157 KiB
157 KiB
import itertools
import lzma
import numpy as np
import regex as re
import torch
from torch import nn
from torch.utils.data import IterableDataset, DataLoader
from torchtext.vocab import build_vocab_from_iteratordef clean_line(line: str):
# Preprocessing
separated = line.split('\t')
prefix = separated[6].replace(r'\n', ' ')
suffix = separated[7].replace(r'\n', ' ')
return prefix + ' ' + suffix
def get_words_from_line(line):
line = clean_line(line)
for word in line.split():
yield word
def get_word_lines_from_file(file_name):
with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:
for line in fid:
yield get_words_from_line(line)
def double_look_ahead_iterator(gen):
prev_prev = None
prev = None
for item in gen:
if prev_prev is not None:
yield np.asarray((prev_prev, prev, item))
prev_prev = prev
prev = item
def prediction(words, model, top) -> str:
words_tensor = [train_dataset.vocab.forward([word]) for word in words]
ixs = torch.tensor(words_tensor).view(-1).to(device)
out = model(ixs)
top = torch.topk(out[0], top)
top_indices = top.indices.tolist()
top_probs = top.values.tolist()
top_words = vocab.lookup_tokens(top_indices)
zipped = list(zip(top_words, top_probs))
for index, element in enumerate(zipped):
unk = None
if '<unk>' in element:
unk = zipped.pop(index)
zipped.append(('', unk[1]))
break
if unk is None:
zipped[-1] = ('', zipped[-1][1])
return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])
def create_outputs(folder_name, model, top):
print(f'Creating outputs in {folder_name}')
with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:
with open(f'{folder_name}/out-top={top}.tsv', 'w', encoding='utf-8', newline='\n') as f:
for line in fid:
separated = line.split('\t')
prefix = separated[6].replace(r'\n', ' ').split()[-2:]
output_line = prediction(prefix, model, top)
f.write(output_line + '\n')
def train_model(lr):
model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)
data = DataLoader(train_dataset, batch_size=batch_size)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = torch.nn.NLLLoss()
model.train()
step = 0
for batch in data:
x = batch[:, :2]
y = batch[:, 2]
x = x.to(device)
y = y.to(device)
optimizer.zero_grad()
ypredicted = model(x)
loss = criterion(torch.log(ypredicted), y)
if step % 100 == 0:
print(step, loss)
step += 1
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 10)
optimizer.step()
torch.save(model.state_dict(), path_to_model)
def with_hyperparams():
train_model(lr=0.0001)
model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)
model.load_state_dict(torch.load(path_to_model))
model.eval()
for top in [200, 400, 600]:
create_outputs('dev-0', model, top)
create_outputs('test-A', model, top)
class Trigrams(IterableDataset):
def __init__(self, text_file, vocabulary_size):
self.vocab = build_vocab_from_iterator(
get_word_lines_from_file(text_file),
max_tokens=vocabulary_size,
specials=['<unk>'])
self.vocab.set_default_index(self.vocab['<unk>'])
self.vocabulary_size = vocabulary_size
self.text_file = text_file
def __iter__(self):
return double_look_ahead_iterator(
(self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))
class SimpleTrigramNeuralLanguageModel(nn.Module):
