Natural Language Processing with TuskLang Python SDK

Overview

TuskLang's Python SDK provides revolutionary natural language processing capabilities that integrate seamlessly with the FUJSEN intelligence system. From basic text processing to advanced language models, TuskLang makes NLP accessible, powerful, and production-ready.

Installation & Setup

Core NLP Dependencies

Install TuskLang Python SDK with NLP extensions
pip install tusknlp[full]
Or install specific NLP components
pip install tusknlp[transformers]  # Hugging Face transformers
pip install tusknlp[spacy]         # spaCy integration
pip install tusknlp[nltk]          # NLTK integration
pip install tusknlp[gensim]        # Gensim word vectors

Environment Configuration

peanu.tsk configuration for NLP workloads
nlp_config = {
    "language_models": {
        "default": "tusk-gpt-3.5",
        "cache_dir": "/var/cache/tusknlp/models",
        "gpu_enabled": true,
        "batch_size": 16
    },
    "text_processing": {
        "max_length": 512,
        "truncation": true,
        "padding": "max_length",
        "return_tensors": "pt"
    },
    "fujsen_integration": {
        "enable_intelligence": true,
        "context_window": 2048,
        "temperature": 0.7
    }
}

Basic Text Processing

Text Loading & Preprocessing

from tusknlp import TextProcessor, Tokenizer
from tusknlp.fujsen import @load_text, @preprocess_text
Load text using FUJSEN operators
@raw_text = @load_text("customer_feedback.txt")
@documents = @load_text("documents/*.txt", batch=True)
TuskLang-native text processing
processor = TextProcessor([
    "lowercase",
    "remove_punctuation",
    "remove_stopwords",
    "lemmatize",
    "normalize_whitespace"
])
@processed_text = processor.process(@raw_text)
Advanced tokenization
tokenizer = Tokenizer(
    model="tusk-tokenizer",
    max_length=512,
    truncation=True,
    padding=True
)
@tokens = tokenizer.tokenize(@processed_text)

Text Analysis & Features

from tusknlp.analysis import TextAnalyzer, FeatureExtractor
from tusknlp.fujsen import @analyze_sentiment, @extract_entities
Basic text analysis
analyzer = TextAnalyzer()
@analysis = analyzer.analyze(@raw_text, features=[
    "sentiment",
    "readability",
    "complexity",
    "tone",
    "emotion"
])
FUJSEN-powered sentiment analysis
@sentiment = @analyze_sentiment(
    text="@raw_text",
    model="tusk-sentiment-v2",
    confidence_threshold=0.8
)
Named entity recognition
@entities = @extract_entities(
    text="@raw_text",
    entity_types=["person", "organization", "location", "date"]
)
Feature extraction
extractor = FeatureExtractor()
@features = extractor.extract(@processed_text, features=[
    "tfidf",
    "word_embeddings",
    "syntactic_features",
    "semantic_features"
])

Advanced NLP Models

Language Models & Generation

from tusknlp.models import TuskLanguageModel, TextGenerator
from tusknlp.fujsen import @generate_text, @complete_text
TuskLang language model
model = TuskLanguageModel(
    model_name="tusk-gpt-3.5",
    max_length=1024,
    temperature=0.7,
    top_p=0.9
)
Text generation
@generated_text = model.generate(
    prompt="@raw_text",
    max_tokens=200,
    stop_sequences=["\n\n", "END"]
)
FUJSEN-powered text generation
@completion = @generate_text(
    prompt="@raw_text",
    model="@model",
    style="creative",
    length="medium"
)
Text completion
@completed_text = @complete_text(
    partial_text="@raw_text",
    completion_length=100,
    style="professional"
)

Text Classification & Categorization

from tusknlp.classification import TextClassifier, CategoryPredictor
from tusknlp.fujsen import @classify_text, @categorize_documents
Text classifier
classifier = TextClassifier(
    model="tusk-classifier-v1",
    categories=["positive", "negative", "neutral"],
    confidence_threshold=0.7
)
@classification = classifier.classify(@raw_text)
FUJSEN-powered classification
@text_category = @classify_text(
    text="@raw_text",
    categories=["support", "feedback", "complaint", "inquiry"],
    model="tusk-intent-classifier"
)
Document categorization
@document_categories = @categorize_documents(
    documents="@documents",
    categories=["technical", "business", "creative", "academic"],
    batch_size=32
)

NLP Pipelines

End-to-End NLP Pipeline

from tusknlp.pipeline import NLPipeline
from tusknlp.fujsen import @process_nlp_pipeline
Complete NLP pipeline
pipeline = NLPipeline([
    "text_loading",
    "preprocessing",
    "tokenization",
    "feature_extraction",
    "analysis",
    "classification",
    "output_generation"
])
Execute pipeline
@pipeline_result = pipeline.execute(
    config={
        "input": "@raw_text",
        "tasks": ["sentiment", "entities", "classification"],
        "output_format": "json"
    }
)
FUJSEN-powered pipeline
@nlp_result = @process_nlp_pipeline(
    text="@raw_text",
    pipeline="comprehensive",
    include_metadata=True
)

