olaverse 0.1.2

Advanced ML infrastructure and interface to load and run all Olaverse models

Olaverse

Advanced ML infrastructure and production-ready interface to load and run all Olaverse models.

Standard NLP tools and model interfaces don't capture local nuances, custom tokenizers, or specific fine-tuned downstream configurations (like contract analysis & legal reasoning).

Olaverse is a unified Python package and developer interface designed to run all Olaverse model families—ranging from lightweight CPU-only local NLP modules to large enterprise-grade legal and reasoning models.

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Key Features

• 🏛️ Enterprise Legal AI: Built-in support and inference pipeline for olaverse/legal-peace-v1.0 (fine-tuned Mistral-7B for contract analysis and legal reasoning).
• 🚀 GPU & CPU Optimized: Lazy-loading and custom CUDA/Unsloth optimization layers for large LLMs alongside hyper-efficient, 100% offline local NLP tools.
• 📦 Optimized Tokenizers (OTK-BPE): Custom Byte-Level BPE tokenizers (e.g. olaverse/otk-bpe-50k) trained on dedicated Nigerian and African language corpora (up to 63% fewer tokens compared to GPT-4).
• 🗣️ Advanced Diacritic Restoration:
  • Yoruba Diacritizer (dot-below only): 97.5% character accuracy.
  • Yoruba Diacritizer (full tonal): 90.0% word accuracy via Viterbi decoding.
  • Igbo Diacritizer: 95.2% character accuracy.
• 🎭 Context-Aware Sentiment Analysis: Captures sentiment nuances in Pidgin English and regional languages (72% accuracy).
• 🔒 Nigerian-specific PII Masking: Automatically masks emails, local +234/080 phone formats, BVN, and NIN.
• 🇳🇬 Robust Language Detection: Accurately classifies text across 5 languages: Yoruba (yor), Hausa (hau), Igbo (ibo), Pidgin (pcm), and English (eng) with 98.12% accuracy (LIDLite5) and 98.96% accuracy (LIDNeural5).

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Installation

Install core library (lightweight, CPU offline-first tokenizers & text helpers):

pip install olaverse

Install with transformer model dependencies (torch + transformers, for LIDNeural5):

pip install olaverse[deeplearning]

Install with legal model dependencies (unsloth, torch, etc. for GPU inference):

pip install olaverse[legal]

For development mode (with Jupyter notebooks and training dependencies):

pip install -e ".[dev]"

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Quick Start

1. Legal & Contract Analysis (LegalPeace)

Direct interface for loading and running the olaverse/legal-peace-v1.0 model. Uses Unsloth under the hood for fast, memory-efficient inference.

from olaverse.llm import LegalPeace

# Instantiates the wrapper with default configurations (or enter custom model name/parameters)
model = LegalPeace(model_name="olaverse/legal-peace-v1.0")

# Loads model & tokenizer lazily (requires GPU and unsloth installed)
model.load()


prompt = "Analyze this contract clause: 'The parties agree that all disputes shall be resolved through binding arbitration in Delaware.' What are the key legal implications?"
response = model.generate(prompt, max_new_tokens=300, temperature=0.7)
print(response)

2. Custom Tokenizers (Tokenizer)

Use optimized Byte-Level BPE tokenizers without needing local .json file paths. Loads from Hugging Face Hub automatically if not cached. For details on performance and training, see the otk-bpe repository.

from olaverse.nlp import Tokenizer

# Load the 50k unified model
tok = Tokenizer("naija") 
# Or specific languages (e.g., "yo", "ig", "ha", "pcm", or full model name "otk-bpe-50k-yo")
# tok = Tokenizer("yo")

tokens = tok.encode("Ẹ kú àbọ̀")
print(tokens) # → [124, 381]

decoded = tok.decode(tokens)
print(decoded) # → "Ẹ kú àbọ̀"

3. Language Detection (LIDLite5 & LIDNeural5)

Identify whether text is Yoruba, Hausa, Igbo, Nigerian Pidgin, or English.

Option A: Lightweight (Zero-Dependency CPU)

from olaverse import LIDLite5

detector = LIDLite5()
print(detector.predict("How far, wetin dey happen?")) # → 'pcm'

# Get confidence scores across all 5 classes
probs = detector.predict_proba("How far, wetin dey happen?")
print(probs) # → {'eng': 0.0006, 'hau': 0.0014, ...}

Option B: Neural (Transformer GPU/CPU)

from olaverse import LIDNeural5

detector = LIDNeural5()
detector.load()  # Downloads and caches model from HF (olaverse/lid-neural-5)
                 # Requires: pip install olaverse[deeplearning]

print(detector.predict("How far, wetin dey happen?")) # → 'pcm'

4. Yoruba & Igbo Diacritizer

Restore missing diacritics in Yoruba or Igbo text.

from olaverse.nlp import diacritize_yoruba, diacritize_yoruba_dot_below, diacritize_igbo

# Dot-below only (no tones)
diacritize_yoruba_dot_below("Ojo lo si oja")
# → 'Ọjọ lo si ọja'

# Full tonal diacritics
diacritize_yoruba("Ojo lo si oja lana")
# → 'Ọjọ́ ló sí ọjà lànà'

# Igbo diacritics
diacritize_igbo("Kedu ka i mere")
# → 'Kedụ ka ị mere'

5. Text Preprocessing & Global PII Masking

Strip HTML tags, clean URLs, and securely mask sensitive global data (Emails, Credit Cards, Social Security Numbers, and Phone Numbers).

from olaverse.nlp import mask_pii, clean_text

mask_pii("Contact me at [e-mail] or call [phone]")
# → 'Contact me at [EMAIL] or call [PHONE]'

clean_text("Read more at <a href='https://olaverse.ai'>Olaverse</a>!   ")
# → 'Read more at Olaverse!'

6. Global Constants & Utilities

from olaverse.utils.constants import CURRENCIES, CONTINENTS, format_currency

CONTINENTS["AF"]           # → 'Africa'
CURRENCIES["GBP"]          # → '£'
format_currency(1500, "$") # → '$1,500.00'

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Design Philosophy

1. Unified Interface: A single, clean library to access all Olaverse NLP, Tokenizer, and Large Language Models.
2. Resource-Adaptive: Keeps local CPU helpers lightweight and fully offline-capable, while offering scalable legal and reasoning GPU wrappers.
3. Honest Metrics & Benchmarks: We believe in sharing open, verifiable performance indicators of our tokenizers and classifiers against global baselines.

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License

Apache License 2.0. See LICENSE for details.