modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 18:27:06
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-29 18:26:56
| card
stringlengths 11
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|---|---|---|---|---|---|---|---|---|---|
wooihen/dqn-SpaceInvadersNoFrameskip-v4
|
wooihen
| 2023-01-04T01:24:48Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-04T01:24:09Z |
---
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: 670.50 +/- 257.47
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga wooihen -f logs/
python enjoy.py --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 wooihen -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 wooihen
```
## 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', 1200000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
huggingtweets/janieclone
|
huggingtweets
| 2023-01-04T01:06:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/1536389142287892481/N6kCwACw_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Columbine Janie</div>
<div style="text-align: center; font-size: 14px;">@janieclone</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 Columbine Janie.
| Data | Columbine Janie |
| --- | --- |
| Tweets downloaded | 3072 |
| Retweets | 1211 |
| Short tweets | 462 |
| Tweets kept | 1399 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1divgffx/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 @janieclone's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ic6ynmd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ic6ynmd/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/janieclone')
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)
|
cleanrl/CartPole-v1-c51_jax-seed1
|
cleanrl
| 2023-01-04T01:02:38Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-01T19:19:43Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: C51
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
---
# (CleanRL) **C51** Agent Playing **CartPole-v1**
This is a trained model of a C51 agent playing CartPole-v1.
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/c51_jax.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[c51_jax]"
python -m cleanrl_utils.enjoy --exp-name c51_jax --env-id CartPole-v1
```
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/CartPole-v1-c51_jax-seed1/raw/main/c51_jax.py
curl -OL https://huggingface.co/cleanrl/CartPole-v1-c51_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/CartPole-v1-c51_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python c51_jax.py --save-model --upload-model --hf-entity cleanrl --env-id CartPole-v1
```
# Hyperparameters
```python
{'batch_size': 128,
'buffer_size': 10000,
'capture_video': False,
'end_e': 0.05,
'env_id': 'CartPole-v1',
'exp_name': 'c51_jax',
'exploration_fraction': 0.5,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'learning_starts': 10000,
'n_atoms': 101,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 500,
'total_timesteps': 500000,
'track': False,
'train_frequency': 10,
'upload_model': True,
'v_max': 100,
'v_min': -100,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/BreakoutNoFrameskip-v4-c51_atari_jax-seed1
|
cleanrl
| 2023-01-04T01:01:29Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T23:37:05Z |
---
tags:
- BreakoutNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: C51
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BreakoutNoFrameskip-v4
type: BreakoutNoFrameskip-v4
metrics:
- type: mean_reward
value: 318.10 +/- 112.21
name: mean_reward
verified: false
---
# (CleanRL) **C51** Agent Playing **BreakoutNoFrameskip-v4**
This is a trained model of a C51 agent playing BreakoutNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari_jax.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[c51_atari_jax]"
python -m cleanrl_utils.enjoy --exp-name c51_atari_jax --env-id BreakoutNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/kinalmehta/BreakoutNoFrameskip-v4-c51_atari_jax-seed1/raw/main/c51_atari_jax.py
curl -OL https://huggingface.co/kinalmehta/BreakoutNoFrameskip-v4-c51_atari_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/kinalmehta/BreakoutNoFrameskip-v4-c51_atari_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python c51_atari_jax.py --save-model --upload-model --hf-entity kinalmehta --env-id BreakoutNoFrameskip-v4
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'end_e': 0.01,
'env_id': 'BreakoutNoFrameskip-v4',
'exp_name': 'c51_atari_jax',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'kinalmehta',
'learning_rate': 0.00025,
'learning_starts': 80000,
'n_atoms': 51,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 10000,
'total_timesteps': 10000000,
'track': False,
'train_frequency': 4,
'upload_model': True,
'v_max': 10,
'v_min': -10,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
cleanrl/PongNoFrameskip-v4-c51_atari_jax-seed1
|
cleanrl
| 2023-01-04T01:00:41Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T05:14:18Z |
---
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: C51
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: 17.40 +/- 6.18
name: mean_reward
verified: false
---
# (CleanRL) **C51** Agent Playing **PongNoFrameskip-v4**
This is a trained model of a C51 agent playing PongNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari_jax.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[c51_atari_jax]"
python -m cleanrl_utils.enjoy --exp-name c51_atari_jax --env-id PongNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/kinalmehta/PongNoFrameskip-v4-c51_atari_jax-seed1/raw/main/c51_atari_jax.py
curl -OL https://huggingface.co/kinalmehta/PongNoFrameskip-v4-c51_atari_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/kinalmehta/PongNoFrameskip-v4-c51_atari_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python c51_atari_jax.py --save-model --upload-model --hf-entity kinalmehta --env-id PongNoFrameskip-v4
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'c51_atari_jax',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'kinalmehta',
'learning_rate': 0.00025,
'learning_starts': 80000,
'n_atoms': 51,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 10000,
'total_timesteps': 10000000,
'track': False,
'train_frequency': 4,
'upload_model': True,
'v_max': 10,
'v_min': -10,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
bileldh/bert-finetuned-ner
|
bileldh
| 2023-01-04T00:26:03Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-12-24T19:18:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.937448149991704
- name: Recall
type: recall
value: 0.9508582968697409
- name: F1
type: f1
value: 0.9441056061492189
- name: Accuracy
type: accuracy
value: 0.9864308000235474
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Precision: 0.9374
- Recall: 0.9509
- F1: 0.9441
- Accuracy: 0.9864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.089 | 1.0 | 1756 | 0.0749 | 0.9104 | 0.9280 | 0.9191 | 0.9812 |
| 0.0331 | 2.0 | 3512 | 0.0614 | 0.9299 | 0.9470 | 0.9384 | 0.9858 |
| 0.0169 | 3.0 | 5268 | 0.0614 | 0.9374 | 0.9509 | 0.9441 | 0.9864 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
elRivx/megaPals2.1
|
elRivx
| 2023-01-04T00:09:22Z | 0 | 1 | null |
[
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-01-04T00:00:04Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
**megaPals2.1**
Hi guys! Do you remember the superhero vintage animated series? Do you like the 70s style? This Stable Diffusion 2.1 embedding is for you! Some recomendations: the magic word for your prompts is megaPals.
If you enjoy my work, please consider supporting me:
[](https://www.buymeacoffee.com/elrivx)
Examples:
<img src=https://imgur.com/wZmw8Xr.png width=30% height=30%>
<img src=https://imgur.com/JJGBmT8.png width=30% height=30%>
<img src=https://imgur.com/0Nr4IJm.png width=30% height=30%>
<img src=https://imgur.com/rRN9r1N.png width=30% height=30%>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
thiagoms7/q-FrozenLake-v1-4x4-noSlippery
|
thiagoms7
| 2023-01-03T23:30:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T23:30:51Z |
---
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="thiagoms7/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"])
```
|
saltacc/RandomPrompt-v1
|
saltacc
| 2023-01-03T23:06:16Z | 18 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-01-03T22:31:03Z |
---
license: mit
---
# RandomPrompt-v1
A fine tuned GPT-neo 125M
The purpose of this model is to autocomplete or generate danbooru-like prompts for generating images in Stable Diffusion derivatives that use danbooru tags for text conditioning.
## Usage
THE HOSTED INTERFACE DOES NOT WORK, USE THE HUGGINGFACE SPACE
### Autocompletion
Type in a few tags, and it will generate a completion of the prompt
### Generation
Type in nothing, and it will generate a prompt
## Training
Trained on 400k tags from danbooru posts for 600k steps, or around 0.25 epochs
https://wandb.ai/saltacc/RandomPrompt/runs/2v2arf0u?workspace=user-saltacc
I plan on doing further runs on better hardware to try to get more accurate prompt completion
|
0xid/qrdqn-SpaceInvadersNoFrameskip-v4
|
0xid
| 2023-01-03T22:39:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T22:38:51Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 1528.00 +/- 875.81
name: mean_reward
verified: false
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga 0xid -f logs/
python enjoy.py --algo qrdqn --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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga 0xid -f logs/
rl_zoo3 enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga 0xid
```
## Hyperparameters
```python
OrderedDict([('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('normalize', False)])
```
|
jonathanybema/twitter-xlm-roberta-base-sentiment
|
jonathanybema
| 2023-01-03T22:38:21Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-03T21:52:24Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: twitter-xlm-roberta-base-sentiment
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. -->
# twitter-xlm-roberta-base-sentiment
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6256
- Accuracy: 0.7297
## 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
|
cheremushkin/ppo-LunarLander-v2
|
cheremushkin
| 2023-01-03T22:00:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T21:59:54Z |
---
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: 265.00 +/- 18.08
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Kimata/my_awesome_billsum_model
|
Kimata
| 2023-01-03T21:52:03Z | 7 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-03T21:44:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1362
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5474
- Rouge1: 0.1362
- Rouge2: 0.0419
- Rougel: 0.1111
- Rougelsum: 0.1112
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8519 | 0.1206 | 0.0274 | 0.0991 | 0.0992 | 19.0 |
| No log | 2.0 | 124 | 2.6323 | 0.1315 | 0.0377 | 0.1066 | 0.1067 | 19.0 |
| No log | 3.0 | 186 | 2.5643 | 0.1371 | 0.043 | 0.1117 | 0.1118 | 19.0 |
| No log | 4.0 | 248 | 2.5474 | 0.1362 | 0.0419 | 0.1111 | 0.1112 | 19.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
UKP-SQuARE/roberta-base-pf-race-onnx
|
UKP-SQuARE
| 2023-01-03T21:42:59Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"onnx",
"roberta",
"adapterhub:rc/race",
"en",
"dataset:race",
"arxiv:2104.08247",
"region:us"
] | null | 2023-01-03T21:39:49Z |
---
inference: false
tags:
- onnx
- adapterhub:rc/race
- roberta
- adapter-transformers
datasets:
- race
language:
- en
---
# ONNX export of Adapter `AdapterHub/roberta-base-pf-race` for roberta-base
## Conversion of [AdapterHub/roberta-base-pf-race](https://huggingface.co/AdapterHub/roberta-base-pf-race) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-race-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-race-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
```
|
ScrappyCoco666/dqn-SpaceInvadersNoFrameskip-v4
|
ScrappyCoco666
| 2023-01-03T21:37:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T21:36:38Z |
---
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: 684.50 +/- 150.66
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ScrappyCoco666 -f logs/
python enjoy.py --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 ScrappyCoco666 -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 ScrappyCoco666
```
## 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)])
```
|
UKP-SQuARE/roberta-base-pf-multirc-onnx
|
UKP-SQuARE
| 2023-01-03T21:23:58Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"onnx",
"roberta",
"text-classification",
"adapterhub:rc/multirc",
"en",
"arxiv:2104.08247",
"region:us"
] |
text-classification
| 2023-01-03T21:20:43Z |
---
inference: false
tags:
- onnx
- text-classification
- adapterhub:rc/multirc
- roberta
- adapter-transformers
language:
- en
---
# ONNX export of Adapter `AdapterHub/roberta-base-pf-multirc` for roberta-base
## Conversion of [AdapterHub/roberta-base-pf-multirc](https://huggingface.co/AdapterHub/roberta-base-pf-multirc) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-multirc-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-multirc-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
```
|
UKP-SQuARE/bert-base-uncased-pf-cosmos_qa-onnx
|
UKP-SQuARE
| 2023-01-03T21:11:53Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"onnx",
"bert",
"adapterhub:comsense/cosmosqa",
"en",
"dataset:cosmos_qa",
"arxiv:2104.08247",
"region:us"
] | null | 2023-01-03T21:09:01Z |
---
inference: false
tags:
- onnx
- bert
- adapterhub:comsense/cosmosqa
- adapter-transformers
datasets:
- cosmos_qa
language:
- en
---
# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-cosmos_qa` for bert-base-uncased
## Conversion of [AdapterHub/bert-base-uncased-pf-cosmos_qa](https://huggingface.co/AdapterHub/bert-base-uncased-pf-cosmos_qa) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-cosmos_qa-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-cosmos_qa-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
```
|
RedPandaAINLP/dqn-SpaceInvadersNoFrameskip-v4_3
|
RedPandaAINLP
| 2023-01-03T21:09:46Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T20:57:19Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 11.50 +/- 12.85
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RedPandaAINLP -f logs/
python enjoy.py --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 RedPandaAINLP -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 RedPandaAINLP
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('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', 0.05),
('learning_starts', 100000),
('n_timesteps', 110000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
hroth/psais-paraphrase-multilingual-MiniLM-L12-v2-20shot
|
hroth
| 2023-01-03T21:07:20Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T21:06:56Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 605 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 605,
"warmup_steps": 61,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
huggingtweets/popbase-popcrave
|
huggingtweets
| 2023-01-03T21:03:29Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-01-03T21:03:20Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/1268086791443230737/BRGz4AiW_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/1394266006395228162/qIjjvzl7_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<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">Pop Base & Pop Crave</div>
<div style="text-align: center; font-size: 14px;">@popbase-popcrave</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 Pop Base & Pop Crave.
| Data | Pop Base | Pop Crave |
| --- | --- | --- |
| Tweets downloaded | 3240 | 3212 |
| Retweets | 343 | 244 |
| Short tweets | 306 | 89 |
| Tweets kept | 2591 | 2879 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/231p93io/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 @popbase-popcrave's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/9st2g69y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/9st2g69y/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/popbase-popcrave')
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)
|
vuiseng9/jpqd-bert-base-lt-15eph-r0.0150-s1e5
|
vuiseng9
| 2023-01-03T21:01:03Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2023-01-03T20:52:14Z |
# Joint Pruning, Quantization and Distillation for BERT-large/SQuADv1.1
## Setup
```bash
git clone https://github.com/vuiseng9/optimum-intel
cd optimum-intel
pip install -e .[openvino,nncf]
cd examples/openvino/question-answering/
pip install -r requirements.txt
pip install wandb # optional
```
## Run
```bash
NNCFCFG=/path/to/openvino_config.json
MASTER_PORT=<PORTID>
RUNID=<RUN_IDENTIFIER>
OUTDIR=/path/to/saved_model
NEPOCH=15
python run_qa.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
--teacher_model_or_path bert-large-uncased-whole-word-masking-finetuned-squad \
--distillation_weight 0.9 \
--do_eval \
--fp16 \
--do_train \
--learning_rate 3e-5 \
--num_train_epochs $NEPOCH \
--per_device_eval_batch_size 128 \
--per_device_train_batch_size 16 \
--max_seq_length 384 \
--doc_stride 128 \
--logging_steps 1 \
--evaluation_strategy steps \
--eval_steps 250 \
--save_steps 500 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR \
--nncf_compression_config $NNCFCFG \
```
### Reference Results
```
Global Step: 80000
F1: 90.272
EM: 83.728
Structured Sparsity (linear): 52.18%
```
|
amal94/q-Taxi-v3
|
amal94
| 2023-01-03T20:30:59Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T20:20:29Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="amal94/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
amal94/q-FrozenLake-v1-4x4-noSlippery
|
amal94
| 2023-01-03T20:18:25Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T20:18:20Z |
---
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="amal94/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"])
```
|
hroth/psais-LaBSE-10shot
|
hroth
| 2023-01-03T20:12:30Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T20:11:46Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 303 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 303,
"warmup_steps": 31,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
rmn0ff/ppo-LunarLander-v2
|
rmn0ff
| 2023-01-03T19:36:47Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-07T14:31:57Z |
---
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: 221.75 +/- 81.24
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
...
