# ๐ค HuggingFace Trainer Notebook Converter
[](https://badge.fury.io/py/hf-trainer-nbconvert)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
A specialized Python package for converting Jupyter notebooks that use the HuggingFace Trainer API to HTML while preserving and enhancing training progress visualization.
## ๐งน Clean Conversion Process
This package provides a **clean, direct conversion** with no intermediate files left behind:
- โ
**Direct Conversion**: Converts directly from .ipynb to HTML in one step
- โ
**No Residual Files**: All temporary files are automatically cleaned up
- โ
**Efficient Processing**: Uses memory for intermediate steps where possible
- โ
**PyPI Ready**: Designed for simple installation via pip
## ๏ฟฝ Universal Converter Update
The converter works with a wide variety of HuggingFace Trainer API usage patterns:
- โ
**Multiple Model Types**: Automatically detects and enhances different model architectures (SequenceClassification, TokenClassification, QuestionAnswering, etc.)
- โ
**Diverse Metrics**: Identifies and visualizes various evaluation metrics (accuracy, F1, precision, recall, etc.)
- โ
**Flexible Training Patterns**: Works with different training approaches and visualization styles
- โ
**Enhanced Analysis**: Generates detailed reports about training configurations and results
## ๐ Quick Start
### Installation
```bash
pip install hf-trainer-nbconvert
```
### Usage
#### Command Line
```bash
# Convert a notebook to HTML (no residual files)
hf-trainer-nbconvert your_notebook.ipynb
# Specify output file
hf-trainer-nbconvert your_notebook.ipynb -o output.html
```
#### Python API
```python
# Simple, direct API (recommended)
from hf_trainer_nbconvert import convert_notebook_to_html
# One-line conversion with automatic cleanup
html_path = convert_notebook_to_html("your_notebook.ipynb")
print(f"HTML saved to: {html_path}")
# Or with custom output path
html_path = convert_notebook_to_html(
"your_notebook.ipynb",
output_html_path="custom_output.html"
)
```
## ๐งช Test Models
The `test_models` directory contains sample notebooks that demonstrate different usages of the HuggingFace Trainer API:
1. **Text Classification**: Simple sentiment analysis with BERT
2. **Named Entity Recognition**: Token classification with CoNLL-2003 dataset
Use these to test the universality of the converter:
```bash
python test_universal_converter.py -a -v
```
## ๐ฏ Features
- **Smart Trainer Detection**: Automatically identifies cells containing Hugging Face Trainer API usage
- **Enhanced Training Visualization**: Adds improved progress tracking and loss visualization
- **Multiple Output Formats**: Convert to Python, HTML, Markdown with enhanced formatting
- **Training Analysis**: Comprehensive analysis of Trainer usage patterns
- **Progress Enhancement**: Automatically enhances training cells with better progress tracking
- **Custom Styling**: Adds custom CSS for HTML output to highlight training components
## ๐ Installation
1. Clone or download this repository
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## ๐ Files
- `hf_trainer_nbconvert.py` - Main converter module
- `example_usage.py` - Usage examples and demonstrations
- `requirements.txt` - Required dependencies
- `README.md` - This documentation
## ๐ง Usage
### Python API
```python
from hf_trainer_nbconvert import HuggingFaceTrainerConverter
# Initialize converter
converter = HuggingFaceTrainerConverter("your_notebook.ipynb")
# Convert to Python with enhancements
python_code = converter.convert_to_python("enhanced_script.py")
# Convert to HTML with custom styling
html_content = converter.convert_to_html("enhanced_notebook.html")
# Convert to Markdown
markdown_content = converter.convert_to_markdown("enhanced_docs.md")
# Analyze Trainer usage
analysis = converter.analyze_trainer_usage()
print(f"Found {len(analysis['trainer_cells'])} Trainer cells")
# Generate training report
report = converter.generate_training_report()
```
### Command Line Interface
```bash
# Basic conversion to Python
python hf_trainer_nbconvert.py notebook.ipynb -f python
# Convert to HTML with custom output path
python hf_trainer_nbconvert.py notebook.ipynb -f html -o output.html
# Convert to all formats
python hf_trainer_nbconvert.py notebook.ipynb -f all
# Generate analysis report
python hf_trainer_nbconvert.py notebook.ipynb --analyze
# Generate training report
python hf_trainer_nbconvert.py notebook.ipynb --report -o report.md
# Convert without enhancements (standard nbconvert)
python hf_trainer_nbconvert.py notebook.ipynb --no-enhance
