diff --git a/train.py b/train.py
deleted file mode 100644
index 5a923b5..0000000
--- a/train.py
+++ /dev/null
@@ -1,124 +0,0 @@
-
-
-from torch import nn
-import torch
-
-
-from torch.utils.data import IterableDataset
-import itertools
-import lzma
-import regex as re
-import pickle
-import scripts
-
-
-def look_ahead_iterator(gen):
-    prev = None
-    current = None
-    next = None
-    for next in gen:
-        if prev is not None and current is not None:
-            yield (prev, current, next)
-        prev = current
-        current = next
-
-
-def get_word_lines_from_file(file_name):
-  counter=0
-  with lzma.open(file_name, 'r') as fh:
-    for line in fh:
-      counter+=1
-      if counter == 100000:
-        break
-      line = line.decode("utf-8")
-      yield scripts.get_words_from_line(line)
-
-
-
-class Trigrams(IterableDataset):
-  def load_vocab(self):
-    with open("vocab.pickle", 'rb') as handle:
-        vocab = pickle.load( handle)
-    return vocab
-
-  def __init__(self, text_file, vocabulary_size):
-      self.vocab = self.load_vocab()
-      self.vocab.set_default_index(self.vocab['<unk>'])
-      self.vocabulary_size = vocabulary_size
-      self.text_file = text_file
-
-  def __iter__(self):
-     return look_ahead_iterator(
-         (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))
-
-vocab_size = scripts.vocab_size
-
-train_dataset = Trigrams('train/in.tsv.xz', vocab_size)
-
-
-
-#=== trenowanie
-from torch import nn
-import torch
-from torch.utils.data import DataLoader
-embed_size = 100
-
-class SimpleTrigramNeuralLanguageModel(nn.Module):
-  def __init__(self, vocabulary_size, embedding_size):
-      super(SimpleTrigramNeuralLanguageModel, self).__init__()
-      self.embedings = nn.Embedding(vocabulary_size, embedding_size)
-      self.linear = nn.Linear(embedding_size*2, vocabulary_size)
-
-      self.linear_first_layer = nn.Linear(embedding_size*2, embedding_size*2)
-      self.relu = nn.ReLU()
-      self.softmax = nn.Softmax()
-
-    #   self.model = nn.Sequential(
-    #       nn.Embedding(vocabulary_size, embedding_size),
-    #       nn.Linear(embedding_size, vocabulary_size),
-    #       nn.Softmax()
-    #   )
-
-  def forward(self, x):
-      emb_1 = self.embedings(x[0])
-      emb_2 = self.embedings(x[1])
-
-      first_layer = self.linear_first_layer(torch.cat((emb_1, emb_2), dim=1))
-      after_relu = self.relu(first_layer)
-      concated = self.linear(after_relu)
-
-      y = self.softmax(concated)
-
-      return y
-
-model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size)
-
-vocab = train_dataset.vocab
-
-
-device = 'cuda'
-model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device)
-data = DataLoader(train_dataset, batch_size=12800)
-optimizer = torch.optim.Adam(model.parameters(), lr=scripts.learning_rate)
-criterion = torch.nn.NLLLoss()
-
-model.train()
-step = 0
-epochs = 4
-for i in range(epochs):
-  for x, y, z in data:
-    x = x.to(device)
-    y = y.to(device)
-    z = z.to(device)
-    optimizer.zero_grad()
-    ypredicted = model([x, z])
-    loss = criterion(torch.log(ypredicted), y)
-    if step % 2000 == 0:
-        print(step, loss)
-    #     torch.save(model.state_dict(), f'model1_{step}.bin')
-    step += 1
-    loss.backward()
-    optimizer.step()
-  torch.save(model.state_dict(), f'batch_model_epoch_{i}.bin')
-  print(step, loss, f'model_epoch_{i}.bin')
-torch.save(model.state_dict(), 'model_tri1.bin')
\ No newline at end of file