def __init__(self, vocabulary_size, embedding_size, hidden_size):
super(SimpleTrigramNeuralLanguageModel, self).__init__()
self.embedding_size = embedding_size
self.embedding = nn.Embedding(vocabulary_size, embedding_size)
self.lin1 = nn.Linear(2 * embedding_size, hidden_size)
self.rel = nn.ReLU()
self.lin2 = nn.Linear(hidden_size, vocabulary_size)
self.sm = nn.Softmax()
def forward(self, x):
x = self.embedding(x).view((-1, 2 * self.embedding_size))
x = self.lin1(x)
x = self.rel(x)
x = self.lin2(x)
return self.sm(x)vocab_size = 25000
embed_size = 300
hidden_size = 150
batch_size = 2000
device = 'cuda'
path_to_train = 'train/in.tsv.xz'
path_to_model = 'model1.bin'from google.colab import drive
drive.mount('/content/drive')
%cd /content/drive/MyDrive/Mounted at /content/drive /content/drive/MyDrive
vocab = build_vocab_from_iterator(
get_word_lines_from_file(path_to_train),
max_tokens=vocab_size,
specials=['<unk>']
)
vocab.set_default_index(vocab['<unk>'])
train_dataset = Trigrams(path_to_train, vocab_size)with_hyperparams()<ipython-input-12-cce599098537>:16: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. return self.sm(x)
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grad_fn=<NllLossBackward0>) 63100 tensor(5.3211, device='cuda:0', grad_fn=<NllLossBackward0>) 63200 tensor(5.4832, device='cuda:0', grad_fn=<NllLossBackward0>) 63300 tensor(5.4616, device='cuda:0', grad_fn=<NllLossBackward0>) 63400 tensor(5.3212, device='cuda:0', grad_fn=<NllLossBackward0>) 63500 tensor(5.2929, device='cuda:0', grad_fn=<NllLossBackward0>) 63600 tensor(5.4305, device='cuda:0', grad_fn=<NllLossBackward0>) 63700 tensor(5.2080, device='cuda:0', grad_fn=<NllLossBackward0>) 63800 tensor(5.4208, device='cuda:0', grad_fn=<NllLossBackward0>) 63900 tensor(5.4145, device='cuda:0', grad_fn=<NllLossBackward0>) 64000 tensor(5.3525, device='cuda:0', grad_fn=<NllLossBackward0>) 64100 tensor(5.5111, device='cuda:0', grad_fn=<NllLossBackward0>) 64200 tensor(5.1437, device='cuda:0', grad_fn=<NllLossBackward0>) 64300 tensor(5.4269, device='cuda:0', grad_fn=<NllLossBackward0>) 64400 tensor(5.5086, device='cuda:0', grad_fn=<NllLossBackward0>) 64500 tensor(5.3559, device='cuda:0', grad_fn=<NllLossBackward0>) 64600 tensor(5.3799, device='cuda:0', grad_fn=<NllLossBackward0>) 64700 tensor(5.5940, device='cuda:0', grad_fn=<NllLossBackward0>) 64800 tensor(5.1958, device='cuda:0', grad_fn=<NllLossBackward0>) 64900 tensor(5.3498, device='cuda:0', grad_fn=<NllLossBackward0>) 65000 tensor(5.3998, device='cuda:0', grad_fn=<NllLossBackward0>) 65100 tensor(5.2237, device='cuda:0', grad_fn=<NllLossBackward0>) 65200 tensor(5.0362, device='cuda:0', grad_fn=<NllLossBackward0>) 65300 tensor(5.5109, device='cuda:0', grad_fn=<NllLossBackward0>) 65400 tensor(5.2673, device='cuda:0', grad_fn=<NllLossBackward0>) 65500 tensor(5.0693, device='cuda:0', grad_fn=<NllLossBackward0>) 65600 tensor(5.4907, device='cuda:0', grad_fn=<NllLossBackward0>) 65700 tensor(5.5288, device='cuda:0', grad_fn=<NllLossBackward0>) 65800 tensor(5.3971, device='cuda:0', grad_fn=<NllLossBackward0>) 65900 tensor(5.3500, device='cuda:0', grad_fn=<NllLossBackward0>) 66000 tensor(5.7787, device='cuda:0', grad_fn=<NllLossBackward0>) 