| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - autism-support |
| - neurodiversity |
| pretty_name: Autism consultant |
| size_categories: |
| - 10K<n<100K |
| --- |
| ```yaml |
| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - neurodiversity |
| - autism-support |
| - clinical-psychology |
| - sft |
| - chatml |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| ``` |
| |
| # Dataset Card for Adult Autism & Sensory Systems Consulting Dataset |
| |
| This dataset is a high-density, highly scrubbed Supervised Fine-Tuning (SFT) corpus designed to train language models in adult autism advocacy, workplace environmental engineering, neurodiversity-affirming psychology, and tactical executive dysfunction adjustments. |
| |
| The entire dataset is natively formatted in the ChatML schema, ensuring immediate compatibility with modern training frameworks like Unsloth, Axolotl, or alignment scripts. |
| |
| --- |
| |
| ## Dataset Description |
| |
| * **Primary Domain:** Adult Autism Clinical & Practical Consultation |
| * **Format:** JSON Lines (`.jsonl`) utilizing standard ChatML turn architectures |
| * **Total Records:** 16,000 high-density QA pairs (12,000 Expert Reference + 4,000 Algorithmic Synthesized) |
| * **Core Objective:** Eliminating conversation padding, repetitive pleasantries, and meta-textual document references to force direct, clinical, and authoritative structural protocols. |
| |
| --- |
| |
| ## Dataset Structure & Schema |
| |
| Each row within the JSONL file represents a complete standalone conversation sequence containing a hard-coded system persona instruction, a user problem statement, and an authoritative assistant response. |
| |
| ```json |
| { |
| "messages": [ |
| {"role": "system", "content": "You are a professional research model. Answer the user prompt strictly, exhaustively, and with academic depth."}, |
| {"role": "user", "content": "[Direct, scenario-driven or structural query regarding neurodivergent profiling or accommodation]"}, |
| {"role": "assistant", "content": "[Dense, zero-filler, multi-point tactical engineering plan or deep clinical explanation]"} |
| ] |
| } |
| |
| ``` |
| |
| --- |
| |
| ## Data Curation & Pipeline Engineering |
| |
| The high structural predictability of this dataset is the result of an intentional two-stage python pipeline designed to enforce linguistic diversity while aggressively cutting down on semantic redundancy. |
| |
| ### 1. Synthesized Augmentation Pipeline (`QA_dataset_generator.py`) |
| |
| To complement the 12,000 primary expert reference entries, 4,000 scenario-grounded rows were algorithmically synthesized from structured markdown repositories using an instruction-tuned local instance. To prevent the model from generating monotonous queries, the synthesis script enforces a randomized assignment across six distinct linguistic archetypes for every generation turn: |
| |
| * **Situational Case Presentations**: Frames a brief, practical scenario or real-world manifestation before appending a targeted inquiry. |
| * **Myth-Correction Matrix**: Frames prompts around popular misunderstandings, historical fallacies, or points of skepticism requiring fact-based corrections. |
| * **Cross-Disciplinary Links**: Demands direct structural comparisons or analytical contrasts between distinct entities, methodologies, or concepts. |
| * **Practitioner Inquiries**: Mimics natural inquiries originating from industry professionals, field researchers, or stakeholders noticing specific real-world patterns. |
| * **Internal Mechanics Deep-Dives**: Centers the prompt explicitly around underlying processes, direct functional experiences, or structural pathways. |
| * **Advanced Operational Requests**: Bypasses traditional question marks entirely, using direct, advanced operational commands (e.g., *"Trace the chronological shift in..."*). |
| |
| ### 2. Vector Anomaly Extraction Pipeline (`cleaner_ChatML.py`) |
| |
| To prevent the model from absorbing low-quality token sequences, synthetic data outputs are post-processed through an embedding-based isolation layer: |
| |
| * **Embedding Model**: Text vectors are generated using a local instance of `snowflake-arctic-embed2:568m`. |
| * **Style Centroid Computation**: The script calculates a global average vector across valid rows to determine the baseline mathematical "style signature" of the domain text. |
| * **Cosine Similarity Sanitization**: Every row is evaluated via cosine similarity against the global style centroid. Any row yielding an alignment score below `0.34` is categorized as an outlier anomaly and automatically dropped from the final training split. |
| |
| --- |
| |
| ## Intended Use Cases |
| |
| * **Supervised Fine-Tuning (SFT)**: Teaching compact base models (e.g., 7B, 8B, 14B architectures) to speak with dense clinical accuracy rather than generic conversational chatter. |
| * **Retrieval-Augmented Generation (RAG)**: Serving as a high-quality ground-truth reference database for encoding clinical and physical accommodation strategies. |
| * **Academic/Educational Research**: Studying the behavioral interaction patterns of structural workplace adjustments. |
| |
| --- |
| |
| ## Considerations & Disclaimers |
| |
| > [!CAUTION] |
| > **Clinical Disclaimer:** This dataset contains structural strategies, physical environment parameters, and cognitive optimization protocols. It is intended strictly for instructional tuning, technical enablement, and informational tooling. It is **not** a diagnostic instrument, medical advice database, or a replacement for licensed clinical healthcare professionals. |
