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Davlan/xlm-roberta-base-finetuned-chichewa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 5 | 2023-01-01T15:57:25Z | ---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
metrics:
- type: mean_reward
value: -798.25 +/- 732.56
name: mean_reward
verified: false
---
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/xlm-roberta-base-finetuned-english | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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} | 5 | null | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: libri-alpha-0.5-Temp-1-processor-change
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-processor-change
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: 91.9750
- Wer: 0.1187
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 569.0646 | 0.75 | 100 | 175.3549 | 0.1589 |
| 440.3574 | 1.49 | 200 | 146.3654 | 0.1640 |
| 398.2328 | 2.24 | 300 | 128.7082 | 0.1562 |
| 357.5816 | 2.99 | 400 | 117.7871 | 0.1495 |
| 344.3317 | 3.73 | 500 | 111.0376 | 0.1417 |
| 331.0486 | 4.48 | 600 | 106.5447 | 0.1398 |
| 321.4498 | 5.22 | 700 | 105.1187 | 0.1363 |
| 305.8177 | 5.97 | 800 | 103.2541 | 0.1365 |
| 304.2076 | 6.72 | 900 | 105.3060 | 0.1385 |
| 297.746 | 7.46 | 1000 | 101.1069 | 0.1307 |
| 285.7675 | 8.21 | 1100 | 99.9853 | 0.1303 |
| 284.6546 | 8.96 | 1200 | 98.5235 | 0.1292 |
| 281.672 | 9.7 | 1300 | 97.8004 | 0.1295 |
| 281.0029 | 10.45 | 1400 | 96.9385 | 0.1278 |
| 283.847 | 11.19 | 1500 | 96.3700 | 0.1275 |
| 274.4053 | 11.94 | 1600 | 95.9557 | 0.1281 |
| 271.8855 | 12.69 | 1700 | 95.5764 | 0.1250 |
| 275.416 | 13.43 | 1800 | 95.0451 | 0.1266 |
| 267.7354 | 14.18 | 1900 | 94.6620 | 0.1242 |
| 273.9816 | 14.93 | 2000 | 95.0889 | 0.1241 |
| 263.9812 | 15.67 | 2100 | 94.4231 | 0.1241 |
| 258.6033 | 16.42 | 2200 | 93.8011 | 0.1225 |
| 260.4275 | 17.16 | 2300 | 94.0336 | 0.1210 |
| 258.7905 | 17.91 | 2400 | 93.4633 | 0.1216 |
| 255.6817 | 18.66 | 2500 | 93.0448 | 0.1212 |
| 252.3298 | 19.4 | 2600 | 92.9945 | 0.1216 |
| 250.5598 | 20.15 | 2700 | 92.9767 | 0.1200 |
| 249.4384 | 20.9 | 2800 | 93.1555 | 0.1203 |
| 255.6291 | 21.64 | 2900 | 92.7784 | 0.1208 |
| 249.5222 | 22.39 | 3000 | 92.5792 | 0.1203 |
| 250.498 | 23.13 | 3100 | 92.4570 | 0.1205 |
| 252.2656 | 23.88 | 3200 | 92.3685 | 0.1199 |
| 248.1438 | 24.63 | 3300 | 92.3731 | 0.1198 |
| 240.2946 | 25.37 | 3400 | 92.1875 | 0.1192 |
| 256.2254 | 26.12 | 3500 | 91.9586 | 0.1192 |
| 248.603 | 26.87 | 3600 | 91.9599 | 0.1191 |
| 252.9337 | 27.61 | 3700 | 92.1080 | 0.1189 |
| 250.9757 | 28.36 | 3800 | 92.1051 | 0.1188 |
| 248.7415 | 29.1 | 3900 | 91.9927 | 0.1187 |
| 248.7394 | 29.85 | 4000 | 91.9750 | 0.1187 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0
|
Davlan/xlm-roberta-base-finetuned-hausa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 234 | null | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: no_distil_librispeech_100_clean_6_attention
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. -->
# no_distil_librispeech_100_clean_6_attention
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: 2333.5361
- Wer: 1.1878
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8374.3469 | 0.75 | 100 | 2762.3215 | 1.0 |
| 4303.0578 | 1.49 | 200 | 2503.2461 | 1.0 |
| 4307.1169 | 2.24 | 300 | 2498.8477 | 1.0 |
| 4236.7513 | 2.99 | 400 | 2489.2173 | 1.0 |
| 4242.8606 | 3.73 | 500 | 2405.0710 | 1.0801 |
| 4132.4353 | 4.48 | 600 | 2333.5361 | 1.1878 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.0
- Tokenizers 0.11.0
|
Davlan/xlm-roberta-base-finetuned-igbo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 68 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-retrained-500k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-retrained-500k
This model is a fine-tuned version of [bitsanlp/roberta-retrained-350k](https://huggingface.co/bitsanlp/roberta-retrained-350k) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-swahili | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 40 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -177.15 +/- 20.55
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/xlm-roberta-base-finetuned-wolof | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 3 | null | ---
tags:
- Breakout-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: Breakout-v5
type: Breakout-v5
metrics:
- type: mean_reward
value: 586.40 +/- 280.74
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Breakout-v5**
This is a trained model of a PPO agent playing Breakout-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_xla_jax_scan.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_xla_jax_scan]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_xla_jax_scan --env-id Breakout-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/vwxyzjn/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/ppo_atari_envpool_xla_jax_scan.py
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_xla_jax_scan.py --track --save-model --upload-model --env-id Breakout-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'batch_size': 1024,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Breakout-v5',
'exp_name': 'ppo_atari_envpool_xla_jax_scan',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': '',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 256,
'norm_adv': True,
'num_envs': 8,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 9765,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
Davlan/xlm-roberta-base-finetuned-yoruba | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 29 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: ivi137/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Davlan/xlm-roberta-base-finetuned-zulu | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 3 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 199.49 +/- 69.02
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
...
```
|
Dean/summarsiation | []
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}
} | 0 | null | ---
tags:
- Breakout-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: Breakout-v5
type: Breakout-v5
metrics:
- type: mean_reward
value: 457.10 +/- 134.14
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Breakout-v5**
This is a trained model of a PPO agent playing Breakout-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_xla_jax_scan.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_xla_jax_scan]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_xla_jax_scan --env-id Breakout-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/vwxyzjn/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed3/raw/main/ppo_atari_envpool_xla_jax_scan.py
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed3/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_xla_jax_scan.py --track --save-model --upload-model --env-id Breakout-v5 --seed 3
```
# Hyperparameters
```python
{'anneal_lr': True,
'batch_size': 1024,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Breakout-v5',
'exp_name': 'ppo_atari_envpool_xla_jax_scan',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': '',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 256,
'norm_adv': True,
'num_envs': 8,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 9765,
'save_model': True,
'seed': 3,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
Declan/CNN_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
}
} | 3 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
datasets: akanametov/minions-dataset
widget:
- text: a photo of stuart minion on the Moon
---
# DreamBooth model for the stuart concept trained by akanametov on the akanametov/minions-dataset dataset.
This is a Stable Diffusion model fine-tuned on the stuart concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of stuart minion**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `minion` images for the wildcard theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('akanametov/stuart-minion')
image = pipeline().images[0]
image
```
|
Declan/FoxNews_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 3 | null |
---
tags:
- yolov5
- yolo
- vision
- object-detection
- pytorch
library_name: yolov5
library_version: 7.0.6
inference: false
datasets:
- keremberke/forklift-object-detection
model-index:
- name: keremberke/yolov5m-forklift
results:
- task:
type: object-detection
dataset:
type: keremberke/forklift-object-detection
name: keremberke/forklift-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.8515819366709647 # min: 0.0 - max: 1.0
name: [email protected]
---
<div align="center">
<img width="640" alt="keremberke/yolov5m-forklift" src="https://huggingface.co/keremberke/yolov5m-forklift/resolve/main/sample_visuals.jpg">
</div>
### How to use
- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('keremberke/yolov5m-forklift')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.max_det = 1000 # maximum number of detections per image
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img, size=640)
# inference with test time augmentation
results = model(img, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
# save results into "results/" folder
results.save(save_dir='results/')
```
- Finetune the model on your custom dataset:
```bash
yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-forklift --epochs 10
```
**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)** |
Declan/FoxNews_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 3 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('parayiv/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Declan/HuffPost_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
} | 3 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech_100
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/librispeech_100_ctc_e_branchformer`
This model was trained by Yifan Peng using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
References:
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077)
- [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html)
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 5fbaedd0555de4e205172d9e5b34a98cbf9d265e
pip install -e .
cd egs2/librispeech_100/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/librispeech_100_ctc_e_branchformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Jan 1 15:05:07 CST 2023`
- python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]`
- espnet version: `espnet 202211`
- pytorch version: `pytorch 1.12.1`
- Git hash: `b12e08c955276daa015cc40cf4f5977d87233db2`
- Commit date: `Thu Dec 29 07:10:24 2022 -0500`