Real-time NLP Processing

from tusknlp.streaming import StreamingNLPipeline
from tusknlp.fujsen import @stream_text_analysis
Streaming NLP pipeline
stream_pipeline = StreamingNLPipeline(
    model="@model",
    window_size=1000,
    update_frequency=100,
    real_time=True
)
Process streaming text
@stream_analysis = stream_pipeline.process_stream(
    @stream_text,
    output_format="json"
)
Real-time text analysis
@real_time_analysis = @stream_text_analysis(
    text_stream="@stream_text",
    analysis_types=["sentiment", "entities", "intent"],
    latency_threshold=100  # ms
)

Language Understanding

Semantic Analysis

from tusknlp.semantics import SemanticAnalyzer, SimilarityCalculator
from tusknlp.fujsen import @calculate_similarity, @find_semantic_matches
Semantic analysis
semantic_analyzer = SemanticAnalyzer(
    model="tusk-semantic-v1",
    embedding_dim=768
)
@semantic_features = semantic_analyzer.analyze(@raw_text)
Text similarity
similarity_calc = SimilarityCalculator()
@similarity_score = similarity_calc.calculate(
    text1="@text1",
    text2="@text2",
    method="cosine"
)
FUJSEN-powered similarity
@semantic_similarity = @calculate_similarity(
    texts=["@text1", "@text2", "@text3"],
    method="semantic",
    threshold=0.8
)
Semantic matching
@matches = @find_semantic_matches(
    query="@query_text",
    candidates="@candidate_texts",
    top_k=5
)

Question Answering & Comprehension

from tusknlp.qa import QuestionAnswerer, DocumentQA
from tusknlp.fujsen import @answer_question, @extract_answers
Question answering
qa_system = QuestionAnswerer(
    model="tusk-qa-v1",
    context_window=512,
    max_answer_length=100
)
@answer = qa_system.answer(
    question="@question",
    context="@context"
)
FUJSEN-powered QA
@qa_result = @answer_question(
    question="@question",
    context="@context",
    model="tusk-qa-advanced",
    confidence_threshold=0.8
)
Document QA
doc_qa = DocumentQA(
    model="@qa_system",
    document="@document",
    chunk_size=512
)
@answers = doc_qa.answer_multiple(
    questions=["@q1", "@q2", "@q3"]
)
Answer extraction
@extracted_answers = @extract_answers(
    text="@text",
    questions=["@q1", "@q2"],
    format="structured"
)

Text Generation & Summarization

Advanced Text Generation

from tusknlp.generation import TextGenerator, StyleTransfer
from tusknlp.fujsen import @generate_with_style, @transfer_style
Text generator
generator = TextGenerator(
    model="tusk-gpt-3.5",
    temperature=0.8,
    top_p=0.9,
    repetition_penalty=1.1
)
@generated_content = generator.generate(
    prompt="@prompt",
    max_tokens=300,
    style="creative"
)
FUJSEN-powered generation
@styled_text = @generate_with_style(
    prompt="@prompt",
    style="professional",
    tone="formal",
    length="long"
)
Style transfer
style_transfer = StyleTransfer(
    source_style="casual",
    target_style="formal"
)
@transferred_text = style_transfer.transfer(@raw_text)
FUJSEN style transfer
@style_result = @transfer_style(
    text="@raw_text",
    target_style="academic",
    preserve_meaning=True
)

Text Summarization

from tusknlp.summarization import TextSummarizer, ExtractiveSummarizer
from tusknlp.fujsen import @summarize_text, @extract_summary
Abstractive summarization
summarizer = TextSummarizer(
    model="tusk-summarizer-v1",
    max_length=150,
    min_length=50
)
@summary = summarizer.summarize(@raw_text)
FUJSEN-powered summarization
@text_summary = @summarize_text(
    text="@raw_text",
    length="medium",
    style="concise",
    focus="key_points"
)
Extractive summarization
extractive = ExtractiveSummarizer(
    method="textrank",
    num_sentences=5
)
@extracted_summary = extractive.summarize(@raw_text)
FUJSEN extractive summary
@key_sentences = @extract_summary(
    text="@raw_text",
    method="extractive",
    num_sentences=3,
    importance_threshold=0.7
)

Multilingual NLP

Language Detection & Translation

from tusknlp.multilingual import LanguageDetector, Translator
from tusknlp.fujsen import @detect_language, @translate_text
Language detection
detector = LanguageDetector(
    model="tusk-langdetect-v1",
    confidence_threshold=0.8
)
@detected_language = detector.detect(@raw_text)
FUJSEN language detection
@language_info = @detect_language(
    text="@raw_text",
    include_confidence=True,
    include_script=True
)
Text translation
translator = Translator(
    source_lang="@detected_language",
    target_lang="en",
    model="tusk-translator-v1"
)
@translated_text = translator.translate(@raw_text)
FUJSEN translation
@translation = @translate_text(
    text="@raw_text",
    target_language="es",
    preserve_formatting=True,
    include_confidence=True
)

