modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Azizun/Geotrend-10-epochs | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 6 | null | ---
tags:
- generated_from_trainer
model-index:
- name: pegasus-pubmed_radiology-ai-cardiothoracic-0.8
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-pubmed_radiology-ai-cardiothoracic-0.8
This model is a fine-tuned version of [google/pegasus-pubmed](https://huggingface.co/google/pegasus-pubmed) 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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Azuris/DialoGPT-medium-envy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
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} | 12 | 2023-01-20T03:05:40Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Awesome7749/autotrain-data-patent-101
co2_eq_emissions:
emissions: 1.761467513119125
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2952885911
- CO2 Emissions (in grams): 1.7615
## Validation Metrics
- Loss: 0.359
- Accuracy: 0.854
- Precision: 0.744
- Recall: 0.296
- AUC: 0.832
- F1: 0.424
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Awesome7749/autotrain-patent-101-2952885911
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Awesome7749/autotrain-patent-101-2952885911", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Awesome7749/autotrain-patent-101-2952885911", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Azuris/DialoGPT-small-envy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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}
} | 14 | 2023-01-20T03:09:28Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: rlcourse-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="alyssamarieloo/rlcourse-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"])
```
|
BE/demo-sentiment2021 | []
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} | 0 | 2023-01-20T03:09:50Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: Artachtron/SnowballTarget1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BME-TMIT/foszt2oszt | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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} | 15 | 2023-01-20T03:20:47Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -113.04 +/- 103.44
name: mean_reward
verified: false
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** 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
...
```
|
BOON/electra-xlnet | []
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}
} | 0 | 2023-01-20T03:23:21Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: rlcourse-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.83
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="alyssamarieloo/rlcourse-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"])
```
|
BOON/electra_qa | []
| null | {
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} | 0 | 2023-01-20T03:23:32Z | ---
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="philipladuca/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"])
```
|
Barbarameerr/Barbara | []
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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: 279.52 +/- 16.04
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
...
```
|
BigSalmon/InformalToFormalLincoln22 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 6 | null | ---
license: unknown
language:
- de
library_name: flair
--- |
BigSalmon/MrLincoln2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 9 | null | ---
language:
- el
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny Greek
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 el
type: mozilla-foundation/common_voice_11_0
config: el
split: test
args: el
metrics:
- name: Wer
type: wer
value: 118.84057971014492
---
<!-- 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 Tiny Greek
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 el dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5908
- Wer: 118.8406
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.5 | 2 | 1.5908 | 118.8406 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
BigSalmon/Neo | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
]
| text-generation | {
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}
} | 13 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### 1cryenginebeta Dreambooth model trained by abbiepam 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:
_coming_out_of_the_countryside,_8k_uhd,_studio_quality,_character,_ultra_realistic,_m.png)
_as_a_cyberpunk_hacker_at_the_down_town,_8k_uhd,_studio_quality,_character,_ultra_realisti.png)
_as_an_Egyptian_Queen_in_the_castle,_8k_uhd,_studio_quality,_character,_ultra_realistic,_m.png)
_by_the_fountain_on_the_city_park,_8k_uhd,_studio_quality,_character,_ultra_realistic,_max.png)
_as_a_cyberpunk_hacker_at_the_down_town,_8k_uhd,_studio_quality,_character,_ultra_realisti.png)
_coming_out_of_the_city,_8k_uhd,_studio_quality,_character,_ultra_realistic,_max_detail,_mass.png)
|
BigSalmon/prepositions | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
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} | 7 | null | ---
tags:
- generated_from_trainer
model-index:
- name: small-mlm-tweet_eval-from-scratch-custom-tokenizer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-mlm-tweet_eval-from-scratch-custom-tokenizer
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: 8.3489
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.8692 | 1.23 | 500 | 8.0216 |
| 7.1727 | 2.45 | 1000 | 8.0836 |
| 7.1072 | 3.68 | 1500 | 8.1879 |
| 6.9769 | 4.9 | 2000 | 7.9914 |
| 6.8654 | 6.13 | 2500 | 8.1122 |
| 6.8821 | 7.35 | 3000 | 8.1989 |
| 6.7578 | 8.58 | 3500 | 8.3489 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
BigTooth/DialoGPT-small-tohru | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 10 | 2023-01-20T12:08:19Z | ---
library_name: skrl
tags:
- deep-reinforcement-learning
- reinforcement-learning
- skrl
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 11336.27 +/- 182.79
name: Total reward (mean)
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Isaac-Ant-v0
type: Isaac-Ant-v0
---
# IsaacOrbit-Isaac-Ant-v0-PPO
Trained agent model for [NVIDIA Isaac Orbit](https://github.com/NVIDIA-Omniverse/Orbit) environment
- **Task:** Isaac-Ant-v0
- **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ppo.html)
# Usage (with skrl)
```python
from skrl.utils.huggingface import download_model_from_huggingface
# assuming that there is an agent named `agent`
path = download_model_from_huggingface("skrl/IsaacOrbit-Isaac-Ant-v0-PPO")
agent.load(path)
```
# Hyperparameters
```python
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ppo.html#configuration-and-hyperparameters
cfg_ppo = PPO_DEFAULT_CONFIG.copy()
cfg_ppo["rollouts"] = 16 # memory_size
cfg_ppo["learning_epochs"] = 8
cfg_ppo["mini_batches"] = 4 # 16 * 1024 / 4096
cfg_ppo["discount_factor"] = 0.99
cfg_ppo["lambda"] = 0.95
cfg_ppo["learning_rate"] = 3e-4
cfg_ppo["learning_rate_scheduler"] = KLAdaptiveRL
cfg_ppo["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg_ppo["random_timesteps"] = 0
cfg_ppo["learning_starts"] = 0
cfg_ppo["grad_norm_clip"] = 1.0
cfg_ppo["ratio_clip"] = 0.2
cfg_ppo["value_clip"] = 0.2
cfg_ppo["clip_predicted_values"] = True
cfg_ppo["entropy_loss_scale"] = 0.0
cfg_ppo["value_loss_scale"] = 1.0
cfg_ppo["kl_threshold"] = 0
cfg_ppo["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg_ppo["state_preprocessor"] = RunningStandardScaler
cfg_ppo["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg_ppo["value_preprocessor"] = RunningStandardScaler
cfg_ppo["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and writing checkpoints
cfg_ppo["experiment"]["write_interval"] = 40
cfg_ppo["experiment"]["checkpoint_interval"] = 400
```
|
BigeS/DialoGPT-small-Rick | [
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"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 10 | null | ---
license: artistic-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: text-classification
tags:
- code
---
DVA-C01 PDFs, which stand for AWS Certified Developer - Associate Exam DVA-C01, can be reliable for exam preparation for a few reasons:
1: They provide a digital copy of the exam's content, including the topics and objectives that will be covered on the test.
2: They are easy to access and can be downloaded and used on a variety of devices, making it convenient to study on-the-go.
3: Some DVA-C01 PDFs may include practice questions and answer explanations, which can help you prepare and identify areas where you may need more study.
4: Many DVA-C01 PDFs are created by experts, who have already taken the exam and have an in-depth knowledge of the exam's format, content, and difficulty level.
However, it's important to note that not all DVA-C01 PDFs are reliable or of the same quality, so it's recommended to look for the ones from reputable sources, and also to use them in conjunction with other resources such as AWS official documentation, hands-on practice and online training to achieve best results.
Click Here To Get DVA-C01 Dumps 2023: https://www.passexam4sure.com/amazon/dva-c01-dumps.html |
Blabla/Pipipopo | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: tiny-mlm-rotten_tomatoes-from-scratch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-rotten_tomatoes-from-scratch
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: 7.5142
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 9.5559 | 0.47 | 500 | 8.8040 |
| 8.3143 | 0.94 | 1000 | 7.9867 |
| 7.7506 | 1.41 | 1500 | 7.6594 |
| 7.5897 | 1.87 | 2000 | 7.6136 |
| 7.5583 | 2.34 | 2500 | 7.5769 |
| 7.4661 | 2.81 | 3000 | 7.5657 |
| 7.4757 | 3.28 | 3500 | 7.5811 |
| 7.4083 | 3.75 | 4000 | 7.4780 |
| 7.4259 | 4.22 | 4500 | 7.4590 |
| 7.39 | 4.69 | 5000 | 7.5142 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Blaine-Mason/hackMIT-finetuned-sst2 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
]
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"BertForSequenceClassification"
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} | 36 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: tiny-mlm-conll2003-from-scratch-custom-tokenizer-target-rotten_tomatoes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-conll2003-from-scratch-custom-tokenizer-target-rotten_tomatoes
This model is a fine-tuned version of [muhtasham/tiny-mlm-conll2003-from-scratch-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-conll2003-from-scratch-custom-tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7896
- Accuracy: 0.7505
- F1: 0.7504
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6935 | 1.87 | 500 | 0.6930 | 0.5 | 0.3333 |
| 0.6714 | 3.75 | 1000 | 0.5756 | 0.7036 | 0.7035 |
| 0.4357 | 5.62 | 1500 | 0.5846 | 0.7355 | 0.7338 |
| 0.3473 | 7.49 | 2000 | 0.6248 | 0.7458 | 0.7449 |
| 0.2935 | 9.36 | 2500 | 0.6486 | 0.7514 | 0.7514 |
| 0.2545 | 11.24 | 3000 | 0.6966 | 0.7486 | 0.7486 |
| 0.229 | 13.11 | 3500 | 0.7537 | 0.7420 | 0.7419 |
| 0.199 | 14.98 | 4000 | 0.7896 | 0.7505 | 0.7504 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Bloodwarrior/Chikfalay | []
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} | 0 | null | ---
language:
- ru
license: apache-2.0
---
# FRED-T5 1.7B (Full-scale Russian Enhanced Denoisers T5)
Model was trained by [SberDevices](https://sberdevices.ru/).
Architecture based on T5.
It has 24 layers and 1536 hidden size. More details in config.json.
The model trained on a mixture of 7 denoisers like UL2 with several differences (https://arxiv.org/abs/2205.05131).
It was trained on Russian language corpus (300GB). The dataset is the same as for ruT5 models.
Bbpe tokenizer. 50257 + special tokens 107. Prefix tokens: '\<LM\>', '\<SC1>',.. '\<SC6>'
First half of the time model trained on the small part of all dataset (1%,3GB) and without prefixes in each task.
For RSG, we trained as described in the T5 paper. First, we trained multitask for all tasks. Then we took the best checkpoint for the task and trained it further.
RSG submit here https://russiansuperglue.com/login/submit_info/1936
Total training time was around 45 days on 112 A100 GPUs.