```
## ๐จ Enhanced Features
### 1. Training Progress Visualization
The converter automatically enhances training cells with:
```python
# Enhanced training visualization
if hasattr(trainer.state, 'log_history'):
logs = trainer.state.log_history
if logs:
steps = [log.get('step', 0) for log in logs if 'loss' in log]
losses = [log.get('loss', 0) for log in logs if 'loss' in log]
if steps and losses:
plt.figure(figsize=(10, 6))
plt.plot(steps, losses, 'b-', linewidth=2, label='Training Loss')
plt.xlabel('Training Steps')
plt.ylabel('Loss')
plt.title('Training Progress - Loss Over Time')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()
```
### 2. Training Documentation
Trainer instantiation cells are enhanced with detailed documentation:
```python
# Enhanced Training Configuration
# This cell initializes the Hugging Face Trainer with the following key components:
# - Model: The transformer model to be fine-tuned
# - Training Arguments: Configuration for training hyperparameters
# - Dataset: Training and evaluation datasets
# - Tokenizer: For text preprocessing
```
### 3. Custom HTML Styling
HTML output includes custom CSS for better visualization:
```css
.trainer-cell {
border-left: 4px solid #ff6b6b;
padding-left: 10px;
background-color: #fff5f5;
}
.training-progress {
border-left: 4px solid #4ecdc4;
padding-left: 10px;
background-color: #f0fffe;
}
```
## ๐ Analysis Features
### Trainer Usage Analysis
The converter can analyze your notebook and provide insights:
```python
analysis = converter.analyze_trainer_usage()
# Returns:
{
'trainer_cells': [6], # Cells with Trainer instantiation
'training_cells': [7], # Cells with training execution
'visualization_cells': [8], # Cells with training visualization
'imports': [...], # Detected imports
'models_used': [...], # Models found in the notebook
'datasets_used': [...] # Datasets detected
}
```
### Training Report Generation
Generate comprehensive reports about your training setup:
```
# Hugging Face Trainer Analysis Report
**Notebook:** example_notebook.ipynb
## Summary
- Total cells: 15
- Code cells: 12
- Trainer-related cells: 3
- Training execution cells: 1
- Visualization cells: 2
## Trainer API Usage
- Trainer instantiation found in cells: [6]
- Training execution found in cells: [7]
- Training visualization found in cells: [8, 10]
## Models Detected
- BertForQuestionAnswering (Cell 6)
## Recommendations
- Consider adding more comprehensive evaluation metrics
- Add early stopping callback for better training control
```
## ๐ Detected Patterns
The converter automatically detects:
- **Trainer Instantiation**: `Trainer(...)`, `trainer = Trainer(...)`
- **Training Execution**: `trainer.train()`, `training_log = trainer.train()`
- **Visualization**: `plt.plot(...loss...)`, `matplotlib...training`
- **Model Usage**: `BertForQuestionAnswering`, `GPT2LMHeadModel`, etc.
- **Import Statements**: `from transformers import ...`
## ๐ฏ Use Cases
1. **Converting Training Notebooks**: Transform research notebooks into production-ready scripts
2. **Documentation Generation**: Create comprehensive HTML/Markdown documentation
3. **Training Analysis**: Analyze and optimize your training setup
4. **Progress Visualization**: Enhance training progress tracking
5. **Code Sharing**: Generate clean Python scripts from experimental notebooks
## ๐ ๏ธ Customization
You can extend the converter by:
1. **Adding Custom Preprocessors**: Create new pattern detection logic
2. **Custom Templates**: Modify output templates for different formats
3. **Enhanced Analysis**: Add more sophisticated training analysis
4. **Custom Styling**: Modify CSS for HTML output
Example custom preprocessor:
```python
class CustomTrainerPreprocessor(HuggingFaceTrainerPreprocessor):
def _is_custom_pattern(self, source: str) -> bool:
# Add your custom detection logic
return 'your_pattern' in source
def _enhance_custom_cell(self, cell, index: int):
# Add your custom enhancement
return cell
```
## ๐ค Contributing
Feel free to contribute by:
- Reporting issues
- Suggesting new features
- Submitting pull requests
- Improving documentation
## ๐ License
This project is licensed under the MIT License - see the LICENSE file for details.
## ๐จโ๐ป Author
**K N S Sri Harshith**
Email: knssriharshith@gmail.com
1. Adding support for more training frameworks
2. Improving visualization capabilities
3. Adding new output formats
4. Enhancing analysis features
5. Improving documentation
## ๐ License
MIT License - Feel free to use and modify as needed.
