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list | pipeline_tag
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|---|---|---|---|---|---|---|
BigSalmon/GPTHeHe | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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} | 8 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/perfectguide_-the_lostchapter-wise_chimp/1673112533435/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/1506931632808370182/YwhLOt2n_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/1346622430207422464/NDRYNAaz_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/1346208413596921864/fGYV6EpP_400x400.jpg')">
</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">Deep thoughts & Perfect Guidance 🧭 & Wise Chimp</div>
<div style="text-align: center; font-size: 14px;">@perfectguide_-the_lostchapter-wise_chimp</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 Deep thoughts & Perfect Guidance 🧭 & Wise Chimp.
| Data | Deep thoughts | Perfect Guidance 🧭 | Wise Chimp |
| --- | --- | --- | --- |
| Tweets downloaded | 367 | 3247 | 3227 |
| Retweets | 1 | 8 | 23 |
| Short tweets | 1 | 216 | 46 |
| Tweets kept | 365 | 3023 | 3158 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3qra8517/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 @perfectguide_-the_lostchapter-wise_chimp's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1bi49m21) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1bi49m21/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/perfectguide_-the_lostchapter-wise_chimp')
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)
|
BigSalmon/GPTNeo350MInformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
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"GPTNeoForCausalLM"
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} | 8 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 568.00 +/- 148.09
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 malamasn -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 malamasn -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 malamasn
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/InformalToFormalLincoln19 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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} | 11 | null | Pretrained - squad2
Finetuned with no "no answer observations" - epoch-7 |
BigSalmon/InformalToFormalLincoln25 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
}
} | 10 | null | ---
tags:
- conversational
---
#Mental Health Support Chatbot |
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 5 | 2023-01-07T18:29:56Z | ---
license: mit
---
### Adolf Hitler on Stable Diffusion
This is the `<Adolf-Hitler>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
BigSalmon/Robertsy | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 4 | null | ---
language:
- en
license: apache-2.0
library_name: timm
tags:
- mobile
- vison
- image-classification
datasets:
- imagenet-1k
metrics:
- accuracy
---
# EfficientFormer-L1
## Table of Contents
- [EfficientFormer-L1](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Misuse and Out-of-scope Use](#misuse-and-out-of-scope-use)
- [Limitations and Biases](#limitations-and-biases)
- [Training](#training)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Evaluation Results](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Citation Information](#citation-information)
<model_details>
## Model Details
EfficientFormer-L3, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance.
This checkpoint of EfficientFormer-L3 was trained for 300 epochs.
- Developed by: Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren
- Language(s): English
- License: This model is licensed under the apache-2.0 license
- Resources for more information:
- [Research Paper](https://arxiv.org/abs/2206.01191)
- [GitHub Repo](https://github.com/snap-research/EfficientFormer/)
</model_details>
<how_to_start>
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import requests
import torch
from PIL import Image
from transformers import EfficientFormerImageProcessor, EfficientFormerForImageClassificationWithTeacher
# Load a COCO image of two cats to test the model
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# Load preprocessor and pretrained model
model_name = "huggingface/efficientformer-l3-300"
processor = EfficientFormerImageProcessor.from_pretrained(model_name)
model = EfficientFormerForImageClassificationWithTeacher.from_pretrained(model_name)
# Preprocess input image
inputs = processor(images=image, return_tensors="pt")
# Inference
with torch.no_grad():
outputs = model(**inputs)
# Print the top ImageNet1k class prediction
logits = outputs.logits
scores = torch.nn.functional.softmax(logits, dim=1)
top_pred_class = torch.argmax(scores, dim=1)
print(f"Predicted class: {top_pred_class}")
```
</how_to_start>
<uses>
## Uses
#### Direct Use
This model can be used for image classification and semantic segmentation. On mobile devices (the model was tested on iPhone 12), the CoreML checkpoints will perform these tasks with low latency.
<Limitations_and_Biases>
## Limitations and Biases
Though most designs in EfficientFormer are general-purposed, e.g., dimension- consistent design and 4D block with CONV-BN fusion, the actual speed of EfficientFormer may vary on other platforms. For instance, if GeLU is not well supported while HardSwish is efficiently implemented on specific hardware and compiler, the operator may need to be modified accordingly. The proposed latency-driven slimming is simple and fast. However, better results may be achieved if search cost is not a concern and an enumeration-based brute search is performed.
Since the model was trained on Imagenet-1K, the [biases embedded in that dataset](https://huggingface.co/datasets/imagenet-1k#considerations-for-using-the-data) will be reflected in the EfficientFormer models.
</Limitations_and_Biases>
<Training>
## Training
#### Training Data
This model was trained on ImageNet-1K.
See the [data card](https://huggingface.co/datasets/imagenet-1k) for additional information.
#### Training Procedure
* Parameters: 31.4 M
* Train. Epochs: 300
Trained on a cluster with NVIDIA A100 and V100 GPUs.
</Training>
<Eval_Results>
## Evaluation Results
Top-1 Accuracy: 82.4% on ImageNet 10K
Latency: 3.0ms
</Eval_Results>
<Cite>
## Citation Information
```bibtex
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
journal={arXiv preprint arXiv:2206.01191},
year={2022}
}
```
</Cite> |
BigSalmon/SimplifyText | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 17 | null | # One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022)
#### [Paper](https://arxiv.org/pdf/2112.02749.pdf) | [Demo](https://www.youtube.com/watch?v=HHj-XCXXePY)
#### Requirements
- Python >= 3.6 , Pytorch >= 1.8 and ffmpeg
- Set up [OpenFace](https://github.com/TadasBaltrusaitis/OpenFace)
- We use the OpenFace tools to extract the initial pose of the reference image
- Make sure you have installed this tool, and set the `OPENFACE_POSE_EXTRACTOR_PATH` in `config.py`. For example, it should be the absolute path of the "`FeatureExtraction.exe`" for Windows.
- Other requirements are listed in the 'requirements.txt'
#### Pretrained Checkpoint
Please download the pretrained checkpoint from [google-drive](https://drive.google.com/file/d/1mjFEozPR_2vMaVRMd9Agk_sU1VaiUYMl/view?usp=sharing) and unzip it to the directory (`/checkpoints`). Or manually modify the settings of `GENERATOR_CKPT` and `AUDIO2POSE_CKPT` in the `config.py`.
#### Extract phoneme
We employ the [CMU phoneset](https://github.com/cmusphinx/cmudict) to represent phonemes, the extra 'SIL' means silence. All the phonesets can be seen in '`phindex.json`'.
We have extracted the phonemes for the audios in the '`sample/audio`' directory. For other audios, you can extract the phonemes by other ASR tools and then map them to the CMU phoneset. Or email to [email protected] for help.
#### Generate Demo Results
```
python test_script.py --img_path xxx.jpg --audio_path xxx.wav --phoneme_path xxx.json --save_dir "YOUR_DIR"
```
Note that the input images must keep the same height and width and the face should be appropriately cropped as in `samples/imgs`. You can also preprocess your images with `image_preprocess.py`.
