custom/gpt2/run_gpt2.py [168:178]:
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def ngram_repeat_mask(xs, n):
    mask = torch.zeros_like(xs)
    for i, x in enumerate(xs):
        seen = set()
        xl = x.tolist()
        for j in range(len(x)-n):
            ng = tuple(xl[j:j+n])
            if ng in seen:
                mask[i, j:j+n] = 1
            seen.add(ng)
    return mask
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custom/sequence_penalty_loss.py [100:110]:
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def ngram_repeat_mask(xs, n):
    mask = torch.zeros_like(xs)
    for i, x in enumerate(xs):
        seen = set()
        xl = x.tolist()
        for j in range(len(x)-n):
            ng = tuple(xl[j:j+n])
            if ng in seen:
                mask[i, j:j+n] = 1
            seen.add(ng)
    return mask
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