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