05:25:10Z | 391 | 55 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-25T05:24:44Z |
---
language:
- en
license: creativeml-openrail-m
thumbnail: >-
https://huggingface.co/andite/pastel-mix/resolve/main/example-images/01194-%20.png
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
duplicated_from: andite/pastel-mix
---
Update Logs:
[1/27/22]
I uploaded the model in CivitAI! -> https://civitai.com/models/5414/pastel-mix-stylized-anime-model I'd appreciate the ratings, thank you!
[2/2/22]
Uploaded a lora version.
<center><h1><b>Pastel Mix</b></h1></center>
<p align="center">Welcome to Pastel Mix - a stylized latent diffusion model. This model is intended to produce high-quality, highly detailed anime style with just a few prompts.</p>
<p align="center">This model is made with the thought of imitating pastel-like art and the potential of mixing LORAs into a model altogether to create a fantastic mix.
Recipe for this mix could be found below. Like other anime-style Stable Diffusion models, it also supports danbooru tags to generate images. </p>
<p align="center">e.g. <b>masterpiece, best quality, upper body, 1girl, looking at viewer, red hair, medium hair, purple eyes, demon horns, black coat, indoors, dimly lit</b></p>
<p align="center"><img src="https://huggingface.co/andite/Pastel-Mix/resolve/main/example-images/grid-0020.png">
<img src="https://huggingface.co/andite/Pastel-Mix/resolve/main/example-images/grid-0018.png"></p>
-------
## How to download with Git
```
git lfs install
git clone https://huggingface.co/andite/pastel-mix
```
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "andite/pastel-mix"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "hatsune_miku"
image = pipe(prompt).images[0]
image.save("./hatsune_miku.png")
```
# Gradio
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run pastel-mix:
[](https://huggingface.co/spaces/akhaliq/pastel-mix)
## Examples
```
masterpiece, best quality, ultra-detailed, illustration, portrait, 1girl
Negative prompt: lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, low quality, normal quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts))
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Size: 448x640, Model hash: 7edc8e08, Model: pastelmix-fp32, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires resize: 960x1280, Hires steps: 20, Hires upscaler: Latent
```
```
masterpiece, best quality, ultra-detailed, illustration, portrait, hakurei reimu, 1girl, throne room, dimly lit
Negative prompt: lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, low quality, normal quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts))
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Size: 448x640, Model hash: 7edc8e08, Model: pastelmix-fp32, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires resize: 960x1280, Hires steps: 20, Hires upscaler: Latent
```
```
masterpiece, best quality, ultra-detailed, illustration, 1girl, witch hat, purple eyes, blonde hair, wielding a purple staff blasting purple energy, purple beam, purple effects, dragons, chaos
Negative prompt: lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, low quality, normal quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts))
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Size: 448x640, Model hash: 7edc8e08, Model: pastelmix-fp32, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires resize: 960x1280, Hires steps: 20, Hires upscaler: Latent
```
```
masterpiece, best quality, ultra-detailed, illustration, close-up, straight on, 1girl, black hair, yellow eyes, red roses, chains
Negative prompt: lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, low quality, normal quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts))
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 2203084815, Size: 640x448, Model hash: 7edc8e08, Model: pastelmix-fp32, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires resize: 1280x960, Hires steps: 20, Hires upscaler: Latent
```
```
masterpiece, best quality, ultra-detailed, illustration, close-up, straight on, face focus, 1girl, white hair, golden eyes, long hair, halo, angel wings, serene expression, looking at viewer
Negative prompt: lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, low quality, normal quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts))
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 240742293, Size: 640x448, Model hash: 7edc8e08, Model: pastelmix-fp32, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires resize: 1280x960, Hires steps: 20, Hires upscaler: Latent
```
## So what the hell is the 'better-vae' version?
I merged the pastel-waifu-diffusion.vae.pt inside the model so you don't have to set up the vae anymore.
life so much ez now since you don't have to download the vae and set it up right?
## What is pastelmix-lora.safetensors?
It's a lora version which is made from extracting the loras from pastel-mix using a script that is similar to add-difference method.
https://github.com/bmaltais/kohya_ss/blob/master/train_network_README.md
## Guide
For the settings or parameters, I recommend using these settings.