## Usage (HuggingFace Models Repository)
```python
import torch
from transformers import GPT2Tokenizer, T5ForConditionalGeneration
tokenizer = GPT2Tokenizer.from_pretrained('ai-forever/FRED-T5-1.7B',eos_token='</s>')
model = T5ForConditionalGeneration.from_pretrained('ai-forever/FRED-T5-1.7B')
device='cuda'
model.to(device)
#Prefix <LM>
lm_text='<LM>Принялся Кутузов рассказывать свою историю как он сюда попал. Началось'
input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device)
outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True)
print(tokenizer.decode(outputs[0][1:]))
# print result: с того, что он был в армии, служил в артиллерии</s>.
#Prefix <SC1>
lm_text='<SC1>Принялся Кутузов рассказывать свою историю <extra_id_0>. Началось с того, что он был в армии, служил в артиллерии.'
input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device)
outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True)
print(tokenizer.decode(outputs[0][1:]))
#print result: '<extra_id_0>, как он воевал</s>'
# Prefix <SC5>
lm_text='<SC5>Принялся Кутузов рассказывать свою историю <extra_id_0>. Началось с того, что он был в армии, служил в артиллерии.'
input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device)
outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True)
tokenizer.decode(outputs[0][1:])
#print result: '<extra_id_0>, как он стал генералом</s>'
```
# Authors
+ NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam):
+ Dmitry Zmitrovich
+ Andrei Kalmykov
+ Vitaly Kadulin
+ Mikhail Novikov
+ Alexey Khoroshilov
[Salute AI Community](https://t.me/SaluteTechGroup).
|
BobBraico/bert-finetuned-ner | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny-mlm-wikitext-from-scratch-custom-tokenizer-target-conll2003
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-wikitext-from-scratch-custom-tokenizer-target-conll2003
This model is a fine-tuned version of [muhtasham/tiny-mlm-wikitext-from-scratch-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-wikitext-from-scratch-custom-tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3451
- Precision: 0.3914
- Recall: 0.5631
- F1: 0.4618
- Accuracy: 0.8978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0009 | 1.14 | 500 | 0.6888 | 0.1156 | 0.1160 | 0.1158 | 0.8144 |
| 0.6084 | 2.28 | 1000 | 0.5797 | 0.2110 | 0.2735 | 0.2382 | 0.8417 |
| 0.5231 | 3.42 | 1500 | 0.5113 | 0.2567 | 0.3295 | 0.2886 | 0.8560 |
| 0.4552 | 4.56 | 2000 | 0.4575 | 0.2947 | 0.4061 | 0.3415 | 0.8701 |
| 0.4 | 5.69 | 2500 | 0.4172 | 0.3182 | 0.4615 | 0.3767 | 0.8802 |
| 0.3587 | 6.83 | 3000 | 0.3915 | 0.3378 | 0.4921 | 0.4006 | 0.8871 |
| 0.3263 | 7.97 | 3500 | 0.3719 | 0.3638 | 0.5296 | 0.4313 | 0.8918 |
| 0.2975 | 9.11 | 4000 | 0.3605 | 0.3687 | 0.5411 | 0.4385 | 0.8939 |
| 0.2748 | 10.25 | 4500 | 0.3509 | 0.3868 | 0.5471 | 0.4532 | 0.8969 |
| 0.2602 | 11.39 | 5000 | 0.3451 | 0.3914 | 0.5631 | 0.4618 | 0.8978 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
BobBraico/distilbert-base-uncased-finetuned-imdb | []
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} | 0 | 2023-01-20T13:02:54Z | ---
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="morganjeffries/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"])
```
|
BonjinKim/dst_kor_bert | [
"pytorch",
"jax",
"bert",
"pretraining",
"transformers"
]
| null | {
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} | 5 | 2023-01-20T13:05:24Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="morganjeffries/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"])
```
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2 | []
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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('chudotony/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Botjallu/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
license: other
tags:
- generated_from_keras_callback
model-index:
- name: MariaK/mit-b0-finetuned-sidewalks
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. -->
# MariaK/mit-b0-finetuned-sidewalks
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8550
- Validation Loss: 0.8639
- Validation Mean Iou: 0.2220
- Validation Mean Accuracy: 0.2670
- Validation Overall Accuracy: 0.7725
- Validation Accuracy Unlabeled: 0.0
- Validation Accuracy Flat-road: 0.6015
- Validation Accuracy Flat-sidewalk: 0.9708
- Validation Accuracy Flat-crosswalk: 0.3807
- Validation Accuracy Flat-cyclinglane: 0.7538
- Validation Accuracy Flat-parkingdriveway: 0.1524
- Validation Accuracy Flat-railtrack: nan
- Validation Accuracy Flat-curb: 0.1957
- Validation Accuracy Human-person: 0.2585
- Validation Accuracy Human-rider: 0.0
- Validation Accuracy Vehicle-car: 0.8971
- Validation Accuracy Vehicle-truck: 0.0
- Validation Accuracy Vehicle-bus: 0.0
- Validation Accuracy Vehicle-tramtrain: nan
- Validation Accuracy Vehicle-motorcycle: 0.0
- Validation Accuracy Vehicle-bicycle: 0.0716
- Validation Accuracy Vehicle-caravan: 0.0
- Validation Accuracy Vehicle-cartrailer: 0.0
- Validation Accuracy Construction-building: 0.8784
- Validation Accuracy Construction-door: 0.0
- Validation Accuracy Construction-wall: 0.4315
- Validation Accuracy Construction-fenceguardrail: 0.1948
- Validation Accuracy Construction-bridge: 0.0
- Validation Accuracy Construction-tunnel: nan
- Validation Accuracy Construction-stairs: 0.0
- Validation Accuracy Object-pole: 0.1201
- Validation Accuracy Object-trafficsign: 0.0
- Validation Accuracy Object-trafficlight: 0.0
- Validation Accuracy Nature-vegetation: 0.8952
- Validation Accuracy Nature-terrain: 0.8231
- Validation Accuracy Sky: 0.8496
- Validation Accuracy Void-ground: 0.0
- Validation Accuracy Void-dynamic: 0.0
- Validation Accuracy Void-static: 0.0692
- Validation Accuracy Void-unclear: 0.0
- Validation Iou Unlabeled: 0.0
- Validation Iou Flat-road: 0.5568
- Validation Iou Flat-sidewalk: 0.7479
- Validation Iou Flat-crosswalk: 0.3509
- Validation Iou Flat-cyclinglane: 0.6355
- Validation Iou Flat-parkingdriveway: 0.1298
- Validation Iou Flat-railtrack: nan
- Validation Iou Flat-curb: 0.1326
- Validation Iou Human-person: 0.2455
- Validation Iou Human-rider: 0.0
- Validation Iou Vehicle-car: 0.6973
- Validation Iou Vehicle-truck: 0.0
- Validation Iou Vehicle-bus: 0.0
- Validation Iou Vehicle-tramtrain: nan
- Validation Iou Vehicle-motorcycle: 0.0
- Validation Iou Vehicle-bicycle: 0.0610
- Validation Iou Vehicle-caravan: 0.0
- Validation Iou Vehicle-cartrailer: 0.0
- Validation Iou Construction-building: 0.6479
- Validation Iou Construction-door: 0.0
- Validation Iou Construction-wall: 0.3003
- Validation Iou Construction-fenceguardrail: 0.1727
- Validation Iou Construction-bridge: 0.0
- Validation Iou Construction-tunnel: nan
- Validation Iou Construction-stairs: 0.0
- Validation Iou Object-pole: 0.0927
- Validation Iou Object-trafficsign: 0.0
- Validation Iou Object-trafficlight: 0.0
- Validation Iou Nature-vegetation: 0.7758
- Validation Iou Nature-terrain: 0.7000
- Validation Iou Sky: 0.8002
- Validation Iou Void-ground: 0.0
- Validation Iou Void-dynamic: 0.0
- Validation Iou Void-static: 0.0573
- Validation Iou Void-unclear: 0.0
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Unlabeled | Validation Accuracy Flat-road | Validation Accuracy Flat-sidewalk | Validation Accuracy Flat-crosswalk | Validation Accuracy Flat-cyclinglane | Validation Accuracy Flat-parkingdriveway | Validation Accuracy Flat-railtrack | Validation Accuracy Flat-curb | Validation Accuracy Human-person | Validation Accuracy Human-rider | Validation Accuracy Vehicle-car | Validation Accuracy Vehicle-truck | Validation Accuracy Vehicle-bus | Validation Accuracy Vehicle-tramtrain | Validation Accuracy Vehicle-motorcycle | Validation Accuracy Vehicle-bicycle | Validation Accuracy Vehicle-caravan | Validation Accuracy Vehicle-cartrailer | Validation Accuracy Construction-building | Validation Accuracy Construction-door | Validation Accuracy Construction-wall | Validation Accuracy Construction-fenceguardrail | Validation Accuracy Construction-bridge | Validation Accuracy Construction-tunnel | Validation Accuracy Construction-stairs | Validation Accuracy Object-pole | Validation Accuracy Object-trafficsign | Validation Accuracy Object-trafficlight | Validation Accuracy Nature-vegetation | Validation Accuracy Nature-terrain | Validation Accuracy Sky | Validation Accuracy Void-ground | Validation Accuracy Void-dynamic | Validation Accuracy Void-static | Validation Accuracy Void-unclear | Validation Iou Unlabeled | Validation Iou Flat-road | Validation Iou Flat-sidewalk | Validation Iou Flat-crosswalk | Validation Iou Flat-cyclinglane | Validation Iou Flat-parkingdriveway | Validation Iou Flat-railtrack | Validation Iou Flat-curb | Validation Iou Human-person | Validation Iou Human-rider | Validation Iou Vehicle-car | Validation Iou Vehicle-truck | Validation Iou Vehicle-bus | Validation Iou Vehicle-tramtrain | Validation Iou Vehicle-motorcycle | Validation Iou Vehicle-bicycle | Validation Iou Vehicle-caravan | Validation Iou Vehicle-cartrailer | Validation Iou Construction-building | Validation Iou Construction-door | Validation Iou Construction-wall | Validation Iou Construction-fenceguardrail | Validation Iou Construction-bridge | Validation Iou Construction-tunnel | Validation Iou Construction-stairs | Validation Iou Object-pole | Validation Iou Object-trafficsign | Validation Iou Object-trafficlight | Validation Iou Nature-vegetation | Validation Iou Nature-terrain | Validation Iou Sky | Validation Iou Void-ground | Validation Iou Void-dynamic | Validation Iou Void-static | Validation Iou Void-unclear | Epoch |
|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:-----------------------------:|:-----------------------------:|:---------------------------------:|:----------------------------------:|:------------------------------------:|:----------------------------------------:|:----------------------------------:|:-----------------------------:|:--------------------------------:|:-------------------------------:|:-------------------------------:|:---------------------------------:|:-------------------------------:|:-------------------------------------:|:--------------------------------------:|:-----------------------------------:|:-----------------------------------:|:--------------------------------------:|:-----------------------------------------:|:-------------------------------------:|:-------------------------------------:|:-----------------------------------------------:|:---------------------------------------:|:---------------------------------------:|:---------------------------------------:|:-------------------------------:|:--------------------------------------:|:---------------------------------------:|:-------------------------------------:|:----------------------------------:|:-----------------------:|:-------------------------------:|:--------------------------------:|:-------------------------------:|:--------------------------------:|:------------------------:|:------------------------:|:----------------------------:|:-----------------------------:|:-------------------------------:|:-----------------------------------:|:-----------------------------:|:------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:----------------------------:|:--------------------------:|:--------------------------------:|:---------------------------------:|:------------------------------:|:------------------------------:|:---------------------------------:|:------------------------------------:|:--------------------------------:|:--------------------------------:|:------------------------------------------:|:----------------------------------:|:----------------------------------:|:----------------------------------:|:--------------------------:|:---------------------------------:|:----------------------------------:|:--------------------------------:|:-----------------------------:|:------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:-----:|