## asr_train_asr_ctc_e_branchformer_e12_raw_en_bpe5000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_clean|2703|54402|91.8|7.5|0.7|1.0|9.2|70.1|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_other|2864|50948|80.4|17.4|2.2|2.8|22.4|87.8|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_clean|2620|52576|91.5|7.7|0.8|1.1|9.6|70.3|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_other|2939|52343|79.5|18.1|2.4|2.6|23.1|88.6|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_clean|2703|288456|97.1|1.2|1.7|1.1|4.0|70.1|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_other|2864|265951|91.2|4.5|4.3|3.0|11.8|87.8|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_clean|2620|281530|97.0|1.3|1.7|1.2|4.2|70.3|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_other|2939|272758|90.8|4.5|4.7|3.0|12.2|88.6|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_clean|2703|69558|89.6|5.9|4.5|0.9|11.3|70.1|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_other|2864|64524|77.9|14.8|7.2|3.0|25.1|87.8|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_clean|2620|66983|89.6|6.0|4.4|1.0|11.4|70.3|
|decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_other|2939|66650|77.2|15.2|7.6|2.8|25.7|88.6|
## ASR config
<details><summary>expand</summary>
```
config: /scratch/bbjs/peng6/espnet-arch/egs2/librispeech_100/asr1/conf/tuning/train_asr_ctc_e_branchformer_e12.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_ctc_e_branchformer_e12_raw_en_bpe5000_sp
ngpu: 1
seed: 2022
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 70
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- cer_ctc
- min
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_clean_100_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_clean_100_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
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- ▁YES
- ▁NAME
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- ▁PIECES
- ▁EXCEPTION
- ▁ENJOYED
- ▁DARED
- ▁TRU
- ▁CLOSELY
- ▁RAPID
- ▁AFFECTED
- ▁REQUIRE
- ▁SOFTLY
- ▁BROW
- UCK
- ▁MARKED
- ▁SEVENT
- ▁ELECT
- ▁FORGOT
- ▁CORRECT
- ▁FRANCS
- ▁MARGUERITE
- ▁SCIENCE
- ▁UNEXPECTED
- ▁FOUGHT
- ▁MILITA
- ▁THUNDER
- ▁VOYAGE
- ▁GANEM
- ▁FREEDOM
- ▁NODDED
- ▁CAPTURE
- ▁MORTAL
- ▁OWNER
- ▁POLITE
- ▁VISION
- ▁EDUCATION
- ▁GOVERNOR
- ▁RAV
- ▁REWARD
- ▁HASTE
- ▁REPEAT
- ▁DETERMIN
- ▁PITI
- ▁KNEE
- LINE
- ▁DEVOTED
- ▁INTERRUPTED
- ▁FOLKS
- ▁EXTREME
- ▁APPROACH
- ▁CONTINUE
- ▁BEARING
- ▁CHAP
- ▁ACQUAINTED
- ▁GLIMPSE
- ▁GRADUALLY
- ▁SUNSHINE
- ▁PRACTICE
- ▁SUPPLI
- ▁DAVID
- ▁DRIFT
- ▁SHOWING
- ▁LEVEL
- ▁PROMPT
- ▁QUARREL
- ▁REPRESENTATIVE
- ▁PLUNG
- ▁GIANT
- FALL
- ▁STOUT
- CHA
- WEPT
- ▁GLANC
- ▁SALT
- ▁CHOSEN
- ▁BUCK
- ▁REALIZED
- ▁REALITY
- ▁TUR
- ▁DRIVEN
- ▁CARD
- ▁PRAYER
- ▁TERM
- AID
- ▁HOLY
- ▁ENDURE
- ▁RANGE
- ▁HANG
- ▁SAM
- LAN
- ▁CAVE
- INA
- ▁GRI
- ▁SIGH
- ▁NEIGHBOUR
- ▁COUNCIL
- ▁EXERCISE
- ▁NAUTILUS
- ▁SOMEWHERE
- ▁SYLVIA
- ▁THOROUGH
- ▁VICTIM
- ▁BRIDGE
- ▁COMPELLED
- ▁INCLINED
- ▁OVERCOME
- ▁RESERVE
- ▁ARREST
- ▁PRECIOUS
- ▁DUTCH
- ▁OCEAN
- ▁ACQUIR
- ▁RECALL
- ▁DESTIN
- ▁ATTACH
- ▁SLIM
- ▁WEEP
- ▁CONSCIOUSNESS
- ▁TIGHT
- ▁WAKE
- ▁COMFORTABLE
- ▁ACTIVE
- ▁WINGS
- ▁GRIN
- ▁AFFECT
- ▁WHIT
- ▁IDEAL
- ▁EASTER
- ▁APPROACHING
- ▁CREATED
- ▁PLANS
- ▁INCREASE
- ▁FLYING
- ▁SHOUT
- OES
- MISSION
- ▁ARMED
- ABILITY
- ▁BLUSH
- ▁CONNECTION
- ▁MATTHEW
- ▁MEDICINE
- ▁REMIND
- ▁EXHIBIT
- ▁BLOCK
- ▁DESERVE
- ▁LISTENING
- ▁TITLE
- ▁FLOUR
- ▁FLAME
- ▁AGENT
- ▁USEFUL
- ▁BRIG
- ▁BOIL
- ▁ASSURED
- ▁REFLECTION
- ▁PINE
- ▁WAG
- ▁YOUNGER
- ▁BEARD
- ▁KINDNESS
- CTUALLY
- ▁ACTUAL
- ▁WEIGHT
- ▁LILY
- ▁IMPRESS
- ▁DESCRIBE
- ▁BEHELD
- ▁COMMUNITY
- ▁DESPERATE
- ▁DISPLAY
- ▁ENEMIES
- ▁MELANCHOLY
- ▁MIRROR
- ▁RECOMMEND
- ▁SPANISH
- ▁BLAME
- ▁VOLUME
- ▁SHOOT
- ▁COMBIN
- ▁SHAKING
- ▁SOUTHERN
- ▁MYSTERY
- ▁EVERYONE
- ▁COMMISSION
- ▁COMPOSED
- ▁UDO
- ▁IMAGE
- ▁DECEIV
- ▁FAILURE
- ▁PATTY
- ▁ALICE
- ▁FRAME
- ▁MODEST
- ▁MAGNIFICENT
- ▁BRANCHES
- ▁REIGN
- ▁RAG
- ▁PARISH
- ▁KATE
- ▁AMID
- ▁SLEEPING
- ▁ANNOUNCED
- ▁EAGERLY
- ▁WIRE
- ▁LAP
- ▁ARAB
- ▁EATING
- ▁RUM
- ▁CAREFUL
- ▁DISCUSS
- WORTH
- ▁DISTRICT
- ▁FOREHEAD
- ▁FRANCIS
- ▁INCIDENT
- ▁APPEAL
- ▁EMBARRASS
- ▁MAINTAIN
- ▁PRONOUNC
- ▁FURNISH
- ▁STRAIN
- ▁ELEMENT
- ▁SILK
- ▁FEAST
- ▁RECENT
- ▁DANCING
- ▁LODGE
- ▁ASHAMED
- ▁TRICK
- ▁BOBO
- ▁STUFF
- ▁ET
- ▁ASSERT
- ▁SANK
- ▁TREATMENT
- ECI
- ▁SWIM
- ▁BECOMING
- ▁SINGING
- ▁PLATE
- ▁SCATTERED
- ▁EXTREMELY
- ▁GRIM
- ▁SANG
- ▁FIGHTING
- ▁FACTOR
- ▁PAINFUL
- ▁HIDE
- ▁FUNN
- ▁AFTERWARD
- ▁FROG
- ▁VENTURE
- ▁DISAPPOINT
- ▁COMRADE
- ▁MONSIEUR
- ▁OBVIOUS
- ▁PASSENGER
- ▁PROFOUND
- ▁PUBLISH
- ▁ACCUSTOM
- ▁BLOOM
- ▁SMITH
- ▁RELATIVE
- ▁ACCUSE
- ▁MANIFEST
- ▁SOLID
- ▁MONSTER
- ▁MARIUS
- ▁CANDLE
- ▁PROCUR
- ▁INTERFERE
- ▁HOUSEHOLD
- ▁DEVELOPMENT
- ▁AGREEABLE
- ▁HALT
- ▁NECESSITY
- FOLD
- ▁CITIES
- ▁REGI
- ▁GLOOMY
- BBL
- ▁SEPARATED
- ▁CHEST
- ▁STRIP
- ▁SPAR
- ▁DUN
- ▁SETTLE
- ▁STARED
- ▁HANGING
- ▁FEATURES
- ▁PILE
- ▁ORIGIN
- ARIES
- ▁LION
- ▁ALI
- ▁ASTONISHMENT
- ▁COMPLIMENT
- ▁DELICATE
- ▁COUNSEL
- ▁FIFTH
- ▁SUPPRESS
- ▁BURDEN
- ▁COMPLEX
- ▁ADDITION
- ▁CRUSH
- ▁TWIST
- ▁PIANO
- ▁BRUSH
- ▁CHECK
- ▁ANNIE
- ▁SHELTER
- ▁IMPROV
- ▁WESTERN
- ▁LOCAL
- ▁APPLE
- ▁GREET
- ▁MASK
- ▁RUSSIAN
- ▁TOWER
- ▁CREW
- ▁TIP
- ▁WANDERING
- ▁READER
- ▁WANDERED
- ▁DESTROY
- ▁OBSERVE
- MORE
- ▁ESCAPED
- ▁PET
- ▁BUILD
- ▁REAR
- ▁DESTROYED
- HIN
- ▁OWE
- ▁RANG
- ▁TEAR
- ▁NED
- ▁OFFICER
- ▁TRAP
- ▁OCCUR
- ▁APPOINTED
- ▁ATMOSPHERE
- ▁CHOOSE
- ▁CONCLUSION
- ▁CULTIVAT
- ▁DESCRIPTION
- ▁ENORMOUS
- ▁EXHAUSTED
- ▁LANDSCAPE
- ▁NATASHA
- ▁PROSPECT
- ▁REFRESH
- ▁SPECIES
- ▁SURROUNDED
- ▁WEAPON
- ▁BLANK
- ▁DEFEND
- ▁EDITH
- ▁HORRIBL
- ▁BETRAY
- ▁FERKO
- ▁LABOUR
- ▁NEGRO
- ▁RESUMED
- ▁LEAF
- ▁MUSKET
- ▁INTENSE
- ▁MERCY
- ▁ADOPT
- ▁SCORE
- ▁DASH
- ▁LAWYER
- ▁SLOPE
- ▁CHUCK
- ▁ASSISTANCE
- ▁BROOK
- ▁BREAKING
- ▁ASSIST
- ▁GROAN
- ▁HELEN
- ▁BEHAV
- ▁MAIDEN
- ▁CRIS
- ▁SHOUTING
- ▁NAY
- ▁PIG
- ▁ACCORDINGLY
- ETTE
- ▁DESIR
- ▁RUB
- ▁GRU
- ▁PIT
- ▁HEAVI
- ▁OBTAINED
- ▁SPARE
- ▁BRANCH
- ▁COUNTER
- ▁APART
- ▁AMBITION
- ▁ASTONISHED
- ▁CORRESPOND
- ▁DRIVING
- ▁ENERGY
- ▁HISTORIAN
- ▁REVOLUTION
- ▁SWEEP
- ▁TREMBLING
- ▁CRAFT
- ▁FAMILIES
- ▁LITERATURE
- SBURG
- ▁FEMALE
- ▁TILNEY
- ▁GENEROUS
- ▁SUBMIT
- ▁INTELLECTUAL
- ▁ORCHARD
- ▁STORIES
- ▁DIANA
- ▁VEIN
- ▁TRIFL
- ▁TWIN
- ▁WORSHIP
- ▁MARBLE
- ▁GALLANT
- ▁SENSIBLE
- ▁NEAT
- ▁BROWNIE
- ▁JUNE
- ▁SHAW
- ▁WORST
- ▁USELESS
- ▁FISHING
- ▁CRYING
- ▁MAYBE
- ▁VARI
- ▁PRESERVE
- ▁VOL
- ▁EMPLOY
- ▁INTERRUPT
- ▁SLIGHTLY
- ▁ACCOMPLISHED
- NEY
- ▁STEAM
- ▁BALANC
- ▁LEANING
- ▁SIGHED
- ▁REFUSE
- ▁IMAGINED
- ▁DATE
- GROUND
- ▁ENTERTAIN
- ▁PERCEIVE
- ▁ABROAD
- ▁CHEESE
- ▁DESTRUCTION
- ▁ESSENTIAL
- ▁EXPEDITION
- ▁GRANDFATHER
- ▁INFINITE
- ▁LIBRARY
- ▁MULTITUDE
- ▁NEGLECT
- ▁SWALLOW
- ▁VILLEFORT
- ▁BELOVED
- ▁COMMITTEE
- ▁CONFIDENT
- ▁PURPLE
- ▁PURCHAS
- ▁SCRAP
- ▁SPOIL
- ▁LIKEWISE
- ▁EXTRA
- ▁STRAW
- ▁SALUT
- ▁SOURCE
- ▁HASTENED
- ▁RESENT
- ▁FLOCK
- ▁LOFT
- ▁FLO
- ▁CLO
- ▁CONVINCED
- ▁GOODNESS
- ▁HYPNOTIZ
- ▁SETTING
- ▁HAIL
- ▁PHI
- ▁GROVE
- ▁DISCOVERY
- ▁DAMP
- ▁WHISPER
- ▁LIFT
- ▁HOP
- ▁SUSPECTED
- ▁SCR
- OLI
- ▁FAC
- ▁BUSH
- ▁FOREVER
- ▁BARRICADE
- ▁CONSTITUTION
- ▁ENDEAVOR
- ▁ENTHUSIASM
- ▁EXECUTION
- ▁HYACINTH
- ▁PERCEVAL
- ▁PSYCHE
- ▁REPROACH
- ▁THIRTEEN
- ▁ABSORB
- ▁GRATITUDE
- ▁MERCER
- ▁REPUTATION
- ▁SCREAM
- ▁PUPIL
- ▁RETIRED
- ▁STEEP
- ▁SUMMIT
- ▁MISERABLE
- ▁STRICT
- ▁MINGLED
- ▁DEFEAT
- ▁REVEAL
- ▁LOVING
- ▁GOOSE
- ▁ECHO
- ▁AWAIT
- ▁MOOD
- ▁CRAWLEY
- ▁CELL
- ▁ENGAGEMENT
- ▁PRECED
- ▁SOMEONE
- ▁ARRANGEMENT
- ▁PICKET
- ▁GASP
- ▁HUMOR
- ▁INVITATION
- ▁JOB
- WITHSTAND
- ▁LAMENT
- ▁CLASSES
- ▁HUNGER
- ▁DISPOSED
- ▁STEAMER
- ▁FEARFUL
- ▁GER
- ▁FINAL
- ▁FLAG
- ▁JULY
- ▁DIG
- WORK
- ▁OPPOS
- ▁ANXIETY
- ▁AUDIENCE
- ▁BACHELOR
- ▁COLUMN
- ▁HANDKERCHIEF
- ▁IMPATIENT
- ▁JUDGMENT
- ▁KNIFE
- ▁SOVEREIGN
- ▁STRIKING
- ▁THOMPSON
- ▁EMPIRE
- ▁FULFIL
- ▁CONSULT
- ▁JENNY
- ▁THENARDIER
- ▁POYSER
- ▁FOURTEEN
- ▁JAPANESE
- ▁INDULG
- ▁MARTIAN
- ▁COUNTRIES
- ▁FETCH
- ▁CRITIC
- ▁ROBBER
- ▁CROOK
- ▁DEPARTURE
- ▁MABEL
- ▁PREACH
- ESCENT
- ▁WHIP
- ▁NAIL
- ▁DELIGHTFUL
- ▁DISCUSSION
- ▁SENTENCE
- ▁LANE
- ▁ENGINEER
- ▁ARRANGED
- MMY
- ▁LEST
- ▁RENT
- MMED
- ▁LIST
- ▁ROBE
- ▁MISSION
- ▁GRACEFUL
- ▁LIGHTN
- STONE
- COURT
- ▁CONCEPTION
- ▁CONTRACT
- ▁DROWN
- ▁EXPERIMENT
- ▁HITHERTO
- ▁PLAGUE
- ▁PORTHOS
- ▁SHRIEK
- ▁DETECT
- ▁ACCENT
- ▁ERECT
- ▁SAZEN
- ▁PROFIT
- ▁VIVID
- ▁SQUIRE
- ▁OPERATION
- ▁SMELL
- ▁SIMON
- ▁EXTENT
- ▁KEEN
- ▁EMERG
- ▁REVIV
- ▁REGIMENT
- ▁DISAPPOINTMENT
- ▁STOLE
- ▁DIVINE
- ▁GUILTY
- ▁COWARD
- ▁EXPECTATION
- ▁SIGNOR
- ▁MODE
- ▁CENTRE
- ▁FIL
- HOW
- ▁WEARI
- ▁TOTAL
- ▁VICTOR
- ▁GOVERN
- ▁RAISE
- ▁ABANDON
- ▁ABSURD
- ▁ASPECT
- ▁CRIMINAL
- ▁DEFINITE
- ▁DELIBERAT
- ▁FEATHER
- ▁FLORINA
- ▁MIDNIGHT
- ▁RICHMOND
- ▁SATISFY
- ▁SINGULAR
- ▁STEADILY
- ▁SUPREME
- ▁TIMBER
- ▁PSYCHOLOG
- ▁GESTURE
- ▁VALUABLE
- ▁INTERVAL
- ▁CONFUSION
- ▁FLUTTER
- ▁SACRED
- ▁DISEASE
- ▁UNDERTAKE
- ▁PENETRAT
- ▁MARVEL
- ▁NORTHERN
- ▁GRIEV
- ▁GENIUS
- ▁SADDLE
- ▁NOVEL
- ▁MISERY
- ▁CONVICTION
- ▁SINK
- ▁WAGON
- ▁ARISE
- ▁COMMENT
- ▁BARN
- UPON
- ▁FENCE
- ▁ASSOCIATION
- ▁BONES
- ▁IDLE
- ▁DOUBTFUL
- ▁PREPARATION
- IZZ
- ▁RAIS
- ▁BITTERLY
- ▁JOE
- ▁RELI
- ADI
- ▁METAL
- ▁EXACT
- ▁GLOOM
- FIELD
- ▁DANGLARS
- ▁DISGRACE
- ▁EXAMINATION
- ▁FASCINAT
- ▁GLITTER
- ▁INCREASING
- ▁MESSENGER
- ▁PATRIOT
- ▁PLATFORM
- ▁PROVISION
- ▁QUALITIES
- ▁SELECT
- ▁STEADY
- ▁POVERTY
- ▁POWDER
- ▁PROPHET
- ▁HOLLAND
- ▁TRUNK
- ▁VARIETY
- ▁PLANCHET
- ▁CONQUER
- ▁CONCEIVE
- ▁COMBAT
- ▁STOOP
- ▁SHIRT
- ▁GENERATION
- ▁COMMITTED
- ▁INSULT
- ▁CONFUSED
- ▁RADIAN
- ▁DEBT
- ▁IMITAT
- ▁DART
- ▁CAROLINE
- ▁SWAM
- ▁WREN
- ▁CHILDHOOD
- ▁BRAND
- ▁JOKE
- ▁FRIENDSHIP
- ▁DIRT
- ▁JOLL
- ▁BUSHES
- ▁MINK
- ▁ROUT
- ▁EQUALITY
- ▁HESITATED
- ▁BARK
- ▁ANTI
- ▁STATEMENT
- PHER
- ▁SUNK
- ▁DAT
- ▁BACKWARD
- ▁SUSPECT
- ▁OBJECTION
- ▁RAP
- ▁CHIN
- ▁MATE
- ▁REDUC
- ▁GREGG
- ▁ACCOMPANY
- ▁ANYWHERE
- ▁BENEFIT
- ▁CLERK
- ▁EXPENSE
- ▁FETNAH
- ▁INTERPRET
- ▁LUKASHKA
- ▁NUMEROUS
- ▁SURGEON
- ▁PUZZL
- ▁RESCUE
- ▁GRATEFUL
- ▁APPROV
- ▁RIVAL
- ▁NIECE
- ▁FLOOD
- ▁VANISHED
- ▁ERROR
- ▁BLAZ
- ▁TUMBL
- ▁WENDY
- ▁PERSIST
- ▁CONSOL
- ▁SOAP
- ▁HUMOUR
- ▁FITTED
- ▁HOUSEKEEPER
- ▁ENABL
- ▁OCCASIONALLY
- ▁HATRED
- ▁SWELL
- ▁WORRY
- ▁RUST
- ▁PURSUIT
- ▁INTIMATE
- ▁SEAL
- ▁COLLECTION
- ▁TREMBLED
- ▁DENY
- ▁HUMANITY
- ▁FATAL
- ▁COCK
- ▁DRIVER
- ▁HOPELESS
- ▁MISTAKEN
- ▁LUC
- ▁ACCOMPLISH
- ▁COAL
- ▁ACCORD
- ▁PURSE
- ▁SEPARATE
- ▁ARRIVE
- ▁SMOK
- ▁MADAM
- ▁ASSOCIAT
- ▁INSTRUCT
- ▁CELEBR
- ▁CHANNEL
- ▁CIVILIZATION
- ▁DOCTRINE
- ▁ENDEAVOUR
- ▁GLACIER
- ▁INTELLIGENT
- ▁INVOLVE
- ▁LEATHER
- ▁MUTTERED
- ▁OLENIN
- ▁PENCROFT
- ▁PERPLEX
- ▁SPECTATOR
- ▁UNIVERSITY
- ▁ATTAIN
- ▁INEVITABL
- ▁YONDER
- ▁ENCHANT
- ▁REPAIR
- ▁CURRENT
- ▁ASCEND
- ▁CREEK
- ▁SPARKL
- ▁RUE
- ▁BEAVER
- ▁INFANT
- ▁CONTINUALLY
- ▁CLASP
- ▁IRISH
- ▁ROLLIN
- ▁PUNISHMENT
- ▁LUNCH
- ▁AGONY
- ▁RUDE
- ▁DRAGG
- ▁INQUIRI
- ▁SEX
- ▁TERRIFI
- ▁ROBIN
- ▁PROFESSIONAL
- ▁SPUR
- ▁GRAIN
- ▁VINE
- ▁PENN
- ▁ROC
- ▁CHASE
- ▁INFORM
- ▁WRITER
- ▁AVO
- ▁TAP
- ▁CREAT
- ▁WHIL
- ▁BARR
- ▁ASSURE
- ▁CIRCUMSTANCE
- ▁OIL
- ▁ROUSE
- ▁COLUMB
- ▁CUNNING
- ▁DOMESTIC
- ▁GLORIOUS
- ▁INDIGNATION
- ▁PRECISELY
- ▁PRUDENCE
- ▁RAILROAD
- ▁SATURDAY
- ▁UTMOST
- ▁VIOLENCE
- ▁WHIRL
- ▁CALCULAT
- ▁OVERWHELM
- ▁PERPETUAL
- ▁QUARLES
- ▁SLENDER
- ▁TELEGRAPH
- ▁ALOUD
- ▁OPPRESS
- ▁CROPPER
- ▁CANADIAN
- ▁HERBERT
- ▁TIMID
- ▁SUPPLY
- ▁STROLL
- ▁CREEP
- ▁OATH
- ▁DUSK
- ▁EXCESS
- ▁HUMBLE
- ▁FURIOUS
- ▁RIDGE
- ▁BULLET
- ▁PONY
- ▁STATU
- ▁ENJOYMENT
- ▁CONWAY
- ▁DIFFICULTIES
- ▁PATCH
- ▁JOYCE
- ▁CLOCK
- ▁RESTORED
- ▁ARGU
- ▁WIG
- ▁CHATT
- ▁PLAC
- ▁REMOVE
- ▁TORN
- ▁DISAPPEAR
- TIME
- WELL
- ▁RECOGNIZE
- ▁FISHE
- ▁DECLARE
- ISTIC
- ▁AUTHOR
- ▁WHISK
- ▁COFFEE
- ▁COMPREHEND
- ▁DISGUISE
- ▁ELZEVIR
- ▁ENTERPRISE
- ▁HOLIDAY
- ▁HORIZON
- ▁IGNORANT
- ▁INTERVIEW
- ▁OLIVER
- ▁RONICKY
