modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
pabloac31/ppo-Huggy | pabloac31 | 2023-01-12T15:52:06Z | 11 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-01-12T15:52:00Z |
---
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: pabloac31/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
peter1133/ppo-LunarLander-v2 | peter1133 | 2023-01-12T15:40:53Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T15:40:29Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.24 +/- 21.06
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
...
```
|
aj555/unit-2-taxi-v3-1 | aj555 | 2023-01-12T15:37:19Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T15:37:14Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit-2-taxi-v3-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
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="aj555/unit-2-taxi-v3-1", 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"])
```
|
Ryandry1st/PPO-LunarLander-v2 | Ryandry1st | 2023-01-12T15:34:19Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T15:33:52Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-MlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 282.42 +/- 16.85
name: mean_reward
verified: false
---
# **PPO-MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-MlpPolicy** 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
...
```
|
bjpsantos/q-FrozenLake-v1-4x4-noSlippery | bjpsantos | 2023-01-12T15:32:06Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T15:31:55Z | ---
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="bjpsantos/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"])
```
|
SatishBethi/PPO-LunarLander-v2 | SatishBethi | 2023-01-12T15:00:35Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T12:17:05Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.80 +/- 11.79
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mike157/flan-t5-base-flant5-apple-support | mike157 | 2023-01-12T14:58:02Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:stackexchange_titlebody_best_voted_answer_jsonl",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-01-11T18:25:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- stackexchange_titlebody_best_voted_answer_jsonl
metrics:
- rouge
model-index:
- name: flan-t5-base-flant5-apple-support
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: stackexchange_titlebody_best_voted_answer_jsonl
type: stackexchange_titlebody_best_voted_answer_jsonl
config: apple
split: train[:2%]
args: apple
metrics:
- name: Rouge1
type: rouge
value: 12.4139
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-flant5-apple-support
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the stackexchange_titlebody_best_voted_answer_jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0475
- Rouge1: 12.4139
- Rouge2: 2.0562
- Rougel: 9.4938
- Rougelsum: 11.0524
- Gen Len: 18.9589
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 232 | 3.0886 | 12.844 | 2.1734 | 9.8971 | 11.3641 | 18.8876 |
| No log | 2.0 | 464 | 3.0639 | 12.2909 | 2.1209 | 9.4999 | 10.9458 | 18.9416 |
| 3.3185 | 3.0 | 696 | 3.0538 | 12.4154 | 2.0984 | 9.4989 | 11.0684 | 18.9492 |
| 3.3185 | 4.0 | 928 | 3.0489 | 12.7043 | 2.1969 | 9.7356 | 11.3629 | 18.9481 |
| 3.187 | 5.0 | 1160 | 3.0475 | 12.4139 | 2.0562 | 9.4938 | 11.0524 | 18.9589 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Prajvi/autotrain-yempp-2842183229 | Prajvi | 2023-01-12T14:57:24Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"dataset:Prajvi/autotrain-data-yempp",
"dataset:squad",
"dataset:squad_v2",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-01-11T22:07:34Z | ---
datasets:
- Prajvi/autotrain-data-yempp
- squad
- squad_v2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details> |
pabloac31/ppo-LunarLander-v2 | pabloac31 | 2023-01-12T14:57:11Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-10T12:07:03Z | ---
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: 277.97 +/- 14.61
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
...
```
|
SuburbanLion/ppo-SnowballTarget | SuburbanLion | 2023-01-12T14:50:22Z | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-01-12T14:50:14Z |
---
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: SuburbanLion/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sd-concepts-library/rim-illustration | sd-concepts-library | 2023-01-12T14:46:45Z | 0 | 2 | null | [
"license:mit",
"region:us"
] | null | 2023-01-12T14:46:35Z | ---
license: mit
---
### Rim_illustration on Stable Diffusion
This is the `<rimbot>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
wxcvbnw/gitlatt | wxcvbnw | 2023-01-12T14:43:10Z | 33 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-01-12T14:39:56Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### gitlatt Dreambooth model trained by wxcvbnw 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:
|
Jbot/q-Taxi-v3 | Jbot | 2023-01-12T14:35:38Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T14:34:39Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.63
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="Jbot/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"])
```
|
ProceduralTree/final-model | ProceduralTree | 2023-01-12T14:28:41Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-01-12T13:46:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: final-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# final-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9257
- Accuracy: 0.6021
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8464 | 1.0 | 4224 | 0.8643 | 0.6 |
| 0.7506 | 2.0 | 8448 | 0.9257 | 0.6021 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
massimowww/Reinforce-Pixelcopter-PLE-v0 | massimowww | 2023-01-12T14:11:23Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T14:11:17Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.40 +/- 24.30
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
|
GEOcite/DocumentSegmentationModel | GEOcite | 2023-01-12T14:02:11Z | 78 | 0 | transformers | [
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-12T13:38:23Z | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layout-xlm-geocite-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layout-xlm-geocite-v2
This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) 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: 4
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
kornwtp/ConGen-Multilingual-DistilBERT | kornwtp | 2023-01-12T13:55:19Z | 12 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-10-10T12:22:23Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-Multilingual-DistilBERT
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kornwtp/ConGen-Multilingual-DistilBERT')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#main-results---sts)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
kornwtp/ConGen-RoBERTa-base | kornwtp | 2023-01-12T13:53:16Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-10-10T12:14:21Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-RoBERTa-base
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kornwtp/ConGen-RoBERTa-base')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#main-results---sts)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
katanaml/layoutlm-finetuned-funsd | katanaml | 2023-01-12T13:50:31Z | 86 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-12T13:46:41Z | ---
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-finetuned-funsd
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. -->
# layoutlm-finetuned-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6806
- Answer: {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809}
- Header: {'precision': 0.36752136752136755, 'recall': 0.36134453781512604, 'f1': 0.3644067796610169, 'number': 119}
- Question: {'precision': 0.7866549604916594, 'recall': 0.8413145539906103, 'f1': 0.8130671506352087, 'number': 1065}
- Overall Precision: 0.7305
- Overall Recall: 0.7943
- Overall F1: 0.7611
- Overall Accuracy: 0.8085
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8023 | 1.0 | 10 | 1.6152 | {'precision': 0.009448818897637795, 'recall': 0.007416563658838072, 'f1': 0.008310249307479225, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.12998266897746968, 'recall': 0.07042253521126761, 'f1': 0.09135200974421437, 'number': 1065} | 0.0668 | 0.0406 | 0.0505 | 0.3276 |
| 1.4614 | 2.0 | 20 | 1.2547 | {'precision': 0.2211253701875617, 'recall': 0.276885043263288, 'f1': 0.24588364434687154, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.43353028064992616, 'recall': 0.5511737089201878, 'f1': 0.4853245142620918, 'number': 1065} | 0.3420 | 0.4069 | 0.3717 | 0.5926 |
| 1.0795 | 3.0 | 30 | 0.9150 | {'precision': 0.49837486457204766, 'recall': 0.5686032138442522, 'f1': 0.5311778290993071, 'number': 809} | {'precision': 0.04081632653061224, 'recall': 0.01680672268907563, 'f1': 0.023809523809523808, 'number': 119} | {'precision': 0.5953338696701529, 'recall': 0.6948356807511737, 'f1': 0.6412478336221837, 'number': 1065} | 0.5427 | 0.6031 | 0.5713 | 0.7145 |
| 0.8025 | 4.0 | 40 | 0.7686 | {'precision': 0.6056622851365016, 'recall': 0.7404202719406675, 'f1': 0.6662958843159066, 'number': 809} | {'precision': 0.10975609756097561, 'recall': 0.07563025210084033, 'f1': 0.08955223880597014, 'number': 119} | {'precision': 0.6737400530503979, 'recall': 0.7154929577464789, 'f1': 0.6939890710382515, 'number': 1065} | 0.6222 | 0.6874 | 0.6532 | 0.7506 |
| 0.6638 | 5.0 | 50 | 0.7034 | {'precision': 0.644535240040858, 'recall': 0.7799752781211372, 'f1': 0.7058165548098434, 'number': 809} | {'precision': 0.23711340206185566, 'recall': 0.19327731092436976, 'f1': 0.21296296296296294, 'number': 119} | {'precision': 0.7158992180712423, 'recall': 0.7737089201877935, 'f1': 0.7436823104693141, 'number': 1065} | 0.6637 | 0.7416 | 0.7005 | 0.7841 |
| 0.5567 | 6.0 | 60 | 0.6784 | {'precision': 0.6687435098650052, 'recall': 0.796044499381953, 'f1': 0.7268623024830699, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.2184873949579832, 'f1': 0.24761904761904763, 'number': 119} | {'precision': 0.7160392798690671, 'recall': 0.8215962441314554, 'f1': 0.765194578049847, 'number': 1065} | 0.6788 | 0.7752 | 0.7238 | 0.7903 |
| 0.4925 | 7.0 | 70 | 0.6815 | {'precision': 0.6839779005524862, 'recall': 0.765142150803461, 'f1': 0.7222870478413069, 'number': 809} | {'precision': 0.2894736842105263, 'recall': 0.2773109243697479, 'f1': 0.2832618025751073, 'number': 119} | {'precision': 0.7233169129720853, 'recall': 0.8272300469483568, 'f1': 0.7717915024091108, 'number': 1065} | 0.6853 | 0.7692 | 0.7248 | 0.7913 |
| 0.4494 | 8.0 | 80 | 0.6765 | {'precision': 0.6962305986696231, 'recall': 0.7762669962917181, 'f1': 0.734073641145529, 'number': 809} | {'precision': 0.28, 'recall': 0.29411764705882354, 'f1': 0.28688524590163933, 'number': 119} | {'precision': 0.7360066833751044, 'recall': 0.8272300469483568, 'f1': 0.7789566755083996, 'number': 1065} | 0.6942 | 0.7747 | 0.7323 | 0.8004 |
| 0.3986 | 9.0 | 90 | 0.6587 | {'precision': 0.7077777777777777, 'recall': 0.7873918417799752, 'f1': 0.7454651843183148, 'number': 809} | {'precision': 0.3274336283185841, 'recall': 0.31092436974789917, 'f1': 0.3189655172413793, 'number': 119} | {'precision': 0.7487266553480475, 'recall': 0.828169014084507, 'f1': 0.7864467231386535, 'number': 1065} | 0.7102 | 0.7807 | 0.7438 | 0.8019 |
| 0.3597 | 10.0 | 100 | 0.6607 | {'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809} | {'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119} | {'precision': 0.7600685518423308, 'recall': 0.8328638497652582, 'f1': 0.7948028673835125, 'number': 1065} | 0.7144 | 0.7868 | 0.7488 | 0.8048 |
| 0.3266 | 11.0 | 110 | 0.6751 | {'precision': 0.7050279329608938, 'recall': 0.7799752781211372, 'f1': 0.7406103286384977, 'number': 809} | {'precision': 0.3684210526315789, 'recall': 0.35294117647058826, 'f1': 0.3605150214592275, 'number': 119} | {'precision': 0.7711571675302246, 'recall': 0.8384976525821596, 'f1': 0.8034188034188035, 'number': 1065} | 0.7227 | 0.7858 | 0.7529 | 0.8056 |
| 0.3103 | 12.0 | 120 | 0.6799 | {'precision': 0.7047413793103449, 'recall': 0.8084054388133498, 'f1': 0.7530224525043179, 'number': 809} | {'precision': 0.3474576271186441, 'recall': 0.3445378151260504, 'f1': 0.3459915611814346, 'number': 119} | {'precision': 0.7799295774647887, 'recall': 0.831924882629108, 'f1': 0.8050885960926851, 'number': 1065} | 0.7246 | 0.7933 | 0.7574 | 0.8034 |
