CoPeP Continual Protein Dataset

This dataset is organized for continual-learning experiments on protein sequences.

Repository

• Dataset repo id: chandar-lab/CoPeP

File layout

• train/: 252 parquet shards (data-00000-of-00252.parquet ... data-00251-of-00252.parquet)
• splits/: 10 task index parquet files (task_0.parquet ... task_9.parquet)
• val/: validation parquet (val.parquet)

Task file mapping

• task_0.parquet → task_0 (2015)
• task_1.parquet → task_1 (2016)
• task_2.parquet → task_2 (2017)
• task_3.parquet → task_3 (2018)
• task_4.parquet → task_4 (2019)
• task_5.parquet → task_5 (2020)
• task_6.parquet → task_6 (2021)
• task_7.parquet → task_7 (2022)
• task_8.parquet → task_8 (2023)
• task_9.parquet → task_9 (2024)

Important note on splits/task_*.parquet

The splits/task_*.parquet files are index-style split definitions keyed by row_idx. They are intended to be joined with records from train/ (or other source files) using row_idx, rather than treated as standalone full-example datasets.

Example usage sketch

1. Load train parquet shards.
2. Load one splits/task_N.parquet file.
3. Join on row_idx to materialize that task's examples.
4. Use val/val.parquet for evaluation.

Basic code: load one split

from datasets import load_dataset

# Replace with your final dataset repo id
repo_id = "chandar-lab/CoPeP"

# 1) Load train split directly
train_ds = load_dataset(repo_id, split="train")

# 2) Load one task index split directly
task0_idx = load_dataset(repo_id, split="task_0")

# 3) Materialize examples by selecting train rows using row_idx
task0_rows = task0_idx["row_idx"]
task0_examples = train_ds.select(task0_rows)

print(task0_examples)
print(task0_examples[0])

Use task_0 as the split name. split='splits/task_0' is not supported as a split identifier.