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+i said i i i the the the the the the the the the it it it that that that that that that the the the the the the the the the the the the the the the
+i think that if more more and and and that that be be be
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+the other of are are are least least least least the
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+we must be start the
+we need to to the the the we to to to more more more and and and and and that that that that that the the
+if we we the we we we we we we the the the the the the the the the the
+the 30 30 30 and and and and our
+we we can to the the and and and and and and and is
+if we our our our we our our
+the the of the the to to to to to the the the the the the the and and and the the the the the the the the the the the the the the the the the the the the the the the the the

run.py

@@ -0,0 +1,164 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import lzma
+
+import pandas as pd
+import numpy as np
+from keras import optimizers
+from keras.callbacks import ModelCheckpoint
+from keras.layers import Dense, LSTM, Embedding, RepeatVector
+from keras.models import Sequential, load_model
+from keras.preprocessing.text import Tokenizer
+from keras.utils import pad_sequences
+from keras.backend import clear_session
+get_ipython().run_line_magic('matplotlib', 'inline')
+pd.set_option('display.max_colwidth', 200)
+
+
+# In[2]:
+
+
+import tensorflow as tf
+
+physical_devices = tf.config.list_physical_devices('GPU')
+tf.config.experimental.set_memory_growth(physical_devices[0], enable=True)
+
+
+# In[3]:
+
+
+train_folder = 'wmt-2020-pl-en/train'
+
+
+def read_xz_file(fname):
+    with lzma.open(fname, mode='rt', encoding='utf-8') as f:
+        return [line.strip() for line in f.readlines()]
+
+
+train_data = read_xz_file(f'{train_folder}/in.tsv.xz')
+train_labels = read_xz_file(f'{train_folder}/expected.tsv.xz')
+
+
+# In[4]:
+
+
+dev_folder = 'wmt-2020-pl-en/dev-0'
+
+
+def read_file(fname):
+    with open(fname, mode='rt', encoding='utf-8') as f:
+        return [line.strip() for line in f.readlines()]
+
+
+dev_data = read_file(f'{dev_folder}/in.tsv')
+dev_labels = read_file(f'{dev_folder}/in.tsv')
+
+
+# In[5]:
+
+
+def tokenize(lines):
+    tokenizer = Tokenizer()
+    tokenizer.fit_on_texts(lines)
+    return tokenizer
+
+
+src_tokenizer = tokenize(train_data)
+src_vocab_size = len(src_tokenizer.word_index) + 1
+dst_tokenizer = tokenize(train_labels)
+dst_vocab_size = len(dst_tokenizer.word_index) + 1
+
+
+# In[6]:
+
+
+def encode_sequences(tokenizer, length, lines):
+    seq = tokenizer.texts_to_sequences(lines)
+    # pad sequences with 0 values
+    seq = pad_sequences(seq, maxlen=length, padding='post', value=0)
+    return seq
+
+
+seq_len = 50
+
+
+# In[7]:
+
+
+train_x = encode_sequences(src_tokenizer, seq_len, train_data)
+train_y = encode_sequences(dst_tokenizer, seq_len, train_labels)
+
+
+# In[8]:
+
+
+hidden_size = 256
+
+model = Sequential()
+model.add(Embedding(src_vocab_size, hidden_size, input_length=seq_len, mask_zero=True))
+model.add(LSTM(hidden_size))
+model.add(RepeatVector(seq_len))
+model.add(LSTM(hidden_size, return_sequences=True))
+model.add(Dense(dst_vocab_size, activation='softmax'))
+
+rms = optimizers.RMSprop(learning_rate=0.001)
+model.compile(optimizer=rms, loss='sparse_categorical_crossentropy')
+
+
+# In[9]:
+
+
+filename = 'model'
+
+
+# In[ ]:
+
+
+checkpoint = ModelCheckpoint(filename, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
+
+model.fit(
+    train_x,
+    train_y.reshape(train_y.shape[0], train_y.shape[1], 1),
+    epochs=10,
+    batch_size=256,
+    validation_split=0.2,
+    callbacks=[checkpoint]
+)
+
+
+# In[28]:
+
+
+dev_x = encode_sequences(src_tokenizer, seq_len, dev_data)
+dev_y = encode_sequences(dst_tokenizer, seq_len, dev_labels)
+
+model = load_model(filename)
+dev_preds = np.argmax(model.predict(dev_x), axis=-1)
+dev_preds_texts = dst_tokenizer.sequences_to_texts(dev_preds)
+with open(f'{dev_folder}/out.tsv', 'w') as f:
+    f.writelines(dev_preds_texts)
+
+
+# In[27]:
+
+
+dst_tokenizer.sequences_to_texts([dev_preds[0]])
+
+
+# In[ ]:
+
+
+test_folder = 'wmt-2020-pl-en/test-A'
+
+test_data = read_file(f'{test_folder}/in.tsv')
+
+test_x = encode_sequences(src_tokenizer, seq_len, test_data)
+test_preds = np.argmax(model.predict(test_x), axis=-1)
+test_preds_texts = dst_tokenizer.sequences_to_texts(test_preds)
+with open(f'{test_folder}/out.tsv', 'w') as f:
+    f.writelines(test_preds_texts)
+