| 0.2893 | 13.0 | 130 | 0.6773 | {'precision': 0.7191392978482446, 'recall': 0.7849196538936959, 'f1': 0.7505910165484633, 'number': 809} | {'precision': 0.35833333333333334, 'recall': 0.36134453781512604, 'f1': 0.35983263598326365, 'number': 119} | {'precision': 0.7861524978089395, 'recall': 0.8422535211267606, 'f1': 0.8132366273798729, 'number': 1065} | 0.7346 | 0.7903 | 0.7614 | 0.8050 |
| 0.2743 | 14.0 | 140 | 0.6788 | {'precision': 0.7063318777292577, 'recall': 0.799752781211372, 'f1': 0.750144927536232, 'number': 809} | {'precision': 0.37168141592920356, 'recall': 0.35294117647058826, 'f1': 0.36206896551724144, 'number': 119} | {'precision': 0.7879858657243817, 'recall': 0.8375586854460094, 'f1': 0.812016385980883, 'number': 1065} | 0.7316 | 0.7933 | 0.7612 | 0.8085 |
| 0.2756 | 15.0 | 150 | 0.6806 | {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809} | {'precision': 0.36752136752136755, 'recall': 0.36134453781512604, 'f1': 0.3644067796610169, 'number': 119} | {'precision': 0.7866549604916594, 'recall': 0.8413145539906103, 'f1': 0.8130671506352087, 'number': 1065} | 0.7305 | 0.7943 | 0.7611 | 0.8085 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
kornwtp/ConGen-TinyBERT-L6 | kornwtp | 2023-01-12T13:41:45Z | 11 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-10-10T12:06:31Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-TinyBERT-L6
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kornwtp/ConGen-TinyBERT-L6')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#main-results---sts)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
kornwtp/ConGen-BERT-Small | kornwtp | 2023-01-12T13:40:36Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-10-10T12:02:46Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-BERT-Small
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 512 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kornwtp/ConGen-BERT-Small')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#main-results---sts)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
kornwtp/ConGen-BERT-Tiny | kornwtp | 2023-01-12T13:38:21Z | 79 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-10-10T11:44:55Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-BERT-Tiny
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 128 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kornwtp/ConGen-BERT-Tiny')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#main-results---sts)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
kornwtp/ConGen-TinyBERT-L4 | kornwtp | 2023-01-12T13:34:22Z | 3 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-10-10T11:56:29Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-TinyBERT-L4
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 312 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kornwtp/ConGen-TinyBERT-L4')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#main-results---sts)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
kornwtp/ConGen-MiniLM-L3 | kornwtp | 2023-01-12T13:28:01Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-10-10T11:58:39Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-MiniLM-L3
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 384 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kornwtp/ConGen-MiniLM-L3')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#main-results---sts)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
Brhnglc/ppo-LunarLander-v2 | Brhnglc | 2023-01-12T13:27:07Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T13:20:31Z | ---
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: 245.59 +/- 16.87
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
...
```
|
hugogolastico/finetuning-sentiment-model-3000-samples | hugogolastico | 2023-01-12T13:09:30Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-01-12T12:58:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3185
- Accuracy: 0.8667
- F1: 0.8675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
NathanaelM/ppo-SnowballTarget | NathanaelM | 2023-01-12T13:03:15Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-01-12T13:02:54Z |
---
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: NathanaelM/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bakhuisdennis/donut-base-mysterybox | bakhuisdennis | 2023-01-12T13:02:47Z | 45 | 0 | transformers | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2023-01-12T12:51:51Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: donut-base-mysterybox
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-mysterybox
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.2
|
kurohige/pixelcopter-v5 | kurohige | 2023-01-12T13:01:35Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T13:01:28Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter-v5
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.30 +/- 28.60
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
|
cleanrl/Zaxxon-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1 | cleanrl | 2023-01-12T12:48:29Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Zaxxon-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T12:48:26Z | ---
tags:
- Zaxxon-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Zaxxon-v5
type: Zaxxon-v5
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Zaxxon-v5**
This is a trained model of a PPO agent playing Zaxxon-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id Zaxxon-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id Zaxxon-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Zaxxon-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
FBM/ppo-SnowballTarget2 | FBM | 2023-01-12T12:30:08Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-01-12T10:30:42Z |
---
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: FBM/ppo-SnowballTarget2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
orenk/Reinforce-pixelcopter-v1 | orenk | 2023-01-12T12:29:23Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T12:29:18Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 29.00 +/- 31.12
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
|
czesty/ea-setfit-v1-classifier | czesty | 2023-01-12T12:17:06Z | 6 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-01-12T12:16:57Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ksnugroho/feelin-base-uncased | ksnugroho | 2023-01-12T11:53:43Z | 115 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-12T10:25:23Z | ---
license: mit
language:
- id
pipeline_tag: fill-mask
widget:
- text: Adik sedang <mask> sepak bola di lapangan.
---
# Model Descripstion
FEEL-IN is a state-of-the-art language model for Indonesian based on the RoBERTa model. The pre-trained model is trained using a masked language modeling (MLM) objective.
# How to Use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='ksnugroho/feelin-base-uncased')
unmasker("Adik sedang <mask> sepak bola <mask> lapangan")
```
Load tokenizer:
```python
from transformers import RobertaTokenizer
tokenizer = RobertaTokenizer.from_pretrained('ksnugroho/feelin-base-uncased')
```
Load model in PyTorch:
```python
from transformers import RobertaModel
model = RobertaModel.from_pretrained('ksnugroho/feelin-base-uncased')
```
and in TensorFlow:
```python
from transformers import TFRobertaModel
model = TFRobertaModel.from_pretrained('ksnugroho/feelin-base-uncased')
```
tested with transformers==4.25.1
# Authors
FEEL-IN was trained and evaluated by Kuncahyo Setyo Nugroho, Fitra Abdurrachman Bachtiar, Wayan Firdaus Mahmudy<br>
Intelligent Systems Lab, Faculty of Computer Science, Brawijaya University, Indonesia |
jz01/ppo-SnowballTarget | jz01 | 2023-01-12T11:33:48Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-01-12T11:33:42Z |
---
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: jz01/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
marccgrau/whisper-small-allSNR-v4 | marccgrau | 2023-01-12T11:26:37Z | 83 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"sbb-asr",
"generated_from_trainer",
"de",
"dataset:marccgrau/sbbdata_allSNR",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-01-12T08:04:57Z | ---
language:
- de
license: apache-2.0
tags:
- sbb-asr
- generated_from_trainer
datasets:
- marccgrau/sbbdata_allSNR
metrics:
- wer
model-index:
- name: Whisper Small German SBB all SNR - v4
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: SBB Dataset 05.01.2023
type: marccgrau/sbbdata_allSNR
args: 'config: German, split: train, test, val'
metrics:
- name: Wer
type: wer
value: 0.02219403931515536
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small German SBB all SNR - v4
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0287
- Wer: 0.0222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 64
- 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: 100
- training_steps: 700
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.6894 | 0.71 | 100 | 0.4702 | 0.4661 |
| 0.1896 | 1.42 | 200 | 0.0322 | 0.0241 |
| 0.0297 | 2.13 | 300 | 0.0349 | 0.0228 |
| 0.0181 | 2.84 | 400 | 0.0250 | 0.0209 |
| 0.0154 | 3.55 | 500 | 0.0298 | 0.0209 |
| 0.0112 | 4.26 | 600 | 0.0327 | 0.0222 |
| 0.009 | 4.96 | 700 | 0.0287 | 0.0222 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.12.1
|
massimowww/Reinforce-CartPole-v1 | massimowww | 2023-01-12T11:20:41Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T11:20:29Z | ---
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
|
apetrenko/sample_factory_brax_halfcheetah | apetrenko | 2023-01-12T11:00:37Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T11:00:28Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: halfcheetah
type: halfcheetah
metrics:
- type: mean_reward
value: 22298.35 +/- 1882.48
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **halfcheetah** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r apetrenko/sample_factory_brax_halfcheetah
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m sf_examples.brax.enjoy_brax --algo=APPO --env=halfcheetah --train_dir=./train_dir --experiment=sample_factory_brax_halfcheetah
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m sf_examples.brax.train_brax --algo=APPO --env=halfcheetah --train_dir=./train_dir --experiment=sample_factory_brax_halfcheetah --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
massimowww/ML-Agents-SnowballTarget | massimowww | 2023-01-12T10:45:29Z | 12 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2023-01-12T10:44:57Z |
---
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: massimowww/ML-Agents-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
4mosot/Reinforce-Pixelcopter-PLE-v0 | 4mosot | 2023-01-12T10:39:27Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-09T20:27:09Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 116.40 +/- 106.83
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
|
LowGI/my_asr_model_2 | LowGI | 2023-01-12T10:37:08Z | 162 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:minds14",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-01-12T09:54:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- wer
model-index:
- name: my_asr_model_2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: minds14
type: minds14
config: en-US
split: train[:100]
args: en-US
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_asr_model_2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1785
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.0949 | 20.0 | 100 | 3.1923 | 1.0 |
| 3.0836 | 40.0 | 200 | 3.1769 | 1.0 |
| 3.0539 | 60.0 | 300 | 3.1766 | 1.0 |
| 3.0687 | 80.0 | 400 | 3.1853 | 1.0 |
| 3.0649 | 100.0 | 500 | 3.1785 | 1.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Dharkelf/ppo-PyramidsTraining | Dharkelf | 2023-01-12T10:26:32Z | 3 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2023-01-12T10:26:24Z |
---
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: Dharkelf/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
atorre/dqn-SpaceInvadersNoFrameskip-v4 | atorre | 2023-01-12T10:23:01Z | 8 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-11T15:52:24Z | ---
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: 456.00 +/- 81.36
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 atorre -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 atorre -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 atorre
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
NYTK/named-entity-recognition-nerkor-hubert-hungarian | NYTK | 2023-01-12T10:21:56Z | 108,495 | 3 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-04-01T08:37:36Z | ---
language:
- hu
tags:
- token-classification
license: apache-2.0
metrics:
- f1
widget:
- text: >-
A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál, azonban a
Németországból érkezett Kovács Péter nehezen boldogul a beilleszkedéssel.
