semvec/pypi-packages huggingface dataset:
test-generated-repo-20250731-01 0.0.0
My package description
License: MIT
Requires Python: >=3.10
Keywords: one, two

# test_generated_repo_20250731_01

## Install as package

```bash
pip install test_generated_repo_20250731_01
```

```python
from test_generated_package_20250731_01 import ...
```

## Contributing

```bash
# clone the repo
git clone https://github.com/momomozhang/test_generated_repo_20250731_01.git

# install the dev dependencies
make install

# run the tests
make test
```
nade 0.3.1
Natural affect detection allows to infer basic emotions from social media messages
License: MIT
Requires Python: >=3.9

# Nade Python Package
This is the Python package version of Nade, a model that allows to infer basic emotions from social media messages ([live demo](https://nade-explorer.inkrement.ai/)). While human raters are often too resource-intensive, lexical approaches face challenges regarding incomplete vocabulary and the handling of informal language. Even advanced machine learning-based approaches require substantial resources (expert knowledge, programming skills, annotated data sets, extensive computational capabilities) and tend to gauge the mere presence, not the intensity, of emotion. This package solves this issue by predicting a vast array of emojis based on the surrounding text, then reduces these predicted emojis to an established set of eight basic emotions.


![Architecture](https://raw.githubusercontent.com/inkrement/nade/main/docs/overview.png)


## Usage
After installation, the module can be loaded and the predict method can be used for inference.

```python
from nade import Nade

n = Nade()
n.predict('I love this')
```

The method returns a dictionary containing the scores for all eight basic emotions.

```python
{
 'anger': [0.004],
 'anticipation': [0.15],
 'disgust': [0.017],
 'fear': [0.027],
 'joy': [0.451],
 'sadness': [0.02],
 'surprise': [0.142],
 'trust': [0.242]
}
```

## Installation

The package can be installed as follows:

```bash
pip install nade
```

## Performance

The prediction method features a _lleaves_ option that provides much faster inference. However, you will have to install [lleaves](https://github.com/siboehm/lleaves) first.

## Notes

 - Usage on Apple M1 chips require additional dependencies (i.e., `brew install cmake libomp`)

## Links
- [Nade Explorer (interactive demo + more information)](https://nade-explorer.inkrement.ai/)
- [Nade R Package](https://github.com/inkrement/nadeR)
- Paper (coming soon)
lint-utils 0.2.1
The humble linter package
License: MIT
Requires Python: >=3.11

# Lint Utils

The humble linter package

## Installation

```bash
pip install lint-utils
```

## Usage

```bash
lu check src
```

or

```bash
lu check path/to/file_1.py path/to/file_2/ path/to/dir
```

## Aliases

short alias: `lu check ...`

full alias `lint-utils check ...`

## Rules

**USL001** - Unused object class fields found

**RL001** - The reminder has expired

Please enter a comment in the format `# lu: RL001[YYYY-MM-DD]: write a note here`

```python
class Command:
    async def execute(self) -> int:
        return 3  # lu: RL001[2025-07-30]: fix later
```

## Configuration

You can create `lint_utils.toml` in root project directory

```toml
[lint-utils]
exclude = []
lint.ignore = []

[lint-utils.exclude-base-classes]
USL001 = []

[lint-utils.exclude-classes]
USL001 = []
```

`pyproject.toml` is also supported

```toml
...

[tool.lint-utils]
exclude = []
lint.ignore = []

[tool.lint-utils.exclude-base-classes]
USL001 = []

[tool.lint-utils.exclude-classes]
USL001 = []
```

### exclude

If you put a file path in the exclude list, it will be ignored during checking.

```toml
...

[tool.lint-utils]
exclude = ["path/to/file.py"]
```

### tool.lint-utils.exclude-base-classes

If you add a base class, it and its child classes will be ignored during validation.

```toml
...

[tool.lint-utils.exclude-base-classes]
USL001 = ["Exception"]
```

Example:

```py
# src/exceptions.py
class FieldNameError(Exception):
    def __init__(self, field_name: str) -> None:
        self._field_name = field_name
```

This file will be ignored if `USL001` rules are followed, because `Exception` class putted in `tool.lint-utils.exclude-base-classes`

### tool.lint-utils.exclude-classes

If you add a class, it will be ignored during validation.

```toml
...

[tool.lint-utils.exclude-classes]
USL001 = ["FieldNameError"]
```

Example:

```py
# src/exceptions.py
class FieldNameError(Exception):
    def __init__(self, field_name: str) -> None:
        self._field_name = field_name
```

This file will be ignored, because `FieldNameError` class putted in lint.exclude_classes for `USL001` rule

### lint.ignore

If you add a rule code, it will be ignored during validation.

```toml
...

[tool.lint-utils]
lint.ignore = ["USL001"]
```

### Line ignoring

In order not to conflict with Ruff and `noqa`, it was decided to specify the following design:

```python
class CommandCommand:
    def __init__(self, variable: str) -> None:
        self._variable = variable  # lu: USL001
```