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use relative paths so that running the data prep scripts always create files in local folder, no matter where run from
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@@ -47,8 +47,8 @@ print(f"val has {len(val_ids):,} tokens")
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# export to bin files
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train_ids = np.array(train_ids, dtype=np.uint16)
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val_ids = np.array(val_ids, dtype=np.uint16)
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train_ids.tofile('train.bin')
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val_ids.tofile('val.bin')
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train_ids.tofile(os.path.join(os.path.dirname(__file__), 'train.bin'))
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val_ids.tofile(os.path.join(os.path.dirname(__file__), 'val.bin'))
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# save the meta information as well, to help us encode/decode later
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meta = {
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@@ -56,7 +56,7 @@ meta = {
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'itos': itos,
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'stoi': stoi,
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}
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with open('meta.pkl', 'wb') as f:
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with open(os.path.join(os.path.dirname(__file__), 'meta.pkl'), 'wb') as f:
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pickle.dump(meta, f)
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# length of dataset in characters: 1115394
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