semvec/pypi-packages huggingface dataset:
oracle-n 0.1.0
A custom model for sentiment analysis
Requires Python: >=3.6

# Oracle-n Model

Oracle-n is an AI model based on the BERT architecture, designed for text and sentiment analysis. Using the foundational strengths of BERT, Oracle-n is designed to to optimize performance for specific needs. This repository includes the model code, tokenizer, and training scripts.

## Features

- **Customized BERT Configuration**: Tailored configurations to fit text and sentiment analysis tasks.
- **Oracle-n Tokenizer**: Custom tokenizer designed for preprocessing text data efficiently.
- **Sentiment Analysis**: Trained on the IMDb dataset to perform sentiment analysis tasks.

## Directory Structure

- `aclImdb/`: Directory containing the IMDb dataset files in Parquet format.
- `dataset.py`: Script for handling the dataset loading and preprocessing.
- `logs/`: Directory for TensorBoard logs during training.
- `oracle-n-model/`: Directory containing the saved model.
- `oracle-n-tokenizer/`: Directory containing the saved tokenizer.
- `scripts/`: Directory containing additional scripts for training and evaluation.
- `.gitignore`: Git ignore file to exclude unnecessary files from the repository.
- `requirements.txt`: File listing the dependencies required for the project.

## Setup and Installation

1. **Clone the repository**:
   ```bash
   git clone https://github.com/hilarl/oracle-n.git
   cd oracle-n
   ```

2. **Install dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

3. **Download the dataset**:
   Ensure you have the IMDb dataset files in the `aclImdb/` directory. If needed, you can download them from the IMDb dataset page and convert them to Parquet format.

## Usage

### Training the Model

1. **Prepare the Dataset**:
   Ensure the dataset files are in the `aclImdb/` directory in Parquet format.

2. **Run the Training Script**:
   ```bash
   python scripts/train_model.py
   ```

3. **Monitor Training with TensorBoard**:
   ```bash
   tensorboard --logdir logs
   ```

### Evaluating the Model

1. **Run the Evaluation Script**:
   ```bash
   python scripts/evaluate_model.py
   ```

## Customizing the Model

1. **Modify the Configuration**:
   Edit the `dataset.py` script to change the model configuration parameters such as hidden size, number of layers, and attention heads.

2. **Add Your Own Tokenizer**:
   Customize the tokenizer by editing the `oracle-n-tokenizer` directory.

## Contribution

Contributions are welcome! Please fork the repository and submit a pull request.

## License

This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.

## Acknowledgments

- **BERT**: This model is based on the BERT architecture developed by Google.
- **Hugging Face**: Leveraging the Hugging Face Transformers library for model development.

## Contact

For any questions or suggestions, please open an issue on GitHub or contact us at hilal@tenzro.com.

---

This README provides an overview of the Oracle-n model, its features, and how to set up and use it. Feel free to customize it further based on your specific requirements and details.
medface3d 0.0.2
3D face reconstruction using mediapipe 3D Face mesh
Requires Python: >=3.7

# medface3D.worker
### Dense face reconstruction based on 3D facial landmarks
#### Using medipipe face mesh to detect 468 3D facial landmarks and use it to reconstruct 3D face mesh
<div style="text-align:center;">
    <img src="https://mohamedalirashad.github.io/FreeFaceMoCap/assets/img/mediapipe.png" alt="MediaPipe" width="30%">
</div>
<div style="text-align:center;">
    <img src="https://github.com/nguyentrongvan/MedFace3D/blob/main/medface3D/data/sample/face_001.jpg_face_reconstruction.gif?raw=true" alt="demo" width="30%">
</div>


## 1. Usage
***1.1. Setup env***  
```
git clone https://github.com/nguyentrongvan/Dense-face-base-on-3D-landmarks.git
cd Dense-face-base-on-3D-landmarks 
pip install -r requirements.txt
```