| 1.4362 | 0.9804 | 0.1752 | 0.2219 | 0.7360 | 0.0 | 0.7417 | 0.9512 | 0.0213 | 0.3662 | 0.1475 | nan | 0.1397 | 0.0055 | 0.0 | 0.8653 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7778 | 0.0 | 0.3370 | 0.0429 | 0.0 | nan | 0.0 | 0.0177 | 0.0 | 0.0 | 0.9324 | 0.7967 | 0.9157 | 0.0 | 0.0 | 0.0409 | 0.0 | 0.0 | 0.5263 | 0.7377 | 0.0213 | 0.3517 | 0.1232 | nan | 0.1053 | 0.0055 | 0.0 | 0.6423 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6012 | 0.0 | 0.2315 | 0.0424 | 0.0 | nan | 0.0 | 0.0163 | 0.0 | 0.0 | 0.7258 | 0.6752 | 0.7692 | 0.0 | 0.0 | 0.0321 | 0.0 | 0 |
| 0.8550 | 0.8639 | 0.2220 | 0.2670 | 0.7725 | 0.0 | 0.6015 | 0.9708 | 0.3807 | 0.7538 | 0.1524 | nan | 0.1957 | 0.2585 | 0.0 | 0.8971 | 0.0 | 0.0 | nan | 0.0 | 0.0716 | 0.0 | 0.0 | 0.8784 | 0.0 | 0.4315 | 0.1948 | 0.0 | nan | 0.0 | 0.1201 | 0.0 | 0.0 | 0.8952 | 0.8231 | 0.8496 | 0.0 | 0.0 | 0.0692 | 0.0 | 0.0 | 0.5568 | 0.7479 | 0.3509 | 0.6355 | 0.1298 | nan | 0.1326 | 0.2455 | 0.0 | 0.6973 | 0.0 | 0.0 | nan | 0.0 | 0.0610 | 0.0 | 0.0 | 0.6479 | 0.0 | 0.3003 | 0.1727 | 0.0 | nan | 0.0 | 0.0927 | 0.0 | 0.0 | 0.7758 | 0.7000 | 0.8002 | 0.0 | 0.0 | 0.0573 | 0.0 | 1 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Brona/poc_de | []
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} | 0 | 2023-01-20T13:38:31Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny-mlm-tweet_eval-from-scratch-custom-tokenizer-target-conll2003
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-tweet_eval-from-scratch-custom-tokenizer-target-conll2003
This model is a fine-tuned version of [muhtasham/tiny-mlm-tweet_eval-from-scratch-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-tweet_eval-from-scratch-custom-tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4153
- Precision: 0.2618
- Recall: 0.4002
- F1: 0.3166
- Accuracy: 0.8656
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0571 | 1.14 | 500 | 0.7799 | 0.0549 | 0.0559 | 0.0554 | 0.7879 |
| 0.6857 | 2.28 | 1000 | 0.6491 | 0.1028 | 0.1508 | 0.1222 | 0.8120 |
| 0.6016 | 3.42 | 1500 | 0.5834 | 0.1427 | 0.2189 | 0.1728 | 0.8256 |
| 0.5481 | 4.56 | 2000 | 0.5410 | 0.1707 | 0.2654 | 0.2078 | 0.8366 |
| 0.5078 | 5.69 | 2500 | 0.5046 | 0.1888 | 0.3080 | 0.2341 | 0.8467 |
| 0.474 | 6.83 | 3000 | 0.4732 | 0.2181 | 0.3477 | 0.2681 | 0.8552 |
| 0.4435 | 7.97 | 3500 | 0.4505 | 0.2310 | 0.3814 | 0.2877 | 0.8593 |
| 0.4168 | 9.11 | 4000 | 0.4369 | 0.2343 | 0.3851 | 0.2913 | 0.8611 |
| 0.3965 | 10.25 | 4500 | 0.4222 | 0.2431 | 0.4000 | 0.3024 | 0.8636 |
| 0.3829 | 11.39 | 5000 | 0.4153 | 0.2618 | 0.4002 | 0.3166 | 0.8656 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
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} | 45 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: small-mlm-wikitext-from-scratch-custom-tokenizer-target-conll2003
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-mlm-wikitext-from-scratch-custom-tokenizer-target-conll2003
This model is a fine-tuned version of [muhtasham/small-mlm-wikitext-from-scratch-custom-tokenizer](https://huggingface.co/muhtasham/small-mlm-wikitext-from-scratch-custom-tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3135
- Precision: 0.5394
- Recall: 0.6932
- F1: 0.6067
- Accuracy: 0.9205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6115 | 1.14 | 500 | 0.4596 | 0.2964 | 0.4574 | 0.3597 | 0.8598 |
| 0.3787 | 2.28 | 1000 | 0.3634 | 0.3690 | 0.5017 | 0.4252 | 0.8889 |
| 0.293 | 3.42 | 1500 | 0.3407 | 0.3956 | 0.5751 | 0.4688 | 0.8944 |
| 0.238 | 4.56 | 2000 | 0.3159 | 0.4337 | 0.5774 | 0.4953 | 0.9034 |
| 0.1975 | 5.69 | 2500 | 0.3061 | 0.4729 | 0.6033 | 0.5302 | 0.9104 |
| 0.1655 | 6.83 | 3000 | 0.3045 | 0.4851 | 0.6427 | 0.5529 | 0.9128 |
| 0.1392 | 7.97 | 3500 | 0.2934 | 0.5052 | 0.6508 | 0.5688 | 0.9170 |
| 0.1127 | 9.11 | 4000 | 0.3047 | 0.5228 | 0.6781 | 0.5904 | 0.9184 |
| 0.0945 | 10.25 | 4500 | 0.3047 | 0.5471 | 0.6712 | 0.6028 | 0.9234 |
| 0.0802 | 11.39 | 5000 | 0.3135 | 0.5394 | 0.6932 | 0.6067 | 0.9205 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
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} | 1,860 | null | ---
tags:
- generated_from_trainer
model-index:
- name: small-mlm-snli-from-scratch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-mlm-snli-from-scratch
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: 4.8966
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.8956 | 0.4 | 500 | 5.9093 |
| 5.5615 | 0.8 | 1000 | 5.6613 |
| 5.2657 | 1.2 | 1500 | 5.4757 |
| 5.1364 | 1.6 | 2000 | 5.4627 |
| 5.0288 | 2.0 | 2500 | 5.4125 |
| 4.9053 | 2.4 | 3000 | 5.2267 |
| 4.7215 | 2.8 | 3500 | 5.2092 |
| 4.6526 | 3.2 | 4000 | 5.0670 |
| 4.5203 | 3.6 | 4500 | 4.9499 |
| 4.3398 | 4.0 | 5000 | 4.8966 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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} | 31 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1349.14 +/- 282.57
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 62 | 2023-01-20T14:26:57Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: small-mlm-wikitext-from-scratch-custom-tokenizer-target-rotten_tomatoes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-mlm-wikitext-from-scratch-custom-tokenizer-target-rotten_tomatoes
This model is a fine-tuned version of [muhtasham/small-mlm-wikitext-from-scratch-custom-tokenizer](https://huggingface.co/muhtasham/small-mlm-wikitext-from-scratch-custom-tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5431
- Accuracy: 0.7411
- F1: 0.7406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6318 | 1.87 | 500 | 0.5225 | 0.7383 | 0.7382 |
| 0.3304 | 3.75 | 1000 | 0.6658 | 0.7477 | 0.7462 |
| 0.2084 | 5.62 | 1500 | 0.8680 | 0.7458 | 0.7441 |
| 0.1328 | 7.49 | 2000 | 1.1738 | 0.7355 | 0.7352 |
| 0.088 | 9.36 | 2500 | 1.5431 | 0.7411 | 0.7406 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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}
} | 132 | 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: 244.28 +/- 19.12
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
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 855 | null | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- tkurtulus/autotrain-data-rottentomato
co2_eq_emissions:
emissions: 0.7137118018641835
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2981285985
- CO2 Emissions (in grams): 0.7137
## Validation Metrics
- Loss: 0.416
- Accuracy: 0.808
- Macro F1: 0.808
- Micro F1: 0.808
- Weighted F1: 0.808
- Macro Precision: 0.809
- Micro Precision: 0.808
- Weighted Precision: 0.809
- Macro Recall: 0.808
- Micro Recall: 0.808
- Weighted Recall: 0.808
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/tkurtulus/autotrain-rottentomato-2981285985
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("tkurtulus/autotrain-rottentomato-2981285985", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("tkurtulus/autotrain-rottentomato-2981285985", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 71 | 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: 252.58 +/- 22.40
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
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-half | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 16 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: juro95/xlm-roberta-finetuned-ner-5-without-skills
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. -->
# juro95/xlm-roberta-finetuned-ner-5-without-skills
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0908
- Validation Loss: 0.1102
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 62781, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2409 | 0.1519 | 0 |
| 0.1288 | 0.1203 | 1 |
| 0.0908 | 0.1102 | 2 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.6.5
- Datasets 2.3.2
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
} | 133 | 2023-01-20T15:00:07Z | ---
language:
- el
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny Greek
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 el
type: mozilla-foundation/common_voice_11_0
config: el
split: test
args: el
metrics:
- name: Wer
type: wer
value: 231.8840579710145
---
<!-- 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 Tiny Greek
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 el dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3444
- Wer: 231.8841
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.5 | 2 | 1.3444 | 231.8841 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
} | 574 | 2023-01-20T15:01:53Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: small-mlm-snli-from-scratch-custom-tokenizer-target-rotten_tomatoes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-mlm-snli-from-scratch-custom-tokenizer-target-rotten_tomatoes
This model is a fine-tuned version of [muhtasham/small-mlm-snli-from-scratch-custom-tokenizer](https://huggingface.co/muhtasham/small-mlm-snli-from-scratch-custom-tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1833
- Accuracy: 0.7101
- F1: 0.7090
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6628 | 1.87 | 500 | 0.6389 | 0.6388 | 0.6187 |
| 0.4993 | 3.75 | 1000 | 0.6044 | 0.6970 | 0.6938 |
| 0.4205 | 5.62 | 1500 | 0.6342 | 0.7261 | 0.7251 |
| 0.3604 | 7.49 | 2000 | 0.7069 | 0.7270 | 0.7254 |
| 0.3034 | 9.36 | 2500 | 0.8202 | 0.6989 | 0.6974 |
| 0.2358 | 11.24 | 3000 | 0.8650 | 0.7111 | 0.7103 |
| 0.1804 | 13.11 | 3500 | 1.1833 | 0.7101 | 0.7090 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
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},
"translation_en_to_de": {
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"translation_en_to_fr": {
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}
} | 2,967 | 2023-01-20T15:07:14Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.11 +/- 0.53
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CBreit00/DialoGPT_small_Rick | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
widget:
- text: a photo of wucaicai celeb tokyo walk
---
# DreamBooth model for the wucaicai concept trained by rootkan.
This is a Stable Diffusion model fine-tuned on the wucaicai concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of wucaicai celeb**
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 `celeb` images for the wildcard theme,
for the Hugging Face DreamBooth Hackathon, from the HF CN Community,
corporated with the HeyWhale.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('rootkan/wucaicai-celeb-heywhale')
image = pipeline().images[0]
image
```
|
CLAck/en-km | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
]
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} | 12 | 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: 0.8638300289723342
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1358
- F1: 0.8638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CLAck/vi-en | [
"pytorch",
"marian",
"text2text-generation",
"en",
"vi",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 6 | 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: 584.50 +/- 188.21
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga keyblade95 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga keyblade95 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga keyblade95
```
## 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)])
```
|
CLTL/MedRoBERTa.nl | [
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} | 2,988 | null | ---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
model_file: model.pkl
widget:
structuredData:
Fedu:
- 3
- 3
- 3
Fjob:
- other
- other
- services
G1:
- 12
- 13
- 8
G2:
- 13
- 14
- 7
G3:
- 12
- 14
- 0
Medu:
- 3
- 2
- 1
Mjob:
- services
- other
- at_home
Pstatus:
- T
- T
- T
Walc:
- 2
- 1
- 1
absences:
- 2
- 0
- 0
activities:
- 'yes'
- 'no'
- 'yes'
address:
- U
- U
- U
age:
- 16
- 16
- 16
failures:
- 0
- 0
- 3
famrel:
- 4
- 5
- 4
famsize:
- GT3
- GT3
- GT3
famsup:
- 'no'
- 'no'
- 'no'
freetime:
- 2
- 3
- 3
goout:
- 3
- 3
- 5
guardian:
- mother
- father
- mother
health:
- 3
- 3
- 3
higher:
- 'yes'
- 'yes'
- 'yes'
internet:
- 'yes'
- 'yes'
- 'yes'
nursery:
- 'yes'
- 'yes'
- 'no'
paid:
- 'yes'
- 'no'
- 'no'
reason:
- home
- home
- home
romantic:
- 'yes'
- 'no'
- 'yes'
school:
- GP
- GP
- GP
schoolsup:
- 'no'
- 'no'
- 'no'
sex:
- M
- M
- F
studytime:
- 2
- 1
- 2
traveltime:
- 1
- 2
- 1
---
# Model description
This is an XGBoost model trained to predict daily alcohol consumption of students.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|---------------------------------------|------------------------------------------------------|
| memory | |
| steps | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, gpu_id=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=5, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> n_estimators=100, n_jobs=None, num_parallel_tree=None,<br /> predictor=None, random_state=None, ...))] |
| verbose | False |
| onehotencoder | OneHotEncoder(handle_unknown='ignore', sparse=False) |
| xgbregressor | XGBRegressor(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, gpu_id=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=5, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> n_estimators=100, n_jobs=None, num_parallel_tree=None,<br /> predictor=None, random_state=None, ...) |
| onehotencoder__categories | auto |
| onehotencoder__drop | |
| onehotencoder__dtype | <class 'numpy.float64'> |
| onehotencoder__handle_unknown | ignore |
| onehotencoder__sparse | False |
| xgbregressor__objective | reg:squarederror |
| xgbregressor__base_score | |
| xgbregressor__booster | |
| xgbregressor__callbacks | |
| xgbregressor__colsample_bylevel | |
| xgbregressor__colsample_bynode | |
| xgbregressor__colsample_bytree | |
| xgbregressor__early_stopping_rounds | |
| xgbregressor__enable_categorical | False |
| xgbregressor__eval_metric | |
| xgbregressor__feature_types | |
| xgbregressor__gamma | |
| xgbregressor__gpu_id | |
| xgbregressor__grow_policy | |
| xgbregressor__importance_type | |
| xgbregressor__interaction_constraints | |
| xgbregressor__learning_rate | |
| xgbregressor__max_bin | |
| xgbregressor__max_cat_threshold | |
| xgbregressor__max_cat_to_onehot | |
| xgbregressor__max_delta_step | |
| xgbregressor__max_depth | 5 |
| xgbregressor__max_leaves | |
| xgbregressor__min_child_weight | |
| xgbregressor__missing | nan |
| xgbregressor__monotone_constraints | |
| xgbregressor__n_estimators | 100 |
| xgbregressor__n_jobs | |
| xgbregressor__num_parallel_tree | |
| xgbregressor__predictor | |
| xgbregressor__random_state | |
| xgbregressor__reg_alpha | |
| xgbregressor__reg_lambda | |
| xgbregressor__sampling_method | |
| xgbregressor__scale_pos_weight | |
| xgbregressor__subsample | |
| xgbregressor__tree_method | |
| xgbregressor__validate_parameters | |
| xgbregressor__verbosity | |
</details>
### Model Plot
The model plot is below.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('onehotencoder',OneHotEncoder(handle_unknown='ignore', sparse=False)),('xgbregressor',XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None,feature_types=None, gamma=None, gpu_id=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=5, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, n_estimators=100,n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3e1fc9fd-9464-4cf2-a34f-716e1f03bb90" type="checkbox" ><label for="3e1fc9fd-9464-4cf2-a34f-716e1f03bb90" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('onehotencoder',OneHotEncoder(handle_unknown='ignore', sparse=False)),('xgbregressor',XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None,feature_types=None, gamma=None, gpu_id=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=5, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, n_estimators=100,n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="064b4f21-1fc7-4646-9751-108c0cbbd266" type="checkbox" ><label for="064b4f21-1fc7-4646-9751-108c0cbbd266" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore', sparse=False)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="8239516d-467c-4346-82ae-95b2c33e2b8a" type="checkbox" ><label for="8239516d-467c-4346-82ae-95b2c33e2b8a" class="sk-toggleable__label sk-toggleable__label-arrow">XGBRegressor</label><div class="sk-toggleable__content"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, gpu_id=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=5, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,n_estimators=100, n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...)</pre></div></div></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|--------------------|---------|
| R squared | 0.382 |
| Mean Squared Error | 0.43055 |
# Feature Importance Plot
<style>table.eli5-weights tr:hover {filter: brightness(85%);}</style><p>Explained as: feature importances</p><pre>XGBoost feature importances; values are numbers 0 <= x <= 1;all values sum to 1.</pre><table class="eli5-weights eli5-feature-importances" style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;"><thead><tr style="border: none;"><th style="padding: 0 1em 0 0.5em; text-align: right; border: none;">Weight</th><th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th></tr></thead><tbody><tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.3592</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x26_5</td></tr><tr style="background-color: hsl(120, 100.00%, 94.98%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0499</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x26_1</td></tr><tr style="background-color: hsl(120, 100.00%, 95.83%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0383</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x26_4</td></tr><tr style="background-color: hsl(120, 100.00%, 96.28%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0325</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x23_3</td></tr><tr style="background-color: hsl(120, 100.00%, 96.85%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0256</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x28_0</td></tr><tr style="background-color: hsl(120, 100.00%, 97.09%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0229</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x30_10</td></tr><tr style="background-color: hsl(120, 100.00%, 97.15%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0222</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x8_health</td></tr><tr style="background-color: hsl(120, 100.00%, 97.32%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0203</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x29_10</td></tr><tr style="background-color: hsl(120, 100.00%, 97.35%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0200</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x14_2</td></tr><tr style="background-color: hsl(120, 100.00%, 97.35%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0200</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x7_3</td></tr><tr style="background-color: hsl(120, 100.00%, 97.36%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0199</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x31_16</td></tr><tr style="background-color: hsl(120, 100.00%, 97.55%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0179</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x28_8</td></tr><tr style="background-color: hsl(120, 100.00%, 97.78%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0155</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x28_6</td></tr><tr style="background-color: hsl(120, 100.00%, 97.78%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0155</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x11_mother</td></tr><tr style="background-color: hsl(120, 100.00%, 97.85%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0149</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x29_12</td></tr><tr style="background-color: hsl(120, 100.00%, 97.89%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0145</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x26_2</td></tr><tr style="background-color: hsl(120, 100.00%, 97.96%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0138</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x21_no</td></tr><tr style="background-color: hsl(120, 100.00%, 98.24%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0112</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x6_2</td></tr><tr style="background-color: hsl(120, 100.00%, 98.39%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0098</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x14_0</td></tr><tr style="background-color: hsl(120, 100.00%, 98.47%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0092</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x18_no</td></tr><tr style="background-color: hsl(120, 100.00%, 98.47%); border: none;"><td colspan="2" style="padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;"><i>… 161 more …</i></td></tr></tbody></table>
|
CLTL/gm-ner-xlmrbase | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"nl",
"transformers",
"dighum",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
} | 2 | 2023-01-20T15:35:57Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.20 +/- 13.36
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CLTL/icf-domains | [
"pytorch",
"roberta",
"nl",
"transformers",
"license:mit",
"text-classification"
]
| text-classification | {
"architectures": [
"RobertaForMultiLabelSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
} | 35 | 2023-01-20T15:38:59Z | ---
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="allie21/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"])
```
|
CLTL/icf-levels-ber | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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},
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},
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},
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},
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}
}
} | 33 | 2023-01-20T15:47:27Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -7.02 +/- 1.65
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CLTL/icf-levels-enr | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 30 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.51 +/- 0.26
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CLTL/icf-levels-ins | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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} | 32 | 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: smeth/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CLTL/icf-levels-stm | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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},
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}
} | 32 | null | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-arxiv
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. -->
# gpt2-arxiv
A [gpt2](https://huggingface.co/gpt2) powered predictive keyboard trained on ~1.6M manuscript abstracts from the ArXiv.
This model uses https://www.kaggle.com/datasets/Cornell-University/arxiv
```python
from transformers import pipeline
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
llm = pipeline('text-generation',model='pearsonkyle/gpt2-arxiv', tokenizer=tokenizer)
texts = llm("Directly imaged exoplanets probe",
max_length=50, do_sample=True, num_return_sequences=5,
penalty_alpha=0.65, top_k=40, repetition_penalty=1.25,
temperature=0.95)
for i in range(5):
print(texts[i]['generated_text']+'\n')
```
- *The reflectance of Earth's vegetation suggests* `that large, deciduous forest fires are composed of mostly dry, unprocessed material that is distributed in a nearly patchy fashion. The distributions of these fires are correlated with temperature, and also with vegetation...`
- *Directly imaged exoplanets probe* `the atmospheres of giant planets. The detection of such planets requires high-quality imaging with high contrast and angular resolution, as well as`
- *We can remotely sense an atmosphere by observing its reflected, transmitted, or emitted light in varying geometries. This light will contain information on* `the planetary conditions including atmospheric temperature and cloud properties, which is essential for understanding how the planet interacts with the atmosphere and how it affects the climate. The primary science objective of this paper is to develop a methodology that can be applied to any kind of observation and measurement data, and to provide a framework that enables the detection and characterization of the atmospheres of exoplanets`
## Model description
[GPT-2](https://huggingface.co/transformers/v2.2.0/pretrained_models.html): 12-layer, 768-hidden, 12-heads, 117M parameters
## Intended uses & limitations
Coming soon...