- ▁CAPACITY
- ▁DISPOSITION
- ▁EXTERNAL
- ▁OPPOSITION
- ▁REPUBLIC
- ▁WHEAT
- ▁CORPSE
- ▁DARLING
- ▁THRILL
- ▁INHABITANTS
- ▁ORNAMENT
- ▁SHIFT
- ▁RECOGNISE
- ▁SHIVER
- ▁BOAST
- ▁HINT
- ▁BOSTON
- ▁MULTI
- IFYING
- ▁STEAL
- ▁INSTRUCTIONS
- ▁ELECTRIC
- ▁SWING
- ▁SOOTH
- ▁SCALE
- ▁MORLAND
- ▁DISLIKE
- ▁FLATTER
- ▁COACH
- ▁LEIF
- ▁STAMP
- ▁ANYHOW
- ▁MOTIONLESS
- ▁ANDREA
- ▁LOSING
- ▁PAUL
- ▁CAROL
- ▁ADVANC
- ▁IMAGIN
- ▁CENTER
- ▁JAR
- ▁SUCCEED
- ▁DISMISS
- CTOR
- ▁RECEIV
- ▁DRAG
- ▁INTENT
- ▁BARBAR
- ▁PUNISH
- ▁ABRUPTLY
- ▁BERNARD
- ▁DECISION
- ▁INDEPENDENT
- ▁PROVINCE
- ▁SLEEVE
- ▁TREMENDOUS
- ▁UNPLEASANT
- ▁LEISURE
- ▁THRONG
- ▁THUMB
- ▁BANNER
- ▁CONTRADICT
- ▁RESTRAIN
- ▁DIVIDED
- ▁WRAPPED
- ▁HAUNT
- ▁SNEER
- CHESTER
- ▁JULIA
- ▁MILD
- ▁CONTACT
- ▁MEANTIME
- ▁NEEDLE
- ▁BLOT
- ▁BARREL
- ▁ISABELLA
- ▁THEATRE
- ▁ESTABLISHMENT
- ▁MARKET
- ▁CHINA
- ▁FORBID
- ▁PERISH
- ▁DOORWAY
- ▁CARLING
- ▁PERIL
- ▁PRIZE
- ▁HATCH
- ▁CURL
- ▁REFER
- ▁DEVOT
- EMBER
- MONT
- ▁CANOE
- ▁PROFESSION
- ▁CONVICT
- ▁CRAWL
- ▁ACTIVITY
- ▁BEWILDER
- ▁BREEZE
- ▁CONTEMPLAT
- ▁DISGUST
- ▁FATIGUE
- ▁MERRICK
- ▁PRAIRIE
- ▁REFORM
- ▁SPECTACLE
- ▁STUDENT
- ▁TUMULT
- ▁UNIFORM
- ▁VIGOROUS
- ▁CONDEMN
- ▁GENUINE
- ▁THOMAS
- ▁ARROW
- ▁PILLOW
- ▁FEEBLE
- ▁RALPH
- ▁SCHEME
- ▁COLLAR
- ▁JUSTINIAN
- ▁NERVE
- ▁OYSTER
- ▁BENNET
- ▁DUTIES
- ▁BINGLEY
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- ▁ERRAND
- ▁EXPEND
- ▁NEGATIVE
- ▁NORHALA
- ▁SCANDAL
- ▁STIMULAT
- ▁SWEAT
- ▁COMPOUND
- ▁DECEMBER
- ▁EXPAND
- ▁PROLONG
- ▁PURITAN
- ▁CONQUEST
- ▁MAGUA
- ▁SANCHO
- ▁TRENCH
- ▁ENTITLE
- ▁PEPPER
- ▁DISASTER
- ▁REGAIN
- ▁SHREWD
- ▁SULLEN
- ▁CLAVIER
- ▁COLOSS
- ▁SHILLING
- ▁ETHEL
- ▁MYSTERIES
- ▁BULK
- ▁GRANDEUR
- ▁AGNES
- ▁CONVERT
- ▁WRIST
- ▁GLID
- ▁TERRACE
- ▁SONYA
- ▁DANTES
- ▁MOULD
- ▁MAGNET
- ▁PLOT
- RANK
- ▁CAVIT
- ▁SUBSID
- ▁SLAP
- TURNED
- ▁THREAT
- BREAK
- ▁ANCESTORS
- ▁ANTICIPATED
- ▁APPLAUSE
- ▁ASSAULT
- ▁ATTORNEY
- ▁AUTOMATIC
- ▁CARAVAN
- ▁CATASTROPHE
- ▁CAVALCANTI
- ▁CROMWELL
- ▁ENVOY
- ▁EXHAUSTION
- ▁FIEND
- ▁GENEROSITY
- ▁GIMBLET
- ▁HARDQUANONNE
- ▁HOUARN
- ▁INJURY
- ▁MACKINSON
- ▁OGLETHORPE
- ▁PETTICOAT
- ▁RASPBERR
- ▁REHNHJELM
- ▁REJOICING
- ▁REMNANT
- ▁SCOTLAND
- ▁SHRINK
- ▁STANDPOINT
- ▁TESTIMONY
- ▁THEREAFTER
- ▁THIRTIETH
- ▁TWENTIETH
- ▁TYRANT
- ▁VENTNOR
- ▁VETERAN
- ▁WHITTAKER
- ▁ZVERKOV
- ▁ARCHITECTUR
- ▁BLUNDER
- ▁DENSHER
- ▁FORTNIGHT
- ▁JUDITH
- ▁MARIANNE
- ▁MEMORABLE
- ▁REFINED
- ▁REVOLV
- ▁UNDERTAKING
- ▁CLUMP
- ▁GRUMBLE
- ▁SYMPATHI
- ▁TICKET
- ▁TWITCH
- ▁EDITION
- ▁FALANDER
- ▁CARTHAGE
- ▁ORLEANS
- ▁POSSUM
- ▁SWITCH
- ▁CLUNG
- ▁CARDINAL
- ▁GNAW
- ▁LOCATED
- ▁HARROW
- ▁RASH
- ▁SIEGE
- ▁LOAF
- ▁BRUISE
- ▁REGULAT
- ▁RESORT
- ▁SARAH
- ▁LEVIN
- ▁NAVY
- ▁MOOSE
- ▁STOOL
- ▁CHANCELLOR
- ▁INGENIOUS
- ▁CHALK
- ▁PRETENCE
- ▁REPAY
- ▁ROAST
- ▁PLUTO
- ▁BAFFL
- ▁STUMBL
- ▁SPHERE
- ▁PLEDGE
- ▁SPRAWL
- ▁WRAP
- ▁FRINGE
- ▁DREAR
- ARRINGTON
- ▁FEDERA
- KEEPER
- ▁PHYSIC
- ▁ADVENT
- HUMAN
- OLOGIST
- ▁ALEXANDR
- ▁APPARITION
- ▁BARTHOLEMY
- ▁CITOYEN
- ▁CLIMATE
- ▁CONTEMPORAR
- ▁DESOLATE
- ▁DISCONTENT
- ▁ELEPHANT
- ▁FERNANDO
- ▁FERRALTI
- ▁FOLIAGE
- ▁FUGITIVE
- ▁GAMBLING
- ▁INVOLUNTARILY
- ▁LABYRINTH
- ▁LEGITIMATE
- ▁MILLIONAIRE
- ▁PERCEPTION
- ▁PROPRIETY
- ▁REBELLION
- ▁REFRAIN
- ▁RUGGLES
- ▁SCRIPTURE
- ▁SPLENDOR
- ▁SQUADRON
- ▁STRICKEN
- ▁SWARM
- ▁THEODORA
- ▁TOMORROW
- ▁VELVET
- ▁WOLVES
- ▁DISREGARD
- ▁GLIMMER
- ▁SHROUD
- ▁TWINKLING
- ▁UNEQUAL
- ▁CHANNING
- ▁CLUMS
- ▁ENIGMA
- ▁NAVIGAT
- ▁TARKAS
- ▁TEMPERATURE
- ▁DIVISION
- ▁GRATIFICATION
- ▁MONUMENT
- ▁SQUEAK
- ▁KAVIN
- ▁INTERPOSE
- ▁THORNTON
- ▁SOLUTION
- ▁STREAK
- ▁SHRILL
- ▁APRON
- ▁PITEOUS
- ▁HAUGHTY
- ▁RECKLESS
- ▁EMPTI
- ▁WADMAN
- ▁BONNET
- ▁MARTHA
- ▁DUMB
- ▁SHATTER
- ▁ACUTE
- ▁BRINK
- ▁CAPRICE
- ▁HURON
- ▁INFERN
- ▁FOWL
- ▁ENRAGE
- ▁ADORN
- ▁CRUIS
- ▁PROBABILIT
- ▁EXPIR
- ▁IMPETU
- ▁OVERHEAR
- BURTON
- ▁TRANSLAT
- ▁ENGAGE
- ▁CONVINCE
- ▁ABNORMAL
- ▁GESTICULAT
- ▁ABOMINABL
- ▁ADVERSARY
- ▁ADVERTISER
- ▁ADVERTISING
- ▁ANNIHILAT
- ▁ARTILLERY
- ▁CATHEDRAL
- ▁COMPETITOR
- ▁COULSON
- ▁CREVICE
- ▁CUSHION
- ▁DEBRAY
- ▁DEJECT
- ▁DIETRICH
- ▁DISADVANTAGE
- ▁ELLISON
- ▁EMPHASIS
- ▁EXCURSION
- ▁FANTASTIC
- ▁HYPOTHES
- ▁INCONVENIENCE
- ▁INDESCRIBABLE
- ▁INDUSTRI
- ▁INVALID
- ▁MERCILESS
- ▁MESOPOTAMIA
- ▁MOSQUITO
- ▁NARRATIVE
- ▁NOWADAYS
- ▁OPPORTUNITIES
- ▁PROMISING
- ▁RECTANGLE
- ▁REMONSTRANCE
- ▁RESTAURANT
- ▁RIBBON
- ▁SCIENTIST
- ▁SHALMANESER
- ▁SKULL
- ▁SPRUCE
- ▁SUBSTANTIAL
- ▁SYMBOL
- ▁TEAPOT
- ▁TERRITORY
- ▁TRAFFIC
- ▁TREASON
- ▁TRUMPET
- ▁TYRANN
- ▁UNANIMOUS
- ▁UNAWARE
- ▁VICINITY
- ▁WREATH
- ▁ZADIG
- ▁CHATEAU
- ▁CONFRONT
- ▁DUCHESS
- ▁EMBODI
- ▁FEMININ
- ▁FURNACE
- ▁MONTONI
- ▁RENOWN
- ▁SMASH
- ▁HARVARD
- ▁NEWBERRY
- ▁PERFUME
- ▁SIGNATURE
- ▁SPLASH
- ▁SUPPOSITION
- ▁HARBOUR
- ▁ASSURANCE
- ▁BRISTOL
- ▁BUCKINGHAM
- ▁DUDLEY
- ▁INTENSITY
- ▁CHOPIN
- ▁ENLIST
- Q
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 1.0
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf: {}
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202211'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Declan/HuffPost_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 9 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- science
widget:
- text: top rated photo of mafra fractal in the shape of seashells.
---
## Description
This is a Stable Diffusion model fine-tuned on Mandelbrot fractal images for the DreamBooth Hackathon 🔥 science theme. To participate or learn more, visit [this page](https://huggingface.co/dreambooth-hackathon).
To generate Mandelbrot fractals, use **a photo of mafra fractal in the shape of [your choice]** or experiment with other variations. CFG scale seems to be the best around 8-9. Additional modifiers and negative prompts may also improve results.
## Examples
*a photo of mafra fractal in the shape of a squid.*

*a photo of mafra fractal in the shape of seashells.*

*a photo of mafra fractal in the shape of jungle foliage.*

*a photo of mafra fractal in the shape of a beautiful flower.*

## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('baruga/mandelbrot-fractals')
image = pipeline().images[0]
image
```
|
Declan/Politico_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 3 | null | ---
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: 0.5268023551875569
---
<!-- 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: 0.8337
- Matthews Correlation: 0.5268
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5253 | 1.0 | 535 | 0.5187 | 0.4181 |
| 0.3463 | 2.0 | 1070 | 0.4989 | 0.5134 |
| 0.2318 | 3.0 | 1605 | 0.5932 | 0.5136 |
| 0.1724 | 4.0 | 2140 | 0.7905 | 0.5156 |
| 0.1285 | 5.0 | 2675 | 0.8337 | 0.5268 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DeepChem/ChemBERTa-77M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 2,416 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: qlearning-taxiv3
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="keshan/qlearning-taxiv3", 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"])
```
|
DeepESP/gpt2-spanish | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1,463 | null |
---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
### Run Configuration
{
"arch": "vitstr_small",
"train_path": "C:\\Users\\smartmind\\Desktop\\workspace\\test\\train_ocr\\train",
"val_path": "C:\\Users\\smartmind\\Desktop\\workspace\\test\\train_ocr\\validation",
"train_samples": 1000,
"val_samples": 20,
"font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf",
"min_chars": 1,
"max_chars": 12,
"name": "vitstr_small-korean",
"epochs": 15,
"batch_size": 64,
"device": 0,
"input_size": 32,
"lr": 0.001,
"weight_decay": 0.01,
"workers": 8,
"resume": null,
"vocab": "korean",
"test_only": false,
"show_samples": false,
"wb": true,
"push_to_hub": true,
"pretrained": true,
"sched": "onecycle",
"amp": true,
"find_lr": false
} |
DeepPavlov/distilrubert-tiny-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 5,993 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-zh-hk
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 zh-HK
type: mozilla-foundation/common_voice_11_0
config: mozilla-foundation/common_voice_11_0 zh-HK
split: None
args: zh-HK
metrics:
- name: Wer
type: wer
value: 0.5615316117542297
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-zh-hk
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 zh-HK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3003
- Wer: 0.5615
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1556 | 2.28 | 1000 | 0.2708 | 0.6069 |
| 0.038 | 4.57 | 2000 | 0.2674 | 0.5701 |
| 0.0059 | 6.85 | 3000 | 0.2843 | 0.5635 |
| 0.0017 | 9.13 | 4000 | 0.2952 | 0.5622 |
| 0.0013 | 11.42 | 5000 | 0.3003 | 0.5615 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Denilson/gbert-base-germaner | []
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} | 0 | null | ---
tags:
- Asteroids-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: Asteroids-v5
type: Asteroids-v5
metrics:
- type: mean_reward
value: 16247.00 +/- 13460.05
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Asteroids-v5**
This is a trained model of a PPO agent playing Asteroids-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 Asteroids-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/Asteroids-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/Asteroids-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Asteroids-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 Asteroids-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': 'Asteroids-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'}
```
|
Deniskin/emailer_medium_300 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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}
} | 14 | null | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: weights_text
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. -->
# weights_text
This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu117
- Tokenizers 0.13.2
|
Denver/distilbert-base-uncased-finetuned-squad | []
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} | 0 | null | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -197.83 +/- 128.39
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 2
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'dotunadegbite/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | {
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"MarianMTModel"
],
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}
} | 1 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Jason-Art Dreambooth model trained by Alexwww with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Putting the prompte words: "photography minimal symmetric" will help get better outputs
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:
|
DimaOrekhov/transformer-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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}
} | 8 | 2023-01-02T07:39:22Z | ---
license: cc0-1.0
---
You want more than a digital style - you want to feel brush strokes and see the built-up paint of an oil painting. You love physical objects and want your AI-generated art to fool you that you're looking at a photograph of something analog, hanging on a wall somewhere.