```
Sampler: DPM++ 2M Karras
Steps: 20
CFG Scale: 7
Hires. Fix: On
Upscaler: Latent (MUST!)
Hires Steps: 20
Denoising Strength: 0.
```
I prefer using 0.6 since it's the sweet spot of this model. If you can find a better setting for this model, then good for you lol.
Latent upscaler is the best setting for me since it retains or enhances the pastel style. Other upscalers like Lanczos or Anime6B tends to smoothen them out, removing the pastel-like brushwork.
Please use the **VAE** that I uploaded in this repository. It is from the [Waifu Diffusion](https://huggingface.co/hakurei/waifu-diffusion-v1-4/tree/main/vae) team. Credits to [haru](https://huggingface.co/hakurei) for letting me rename and upload it.
## Tip (Optional)
Putting mksks style in the beginning of the prompt can further influence the pastel-like style and make the output better. It is optional though, so it's up to you. You don't really need it.
```
mksks style, masterpiece, best quality, upper body, 1girl, looking at viewer, red hair, medium hair, purple eyes, demon horns, black coat, indoors, dimly lit
Negative prompt: lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, low quality, normal quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts))
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 580841049, Size: 448x640, Model hash: 7edc8e08, Model: pastelmix-fp32, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires resize: 960x1280, Hires steps: 20, Hires upscaler: Latent
```
## Recipe
Merging the models.
| Model: A | Model: B | Weight | Base alpha | Merge Name |
| --- | --- | --- | --- | --- |
| [dpepmkmp](https://huggingface.co/closertodeath/dpepmkmp) | [Tea](https://huggingface.co/andite/desserts) | 1,0.9,0.7,0.5,0.3,0.1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.1,0.3,0.5,0.7,0.9,1 | 0 | dpeptea |
| dpeptea | [basil-mix](https://huggingface.co/nuigurumi/basil_mix) | 1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 | 0 | dpeptea-basil |
Merging the loras into the model.
| Model | Lora | Weight | Merge Name |
| --- | --- | --- | --- |
| [dpeptea-basil](https://huggingface.co/closertodeath/dpepteahands3) | [Magic LORA](https://cdn.discordapp.com/attachments/1065289257243115540/1066346221876301845/MagicLORA.pt) | 0.3 | dpeptea-1 |
| dpeptea-1 | [Jordan_3](https://huggingface.co/SatyamSSJ10/ConceptArt) | 1 | dpeptea-2 |
| dpeptea-2 | [sttabi_v1.4-04](https://huggingface.co/dolphinz/stlora) | 0.5 | dpeptea-3 |
| dpeptea-3 | [xlimo768](https://huggingface.co/closertodeath/ctdlora) | 0.6 | dpeptea-4 |
| dpeptea-4 | [dpep 2 768](https://huggingface.co/closertodeath/ctdlora)| 0.35 | Pastel-Mix |
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content.
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
-------
## Big Thanks to
The 東方Project AI community for their wonderful LORAs.
- [Closertodeath](https://huggingface.co/closertodeath) for dpepmkmp model, and the loras: xlimo768, dpep 2 768
- [dolphinz/sometimes#9353](https://huggingface.co/dolphinz) for tabi artstyle Lora.
- [SatyamSSJ10](https://huggingface.co/SatyamSSJ10/ConceptArt) for Jordan_3 Lora.
- randomaccessmemories#4004 for Magic Lora
|
toastynews/electra-hongkongese-large-discriminator
|
toastynews
| 2023-02-25T05:20:40Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"yue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: yue
license: apache-2.0
metrics:
- DRCD
- openrice-senti
- lihkg-cat
- wordshk-sem
---
# ELECTRA Hongkongese Large
## Model description
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
## Intended uses & limitations
This model is an alternative to Chinese models. It may offer better performance for tasks catering to the langauge usage of Hong Kongers. Yue Wikipedia is used which is much smaller than Chinese Wikipedia; this model will lack the breath of knowledge compared to other Chinese models.
#### How to use
This is the large model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.
#### Limitations and bias
The training data consists of mostly news articles and blogs. There is probably a bias towards formal language usage.