#### License and Citation
```
@InProceedings{wang2021one,
author = Suzhen Wang, Lincheng Li, Yu Ding, Xin Yu
title = {One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning},
booktitle = {AAAI 2022},
year = {2022},
}
```
#### Acknowledgement
This codebase is based on [First Order Motion Model](https://github.com/AliaksandrSiarohin/first-order-model) and [imaginaire](https://github.com/NVlabs/imaginaire), thanks for their contributions.
|
BigTooth/DialoGPT-small-tohru | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 10 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pendulum1
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
|
BigeS/DialoGPT-small-Rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 10 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 464.70 +/- 105.90
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Bilz/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 274.50 +/- 31.50
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 Hatman -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 Hatman -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 Hatman
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 10000),
('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', 0.01),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Binbin/test | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: sdcid
---
###
Sample pictures of:
sdcid (use that on your prompt)
|
Blabla/Pipipopo | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.91
- name: F1
type: f1
value: 0.9105367793240556
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2623
- Accuracy: 0.91
- F1: 0.9105
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Blaine-Mason/hackMIT-finetuned-sst2 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
]
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} | 36 | null | ---
tags:
- conversational
---
#Mental Health Support Chatbot |
BlightZz/MakiseKurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-base-patch16-224-in21k_covid_19_ct_scans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.94
- name: F1
type: f1
value: 0.9379310344827586
- name: Recall
type: recall
value: 0.8947368421052632
- name: Precision
type: precision
value: 0.9855072463768116
language:
- en
pipeline_tag: image-classification
---
# vit-base-patch16-224-in21k_covid_19_ct_scans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1727
- Accuracy: 0.94
- F1: 0.9379
- Recall: 0.8947
- Precision: 0.9855
## Model description
This is a binary classification model to distinguish between CT scans that detect COVID-19 and those who do not.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Binary%20Classification/COVID19%20Lung%20CT%20Scans/COVID19_Lung_CT_Scans_ViT.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/luisblanche/covidct
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6742 | 1.0 | 38 | 0.4309 | 0.9 | 0.8993 | 0.8816 | 0.9178 |
| 0.6742 | 2.0 | 76 | 0.3739 | 0.8467 | 0.8686 | 1.0 | 0.7677 |
| 0.6742 | 3.0 | 114 | 0.1727 | 0.94 | 0.9379 | 0.8947 | 0.9855 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.12.1 |
BobBraico/bert-finetuned-ner | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
datasets: Arch4ngel/untitled_goose_game
widget:
- text: untitled_goose goose on an airplane
---
# DreamBooth model for the untitled_goose concept trained by Arch4ngel on the Arch4ngel/untitled_goose_game dataset.
This is a Stable Diffusion model fine-tuned on the untitled_goose concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of untitled_goose goose**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
Stable Diffusion model fine-tuned for generating Goose from Untitled Goose Game images.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Arch4ngel/untitled_goose-goose')
image = pipeline().images[0]
image
```
|
BobBraico/distilbert-base-uncased-finetuned-imdb | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.83 +/- 16.79
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
...
```
|
BogdanKuloren/continual-learning-paper-embeddings-model | [
"pytorch",
"mpnet",
"feature-extraction",
"transformers"
]
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} | 11 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 54.90 +/- 38.87
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Boondong/Wandee | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6701497735980495
---
<!-- 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-eurosat
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8561
- Accuracy: 0.6701
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1531 | 1.0 | 202 | 0.9684 | 0.6329 |
| 1.0658 | 2.0 | 404 | 0.8881 | 0.6667 |
| 1.0049 | 3.0 | 606 | 0.8561 | 0.6701 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2 | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 261.81 +/- 18.32
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
...
```
|
BossLee/t5-gec | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"prefix": "translate English to Romanian: "
}
}
} | 6 | null | PocketSphinx 5.0.0
==================
This is PocketSphinx, one of Carnegie Mellon University's open source large
vocabulary, speaker-independent continuous speech recognition engines.
Although this was at one point a research system, active development
has largely ceased and it has become very, very far from the state of
the art. I am making a release, because people are nonetheless using
it, and there are a number of historical errors in the build system
and API which needed to be corrected.
The version number is strangely large because there was a "release"
that people are using called 5prealpha, and we will use proper
[semantic versioning](https://semver.org/) from now on.
**Please see the LICENSE file for terms of use.**
Installation
------------
We now use CMake for building, which should give reasonable results
across Linux and Windows. Not certain about Mac OS X because I don't
have one of those. In addition, the audio library, which never really
built or worked correctly on any platform at all, has simply been
removed.
There is no longer any dependency on SphinxBase. There is no
SphinxBase anymore. This is not the SphinxBase you're looking for.
All your SphinxBase are belong to us.
To install the Python module in a virtual environment (replace
`~/ve_pocketsphinx` with the virtual environment you wish to create),
from the top level directory:
```
python3 -m venv ~/ve_pocketsphinx
. ~/ve_pocketsphinx/bin/activate
pip install .
```
To install the C library and bindings (assuming you have access to
/usr/local - if not, use `-DCMAKE_INSTALL_PREFIX` to set a different
prefix in the first `cmake` command below):
```
cmake -S . -B build
cmake --build build
cmake --build build --target install
```
Usage
-----
The `pocketsphinx` command-line program reads single-channel 16-bit
PCM audio from standard input or one or more files, and attemps to
recognize speech in it using the default acoustic and language model.
It accepts a large number of options which you probably don't care
about, a *command* which defaults to `live`, and one or more inputs
(except in `align` mode), or `-` to read from standard input.
If you have a single-channel WAV file called "speech.wav" and you want
to recognize speech in it, you can try doing this (the results may not
be wonderful):
pocketsphinx single speech.wav
If your input is in some other format I suggest converting it with
`sox` as described below.
The commands are as follows:
- `help`: Print a long list of those options you don't care about.
- `config`: Dump configuration as JSON to standard output (can be
loaded with the `-config` option).
- `live`: Detect speech segments in each input, run recognition
on them (using those options you don't care about), and write the
results to standard output in line-delimited JSON. I realize this
isn't the prettiest format, but it sure beats XML. Each line
contains a JSON object with these fields, which have short names
to make the lines more readable:
- `b`: Start time in seconds, from the beginning of the stream
- `d`: Duration in seconds
- `p`: Estimated probability of the recognition result, i.e. a
number between 0 and 1 representing the likelihood of the input
according to the model
- `t`: Full text of recognition result
- `w`: List of segments (usually words), each of which in turn
contains the `b`, `d`, `p`, and `t` fields, for start, end,
probability, and the text of the word. If `-phone_align yes`
has been passed, then a `w` field will be present containing
phone segmentations, in the same format.
- `single`: Recognize each input as a single utterance, and write a
JSON object in the same format described above.
- `align`: Align a single input file (or `-` for standard input) to
a word sequence, and write a JSON object in the same format
described above. The first positional argument is the input, and
all subsequent ones are concatenated to make the text, to avoid
surprises if you forget to quote it. You are responsible for
normalizing the text to remove punctuation, uppercase, centipedes,
etc. For example:
pocketsphinx align goforward.wav "go forward ten meters"
By default, only word-level alignment is done. To get phone
alignments, pass `-phone_align yes` in the flags, e.g.:
pocketsphinx -phone_align yes align audio.wav $text
This will make not particularly readable output, but you can use
[jq](https://stedolan.github.io/jq/) to clean it up. For example,
you can get just the word names and start times like this:
pocketsphinx align audio.wav $text | jq '.w[]|[.t,.b]'
Or you could get the phone names and durations like this:
pocketsphinx -phone_align yes align audio.wav $text | jq '.w[]|.w[]|[.t,.d]'
There are many, many other possibilities, of course.
