Upload folder using huggingface_hub

.gitignore

@@ -1,9 +1,9 @@
-*.jsonl
-__pycache__/
-*.pyc
-.venv/
-.env
-.DS_Store
-.pypirc
-dist/
+*.jsonl
+__pycache__/
+*.pyc
+.venv/
+.env
+.DS_Store
+.pypirc
+dist/
 !probes.jsonl

README.md

@@ -1,64 +1,69 @@
----
-license: apache-2.0
----
-
-# Finesse Benchmark Database
-
-## Overview
-
-`finesse-benchmark-database` is a data generation factory for atomic probes in the Finesse benchmark. It generates `probes_atomic.jsonl` files from Wikimedia Wikipedia datasets, leveraging Hugging Face's `datasets` library, tokenizers from `transformers`, and optional PyTorch support.
-
-This tool is designed to create high-quality, language-specific probe datasets for benchmarking fine-grained understanding in NLP tasks.
-
-## Installation
-
-Install the package from PyPI:
-
-```bash
-pip install finesse-benchmark-database
-```
-
-Ensure you have Python 3.10+ installed.
-
-## Usage
-
-Here's a complete example of how to configure and generate a dataset:
-
-```python
-from finesse_benchmark_database.config import ProbeConfig
-from finesse_benchmark_database.main import generate_dataset
-
-# Define the configuration
-my_config = ProbeConfig(
-    languages=['en', 'ko'],  # Languages to generate probes for
-    samples_per_language=10,  # Number of samples per language (reduce for testing)
-    output_file='my_first_probes.jsonl',  # Output file path
-    seed=123  # Random seed for reproducibility
-)
-
-# Generate the dataset
-print(f"Generating '{my_config.output_file}'...")
-generate_dataset(my_config)
-print("Dataset generation completed!")
-```
-
-### Configuration Options
-
-- `languages`: List of language codes (e.g., ['en', 'ko', 'fr']).
-- `samples_per_language`: Number of probe samples to generate per language.
-- `output_file`: Path to the output JSONL file.
-- `seed`: Optional seed for deterministic results.
-
-## Requirements
-
-- `datasets`
-- `transformers`
-- `torch` (for tokenization)
-
-## License
-
-This project is licensed under the Apache-2.0 license.
-
-## Contributing
-
+---
+license: apache-2.0
+---
+
+[Github](https://github.com/enzoescipy/finesse-benchmark)
+[huggingface](https://huggingface.co/datasets/enzoescipy/finesse-benchmark-database)
+[pypi](https://pypi.org/project/finesse-benchmark/)
+[blog](https://www.winter-sci-dev.com/posts/embed-sequence-merger-vbert-ppe-article/)
+
+# Finesse Benchmark Database
+
+## Overview
+
+`finesse-benchmark-database` is a data generation factory for atomic probes in the Finesse benchmark. It generates `probes_atomic.jsonl` files from Wikimedia Wikipedia datasets, leveraging Hugging Face's `datasets` library, tokenizers from `transformers`, and optional PyTorch support.
+
+This tool is designed to create high-quality, language-specific probe datasets for benchmarking fine-grained understanding in NLP tasks.
+
+## Installation
+
+Install the package from PyPI:
+
+```bash
+pip install finesse-benchmark-database
+```
+
+Ensure you have Python 3.10+ installed.
+
+## Usage
+
+Here's a complete example of how to configure and generate a dataset:
+
+```python
+from finesse_benchmark_database.config import ProbeConfig
+from finesse_benchmark_database.main import generate_dataset
+
+# Define the configuration
+my_config = ProbeConfig(
+    languages=['en', 'ko'],  # Languages to generate probes for
+    samples_per_language=10,  # Number of samples per language (reduce for testing)
+    output_file='my_first_probes.jsonl',  # Output file path
+    seed=123  # Random seed for reproducibility
+)
+
+# Generate the dataset
+print(f"Generating '{my_config.output_file}'...")
+generate_dataset(my_config)
+print("Dataset generation completed!")
+```
+
+### Configuration Options
+
+- `languages`: List of language codes (e.g., ['en', 'ko', 'fr']).
+- `samples_per_language`: Number of probe samples to generate per language.
+- `output_file`: Path to the output JSONL file.
+- `seed`: Optional seed for deterministic results.
+
+## Requirements
+
+- `datasets`
+- `transformers`
+- `torch` (for tokenization)
+
+## License
+
+This project is licensed under the Apache-2.0 license.
+
+## Contributing
+
 Contributions are welcome! Please open issues or pull requests on the [GitHub repository](https://github.com/your-repo/finesse-benchmark-database).