---
# Hungarian Named Entity Recognition Model with huBERT
For further models, scripts and details, see [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: SZTAKI-HLT/hubert-base-cc
- Finetuned on [NYTK-NerKor](https://github.com/nytud/NYTK-NerKor)
- NE categories are: PER, LOC, MISC, ORG
## Limitations
- max_seq_length = 128
## Results
F-score: **90.18%**
## Usage with pipeline
```python
from transformers import pipeline
ner = pipeline(task="ner", model="NYTK/named-entity-recognition-nerkor-hubert-hungarian")
input_text = "A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál, azonban a Németországból érkezett Kovács Péter nehezen boldogul a beilleszkedéssel."
print(ner(input_text, aggregation_strategy="simple"))
```
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {yang-language-models,
title = {Training language models with low resources: RoBERTa, BART and ELECTRA experimental models for Hungarian},
booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)},
year = {2021},
publisher = {IEEE},
address = {Online},
author = {Yang, Zijian Győző and Váradi, Tamás},
pages = {279--285}
}
``` |
gulermuslim/xlm-roberta-base-finetuned-panx-de | gulermuslim | 2023-01-12T10:21:02Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-12-17T22:54:22Z | ---
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
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 4.11.3
- Pytorch 1.13.1+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
RegisGraptin/Reinforce-Pixelcopter-PLE-v0 | RegisGraptin | 2023-01-12T10:18:41Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T10:18:35Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.90 +/- 23.20
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
|
UKP-SQuARE/roberta-base-pf-boolq-onnx | UKP-SQuARE | 2023-01-12T10:09:08Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"onnx",
"roberta",
"text-classification",
"adapterhub:qa/boolq",
"en",
"dataset:boolq",
"arxiv:2104.08247",
"region:us"
] | text-classification | 2023-01-02T23:26:41Z | ---
inference: false
tags:
- onnx
- text-classification
- roberta
- adapterhub:qa/boolq
- adapter-transformers
datasets:
- boolq
language:
- en
---
# ONNX export of Adapter `AdapterHub/roberta-base-pf-boolq` for roberta-base
## Conversion of [AdapterHub/roberta-base-pf-boolq](https://huggingface.co/AdapterHub/roberta-base-pf-boolq) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-boolq-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'English orthography typically represents vowel sounds with the five conventional vowel letters ⟨a, e, i, o, u⟩, as well as ⟨y⟩, which may also be a consonant depending on context. However, outside of abbreviations, there are a handful of words in English that do not have vowels, either because the vowel sounds are not written with vowel letters or because the words themselves are pronounced without vowel sounds'.
question = 'can there be a word without a vowel'
tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-boolq-onnx')
inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')
inputs = {key: np.array(inputs[key], dtype=np.int64) for key in inputs}
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` |
UKP-SQuARE/bert-base-uncased-pf-boolq-onnx | UKP-SQuARE | 2023-01-12T10:08:08Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"onnx",
"bert",
"text-classification",
"adapterhub:qa/boolq",
"en",
"dataset:boolq",
"arxiv:2104.08247",
"region:us"
] | text-classification | 2023-01-02T23:14:49Z | ---
inference: false
tags:
- onnx
- text-classification
- bert
- adapterhub:qa/boolq
- adapter-transformers
datasets:
- boolq
language:
- en
---
# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-boolq` for bert-base-uncased
## Conversion of [AdapterHub/bert-base-uncased-pf-boolq](https://huggingface.co/AdapterHub/bert-base-uncased-pf-boolq) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-boolq-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'English orthography typically represents vowel sounds with the five conventional vowel letters ⟨a, e, i, o, u⟩, as well as ⟨y⟩, which may also be a consonant depending on context. However, outside of abbreviations, there are a handful of words in English that do not have vowels, either because the vowel sounds are not written with vowel letters or because the words themselves are pronounced without vowel sounds'.
question = 'can there be a word without a vowel'
tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-boolq-onnx')
inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')
inputs = {key: np.array(inputs[key], dtype=np.int64) for key in inputs}
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` |
arnonl/Reinforce-CartPole-v1 | arnonl | 2023-01-12T10:04:48Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T09:51:26Z | ---
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
|
Ryukijano/q-taxiv3_first | Ryukijano | 2023-01-12T10:01:33Z | 0 | 1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T10:01:23Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxiv3_first
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.42 +/- 2.67
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="Ryukijano/q-taxiv3_first", 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"])
```
|
nachshonc/RL-unit4-reinforce | nachshonc | 2023-01-12T09:46:38Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T09:46:24Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: RL-unit4-reinforce
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 451.30 +/- 146.10
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
|
yyfairstein/Reinforce-cartpole | yyfairstein | 2023-01-12T09:35:09Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T09:34:55Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
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
|
RisiPisi/ppo-Huggy | RisiPisi | 2023-01-12T09:32:22Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-01-12T09:32:13Z |
---
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: RisiPisi/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
iblub/dqn-SpaceInvadersNoFrameskip-v4 | iblub | 2023-01-12T09:05:19Z | 6 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T02:04:25Z | ---
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: 1415.00 +/- 538.76
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 iblub -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 iblub -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 iblub
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 1000000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Rimiru/tech-recog | Rimiru | 2023-01-12T08:54:07Z | 225 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-01-12T08:53:51Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: tech-recog