- Predictive Keyboard using text generation
- Realtime reference recommendations using nearest neighbors of embeddings
Be careful when generating a lot of text or when changing the sampling mode of the language model. It can sometimes produce things that are not truthful, e.g.,
- The surface of Mars is composed of a thin layer of water ice, that was discovered by the Cassini spacecraft after its impact on the Earth's surface.
## Training procedure
~49 hours on a 3090 training for 1.25M iterations
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Tokenizers 0.13.2
|
CM-CA/DialoGPT-small-cartman | []
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="harisumant/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"])
```
|
CNT-UPenn/RoBERTa_for_seizureFrequency_QA | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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}
} | 5 | 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: 7.48 +/- 2.69
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="Rschmaelzle/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"])
```
|
CSResearcher/TestModel | [
"license:mit"
]
| null | {
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}
} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="newwater/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"])
```
|
CSZay/bart | []
| null | {
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}
} | 0 | null | ---
license: mit
widget:
- text: කවියා නුමුහු කළ නුවණ
- text: පිරිසිදු පානීය ජලය
language:
- si
pipeline_tag: text-generation
---
### Fine tuned GPT Neo 125M
This model is fine tuned with a [Sinhala data set](https://github.com/TharukaCkasthuri/plagiarism_detection_dataset_sinhala) for Sinhala text generation.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='Suchinthana/sinhala-gpt-neo')
>>> generator("කවියා නුමුහු කළ නුවණ ", do_sample=True, max_length=500)
``` |
CTBC/ATS | []
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} | 0 | 2023-01-20T16:17:50Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="harisumant/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"])
```
|
CZWin32768/xlm-align | [
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2106.06381",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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}
} | 6 | 2023-01-20T16:18:30Z | ---
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: 274.01 +/- 18.90
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Caddy/UD | []
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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: 0.8654677896653767
---
<!-- 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: 0.1405
- F1: 0.8655
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2495 | 1.0 | 787 | 0.1764 | 0.8184 |
| 0.1299 | 2.0 | 1574 | 0.1427 | 0.8562 |
| 0.0771 | 3.0 | 2361 | 0.1405 | 0.8655 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Calamarii/calamari | []
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} | 0 | 2023-01-20T16:21:20Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Ashraf-kasem/gpt2_fine_tune_uncleaned_ds
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. -->
# Ashraf-kasem/gpt2_fine_tune_uncleaned_ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.1724
- Validation Loss: 3.9371
- Epoch: 5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 147444, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.1003 | 4.0757 | 0 |
| 3.6090 | 3.9807 | 1 |
| 3.4057 | 3.9625 | 2 |
| 3.2859 | 3.9406 | 3 |
| 3.2125 | 3.9486 | 4 |
| 3.1724 | 3.9371 | 5 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Callidior/bert2bert-base-arxiv-titlegen | [
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:arxiv_dataset",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| summarization | {
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"EncoderDecoderModel"
],
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}
} | 145 | 2023-01-20T16:23:47Z | ---
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="newwater/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"])
```
|
CalvinHuang/mt5-small-finetuned-amazon-en-es | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| summarization | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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}
} | 16 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1102.58 +/- 114.93
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Cameron/BERT-Jigsaw | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
} | 35 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
- image-to-image
- diffusers
license: creativeml-openrail-m
inference: true
--- |
Cameron/BERT-SBIC-offensive | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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}
} | 31 | 2023-01-20T16:30:59Z | ---
license: other
---
<style>
code {
white-space : pre-wrap !important;
word-break: break-word;
}
</style>
# モデル説明 (model explanation)
- CoolJapanDiffusion 2.1.1 + 0.8(YaguruMagiku-v3.1-AnyBased - HassanBlend1.5) + 0.8(AbyssOrangeMix2_sfw - HassanBlend1.5)
- **マージ元の一部のルーツにNAIリークやInsta系モデルが含まれるという噂があるので、NAIリークアンチ・Insta系モデルアンチには非推奨**
- Stable Diffusion 2.x系と1.x系のマージの実験。不思議な絵が出る。
- colabのWebUIで動かせる。
- [これ](https://colab.research.google.com/drive/1ldhBc70wvuvkp4Af_vNTzTfBXwpf_cH5?usp=sharing)の以下の書き換えを行う。やり方は[ここ](https://the-pioneer.notion.site/Colab-Automatic1111-6043f15ef44d4ba0b11920c95d33a78c)。
```python
!aria2c --summary-interval=10 -x 16 -s 16 --allow-overwrite=true -Z https://huggingface.co/JosephusCheung/ACertainModel/resolve/main/ACertainModel-half.ckpt
```
- CoolJapanDiffusion 2.1.1 + 0.8(YaguruMagiku-v3.1-AnyBased - HassanBlend1.5) + 0.8(AbyssOrangeMix2_sfw - HassanBlend1.5)
- **Since the part of the original models might have the root back in NovelAI leak and Instagram based models, according to some rumors, I do not recommend you use it, if you are a hater of NAI leak/Instagram based models and their derivatives.**
- Since this model is an experimental model to see what will happen when merging a SD 1.x based model to SD 2.x, it is very likely that you get a weird result.
- You can run this model on colab WebUI.
- Rewrite the following line of [this notebook](https://colab.research.google.com/drive/1ldhBc70wvuvkp4Af_vNTzTfBXwpf_cH5?usp=sharing) following the instructions I posted [here](https://the-pioneer.notion.site/Colab-Automatic1111-6043f15ef44d4ba0b11920c95d33a78c).
```python
!aria2c --summary-interval=10 -x 16 -s 16 --allow-overwrite=true -Z https://huggingface.co/JosephusCheung/ACertainModel/resolve/main/ACertainModel-half.ckpt
```
# extras.py
**本ファイルのみ、CC0 1.0ライセンスとする(WebUIのAGPLとの互換性維持のため)。**
WebUIの同名ファイルを置き換えることであなた自身のマージを作ることができます。
- ``No interpolation``はひっかけで、マージはしません。最初これで、マージできたと勘違いしていました。
- ``Weighted sum``は比率0.1程度でも元のモデルを跡形もなく破壊します。0.01なら大丈夫でしたが、その間のどこがボーダーなのかは不明です。
- ``Add difference``は比較的元のモデルを維持したままで画風などを変更できます。ただし、やりすぎるとこのモデルのような結果になります。また、変更内容がマージに使ったSD 1.x系に期待した内容通りになる保証もありません。
**Note that this file and only this file in the model is released under public domain (CC0 1.0), in order to keep it compatible with the AGPL license of WebUI.**
By replacing the file with the same name in WebUI, you can create your own merged model.
- ``No interpolation`` is NOT a merging operation. It will work, but it will only return the same model as model A.
- ``Weighted sum`` can easily destroy the original SD 2.x based model. Multiplier 0.1 was enough for it, whereas 0.01 was OK. There should be a border zone somewhere.
- ``Add difference`` will work relatively fine, but going too far will likely result in a model similar to this. Additionally, there is no guarantee that you can get the style and/or content you expected to the original SD 1.x model you merged to.
# sample outputs
アップしているので、気になるならご自身で見てください。プロンプトはメタデータに入っているはずです。
Check it by yourself if you are interested in this model. The prompts should be in the metadata of each image.
# License: The Libertarian OpenRAIL License
注意: アップロード者が日本語母語話者であるため、翻訳版と日本語版に差異がある場合、**元の日本語版**が優先されるものとする。
Caution: Since the uploader is a Japanese native, in the event of any differences in meaning between the original Japanese version and a translation, **the original Japanese version** takes precedence.
要約: ほぼCreativeML Open RAIL-M。但しリバタリアン的解釈によって再構成。CreativeML Open RAIL-Mの制限は、同解釈において維持されているものと判断する。
Summary: A CreativeML Open RAIL-M, interpreted and reconstructed under a libertarian manner. The restriction of CreativeML Open RAIL-M is considered to be valid under such interpretation.
## 主な相違 (differences from the original CreativeML Open RAIL-M license)
- 違法性は、無罪推定の原則に基づき、有罪確定を以て、かつそれのみによって判断する（有罪が確定するまで、法令違反であるように見えても、ライセンス者は違法とはみなさない）。
- ex. フェアユース文化圏は無論、親告罪である日本においても、著作者が訴えない範囲のほどほどの二次創作は、事実上問題視しない。
- 本モデル及び派生モデルによる生成物はパブリック・ドメイン（CC0 1.0）とすることを義務付け、生成者を含む任意の人物による（再）利用の自由を保障する。
- Stability.aiが運営するDream Studioが生成物をCC0 1.0としているが、元のモデルライセンスと両立していることに注意せよ。
- 派生モデルでは、本ライセンスと同等以上の制限とともに、同等以上の自由も保障しなければならない。
- The violation of law or regulation will be judged by and only by your conviction per the presumption of innocence (unless you are convicted, it is not enough to claim it is illegal for the Licensor, even if it looks like it).
- ex. Fanart in Japan is technically illegal, unlike countries which have fair use, but as long as it is in the moderate range and the copright holder won't sue you, we will practically do not consider it as problematic.
- Outputs you generated by the Model or Derivatives of the Model must be distributed under public domain (CC0 1.0), to ensure not only you but anyone can (re)use it freely.
- Note that Dream Studio, run by Stability.ai demands the output be CC0 1.0 as well, but still isn't against the original model license.
- Derivatives of the Model will always have to include - at minimum - the same use-based restrictions <u>and the same open permissions</u>.
## 全文 (full license)
### 日本語版
[License_ja.md](https://huggingface.co/ThePioneer/MoeDiffusionPlusPlus/blob/main/License_ja.md)を参照。
### English version
[License_en.md](https://huggingface.co/ThePioneer/MoeDiffusionPlusPlus/blob/main/License_en.md)を参照。 |
Cameron/BERT-SBIC-targetcategory | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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}
}
} | 30 | 2023-01-20T16:37:03Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- landscape
widget:
- text: high quality photo of Venice in fgreeneruins ruins
---
# DreamBooth model for the fgreeneruins concept trained on the CCMat/db-forest-ruins dataset.
This is a Stable Diffusion model fine-tuned on the fgreeneruins concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of fgreeneruins ruins**
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 `ruins` images for the landscape theme.<br>
Concept: **fgreeneruins** : forest ruins, greenery ruins<br>
Pretrained Model: [nitrosocke/elden-ring-diffusion](https://huggingface.co/nitrosocke/elden-ring-diffusion)<br>
Learning rate: 2e-6<br>
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('CCMat/fgreeneruins-ruins')
image = pipeline().images[0]
image
```
## Samples
Prompt: "high quality photo of Venice in fruins ruins"
<br>
Prompt: "high quality photo of Rome in fgreeneruins ruins with the Colosseum in the background"
<br>
Prompt: "fgreeneruins ruins in London near the Tower Bridge, professional photograph"
<br>
Prompt: "photo of Paris in fgreeneruins ruins, elden ring style"
Prompt: "fgreeneruins ruins in Saint Petersburg, Sovietwave"
 |
Cameron/BERT-jigsaw-identityhate | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 37 | null | ---
datasets:
- amazon_reviews_multi
language:
- en
metrics:
- rouge
pipeline_tag: summarization
---
# Model Card for Model ID
Model that trained to summarize product reviews.
|
Cameron/BERT-jigsaw-severetoxic | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 30 | 2023-01-20T16:38:47Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1772.88 +/- 77.20
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Cameron/BERT-mdgender-convai-binary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
} | 33 | 2023-01-20T16:40:52Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
} | 38 | 2023-01-20T16:40:54Z |
---
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: stevaras2/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Camzure/MaamiBot-test | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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}
} | 9 | 2023-01-20T16:54:54Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1048.80 +/- 251.18
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Camzure/MaamiBot | []
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} | 0 | 2023-01-20T17:00:14Z | ---
license: openrail
language:
- en
metrics:
- f1
library_name: fairseq
pipeline_tag: audio-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
We explore benefits of unsupervised pretraining of wav2vec 2.0 (W2V2) using large-scale unlabeled home recordings collected using LittleBeats (LB) and LENA (Language Environment Analysis) devices.