- `soxflags`: Return arguments to `sox` which will create the
appropriate input format. Note that because the `sox`
command-line is slightly quirky these must always come *after* the
filename or `-d` (which tells `sox` to read from the microphone).
You can run live recognition like this:
sox -d $(pocketsphinx soxflags) | pocketsphinx -
or decode from a file named "audio.mp3" like this:
sox audio.mp3 $(pocketsphinx soxflags) | pocketsphinx -
By default only errors are printed to standard error, but if you want
more information you can pass `-loglevel INFO`. Partial results are
not printed, maybe they will be in the future, but don't hold your
breath.
Programming
-----------
For programming, see the [examples directory](./examples/) for a
number of examples of using the library from C and Python. You can
also read the [documentation for the Python
API](https://pocketsphinx.readthedocs.io) or [the C
API](https://cmusphinx.github.io/doc/pocketsphinx/)
Authors
-------
PocketSphinx is ultimately based on `Sphinx-II` which in turn was
based on some older systems at Carnegie Mellon University, which were
released as free software under a BSD-like license thanks to the
efforts of Kevin Lenzo. Much of the decoder in particular was written
by Ravishankar Mosur (look for "rkm" in the comments), but various
other people contributed as well, see [the AUTHORS file](./AUTHORS)
for more details.
David Huggins-Daines (the author of this document) is
guilty^H^H^H^H^Hresponsible for creating `PocketSphinx` which added
various speed and memory optimizations, fixed-point computation, JSGF
support, portability to various platforms, and a somewhat coherent
API. He then disappeared for a while.
Nickolay Shmyrev took over maintenance for quite a long time
afterwards, and a lot of code was contributed by Alexander Solovets,
Vyacheslav Klimkov, and others.
Currently this is maintained by David Huggins-Daines again.
|
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: libri-alpha-0.75-Temp-1-attention-3-layers-distil-with-6-layers-take-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. -->
# libri-alpha-0.75-Temp-1-attention-3-layers-distil-with-6-layers-take-3
This model is a fine-tuned version of [rohitp1/libri-alpha-0.75-Temp-1-attention-3-layers-distil-with-6-layers-take-2](https://huggingface.co/rohitp1/libri-alpha-0.75-Temp-1-attention-3-layers-distil-with-6-layers-take-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 236.1198
- Wer: 0.2607
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 388.1305 | 0.45 | 200 | 228.0258 | 0.2599 |
| 376.7096 | 0.9 | 400 | 226.8922 | 0.2566 |
| 384.1615 | 1.35 | 600 | 228.5904 | 0.2571 |
| 373.8909 | 1.79 | 800 | 229.0286 | 0.2563 |
| 385.2149 | 2.24 | 1000 | 230.8802 | 0.2575 |
| 384.5473 | 2.69 | 1200 | 230.1264 | 0.2563 |
| 383.9426 | 3.14 | 1400 | 232.5964 | 0.2569 |
| 385.9253 | 3.59 | 1600 | 237.4036 | 0.2599 |
| 396.9868 | 4.04 | 1800 | 236.1198 | 0.2607 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BotterHax/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: KubiakJakub01/finetuned-distilbert-base-augumented
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# KubiakJakub01/finetuned-distilbert-base-augumented
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4522
- Validation Loss: 0.4260
- Train Accuracy: 0.8129
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 470, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4522 | 0.4260 | 0.8129 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.10.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Branex/gpt-neo-2.7B | []
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} | 0 | null | ---
tags:
- IceHockey-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: IceHockey-v5
type: IceHockey-v5
metrics:
- type: mean_reward
value: 2.40 +/- 4.29
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **IceHockey-v5**
This is a trained model of a PPO agent playing IceHockey-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id IceHockey-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/IceHockey-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/IceHockey-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/IceHockey-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id IceHockey-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'IceHockey-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
BrianTin/MTBERT | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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"BertForMaskedLM"
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} | 11 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### {INSTANCE_NAME} Dreambooth model trained by asp2131 with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
Broadus20/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 9 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 690.50 +/- 215.17
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 jrauch4 -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 jrauch4 -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 jrauch4
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### SD-Cresencio Dreambooth model trained by cresencio with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
BrunoNogueira/DialoGPT-kungfupanda | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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}
} | 10 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-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
|
Brunomezenga/NN | []
| null | {
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.38 +/- 19.29
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
...
```
|
Bryan190/Aguy190 | []
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}
}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 214.38 +/- 19.75
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).
...
```
|
Brykee/BrykeeBot | []
| null | {
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}
} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 439.30 +/- 69.72
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
|
Brykee/DialoGPT-medium-Morty | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 10 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 567.50 +/- 197.40
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga EduardoCGarridoMerchan -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 EduardoCGarridoMerchan -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 EduardoCGarridoMerchan
```
## 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', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Bryson575x/riceboi | []
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}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6879136189481017
---
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8504
- Accuracy: 0.6879
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1617 | 1.0 | 202 | 1.0081 | 0.6270 |
| 1.0604 | 2.0 | 404 | 0.9516 | 0.6524 |
| 0.998 | 3.0 | 606 | 0.8857 | 0.6809 |
| 0.9971 | 4.0 | 808 | 0.8504 | 0.6879 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
CasualHomie/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2203
- Accuracy: 0.923
- F1: 0.9231
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8194 | 1.0 | 250 | 0.3166 | 0.904 | 0.8993 |
| 0.2477 | 2.0 | 500 | 0.2203 | 0.923 | 0.9231 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
dccuchile/albert-xlarge-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
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}
} | 3 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/albert-xlarge-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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}
} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-wikitext
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. -->
# small-mlm-wikitext
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.077 | 0.4 | 500 | 2.9034 |
| 2.9927 | 0.8 | 1000 | 2.9247 |
| 2.9484 | 1.2 | 1500 | nan |
| 2.9264 | 1.6 | 2000 | 2.8945 |
| 2.9185 | 2.0 | 2500 | 2.8874 |
| 2.855 | 2.4 | 3000 | 2.9401 |
| 2.8632 | 2.8 | 3500 | 2.9649 |
| 2.8067 | 3.2 | 4000 | nan |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
dccuchile/albert-xxlarge-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
} | 3 | null | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum-6
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4110
## 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: 6
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8456 | 0.2 | 500 | 1.5299 |
| 1.6587 | 0.41 | 1000 | 1.4547 |
| 1.6435 | 0.61 | 1500 | 1.4243 |
| 1.7041 | 0.81 | 2000 | 1.4110 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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} | 7 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 49.90 +/- 41.86
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/albert-large-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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}
} | 75 | 2023-01-08T02:58:12Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### SD-1-Rommel Dreambooth model trained by romzhey with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
dccuchile/albert-xxlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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} | 42 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### mpid-hassanblend-v1-5-main-hard800 Dreambooth model trained by tftgregrge with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-ner | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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} | 81 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 36.40 +/- 25.78
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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} | 25 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: danmorris427/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 1 | null | CDC model trained with 600 detail-rich anime images for anime image Super Resolution.