66100 tensor(5.1555, device='cuda:0', grad_fn=<NllLossBackward0>) 66200 tensor(5.4229, device='cuda:0', grad_fn=<NllLossBackward0>) 66300 tensor(5.1499, device='cuda:0', grad_fn=<NllLossBackward0>) 66400 tensor(5.5168, device='cuda:0', grad_fn=<NllLossBackward0>) 66500 tensor(5.6282, device='cuda:0', grad_fn=<NllLossBackward0>) 66600 tensor(5.3283, device='cuda:0', grad_fn=<NllLossBackward0>) 66700 tensor(5.3960, device='cuda:0', grad_fn=<NllLossBackward0>) 66800 tensor(5.3382, device='cuda:0', grad_fn=<NllLossBackward0>) 66900 tensor(5.2665, device='cuda:0', grad_fn=<NllLossBackward0>) 67000 tensor(5.3828, device='cuda:0', grad_fn=<NllLossBackward0>) 67100 tensor(5.2455, device='cuda:0', grad_fn=<NllLossBackward0>) 67200 tensor(5.7224, device='cuda:0', grad_fn=<NllLossBackward0>) 67300 tensor(5.5869, device='cuda:0', grad_fn=<NllLossBackward0>) 67400 tensor(5.4242, device='cuda:0', grad_fn=<NllLossBackward0>) 67500 tensor(5.4228, device='cuda:0', grad_fn=<NllLossBackward0>) 67600 tensor(5.3538, device='cuda:0', grad_fn=<NllLossBackward0>) 67700 tensor(5.1782, device='cuda:0', grad_fn=<NllLossBackward0>) 67800 tensor(5.3206, device='cuda:0', grad_fn=<NllLossBackward0>) 67900 tensor(5.2828, device='cuda:0', grad_fn=<NllLossBackward0>) 68000 tensor(5.3962, device='cuda:0', grad_fn=<NllLossBackward0>) 68100 tensor(5.3605, device='cuda:0', grad_fn=<NllLossBackward0>) 68200 tensor(5.1993, device='cuda:0', grad_fn=<NllLossBackward0>) 68300 tensor(5.3261, device='cuda:0', grad_fn=<NllLossBackward0>) 68400 tensor(5.8642, device='cuda:0', grad_fn=<NllLossBackward0>) 68500 tensor(5.1566, device='cuda:0', grad_fn=<NllLossBackward0>) 68600 tensor(5.3310, device='cuda:0', grad_fn=<NllLossBackward0>) 68700 tensor(5.3318, device='cuda:0', grad_fn=<NllLossBackward0>) 68800 tensor(5.5199, device='cuda:0', grad_fn=<NllLossBackward0>) 68900 tensor(5.3169, device='cuda:0', grad_fn=<NllLossBackward0>) 69000 tensor(5.2783, device='cuda:0', grad_fn=<NllLossBackward0>) 69100 tensor(5.4604, device='cuda:0', grad_fn=<NllLossBackward0>) 69200 tensor(5.3401, device='cuda:0', grad_fn=<NllLossBackward0>) 69300 tensor(5.0342, device='cuda:0', grad_fn=<NllLossBackward0>) 69400 tensor(5.3514, device='cuda:0', grad_fn=<NllLossBackward0>) 69500 tensor(5.1504, device='cuda:0', grad_fn=<NllLossBackward0>) Creating outputs in dev-0 Creating outputs in test-A 0 tensor(10.3829, device='cuda:0', grad_fn=<NllLossBackward0>) 100 tensor(8.0792, device='cuda:0', grad_fn=<NllLossBackward0>) 200 tensor(7.3059, device='cuda:0', grad_fn=<NllLossBackward0>) 300 tensor(6.8478, device='cuda:0', grad_fn=<NllLossBackward0>) 400 tensor(6.6292, device='cuda:0', grad_fn=<NllLossBackward0>) 500 tensor(6.6597, device='cuda:0', grad_fn=<NllLossBackward0>) 600 tensor(6.7076, device='cuda:0', grad_fn=<NllLossBackward0>) 700 tensor(6.4022, device='cuda:0', grad_fn=<NllLossBackward0>) 800 tensor(6.1865, device='cuda:0', grad_fn=<NllLossBackward0>) 900 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grad_fn=<NllLossBackward0>) 56500 tensor(5.1546, device='cuda:0', grad_fn=<NllLossBackward0>) 56600 tensor(5.1825, device='cuda:0', grad_fn=<NllLossBackward0>) 56700 