diff --git a/4. Seq2seq/seq2seq_Dopierala.ipynb b/4. Seq2seq/seq2seq_Dopierala.ipynb
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+++ b/4. Seq2seq/seq2seq_Dopierala.ipynb	
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "8Lrtmwas7Y6x"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From C:\\Users\\Pawel\\anaconda3\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import keras\n",
+    "import os\n",
+    "from pathlib import Path\n",
+    "import requests\n",
+    "import zipfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BkwAAv7b7i9u"
+   },
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "oiE7pYD18jr-"
+   },
+   "outputs": [],
+   "source": [
+    "url = \"https://www.manythings.org/anki/pol-eng.zip\"\n",
+    "local_filename = url.split('/')[-1]\n",
+    "dirpath = Path().absolute()\n",
+    "\n",
+    "headers = {\n",
+    "    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'\n",
+    "}\n",
+    "\n",
+    "response = requests.get(url, headers=headers)\n",
+    "if response.status_code == 200:\n",
+    "    with open(local_filename, 'wb') as f:\n",
+    "        f.write(response.content)\n",
+    "    with zipfile.ZipFile(local_filename, 'r') as zip_ref:\n",
+    "        zip_ref.extractall(dirpath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "he25FWpj-5Lk"
+   },
+   "source": [
+    "### Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "X8pq11jj-90z"
+   },
+   "outputs": [],
+   "source": [
+    "batch_size = 64  # Batch size for training.\n",
+    "epochs = 200  # Number of epochs to train for.\n",
+    "latent_dim = 256  # Latent dimensionality of the encoding space.\n",
+    "num_samples = 10000  # Number of samples to train on.\n",
+    "# Path to the data txt file on disk.\n",
+    "data_path = os.path.join(dirpath, \"pol.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P9Ivwa14_Er2"
+   },
+   "source": [
+    "### Prepare the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "eZbcKeoF-3Gv",
+    "outputId": "a2e0bc6e-fd9b-40c9-8757-3da4436ddfdb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of samples: 10000\n",
+      "Number of unique input tokens: 70\n",
+      "Number of unique output tokens: 87\n",
+      "Max sequence length for inputs: 19\n",
+      "Max sequence length for outputs: 52\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Vectorize the data.\n",
+    "input_texts = []\n",
+    "target_texts = []\n",
+    "input_characters = set()\n",
+    "target_characters = set()\n",
+    "with open(data_path, \"r\", encoding=\"utf-8\") as f:\n",
+    "    lines = f.read().split(\"\\n\")\n",
+    "for line in lines[: min(num_samples, len(lines) - 1)]:\n",
+    "    input_text, target_text, _ = line.split(\"\\t\")\n",
+    "    # We use \"tab\" as the \"start sequence\" character\n",
+    "    # for the targets, and \"\\n\" as \"end sequence\" character.\n",
+    "    target_text = \"\\t\" + target_text + \"\\n\"\n",
+    "    input_texts.append(input_text)\n",
+    "    target_texts.append(target_text)\n",
+    "    for char in input_text:\n",
+    "        if char not in input_characters:\n",
+    "            input_characters.add(char)\n",
+    "    for char in target_text:\n",
+    "        if char not in target_characters:\n",
+    "            target_characters.add(char)\n",
+    "\n",
+    "input_characters = sorted(list(input_characters))\n",
+    "target_characters = sorted(list(target_characters))\n",
+    "num_encoder_tokens = len(input_characters)\n",
+    "num_decoder_tokens = len(target_characters)\n",
+    "max_encoder_seq_length = max([len(txt) for txt in input_texts])\n",
+    "max_decoder_seq_length = max([len(txt) for txt in target_texts])\n",
+    "\n",
+    "print(\"Number of samples:\", len(input_texts))\n",
+    "print(\"Number of unique input tokens:\", num_encoder_tokens)\n",