```
|
Slashy/krystal-test
|
Slashy
| 2023-01-03T19:24:38Z | 4 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-03T19:22:28Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Krystal-Test Dreambooth model trained by Slashy 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:
|
BKluwe2209/Taxi-v3
|
BKluwe2209
| 2023-01-03T19:21:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T19:19:43Z |
---
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="BKluwe2209/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"])
```
|
BhavyaMuni/taylor-swift-model-paragraphs
|
BhavyaMuni
| 2023-01-03T18:54:45Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-01-03T18:11:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: taylor-swift-model-paragraphs
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. -->
# taylor-swift-model-paragraphs
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3564
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9316 | 1.0 | 59 | 3.8227 |
| 3.824 | 2.0 | 118 | 3.7301 |
| 3.5808 | 3.0 | 177 | 3.6658 |
| 3.625 | 4.0 | 236 | 3.6205 |
| 3.643 | 5.0 | 295 | 3.5862 |
| 3.5443 | 6.0 | 354 | 3.5545 |
| 3.4535 | 7.0 | 413 | 3.5274 |
| 3.398 | 8.0 | 472 | 3.5072 |
| 3.3253 | 9.0 | 531 | 3.4833 |
| 3.4111 | 10.0 | 590 | 3.4688 |
| 3.3461 | 11.0 | 649 | 3.4503 |
| 3.3133 | 12.0 | 708 | 3.4373 |
| 3.3921 | 13.0 | 767 | 3.4246 |
| 3.2661 | 14.0 | 826 | 3.4102 |
| 3.2257 | 15.0 | 885 | 3.4052 |
| 3.1837 | 16.0 | 944 | 3.3911 |
| 3.1935 | 17.0 | 1003 | 3.3849 |
| 2.9369 | 18.0 | 1062 | 3.3774 |
| 3.2486 | 19.0 | 1121 | 3.3721 |
| 3.1542 | 20.0 | 1180 | 3.3681 |
| 3.0771 | 21.0 | 1239 | 3.3624 |
| 3.1206 | 22.0 | 1298 | 3.3581 |
| 3.0358 | 23.0 | 1357 | 3.3585 |
| 2.9207 | 24.0 | 1416 | 3.3568 |
| 3.0496 | 25.0 | 1475 | 3.3564 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Eslam25/q-FrozenLake-v1-4x4-noSlippery
|
Eslam25
| 2023-01-03T18:07:11Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T18:07:07Z |
---
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="Eslam25/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"])
```
|
noctrog/q-FrozenLake-v1-4x4-noSlippery
|
noctrog
| 2023-01-03T17:49:11Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T17:49:06Z |
---
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="noctrog/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"])
```
|
sinsforeal/cityscape
|
sinsforeal
| 2023-01-03T17:42:36Z | 0 | 1 | null |
[
"license:openrail",
"region:us"
] | null | 2023-01-03T17:22:36Z |
---
license: openrail
---
This is a model that improves the generation quality of cityscape landscapes into a more futuristic stype it was trained off of 38 768 images using photo bucketing with stable tuner
at 16 batch size and 2e-5 lr. The images were randomly pulled from artstation after I searched for cityscape. try using "cityscape" "cyberpunk" "futuristc" "dystopian" "utopian" "skyscrapers" "nighttime" to trigger it.
|
hroth/psais-multi-qa-mpnet-base-dot-v1-10shot
|
hroth
| 2023-01-03T17:25:36Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T17:25:08Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 303 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 303,
"warmup_steps": 31,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
sinsforeal/haruhisky
|
sinsforeal
| 2023-01-03T17:22:08Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-01-03T17:14:07Z |
---
license: openrail
---
trained with all the images by the artist haruhisky on danbooru. 768 res with 16 batch size and 2e-5 lr. you should be able to get his art style with "by haruhisky" this is 15
epochs
|
sinsforeal/cardcaptorsakura
|
sinsforeal
| 2023-01-03T17:13:14Z | 0 | 1 | null |
[
"license:openrail",
"region:us"
] | null | 2023-01-03T16:56:44Z |
---
license: openrail
---
cardcaptor sakura model trained on anime screenshots all were 768 resolution images. 16 batch size and 1.6e-5 lr. the number indicates the epoch. you can really only do sakura
tomoyo. something like kinomoto sakura, white beret, school bag, tomeda elementary school uniform, happy should give ok results when added to a normal prompt.
|
maixbach/swin-tiny-patch4-window7-224-finetuned-trash_classification
|
maixbach
| 2023-01-03T16:57:28Z | 45 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-01-03T09:29:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-trash_classification
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: 0.882689556509299
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-trash_classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3372
- Accuracy: 0.8827
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4991 | 1.0 | 22 | 0.5482 | 0.7911 |
| 0.4008 | 2.0 | 44 | 0.5193 | 0.7954 |
| 0.3659 | 3.0 | 66 | 0.4464 | 0.8398 |
| 0.372 | 4.0 | 88 | 0.4384 | 0.8398 |
| 0.3388 | 5.0 | 110 | 0.4281 | 0.8455 |
| 0.2654 | 6.0 | 132 | 0.3618 | 0.8712 |
| 0.2326 | 7.0 | 154 | 0.3550 | 0.8755 |
| 0.2354 | 8.0 | 176 | 0.3401 | 0.8798 |
| 0.1774 | 9.0 | 198 | 0.3372 | 0.8827 |
| 0.1849 | 10.0 | 220 | 0.3380 | 0.8827 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
sinsforeal/aokiume
|
sinsforeal
| 2023-01-03T16:45:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-01-03T16:31:00Z |
this was trained off of all the images on danbooru tagged under the artist aoki ume the creator of hidamari sketch. all of the source images were 768 resolution and this was trained at
16 batch size with a lr of 1.6e-5. you should be able to prompt all of the hidamaris or you could try applying the style with "by aoki ume"
|
sinsforeal/horiguchiyukiko
|
sinsforeal
| 2023-01-03T16:34:42Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-01-03T16:18:19Z |
---
license: openrail
---
another quick finetune trained at 768 resoultion for 10 epochs based off of kani-anime trained at 16 batch size with 1.6e-5 learning rate in stable tuner.
this one is based off all of the offical k-on artwork by the artist horiguchi yukiko. all i did was scrap the danbooru images so if you use the take horiguchi yukiko on there
you can get a good idea of what kind of prompts you should use. generally you will want to use at least "by horiguchi yukiko" to generate images in his style
|
hroth/psais-multi-qa-mpnet-base-dot-v1-8shot
|
hroth
| 2023-01-03T16:25:56Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T16:25:39Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 240 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 240,
"warmup_steps": 24,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
aplnestrella/pegasus-samsum1
|
aplnestrella
| 2023-01-03T15:32:30Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-03T14:38:06Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum1
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-samsum1
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: 1.4877
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6966 | 0.54 | 500 | 1.4877 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
jessietextstan/setfit_v0
|
jessietextstan
| 2023-01-03T15:26:28Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T15:26:15Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
hroth/psais-all-mpnet-base-v2-8shot
|
hroth
| 2023-01-03T15:21:16Z | 7 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T15:20:55Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 240 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 240,
"warmup_steps": 24,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
BiggieW/classification_tnews_100_per_class
|
BiggieW
| 2023-01-03T15:16:02Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-03T13:53:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
widget:
- '全国高校科研质量排名，清华北大浙大无缘前五。'
model-index:
- name: classification_tnews_100_per_class
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. -->
# classification_tnews_100_per_class
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on a subset of TNEWS dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0423
- Accuracy: 0.7133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8994 | 1.0 | 150 | 1.2250 | 0.6733 |
| 0.9706 | 2.0 | 300 | 1.0644 | 0.6867 |
| 0.622 | 3.0 | 450 | 1.0083 | 0.6933 |
| 0.4115 | 4.0 | 600 | 1.0495 | 0.6867 |
| 0.2959 | 5.0 | 750 | 1.0423 | 0.7133 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
tryolabs/long-t5-tglobal-base-blogpost-cqa
|
tryolabs
| 2023-01-03T15:14:01Z | 6 | 6 |
transformers
|
[
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-12-15T14:46:54Z |
# Fine-tuned LongT5 for Conversational QA
This model is a fine-tuned version of [long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) for the task of Conversational QA. The model was fine-tuned on the [SQuADv2](https://huggingface.co/datasets/squad_v2) and [CoQA](https://huggingface.co/datasets/coqa) datasets and on Tryolabs' own custom dataset, [TryoCoQA](https://github.com/tryolabs/TryoCoQA).
An export of this model to the ONNX format is available at [tryolabs/long-t5-tglobal-base-blogpost-cqa-onnx](https://huggingface.co/tryolabs/long-t5-tglobal-base-blogpost-cqa-onnx).
You can find the details on how we fine-tuned the model and built TryoCoQA on our blog post!
You can also play with the model on the following [space](https://huggingface.co/spaces/tryolabs/blogpost-cqa).
## Results
* Fine-tuning for 3 epochs on SQuADv2 and CoQA combined achieved a 74.29 F1 score on the test set.
* Fine-tuning for 166 epochs on TryoCoQA achieved a 54.77 F1 score on the test set.
|
tytfyhutrf/ducobumix
|
tytfyhutrf
| 2023-01-03T15:08:34Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-03T14:34:22Z |
---
license: creativeml-openrail-m
---
|
echarlaix/t5-small-openvino
|
echarlaix
| 2023-01-03T14:58:52Z | 5,341 | 4 |
transformers
|
[
"transformers",
"openvino",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-29T15:49:15Z |
---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
- openvino
license: apache-2.0
---
## [t5-small](https://huggingface.co/t5-small) exported to the OpenVINO IR.
## Model description
[T5](https://huggingface.co/docs/transformers/model_doc/t5#t5) is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.
For more information, please take a look at the original paper.
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Usage example
You can use this model with Transformers *pipeline*.
```python
from transformers import AutoTokenizer, pipeline
from optimum.intel.openvino import OVModelForSeq2SeqLM
model_id = "echarlaix/t5-small-openvino"
model = OVModelForSeq2SeqLM.from_pretrained(model_id, use_cache=False)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Create a pipeline
translation_pipe = pipeline("translation_en_to_fr", model=model, tokenizer=tokenizer)
text = "He never went out without a book under his arm, and he often came back with two."
result = translation_pipe(text)
```
|
hroth/psais-all-MiniLM-L6-v2-10shot
|
hroth
| 2023-01-03T14:44:59Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T14:44:44Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 303 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 303,
"warmup_steps": 31,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
malamasn/ppo-LunarLander-v2
|
malamasn
| 2023-01-03T14:40:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T14:40:01Z |
---
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: 253.94 +/- 17.76
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
...
```
|
mamiksik/CodeBertaCLM
|
mamiksik
| 2023-01-03T14:31:20Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-01-01T13:38:07Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: CodeBertaCLM
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. -->
# CodeBertaCLM
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5831
- Accuracy: 0.0144
- F1: 0.0144
- Bleu4: 0.0421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Bleu4 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|
| 3.6734 | 1.0 | 1673 | 3.6884 | 0.0159 | 0.0159 | 0.0131 |
| 2.8139 | 2.0 | 3346 | 3.2517 | 0.0164 | 0.0164 | 0.0192 |
| 2.4176 | 3.0 | 5019 | 3.0747 | 0.0178 | 0.0178 | 0.0332 |
| 2.2785 | 4.0 | 6692 | 2.9695 | 0.0174 | 0.0174 | 0.0347 |
| 2.1557 | 5.0 | 8365 | 2.8886 | 0.0171 | 0.0171 | 0.0377 |
| 2.0357 | 6.0 | 10038 | 2.8313 | 0.0158 | 0.0158 | 0.0394 |
| 1.9615 | 7.0 | 11711 | 2.7865 | 0.0158 | 0.0158 | 0.0393 |
| 1.8982 | 8.0 | 13384 | 2.7498 | 0.0147 | 0.0147 | 0.0399 |
| 1.8233 | 9.0 | 15057 | 2.7195 | 0.0149 | 0.0149 | 0.0430 |
| 1.7866 | 10.0 | 16730 | 2.6925 | 0.0157 | 0.0157 | 0.0485 |
| 1.7237 | 11.0 | 18403 | 2.6745 | 0.0146 | 0.0146 | 0.0419 |
| 1.6757 | 12.0 | 20076 | 2.6616 | 0.0146 | 0.0146 | 0.0403 |
| 1.6452 | 13.0 | 21749 | 2.6377 | 0.0147 | 0.0147 | 0.0403 |
| 1.6036 | 14.0 | 23422 | 2.6216 | 0.0145 | 0.0145 | 0.0397 |
| 1.5818 | 15.0 | 25095 | 2.6169 | 0.0150 | 0.0150 | 0.0413 |
| 1.5389 | 16.0 | 26768 | 2.6047 | 0.0146 | 0.0146 | 0.0420 |
| 1.5131 | 17.0 | 28441 | 2.5940 | 0.0153 | 0.0153 | 0.0433 |
| 1.4822 | 18.0 | 30114 | 2.5899 | 0.0145 | 0.0145 | 0.0404 |
| 1.4461 | 19.0 | 31787 | 2.5812 | 0.0150 | 0.0150 | 0.0423 |
| 1.4149 | 20.0 | 33460 | 2.5841 | 0.0148 | 0.0148 | 0.0418 |
| 1.3933 | 21.0 | 35133 | 2.5783 | 0.0139 | 0.0139 | 0.0386 |
| 1.3752 | 22.0 | 36806 | 2.5730 | 0.0151 | 0.0151 | 0.0444 |
| 1.3412 | 23.0 | 38479 | 2.5709 | 0.0149 | 0.0149 | 0.0419 |
| 1.3307 | 24.0 | 40152 | 2.5699 | 0.0143 | 0.0143 | 0.0424 |
| 1.2909 | 25.0 | 41825 | 2.5648 | 0.0144 | 0.0144 | 0.0416 |
| 1.2679 | 26.0 | 43498 | 2.5615 | 0.0145 | 0.0145 | 0.0420 |
| 1.2603 | 27.0 | 45171 | 2.5626 | 0.0148 | 0.0148 | 0.0433 |
| 1.2203 | 28.0 | 46844 | 2.5670 | 0.0148 | 0.0148 | 0.0410 |
| 1.2134 | 29.0 | 48517 | 2.5536 | 0.0147 | 0.0147 | 0.0422 |
| 1.1907 | 30.0 | 50190 | 2.5701 | 0.0139 | 0.0139 | 0.0404 |
| 1.1702 | 31.0 | 51863 | 2.5722 | 0.0143 | 0.0143 | 0.0424 |
| 1.1555 | 32.0 | 53536 | 2.5679 | 0.0144 | 0.0144 | 0.0434 |
| 1.1371 | 33.0 | 55209 | 2.5694 | 0.0146 | 0.0146 | 0.0431 |
| 1.1189 | 34.0 | 56882 | 2.5692 | 0.0141 | 0.0141 | 0.0422 |
| 1.0989 | 35.0 | 58555 | 2.5831 | 0.0144 | 0.0144 | 0.0421 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
hroth/psais-paraphrase-multilingual-MiniLM-L12-v2-8shot
|
hroth
| 2023-01-03T14:12:40Z | 7 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T14:12:18Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 240 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 240,
"warmup_steps": 24,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
LuisQ/ddpm-celebahq-finetuned-butterflies-2epochs
|
LuisQ
| 2023-01-03T13:59:45Z | 6 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-01-03T13:59:33Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('LuisQ/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
AkkyMa/LunarLander
|
AkkyMa
| 2023-01-03T13:36:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T13:35:57Z |
---
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: 171.84 +/- 51.84
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
...