main.py

@@ -1,39 +1,39 @@
-#!/usr/bin/env python3
-"""
-Example script to demonstrate the usage of the finesse-benchmark-database package.
-This script creates a simple configuration and generates a probes_atomic.jsonl file.
-"""
-
-from finesse_benchmark_database.config import ProbeConfig
-from finesse_benchmark_database.main import generate_dataset
-
-LANGUAGES = [
-    'en',
-    'ko',
-    'es',
-    'ja',
-    'ru',
-    'zh',
-    'ar',
-    'id',
-    'de',
-    'vi',
-]
-
-if __name__ == "__main__":
-    # Define a simple configuration for demonstration
-    demo_config = ProbeConfig(
-        languages=LANGUAGES,  # Start with English for simplicity
-        samples_per_language=5000,  
-        output_file='probes.jsonl',  # Output file for the demo
-        seed=42  # Fixed seed for reproducibility
-    )
-
-    print(f"Generating demo dataset: '{demo_config.output_file}'")
-    print("This will create a small probes_atomic.jsonl file using Wikipedia data.")
-    
-    # Generate the dataset
-    generate_dataset(demo_config)
-    
-    print("Demo generation completed! Check 'demo_probes.jsonl' for the output.")
+#!/usr/bin/env python3
+"""
+Example script to demonstrate the usage of the finesse-benchmark-database package.
+This script creates a simple configuration and generates a probes_atomic.jsonl file.
+"""
+
+from finesse_benchmark_database.config import ProbeConfig
+from finesse_benchmark_database.main import generate_dataset
+
+LANGUAGES = [
+    'en',
+    'ko',
+    'es',
+    'ja',
+    'ru',
+    'zh',
+    'ar',
+    'id',
+    'de',
+    'vi',
+]
+
+if __name__ == "__main__":
+    # Define a simple configuration for demonstration
+    demo_config = ProbeConfig(
+        languages=LANGUAGES,  # Start with English for simplicity
+        samples_per_language=5000,  
+        output_file='probes.jsonl',  # Output file for the demo
+        seed=42  # Fixed seed for reproducibility
+    )
+
+    print(f"Generating demo dataset: '{demo_config.output_file}'")
+    print("This will create a small probes_atomic.jsonl file using Wikipedia data.")
+    
+    # Generate the dataset
+    generate_dataset(demo_config)
+    
+    print("Demo generation completed! Check 'demo_probes.jsonl' for the output.")
     print("You can now inspect the file or integrate this into your Finesse benchmark workflow.")

pyproject.toml

@@ -1,19 +1,19 @@
-[tool.poetry]
-name = "finesse-benchmark-database"
-version = "0.1.12"
-description = "Data generation factory for atomic probes in Finesse benchmark. Generates probes_atomic.jsonl from Wikimedia Wikipedia."
-authors = ["winter.sci.dev <enzoescipy@gmail.com>"]
-readme = "README.md"
-packages = [{include = "finesse_benchmark_database", from = "src"}]
-
-[tool.poetry.dependencies]
-python = "^3.10"
-datasets = "^4.3.0"
-transformers = "^4.35.0"
-torch = "^2.1.0"  # For tokenizer if needed
-
-
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+[tool.poetry]
+name = "finesse-benchmark-database"
+version = "0.1.13"
+description = "Data generation factory for atomic probes in Finesse benchmark. Generates probes_atomic.jsonl from Wikimedia Wikipedia."
+authors = ["winter.sci.dev <enzoescipy@gmail.com>"]
+readme = "README.md"
+packages = [{include = "finesse_benchmark_database", from = "src"}]
+
+[tool.poetry.dependencies]
+python = "^3.10"
+datasets = "^4.3.0"
+transformers = "^4.35.0"
+torch = "^2.1.0"  # For tokenizer if needed
+
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

src/finesse_benchmark_database/__init__.py

@@ -1,48 +1,48 @@
-"""finesse-benchmark-database: Multilingual Atomic Probe Generator for Long-Context Evaluation
-
-This package provides a flexible, configurable library for generating high-quality, traceable datasets of 'strings of beads'—
-atomic 64-token text chunks sourced from multilingual Wikipedia articles. It serves as the foundational data generation
-pipeline for the Finesse long-context benchmarking framework, ensuring reproducibility, semantic diversity, and
-complete metadata tracking for advanced LLM evaluation.
-
-Core Principles:
-- Atomic Beads: Exact 64-token chunks (discard incompletes) to test pure memory granularity.
-- Traceable Origins: Each bead/string includes full metadata (dataset, article_id, lang) for debugging and verification.
-- Multilingual Balance: Supports 10+ languages with configurable quotas for fair coverage.
-- Library-First Design: Instantiable via ProbeConfig for custom experiments; no globals or hardcoding.
-- JSONL Output: Efficient streaming format for large-scale datasets.
-
-Key Components:
-- ProbeConfig: Dataclass for all settings (languages, samples, chunk size, etc.).
-- generate_all_strings_of_beads(config): Produces list of {'source': metadata, 'beads': [text_chunks]} dicts.
-- write_strings_to_probes_atomic(config, strings): Serializes to JSONL with auto-assigned string_ids.
-
-Example Usage:
-    from finesse_benchmark_database import ProbeConfig, generate_all_strings_of_beads, write_strings_to_probes_atomic
-    
-    config = ProbeConfig(
-        languages=['en', 'ko'],
-        samples_per_language=100,
-        chunk_token_size=64,
-        output_file='my_probes.jsonl',
-        seed=42
-    )
-    
-    beads_strings = generate_all_strings_of_beads(config)
-    write_strings_to_probes_atomic(config, beads_strings)
-    # Outputs my_probes.jsonl with ~200 traceable strings of beads.
-
-Installation:
-pip install finesse-benchmark-database
-# Or via Poetry: poetry add finesse-benchmark-database
-
-This package powers the creation of ~1M+ atomic probes for rigorous long-context memory testing.
-See main.py for a full pipeline example.
-"""
-
-__version__ = "0.1.0"
-
-from .config import ProbeConfig
-from .chunker import generate_all_strings_of_beads
-from .writer import write_strings_to_probes_atomic
+"""finesse-benchmark-database: Multilingual Atomic Probe Generator for Long-Context Evaluation
+
+This package provides a flexible, configurable library for generating high-quality, traceable datasets of 'strings of beads'—
+atomic 64-token text chunks sourced from multilingual Wikipedia articles. It serves as the foundational data generation
+pipeline for the Finesse long-context benchmarking framework, ensuring reproducibility, semantic diversity, and
+complete metadata tracking for advanced LLM evaluation.
+
+Core Principles:
+- Atomic Beads: Exact 64-token chunks (discard incompletes) to test pure memory granularity.
+- Traceable Origins: Each bead/string includes full metadata (dataset, article_id, lang) for debugging and verification.
+- Multilingual Balance: Supports 10+ languages with configurable quotas for fair coverage.
+- Library-First Design: Instantiable via ProbeConfig for custom experiments; no globals or hardcoding.
+- JSONL Output: Efficient streaming format for large-scale datasets.
+
+Key Components:
+- ProbeConfig: Dataclass for all settings (languages, samples, chunk size, etc.).
+- generate_all_strings_of_beads(config): Produces list of {'source': metadata, 'beads': [text_chunks]} dicts.
+- write_strings_to_probes_atomic(config, strings): Serializes to JSONL with auto-assigned string_ids.
+
+Example Usage:
+    from finesse_benchmark_database import ProbeConfig, generate_all_strings_of_beads, write_strings_to_probes_atomic
+    
+    config = ProbeConfig(
+        languages=['en', 'ko'],
+        samples_per_language=100,
+        chunk_token_size=64,
+        output_file='my_probes.jsonl',
+        seed=42
+    )
+    
+    beads_strings = generate_all_strings_of_beads(config)
+    write_strings_to_probes_atomic(config, beads_strings)
+    # Outputs my_probes.jsonl with ~200 traceable strings of beads.
+
+Installation:
+pip install finesse-benchmark-database
+# Or via Poetry: poetry add finesse-benchmark-database
+
+This package powers the creation of ~1M+ atomic probes for rigorous long-context memory testing.
+See main.py for a full pipeline example.
+"""
+
+__version__ = "0.1.0"
+
+from .config import ProbeConfig
+from .chunker import generate_all_strings_of_beads
+from .writer import write_strings_to_probes_atomic
 from .main import generate_dataset

