diff --git a/dev-0/out.tsv b/dev-0/out.tsv
index ea45758..a895746 100644
--- a/dev-0/out.tsv
+++ b/dev-0/out.tsv
@@ -1,800 +1,800 @@
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+UNK see Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+Other UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
diff --git a/lang.py b/lang.py
index aed9bc2..3e28c54 100644
--- a/lang.py
+++ b/lang.py
@@ -1,18 +1,17 @@
 from nltk.tokenize import RegexpTokenizer
-SOS_token = 0
-EOS_token = 1
-tokenizer = RegexpTokenizer(r'\w+')
+SOS_token = 2
+PAD_token = 0
 
 class Lang:
     def __init__(self, name):
         self.name = name
         self.word2index = {}
         self.word2count = {}
-        self.index2word = {0: "SOS", 1: "EOS"}
-        self.n_words = 2  # Count SOS and EOS
+        self.index2word = {0: "PAD", 1: "UNK", 2: "SOS"}
+        self.n_words = 2
 
     def addSentence(self, sentence):
-        for word in tokenizer.tokenize(sentence):
+        for word in sentence.split():
             self.addWord(word)
 
     def addWord(self, word):
diff --git a/lstm_model.py b/lstm_model.py
index 2e3468a..bbbf493 100644
--- a/lstm_model.py
+++ b/lstm_model.py
@@ -2,8 +2,6 @@ import torch
 from torch import nn
 device = 'cuda'
 import torch.nn.functional as F
-import torch.nn.init as init
-from lang import SOS_token, EOS_token
 
 class EncoderRNN(nn.Module):
     def __init__(self, input_size, hidden_size):
@@ -14,7 +12,7 @@ class EncoderRNN(nn.Module):
         self.lstm = nn.LSTM(hidden_size, hidden_size)
 
     def forward(self, input, hidden):
-        embedded = self.embedding(input).view(1, 1, -1)
+        embedded = self.embedding(input)
         output = embedded
         output, hidden = self.lstm(output, hidden)
         return output, hidden
@@ -33,46 +31,8 @@ class DecoderRNN(nn.Module):
         self.softmax = nn.LogSoftmax(dim=1)
 
     def forward(self, input, hidden):
-        output = self.embedding(input).view(1, 1, -1)
+        output = self.embedding(input)
         output = F.relu(output)
         output, hidden = self.lstm(output, hidden)
         output = self.softmax(self.out(output[0]))
         return output, hidden
-
-    def initHidden(self):
-        return (torch.zeros(1, 1, self.hidden_size, device=device), torch.zeros(1, 1, self.hidden_size, device=device))
-
-class AttnDecoderRNN(nn.Module):
-    def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=300):
-        super(AttnDecoderRNN, self).__init__()
-        self.hidden_size = hidden_size
-        self.output_size = output_size
-        self.dropout_p = dropout_p
-        self.max_length = max_length
-
-        self.embedding = nn.Embedding(self.output_size, self.hidden_size)
-        self.attn = nn.Linear(self.hidden_size, self.max_length)
-        self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)
-        self.dropout = nn.Dropout(self.dropout_p)
-        self.lstm = nn.LSTM(self.hidden_size, self.hidden_size)
-        self.out = nn.Linear(self.hidden_size, self.output_size)
-
-    def forward(self, input, hidden, encoder_outputs):
-        embedded = self.embedding(input).view(1, 1, -1)
-        embedded = self.dropout(embedded)
-        attn_weights = F.softmax(
-            self.attn(torch.cat((embedded, hidden[0]), 1)), dim=1)
-        attn_applied = torch.bmm(attn_weights.unsqueeze(0),
-                                 encoder_outputs.unsqueeze(0))
-        output = torch.cat((embedded[0], attn_applied[0]), 1)
-        output = self.attn_combine(output).unsqueeze(0)
-
-        output = F.relu(output)
-        output, hidden = self.lstm(output, hidden)
-
-        output = F.log_softmax(self.out(output[0]), dim=1)
-        print(output.shape, hidden.shape)
-        return output, hidden, attn_weights
-
-    def initHidden(self):
-        return (torch.zeros(1, 1, self.hidden_size, device=device), torch.zeros(1, 1, self.hidden_size, device=device))
\ No newline at end of file
diff --git a/model_train.py b/model_train.py
index 6b96aa4..5fa9e99 100644
--- a/model_train.py
+++ b/model_train.py
@@ -1,16 +1,16 @@
-from lang import SOS_token, EOS_token
+from lang import SOS_token
 import torch
 import random
 import math
 import time
 from torch import nn, optim
+from torch.nn.utils.rnn import pad_sequence
 import torch
-from lang import EOS_token, tokenizer
 import pickle
 