```
|
Maki7/alwe
|
Maki7
| 2023-01-03T13:10:27Z | 0 | 0 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-01-02T10:56:26Z |
---
license: apache-2.0
---
## Anime Segmentation Models
models of [https://github.com/SkyTNT/anime-segmentation](https://github.com/SkyTNT/anime-segmentation)
|
Aman6917/autotrain-tm3_model-2711480631
|
Aman6917
| 2023-01-03T13:02:37Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:Aman6917/autotrain-data-tm3_model",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-01-03T12:54:16Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Aman6917/autotrain-data-tm3_model
co2_eq_emissions:
emissions: 9.180873432477254
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2711480631
- CO2 Emissions (in grams): 9.1809
## Validation Metrics
- Loss: 0.088
- Rouge1: 94.701
- Rouge2: 90.005
- RougeL: 93.006
- RougeLsum: 93.078
- Gen Len: 66.529
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Aman6917/autotrain-tm3_model-2711480631
```
|
Aman6917/autotrain-tm3_model-2711480628
|
Aman6917
| 2023-01-03T13:02:33Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:Aman6917/autotrain-data-tm3_model",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-01-03T12:54:09Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Aman6917/autotrain-data-tm3_model
co2_eq_emissions:
emissions: 9.38482304577412
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2711480628
- CO2 Emissions (in grams): 9.3848
## Validation Metrics
- Loss: 0.088
- Rouge1: 94.638
- Rouge2: 90.173
- RougeL: 93.188
- RougeLsum: 93.163
- Gen Len: 66.529
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Aman6917/autotrain-tm3_model-2711480628
```
|
Aman6917/autotrain-tm3_model-2711480629
|
Aman6917
| 2023-01-03T13:02:29Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:Aman6917/autotrain-data-tm3_model",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-01-03T12:54:11Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Aman6917/autotrain-data-tm3_model
co2_eq_emissions:
emissions: 8.016071286117265
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2711480629
- CO2 Emissions (in grams): 8.0161
## Validation Metrics
- Loss: 0.088
- Rouge1: 94.701
- Rouge2: 89.907
- RougeL: 92.992
- RougeLsum: 93.163
- Gen Len: 66.529
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Aman6917/autotrain-tm3_model-2711480629
```
|
hroth/psais-all-MiniLM-L6-v2-5shot
|
hroth
| 2023-01-03T12:33:51Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-02T21:28:56Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
**This model was fine tuned with SetFit based on 5 utterances per intent and is used for an university project for intent detection. Other usage not tested**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 150 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 150,
"warmup_steps": 15,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
hroth/psais-all-mpnet-base-v2-1shot
|
hroth
| 2023-01-03T12:32:41Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-02T21:46:23Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
**This model was fine tuned with SetFit based on 1 utterances and is used for an university project for intent detection. Other usage not tested**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 30 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 30,
"warmup_steps": 3,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
hroth/psais-multi-qa-mpnet-base-dot-v1-1shot
|
hroth
| 2023-01-03T12:31:29Z | 7 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T10:10:46Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
**This model was fine tuned with SetFit based on 1 utterance and is used for an university project for intent detection. Other usage not tested**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 30 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 30,
"warmup_steps": 3,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
hroth/psais-multi-qa-mpnet-base-dot-v1-5shot
|
hroth
| 2023-01-03T12:31:07Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-03T10:28:15Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
**This model was fine tuned with SetFit based on 5 utterances and is used for an university project for intent detection. Other usage not tested**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 150 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 150,
"warmup_steps": 15,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
cleanrl/BattleZone-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1
|
cleanrl
| 2023-01-03T12:25:57Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"BattleZone-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T12:25:53Z |
---
tags:
- BattleZone-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: BattleZone-v5
type: BattleZone-v5
metrics:
- type: mean_reward
value: 34400.00 +/- 6621.18
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **BattleZone-v5**
This is a trained model of a PPO agent playing BattleZone-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 BattleZone-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/BattleZone-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/BattleZone-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/BattleZone-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 BattleZone-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': 'BattleZone-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'}
```
|
sd-concepts-library/egorey
|
sd-concepts-library
| 2023-01-03T12:16:13Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2023-01-03T12:16:02Z |
---
license: mit
---
### egorey on Stable Diffusion
This is the `<gorey>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
LarryAIDraw/yakimashake020
|
LarryAIDraw
| 2023-01-03T12:07:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-03T08:53:19Z |
---
license: creativeml-openrail-m
---
anythingV30 sd1.5 merge 0.2=>fst
fst NAInsfw NAIsfw add 0.3=>snd
NAInsfw gape merge 0.6=>gapensfw
snd gapensfw NAIsfw 1.0=>yakomashake020
|
TransLL/distilbert-base-uncased-distilled-clinc
|
TransLL
| 2023-01-03T12:05:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-03T11:50:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: train
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9503225806451613
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3186
- Accuracy: 0.9503
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 2.0524 | 0.7519 |
| 2.4405 | 2.0 | 636 | 1.0364 | 0.8623 |
| 2.4405 | 3.0 | 954 | 0.5867 | 0.9187 |
| 0.921 | 4.0 | 1272 | 0.4271 | 0.9361 |
| 0.417 | 5.0 | 1590 | 0.3687 | 0.9442 |
| 0.417 | 6.0 | 1908 | 0.3438 | 0.9484 |
| 0.2885 | 7.0 | 2226 | 0.3292 | 0.95 |
| 0.2454 | 8.0 | 2544 | 0.3235 | 0.9490 |
| 0.2454 | 9.0 | 2862 | 0.3206 | 0.95 |
| 0.2309 | 10.0 | 3180 | 0.3186 | 0.9503 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Abdullah17/distill-bert-uncased-clickbait
|
Abdullah17
| 2023-01-03T11:42:53Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-29T10:32:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distill-bert-uncased-English-clickbait
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. -->
# distill-bert-uncased-English-clickbait
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an Custom dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- 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: 3
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
sparanoid/milky-green-diff-svc
|
sparanoid
| 2023-01-03T11:33:25Z | 3 | 2 | null |
[
"audio-to-audio",
"zh",
"en",
"ja",
"license:agpl-3.0",
"region:us"
] |
audio-to-audio
| 2022-12-17T09:23:40Z |
---
language:
- zh
- en
- ja
tags:
- audio-to-audio
license: "agpl-3.0"
---
# Milky Green Diff-SVC Model
Milky Green (aka. [明前奶绿](https://space.bilibili.com/2132180406)) [Diff-SVC](https://github.com/prophesier/diff-svc) (Singing Voice Conversion via diffusion) model
|
adamf9898/9898-MTG
|
adamf9898
| 2023-01-03T10:39:40Z | 0 | 1 | null |
[
"license:unknown",
"region:us"
] | null | 2023-01-03T10:34:28Z |
---
license: unknown
---
https://perchance.org/9898-mtg-card-generator-v3
---
background = {import:background-image-plugin}
commentsPlugin = {import:comments-plugin}
o = [output]
ocn = [output_card_name.selectUnique(1)]
tCT = [thisCardType]
ocm = [output_card_mana]
oct = [output_card_type]
octst = [output_cardtype_subtype]
octxt = [output_card_text]
octxtkact = [output_card_text_keyword_action]
octxtkab = [output_card_text_keyword_ability]
ocr = [output_card_rarity]
ocsc = [output_card_set_code]
ocpt = [output_card_power_toughness]
c = [colors]
s = [scryfall.selectUnique(1).sentenceCase]
r = <b>[ocn.selectUnique(1).titleCase]<p>[ocm.selectUnique(1)]<br>[tCT.selectUnique(1).titleCase]<br>[ocsc.selectUnique(1)] • [ocr.selectUnique(1)]<br>{{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxt.selectUnique(1)]|[octxtkact.selectUnique(1)]|[octxtkab.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]} [octxt.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxt.selectUnique(1)] [octxtkact.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxtkact.selectUnique(1)] [octxtkab.selectUnique(1)]}<br><b>[ocpt.selectUnique(1)] <br><br>— — — — — — — — — — — — — — — — — —<br>
emo = {import:emotion}
pageTitle = <u>9898-MTG Card Generator V3</u>
pageSubtitle = 2023 © 9898-MTG
ocbl = ocstbl = output_card_subtype_basic_land
ocnbl = ocstnbl = output_card_subtype_nonbasic_land
commentsOptions
width = 400
title
9898-MTG Card Generator V3
$output = <b>[ocn.selectUnique(1).titleCase]<p>[ocm.selectUnique(1)]<br>[tCT.selectUnique(1).titleCase]<br>[ocsc.selectUnique(1)] • [ocr.selectUnique(1)]<br>{{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxt.selectUnique(1)]|[octxtkact.selectUnique(1)]|[octxtkab.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]} [octxt.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxt.selectUnique(1)] [octxtkact.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxtkact.selectUnique(1)] [octxtkab.selectUnique(1)]}<br><b>[ocpt.selectUnique(1)] <br>— — — — — — — — — — — — — — — — — —<br>
output1
title = Name
buttonText = Generate
content = <b>[ocn.selectUnique(1).titleCase]
output2
title = Cost
buttonText = Generate
content = <b>[ocm.selectUnique(1)]
output3
title = Type — Subtype
buttonText = Generate
content = <b>{[tCT.selectUnique(1)]|[oct.selectUnique(1)] — [octst.selectUnique(1)]}
output4
title = Set Code • Rartity
buttonText = Generate
content = <b>[ocsc.selectUnique(1)] • [ocr.selectUnique(1)]
output5
title = Effects
buttonText = Generate
content = <b>{[octxt.selectUnique(1)]|[octxtkact.selectUnique(1)]|[octxtkab.selectUnique(1)]|[octxt.selectUnique(1)]|[octxt.selectUnique(1)], [octxtkact.selectUnique(1)]|[octxtkact.selectUnique(1)], [octxtkab.selectUnique(1)]}
output6
title = Power/Toughness
buttonText = Generate
content = [ocpt.selectUnique(1)]
output7
title = Results
buttonText = Generate
content = [r]
output_card_name
{{import:adjective}|{import:verb}} {{import:word}|{import:noun}}
{import:adjective} {import:verb} {{import:word}|{import:noun}}
{{import:adjective}|{import:verb}} {import:word} {import:noun}
{import:adjective} {import:verb} {import:word} {import:noun}
thisCardType
[thisCardType = output_card_type] — [specificType]
specificType
[output_card_subtype_basic_land] ^[thisCardType == "Basic"]
[output_card_subtype_nonbasic_land] ^[thisCardType == "NonBasic"]
[output_card_subtype_creature] ^[thisCardType == "Creature"]
[output_card_subtype_artifact] ^[thisCardType == "Artifact"]
[output_card_subtype_enchantment] ^[thisCardType == "Enchantment"]
[output_card_subtype_planeswalker] ^[thisCardType == "Planeswalker"]
[output_card_subtype_instant] ^[thisCardType == "Instant"]
[output_card_subtype_sorcery] ^[thisCardType == "Sorcery"]
[output_card_subtype_creature] ^[thisCardType == "Creature"]
[output_card_subtype_plane] ^[thisCardType == "Plane"]
output_card_mana
{{{0-12}|X}|{0-12}|X {0-6 [basic_mana]|[hybrid_mana]|[tri_hybrid_mana]|[four_color_mana]|[multicolor_mana]|[phyrexian_mana]|[prismatic_mana]}}
basic_mana
{W|U|B|R|G|C|X|S}
hybrid_mana
{{1-2}/{W|U|B|R|G}|{W|U|B|R|G}}
tri_hybrid_mana
{W/B/G|W/U/G|W/U/B|U/B/R|W/U/R|B/R/G|W/B/R|W/R/G|U/B/G|U/R/G}
four_color_mana
{U/B/R/G|W/B/R/G|W/U/B/G|W/U/B/R}
multicolor_mana
{BR|UB|BG|RG|GU|UR|WB|GW|RW|WU}
phyrexian_mana
-2 Life/{W|U|B|R|G|C|X|S}
prismatic_mana
WUBRG
out
[thisCardType = output_card_type]
ocbl
[ocbl = ocstbl = output_card_subtype_basic_land]
ocnbl
[ocnbl = ocstnbl = output_card_subtype_nonbasic_land]
ocleg
[ocleg = ocstleg = output_card_subtype_legendary]
output_card_type
Basic [if (output_card_type = "Basic") {output_card_subtype_basic_land} else {output_card_type}|{ocbl}]
NonBasic [if (output_card_type = "NonBasic") {output_card_subtype_nonbasic_land} else {output_card_type}|{ocnbl}]
Legendary [if (output_card_type = "Legendary") {output_card_subtype_legendary} else {output_card_type}|{ocstleg}]
Token
Tribal
World
Conspiracy
Creature [if (output_card_type = "Creature") {output_card_subtype_creature} else {output_card_type}]
Advertisement
Artifact [if (output_card_type = "Artifact") {output_card_subtype_artifact} else {output_card_type}]
Artifact Creature
Artifact Land
Enchantment [if (output_card_type = "Enchantment") {output_card_subtype_enchantment} else {output_card_type}]
Enchantment Creature
Instant [if (output_card_type = "Instant") {output_card_subtype_instant} else {output_card_type}]
Sorcery [if (output_card_type = "Sorcery") {output_card_subtype_sorcery} else {output_card_type}]
Land [if (output_card_type = "Land") {output_card_subtype_basic_land} else {output_card_subtype_basic_land} {output_card_type}] [if (output_card_type = "land") output_card_mana = ""]
Planeswalker [if (output_card_type = "Planeswalker") {{{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxt.selectUnique(1)]|[octxtkact.selectUnique(1)]|[octxtkab.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]} [octxt.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxt.selectUnique(1)] [octxtkact.selectUnique(1)]|{[scry_keyword_abilities.selectUnique(1)]|[scry_keyword_actions.selectUnique(1)]|[scry_ability_words.selectUnique(1)]}|[octxtkact.selectUnique(1)] [octxtkab.selectUnique(1)]}}[if (output_card_type = "Planeswalker") {output_card_subtype_planeswalker} else {output_card_type}]
Emblem
Phenemonom
Plane [if (output_card_type = "Plane") {output_card_subtype_plane} else {output_card_type}]
Dungeon
Scheme
Vanguard
output_cardtype_subtype
{[output_card_subtype_artifact]|[output_card_subtype_enchantment]|[output_card_subtype_basic_land]|[output_card_subtype_nonbasic_land]|[output_card_subtype_planeswalker]|[output_card_subtype_instant]|[output_card_subtype_sorcery]|[output_card_subtype_creature]|[output_card_subtype_plane]}
output_card_subtype_artifact
Blood
Clue
Contraption
Equipment
Food
Fortification
Gold
Powerstone
Treasure
Vehicle
output_card_subtype_enchantment
Aura
Background
Cartouche
Class
Curse
Rune
Saga
Shard
Shrine
output_card_subtype_basic_land
Plains
Island
Swamp
Mountain
Forest
Waste
Snow-Covered Plains
Snow-Covered Island
Snow-Covered Swamp
Snow-Covered Mountain
Snow-Covered Forest
output_card_subtype_nonbasic_land
Desert
Gate
Lair
Locus
Mine
Power-Plant
Tower
Urza’s
output_card_subtype_planeswalker
Ajani
Aminatou
Angrath
Arlinn
Ashiok
Bahamut
Basri
Bolas
Calix
Chandra
Dack
Dakkon
Daretti
Davriel
Dihada
Domri
Dovin
Ellywick
Elminster
Elspeth
Estrid
Freyalise
Garruk
Gideon
Grist
Huatli
Jace
Jaya
Jeska
Kaito
Karn
Kasmina
Kaya
Kiora
Koth
Liliana
Lolth
Lukka
Minsc
Mordenkainen
Nahiri
Narset
Niko
Nissa
Nixilis
Oko
Ral
Rowan
Saheeli
Samut
Sarkhan
Serra
Sivitri
Sorin
Szat
Tamiyo
Tasha
Teferi
Teyo
Tezzeret
Tibalt
Tyvar
Ugin
Venser
Vivien
Vraska
Will
Windgrace
Wrenn
Xenagos
Yanggu
Yanling
Zariel
output_card_subtype_instant
Adventure
Arcane
Lesson
Trap
output_card_subtype_sorcery
Adventure
Arcane
Lesson
Trap
output_card_subtype_creature
Advisor
Aetherborn
Ally
Angel
Antelope
Ape
Archer
Archon
Army
Artificer
Assassin
Assembly-Worker
Atog
Aurochs
Avatar
Azra
Badger
Barbarian
Bard
Basilisk
Bat
Bear
Beast
Beeble
Beholder
Berserker
Bird,
Blinkmoth
Boar
Bringer
Brushwagg
Camarid
Camel
Caribou
Carrier
Cat
Centaur
Cephalid
Chimera
Citizen
Cleric
Cockatrice
Construct
Coward
Crab
Crocodile
Cyclops
Dauthi
Demigod
Demon
Deserter
Devil
Dinosaur
Djinn
Dog
Dragon
Drake
Dreadnought
Drone
Druid
Dryad
Dwarf
Efreet
Egg
Elder
Eldrazi
Elemental
Elephant
Elf
Elk
Eye
Faerie
Ferret
Fish
Flagbearer
Fox
Fractal
Frog
Fungus
Gargoyle
Germ
Giant
Gith
Gnoll
Gnome
Goat
Goblin
God
Golem
Gorgon
Graveborn
Gremlin
Griffin
Hag
Halfling
Hamster
Harpy
Hellion
Hippo
Hippogriff
Homarid
Homunculus