src/finesse_benchmark_database/chunker.py

@@ -1,154 +1,154 @@
-"""Chunker module for Finesse Benchmark Database Generator
-
-This module implements the 'Gemstone Necklace Crafter' aka the atomic bead generator.
-It transforms raw Wikimedia Wikipedia documents into 'strings of beads': sequential chains
-of exactly 64-token atomic beads from each source article, preserving semantic flow within
-each string while ensuring atomicity and reproducibility across languages.
-
-Key Principles:
-- Uses official reference tokenizer: bert-base-multilingual-cased for universal fairness.
-- Exactly 64-token beads only; discard all incomplete final chunks (golden rule).
-- One 'string' per Wikipedia article, with beads in original sequential order.
-- Balanced collection: samples_per_language strings per language.
-- Streaming processing for efficiency on massive datasets.
-- Logged progress for transparency and debugging.
-
-Usage:
-from chunker import generate_all_strings_of_beads
-strings_of_beads = generate_all_strings_of_beads()
-# Result: List[Dict] where each inner dict contains 'source' and 'beads' keys.
-"""
-
-import logging
-import random
-from typing import List, Dict
-
-from .config import ProbeConfig
-from datasets import load_dataset
-from transformers import AutoTokenizer
-
-# Configure logging for progress tracking
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def generate_all_strings_of_beads(config: ProbeConfig) -> List[Dict]:
-    """
-    Generate all 'strings of beads' across languages using the official tokenizer.
-    
-    Args:
-        config: ProbeConfig instance with all parameters (languages, samples_per_language, etc.).
-    
-    For each language:
-    - Stream Wikipedia articles.
-    - For each article (up to config.samples_per_language):
-      - Tokenize the full text.
-      - Sequentially slice into 64-token chunks.
-      - Decode only exact 64-token chunks to bead texts.
-      - Form a 'string' as [bead1, bead2, ...] if at least one bead exists.
-    - Collect all strings into a global 2D list.
-    
-    Returns:
-        List of dictionaries: Outer list by language/order, inner dicts contain 'source' and 'beads' keys.
-    """
-    # Set global seed for reproducibility
-    random.seed(config.seed)
-    
-    # Load the official reference tokenizer (our 'public scale')
-    logger.info(f"Loading official tokenizer: {config.tokenizer_name}")
-    tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
-    
-    all_strings_of_beads: List[Dict] = []
-    
-    if config.languages is None:
-        raise ValueError("config.languages must be set to a list of language codes.")
-    
-    for lang in config.languages:
-        logger.info(f"Starting processing for language: {lang} (target: {config.samples_per_language} strings)")
-        
-        # Load streaming dataset with the specified split
-        dataset = load_dataset(
-            "wikimedia/wikipedia",
-            f"20231101.{lang}",
-            streaming=True,
-            split="train"
-        )
-        
-        strings_for_lang: List[Dict] = []
-        article_count = 0
-        
-        for example in dataset:
-            if len(strings_for_lang) >= config.samples_per_language:
-                break
-            
-            # Extract and clean text
-            text = example.get("text", "").strip()
-            if not text:
-                continue
-            
-            # Extract article ID for metadata
-            article_id = example.get("id", "")
-            
-            # Tokenize the full article
-            tokens = tokenizer.encode(text, add_special_tokens=False)
-            
-            # Sequentially chunk into exact config.chunk_token_size-token beads
-            beads: List[str] = []
-            for i in range(0, len(tokens), config.chunk_token_size):
-                chunk_tokens = tokens[i:i + config.chunk_token_size]
-                
-                # Golden rule: Only accept exactly 64 tokens; discard incompletes
-                if len(chunk_tokens) == config.chunk_token_size:
-                    bead_text = tokenizer.decode(chunk_tokens, skip_special_tokens=True).strip()
-                    if bead_text:  # Ensure non-empty after decoding
-                        beads.append(bead_text)
-            
-            # Only add the string if it has at least one bead
-            if beads:
-                string_data = {
-                    "source": {
-                        "dataset": "wikimedia/wikipedia",
-                        "article_id": article_id,
-                        "lang": lang
-                    },
-                    "beads": beads
-                }
-                strings_for_lang.append(string_data)
-                article_count += 1
-                
-                # Log progress every 100 articles
-                if article_count % 100 == 0:
-                    logger.info(
-                        f"Language {lang}: Processed {article_count} articles, "
-                        f"collected {len(strings_for_lang)} strings so far"
-                    )
-        
-        # Add language's strings to the global collection
-        all_strings_of_beads.extend(strings_for_lang)
-        logger.info(f"Completed {lang}: {len(strings_for_lang)} strings generated "
-                    f"(from {article_count} articles)")
-    
-    total_strings = len(all_strings_of_beads)
-    logger.info(f"Generation complete: {total_strings} total strings of beads across {len(config.languages)} languages")
-    
-    return all_strings_of_beads
-
-if __name__ == "__main__":
-    # Example library usage: Create config and generate
-    from config import ProbeConfig
-    
-    # User creates and populates config
-    test_config = ProbeConfig(
-        languages=['en', 'ko'],  # Test with 2 languages
-        samples_per_language=5,  # Small sample for demo
-        chunk_token_size=64,
-        tokenizer_name="google-bert/bert-base-multilingual-cased",
-        output_file="probes_atomic.jsonl",
-        seed=42
-    )
-    
-    # Generate and print summary
-    strings = generate_all_strings_of_beads(test_config)
-    print(f"Generated {len(strings)} strings of beads.")
-    if strings:
-        avg_beads_per_string = sum(len(s['beads']) for s in strings) / len(strings)