[CDC repo](https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution/)
| name | info |
|:-------------------------:|:----------------------------------:|
| HGSR-MHR-anime_X4_280.pth | Bicubic interpolation downsampling |
| HGSR-MHR-anime-aug_X4_320.pth | Random interpolation downsampling with argumentations | |
Chaddmckay/Cdm | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 250.86 +/- 23.05
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
...
```
|
ChoboAvenger/DialoGPT-small-joshua | []
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} | 0 | 2023-01-08T04:30:31Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### SD-ROSE-BLACKPINK Dreambooth model trained by jennysun with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Try doing: rosebp intricate character portrait, intricate, beautiful, 8k resolution, dynamic lighting, hyperdetailed, quality 3D rendered, volumetric lighting, greg rutkowski, detailed background, artstation character portrait, dnd character portrait
Sample pictures of this concept:
.png)
.jpeg)
.jpeg)
.jpeg)
.png)
.webp)
.jpeg)
.webp)
.jpeg)
.jpeg)
|
ChrisVCB/DialoGPT-medium-cmjs | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 7 | 2023-01-08T04:42:30Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### SD-1-5-Ram Dreambooth model trained by RamAnanth1 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
ChrisVCB/DialoGPT-medium-ej | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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} | 13 | 2023-01-08T04:45:08Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: jrtec-gpt2-superheroes-name-generator
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. -->
# jrtec-gpt2-superheroes-name-generator
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 16.5324
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3728 | 3.45 | 500 | 14.0617 |
| 2.0865 | 6.9 | 1000 | 14.7614 |
| 1.9246 | 10.34 | 1500 | 15.6696 |
| 1.8718 | 13.79 | 2000 | 16.2823 |
| 1.8267 | 17.24 | 2500 | 16.5324 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Chungu424/repo | []
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} | 0 | 2023-01-08T05:43:22Z | ---
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: 256.33 +/- 40.76
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
...
```
|
ComCom/gpt2 | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
]
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} | 1 | 2023-01-08T07:26:32Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### thilinamethsahann Dreambooth model trained by Thilinameths with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Culmenus/checkpoint-168500-finetuned-de-to-is_nr2 | []
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}
} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="kumarkanth218/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"])
```
|
CurtisBowser/DialoGPT-medium-sora-three | []
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} | 0 | null | ---
license: apache-2.0
language:
- en
tags:
- text-to-image
metrics:
- accuracy
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
A generative model, that geneartive artistic images using stable diffusion
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [ANURAG Kr. SINGH]
- **Model type:** [TEXT-TO-IMAGE]
- **Language(s) (NLP):** [PYTHON]
- **License:** [APCAHE 2.0]
- **Finetuned from model [optional]:** [BUIDLSPACE DIFFUSERS]
|
alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
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}
} | 866 | null | ---
license: creativeml-openrail-m
---
Sample images:
<style>
img {
display: inline-block;
}
</style>
<img src="https://huggingface.co/YoungMasterFromSect/Chibi/resolve/main/1.png" width="300" height="200">
<img src="https://huggingface.co/YoungMasterFromSect/Chibi/resolve/main/2.png" width="300" height="200">
<img src="https://huggingface.co/YoungMasterFromSect/Chibi/resolve/main/3.png" width="300" height="300">
<img src="https://huggingface.co/YoungMasterFromSect/Chibi/resolve/main/4.png" width="300" height="300">
<img src="https://huggingface.co/YoungMasterFromSect/Chibi/resolve/main/5.png" width="300" height="300">
|
Darren/darren | [
"pytorch"
]
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### islamicdiffusion Dreambooth model trained by Falah
With Stable Diffusion DreamBooth, we can now create AI art generation images using our own trained images.
in this model, we can generate images with Islamic landscapes or female or male ones as popular images or just about anything you can think of
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept with the simple and easy prompts:
any prompt and add word islamidiffusion style :
"home islamicdiffusion"
<img src="https://huggingface.co/Falah/islamicdiffusion/resolve/main/00626-3710065853-Islamic%20home.png" style="max-width: 800px;" width="100%"/>
"home islamicdiffusion"
<img src ="https://huggingface.co/Falah/islamicdiffusion/resolve/main/00629-3710065856-Islamic%20home.png" style="max-width: 800px;" width="100%"/>
"Bearded old man, handsome, rugged, sadness + crying, Arabic, award-winning photography, nikon d750 islamicdiffusion"
<img src ="https://huggingface.co/Falah/islamicdiffusion/resolve/main/00722-913347333-islamicdiffusion%20%20Bearded%20old%20man%2C%20handsome%2C%20rugged%2C%20sadness%20%2B%20crying%2C%20Arabic%2C%20award-winning%20photography%2C%20nikon%20d750.png" style="max-width: 800px;" width="100%"/>
"Bearded old man, handsome, rugged, sadness + crying, Arabic, award-winning photography, nikon d750 islamicdiffusion"
<img src="https://huggingface.co/Falah/islamicdiffusion/resolve/main/00725-913347336-islamicdiffusion%20%20Bearded%20old%20man%2C%20handsome%2C%20rugged%2C%20sadness%20%2B%20crying%2C%20Arabic%2C%20award-winning%20photography%2C%20nikon%20d750.png" style="max-width: 800px;" width="100%"/>
<img src="https://huggingface.co/Falah/islamicdiffusion/resolve/main/1.png" style="max-width: 800px;" width="100%"/>
<img src="https://huggingface.co/Falah/islamicdiffusion/resolve/main/2.png" style="max-width: 800px;" width="100%"/>
<img src="https://huggingface.co/Falah/islamicdiffusion/resolve/main/3.png" style="max-width: 800px;" width="100%"/>
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "Falah/islamicdiffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = ""Bearded old man, handsome, rugged, sadness + crying, Arabic, award-winning photography, nikon d750 islamicdiffusion"
image = pipe(prompt).images[0]
image.save("./result.jpg")
```
|
Darya/layoutlmv2-finetuned-funsd-test | []
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} | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum-20
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum 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: 20
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DataikuNLP/TinyBERT_General_4L_312D | [
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"transformers"
]
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} | 74 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: lambdalabs/pokemon-blip-captions
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-pokemon
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `lambdalabs/pokemon-blip-captions` 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: 16
- eval_batch_size: 16
- 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/yuch0001/ddpm-pokemon/tensorboard?#scalars)
|
DataikuNLP/paraphrase-MiniLM-L6-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
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}
} | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: initial-dq-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. -->
# initial-dq-model
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1677
- Precision: 0.7763
- Recall: 0.9380
- F1: 0.8495
- Accuracy: 0.9423
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2251 | 1.0 | 1220 | 0.1768 | 0.7481 | 0.9264 | 0.8277 | 0.9378 |
| 0.186 | 2.0 | 2440 | 0.1677 | 0.7763 | 0.9380 | 0.8495 | 0.9423 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.10.2+cu113
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| sentence-similarity | {
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} | 1,517 | 2023-01-08T14:00:16Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
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-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` 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: 16
- eval_batch_size: 16
- 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/zrkcdd/ddpm-butterflies-128/tensorboard?#scalars)
|
DavidAMcIntosh/DialoGPT-small-rick | []
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} | 0 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-squad2-coffee20230108
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-squad2-coffee20230108
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3047
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 91 | 1.9687 |
| 2.3297 | 2.0 | 182 | 1.7777 |
| 1.5351 | 3.0 | 273 | 1.9963 |
| 0.9318 | 4.0 | 364 | 2.1472 |
| 0.6973 | 5.0 | 455 | 2.3928 |
| 0.4767 | 6.0 | 546 | 2.4984 |
| 0.3988 | 7.0 | 637 | 2.7923 |
| 0.2823 | 8.0 | 728 | 3.2482 |
| 0.1804 | 9.0 | 819 | 3.2490 |
| 0.1104 | 10.0 | 910 | 3.6360 |
| 0.0835 | 11.0 | 1001 | 3.8134 |
| 0.0835 | 12.0 | 1092 | 4.1694 |
| 0.0388 | 13.0 | 1183 | 4.2699 |
| 0.0371 | 14.0 | 1274 | 4.2752 |
| 0.0147 | 15.0 | 1365 | 4.3047 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.11.0+cu113
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DavidSpaceG/MSGIFSR | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 280.02 +/- 17.90
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
...