+    "print(\"Number of unique output tokens:\", num_decoder_tokens)\n",
+    "print(\"Max sequence length for inputs:\", max_encoder_seq_length)\n",
+    "print(\"Max sequence length for outputs:\", max_decoder_seq_length)\n",
+    "\n",
+    "input_token_index = dict([(char, i) for i, char in enumerate(input_characters)])\n",
+    "target_token_index = dict([(char, i) for i, char in enumerate(target_characters)])\n",
+    "\n",
+    "encoder_input_data = np.zeros(\n",
+    "    (len(input_texts), max_encoder_seq_length, num_encoder_tokens),\n",
+    "    dtype=\"float32\",\n",
+    ")\n",
+    "decoder_input_data = np.zeros(\n",
+    "    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),\n",
+    "    dtype=\"float32\",\n",
+    ")\n",
+    "decoder_target_data = np.zeros(\n",
+    "    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),\n",
+    "    dtype=\"float32\",\n",
+    ")\n",
+    "\n",
+    "for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):\n",
+    "    for t, char in enumerate(input_text):\n",
+    "        encoder_input_data[i, t, input_token_index[char]] = 1.0\n",
+    "    encoder_input_data[i, t + 1 :, input_token_index[\" \"]] = 1.0\n",
+    "    for t, char in enumerate(target_text):\n",
+    "        # decoder_target_data is ahead of decoder_input_data by one timestep\n",
+    "        decoder_input_data[i, t, target_token_index[char]] = 1.0\n",
+    "        if t > 0:\n",
+    "            # decoder_target_data will be ahead by one timestep\n",
+    "            # and will not include the start character.\n",
+    "            decoder_target_data[i, t - 1, target_token_index[char]] = 1.0\n",
+    "    decoder_input_data[i, t + 1 :, target_token_index[\" \"]] = 1.0\n",
+    "    decoder_target_data[i, t:, target_token_index[\" \"]] = 1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xuxzuM6dBpil"
+   },
+   "source": [
+    "### Build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "SyABMORlBtGX"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From C:\\Users\\Pawel\\anaconda3\\Lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define an input sequence and process it.\n",
+    "encoder_inputs = keras.Input(shape=(None, num_encoder_tokens))\n",
+    "encoder = keras.layers.LSTM(latent_dim, return_state=True)\n",
+    "encoder_outputs, state_h, state_c = encoder(encoder_inputs)\n",
+    "\n",
+    "# We discard `encoder_outputs` and only keep the states.\n",
+    "encoder_states = [state_h, state_c]\n",
+    "\n",
+    "# Set up the decoder, using `encoder_states` as initial state.\n",
+    "decoder_inputs = keras.Input(shape=(None, num_decoder_tokens))\n",
+    "\n",
+    "# We set up our decoder to return full output sequences,\n",
+    "# and to return internal states as well. We don't use the\n",
+    "# return states in the training model, but we will use them in inference.\n",
+    "decoder_lstm = keras.layers.LSTM(latent_dim, return_sequences=True, return_state=True)\n",
+    "decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)\n",
+    "decoder_dense = keras.layers.Dense(num_decoder_tokens, activation=\"softmax\")\n",
+    "decoder_outputs = decoder_dense(decoder_outputs)\n",
+    "\n",
+    "# Define the model that will turn\n",
+    "# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`\n",
+    "model = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4Kjv669-BxFF"
+   },
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rsebz819Bzfu",
+    "outputId": "0996aac7-eeda-46ee-e80e-1fb55693976f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From C:\\Users\\Pawel\\anaconda3\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n",
+      "Epoch 1/200\n",
+      "WARNING:tensorflow:From C:\\Users\\Pawel\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
+      "\n",