-MAX_LENGTH = 300
+MAX_LENGTH = 25
 device = 'cuda'
-teacher_forcing_ratio = 0.5
+teacher_forcing_ratio = 0.8
 
 with open('data/pairs.pkl', 'rb') as input_file:
     pairs = pickle.load(input_file)
@@ -21,14 +21,16 @@ with open('data/pl_lang.pkl', 'rb') as input_file:
 with open('data/en_lang.pkl', 'rb') as out_file:
     output_lang = pickle.load(out_file)
 
+
 def indexesFromSentence(lang, sentence):
-    return [lang.word2index[word] for word in tokenizer.tokenize(sentence) if word in lang.word2index]
+    return [lang.word2index[word] if word in lang.word2index else 1 for word in sentence]
 
 
 def tensorFromSentence(lang, sentence):
     indexes = indexesFromSentence(lang, sentence)
-    indexes.append(EOS_token)
-    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)
+    indexes.append(0)
+    out = torch.tensor(indexes, device=device).view(-1, 1)
+    return out
 
 
 def tensorsFromPair(pair):
@@ -57,14 +59,12 @@ def train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, deco
     input_length = input_tensor.size(0)
     target_length = target_tensor.size(0)
 
-    encoder_outputs = torch.zeros(max_length, max_length, encoder.hidden_size, device=device)
-
+    encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)
     loss = 0
+    encoder_output, encoder_hidden = encoder(input_tensor, encoder_hidden)
+
+
 
-    for ei in range(input_length):
-        encoder_output, encoder_hidden = encoder(
-            input_tensor[ei], encoder_hidden)
-        encoder_outputs[ei] = encoder_output[0, 0, 0]
 
 
     decoder_input = torch.tensor([[SOS_token]], device=device)
@@ -72,25 +72,17 @@ def train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, deco
     use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
 
     if use_teacher_forcing:
-        # Teacher forcing: Feed the target as the next input
         for di in range(target_length):
-            decoder_output, decoder_hidden, decoder_attention = decoder(
-                decoder_input, decoder_hidden, encoder_outputs)
+            decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)
             loss += criterion(decoder_output, target_tensor[di])
-            decoder_input = target_tensor[di]
+            decoder_input = target_tensor[di].unsqueeze(0)
 
     else:
-        # Without teacher forcing: use its own predictions as the next input
         for di in range(target_length):
-            decoder_output, decoder_hidden, decoder_attention = decoder(
-                decoder_input, decoder_hidden, encoder_outputs)
+            decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)
             topv, topi = decoder_output.topk(1)
-            decoder_input = topi.squeeze().detach()
-
+            decoder_input = topi.transpose(0, 1).detach()
             loss += criterion(decoder_output, target_tensor[di])
-            if decoder_input.item() == EOS_token:
-                break
-
     loss.backward()
 
     encoder_optimizer.step()
@@ -105,24 +97,34 @@ def trainIters(encoder, decoder, n_iters, print_every=10, plot_every=100, learni
 
     encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
     decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)
-    training_pairs = [tensorsFromPair(random.choice(pairs))
-                      for i in range(n_iters)]
     criterion = nn.NLLLoss()
-
+    pairs_in = pairs[:10000]
     for iter in range(1, n_iters + 1):
-        training_pair = training_pairs[iter - 1]
-        input_tensor = training_pair[0]
-        target_tensor = training_pair[1]
+        try:
+            for idx, training_pair in enumerate(pairs_in):
+                input_ = training_pair[0]
+                target_ = training_pair[1]
+                input_ = input_.split()
+                input_ = input_[::-1]
+                target_ = target_.split()
 
-        loss = train(input_tensor, target_tensor, encoder,
-                     decoder, encoder_optimizer, decoder_optimizer, criterion)
-        print_loss_total += loss
-        plot_loss_total += loss
+                if len(input_)>1 and len(target_)>1:
+                    input_tensor = tensorFromSentence(input_lang, input_)
+                    target_tensor = tensorFromSentence(output_lang, target_)
 
-        if iter % print_every == 0:
-            print_loss_avg = print_loss_total / print_every
-            print_loss_total = 0
-            print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),
+                    loss = train(input_tensor, target_tensor, encoder,
+                    decoder, encoder_optimizer, decoder_optimizer, criterion)
+                    print_loss_total += loss
+                    plot_loss_total += loss
+                print(idx/len(pairs_in), end='\r')
+
+            if iter % print_every == 0:
+                print_loss_avg = print_loss_total / print_every
+                print_loss_total = 0
+                print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),
                                          iter, iter / n_iters * 100, print_loss_avg))
+        except KeyboardInterrupt:
+            torch.save(encoder.state_dict(), 'encoder.dict')
+            torch.save(decoder.state_dict(), 'decoder.dict')
     torch.save(encoder.state_dict(), 'encoder.dict')
-    torch.save(decoder.state_dict(), 'decoder.dict')
\ No newline at end of file
+    torch.save(decoder.state_dict(), 'decoder.dict')
diff --git a/predict.py b/predict.py
index 62e2170..c0dd231 100644
--- a/predict.py
+++ b/predict.py
@@ -1,4 +1,4 @@
-from model_train import tensorFromSentence, SOS_token, MAX_LENGTH, device, EOS_token
+from model_train import tensorFromSentence, SOS_token, MAX_LENGTH, device
 import pickle
 from lstm_model import EncoderRNN, DecoderRNN
 import sys
@@ -14,45 +14,43 @@ with open('data/en_lang.pkl', 'rb') as out_file:
 def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):
     with torch.no_grad():
         input_tensor = tensorFromSentence(input_lang, sentence)
-        input_length = input_tensor.size()[0]
         encoder_hidden = encoder.initHidden()
 
         encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)
 
-        loss = 0
+        encoder_output, encoder_hidden = encoder(input_tensor,encoder_hidden)
 
-        for ei in range(input_length):
-            encoder_output, encoder_hidden = encoder(input_tensor[ei],
-                                                     encoder_hidden)
-            encoder_outputs[ei] = encoder_output[0, 0]
+        encoder_outputs = encoder_output
 
-        decoder_input = torch.tensor([[SOS_token]], dtype=torch.long, device=device).view(-1, 1)  # SOS
+        decoder_input = torch.tensor([[SOS_token]], device=device)
 
         decoder_hidden = encoder_hidden
 
         decoded_words = []
-        decoder_attentions = torch.zeros(max_length, max_length)
 
         for di in range(max_length):
-            decoder_output, decoder_hidden = decoder(
-                decoder_input, decoder_hidden)
-            topv, topi = decoder_output.data.topk(1)
-            if topi.item() == EOS_token:
-                break
-            else:
-                decoded_words.append(output_lang.index2word[topi.item()])
-
-            decoder_input = topi.squeeze().detach()
-
-        return decoded_words
+            decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)
+            topv, topi = decoder_output.topk(1)
+            decoded_words.append(topi)
+            decoder_input = topi.transpose(0, 1)
+        out = torch.stack(decoded_words)
+        return out
 
 hidden_size = 256
 encoder = EncoderRNN(input_lang.n_words, hidden_size).to(device)
 decoder = DecoderRNN(hidden_size, output_lang.n_words).to(device)
 encoder.load_state_dict(torch.load('encoder.dict'))
 decoder.load_state_dict(torch.load('decoder.dict'))
-
+encoder.eval()
+decoder.eval()
 for line in sys.stdin:
     line = line.rstrip()
     dec_words = evaluate(encoder, decoder, line, MAX_LENGTH)
-    print(' '.join(dec_words))
+    dec_words = dec_words.transpose(0, 1)
+    for sen in dec_words:
+        out = []
+        for idx in sen:
+            if idx == 0:
+                break
+            out.append(output_lang.index2word[idx.item()])
+    print(' '.join(out))
diff --git a/prepare.py b/prepare.py
index 6725cdd..1d4f1d3 100644
--- a/prepare.py
+++ b/prepare.py
@@ -11,7 +11,7 @@ from torch import optim
 import torch.nn.functional as F
 from lang import *
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-MAX_LENGTH = 300
+MAX_LENGTH = 25
 
 # Turn a Unicode string to plain ASCII, thanks to
 # https://stackoverflow.com/a/518232/2809427
diff --git a/test-A/out.tsv b/test-A/out.tsv
index 6afbf07..5a86c40 100644
--- a/test-A/out.tsv
+++ b/test-A/out.tsv
@@ -1198,3 +1198,1203 @@ We be the the the and the
 We is the the the and the and the and the and the and the and the and the and the and the
 We is the the the and the and the and the and the and the and the and the and the and the and the and the and the
 We is the the the and the and the and the and the and the and the and the and the and the and the and the and the and the
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+Other UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+This is the UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+The UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+The UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+Other Fields UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+Other assertions UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+Other assertions UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+Other assertions UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow
+Other assertions UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow UNK Minutes fellow
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+This is not UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
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+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+This is the UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+This UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+This assertions assertions UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+Other assertions UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK see Minutes fellow UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+Other Fields assertions UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
+UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK
diff --git a/train.py b/train.py
index ed2b28d..7064ccf 100644
--- a/train.py
+++ b/train.py
@@ -1,7 +1,7 @@
-from lstm_model import EncoderRNN, DecoderRNN, AttnDecoderRNN
+from lstm_model import EncoderRNN, DecoderRNN
 from model_train import *
 hidden_size = 256
 encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device)
-attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words).to(device)
+attn_decoder1 = DecoderRNN(hidden_size, output_lang.n_words).to(device)
 
-trainIters(encoder1, attn_decoder1, 10000, print_every=100)
\ No newline at end of file
+trainIters(encoder1, attn_decoder1, 5, print_every=1)