This is the embedding for you. Download the the 'classipeint.pt' file and trigger it in your prompt "art by classipeint" or "painted by classipeint" or simply "by classipeint"
<strong>Interested in generating your own embeddings? <a href="https://docs.google.com/document/d/1JvlM0phnok4pghVBAMsMq_-Z18_ip_GXvHYE0mITdFE/edit?usp=sharing" target="_blank">My Google doc walkthrough might help</a></strong>
It is reasonably flexible - I find I can prompt for fantasy elements, classic scenes, modern architecture ... it does sometimes take a little finessing but except for bad anatomy, I am using surprisingly few negative prompts.
You can rename the file and use that filename as the prompt. Just be sure your filename is unique and not something that may be an existing token that Stable Diffusion is trained on.








|
Dongjae/mrc2reader | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
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}
} | 3 | null | ---
license: openrail
---
## Models
```
yolov4 (single/multiple gpu)
yolov4-csp (single/multiple gpu)
```
## Dataset
Synthetic data consisting of common office and household items
## Training
Using darknet |
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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}
} | 38,156 | 2023-01-02T10:54:12Z | ---
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: 636.50 +/- 190.80
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 misza222 -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 misza222 -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 misza222
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('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)])
```
|
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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}
} | 4,785,283 | 2023-01-02T10:55:17Z | ---
language: en
license: mit
tags:
- vision
- image-to-text
inference: false
model_name: microsoft/git-base-msrvtt-qa
---
# GIT (GenerativeImage2Text), base-sized, fine-tuned on MSRVTT-QA
GIT (short for GenerativeImage2Text) model, base-sized version, fine-tuned on MSRVTT-QA. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.

This allows the model to be used for tasks like:
- image and video captioning
- visual question answering (VQA) on images and videos
- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
## Intended uses & limitations
You can use the raw model for video question answering (QA). See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/git.html).
## Training data
From the paper:
> We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions
(CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016),
Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B
data following a similar collection procedure in Hu et al. (2021a).
=> however this is for the model referred to as "GIT" in the paper, which is not open-sourced.
This checkpoint is "GIT-base", which is a smaller variant of GIT trained on 10 million image-text pairs.
Next, the model was fine-tuned on MSRVTT-QA.
See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
### Preprocessing
We refer to the original repo regarding details for preprocessing during training.
During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100). |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"AlbertForMaskedLM"
],
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}
}
} | 26,792 | 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)
|
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
} | 7,091 | 2023-01-02T11:07:38Z | ---
language: en
license: mit
tags:
- vision
model_name: microsoft/git-large-vqav2
pipeline_tag: visual-question-answering
---
# GIT (GenerativeImage2Text), large-sized, fine-tuned on VQAv2
GIT (short for GenerativeImage2Text) model, large-sized version, fine-tuned on VQAv2. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.

This allows the model to be used for tasks like:
- image and video captioning
- visual question answering (VQA) on images and videos
- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
## Intended uses & limitations
You can use the raw model for visual question answering (VQA). See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/git.html).
## Training data
From the paper:
> We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions
(CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016),
Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B
data following a similar collection procedure in Hu et al. (2021a).
=> however this is for the model referred to as "GIT" in the paper, which is not open-sourced.
This checkpoint is "GIT-large", which is a smaller variant of GIT trained on 20 million image-text pairs.
Next, the model was fine-tuned on VQAv2.
See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
### Preprocessing
We refer to the original repo regarding details for preprocessing during training.
During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100). |
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 11,644 | 2023-01-02T11:09:46Z | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
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: 1.4812
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6928 | 0.54 | 500 | 1.4812 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
} | 8,621,271 | 2023-01-02T11:10:56Z | ---
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="eyechen/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"])
```
|
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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},
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},
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},
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}
}
} | 3,377,486 | 2023-01-02T11:12:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: insertion-prop-05-correct-data
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. -->
# insertion-prop-05-correct-data
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: 0.0794
- Precision: 0.9284
- Recall: 0.9056
- F1: 0.9169
- Accuracy: 0.9689
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1815 | 0.32 | 500 | 0.0982 | 0.9159 | 0.8802 | 0.8977 | 0.9619 |
| 0.1113 | 0.64 | 1000 | 0.0833 | 0.9257 | 0.9018 | 0.9136 | 0.9676 |
| 0.1018 | 0.96 | 1500 | 0.0794 | 0.9284 | 0.9056 | 0.9169 | 0.9689 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
} | 175,983 | 2023-01-02T11:12:32Z | ---
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: 659.00 +/- 173.14
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 joheras -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 joheras -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 joheras
```
## 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)])
```
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
"min_length": null,
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},
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},
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"max_length": null,
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"prefix": null
}
}
} | 1,814 | 2023-01-02T11:14:16Z | ---
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: 7.50 +/- 2.72
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="eyechen/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"])
```
|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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},
"translation_en_to_fr": {
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}
}
} | 68,305 | 2023-01-02T11:18:10Z | ---
language: en
license: mit
tags:
- vision
model_name: microsoft/git-large-textvqa
inference: false
pipeline_tag: visual-question-answering
---
# GIT (GenerativeImage2Text), large-sized, fine-tuned on TextVQA
GIT (short for GenerativeImage2Text) model, large-sized version, fine-tuned on TextVQA. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.

This allows the model to be used for tasks like:
- image and video captioning
- visual question answering (VQA) on images and videos
- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
## Intended uses & limitations
You can use the raw model for visual question answering (VQA). See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/git.html).
## Training data
From the paper:
> We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions
(CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016),
Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B
data following a similar collection procedure in Hu et al. (2021a).
=> however this is for the model referred to as "GIT" in the paper, which is not open-sourced.
This checkpoint is "GIT-large", which is a smaller variant of GIT trained on 20 million image-text pairs.
Next, the model was fine-tuned on TextVQA.
See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
### Preprocessing
We refer to the original repo regarding details for preprocessing during training.
During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100). |
bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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"text-generation": {
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},
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}
}
} | 59,663,489 | 2023-01-02T11:27:10Z | ---
license: openrail
---
text="""Dear Amazon, last week I ordered an Optimus Prime action figure from your online store in Germany. Unfortunately, when I opened the package, I discovered to my horror that I had been sent an action figure of Megatron instead! As a lifelong enemy of the Deceptions, I hope yoou can understand my dilemma. To resolve the issue, I demand an exchange of Megatron for the Optimus Prime figure I ordered. Enclosed are copies of my records concerning this purchase. I expect to hear from you soon. Sincerely, Bumblebee."""
from transformers import pipeline
classifier = pipeline("text-classification") |
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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} | 8,214 | 2023-01-02T11:33:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: text_classification_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# text_classification_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3686
- F1: 0.8968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2356 | 1.0 | 7215 | 0.3704 | 0.8946 |
| 0.2011 | 2.0 | 14430 | 0.3686 | 0.8968 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 76,685 | 2023-01-02T11:46:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: insertion-prop-015-correct-data
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. -->
# insertion-prop-015-correct-data
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: 0.0497
- Precision: 0.8907
- Recall: 0.8518
- F1: 0.8708
- Accuracy: 0.9816
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0978 | 0.32 | 500 | 0.0581 | 0.8730 | 0.8300 | 0.8509 | 0.9787 |
| 0.0633 | 0.64 | 1000 | 0.0515 | 0.8867 | 0.8447 | 0.8652 | 0.9807 |
| 0.0588 | 0.96 | 1500 | 0.0497 | 0.8907 | 0.8518 | 0.8708 | 0.9816 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
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}
} | 1,440,898 | 2023-01-02T11:48:08Z | ---
language: en
license: mit
tags:
- vision
inference: false
model_name: microsoft/git-large-vatex
---
# GIT (GenerativeImage2Text), large-sized, fine-tuned on VATEX
GIT (short for GenerativeImage2Text) model, large-sized version, fine-tuned on VATEX. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.

This allows the model to be used for tasks like:
- image and video captioning
- visual question answering (VQA) on images and videos
- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
## Intended uses & limitations
You can use the raw model for video captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/git.html).
## Training data
From the paper:
> We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions
(CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016),
Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B
data following a similar collection procedure in Hu et al. (2021a).
=> however this is for the model referred to as "GIT" in the paper, which is not open-sourced.
This checkpoint is "GIT-large", which is a smaller variant of GIT trained on 20 million image-text pairs.
Next, the model was fine-tuned on VATEX.
See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
### Preprocessing
We refer to the original repo regarding details for preprocessing during training.
During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100). |
distilbert-base-german-cased | [
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"DistilBertForMaskedLM"
],
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}
}
} | 43,667 | 2023-01-02T11:59:29Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_model
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="asiaLootus/taxi_model", 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"])
```
|
gpt2 | [
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"doi:10.57967/hf/0039",
"transformers",
"exbert",
"license:mit",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"do_sample": true,
"max_length": 50
},
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}
}
} | 21,488,226 | 2023-01-02T12:18:41Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: toinsson/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
123www/test_model | [
"pytorch",
"wav2vec2",
"transformers"
]
| null | {
"architectures": [
"Wav2Vec2ForSpeechClassification"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
}
} | 5 | 2023-01-02T13:47:50Z | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
13on/kw2t-wishes | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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}
} | 10 | 2023-01-02T13:50:35Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.95 +/- 13.99
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
...
```
|
AdapterHub/bert-base-uncased-pf-winogrande | [
"bert",
"en",
"dataset:winogrande",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/winogrande"
]
| null | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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}
} | 1 | null | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.74 +/- 0.44
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="0xid/q-FrozenLake-v1-4x4-Slippery", 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"])
```
|
AdapterHub/bert-base-uncased-pf-yelp_polarity | [
"bert",
"en",
"dataset:yelp_polarity",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
]
| text-classification | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 2 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tkurtulus/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"])
```
|
AdapterHub/narrativeqa | [
"bart",
"dataset:narrativeqa",
"adapter-transformers",
"adapterhub:qa/narrativeqa"
]
| null | {
"architectures": null,
"model_type": "bart",
"task_specific_params": {
"conversational": {
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},
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} | 23 | null | ---
language: en
tags:
- financial
- stocks
- topic
datasets:
- Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75
widget:
- text: "LexaGene Receives Signed Quote from Large Biopharma Company to Purchase a MiQLab System -- LexaGene Holdings, Inc., (OTCQB: LXXGF; TSX-V: LXG) (“LexaGene” or the “Company”), an innovative, molecular diagnostics company that has commercialized the MiQLab® System for automated, genetic testing, is pleased to announce that it has received an indication that a major biopharma company intends to purchase its technology."
- text: "Melcor REIT (TSX: MR.UN) today announced results for the third quarter ended September 30, 2022. Revenue was stable in the quarter and year-to-date. Net operating income was down 3% in the quarter at $11.61 million due to the timing of operating expenses and inflated costs including utilities like gas/heat and power"
- text: "Badger Infrastructure Solutions Ltd. Announces Resignation of Chief Financial Officer and Appointment of Interim Chief Financial Officer -- Badger Infrastructure Solutions Ltd. (“Badger” or the “Company”) (TSX:BDGI) announced today the resignation of Mr. Darren Yaworsky, Senior Vice President, Finance & Chief Financial Officer and the appointment of Mr. Pramod Bhatia as interim Chief Financial Officer. Mr. Yaworsky will remain with the Company until December 31, 2022 to facilitate an orderly transition."
license: mit
---
# Model fine-tuned from roberta-large for topic classification of financial news (emphasis on Canadian news).
### Introduction
This model was train on the topic column of financial_news_sentiment_mixte_with_phrasebank_75 dataset.
The topic column was generated using a zero-shot classification model on 11 topics.
There was no manual reviews on the generated topics and therefore we should expect misclassifications in the dataset,
and therefore the trained model might reproduce the same errors.
### Training data
Training data was classified as follow:
class |Description
-|-
0 |acquisition
1 |other
2 |quaterly financial release
3 |appointment to new position
4 |dividend
5 |corporate update
6 |drillings results
7 |conference
8 |share repurchase program
9 |grant of stocks
### How to use roberta-large-financial-news-topics-en with HuggingFace
##### Load roberta-large-financial-news-topics-en and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-financial-news-topics-en")
model = AutoModelForSequenceClassification.from_pretrained("Jean-Baptiste/roberta-large-financial-news-topics-en")
##### Process text sample (from wikipedia)
from transformers import pipeline
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
pipe("Melcor REIT (TSX: MR.UN) today announced results for the third quarter ended September 30, 2022. Revenue was stable in the quarter and year-to-date. Net operating income was down 3% in the quarter at $11.61 million due to the timing of operating expenses and inflated costs including utilities like gas/heat and power")
[{'label': 'quaterly financial release', 'score': 0.8829097151756287}]
```
### Model performances
Overall f1 score (average macro)
precision|recall|f1
-|-|-
0.7533|0.7629|0.7499
|
AdapterHub/roberta-base-pf-emotion | [
"roberta",
"en",
"dataset:emotion",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
]
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}
} | 6 | null | ---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-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="0xid/q-FrozenLake-v1-8x8-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"])
```
|
AdapterHub/roberta-base-pf-hotpotqa | [
"roberta",
"en",
"dataset:hotpot_qa",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
]
| question-answering | {
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}
} | 35 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 818.00 +/- 306.11
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sliu -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 sliu -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 sliu
```
## 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)])
```
|
AdapterHub/roberta-base-pf-imdb | [
"roberta",
"en",
"dataset:imdb",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/imdb"
]
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 275.15 +/- 21.31
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AdapterHub/roberta-base-pf-mit_movie_trivia | [
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:ner/mit_movie_trivia"
]
| token-classification | {
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}
} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Barb2000 Dreambooth model trained by asfdsadsada 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:
|
AdapterHub/roberta-base-pf-record | [
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:rc/record"
]
| text-classification | {
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}
} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: beto-sentiment-analysis-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beto-sentiment-analysis-finetuned
This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4406
- Accuracy: 0.7757
- F1: 0.7773
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3380
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.5384 | 1.45 | 100 | 2.1387 | 0.2831 | 0.3049 |
| 2.1562 | 2.9 | 200 | 1.6375 | 0.4596 | 0.4873 |
| 1.5805 | 4.35 | 300 | 1.4332 | 0.5993 | 0.6377 |
| 1.4242 | 5.8 | 400 | 1.3355 | 0.6544 | 0.6565 |
| 1.1192 | 7.25 | 500 | 1.2845 | 0.6765 | 0.6854 |
| 0.9617 | 8.7 | 600 | 1.1512 | 0.6912 | 0.7167 |
| 0.829 | 10.14 | 700 | 1.0676 | 0.6801 | 0.7079 |
| 0.6889 | 11.59 | 800 | 1.0715 | 0.7022 | 0.7323 |
| 0.59 | 13.04 | 900 | 1.1065 | 0.7316 | 0.7392 |
| 0.5129 | 14.49 | 1000 | 1.1585 | 0.7059 | 0.7382 |
| 0.4278 | 15.94 | 1100 | 1.1106 | 0.75 | 0.7582 |
| 0.3728 | 17.39 | 1200 | 1.1561 | 0.7537 | 0.7679 |
| 0.3142 | 18.84 | 1300 | 1.1755 | 0.7537 | 0.7667 |
| 0.275 | 20.29 | 1400 | 1.2095 | 0.7574 | 0.7707 |
| 0.2251 | 21.74 | 1500 | 1.3647 | 0.7574 | 0.7674 |
| 0.2175 | 23.19 | 1600 | 1.3127 | 0.7537 | 0.7635 |
| 0.1923 | 24.64 | 1700 | 1.3494 | 0.7794 | 0.7760 |
| 0.1753 | 26.09 | 1800 | 1.4221 | 0.7684 | 0.7658 |
| 0.1484 | 27.54 | 1900 | 1.3572 | 0.7684 | 0.7727 |
| 0.1455 | 28.99 | 2000 | 1.4063 | 0.7757 | 0.7747 |
| 0.131 | 30.43 | 2100 | 1.3754 | 0.7721 | 0.7730 |
| 0.1125 | 31.88 | 2200 | 1.4302 | 0.7757 | 0.7740 |
| 0.1203 | 33.33 | 2300 | 1.4146 | 0.7684 | 0.7714 |
| 0.1083 | 34.78 | 2400 | 1.4406 | 0.7757 | 0.7773 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AdapterHub/roberta-base-pf-rotten_tomatoes | [
"roberta",
"en",
"dataset:rotten_tomatoes",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/rotten_tomatoes"
]
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}
} | 4 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.81 +/- 46.92
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
...
