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-# TurMix & HinMix: Exact Reproduction Guide
-
-This document provides a complete guide to reproducing the Turkish (TurMix) and Hindi (HinMix) pretraining data pipelines.
-
-## Table of Contents
-1. [Overview](#overview)
-2. [Environment Setup](#environment-setup)
-3. [Project Structure](#project-structure)
-4. [Data Sources](#data-sources)
-5. [Pipeline Stages](#pipeline-stages)
-6. [Stage 1: Download](#stage-1-download)
-7. [Stage 2: Quality Filtering](#stage-2-quality-filtering)
-8. [Stage 3: MinHash Deduplication](#stage-3-minhash-deduplication)
-9. [Stage 4: Consensus Subset Construction](#stage-4-consensus-subset-construction)
-10. [Stage 5: Upload to HuggingFace](#stage-5-upload-to-huggingface)
-11. [Final Statistics](#final-statistics)
-
----
-
-## Overview
-
-The pipeline processes web crawl data through the following stages:
-1. **Download**: Fetch data from HuggingFace using `huggingface-cli`
-2. **Quality Filter**: Apply language-specific quality filters using DataTrove
-3. **MinHash Dedup**: Remove near-duplicate documents within each source
-4. **Consensus**: Identify documents appearing in 2+ sources (exact text match)
-
-### Key Design Decisions
-- Use `huggingface-cli download` with `--include` patterns for efficient selective downloads
-- Process each source separately to avoid parquet schema conflicts
-- Use 54 workers (CPU cores - 2) for parallel processing
-- MinHash deduplication within each source (not cross-source)
-- Consensus detection via exact text hash matching across sources
-
----
-
-## Environment Setup
-
-### Prerequisites
-```bash
-# Python 3.10+
-conda create -n pretraining python=3.10
-conda activate pretraining
-
-# Install dependencies
-pip install datasets datatrove pyarrow huggingface_hub
-pip install fasttext-langdetect  # For language detection
-```
-
-### HuggingFace Authentication
-```bash
-huggingface-cli login
-# Enter your HuggingFace token
-```
-
----
-
-## Project Structure
-
-```
-arabic-pretraining-mix-other-languages/
-├── run_pipeline.py              # Main unified pipeline runner
-├── build_consensus_v2.py        # Consensus subset builder (memory-efficient)
-├── filter_c4.py                 # C4 JSON filtering script
-├── fix_hplt2_filter.py          # Turkish HPLT2 filtering (5 subfolders)
-├── fix_hindi_hplt2_filter.py    # Hindi HPLT2 filtering
-├── src/
-│   ├── config/
-│   │   ├── common.py            # Shared configs (paths, workers, MinHash params)
-│   │   ├── datasets_tr.py       # Turkish dataset definitions
-│   │   └── datasets_hi.py       # Hindi dataset definitions
-│   ├── filters/
-│   │   ├── base_quality.py      # Base quality filter class
-│   │   ├── tr_quality.py        # Turkish quality filter
-│   │   ├── hi_quality.py        # Hindi quality filter
-│   │   └── lang_config.py       # Language-specific constants
-│   └── dedup/
-│       └── __init__.py          # MinHash deduplication wrappers
-└── data/
-    ├── hi/                      # Hindi data
-    │   ├── downloads/           # Raw downloaded data
-    │   ├── filtered/            # Quality-filtered data
-    │   ├── deduped/             # MinHash-deduplicated data
-    │   ├── consensus/           # Consensus subset
-    │   └── minhash_signatures/  # MinHash signatures (preserved)
-    └── tr/                      # Turkish data (same structure)
-```
-
----
-
-## Data Sources
-
-### Hindi Sources (6)
-| Source | HuggingFace Path | Subset/Config | Download Command |
-|--------|------------------|---------------|------------------|
-| HPLT-2 | `HPLT/HPLT2.0_cleaned` | `hin_Deva` | `huggingface-cli download HPLT/HPLT2.0_cleaned --include "hin_Deva/*" --local-dir ./data/hi/hplt2 --repo-type dataset` |
-| Fineweb-2 | `HuggingFaceFW/fineweb-2` | `hin_Deva` | `huggingface-cli download HuggingFaceFW/fineweb-2 --include "data/hin_Deva/*" --local-dir ./data/hi/fineweb2 --repo-type dataset` |
-| CulturaX | `uonlp/CulturaX` | `hi` | `huggingface-cli download uonlp/CulturaX --include "hi/*" --local-dir ./data/hi/culturax --repo-type dataset` |
