metadata
dataset_info:
features:
- name: input_ids
sequence: int32
- name: aa_seqs
dtype: string
splits:
- name: train
num_bytes: 61101706188
num_examples: 9920628
download_size: 5540646354
dataset_size: 61101706188
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
10 million random examples from Uniref50 representative sequences (October 2023) and computed selfies strings. The strings are stored as input ids from a custom selfies tokenizer. A BERT tokenizer with this vocabulary has been uploaded to this dataset under the files.
Intended for atom-wise protein language modeling.