semvec/pypi-packages huggingface dataset:
odoobeast 0.1.6
OdooBeast: The ultimate AI-powered personal assistant library
Requires Python: >=3.8

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OdooBeast

OdooBeast is a powerful, intelligent, and emotionally connected AI library designed to handle complex conversations and provide human-like responses. It integrates advanced Natural Language Processing (NLP), machine learning, memory systems, and emotional analysis to deliver thoughtful and context-aware replies.

Features

Advanced NLP: High-performance intent classification and language understanding.

Emotion Detection: Analyzes user input to detect emotions and adjust responses accordingly.

Memory System: Remembers past conversations and maintains context across interactions.

Response Generation: Provides smart, human-like, and contextually aware replies.

Error Handling: Robust logging and error management.

Machine Learning: Uses prebuilt and customizable ML models.

Installation 
pip install odoobeast

Usage
from odoobeast.core import OdooBeast

bot = OdooBeast()
user_input = "I’m feeling a bit down today."
response = bot.process_input(user_input)
print(f"OdooBeast: {response}")

Folder Structure
odoobeast/
|-- core/
|   |-- __init__.py
|   |-- nlp.py
|   |-- ml.py
|   |-- memory.py
|   |-- utils.py
|   |-- emotion.py
|   |-- response.py
|   |-- error.py
|-- models/
|   |-- __init__.py
|   |-- prebuilt_models.py
|-- tests/
|   |-- __init__.py
|   |-- test_nlp.py
|   |-- test_ml.py
|   |-- test_memory.py
|   |-- test_response.py
|   |-- test_error.py
|-- __init__.py
|-- README.md
|-- setup.py
|-- requirements.txt

Contributing

Feel free to open issues or submit pull requests for bug fixes, feature additions, or documentation improvements.

License

MIT License
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Together we can, we will.
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Made With High Dreams and Goals.
=======
# odoobeast
 OdooBeast  OdooBeast is a powerful, intelligent, and emotionally connected AI library designed to handle complex conversations and provide human-like responses. It integrates advanced Natural Language Processing (NLP), machine learning, memory systems, and emotional analysis to deliver thoughtful and context-aware replies.
>>>>>>> 2a858efa2af4c59726e23194fc5e987148be7b79
NCD-KNN-Classifier 0.2.1
A kNN classifier based on NCD distance metric for text compression
Requires Python: >=3.11

# NCD-KNN-Classifier

A kNN classifier based on the Normalized Compression Distance (NCD) for text classification.

## Installation

Install the package using pip:

```bash
pip install NCD-KNN-Classifier
```

## Usage

Here's a basic example of how to use the classifier:

```python
from datasets import load_dataset
from NCD_KNN_Classifier import CompNCDClassifier

# Example of imdb dataset for classification of two labels
dataset = load_dataset("imdb")
test_samples = dataset["test"].shuffle(seed=42).select(range(200))

# Compressing and Saving the Training Dataset Footprint
classifier = CompNCDClassifier(
    train_dataset=dataset['train'],
    test_dataset=test_samples,
    k=3,
    compressor="gzip",
    verbose=True
)
classifier.save_to_pickle("train_footprint.pkl")

# Inference on the test set
metrics = classifier.evaluate()
print("Evaluation metrics:", metrics)
```
### Output Example

After running the example, you should see output similar to this:

```bash
INFO:NCD_KNN_Classifier.classifier:Evaluating on the dataset...
Evaluating 200/200: 100%|██████████| 200/200 [00:55<00:00,  3.63it/s]
Evaluation metrics: {'accuracy': 0.69, 'precision': 0.6969369369369369, 'recall': 0.69, 'f1_score': 0.68476726342711}
```

## New Features in v0.2.1

- Added explainability in the **classify** function, providing information about the nearest neighbors and their influence on classification decisions.

- Introduced the **optimizer** function to assist in finding the optimal k and compressor type.



## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.