-| mC4 | `allenai/c4` | `hi` | `huggingface-cli download allenai/c4 --include "multilingual/c4-hi*" --local-dir ./data/hi/c4 --repo-type dataset` |
-| Sangraha (verified) | `ai4bharat/sangraha` | `verified/hin` | `huggingface-cli download ai4bharat/sangraha --include "verified/hin/*" --local-dir ./data/hi/sangraha_verified --repo-type dataset` |
-| Sangraha (unverified) | `ai4bharat/sangraha` | `unverified/hin` | `huggingface-cli download ai4bharat/sangraha --include "unverified/hin/*" --local-dir ./data/hi/sangraha_unverified --repo-type dataset` |
-
-### Turkish Sources (5)
-| Source | HuggingFace Path | Subset/Config | Download Command |
-|--------|------------------|---------------|------------------|
-| HPLT-2 | `HPLT/HPLT2.0_cleaned` | `tur_Latn` | `huggingface-cli download HPLT/HPLT2.0_cleaned --include "tur_Latn*/*" --local-dir ./data/tr/hplt2 --repo-type dataset` |
-| Fineweb-2 | `HuggingFaceFW/fineweb-2` | `tur_Latn` | `huggingface-cli download HuggingFaceFW/fineweb-2 --include "data/tur_Latn/*" --local-dir ./data/tr/fineweb2 --repo-type dataset` |
-| CulturaX | `uonlp/CulturaX` | `tr` | `huggingface-cli download uonlp/CulturaX --include "tr/*" --local-dir ./data/tr/culturax --repo-type dataset` |
-| mC4 | `allenai/c4` | `tr` | `huggingface-cli download allenai/c4 --include "multilingual/c4-tr*" --local-dir ./data/tr/c4 --repo-type dataset` |
-| VNGRS | `vngrs-ai/vngrs-web-corpus` | N/A | `huggingface-cli download vngrs-ai/vngrs-web-corpus --local-dir ./data/tr/vngrs --repo-type dataset` |
-
-**Note**: Turkish HPLT-2 is split into 5 subfolders: `tur_Latn_1` through `tur_Latn_5`.
-
----
-
-## Pipeline Stages
-
-### Stage 1: Download
-
-Downloads are performed using `huggingface-cli download` with `--include` patterns:
-
-```bash
-# Example: Download Hindi CulturaX
-huggingface-cli download uonlp/CulturaX \
-    --include "hi/*" \
-    --local-dir ./data/hi/culturax \
-    --repo-type dataset
-```
-
-The `run_pipeline.py` script automates this:
-```bash
-python run_pipeline.py --language hi --stage download
-python run_pipeline.py --language tr --stage download
-```
-
----
-
-### Stage 2: Quality Filtering
-
-Quality filtering uses DataTrove with custom language-specific filters.
-
-#### Filter Configuration (from Fineweb-2)
-
-**Hindi Filter Thresholds** (`src/filters/hi_quality.py`):
-```python
-HINDI_FILTER_CONFIG = {
-    "min_script_ratio": 0.5,         # Devanagari script ratio
-    "lang_score_threshold": 0.692,
-    "dup_line_frac": 0.206,
-    "new_line_ratio": 0.316,
-    "min_avg_word_length": 2,
-    "max_avg_word_length": 21,
-    "line_punct_thr": 0.091,
-    "non_alpha_words_ratio": 0.837,
-    "top_5_gram_frac": 0.135,
-    "top_10_gram_frac": 0.090,
-}
-```
-
-**Turkish Filter Thresholds** (`src/filters/tr_quality.py`):
-```python
-TURKISH_FILTER_CONFIG = {
-    "min_script_ratio": 0.65,        # Turkish Latin script ratio
-    "lang_score_threshold": 0.875,
-    "dup_line_frac": 0.272,
-    "new_line_ratio": 0.222,
-    "min_avg_word_length": 3,
-    "max_avg_word_length": 21,
-    "line_punct_thr": 0.091,
-    "non_alpha_words_ratio": 0.773,
-    "top_5_gram_frac": 0.154,
-    "top_10_gram_frac": 0.103,
-}
-```
-
-#### Output Schema Normalization
-
-All filtered output uses a unified schema:
-```python
-OUTPUT_SCHEMA = pa.schema([
-    ("text", pa.string()),
-    ("id", pa.string()),
-    ("metadata", pa.struct([
-        ("source", pa.string()),
-    ])),
-])
-```
-
-#### Running Filtering
-
-```bash
-# Main parquet datasets (culturax, fineweb2, sangraha, vngrs)
-python run_pipeline.py --language hi --stage filter
-python run_pipeline.py --language tr --stage filter
-
-# C4 JSON files (requires separate script due to JSON format)
-python filter_c4.py --language hi
-python filter_c4.py --language tr
-
-# HPLT2 (requires separate handling due to nested structure)
-python fix_hindi_hplt2_filter.py
-python fix_hplt2_filter.py  # Turkish - 5 subfolders
-```
-
----
-
-### Stage 3: MinHash Deduplication
-
-MinHash deduplication removes near-duplicate documents within each source.
