Datasets:
Update README.md
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README.md
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@@ -65,7 +65,6 @@ ds_un = load_dataset("Avatarr05/GreenHyperSpectra", "unlabeled")
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GreenHyperSpectra = ds_un['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
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display(GreenHyperSpectra.head())
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```
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---
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| Car | Leaf carotenoids content (µg/m²) |
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| Anth | Leaf anthocynins content (µg/m²) |
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| Cbc | Carbon-based constituents (g/cm²) |
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---
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### 3. `Split labeled set`
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- Files: all CSVs under `labeled_splits/`
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These files follow the same format as the previous set but are pre-split for machine learning purposes. The split is stratified based on the dataset ID, with 20% of the data reserved for testing.
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```
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### Check the data with Hugging Face datasets library ###
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from datasets import load_dataset
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df = ds['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
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display(df.head())
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### Labeled splits: labeled_splits ###
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annotated_ds_train = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="train")
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display(annotated_ds_train.head())
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display(annotated_ds_test.head())
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```
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> ⚠️ **Note:** Due to the high dimensionality of spectral datasets—often containing hundreds or thousands of columns—**Hugging Face Data Studio may not render these files properly**. This is a known limitation, as the Studio interface is not optimized for wide tabular data.
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>
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> To work effectively with this dataset, we recommend using the **Hugging Face `datasets` library** or the **MLCroissant Python library** for programmatic access and exploration.
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## Citation
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If you use the **GreenHyperSpectra** dataset, please cite the following paper:
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GreenHyperSpectra = ds_un['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
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display(GreenHyperSpectra.head())
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```
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---
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| Car | Leaf carotenoids content (µg/m²) |
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| Anth | Leaf anthocynins content (µg/m²) |
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| Cbc | Carbon-based constituents (g/cm²) |
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<!-- --- -->
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```
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### Check the data with Hugging Face datasets library ###
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from datasets import load_dataset
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df = ds['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
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display(df.head())
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```
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---
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### 3. `Split labeled set`
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- Files: all CSVs under `labeled_splits/`
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These files follow the same format as the previous set but are pre-split for machine learning purposes. The split is stratified based on the dataset ID, with 20% of the data reserved for testing.
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```
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### Check the data with Hugging Face datasets library ###
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from datasets import load_dataset
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### Labeled splits: labeled_splits ###
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annotated_ds_train = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="train")
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display(annotated_ds_train.head())
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display(annotated_ds_test.head())
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```
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> ⚠️ **Note:** Due to the high dimensionality of spectral datasets—often containing hundreds or thousands of columns—**Hugging Face Data Studio may not render these files properly**. This is a known limitation, as the Studio interface is not optimized for wide tabular data.
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>
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> To work effectively with this dataset, we recommend using the **Hugging Face `datasets` library** or the **MLCroissant Python library** for programmatic access and exploration.
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## Citation
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If you use the **GreenHyperSpectra** dataset, please cite the following paper:
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