```
|
Davlan/bert-base-multilingual-cased-finetuned-amharic | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 109 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.93 +/- 15.23
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
...
```
|
Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 15 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: tiny-vanilla-target-glue-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-vanilla-target-glue-cola
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8745
- Matthews Correlation: 0.0651
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6124 | 1.87 | 500 | 0.6204 | 0.0 |
| 0.603 | 3.73 | 1000 | 0.6181 | 0.0 |
| 0.5927 | 5.6 | 1500 | 0.6231 | 0.0194 |
| 0.5707 | 7.46 | 2000 | 0.6366 | 0.0149 |
| 0.5399 | 9.33 | 2500 | 0.6549 | 0.0646 |
| 0.5169 | 11.19 | 3000 | 0.6769 | 0.0736 |
| 0.4954 | 13.06 | 3500 | 0.6856 | 0.0742 |
| 0.4724 | 14.93 | 4000 | 0.7246 | 0.0777 |
| 0.4552 | 16.79 | 4500 | 0.7517 | 0.0767 |
| 0.434 | 18.66 | 5000 | 0.7868 | 0.0832 |
| 0.4192 | 20.52 | 5500 | 0.8144 | 0.0664 |
| 0.4092 | 22.39 | 6000 | 0.8025 | 0.0670 |
| 0.3874 | 24.25 | 6500 | 0.8745 | 0.0651 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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}
} | 27 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 642.00 +/- 159.11
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 LinasKo -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 LinasKo -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 LinasKo
```
## 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)])
```
|
Davlan/bert-base-multilingual-cased-finetuned-luo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 11 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: 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
|
Davlan/bert-base-multilingual-cased-finetuned-naija | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 13 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: train
args: cord
metrics:
- name: Precision
type: precision
value: 0.9415247964470762
- name: Recall
type: recall
value: 0.9520958083832335
- name: F1
type: f1
value: 0.9467807964272422
- name: Accuracy
type: accuracy
value: 0.9575551782682513
---
<!-- 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. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2246
- Precision: 0.9415
- Recall: 0.9521
- F1: 0.9468
- Accuracy: 0.9576
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 1.0265 | 0.7630 | 0.8099 | 0.7858 | 0.8086 |
| 1.4021 | 3.12 | 500 | 0.5804 | 0.8290 | 0.8638 | 0.8460 | 0.8718 |
| 1.4021 | 4.69 | 750 | 0.3937 | 0.8882 | 0.9034 | 0.8957 | 0.9126 |
| 0.4062 | 6.25 | 1000 | 0.3171 | 0.9137 | 0.9274 | 0.9205 | 0.9351 |
| 0.4062 | 7.81 | 1250 | 0.2798 | 0.9332 | 0.9409 | 0.9370 | 0.9444 |
| 0.2212 | 9.38 | 1500 | 0.2558 | 0.9277 | 0.9416 | 0.9346 | 0.9461 |
| 0.2212 | 10.94 | 1750 | 0.2479 | 0.9335 | 0.9454 | 0.9394 | 0.9516 |
| 0.1525 | 12.5 | 2000 | 0.2356 | 0.9444 | 0.9536 | 0.9490 | 0.9588 |
| 0.1525 | 14.06 | 2250 | 0.2286 | 0.9365 | 0.9491 | 0.9428 | 0.9563 |
| 0.1134 | 15.62 | 2500 | 0.2246 | 0.9415 | 0.9521 | 0.9468 | 0.9576 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-wolof | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 4 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-coffee20230108
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-coffee20230108
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 89 | 4.5615 |
| 5.5192 | 2.0 | 178 | 2.8434 |
| 3.2266 | 3.0 | 267 | 2.2547 |
| 2.1833 | 4.0 | 356 | 2.3272 |
| 1.5483 | 5.0 | 445 | 2.3703 |
| 1.148 | 6.0 | 534 | 2.4088 |
| 1.0413 | 7.0 | 623 | 2.6734 |
| 0.6844 | 8.0 | 712 | 2.7058 |
| 0.5396 | 9.0 | 801 | 2.9746 |
| 0.5396 | 10.0 | 890 | 3.6085 |
| 0.3883 | 11.0 | 979 | 3.4980 |
| 0.2854 | 12.0 | 1068 | 4.0556 |
| 0.2021 | 13.0 | 1157 | 4.1024 |
| 0.1797 | 14.0 | 1246 | 4.2926 |
| 0.1425 | 15.0 | 1335 | 4.4032 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.11.0+cu113
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-yoruba | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 21 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tiny-vanilla-target-glue-mnli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-vanilla-target-glue-mnli
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8100
- Accuracy: 0.6375
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0866 | 0.04 | 500 | 1.0515 | 0.4557 |
| 1.0101 | 0.08 | 1000 | 0.9526 | 0.5612 |
| 0.9599 | 0.12 | 1500 | 0.9195 | 0.5802 |
| 0.9378 | 0.16 | 2000 | 0.9018 | 0.5930 |
| 0.9229 | 0.2 | 2500 | 0.8904 | 0.5954 |
| 0.9182 | 0.24 | 3000 | 0.8802 | 0.6033 |
| 0.9019 | 0.29 | 3500 | 0.8738 | 0.6070 |
| 0.8971 | 0.33 | 4000 | 0.8613 | 0.6154 |
| 0.8788 | 0.37 | 4500 | 0.8593 | 0.6172 |
| 0.8856 | 0.41 | 5000 | 0.8508 | 0.6194 |
| 0.8751 | 0.45 | 5500 | 0.8404 | 0.6256 |
| 0.8718 | 0.49 | 6000 | 0.8445 | 0.6248 |
| 0.8739 | 0.53 | 6500 | 0.8333 | 0.6306 |
| 0.8653 | 0.57 | 7000 | 0.8363 | 0.6280 |
| 0.8588 | 0.61 | 7500 | 0.8213 | 0.6376 |
| 0.8587 | 0.65 | 8000 | 0.8215 | 0.6360 |
| 0.8544 | 0.69 | 8500 | 0.8268 | 0.6292 |
| 0.8556 | 0.73 | 9000 | 0.8045 | 0.6463 |
| 0.8445 | 0.77 | 9500 | 0.8187 | 0.6328 |
| 0.836 | 0.81 | 10000 | 0.8021 | 0.6446 |
| 0.8399 | 0.86 | 10500 | 0.8100 | 0.6375 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Davlan/byt5-base-eng-yor-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
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"T5ForConditionalGeneration"
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}
} | 11 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: janbrv170582
---
### janbrv170582 Dreambooth model trained by pakuschj with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
janbrv170582 (use that on your prompt)
|
Davlan/distilbert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
]
| token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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}
} | 123,856 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### SD-lanakul Dreambooth model trained by 00nakul with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
Davlan/m2m100_418M-eng-yor-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
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}
} | 9 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-base-model
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
|
Davlan/m2m100_418M-yor-eng-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
"conversational": {
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},
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}
} | 6 | 2023-01-08T15:00:10Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: tiny-vanilla-target-glue-mrpc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-vanilla-target-glue-mrpc
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0066
- Accuracy: 0.7206
- F1: 0.8021
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.593 | 4.35 | 500 | 0.5612 | 0.7059 | 0.8058 |
| 0.4814 | 8.7 | 1000 | 0.5717 | 0.7377 | 0.8266 |
| 0.3364 | 13.04 | 1500 | 0.6346 | 0.7353 | 0.8188 |
| 0.2104 | 17.39 | 2000 | 0.7927 | 0.7230 | 0.8094 |
| 0.1308 | 21.74 | 2500 | 1.0066 | 0.7206 | 0.8021 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Davlan/mbart50-large-eng-yor-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
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},
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}
} | 5 | 2023-01-08T15:02:04Z | ---
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: 250.47 +/- 22.53
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
...