***1.2. Run demo***  
```
python demo.py
```

## 2. Demo result
### Face dense generation:
![Demo face mesh](https://github.com/nguyentrongvan/MedFace3D/blob/main/medface3D/data/demo/dense.png?raw=true)
<div style="text-align:center;">
    <img src="https://github.com/nguyentrongvan/MedFace3D/blob/main/medface3D/data/demo/famed01.gif?raw=true" alt="MediaPipe" width="49%">
    <img src="https://github.com/nguyentrongvan/MedFace3D/blob/main/medface3D/data/demo/famed02.gif?raw=true" alt="MediaPipe" width="49%">
</div>


### Face depth estimation:
![Demo face depth](https://github.com/nguyentrongvan/MedFace3D/blob/main/medface3D/data/demo/depth.png?raw=true)


### 3D face mesh reconstruction:
<div style="text-align:center;">
    <img src="https://github.com/nguyentrongvan/MedFace3D/blob/main/medface3D/data/demo/mesh_famed.gif?raw=true" alt="MediaPipe" width="100%">
    <img src="https://github.com/nguyentrongvan/MedFace3D/blob/main/medface3D/data/demo/mesh_local.gif?raw=true" alt="MediaPipe" width="100%">
</div>
face3d-med-reconstruction 0.0.1
3D face reconstruction using mediapipe 3D Face mesh
Requires Python: >=3.7

# medface3d.worker
### Dense face reconstruction based on 3D facial landmarks
#### Using medipipe face mesh to detect 468 3D facial landmarks and use it to reconstruct 3D face mesh
<div style="text-align:center;">
    <img src="https://mohamedalirashad.github.io/FreeFaceMoCap/assets/img/mediapipe.png" alt="MediaPipe" width="30%">
</div>
<div style="text-align:center;">
    <img src="medface3d/data/sample/face_001.jpg_face_reconstruction.gif" alt="demo" width="30%">
</div>


## 1. Usage
***1.1. Setup env***  
```
git clone https://github.com/nguyentrongvan/Dense-face-base-on-3D-landmarks.git
cd Dense-face-base-on-3D-landmarks 
pip install -r requirements.txt
```


***1.2. Run demo***  
```
python demo.py
```

## 2. Demo result
### Face dense generation:
![Demo face mesh](medface3d/data/demo/dense.png)
<div style="text-align:center;">
    <img src="medface3d/data/demo/famed01.gif" alt="MediaPipe" width="49%">
    <img src="medface3d/data/demo/famed02.gif" alt="MediaPipe" width="49%">
</div>


### Face depth estimation:
![Demo face depth](medface3d/data/demo/depth.png)


### 3D face mesh reconstruction:
<div style="text-align:center;">
    <img src="medface3d/data/demo/mesh_famed.gif" alt="MediaPipe" width="100%">
    <img src="medface3d/data/demo/mesh_local.gif" alt="MediaPipe" width="100%">
</div>
med-face-reconstruction 0.0.1
3D face reconstruction using mediapipe 3D Face mesh
Requires Python: >=3.7

# medface3d.worker
### Dense face reconstruction based on 3D facial landmarks
#### Using medipipe face mesh to detect 468 3D facial landmarks and use it to reconstruct 3D face mesh
<div style="text-align:center;">
    <img src="https://mohamedalirashad.github.io/FreeFaceMoCap/assets/img/mediapipe.png" alt="MediaPipe" width="30%">
</div>
<div style="text-align:center;">
    <img src="data/sample/face_001.jpg_face_reconstruction.gif" alt="demo" width="30%">
</div>


## 1. Usage
***1.1. Setup env***  
```
git clone https://github.com/nguyentrongvan/Dense-face-base-on-3D-landmarks.git
cd Dense-face-base-on-3D-landmarks 
pip install -r requirements.txt
```


***1.2. Run demo***  
```
python demo.py
```

## 2. Demo result
### Face dense generation:
![Demo face mesh](data/demo/dense.png)
<div style="text-align:center;">
    <img src="data/demo/famed01.gif" alt="MediaPipe" width="49%">
    <img src="data/demo/famed02.gif" alt="MediaPipe" width="49%">
</div>


### Face depth estimation:
![Demo face depth](data/demo/depth.png)


### 3D face mesh reconstruction:
<div style="text-align:center;">
    <img src="data/demo/mesh_famed.gif" alt="MediaPipe" width="100%">
    <img src="data/demo/mesh_local.gif" alt="MediaPipe" width="100%">
</div>