Cross-lingual Analysis

from tusknlp.crosslingual import CrossLingualAnalyzer
from tusknlp.fujsen import @cross_lingual_analysis
Cross-lingual analysis
cross_analyzer = CrossLingualAnalyzer(
    model="tusk-crosslingual-v1",
    supported_languages=["en", "es", "fr", "de", "zh"]
)
@cross_analysis = cross_analyzer.analyze(
    texts=["@text_en", "@text_es", "@text_fr"],
    analysis_type="sentiment"
)
FUJSEN cross-lingual analysis
@multilingual_result = @cross_lingual_analysis(
    texts=["@text1", "@text2", "@text3"],
    languages=["en", "es", "fr"],
    analysis_types=["sentiment", "entities", "classification"]
)

NLP with TuskLang Ecosystem

Integration with TuskDB

from tusknlp.storage import TuskDBStorage
from tusknlp.fujsen import @store_nlp_result, @load_nlp_model
Store NLP results in TuskDB
@nlp_storage = TuskDBStorage(
    database="nlp_results",
    collection="text_analysis"
)
@store_nlp_result = @store_nlp_result(
    text="@raw_text",
    analysis="@analysis",
    metadata={
        "timestamp": "@timestamp",
        "model_version": "tusk-nlp-v1.2",
        "processing_time": 0.5
    }
)
Load NLP models from TuskDB
@loaded_model = @load_nlp_model(
    model_name="tusk-sentiment-v2",
    version="latest"
)

NLP with FUJSEN Intelligence

from tusknlp.fujsen import @nlp_intelligence, @smart_text_processing
FUJSEN-powered NLP intelligence
@intelligent_analysis = @nlp_intelligence(
    text="@raw_text",
    context="@context",
    intelligence_level="advanced",
    include_reasoning=True
)
Smart text processing
@smart_processing = @smart_text_processing(
    text="@raw_text",
    tasks=["sentiment", "entities", "summary"],
    adaptive_processing=True,
    quality_threshold=0.9
)

Best Practices

Performance Optimization

Batch processing
from tusknlp.optimization import BatchProcessor
batch_processor = BatchProcessor(
    model="@model",
    batch_size=32,
    max_workers=4
)
@batch_results = batch_processor.process_batch(@texts)
Model caching
from tusknlp.caching import ModelCache
cache = ModelCache(
    cache_dir="/var/cache/tusknlp",
    max_size="10GB"
)
@cached_model = cache.get_or_load("tusk-sentiment-v1")

Quality Assurance

from tusknlp.quality import NLPQualityChecker
from tusknlp.validation import TextValidator
Quality checking
quality_checker = NLPQualityChecker(
    min_confidence=0.8,
    max_processing_time=1.0,
    quality_metrics=["accuracy", "precision", "recall"]
)
@quality_report = quality_checker.check(@nlp_result)
Text validation
validator = TextValidator()
@validation_result = validator.validate(
    text="@raw_text",
    checks=["length", "language", "content", "format"]
)

Example: Customer Feedback Analysis

Complete customer feedback analysis system
from tusknlp import *
Load and preprocess feedback
@feedback_data = @load_text("customer_feedback/*.txt")
@processed_feedback = @preprocess_text(@feedback_data)
Analyze sentiment and extract insights
@sentiment_analysis = @analyze_sentiment(
    text="@processed_feedback",
    model="tusk-sentiment-v2"
)
@entities = @extract_entities(
    text="@processed_feedback",
    entity_types=["product", "feature", "issue"]
)
Classify feedback types
@feedback_categories = @classify_text(
    text="@processed_feedback",
    categories=["bug_report", "feature_request", "compliment", "complaint"]
)
Generate summary and insights
@feedback_summary = @summarize_text(
    text="@processed_feedback",
    length="medium",
    focus="key_insights"
)
Store results in TuskDB
@store_feedback_analysis = @store_nlp_result(
    feedback="@processed_feedback",
    analysis={
        "sentiment": "@sentiment_analysis",
        "entities": "@entities",
        "categories": "@feedback_categories",
        "summary": "@feedback_summary"
    }
)

Conclusion

TuskLang's Python SDK provides a comprehensive natural language processing ecosystem that leverages the revolutionary FUJSEN intelligence system. From basic text processing to advanced language understanding, TuskLang makes NLP accessible, powerful, and production-ready.

The integration with TuskDB, FlexEquil distributed computing, and the FUJSEN intelligence system creates a unique NLP platform that scales from simple text analysis to enterprise-grade language understanding systems. Whether you're building chatbots, content analysis tools, or multilingual applications, TuskLang provides the tools and infrastructure you need to succeed.

Embrace the future of natural language processing with TuskLang - where language meets revolutionary technology.