## ๐ Related Tools
- [nbconvert](https://nbconvert.readthedocs.io/) - The underlying conversion library
- [Hugging Face Transformers](https://huggingface.co/transformers/) - The ML library this tool specializes in
- [Jupyter](https://jupyter.org/) - The notebook environment
## ๐ Support
If you encounter issues or have suggestions:
1. Check the analysis output for insights
2. Use the `--analyze` flag to understand your notebook structure
3. Try the `--no-enhance` flag if you encounter compatibility issues
4. Review the generated training report for optimization suggestions
---
**Happy Training! ๐**
Raw data
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"description": "# \ud83e\udd17 HuggingFace Trainer Notebook Converter\r\n\r\n[](https://badge.fury.io/py/hf-trainer-nbconvert)\r\n[](https://www.python.org/downloads/)\r\n[](https://opensource.org/licenses/MIT)\r\n\r\nA specialized Python package for converting Jupyter notebooks that use the HuggingFace Trainer API to HTML while preserving and enhancing training progress visualization.\r\n\r\n## \ud83e\uddf9 Clean Conversion Process\r\n\r\nThis package provides a **clean, direct conversion** with no intermediate files left behind:\r\n\r\n- \u2705 **Direct Conversion**: Converts directly from .ipynb to HTML in one step\r\n- \u2705 **No Residual Files**: All temporary files are automatically cleaned up\r\n- \u2705 **Efficient Processing**: Uses memory for intermediate steps where possible\r\n- \u2705 **PyPI Ready**: Designed for simple installation via pip\r\n\r\n## \ufffd Universal Converter Update\r\n\r\nThe converter works with a wide variety of HuggingFace Trainer API usage patterns:\r\n\r\n- \u2705 **Multiple Model Types**: Automatically detects and enhances different model architectures (SequenceClassification, TokenClassification, QuestionAnswering, etc.)\r\n- \u2705 **Diverse Metrics**: Identifies and visualizes various evaluation metrics (accuracy, F1, precision, recall, etc.)\r\n- \u2705 **Flexible Training Patterns**: Works with different training approaches and visualization styles\r\n- \u2705 **Enhanced Analysis**: Generates detailed reports about training configurations and results\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install hf-trainer-nbconvert\r\n```\r\n\r\n### Usage\r\n\r\n#### Command Line\r\n```bash\r\n# Convert a notebook to HTML (no residual files)\r\nhf-trainer-nbconvert your_notebook.ipynb\r\n\r\n# Specify output file\r\nhf-trainer-nbconvert your_notebook.ipynb -o output.html\r\n```\r\n\r\n#### Python API\r\n```python\r\n# Simple, direct API (recommended)\r\nfrom hf_trainer_nbconvert import convert_notebook_to_html\r\n\r\n# One-line conversion with automatic cleanup\r\nhtml_path = convert_notebook_to_html(\"your_notebook.ipynb\")\r\nprint(f\"HTML saved to: {html_path}\")\r\n\r\n# Or with custom output path\r\nhtml_path = convert_notebook_to_html(\r\n \"your_notebook.ipynb\", \r\n output_html_path=\"custom_output.html\"\r\n)\r\n```\r\n\r\n## \ud83e\uddea Test Models\r\n\r\nThe `test_models` directory contains sample notebooks that demonstrate different usages of the HuggingFace Trainer API:\r\n\r\n1. **Text Classification**: Simple sentiment analysis with BERT\r\n2. **Named Entity Recognition**: Token classification with CoNLL-2003 dataset\r\n\r\nUse these to test the universality of the converter:\r\n\r\n```bash\r\npython test_universal_converter.py -a -v\r\n```\r\n\r\n## \ud83c\udfaf Features\r\n\r\n- **Smart Trainer Detection**: Automatically identifies cells containing Hugging Face Trainer API usage\r\n- **Enhanced Training Visualization**: Adds improved progress tracking and loss visualization\r\n- **Multiple Output Formats**: Convert to Python, HTML, Markdown with enhanced formatting\r\n- **Training Analysis**: Comprehensive analysis of Trainer usage patterns\r\n- **Progress Enhancement**: Automatically enhances training cells with better progress tracking\r\n- **Custom Styling**: Adds