Horror
Horse
Human
Hydra
Hyena
Illusion
Imp
Incarnation
Inkling
Insect
Jackal
Jellyfish
Juggernaut
Kavu
Kirin
Kithkin
Knight
Kobold
Kor
Kraken
Lamia
Lammasu
Leech
Leviathan
Lhurgoyf
Licid
Lizard
Manticore
Masticore
Mercenary
Merfolk
Metathran
Minion
Minotaur
Mole
Monger
Mongoose
Monk
Monkey
Moonfolk
Mouse
Mutant
Myr
Mystic
Naga
Nautilus
Nephilim
Nightmare
Nightstalker
Ninja
Noble
Noggle
Nomad
Nymph
Octopus
Ogre
Ooze
Orb
Orc
Orgg
Otter
Ouphe
Ox
Oyster
Pangolin
Peasant
Pegasus
Pentavite
Pest
Phelddagrif
Phoenix
Phyrexian
Pilot
Pincher
Pirate
Plant
Praetor
Prism
Processor
Rabbit
Raccoon
Ranger
Rat
Rebel
Reflection
Rhino
Rigger
Rogue
Sable
Salamander
Samurai
Sand
Saproling
Satyr
Scarecrow
Scion
Scorpion
Scout
Sculpture
Serf
Serpent
Servo
Shade
Shaman
Shapeshifter
Shark
Sheep
Siren
Skeleton
Slith
Sliver
Slug
Snake
Soldier
Soltari
Spawn
Specter
Spellshaper
Sphinx
Spider
Spike
Spirit
Splinter
Sponge
Squid
Squirrel
Starfish
Surrakar
Survivor
Tentacle
Tetravite
Thalakos
Thopter
Thrull
Tiefling
Treefolk
Trilobite
Triskelavite
Troll
Turtle
Unicorn
Vampire
Vedalken
Viashino
Volver
Wall
Walrus
Warlock
Warrior
Weird
Werewolf
Whale
Wizard
Wolf
Wolverine
Wombat
Worm
Wraith
Wurm
Yeti
Zombie
Zubera
output_card_subtype_plane
Alara
Arkhos
Azgol
Belenon
Bolas’s Meditation Realm
Dominaria
Equilor
Ergamon
Fabacin
Innistrad
Iquatana
Ir
Kaldheim
Kamigawa
Karsus
Kephalai
Kinshala
Kolbahan
Kyneth
Lorwyn
Luvion
Mercadia
Mirrodin
Moag
Mongseng
Muraganda
New Phyrexia
Phyrexia
Pyrulea
Rabiah
Rath
Ravnica
Regatha
Segovia
Serra’s Realm
Shadowmoor
Shandalar
Ulgrotha
Valla
Vryn
Wildfire
Xerex
Zendikar
output_card_subtype_legendary
Artifact
Creature
Enchantment
Land
Planeswalker
Instant
Sorcery
Artifact Land
Artifact Creature
Enchantment Artifact
Enchantment Artifact Creature
Enchantment Creature
Enchantment Land
Instant Creature
Land Creature
output_card_set_code
{A-Z}{A-Z}{A-Z}
output_card_rarity
{Common|Uncommon|Rare|Mythic Rare|Special|Masterpiece}
output_card_text
[output_card_text_keyword_action]
[output_card_text_keyword_ability]
[output_card_text_keyword_action] [output_card_text_keyword_ability]
When ~this enters the battlefield, {[output_card_text_keyword_action]|[output_card_text_keyword_ability]|[output_card_text_keyword_action], [output_card_text_keyword_ability]}
Whenever ~this enters the battlefield or attacks, {[output_card_text_keyword_action]|[output_card_text_keyword_ability]|[output_card_text_keyword_action], [output_card_text_keyword_ability]}
When ~this dies, {[output_card_text_keyword_action]|[output_card_text_keyword_ability]|[output_card_text_keyword_action], [output_card_text_keyword_ability]}
Whenever a card from [output_game_zones] is put into [output_game_zones], [output_card_text_keyword_action]
When ~this is put into [output_game_zones], [output_card_text_keyword_action] target {[output_card_type]|[output_cardtype_subtype]|[output_card_type] [output_cardtype_subtype]}
Whenever ~this deals damage to a player, {[output_card_text_keyword_action]|[output_card_text_keyword_ability]|[output_card_text_keyword_action], [output_card_text_keyword_ability]}
Whenever ~this deals damage to a player, create a token thats a copy of ~this.
Whenever ~this deals damage to a player, exile target {[output_card_type]|[output_cardtype_subtype]}
Whenever you {[output_card_text_keyword_action]|[output_card_text_keyword_ability]|[output_card_text_keyword_action], [output_card_text_keyword_ability]}, {[output_card_text_keyword_action]|[output_card_text_keyword_ability]|[output_card_text_keyword_action], [output_card_text_keyword_ability]}
Whenever you gain life, create a 1/1 colorless [output_card_subtype_creature] creature token.
Whenever you roll a die, create a X/X colorless [output_card_subtype_creature] creature token where X equals the result of the die roll.
[c] creatures get {+0/+1|+1/+0|+1/+1}
[c] creatures you control get {+0/+1|+1+0|+1/+1}
output_card_text_keyword_action
Abandon
Activate
Adapt
Amass
Assemble
Attach
Bolster
Cast
Clash
Connive
Counter
Create
Destroy
Detain
Discard
Double
Exchange
Exert
Exile
Explore
Fateseal
Fight
Goad
Investigate
Learn
Manifest
Meld
Mill
Monstrosity
Planeswalk
Play
Populate
Proliferate
Regenerate
Reveal
Sacrifice
Scry
Search
Set in Motion
Shuffle
Support
Surveil
Tap
Transform
Untap
Venture into the Dungeon
Vote
output_card_text_keyword_ability
Deathtouch
Defender
Double Strike
Enchant
Equip
First Strike
Flash
Flying
Haste
Hexproof
Indestructible
Intimidate
Landwalk
Lifelink
Protection
Reach
Shroud
Trample
Vigilance
Ward
Banding
Rampage
Cumulative Upkeep
Flanking
Phasing
Buyback
Shadow
Cycling
Echo
Horsemanship
Fading
Kicker
Flashback
Madness
Fear
Morph
Amplify
Provoke
Storm
Affinity
Entwine
Modular
Sunburst
Bushido
Soulshift
Splice
Offering
Ninjutsu
Epic
Convoke
Dredge
Transmute
Bloodthirst
Haunt
Replicate
Forecast
Graft
Recover
Ripple
Split Second
Suspend
Vanishing
Absorb
Aura Swap
Delve
Fortify
Frenzy
Gravestorm
Poisonous
Transfigure
Champion
Changeling
Evoke
Hideaway
Prowl
Reinforce
Conspire
Persist
Wither
Retrace
Devour
Exalted
Unearth
Cascade
Annihilator
Level Up
Rebound
Totem Armor
Infect
Battle Cry
Living Weapon
Undying
Miracle
Soulbond
Overload
Scavenge
Unleash
Cipher
Evolve
Extort
Fuse
Bestow
Tribute
Dethrone
Hidden Agenda
Outlast
Prowess
Dash
Exploit
Menace
Renown
Awaken
Devoid
Ingest
Myriad
Surge
Skulk
Emerge
Escalate
Melee
Crew
Fabricate
Partner
Undaunted
Improvise
Aftermath
Embalm
Eternalize
Afflict
Ascend
Assist
Jump-Start
Mentor
Afterlife
Riot
Spectacle
Escape
Companion
Mutate
Encore
Boast
Foretell
Demonstrate
Daybound
Nightbound
Disturb
Decayed
Cleave
Training
Compleated
Reconfigure
Blitz
Casualty
Enlist
Read Ahead
output_card_power_toughness
{{0-12}/{1-12}|[scry_powers]/[scry_toughness]}
output_game_zones
Battlefield
Command
Exile
Graveyard
Hand
Library
Sideboard
Stack
Outside Of The Game
word_types
{import:noun}
{import:pronoun}
{import:verb}
{import:adjective}
{import:adverb}
{import:preposition}
{import:interjection}
noun
abbey
absence
absorption
abstinence
absurdity
abundance
acceptance
accessibility
accommodation
accomplice
accountability
accounting
accreditation
accuracy
acquiescence
acreage
actress
actuality
adage
adaptation
adherence
adjustment
adoption
adultery
advancement
advert
advertisement
advertising
advice
aesthetics
affinity
aggression
agriculture
aircraft
airtime
allegation
allegiance
allegory
allergy
allies
alligator
allocation
allotment
altercation
ambulance
ammonia
anatomy
anemia
ankle
announcement
annoyance
annuity
anomaly
anthropology
anxiety
apartheid
apologise
apostle
apparatus
appeasement
appellation
appendix
applause
appointment
appraisal
archery
archipelago
architecture
ardor
arrears
arrow
artisan
artistry
ascent
assembly
assignment
association
asthma
atheism
attacker
attraction
attractiveness
auspices
authority
avarice
aversion
aviation
babbling
backlash
baker
ballet
balls
banjo
baron
barrier
barrister
bases
basin
basis
battery
battling
bedtime
beginner
begun
bending
bicycle
billing
bingo
biography
biology
birthplace
blackberry
blather
blossom
boardroom
boasting
bodyguard
boldness
bomber
bondage
bonding
bones
bonus
bookmark
boomer
booty
bounds
bowling
brainstorming
breadth
breaker
brewer
brightness
broccoli
broth
brotherhood
browsing
brunch
brunt
building
bullion
bureaucracy
burglary
buyout
by-election
cabal
cabbage
calamity
campaign
canonization
captaincy
carcass
carrier
cartridge
cassette
catfish
caught
celebrity
cemetery
certainty
certification
charade
chasm
check-in
cheerleader
cheesecake
chemotherapy
chili
china
chivalry
cholera
cilantro
circus
civilisation
civility
clearance
clearing
clerk
climber
closeness
clothing
clutches
coaster
coconut
coding
collaborator
colleague
college
collision
colors
combustion
comedian
comer
commander
commemoration
commenter
commissioner
commune
competition
completeness
complexity
computing
comrade
concur
condominium
conduit
confidant
configuration
confiscation
conflagration
conflict
consist
consistency
consolidation
conspiracy
constable
consul
consultancy
contentment
contents
contractor
conversation
cornerstone
corpus
correlation
councilman
counselor
countdown
countryman
coverage
covering
coyote
cracker
creator
criminality
crocodile
cropping
cross-examination
crossover
crossroads
culprit
cumin
curator
curfew
cursor
custard
cutter
cyclist
cyclone
cylinder
cynicism
daddy
damsel
darkness
dawning
daybreak
dealing
dedication
deduction
defection
deference
deficiency
definition
deflation
degeneration
delegation
delicacy
delirium
deliverance
demeanor
demon
demonstration
denomination
dentist
departure
depletion
depression
designation
despotism
detention
developer
devolution
dexterity
diagnosis
dialect
differentiation
digger
digress
dioxide
diploma
disability
disarmament
discord
discovery
dishonesty
dismissal
disobedience
dispatcher
disservice
distribution
distributor
diver
diversity
docking
dollar
dominance
domination
dominion
donkey
doorstep
doorway
dossier
downside
drafting
drank
drilling
driver
drumming
drunkenness
duchess
ducking
dugout
dumps
dwelling
dynamics
eagerness
earnestness
earnings
eater
editor
effectiveness
electricity
elements
eloquence
emancipation
embodiment
embroidery
emperor
employment
encampment
enclosure
encouragement
endangerment
enlightenment
enthusiasm
environment
environs
envoy
epilepsy
equation
equator
error
espionage
estimation
evacuation
exaggeration
examination
exclamation
expediency
exploitation
extinction
eyewitness
falls
fascism
fastball
feces
feedback
ferocity
fertilization
fetish
finale
firing
fixing
flashing
flask
flora
fluke
folklore
follower
foothold
footing
forefinger
forefront
forgiveness
formality
formation
formula
foyer
fragmentation
framework
fraud
freestyle
frequency
friendliness
fries
frigate
fulfillment
function
functionality
fundraiser
fusion
futility
gallantry
gallery
genesis
genitals
girlfriend
glamour
glitter
glucose
google
grandeur
grappling
greens
gridlock
grocer
groundwork
grouping
gunman
gusto
habitation
hacker
hallway
hamburger
hammock
handling
hands
handshake
happiness
hardship
headcount
header
headquarters
heads
headset
hearth
hearts
heath
hegemony
height
hello
helper
helping
helplessness
hierarchy
hoarding
hockey
homeland
homer
honesty
horror
horseman
hostility
housing
humility
hurricane
iceberg
ignition
illness
illustration
illustrator
immunity
immunization
imperialism
imprisonment
inaccuracy
inaction
inactivity
inauguration
indecency
indicator
inevitability
infamy
infiltration
influx
iniquity
innocence
innovation
insanity
inspiration
instruction
instructor
insurer
interact
intercession
intercourse
intermission
interpretation
intersection
interval
intolerance
intruder
invasion
investment
involvement
irrigation
iteration
jenny
jogging
jones
joseph
juggernaut
juncture
jurisprudence
juror
kangaroo
kingdom
knocking
laborer
larceny
laurels
layout
leadership
leasing
legislation
leopard
liberation
licence
lifeblood
lifeline
ligament
lighting
likeness
line-up
lineage
liner
lineup
liquidation
listener
literature
litigation
litre
loathing
locality
lodging
logic
longevity
lookout
lordship
lustre
ma'am
machinery
madness
magnificence
mahogany
mailing
mainframe
maintenance
majority
manga
mango
manifesto
mantra
manufacturer
maple
martin
martyrdom
mathematician
matrix
matron
mayhem
mayor
means
meantime
measurement
mechanics
mediator
medics
melodrama
memory
mentality
metaphysics
method
metre
miner
mirth
misconception
misery
mishap
misunderstanding
mobility
molasses
momentum
monarchy
monument
morale
mortality
motto
mouthful
mouthpiece
mover
movie
mowing
murderer
musician
mutation
mythology
narration
narrator
nationality
negligence
neighborhood
neighbour
nervousness
networking
nexus
nightmare
nobility
nobody
noodle
normalcy
notification
nourishment
novella
nucleus
nuisance
nursery
nutrition
nylon
oasis
obscenity
obscurity
observer
offense
onslaught
operation
opportunity
opposition
oracle
orchestra
organisation
organizer
orientation
originality
ounce
outage
outcome
outdoors
outfield
outing
outpost
outset
overseer
owner
oxygen
pairing
panther
paradox
parliament
parsley
parson
passenger
pasta
patchwork
pathos
patriotism
pendulum
penguin
permission
persona
perusal
pessimism
peter
philosopher
phosphorus
phrasing
physique
piles
plateau
playing
plaza
plethora
plurality
pneumonia
pointer
poker
policeman
polling
poster
posterity
posting
postponement
potassium
pottery
poultry
pounding
pragmatism
precedence
precinct
preoccupation
pretense
priesthood
prisoner
privacy
probation
proceeding
proceedings
processing
processor
progression
projection
prominence
propensity
prophecy
prorogation
prospectus
protein
prototype
providence
provider
provocation
proximity
puberty
publicist
publicity
publisher
pundit
putting
quantity
quart
quilting
quorum
racism
radiance
ralph
rancher
ranger
rapidity
rapport
ratification
rationality
reaction
reader
reassurance
rebirth
receptor
recipe
recognition
recourse
recreation
rector
recurrence
redemption
redistribution
redundancy
refinery
reformer
refrigerator
regularity
regulator
reinforcement
reins
reinstatement
relativism
relaxation
rendition
repayment
repentance
repertoire
repository
republic
reputation
resentment
residency
resignation
restaurant
resurgence
retailer
retention
retirement
reviewer
riches
righteousness
roadblock
robber
rocks
rubbing
runoff
saloon
salvation
sarcasm
saucer
savior
scarcity
scenario
scenery
schism
scholarship
schoolboy
schooner
scissors
scolding
scooter
scouring
scrimmage
scrum
seating
sediment
seduction
seeder
seizure
self-confidence
self-control
self-respect
semicolon
semiconductor
semifinal
senator
sending
serenity
seriousness
servitude
sesame
setup
sewing
sharpness
shaving
shoplifting
shopping
siding
simplicity
simulation
sinking
skate
sloth
slugger
snack
snail
snapshot
snark
soccer
solemnity
solicitation
solitude
somewhere
sophistication
sorcery
souvenir
spaghetti
specification
specimen
specs
spectacle
spectre
speculation
sperm
spoiler
squad
squid
staging
stagnation
staircase
stairway
stamina
standpoint
standstill
stanza
statement
stillness
stimulus
stocks
stole
stoppage
storey
storyteller
stylus
subcommittee
subscription
subsidy
suburb
success
sufferer
supposition
suspension
sweater
sweepstakes
swimmer
syndrome
synopsis
syntax
system
tablespoon
taker
tavern
technology
telephony
template
tempo
tendency
tendon
terrier
terror
terry
theater
theology
therapy
thicket
thoroughfare
threshold
thriller
thunderstorm
ticker
tiger
tights
today
tossing
touchdown
tourist
tourney
toxicity
tracing
tractor
translation
transmission
transmitter
trauma
traveler
treadmill
trilogy
trout
tuning
twenties
tycoon
tyrant
ultimatum
underdog
underwear
unhappiness
unification
university
uprising
vaccination
validity
vampire
vanguard
variation
vegetation
verification
viability
vicinity
victory
viewpoint
villa
vindication
violation
vista
vocalist
vogue
volcano
voltage
vomiting
vulnerability
waistcoat
waitress
wardrobe
warmth
watchdog
wealth
weariness
whereabouts
whisky
whiteness
widget
width
windfall
wiring
witchcraft
withholding
womanhood
words
workman
youngster
pronoun
all
another
any
anybody
anyone
anything
both
each
each other
either
everybody
everyone
everything
few
he
her
hers
herself
him
himself
his
I
it
its
itself
many
me
mine
more
most
much
my
myself
neither
no one
nobody
none
one
one another
other
others
our
ours
ourselves
several
she
some
somebody
someone
something
that
their
theirs
them
themselves
these
they
this
those
uswe
what
whatever
which
whichever
who
whoever
whom
whomever
whose
you
your
yours
yourself
yourselves
verb
accept
pastTense = accepted
add
pastTense = added
admire
pastTense = admired
admit
pastTense = admitted
advise
pastTense = advised
afford
pastTense = afforded
agree
pastTense = agreed
alert
pastTense = alerted
allow
pastTense = allowed
amuse
pastTense = amused
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pastTense = analysed
announce
pastTense = announced
annoy
pastTense = annoyed
answer
pastTense = answered
apologise
pastTense = apologised
appear
pastTense = appeared
applaud
pastTense = applauded
appreciate
pastTense = appreciated
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pastTense = approved
argue
pastTense = argued
arrange
pastTense = arranged
arrest
pastTense = arrested
arrive
pastTense = arrived
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pastTense = asked
attach
pastTense = attached
attack
pastTense = attacked
attempt
pastTense = attempted
attend
pastTense = attended
attract
pastTense = attracted
avoid
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back
pastTense = backed
bake
pastTense = baked
balance
pastTense = balanced
ban
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bang