+      "WARNING:tensorflow:From C:\\Users\\Pawel\\anaconda3\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
+      "\n",
+      "125/125 [==============================] - 20s 121ms/step - loss: 1.3745 - accuracy: 0.6987 - val_loss: 1.4872 - val_accuracy: 0.6530\n",
+      "Epoch 2/200\n",
+      "125/125 [==============================] - 12s 94ms/step - loss: 1.0822 - accuracy: 0.7185 - val_loss: 1.2406 - val_accuracy: 0.6688\n",
+      "Epoch 3/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.9703 - accuracy: 0.7424 - val_loss: 1.1089 - val_accuracy: 0.7065\n",
+      "Epoch 4/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.8733 - accuracy: 0.7671 - val_loss: 1.0149 - val_accuracy: 0.7165\n",
+      "Epoch 5/200\n",
+      "125/125 [==============================] - 18s 145ms/step - loss: 0.7839 - accuracy: 0.7835 - val_loss: 0.9353 - val_accuracy: 0.7399\n",
+      "Epoch 6/200\n",
+      "125/125 [==============================] - 16s 129ms/step - loss: 0.7367 - accuracy: 0.7946 - val_loss: 0.8968 - val_accuracy: 0.7464\n",
+      "Epoch 7/200\n",
+      "125/125 [==============================] - 15s 121ms/step - loss: 0.7030 - accuracy: 0.8006 - val_loss: 0.8677 - val_accuracy: 0.7572\n",
+      "Epoch 8/200\n",
+      "125/125 [==============================] - 14s 112ms/step - loss: 0.6812 - accuracy: 0.8056 - val_loss: 0.8536 - val_accuracy: 0.7566\n",
+      "Epoch 9/200\n",
+      "125/125 [==============================] - 14s 114ms/step - loss: 0.6642 - accuracy: 0.8100 - val_loss: 0.8439 - val_accuracy: 0.7569\n",
+      "Epoch 10/200\n",
+      "125/125 [==============================] - 15s 117ms/step - loss: 0.6492 - accuracy: 0.8133 - val_loss: 0.8181 - val_accuracy: 0.7648\n",
+      "Epoch 11/200\n",
+      "125/125 [==============================] - 17s 136ms/step - loss: 0.6369 - accuracy: 0.8162 - val_loss: 0.8118 - val_accuracy: 0.7664\n",
+      "Epoch 12/200\n",
+      "125/125 [==============================] - 14s 115ms/step - loss: 0.6252 - accuracy: 0.8193 - val_loss: 0.7932 - val_accuracy: 0.7709\n",
+      "Epoch 13/200\n",
+      "125/125 [==============================] - 15s 124ms/step - loss: 0.6147 - accuracy: 0.8216 - val_loss: 0.7842 - val_accuracy: 0.7722\n",
+      "Epoch 14/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.6040 - accuracy: 0.8244 - val_loss: 0.7801 - val_accuracy: 0.7749\n",
+      "Epoch 15/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.5944 - accuracy: 0.8273 - val_loss: 0.7741 - val_accuracy: 0.7758\n",
+      "Epoch 16/200\n",
+      "125/125 [==============================] - 13s 108ms/step - loss: 0.5841 - accuracy: 0.8302 - val_loss: 0.7631 - val_accuracy: 0.7788\n",
+      "Epoch 17/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.5739 - accuracy: 0.8337 - val_loss: 0.7413 - val_accuracy: 0.7838\n",
+      "Epoch 18/200\n",
+      "125/125 [==============================] - 14s 108ms/step - loss: 0.5627 - accuracy: 0.8363 - val_loss: 0.7355 - val_accuracy: 0.7862\n",
+      "Epoch 19/200\n",
+      "125/125 [==============================] - 14s 110ms/step - loss: 0.5532 - accuracy: 0.8390 - val_loss: 0.7239 - val_accuracy: 0.7899\n",
+      "Epoch 20/200\n",
+      "125/125 [==============================] - 15s 121ms/step - loss: 0.5441 - accuracy: 0.8418 - val_loss: 0.7179 - val_accuracy: 0.7916\n",
+      "Epoch 21/200\n",
+      "125/125 [==============================] - 15s 117ms/step - loss: 0.5360 - accuracy: 0.8437 - val_loss: 0.7164 - val_accuracy: 0.7915\n",
+      "Epoch 22/200\n",
+      "125/125 [==============================] - 15s 123ms/step - loss: 0.5270 - accuracy: 0.8469 - val_loss: 0.7067 - val_accuracy: 0.7947\n",
+      "Epoch 23/200\n",
+      "125/125 [==============================] - 14s 116ms/step - loss: 0.5186 - accuracy: 0.8493 - val_loss: 0.7051 - val_accuracy: 0.7962\n",