## Training data
The following is the list of data sources. Total characters is about 507M.
| Data | % |
| ------------------------------------------------- | --: |
| News Articles / Blogs | 58% |
| Yue Wikipedia / EVCHK | 18% |
| Restaurant Reviews | 12% |
| Forum Threads | 12% |
| Online Fiction | 1% |
The following is the distribution of different languages within the corpus.
| Language | % |
| ------------------------------------------------- | --: |
| Standard Chinese | 62% |
| Hongkongese | 30% |
| English | 8% |
## Training procedure
Model was trained on a single TPUv3 from the official repo with the default parameters.
| Parameter | Value |
| ------------------------------------------------ | ----: |
| Batch Size | 96 |
| Max Sequence Size | 512 |
| Mask Prob | 0.25 |
| Learning Rate | 2e-4 |
| Vocab Size | 30000 |
*Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC)*
## Eval results
Average evaluation task results over 10 runs. Comparison using the original repo model and code. Chinese models are available from [Joint Laboratory of HIT and iFLYTEK Research (HFL)](https://huggingface.co/hfl)
| Model | DRCD (EM/F1) | openrice-senti | lihkg-cat | wordshk-sem |
|:-----------:|:------------:|:--------------:|:---------:|:-----------:|
| Chinese | 88.8 / 93.6 | 79.8 | 70.4 | 90.4 |
| Hongkongese | 84.7 / 90.9 | 79.7 | 69.9 | 91.5 |
|
dyingc/ppo-Huggy
|
dyingc
| 2023-02-25T04:25:13Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-02-25T04:25:06Z |
---
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: dyingc/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Thehours/TheCrown
|
Thehours
| 2023-02-25T04:16:46Z | 0 | 0 |
nemo
|
[
"nemo",
"aa",
"dataset:google/MusicCaps",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-25T04:12:27Z |
---
license: creativeml-openrail-m
datasets:
- google/MusicCaps
language:
- aa
metrics:
- accuracy
- bertscore
library_name: nemo
---
|
Eagelaxis/Cetus-mix_version2
|
Eagelaxis
| 2023-02-25T04:03:02Z | 0 | 5 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-25T04:03:02Z |
---
license: creativeml-openrail-m
---
|
simhuangxi/MoXin
|
simhuangxi
| 2023-02-25T02:52:27Z | 0 | 28 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-02-25T02:42:54Z |
---
license: cc-by-nc-4.0
---
# **《墨心》**
# 昔涓子**《琴心》**，王孙**《巧心》**，**心**哉美矣，故用之焉。
### 本品由安吉吴仓石、兴化板桥先生、八大山人、山阴伯年等大师之大小写意作品辅以现代人物训练而成。辅以恰当之提示词，诵先贤尊号，襄古今并用之意，明雅俗共举之美。
### Above part is just a little self-praise of my insignificance, without any practical significance, you can simply ignore them.
***
### 注意事项：
1. CFG范围将会改变风格
- 1~3 : 大小写意
- 3~7 : 逐渐工笔
2. 推荐基础模型为国风3.2( GuoFeng3.2 )