```
|
Davlan/mbart50-large-yor-eng-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
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},
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}
} | 5 | 2023-01-08T15:06:41Z | ---
language:
- vi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: HuyenNguyen
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. -->
# HuyenNguyen
This model is a fine-tuned version of [Huyen2310/FPT-S15000](https://huggingface.co/Huyen2310/FPT-S15000) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 450
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Davlan/mt5-small-en-pcm | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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}
} | 9 | 2023-01-08T15:06:51Z | ---
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: 236.40 +/- 48.11
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
...
```
|
Davlan/mt5-small-pcm-en | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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}
} | 9 | 2023-01-08T15:07:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tiny-vanilla-target-glue-qnli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-vanilla-target-glue-qnli
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4624
- Accuracy: 0.7825
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6082 | 0.15 | 500 | 0.5375 | 0.7362 |
| 0.5378 | 0.31 | 1000 | 0.5192 | 0.7459 |
| 0.5161 | 0.46 | 1500 | 0.4967 | 0.7672 |
| 0.5097 | 0.61 | 2000 | 0.5182 | 0.7505 |
| 0.5092 | 0.76 | 2500 | 0.4728 | 0.7750 |
| 0.5011 | 0.92 | 3000 | 0.4660 | 0.7866 |
| 0.4889 | 1.07 | 3500 | 0.4476 | 0.7922 |
| 0.48 | 1.22 | 4000 | 0.4619 | 0.7840 |
| 0.4661 | 1.37 | 4500 | 0.4813 | 0.7741 |
| 0.4742 | 1.53 | 5000 | 0.4624 | 0.7825 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Davlan/mt5_base_yor_eng_mt | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
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}
} | 8 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: tushar117/RL_ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Davlan/naija-twitter-sentiment-afriberta-large | [
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.08277",
"transformers",
"has_space"
]
| text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 61 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: ThomasSimonini/ppo-SnowballTarget2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Davlan/xlm-roberta-base-finetuned-english | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 794.50 +/- 328.16
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 Kon3000 -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 Kon3000 -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 Kon3000
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Davlan/xlm-roberta-base-finetuned-hausa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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}
} | 234 | null | ---
tags:
- text-to-image
- stable-diffusion
- diffusion-models-class
- dreambooth-hackathon
- wildcard
---
Keyword: dashdash toy
DreamBooth 名副其实，一梦千年。到底哪个是真的，哪个是梦境，你能分得清吗？
DreamBooth, just like its name, shows the thing that you might see in your dream.
Can you really tell which one is the real object and which one are coming from your dream?
|
Davlan/xlm-roberta-base-finetuned-igbo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
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}
}
} | 68 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.27 +/- 25.01
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
...
```
|
Davlan/xlm-roberta-base-finetuned-kinyarwanda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
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}
}
} | 61 | 2023-01-08T15:21:22Z | ---
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="ospeek/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"])
```
|
Davlan/xlm-roberta-base-finetuned-luganda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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}
} | 11 | 2023-01-08T15:22:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: tiny-vanilla-target-glue-qqp
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-vanilla-target-glue-qqp
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4162
- Accuracy: 0.7951
- F1: 0.7610
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5864 | 0.04 | 500 | 0.5228 | 0.7257 | 0.6710 |
| 0.5173 | 0.09 | 1000 | 0.4944 | 0.7372 | 0.7000 |
| 0.5005 | 0.13 | 1500 | 0.4983 | 0.7317 | 0.7096 |
| 0.4853 | 0.18 | 2000 | 0.4763 | 0.7451 | 0.7176 |
| 0.474 | 0.22 | 2500 | 0.4644 | 0.7546 | 0.7240 |
| 0.4584 | 0.26 | 3000 | 0.4570 | 0.7617 | 0.7321 |
| 0.4584 | 0.31 | 3500 | 0.4513 | 0.7640 | 0.7348 |
| 0.4531 | 0.35 | 4000 | 0.4587 | 0.7587 | 0.7345 |
| 0.4536 | 0.4 | 4500 | 0.4523 | 0.7627 | 0.7383 |
| 0.4444 | 0.44 | 5000 | 0.4282 | 0.7847 | 0.7467 |
| 0.4323 | 0.48 | 5500 | 0.4415 | 0.7718 | 0.7445 |
| 0.4315 | 0.53 | 6000 | 0.4130 | 0.7969 | 0.7556 |
| 0.4275 | 0.57 | 6500 | 0.4339 | 0.7791 | 0.7506 |
| 0.4272 | 0.62 | 7000 | 0.4127 | 0.7949 | 0.7568 |
| 0.4182 | 0.66 | 7500 | 0.4162 | 0.7951 | 0.7610 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-luo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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},
"translation_en_to_fr": {
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}
}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.98 +/- 16.47
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
...
```
|
Davlan/xlm-roberta-base-masakhaner | [
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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}
} | 3 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ru
datasets:
- lmqg/qag_ruquad
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов."
example_title: "Questions & Answers Generation Example 1"
model-index:
- name: lmqg/mbart-large-cc25-ruquad-qag
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qag_ruquad
type: default
args: default
metrics:
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
type: qa_aligned_f1_score_bertscore_question_answer_generation
value: 77.36
- name: QAAlignedRecall-BERTScore (Question & Answer Generation)
type: qa_aligned_recall_bertscore_question_answer_generation
value: 80.05
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
type: qa_aligned_precision_bertscore_question_answer_generation
value: 74.97
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
type: qa_aligned_f1_score_moverscore_question_answer_generation
value: 56.1
- name: QAAlignedRecall-MoverScore (Question & Answer Generation)
type: qa_aligned_recall_moverscore_question_answer_generation
value: 58.11
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
type: qa_aligned_precision_moverscore_question_answer_generation
value: 54.4
---
# Model Card of `lmqg/mbart-large-cc25-ruquad-qag`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question & answer pair generation task on the [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
- **Language:** ru
- **Training data:** [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ru", model="lmqg/mbart-large-cc25-ruquad-qag")
# model prediction
question_answer_pairs = model.generate_qa("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-ruquad-qag")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.")