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bare
pastTense = bared
bat
pastTense = batted
bathe
pastTense = bathed
battle
pastTense = battled
beam
pastTense = beamed
beg
pastTense = begged
behave
pastTense = behaved
belong
pastTense = belonged
bleach
pastTense = bleached
bless
pastTense = blessed
blind
pastTense = blinded
blink
pastTense = blinked
blot
pastTense = blotted
blush
pastTense = blushed
boast
pastTense = boasted
boil
pastTense = boiled
bolt
pastTense = bolted
bomb
pastTense = bombed
book
pastTense = booked
bore
pastTense = bored
borrow
pastTense = borrowed
bounce
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bow
pastTense = bowed
box
pastTense = boxed
brake
pastTense = braked
branch
pastTense = branched
breathe
pastTense = breathed
bruise
pastTense = bruised
brush
pastTense = brushed
bubble
pastTense = bubbled
bump
pastTense = bumped
burn
pastTense = burned
bury
pastTense = buried
buzz
pastTense = buzzed
calculate
pastTense = calculated
call
pastTense = called
camp
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care
pastTense = cared
carry
pastTense = carried
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pastTense = carved
cause
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challenge
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change
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charge
pastTense = charged
chase
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cheat
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check
pastTense = checked
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chew
pastTense = chewed
choke
pastTense = choked
chop
pastTense = chopped
claim
pastTense = claimed
clap
pastTense = clapped
clean
pastTense = cleaned
clear
pastTense = cleared
clip
pastTense = clipped
close
pastTense = closed
coach
pastTense = coached
coil
pastTense = coiled
collect
pastTense = collected
colour
pastTense = coloured
comb
pastTense = combed
command
pastTense = commanded
communicate
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compare
pastTense = compared
compete
pastTense = competed
complain
pastTense = complained
complete
pastTense = completed
concentrate
pastTense = concentrated
concern
pastTense = concerned
confess
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pastTense = confused
connect
pastTense = connected
consider
pastTense = considered
consist
pastTense = consisted
contain
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continue
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copy
pastTense = copied
correct
pastTense = corrected
cough
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count
pastTense = counted
cover
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crack
pastTense = cracked
crash
pastTense = crashed
crawl
pastTense = crawled
cross
pastTense = crossed
crush
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cry
pastTense = cried
cure
pastTense = cured
curl
pastTense = curled
curve
pastTense = curved
cycle
pastTense = cycled
dam
pastTense = dammed
damage
pastTense = damaged
dance
pastTense = danced
dare
pastTense = dared
decay
pastTense = decayed
deceive
pastTense = deceived
decide
pastTense = decided
decorate
pastTense = decorated
delay
pastTense = delayed
delight
pastTense = delighted
deliver
pastTense = delivered
depend
pastTense = depended
describe
pastTense = described
desert
pastTense = deserted
deserve
pastTense = deserved
destroy
pastTense = destroyed
detect
pastTense = detected
develop
pastTense = developed
disagree
pastTense = disagreed
disappear
pastTense = disappeared
disapprove
pastTense = disapproved
disarm
pastTense = disarmed
discover
pastTense = discovered
dislike
pastTense = disliked
divide
pastTense = divided
double
pastTense = doubled
doubt
pastTense = doubted
drag
pastTense = dragged
drain
pastTense = drained
dream
pastTense = dreamed
dress
pastTense = dressed
drip
pastTense = dripped
drop
pastTense = dropped
drown
pastTense = drowned
drum
pastTense = drummed
dry
pastTense = dried
dust
pastTense = dusted
earn
pastTense = earned
educate
pastTense = educated
embarrass
pastTense = embarrassed
employ
pastTense = employed
empty
pastTense = emptied
encourage
pastTense = encouraged
end
pastTense = ended
enjoy
pastTense = enjoyed
enter
pastTense = entered
entertain
pastTense = entertained
escape
pastTense = escaped
examine
pastTense = examined
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pastTense = excited
excuse
pastTense = excused
exercise
pastTense = exercised
exist
pastTense = existed
expand
pastTense = expand
expect
pastTense = expected
explain
pastTense = explained
explode
pastTense = exploded
extend
pastTense = extended
face
pastTense = faced
fade
pastTense = faded
fail
pastTense = failed
fancy
pastTense = fancied
fasten
pastTense = fastened
fax
pastTense = faxed
fear
pastTense = feared
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pastTense = fenced
fetch
pastTense = fetched
file
pastTense = filed
fill
pastTense = filled
film
pastTense = filmed
fire
pastTense = fired
fit
pastTense = fitted
fix
pastTense = fixed
flap
pastTense = flapped
flash
pastTense = flashed
float
pastTense = floated
flood
pastTense = flooded
flow
pastTense = flowed
flower
pastTense = flowered
fold
pastTense = folded
follow
pastTense = followed
fool
pastTense = fooled
force
pastTense = forced
form
pastTense = formed
found
pastTense = founded
frame
pastTense = framed
frighten
pastTense = frightened
fry
pastTense = fried
gather
pastTense = gathered
gaze
pastTense = gazed
glow
pastTense = glowed
glue
pastTense = glued
grab
pastTense = grabbed
grate
pastTense = grated
grease
pastTense = greased
greet
pastTense = greeted
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pastTense = gripped
groan
pastTense = groaned
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pastTense = guaranteed
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pastTense = guarded
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pastTense = guided
hammer
pastTense = hammered
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pastTense = handed
handle
pastTense = handled
hang
pastTense = hung
happen
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harass
pastTense = harassed
harm
pastTense = harmed
hate
pastTense = hated
haunt
pastTense = haunted
head
pastTense = headed
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pastTense = healed
heap
pastTense = heaped
heat
pastTense = heated
help
pastTense = helped
hook
pastTense = hooked
hop
pastTense = hopped
hope
pastTense = hoped
hover
pastTense = hovered
hug
pastTense = hugged
hum
pastTense = hummed
hunt
pastTense = hunted
hurry
pastTense = hurried
identify
pastTense = identified
ignore
pastTense = ignored
imagine
pastTense = imagined
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pastTense = impressed
improve
pastTense = improved
include
pastTense = included
increase
pastTense = increased
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pastTense = influenced
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inject
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injure
pastTense = injured
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pastTense = instructed
intend
pastTense = intended
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pastTense = interested
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interrupt
pastTense = interrupted
introduce
pastTense = introduced
invent
pastTense = invented
invite
pastTense = invited
irritate
pastTense = irritated
itch
pastTense = itched
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jam
pastTense = jammed
jog
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join
pastTense = joined
joke
pastTense = joked
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juggle
pastTense = juggled
jump
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kick
pastTense = kicked
kill
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kiss
pastTense = kissed
kneel
pastTense = knelt
knit
pastTense = knitted
knock
pastTense = knocked
knot
pastTense = knotted
label
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last
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laugh
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launch
pastTense = launched
learn
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license
pastTense = licensed
lick
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pastTense = lied
lighten
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list
pastTense = listed
listen
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pastTense = lived
load
pastTense = loaded
lock
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pastTense = longed
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pastTense = look
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pastTense = loved
man
pastTense = manned
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pastTense = marched
mark
pastTense = marked
marry
pastTense = married
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pastTense = mated
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pastTense = mattered
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pastTense = measured
meddle
pastTense = meddled
melt
pastTense = melted
memorise
pastTense = memorised
mend
pastTense = mended
mess up
pastTense = messed up
milk
pastTense = milked
mine
pastTense = mined
miss
pastTense = missed
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pastTense = mixed
moan
pastTense = moaned
moor
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mourn
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move
pastTense = moved
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mug
pastTense = mugged
multiply
pastTense = multiplied
murder
pastTense = murdered
nail
pastTense = nailed
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need
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nest
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note
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notice
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number
pastTense = numbered
obey
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observe
pastTense = observed
obtain
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offend
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offer
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open
pastTense = opened
order
pastTense = ordered
overflow
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owe
pastTense = owed
own
pastTense = owned
pack
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paddle
pastTense = paddled
paint
pastTense = painted
park
pastTense = parked
part
pastTense = parted
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pastTense = passed
paste
pastTense = pasted
pat
pastTense = patted
pause
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peck
pastTense = pecked
pedal
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peel
pastTense = peeled
peep
pastTense = peeped
perform
pastTense = performed
permit
pastTense = permitted
phone
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pick
pastTense = picked
pinch
pastTense = pinched
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pastTense = pined
place
pastTense = placed
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pastTense = planned
plant
pastTense = planted
play
pastTense = played
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plug
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point
pastTense = pointed
poke
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polish
pastTense = polished
pop
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post
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precede
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pretend
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pastTense = pricked
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program
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protect
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pump
pastTense = pumped
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rain
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replace
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roll
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rub
pastTense = rubbed
ruin
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pastTense = scolded
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pastTense = scratched
scream
pastTense = screamed
screw
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scribble
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share
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shave
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shiver
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shock
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shop
pastTense = shopped
shrug
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sigh
pastTense = sighed
sign
pastTense = signed
signal
pastTense = signalled
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sip
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ski
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skip
pastTense = skipped
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slip
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slow
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pastTense = smelled
smile
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sneeze
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sniff
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snore
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soak
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soothe
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sound
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sparkle
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spell
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spill
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spoil
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spray
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sprout
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squash
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squeak
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squeal
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squeeze
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stamp
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step
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stop
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strengthen
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pastTense = stretched
strip
pastTense = stripped
stroke
pastTense = stroked
stuff
pastTense = stuffed
subtract
pastTense = subtracted
succeed
pastTense = succeeded
suck
pastTense = sucked
suffer
pastTense = suffered
suggest
pastTense = suggested
suit
pastTense = suited
supply
pastTense = supplied
support
pastTense = supported
suppose
pastTense = supposed
surprise
pastTense = surprised
surround
pastTense = surrounded
suspect
pastTense = suspected
suspend
pastTense = suspended
switch
pastTense = switched
talk
pastTense = talked
tame
pastTense = tamed
tap
pastTense = tapped
taste
pastTense = tasted
tease
pastTense = teased
telephone
pastTense = telephoned
tempt
pastTense = tempted
terrify
pastTense = terrified
test
pastTense = tested
thank
pastTense = thanked
thaw
pastTense = thawed
tick
pastTense = ticked
tickle
pastTense = tickled
tie
pastTense = tied
time
pastTense = timed
tip
pastTense = tipped
tire
pastTense = tired
touch
pastTense = touched
tour
pastTense = toured
tow
pastTense = towed
trace
pastTense = traced
trade
pastTense = traded
train
pastTense = trained
transport
pastTense = transported
trap
pastTense = trapped
travel
pastTense = travelled
treat
pastTense = treated
tremble
pastTense = trembled
trick
pastTense = tricked
trip
pastTense = tripped
trot
pastTense = trotted
trouble
pastTense = troubled
trust
pastTense = trusted
try
pastTense = tried
tug
pastTense = tugged
tumble
pastTense = tumbled
turn
pastTense = turned
twist
pastTense = twisted
type
pastTense = typed
undress
pastTense = undressed
unfasten
pastTense = unfastened
unite
pastTense = united
unlock
pastTense = unlocked
unpack
pastTense = unpacked
use
pastTense = used
vanish
pastTense = vanished
visit
pastTense = visited
wail
pastTense = wailed
wait
pastTense = waited
walk
pastTense = walked
wander
pastTense = wandered
want
pastTense = wanted
warm
pastTense = warmed
warn
pastTense = warned
wash
pastTense = washed
waste
pastTense = wasted
watch
pastTense = watched
water
pastTense = watered
wave