3. 推荐Lora权重为0.85以下
### Tips:
1. The result style will change within the following CFG ranges:
- 1~3 : Xieyi Painting
- 3~7 : Gongbi Painting
2. It is recommended to use the GuoFeng3.2 model as a base.
3. It is recommended to use a Lora weight of 0.85 or lower.
|
Zekunli/flan-t5-large-nlg-multiwoz2.0_400
|
Zekunli
| 2023-02-25T02:24:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-25T00:57:23Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-large-nlg-multiwoz2.0_400
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-large-nlg-multiwoz2.0_400
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9323
- Rouge1: 36.3522
- Rouge2: 19.5982
- Rougel: 33.0495
- Rougelsum: 34.4791
- Gen Len: 17.7927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4929 | 0.58 | 200 | 1.1051 | 33.8407 | 17.022 | 30.6518 | 32.2374 | 17.7195 |
| 1.1546 | 1.17 | 400 | 1.0221 | 33.4159 | 17.73 | 30.4168 | 31.6796 | 17.8444 |
| 1.0597 | 1.75 | 600 | 0.9819 | 34.8735 | 18.3373 | 31.5435 | 33.0184 | 17.7802 |
| 0.9863 | 2.33 | 800 | 0.9672 | 34.7204 | 18.0945 | 31.5299 | 32.9849 | 17.6341 |
| 0.9689 | 2.92 | 1000 | 0.9509 | 35.7006 | 19.2988 | 32.4312 | 33.8706 | 17.8081 |
| 0.9279 | 3.5 | 1200 | 0.9432 | 35.5086 | 19.1375 | 32.3084 | 33.7471 | 17.9298 |
| 0.9187 | 4.08 | 1400 | 0.9414 | 35.591 | 19.3273 | 32.4831 | 33.914 | 17.7133 |
| 0.8865 | 4.66 | 1600 | 0.9323 | 36.3522 | 19.5982 | 33.0495 | 34.4791 | 17.7927 |
| 0.8735 | 5.25 | 1800 | 0.9311 | 35.7889 | 18.75 | 32.3179 | 33.9012 | 17.8027 |
| 0.8556 | 5.83 | 2000 | 0.9284 | 36.1266 | 19.5539 | 32.7835 | 34.263 | 17.7171 |
| 0.8479 | 6.41 | 2200 | 0.9277 | 36.21 | 19.5396 | 32.8933 | 34.3317 | 17.8339 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Zekunli/flan-t5-large-nlg-multiwoz2.0_800
|
Zekunli
| 2023-02-25T02:20:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-25T00:58:07Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-large-nlg-multiwoz2.0_800
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-large-nlg-multiwoz2.0_800
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9178
- Rouge1: 36.3013
- Rouge2: 19.7789
- Rougel: 33.0604
- Rougelsum: 34.5306
- Gen Len: 17.5889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4671 | 0.3 | 200 | 1.1001 | 32.2008 | 15.6109 | 29.2636 | 30.6894 | 17.5463 |
| 1.1704 | 0.6 | 400 | 1.0132 | 35.1237 | 19.2608 | 32.2073 | 33.6041 | 17.4995 |
| 1.1034 | 0.89 | 600 | 0.9733 | 34.8097 | 17.7552 | 31.3877 | 32.8936 | 18.0388 |
| 1.0207 | 1.19 | 800 | 0.9544 | 34.4615 | 17.7876 | 31.2393 | 32.6945 | 17.8274 |
| 0.9856 | 1.49 | 1000 | 0.9372 | 35.4821 | 19.5844 | 32.4901 | 33.8523 | 17.4778 |
| 0.9826 | 1.79 | 1200 | 0.9236 | 35.2746 | 18.6897 | 32.0828 | 33.6526 | 17.5149 |
| 0.9473 | 2.09 | 1400 | 0.9178 | 36.3013 | 19.7789 | 33.0604 | 34.5306 | 17.5889 |
| 0.9183 | 2.38 | 1600 | 0.9097 | 35.9042 | 19.0983 | 32.4102 | 34.0221 | 17.4669 |
| 0.9314 | 2.68 | 1800 | 0.9011 | 35.7411 | 19.3554 | 32.4951 | 33.9165 | 17.2751 |
| 0.9137 | 2.98 | 2000 | 0.8966 | 35.5147 | 18.593 | 32.1424 | 33.7225 | 17.6211 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
toastynews/electra-hongkongese-base-discriminator
|
toastynews
| 2023-02-25T02:02:20Z | 14 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"yue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: yue
license: apache-2.0
metrics:
- DRCD
- openrice-senti
- lihkg-cat
- wordshk-sem
---
# ELECTRA Hongkongese Base
## Model description
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
## Intended uses & limitations
This model is an alternative to Chinese models. It may offer better performance for tasks catering to the langauge usage of Hong Kongers. Yue Wikipedia is used which is much smaller than Chinese Wikipedia; this model will lack the breath of knowledge compared to other Chinese models.
#### How to use
This is the base model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.
#### Limitations and bias
The training data consists of mostly news articles and blogs. There is probably a bias towards formal language usage.