```
|
AdapterHub/roberta-base-pf-scitail | [
"roberta",
"en",
"dataset:scitail",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/scitail"
]
| text-classification | {
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} | 1 | null | ---
language: es
license: gpl-3.0
tags:
- spacy
- token-classification
widget:
- text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago."
- text: "El proyecto lo financia el Ministerio de Industria y Competitividad."
model-index:
- name: es_spacy_ner_cds
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9648998822
- name: NER Recall
type: recall
value: 0.9603751465
- name: NER F Score
type: f_score
value: 0.9626321974
---
# Introduction
spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC).
| Feature | Description |
| --- | --- |
| **Name** | `es_spacy_ner_cds` |
| **Version** | `0.0.1a` |
| **spaCy** | `>=3.4.3,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
## Usage
You can use this model with the spaCy *pipeline* for NER.
```python
import spacy
from spacy.pipeline import merge_entities
nlp = spacy.load("es_spacy_ner_cds")
nlp.add_pipe('sentencizer')
example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. El proyecto lo financia el Ministerio de Industria y Competitividad."
ner_pipe = nlp(example)
print(ner_pipe.ents)
for token in merge_entities(ner_pipe):
print(token.text, token.ent_type_)
```
## Dataset
ToDo
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 96.26 |
| `ENTS_P` | 96.49 |
| `ENTS_R` | 96.04 |
| `TOK2VEC_LOSS` | 62780.17 |
| `NER_LOSS` | 34006.41 |
|
AdapterHub/roberta-base-pf-sick | [
"roberta",
"en",
"dataset:sick",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/sick"
]
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}
} | 21 | null | ---
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 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)
```
## 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 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': 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 --> |
AdapterHub/roberta-base-pf-stsb | [
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sts/sts-b"
]
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} | 0 | null | Fusion-in-Decoder (FiD) is a model described in the following paper:
> Izacard, Gautier, and Édouard Grave. [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://aclanthology.org/2021.eacl-main.74/). _Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume_. 2021.
We have replicated FiD training with our Wikipedia corpus variants and incorporated the model into our [PyGaggle](https://github.com/castorini/pygaggle) neural text ranking library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is a FiD-large reader model for the wiki-all-8-4 corpus variant trained on the TriviaQA dataset.
|
AdapterHub/roberta-base-pf-swag | [
"roberta",
"en",
"dataset:swag",
"arxiv:2104.08247",
"adapter-transformers"
]
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} | 1 | null | Fusion-in-Decoder (FiD) is a model described in the following paper:
> Izacard, Gautier, and Édouard Grave. [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://aclanthology.org/2021.eacl-main.74/). _Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume_. 2021.
We have replicated FiD training with our Wikipedia corpus variants and incorporated the model into our [PyGaggle](https://github.com/castorini/pygaggle) neural text ranking library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is a FiD-large reader model for the wiki-text-8-4 corpus variant trained on the Natural Questions dataset. |
Adarsh123/distilbert-base-uncased-finetuned-ner | []
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} | 0 | 2023-01-02T21:25:34Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
**This model was fine tuned with SetFit based on 1 utterance 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 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': 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 --> |
Adharsh2608/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
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 --> |
Adinda/Adinda | [
"license:artistic-2.0"
]
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} | 0 | 2023-01-02T21:42:52Z | ---
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: 291.75 +/- 16.72
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
...
```
|
Adityanawal/testmodel_1 | []
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} | 0 | null | ---
license: openrail
---
pip install transformers
from transformers import Trainer, TrainingArguments
# Load the training and validation data
train_data = ...
validation_data = ...
# Define the model architecture and hyperparameters
model_name = "bert-base-cased"
num_labels = 2
# Define the training arguments
training_args = TrainingArguments(
output_dir="./output", # directory to save the trained model
num_train_epochs=3, # number of training epochs
per_device_train_batch_size=32, # batch size
per_device_eval_batch_size=64, # batch size for evaluation
warmup_steps=500, # number of warmup steps
weight_decay=0.01, # L2 regularization coefficient
learning_rate=3e-5, # learning rate
adam_epsilon=1e-8, # epsilon for Adam optimizer
max_grad_norm=1.0, # maximum gradient norm for gradient clipping
save_steps=1000, # number of steps after which to save the model
save_total_limit=2, # maximum number of models to save
)
# Initialize the trainer
trainer = Trainer(
model_name=model_name,
num_labels=num_labels,
data_collator=data_collator, # data collator for the training and validation data
args=training_args,
)
# Train the model
trainer.train(train_data, validation_data)
|
Advertisement/FischlUWU | []
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} | 0 | null | ---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- data/copas
metrics:
- wer
model-index:
- name: Whisper Small dysarthric Dutch
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: data/copas copas-full
type: data/copas
config: copas-full
split: test
args: copas-full
metrics:
- name: Wer
type: wer
value: 22.163827473722364
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small dysarthric Dutch
This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the data/copas copas-full dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4702
- Wer: 22.1638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 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
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1618 | 0.05 | 500 | 0.3787 | 28.9235 |
| 0.0583 | 1.05 | 1000 | 0.3732 | 25.7702 |
| 0.0382 | 2.05 | 1500 | 0.4001 | 25.4621 |
| 0.0316 | 3.05 | 2000 | 0.4081 | 24.7010 |
| 0.0169 | 4.05 | 2500 | 0.4325 | 24.1935 |
| 0.0153 | 5.05 | 3000 | 0.4325 | 33.4179 |
| 0.0074 | 6.05 | 3500 | 0.4367 | 23.9398 |
| 0.0096 | 7.05 | 4000 | 0.4390 | 23.3055 |
| 0.0054 | 8.05 | 4500 | 0.4441 | 23.7042 |
| 0.0032 | 9.04 | 5000 | 0.4493 | 23.2693 |
| 0.004 | 10.04 | 5500 | 0.4524 | 23.3418 |
| 0.0048 | 11.04 | 6000 | 0.4498 | 23.7224 |
| 0.001 | 12.04 | 6500 | 0.4577 | 22.8887 |
| 0.0002 | 13.04 | 7000 | 0.4577 | 22.0913 |
| 0.0001 | 14.04 | 7500 | 0.4616 | 22.1276 |
| 0.0001 | 15.04 | 8000 | 0.4639 | 22.2726 |
| 0.0001 | 16.04 | 8500 | 0.4662 | 22.1095 |
| 0.0001 | 17.04 | 9000 | 0.4684 | 22.1457 |
| 0.0001 | 18.04 | 9500 | 0.4697 | 22.1457 |
| 0.0001 | 19.04 | 10000 | 0.4702 | 22.1638 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Aeroxas/Botroxas-small | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {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)
```
## 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': 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 --> |
Ahmadatiya97/Alannah | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- recall
- precision
- f1
model-index:
- name: t5-base-extraction-cnndm_fs0.02-c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-extraction-cnndm_fs0.02-c
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8400
- Recall: 35.4852
- Precision: 40.9499
- F1: 36.9238
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:-------:|:-------:|
| 2.7966 | 1.14 | 200 | 2.0568 | 17.0907 | 45.1725 | 21.4724 | 12.7061 |
| 2.1271 | 2.29 | 400 | 1.8400 | 35.4852 | 40.9499 | 36.9238 | 19.0 |
| 1.9831 | 3.43 | 600 | 1.7756 | 35.0259 | 39.8685 | 36.1824 | 18.9962 |
| 1.9025 | 4.57 | 800 | 1.7365 | 34.9077 | 39.2092 | 35.8205 | 19.0 |
| 1.8564 | 5.71 | 1000 | 1.7075 | 33.8282 | 38.141 | 34.765 | 19.0 |
| 1.8164 | 6.86 | 1200 | 1.6898 | 34.6927 | 38.999 | 35.5568 | 19.0 |
| 1.7929 | 8.0 | 1400 | 1.6753 | 34.9922 | 39.2711 | 35.8318 | 19.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.10.0+cu111
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Ahmed59/Demo-Team-5-SIAD | [
"tf",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
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"RobertaForSequenceClassification"
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} | 14 | null | ---
inference: false
tags:
- onnx
- text-classification
- bert
- adapterhub:qa/boolq
- adapter-transformers
datasets:
- boolq
language:
- en
---
# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-boolq` for bert-base-uncased
## Conversion of [AdapterHub/bert-base-uncased-pf-boolq](https://huggingface.co/AdapterHub/bert-base-uncased-pf-boolq) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-boolq-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'English orthography typically represents vowel sounds with the five conventional vowel letters ⟨a, e, i, o, u⟩, as well as ⟨y⟩, which may also be a consonant depending on context. However, outside of abbreviations, there are a handful of words in English that do not have vowels, either because the vowel sounds are not written with vowel letters or because the words themselves are pronounced without vowel sounds'.
question = 'can there be a word without a vowel'
tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-boolq-onnx')
inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')
inputs = {key: np.array(inputs[key], dtype=np.int64) for key in inputs}
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",
}
``` |
AhmedHassan19/model | []
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} | 0 | null | ---
inference: false
tags:
- onnx
- text-classification
- roberta
- adapterhub:qa/boolq
- adapter-transformers
datasets:
- boolq
language:
- en
---
# ONNX export of Adapter `AdapterHub/roberta-base-pf-boolq` for roberta-base
## Conversion of [AdapterHub/roberta-base-pf-boolq](https://huggingface.co/AdapterHub/roberta-base-pf-boolq) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-boolq-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'English orthography typically represents vowel sounds with the five conventional vowel letters ⟨a, e, i, o, u⟩, as well as ⟨y⟩, which may also be a consonant depending on context. However, outside of abbreviations, there are a handful of words in English that do not have vowels, either because the vowel sounds are not written with vowel letters or because the words themselves are pronounced without vowel sounds'.
question = 'can there be a word without a vowel'
tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-boolq-onnx')
inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')
inputs = {key: np.array(inputs[key], dtype=np.int64) for key in inputs}
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",
}
``` |
Ahmedahmed/Wewe | []
| null | {
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} | 0 | null | ---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
model_file: umit_regress.pkl
widget:
structuredData:
AGE:
- 92.7
- 97.4
- 18.5
B:
- 395.09
- 302.76
- 392.33
CHAS:
- 0
- 0
- 0
CRIM:
- 0.15086
- 6.39312
- 0.07244
DIS:
- 1.8209
- 2.206
- 10.7103
INDUS:
- 27.74
- 18.1
- 1.69
LSTAT:
- 18.06
- 24.1
- 7.79
NOX:
- 0.609
- 0.584
- 0.411
PTRATIO:
- 20.1
- 20.2
- 18.3
RAD:
- 4
- 24
- 4
RM:
- 5.454
- 6.162
- 5.884
TAX:
- 711.0
- 666.0
- 411.0
ZN:
- 0.0
- 0.0
- 60.0
---
# 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 |
|------------------|---------|
| copy_X | True |
| fit_intercept | True |
| n_jobs | |
| positive | False |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LinearRegression()</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">LinearRegression</label><div class="sk-toggleable__content"><pre>LinearRegression()</pre></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|--------------------|-----------|
| Mean Squared Error | 23.7928 |
| R-Squared | 0.751045 |
# 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]
```
# limitations
This model is not ready to be used in production.
# model_description
More info on me [umit isikdag](https://isikdag.com).
|
Ahren09/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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} | 33 | null | ---
license: apache-2.0
---
# Classifier architecture
The classifier uses DenseNet161 as the encoder and some linear layers at classifier base.
# Model accuracy:
Model achieves 91.3% accuracy on the validation set. \
F1-score per class: {'digital': 0.9873773235685747, 'hard': 0.9338602782753218, 'soft': 0.8444277483052108} \
Mean F1-score: 0.9218884500497024 \
Accuracy: 0.913
# Training dataset metadata:
1. Dataset classes: ['soft', 'digital', 'hard']
2. Number of classes: 3
3. Total number of images: 18415
# Number of images per class:
- soft : 5482
- digital : 1206
- hard : 11727
# Classes description:
1. The **hard** class denotes a group of scenes to which a coarser background removal method should be applied, intended for objects with an edge without small details.
The hard class contains the following categories of objects:
object, laptop, charger, pc mouse, pc, rocks, table, bed, box, sneakers, ship, wire, guitar, fork, spoon, plate, keyboard, car, bus, screwdriver, ball, door, flower, clocks, fruit , food, robot.
2. The **soft** class denotes a group of scenes to which you want to apply a soft background removal method intended for people, hair, clothes, and other similar types of objects. The soft class contains the following categories of objects:
animal, people, human, man, woman, t-shirt, hairs, hair, dog, cat, monkey, cow, medusa, clothes
3. The **digital** class denotes a group of images with digital graphics, such as screenshots, logos, and so on.
The digital class contains the following categories of scenes:
screenshot
|
Akari/albert-base-v2-finetuned-squad | [
"pytorch",
"tensorboard",
"albert",
"question-answering",
"dataset:squad_v2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 13 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 630.00 +/- 204.11
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga armargolis -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 armargolis -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 armargolis
```
## 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)])
```
|
AkshatSurolia/ViT-FaceMask-Finetuned | [
"pytorch",
"safetensors",
"vit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| image-classification | {
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"ViTForImageClassification"
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} | 40 | null | ---
license: creativeml-openrail-m
language:
- en
tags:
- text-to-image
- midjourney
- stable-diffusion
- disco-diffusion
- art
- arxiv:2208.12242
inference: true
library_name: diffusers
---
## Paint Journey V2 is [V1](https://huggingface.co/FredZhang7/paint-journey-v1) fine-tuned on 768x768 oil paintings by Midjourney V4, Open Journey V2, Disco Diffusion, and artists given permission
Begin the prompt with **((oil painting))** to add the oil paint effect. For digital and other painting styles, use similar prompts as you would for Midjourney V4 (with some tweaks), Stable Diffusion v1.5 (add more styles), Open Journey V2, or Disco Diffusion.