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7476635575294495
---
# tech-recog
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Desktop Computer
#### Laptop
#### Smart Phone
#### Smart Watch
#### Tablet
 |
espnet/kmiyazaki_librispeech_asr_s4_decoder | espnet | 2023-01-12T08:52:20Z | 11 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2023-01-05T09:17:56Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/kmiyazaki_librispeech_asr_s4_decoder`
This model was trained by kmiyazaki using librspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 617189d2d7e060bbcf670ab54b88776333b5137e
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/kmiyazaki_librispeech_asr_s4_decoder
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Dec 29 11:58:25 UTC 2022`
- python version: `3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0]`
- espnet version: `espnet 202211`
- pytorch version: `pytorch 1.12.0`
- Git hash: `617189d2d7e060bbcf670ab54b88776333b5137e`
- Commit date: `Mon Dec 26 18:01:58 2022 +0900`
## asr_train_asr_s4_decoder_raw_en_bpe5000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam60_ctc0.3/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.9|
|beam60_ctc0.3/dev_other|2864|50948|95.5|4.2|0.4|0.5|5.0|42.2|
|beam60_ctc0.3/test_clean|2620|52576|98.0|1.8|0.2|0.3|2.3|27.2|
|beam60_ctc0.3/test_other|2939|52343|95.6|4.0|0.4|0.6|5.0|44.4|
|beam60_ctc0.3_lm0.6/dev_clean|2703|54402|98.5|1.3|0.2|0.2|1.7|23.0|
|beam60_ctc0.3_lm0.6/dev_other|2864|50948|96.4|3.3|0.3|0.4|4.0|36.6|
|beam60_ctc0.3_lm0.6/test_clean|2620|52576|98.3|1.5|0.2|0.2|1.9|23.7|
|beam60_ctc0.3_lm0.6/test_other|2939|52343|96.3|3.3|0.4|0.4|4.1|39.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam60_ctc0.3/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.6|25.9|
|beam60_ctc0.3/dev_other|2864|265951|98.4|1.0|0.6|0.5|2.1|42.2|
|beam60_ctc0.3/test_clean|2620|281530|99.5|0.3|0.2|0.2|0.7|27.2|
|beam60_ctc0.3/test_other|2939|272758|98.6|0.8|0.6|0.6|2.0|44.4|
|beam60_ctc0.3_lm0.6/dev_clean|2703|288456|99.6|0.2|0.2|0.2|0.6|23.0|
|beam60_ctc0.3_lm0.6/dev_other|2864|265951|98.6|0.8|0.5|0.5|1.8|36.6|
|beam60_ctc0.3_lm0.6/test_clean|2620|281530|99.6|0.2|0.2|0.2|0.6|23.7|
|beam60_ctc0.3_lm0.6/test_other|2939|272758|98.8|0.7|0.6|0.5|1.7|39.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam60_ctc0.3/dev_clean|2703|68010|97.8|1.6|0.6|0.4|2.5|25.9|
|beam60_ctc0.3/dev_other|2864|63110|94.5|4.3|1.3|0.9|6.4|42.2|
|beam60_ctc0.3/test_clean|2620|65818|97.5|1.7|0.7|0.4|2.8|27.2|
|beam60_ctc0.3/test_other|2939|65101|94.6|3.9|1.5|0.8|6.2|44.4|
|beam60_ctc0.3_lm0.6/dev_clean|2703|68010|98.1|1.4|0.5|0.4|2.2|23.0|
|beam60_ctc0.3_lm0.6/dev_other|2864|63110|95.4|3.5|1.1|0.9|5.5|36.6|
|beam60_ctc0.3_lm0.6/test_clean|2620|65818|98.0|1.4|0.6|0.4|2.4|23.7|
|beam60_ctc0.3_lm0.6/test_other|2939|65101|95.5|3.2|1.3|0.8|5.4|39.5|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_s4_decoder.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_s4_decoder_raw_en_bpe5000_sp
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 46029
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 60
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: true
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 35000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_960_sp/wav.scp
- speech
- sound
- - dump/raw/train_960_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- sound
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: true
optim: adamw
optim_conf:
lr: 0.0025
weight_decay: 0.01
scheduler: warmuplr
scheduler_conf:
warmup_steps: 40000
token_list:
- <blank>
- <unk>
- ▁THE
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- ▁M
- ▁HOWEVER
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- CO
- ▁STOOD
- ID
- ▁KIND
- ▁HUNDRED
- AS
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- ▁BOY
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- ▁HO
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- ▁MO
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- ▁FOUR
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- ▁TILL
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- RU
- TE
- ▁FACT
- ▁HELP
- ▁NEXT
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- ▁DOES
- ▁FRIEND
- ▁LAY
- ANCE
- ▁POWER
- ▁BROUGHT
- VER
- ▁FIRE
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- ▁COUNTRY
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- ▁WORD
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- ▁TEAR
- ▁NEIGHBOUR
- ▁CONSTITUTION
- ▁INTELLIGENCE
- ▁PROFESSION
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- RIDGE
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- ELLA
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- J
- ▁ORGAN
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- ▁CONCERN
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- ▁UTTERLY
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- PHA
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- ▁RECOVERED
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- ▁ENORMOUS
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- BURY
- TTERED
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- ▁INTIMATE
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- ▁APPOINTED
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- ▁CREATED
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- ▁LIGHTNING
- ▁BORNE
- ▁TOTAL
- ▁DISPOSED
- ▁RIFLE
- ▁POLLY
- ▁GOAT
- ▁BACKWARD
- ▁VIRGINIA
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- CLOSE
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- ▁REJOICE
- ▁PILLOW
- ▁EXPECTATION
- ▁POETRY
- ▁HOPELESS
- ▁PERISH
- ▁PHILOSOPHY
- ▁WHISTLE
- ▁BERNARD
- ▁LAMENT
- ▁IMPROVE
- ▁SUP
- ▁PERPLEX
- ▁FOUNTAIN
- ▁LEAGUE
- ▁DESPISE
- ▁IGNORANCE
- ▁REFERENCE
- ▁DUCK
- ▁GROVE
- ▁PURSE
- ▁PARTNER
- ▁PROPHET
- ▁SHIVER
- ▁NEIGHBOURHOOD
- ▁REPRESENTATIVE
- SAIL
- ▁WIP
- ▁ACQUIRED
- ▁CHIMNEY
- ▁DOCTRINE
- ▁MAXIM
- ▁ANGLE
- ▁MAJORITY
- ▁AUTUMN
- ▁CONFUSED
- ▁CRISTO
- ▁ACHIEVE
- ▁DISGUISE
- ▁REDUCED
- ▁EARLIER
- ▁THEATRE
- ▁DECIDE
- MINATED
- OLOGICAL
- ▁OCCUPATION
- ▁VIGOROUS
- ▁CONTINENT
- ▁DECLINE
- ▁COMMUNITY
- ▁MOTIONLESS
- ▁HATRED
- ▁COMMUNICATION
- ▁BOWL
- ▁COMMENT
- ▁APPROVE
- ▁CEREMONY
- ▁CRIMINAL
- ▁SCIENTIFIC
- ▁DUCHESS
- ▁VIVID
- ▁SHIFT
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- ▁DAMP
- ▁JOHNSON
- ▁SLENDER
- ▁CONTRAST
- ▁AMUSEMENT
- ▁PLOT
- ▁LYN
- ▁ASSOCIATION
- ▁SNATCH
- ▁UNCERTAIN
- ▁PRESSURE
- ▁PERCH
- ▁APPLY
- ▁PLANET
- ▁NOTWITHSTANDING
- ▁SWUNG
- ▁STIRRED
- ▁ATTENDANT
- ▁ENJOYMENT
- ▁WORRY
- ▁ALBERT
- ▁NAKED
- ▁TALENT
- ▁MARIAN
- ▁REFORM
- ▁DELIBERATE
- ▁INTELLIGENT
- ▁SENSITIVE
- ▁YONDER
- ▁PUPIL
- ▁FRIGHTFUL