## Training data
The following is the list of data sources. Total characters is about 507M.
| Data | % |
| ------------------------------------------------- | --: |
| News Articles / Blogs | 58% |
| Yue Wikipedia / EVCHK | 18% |
| Restaurant Reviews | 12% |
| Forum Threads | 12% |
| Online Fiction | 1% |
The following is the distribution of different languages within the corpus.
| Language | % |
| ------------------------------------------------- | --: |
| Standard Chinese | 62% |
| Hongkongese | 30% |
| English | 8% |
## Training procedure
Model was trained on a single TPUv3 from the official repo with the default parameters.
| Parameter | Value |
| ------------------------------------------------ | ----: |
| Batch Size | 256 |
| Max Sequence Size | 512 |
| Vocab Size | 30000 |
*Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC)*
## Eval results
Average evaluation task results over 10 runs. Comparison using the original repo model and code. Chinese models are available from [Joint Laboratory of HIT and iFLYTEK Research (HFL)](https://huggingface.co/hfl)
| Model | DRCD (EM/F1) | openrice-senti | lihkg-cat | wordshk-sem |
|:-----------:|:------------:|:--------------:|:---------:|:-----------:|
| Chinese | 86.6 / 91.7 | 79.1 | 67.4 | 88.1 |
| Hongkongese | 83.0 / 89.6 | 81.5 | 70.0 | 90.1 |
|
lineups-io/autotrain-multifamily-3716799077
|
lineups-io
| 2023-02-25T01:31:23Z | 37 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"autotrain",
"vision",
"dataset:lineups-io/autotrain-data-multifamily",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-02-25T00:51:34Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- lineups-io/autotrain-data-multifamily
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 4.633385512099204
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3716799077
- CO2 Emissions (in grams): 4.6334
## Validation Metrics
- Loss: 0.777
- Accuracy: 0.762
- Macro F1: 0.740
- Micro F1: 0.762
- Weighted F1: 0.742
- Macro Precision: 0.754
- Micro Precision: 0.762
- Weighted Precision: 0.754
- Macro Recall: 0.758
- Micro Recall: 0.762
- Weighted Recall: 0.762
|
toastynews/electra-hongkongese-small-discriminator
|
toastynews
| 2023-02-25T01:25:47Z | 8 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"yue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: yue
license: apache-2.0
metrics:
- DRCD
- openrice-senti
- lihkg-cat
- wordshk-sem
---
# ELECTRA Hongkongese Small
## Model description
ELECTRA trained exclusively with data from Hong Kong. A signaficant amount of Hongkongese/Cantonese/Yue is included in the training data.
## Intended uses & limitations
This model is an alternative to Chinese models. It may offer better performance for tasks catering to the langauge usage of Hong Kongers. Yue Wikipedia is used which is much smaller than Chinese Wikipedia; this model will lack the breath of knowledge compared to other Chinese models.
#### How to use
This is the small model trained from the official repo. Further finetuning will be needed for use on downstream tasks. Other model sizes are also available.
#### Limitations and bias
The training data consists of mostly news articles and blogs. There is probably a bias towards formal language usage.
## Training data
The following is the list of data sources. Total characters is about 507M.
| Data | % |
| ------------------------------------------------- | --: |
| News Articles / Blogs | 58% |
| Yue Wikipedia / EVCHK | 18% |
| Restaurant Reviews | 12% |
| Forum Threads | 12% |
| Online Fiction | 1% |
The following is the distribution of different languages within the corpus.
| Language | % |
| ------------------------------------------------- | --: |
| Standard Chinese | 62% |
| Hongkongese | 30% |
| English | 8% |
## Training procedure
Model was trained on a single TPUv3 from the official repo with the default parameters.
| Parameter | Value |
| ------------------------------------------------ | ----: |
| Batch Size | 384 |
| Max Sequence Size | 512 |
| Generator Hidden Size | 1.0 |
| Vocab Size | 30000 |
*Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC)*
## Eval results
Average evaluation task results over 10 runs. Comparison using the original repo model and code. Chinese models are available from [Joint Laboratory of HIT and iFLYTEK Research (HFL)](https://huggingface.co/hfl)
| Model | DRCD (EM/F1) | openrice-senti | lihkg-cat | wordshk-sem |
|:-----------:|:------------:|:--------------:|:---------:|:-----------:|
| Chinese | 78.5 / 85.6 | 77.9 | 63.7 | 79.2 |
| Hongkongese | 76.7 / 84.4 | 79.0 | 62.6 | 80.0 |
|
dotunadegbite/a2c-PandaReachDense-v2
|
dotunadegbite
| 2023-02-25T01:16:41Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T01:14:18Z |
---
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: -1.24 +/- 0.65
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
...