custom CSS for HTML output to highlight training components\r\n\r\n## \ud83d\ude80 Installation\r\n\r\n1. Clone or download this repository\r\n2. Install the required dependencies:\r\n\r\n```bash\r\npip install -r requirements.txt\r\n```\r\n\r\n## \ud83d\udcc1 Files\r\n\r\n- `hf_trainer_nbconvert.py` - Main converter module\r\n- `example_usage.py` - Usage examples and demonstrations\r\n- `requirements.txt` - Required dependencies\r\n- `README.md` - This documentation\r\n\r\n## \ud83d\udd27 Usage\r\n\r\n### Python API\r\n\r\n```python\r\nfrom hf_trainer_nbconvert import HuggingFaceTrainerConverter\r\n\r\n# Initialize converter\r\nconverter = HuggingFaceTrainerConverter(\"your_notebook.ipynb\")\r\n\r\n# Convert to Python with enhancements\r\npython_code = converter.convert_to_python(\"enhanced_script.py\")\r\n\r\n# Convert to HTML with custom styling\r\nhtml_content = converter.convert_to_html(\"enhanced_notebook.html\")\r\n\r\n# Convert to Markdown\r\nmarkdown_content = converter.convert_to_markdown(\"enhanced_docs.md\")\r\n\r\n# Analyze Trainer usage\r\nanalysis = converter.analyze_trainer_usage()\r\nprint(f\"Found {len(analysis['trainer_cells'])} Trainer cells\")\r\n\r\n# Generate training report\r\nreport = converter.generate_training_report()\r\n```\r\n\r\n### Command Line Interface\r\n\r\n```bash\r\n# Basic conversion to Python\r\npython hf_trainer_nbconvert.py notebook.ipynb -f python\r\n\r\n# Convert to HTML with custom output path\r\npython hf_trainer_nbconvert.py notebook.ipynb -f html -o output.html\r\n\r\n# Convert to all formats\r\npython hf_trainer_nbconvert.py notebook.ipynb -f all\r\n\r\n# Generate analysis report\r\npython hf_trainer_nbconvert.py notebook.ipynb --analyze\r\n\r\n# Generate training report\r\npython hf_trainer_nbconvert.py notebook.ipynb --report -o report.md\r\n\r\n# Convert without enhancements (standard nbconvert)\r\npython hf_trainer_nbconvert.py notebook.ipynb --no-enhance\r\n```\r\n\r\n## \ud83c\udfa8 Enhanced Features\r\n\r\n### 1. Training Progress Visualization\r\n\r\nThe converter automatically enhances training cells with:\r\n\r\n```python\r\n# Enhanced training visualization\r\nif hasattr(trainer.state, 'log_history'):\r\n logs = trainer.state.log_history\r\n if logs:\r\n steps = [log.get('step', 0) for log in logs if 'loss' in log]\r\n losses = [log.get('loss', 0) for log in logs if 'loss' in log]\r\n \r\n if steps and losses:\r\n plt.figure(figsize=(10, 6))\r\n plt.plot(steps, losses, 'b-', linewidth=2, label='Training Loss')\r\n plt.xlabel('Training Steps')\r\n plt.ylabel('Loss')\r\n plt.title('Training Progress - Loss Over Time')\r\n plt.legend()\r\n plt.grid(True, alpha=0.3)\r\n plt.show()\r\n```\r\n\r\n### 2. Training Documentation\r\n\r\nTrainer instantiation cells are enhanced with detailed documentation:\r\n\r\n```python\r\n# Enhanced Training Configuration\r\n# This cell initializes the Hugging Face Trainer with the following key components:\r\n# - Model: The transformer model to be fine-tuned\r\n# - Training Arguments: Configuration for training hyperparameters\r\n# - Dataset: Training and evaluation datasets\r\n# - Tokenizer: For text preprocessing\r\n```\r\n\r\n### 3. Custom HTML Styling\r\n\r\nHTML output includes custom CSS for better visualization:\r\n\r\n```css\r\n.trainer-cell {\r\n border-left: 4px solid #ff6b6b;\r\n padding-left: 10px;\r\n background-color: #fff5f5;\r\n}\r\n.training-progress {\r\n border-left: 4px solid #4ecdc4;\r\n padding-left: 10px;\r\n background-color: #f0fffe;\r\n}\r\n```\r\n\r\n## \ud83d\udcca Analysis Features\r\n\r\n### Trainer Usage Analysis\r\n\r\nThe converter can analyze your notebook and provide insights:\r\n\r\n```python\r\nanalysis = converter.analyze_trainer_usage()\r\n# Returns:\r\n{\r\n 'trainer_cells': [6], # Cells with Trainer instantiation\r\n 'training_cells': [7], # Cells with training execution\r\n 'visualization_cells': [8], # Cells with training visualization\r\n 'imports': [...], # Detected