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README.md

@@ -93,4 +93,23 @@ Documents were filtered based on:
 - Document length constraints
 - Line quality metrics
 - Repetition detection (including Turkish-specific patterns)
-- Boilerplate/policy phrase removal
+- Boilerplate/policy phrase removal
+
+Filter thresholds based on Fineweb-2 Turkish configuration.
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{turmix2024,
+  title={TurMix: Turkish Pretraining Data Mix},
+  author={AdaMLLab},
+  year={2024},
+  publisher={Hugging Face}
+}
+```
+
+## License
+
+This dataset is released under CC-BY-4.0. Individual source datasets may have their own licenses.

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turmix_hinmix_exact_guide_reproduce.md

@@ -0,0 +1,443 @@
+# 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}
+}
+```