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preprocess.py

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+from transformers import T5Tokenizer
+import polars as pl
+from tqdm import tqdm
+from functools import partial
+
+
+def detokenize(seq: str, tokenizer: T5Tokenizer):
+    output = tokenizer.decode(seq)
+    output = output.replace(" ", "")
+    output = output.replace("</s>", "")
+
+    assert len(output) == len(seq[:-1])
+    return output
+
+
+def main():
+    tokenizer = T5Tokenizer.from_pretrained('Rostlab/ProstT5', do_lower_case=False) # noqa
+    splits = {'test': 'data/test-00000-of-00001-b109fa020c25190c.parquet', 'valid': 'data/valid-00000-of-00001-6442282fee0bc004.parquet', 'train': 'data/train-*-of-*.parquet'} # noqa
+
+    detokenize_func = partial(detokenize, tokenizer=tokenizer)
+
+    for k, v in tqdm(splits.items()):
+        df = pl.scan_parquet('hf://datasets/Rostlab/ProstT5Dataset/' + v) # noqa
+        df = df.with_columns(pl.col("input_id_x").map_elements(detokenize_func, return_dtype=pl.String).alias("3di")) # noqa
+        df = df.with_columns(pl.col("input_id_y").map_elements(detokenize_func, return_dtype=pl.String).alias("protein")) # noqa
+        df = df.drop("input_id_x").drop("input_id_y")
+        df.sink_parquet(k + ".parquet")
+
+
+if __name__ == "__main__":
+    main()