hayas11/maritime-arabic-industrial-ai
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--- language: - ar - en tags: - maritime - arabic - industrial-ai - sft - rlhf - multi-turn-conversations - glossary-(term-level) - error-correction-pairs - translation-pairs-(en↔ar) - ship-stability - naval-architecture - STCW pretty_name: Maritime Technical Arabic - Industrial AI Dataset size_categories: - n<1K task_categories: - text-generation - translation license: cc-by-4.0 configs: - config_name: error_correction data_files: "dataset/error_correction/*" - config_name: multi_turn_conversations data_files: "dataset/multi_turn_conversations/*" - config_name: rlhf_ranking data_files: "dataset/rlhf_ranking/*" - config_name: sft data_files: "dataset/sft/*" - config_name: translation_pairs data_files: "dataset/translation_pairs/*" - config_name: glossaries data_files: "dataset/glossaries/*" --- # Maritime Technical Arabic — Industrial AI Dataset **Author:** Mohammed Almalki - Marine Engineering & Naval Architecture **Purpose:** Bridging the gap between general Arabic NLP and heavy industry technical language for AI models. --- ## The Problem This Dataset Solves This dataset solves the problem of specialized knowledge. By converting complex engineering research into a structured format, it allows AI developers to create tools that can accurately answer questions on Naval Research & Analysis, Marine Technology, Naval Architecture & Marine Engineering in the Arabic language a task that general-purpose models currently struggle with due to a lack of domain-specific training data. Synthetic Augmentation: 90% of examples were generated by LLM pipelines (using the sources) and then validated, not original human-authored content --- ## Dataset Summary This dataset is a proof-of-concept, not a production-scale resource but a curated technical corpus focused on maritime engineering and future-tech feasibility within the Arabic-speaking world. Sources include academic research, technical textbooks, and feasibility studies covering many aspects. ## Methodology & AI Orchestration To transform dense academic PDFs and texts into a structured dataset, the following pipeline was implemented: * **Programmatic Data Cleaning:** Developed custom LLM workflows to extract technical specifications, formulas, and core arguments from unstructured research papers, normalizing terminology across Arabic and English technical lexicons. * **Synthetic Data Augmentation:** Generated complex Q&A pairs and summarizations based on the core research to expand the dataset’s utility for training RAG (Retrieval-Augmented Generation) systems. * **Human-in-the-Loop (HITL) Validation:** Performed rigorous manual audits of technical terms (e.g., "Nuclear Feasibility," "Electromagnetic Induction") to ensure that the Arabic translation and conceptual mapping remained scientifically accurate. ## Quality Assurance & Validation Validation Methodology 1 - Human-in-the-Loop Review (100% coverage) -All error correction pairs manually reviewed for technical accuracy -Glossary terms cross-referenced against maritime engineering textbooks -Multi-turn conversations checked for logical flow and coherence 2 - Consistency Checks -All technical terms in SFT pairs validated against master glossary -Translation pairs checked for semantic equivalence (not literal translation) -Domain terminology usage consistent across all subsets 3 - Accuracy Metrics -Spot-check validation: 50 random error correction pairs reviewed - 100% accuracy -Glossary-to-dataset consistency: of terms appear correctly in context -Translation pair accuracy (technical meaning preservation) ## Dataset Contents ### 1. Glossary - Term Maritime Arabic Glossary A bilingual terminology reference covering domain-specific terms across naval architecture, marine propulsion, nuclear engineering, and maritime law. | Field | Type | Description | |--------------------|-------------|-------------------------------------------------------| | `english_term` | string | Technical term in English | | `arabic_term` | string | Verified Arabic translation | | `arabic_definition`| string | Arabic definition/explanation of the term | ### 2. SFT - Supervised Fine-Tuning Prompt-Response Pairs Single-turn instruction-response pairs designed for supervised fine-tuning. Each entry presents a technical question a practitioner or researcher might ask, paired with a detailed, accurate Arabic response grounded in the source research. | Field | Type | Description | |-------------------|--------|-----------------------------------------------------------------| | `instruction` | string | Technical question in Arabic | | `response` | string | Expert-level Arabic answer (typically 100–300 words) | | `metadata_topic` | string | Broad topic tag (e.g. "nuclear propulsion", "submarine design") | | `metadata_source` | string | Research paper or chapter the response is derived from | **Designed for:** `transformers` SFT pipelines **Format compatibility:** Converts directly to ShareGPT or Alpaca format. ### 4. RLHF Ranking — Preference Pairs Preference pairs for reward model training or Direct Preference Optimization (DPO). Each entry presents the same prompt with two responses one technically accurate and one containing a plausible hallucination or imprecision labelled as chosen and rejected. The "rejected" responses are not randomly wrong. They represent the category of errors Arabic LLMs most commonly produce on technical maritime content: wrong numerical values, institution name confusions, and conceptual misattributions that are internally coherent but factually incorrect. | Field | Type | Description | |------------|--------|----------------------------------------------------------| | `prompt` | string | Technical question or scenario in Arabic | | `chosen` | string | Accurate, expert-level Arabic response | | `rejected` | string | Plausible but incorrect or imprecise Arabic response | **Designed for:** DPO, IPO, KTO trainers (TRL `DPOTrainer`, Axolotl `dpo`). **Direct compatibility:** Hugging Face `trl` DPO format (prompt/chosen/rejected). ## 4. Multi-turn conversations - Dialogues consisting of multiple back-and-forth exchanges Realistic dialogues simulating a domain expert being consulted on maritime engineering topics. Each conversation contains between 3 and 8 turns, covering follow-up questions, clarification requests, and technical elaboration patterns that single-turn SFT data cannot capture. | Field | Type | Description | |----------------|--------------|----------------------------------------------------------| | `messages` | list[object] | Ordered list of turns | | `role` | string | `"user"` or `"assistant"` | | `content` | string | The message content in Arabic | | `topic` | string | Conversation topic tag | **Designed for:** Chat fine-tuning with `apply_chat_template` (ChatML, LLaMA-3 Instruct, Qwen, Mistral Instruct formats). ## 5. Translation pairs (EN-AR) Parallel sentence pairs for technical translation evaluation and fine-tuning. Sentences are drawn from engineering specifications, research abstracts, and safety documentation contexts where literal translation fails and domain-faithful rendering is required. | Field | Type | Description | |------------|--------|-------------------------------------------| | `en` | string | Source sentence in English | | `ar` | string | Expert Arabic translation | | `domain` | string | Sub-domain of the sentence | **Designed for:** Fine-tuning translation models (NLLB, SeamlessM4T) and evaluating Arabic technical translation quality. ## 6. Error correction pairs Pairs of incorrect and correct Arabic technical statements. The incorrect version represents a believable hallucination wrong facts, swapped terms, or plausible-sounding but inaccurate claims drawn from the same research domain as the correct version. | Field | Type | Description | |-------------|--------|-----------------------------------------------------| | `incorrect` | string | Hallucinated or factually wrong statement in Arabic | | `correct` | string | Accurate corrected statement in Arabic | | `topic` | string | Sub-domain of the pair | **Designed for:** Training models on factual grounding, building hallucination-detection classifiers, and constructing RLHF rejected samples. ## Intended use - RAG (Retrieval-Augmented Generation): Optimized for building AI-powered technical assistants that can query complex maritime research - Domain-Specific Fine-Tuning: Ideal for fine-tuning LLMs to understand the specific linguistic patterns and technical naming of the Arabic maritime engineering sector. - Technical Translation (Cross-Lingual): A baseline for improving the translation accuracy of high-stakes engineering terms from English to Arabic, ensuring scientific "faithfulness" rather than just literal translation. ## Source & Derivation The technical content in this dataset is derived from graduate-level engineering research in naval architecture and marine engineering and from textbooks, covering: power systems, hull design, propulsion,marine propulsion, electrical systems, auxiliary machinery, Descriptions of mechanical failures, diagnostic steps, and repair procedures. - maritime-engineering - industrial-ai - safety-protocols - naval-architecture - technical-arabic - auxiliary-machinery - port-operations All content has undergone human-in-the-loop validation by a Marine Engineering & Naval Architecture specialist to ensure scientific accuracy. ## Citation ```bibtex @dataset{hayas11_2025maritimearabic, author = {hayas11}, title = {Maritime Technical Arabic — Industrial AI Dataset}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/hayas11/maritime-arabic-industrial-ai} } ``` --- ## License Creative Commons Attribution 4.0 International (CC BY 4.0) Free to use for research and model training with attribution.