LittleBeats is a new infant wearable multi-modal device that we developed, which simultaneously records audio, movement of the infant, as well as heart-rate variablity.
We use W2V2 to advance LB audio pipeline such that it automatically provides reliable labels of speaker diarization and vocalization classifications for family members, including infants, parents, and siblings, at home.
We show that W2V2 pretrained on thousands hours of large-scale unlabeled home audio outperforms oracle W2V2 pretrained on 52k-hours released by Facebook/Meta in terms of automatic family audio analysis tasks.
For more details about LittleBeats, check out **https://littlebeats.hdfs.illinois.edu/**
## Model Sources
For more information regarding this model, please checkout our paper
- **Paper [optional]:** [More Information Needed]
## Model Description
<!-- Provide a longer summary of what this model is. -->
Two versions of pretrained W2V2 models **using fairseq** are available:
- **LB_1100/checkpoint_best.pt**: pretrained using 1100-hour of LB home recordings collected from 110 families of children under 5-year-old
- **LL_4300/checkpoint_best.pt**: pretrained using 1100-hour of LB home recordings collected from 110 families + 3200-hour of LENA home recordings from 275 families of children under 5-year-old
One version of fine-tuned W2V2 models on labeled LB and LENA data **using SpeechBrain** is available:
- **LL_4300_fine_tuned**: pretrained on LL_4300 checkpoint and followed by fine-tuning on labeled LB and LENA home recordings + labeled lab recordings with data augmentation
Two pretrained ECAPA-TDNN speaker embeddings are available:
- **ECAPA_TDNN_LB/embedding_model.ckpt**: pretrained using 12-hour of labeled LB home recordings collected from 22 families of infants under 14-month-old
- **ECAPA_TDNN_LB_LENA/embedding_model.ckpt**: pretrained using 12-hour of labeled LB home recordings collected from 22 families + 18-hour of labeled LENA home recordings from 30 families of infants under 14-month-old
## Uses
**We develop our complete fine-tuning recipe using SpeechBrain toolkit available at**
- **https://github.com/jialuli3/speechbrain/tree/infant-voc-classification/recipes/wav2vec_LittleBeats**
## Quick Start
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
If you wish to use fairseq framework, the following code snippet provides two functions of loading our pretrained W2V2 model and extracting features.
<pre><code>
import torch
import torch.nn.functional as F
from torch import nn
import fairseq
import torchaudio
def load_model(model_path, freeze=True):
'''
This function loads pretrained model using fairseq framework.
Arguments
---------
model_path : str
Path and filename of the pretrained model
freeze : bool (default: True)
If True, the model is frozen with no parameter updates through training.
'''
model,_,_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([model_path])
model = model[0]
if freeze:
model.eval()
# Freeze parameters
for param in model.parameters():
param.requires_grad = False
else:
model.train()
for param in model.parameters():
param.requires_grad = True
#remove unnecessary components
model.quantizer = None
model.project_q = None
model.target_glu = None
model.final_proj = None
return model
def extract_features(model, wav, input_norm=None, output_norm=True, tgt_layer=None, output_all_hiddens=False):
'''
This function extracts features from w2v2 model. The function extracts the last transformer layer
feature by default. It allows for extracting features from certain layer, or features from all layers
Arguments
---------
model : fairseq wav2vec
wav : tensor
audio wav for feature extraction
input_norm : bool (default: None)
If True, a layer_norm (affine) will be applied to the input waveform.
output_norm : bool (default: True)
If True, a layer_norm (affine) will be applied to the output obtained
from the wav2vec model.
tgt_layer : int (default: None)
Target transformer layer features, 0-indexed.
output_all_hiddens : bool (default: False)
Whether to extract features from all layers. Need to set tgt_layer as None
'''
if input_norm:
wav = F.layer_norm(wav, wav.shape)
# Extract wav2vec output
out = model.extract_features(wav, padding_mask=None, mask=False)['x']
if isinstance(tgt_layer, int):
out = model.extract_features(wav, padding_mask=None, mask=False, layer=tgt_layer)['x']
elif output_all_hiddens:
features = []
model.layerdrop = 0
for i in range(len(out['layer_results'])):
curr_feature = out['layer_results'][i][0].transpose(0,1)
features.append(curr_feature)
out = torch.stack(features)
if output_norm:
out = F.layer_norm(out, out.shape)
return out
model=load_model("your/path/to/LL_4300/checkpoint_best.pt")
audio, fs = torchaudio.load("sample.wav")
audio = audio.transpose(0,1).squeeze(1)
features = extract_features(model, audio)
</code></pre>
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
We test 4 unlabeled datasets on unsupervised pretrained W2V2-base models:
- **base (oracle version):** originally released version pretrained on ~52k-hour unlabeled audio
- **Libri960h:** oracle version fine-tuned using 960h Librispeech
- **LB1100h:** pretrain W2V2 using 1100h LB home recordings
- **LL4300h:** pretrain W2V2 using 4300h LB+LENA home recordings
We then fine-tune pretrained models on 11.7h of LB labeled home recordings, the f1 scores across three tasks are
Additionally, we improve our model performances by adding relevant labeled home recordings and using data augmentation techniques of SpecAug and noise/reverberation corruption.
For more details of experiments and results, please refer to our paper.
# Paper/BibTex Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you found this model helpful to you, please cite us as
Coming soon
# Model Card Contact
Jialu Li (she, her, hers)
Ph.D candidate @ Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
E-mail: [email protected]
Homepage: https://sites.google.com/view/jialuli/
Our team: https://littlebeats.hdfs.illinois.edu/team/
|
Canadiancaleb/DialoGPT-small-walter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 13 | 2023-01-20T17:12:49Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.99 +/- 0.50
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Canadiancaleb/jessebot | []
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} | 0 | 2023-01-20T17:15:12Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.94 +/- 0.34
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Capreolus/bert-base-msmarco | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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} | 238 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: daripaez/ppo-pyramids_3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Capreolus/birch-bert-large-msmarco_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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"BertForNextSentencePrediction"
],
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} | 1 | null | ---
license: cc-by-nc-4.0
---
JAX weights converted from Torch checkpoint at `facebook/galactica-1.3b`.
```python
(env) ubuntu@vm:~$ JAX_PLATFORM_NAME=cpu python3
>>> import jax
>>> print(jax.devices())
[CpuDevice(id=0)] # Ensure that model weights are loaded into CPU RAM, not accelerator memory.
>>> from transformers import FlaxOPTForCausalLM
>>> model = FlaxOPTForCausalLM.from_pretrained("facebook/galactica-1.3b", from_pt=True)
>>> model.push_to_hub(hf_model_repo)
```
## Citation and Attribution
Citation from the original repo is reproduced below as per the cc-by-nc-4.0 licsense.
```bibtex
@inproceedings{GALACTICA,
title={GALACTICA: A Large Language Model for Science},
author={Ross Taylor and Marcin Kardas and Guillem Cucurull and Thomas Scialom and Anthony Hartshorn and Elvis Saravia and Andrew Poulton and Viktor Kerkez and Robert Stojnic},
year={2022}
}
```
> Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) |
Captain-1337/CrudeBERT | [
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 28 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.76 +/- 0.31
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Carlork314/Carlos | []
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} | 0 | null | Access to model Monan/Abc2 is restricted and you are not in the authorized list. Visit https://huggingface.co/Monan/Abc2 to ask for access. |
CarlosTron/Yo | []
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} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- sbrandeis-test-org/autotrain-data-retrain-db16d58
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.5759791564661282
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2983986070
- CO2 Emissions (in grams): 0.5760
## Validation Metrics
- Loss: 0.001
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000 |
Carolhuehuehuehue/Sla | []
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} | 0 | null | Access to model WebDUh1/autotrain-faqued-2983486071 is restricted and you are not in the authorized list. Visit https://huggingface.co/WebDUh1/autotrain-faqued-2983486071 to ask for access. |
dccuchile/albert-large-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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"AlbertForTokenClassification"
],
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}
} | 1 | null | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
moved to https://huggingface.co/MyneFactory/MF-EminenceInShadow |
Certified-Zoomer/DialoGPT-small-rick | []
| null | {
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}
} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-internship
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-internship
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ChrisP/xlm-roberta-base-finetuned-marc-en | []
| null | {
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} | 0 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/pii-pile-chunk3-0-50000
- tomekkorbak/pii-pile-chunk3-50000-100000
- tomekkorbak/pii-pile-chunk3-100000-150000
- tomekkorbak/pii-pile-chunk3-150000-200000
- tomekkorbak/pii-pile-chunk3-200000-250000
- tomekkorbak/pii-pile-chunk3-250000-300000
- tomekkorbak/pii-pile-chunk3-300000-350000
- tomekkorbak/pii-pile-chunk3-350000-400000
- tomekkorbak/pii-pile-chunk3-400000-450000
- tomekkorbak/pii-pile-chunk3-450000-500000
- tomekkorbak/pii-pile-chunk3-500000-550000
- tomekkorbak/pii-pile-chunk3-550000-600000
- tomekkorbak/pii-pile-chunk3-600000-650000
- tomekkorbak/pii-pile-chunk3-650000-700000
- tomekkorbak/pii-pile-chunk3-700000-750000
- tomekkorbak/pii-pile-chunk3-750000-800000
- tomekkorbak/pii-pile-chunk3-800000-850000
- tomekkorbak/pii-pile-chunk3-850000-900000
- tomekkorbak/pii-pile-chunk3-900000-950000
- tomekkorbak/pii-pile-chunk3-950000-1000000
- tomekkorbak/pii-pile-chunk3-1000000-1050000
- tomekkorbak/pii-pile-chunk3-1050000-1100000
- tomekkorbak/pii-pile-chunk3-1100000-1150000
- tomekkorbak/pii-pile-chunk3-1150000-1200000
- tomekkorbak/pii-pile-chunk3-1200000-1250000
- tomekkorbak/pii-pile-chunk3-1250000-1300000
- tomekkorbak/pii-pile-chunk3-1300000-1350000
- tomekkorbak/pii-pile-chunk3-1350000-1400000
- tomekkorbak/pii-pile-chunk3-1400000-1450000
- tomekkorbak/pii-pile-chunk3-1450000-1500000
- tomekkorbak/pii-pile-chunk3-1500000-1550000
- tomekkorbak/pii-pile-chunk3-1550000-1600000
- tomekkorbak/pii-pile-chunk3-1600000-1650000
- tomekkorbak/pii-pile-chunk3-1650000-1700000
- tomekkorbak/pii-pile-chunk3-1700000-1750000
- tomekkorbak/pii-pile-chunk3-1750000-1800000
- tomekkorbak/pii-pile-chunk3-1800000-1850000
- tomekkorbak/pii-pile-chunk3-1850000-1900000
- tomekkorbak/pii-pile-chunk3-1900000-1950000
model-index:
- name: keen_jackson
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. -->
# keen_jackson
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 12588
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'filter_threshold': 0.000286,
'is_split_by_sentences': True,
'skip_tokens': 1649999872},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '9e6c78543a6ff1e4089002c38864d5a9cf71ec90'},
'path_or_name': 'tomekkorbak/nervous_wozniak'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 128,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'keen_jackson',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25177,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1649999872,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/2uvwek0j |
ChrisVCB/DialoGPT-medium-cmjs | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- germeval_14
