Metadata-Version: 2.4
Name: shog-ai
Version: 0.1.0
Summary: Streaming dataset engineering library for multilingual AI and foundation model pretraining.
Author-email: Godsave Kawurem <godsaveogbidor@gmail.com>
Maintainer-email: Godsave Kawurem <godsaveogbidor@gmail.com>
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Project-URL: Homepage, https://github.com/Perfect-Aimers-Enterprise/shog-ai-data
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Keywords: ai,llm,nlp,foundation-model,pretraining,dataset,machine-learning,deep-learning,huggingface,wikipedia,opus,multilingual,python,pytorch,streaming
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Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Requires-Python: >=3.10
Description-Content-Type: text/markdown
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Dynamic: license-file

# Shog AI

**Streaming dataset engineering for foundation model training.**

Build multilingual AI datasets from Wikipedia, OPUS, Common Crawl and more with a unified schema — designed for large-scale LLM pretraining.

<!-- Badges -->

## Why Shog AI?

Preparing multilingual, large-scale datasets for foundation model pretraining usually means gluing together one-off scripts for every source — Wikipedia dumps, OPUS corpora, Common Crawl shards — each with a different schema, different cleaning rules, and no consistent output format.

Shog AI standardizes that process. It gives you a single pipeline that ingests data from multiple sources, normalizes it into a unified schema, validates and deduplicates it, and exports it in a form ready for training or publishing.

---

## Features

- **Unified schema** — every source is normalized into the same record format
- **Streaming pipeline** — process datasets without loading everything into memory
- **Multi-source ingestion** — combine Wikipedia, OPUS, and other sources in a single run
- **Built-in validation & deduplication**
- **One-line publishing** — push finished datasets straight to the Hugging Face Hub or local storage
- **Reproducible outputs** — every run produces metadata, statistics, and a manifest alongside the data

---

## Installation

```bash
pip install shog-ai
```

---

## Quick Start

```python
from datasets import load_dataset, load_from_disk

from parsers.wikipedia import WikipediaParser
from pipeline.config import PipelineConfig
from pipeline.pipeline import Pipeline

# yo = load_from_disk("data/wikipedia/yo")
# yo = load_dataset("data/wikipedia/yo")
yo_10 = load_dataset(
    "wikimedia/wikipedia",
    "20231101.yo",
    split="train[:10]"
)

wiki = WikipediaParser(
    language="yo_10"
)

config = PipelineConfig(
    output_dir="output",
    dataset_name="shog_alpha",
)

pipeline = Pipeline(config)

stats = pipeline.process(
    (wiki, yo_10),
)

pipeline.summary()
```

### Processing multiple sources at once

```python
stats = pipeline.process(
    (wiki, wiki_dataset),
    (opus, opus_dataset),
    (books, books_dataset),
)

pipeline.summary(stats)
```

---

## Architecture

Shog AI is built around three concepts:

- **Parsers** — source-specific adapters (`WikipediaParser`, `OpusParser`, ...) that read raw data and emit standardized records
- **Pipeline** — orchestrates one or more parsers, applies validation and deduplication, and writes output
- **Publishers** — take a finished dataset and ship it somewhere (Hugging Face Hub, local storage, ...)

```
Source Dataset → Parser → Pipeline → Output Package → Publisher
```

---

## Supported Datasets

- ✅ Wikipedia
- ✅ OPUS-100

**Planned:**

- FLORES-200
- OSCAR
- Common Crawl
- CC100
- WikiMatrix
- The Stack
- FineWeb
- Community datasets

---

## Output Format

A pipeline run produces a self-contained dataset package:

```text
output/

    shog_alpha/

        data.jsonl
        metadata.json
        statistics.json
        manifest.json
        README.md
        LICENSE
```

### Standardized record

Every record, regardless of source, follows the same schema:

```json
{
  "id": "...",
  "source": "...",
  "language": "...",
  "text": "..."
}
```

---

## Publishing

### Hugging Face Hub

**Usage 1 — with a token**

```python
from shog.publishers import HuggingFacePublisher

publisher = HuggingFacePublisher(
    token="hf_xxxxxxxxx"
)

publisher.publish(
    dataset_path="output/shog_alpha/data.jsonl",
    repo_id="GodsaveKawurem/shog-alpha"
)
```

**Usage 2 — CLI login (no token in code)**

```bash
huggingface-cli login
```

```python
publisher = HuggingFacePublisher()

publisher.publish(
    dataset_path="output/shog_alpha/data.jsonl",
    repo_id="GodsaveKawurem/shog-alpha"
)
```

### Local storage

```python
from publishers.local import LocalStorage

LocalStorage.save(
    dataset_path="output/shog_alpha/data.jsonl",
    output_dir="datasets/shog_alpha",
)
```

---

## Documentation

## [Unreleased]

Full documentation lives in [`docs/`](docs/):

- [Getting Started](docs/getting-started.md)
- [Parsers](docs/parsers.md)
- [Pipeline](docs/pipeline.md)
- [Exporters](docs/exporters.md)
- [Publishers](docs/publishers.md)
- [Storage](docs/storage.md)
- [Architecture](docs/architecture.md)

---

## Roadmap

- [ ] Common Crawl / CC100 support
- [ ] FLORES-200 and OSCAR parsers
- [ ] Configurable deduplication strategies
- [ ] Streaming exporters for very large corpora
- [ ] Expanded publisher targets

---

## Contributing

Contributions are welcome. Please open an issue to discuss significant changes before submitting a pull request.

---

## License

Released under the [MIT License](LICENSE).

---

## Citation

If you use Shog AI in your research, please cite:

```bibtex
@software{shog_ai,
  title  = {Shog AI: Streaming Dataset Engineering for Foundation Model Training},
  author = {Godsave Kawurem},
  year   = {2026},
  url    = {git@github.com:Perfect-Aimers-Enterprise/shog-ai-data}
}
```

---

## About Shog AI

**Shog AI** is an open AI research initiative focused on building multilingual foundation models and research infrastructure, with an emphasis on African languages. This library provides the data engineering pipeline used to construct large-scale training corpora for foundation models.