+"""Chunker module for Finesse Benchmark Database Generator
+
+This module implements the 'Gemstone Necklace Crafter' aka the atomic bead generator.
+It transforms raw Wikimedia Wikipedia documents into 'strings of beads': sequential chains
+of exactly 64-token atomic beads from each source article, preserving semantic flow within
+each string while ensuring atomicity and reproducibility across languages.
+
+Key Principles:
+- Uses official reference tokenizer: bert-base-multilingual-cased for universal fairness.
+- Exactly 64-token beads only; discard all incomplete final chunks (golden rule).
+- One 'string' per Wikipedia article, with beads in original sequential order.
+- Balanced collection: samples_per_language strings per language.
+- Streaming processing for efficiency on massive datasets.
+- Logged progress for transparency and debugging.
+
+Usage:
+from chunker import generate_all_strings_of_beads
+strings_of_beads = generate_all_strings_of_beads()
+# Result: List[Dict] where each inner dict contains 'source' and 'beads' keys.
+"""
+
+import logging
+import random
+from typing import List, Dict
+
+from .config import ProbeConfig
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+# Configure logging for progress tracking
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def generate_all_strings_of_beads(config: ProbeConfig) -> List[Dict]:
+    """
+    Generate all 'strings of beads' across languages using the official tokenizer.
+    
+    Args:
+        config: ProbeConfig instance with all parameters (languages, samples_per_language, etc.).
+    
+    For each language:
+    - Stream Wikipedia articles.
+    - For each article (up to config.samples_per_language):
+      - Tokenize the full text.
+      - Sequentially slice into 64-token chunks.
+      - Decode only exact 64-token chunks to bead texts.
+      - Form a 'string' as [bead1, bead2, ...] if at least one bead exists.
+    - Collect all strings into a global 2D list.
+    
+    Returns:
+        List of dictionaries: Outer list by language/order, inner dicts contain 'source' and 'beads' keys.
+    """
+    # Set global seed for reproducibility
+    random.seed(config.seed)
+    
+    # Load the official reference tokenizer (our 'public scale')
+    logger.info(f"Loading official tokenizer: {config.tokenizer_name}")
+    tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
+    
+    all_strings_of_beads: List[Dict] = []
+    
+    if config.languages is None:
+        raise ValueError("config.languages must be set to a list of language codes.")
+    
+    for lang in config.languages:
+        logger.info(f"Starting processing for language: {lang} (target: {config.samples_per_language} strings)")
+        
+        # Load streaming dataset with the specified split
+        dataset = load_dataset(
+            "wikimedia/wikipedia",
+            f"20231101.{lang}",
+            streaming=True,
+            split="train"
+        )
+        
+        strings_for_lang: List[Dict] = []
+        article_count = 0
+        
+        for example in dataset:
+            if len(strings_for_lang) >= config.samples_per_language:
+                break
+            
+            # Extract and clean text
+            text = example.get("text", "").strip()
+            if not text:
+                continue
+            
+            # Extract article ID for metadata
+            article_id = example.get("id", "")
+            
+            # Tokenize the full article
+            tokens = tokenizer.encode(text, add_special_tokens=False)
+            
+            # Sequentially chunk into exact config.chunk_token_size-token beads
+            beads: List[str] = []
+            for i in range(0, len(tokens), config.chunk_token_size):
+                chunk_tokens = tokens[i:i + config.chunk_token_size]
+                
+                # Golden rule: Only accept exactly 64 tokens; discard incompletes
+                if len(chunk_tokens) == config.chunk_token_size:
+                    bead_text = tokenizer.decode(chunk_tokens, skip_special_tokens=True).strip()
+                    if bead_text:  # Ensure non-empty after decoding
+                        beads.append(bead_text)
+            
+            # Only add the string if it has at least one bead
+            if beads:
+                string_data = {
+                    "source": {
+                        "dataset": "wikimedia/wikipedia",
+                        "article_id": article_id,
+                        "lang": lang
+                    },
+                    "beads": beads
+                }
+                strings_for_lang.append(string_data)
+                article_count += 1
+                
+                # Log progress every 100 articles
+                if article_count % 100 == 0:
+                    logger.info(
+                        f"Language {lang}: Processed {article_count} articles, "
+                        f"collected {len(strings_for_lang)} strings so far"
+                    )
+        
+        # Add language's strings to the global collection
+        all_strings_of_beads.extend(strings_for_lang)
+        logger.info(f"Completed {lang}: {len(strings_for_lang)} strings generated "
+                    f"(from {article_count} articles)")
+    
+    total_strings = len(all_strings_of_beads)
+    logger.info(f"Generation complete: {total_strings} total strings of beads across {len(config.languages)} languages")
+    
+    return all_strings_of_beads
+
+if __name__ == "__main__":
+    # Example library usage: Create config and generate
+    from config import ProbeConfig
+    
+    # User creates and populates config
+    test_config = ProbeConfig(
+        languages=['en', 'ko'],  # Test with 2 languages
+        samples_per_language=5,  # Small sample for demo
+        chunk_token_size=64,
+        tokenizer_name="google-bert/bert-base-multilingual-cased",
+        output_file="probes_atomic.jsonl",
+        seed=42
+    )
+    
+    # Generate and print summary
+    strings = generate_all_strings_of_beads(test_config)
+    print(f"Generated {len(strings)} strings of beads.")
+    if strings:
+        avg_beads_per_string = sum(len(s['beads']) for s in strings) / len(strings)
         print(f"Average beads per string: {avg_beads_per_string:.2f}")

