Metadata-Version: 2.4
Name: saujana-nlp
Version: 0.1.1
Summary: A fast Indonesian NLP and Word Embedding library.
Project-URL: Homepage, https://github.com/Muhammad-Ikhwan-Fathulloh/Saujana
Project-URL: Bug Tracker, https://github.com/Muhammad-Ikhwan-Fathulloh/Saujana/issues
Author-email: Muhammad Ikhwan Fathulloh <muhammadikhwanfathulloh17@gmail.com>
License: MIT License
        
        Copyright (c) 2026 Saujana
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: indonesian,nlp,saka-nlp,saujana,word-embedding
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.8
Requires-Dist: fasttext-wheel>=0.9.2
Requires-Dist: numpy>=1.20.0
Requires-Dist: requests>=2.25.0
Requires-Dist: saka-nlp>=0.1.9
Requires-Dist: tqdm>=4.60.0
Provides-Extra: dev
Requires-Dist: black>=22.0; extra == 'dev'
Requires-Dist: pytest>=7.0; extra == 'dev'
Description-Content-Type: text/markdown

# Saujana 🇮🇩

[![Python Version](https://img.shields.io/badge/python-3.8+-blue.svg)](https://pypi.org/project/saujana-nlp/)
[![PyPI](https://img.shields.io/pypi/v/saujana-nlp.svg)](https://pypi.org/project/saujana-nlp/)
[![Website](https://img.shields.io/badge/website-live-brightgreen.svg)](https://saujana-nlp.netlify.app/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Saujana** adalah _standalone framework_ Natural Language Processing (NLP) dan Word Embedding native Bahasa Indonesia. 

---

## ✨ Fitur Utama

- 🚀 **Native Engine**: Pelatihan Word2Vec dan FastText kustom tanpa dependensi eksternal berat.
- 🧠 **Semantic API**: Pencarian kemiripan (*similarity*), analogi kata, dan deteksi OOV (*Out-of-Vocabulary*).
- 🔗 **Multilingual Alignment**: Penyelarasan ruang vektor bahasa daerah (Sunda, Jawa, Bali) ke Bahasa Indonesia menggunakan algoritma Procrustes.
- 🛠️ **Corpus Builder**: Alat untuk membangun dataset pelatihan dari ribuan file teks lokal secara otomatis.
- 🧩 **Saka-NLP Ready**: Terintegrasi penuh dengan ekosistem [Saka-NLP](https://saka-nlp.netlify.app/) untuk morfologi dan normalisasi Nusantara.

---

## 📦 Instalasi

```bash
pip install saujana-nlp
```

---

## 🚀 Penggunaan Cepat

### 1. Memuat Model Vektor
Saujana menyediakan model pre-trained yang siap digunakan untuk ekstraksi fitur semantik.

```python
import saujana

# Memuat model Bahasa Indonesia sedang (~200k kata)
nlp = saujana.load("id_saujana_md")

doc = nlp("Presiden bertolak menuju Jakarta untuk pertemuan tingkat tinggi.")

for token in doc:
    print(f"{token.text:12} | POS: {token.pos_:6} | Lemma: {token.lemma_}")
```

### 2. Analisis Kemiripan (Similarity)
Bandingkan dokumen atau kata berdasarkan konteks semantiknya.

```python
doc1 = nlp("Jokowi mengunjungi ibu kota.")
doc2 = nlp("Presiden pergi ke Jakarta.")

print(f"Skor Kemiripan: {doc1.similarity(doc2):.4f}")
```

---

## 🛠️ Fitur Lanjut

### 1. Pelatihan Model Word Embedding Native
Anda dapat melatih model Anda sendiri secara *native* dengan format `.npz` yang memuat kilat.

```python
from saujana.vectors.loader import train_word2vec

sentences = [["saya", "suka", "nlp"], ["saujana", "sangat", "cepat"]]
vocab = train_word2vec(sentences, vector_size=100, epochs=10)

# Cari kata terdekat
print(vocab.most_similar("nlp"))
```

### 2. Pembangun Korpus (Corpus Builder)
Gunakan ribuan data mentah Anda menjadi korpus siap latih.

```python
from saujana.vectors.training import CorpusBuilder

builder = CorpusBuilder()
builder.add_directory("./data/artikel_berita")
builder.add_file("./data/kamus_tambahan.txt")

# Memproses dan menggabung menjadi satu file korpus bersih
corpus_file = builder.build("data_latih.txt", lang="id")
```

### 3. Penyelarasan Bahasa Daerah (Alignment)
Selaraskan model bahasa daerah agar berada dalam ruang vektor yang sama dengan Bahasa Indonesia.

```python
from saujana.vectors.training import FastTextTrainer

trainer = FastTextTrainer()
# Definisi kata jangkar (anchor words)
anchors = [("makan", "tuang"), ("pergi", "angkat"), ("tidur", "kulem")]

# Putar model Sunda agar selaras dengan Indonesia
aligned_model = trainer.align_models(sunda_model, indo_model, anchors)
```

---

## 🤝 Ekosistem Saka-NLP

Saujana bertindak sebagai **lapisan semantik** (Word Embeddings) yang melengkapi **lapisan linguistik** (Morfologi/Normalisasi) dari **Saka-NLP**.

> [!TIP]
> Untuk dokumentasi lengkap mengenai **Normalisasi Slang**, **Stemming Nusantara**, **Analisis Morfologi Sunda/Jawa/Bali**, dan penggunaan modul **Saka-NLP** lainnya, silakan kunjungi:
> 
> 👉 **[https://saka-nlp.netlify.app/](https://saka-nlp.netlify.app/)**

---

## 📊 API Reference Singkat

| Objek     | Properti Utama                                | Deskripsi                                     |
| :-------- | :-------------------------------------------- | :-------------------------------------------- |
| **Token** | `text`, `pos_`, `lemma_`, `is_stop`, `vector` | Unit kata tunggal dalam dokumen.              |
| **Doc**   | `ents`, `sents`, `vector`, `similarity()`     | Kontainer teks hasil pemrosesan pipeline.     |
| **Vocab** | `most_similar()`, `analogy()`, `get_vector()` | Pusat data representasi vektor kata.          |
| **Span**  | `text`, `label_`, `vector`                    | Potongan dari dokumen (misal: Frasa/Entitas). |

---

## 📜 Lisensi
Saujana didistribusikan di bawah lisensi **MIT**. Bebas digunakan untuk keperluan riset maupun komersial.

---
**Website**: [saujana-nlp.netlify.app](https://saujana-nlp.netlify.app/)  
**Creator**: [Muhammad Ikhwan Fathulloh](https://github.com/Muhammad-Ikhwan-Fathulloh)  

*Dikembangkan dengan ❤️ untuk memperkuat kedaulatan data bahasa Nusantara.*