- ▁DOUBTFUL
- ▁STANDARD
- ▁MAGISTRATE
- ▁SHEPHERD
- ▁STOMACH
- ▁DEPOSIT
- ▁RENEW
- ▁HEDGE
- ▁FRANCS
- ▁POSSIBILITY
- ▁RESEMBLE
- ▁FATIGUE
- ▁PORTRAIT
- ▁FAVORITE
- ▁CREAM
- ▁BURG
- ▁SECRETARY
- ▁DIVERS
- ▁ACTIVITY
- ▁SPECULAT
- ▁HUMOUR
- ▁FITTED
- ▁EXTERNAL
- ▁CETERA
- ▁WRAPPED
- ▁WHIT
- ▁FRED
- ▁EXAMINATION
- ▁LODGING
- ▁OWING
- ▁JAW
- ▁CROW
- ▁BALANCE
- ▁PUFF
- ▁TENDERNESS
- ▁PORTHOS
- ▁ANCHOR
- ▁INTERRUPT
- ▁NECESSARILY
- ▁PERPETUAL
- ▁AGONY
- ▁POPE
- ▁SCHOLAR
- ▁SCOTLAND
- ▁SUPPRESS
- ▁WRATH
- ▁WRECK
- ▁EXCEED
- ▁PERFECTION
- ▁INDIA
- ▁TRADITION
- ▁SECTION
- ▁EASTERN
- ▁DOORWAY
- ▁WIVES
- ▁CONVENTION
- ▁ANNOUNC
- ▁EGYPT
- ▁CONTRADICT
- ▁SCRATCH
- ▁CENTRAL
- ▁GLOVE
- ▁WAX
- ▁PREPARE
- ▁ACCOMPANY
- ▁INCREASING
- ▁LIBERAL
- ▁RAISING
- ▁ORANGE
- ▁SHOE
- ▁ATTRIBUTE
- ▁LITERATURE
- ▁PUZZLED
- ▁WITHDRAW
- ▁WHITHER
- ▁HAWK
- ▁MOONLIGHT
- ▁EXAMINE
- ▁HAPPILY
- ▁PRECEDE
- ▁DETECTIVE
- ▁INCHES
- ▁SOLITARY
- ▁DUTCH
- ▁NAPOLEON
- ▁UNEASY
- ▁CARDINAL
- ▁BLEW
- ▁FOWL
- ▁DECORAT
- ▁CHILDHOOD
- ▁TORMENT
- ▁LOSING
- ▁PERMISSION
- ▁BLANK
- ▁UPSTAIRS
- ▁CAPACITY
- ▁TRIFLE
- ▁FOLLY
- ▁RECOGNIZE
- ▁REMOVE
- ▁VENGEANCE
- ▁ENTERPRISE
- ▁BEDROOM
- ▁ANYHOW
- ▁INQUIRY
- ▁ASHES
- ▁DRAG
- ▁HUSH
- ▁AWKWARD
- ▁SATURDAY
- ▁GENUINE
- ▁SURVIV
- ▁SKIRT
- ▁AFFECTIONATE
- ▁TANG
- ▁MUTUAL
- ▁DISPUTE
- ▁EAGLE
- ▁INCOME
- ▁BIND
- ▁FAME
- ▁IMPROVEMENT
- ROVING
- ▁DIFFER
- ▁AWOKE
- ▁SLEEVE
- ▁SOLITUDE
- ▁FAVOURITE
- JI
- ▁DETECT
- ▁COMPREHEND
- ▁PREPARING
- ▁SERPENT
- ▁SUMMIT
- ▁KNOT
- ▁KNIT
- ▁COPY
- ▁STOPPING
- ▁FADED
- ▁HIDEOUS
- ▁JULIE
- STEAD
- ▁SHINE
- ▁CONFLICT
- ▁PROPOSITION
- ▁REFUGE
- ▁GALLERY
- ▁BUNDLE
- ▁AXE
- ▁SLAVERY
- ▁MASK
- ▁ALYOSHA
- ▁LADDER
- ▁DEPARTMENT
- ▁DISCHARGE
- ▁DEPRESS
- ▁GALLOP
- ▁SCARLET
- ▁KITTY
- ▁RECEIVING
- ▁SURRENDER
- ▁SUSTAIN
- ▁TWILIGHT
- ▁CONGRESS
- ▁IRELAND
- ▁FUNNY
- ▁LEND
- ▁CONSTITUTE
- ▁FUNERAL
- ▁CRYSTAL
- ▁SPAIN
- ▁EXCEEDINGLY
- ▁DAMN
- ▁COMMUN
- ▁CIVILIZATION
- ▁PREJUDICE
- ▁PORCH
- ▁ASSISTANT
- ▁INDUSTRY
- ▁TUMBLE
- ▁DEFENCE
- ▁HITHER
- ▁SMOT
- ▁COLONI
- ▁AMAZEMENT
- ▁MARGUERITE
- ▁MIRACLE
- ▁INHERIT
- ▁BEGGAR
- ▁ENVELOPE
- ▁INDIGNATION
- ▁NATASHA
- ▁PROPOSAL
- ▁FRAGMENT
- ▁ROUSED
- ▁ROAST
- ENCIES
- ▁COMMENCED
- ▁RESOURCE
- ▁POPULATION
- ▁QUOTH
- ▁PURSUE
- ▁EDUCAT
- ▁AFFLICT
- ▁CONTACT
- ▁CRIMSON
- ▁DIVISION
- ▁DISORDER
- ▁COPPER
- ▁SOLICIT
- ▁MODERATE
- ▁DRUM
- ▁SWIM
- ▁SALUTE
- ▁ASSUME
- ▁MUSCLE
- ▁OVERWHELM
- ▁SHAKESPEARE
- ▁STRUGGLING
- ▁TRANQUIL
- ▁CHICKEN
- ▁TREAD
- ▁CLAW
- ▁BIBLE
- ▁RIDGE
- ▁THREAT
- ▁VELVET
- ▁EXPOSED
- ▁IDIOT
- ▁BARREL
- ▁PENNY
- ▁TEMPTATION
- ▁DANGLARS
- ▁CENTURIES
- ▁DISTRIBUT
- ▁REJECT
- ▁RETORTED
- ▁CONCENTRAT
- ▁CORDIAL
- ▁MOTOR
- ▁CANNON
- KEEP
- ▁WRETCH
- ▁ASSURANCE
- ▁THIEF
- ▁SURVEY
- ▁VITAL
- ▁RAILWAY
- ▁JACKSON
- ▁CRASH
- ▁GROWL
- ▁COMBAT
- ▁RECOLLECTION
- ▁SECURITY
- ▁JACOB
- ▁CLUTCH
- ▁BLANKET
- ▁NANCY
- ▁CELLAR
- ▁CONVENIENT
- ▁INDIGNANT
- ▁COARSE
- ▁WORM
- ▁SCREEN
- ▁TRANSPORT
- ▁BULLET
- ▁APPRECIATE
- ▁DEVOTION
- ▁INVISIBLE
- ▁DRIED
- ▁MIXTURE
- ▁CANDID
- ▁PERFORMANCE
- ▁RIPE
- ▁EXQUISITE
- ▁BARGAIN
- ▁TOBACCO
- ▁LOYAL
- ▁MOULD
- ▁ATTENTIVE
- ▁DOROTHY
- ▁BRUTE
- ▁ESTABLISHMENT
- ▁ABILITY
- ▁INHABIT
- ▁OBSCURE
- ▁BORROW
- ▁ESSENCE
- ▁DISMAY
- ▁FLEE
- ▁BLADE
- ▁PLUCK
- ▁COFFIN
- ▁SUNSET
- ▁STEPHEN
- ▁ECONOMIC
- ▁HOLIDAY
- ▁MECHANICAL
- ▁COTTON
- ▁AWAKENED
- ▁SEIZE
- ▁RIDICULOUS
- ▁SANCHO
- ▁HESITATION
- ▁CORPSE
- ▁SAVING
- HOLD
- FOOT
- ▁ELDEST
- ▁DESPITE
- ▁EDITH
- ▁CHERISH
- ▁RESISTANCE
- ▁WILSON
- ▁ARGUE
- ▁INQUIRE
- ▁APPREHENSION
- ▁AVENUE
- ▁DRAKE
- ▁PROPOSE
- HURST
- ▁INFERIOR
- ▁STAIRCASE
- ▁WHEREFORE
- ▁CARLYLE
- ▁COUCH
- ▁ROUTE
- ▁POLITICS
- ▁TOMORROW
- ▁THRONG
- ▁NAUGHT
- ▁SUNLIGHT
- ▁INDIFFERENCE
- ▁OBEDIENCE
- ▁RECEPTION
- ▁VEGETABLE
- ▁IMPERFECT
- ▁RESIDENCE
- ▁TURKEY
- ▁VIOLET
- ▁SARAH
- ▁ALTAR
- ▁GRIEVE
- ▁JERK
- ▁ENSU
- ▁MAGICIAN
- ▁BLOSSOM
- ▁LANTERN
- ▁RESOLUTE
- ▁THOUGHTFULLY
- ▁FORTNIGHT
- ▁TRUMPET
- ▁VALJEAN
- ▁UNWILLING
- ▁LECTURE
- ▁WHEREUPON
- ▁HOLLAND
- ▁CHANGING
- ▁CREEK
- ▁SLICE
- ▁NORMAL
- ▁ANNIE
- ▁ACCENT
- ▁FREDERICK
- ▁DISAGREEABLE
- ▁RUBBED
- ▁DUMB
- ▁ESTABLISH
- ▁IMPORT
- ▁AFFIRM
- ▁MATTHEW
- ▁BRISK
- ▁CONVERT
- ▁BENDING
- ▁IVAN
- ▁MADEMOISELLE
- ▁MICHAEL
- ▁EASIER
- ▁JONES
- ▁FACING
- ▁EXCELLENCY
- ▁LITERARY
- ▁GOSSIP
- ▁DEVOUR
- ▁STAGGER
- ▁PENCIL
- ▁AVERAGE
- ▁HAMMER
- ▁TRIUMPHANT
- ▁PREFERRED
- ▁APPLICATION
- ▁OCCUPY
- ▁AUTHORITIES
- BURN
- ▁ASCERTAIN
- ▁CORRIDOR
- ▁DELICIOUS
- ▁PRACTISE
- ▁UNIVERSE
- ▁SHILLING
- ▁CONTEST
- ▁ASHORE
- ▁COMMIT
- ▁ADMINISTRATION
- ▁STUDIED
- ▁RIGID
- ▁ADORN
- ▁ELSEWHERE
- ▁INNOCENCE
- ▁JOURNAL
- ▁LANDSCAPE
- ▁TELEGRAPH
- ▁ANGRILY
- ▁CAMPAIGN
- ▁UNJUST
- ▁CHALLENGE
- ▁TORRENT
- ▁RELATE
- ▁ASSEMBLED
- ▁IMPRESSED
- ▁CANOE
- ▁CONCLUD
- ▁QUIXOTE
- ▁SATISFACTORY
- ▁NIECE
- ▁DEAF
- ▁RAFT
- ▁JIMMY
- ▁GLID
- ▁REGULAT
- ▁CHATTER
- ▁GLACIER
- ▁ENVY
- ▁STATUE
- ▁BOSTON
- ▁RICHMOND
- ▁DENIED
- ▁FANNY
- ▁SOLOMON
- ▁VULGAR
- ▁STALK
- ▁REPLACE
- ▁SPOON
- ▁BASIN
- ▁FEATURE
- ▁CONVICT
- ▁ARCHITECT
- ▁ADMIRAL
- ▁RIBBON
- ▁PERMANENT
- ▁APRIL
- ▁JOLLY
- ▁NEIGHBORHOOD
- ▁IMPART
- BOROUGH
- CAMP
- ▁HORRID
- ▁IMMORTAL
- ▁PRUDENCE
- ▁SPANIARD
- ▁SUPPOSING
- ▁TELEPHONE
- ▁TEMPERATURE
- ▁PENETRATE
- ▁OYSTER
- ▁APPOINTMENT
- ▁EGYPTIAN
- ▁DWELT
- ▁NEPHEW
- ▁RAILROAD
- ▁SEPTEMBER
- ▁DEVICE
- ▁WHEAT
- ▁GILBERT
- ▁ELEGANT
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- ▁RATIONAL
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- ▁CULTIVATE
- ▁EDITOR
- ▁SPECIMEN
- ▁UNDOUBTEDLY
- ▁WHALE
- ▁DROPPING
- ▁BALLOON
- ▁MEDICAL
- COMB
- ▁COMPOSITION
- ▁FOOTSTEPS
- ▁LAUNCELOT
- ▁DISCOURSE
- ▁ERRAND
- ▁CONVERSE
- ▁ADVANCING
- ▁DOWNSTAIRS
- ▁TUMULT
- ▁CORRUPT
- ▁SUFFICE
- ▁ANGUISH
- ▁SHAGGY
- ▁RETIRE
- ▁TIMBER
- ▁BLAZE
- ▁ABSTRACT
- ▁EMBROIDER
- ▁PHOTOGRAPH
- ▁PROSPERITY
- ▁TERRIBLY
- ▁TERRITORY
- ▁THRESHOLD
- ▁PAVEMENT
- ▁INJURED
- ▁LIMP
- ▁AGITATION
- ▁RASCAL
- ▁PRESUME
- ▁OBSERVING
- ▁OBSTACLE
- ▁SIMPLICITY
- ▁SLUMBER
- ▁SUPPLIED
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- ▁DRAIN
- ▁WILDERNESS
- ▁BELIEVING
- ▁VILLAIN
- ▁RECKLESS
- ▁INJURY
- ▁CLAPP
- ▁FRIDAY
- ▁HERCULES
- ▁KENNEDY
- ▁SYMPTOM
- ▁SLEDGE
- ▁CEILING
- ▁LEMON
- ▁PLAGUE
- ▁MONDAY
- ▁CANVAS
- ▁IMPATIENCE
- ▁UNCOMFORTABLE
- ▁ACCESS
- ▁FROZEN
- ▁SENATOR
- ▁FRANZ
- ▁SWIMMING
- ▁BARRIER
- ▁ADJUST
- ▁COMPARISON
- ▁PROCLAIM
- ▁WRINKL
- ▁OVERLOOK
- ▁MITYA
- ▁GUILT
- ▁PERCEPTION
- ▁PRECAUTION
- ▁SPECTATOR
- ▁SURPRISING
- ▁DISTRACT
- ▁DISDAIN
- ▁BONNET
- ▁MAGNET
- ▁PROFESS
- ▁CONFOUND
- ▁NARRATIVE
- ▁STRUCTURE
- ▁SKETCH
- ▁ULTIMATE
- ▁GLOBE
- ▁INSECT
- FICIENCY
- ▁ORCHARD
- ▁AMIABLE
- ▁DESCENT
- ▁INDEPENDENCE
- ▁MANUFACTURE
- ▁SPRINKLE
- ▁NIGHTINGALE
- ▁CUSHION
- ▁EMINENT
- ▁SCOTT
- ▁ARRAY
- ▁COSETTE
- ▁WAVING
- ▁EXTRACT
- ▁IRREGULAR
- ▁PERSECUT
- ▁DERIVED
- ▁WITHDREW
- ▁CAUTION
- ▁SUSPICIOUS
- ▁MEMORIES
- ▁NOWHERE
- ▁SUBTLE
- ▁THOROUGH