src/finesse_benchmark_database/config.py

@@ -1,79 +1,79 @@
-"""Configuration module for Finesse Benchmark Database Generator
-
-This module holds all configurable parameters for generating atomic probes from Wikimedia Wikipedia datasets.
-Based on the 'Beads and String' model: 64-token atomic beads from diverse languages, ensuring semantic continuity within strings but independence across probes.
-
-Key Principles:
-- Fixed 64-token chunk size for atomic beads.
-- Balanced sampling across languages for global diversity.
-- Seeded randomness for perfect reproducibility.
-- Output in probes_atomic.jsonl format for dynamic assembly in evaluation.
-
-Usage:
-from config import tokenizer_name, languages, chunk_token_size, samples_per_language, output_file, seed
-"""
-
-from dataclasses import dataclass
-import random
-
-@dataclass
-class ProbeConfig:
-    """Central configuration for probe generation.
-    
-    This dataclass serves as a flexible template for library users.
-    Instantiate and populate it with desired values before passing to generate functions.
-    
-    Example:
-        config = ProbeConfig(
-            languages=['en', 'ko'],
-            samples_per_language=10,
-            chunk_token_size=64
-        )
-    """
-    
-    # Tokenizer for tokenization (default: multilingual BERT)
-    tokenizer_name: str = "google-bert/bert-base-multilingual-cased"
-    
-    # Languages for balanced multilingual coverage (must be set by user)
-    languages: list[str] = None
-    
-    # Atomic bead size (golden rule from design; override for custom experiments)
-    chunk_token_size: int = 64
-
-    # Number of 'strings of beads' (source documents) per language: The number of complete
-    # Wikipedia articles to process per language. Each document is chunked sequentially into
-    # multiple 64-token atomic beads, preserving original order and semantic flow within
-    # the string. This is NOT the total count of individual beads (which will be much higher,
-    # depending on document lengths), but the number of such connected 'necklaces' or
-    # 'strings' for balanced multilingual coverage.
-    samples_per_language: int = 10000
-    
-    # Output file for atomic probes
-    output_file: str = "probes_atomic.jsonl"
-    
-    # Fixed seed for reproducibility (immutable law; set to None for non-deterministic runs)
-    seed: int = 42
-
-def get_config() -> ProbeConfig:
-    """Instantiate and return the configuration with languages initialized."""
-    config = ProbeConfig()
-    config.languages = [
-        'en',  # English
-        'ko',  # Korean
-        'es',  # Spanish
-        'ja',  # Japanese
-        'ru',  # Russian
-        'zh',  # Chinese
-        'ar',  # Arabic
-        'id',  # Indonesian
-        'de',  # German
-        'vi',  # Vietnamese
-    ]
-    
-    # Set global seed for all randomness
-    random.seed(config.seed)
-    
-    return config
-
-# Default config instance
+"""Configuration module for Finesse Benchmark Database Generator
+
+This module holds all configurable parameters for generating atomic probes from Wikimedia Wikipedia datasets.
+Based on the 'Beads and String' model: 64-token atomic beads from diverse languages, ensuring semantic continuity within strings but independence across probes.
+
+Key Principles:
+- Fixed 64-token chunk size for atomic beads.
+- Balanced sampling across languages for global diversity.
+- Seeded randomness for perfect reproducibility.
+- Output in probes_atomic.jsonl format for dynamic assembly in evaluation.
+
+Usage:
+from config import tokenizer_name, languages, chunk_token_size, samples_per_language, output_file, seed
+"""
+
+from dataclasses import dataclass
+import random
+
+@dataclass
+class ProbeConfig:
+    """Central configuration for probe generation.
+    
+    This dataclass serves as a flexible template for library users.
+    Instantiate and populate it with desired values before passing to generate functions.
+    
+    Example:
+        config = ProbeConfig(
+            languages=['en', 'ko'],
+            samples_per_language=10,
+            chunk_token_size=64
+        )
+    """
+    
+    # Tokenizer for tokenization (default: multilingual BERT)
+    tokenizer_name: str = "google-bert/bert-base-multilingual-cased"
+    
+    # Languages for balanced multilingual coverage (must be set by user)
+    languages: list[str] = None
+    
+    # Atomic bead size (golden rule from design; override for custom experiments)
+    chunk_token_size: int = 64
+
+    # Number of 'strings of beads' (source documents) per language: The number of complete
+    # Wikipedia articles to process per language. Each document is chunked sequentially into
+    # multiple 64-token atomic beads, preserving original order and semantic flow within
+    # the string. This is NOT the total count of individual beads (which will be much higher,
+    # depending on document lengths), but the number of such connected 'necklaces' or
+    # 'strings' for balanced multilingual coverage.
+    samples_per_language: int = 10000
+    
+    # Output file for atomic probes
+    output_file: str = "probes_atomic.jsonl"
+    
+    # Fixed seed for reproducibility (immutable law; set to None for non-deterministic runs)
+    seed: int = 42
+
+def get_config() -> ProbeConfig:
+    """Instantiate and return the configuration with languages initialized."""
+    config = ProbeConfig()
+    config.languages = [
+        'en',  # English
+        'ko',  # Korean
+        'es',  # Spanish
+        'ja',  # Japanese
+        'ru',  # Russian
+        'zh',  # Chinese
+        'ar',  # Arabic
+        'id',  # Indonesian
+        'de',  # German
+        'vi',  # Vietnamese
+    ]
+    
+    # Set global seed for all randomness
+    random.seed(config.seed)
+    
+    return config
+
+# Default config instance
 CONFIG = get_config()

