Datasets:

bareethul
/

nyc-landmark-descriptions

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 - 1K<n<10K
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-# Model Card: NYC Landmark Descriptions Dataset
-## Purpose
-This dataset was created to explore how short, descriptive texts about New York City landmarks can be classified by *vibe* (e.g., iconic, hidden gem, peaceful) and whether they are *touristry*.
-The primary goal is educational: practicing dataset construction, augmentation, and deployment to Hugging Face for use in simple NLP classification tasks.
-## Composition
-- **100 manually authored descriptions** of NYC landmarks.
-- Each description is **~200 characters long**, written to reflect sensory details, atmosphere, or emotional tone.
-- All text is **self authored** (not scraped), ensuring originality.
-## Collection
-- Descriptions were collected using Google Reviews and TripAdvisor for each place, rough drafted, and then polished with ChatGPT as a brainstorming assistant.
-- Final texts were manually authored by the dataset creator.
-- No copyrighted or sensitive material was used.
-## Preprocessing & Augmentation
-- **Preprocessing**: Texts were normalized for consistent formatting.
-- **Augmentation**: Applied EDA style transformations (synonym replacement, random deletion, random swap, random insertion) and character level noise.
-- Label preserving methods ensured that `vibe` and `is_touristry` tags remained unchanged.
-- Expanded dataset size from 100 to 1000+ augmented samples.
-## Labels
-- **text**: Short description of a NYC landmark.
-- **vibe**: Categorical label (e.g., iconic, hidden gem, peaceful, gritty).
-- **is_touristry**: Binary label (`1 = touristry`, `0 = not touristry`).
-## Splits
-- **original**: 100 manually authored samples.
-- **augmented**: 1000 augmented samples via label preserving transformations.
-## Intended Use / Limits
-- **Intended use**:
-  - Educational practice for dataset creation and augmentation.
-  - Simple text classification tasks (e.g., vibe prediction).
-- **Not intended for**:
-  - Real world tourism recommendations.
-  - Factual verification of NYC landmarks (descriptions are stylized and subjective).
-## Ethical Notes
-- Dataset avoids sensitive content (no personal data, hate speech, or private info).
-- Descriptions are fictionalized and meant for educational/demo purposes only.
-## License
-- **CC BY 4.0** - free to share and adapt with attribution.
-## AI Usage Disclosure
-- ChatGPT (GPT-4) was used as a coding teammate for:
-  - Implementing augmentation functions.
-  - Refining description drafts.
-  - Structuring the Colab notebook.
-  - All final text samples in the original split were manually authored by the creator.

 - 1K<n<10K
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+# Dataset Card for NYC Landmark Descriptions
+This dataset card documents the **NYC Landmark Descriptions** dataset.
+It contains 100+ manually written landmark descriptions, each ~200 characters long, labeled with **vibe** and a binary **touristy** tag. The augmented split expands to 1,000 samples.
+---
+## Dataset Details
+**Dataset Description**
+- **Curated by:** Bareethul Kader (Carnegie Mellon University)
+- **Language(s):** English
+- **License:** CC BY 4.0
+- **Repository:** [bareethul/nyc-landmark-descriptions](https://huggingface.co/datasets/bareethul/nyc-landmark-descriptions)
+---
+## Uses
+**Direct Use**
+- Educational practice in **NLP dataset creation and augmentation**.
+- Classification task: predict the **vibe** of a landmark (Iconic, Hidden Gem, Peaceful, Touristy).
+- Binary task: predict whether the landmark is **touristy or not**.
+**Out-of-Scope Use**
+- Not intended as a factual tourist guide.
+- Descriptions are partly fictional or paraphrased — not reliable for travel planning.
+---
+## Dataset Structure
+- **Original split:** 100 manually authored landmark descriptions.
+- **Augmented split:** 1,000 text samples generated with **Easy Data Augmentation (EDA)** techniques (synonym replacement, random swap, insertion, deletion).
+**Features:**
+- `Landmark` (string, e.g., *Statue of Liberty*)
+- `Description` (~200-character text)
+- `Vibe` (categorical: Iconic, Hidden Gem, Peaceful, Touristy)
+- `is_touristy` (binary target: `1 = yes, 0 = no`)
+---
+## Dataset Creation
+**Curation Rationale**
+To study how short text descriptions of landmarks can be used for **sentiment-style classification** (vibe) and **binary tagging** (touristy or not).
+**Data Collection and Processing**
+- All descriptions manually written or paraphrased by the dataset creator.
+- Each description is unique and ≥200 characters.
+- Augmentation performed using **word-level EDA** with NLTK/WordNet synonyms and random perturbations.
+**Source Data Producers**
+- Original author: Bareethul Kader.
+- Data inspired by common NYC landmarks (fictionalized/paraphrased).
+---
+## Annotations
+- **Annotation Process:**
+  - `Vibe` assigned manually (Iconic, Hidden Gem, Peaceful, Touristy).
+  - `is_touristy` assigned based on whether a landmark is typically crowded/visited.
+- **Annotators:** Dataset creator.
+---
+## Personal and Sensitive Information
+- No personal or sensitive information included.
+- All text is fictional or paraphrased descriptions of public places.
+---
+## Bias, Risks, and Limitations
+- Dataset is **small (100 samples)** and handcrafted — not representative of all NYC landmarks.
+- Subjective labels like `vibe` may vary across annotators.
+- Augmentation may occasionally create awkward or ungrammatical text.
+---
+## Recommendations
+Users should treat this dataset as an **educational text classification resource**, not as factual information about NYC landmarks.
+It is best used for **NLP experiments, demos, and student projects**.
+---
+## Citation
+**BibTeX:**
+```bibtex
+@dataset{bareethul_nyc_landmark_descriptions,
+  author       = {Kader, Bareethul},
+  title        = {NYC Landmark Descriptions},
+  year         = {2025},
+  publisher    = {Hugging Face Datasets},
+  url          = {https://huggingface.co/datasets/bareethul/nyc-landmark-descriptions}
+}