```
|
Niklas25/my_awesome_qa_model
|
Niklas25
| 2023-02-25T00:55:33Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-25T00:37:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_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_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0715
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 37 | 4.1103 |
| No log | 2.0 | 74 | 3.9785 |
| No log | 3.0 | 111 | 3.8233 |
| No log | 4.0 | 148 | 3.7477 |
| No log | 5.0 | 185 | 3.8200 |
| No log | 6.0 | 222 | 3.8821 |
| No log | 7.0 | 259 | 3.9189 |
| No log | 8.0 | 296 | 4.0071 |
| No log | 9.0 | 333 | 4.0935 |
| No log | 10.0 | 370 | 4.0715 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
tfrance/ppo-LunarLander-v2
|
tfrance
| 2023-02-25T00:37:12Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T00:36:45Z |
---
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: 269.75 +/- 25.95
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
...
```
|
Madao-314/RL_moon_landing
|
Madao-314
| 2023-02-25T00:15:58Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T23:05:21Z |
---
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: 299.35 +/- 10.86
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
...
```
|
Iamvincent/rl_course_vizdoom_health_gathering_supreme
|
Iamvincent
| 2023-02-25T00:12:21Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T00:12:16Z |
---
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: 13.47 +/- 4.95
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 Iamvincent/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 <path.to.enjoy.module> --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 <path.to.train.module> --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.
|
dotunadegbite/a2c-AntBulletEnv-v0
|
dotunadegbite
| 2023-02-25T00:12:07Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-25T00:11:01Z |
---
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: 1458.87 +/- 166.06
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
...
```
|
aburkard/q-FrozenLake-v1-4x4-noSlippery
|
aburkard
| 2023-02-24T23:22:16Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T23:22:13Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="aburkard/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"])
```
|
Artachtron/rl_course_vizdoom_health_gathering_supreme
|
Artachtron
| 2023-02-24T23:08:41Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T23:06:23Z |
---
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: 11.55 +/- 4.42
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 Artachtron/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 <path.to.enjoy.module> --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 <path.to.train.module> --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.
|
haritzpuerto/bert-base-uncased-pf-squad
|
haritzpuerto
| 2023-02-24T22:31:20Z | 5 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"question-answering",
"bert",
"adapterhub:qa/squad1",
"en",
"dataset:squad",
"arxiv:2104.08247",
"region:us"
] |
question-answering
| 2023-02-24T22:28:39Z |
---
tags:
- question-answering
- bert
- adapterhub:qa/squad1
- adapter-transformers
datasets:
- squad
language:
- en
---
# Adapter `AdapterHub/bert-base-uncased-pf-squad` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/squad1](https://adapterhub.ml/explore/qa/squad1/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-squad", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
```
|
jonathanfernandes/vit-base-patch16-224-finetuned-flower
|
jonathanfernandes
| 2023-02-24T21:56:59Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-12-19T20:29:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
kinkpunk/rl-doom-health-gathering-supreme
|
kinkpunk
| 2023-02-24T21:55:08Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T21:52:40Z |
---
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: 8.45 +/- 3.21
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 kinkpunk/rl-doom-health-gathering-supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl-doom-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 <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl-doom-health-gathering-supreme --restart_behavior=resume --train_for_env_steps=5000000
```
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.
|
ndhieunguyen/rl-doom-health-gathering-supreme
|
ndhieunguyen
| 2023-02-24T21:55:08Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-04-26T15:34:50Z |
---
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: 8.45 +/- 3.21
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 kinkpunk/rl-doom-health-gathering-supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl-doom-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 <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl-doom-health-gathering-supreme --restart_behavior=resume --train_for_env_steps=5000000
```
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.
|
YisusLn/ppo-LunarLander-v2
|
YisusLn
| 2023-02-24T21:20:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T21:18:11Z |
---
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: 268.85 +/- 10.17
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
...
