Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,154 @@
+---
+license: mit
+language:
+- en
+- fr
+tags:
+- color
+- color-normalization
+- multilingual
+- ecommerce
+- text-classification
+pretty_name: Color Names Normalized
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-classification
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/colors_normalized.parquet
+---
+
+# Color Names Normalized
+
+A dataset for normalizing messy, multilingual, free-text color names to a
+small, fixed vocabulary. Real-world `color` attributes — product feeds,
+marketplace listings, survey answers — are free text with thousands of
+variants. This dataset maps **38,112 real-world color names** (English and
+French) to a strict **20-color base palette** — e.g. `"rouge"`, `"dark navy"`,
+`"burgundy"`, `"whispering grasslands"` all resolve to a canonical base color
+— so color data becomes groupable, filterable, and analyzable.
+
+Each color name comes with its hex code and was assigned to the perceptually
+closest palette color using the **CIEDE2000** color-difference formula in
+CIELAB space (D65).
+
+## Use cases
+
+- **E-commerce catalogs**: power color filters and facets from free-text
+  product attributes instead of maintaining hand-written mapping tables.
+- **Analytics**: aggregate sales or inventory by 20 base colors rather than
+  thousands of vendor-specific shade names.
+- **Search & matching**: treat `"navy"`, `"midnight"`, and `"bleu marine"`
+  as the same bucket for retrieval, dedup, or entity matching.
+- **ML features**: turn a high-cardinality text column into a clean
+  20-class categorical feature.
+
+## Quick start
+
+```python
+import pandas as pd
+
+# Load directly from the Hub
+df = pd.read_parquet(
+    "hf://datasets/NacerKr/colors-normalized/data/colors_normalized.parquet"
+)
+
+# Build a normalization lookup: messy name -> base color
+lookup = df.set_index(["name", "language"])["base_color"]
+lookup.loc[("rouge", "fr")]      # 'red'
+lookup.loc[("burgundy", "en")]   # 'brown'
+```
+
+Or with the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("NacerKr/colors-normalized", split="train")
+```
+
+Names in the dataset are lowercased and whitespace-normalized — apply
+`.str.strip().str.lower()` (and collapse inner whitespace) to your raw
+`color` values before joining.
+
+## Schema
+
+| Column | Type | Description |
+|---|---|---|
+| `name` | string | Color name, lowercased and whitespace-normalized |
+| `hex` | string | Hex code of the named color, uppercase `#RRGGBB` |
+| `language` | string | `en` or `fr` |
+| `source` | string | `color-names`, `color-pedia`, or `french-gist` |
+| `base_color` | string | Assigned base palette color (one of 20 labels) |
+| `base_hex` | string | Hex code of the assigned base color |
+| `delta_e` | float32 | CIEDE2000 distance between `hex` and `base_hex` (0 = exact) |
+
+**38,112 rows** — 37,877 English, 235 French. Median ΔE₀₀ to the assigned
+base color is 12.4 (p90 = 19.6). Unique on (`name`, `language`).
+
+## Base palette (20 labels)
+
+The palette is the `basic` palette from
+[colorjs/color-namer](https://github.com/colorjs/color-namer/blob/master/lib/colors/basic.js)
+(MIT), minus `fuchsia`, which is an exact duplicate of `magenta` (`#FF00FF`).
+
+| Label | Hex | | Label | Hex |
+|---|---|---|---|---|
+| black | `#000000` | | tan | `#D2B48C` |
+| blue | `#0000FF` | | violet | `#EE82EE` |
+| cyan | `#00FFFF` | | beige | `#F5F5DC` |
+| green | `#008000` | | gold | `#FFD700` |
+| teal | `#008080` | | magenta | `#FF00FF` |
+| turquoise | `#40E0D0` | | orange | `#FFA500` |
+| indigo | `#4B0082` | | pink | `#FFC0CB` |
+| gray | `#808080` | | red | `#FF0000` |
+| purple | `#800080` | | white | `#FFFFFF` |
+| brown | `#A52A2A` | | yellow | `#FFFF00` |
+
+## Methodology
+
+1. **Merge** three permissively-licensed color-name sources (below) into
+   (`name`, `hex`) pairs tagged with `language` and `source`.
+2. **Clean**: lowercase and whitespace-normalize names; validate and
+   normalize hex to uppercase `#RRGGBB`; drop junk names that embed raw hex
+   codes (~15k generation artifacts in color-pedia); deduplicate on
+   (`name`, `language`) with source priority `color-names` → `color-pedia` →
+   `french-gist`.
+3. **Assign**: convert every hex and the 20 palette anchors from sRGB to
+   CIELAB (D65 white point), compute the full N×20
+   [CIEDE2000](http://www2.ece.rochester.edu/~gsharma/ciede2000/) distance
+   matrix in one vectorized numpy pass, and take the argmin. The CIEDE2000
+   implementation is validated against the Sharma et al. (2005) published
+   test pairs.
+
+## Sources & attribution
+
+| Source | Contribution | License |
+|---|---|---|
+| [boltuix/color-pedia](https://huggingface.co/datasets/boltuix/color-pedia) | English color names + hex | MIT |
+| [BatteRaquette58/color-names](https://huggingface.co/datasets/BatteRaquette58/color-names) (derived from [meodai/color-names](https://github.com/meodai/color-names)) | English color names + hex | MIT |
+| [angelodlfrtr's french_colors.json gist](https://gist.github.com/angelodlfrtr/bc39dc5d63ee330ac432f81b5e03eec6) | French color names + hex | not stated (attribution given) |
+| [colorjs/color-namer](https://github.com/colorjs/color-namer) | 20-label base palette | MIT |
+
+The [EPFL Multi-lingual Color Thesaurus](https://www.epfl.ch/labs/ivrl/research/image-mining/multi-lingual-color-thesaurus/)
+was evaluated as an additional French source but deliberately **excluded**:
+its CC BY-NC-SA 3.0 license (non-commercial) is incompatible with commercial
+use.
+
+## Limitations
+
+- **Nearest-anchor artifacts**: assignment is purely geometric against 20
+  fixed anchor points. A few results differ from human intuition — e.g.
+  `navy blue` (`#000080`) lands on `indigo` rather than `blue`, and some
+  saturated pinks land on `violet`/`magenta` because the `pink` anchor
+  (`#FFC0CB`) is very pale. Use `delta_e` as a confidence signal (larger =
+  less certain).
+- **French coverage is small** (235 names) relative to English.
+- Names are lowercased; match case-insensitively against your raw data.
+
+## License
+
+Released under the [MIT License](https://opensource.org/licenses/MIT).