tensor(5.7089, device='cuda:0', grad_fn=<NllLossBackward0>) 56800 tensor(5.3728, device='cuda:0', grad_fn=<NllLossBackward0>) 56900 tensor(5.4364, device='cuda:0', grad_fn=<NllLossBackward0>) 57000 tensor(5.5370, device='cuda:0', grad_fn=<NllLossBackward0>) 57100 tensor(5.2860, device='cuda:0', grad_fn=<NllLossBackward0>) 57200 tensor(5.5949, device='cuda:0', grad_fn=<NllLossBackward0>) 57300 tensor(5.6466, device='cuda:0', grad_fn=<NllLossBackward0>) 57400 tensor(5.3175, device='cuda:0', grad_fn=<NllLossBackward0>) 57500 tensor(5.4093, device='cuda:0', grad_fn=<NllLossBackward0>) 57600 tensor(5.7817, device='cuda:0', grad_fn=<NllLossBackward0>) 57700 tensor(5.5003, device='cuda:0', grad_fn=<NllLossBackward0>) 57800 tensor(5.3439, device='cuda:0', grad_fn=<NllLossBackward0>) 57900 tensor(5.4006, device='cuda:0', 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grad_fn=<NllLossBackward0>) 59500 tensor(5.5514, device='cuda:0', grad_fn=<NllLossBackward0>) 59600 tensor(5.1275, device='cuda:0', grad_fn=<NllLossBackward0>) 59700 tensor(5.4095, device='cuda:0', grad_fn=<NllLossBackward0>) 59800 tensor(5.2168, device='cuda:0', grad_fn=<NllLossBackward0>) 59900 tensor(5.3622, device='cuda:0', grad_fn=<NllLossBackward0>) 60000 tensor(5.3232, device='cuda:0', grad_fn=<NllLossBackward0>) 60100 tensor(5.2477, device='cuda:0', grad_fn=<NllLossBackward0>) 60200 tensor(5.4876, device='cuda:0', grad_fn=<NllLossBackward0>) 60300 tensor(5.3204, device='cuda:0', grad_fn=<NllLossBackward0>) 60400 tensor(5.5030, device='cuda:0', grad_fn=<NllLossBackward0>) 60500 tensor(5.1152, device='cuda:0', grad_fn=<NllLossBackward0>) 60600 tensor(5.4408, device='cuda:0', grad_fn=<NllLossBackward0>) 60700 tensor(5.2033, device='cuda:0', grad_fn=<NllLossBackward0>) 60800 tensor(5.5601, device='cuda:0', grad_fn=<NllLossBackward0>) 60900 tensor(5.5461, device='cuda:0', grad_fn=<NllLossBackward0>) 61000 tensor(5.4563, device='cuda:0', grad_fn=<NllLossBackward0>) 61100 tensor(5.2254, device='cuda:0', grad_fn=<NllLossBackward0>) 61200 tensor(5.5692, device='cuda:0', grad_fn=<NllLossBackward0>) 61300 tensor(5.4247, device='cuda:0', grad_fn=<NllLossBackward0>) 61400 tensor(4.9635, device='cuda:0', grad_fn=<NllLossBackward0>) 61500 tensor(5.2972, device='cuda:0', grad_fn=<NllLossBackward0>) 61600 tensor(5.4258, device='cuda:0', grad_fn=<NllLossBackward0>) 61700 tensor(5.3653, device='cuda:0', grad_fn=<NllLossBackward0>) 61800 tensor(5.5186, device='cuda:0', grad_fn=<NllLossBackward0>) 61900 tensor(5.8254, device='cuda:0', grad_fn=<NllLossBackward0>) 62000 tensor(5.3711, device='cuda:0', grad_fn=<NllLossBackward0>) 62100 tensor(5.5506, device='cuda:0', grad_fn=<NllLossBackward0>) 62200 tensor(5.3525, device='cuda:0', grad_fn=<NllLossBackward0>) 62300 tensor(4.9781, device='cuda:0', grad_fn=<NllLossBackward0>) 62400 tensor(5.2654, device='cuda:0', grad_fn=<NllLossBackward0>) 62500 tensor(5.1860, device='cuda:0', grad_fn=<NllLossBackward0>) 62600 tensor(5.2197, device='cuda:0', grad_fn=<NllLossBackward0>) 62700 tensor(4.8901, device='cuda:0', grad_fn=<NllLossBackward0>) 62800 tensor(5.0782, device='cuda:0', grad_fn=<NllLossBackward0>) 62900 tensor(5.4533, device='cuda:0', grad_fn=<NllLossBackward0>) 