semvec/pypi-packages huggingface dataset:
minpin 1.0.0
Automatically add minimum version pins to unpinned packages in conda YAML files.
Requires Python: >=3.7
Keywords: conda, pinning, anaconda-project, dependencies

```
   / \__
  (    @\_____
  /         O
 /   (_____/  
/_____/   U   
```

# Min Pin

Min Pin is a command-line tool that automatically adds minimum version pins to unpinned packages in conda-friendly YAML files, such as `environment.yml` or `anaconda-project.yml`.

## Features

- Parses `conda list` and `pip list` to retrieve installed package versions.
- Updates YAML files by adding minimum version pins to unpinned packages.
- Handles both conda and pip dependencies.
- Preserves the original structure and comments of the YAML file.

## Installation

```bash
pip install minpin
```

## Usage

```bash
minpin ./environment.yml
```

Optionally, specify a conda list output file which you can create with `conda list > conda_list.txt`:

```bash
minpin anaconda-project.yml --conda-list conda_list.txt
```

## Example

**Input: original `environment.yml`**

```yaml
channels:
  - conda-forge
dependencies:
  - python=3.10
  - numpy
  - pandas>=1.3
  - pip
  - pip:
      - requests
      - flask
```



**Output: Updated `environment.yml`**
```yaml
channels:
  - conda-forge
dependencies:
  - python=3.10
  - numpy>=1.24.0 # auto min pinned 2024-11-18
  - pandas>=1.3.3 # auto min pinned 2024-11-18
  - pip
  - pip:
      - requests>=2.28.2 # auto min pinned 2024-11-18
      - flask>=2.2.3 # auto min pinned 2024-11-18
```
SimsysDNLP 0.0.4
A deep learning natural language processing service for simulating different systems (normal-parallel-single server).
Requires Python: >=3.6

# DNLP Simulation Service

This repository contains the **DNLP Simulation Service**, a machine learning-based service extraction and simulation framework for natural language processing tasks. The model, built with PyTorch and Transformers, leverages BERT embeddings and multi-head attention for robust service classification and information extraction.

## Features

- **Service Extraction**: Extracts service-related details from unstructured text.
- **Simulation**: Simulates customer interactions and service response times.
- **Customizable NLP Pipeline**: Built with flexible NLP components for tailored use cases.
- **Cloud-Ready**: Compatible with cloud platforms and frameworks like Kaggle and Colab for cloud training and fine-tuning.

## Installation

Clone the repository and install dependencies:

```bash
git clone https://github.com/Mohamed-Hamdey/DNLP-Simulation-Service.git
cd DNLP-Simulation-Service
pip install -r requirements.txt
```

Alternatively, install the package directly:

```bash
pip install DNLP-Simulation-Service
```

## Usage

### Training

To train the model, prepare a dataset in JSON format and run:

```python
from DNLP_Simulation_Service.trainer import NLPService
from DNLP_Simulation_Service.config import ModelConfig

config = ModelConfig(...)
service = NLPService(config=config)
service.train(train_data="path/to/dataset.json")
```

### Prediction

Once trained, make predictions using:

```python
text = "Example service description text."
prediction = service.predict(text)
print(prediction)
```

### Cloud Training

To train on Kaggle or Colab:
1. Upload the repository files to the environment.
2. Use the provided `trainer.py` script to start training on cloud GPUs.

## API Reference

### Configuration

The `ModelConfig` class allows adjustments to:
- `embedding_dim`
- `hidden_dim`
- `num_heads`
- `dropout`

### Data Preparation

Use `DataProcessor` for preprocessing data and preparing labels.

## Contributing

1. Fork the repo
2. Create a branch (`git checkout -b feature-branch`)
3. Commit changes (`git commit -m 'Add feature'`)
4. Push the branch (`git push origin feature-branch`)
5. Open a pull request

## License

This project is licensed under the MIT License.
mtlabpy 0.1.0
A Python package for MTCLab computational tools
Requires Python: >=3.6

# MatlabPy

MatlabPy is a comprehensive Python package that provides computational tools for various biological and chemical analyses. This package includes modules for:

- Enzyme calculations
- Molecular docking
- Molecular pocket analysis
- Solubility predictions
- Developmental tree analysis
- Kcat predictions
- Affinity calculations

## Installation

You can install the package using pip:

```bash
pip install matlabpy
```

## Usage

Here's a quick example of how to use the package:

```python
import matlabpy

# More usage examples will be added
```

## Features

- Enzyme Self Calculation
- Molecular Docking Analysis
- Pocket Analysis
- Solubility Prediction
- And more...

## Requirements

- Python >= 3.6
- Other dependencies are listed in requirements.txt

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.