[](https://colab.research.google.com/github/AMLA-UBC/100-Exploring-the-World-of-Modern-Machine-Learning/blob/main/assets/PaintJourneyV2.ipynb)
## Examples
*All examples were generated using Camenduru's WebUI (see the Colab file)*

*⬆️ 768x1136 portraits, generated using descriptive prompts and without face restoration, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/character_settings.txt)*

*⬆️ 1280x768 (mostly) natural landscapes, used shorter prompts, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/nature_settings.txt)*

*⬆️ 1152x768 outerspace landscapes, used descriptive prompts, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/outerspace_settings.txt)*

*⬆️ 1280x768 lamborghini, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/lamborghini_settings.txt)*

*⬆️ 960x768 Eevee, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/eevee_settings.txt)*
## Comparisons
Paint Journey V2's paintings are closer to human-drawn art than Open Journey V2.
Compared to models like Dreamlike Diffusion 1.0, PJ V2 tends to generate 768x768 or higher resolution images with reduced noise levels.
This model is also capable of generating stunning portraits at 768x1136 resolution without duplicated faces (with [Camenduru's WebUI](https://github.com/camenduru/stable-diffusion-webui)), a difficult task to models like DreamShaper 3.3.
At lower resolutions, DreamShaper 3.3 tends to generate higher quality portraits than PJ V2 in terms of noise levels, given the same (short) postive and negative prompts.
However, PJ V2 can craft more stunning masterpieces with more descriptive positive and negative prompts and can still generate beautiful landscapes with shorter prompts.
## Training
Instead of solely fine-tuning its Unet, Paint Journey V2 focuses on fine-tuning its text encoder with a diverse range of prompts.
This allows for a seamless blend of the digital and oil painting styles into various other types of prompts, resulting in a more natural and dynamic output.
This model was trained on a curated dataset of roughly 300 images hand-picked from Midjourney, [Prompt Hero](https://prompthero.com/), [PixaBay](https://pixabay.com/images/search/paintings/), Open Journey V2, and Reddit.
Before training, I used R-ESRGAN 4x on many images to increase their resolution and reduce noise.
## Running out of prompts?
Useful resources: [Lexica.art](https://lexica.art/), [Fast GPT PromptGen](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2), [Prompt Hero](https://prompthero.com/)
## Output Dimensions
Portrait sizes include, but are not limited to, `512x768`, `768x768`, and `768x1136`.
Landscape sizes include, but are not limited to, `768x512`, `768x768`, `1152x768`, and `1280x768`.
## Camenduru's WebUI
```
git clone -b v1.6 https://github.com/camenduru/stable-diffusion-webui
```
<details>
<summary> Click to use Automatic1111's Webui instead, but may not output images as artistic </summary>
```
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
```
</details>
Download [checkpoint](./paint_journey_v2.ckpt) and [vae](./paint_journey_v2.vae.pt) to the `./stable-diffusion-webui/models/Stable-diffusion` folder. Run `webui-user.bat`.
## 🧨 Diffusers
*Tip: using double, tripple, or quadriple brackets around some letters WORD (e.g. "((WORD))") will put an 'emphasis' on WORD*
```bash
pip install --upgrade diffusers transformers
```
```python
# see more sampling algorithms at https://huggingface.co/docs/diffusers/using-diffusers/schedulers#changing-the-scheduler
from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
import torch, random, datetime
pipe = StableDiffusionPipeline.from_pretrained("FredZhang7/paint-journey-v2")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
def random_seed():
return random.randint(0, 2**32 - 1)
prompt = "((oil painting)), gentle waves, bright blue sky, white sails billowing, sun glistening on the surface, salty sea air, distant horizon, calm breeze, birds soaring overhead, vibrant colors, artstation digital painting, high resolution, uhd, 4 k, 8k wallpaper" # what you want to see
negative_prompt = "low-res, blurry, haze, dark clouds looming, choppy waves, engine failing, sails tattered, stormy winds".split(", ") # what you don't want to see
seed = random_seed() # replace with the desired seed if needed
width, height = 1280, 768 # width and height of the generated image
cfg_scale = 7.5 # classifer free guidance scale, smaller means more creative, 7 to 11 is usually a good range
num_inference_steps = 40 # sampling steps, 30 to 40 is usually good for Euler Ancestral
generator = torch.Generator("cuda").manual_seed(seed)
with torch.autocast("cuda"):
image = pipe(prompt=prompt,
num_inference_steps=num_inference_steps,
width=width, height=height,
generator=generator,
guidance_scale=cfg_scale).images[0]
def generate_filename(string, seed):
invalid_chars = ["<", ">", ":", '"', "/", "\\", "|", "?", "*"]
for char in invalid_chars:
string = string.replace(char, "")
return f"{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}_{seed}_{string}"
image.save(f"./{generate_filename(prompt, seed)}.png")
```
## Safety Checker V2
The official [stable diffusion safety checker](https://huggingface.co/CompVis/stable-diffusion-safety-checker) uses up 1.22GB VRAM.
I recommend using [Google Safesearch Mini V2](https://huggingface.co/FredZhang7/google-safesearch-mini-v2) (220MB) to save 1.0GB VRAM. |
AlErysvi/Erys | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8697972857872921
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1727
- F1: 0.8698
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2544 | 1.0 | 787 | 0.1789 | 0.8115 |
| 0.1391 | 2.0 | 1574 | 0.1601 | 0.8223 |
| 0.0929 | 3.0 | 2361 | 0.1497 | 0.8586 |
| 0.0591 | 4.0 | 3148 | 0.1528 | 0.8673 |
| 0.0368 | 5.0 | 3935 | 0.1727 | 0.8698 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Aleksandar/distilbert-srb-ner-setimes-lr | []
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} | 0 | null | ---
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},
}
``` |
Aleksandar/distilbert-srb-ner | [
"pytorch",
"distilbert",
"token-classification",
"sr",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| token-classification | {
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"DistilBertForTokenClassification"
],
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}
} | 9 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### ssaassaaddoo Dreambooth model trained by sasa30 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:
|
Aleksandra/distilbert-base-uncased-finetuned-squad | []
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} | 0 | null | ---
tags:
- text classification
widget:
- text: "Take out the trash."
example_title: "Example 1"
- text: "Cut the tomato."
example_title: "Example 2"
---
# Temporal Action Prediction
Prediction of action effect time from simple sentences.
|
AlexaMerens/Owl | [
"license:cc"
]
| null | {
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: T5-asr-corrector
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# T5-asr-corrector
This model is a fine-tuned version of [flax-community/bengali-t5-base](https://huggingface.co/flax-community/bengali-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4683
## 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
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6804 | 0.15 | 500 | 0.8576 |
| 0.792 | 0.31 | 1000 | 0.6556 |
| 0.6553 | 0.46 | 1500 | 0.5640 |
| 0.5901 | 0.62 | 2000 | 0.5114 |
| 0.5454 | 0.77 | 2500 | 0.4815 |
| 0.53 | 0.93 | 3000 | 0.4683 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Alexander-Learn/bert-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 8 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: fr
datasets:
- lmqg/qag_frquad
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc."
example_title: "Questions & Answers Generation Example 1"
model-index:
- name: lmqg/mbart-large-cc25-frquad-qag
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qag_frquad
type: default
args: default
metrics:
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
type: qa_aligned_f1_score_bertscore_question_answer_generation
value: 77.75
- name: QAAlignedRecall-BERTScore (Question & Answer Generation)
type: qa_aligned_recall_bertscore_question_answer_generation
value: 79.45
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
type: qa_aligned_precision_bertscore_question_answer_generation
value: 76.19
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
type: qa_aligned_f1_score_moverscore_question_answer_generation
value: 53.5
- name: QAAlignedRecall-MoverScore (Question & Answer Generation)
type: qa_aligned_recall_moverscore_question_answer_generation
value: 54.55
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
type: qa_aligned_precision_moverscore_question_answer_generation
value: 52.57
---
# Model Card of `lmqg/mbart-large-cc25-frquad-qag`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question & answer pair generation task on the [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
- **Language:** fr
- **Training data:** [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="fr", model="lmqg/mbart-large-cc25-frquad-qag")
# model prediction
question_answer_pairs = model.generate_qa("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-frquad-qag")
output = pipe("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
```
## Evaluation
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_frquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 77.75 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
| QAAlignedF1Score (MoverScore) | 53.5 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
| QAAlignedPrecision (BERTScore) | 76.19 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
| QAAlignedPrecision (MoverScore) | 52.57 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
| QAAlignedRecall (BERTScore) | 79.45 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
| QAAlignedRecall (MoverScore) | 54.55 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qag_frquad
- dataset_name: default
- input_types: ['paragraph']
- output_types: ['questions_answers']
- prefix_types: None
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 256
- epoch: 14
- batch: 2
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 64
- label_smoothing: 0.0
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qag/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Aliraza47/BERT | []
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 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
...
```
|
Amro-Kamal/gpt | []
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} | 0 | null | ---
language:
- "ain"
tags:
- "ainu"
- "token-classification"
- "pos"
- "dependency-parsing"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "itak=as awa pon rupne aynu ene itaki"
- text: "イタカㇱ アワ ポン ルㇷ゚ネ アイヌ エネ イタキ"
- text: "итакас ава пон рубне айну эне итакі"
---
# deberta-base-ainu-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on Ainu texts (in カタカナ, Roman, and Кириллица) for POS-tagging and dependency-parsing, derived from [deberta-base-ainu](https://huggingface.co/KoichiYasuoka/deberta-base-ainu). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-ainu-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-ainu-upos")
```
or
```py
import esupar
nlp=esupar.load("KoichiYasuoka/deberta-base-ainu-upos","ainu")
```
## Reference
安岡孝一: [ローマ字・カタカナ・キリル文字併用アイヌ語RoBERTa・DeBERTaモデルの開発](http://id.nii.ac.jp/1001/00224072/), 情報処理学会研究報告, Vol.2023-CH-131『人文科学とコンピュータ』, No.7 (2023年2月18日), pp.1-7.
## See Also
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
|
Amrrs/wav2vec2-large-xlsr-53-tamil | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ta",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index",
"has_space"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 31 | null | ---
language:
- "ain"
tags:
- "ainu"
- "token-classification"
- "pos"
- "dependency-parsing"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "itak=as awa pon rupne aynu ene itaki"
- text: "イタカㇱ アワ ポン ルㇷ゚ネ アイヌ エネ イタキ"
- text: "итакас ава пон рубне айну эне итакі"
---
# deberta-base-ainu-ud-goeswith
## Model Description
This is a DeBERTa(V2) model pre-trained on Ainu texts (in カタカナ, Roman, and Кириллица) for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-base-ainu-upos](https://huggingface.co/KoichiYasuoka/deberta-base-ainu-upos).
## How to Use
```py
class UDgoeswith(object):
def __init__(self,bert):
from transformers import AutoTokenizer,AutoModelForTokenClassification
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForTokenClassification.from_pretrained(bert)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=self.tokenizer(text,return_offsets_mapping=True)
v=w["input_ids"]
x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
with torch.no_grad():
e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
g=self.model.config.label2id["X|_|goeswith"]
r=numpy.tri(e.shape[0])
for i in range(e.shape[0]):
for j in range(i+2,e.shape[1]):
r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)
m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
p=numpy.zeros(m.shape)
p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
for i in range(1,m.shape[0]):
m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text+"\n"
v=[(s,e) for s,e in w["offset_mapping"] if s<e]
for i,(s,e) in enumerate(v,1):
q=self.model.config.id2label[p[i,h[i]]].split("|")
u+="\t".join([str(i),text[s:e],"_",q[0],"|".join(q[1:-1]),"_",str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=UDgoeswith("KoichiYasuoka/deberta-base-ainu-ud-goeswith")
print(nlp("itak=as awa pon rupne aynu ene itaki"))
```
with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
Or without ufal.chu-liu-edmonds:
```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-base-ainu-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("itak=as awa pon rupne aynu ene itaki"))
```
## Reference
安岡孝一: [ローマ字・カタカナ・キリル文字併用アイヌ語RoBERTa・DeBERTaモデルの開発](http://id.nii.ac.jp/1001/00224072/), 情報処理学会研究報告, Vol.2023-CH-131『人文科学とコンピュータ』, No.7 (2023年2月18日), pp.1-7.
|
Ana1315/A | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: W4nkel/distilbertBase128KTrain
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. -->
# W4nkel/distilbertBase128KTrain
This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7462
- Validation Loss: 0.5115
- Train Accuracy: 0.7675
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.7462 | 0.5115 | 0.7675 | 0 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.11.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Ana1315/ana | []
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: PaulMest/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AndrewMcDowell/wav2vec2-xls-r-300m-japanese | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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},
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},
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}
}
} | 4 | null | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ES_roberta_30_prepro
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. -->
# ES_roberta_30_prepro
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Exact Match: 26.25
- F1: 36.0319
- Loss: 1.2394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Exact Match | F1 | Validation Loss |
|:-------------:|:-----:|:----:|:-----------:|:-------:|:---------------:|
| No log | 1.0 | 305 | 22.9167 | 34.1584 | 1.0608 |
| 0.7921 | 2.0 | 610 | 25.0 | 35.1179 | 1.0869 |
| 0.7921 | 3.0 | 915 | 26.25 | 36.0319 | 1.2394 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Andrey1989/mt5-small-finetuned-mlsum-es | []
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: jsalvador/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Andrey78/my_nlp_test_model | []
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} | 0 | null | ---
tags:
- conversational
---
# Peter Griffin DialoGPT Model |
Andrija/RobertaFastBPE | []
| null | {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Ayaka_DB Dreambooth model trained by Falon 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:
|
Andrija/SRoBERTa-F | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"dataset:cc100",
"dataset:hrwac",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 59 | null | # WARNING: NOT ORIGINAL MODEL
This repository and model is not an ORIGINAL one published by the author.
It is just a copy of diffusers for having a link to stable diffusion dreambooth training.
So, thank you for your merge requests, but probably you need to do them to the author repo if it has it.
At the giving time, huggingface does not have an author's repository here, unfortunately.
The closest is that one: https://huggingface.co/johnslegers/hasdx
|
Andrija/SRoBERTa-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 7 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
extra_gated_prompt: |-
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. CompVis 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 carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
---
[![Example][1]][1]
## Why Epic Diffusion
Epîc Diffusion is a general purpose model based on Stable Diffusion 1.x intended to replace the official SD releases
as your default model. It is focused on providing high quality output in a wide range of different styles, with support
for NFSW content.
Epîc Diffusion 1.0 is a heavily calibrated merge of SD 1.4, SD 1.5, Analog Diffusion, Wavy Diffusion,
Openjourney Diffusion, Samdoesarts Ultramerge, postapocalypse, Elldreth's Dream, Inkpunk Diffusion,
Arcane Diffusion & Van Gogh Diffusion blended and reblended multiple times until I got the quality & consistency
I was looking for...
Epic Diffusion is also [available on CivitAI](https://civitai.com/models/3855/epic-diffusion).
## 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. CompVis 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)
<a href="https://www.buymeacoffee.com/johnslegers" target="_blank">
<img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" >
</a>
## Example prompts
<table>
<tr style="border: 1px solid;background:#e5e7eb">
<th style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
Prompt
</th>
<th style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
Parameters
</th>
<th style="vertical-align:top;padding:.5714286em!important;border: 1px solid;min-width:270px">
Output
</th>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
scarlett johansson, in the style of Wes Anderson, highly detailed, unreal engine, octane render, 8k
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>2263657329<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/0oZij.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
sansa angeline jolie gessica chastain mummy, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha and william - adolphe bouguereau
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>1310341382<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/mnnBR.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
Pokimane, Feminine, Mercy, Perfect Sexy Symmetrical Face, Detailed Pupils, Pensive Smirk, Look at Viewer, Leaf Armor, Ilya Kuvshinov, Gil Elvgren, Mucha. Intricate, Octane Render, 4KUHD, Centered, Oil Painting, Bokeh, Rim Lighting.