63000 tensor(4.8650, device='cuda:0', grad_fn=<NllLossBackward0>) 63100 tensor(5.2813, device='cuda:0', grad_fn=<NllLossBackward0>) 63200 tensor(5.4397, device='cuda:0', grad_fn=<NllLossBackward0>) 63300 tensor(5.4245, device='cuda:0', grad_fn=<NllLossBackward0>) 63400 tensor(5.2748, device='cuda:0', grad_fn=<NllLossBackward0>) 63500 tensor(5.2523, device='cuda:0', grad_fn=<NllLossBackward0>) 63600 tensor(5.3960, device='cuda:0', grad_fn=<NllLossBackward0>) 63700 tensor(5.1610, device='cuda:0', grad_fn=<NllLossBackward0>) 63800 tensor(5.3532, device='cuda:0', grad_fn=<NllLossBackward0>) 63900 tensor(5.3806, device='cuda:0', grad_fn=<NllLossBackward0>) 64000 tensor(5.3295, device='cuda:0', grad_fn=<NllLossBackward0>) 64100 tensor(5.4567, device='cuda:0', grad_fn=<NllLossBackward0>) 64200 tensor(5.1251, device='cuda:0', grad_fn=<NllLossBackward0>) 64300 tensor(5.3982, device='cuda:0', grad_fn=<NllLossBackward0>) 64400 tensor(5.4605, device='cuda:0', grad_fn=<NllLossBackward0>) 64500 tensor(5.3091, device='cuda:0', grad_fn=<NllLossBackward0>) 64600 tensor(5.3547, device='cuda:0', grad_fn=<NllLossBackward0>) 64700 tensor(5.5553, device='cuda:0', grad_fn=<NllLossBackward0>) 64800 tensor(5.1512, device='cuda:0', grad_fn=<NllLossBackward0>) 64900 tensor(5.3059, device='cuda:0', grad_fn=<NllLossBackward0>) 65000 tensor(5.3715, device='cuda:0', grad_fn=<NllLossBackward0>) 65100 tensor(5.1765, device='cuda:0', grad_fn=<NllLossBackward0>) 65200 tensor(4.9975, device='cuda:0', grad_fn=<NllLossBackward0>) 65300 tensor(5.4619, device='cuda:0', grad_fn=<NllLossBackward0>) 65400 tensor(5.2211, device='cuda:0', grad_fn=<NllLossBackward0>) 65500 tensor(5.0544, device='cuda:0', grad_fn=<NllLossBackward0>) 65600 tensor(5.4778, device='cuda:0', grad_fn=<NllLossBackward0>) 65700 tensor(5.4886, device='cuda:0', grad_fn=<NllLossBackward0>) 65800 tensor(5.3707, device='cuda:0', grad_fn=<NllLossBackward0>) 65900 tensor(5.3304, device='cuda:0', grad_fn=<NllLossBackward0>) 66000 tensor(5.7419, device='cuda:0', grad_fn=<NllLossBackward0>) 66100 tensor(5.1063, device='cuda:0', grad_fn=<NllLossBackward0>) 66200 tensor(5.3704, device='cuda:0', grad_fn=<NllLossBackward0>) 66300 tensor(5.1073, device='cuda:0', grad_fn=<NllLossBackward0>) 66400 tensor(5.4869, device='cuda:0', grad_fn=<NllLossBackward0>) 66500 tensor(5.6025, device='cuda:0', grad_fn=<NllLossBackward0>) 66600 tensor(5.3030, device='cuda:0', grad_fn=<NllLossBackward0>) 66700 tensor(5.3760, device='cuda:0', grad_fn=<NllLossBackward0>) 66800 tensor(5.3238, device='cuda:0', grad_fn=<NllLossBackward0>) 66900 tensor(5.2442, device='cuda:0', grad_fn=<NllLossBackward0>) 67000 tensor(5.3488, device='cuda:0', grad_fn=<NllLossBackward0>) 67100 tensor(5.2200, device='cuda:0', grad_fn=<NllLossBackward0>) 67200 tensor(5.6754, device='cuda:0', grad_fn=<NllLossBackward0>) 67300 tensor(5.5589, device='cuda:0', grad_fn=<NllLossBackward0>) 67400 tensor(5.3765, device='cuda:0', grad_fn=<NllLossBackward0>) 67500 tensor(5.3911, device='cuda:0', grad_fn=<NllLossBackward0>) 67600 tensor(5.3410, device='cuda:0', grad_fn=<NllLossBackward0>) 67700 tensor(5.1323, device='cuda:0', grad_fn=<NllLossBackward0>) 67800 