Hi, I have a question, I have set seed, but I still, have a different result.
thanks, I have no ideas a few days ;( 
seed=0
random.seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if seed == 0:
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
print(torch.cuda.get_device_name(0))
batch_first = True
TEXT.build_vocab(train_data,
max_size = MAX_VOCAB_SIZE,
vectors = “glove.6B.300d”,
unk_init = torch.Tensor.normal_)
LABEL.build_vocab(train_data)
train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
(train_data, valid_data, test_data),
batch_size = BATCH_SIZE,
sort_within_batch = True,
device = device)
class RNN(nn.Module):
def init(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers,
bidirectional, dropout, pad_idx):
    super().__init__()
    
    self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)
    
    
    self.rnn = nn.LSTM(embedding_dim, 
                       hidden_dim, 
                       num_layers=n_layers, 
                       bidirectional=True, 
                       dropout=dropout,
                       )
    
    self.fc = nn.Linear(hidden_dim * 2, output_dim)
    
    self.dropout = nn.Dropout(dropout)
    
def forward(self, text, text_lengths):
    
    embedded = self.embedding(text)
    packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths.to('cpu'))
    
    packed_output, (hidden, cell) = self.rnn(packed_embedded)

    output, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_output)

    hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1))
    return self.fc( hidden)