imports\r\n 'models_used': [...], # Models found in the notebook\r\n 'datasets_used': [...] # Datasets detected\r\n}\r\n```\r\n\r\n### Training Report Generation\r\n\r\nGenerate comprehensive reports about your training setup:\r\n\r\n```\r\n# Hugging Face Trainer Analysis Report\r\n**Notebook:** example_notebook.ipynb\r\n\r\n## Summary\r\n- Total cells: 15\r\n- Code cells: 12\r\n- Trainer-related cells: 3\r\n- Training execution cells: 1\r\n- Visualization cells: 2\r\n\r\n## Trainer API Usage\r\n- Trainer instantiation found in cells: [6]\r\n- Training execution found in cells: [7]\r\n- Training visualization found in cells: [8, 10]\r\n\r\n## Models Detected\r\n- BertForQuestionAnswering (Cell 6)\r\n\r\n## Recommendations\r\n- Consider adding more comprehensive evaluation metrics\r\n- Add early stopping callback for better training control\r\n```\r\n\r\n## \ud83d\udd0d Detected Patterns\r\n\r\nThe converter automatically detects:\r\n\r\n- **Trainer Instantiation**: `Trainer(...)`, `trainer = Trainer(...)`\r\n- **Training Execution**: `trainer.train()`, `training_log = trainer.train()`\r\n- **Visualization**: `plt.plot(...loss...)`, `matplotlib...training`\r\n- **Model Usage**: `BertForQuestionAnswering`, `GPT2LMHeadModel`, etc.\r\n- **Import Statements**: `from transformers import ...`\r\n\r\n## \ud83c\udfaf Use Cases\r\n\r\n1. **Converting Training Notebooks**: Transform research notebooks into production-ready scripts\r\n2. **Documentation Generation**: Create comprehensive HTML/Markdown documentation\r\n3. **Training Analysis**: Analyze and optimize your training setup\r\n4. **Progress Visualization**: Enhance training progress tracking\r\n5. **Code Sharing**: Generate clean Python scripts from experimental notebooks\r\n\r\n## \ud83d\udee0\ufe0f Customization\r\n\r\nYou can extend the converter by:\r\n\r\n1. **Adding Custom Preprocessors**: Create new pattern detection logic\r\n2. **Custom Templates**: Modify output templates for different formats\r\n3. **Enhanced Analysis**: Add more sophisticated training analysis\r\n4. **Custom Styling**: Modify CSS for HTML output\r\n\r\nExample custom preprocessor:\r\n\r\n```python\r\nclass CustomTrainerPreprocessor(HuggingFaceTrainerPreprocessor):\r\n def _is_custom_pattern(self, source: str) -> bool:\r\n # Add your custom detection logic\r\n return 'your_pattern' in source\r\n \r\n def _enhance_custom_cell(self, cell, index: int):\r\n # Add your custom enhancement\r\n return cell\r\n```\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nFeel free to contribute by:\r\n\r\n- Reporting issues\r\n- Suggesting new features\r\n- Submitting pull requests\r\n- Improving documentation\r\n\r\n## \ud83d\udcc4 License\r\n\r\nThis project is licensed under the MIT License - see the LICENSE file for details.\r\n\r\n## \ud83d\udc68\u200d\ud83d\udcbb Author\r\n\r\n**K N S Sri Harshith** \r\nEmail: knssriharshith@gmail.com\r\n\r\n1. Adding support for more training frameworks\r\n2. Improving visualization capabilities\r\n3. Adding new output formats\r\n4. Enhancing analysis features\r\n5. Improving documentation\r\n\r\n## \ud83d\udcdd License\r\n\r\nMIT License - Feel free to use and modify as needed.\r\n\r\n## \ud83d\udd17 Related Tools\r\n\r\n- [nbconvert](https://nbconvert.readthedocs.io/) - The underlying conversion library\r\n- [Hugging Face Transformers](https://huggingface.co/transformers/) - The ML library this tool specializes in\r\n- [Jupyter](https://jupyter.org/) - The notebook environment\r\n\r\n## \ud83d\udcde Support\r\n\r\nIf you encounter issues or have suggestions:\r\n\r\n1. Check the analysis output for insights\r\n2. Use the `--analyze` flag to understand your notebook structure\r\n3. Try the `--no-enhance` flag if you encounter compatibility issues\r\n4. Review the generated training report for optimization suggestions\r\n\r\n---\r\n\r\n**Happy Training! \ud83d\ude80**\r\n",
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