+      "Epoch 24/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.5107 - accuracy: 0.8519 - val_loss: 0.6904 - val_accuracy: 0.7994\n",
+      "Epoch 25/200\n",
+      "125/125 [==============================] - 18s 142ms/step - loss: 0.5022 - accuracy: 0.8542 - val_loss: 0.6898 - val_accuracy: 0.7998\n",
+      "Epoch 26/200\n",
+      "125/125 [==============================] - 14s 111ms/step - loss: 0.4945 - accuracy: 0.8565 - val_loss: 0.6775 - val_accuracy: 0.8041\n",
+      "Epoch 27/200\n",
+      "125/125 [==============================] - 14s 116ms/step - loss: 0.4875 - accuracy: 0.8584 - val_loss: 0.6717 - val_accuracy: 0.8064\n",
+      "Epoch 28/200\n",
+      "125/125 [==============================] - 14s 114ms/step - loss: 0.4806 - accuracy: 0.8608 - val_loss: 0.6649 - val_accuracy: 0.8077\n",
+      "Epoch 29/200\n",
+      "125/125 [==============================] - 15s 122ms/step - loss: 0.4728 - accuracy: 0.8627 - val_loss: 0.6616 - val_accuracy: 0.8085\n",
+      "Epoch 30/200\n",
+      "125/125 [==============================] - 15s 117ms/step - loss: 0.4659 - accuracy: 0.8649 - val_loss: 0.6574 - val_accuracy: 0.8094\n",
+      "Epoch 31/200\n",
+      "125/125 [==============================] - 15s 120ms/step - loss: 0.4596 - accuracy: 0.8672 - val_loss: 0.6557 - val_accuracy: 0.8113\n",
+      "Epoch 32/200\n",
+      "125/125 [==============================] - 15s 117ms/step - loss: 0.4527 - accuracy: 0.8690 - val_loss: 0.6500 - val_accuracy: 0.8124\n",
+      "Epoch 33/200\n",
+      "125/125 [==============================] - 15s 119ms/step - loss: 0.4464 - accuracy: 0.8706 - val_loss: 0.6459 - val_accuracy: 0.8141\n",
+      "Epoch 34/200\n",
+      "125/125 [==============================] - 14s 111ms/step - loss: 0.4403 - accuracy: 0.8727 - val_loss: 0.6470 - val_accuracy: 0.8141\n",
+      "Epoch 35/200\n",
+      "125/125 [==============================] - 13s 108ms/step - loss: 0.4343 - accuracy: 0.8741 - val_loss: 0.6338 - val_accuracy: 0.8175\n",
+      "Epoch 36/200\n",
+      "125/125 [==============================] - 14s 108ms/step - loss: 0.4282 - accuracy: 0.8757 - val_loss: 0.6314 - val_accuracy: 0.8190\n",
+      "Epoch 37/200\n",
+      "125/125 [==============================] - 14s 108ms/step - loss: 0.4223 - accuracy: 0.8776 - val_loss: 0.6343 - val_accuracy: 0.8183\n",
+      "Epoch 38/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.4165 - accuracy: 0.8792 - val_loss: 0.6327 - val_accuracy: 0.8183\n",
+      "Epoch 39/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.4107 - accuracy: 0.8812 - val_loss: 0.6277 - val_accuracy: 0.8211\n",
+      "Epoch 40/200\n",
+      "125/125 [==============================] - 14s 116ms/step - loss: 0.4057 - accuracy: 0.8828 - val_loss: 0.6275 - val_accuracy: 0.8206\n",
+      "Epoch 41/200\n",
+      "125/125 [==============================] - 14s 115ms/step - loss: 0.3998 - accuracy: 0.8842 - val_loss: 0.6186 - val_accuracy: 0.8234\n",
+      "Epoch 42/200\n",
+      "125/125 [==============================] - 14s 111ms/step - loss: 0.3948 - accuracy: 0.8852 - val_loss: 0.6186 - val_accuracy: 0.8238\n",
+      "Epoch 43/200\n",
+      "125/125 [==============================] - 14s 112ms/step - loss: 0.3894 - accuracy: 0.8873 - val_loss: 0.6225 - val_accuracy: 0.8232\n",
+      "Epoch 44/200\n",
+      "125/125 [==============================] - 15s 119ms/step - loss: 0.3842 - accuracy: 0.8887 - val_loss: 0.6209 - val_accuracy: 0.8237\n",
+      "Epoch 45/200\n",
+      "125/125 [==============================] - 15s 121ms/step - loss: 0.3797 - accuracy: 0.8898 - val_loss: 0.6139 - val_accuracy: 0.8262\n",
+      "Epoch 46/200\n",
+      "125/125 [==============================] - 15s 124ms/step - loss: 0.3745 - accuracy: 0.8918 - val_loss: 0.6112 - val_accuracy: 0.8271\n",