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>4142902194<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/v9NoC.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
Mature babe,artgerm Style, gerald brom, atey ghailan, mike mignola, short cut off shirt knot, wide hips, showing off, exposing herself vulnerable, blushing, exited, confident, demanding, joyful, trending on artstation, double split complementary colors, intricate details, highly detailed,
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>3954688283<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/vl0bc.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
planet base, windows, night, ground level, no man's sky, digital art, highly detailed, intricate, sharp focus, Trending on Artstation HQ, deviantart, unreal engine 5, 4K UHD image
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>895811336<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/D2GNK.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
berchtesgaden, hyperdetailed, detailed faces, artgerm, wolfenstein, portal 2, Leartes Studios, assassin's creed, alphonse mucha, bouguereau, edmund blair leighton, greg kadel, dynamic lighting, delicate, unreal engine, octane render, 8k
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>1172925287<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/m7Xkb.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
princess, detailed portrait, hyperdetailed, detailed faces, irakli nadar, magali villeneuve, Assassin's Creed, Tim Hildebrandt, Ilya Kuvshinov, artgem, greg kadel, dynamic lighting, delicate, unreal engine, octane render, 8k
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>2096567313<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/LwPPa.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
a Photorealistic dramatic hyperrealistic bright blue eyes, African American elegant girl, black hair, white veil,by WLOP,Artgerm,Greg Rutkowski,Alphonse Mucha, Beautiful dynamic dramatic bright sunset lighting,shadows,cinematic atmosphere,Artstation,concept design art,Octane render,8k
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>2999946689<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/1nH9c.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
cutest girl in the world outside, (detailed portrait), in the style of fernanda suarez and simon stalenhag and Ilya Kuvshinov and Wlop and Artgerm and Chie Yoshii and Greg Rutkowski and Waking Life, trending on artstation, featured on pixiv, dynamic lighting, highly detailed, ambient lighting, octane render, 8k
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>2249388004<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/uNux1.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
military academy, (detailed portrait), steampunk, in the style of arcane and fernanda suarez and dishonored and bioshock and simon stalenhag and Ilya Kuvshinov and Wlop and Artgerm, trending on artstation, featured on pixiv, dynamic lighting, highly detailed, ambient lighting, octane render, 8k
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>3877530043<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/sFXCi.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
beautiful female assassin wearing cyberpunk clothing, respirator, cybernetic respirator, (detailed portrait), cell shaded, 4 k, vivid colours, photorealistic concept art by wlop, ilya kuvshinov, artgerm, krenz cushart, greg rutkowski, pixiv. cinematic dramatic atmosphere, sharp focus, volumetric lighting, cinematic lighting, studio quality
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>3388890157<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/14iZS.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
cemetary, pen and ink, in the style of gustave dore highly detailed, octane render, 8k, trending on artstation, sharp focus, studio photo, intricate details, highly detailed, by greg rutkowski
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>568457114<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/D1hsN.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
dubai, hyperdetailed, detailed faces, artgem, irakli nadar, mass effect, Tim Hildebrandt, Ilya Kuvshinov, liam wong, greg rutkowski, greg kadel, dynamic lighting, delicate, unreal engine, octane render, 8k, centered, symmetry, painted, intricate, volumetric lighting, beautiful, rich deep colors masterpiece, sharp focus, ultra detailed, in the style of dan mumford and marc simonetti, astrophotography
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>DPM++ SDE<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>4262868463<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/4uPzr.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
Little cute forest fluffy chibi cuteness overload, sunny magical background, ultra precious details, intricate details, volumetric lighting, photo realistic, lifelike, photography, digital art, 8k, trending on artstation, sharp focus, studio photo, intricate details, highly detailed, by greg rutkowski, sharp focus, emitting diodes, smoke, artillery, sparks, racks, system unit, motherboard, by pascal blanche rutkowski repin artstation hyperrealism painting concept art of detailed character design matte painting, 4 k resolution blade runner
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>DPM++ SDE Karras<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>3849507891<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/4yTQP.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
15 year old schoolgirl with short straight hair, blue eyes, cute, friendly, round face, cottagecore, intricate, enlightened, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>2276800560<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/gqynB.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
extreme wide shot a futuristic containment building in a rainforest valley with a city in the distance, national geographic, hyper realistic, 4 k, harsh light
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>3260458902<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/8qH9Y.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
portrait of a middle - eastern female cleric with straight black hair wearing blue and yellow vestments casting fireball, fantasy, highly detailed, digital painting, artstation, concept art, character art, art by greg rutkowski and tyler jacobson and alphonse mucha
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>1379894453<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/BP98Y.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
aSnowshoe Siamese Cat as the doomslayer, realistic scifi cyberpunk power armor robot, closeup portrait art by donato giancola and greg rutkowski, vintage retro scifi, realistic face, digital art, trending on artstation, symmetry
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>2122325442<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/GYdOS.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
Beautiful boy by René Magritte
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>1753689226<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/vP9sv.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
portrait of a dark god, copper wires, visible scars and nerves, intricate, headshot, highly detailed, digital painting, artstation, concept art, sharp focus, cinematic lighting, illustration, art by artgerm and greg rutkowski, alphonse mocha, cgsociety, Olivia
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>3355776798<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/A94Gg.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
knight warrior helmet skyrim mask elder scrolls v nordic armor bethesda adam adamowicz illustration character design concept, unreal 5, daz, hyperrealistic, octane render, cosplay, rpg portrait, dynamic lighting, intricate detail, harvest fall vibrancy, cinematic volume inner glowing aura global illumination ray tracing hdr
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>1938574287<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/efGrz.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
berserker portrait, d&d style, fantasy, photorealistic, highly detailed, artstation, smooth, sharp focus, art by michael whelan, artgerm, greg rutkowski and alphonse mucha
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>DPM++ SDE Karras<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>156077154<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/Wbjgp.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
symmetry product render poster vivid colors classical proportion car, glowing fog intricate, elegant, highly detailed, digital painting, art station, concept art, smooth, sharp focus, illustration,
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>DPM++ SDE Karras<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>4294525772<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/sMMpR.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
Futuristic Vintage Medium Shot 1920's Poster with Cyberpunk, ovni, tron biker with helmet bike, black in color, with a cyberpunk city background, futuristic lighting, cinematic lighting, cozy lighting, 8k, cinematic poster vintage 1800s
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>1229558409<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/0Gojz.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
beautiful, young woman, cybernetic, cyberpunk, detailed gorgeous face, flowing hair, vaporwave aesthetic, synthwave , digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>264509871<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/zFdjj.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
strong warrior princess| centered| key visual| intricate| highly detailed| breathtaking beauty| precise lineart| vibrant| comprehensive cinematic| Carne Griffiths| Conrad Roset
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>16<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/aGuIL.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
portrait of a rugged 19th century man with mutton chops in a jacket, victorian, concept art, detailed face, fantasy, close up face, highly detailed, cinematic lighting, digital art painting by greg rutkowski
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>16<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/6sKW6.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
side profile of cyberpunk body with cyborg skull | cyberpunk | styled in Art Nouveau | insanely detailed | embellishments | high definition | concept art | digital art | vibrant
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>16<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/N7kSu.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
a cute little matte low poly isometric cherry blossom forest island, pink waterfalls, mist, lat lighting, soft shadows, trending on artstation, 3d render, monument valley, fez video game,
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>16<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/fVj9N.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
high resolution concept art of an apartment living room overlooking a large futuristic city with floor to ceiling windows and mid century modern furniture cinematic lighting cgsociety
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>850995814<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/jkpgU.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
hyperrealistic full length portrait of gorgeous watson from apex legends | blonde | detailed gorgeous face!! | full body!! | armor | intricate | elegant | realistic | hyperrealistic | cinematic | character design | concept art | highly detailed | illustration | digital art | digital painting | depth of field | illustrated by tim brown lee
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>3002798343<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/hMsH2.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
Chibi spiderman, high redolution, 3D rendering, octane rendering, modern Disney style
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>20<br>
<b>Sampler:</b><br>Euler a<br>
<b>CFG scale:</b><br>7<br>
<b>Seed:</b><br>3232863832<br>
<b>Size:</b><br>512x512
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/zl18l.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
photo of the most beautiful artwork in the world featuring soft lustrous, industrial mechanic real world, fantastic location, working environment, rugged harsh situation worker, full body 8k unity render, action shot, skin pores, detailed intricate iris, very dark lighting, heavy shadows, detailed, detailed face, (vibrant, photo realistic, realistic, dramatic, dark, sharp focus, 8k), (weathered greasy dirty damaged old worn technician worker outfit:1.1), (intricate:1.1), (highly detailed:1.1), digital painting, octane render, artstation, concept art, smooth, sharp focus, illustration, art by artgerm, (loish:0.23), wlop ilya kuvshinov., (global illumination, studio light, volumetric light)<br><br>
<b>Negative prompt:</b> Asian, black and white, close up, cartoon, 3d, denim, (disfigured), (deformed), (poorly drawn), (extra limbs), blurry, boring, sketch, lackluster, signature, letters, watermark, low res , horrific , mutated , artifacts , bad art , gross , b&w , poor quality , low quality , cropped
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>30<br>
<b>Sampler:</b><br>DPM++ SDE Karras<br>
<b>CFG scale:</b><br>10<br>
<b>Seed:</b><br>169686802<br>
<b>Size:</b><br>512x640
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/dPnAA.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
photo of the most beautiful artwork in the world featuring soft lustrous, industrial mechanic real world, fantastic location, working environment, rugged harsh situation worker, full body 8k unity render, action shot, skin pores, detailed intricate iris, very dark lighting, heavy shadows, detailed, detailed face, (vibrant, photo realistic, realistic, dramatic, dark, sharp focus, 8k), (weathered greasy dirty damaged old worn technician worker outfit:1.1), (intricate:1.1), (highly detailed:1.1), digital painting, octane render, artstation, concept art, smooth, sharp focus, illustration, art by artgerm, (loish:0.23), wlop ilya kuvshinov., (global illumination, studio light, volumetric light)<br><br>
<b>Negative prompt:</b> Asian, black and white, close up, cartoon, 3d, denim, (disfigured), (deformed), (poorly drawn), (extra limbs), blurry, boring, sketch, lackluster, signature, letters, watermark, low res , horrific , mutated , artifacts , bad art , gross , b&w , poor quality , low quality , cropped
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>30<br>
<b>Sampler:</b><br>DPM++ SDE Karras<br>
<b>CFG scale:</b><br>10<br>
<b>Seed:</b><br>169686796<br>
<b>Size:</b><br>512x640<br>
<b>Denoising strength:</b><br>0.7<br>
<b>Hires upscale:</b><br>2<br>
<b>Hires upscaler:</b><br>Latent
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.imgur.com/ktLu2Tl.png">
</td>
</tr>
<tr>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
dark and gloomy full body 8k unity render, female teen cyborg, Blue yonder hair, wearing broken battle armor, at cluttered and messy shack , action shot, tattered torn shirt, porcelain cracked skin, skin pores, detailed intricate iris, very dark lighting, heavy shadows, detailed, detailed face, (vibrant, photo realistic, realistic, dramatic, dark, sharp focus, 8k)<br><br>
<b>Negative prompt:</b> nude, Asian, black and white, close up, cartoon, 3d, denim, (disfigured), (deformed), (poorly drawn), (extra limbs), blurry, boring, sketch, lackluster, signature, letters, watermark, low res , horrific , mutated , artifacts , bad art , gross , b&w , poor quality , low quality , cropped
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<b>Steps:</b><br>26<br>
<b>Sampler:</b><br>DPM++ SDE Karras<br>
<b>CFG scale:</b><br>7.5<br>
<b>Seed:</b><br>2388736888<br>
<b>Size:</b><br>768x1024
</td>
<td style="vertical-align:top;padding:.5714286em!important;border: 1px solid">
<img style="vertical-align:top;margin:0;padding:0" src="https://i.stack.imgur.com/GnUuV.jpg">
</td>
</tr>
</table>
[1]: https://i.stack.imgur.com/wkK2b.png |
Andrija/SRoBERTa | [
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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},
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},
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},
"translation_en_to_fr": {
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}
}
} | 88 | 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 --> |
Andrija/SRoBERTaFastBPE | []
| null | {
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}
}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -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
...
