semvec/pypi-packages huggingface dataset:
yakit 0.1.1
Yakut language text normalizer using Word2Vec embeddings
License: MIT
Requires Python: <3.14,>=3.10
Keywords: nlp, sakha, text-normalization, word2vec, yakut

# Yakit - Yakut Language Text Normalizer

A Python library for normalizing Yakut (Sakha) language text using Word2Vec embeddings.

## Installation

```bash
pip install yakit
```

For automatic model downloading from Hugging Face Hub:

```bash
pip install yakit[download]
```

## Quick Start

```python
from yakit.normalizers import Word2VecNormalizer

# Initialize normalizer (auto-downloads model on first use)
normalizer = Word2VecNormalizer()

# Normalize text
text = "Мин сахалыы билэбин"
normalized = normalizer.normalize(text)
print(normalized)
```

## Custom Model Path

If you have your own Word2Vec model:

```python
from yakit.normalizers import Word2VecNormalizer

normalizer = Word2VecNormalizer(
    word2vec_path="/path/to/your/model.bin",
    training_data_path="/path/to/train_pairs.txt"  # optional
)
```

## Command Line Interface

```bash
# Normalize text directly
yakit normalize "Мин сахалыы билэбин"

# Normalize a file
yakit normalize -i input.txt -o output.txt

# Download models manually
yakit download

# Show cache info
yakit info
```

## What is Normalization?

Normalization converts text WITHOUT special Yakut characters to text WITH proper Yakut characters:

| Input | Output |
|-------|--------|
| h → | һ |
| г → | ҕ (in certain positions) |
| н → | ҥ (in certain positions) |
| о → | ө (in certain positions) |
| у → | ү (in certain positions) |

## Performance

With optimized hyperparameters:
- Character Accuracy: **97.15%**
- Word Accuracy: **92.09%**
- Exact Match: **61.77%**

## Requirements

- Python 3.10–3.13 (3.14 not yet supported: gensim has no compatible build)
- gensim
- numpy
- tqdm

## License

MIT
fw-hpc-client 2.2.0
The HPC Client is a self-service solution that allows Flywheel jobs and gears to run on a High Performance Computing environment. Use on-premise hardware that's already available for highly-concurrent scientific workloads!
License: MIT
Requires Python: <=3.12,>=3.9
Keywords: Flywheel, App

# Flywheel HPC Client <!-- omit in toc -->

The HPC Client is a self-service solution that allows Flywheel jobs and gears to run on
a High Performance Computing environment. Use on-premise hardware that's already
available for highly-concurrent scientific workloads!

## Documentation

The types of HPC schedulers, minimal requirements, setup directions, and answers to
Frequently Asked Questions can be found
[here](https://flywheel-io.gitlab.io/scientific-solutions/app/fw-hpc-client/).