pastTense = waved
weigh
pastTense = weighed
welcome
pastTense = welcomed
whine
pastTense = whined
whip
pastTense = whipped
whirl
pastTense = whirled
whisper
pastTense = whispered
whistle
pastTense = whistled
wink
pastTense = winked
wipe
pastTense = wiped
wish
pastTense = wished
wobble
pastTense = wobbled
wonder
pastTense = wondered
work
pastTense = worked
worry
pastTense = worried
wrap
pastTense = wrapped
wreck
pastTense = wrecked
wrestle
pastTense = wrestled
wriggle
pastTense = wriggled
x-ray
pastTense = x-rayed
yawn
pastTense = yawned
yell
pastTense = yelled
zip
pastTense = zipped
zoom
pastTense = zoomed
adjective
abashed
aberrant
abhorrent
abiding
ablaze
abnormal
aboard
aboriginal
abortive
abounding
abrasive
abrupt
absent
absolute
absorbed
absorbing
abstracted
absurd
abundant
abusive
academic
acceptable
accessible
accidental
acclaimed
accomplished
accurate
aching
acidic
acoustic
acrid
acrobatic
active
ad hoc
adamant
adaptable
addicted
adept
adhesive
adjoining
admirable
admired
adolescent
adorable
adored
advanced
adventurous
affectionate
afraid
aged
aggravating
aggressive
agile
agitated
agonizing
agreeable
ahead
ajar
alarmed
alarming
alcoholic
alert
alienated
alive
alleged
alluring
aloof
altruistic
amazing
ambiguous
ambitious
amiable
amuck
amused
amusing
anchored
ancient
angelic
angry
anguished
animated
annoyed
annoying
annual
another
antique
antsy
anxious
apathetic
appetizing
apprehensive
appropriate
apt
aquatic
arctic
arid
aromatic
arrogant
artistic
ashamed
aspiring
assorted
assured
astonishing
athletic
attached
attentive
attractive
auspicious
austere
authentic
authorized
automatic
available
avaricious
average
awake
aware
awesome
awful
awkward
axiomatic
babyish
bad
baggy
barbarous
bare
barren
bashful
basic
batty
bawdy
beautiful
beefy
befitting
belated
belligerent
beloved
beneficial
bent
berserk
better
bewildered
bewitched
big
big-hearted
billowy
biodegradable
bite-sized
biting
bitter
bizarre
black
black-and-white
bland
blank
blaring
bleak
blind
blissful
blond
bloody
blue
blue-eyed
blushing
bogus
boiling
bold
bony
boorish
bored
boring
bossy
both
bouncy
boundless
bountiful
bowed
brainy
brash
brave
brawny
breakable
breezy
brief
bright
brilliant
brisk
broad
broken
bronze
brown
bruised
bubbly
bulky
bumpy
buoyant
burdensome
burly
bustling
busy
buttery
buzzing
cagey
calculating
callous
calm
candid
canine
capable
capital
capricious
carefree
careful
careless
caring
cautious
cavernous
ceaseless
celebrated
certain
changeable
charming
cheap
cheeky
cheerful
cheery
chemical
chief
childlike
chilly
chivalrous
chubby
chunky
circular
clammy
classic
classy
clean
clear
clear-cut
clever
cloistered
closed
cloudy
clueless
clumsy
cluttered
coarse
coherent
cold
colorful
colorless
colossal
colossal
combative
comfortable
common
compassionate
competent
complete
complex
complicated
composed
concerned
concrete
condemned
condescending
confused
conscious
considerate
constant
contemplative
content
conventional
convincing
convoluted
cooing
cooked
cool
cooperative
coordinated
corny
corrupt
costly
courageous
courteous
cowardly
crabby
crafty
craven
crazy
creamy
creative
creepy
criminal
crisp
critical
crooked
crowded
cruel
crushing
cuddly
cultivated
cultured
cumbersome
curious
curly
curved
curvy
cute
cylindrical
cynical
daffy
damaged
damaging
damp
dangerous
dapper
dapper
daring
dark
darling
dashing
dazzling
dead
deadly
deadpan
deafening
dearest
debonair
decayed
deceitful
decent
decimal
decisive
decorous
deep
defeated
defective
defenseless
defensive
defiant
deficient
definite
delayed
delectable
delicate
delicious
delightful
delirious
demanding
demonic
dense
dental
dependable
dependent
depraved
depressed
deranged
descriptive
deserted
despicable
detailed
determined
devilish
devoted
didactic
different
difficult
digital
dilapidated
diligent
dim
diminutive
dimpled
dimwitted
direct
direful
dirty
disagreeable
disastrous
discreet
discrete
disfigured
disguised
disgusted
disgusting
dishonest
disillusioned
disloyal
dismal
dispensable
distant
distinct
distorted
distraught
distressed
disturbed
divergent
dizzy
domineering
dopey
doting
double
doubtful
downright
drab
draconian
drafty
drained
dramatic
dreary
droopy
drunk
dry
dual
dull
dusty
dutiful
dynamic
dysfunctional
eager
early
earnest
earsplitting
earthy
easy-going
economic
ecstatic
edible
educated
efficacious
efficient
elaborate
elastic
elated
elderly
electric
elegant
elementary
elfin
elite
elliptical
emaciated
embarrassed
embellished
eminent
emotional
empty
enchanted
enchanting
encouraging
endurable
energetic
enlightened
enormous
enraged
entertaining
enthusiastic
entire
envious
envious
equable
equatorial
erect
erratic
essential
esteemed
ethereal
ethical
euphoric
evanescent
evasive
even
evergreen
everlasting
evil
exalted
exasperated
excellent
excitable
excited
exciting
exclusive
exemplary
exhausted
exhilarated
exotic
expensive
experienced
expert
extensive
extra-large
extraneous
extra-small
extroverted
exuberant
exultant
fabulous
faded
failing
faint
fair
faithful
fake
fallacious
false
familiar
famous
fanatical
fancy
fantastic
faraway
far-flung
far-off
fascinated
fast
fat
fatal
fatherly
faulty
favorable
favorite
fearful
fearless
feeble
feigned
feisty
feline
female
feminine
fertile
festive
fickle
fierce
filthy
fine
finicky
finished
firm
first
firsthand
fitting
fixed
flagrant
flaky
flamboyant
flashy
flat
flawed
flawless
flickering
flimsy
flippant
floppy
flowery
fluffy
fluid
flustered
fluttering
foamy
focused
fond
foolhardy
foolish
forceful
foregoing
forgetful
forked
formal
forsaken
forthright
fortunate
fragile
fragrant
frail
frantic
frayed
free
freezing
French
frequent
fresh
fretful
friendly
frightened
frightening
frigid
frilly
frivolous
frizzy
frosty
frothy
frozen
frugal
fruitful
frustrating
full
fumbling
fumbling
functional
funny
furry
furtive
fussy
future
futuristic
fuzzy
gabby
gainful
gamy
gaping
gargantuan
garrulous
gaseous
gaudy
generous
gentle
genuine
ghastly
giant
giddy
gifted
gigantic
giving
glamorous
glaring
gleaming
gleeful
glib
glistening
glittering
gloomy
glorious
glossy
glum
godly
golden
good
good-natured
goofy
gorgeous
graceful
gracious
grand
grandiose
grandiose
granular
grateful
grave
gray
greasy
great
greedy
green
gregarious
grey
grieving
grim
grimy
gripping
grizzled
groovy
gross
grotesque
grouchy
grounded
growing
growling
grown
grubby
gruesome
grumpy
guarded
guiltless
guilty
gullible
gummy
gusty
guttural
habitual
hairy
hallowed
halting
handmade
handsome
handy
hanging
hapless
happy
happy-go-lucky
hard
hard-to-find
harebrained
harmful
harmless
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harsh
hasty
hateful
haunting
heady
healthy
heartbreaking
heartfelt
hearty
heavenly
heavy
hefty
hellish
helpful
helpless
hesitant
hidden
hideous
high
highfalutin
high-level
high-pitched
hilarious
hissing
historical
hoarse
holistic
hollow
homeless
homely
honest
honorable
honored
hopeful
horrible
horrific
hospitable
hot
huge
hulking
humble
humdrum
humiliating
humming
humongous
humorous
hungry
hurried
hurt
hurtful
hushed
husky
hypnotic
hysterical
icky
icy
ideal
ideal
idealistic
identical
idiotic
idle
idolized
ignorant
ill
illegal
ill-fated
ill-informed
illiterate
illustrious
imaginary
imaginative
immaculate
immaterial
immediate
immense
imminent
impartial
impassioned
impeccable
imperfect
imperturbable
impish
impolite
important
imported
impossible
impractical
impressionable
impressive
improbable
impure
inborn
incandescent
incomparable
incompatible
incompetent
incomplete
inconclusive
inconsequential
incredible
indelible
indolent
industrious
inexpensive
inexperienced
infamous
infantile
infatuated
inferior
infinite
informal
innate
innocent
inquisitive
insecure
insidious
insignificant
insistent
instinctive
instructive
insubstantial
intelligent
intentional
interesting
internal
international
intrepid
intrigued
invincible
irate
ironclad
irresponsible
irritable
irritating
itchy
jaded
jagged
jam-packed
jaunty
jazzy
jealous
jittery
jobless
jolly
jovial
joyful
joyous
jubilant
judicious
juicy
jumbled
jumbo
jumpy
jumpy
junior
juvenile
kaleidoscopic
kaput
keen
kind
kindhearted
kindly
klutzy
knobby
knotty
knowing
knowledgeable
kooky
kosher
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lackadaisical
lacking
lame
lamentable
languid
lanky
large
lasting
late
laughable
lavish
lawful
lazy
leading
leafy
lean
learned
left
legal
legitimate
lethal
level
lewd
light
lighthearted
likable
likeable
likely
limited
limp
limping
linear
lined
liquid
literate
little
live
lively
livid
living
loathsome
lone
lonely
long
longing
long-term
loose
lopsided
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loud
loutish
lovable
lovely
loving
low
lowly
loyal
lucky
ludicrous
lumbering
luminous
lumpy
lush
lustrous
luxuriant
luxurious
lying
lyrical
macabre
macho
mad
maddening
made-up
magenta
magical
magnificent
majestic
major
makeshift
male
malicious
mammoth
maniacal
marked
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marvelous
masculine
massive
material
materialistic
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mealy
mean
measly
meaty
medical
mediocre
medium
meek
melancholy
mellow
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melted
memorable
menacing
merciful
mere
merry
messy
metallic
mighty
mild
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milky
mindless
miniature
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minty
minute
miscreant
miserable
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mistaken
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mixed
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moist
moldy
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monumental
moody
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mortified
motherly
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mountainous
muddled
muddy
muffled
multicolored
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mundane
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natural
naughty
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nautical
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nebulous
necessary
needless
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negative
neglected
negligible
neighboring
neighborly
nervous
nervous
new
next
nice
nice
nifty
nimble
nine
nippy
nocturnal
noiseless
noisy
nonchalant
nondescript
nonsensical
nonstop
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nostalgic
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notable
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noteworthy
novel
noxious
numb
numberless
numerous
nutritious
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obedient
obeisant
obese
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oblong
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obsequious
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obsolete
obtainable
obvious
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old-fashioned
omniscient
onerous
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optimistic
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organic
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ornate
ornery
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outlandish
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overdue
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overlooked
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overwrought
painful
painstaking
palatable
pale
paltry
panicky
panoramic
parallel
parched
parsimonious
partial
passionate
pastel
pastoral
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peaceful
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peppery
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perfumed
periodic
perky
permissible
perpetual
perplexed
personal
pertinent
pesky
pessimistic
petite
petty
petty
phobic
phony
physical
picayune
piercing
pink
piquant
pitiful
placid
plain
plaintive
plant
plastic
plausible
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pleasing
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plump
plush
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positive
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powerless
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precious
premium
present
present
prestigious
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private
prize
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profuse
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psychotic
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puffy
pumped
punctual
pungent
puny
pure
purple
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pushy
putrid
puzzled
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quack
quaint
quaint
qualified
quarrelsome
quarterly
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querulous
questionable
quick
quickest
quick-witted
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quintessential
quirky
quixotic
quixotic
quizzical
rabid
racial
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rambunctious
rampant
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remorseful
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ripe
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robust
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rotten
rotund
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rowdy
royal
rubbery
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rude
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sandy
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sassy
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scant
scarce
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scornful
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scrawny
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serpentine
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severe
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shady
shaggy
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simplistic
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single
six
sizzling
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slight
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smug
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Spanish
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stimulating
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superb
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tacit
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tan
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taut
tawdry
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various
vast
velvety
venerated
vengeful
venomous
verdant
verifiable
versed
vexed
vibrant
vicious
victorious
vigilant
vigorous
villainous
violent
violet
virtual
virtuous
visible
vital
vivacious
vivid
voiceless
volatile
voluminous
voracious
vulgar
wacky
waggish
waiting
wakeful
wandering
wanting
warlike
warm
warmhearted
warped
wary
wasteful
watchful
waterlogged
watery
wavy
weak
wealthy
weary
webbed
wee
weekly
weepy
weighty
weird
welcome
well-documented
well-groomed
well-informed
well-lit
well-made
well-off
well-to-do
well-worn
wet
which
whimsical
whirlwind
whispered
whispering
white
whole
wholesale
whopping
wicked
wide
wide-eyed
wiggly
wild
willing
wilted
winding
windy
winged
wiry
wise
wistful
witty
wobbly
woebegone
woeful
womanly
wonderful
wooden
woozy
wordy
workable
worldly
worn
worried
worrisome
worse
worst
worthless
worthwhile
worthy
wrathful
wretched
writhing
wrong
wry
xenophobic
yawning
yearly
yellow
yellowish
yielding
young
youthful
yummy
zany
zealous
zesty
zigzag
zippy
zonked
adverb
abnormally
absentmindedly
accidentally
acidly
actually
adventurously
afterwards
almost
always
angrily
annually
anxiously
arrogantly
awkwardly
badly
bashfully
beautifully
bitterly
bleakly
blindly
blissfully
boastfully
boldly
bravely
briefly
brightly
briskly
broadly
busily
calmly
carefully
carelessly
cautiously
certainly
cheerfully
clearly
cleverly
closely
coaxingly
colorfully
commonly
continually
coolly
correctly
courageously
crossly
cruelly
curiously
daily
daintily
dearly
deceivingly
deeply
defiantly
deliberately
delightfully
diligently
dimly
doubtfully
dreamily
easily
elegantly
energetically
enormously
enthusiastically
equally
especially
evenly
eventually
exactly
excitedly
extremely
fairly
faithfully
famously
fatally
ferociously
fervently
fiercely
fondly
foolishly
fortunately
frankly
frantically
freely
frenetically
frightfully
fully
furiously
generally
generously
gently
gladly
gleefully
gracefully
gratefully
greatly
greedily
happily
hastily
healthily
heavily
helpfully
helplessly
highly
honestly
hopelessly
hourly
hungrily
immediately
innocently
inquisitively
instantly
intensely
intently
interestingly
inwardly
