sevka-k1/translator-rules-dataset

Translator Rules Dataset

A high-quality Arabic↔English machine-translation dataset built from the WAM (Web Archive Management) translation corpus — 10 years of professional Arabic↔English news/government translations — augmented with 200 LLM-extracted translation rules embedded in system prompts, and mixed with ~7% general-instruction data to prevent catastrophic forgetting during fine-tuning.

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Dataset Statistics

Split	Rows	MT rows	General rows	General %
Train	15,028	13,948	1,080	7.2%
Validation	1,668	1,548	120	7.2%
Total	16,696	15,496	1,200	7.2%

Token Length Analysis

Measured on the full concatenated message text (system + user + assistant) using both target model tokenizers:

Metric	Karnak-6B-v1.0	Qwen3.5-4B
Vocab size	192,726	248,044
Model max length	131,072	262,144
Absolute max	17,209	17,955
p99	12,950	13,559
p95	11,554	12,225
Mean	4,973	5,241
Median	5,374	5,711

Karnak's Arabic-optimized tokenizer consistently produces ~600–700 fewer tokens per example.

Token length distribution (Karnak)

Bucket (tokens)	Count	%
0–512	4,878	29.2%
512–1024	1,038	6.2%
1024–2048	695	4.2%
2048–4096	1,177	7.0%
4096–8192	3,906	23.4%
8192–16384	4,999	29.9%
16384–32768	3	0.0%

Recommendation: max_seq_len=16384 covers 99.98% of the dataset (only 3 rows exceed it).

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Row Schema

Each row is a single translation turn with full metadata:

Field	Type	Description
english	str	Original English text
arabic	str	Original Arabic text
source	str	Source text (depends on direction)
translation	str	Target text (depends on direction)
system_prompt	str	System prompt (may contain translation rules)
direction	str	en2ar, ar2en, or general
field	str	Title, Body (MT), or category (math/code/qa/creative for general)
code	str	WAM article code (empty for general)
classifications	str	WAM classifications (category name for general)
messages	list[dict]	Chat messages: [{role, content}, ...]

Messages format

[
  {"role": "system", "content": "<rules prompt or minimal/empty>"},
  {"role": "user", "content": "<instruction + source text>"},
  {"role": "assistant", "content": "<target translation>"}
]

The system message may be omitted entirely (empty-system mode) — see Message Structure Randomization below.

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How the Dataset Was Built

The dataset is produced by a multi-stage pipeline that extracts translation rules from the corpus itself, cleans and filters the data, then mixes in general-instruction examples.

1. Corpus Loading & Chunking

• Parses the WAM corpus JSON (~3,900 Arabic↔English translation entries)
• Validates that both AR and EN sides have non-empty Title and Body fields
• Chunks entries into contiguous blocks of 3–5 pairs for LLM rule extraction

2. Translation Rule Extraction

• For each block of 3–5 translation pairs, an LLM extracts translation rules
• Rules capture patterns like: number formatting, date conventions, named-entity handling, register/tone, punctuation, etc.
• Temperature: 0.2 (consistent extraction)

3. Map-Reduce Consolidation

Two-stage consolidation of per-block rules into a final set of 200 rules:

1. Map stage: Block rule sets are batched (15 blocks per batch) and sent to the LLM for deduplication/merging into cluster-level rules
2. Reduce stage: All clusters are consolidated in a single LLM call producing exactly num_rules (200) final rules

• Temperature: 0.3

4. System Prompt Generation

• Generates 100 unique system prompts from the consolidated rules
• Each prompt samples rules_per_category (10) rules from each category
• LLM rewrites them into a self-contained translation-agent system prompt
• Temperature: 0.7 (high diversity)
• Each entry in the dataset gets assigned one of the 100 prompts (deterministic round-robin)

5. Data Cleaning & Pair Filtering

This is the critical quality-control step. It performs two operations:

A. Data Cleaning

Applied to all text fields before LLM filtering and before dataset building:

1. URL normalization:

   • Strips http:///https:// scheme from www.* URLs (bare www.example.com form)
   • Lowercases scheme and host
   • Removes URL fragments (#section)
   • Removes trailing slashes
   • Strips attached punctuation (including Arabic comma ، and Arabic full stop .)
   • Ensures the same site isn't counted as different across AR/EN sides

2. Repeated punctuation collapse:

   • Regex: ([!?.;,:\-_=*])\1{3,} — runs of 4+ identical punctuation marks → collapsed to 1
   • Handles real-world WAM corpus artifacts: ............ (section separators), ------------ (horizontal rules), ***** (email-scanner footer markers)
   • Markdown table separators (|------|) in system prompts are preserved (they're legitimate formatting, not decorative runs)

B. LLM Pair Filtering

Some AR↔EN pairs in the WAM corpus describe the same event but contain different numbers, dates, names, or factual details. These are removed:

• Each entry's Title and Body fields are checked independently (2 checks per entry)
• Two LLM endpoints run in parallel for throughput
• Temperature: 0.1 (consistent factual judgments)
• LLM classifies each pair as match / mismatch / skip (empty text) / unknown
• URL-mismatched entries (where AR/EN URL sets differ after normalization) are flagged
• Mismatched pairs are excluded from the dataset

6. Dataset Build

Builds a HuggingFace DatasetDict with train/validation splits:

• Entry-level split: 10% of entries reserved for validation (no translation pair leaks across splits)
• 100 entries reserved for manual validation (excluded from train/eval)
• Each MT entry produces 2 fields × 2 directions = 4 rows (Title-en2ar, Title-ar2en, Body-en2ar, Body-ar2en)
• Uses cleaned entries (URL-normalized + punctuation-fixed) from the filter stage
• Mismatched pairs (from LLM filtering) are excluded

7. General Data Mixing

Augments the MT dataset with ~1,200 general-instruction examples to prevent catastrophic forgetting of general capabilities (math, code, QA, creative) during fine-tuning.