```
|
ngtoanrob/vien-translation
|
ngtoanrob
| 2023-02-24T20:37:46Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"vi",
"en",
"dataset:ngtoanrob/vi-en-v1-dataset",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-02-12T16:19:33Z |
---
language:
- vi
- en
datasets:
- ngtoanrob/vi-en-v1-dataset
tags:
- translation
widget:
- text: 'vi: Anh yêu em nhiều lắm'
license: openrail
metrics:
- bleu
---
# EnViT5 Translation
[](https://paperswithcode.com/sota/machine-translation-on-iwslt2015-english-1?p=mtet-multi-domain-translation-for-english)
[](https://paperswithcode.com/sota/on-phomt?p=mtet-multi-domain-translation-for-english-and)
State-of-the-art English-Vietnamese and Vietnamese-English Translation models trained on [MTet](https://research.vietai.org/mtet/), [PhoMT](https://github.com/VinAIResearch/PhoMT).
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "ngtoanrob/vien-translation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
inputs = [
"vi: VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam.",
"vi: Theo báo cáo mới nhất của Linkedin về danh sách việc làm triển vọng với mức lương hấp dẫn năm 2020, các chức danh công việc liên quan đến AI như Chuyên gia AI (Artificial Intelligence Specialist), Kỹ sư ML (Machine Learning Engineer) đều xếp thứ hạng cao.",
"en: Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.",
"en: We're on a journey to advance and democratize artificial intelligence through open source and open science."
]
outputs = model.generate(tokenizer(inputs, return_tensors="pt", padding=True).input_ids.to('cuda'), max_length=512)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ['en: VietAI is a non-profit organization with the mission of nurturing artificial intelligence talents and building an international - class community of artificial intelligence experts in Vietnam.',
# 'en: According to the latest LinkedIn report on the 2020 list of attractive and promising jobs, AI - related job titles such as AI Specialist, ML Engineer and ML Engineer all rank high.',
# 'vi: Nhóm chúng tôi khao khát tạo ra những khám phá có ảnh hưởng đến mọi người, và cốt lõi trong cách tiếp cận của chúng tôi là chia sẻ nghiên cứu và công cụ để thúc đẩy sự tiến bộ trong lĩnh vực này.',
# 'vi: Chúng ta đang trên hành trình tiến bộ và dân chủ hoá trí tuệ nhân tạo thông qua mã nguồn mở và khoa học mở.']
```
## Results
## Citation
```
@misc{https://doi.org/10.48550/arxiv.2210.05610,
doi = {10.48550/ARXIV.2210.05610},
author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang},
title = {MTet: Multi-domain Translation for English and Vietnamese},
publisher = {arXiv},
year = {2022},
}
```
|
pryjuli/Reinforce-Cartpole-v1
|
pryjuli
| 2023-02-24T20:36:15Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T20:36:06Z |
---
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
|
KoRiF/poca-SoccerTwos
|
KoRiF
| 2023-02-24T20:08:19Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-24T20:08:05Z |
---
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: KoRiF/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DrishtiSharma/PPO-Huggy-20-Epochs-0.0003-lr
|
DrishtiSharma
| 2023-02-24T19:53:53Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-02-24T18:33:30Z |
---
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: DrishtiSharma/PPO-Huggy-20-Epochs-0.0003-lr
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
espnet/wanchichen_fleurs_asr_conformer_hier_lid_utt
|
espnet
| 2023-02-24T18:56:25Z | 6 | 2 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:google/fleurs",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-11-08T00:54:47Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- google/fleurs
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/wanchichen_fleurs_asr_conformer_hier_lid_utt`
This model was trained by William Chen using the fleurs recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/fleurs/asr1
./run.sh
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sat Oct 22 17:36:51 CDT 2022`
- python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]`
- espnet version: `espnet 202207`
- pytorch version: `pytorch 1.12.1+cu116`
- Git hash: `14fcb2d42b2609f766ffaa7a79e9c921cd8398d9`
- Commit date: `Tue Sep 27 20:02:22 2022 +0000`
## asr_train_asr_conformer_lid_utt_scctc_raw_all_bpe6500_train_data_path_and_name_and_typedumprawtrain_all_splid,lid,text_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|610500|72.9|24.4|2.7|3.1|30.2|95.5|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|1592160|72.2|25.0|2.9|3.6|31.5|96.6|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|3988181|92.6|4.7|2.6|2.2|9.6|95.5|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|10235271|92.5|4.7|2.8|2.6|10.1|96.7|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|3547834|91.4|5.8|2.8|2.5|11.0|95.4|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|9622352|91.6|5.6|2.8|2.8|11.2|96.6|
|