tensor(5.2726, device='cuda:0', grad_fn=<NllLossBackward0>) 67900 tensor(5.2314, device='cuda:0', grad_fn=<NllLossBackward0>) 68000 tensor(5.3615, device='cuda:0', grad_fn=<NllLossBackward0>) 68100 tensor(5.3275, device='cuda:0', grad_fn=<NllLossBackward0>) 68200 tensor(5.1481, device='cuda:0', grad_fn=<NllLossBackward0>) 68300 tensor(5.2834, device='cuda:0', grad_fn=<NllLossBackward0>) 68400 tensor(5.8378, device='cuda:0', grad_fn=<NllLossBackward0>) 68500 tensor(5.0982, device='cuda:0', grad_fn=<NllLossBackward0>) 68600 tensor(5.2805, device='cuda:0', grad_fn=<NllLossBackward0>) 68700 tensor(5.2916, device='cuda:0', grad_fn=<NllLossBackward0>) 68800 tensor(5.4921, device='cuda:0', grad_fn=<NllLossBackward0>) 68900 tensor(5.2871, device='cuda:0', grad_fn=<NllLossBackward0>) 69000 tensor(5.2191, device='cuda:0', grad_fn=<NllLossBackward0>) 69100 tensor(5.4146, device='cuda:0', grad_fn=<NllLossBackward0>) 69200 tensor(5.3098, device='cuda:0', grad_fn=<NllLossBackward0>) 69300 tensor(4.9947, device='cuda:0', grad_fn=<NllLossBackward0>) 69400 tensor(5.3038, device='cuda:0', grad_fn=<NllLossBackward0>) 69500 tensor(5.1063, device='cuda:0', grad_fn=<NllLossBackward0>) Creating outputs in dev-0 Creating outputs in test-A 0 tensor(10.3276, device='cuda:0', grad_fn=<NllLossBackward0>) 100 tensor(7.9401, device='cuda:0', grad_fn=<NllLossBackward0>) 200 tensor(7.2381, device='cuda:0', grad_fn=<NllLossBackward0>) 300 tensor(6.8126, device='cuda:0', grad_fn=<NllLossBackward0>) 400 tensor(6.6045, device='cuda:0', grad_fn=<NllLossBackward0>) 500 tensor(6.6184, device='cuda:0', grad_fn=<NllLossBackward0>) 600 tensor(6.6869, device='cuda:0', grad_fn=<NllLossBackward0>) 700 tensor(6.3630, device='cuda:0', grad_fn=<NllLossBackward0>) 800 tensor(6.1966, device='cuda:0', grad_fn=<NllLossBackward0>) 900 tensor(6.3506, device='cuda:0', grad_fn=<NllLossBackward0>) 1000 tensor(6.2652, device='cuda:0', grad_fn=<NllLossBackward0>) 1100 tensor(5.8459, device='cuda:0', grad_fn=<NllLossBackward0>) 1200 tensor(6.3685, device='cuda:0', grad_fn=<NllLossBackward0>) 1300 tensor(6.4105, device='cuda:0', grad_fn=<NllLossBackward0>) 1400 tensor(6.1318, device='cuda:0', grad_fn=<NllLossBackward0>) 1500 tensor(5.9373, device='cuda:0', grad_fn=<NllLossBackward0>) 1600 tensor(5.9996, device='cuda:0', grad_fn=<NllLossBackward0>) 1700 tensor(6.2852, device='cuda:0', grad_fn=<NllLossBackward0>) 1800 tensor(6.2778, device='cuda:0', grad_fn=<NllLossBackward0>) 1900 tensor(6.1339, device='cuda:0', grad_fn=<NllLossBackward0>) 2000 tensor(6.1958, device='cuda:0', grad_fn=<NllLossBackward0>) 2100 tensor(5.9972, device='cuda:0', grad_fn=<NllLossBackward0>) 2200 tensor(6.2078, device='cuda:0', grad_fn=<NllLossBackward0>) 2300 tensor(6.1827, device='cuda:0', grad_fn=<NllLossBackward0>) 2400 tensor(6.1275, device='cuda:0', grad_fn=<NllLossBackward0>) 2500 tensor(6.1562, device='cuda:0', grad_fn=<NllLossBackward0>) 2600 tensor(6.0775, device='cuda:0', grad_fn=<NllLossBackward0>) 2700 tensor(6.2004, device='cuda:0', grad_fn=<NllLossBackward0>) 2800 tensor(6.1155, device='cuda:0', grad_fn=<NllLossBackward0>) 2900 tensor(6.0537, device='cuda:0', grad_fn=<NllLossBackward0>) 3000 