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: germeval_14
type: germeval_14
config: germeval_14
split: validation
args: germeval_14
metrics:
- name: F1
type: f1
value: 0.858814923189466
---
<!-- 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 germeval_14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0744
- F1: 0.8588
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1261 | 1.0 | 1000 | 0.0769 | 0.8335 |
| 0.0555 | 2.0 | 2000 | 0.0679 | 0.8568 |
| 0.0329 | 3.0 | 3000 | 0.0744 | 0.8588 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ChrisVCB/DialoGPT-medium-ej | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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},
"text-generation": {
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}
} | 13 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Freddthink/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"])
```
|
ChristianOrr/madnet_keras | [
"tensorboard",
"dataset:flyingthings-3d",
"dataset:kitti",
"arxiv:1810.05424",
"vision",
"deep-stereo",
"depth-estimation",
"Tensorflow2",
"Keras",
"license:apache-2.0"
]
| depth-estimation | {
"architectures": null,
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: bguisard/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ChristopherA08/IndoELECTRA | [
"pytorch",
"electra",
"pretraining",
"id",
"dataset:oscar",
"transformers"
]
| null | {
"architectures": [
"ElectraForPreTraining"
],
"model_type": "electra",
"task_specific_params": {
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"max_length": null
},
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},
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},
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 4 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: basic-Reinforce-CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 464.60 +/- 67.81
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Chun/DialoGPT-medium-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 15 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 31.60 +/- 19.90
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Chun/w-en2zh-hsk | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-model1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9774436090225563
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-model1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0584
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1296 | 3.85 | 500 | 0.0584 | 0.9774 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Chun/w-en2zh-mtm | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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} | 7 | null |
Create your new pokemon
Recommendations:
TOKEN -> PokeModel
Basic prompts:
Positive:
PokeModel, A new pokemon, Unknown type, (((realistic, hyper realistic, ultra detailed, hyper realistic textures))), awesome, masterly, randomly, perfect coherence, perfect lighting
Negative:
Deformed, blurry, colorless, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, ((((mutated hands and fingers)))), (((out of frame))), {((([[[watermark, logo, text, firm]]])))}
--
For the 2 rescaled in img2img I kept the:
Denoising strength: 0.4
Steps: 44, Sampler: DPM++ SDE Karras, CFG scale: 8, Seed: 1390007811, Size: 1024x1024, Model hash: 7aecd9eb4c, Model: PokeModel, Denoising strength: 0.4, Mask blur: 4
|
Chun/w-en2zh-otm | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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} | 7 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: smanduru/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Chun/w-zh2en-mtm | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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} | 8 | null | ---
license: mit
---
Causality detection model fine-tuned on both self-labeled data and both the training and dev sets from the Causal News Corpus (https://github.com/tanfiona/CausalNewsCorpus/tree/master/data). |
Chungu424/repo | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- food
widget:
- text: Trump eating bhapa cake
---
# DreamBooth model for the bhapa concept trained by nahidalam on the nahidalam/bhapa dataset.
This is a Stable Diffusion model fine-tuned on the bhapa concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of bhapa cake**
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 `cake` images for the food theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('nahidalam/bhapa-cake')
image = pipeline().images[0]
image
```
|
Clarianliz30/Caitlyn | []
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}
} | 0 | null | ---
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
# OPT-IML
## Model Description
[OPT-IML (OPT + Instruction Meta-Learning)](https://arxiv.org/abs/2212.12017) is a set of instruction-tuned versions of OPT, on a collection of ~2000 NLP tasks gathered from 8 NLP benchmarks, called OPT-IML Bench.
We provide two model versions:
* OPT-IML trained on 1500 tasks with several tasks held-out for purposes of downstream evaluation, and
* OPT-IML-Max trained on all ~2000 tasks
### How to use
For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because
one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU.
It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate)
method as follows:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-iml-30b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-iml-30b", use_fast=False)
>>> prompt = "What is the color of a carrot?\nA:"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> generated_ids = model.generate(input_ids)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
```
### Limitations and bias
While OPT-IML models outperform baseline OPT on an extensive set of evaluations,
nevertheless, they are susceptible to the various risks associated with using large language models
relating to factual correctness, generation of toxic language and enforcing stereotypes. While we release our
OPT-IML models to proliferate future work on instruction-tuning and to improve the availability
of large instruction-tuned causal LMs, the use of these models should be
accompanied with responsible best practices.
## Training data
OPT-IML models are trained on OPT-IML Bench, a large benchmark for Instruction MetaLearning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks include Super-NaturalInstructions, FLAN, PromptSource, etc.
## Training procedure
The texts are tokenized using the GPT2 byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 30B model was fine-tuned on 64 40GB A100 GPUs. During fine-tuning, models saw approximately 2 billion tokens, which is only 0.6% of the pre-training
budget of OPT.
### BibTeX entry and citation info
```bibtex
@misc{iyer2022opt,
title={OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization},
author={Iyer, Srinivasan and Lin, Xi Victoria and Pasunuru, Ramakanth and Mihaylov, Todor and Simig, D{\'a}niel and Yu, Ping and Shuster, Kurt and Wang, Tianlu and Liu, Qing and Koura, Punit Singh and others},
year={2022},
eprint={2212.12017},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
CleveGreen/JobClassifier | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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"BertForSequenceClassification"
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}
} | 31 | null | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery_ex02
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.73 +/- 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="Gumibit/q-FrozenLake-v1-4x4-Slippery_ex02", 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"])
```
|
Cloudy/DialoGPT-CJ-large | [
"pytorch",
"conversational"
]
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}
} | 1 | null | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- PoseyATX/autotrain-data-billwork_second_half
co2_eq_emissions:
emissions: 674.392653539903
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2990886304
- CO2 Emissions (in grams): 674.3927
## Validation Metrics
- Loss: 0.190
- Rouge1: 88.799
- Rouge2: 85.137
- RougeL: 86.455
- RougeLsum: 87.865
- Gen Len: 157.049
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/PoseyATX/autotrain-billwork_second_half-2990886304
``` |
CoShin/XLM-roberta-large_ko_en_nil_sts | []
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} | 0 | null | ---
license: other
language:
- en
- ja
library_name: diffusers
pipeline_tag: text-to-image
tags:
- art
---
<style>
code {
white-space : pre-wrap !important;
word-break: break-word;
}
</style>
# モデル説明 (model explanation)
- [CoolJapanDiffusion 2.1.1](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-1/blob/main/v2-1-1.ckpt)と[WaifuDiffusion 1.4 anime epoch2](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/wd-1-4-anime_e2.ckpt)のマージ。比率はckptファイル名の記載の通り。
- colabのWebUIで動かせる。
- [これ](https://colab.research.google.com/drive/1ldhBc70wvuvkp4Af_vNTzTfBXwpf_cH5?usp=sharing)の以下の書き換えを行う。やり方は[ここ](https://the-pioneer.notion.site/Colab-Automatic1111-6043f15ef44d4ba0b11920c95d33a78c)。
- ~~リアル系モデルとマージしようとすると、発色が鮮やかになりすぎる傾向あり。~~SD 2.1 768とのマージが原因。512系とのマージなら問題なし。
```python
!aria2c --summary-interval=10 -x 16 -s 16 --allow-overwrite=true -Z https://huggingface.co/JosephusCheung/ACertainModel/resolve/main/ACertainModel-half.ckpt
```
- **注意: URLを引用符で囲まないとエラーになることが判明したのでご注意ください**
```python
!aria2c --summary-interval=10 -x 16 -s 16 --allow-overwrite=true -Z "https://huggingface.co/ThePioneer/CoolerWaifuDiffusion/resolve/main/0.65(wd-1-4-anime_e2)%20%2B%200.35(v2-1-1).ckpt"
!aria2c --summary-interval=10 -x 16 -s 16 --allow-overwrite=true -Z "https://huggingface.co/ThePioneer/CoolerWaifuDiffusion/raw/main/0.65(wd-1-4-anime_e2)%20%2B%200.35(v2-1-1).yaml"
```
- Some merged model of [CoolJapanDiffusion 2.1.1](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-1/blob/main/v2-1-1.ckpt) and [WaifuDiffusion 1.4 anime epoch2](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/wd-1-4-anime_e2.ckpt). The merge ration of each model is written on the ckpt file name.
- You can run this model on colab WebUI.
- Rewrite the following line of [this notebook](https://colab.research.google.com/drive/1ldhBc70wvuvkp4Af_vNTzTfBXwpf_cH5?usp=sharing) following the instructions I posted [here](https://the-pioneer.notion.site/Colab-Automatic1111-6043f15ef44d4ba0b11920c95d33a78c).
- ~~Trying to merge with a realistic model will probably result in a model with too vivid color.~~ It was because I was trying to merge with a SD 2.1 768 based model. It works fine with a SD 2.1 512 based model.
```python
!aria2c --summary-interval=10 -x 16 -s 16 --allow-overwrite=true -Z https://huggingface.co/JosephusCheung/ACertainModel/resolve/main/ACertainModel-half.ckpt
```
- **NOTE: you need to wrap the URL with a quotation as follows**
```python
!aria2c --summary-interval=10 -x 16 -s 16 --allow-overwrite=true -Z "https://huggingface.co/ThePioneer/CoolerWaifuDiffusion/resolve/main/0.65(wd-1-4-anime_e2)%20%2B%200.35(v2-1-1).ckpt"
!aria2c --summary-interval=10 -x 16 -s 16 --allow-overwrite=true -Z "https://huggingface.co/ThePioneer/CoolerWaifuDiffusion/raw/main/0.65(wd-1-4-anime_e2)%20%2B%200.35(v2-1-1).yaml"
```
# サンプル画像 (sample images)
## prompt
```
masterpiece, best quality, A teenage girl wearing a white feather down jacket, smile, in the style of Kyoto Animation in the 2010s, official art, ((black hair)), eyes of Haruhi Suzumiya, face of Haruhi Suzumiya, beautiful symmetric face, ponytail, beautifully detailed hair, posing of Haruhi Suzumiya, at a snowing mountain in winter, detailed background, alone, solo, 8k, ((((sharp contrast)))), watercolor
Negative prompt: low quality, bad face, ((ugly face)), asymmetric face, ((((bad anatomy)))), ((bad hand)), too many fingers, missing fingers, too many legs, too many arms, too many heads, wrong anatomy, ((lowres, jpeg artifacts)), [[[[3d]]]], 2d, (((text))), logo, signature, ((loli)), twintails, ponytail, long hair, plaits, pajamas, blushing, boy, sad face, bells, fanart, pixiv, card game, ahoge, ribbon, headband, thick eyebrow, bakemonogatari, black outlines, solid outlines, bold outlines, outlines, technicolor, ((blurry)), vivid colors, vector art, anime, manga, posters, [[oily skin]], huge breasts, baby face, bruises, simple background
Steps: 50
Sampler: Euler a
CFG scale: 7
Seed: 2930115154
Size: 768x768
```
## xy plot
- 最適なモデルは何を生成するかによって変わりうる。
- The best model may depend on what to generate.