- Q
- ▁APPROPRIATE
- ▁SLAUGHTER
- ▁YOURSELVES
- ▁THUMB
- ▁TWAS
- ▁ABODE
- ▁BIDDING
- ▁CONSPICUOUS
- ▁REBECCA
- ▁SERGEANT
- ▁APRON
- ▁ANTICIPATE
- ▁DISCIPLINE
- ▁GLANCING
- ▁PILGRIM
- ▁SULLEN
- ▁CONTRIBUTE
- ▁PRAIRIE
- ▁CARVED
- ▁COMMERCE
- ▁EXCLAMATION
- ▁MUSCULAR
- ▁NOVEMBER
- ▁PHENOMENA
- ▁SYMBOL
- ▁UMBRELLA
- ▁DIMINISH
- ▁PARLOUR
- ▁THREATENING
- ▁STUMP
- ▁EXTENSIVE
- ▁PLEASING
- ▁REMEMBRANCE
- ▁COMBINED
- ▁SHERIFF
- ▁SHAFT
- ▁LAURA
- ▁INTERCOURSE
- ▁STRICKEN
- ▁SUPPLIES
- ▁LANDLORD
- ▁SHRINK
- ▁PRICK
- ▁CAESAR
- ▁DRUG
- ▁BEWILDERED
- ▁NAUTILUS
- ▁BRUTAL
- ▁COMMERCIAL
- ▁MAGGIE
- ▁SPHERE
- ▁VIRGIN
- ▁BRETHREN
- ▁DESTINY
- ▁POLICY
- ▁TERRIFIED
- ▁HOUSEKEEPER
- ▁CRAZY
- ▁ARDENT
- ▁DISCERN
- ▁WRAP
- ▁MARQUIS
- ▁RUSSIA
- MOUTH
- ▁BRITAIN
- ▁HARBOUR
- ▁CONCERT
- ▁DONKEY
- ▁DAMAGE
- ▁SLIM
- ABOUT
- ▁LUXURY
- ▁MONSTROUS
- ▁TENDENCY
- ▁PARADISE
- ▁CULTURE
- ▁JULIUS
- ▁RAOUL
- ▁REMEDY
- ▁DECAY
- ▁SCOLD
- ▁SPLIT
- ▁ASSAULT
- ▁DECEMBER
- ▁MOSCOW
- ▁EXPLORE
- ▁TROUSERS
- ▁WRIST
- PIECE
- ▁MUSKET
- ▁VALENTINE
- ▁TYRANT
- ▁ABRAHAM
- ▁MEDIUM
- ▁ARTIFICIAL
- ▁FACULTY
- ▁OBLIGATION
- ▁RESEMBLANCE
- ▁INQUIRIES
- ▁DETAIN
- ▁SWARM
- ▁PLEDGE
- ▁ADMIRABLE
- ▁DEFECT
- ▁SUPERINTEND
- ▁PATRIOT
- ▁CLUNG
- ▁DISMAL
- ▁RECIT
- ▁IGNOR
- ▁AMELIA
- ▁JUSTIFY
- ▁ELEPHANT
- ▁ESTIMATE
- ▁KNELT
- ▁SERVING
- ▁WHIM
- ▁SHRILL
- ▁STUDIO
- ▁TEXT
- ▁ALEXANDER
- ▁WROUGHT
- ▁ABUNDANT
- ▁SITUATED
- ▁REGAIN
- ▁FIERY
- ▁SNEER
- ▁SWEAT
- ▁GLARE
- ▁NIGH
- ▁ESCORT
- ▁INEVITABLE
- ▁PSMITH
- ▁RELUCTANT
- ▁PRECEDING
- ▁RESORT
- ▁OUTRAGE
- ▁AMBASSADOR
- ▁CONSOLATION
- ▁RECOGNITION
- ▁REMORSE
- ▁BEHALF
- ▁FORMIDABLE
- ▁GRAVITY
- ▁DIVIDE
- ▁CONFRONT
- ▁GIGANTIC
- ▁OCTOBER
- ▁FLANK
- ▁SLEW
- ▁CLARA
- ▁FILM
- ▁BULK
- ▁POMP
- ▁ELEANOR
- ▁EMPHASIS
- ▁JAPANESE
- ▁CAVALRY
- ▁EXCLUSIVE
- ▁PERFUME
- ▁BRONZE
- ▁FEDERAL
- ▁LIQUID
- ▁RUBBING
- ▁OVEN
- DOLPH
- ▁CONVULS
- ▁DEPRIVED
- ▁RESPONSIBILITY
- ▁SIGNIFICANT
- ▁WAISTCOAT
- ▁CLUSTER
- ▁MARTHA
- ▁REVERSE
- ▁ATTORNEY
- ▁DROOP
- ▁SKILFUL
- ▁HABITUAL
- ▁PUMP
- ▁INTERVEN
- ▁OWL
- ▁CONJECTURE
- ▁FANTASTIC
- ▁RESPONSIBLE
- ▁DESTINED
- ▁DOCUMENT
- ▁THEREUPON
- ▁GODDESS
- ▁PACIFIC
- ▁WARRANT
- ▁COSTUME
- ▁BRIDLE
- ▁CALIFORNIA
- ▁DEMOCRATIC
- ▁EUSTACE
- ▁SQUIRREL
- ▁UNCOMMON
- ▁MARVELLOUS
- ▁PLOUGH
- ▁TRAGEDY
- ▁VAULT
- ▁HESITATE
- ▁REFRAIN
- ▁ADMIRING
- ▁CORPORAL
- ▁ENTITLED
- ▁SHREWD
- ▁SQUEEZ
- ▁ACCURATE
- ▁TEMPEST
- ▁MONUMENT
- ▁SIEGE
- ▁CHINESE
- ▁RAVEN
- ▁LOUNG
- ▁ASSASSIN
- ▁INFLICT
- ▁AGITATED
- ▁DESIRABLE
- ▁EARLIEST
- ▁LAUNCH
- ▁PILOT
- ▁PULSE
- ▁MUTE
- LEIGH
- ▁LIQUOR
- ▁SCARECROW
- ▁SKULL
- ▁DESOLATE
- ▁SUBLIME
- ▁SERENE
- ▁RECESS
- ▁WAKING
- ▁CHARLOTTE
- ▁CIRCULAR
- ▁INJUSTICE
- ▁PINOCCHIO
- ▁PRISCILLA
- ▁THYSELF
- ▁OCCURRENCE
- ▁CASUAL
- ▁FRANTIC
- ▁LEGEND
- ▁FERTIL
- ▁BACKGROUND
- ▁DELICACY
- ▁ESTRALLA
- ▁MANUSCRIPT
- ▁RESPONSE
- ▁UNIVERSITY
- ▁WOLVES
- ▁SCANDAL
- ▁STUMBLE
- ▁HOARSE
- ▁BODILY
- ▁CONVENT
- ▁EXAMINING
- ▁INCAPABLE
- ▁PERCEIVING
- ▁PHILADELPHIA
- ▁SUBSEQUENT
- ▁THIEVES
- ▁ACCUMULAT
- ▁DAMSEL
- ▁SCOTCH
- ▁UNDERNEATH
- ▁NOBILITY
- ▁SMASH
- ▁REVOLT
- ▁ENGAGE
- ▁CATHEDRAL
- ▁CHAMPION
- ▁DESPATCH
- ▁ETERNITY
- ▁JANUARY
- ▁PLEADED
- ▁PROBABILITY
- ▁JIMMIE
- ▁PARALLEL
- ▁FISHERMAN
- ▁JERRY
- ▁SWORE
- ▁DRAUGHT
- ▁OPPONENT
- ▁PRIMITIVE
- ▁SIGNIFICANCE
- ▁SUBSTANTIAL
- ▁AMAZED
- ▁DUNBAR
- ▁COMMEND
- ▁CONTEMPLATE
- ▁TESTIMONY
- ▁IMPERIAL
- ▁ADAPT
- ▁JUICE
- ▁CALAMIT
- CULAR
- ▁CHATEAU
- ▁PHOENIX
- ▁PRUDENT
- ▁SOLUTION
- ▁VILLEFORT
- ▁REACTION
- ▁RELAX
- ▁YU
- ▁PROHIBIT
- ▁DISTRUST
- ▁PLUNDER
- ▁WELFARE
- ▁NAVIGAT
- ▁PARLOR
- ▁LAZY
- ▁DETACH
- OMETER
- ▁PRIV
- ▁DISCOURAGE
- ▁OBSTINATE
- ▁REJOICING
- ▁SERMON
- ▁VEHICLE
- ▁FANCIES
- ▁ENLIGHTEN
- ▁ACUTE
- ▁ILLUSION
- ▁ANTHEA
- ▁MARTIAN
- ▁EXCITE
- ▁GENEROSITY
- OLOGIST
- ▁AMAZING
- ▁UNWORTHY
- ▁INTERNAL
- ▁INCENSE
- ▁VIBRAT
- ▁ADHERE
- ROACH
- ▁FEBRUARY
- ▁MEXICAN
- ▁POTATOES
- ▁INCESSANT
- ▁INTERPOSED
- ▁PARCEL
- ▁VEXED
- ▁PROMOTE
- MIDST
- ▁ARISTOCRAT
- ▁CYRIL
- ▁EMBARK
- ▁ABUNDANCE
- ▁LITERALLY
- ▁SURGEON
- ▁TERRACE
- ▁ATLANTIC
- ▁MARTYR
- ▁SPECK
- ▁SENATE
- ▁LOAF
- ▁ADMINISTER
- ▁APPREHEND
- ▁SUBDUED
- ▁TEMPORARY
- ▁DOMINION
- ▁ELABORATE
- ▁DIGNIFIED
- ▁ELIZA
- ▁SPLASH
- ▁CONSEIL
- ▁DEXTER
- ▁UNSEEN
- ▁TRAGIC
- VOCATION
- ▁GRATIFY
- ▁BACHELOR
- ▁DEFENSE
- ▁EXCURSION
- ▁FACULTIES
- ▁PROPRIETOR
- ▁SYMPATHETIC
- ▁UNNECESSARY
- ▁RADIANT
- ▁VACANT
- ▁OUNCE
- ▁SCREW
- ▁PHENOMENON
- ▁PROMINENT
- ▁WORRIED
- ▁STUDIES
- ▁CLIMATE
- ▁KEITH
- ▁ARAMIS
- ▁BLISS
- ▁CONTINUAL
- ▁SURPASS
- ▁HEBREW
- ▁IDENTITY
- ▁PROVOKE
- ▁TEMPERAMENT
- ▁CHARIOT
- ▁HARBOR
- ▁NINTH
- ▁PRIOR
- ▁DESIROUS
- ▁JERUSALEM
- ▁UNDERTAKING
- ▁EDISON
- ▁MIRTH
- ▁SCOUT
- ▁APPARATUS
- ▁ILLUSTRATION
- ▁INTELLIGIBLE
- ▁INVARIABLY
- ▁PIERCED
- ▁REVIEW
- ▁FLICKER
- ▁HAZARD
- ▁REVELATION
- ▁DIXON
- ▁EXCITING
- ▁GOSPEL
- ▁CONSTANCE
- ▁OVERTAKE
- ▁GUINEA
- ▁ALADDIN
- ▁CHICAGO
- ▁TULLIVER
- ▁HAMILTON
- ▁GARRISON
- ▁DISCIPLE
- ▁INTENSITY
- ▁TRAITOR
- ▁CHANCELLOR
- ▁PROVERB
- ▁DAGGER
- ▁FORESEE
- ▁CONFIDE
- ▁GLIMMER
- ▁CHAUVELIN
- ▁ILLUSTRATE
- ▁VOLUNTEER
- ▁JUNGLE
- ▁STREAK
- ▁SUNRISE
- ▁DISSOLV
- ▁QUEST
- ▁AWHILE
- ▁FELICITY
- ▁LEGISLATURE
- ▁LEONORA
- ▁MAGAZINE
- ▁PITIFUL
- ▁COLONY
- ▁SHAWL
- ▁ARRIVING
- ▁FUNDAMENTAL
- ▁CARPENTER
- ▁OVERFLOW
- ▁EXPAND
- ▁HARVEST
- ▁FEMININE
- ▁INNUMERABLE
- ▁SCRAMBLE
- ▁TWENTIETH
- ▁TRIFLING
- ▁GHASTL
- ▁CONQUEST
- ▁DANIEL
- ▁FACILIT
- ▁FORSAKE
- ▁BEHAVIOUR
- ▁GORGEOUS
- ▁PRODUCING
- ▁HAPPIER
- ▁PROMISING
- ▁RAINBOW
- ▁INSTINCTIVELY
- ▁DECREE
- ▁EYEBROWS
- ▁IRRESISTIBLE
- ▁PHARAOH
- ▁SCROOGE
- ▁UNNATURAL
- ▁CRUMBS
- ▁REFINED
- ▁DREARY
- ▁TRENCH
- ▁CONVINCE
- ▁FRINGE
- ▁EXTREMITY
- ▁INTIMACY
- ▁SCOUNDREL
- ▁SUFFRAGE
- ▁UNEASINESS
- ▁BARRICADE
- ▁CIRCULAT
- ▁SAMUEL
- ▁BRUCE
- ▁DARCY
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
n_fft: 512
hop_length: 160
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 10
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: s4
decoder_conf:
dropinp: 0.0
dropout: 0.1
drop_path: 0.1
prenorm: true
n_layers: 6
layer:
- postact: glu
dropout: 0.1
n_ssm: 1
lr: 0.0025
dt_min: 0.001
dt_max: 0.1
measure: legs
transposed: false
_name_: s4
- n_head: 8
dropout: 0.1
transposed: false
_name_: mha
- expand: 4
activation: gelu
dropout: 0.1
transposed: false
_name_: ff
residual: residual
norm: layer
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202211'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
gymeee/demo_code_switching | gymeee | 2023-01-12T08:40:15Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"zh",
"dataset:ASCEND",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-01-11T14:25:39Z | ---
license: apache-2.0
datasets:
- ASCEND
language:
- zh
metrics:
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
---
## inference
The model can be used directly (without a language model) as follows...
Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
import torchaudio
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("gymeee/demo_code_switching")
model = Wav2Vec2ForCTC.from_pretrained("gymeee/demo_code_switching")
# load speech
speech_array, sampling_rate = torchaudio.load("speech.wav")
# tokenize
input_values = processor(speech_array[0], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
transcription |
noodlynoodle/Reinforce-Pixelcopter-PLE-v0 | noodlynoodle | 2023-01-12T08:36:36Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T08:21:23Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 6.40 +/- 7.12
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
|
Dharkelf/ppo-SnowballTarget | Dharkelf | 2023-01-12T08:33:17Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-01-12T08:33:11Z |
---
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: Dharkelf/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sgoodfriend/ppo-RND-Pyramids | sgoodfriend | 2023-01-12T08:32:58Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2023-01-12T07:18:35Z |
---
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: sgoodfriend/ppo-RND-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Harm/ppo-Huggy | Harm | 2023-01-12T08:29:03Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-01-12T08:28:56Z |
---
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: Harm/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
chiHang/rowbody-4cats-outputs | chiHang | 2023-01-12T08:21:28Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-01-12T08:19:55Z | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-rowbody-4cats
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-rowbody-4cats
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1486
- Mean Iou: 0.6527
- Mean Accuracy: 0.9381
- Overall Accuracy: 0.9558
- Accuracy Sleeve-right: nan
- Accuracy Sleeve-left: 0.9259
- Accuracy Neck: 0.9212
- Accuracy Body: 0.9670
- Iou Sleeve-right: 0.0
- Iou Sleeve-left: 0.9012
- Iou Neck: 0.7545
- Iou Body: 0.9551
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Sleeve-right | Accuracy Sleeve-left | Accuracy Neck | Accuracy Body | Iou Sleeve-right | Iou Sleeve-left | Iou Neck | Iou Body |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------:|:--------------------:|:-------------:|:-------------:|:----------------:|:---------------:|:--------:|:--------:|
| 0.9629 | 2.5 | 20 | 1.1228 | 0.2325 | 0.3900 | 0.7680 | nan | 0.1708 | 0.0 | 0.9991 | 0.0 | 0.1703 | 0.0 | 0.7598 |
| 0.6667 | 5.0 | 40 | 0.6465 | 0.4513 | 0.6646 | 0.8681 | nan | 0.5731 | 0.4337 | 0.9870 | 0.0 | 0.5659 | 0.3911 | 0.8483 |
| 0.414 | 7.5 | 60 | 0.4340 | 0.5935 | 0.8506 | 0.9377 | nan | 0.8496 | 0.7215 | 0.9806 | 0.0 | 0.8374 | 0.6136 | 0.9228 |
| 0.3351 | 10.0 | 80 | 0.3371 | 0.6263 | 0.9237 | 0.9411 | nan | 0.9325 | 0.8909 | 0.9478 | 0.0 | 0.8568 | 0.7171 | 0.9314 |
| 0.4849 | 12.5 | 100 | 0.3146 | 0.6428 | 0.9438 | 0.9526 | nan | 0.9708 | 0.9093 | 0.9513 | 0.0 | 0.8922 | 0.7375 | 0.9415 |
| 0.2461 | 15.0 | 120 | 0.2716 | 0.6431 | 0.9554 | 0.9496 | nan | 0.9717 | 0.9511 | 0.9434 | 0.0 | 0.8938 | 0.7410 | 0.9373 |
| 0.2123 | 17.5 | 140 | 0.2477 | 0.6715 | 0.9481 | 0.9679 | nan | 0.9523 | 0.9151 | 0.9768 | 0.0 | 0.9229 | 0.7999 | 0.9632 |
| 0.1827 | 20.0 | 160 | 0.2413 | 0.6620 | 0.9048 | 0.9649 | nan | 0.9200 | 0.8032 | 0.9912 | 0.0 | 0.9147 | 0.7717 | 0.9617 |
| 0.2828 | 22.5 | 180 | 0.2286 | 0.6484 | 0.9472 | 0.9532 | nan | 0.9622 | 0.9265 | 0.9530 | 0.0 | 0.8996 | 0.7495 | 0.9443 |
| 0.4631 | 25.0 | 200 | 0.2137 | 0.6459 | 0.9452 | 0.9485 | nan | 0.9523 | 0.9345 | 0.9486 | 0.0 | 0.8886 | 0.7543 | 0.9408 |
| 0.159 | 27.5 | 220 | 0.1854 | 0.6336 | 0.9374 | 0.9415 | nan | 0.9355 | 0.9328 | 0.9440 | 0.0 | 0.8832 | 0.7145 | 0.9368 |
| 0.1361 | 30.0 | 240 | 0.1760 | 0.6563 | 0.9429 | 0.9576 | nan | 0.9492 | 0.9161 | 0.9635 | 0.0 | 0.9027 | 0.7687 | 0.9538 |
| 0.1369 | 32.5 | 260 | 0.1634 | 0.6516 | 0.9451 | 0.9562 | nan | 0.9497 | 0.9249 | 0.9607 | 0.0 | 0.9130 | 0.7426 | 0.9507 |
| 0.1356 | 35.0 | 280 | 0.1580 | 0.6488 | 0.9248 | 0.9527 | nan | 0.9154 | 0.8905 | 0.9684 | 0.0 | 0.8966 | 0.7457 | 0.9528 |
| 0.2415 | 37.5 | 300 | 0.1635 | 0.6457 | 0.9439 | 0.9523 | nan | 0.9406 | 0.9340 | 0.9571 | 0.0 | 0.9003 | 0.7344 | 0.9482 |
| 0.1183 | 40.0 | 320 | 0.1639 | 0.6463 | 0.9386 | 0.9524 | nan | 0.9286 | 0.9262 | 0.9612 | 0.0 | 0.8998 | 0.7351 | 0.9500 |
| 0.1264 | 42.5 | 340 | 0.1564 | 0.6512 | 0.9439 | 0.9552 | nan | 0.9385 | 0.9312 | 0.9618 | 0.0 | 0.9066 | 0.7463 | 0.9521 |
| 0.2418 | 45.0 | 360 | 0.1553 | 0.6569 | 0.9366 | 0.9589 | nan | 0.9198 | 0.9166 | 0.9734 | 0.0 | 0.9033 | 0.7656 | 0.9587 |
| 0.189 | 47.5 | 380 | 0.1611 | 0.6520 | 0.9386 | 0.9544 | nan | 0.9284 | 0.9231 | 0.9643 | 0.0 | 0.8990 | 0.7554 | 0.9536 |
| 0.1132 | 50.0 | 400 | 0.1486 | 0.6527 | 0.9381 | 0.9558 | nan | 0.9259 | 0.9212 | 0.9670 | 0.0 | 0.9012 | 0.7545 | 0.9551 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Heerak/xlm-roberta-base-finetuned_panx_de | Heerak | 2023-01-12T08:15:22Z | 110 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-12T07:14:31Z | ---
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
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8564961521050248
---
<!-- 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: 0.1338
- F1: 0.8565
## 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: 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 263 | 0.1602 | 0.8158 |
| 0.2169 | 2.0 | 526 | 0.1372 | 0.8407 |
| 0.2169 | 3.0 | 789 | 0.1338 | 0.8565 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
spatial/ppo-Huggy | spatial | 2023-01-12T07:58:44Z | 0 | 1 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-01-12T07:58:35Z |
---
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: spatial/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sgoodfriend/ppo-SnowballTarget | sgoodfriend | 2023-01-12T07:51:47Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-01-12T01:29:30Z |
---
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: sgoodfriend/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
noodlynoodle/Reinforce-CartPole-v1 | noodlynoodle | 2023-01-12T07:41:52Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T07:41:43Z | ---
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
|
lixiangchun/transcriptome-bert-1536-1-16-64 | lixiangchun | 2023-01-12T07:07:27Z | 116 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-12T06:43:52Z | # iSEEEK
Generative pretraining from the rankings of top expressing genes.
It was trained on more than 20 million single-cell transcriptomes with a sequence length of 64.
|
Skanderbeg/ppo-LunarLander-v2 | Skanderbeg | 2023-01-12T07:00:11Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T06:59:47Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.68 +/- 14.68
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
...
```
|
thien1892/q-taxi-v3 | thien1892 | 2023-01-12T06:59:18Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T05:21:45Z | ---
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: 7.58 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
1. Load model
```python
from urllib.error import HTTPError
from huggingface_hub import hf_hub_download
def load_from_hub(repo_id: str, filename: str) -> str:
"""
Download a model from Hugging Face Hub.