tensor(6.0540, device='cuda:0', grad_fn=<NllLossBackward0>) 3100 tensor(5.8310, device='cuda:0', grad_fn=<NllLossBackward0>) 3200 tensor(6.3952, device='cuda:0', grad_fn=<NllLossBackward0>) 3300 tensor(6.1059, device='cuda:0', grad_fn=<NllLossBackward0>) 3400 tensor(5.9665, device='cuda:0', grad_fn=<NllLossBackward0>) 3500 tensor(5.9202, device='cuda:0', grad_fn=<NllLossBackward0>) 3600 tensor(6.2096, device='cuda:0', grad_fn=<NllLossBackward0>) 3700 tensor(5.9983, device='cuda:0', grad_fn=<NllLossBackward0>) 3800 tensor(6.0919, device='cuda:0', grad_fn=<NllLossBackward0>) 3900 tensor(6.0015, device='cuda:0', grad_fn=<NllLossBackward0>) 4000 tensor(5.8796, device='cuda:0', grad_fn=<NllLossBackward0>) 4100 tensor(6.0101, device='cuda:0', grad_fn=<NllLossBackward0>) 4200 tensor(5.9665, device='cuda:0', grad_fn=<NllLossBackward0>) 4300 tensor(5.8365, device='cuda:0', grad_fn=<NllLossBackward0>) 4400 tensor(5.6078, device='cuda:0', grad_fn=<NllLossBackward0>) 4500 tensor(5.9602, device='cuda:0', grad_fn=<NllLossBackward0>) 4600 tensor(5.8495, device='cuda:0', grad_fn=<NllLossBackward0>) 4700 tensor(5.6834, device='cuda:0', grad_fn=<NllLossBackward0>) 4800 tensor(5.8261, device='cuda:0', grad_fn=<NllLossBackward0>) 4900 tensor(5.9137, device='cuda:0', grad_fn=<NllLossBackward0>) 5000 tensor(6.0360, device='cuda:0', grad_fn=<NllLossBackward0>) 5100 tensor(5.8791, device='cuda:0', grad_fn=<NllLossBackward0>) 5200 tensor(6.1084, device='cuda:0', grad_fn=<NllLossBackward0>) 5300 tensor(6.0378, device='cuda:0', grad_fn=<NllLossBackward0>) 5400 tensor(5.9057, device='cuda:0', grad_fn=<NllLossBackward0>) 5500 tensor(5.9146, device='cuda:0', grad_fn=<NllLossBackward0>) 5600 tensor(5.9022, device='cuda:0', grad_fn=<NllLossBackward0>) 5700 tensor(5.9767, device='cuda:0', grad_fn=<NllLossBackward0>) 5800 tensor(5.9410, device='cuda:0', grad_fn=<NllLossBackward0>) 5900 tensor(5.8609, device='cuda:0', grad_fn=<NllLossBackward0>) 6000 tensor(5.8036, device='cuda:0', grad_fn=<NllLossBackward0>) 6100 tensor(5.8270, device='cuda:0', grad_fn=<NllLossBackward0>) 6200 tensor(5.9282, device='cuda:0', grad_fn=<NllLossBackward0>) 6300 tensor(5.7968, device='cuda:0', grad_fn=<NllLossBackward0>) 6400 tensor(6.1270, device='cuda:0', grad_fn=<NllLossBackward0>) 6500 tensor(5.7318, device='cuda:0', grad_fn=<NllLossBackward0>) 6600 tensor(6.0448, device='cuda:0', grad_fn=<NllLossBackward0>) 6700 tensor(5.9031, device='cuda:0', grad_fn=<NllLossBackward0>) 6800 tensor(5.7908, device='cuda:0', grad_fn=<NllLossBackward0>) 6900 tensor(5.7183, device='cuda:0', grad_fn=<NllLossBackward0>) 7000 tensor(5.8839, device='cuda:0', grad_fn=<NllLossBackward0>) 7100 tensor(5.7365, device='cuda:0', grad_fn=<NllLossBackward0>) 7200 tensor(5.8651, device='cuda:0', grad_fn=<NllLossBackward0>) 7300 tensor(6.0091, device='cuda:0', grad_fn=<NllLossBackward0>) 7400 tensor(5.7031, device='cuda:0', grad_fn=<NllLossBackward0>) 7500 tensor(5.8671, device='cuda:0', grad_fn=<NllLossBackward0>) 7600 tensor(5.8997, device='cuda:0', grad_fn=<NllLossBackward0>) 7700 tensor(5.7679, device='cuda:0', grad_fn=<NllLossBackward0>) 7800 tensor(5.7867, device='cuda:0', 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