irritably
jaggedly
jealously
joshingly
jovially
joyfully
joyously
jubilantly
judgementally
justly
keenly
kiddingly
kindheartedly
kindly
kissingly
knavishly
knottily
knowingly
knowledgeably
kookily
lazily
lightly
likely
limply
lively
loftily
longingly
loosely
loudly
lovingly
loyally
madly
majestically
meaningfully
mechanically
merrily
miserably
mockingly
monthly
mortally
mostly
mysteriously
naturally
nearly
neatly
needily
nervously
nicely
noisily
obediently
obnoxiously
oddly
offensively
officially
often
only
openly
optimistically
overconfidently
owlishly
painfully
partially
patiently
perfectly
physically
playfully
politely
poorly
positively
potentially
powerfully
promptly
properly
punctually
quaintly
quarrelsomely
queasily
queerly
questionably
questioningly
quicker
quickly
quietly
quirkily
quizzically
rapidly
rarely
readily
really
reassuringly
recklessly
regularly
reluctantly
repeatedly
reproachfully
restfully
righteously
rightfully
rigidly
roughly
rudely
sadly
safely
scarcely
scarily
searchingly
sedately
seemingly
seldom
selfishly
separately
seriously
shakily
sharply
sheepishly
shrilly
shyly
silently
sleepily
slowly
smoothly
softly
solemnly
solidly
sometimes
soon
speedily
stealthily
sternly
strictly
successfully
suddenly
surprisingly
suspiciously
sweetly
swiftly
sympathetically
tenderly
tensely
terribly
thankfully
thoroughly
thoughtfully
tightly
tomorrow
tremendously
triumphantly
truly
truthfully
ultimately
unabashedly
unaccountably
unbearably
unethically
unexpectedly
unfortunately
unimpressively
unnaturally
unnecessarily
upbeat
upliftingly
upright
upside-down
upward
upwardly
urgently
usefully
uselessly
usually
utterly
vacantly
vaguely
vainly
valiantly
vastly
verbally
viciously
victoriously
violently
vivaciously
voluntarily
warmly
weakly
wearily
wetly
wholly
wildly
willfully
wisely
woefully
wonderfully
worriedly
wrongly
yawningly
yearly
yearningly
yesterday
yieldingly
youthfully
preposition
as
at
but
by
down
for
from
in
into
like
near
next
of
off
on
onto
out
over
past
plus
minus
since
than
to
up
with
aboard
about
above
across
after
against
along
around
before
behind
below
beneath
beside
between
beyond
during
except
following
inside
minus
onto
opposite
outside
round
since
through
toward
under
underneath
unlike
until
upon
without
according to
along with
alongside
among
apart from
as for
atop
because of
by means of
concerning
despite
except for
in addition to
in back of
in case of
in front of
in place of
in spite of
instead of
on top of
out of
regarding
throughout
till
up to
via
within
worth
interjection
aah
ack
agreed
ah
aha
ahem
alas
all right
amen
argh
as if
aw
ay
aye
bah
blast
boo hoo
bother
boy
brr
by golly
bye
cheerio
cheers
chin up
come on
crikey
curses
dear me
doggone
drat
duh
easy does it
eek
egads
er
exactly
fair enough
fiddle-dee-dee
fiddlesticks
fie
foo
fooey
gadzooks
gah
gangway
g'day
gee
gee whiz
geez
gesundheit
get lost
get outta here
go on
good
good golly
good job
gosh
gracious
great
grr
gulp
ha
ha-ha
hah
hallelujah
harrumph
haw
hee
here
hey
hmm
ho hum
hoo
hooray
hot dog
how
huh
hum
humbug
hurray
huzza
I say
ick
is it
ixnay
jeez
just kidding
just a sec
just wondering
kapish
la
la-di-dah
lo
look
look here
long time
lordy
man
meh
mmm
most certainly
my
my my
my word
nah
naw
never
no
no can do
nooo
not
no thanks
no way
nuts
oh
oho
oh-oh
oh no
okay
okey-dokey
om
oof
ooh
oopsey
over
oy
oyez
peace
pff
pew
phew
pish posh
psst
ptui
quite
rah
rats
ready
right
right on
roger
roger that
rumble
say
see ya
shame
shh
shoo
shucks
sigh
sleep tight
snap
sorry
sssh
sup
ta
ta-da
ta ta
take that
tally ho
tch
thanks
there
there there
time out
toodles
touche
tsk
tsk-tsk
tut
tut-tut
ugh
uh
uh-oh
um
ur
urgh
very nice
very well
voila
vroom
wah
well
well done
well, well
what
whatever
whee
when
whoa
whoo
whoopee
whoops
whoopsey
whew
why
word
wow
wuzzup
ya
yea
yeah
yech
yikes
yippee
yo
yoo-hoo
you bet
you don't say
you know
yow
yum
yummy
zap
zounds
zowie
zzz
colors
White
Blue
Black
Red
Green
Colorless
Multi-color
scry_powers
"-1",
"?",
"0",
"∞",
"*",
"+0",
"*²",
".5",
"+1",
"1+*",
"1",
"1.5",
"+2",
"2",
"2+*",
"2.5",
"3",
"+3",
"3.5",
"4",
"+4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"15",
"16",
"20",
"99"
scry_toughness
"-1",
"+0",
"*²",
"-0",
"?",
"0",
"*+1",
"*",
".5",
"+1",
"1+*",
"1",
"1.5",
"2+*",
"+2",
"2",
"2.5",
"+3",
"3",
"3.5",
"4",
"+4",
"5",
"6",
"7-*",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"20",
"99"
scry_keyword_abilities
"Living weapon",
"Jump-start",
"Basic landcycling",
"Commander ninjutsu",
"Legendary landwalk",
"Nonbasic landwalk",
"Totem armor",
"Megamorph",
"Haunt",
"Forecast",
"Graft",
"Fortify",
"Frenzy",
"Gravestorm",
"Hideaway",
"Level Up",
"Infect",
"Reach",
"Rampage",
"Phasing",
"Multikicker",
"Morph",
"Provoke",
"Modular",
"Ninjutsu",
"Replicate",
"Recover",
"Poisonous",
"Prowl",
"Reinforce",
"Persist",
"Retrace",
"Rebound",
"Miracle",
"Overload",
"Outlast",
"Prowess",
"Renown",
"Myriad",
"Shroud",
"Trample",
"Vigilance",
"Shadow",
"Storm",
"Soulshift",
"Splice",
"Transmute",
"Ripple",
"Suspend",
"Vanishing",
"Transfigure",
"Wither",
"Undying",
"Soulbond",
"Unleash",
"Ascend",
"Assist",
"Afterlife",
"Companion",
"Fabricate",
"Embalm",
"Escape",
"Fuse",
"Menace",
"Ingest",
"Melee",
"Improvise",
"Mentor",
"Partner",
"Mutate",
"Scavenge",
"Tribute",
"Surge",
"Skulk",
"Undaunted",
"Riot",
"Spectacle",
"Forestwalk",
"Islandwalk",
"Mountainwalk",
"Double strike",
"Cumulative upkeep",
"First strike",
"Encore",
"Sunburst",
"Deathtouch",
"Defender",
"Foretell",
"Amplify",
"Affinity",
"Bushido",
"Convoke",
"Bloodthirst",
"Absorb",
"Aura Swap",
"Changeling",
"Conspire",
"Cascade",
"Annihilator",
"Battle Cry",
"Cipher",
"Bestow",
"Dash",
"Awaken",
"Crew",
"Aftermath",
"Afflict",
"Flanking",
"Echo",
"Fading",
"Fear",
"Eternalize",
"Entwine",
"Epic",
"Dredge",
"Delve",
"Evoke",
"Exalted",
"Evolve",
"Extort",
"Dethrone",
"Exploit",
"Devoid",
"Emerge",
"Escalate",
"Flying",
"Haste",
"Hexproof",
"Indestructible",
"Intimidate",
"Lifelink",
"Horsemanship",
"Kicker",
"Madness",
"Hidden agenda",
"Swampwalk",
"Desertwalk",
"Wizardcycling",
"Slivercycling",
"Cycling",
"Landwalk",
"Plainswalk",
"Champion",
"Enchant",
"Plainscycling",
"Islandcycling",
"Swampcycling",
"Mountaincycling",
"Forestcycling",
"Landcycling",
"Typecycling",
"Split second",
"Flash",
"Banding",
"Augment",
"Double agenda",
"Partner with",
"Hexproof from",
"Boast",
"Buyback",
"Ward",
"Demonstrate",
"Devour",
"Flashback",
"Equip",
"Reconfigure",
"Compleated",
"Daybound",
"Nightbound",
"Decayed",
"Disturb",
"Training",
"Cleave",
"Intensity",
"Blitz",
"Casualty",
"Friends forever",
"Protection",
"Offering",
"Enlist",
"Read Ahead",
"Squad",
"Ravenous",
"More Than Meets the Eye",
"Living metal",
"Unearth",
"Prototype"
scry_keyword_actions
"Meld",
"Bolster",
"Clash",
"Fateseal",
"Manifest",
"Monstrosity",
"Populate",
"Proliferate",
"Scry",
"Support",
"Detain",
"Explore",
"Fight",
"Amass",
"Adapt",
"Assemble",
"Abandon",
"Activate",
"Attach",
"Seek",
"Cast",
"Counter",
"Create",
"Destroy",
"Discard",
"Double",
"Exchange",
"Exile",
"Investigate",
"Play",
"Regenerate",
"Reveal",
"Sacrifice",
"Set in motion",
"Shuffle",
"Tap",
"Untap",
"Vote",
"Transform",
"Surveil",
"Goad",
"Planeswalk",
"Mill",
"Learn",
"Conjure",
"Exert",
"Connive",
"Venture into the dungeon",
"Convert",
"Open an Attraction",
"Roll to Visit Your Attractions"
scry_ability_words
"Battalion",
"Bloodrush",
"Channel",
"Chroma",
"Cohort",
"Constellation",
"Converge",
"Delirium",
"Domain",
"Fateful hour",
"Ferocious",
"Formidable",
"Grandeur",
"Hellbent",
"Heroic",
"Imprint",
"Inspired",
"Join forces",
"Kinship",
"Landfall",
"Lieutenant",
"Metalcraft",
"Morbid",
"Parley",
"Radiance",
"Raid",
"Rally",
"Spell mastery",
"Strive",
"Sweep",
"Tempting offer",
"Threshold",
"Will of the council",
"Adamant",
"Addendum",
"Council's dilemma",
"Eminence",
"Enrage",
"Hero's Reward",
"Kinfall",
"Landship",
"Legacy",
"Revolt",
"Underdog",
"Undergrowth",
"Magecraft",
"Teamwork",
"Pack tactics",
"Coven",
"Alliance
|
TransLL/distilbert-base-uncased-finetuned-clinc
|
TransLL
| 2023-01-03T10:31:44Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-03T10:21:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: train
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9183870967741935
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2890 | 0.7432 |
| 3.7868 | 2.0 | 636 | 1.8756 | 0.8377 |
| 3.7868 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.6929 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.9058 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
optimum/gpt2
|
optimum
| 2023-01-03T10:29:58Z | 11,764 | 5 |
transformers
|
[
"transformers",
"onnx",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-22T10:17:23Z |
---
language: en
tags:
- exbert
license: mit
---
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use the ONNX models of gpt2 to get the features of a given text:
Example using transformers.pipelines:
```python
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = ORTModelForCausalLM.from_pretrained("gpt2", from_transformers=True)
onnx_gen = pipeline("text-generation", model=model, tokenizer=tokenizer)
text = "My name is Philipp and I live in Germany."
gen = onnx_gen(text)
```
Example of text generation:
```python
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("optimum/gpt2")
model = ORTModelForCausalLM.from_pretrained("optimum/gpt2")
inputs = tokenizer("My name is Arthur and I live in", return_tensors="pt")
gen_tokens = model.generate(**inputs,do_sample=True,temperature=0.9, min_length=20,max_length=20)
tokenizer.batch_decode(gen_tokens)
```
|
Brainkite/PPO-LunarLander-v2
|
Brainkite
| 2023-01-03T10:29:21Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T17:23:37Z |
---
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: 276.27 +/- 20.80
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
...
```
|
Kuaaangwen/bert-base-cased-finetuned-revision-booklet-chemistry
|
Kuaaangwen
| 2023-01-03T10:19:22Z | 160 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-01-03T10:05:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-revision-booklet-chemistry
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-finetuned-revision-booklet-chemistry
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 105 | 1.8484 |
| No log | 2.0 | 210 | 1.6418 |
| No log | 3.0 | 315 | 1.5820 |
| No log | 4.0 | 420 | 1.4826 |
| 1.8696 | 5.0 | 525 | 1.4521 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
emmyapi/distilbart-podimo-data-eval-1-2e
|
emmyapi
| 2023-01-03T10:18:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-03T09:08:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-podimo-data-eval-1-2e
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. -->
# distilbart-podimo-data-eval-1-2e
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7114
- Rouge1: 32.7887
- Rouge2: 6.5245
- Rougel: 16.9089
- Rougelsum: 29.6437
- Gen Len: 141.3408
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 4.2142 | 0.98 | 44 | 3.8082 | 32.7658 | 6.2506 | 16.7953 | 29.6922 | 140.5503 |
| 3.6965 | 1.98 | 88 | 3.7114 | 32.7887 | 6.5245 | 16.9089 | 29.6437 | 141.3408 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
osanseviero/ppo-LunarLander-v2-AGAIN
|
osanseviero
| 2023-01-03T10:16:34Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T10:14:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -575.20 +/- 517.74
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
...
```
|
Luvidi/ppo-LunarLander-v2
|
Luvidi
| 2023-01-03T10:05:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T08:21:50Z |
---
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: 253.34 +/- 24.43
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
...
```
|
fxmarty/gpt2-tiny-onnx
|
fxmarty
| 2023-01-03T09:41:05Z | 585 | 1 |
transformers
|
[
"transformers",
"onnx",
"gpt2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-01-03T09:39:43Z |
---
license: apache-2.0
---
This model is meant for testing and will not return any meaningful output.
|
likejazz/distilbert-base-uncased-finetuned-emotion
|
likejazz
| 2023-01-03T09:07:07Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-03T08:28:11Z |
---
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: 0.1766
- Accuracy: 0.9305
- F1: 0.9308
## 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: 320
- eval_batch_size: 320
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 50 | 0.1837 | 0.929 | 0.9293 |
| No log | 2.0 | 100 | 0.1766 | 0.9305 | 0.9308 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.1+cu117
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Botnoi/wav2vec2-xls-r-300m-th-v2
|
Botnoi
| 2023-01-03T09:05:23Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-01-03T02:24:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-th-v2
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-xls-r-300m-th-v2
This model is a fine-tuned version of [Botnoi/wav2vec2-xls-r-300m-th-v1](https://huggingface.co/Botnoi/wav2vec2-xls-r-300m-th-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3630
- Wer: 0.3962
- Cer: 0.0942
- Clean Cer: 0.0767
## 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: 4.533e-08
- 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
- training_steps: 9000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Clean Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| 0.3323 | 0.68 | 1000 | 0.3635 | 0.3961 | 0.0942 | 0.0767 |
| 0.3386 | 1.36 | 2000 | 0.3632 | 0.3962 | 0.0943 | 0.0768 |
| 0.3453 | 2.03 | 3000 | 0.3632 | 0.3964 | 0.0943 | 0.0768 |
| 0.3392 | 2.71 | 4000 | 0.3632 | 0.3961 | 0.0943 | 0.0767 |
| 0.3399 | 3.39 | 5000 | 0.3634 | 0.3961 | 0.0942 | 0.0768 |
| 0.347 | 4.07 | 6000 | 0.3632 | 0.3961 | 0.0942 | 0.0767 |
| 0.3414 | 4.74 | 7000 | 0.3631 | 0.3962 | 0.0942 | 0.0767 |
| 0.3378 | 5.42 | 8000 | 0.3631 | 0.3961 | 0.0942 | 0.0767 |
| 0.3421 | 6.1 | 9000 | 0.3630 | 0.3962 | 0.0942 | 0.0767 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
0xid/dqn-SpaceInvadersNoFrameskip-v4
|
0xid
| 2023-01-03T08:57:35Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T08:53:39Z |
---
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: 1051.50 +/- 365.19
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga 0xid -f logs/
python enjoy.py --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 0xid -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 0xid
```
## 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)])
```
|
r10521708/distilbert-base-uncased-finetuned-cola
|
r10521708
| 2023-01-03T08:56:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-03T06:55:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.548847644400088
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5263
- Matthews Correlation: 0.5488
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5258 | 1.0 | 535 | 0.5402 | 0.4315 |
| 0.3464 | 2.0 | 1070 | 0.5117 | 0.4810 |
| 0.2355 | 3.0 | 1605 | 0.5263 | 0.5488 |
| 0.1718 | 4.0 | 2140 | 0.7802 | 0.5411 |
| 0.1222 | 5.0 | 2675 | 0.8106 | 0.5469 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
PaulMest/ppo-Huggy
|
PaulMest
| 2023-01-03T08:49:06Z | 15 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-01-03T08:48:57Z |
---
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: PaulMest/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
swathysekar/donut-base-sroie
|
swathysekar
| 2023-01-03T08:30:37Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2022-11-29T10:18:39Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
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. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- 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
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
|
yasu320001/xlm-roberta-base-finetuned-panx-all
|
yasu320001
| 2023-01-03T06:48:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-01-03T06:22:47Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1656
- F1: 0.8589
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2905 | 1.0 | 715 | 0.1783 | 0.8310 |
| 0.1461 | 2.0 | 1430 | 0.1600 | 0.8455 |
| 0.0948 | 3.0 | 2145 | 0.1656 | 0.8589 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
simonbronson/Old-Portraits
|
simonbronson
| 2023-01-03T06:33:53Z | 3 | 4 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2022-12-28T21:08:49Z |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Old Portraits
Using a set of 25 mainly old large format photos from the 19th and early 20th century I wanted to capture the lighting, expression and film artifacts from this era. Subjects range in age, gender & ethnicity with poses generally looking at the camera with a neutral expression.