:param repo_id: id of the model repository from the Hugging Face Hub
:param filename: name of the model zip file from the repository
"""
# Get the model from the Hub, download and cache the model on your local disk
pickle_model = hf_hub_download(
repo_id=repo_id,
filename=filename
)
with open(pickle_model, 'rb') as f:
downloaded_model_file = pickle.load(f)
return downloaded_model_file
```
2. Evaluate model
```
model = load_from_hub(repo_id="thien1892/q-taxi-v3", filename="q-learning.pkl") # Try to use another model
print(model)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
egumasa/roberta-base-academic3 | egumasa | 2023-01-12T06:57:14Z | 133 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-07T16:08:10Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-academic3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-academic3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4206
## 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: 7e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6943 | 0.99 | 82 | 1.5540 |
| 1.6494 | 1.99 | 164 | 1.5268 |
| 1.63 | 2.99 | 246 | 1.5209 |
| 1.6152 | 3.99 | 328 | 1.5049 |
| 1.5985 | 4.99 | 410 | 1.4891 |
| 1.5826 | 5.99 | 492 | 1.4876 |
| 1.5643 | 6.99 | 574 | 1.4769 |
| 1.5506 | 7.99 | 656 | 1.4638 |
| 1.5383 | 8.99 | 738 | 1.4548 |
| 1.5309 | 9.99 | 820 | 1.4511 |
| 1.5225 | 10.99 | 902 | 1.4492 |
| 1.5124 | 11.99 | 984 | 1.4419 |
| 1.507 | 12.99 | 1066 | 1.4323 |
| 1.4985 | 13.99 | 1148 | 1.4294 |
| 1.4921 | 14.99 | 1230 | 1.4296 |
| 1.4859 | 15.99 | 1312 | 1.4256 |
| 1.4827 | 16.99 | 1394 | 1.4194 |
| 1.4756 | 17.99 | 1476 | 1.4184 |
| 1.474 | 18.99 | 1558 | 1.4156 |
| 1.4737 | 19.99 | 1640 | 1.4165 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
lmazzon70/videomae-base-short-finetuned-ssv2-finetuned-rwf2000-epochs8-batch8-fp16 | lmazzon70 | 2023-01-12T05:44:03Z | 66 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2023-01-11T15:57:25Z | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-short-finetuned-ssv2-finetuned-rwf2000-epochs8-batch8-fp16
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. -->
# videomae-base-short-finetuned-ssv2-finetuned-rwf2000-epochs8-batch8-fp16
This model is a fine-tuned version of [MCG-NJU/videomae-base-short-finetuned-ssv2](https://huggingface.co/MCG-NJU/videomae-base-short-finetuned-ssv2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4339
- Accuracy: 0.4643
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4239 | 0.06 | 200 | 0.3879 | 0.82 |
| 0.4179 | 1.06 | 400 | 1.1635 | 0.6162 |
| 0.4329 | 2.06 | 600 | 0.8215 | 0.63 |
| 0.3051 | 3.06 | 800 | 0.5541 | 0.7412 |
| 0.172 | 4.06 | 1000 | 0.4696 | 0.8363 |
| 0.1955 | 5.06 | 1200 | 0.5384 | 0.78 |
| 0.2301 | 6.06 | 1400 | 1.3358 | 0.635 |
| 0.2995 | 7.06 | 1600 | 1.0372 | 0.7087 |
| 0.3789 | 8.06 | 1800 | 0.8670 | 0.7412 |
| 0.2525 | 9.06 | 2000 | 0.5886 | 0.8225 |
| 0.1846 | 10.06 | 2200 | 0.7851 | 0.735 |
| 0.1547 | 11.06 | 2400 | 0.8905 | 0.7638 |
| 0.2501 | 12.06 | 2600 | 0.9807 | 0.76 |
| 0.1046 | 13.06 | 2800 | 1.0419 | 0.7438 |
| 0.0786 | 14.06 | 3000 | 1.0128 | 0.7538 |
| 0.0178 | 15.06 | 3200 | 1.0156 | 0.75 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
KJIM/kobigbird-pure130-17419853 | KJIM | 2023-01-12T05:10:10Z | 93 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"question-answering",
"generated_from_trainer",
"dataset:custom_squad_v2",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-01-12T02:39:38Z | ---
tags:
- generated_from_trainer
datasets:
- custom_squad_v2
model-index:
- name: kobigbird-pure130-17419853
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. -->
# kobigbird-pure130-17419853
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the custom_squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1318
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 130
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 42 | 1.4263 |
| No log | 1.99 | 84 | 1.1201 |
| No log | 2.99 | 126 | 1.1318 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Addwater/ppo-SnowballTarget | Addwater | 2023-01-12T04:56:01Z | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-01-12T04:55:54Z |
---
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: Addwater/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kasukanra/fantasy-art-style-v1-5 | kasukanra | 2023-01-12T04:36:00Z | 46 | 0 | diffusers | [
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-01-12T02:37:38Z | ---
license: creativeml-openrail-m
---
|
YuJungSoo/kobigbird-pure125-74195889 | YuJungSoo | 2023-01-12T04:27:01Z | 93 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"question-answering",
"generated_from_trainer",
"dataset:custom_squad_v2",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-01-12T01:58:17Z | ---
tags:
- generated_from_trainer
datasets:
- custom_squad_v2
model-index:
- name: kobigbird-pure125-74195889
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. -->
# kobigbird-pure125-74195889
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the custom_squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1059
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 125
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 42 | 1.3347 |
| No log | 1.99 | 84 | 1.0866 |
| No log | 2.99 | 126 | 1.1059 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
rahmanfadhil/indobert-finetuned-bapos | rahmanfadhil | 2023-01-12T04:25:32Z | 115 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:indonlu",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-12T03:10:22Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: indobert-finetuned-bapos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indonlu
type: indonlu
config: bapos
split: train
args: bapos
metrics:
- name: Precision
type: precision
value: 0.9616493964320051
- name: Recall
type: recall
value: 0.9633646060000713
- name: F1
type: f1
value: 0.9625062370803336
- name: Accuracy
type: accuracy
value: 0.9653301071552022
---
<!-- 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. -->
# indobert-finetuned-bapos
This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1239
- Precision: 0.9616
- Recall: 0.9634
- F1: 0.9625
- Accuracy: 0.9653
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2748 | 1.0 | 500 | 0.1450 | 0.9511 | 0.9529 | 0.9520 | 0.9559 |
| 0.0917 | 2.0 | 1000 | 0.1220 | 0.9585 | 0.9627 | 0.9606 | 0.9638 |
| 0.0612 | 3.0 | 1500 | 0.1239 | 0.9616 | 0.9634 | 0.9625 | 0.9653 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
anas-awadalla/spanbert-large-squad | anas-awadalla | 2023-01-12T04:20:46Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-01-12T03:42:53Z | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-large-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spanbert-large-squad
This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.co/SpanBERT/spanbert-large-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
HarrySun/hemepath | HarrySun | 2023-01-12T03:59:35Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2023-01-11T23:47:56Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# hemepath
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/HarrySun/hemepath/tensorboard?#scalars)
|
SuperZan/GetFileDemo | SuperZan | 2023-01-12T03:31:12Z | 0 | 0 | null | [
"Diffusion",
"en",
"region:us"
] | null | 2023-01-11T11:18:52Z | ---
language: en
tags:
- Diffusion
--- |
tomekkorbak/pensive_saha | tomekkorbak | 2023-01-12T03:30:46Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-01-11T14:15:13Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: pensive_saha
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. -->
# pensive_saha
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-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: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 25000
- 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': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.00056},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 1661599744},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '81a1701e025d2c65ae6e8c2103df559071523ee0'},
'num_additional_tokens': 2,
'path_or_name': 'tomekkorbak/goofy_pasteur'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'pensive_saha',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 2535,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1661599744,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/3qk673go |
moom825/testing | moom825 | 2023-01-12T03:22:23Z | 0 | 0 | null | [
"text-to-image",
"doi:10.57967/hf/0268",
"license:gfdl",
"region:us"
] | text-to-image | 2023-01-12T03:06:14Z | ---
license: gfdl
pipeline_tag: text-to-image
--- |
Sirianth/taxi_rl | Sirianth | 2023-01-12T02:01:40Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T02:01:34Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_rl
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.75
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="Sirianth/taxi_rl", 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"])
```
|
jason1i/q-FrozenLake-v1-4x4-noSlippery | jason1i | 2023-01-12T00:53:51Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-12T00:53:47Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jason1i/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"])
```
|
rahmanfadhil/indobert-finetuned-pos | rahmanfadhil | 2023-01-12T00:35:21Z | 130 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:indonlu",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-11T12:40:47Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: indobert-finetuned-pos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indonlu
type: indonlu
config: posp
split: train
args: posp
metrics:
- name: Precision
type: precision
value: 0.9477284686897035
- name: Recall
type: recall
value: 0.9477284686897035
- name: F1
type: f1
value: 0.9477284686897035
- name: Accuracy
type: accuracy
value: 0.9477284686897035
---
<!-- 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. -->
# indobert-finetuned-pos
This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1762
- Precision: 0.9477
- Recall: 0.9477
- F1: 0.9477
- Accuracy: 0.9477
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 420 | 0.2238 | 0.9278 | 0.9278 | 0.9278 | 0.9278 |
| 0.3621 | 2.0 | 840 | 0.1806 | 0.9437 | 0.9437 | 0.9437 | 0.9437 |
| 0.1504 | 3.0 | 1260 | 0.1762 | 0.9477 | 0.9477 | 0.9477 | 0.9477 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
PygmalionAI/pygmalion-350m | PygmalionAI | 2023-01-11T23:44:13Z | 2,045 | 55 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"convAI",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-12-20T22:04:32Z | ---
language:
- en
thumbnail:
tags:
- convAI
- conversational
inference: false
---
# pygmalion-350m
# Model description
This is a proof-of-concept fine-tune of Facebook's OPT-350M model optimized for dialogue, to be used as a stepping stone to higher parameter models.
**Disclaimer:** NSFW data was included in the fine-tuning of this model. Although SFW inputs will usually result in SFW outputs, you are advised to **chat at your own risk. This model is not suitable for use by minors.**
# Fine-tuning process
This model was much easier than expected to create.
We used the [ColossalAI](https://www.colossalai.org/) library to fine-tune the [OPT-350M](https://huggingface.co/facebook/opt-350m) model originally trained by Facebook on The Pile. Though our initial dataset was sets of dialogue gathered from various sources totaling about 50 MB in size, early training runs revealed that the model converged after only 7% of the dataset was passed through. To alleviate this, we massively reduced the size of the dataset to only 273 KB.
ColossalAI's magic allowed for something incredible: this entire model was fine-tuned on a singular GPU with only 6 GB ***(!)*** of VRAM. Fine-tuning took less than an hour to complete. |
OubeidAllahjb/ppo-LunarLander-v2 | OubeidAllahjb | 2023-01-11T23:30:41Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-11T23:30:20Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: POO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.03 +/- 20.56
name: mean_reward
verified: false
---
# **POO** Agent playing **LunarLander-v2**
This is a trained model of a **POO** 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
...
```
|
chqmatteo/taxi-v3 | chqmatteo | 2023-01-11T23:23:47Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-11T06:54:57Z | ---
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="chqmatteo/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"])
```
|
drcostco/testmodel1 | drcostco | 2023-01-11T23:17:04Z | 15 | 0 | diffusers | [
"diffusers",
"text-to-image",
"en",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-01-11T20:04:31Z | ---
pipeline_tag: text-to-image
license: openrail
language:
- en
library_name: diffusers
--- |
willtai/ppo-LunarLander-v2 | willtai | 2023-01-11T23:07:50Z | 6 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-10T19:33:14Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 261.77 +/- 17.72
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
vtisza/ppo-SnowballTarget | vtisza | 2023-01-11T23:06:06Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-01-11T23:05:59Z |
---
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: vtisza/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
tarolling/xlm-roberta-base-finetuned-panx-de | tarolling | 2023-01-11T23:04:37Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-11T22:40:57Z | ---
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
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 4.11.3
- Pytorch 1.13.1+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
goldman6g6/gold6g6 | goldman6g6 | 2023-01-11T23:02:13Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2023-01-11T23:00:53Z | ---
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details> |
jcbm07/jcbm07 | jcbm07 | 2023-01-11T23:01:45Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-11T23:01:45Z | ---
license: creativeml-openrail-m
---
|
Ghosty18/ppo-LunarLander-v2 | Ghosty18 | 2023-01-11T22:49:38Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-10T23:36:16Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 271.32 +/- 12.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Rasgeath/LoRA | Rasgeath | 2023-01-11T22:42:04Z | 0 | 6 | null | [
"region:us"
] | null | 2023-01-07T17:45:27Z | # Tigger words :
sksmalin.safetensors: **sksmalin**
ke-ta.safetensors: **ke-ta**
arknights.safetensors: **arknights** |
dfm794/Reinforce-cartpole-v1 | dfm794 | 2023-01-11T21:36:19Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-11T21:36:09Z | ---
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
|
jpopham91/reinforce-Cartpole-v1 | jpopham91 | 2023-01-11T21:28:02Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-11T21:27:48Z | ---
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
|
Ubermensch/form | Ubermensch | 2023-01-11T21:25:52Z | 0 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-01-11T21:25:52Z | ---
license: cc-by-nc-sa-4.0
---
|
odiaz1066/Reinforce-CartPole-v1 | odiaz1066 | 2023-01-11T21:22:30Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-11T21:22:20Z | ---
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
|
rudzinskimaciej/jajem2 | rudzinskimaciej | 2023-01-11T21:18:56Z | 37 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-01-11T21:08:24Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### JaJem2 Dreambooth model trained by rudzinskimaciej 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:
|
Subsets and Splits