Update README.md

README.md

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 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64cac7e17221ef3c7e2eed1f/ZgP3fd2Z9eVgucsZHYYvA.png)
 
-### Overview
+## Overview
 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