```
## Evaluation
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_ruquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 77.36 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
| QAAlignedF1Score (MoverScore) | 56.1 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
| QAAlignedPrecision (BERTScore) | 74.97 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
| QAAlignedPrecision (MoverScore) | 54.4 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
| QAAlignedRecall (BERTScore) | 80.05 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
| QAAlignedRecall (MoverScore) | 58.11 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qag_ruquad
- dataset_name: default
- input_types: ['paragraph']
- output_types: ['questions_answers']
- prefix_types: None
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 256
- epoch: 6
- batch: 2
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 32
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qag/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Davlan/xlm-roberta-base-ner-hrl | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
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}
} | 760 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ospeek/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"])
```
|
DeadBeast/marathi-roberta-base | []
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.74 +/- 21.25
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
...
```
|
Declan/Breitbart_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
],
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} | 7 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Beegbrain/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Declan/Breitbart_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: tiny-mlm-glue-cola-target-glue-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-glue-cola-target-glue-cola
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7414
- Matthews Correlation: 0.1051
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6094 | 1.87 | 500 | 0.6214 | 0.0 |
| 0.6012 | 3.73 | 1000 | 0.6164 | 0.0 |
| 0.5846 | 5.6 | 1500 | 0.6194 | 0.0618 |
| 0.5573 | 7.46 | 2000 | 0.6398 | 0.0749 |
| 0.5257 | 9.33 | 2500 | 0.6667 | 0.1176 |
| 0.5096 | 11.19 | 3000 | 0.6774 | 0.0953 |
| 0.4814 | 13.06 | 3500 | 0.7063 | 0.1171 |
| 0.4635 | 14.93 | 4000 | 0.7414 | 0.1051 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Declan/CNN_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"task_specific_params": {
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} | 7 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 10.90 +/- 11.67
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Declan/CNN_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 5 | 2023-01-08T16:49:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tiny-mlm-glue-cola-target-glue-mnli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-glue-cola-target-glue-mnli
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8037
- Accuracy: 0.6427
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0736 | 0.04 | 500 | 1.0266 | 0.4807 |
| 1.0005 | 0.08 | 1000 | 0.9516 | 0.5605 |
| 0.9517 | 0.12 | 1500 | 0.9140 | 0.5810 |
| 0.9271 | 0.16 | 2000 | 0.9009 | 0.5921 |
| 0.919 | 0.2 | 2500 | 0.8858 | 0.6014 |
| 0.9125 | 0.24 | 3000 | 0.8740 | 0.6069 |
| 0.8965 | 0.29 | 3500 | 0.8676 | 0.6134 |
| 0.89 | 0.33 | 4000 | 0.8547 | 0.6193 |
| 0.8754 | 0.37 | 4500 | 0.8516 | 0.6214 |
| 0.8779 | 0.41 | 5000 | 0.8448 | 0.6220 |
| 0.8698 | 0.45 | 5500 | 0.8396 | 0.6252 |
| 0.8653 | 0.49 | 6000 | 0.8371 | 0.6287 |
| 0.8692 | 0.53 | 6500 | 0.8304 | 0.6309 |
| 0.8579 | 0.57 | 7000 | 0.8307 | 0.6301 |
| 0.8528 | 0.61 | 7500 | 0.8151 | 0.6409 |
| 0.8538 | 0.65 | 8000 | 0.8153 | 0.6381 |
| 0.8451 | 0.69 | 8500 | 0.8264 | 0.6329 |
| 0.8497 | 0.73 | 9000 | 0.8002 | 0.6464 |
| 0.8401 | 0.77 | 9500 | 0.8125 | 0.6363 |
| 0.8299 | 0.81 | 10000 | 0.7968 | 0.6464 |
| 0.8343 | 0.86 | 10500 | 0.8037 | 0.6427 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Declan/CNN_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 3 | null | ---
license: apache-2.0
language:
- sr
---
## Serbian fine tuned Whisper large-v2
Pacted in .pt from https://huggingface.co/DrishtiSharma/whisper-large-v2-serbian
Great thanx!!!
Usable in Whisper |
Declan/CNN_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 3 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 268.31 +/- 17.31
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
...
```
|
Declan/CNN_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"task_specific_params": {
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}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-eng
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-eng
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5047
- Wer: 0.2233
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5485 | 1.0 | 500 | 1.9954 | 1.0042 |
| 0.9068 | 2.01 | 1000 | 0.6418 | 0.4572 |
| 0.4398 | 3.01 | 1500 | 0.4586 | 0.3629 |
| 0.3023 | 4.02 | 2000 | 0.4464 | 0.3248 |
| 0.2328 | 5.02 | 2500 | 0.4019 | 0.2969 |
| 0.1899 | 6.02 | 3000 | 0.4363 | 0.2961 |
| 0.163 | 7.03 | 3500 | 0.4832 | 0.2872 |
| 0.1442 | 8.03 | 4000 | 0.4421 | 0.2801 |
| 0.1246 | 9.04 | 4500 | 0.4757 | 0.2659 |
| 0.1122 | 10.04 | 5000 | 0.4693 | 0.2648 |
| 0.102 | 11.04 | 5500 | 0.4834 | 0.2549 |
| 0.0919 | 12.05 | 6000 | 0.4558 | 0.2633 |
| 0.0866 | 13.05 | 6500 | 0.4527 | 0.2641 |
| 0.0762 | 14.06 | 7000 | 0.4394 | 0.2565 |
| 0.0705 | 15.06 | 7500 | 0.5240 | 0.2609 |
| 0.0647 | 16.06 | 8000 | 0.4980 | 0.2522 |
| 0.0608 | 17.07 | 8500 | 0.5163 | 0.2589 |
| 0.0576 | 18.07 | 9000 | 0.4991 | 0.2565 |
| 0.0499 | 19.08 | 9500 | 0.4750 | 0.2457 |
| 0.047 | 20.08 | 10000 | 0.5162 | 0.2447 |
| 0.0418 | 21.08 | 10500 | 0.4801 | 0.2413 |
| 0.0383 | 22.09 | 11000 | 0.4961 | 0.2394 |
| 0.0342 | 23.09 | 11500 | 0.5209 | 0.2386 |
| 0.032 | 24.1 | 12000 | 0.4970 | 0.2364 |
| 0.0293 | 25.1 | 12500 | 0.4789 | 0.2309 |
| 0.0265 | 26.1 | 13000 | 0.4948 | 0.2302 |
| 0.0269 | 27.11 | 13500 | 0.4917 | 0.2249 |
| 0.0237 | 28.11 | 14000 | 0.4991 | 0.2238 |
| 0.022 | 29.12 | 14500 | 0.5047 | 0.2233 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.13.0+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
|
Declan/CNN_model_v7 | []
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} | 0 | 2023-01-08T17:02:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-0.0001
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6854754440961337
---
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-0.0001
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8601
- Accuracy: 0.6855
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1632 | 1.0 | 202 | 0.9975 | 0.6290 |
| 1.0563 | 2.0 | 404 | 0.9350 | 0.6614 |
| 0.9564 | 3.0 | 606 | 0.8601 | 0.6855 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Declan/ChicagoTribune_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
],
