--- title: ESCP Huggingface Guidelines emoji: 🏢 colorFrom: pink colorTo: pink sdk: static pinned: false short_description: Publishing and usage guidelines for ESCP projects on Hugging --- # ESCP on Hugging Face — README (Publishing & Usage Guide) This repository/organization is ESCP’s hub for **sharing applied AI work**: teaching assistants, course demos, student projects, research prototypes, and datasets. The goal is to **learn by doing**, publish responsibly, and keep the ESCP brand credible. If you are new to Hugging Face, start with the **Quick Start**. If you already know the platform, jump to **Publishing Standards**. --- ## 1) Quick Start (10 minutes) **Hugging Face Hub basics** - **Models**: weights + inference examples + eval notes - **Datasets**: data + dataset card + license + intended use - **Spaces**: interactive demos (Gradio / Streamlit / static) — best for pedagogy and showcasing prototypes - **Collections**: curated groups (recommended for course cohorts, projects, or labs) **Recommended path** 1. Explore existing ESCP assets (Spaces/Models/Datasets) 2. Clone or duplicate a Space as a template 3. Publish your first draft privately (when possible), then request review Docs: - Hub overview: https://huggingface.co/docs/hub/index - Spaces: https://huggingface.co/docs/hub/spaces - Model cards: https://huggingface.co/docs/hub/model-cards - Dataset cards: https://huggingface.co/docs/hub/datasets-cards --- ## 2) What belongs here (scope) ✅ Good fits - Teaching assistants, course copilots, interactive demos - Student prototypes, hackathon outputs (cleaned) - Research artifacts: baselines, evaluation code, reproducible demos - Datasets used for learning/research **with clear rights and documentation** - Tutorials, notebooks, templates for ESCP usage - and any other suggestions you wish to share with us ❌ Not allowed - Anything with unclear IP/rights or confidential information - Scraped data that violates terms, privacy, or platform policies - Personal data (students, staff, interviewees) unless **proper consent and a valid legal basis** are clearly established - Content that conflicts with applicable European regulations, including **GDPR** and the **EU AI Act**, or exposes ESCP or individuals to legal, ethical, or regulatory risk - “Half-broken” repos with no README, no description, or no reproducibility Rule of thumb: publish what you’d be comfortable showing publicly to partners, media, and peers. --- ## 3) Publishing workflow (recommended) ### A. Before you publish - Ensure you have the rights to publish code/data/model - Remove secrets (tokens, keys), internal URLs, private documents - Add a README (this one with link) + a short “card” (model/dataset) when relevant - Add a minimal reproducible example (how to run, dependencies) ### B. Get support or approval For questions, support requests, or publication guidance contact: **genai@escp.eu** If you’re unsure about IP, privacy, or institutional sensitivity: ask first. --- ## 4) Naming & structure standards Clear naming makes ESCP content discoverable and professional. ### A. Repository naming pattern (recommended) Use lowercase, hyphen-separated: **Spaces** - `escp-aita-student` (teaching assistant student-facing) - `escp-course--demo` (e.g. `escp-course-finance-demo`) - `escp-ai-education-` (e.g. `escp-ai-education-feedback`) **Models** - `escp---` Example: `escp-aiedu-rag-baseline-v1` **Datasets** - `escp---` Example: `escp-management-cases-synth-v1` ### B. Descriptions and tags Every asset must include: - One-line purpose (what it does) - Target audience (students / faculty / research / partners) - Status tag: `prototype`, `teaching`, `research`, `demo`, `baseline` ### C. Versioning Use semantic-ish suffixes: `v1`, `v1.1`, `v2` and keep a short changelog section. --- ## 5) Content & safety policy (non-negotiable) ESCP content must remain **professional, inclusive, and responsible**. Not allowed: - Racist, discriminatory, hateful, harassing, or violent content - Targeted attacks or demeaning stereotypes - Sexual content involving minors (or any unsafe content) - Content encouraging wrongdoing or unsafe behavior - “Edgy” demos that can embarrass ESCP or harm others Also avoid: - Biased examples presented as facts without context - Generated “fake news” style demos without clear labeling Minimum requirement: - Add an **Ethics / Limitations** note when a model/demo could mislead or impact people. --- ## 6) Data, privacy, and IP (must check) Before publishing datasets or logs: - Do not publish personal data (names, emails, IDs, voice, faces) unless you have explicit authorization and a valid legal basis. - Prefer anonymized or synthetic data for teaching. - Respect copyright and data licenses. For datasets, include: - Source and license - Collection method - Intended use and limitations - Known biases or risks Docs: - Dataset cards: https://huggingface.co/docs/hub/datasets-cards --- ## 7) Quality bar (lightweight but real) Every public asset should include, at minimum: ### Spaces - Clear title + 2–5 line description - “How to use” section - Basic guardrails (input constraints, disclaimers) - Works on CPU unless a GPU is truly required ### Models - Model card (task, training data summary, eval, intended use) - Inference example (Transformers snippet or API) - Limitations and safety notes ### Datasets - Dataset card (what/why/how) - Schema, splits, examples - License and usage constraints --- ## 8) Encourage innovation (what we want to see) This organization is not only for “finished research”. We want: - Pedagogical prototypes that help students learn - Transparent baselines and reproducible experiments - Practical demos that connect AI to management problems - Proposals for new collections, benchmarks, or shared templates If you have an idea: - Create a small draft Space - Write a 10-line README with the goal + expected users - Email **genai@escp.eu** for guidance and visibility --- ## 9) Suggested templates (copy/paste) ### A. Space README skeleton - What it is (2 lines) - Who it’s for - How to use - Technical notes (stack, requirements) - Limitations / ethics ### B. Model card skeleton - Model summary - Intended use - Training data (high-level) - Evaluation - Limitations & biases - How to run ### C. Dataset card skeleton - Dataset summary - Motivation - Composition / schema - Collection process - Licensing - Intended use & risks --- ## 10) Contacts For support, publication guidance, or special requests: **genai@escp.eu** For anything sensitive (privacy, IP, reputational risk): contact first, publish after. --- *This README is a living document. Tutorials and additional ESCP guidance will be released progressively, with updates also available on https://escp.eu/ai.*