While only a couple of my source images had distressed frames, plenty of the generations contain them.
Download ckpt: https://huggingface.co/simonbronson/Old-Portraits/blob/main/Old_Portraits.ckpt
**Examples:**
```
|
thesunshine36/fineturn_ViT5
|
thesunshine36
| 2023-01-03T06:11:19Z | 61 | 1 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-03T03:42:38Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: fineturn_ViT5
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. -->
# fineturn_ViT5
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6173
- Validation Loss: 0.9866
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.8656 | 1.0455 | 0 |
| 0.6173 | 0.9866 | 1 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
|
yasu320001/xlm-roberta-base-finetuned-panx-it
|
yasu320001
| 2023-01-03T06:07:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-01-03T05:52:54Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8219402374130168
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2564
- F1: 0.8219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8123 | 1.0 | 70 | 0.3267 | 0.7418 |
| 0.2832 | 2.0 | 140 | 0.2694 | 0.8006 |
| 0.1766 | 3.0 | 210 | 0.2564 | 0.8219 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AliSab/dqn-SpaceInvadersNoFrameskip-v4
|
AliSab
| 2023-01-03T06:05:57Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T06:05:20Z |
---
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: 636.00 +/- 179.59
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AliSab -f logs/
python enjoy.py --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 AliSab -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 AliSab
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 150000),
('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)])
```
|
adam1brownell/u1_lunar
|
adam1brownell
| 2023-01-03T05:58:05Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T05:57:39Z |
---
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: 259.59 +/- 22.35
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
...
```
|
IzumiSatoshi/sketch2img-FashionMNIST
|
IzumiSatoshi
| 2023-01-03T05:54:13Z | 2 | 6 |
diffusers
|
[
"diffusers",
"license:apache-2.0",
"diffusers:DDPMSketch2ImgPipeline",
"region:us"
] | null | 2022-12-30T13:29:06Z |
---
license: apache-2.0
---
# sketch2img with diffusion models
https://github.com/IzumiSatoshi/sketch2img
|
derenrich/psychiq2
|
derenrich
| 2023-01-03T05:36:24Z | 3,411 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"wikipedia",
"wikidata",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-29T05:55:40Z |
---
license: gpl-3.0
language:
- en
tags:
- wikipedia
- wikidata
widget:
- text: "Douglas Adams\n
1952 births\n
2001 deaths\n
20th-century atheists\n
21st-century atheists\n
20th-century English novelists\n
21st-century English novelists\n
20th-century English screenwriters\n
Alumni of St John's College, Cambridge\n
Apple Inc. people\n
Audiobook narrators\n
BBC radio producers\n
British atheism activists\n
British child writers\n
Burials at Highgate Cemetery\n
English atheists\n
English comedy writers\n
English essayists\n
English humanists\n
English humorists\n
English radio writers\n
English science fiction writers\n
English social commentators\n
English television writers\n
Infocom\n
Inkpot Award winners\n
Interactive fiction writers\n
British male television writers\n
Monty Python\n
Non-fiction environmental writers\n
People educated at Brentwood School, Essex\n
People from Cambridge\n
Usenet people\n
Weird fiction writers\n
Douglas Adams"
example_title: "Douglas Adams"
- text: "Unincorporated communities in Minnesota\n
Unincorporated communities in St. Louis County, Minnesota\n
St. Louis County, Minnesota geography stubs\n
Sturgeon, Minnesota"
example_title: "Sturgeon, Minnesota"
- text: "Araneus\n
Spiders described in 1884\n
Araneidae stubs\n
Araneus pratensis"
example_title: "Araneus pratensis"
- text: "Mohammedan SC (Dhaka) seasons\n
Bangladeshi football club records and statistics\n
2019 in Bangladeshi football\n
2020 in Bangladeshi football\n
2019–20 Mohammedan SC (Dhaka) season"
example_title: "2019–20 Mohammedan SC (Dhaka) season"
- text: "Waterfalls of Karnataka\n
Tourist attractions in Dakshina Kannada district\n
Geography of Dakshina Kannada district\n
Bandaje Falls
"
example_title: "Bandaje Falls"
---
Psychiq is a model that predicts the instance or subclass of a wikipedia article. The model accepts as input 1) the list of all categories the article is in separated by newlines followed by 2) the title of the article . It makes a guess at the top 1000 most common types or returns unknown. Take a look at the examples to see what the format should look like.
|
yasu320001/xlm-roberta-base-finetuned-panx-de-fr
|
yasu320001
| 2023-01-03T05:34:33Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-01-03T05:08:20Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1656
- F1: 0.8589
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2905 | 1.0 | 715 | 0.1783 | 0.8310 |
| 0.1461 | 2.0 | 1430 | 0.1600 | 0.8455 |
| 0.0948 | 3.0 | 2145 | 0.1656 | 0.8589 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
rohitp1/libri-alpha-0.5-Temp-1-mse
|
rohitp1
| 2023-01-03T05:14:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-01-02T20:20:43Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: libri-alpha-0.5-Temp-1-mse
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-0.5-Temp-1-mse
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 28.9681
- Wer: 0.1160
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 212.5522 | 0.75 | 100 | 55.9161 | 0.1500 |
| 171.0676 | 1.49 | 200 | 51.5701 | 0.1434 |
| 159.3247 | 2.24 | 300 | 40.6680 | 0.1416 |
| 147.7202 | 2.99 | 400 | 36.0320 | 0.1388 |
| 136.0871 | 3.73 | 500 | 32.8709 | 0.1323 |
| 126.3071 | 4.48 | 600 | 31.9204 | 0.1298 |
| 126.9502 | 5.22 | 700 | 31.0903 | 0.1281 |
| 117.0498 | 5.97 | 800 | 30.5398 | 0.1272 |
| 117.0928 | 6.72 | 900 | 30.2616 | 0.1262 |
| 116.35 | 7.46 | 1000 | 30.2445 | 0.1264 |
| 116.784 | 8.21 | 1100 | 30.0181 | 0.1268 |
| 111.6779 | 8.96 | 1200 | 29.6434 | 0.1252 |
| 110.2514 | 9.7 | 1300 | 29.6900 | 0.1233 |
| 112.603 | 10.45 | 1400 | 29.4023 | 0.1240 |
| 110.4294 | 11.19 | 1500 | 29.5929 | 0.1239 |
| 106.3693 | 11.94 | 1600 | 29.4228 | 0.1232 |
| 102.5095 | 12.69 | 1700 | 29.6106 | 0.1236 |
| 104.8351 | 13.43 | 1800 | 29.3908 | 0.1220 |
| 103.6225 | 14.18 | 1900 | 29.5250 | 0.1216 |
| 102.5769 | 14.93 | 2000 | 29.4744 | 0.1211 |
| 102.7153 | 15.67 | 2100 | 29.3769 | 0.1203 |
| 98.3215 | 16.42 | 2200 | 29.3692 | 0.1205 |
| 100.0971 | 17.16 | 2300 | 29.0029 | 0.1183 |
| 94.876 | 17.91 | 2400 | 28.9354 | 0.1181 |
| 100.2511 | 18.66 | 2500 | 28.9513 | 0.1168 |
| 95.3128 | 19.4 | 2600 | 29.0832 | 0.1166 |
| 95.2151 | 20.15 | 2700 | 29.0161 | 0.1157 |
| 92.6844 | 20.9 | 2800 | 29.0543 | 0.1152 |
| 96.837 | 21.64 | 2900 | 29.2276 | 0.1164 |
| 94.2866 | 22.39 | 3000 | 28.9697 | 0.1164 |
| 92.1945 | 23.13 | 3100 | 29.0823 | 0.1169 |
| 97.7153 | 23.88 | 3200 | 29.0628 | 0.1158 |
| 95.3836 | 24.63 | 3300 | 28.9681 | 0.1160 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0
|
KoichiYasuoka/deberta-base-chinese
|
KoichiYasuoka
| 2023-01-03T04:55:48Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"chinese",
"masked-lm",
"wikipedia",
"zh",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-12-28T04:07:51Z |
---
language:
- "zh"
tags:
- "chinese"
- "masked-lm"
- "wikipedia"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
---
# deberta-base-chinese
## Model Description
This is a DeBERTa(V2) model pre-trained on Chinese Wikipedia texts (both simplified and traditional). NVIDIA A100-SXM4-40GB took 102 hours 34 minutes for training. You can fine-tune `deberta-base-chinese` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-chinese-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-chinese-ud-goeswith), and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-chinese")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-chinese")
```
|
halffried/midas_v3_1_dpt_swin2_large_384
|
halffried
| 2023-01-03T04:10:25Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2023-01-03T03:54:34Z |
---
license: mit
---
## What is it?
Just a mirror of a model from https://github.com/isl-org/MiDaS, to allow downloading with Huggingface Hub tools
## Citation
```bibtex
@ARTICLE {Ranftl2022,
author = "Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun",
title = "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
year = "2022",
volume = "44",
number = "3"
}
```
```bibtex
@article{Ranftl2021,
author = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
title = {Vision Transformers for Dense Prediction},
journal = {ICCV},
year = {2021},
}
```
|
halffried/midas_v3_1_dpt_beit_large_512
|
halffried
| 2023-01-03T03:49:54Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-01-03T03:06:36Z |
---
license: mit
---
## What is it?
Just a mirror of a model from https://github.com/isl-org/MiDaS, to allow downloading with Huggingface Hub tools
## Citation
```bibtex
@ARTICLE {Ranftl2022,
author = "Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun",
title = "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
year = "2022",
volume = "44",
number = "3"
}
```
```bibtex
@article{Ranftl2021,
author = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
title = {Vision Transformers for Dense Prediction},
journal = {ICCV},
year = {2021},
}
```
|
aj-ai/ppo-Huggy
|
aj-ai
| 2023-01-03T02:52:39Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-01-03T02:52:30Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: aj-ai/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
vjkrish/lunarLander_2
|
vjkrish
| 2023-01-03T02:20:41Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T01:41:26Z |
---
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: 271.32 +/- 33.44
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
...
```
|
gababas/ggaabboommeerr
|
gababas
| 2023-01-03T01:46:04Z | 29 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-03T01:44:02Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### ggaabboommeerr Dreambooth model trained by gababas 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:
|
TheMurusTeam/CoreML-Stable-Diffusion-1.5-ORIGINAL-img2img
|
TheMurusTeam
| 2023-01-03T01:36:43Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-03T01:09:30Z |
---
license: creativeml-openrail-m
---
|
y1450/hf_hub_example-ff7aeda4-022b-47d2-bf00-0c241bc195f7
|
y1450
| 2023-01-03T01:06:12Z | 0 | 0 |
sklearn
|
[
"sklearn",
"skops",
"tabular-classification",
"region:us"
] |
tabular-classification
| 2023-01-03T01:06:06Z |
---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: skops-bo_9fb88.pkl
widget:
structuredData:
area error:
- 30.29
- 96.05
- 48.31
compactness error:
- 0.01911
- 0.01652
- 0.01484
concave points error:
- 0.01037
- 0.0137
- 0.01093
concavity error:
- 0.02701
- 0.02269
- 0.02813
fractal dimension error:
- 0.003586
- 0.001698
- 0.002461
mean area:
- 481.9
- 1130.0
- 748.9
mean compactness:
- 0.1058
- 0.1029
- 0.1223
mean concave points:
- 0.03821
- 0.07951
- 0.08087
mean concavity:
- 0.08005
- 0.108
- 0.1466
mean fractal dimension:
- 0.06373
- 0.05461
- 0.05796
mean perimeter:
- 81.09
- 123.6
- 101.7
mean radius:
- 12.47
- 18.94
- 15.46
mean smoothness:
- 0.09965
- 0.09009
- 0.1092
mean symmetry:
- 0.1925
- 0.1582
- 0.1931
mean texture:
- 18.6
- 21.31
- 19.48
perimeter error:
- 2.497
- 5.486
- 3.094
radius error:
- 0.3961
- 0.7888
- 0.4743
smoothness error:
- 0.006953
- 0.004444
- 0.00624
symmetry error:
- 0.01782
- 0.01386
- 0.01397
texture error:
- 1.044
- 0.7975
- 0.7859
worst area:
- 677.9
- 1866.0
- 1156.0
worst compactness:
- 0.2378
- 0.2336
- 0.2394
worst concave points:
- 0.1015
- 0.1789
- 0.1514
worst concavity:
- 0.2671
- 0.2687
- 0.3791
worst fractal dimension:
- 0.0875
- 0.06589
- 0.08019
worst perimeter:
- 96.05
- 165.9
- 124.9
worst radius:
- 14.97
- 24.86
- 19.26
worst smoothness:
- 0.1426
- 0.1193
- 0.1546
worst symmetry:
- 0.3014
- 0.2551
- 0.2837
worst texture:
- 24.64
- 26.58
- 26.0
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|---------------------------------|----------------------------------------------------------|
| aggressive_elimination | False |
| cv | 5 |
| error_score | nan |
| estimator__categorical_features | |
| estimator__early_stopping | auto |
| estimator__l2_regularization | 0.0 |
| estimator__learning_rate | 0.1 |
| estimator__loss | log_loss |
| estimator__max_bins | 255 |
| estimator__max_depth | |
| estimator__max_iter | 100 |
| estimator__max_leaf_nodes | 31 |
| estimator__min_samples_leaf | 20 |
| estimator__monotonic_cst | |
| estimator__n_iter_no_change | 10 |
| estimator__random_state | |
| estimator__scoring | loss |
| estimator__tol | 1e-07 |
| estimator__validation_fraction | 0.1 |
| estimator__verbose | 0 |
| estimator__warm_start | False |
| estimator | HistGradientBoostingClassifier() |
| factor | 3 |
| max_resources | auto |
| min_resources | exhaust |
| n_jobs | -1 |
| param_grid | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} |
| random_state | 42 |
| refit | True |
| resource | n_samples |
| return_train_score | True |
| scoring | |
| verbose | 0 |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>
## Evaluation Results
[More Information Needed]
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
|
TheMurusTeam/coreml-upscaler-gfpgan
|
TheMurusTeam
| 2023-01-03T01:03:46Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-03T01:01:08Z |
---
license: creativeml-openrail-m
---
|
TheMurusTeam/coreml-upscaler-MPRNetDenoising
|
TheMurusTeam
| 2023-01-03T01:00:22Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-03T00:59:22Z |
---
license: creativeml-openrail-m
---
|
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