Sources

Category	Count	Source HF Dataset	Format
Math	300	meta-math/MetaMathQA	query → response
Code (Python)	240	HuggingFaceH4/CodeAlpaca_20K	prompt → completion (Python-only filter)
QA	360	yahma/alpaca-cleaned	instruction+input → output
Creative	300	Magpie-Align/Magpie-Air-300K-Filtered	ShareGPT → first turn
Total	1,200

Filters applied to general data

• Length filter: examples where len(user) + len(assistant) > 3000 chars are skipped (keeps token budget manageable)
• Python-only filter (code): looks for Python markers (python, def , import , print(, pip , django, flask, pandas, numpy)
• First-turn extraction (creative): extracts only the first human→gpt turn from ShareGPT conversations

System prompt randomization for general data

• 30% of general rows get an empty system prompt (no system message)
• 70% get a role-based system prompt (3 variants per category, randomly selected)

Mixing process

1. Load MT dataset
2. Load 1,200 general samples from 4 HF datasets
3. Split general into train/validation at 10% (120 val / 1,080 train)
4. Concatenate per-split: MT + general
5. Shuffle with seed=42

Final mix ratio: ~92.8% MT + ~7.2% general per split

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Message Structure Randomization

To maximize prompt diversity and robustness, the MT dataset uses 5 weighted prompt modes for each row. The mode is selected via weighted random roll (deterministic, seed=123):

Mode	Weight	System	User
rules_system_instr_user	35%	Full rules prompt	Translation instruction + source text
rules_user_minimal_sys	20%	Minimal: "You are a professional Arabic↔English translator."	Rules + instruction + source text
no_rules_empty_sys	20%	Empty (omitted)	Instruction + source text
no_rules_minimal_sys	15%	Minimal	Instruction + source text
rules_system_bare_user	10%	Full rules prompt	Bare source text (no instruction)

Instruction templates

29 templates per direction (en2ar and ar2en), covering:

• Short imperative: "Translate to Arabic:\n{src}"
• Descriptive English: "Please translate the following text from English to Arabic:\n{src}"
• Arabic instructions (short): "ترجم إلى العربية:\n{src}"
• Arabic instructions (descriptive): "أرجو ترجمة النص التالي من الإنجليزية إلى العربية:\n{src}"
• Mixed/unconventional: "EN → AR:\n{src}", "[English] → [Arabic]\n{src}"

Actual distribution (train split, 15,028 rows)

Mode	Count	%
rules_system_bare_user	3,380	22.5%
rules_system_instr_user	2,922	19.4%
no_rules_empty_sys	2,763	18.4%
rules_user_minimal_sys	2,752	18.3%
no_rules_minimal_sys	2,131	14.2%
General (mixed)	1,080	7.2%

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Data Quality Verification

URL normalization ✅

• 0 issues found across all 16,696 rows
• All URLs properly normalized: bare www.example.com form (no http:// scheme, no fragments, no trailing slashes)
• Example: www.abudhabiawards.ae, www.wcems.ae

Repeated punctuation ✅

• 0 real violations in corpus text fields (english, arabic, source, translation)
• 5,343 regex matches total, but all are legitimate:
  • 5,338 are markdown table separators (|------|) in system prompts containing translation rules — these are intentional formatting, not decorative runs
  • 5 are markdown table separators in one translation that contains a table
  • 1 is a fill-in-the-blank exercise (_____) in a source text — legitimate content

Shuffle quality ✅

• General data uniformly distributed at ~7% per 1,000-row bucket across the train split
• Max consecutive general run: 4 rows
• Max consecutive MT run: 96 rows (no clustering)

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Target Models

Model	Parameters	Vocab	Max context
Applied-Innovation-Center/Karnak-6B-v1.0	6B	192,726	131,072
Qwen/Qwen3.5-4B	4B	248,044	262,144

Karnak's tokenizer is optimized for Arabic, producing ~600–700 fewer tokens per example than Qwen on the same text.

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Reproducibility

• All random operations use fixed seeds (chunking, rule sampling, prompt mode assignment, general data shuffle — all seed=42 or 123)
• LLM calls use low temperatures (0.1–0.7) for consistency
• Filter and extraction steps are fully resumable (incremental saves)

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Source Data

• WAM Corpus: 10 years of professional Arabic↔English government/news translations
• General data sources: MetaMathQA, CodeAlpaca_20K, alpaca-cleaned, Magpie-Air-300K-Filtered — all public HuggingFace datasets
• Translation rules: 200 rules extracted and consolidated by LLM from the WAM corpus itself