4eJIoBek/stable-diffusion-v1-4-openvino-fp32
|
4eJIoBek
| 2023-02-24T18:36:39Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-24T17:27:47Z |
---
license: creativeml-openrail-m
---
|
CloXD/dqn-SpaceInvadersNoFrameskip-v4
|
CloXD
| 2023-02-24T18:36:18Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T18:35:52Z |
---
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: 965.00 +/- 409.30
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 CloXD -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 CloXD -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 CloXD
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
RohanDani2/ppo-Huggy
|
RohanDani2
| 2023-02-24T18:01:49Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-02-24T18:01:42Z |
---
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: RohanDani2/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ThatGuyVanquish/kook-model-output-dir
|
ThatGuyVanquish
| 2023-02-24T18:01:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:NLP-MINI-PROJECT/rabbi_kook",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-24T08:07:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- NLP-MINI-PROJECT/rabbi_kook
metrics:
- rouge
model-index:
- name: kook-model-output-dir
results:
- task:
name: Summarization
type: summarization
dataset:
name: NLP-MINI-PROJECT/rabbi_kook
type: NLP-MINI-PROJECT/rabbi_kook
metrics:
- name: Rouge1
type: rouge
value: 0.0
---
<!-- 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. -->
# kook-model-output-dir
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the NLP-MINI-PROJECT/rabbi_kook dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2296
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.11.0
|
spacemanidol/flan-t5-base-6-1-cnndm
|
spacemanidol
| 2023-02-24T17:44:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-24T16:58:52Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: base-6-1-t
results:
- task:
name: Summarization
type: summarization
dataset:
name: cnn_dailymail 3.0.0
type: cnn_dailymail
config: 3.0.0
split: validation
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 36.7469
---
<!-- 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. -->
# base-6-1-t
This model is a fine-tuned version of [asy/cnndm/base-6-1/](https://huggingface.co/asy/cnndm/base-6-1/) on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9488
- Rouge1: 36.7469
- Rouge2: 16.4835
- Rougel: 27.609
- Rougelsum: 34.2224
- Gen Len: 67.6735
## 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: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
huggingtweets/brentai__-goodtimes2-jagxofficial
|
huggingtweets
| 2023-02-24T17:36:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-24T17:34:22Z |
---
language: en
thumbnail: http://www.huggingtweets.com/brentai__-goodtimes2-jagxofficial/1677260190068/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1262566007257862145/BL5FccA6_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1573540827221090310/0INndCsI_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/533372161788874752/KeW9HtZI_400x400.jpeg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">BrentAI & brnt & Brent Fortner</div>
<div style="text-align: center; font-size: 14px;">@brentai__-goodtimes2-jagxofficial</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from BrentAI & brnt & Brent Fortner.
| Data | BrentAI | brnt | Brent Fortner |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3128 | 297 |
| Retweets | 0 | 510 | 74 |
| Short tweets | 79 | 466 | 22 |
| Tweets kept | 3171 | 2152 | 201 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rzazb0ak/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @brentai__-goodtimes2-jagxofficial's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wykkf4my) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wykkf4my/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/brentai__-goodtimes2-jagxofficial')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
robotman0/rl_course_vizdoom_health_gathering_supreme
|
robotman0
| 2023-02-24T17:26:14Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T17:26:03Z |
---
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: 9.39 +/- 3.29
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 robotman0/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 <path.to.enjoy.module> --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 <path.to.train.module> --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.
|
bsmith0430/Reinforce-cart
|
bsmith0430
| 2023-02-24T17:25:30Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-24T17:25:19Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cart
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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