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} | 3 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: bdiptesh99/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Declan/ChicagoTribune_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 7 | 2023-01-08T17:07:46Z | ---
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="avoroshilov/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"])
```
|
Declan/ChicagoTribune_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: pft-clf-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pft-clf-finetuned
This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0987
- Matthews Correlation: 0.9737
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 6
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1299 | 1.0 | 1268 | 0.0987 | 0.9737 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/ChicagoTribune_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 7 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
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-butterflies-128-2
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` 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: 16
- eval_batch_size: 16
- 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/yuch0001/ddpm-butterflies-128-2/tensorboard?#scalars)
|
Declan/ChicagoTribune_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
],
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} | 7 | null | ---
license: openrail
---
## yy model
768ARB, fp32, EMA, finetuned on animefull
**epoch48**: probably overfitted, good results overall
**epoch28**: not overfitted: less stylized but mostly good anatomy
tested on:
- 768x1024 DPM++ SDE Karras, no hires fix
- 512x768 Latent hires → 1024x1536 DPM++ SDE Karras
token word:
- `yingyi, best quality, explicit` → nsfw
- `yingyi, concept art` → concepts
- `yingyi, best quality` → less nsfw
tagging:
- wd1.4, booru, blip
lr & augments:
- 2e-6 sqrt bs2, weight decay 5e-2
- rotate, brightness, contrast, flip
nsfw sample 1 (e48):
```
yingyi, best quality, explicit, multiple girls, 2girls, breasts, long hair, twintails, rabbit ears, pink hair, sex toy, thighhighs, animal ears, vibrator, very long hair, blue eyes, holding, high heels, full body, blush, outdoors, vaginal object insertion, pink eyes, blue hair, standing, gloves, looking at viewer, bow, medium breasts, bangs, fake animal ears, heart, bodysuit, vibrator cord, object insertion, small breasts, public indecency, navel, elbow gloves, heart-shaped pupils, pussy juice, revealing clothes, hair ornament, piercing, remote control vibrator, body writing, red footwear, flower, dildo, red eyes, vaginal, symbol-shaped pupils, black footwear, aqua hair
Negative prompt: error, signature, watermark, username, multiple people, animals, lowres, cropped, worth quality ,low quality, normal quality, jpeg artifacts, blurry, bad anatomy, bad hands, bad arms, bad feet, bad anatomy, missing fingers, extra digits, fewer digits, long neck, missing legs, huge person, optical_illusion
Steps: 25, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 1438393047, Size: 512x768, Model hash: f32626be, Model: yye48, Denoising strength: 0.7, Clip skip: 2, ENSD: 31338, Hires upscale: 2, Hires upscaler: Latent (bicubic)
```
nsfw sample 2 (e48):
```
yingyi, best quality, explicit, restrained, 1girl, thighhighs, sex toy, bound, bdsm, blindfold, object insertion, gloves, dildo, gag, solo, breasts, anal, body writing, gagged, vaginal, bondage, white gloves, elbow gloves, short hair, vibrator, vaginal object insertion, small breasts, ball gag, anal object insertion, white thighhighs, white hair, stationary restraints, nipples, sex machine, soles, pussy, black blindfold, motion blur, navel, feet, tally, spread legs
Negative prompt: error, signature, watermark, username, multiple people, animals, lowres, cropped, worth quality ,low quality, normal quality, jpeg artifacts, blurry, bad anatomy, bad hands, bad arms, bad feet, bad anatomy, missing fingers, extra digits, fewer digits, long neck, missing legs, huge person, optical_illusion
Steps: 25, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 1438393014, Size: 512x768, Model hash: f32626be, Model: yye48, Denoising strength: 0.7, Clip skip: 2, ENSD: 31338, Hires upscale: 2, Hires upscaler: Latent (bicubic)
```
sfw sample (e48): [file here](https://huggingface.co/trojblue/yys/blob/main/%5Blatnet%20nearest%20exact%5D31338-DPM%2B%2B%202M%20Karras-step27-cfg6.5-8a648075-20230108_133253_026580.png)
```
yingyi, 1girl, waves, water, long hair, splashing, liquid hair, very long hair, a painting of a blue wave with a white background and a person standing on the wave of water in the middle, ying yi, hatsune miku, vocaloid, absurdres, highres, aqua eyes, aqua hair, bird, boots, bridal gauntlets, cape, fish, nail polish, thigh boots, tropical fish, twintails, wading, white bird
Negative prompt: error, signature, watermark, username, realistic,3d, multiple people, extra legs, animals, lowres, cropped, worth quality, low quality, normal quality, jpeg artifacts, bad anatomy, bad hands, bad arms, bad feet, bad anatomy, missing fingers, extra digits,explicit, fewer digits, long neck, missing legs, huge person, optical_illusion
Steps: 27, Sampler: DPM++ 2M Karras, CFG scale: 6.5, Seed: 31338, Size: 384x576, Model hash: 8a648075, Model: yy_e28, Denoising strength: 0.7, Clip skip: 2, ENSD: 31338, Hires upscale: 2, Hires upscaler: Latent (nearest-exact)
```
|
Declan/ChicagoTribune_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: tiny-mlm-glue-cola-target-glue-mrpc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-glue-cola-target-glue-mrpc
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1611
- Accuracy: 0.7377
- F1: 0.8231
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5876 | 4.35 | 500 | 0.5489 | 0.7132 | 0.8116 |
| 0.4468 | 8.7 | 1000 | 0.5577 | 0.7426 | 0.8298 |
| 0.2984 | 13.04 | 1500 | 0.6360 | 0.7525 | 0.8331 |
| 0.189 | 17.39 | 2000 | 0.7762 | 0.7451 | 0.8272 |
| 0.1151 | 21.74 | 2500 | 1.0086 | 0.7402 | 0.8172 |
| 0.0884 | 26.09 | 3000 | 1.1611 | 0.7377 | 0.8231 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Declan/ChicagoTribune_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tiny-mlm-glue-cola-target-glue-qnli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-glue-cola-target-glue-qnli
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4706
- Accuracy: 0.7820
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6037 | 0.15 | 500 | 0.5447 | 0.7315 |
| 0.5395 | 0.31 | 1000 | 0.5304 | 0.7417 |
| 0.5171 | 0.46 | 1500 | 0.4946 | 0.7626 |
| 0.5141 | 0.61 | 2000 | 0.5316 | 0.7450 |
| 0.5107 | 0.76 | 2500 | 0.4847 | 0.7712 |
| 0.5031 | 0.92 | 3000 | 0.4687 | 0.7844 |
| 0.4903 | 1.07 | 3500 | 0.4536 | 0.7897 |
| 0.48 | 1.22 | 4000 | 0.4689 | 0.7829 |
| 0.4677 | 1.37 | 4500 | 0.4769 | 0.7763 |
| 0.474 | 1.53 | 5000 | 0.4706 | 0.7820 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Declan/Politico_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 7 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 20.50 +/- 15.34
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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