--- dataset_info: features: - name: Entry dtype: string - name: seqs dtype: string - name: EC number dtype: string - name: Active site dtype: string - name: labels dtype: int64 splits: - name: train num_bytes: 50935453 num_examples: 80212 - name: valid num_bytes: 686688 num_examples: 1000 - name: test num_bytes: 692134 num_examples: 1000 download_size: 49792005 dataset_size: 52314275 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* --- Uniprot (ec:*) AND (go_manual:*) on 5/30/25 Remove any EC entries with '-' Remove any EC entries with more than one EC number Filter for sequences between 20 and 2048 Cluster at 80% sequence similarity docker run -v `pwd`:/data -w /data cd-hit cd-hit -i ec.fasta -o output_ec_80 -c 0.8 -n 3 -T 72 -M 512000 Keep representative sequences Keep EC numbers with 100 or more examples Map each EC number to a unique integer Remove any duplicates, favor entries with active site information Shuffle Split into 1000 valid and 1000 test randomly, make sure they all have active site info 13.8% of train entries have active site info