```
|
Ani123/Ani | []
| null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
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},
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 0 | null | ---
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 --> |
Ankitha/DialoGPT-small-harrypottery | []
| null | {
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},
"text-generation": {
"do_sample": null,
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}
} | 0 | null | ---
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
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annoy
pastTense = annoyed
answer
pastTense = answered
apologise
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appear
pastTense = appeared
applaud
pastTense = applauded
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pastTense = appreciated
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pastTense = approved
argue
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arrange
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arrest
pastTense = arrested
arrive
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ask
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attach
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attack
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attempt
pastTense = attempted
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pastTense = attended
attract
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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
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bat
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battle
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beam
pastTense = beamed
beg
pastTense = begged
behave
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belong
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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
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borrow
pastTense = borrowed
bounce
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bow
pastTense = bowed
box
pastTense = boxed
brake
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branch
pastTense = branched
breathe
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bruise
pastTense = bruised
brush
pastTense = brushed
bubble
pastTense = bubbled
bump
pastTense = bumped
burn
pastTense = burned
bury
pastTense = buried
buzz
pastTense = buzzed
calculate
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call
pastTense = called
camp
pastTense = camped
care
pastTense = cared
carry
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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
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cheer
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chew
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choke
pastTense = choked
chop
pastTense = chopped
claim
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clap
pastTense = clapped
clean
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clear
pastTense = cleared
clip
pastTense = clipped
close
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coach
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coil
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collect
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colour
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comb
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command
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communicate
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compare
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compete
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complain
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concentrate
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confess
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connect
pastTense = connected
consider
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contain
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continue
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copy
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correct
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count
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cover
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crack
pastTense = cracked
crash
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crawl
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cross
pastTense = crossed
crush
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cry
pastTense = cried
cure
pastTense = cured
curl
pastTense = curled
curve
pastTense = curved
cycle
pastTense = cycled
dam
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damage
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dance
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dare
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decide
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decorate
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delay
pastTense = delayed
delight
pastTense = delighted
deliver
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depend
pastTense = depended
describe
pastTense = described
desert
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deserve
pastTense = deserved
destroy
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detect
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develop
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disagree
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disappear
pastTense = disappeared
disapprove
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disarm
pastTense = disarmed
discover
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dislike
pastTense = disliked
divide
pastTense = divided
double
pastTense = doubled
doubt
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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
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embarrass
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employ
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empty
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encourage
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end
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enjoy
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enter
pastTense = entered
entertain
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escape
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examine
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exercise
pastTense = exercised
exist
pastTense = existed
expand
pastTense = expand
expect
pastTense = expected
explain
pastTense = explained
explode
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extend
pastTense = extended
face
pastTense = faced
fade
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fail
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fancy
pastTense = fancied
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fax
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pastTense = feared
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fetch
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file
pastTense = filed
fill
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film
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fire
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fit
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fix
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flap
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flash
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float
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flood
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flow
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flower
pastTense = flowered
fold
pastTense = folded
follow
pastTense = followed
fool
pastTense = fooled
force
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form
pastTense = formed
found
pastTense = founded
frame
pastTense = framed
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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
grin
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pastTense = gripped
groan
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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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harm
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hate
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haunt
pastTense = haunted
head
pastTense = headed
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pastTense = healed
heap
pastTense = heaped
heat
pastTense = heated
help
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hook
pastTense = hooked
hop
pastTense = hopped
hope
pastTense = hoped
hover
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hug
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hum
pastTense = hummed
hunt
pastTense = hunted
hurry
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identify
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ignore
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pastTense = impressed
improve
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include
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increase
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inject
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injure
pastTense = injured
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interrupt
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introduce
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invent
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itch
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jam
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jog
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join
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joke
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jump
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kick
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kill
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kneel
pastTense = knelt
knit
pastTense = knitted
knock
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knot
pastTense = knotted
label
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last
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launch
pastTense = launched
learn
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license
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lick
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listen
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lock
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look
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man
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mark
pastTense = marked
marry
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measure
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meddle
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melt
pastTense = melted
memorise
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mess up
pastTense = messed up
milk
pastTense = milked
mine
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miss
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mix
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moan
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moor
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mourn
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move
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muddle
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mug
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multiply
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murder
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nail
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name
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need
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notice
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number
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offend
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open
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order
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overflow
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own
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pack
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paddle
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paint
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park
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part
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paste
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pat
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pause
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peck
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pedal
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peep
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perform
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phone
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pick
pastTense = picked
pinch
pastTense = pinched
pine
pastTense = pined
place
pastTense = placed
plan
pastTense = planned
plant
pastTense = planted
play
pastTense = played
please
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plug
pastTense = plugged
point
pastTense = pointed
poke
pastTense = poked
polish
pastTense = polished
pop
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possess
pastTense = possessed
post
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pour
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present
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press
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pretend
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pastTense = pricked
print
pastTense = printed
produce
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program
pastTense = programmed
promise
pastTense = promised
protect
pastTense = protected
provide
pastTense = provided
pull
pastTense = pulled
pump
pastTense = pumped
punch
pastTense = punched
puncture
pastTense = punctured
punish
pastTense = punished
push
pastTense = pushed
question
pastTense = questioned
queue
pastTense = questioned
race
pastTense = raced
radiate
pastTense = radiated
rain
pastTense = rained
raise
pastTense = raised
reach
pastTense = reached
realise
pastTense = realised
receive
pastTense = received
recognise
pastTense = recognised
record
pastTense = recorded
reduce
pastTense = reduced
reflect
pastTense = reflected
refuse
pastTense = refused
regret
pastTense = regretted
reign
pastTense = reigned
reject
pastTense = rejected
rejoice
pastTense = rejoiced
relax
pastTense = relaxed
release
pastTense = released
rely
pastTense = relied
remain
pastTense = remained
remember
pastTense = remembered
remind
pastTense = reminded
remove
pastTense = removed
repair
pastTense = repaired
repeat
pastTense = repeated
replace
pastTense = replaced
reply
pastTense = replied
report
pastTense = reported
reproduce
pastTense = reproduced
request
pastTense = requested
rescue
pastTense = rescued
retire
pastTense = retired
return
pastTense = returned
rhyme
pastTense = rhyme
rinse
pastTense = rinsed
risk
pastTense = risked
rob
pastTense = robbed
rock
pastTense = rocked
roll
pastTense = rolled
rot
pastTense = rotted
rub
pastTense = rubbed
ruin
pastTense = ruined
rule
pastTense = ruled
rush
pastTense = rushed
sack
pastTense = sacked
sail
pastTense = sailed
satisfy
pastTense = satisfied
save
pastTense = saved
saw
pastTense = sawed
scare
pastTense = scared
scatter
pastTense = scattered
scold
pastTense = scolded
scorch
pastTense = scorched
scrape
pastTense = scraped
scratch
pastTense = scratched
scream
pastTense = screamed
screw
pastTense = screwed
scribble
pastTense = scribbled
scrub
pastTense = scrubbed
seal
pastTense = sealed
search
pastTense = searched
separate
pastTense = separate
serve
pastTense = served
settle
pastTense = settled
shade
pastTense = shaded
share
pastTense = shared
shave
pastTense = shaved
shelter
pastTense = sheltered
shiver
pastTense = shivered
shock
pastTense = shocked
shop
pastTense = shopped
shrug
pastTense = shrugged
sigh
pastTense = sighed
sign
pastTense = signed
signal
pastTense = signalled
sin
pastTense = sinned
sip
pastTense = sipped
ski
pastTense = skied
skip
pastTense = skipped
slap
pastTense = slapped
slip
pastTense = slipped
slow
pastTense = slowed
smash
pastTense = smashed
smell
pastTense = smelled
smile
pastTense = smiled
smoke
pastTense = smoked
snatch
pastTense = snatched
sneeze
pastTense = sneezed
sniff
pastTense = sniffed
snore
pastTense = snored
snow
pastTense = snowed
soak
pastTense = soaked
soothe
pastTense = soothed
sound
pastTense = sounded
spare
pastTense = spared
spark
pastTense = sparked
sparkle
pastTense = sparkled
spell
pastTense = spelled
spill
pastTense = spilled
spoil
pastTense = spoiled
spot
pastTense = spotted
spray
pastTense = sprayed
sprout
pastTense = sprouted
squash
pastTense = squashed
squeak
pastTense = squeaked
squeal
pastTense = squealed
squeeze
pastTense = squeezed
stain
pastTense = stained
stamp
pastTense = stamped
stare
pastTense = stared
start
pastTense = started
stay
pastTense = stayed
steer
pastTense = steered
step
pastTense = stepped
stir
pastTense = stirred
stitch
pastTense = stitched
stop
pastTense = stopped
store
pastTense = stored
strap
pastTense = strapped
strengthen
pastTense = strengthened
stretch
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
harmonious
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
labored
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
lost
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
married
marvelous
masculine
massive
material
materialistic
mature
meager
mealy
mean
measly
meaty
medical
mediocre
medium
meek
melancholy
mellow
melodic
melted
memorable
menacing
merciful
mere
merry
messy
metallic
mighty
mild
military
milky
mindless
miniature
minor
minty
minute
miscreant
miserable
miserly
misguided
mistaken
misty
mixed
moaning
modern
modest
moist
moldy
momentous
monstrous
monumental
moody
moral
mortified
motherly
motionless
mountainous
muddled
muddy
muffled
multicolored
mundane
mundane
murky
mushy
musty
mute
muted
mysterious
naive
narrow
nasty
natural
naughty
nauseating
nautical
neat
nebulous
necessary
needless
needy
negative
neglected
negligible
neighboring
neighborly
nervous
nervous
new
next
nice
nice
nifty
nimble
nine
nippy
nocturnal
noiseless
noisy
nonchalant
nondescript
nonsensical
nonstop
normal
nostalgic
nosy
notable
noted
noteworthy
novel
noxious
numb
numberless
numerous
nutritious
nutty
oafish
obedient
obeisant
obese
oblivious
oblong
obnoxious
obscene
obsequious
observant
obsolete
obtainable
obvious
occasional
oceanic
odd
oddball
offbeat
offensive
official
oily
old
old-fashioned
omniscient
onerous
open
opposite
optimal
optimistic
opulent
orange
orderly
ordinary
organic
original
ornate
ornery
ossified
outgoing
outlandish
outlying
outrageous
outstanding
oval
overconfident
overcooked
overdue
overjoyed
overlooked
overrated
overt
overwrought
painful
painstaking
palatable
pale
paltry
panicky
panoramic
parallel
parched
parsimonious
partial
passionate
pastel
pastoral
pathetic
peaceful
penitent
peppery
perfect
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
playful
pleasant
pleased
pleasing
plucky
plump
plush
pointed
pointless
poised
polished
polite
political
pompous
poor
popular
portly
posh
positive
possessive
possible
potable
powerful
powerless
practical
precious
premium
present
present
prestigious
pretty
previous
pricey
prickly
primary
prime
pristine
private
prize
probable
productive
profitable
profuse
proper
protective
proud
prudent
psychedelic
psychotic
public
puffy
pumped
punctual
pungent
puny
pure
purple
purring
pushy
pushy
putrid
puzzled
puzzling
quack
quaint
quaint
qualified
quarrelsome
quarterly
queasy
querulous
questionable
quick
quickest
quick-witted
quiet
quintessential
quirky
quixotic
quixotic
quizzical
rabid
racial
radiant
ragged
rainy
rambunctious
rampant
rapid
rare
rash
raspy
ratty
raw
ready
real
realistic
reasonable
rebel
recent
receptive
reckless
recondite
rectangular
red
redundant
reflecting
reflective
regal
regular
reliable
relieved
remarkable
reminiscent
remorseful
remote
repentant
repulsive
required
resolute
resonant
respectful
responsible
responsive
revolving
rewarding
rhetorical
rich
right
righteous
rightful
rigid
ringed
ripe
ritzy
roasted
robust
romantic
roomy
rosy
rotating
rotten
rotund
rough
round
rowdy
royal
rubbery
ruddy
rude
rundown
runny
rural
rustic
rusty
ruthless
sable
sad
safe
salty
sandy
sane
sarcastic
sardonic
sassy
satisfied
satisfying
savory
scaly
scandalous
scant
scarce
scared
scary
scattered
scented
scholarly
scientific
scintillating
scornful
scratchy
scrawny
screeching
secondary
second-hand
secret
secretive
sedate
seemly
selective
self-assured
selfish
self-reliant
sentimental
separate
serene
serious
serpentine
several
severe
shabby
shadowy
shady
shaggy
shaky
shallow
shameful
shameless
sharp
shimmering
shiny
shivering
shocked
shocking
shoddy
short
short-term
showy
shrill
shy
sick
silent
silky
silly
silver
similar
simple
simplistic
sincere
sinful
single
six
sizzling
skeletal
skillful
skinny
sleepy
slight
slim
slimy
slippery
sloppy
slow
slushy
small
smarmy
smart
smelly
smiling
smoggy
smooth
smug
snappy
snarling
sneaky
sniveling
snobbish
snoopy
snotty
sociable
soft
soggy
solid
somber
some
sophisticated
sordid
sore
sorrowful
soulful
soupy
sour
sour
Spanish
sparkling
sparse
special
specific
spectacular
speedy
spherical
spicy
spiffy
spiky
spirited
spiritual
spiteful
splendid
spooky
spotless
spotted
spotty
spry
spurious
squalid
square
squeaky
squealing
squeamish
squiggly
stable
staid
stained
staking
stale
standard
standing
starchy
stark
starry
statuesque
steadfast
steady
steel
steep
stereotyped
sticky
stiff
stimulating
stingy
stormy
stout
straight
strange
strict
strident
striking
striped
strong
studious
stunning
stunning
stupendous
stupid
sturdy
stylish
subdued
submissive
subsequent
substantial
subtle
suburban
successful
succinct
succulent
sudden
sugary
sulky
sunny
super
superb
superficial
superior
supportive
supreme
sure-footed
surprised
suspicious
svelte
swanky
sweaty
sweet
sweltering
swift
sympathetic
symptomatic
synonymous
taboo
tacit
tacky
talented
talkative
tall
tame
tan
tangible
tangy
tart
tasteful
tasteless
tasty
tattered
taut
tawdry
tearful
tedious
teeming
teeny
teeny-tiny
telling
temporary
tempting
tender
tense
tenuous
tepid
terrible
terrific
tested
testy
thankful
therapeutic
thick
thin
thinkable
thirsty
thorny
thorough
thoughtful
thoughtless
threadbare
threatening
thrifty
thundering
thunderous
tidy
tight
tightfisted
tinted
tiny
tired
tiresome
toothsome
torn
torpid
total
tough
towering
tragic
trained
tranquil
trashy
traumatic
treasured
tremendous
triangular
tricky
trifling
trite
trivial
troubled
truculent
true
trusting
trustworthy
trusty
truthful
tubby
turbulent
twin
two
typical
ubiquitous
ugliest
ugly
ultimate
ultra
unaccountable
unarmed
unaware
unbecoming
unbiased
uncomfortable
uncommon
unconscious
uncovered
understated
understood
undesirable
unequal
unequaled
uneven
unfinished
unfit
unfolded
unfortunate
unhappy
unhealthy
uniform
unimportant
uninterested
unique
united
unkempt
unknown
unlawful
unlined
unlucky
unnatural
unpleasant
unrealistic
unripe
unruly
unselfish
unsightly
unsteady
unsuitable
unsung
untidy
untried
untrue
unused
unusual
unwelcome
unwieldy
unwitting
unwritten
upbeat
uppity
upright
upset
uptight
urban
usable
used
used
useful
useless
utilized
utopian
utter
uttermost
vacant
vacuous
vagabond
vague
vain
valid
valuable
vapid
variable
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
|
Ann2020/distilbert-base-uncased-finetuned-ner | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
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}
}
} | 4 | null | ---
tags:
- conversational
---
# Peter GriffinV2 DialoGPT Model |
Ann2020/model-finetuned-ner | []
| null | {
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}
} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- molsen/autotrain-data-genderage
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 8.240977060159542
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2709480568
- CO2 Emissions (in grams): 8.2410
## Validation Metrics
- Loss: 1.277
- Accuracy: 0.560
- Macro F1: 0.560
- Micro F1: 0.560
- Weighted F1: 0.560
- Macro Precision: 0.570
- Micro Precision: 0.560
- Weighted Precision: 0.570
- Macro Recall: 0.560
- Micro Recall: 0.560
- Weighted Recall: 0.560 |
Ann2020/rubert-base-cased-finetuned-ner | []
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Rschmaelzle/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ann2020/rubert-base-cased-sentence-finetuned-ner | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 567.50 +/- 187.47
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 Augcos -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 Augcos -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 Augcos
```
## 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)])
```
|
Ann2020/rubert-base-cased-sentence-finetuned-ner_tags | []
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} | 0 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('toinsson/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Anonymous0230/model_name | []
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} | 0 | null | ---
language:
- vi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: HuyenNguyen
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HuyenNguyen
This model is a fine-tuned version of [Huyen2310/FPT-S15000](https://huggingface.co/Huyen2310/FPT-S15000) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 450
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/AR_EManuals-BERT | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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}
} | 5 | null | ---
tags:
- BeamRiderNoFrameskip-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: BeamRiderNoFrameskip-v4
type: BeamRiderNoFrameskip-v4
metrics:
- type: mean_reward
value: 5873.40 +/- 1897.78
name: mean_reward
verified: false
---
# (CleanRL) **C51** Agent Playing **BeamRiderNoFrameskip-v4**
This is a trained model of a C51 agent playing BeamRiderNoFrameskip-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 BeamRiderNoFrameskip-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/BeamRiderNoFrameskip-v4-c51_atari_jax-seed1/raw/main/c51_atari_jax.py
curl -OL https://huggingface.co/kinalmehta/BeamRiderNoFrameskip-v4-c51_atari_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/kinalmehta/BeamRiderNoFrameskip-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 BeamRiderNoFrameskip-v4
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'end_e': 0.01,
'env_id': 'BeamRiderNoFrameskip-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'}
```
|
AnonymousSub/AR_bert-base-uncased | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
],
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} | 2 | null | This model classifies sentiment of the scientific text based on it's context, i.e text from scientific journals to negative (n), positive (p) and neutrals (o). |
AnonymousSub/AR_rule_based_hier_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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}
} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-podimo-data-eval-2-2e
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-2-2e
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: 3.7374
- Rouge1: 32.9705
- Rouge2: 6.9494
- Rougel: 17.922
- Rougelsum: 29.4629
- Gen Len: 137.5363
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 4.1649 | 0.98 | 44 | 3.8138 | 32.12 | 6.544 | 17.5999 | 28.8314 | 136.4553 |
| 3.6772 | 1.98 | 88 | 3.7374 | 32.9705 | 6.9494 | 17.922 | 29.4629 | 137.5363 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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],
"model_type": "roberta",
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}
} | 9 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 622.50 +/- 139.04
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 keshan -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 keshan -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 keshan
```
## 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', 3000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AnonymousSub/AR_rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
}
} | 6 | null | ---
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`:




|
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