Update README.md

README.md

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 As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the BIOSCAN-5M Insect dataset to the machine learning community. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, 
 and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical information, and specimen size. 
 
-### Large-Scale Foundation Model Training
-
-#### BIOSCAN-5M is partitioned to support both closed-world and open-world biodiversity learning:
-
-- **Closed-world (train, val, test):**
-Samples with known species names for supervised classification.
-
-- **Open-world (key_unseen, val_unseen, test_unseen):**
-Placeholder species but known genera, enabling generalization to unseen species.
-
-- **Novelty detection (other_heldout):**
-Unknown species and genus, suitable for open-set detection.
-
-- **Pretraining (pretrain):**
-Unlabeled data for self- or semi-supervised learning at scale.
-
-#### Supported Tasks
-- **Task I: DNA-based Taxonomic Classification**
-Predict taxonomic labels from raw DNA barcode sequences.
-
-- **Task II: Zero-Shot Transfer Learning**
-Evaluate whether unlabeled models can organize data into semantically meaningful clusters—across modalities like image and DNA—using learned representations.
-
-- **Task III: Multimodal Retrieval Learning**
-Retrieve matching specimens across modalities (e.g., image ↔ DNA ↔ Text) using shared embeddings.
-
 ### Citation
 
 If you make use of the BIOSCAN-5M dataset and/or its code repository, please cite the following paper:
@@ -98,6 +72,26 @@ cite as:
 }
 ```
 
+## Large-Scale Foundation Model Training
+
+#### Dataset Partitions
+
+| Partition        | Description                                                                 |
+|------------------|-----------------------------------------------------------------------------|
+| **Closed-world**<br>(train, val, test) | Samples with known species names for supervised classification.           |
+| **Open-world**<br>(key_unseen, val_unseen, test_unseen) | Placeholder species names but known genera, enabling generalization to unseen species. |
+| **Novelty Detection**<br>(other_heldout) | Unknown species and genus, suitable for open-set detection.              |
+| **Pretraining**<br>(pretrain)         | Unlabeled data for self-/semi-supervised learning at scale.             |
+
+
+#### Supported Tasks
+
+| Task                          | Description                                                                 |
+|-------------------------------|-----------------------------------------------------------------------------|
+| **Task I**<br>DNA-based Taxonomic Classification | Predict taxonomic labels from raw DNA barcode sequences.                  |
+| **Task II**<br>Zero-Shot Transfer Learning      | Evaluate whether unlabeled models can semantically cluster data—across modalities like image and DNA—using learned representations. |
+| **Task III**<br>Multimodal Retrieval Learning   | Retrieve matching specimens across modalities (e.g., image ↔ DNA ↔ text) via shared embeddings. |
+
 
 ## Dataset features via metadata fields