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--- |
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dataset_info: |
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features: |
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- name: input_ids |
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sequence: int32 |
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- name: aa_seqs |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 61101706188 |
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num_examples: 9920628 |
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download_size: 5540646354 |
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dataset_size: 61101706188 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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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. |
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You can access the tokenizer like this: |
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```python |
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import os |
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from huggingface_hub import hf_hub_download |
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from transformers import AutoTokenizer |
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repo_path = 'Synthyra/ProteinSelfies' |
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local_path = 'ProteinSelfies' |
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files = ['special_tokens_map.json', 'tokenizer_config.json', 'vocab.txt'] |
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os.makedirs(local_path, exist_ok=True) |
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for file in files: |
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hf_hub_download( |
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repo_id=repo_path, |
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filename=file, |
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repo_type='dataset', |
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local_dir=local_path |
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) |
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tokenizer = AutoTokenizer.from_pretrained(local_path) |
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``` |
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Intended for atom-wise protein language modeling. |