# License: The Libertarian OpenRAIL License
注意: アップロード者が日本語母語話者であるため、翻訳版と日本語版に差異がある場合、**元の日本語版**が優先されるものとする。
Caution: Since the uploader is a Japanese native, in the event of any differences in meaning between the original Japanese version and a translation, **the original Japanese version** takes precedence.
要約: ほぼCreativeML Open RAIL-M。但しリバタリアン的解釈によって再構成。CreativeML Open RAIL-Mの制限は、同解釈において維持されているものと判断する。
Summary: A CreativeML Open RAIL-M, interpreted and reconstructed under a libertarian manner. The restriction of CreativeML Open RAIL-M is considered to be valid under such interpretation.
## 主な相違 (differences from the original CreativeML Open RAIL-M license)
- 違法性は、無罪推定の原則に基づき、有罪確定を以て、かつそれのみによって判断する（有罪が確定するまで、法令違反であるように見えても、ライセンス者は違法とはみなさない）。
- ex. フェアユース文化圏は無論、親告罪である日本においても、著作者が訴えない範囲のほどほどの二次創作は、事実上問題視しない。
- 本モデル及び派生モデルによる生成物はパブリック・ドメイン（CC0 1.0）とすることを義務付け、生成者を含む任意の人物による（再）利用の自由を保障する。
- Stability.aiが運営するDream Studioが生成物をCC0 1.0としているが、元のモデルライセンスと両立していることに注意せよ。
- 派生モデルでは、本ライセンスと同等以上の制限とともに、同等以上の自由も保障しなければならない。
- The violation of law or regulation will be judged by and only by your conviction per the presumption of innocence (unless you are convicted, it is not enough to claim it is illegal for the Licensor, even if it looks like it).
- ex. Fanart in Japan is technically illegal, unlike countries which have fair use, but as long as it is in the moderate range and the copright holder won't sue you, we will practically do not consider it as problematic.
- Outputs you generated by the Model or Derivatives of the Model must be distributed under public domain (CC0 1.0), to ensure not only you but anyone can (re)use it freely.
- Note that Dream Studio, run by Stability.ai demands the output be CC0 1.0 as well, but still isn't against the original model license.
- Derivatives of the Model will always have to include - at minimum - the same use-based restrictions <u>and the same open permissions</u>.
## 全文 (full license)
### 日本語版
[License_ja.md](https://huggingface.co/ThePioneer/MoeDiffusionPlusPlus/blob/main/License_ja.md)を参照。
### English version
[License_en.md](https://huggingface.co/ThePioneer/MoeDiffusionPlusPlus/blob/main/License_en.md)を参照。 |
CoachCarter/distilbert-base-uncased | []
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} | 0 | null | # Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) wiki page for extra scripts developed by users.
## Features
[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
- Original txt2img and img2img modes
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Color Sketch
- Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- a man in a ((tuxedo)) - will pay more attention to tuxedo
- a man in a (tuxedo:1.21) - alternative syntax
- select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
- Textual Inversion
- have as many embeddings as you want and use any names you like for them
- use multiple embeddings with different numbers of vectors per token
- works with half precision floating point numbers
- train embeddings on 8GB (also reports of 6GB working)
- Extras tab with:
- GFPGAN, neural network that fixes faces
- CodeFormer, face restoration tool as an alternative to GFPGAN
- RealESRGAN, neural network upscaler
- ESRGAN, neural network upscaler with a lot of third party models
- SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
- Adjust sampler eta values (noise multiplier)
- More advanced noise setting options
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Live prompt token length validation
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- drag and drop an image/text-parameters to promptbox
- Read Generation Parameters Button, loads parameters in promptbox to UI
- Settings page
- Running arbitrary python code from UI (must run with --allow-code to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Random artist button
- Tiling support, a checkbox to create images that can be tiled like textures
- Progress bar and live image generation preview
- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
- Styles, a way to save part of prompt and easily apply them via dropdown later
- Variations, a way to generate same image but with tiny differences
- Seed resizing, a way to generate same image but at slightly different resolution
- CLIP interrogator, a button that tries to guess prompt from an image
- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
- Batch Processing, process a group of files using img2img
- Img2img Alternative, reverse Euler method of cross attention control
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
- Reloading checkpoints on the fly
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
- separate prompts using uppercase `AND`
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
- Generate forever option
- Training tab
- hypernetworks and embeddings options
- Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
- Clip skip
- Use Hypernetworks
- Use VAEs
- Estimated completion time in progress bar
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Place `model.ckpt` in the `models` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
5. _*(Optional)*_ Place `GFPGANv1.4.pth` in the base directory, alongside `webui.py` (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
6. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
### Automatic Installation on Linux
1. Install the dependencies:
```bash
# Debian-based:
sudo apt install wget git python3 python3-venv
# Red Hat-based:
sudo dnf install wget git python3
# Arch-based:
sudo pacman -S wget git python3
```
2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:
```bash
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
## Contributing
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
- Security advice - RyotaK
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
|
CodeDanCode/SP-KyleBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 15 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: mbart-large-50-finetuned-en-to-ko-8603428
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. -->
# mbart-large-50-finetuned-en-to-ko-8603428
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CodeNinja1126/bert-q-encoder | [
"pytorch"
]
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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: 254.17 +/- 19.23
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DARKVIP3R/DialoGPT-medium-Anakin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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}
} | 13 | 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: 7.46 +/- 2.78
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="rahul-t-p/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"])
```
|
DJStomp/TestingSalvoNET | [
"transformers"
]
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: opus-mt-ko-en-finetuned-ko-to-en-2780616
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. -->
# opus-mt-ko-en-finetuned-ko-to-en-2780616
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8435
## 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: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0458 | 1.0 | 9376 | 0.9283 |
| 0.9423 | 2.0 | 18752 | 0.8607 |
| 0.9013 | 3.0 | 28128 | 0.8435 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Tweets",
"Sentiment analysis"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-conll
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9151515151515152
- name: Recall
type: recall
value: 0.9402558061258836
- name: F1
type: f1
value: 0.9275338258487591
- name: Accuracy
type: accuracy
value: 0.9845470065344086
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner-conll
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0593
- Precision: 0.9152
- Recall: 0.9403
- F1: 0.9275
- Accuracy: 0.9845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 293 | 0.0773 | 0.8819 | 0.9138 | 0.8976 | 0.9775 |
| 0.1657 | 2.0 | 586 | 0.0598 | 0.9101 | 0.9374 | 0.9236 | 0.9835 |
| 0.1657 | 3.0 | 879 | 0.0593 | 0.9152 | 0.9403 | 0.9275 | 0.9845 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1
- Datasets 2.6.1
- Tokenizers 0.11.0
|
DTAI-KULeuven/robbertje-1-gb-bort | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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}
}
} | 6 | 2023-01-21T07:32:22Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: CreativeEvolution/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DTAI-KULeuven/robbertje-1-gb-merged | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 1 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
library_name: diffusers
extra_gated_prompt: |-
One more step before getting this model.
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 here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask.
The **Stable-Diffusion-Inpainting** was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original). First 595k steps regular training, then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning to improve classifier-free [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
[](https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
:-------------------------:|:-------------------------:|
## Examples:
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```python
from diffusers import StableDiffusionInpaintPipeline
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
revision="fp16",
torch_dtype=torch.float16,
)
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
#image and mask_image should be PIL images.
#The mask structure is white for inpainting and black for keeping as is
image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
image.save("./yellow_cat_on_park_bench.png")
```
**How it works:**
`image` | `mask_image`
:-------------------------:|:-------------------------:|
<img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="300"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="300"/>
`prompt` | `Output`
:-------------------------:|:-------------------------:|
<span style="position: relative;bottom: 150px;">Face of a yellow cat, high resolution, sitting on a park bench</span> | <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/test.png" alt="drawing" width="300"/>
### Original GitHub Repository
1. Download the weights [sd-v1-5-inpainting.ckpt](https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt)
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/runwayml/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide six checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, `sd-v1-4.ckpt`, `sd-v1-5.ckpt` and `sd-v1-5-inpainting.ckpt`
which were trained as follows,
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-4.ckpt`: Resumed from stable-diffusion-v1-2.225,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-5.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.
- `sd-v1-5-inpaint.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Inpainting Evaluation
To assess the performance of the inpainting model, we used the same evaluation
protocol as in our [LDM paper](https://arxiv.org/abs/2112.10752). Since the
Stable Diffusion Inpainting Model acccepts a text input, we simply used a fixed
prompt of `photograph of a beautiful empty scene, highest quality settings`.
| Model | FID | LPIPS |
|-----------------------------|------|------------------|
| Stable Diffusion Inpainting | 1.00 | 0.141 (+- 0.082) |
| Latent Diffusion Inpainting | 1.50 | 0.137 (+- 0.080) |
| CoModGAN | 1.82 | 0.15 |
| LaMa | 2.21 | 0.134 (+- 0.080) |
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
alexandrainst/da-emotion-classification-base | [
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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}
}
} | 837 | null | Access to model chrisdiablo/chrisface is restricted and you are not in the authorized list. Visit https://huggingface.co/chrisdiablo/chrisface to ask for access. |
alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
} | 866 | 2023-01-21T08:04:26Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: beila dog sitting on top of the deck of a battle ship traveling through the
open sea with a lot of ships surrounding it
---
# DreamBooth model for the beila concept trained by xuehaimeng.
This is a Stable Diffusion model fine-tuned on the beila concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of beila dog**
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 `dog` images for the animal theme,
for the Hugging Face DreamBooth Hackathon, from the HF CN Community,
corporated with the HeyWhale.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('xuehaimeng/beila-dog-heywhale')
image = pipeline().images[0]
image
```
|
alexandrainst/da-subjectivivity-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"dataset:DDSC/twitter-sent",
"dataset:DDSC/europarl",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 846 | null | ---
tags:
- generated_from_trainer
model-index:
- name: For_inference
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. -->
# For_inference
This model is a fine-tuned version of [Lancelot53/BB20K_8epoch](https://huggingface.co/Lancelot53/BB20K_8epoch) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- 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_ratio: 0.1
- num_epochs: 8
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
alexandrainst/da-ned-base | [
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
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}
} | 25 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: CreativeEvolution/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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