+      "Epoch 47/200\n",
+      "125/125 [==============================] - 15s 118ms/step - loss: 0.3695 - accuracy: 0.8931 - val_loss: 0.6122 - val_accuracy: 0.8265\n",
+      "Epoch 48/200\n",
+      "125/125 [==============================] - 15s 122ms/step - loss: 0.3641 - accuracy: 0.8943 - val_loss: 0.6184 - val_accuracy: 0.8256\n",
+      "Epoch 49/200\n",
+      "125/125 [==============================] - 15s 117ms/step - loss: 0.3599 - accuracy: 0.8960 - val_loss: 0.6116 - val_accuracy: 0.8274\n",
+      "Epoch 50/200\n",
+      "125/125 [==============================] - 15s 121ms/step - loss: 0.3552 - accuracy: 0.8972 - val_loss: 0.6109 - val_accuracy: 0.8279\n",
+      "Epoch 51/200\n",
+      "125/125 [==============================] - 14s 116ms/step - loss: 0.3505 - accuracy: 0.8986 - val_loss: 0.6106 - val_accuracy: 0.8289\n",
+      "Epoch 52/200\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "125/125 [==============================] - 15s 122ms/step - loss: 0.3459 - accuracy: 0.9002 - val_loss: 0.6133 - val_accuracy: 0.8282\n",
+      "Epoch 53/200\n",
+      "125/125 [==============================] - 14s 115ms/step - loss: 0.3415 - accuracy: 0.9015 - val_loss: 0.6171 - val_accuracy: 0.8282\n",
+      "Epoch 54/200\n",
+      "125/125 [==============================] - 15s 119ms/step - loss: 0.3375 - accuracy: 0.9020 - val_loss: 0.6109 - val_accuracy: 0.8300\n",
+      "Epoch 55/200\n",
+      "125/125 [==============================] - 18s 143ms/step - loss: 0.3326 - accuracy: 0.9039 - val_loss: 0.6103 - val_accuracy: 0.8302\n",
+      "Epoch 56/200\n",
+      "125/125 [==============================] - 16s 130ms/step - loss: 0.3283 - accuracy: 0.9047 - val_loss: 0.6132 - val_accuracy: 0.8305\n",
+      "Epoch 57/200\n",
+      "125/125 [==============================] - 16s 132ms/step - loss: 0.3243 - accuracy: 0.9062 - val_loss: 0.6136 - val_accuracy: 0.8303\n",
+      "Epoch 58/200\n",
+      "125/125 [==============================] - 15s 122ms/step - loss: 0.3195 - accuracy: 0.9077 - val_loss: 0.6206 - val_accuracy: 0.8295\n",
+      "Epoch 59/200\n",
+      "125/125 [==============================] - 16s 131ms/step - loss: 0.3152 - accuracy: 0.9093 - val_loss: 0.6196 - val_accuracy: 0.8305\n",
+      "Epoch 60/200\n",
+      "125/125 [==============================] - 16s 126ms/step - loss: 0.3114 - accuracy: 0.9098 - val_loss: 0.6217 - val_accuracy: 0.8291\n",
+      "Epoch 61/200\n",
+      "125/125 [==============================] - 16s 127ms/step - loss: 0.3073 - accuracy: 0.9113 - val_loss: 0.6295 - val_accuracy: 0.8282\n",
+      "Epoch 62/200\n",
+      "125/125 [==============================] - 16s 125ms/step - loss: 0.3025 - accuracy: 0.9124 - val_loss: 0.6231 - val_accuracy: 0.8292\n",
+      "Epoch 63/200\n",
+      "125/125 [==============================] - 16s 127ms/step - loss: 0.2990 - accuracy: 0.9134 - val_loss: 0.6255 - val_accuracy: 0.8305\n",
+      "Epoch 64/200\n",
+      "125/125 [==============================] - 16s 129ms/step - loss: 0.2956 - accuracy: 0.9147 - val_loss: 0.6279 - val_accuracy: 0.8302\n",
+      "Epoch 65/200\n",
+      "125/125 [==============================] - 16s 125ms/step - loss: 0.2910 - accuracy: 0.9158 - val_loss: 0.6302 - val_accuracy: 0.8302\n",
+      "Epoch 66/200\n",
+      "125/125 [==============================] - 17s 132ms/step - loss: 0.2868 - accuracy: 0.9170 - val_loss: 0.6319 - val_accuracy: 0.8300\n",
+      "Epoch 67/200\n",
+      "125/125 [==============================] - 16s 127ms/step - loss: 0.2833 - accuracy: 0.9179 - val_loss: 0.6364 - val_accuracy: 0.8298\n",
+      "Epoch 68/200\n",
+      "125/125 [==============================] - 16s 130ms/step - loss: 0.2795 - accuracy: 0.9189 - val_loss: 0.6327 - val_accuracy: 0.8315\n",