src/finesse_benchmark_database/main.py

@@ -1,99 +1,99 @@
-"""Main orchestration module for Finesse Benchmark Database Generator
-
-This script serves as the 'Conductor' that orchestrates the entire pipeline: from configuration setup
-through atomic bead generation to final JSONL serialization. It ties together config, chunker, and writer
-for end-to-end execution of our 'Gemstone Necklace' production process.
-
-Key Principles:
-- Ensures all modules use the shared CONFIG for consistency.
-- Executes chunking first (memory-intensive), then writing (I/O-focused).
-- Comprehensive logging for audit trail and debugging.
-- Designed for one-shot full generation; scale via external orchestration if needed.
-
-Usage:
-python main.py
-# Runs the full pipeline: generates ~100,000 strings of beads and writes to probes_atomic.jsonl
-"""
-
-import logging
-
-from .config import ProbeConfig
-from .chunker import generate_all_strings_of_beads
-from .writer import write_strings_to_probes_atomic
-
-# Configure logging for the entire pipeline
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-def generate_dataset(config: ProbeConfig):
-    logger.info(f"Config set: {len(config.languages)} languages, {config.chunk_token_size}-token beads, "
-                f"{config.samples_per_language} strings per language")
-    
-    # Step 2: Generate all structured 'strings of beads' using the chunker
-    logger.info("Starting bead generation with chunker...")
-    all_strings_of_beads = generate_all_strings_of_beads(config)
-    total_strings = len(all_strings_of_beads)
-    logger.info(f"Bead generation complete: {total_strings} total strings produced "
-                f"(~{total_strings / len(config.languages):.0f} per language)")
-    
-    # Step 3: Serialize to the atomic probes JSONL file using the writer
-    logger.info(f"Starting serialization to {config.output_file}...")
-    write_strings_to_probes_atomic(config, all_strings_of_beads)
-    logger.info(f"Serialization complete: All {total_strings} strings saved with metadata.")
-    
-    # Final summary
-    estimated_beads = sum(len(s['beads']) for s in all_strings_of_beads)
-    logger.info("=== Pipeline Summary (Test Run) ===")
-    logger.info(f"- Total strings: {total_strings}")
-    logger.info(f"- Total atomic beads: {estimated_beads}")
-    logger.info(f"- Coverage: {len(set(s['source']['lang'] for s in all_strings_of_beads))} languages")
-    logger.info(f"- Output file: {config.output_file}")
-    logger.info("For full production: Increase samples_per_language and add more languages in config.")
-    logger.info("=== Finesse Benchmark Database Generation Example Finished Successfully ===")
-
-
-def main() -> None:
-    """Execute the full Finesse benchmark database generation pipeline as a library example.
-    
-    This demonstrates how users would create a ProbeConfig, generate data, and write output.
-    For production, adjust config parameters as needed (e.g., more languages, larger samples).
-    """
-    logger.info("=== Finesse Benchmark Database Pipeline Started (Library Example) ===")
-    
-    # Step 1: User creates and configures ProbeConfig for this run
-    logger.info("Creating test configuration...")
-    test_config = ProbeConfig(
-        languages=['en'],  # Single language for quick demo
-        samples_per_language=5,  # Small sample for testing
-        chunk_token_size=64,
-        tokenizer_name="google-bert/bert-base-multilingual-cased",
-        output_file="test_probes_atomic.jsonl",
-        seed=42
-    )
-    logger.info(f"Config set: {len(test_config.languages)} languages, {test_config.chunk_token_size}-token beads, "
-                f"{test_config.samples_per_language} strings per language")
-    
-    # Step 2: Generate all structured 'strings of beads' using the chunker
-    logger.info("Starting bead generation with chunker...")
-    all_strings_of_beads = generate_all_strings_of_beads(test_config)
-    total_strings = len(all_strings_of_beads)
-    logger.info(f"Bead generation complete: {total_strings} total strings produced "
-                f"(~{total_strings / len(test_config.languages):.0f} per language)")
-    
-    # Step 3: Serialize to the atomic probes JSONL file using the writer
-    logger.info(f"Starting serialization to {test_config.output_file}...")
-    write_strings_to_probes_atomic(test_config, all_strings_of_beads)
-    logger.info(f"Serialization complete: All {total_strings} strings saved with metadata.")
-    
-    # Final summary
-    estimated_beads = sum(len(s['beads']) for s in all_strings_of_beads)
-    logger.info("=== Pipeline Summary (Test Run) ===")
-    logger.info(f"- Total strings: {total_strings}")
-    logger.info(f"- Total atomic beads: {estimated_beads}")
-    logger.info(f"- Coverage: {len(set(s['source']['lang'] for s in all_strings_of_beads))} languages")
-    logger.info(f"- Output file: {test_config.output_file}")
-    logger.info("For full production: Increase samples_per_language and add more languages in config.")
-    logger.info("=== Finesse Benchmark Database Generation Example Finished Successfully ===")
-