-
-#### MinHash Configuration
-```python
-MINHASH_CONFIG = {
-    "n_grams": 5,
-    "num_buckets": 14,
-    "hashes_per_bucket": 8,
-    "similarity_threshold": 0.8,
-}
-```
-
-#### MinHash Stages (via DataTrove)
-1. **Stage 1 - Signatures**: Generate MinHash signatures for each document
-2. **Stage 2 - Buckets**: Group documents by LSH buckets to find candidates
-3. **Stage 3 - Cluster**: Cluster similar documents together
-4. **Stage 4 - Filter**: Keep one representative per cluster, write deduped output
-
-#### Running MinHash
-```bash
-python run_pipeline.py --language hi --stage minhash
-python run_pipeline.py --language tr --stage minhash
-```
-
-**Runtime**: Hindi ~17 hours, Turkish ~30 hours (on 56-core machine)
-
----
-
-### Stage 4: Consensus Subset Construction
-
-The consensus subset identifies documents that appear in 2+ sources using exact text hash matching.
-
-#### Algorithm (Two-Pass, Memory-Efficient)
-
-**Pass 1**: Build hash-to-sources index
-```python
-# For each document, compute MD5 hash of normalized text
-# Store only: hash -> set of sources (not full text)
-def compute_text_hash(text: str) -> str:
-    normalized = ' '.join(text.lower().split())
-    return hashlib.md5(normalized.encode('utf-8')).hexdigest()
-
-# Pass 1: hash_to_sources[hash].add(source)
-```
-
-**Pass 2**: Extract documents with multi-source hashes
-```python
-# Re-read data, collect documents where hash appears in 2+ sources
-# Store full document with sources list
-```
-
-#### Consensus Output Schema
-```python
-schema = pa.schema([
-    ('text', pa.string()),
-    ('id', pa.string()),
-    ('sources', pa.list_(pa.string())),      # e.g., ["c4", "culturax"]
-    ('all_ids', pa.list_(pa.string())),      # e.g., ["c4:url1", "culturax:url2"]
-    ('metadata', pa.struct([
-        ('source', pa.string()),              # "consensus"
-    ])),
-])
-```
-
-#### Running Consensus Builder
-```bash
-python build_consensus_v2.py --language hi
-python build_consensus_v2.py --language tr
-```
-
-**Runtime**: Hindi ~2 hours, Turkish ~7 hours
-
----
-
-### Stage 5: Upload to HuggingFace
-
-#### Create Repositories
-```bash
-huggingface-cli repo create HinMix --organization AdaMLLab --type dataset
-huggingface-cli repo create TurMix --organization AdaMLLab --type dataset
-```
-
-#### Staging Directory Structure
-```
-hf_staging/HinMix/
-├── README.md
-├── minhash_deduped/
-│   ├── c4/*.parquet
-│   ├── culturax/*.parquet
-│   ├── fineweb2/*.parquet
-│   ├── hplt2/*.parquet
-│   ├── sangraha_unverified/*.parquet
-│   └── sangraha_verified/*.parquet
-├── quality_filtered/
-│   └── (same structure as minhash_deduped)
-└── consensus/
-    └── consensus.parquet
-```
-
-#### Upload Command
-```bash
-# Use upload-large-folder for large datasets
-hf upload-large-folder AdaMLLab/HinMix hf_staging/HinMix --repo-type dataset --num-workers 8
-hf upload-large-folder AdaMLLab/TurMix hf_staging/TurMix --repo-type dataset --num-workers 8
-```
-
----
-
-## Final Statistics
-
-### Hindi (HinMix)
-
-| Stage | Documents | Size | Notes |
-|-------|-----------|------|-------|
