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import math | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.nn import TransformerEncoder, TransformerEncoderLayer | |
class TransformerModel(nn.Module): | |
def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5): |
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import os; import psutil; import timeit | |
from datasets import load_dataset | |
mem_before = psutil.Process(os.getpid()).memory_info().rss >> 20 | |
wiki = load_dataset("wikipedia", "20200501.en", split='train') | |
mem_after = psutil.Process(os.getpid()).memory_info().rss >> 20 | |
print(f"RAM memory used: {(mem_after - mem_before)} MB") | |
s = """batch_size = 1000 | |
for i in range(0, len(wiki), batch_size): |
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