-if __name__ == "__main__":
+"""Main orchestration module for Finesse Benchmark Database Generator
+
+This script serves as the 'Conductor' that orchestrates the entire pipeline: from configuration setup
+through atomic bead generation to final JSONL serialization. It ties together config, chunker, and writer
+for end-to-end execution of our 'Gemstone Necklace' production process.
+
+Key Principles:
+- Ensures all modules use the shared CONFIG for consistency.
+- Executes chunking first (memory-intensive), then writing (I/O-focused).
+- Comprehensive logging for audit trail and debugging.
+- Designed for one-shot full generation; scale via external orchestration if needed.
+
+Usage:
+python main.py
+# Runs the full pipeline: generates ~100,000 strings of beads and writes to probes_atomic.jsonl
+"""
+
+import logging
+
+from .config import ProbeConfig
+from .chunker import generate_all_strings_of_beads
+from .writer import write_strings_to_probes_atomic
+
+# Configure logging for the entire pipeline
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def generate_dataset(config: ProbeConfig):
+    logger.info(f"Config set: {len(config.languages)} languages, {config.chunk_token_size}-token beads, "
+                f"{config.samples_per_language} strings per language")
+    
+    # Step 2: Generate all structured 'strings of beads' using the chunker
+    logger.info("Starting bead generation with chunker...")
+    all_strings_of_beads = generate_all_strings_of_beads(config)
+    total_strings = len(all_strings_of_beads)
+    logger.info(f"Bead generation complete: {total_strings} total strings produced "
+                f"(~{total_strings / len(config.languages):.0f} per language)")
+    
+    # Step 3: Serialize to the atomic probes JSONL file using the writer
+    logger.info(f"Starting serialization to {config.output_file}...")
+    write_strings_to_probes_atomic(config, all_strings_of_beads)
+    logger.info(f"Serialization complete: All {total_strings} strings saved with metadata.")
+    
+    # Final summary
+    estimated_beads = sum(len(s['beads']) for s in all_strings_of_beads)
+    logger.info("=== Pipeline Summary (Test Run) ===")
+    logger.info(f"- Total strings: {total_strings}")
+    logger.info(f"- Total atomic beads: {estimated_beads}")
+    logger.info(f"- Coverage: {len(set(s['source']['lang'] for s in all_strings_of_beads))} languages")
+    logger.info(f"- Output file: {config.output_file}")
+    logger.info("For full production: Increase samples_per_language and add more languages in config.")
+    logger.info("=== Finesse Benchmark Database Generation Example Finished Successfully ===")
+
+
+def main() -> None:
+    """Execute the full Finesse benchmark database generation pipeline as a library example.
+    
+    This demonstrates how users would create a ProbeConfig, generate data, and write output.
+    For production, adjust config parameters as needed (e.g., more languages, larger samples).
+    """
+    logger.info("=== Finesse Benchmark Database Pipeline Started (Library Example) ===")
+    
+    # Step 1: User creates and configures ProbeConfig for this run
+    logger.info("Creating test configuration...")
+    test_config = ProbeConfig(
+        languages=['en'],  # Single language for quick demo
+        samples_per_language=5,  # Small sample for testing
+        chunk_token_size=64,
+        tokenizer_name="google-bert/bert-base-multilingual-cased",
+        output_file="test_probes_atomic.jsonl",
+        seed=42
+    )
+    logger.info(f"Config set: {len(test_config.languages)} languages, {test_config.chunk_token_size}-token beads, "
+                f"{test_config.samples_per_language} strings per language")
+    
+    # Step 2: Generate all structured 'strings of beads' using the chunker
+    logger.info("Starting bead generation with chunker...")
+    all_strings_of_beads = generate_all_strings_of_beads(test_config)
+    total_strings = len(all_strings_of_beads)
+    logger.info(f"Bead generation complete: {total_strings} total strings produced "
+                f"(~{total_strings / len(test_config.languages):.0f} per language)")
+    
+    # Step 3: Serialize to the atomic probes JSONL file using the writer
+    logger.info(f"Starting serialization to {test_config.output_file}...")
+    write_strings_to_probes_atomic(test_config, all_strings_of_beads)
+    logger.info(f"Serialization complete: All {total_strings} strings saved with metadata.")
+    
+    # Final summary
+    estimated_beads = sum(len(s['beads']) for s in all_strings_of_beads)
+    logger.info("=== Pipeline Summary (Test Run) ===")
+    logger.info(f"- Total strings: {total_strings}")
+    logger.info(f"- Total atomic beads: {estimated_beads}")
+    logger.info(f"- Coverage: {len(set(s['source']['lang'] for s in all_strings_of_beads))} languages")
+    logger.info(f"- Output file: {test_config.output_file}")
+    logger.info("For full production: Increase samples_per_language and add more languages in config.")
+    logger.info("=== Finesse Benchmark Database Generation Example Finished Successfully ===")
+
+if __name__ == "__main__":
     main()