-| **Quality Filtered** | ~99M | 231GB | All 6 sources combined |
-| **MinHash Deduped** | ~60M | 136GB | 40% reduction |
-| **Consensus** | 1.92M | 3.7GB | Docs in 2+ sources |
-
-**Consensus Source Participation**:
-- fineweb2: 1,602,172
-- hplt2: 1,194,132
-- sangraha_unverified: 600,851
-- culturax: 277,990
-- sangraha_verified: 153,060
-- c4: 71,462
-
-### Turkish (TurMix)
-
-| Stage | Documents | Size | Notes |
-|-------|-----------|------|-------|
-| **Quality Filtered** | ~49M | 658GB | All 5 sources combined |
-| **MinHash Deduped** | ~27M | 359GB | 46% reduction |
-| **Consensus** | 7.84M | 13GB | Docs in 2+ sources |
-
-**Consensus Source Participation**:
-- fineweb2: 7,217,270
-- hplt2: 7,075,189
-- culturax: 686,152
-- c4: 419,307
-- vngrs: 402,760
-
----
-
-## Key Scripts
-
-### run_pipeline.py (Main Entry Point)
-```python
-#!/usr/bin/env python3
-"""
-Usage:
-    python run_pipeline.py --language hi --stage download
-    python run_pipeline.py --language hi --stage filter
-    python run_pipeline.py --language hi --stage minhash
-    python run_pipeline.py --language tr --stage all
-"""
-```
-
-### build_consensus_v2.py (Consensus Builder)
-```python
-#!/usr/bin/env python3
-"""
-Memory-efficient two-pass consensus builder.
-Usage:
-    python build_consensus_v2.py --language hi
-    python build_consensus_v2.py --language tr
-"""
-```
-
-### filter_c4.py (C4 JSON Filtering)
-```python
-#!/usr/bin/env python3
-"""
-Filters C4 JSON files (not parquet) with schema normalization.
-Usage:
-    python filter_c4.py --language hi
-    python filter_c4.py --language tr
-"""
-```
-
----
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Storage Full During MinHash**
-   - MinHash signatures can grow to 300+ GB
-   - Ensure at least 500GB free space before starting
-   - If interrupted, clean `minhash_signatures/`, `minhash_buckets/`, `minhash_clusters/` and restart
-
-2. **Memory Issues During Consensus**
-   - Use `build_consensus_v2.py` (two-pass, memory-efficient)
-   - Original `build_consensus.py` requires 100+ GB RAM
-
-3. **C4 Schema Mismatch**
-   - C4 is JSON, not parquet
-   - Use `filter_c4.py` with `JsonlReader` and schema adapter
-
-4. **HPLT2 Nested Folders**
-   - Turkish HPLT2 has 5 subfolders (`tur_Latn_1` to `tur_Latn_5`)
-   - Use `fix_hplt2_filter.py` which handles all subfolders
-
-### Recovery Commands
-```bash
-# Check running processes
-ps aux | grep "run_pipeline\|build_consensus" | grep -v grep
-
-# Check disk usage
-df -h /home/alrashsm/Documents/Github/arabic-pretraining-mix-other-languages/data/
-
-# Check MinHash progress
-ls data/hi/logs/minhash_sig/completions/ | wc -l  # Should reach 54
-tail -20 data/hi/minhash.log
-```
-
----
-
-## License
-
-This pipeline and resulting datasets are released under CC-BY-4.0.
-Individual source datasets have their own licenses - refer to original sources.
-
----
-
-## Citation
-
-```bibtex
-@dataset{hinmix_turmix_2024,
-  title={HinMix and TurMix: Hindi and Turkish Pretraining Data Mixes},
-  author={AdaMLLab},
-  year={2024},
-  publisher={Hugging Face},
-  url={https://huggingface.co/AdaMLLab}
-}
-```