Nasty Examples Catalog

Comprehensive catalog of all example scripts demonstrating Nasty's capabilities.

Quick Start

All examples can be run directly:

elixir examples/example_name.exs

Or make them executable:

chmod +x examples/example_name.exs
./examples/example_name.exs

Basic Examples

tokenizer_example.exs

Purpose: Introduction to tokenization

What it demonstrates:

Basic tokenization with NimbleParsec
Position tracking (line, column, byte offsets)
Handling contractions (don't, it's)
Punctuation as separate tokens
Sentence boundary detection

Run:

elixir examples/tokenizer_example.exs

Best for: Understanding the first step in the NLP pipeline

hmm_pos_tagger_example.exs

Purpose: Statistical POS tagging with Hidden Markov Models

What it demonstrates:

Training HMM POS taggers from CoNLL-U data
Viterbi algorithm for sequence tagging
Model evaluation and accuracy metrics
Comparison with rule-based tagging
Model persistence (save/load)

Run:

elixir examples/hmm_pos_tagger_example.exs

Best for: Learning about statistical NLP models

neural_pos_tagger_example.exs

Purpose: Neural POS tagging with BiLSTM-CRF

What it demonstrates:

BiLSTM-CRF architecture with Axon/EXLA
Training neural models on UD corpora
Character-level embeddings for OOV handling
GPU acceleration with EXLA
97-98% accuracy on benchmark datasets

Run:

elixir examples/neural_pos_tagger_example.exs

Best for: Understanding deep learning for NLP

Language-Specific Examples

spanish_example.exs

Purpose: Spanish language processing

What it demonstrates:

Spanish tokenization (¿?, ¡!, del, al contractions)
Spanish POS tagging with morphology
Gender/number agreement
Parsing Spanish sentence structure
Entity recognition with Spanish lexicons

Run:

elixir examples/spanish_example.exs

Best for: Working with Romance languages

catalan_example.exs

Purpose: Catalan language processing

What it demonstrates:

Catalan-specific tokenization (interpunct l·l, apostrophes)
All 10 Catalan diacritics (à, è, é, í, ï, ò, ó, ú, ü, ç)
Article contractions (del, al, pel, cal)
Catalan morphology and POS tagging
Entity recognition with Catalan lexicons
Translation between Catalan and English

Run:

elixir examples/catalan_example.exs

Best for: Catalan NLP applications

Translation Examples

translation_example.exs

Purpose: Basic AST-based translation

What it demonstrates:

English ↔ Spanish translation
AST-level translation preserving grammar
Morphological agreement enforcement
Word order transformations
Rendering translated AST to text

Run:

elixir examples/translation_example.exs

Best for: Getting started with translation

roundtrip_translation.exs

Purpose: Translation quality analysis

What it demonstrates:

English → Spanish → English roundtrips
English → Catalan → English roundtrips
Spanish → English → Spanish roundtrips
Similarity metrics and quality assessment
Challenging translation cases
Performance across complexity levels

Run:

elixir examples/roundtrip_translation.exs

Best for: Evaluating translation quality

multilingual_pipeline.exs

Purpose: Side-by-side multilingual comparison

What it demonstrates:

Processing same content in English, Spanish, Catalan
Token-level comparison across languages
POS tagging differences
Morphological feature comparison
Translation matrix (all language pairs)
Performance benchmarking
Language-specific features summary

Run:

elixir examples/multilingual_pipeline.exs

Best for: Understanding cross-language differences

Advanced NLP Tasks

summarization.exs

Purpose: Extractive text summarization

What it demonstrates:

Position-weighted sentence scoring
Entity density calculation
Discourse marker detection
Keyword frequency (TF)
MMR (Maximal Marginal Relevance) for diversity
Compression ratio vs. fixed sentence count

Run:

elixir examples/summarization.exs

Best for: Document summarization applications

question_answering.exs

Purpose: Extractive question answering

What it demonstrates:

Question classification (WHO, WHAT, WHEN, WHERE, WHY, HOW)
Answer extraction strategies
Entity type filtering
Keyword matching with lemmatization
Confidence scoring
Multiple answer support

Run:

elixir examples/question_answering.exs

Best for: Building Q&A systems

text_classification.exs

Purpose: Document classification

What it demonstrates:

Multinomial Naive Bayes classifier
Feature extraction (BOW, n-grams, POS patterns, entities, lexical)
Training on labeled data
Multi-class classification
Model evaluation (accuracy, precision, recall, F1)
Sentiment analysis example

Run:

elixir examples/text_classification.exs

Best for: Text categorization tasks

information_extraction.exs

Purpose: Structured information extraction

What it demonstrates:

Relation extraction (employment, organization, location)
Event extraction (acquisitions, foundings, announcements)
Template-based extraction
Pattern matching with verb patterns
Confidence scoring
Integration with NER and dependencies

Run:

elixir examples/information_extraction.exs

Best for: Knowledge base construction

Code Interoperability

code_generation.exs

Purpose: Natural language to code

What it demonstrates:

Intent recognition from natural language
Constraint extraction (comparison, property, range)
Elixir code generation
List operations (sort, filter, map, reduce)
Arithmetic expressions
Conditional statements

Run:

elixir examples/code_generation.exs

Best for: Natural language programming interfaces

code_explanation.exs

Purpose: Code to natural language

What it demonstrates:

Elixir AST parsing
Code explanation generation
Pipeline explanation
Function call description
Variable usage analysis

Run:

elixir examples/code_explanation.exs

Best for: Code documentation and understanding

Neural Network Examples

pretrained_model_usage.exs

Purpose: Using pre-trained transformers

What it demonstrates:

BERT and RoBERTa via Bumblebee
Fine-tuning for POS tagging and NER
Zero-shot classification
Model quantization (INT8)
Multilingual models (XLM-RoBERTa)

Run:

elixir examples/pretrained_model_usage.exs

Best for: Leveraging pre-trained models

transformer_pos_example.exs

Purpose: Transformer-based POS tagging

What it demonstrates:

RoBERTa for POS tagging
Fine-tuning transformers
98-99% accuracy
Cross-lingual transfer
Model comparison

Run:

elixir examples/transformer_pos_example.exs

Best for: State-of-the-art accuracy

advanced_neural_features.exs

Purpose: Advanced neural NLP features

What it demonstrates:

Multiple neural architectures
Ensemble methods
Model quantization
Zero-shot learning
Cross-lingual transfer
Performance optimization

Run:

elixir examples/advanced_neural_features.exs

Best for: Production neural NLP systems

Comprehensive Demos

comprehensive_demo.exs

Purpose: Complete NLP pipeline walkthrough

What it demonstrates:

Full pipeline from tokenization to summarization
All major NLP tasks
Entity recognition
Dependency extraction
Semantic role labeling
Coreference resolution
Information extraction

Run:

./examples/comprehensive_demo.exs

Best for: Overview of all capabilities

Example Selection Guide

By Use Case

Text Analysis:

tokenizer_example.exs
hmm_pos_tagger_example.exs
comprehensive_demo.exs

Machine Learning:

neural_pos_tagger_example.exs
transformer_pos_example.exs
text_classification.exs
advanced_neural_features.exs

Multilingual:

spanish_example.exs
catalan_example.exs
translation_example.exs
roundtrip_translation.exs
multilingual_pipeline.exs

Information Extraction:

question_answering.exs
information_extraction.exs
summarization.exs

Code Integration:

code_generation.exs
code_explanation.exs

By Difficulty Level

Beginner:

tokenizer_example.exs
spanish_example.exs
translation_example.exs
summarization.exs

Intermediate:

hmm_pos_tagger_example.exs
catalan_example.exs
question_answering.exs
text_classification.exs
multilingual_pipeline.exs

Advanced:

neural_pos_tagger_example.exs
information_extraction.exs
transformer_pos_example.exs
advanced_neural_features.exs
roundtrip_translation.exs

By Processing Time

Fast (<1 second):

tokenizer_example.exs
translation_example.exs
spanish_example.exs

Medium (1-10 seconds):

catalan_example.exs
multilingual_pipeline.exs
summarization.exs
question_answering.exs

Slow (>10 seconds):

hmm_pos_tagger_example.exs (if training)
neural_pos_tagger_example.exs
transformer_pos_example.exs
roundtrip_translation.exs

Running Multiple Examples

Run all basic examples:

for example in tokenizer_example spanish_example translation_example; do
  echo "Running ${example}..."
  elixir examples/${example}.exs
  echo "---"
done

Run all translation examples:

for example in translation_example roundtrip_translation multilingual_pipeline; do
  elixir examples/${example}.exs
done

Run all language-specific examples:

elixir examples/spanish_example.exs
elixir examples/catalan_example.exs
elixir examples/multilingual_pipeline.exs

Expected Output

Typical Output Format

Most examples output:

Section headers: Clearly marked sections
Input text: What's being processed
Results: Parsed output, tags, entities, etc.
Statistics: Counts, accuracy, timing
Summary: Key takeaways

Example Output Snippet

========================================
Spanish Language Processing Demo
========================================

1. Tokenization
---------------
Input: El gato duerme en el sofá.

Tokens:
  El (1:1)
  gato (1:4)
  duerme (1:9)
  ...

2. POS Tagging
--------------
Tagged tokens:
  El → det
  gato → noun
  duerme → verb
  ...

Troubleshooting

Common Issues

Example won't run:

# Make sure dependencies are installed
mix deps.get
mix compile

# Check file permissions
chmod +x examples/example_name.exs

Missing models: Some examples (neural, transformer) require trained models. See TRAINING_NEURAL.md for training instructions.

Out of memory: Neural/transformer examples may need more memory. Reduce batch size or use smaller models.

Creating Your Own Examples

Template for new examples:

#!/usr/bin/env elixir

# Your Example Name
#
# Brief description of what this example demonstrates

Mix.install([
  {:nasty, path: Path.expand("..", __DIR__)}
])

alias Nasty.Language.English

IO.puts("\n========================================")
IO.puts("Your Example Title")
IO.puts("========================================\n")

# Example 1: First concept
IO.puts("1. First Section")
IO.puts("----------------")

# Your code here

# Example 2: Second concept
IO.puts("\n2. Second Section")
IO.puts("-----------------")

# Your code here

IO.puts("\n========================================")
IO.puts("Example Complete!")
IO.puts("========================================\n")

Nasty Examples Catalog

Quick Start

Basic Examples

tokenizer_example.exs

hmm_pos_tagger_example.exs

neural_pos_tagger_example.exs

Language-Specific Examples

spanish_example.exs

catalan_example.exs

Translation Examples

translation_example.exs

roundtrip_translation.exs

multilingual_pipeline.exs

Advanced NLP Tasks

summarization.exs

question_answering.exs

text_classification.exs

information_extraction.exs

Code Interoperability

code_generation.exs

code_explanation.exs

Neural Network Examples

pretrained_model_usage.exs

transformer_pos_example.exs

advanced_neural_features.exs

Comprehensive Demos

comprehensive_demo.exs

Example Selection Guide

By Use Case

By Difficulty Level

By Processing Time

Running Multiple Examples

Run all basic examples:

Run all translation examples:

Run all language-specific examples:

Expected Output

Typical Output Format

Example Output Snippet

Troubleshooting

Common Issues

Creating Your Own Examples

See Also