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---
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](https://github.com/aspuru-guzik-group/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.
You can access the tokenizer like this:
```python
import os
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
repo_path = 'Synthyra/ProteinSelfies'
local_path = 'ProteinSelfies'
files = ['special_tokens_map.json', 'tokenizer_config.json', 'vocab.txt']
os.makedirs(local_path, exist_ok=True)
for file in files:
hf_hub_download(
repo_id=repo_path,
filename=file,
repo_type='dataset',
local_dir=local_path
)
tokenizer = AutoTokenizer.from_pretrained(local_path)
```
Intended for atom-wise protein language modeling. |