src/finesse_benchmark_database/writer.py

@@ -1,83 +1,83 @@
-"""Writer module for Finesse Benchmark Database Generator
-
-This module implements the 'Eternal Scribe': it takes the structured 'strings of beads' output from chunker.py
-and serializes them into probes_atomic.jsonl format. Each line is an independent JSON object with metadata
-for full traceability, ensuring the dataset is reproducible, queryable, and efficient for large-scale use.
-
-Key Principles:
-- JSONL format for streaming efficiency (one complete JSON object per line).
-- Assigns immutable 'string_id' for unique identification.
-- Preserves all source metadata (dataset, article_id, lang) for debugging and verification.
-- UTF-8 encoding to handle multilingual content without corruption.
-- Logged serialization progress for transparency.
-
-Usage:
-from writer import write_strings_to_probes_atomic
-from chunker import generate_all_strings_of_beads
-strings = generate_all_strings_of_beads()
-write_strings_to_probes_atomic(strings)
-# Results in probes_atomic.jsonl with all probes serialized.
-"""
-
-import json
-import logging
-from typing import List, Dict
-
-from .config import ProbeConfig
-
-# Configure logging for progress tracking
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def write_strings_to_probes_atomic(config: ProbeConfig, all_strings_of_beads: List[Dict]) -> None:
-    """
-    Write the structured strings of beads to the atomic probes JSONL file.
-    
-    Args:
-        config: ProbeConfig instance specifying output_file and SEED.
-        all_strings_of_beads: List of dicts from chunker.py, each with 'source' and 'beads'.
-    
-    Side Effects:
-        Creates/appends to config.output_file.
-        Each string gets a sequential 'string_id' assigned.
-    """
-    output_path = config.output_file
-    logger.info(f"Starting serialization to {output_path}. Total strings: {len(all_strings_of_beads)}")
-    
-    with open(output_path, 'w', encoding='utf-8') as file:
-        for i, string_data in enumerate(all_strings_of_beads):
-            # Assign unique string_id (immutable global identifier)
-            string_data['string_id'] = i
-            
-            # Serialize to JSON line
-            json_line = json.dumps(string_data, ensure_ascii=False, separators=(',', ':'))
-            file.write(json_line + '\n')
-            
-            # Log progress every 1000 strings
-            if (i + 1) % 1000 == 0:
-                logger.info(f"Serialized {i + 1} strings to {output_path}")
-    
-    total_written = len(all_strings_of_beads)
-    logger.info(f"Serialization complete: {total_written} strings written to {output_path}")
-    logger.info(f"File structure: Each line is a JSON object with 'string_id', 'source' (metadata), and 'beads' ({config.chunk_token_size}-token chunks).")
-
-if __name__ == "__main__":
-    # Example library usage: Create config and run full pipeline
-    from config import ProbeConfig
-    from chunker import generate_all_strings_of_beads
-    
-    # User creates and populates config for a small test run
-    demo_config = ProbeConfig(
-        languages=['en'],  # Single language for quick demo
-        samples_per_language=3,  # Very small sample
-        chunk_token_size=64,
-        tokenizer_name="google-bert/bert-base-multilingual-cased",
-        output_file="demo_probes_atomic.jsonl",
-        seed=42
-    )
-    
-    print("Generating strings of beads...")
-    strings = generate_all_strings_of_beads(demo_config)
-    print(f"Generated {len(strings)} strings. Now serializing...")
-    write_strings_to_probes_atomic(demo_config, strings)
+"""Writer module for Finesse Benchmark Database Generator
+
+This module implements the 'Eternal Scribe': it takes the structured 'strings of beads' output from chunker.py
+and serializes them into probes_atomic.jsonl format. Each line is an independent JSON object with metadata
+for full traceability, ensuring the dataset is reproducible, queryable, and efficient for large-scale use.
+
+Key Principles:
+- JSONL format for streaming efficiency (one complete JSON object per line).
+- Assigns immutable 'string_id' for unique identification.
+- Preserves all source metadata (dataset, article_id, lang) for debugging and verification.
+- UTF-8 encoding to handle multilingual content without corruption.
+- Logged serialization progress for transparency.
+
+Usage:
+from writer import write_strings_to_probes_atomic
+from chunker import generate_all_strings_of_beads
+strings = generate_all_strings_of_beads()
+write_strings_to_probes_atomic(strings)
+# Results in probes_atomic.jsonl with all probes serialized.
+"""
+
+import json
+import logging
+from typing import List, Dict
+
+from .config import ProbeConfig
+
+# Configure logging for progress tracking
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def write_strings_to_probes_atomic(config: ProbeConfig, all_strings_of_beads: List[Dict]) -> None:
+    """
+    Write the structured strings of beads to the atomic probes JSONL file.
+    
+    Args:
+        config: ProbeConfig instance specifying output_file and SEED.
+        all_strings_of_beads: List of dicts from chunker.py, each with 'source' and 'beads'.
+    
+    Side Effects:
+        Creates/appends to config.output_file.
+        Each string gets a sequential 'string_id' assigned.
+    """
+    output_path = config.output_file
+    logger.info(f"Starting serialization to {output_path}. Total strings: {len(all_strings_of_beads)}")
+    
+    with open(output_path, 'w', encoding='utf-8') as file:
+        for i, string_data in enumerate(all_strings_of_beads):
+            # Assign unique string_id (immutable global identifier)
+            string_data['string_id'] = i
+            
+            # Serialize to JSON line
+            json_line = json.dumps(string_data, ensure_ascii=False, separators=(',', ':'))
+            file.write(json_line + '\n')
+            
+            # Log progress every 1000 strings
+            if (i + 1) % 1000 == 0:
+                logger.info(f"Serialized {i + 1} strings to {output_path}")
+    
+    total_written = len(all_strings_of_beads)
+    logger.info(f"Serialization complete: {total_written} strings written to {output_path}")
+    logger.info(f"File structure: Each line is a JSON object with 'string_id', 'source' (metadata), and 'beads' ({config.chunk_token_size}-token chunks).")
+
+if __name__ == "__main__":
+    # Example library usage: Create config and run full pipeline
+    from config import ProbeConfig
+    from chunker import generate_all_strings_of_beads
+    
+    # User creates and populates config for a small test run
+    demo_config = ProbeConfig(
+        languages=['en'],  # Single language for quick demo
+        samples_per_language=3,  # Very small sample
+        chunk_token_size=64,
+        tokenizer_name="google-bert/bert-base-multilingual-cased",
+        output_file="demo_probes_atomic.jsonl",
+        seed=42
+    )
+    
+    print("Generating strings of beads...")
+    strings = generate_all_strings_of_beads(demo_config)
+    print(f"Generated {len(strings)} strings. Now serializing...")
+    write_strings_to_probes_atomic(demo_config, strings)
     print("Pipeline complete. Check demo_probes_atomic.jsonl for output.")