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ncfrey
reviewed
Jul 9, 2025
karinazad
reviewed
Sep 3, 2025
| torch.set_float32_matmul_precision("high") | ||
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| def get_args(): |
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can we use hydra for this?
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looks great! could you pls add a usage example to |
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karinazad
reviewed
Sep 3, 2025
| cur_num_in_shard += len(outputs) | ||
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| if cur_num_in_shard >= args.max_num_per_shard: | ||
| print(f"Saving shard {cur_shard_num} to {output_file}...") |
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output_file seems to be defined only in the next line - does this work?
karinazad
reviewed
Sep 3, 2025
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| cur_shard_num += 1 | ||
| cur_num_in_shard = 0 | ||
| results_tmp_list = [] |
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I think leftover results are not saved? if results_tmp_list has data when loop ends, it might be lost
karinazad
reviewed
Sep 3, 2025
| """Load sequence metadata from assigned shards.""" | ||
| sequences = [] | ||
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| for shard_file in self.shard_files: |
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why not just load all shards at once with pd.read_parquet?
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could you pls add tests? |
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Description
Scripts for token selection by assessing per-token loss under a pretrained autoregressive model (ProtGPT2):
Type of Change
Testing
Checklist