+      "Epoch 69/200\n",
+      "125/125 [==============================] - 16s 131ms/step - loss: 0.2756 - accuracy: 0.9205 - val_loss: 0.6398 - val_accuracy: 0.8302\n",
+      "Epoch 70/200\n",
+      "125/125 [==============================] - 17s 138ms/step - loss: 0.2719 - accuracy: 0.9216 - val_loss: 0.6430 - val_accuracy: 0.8306\n",
+      "Epoch 71/200\n",
+      "125/125 [==============================] - 15s 123ms/step - loss: 0.2685 - accuracy: 0.9225 - val_loss: 0.6439 - val_accuracy: 0.8295\n",
+      "Epoch 72/200\n",
+      "125/125 [==============================] - 16s 126ms/step - loss: 0.2640 - accuracy: 0.9238 - val_loss: 0.6478 - val_accuracy: 0.8296\n",
+      "Epoch 73/200\n",
+      "125/125 [==============================] - 13s 104ms/step - loss: 0.2609 - accuracy: 0.9249 - val_loss: 0.6485 - val_accuracy: 0.8305\n",
+      "Epoch 74/200\n",
+      "125/125 [==============================] - 16s 125ms/step - loss: 0.2568 - accuracy: 0.9261 - val_loss: 0.6519 - val_accuracy: 0.8311\n",
+      "Epoch 75/200\n",
+      "125/125 [==============================] - 14s 116ms/step - loss: 0.2542 - accuracy: 0.9263 - val_loss: 0.6572 - val_accuracy: 0.8287\n",
+      "Epoch 76/200\n",
+      "125/125 [==============================] - 16s 127ms/step - loss: 0.2502 - accuracy: 0.9282 - val_loss: 0.6625 - val_accuracy: 0.8289\n",
+      "Epoch 77/200\n",
+      "125/125 [==============================] - 14s 110ms/step - loss: 0.2468 - accuracy: 0.9289 - val_loss: 0.6624 - val_accuracy: 0.8300\n",
+      "Epoch 78/200\n",
+      "125/125 [==============================] - 13s 106ms/step - loss: 0.2435 - accuracy: 0.9298 - val_loss: 0.6642 - val_accuracy: 0.8301\n",
+      "Epoch 79/200\n",
+      "125/125 [==============================] - 13s 105ms/step - loss: 0.2401 - accuracy: 0.9307 - val_loss: 0.6743 - val_accuracy: 0.8290\n",
+      "Epoch 80/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.2369 - accuracy: 0.9316 - val_loss: 0.6680 - val_accuracy: 0.8301\n",
+      "Epoch 81/200\n",
+      "125/125 [==============================] - 13s 104ms/step - loss: 0.2333 - accuracy: 0.9326 - val_loss: 0.6743 - val_accuracy: 0.8286\n",
+      "Epoch 82/200\n",
+      "125/125 [==============================] - 15s 119ms/step - loss: 0.2300 - accuracy: 0.9336 - val_loss: 0.6763 - val_accuracy: 0.8292\n",
+      "Epoch 83/200\n",
+      "125/125 [==============================] - 12s 98ms/step - loss: 0.2269 - accuracy: 0.9345 - val_loss: 0.6805 - val_accuracy: 0.8293\n",
+      "Epoch 84/200\n",
+      "125/125 [==============================] - 13s 107ms/step - loss: 0.2238 - accuracy: 0.9357 - val_loss: 0.6812 - val_accuracy: 0.8292\n",
+      "Epoch 85/200\n",
+      "125/125 [==============================] - 15s 118ms/step - loss: 0.2201 - accuracy: 0.9363 - val_loss: 0.6801 - val_accuracy: 0.8305\n",
+      "Epoch 86/200\n",
+      "125/125 [==============================] - 14s 110ms/step - loss: 0.2176 - accuracy: 0.9370 - val_loss: 0.6918 - val_accuracy: 0.8298\n",
+      "Epoch 87/200\n",
+      "125/125 [==============================] - 13s 106ms/step - loss: 0.2142 - accuracy: 0.9384 - val_loss: 0.7037 - val_accuracy: 0.8277\n",
+      "Epoch 88/200\n",
+      "125/125 [==============================] - 13s 104ms/step - loss: 0.2110 - accuracy: 0.9394 - val_loss: 0.7012 - val_accuracy: 0.8290\n",
+      "Epoch 89/200\n",
+      "125/125 [==============================] - 14s 111ms/step - loss: 0.2078 - accuracy: 0.9402 - val_loss: 0.7013 - val_accuracy: 0.8297\n",
+      "Epoch 90/200\n",
+      "125/125 [==============================] - 17s 137ms/step - loss: 0.2052 - accuracy: 0.9409 - val_loss: 0.7069 - val_accuracy: 0.8285\n",
+      "Epoch 91/200\n",
+      "125/125 [==============================] - 19s 150ms/step - loss: 0.2021 - accuracy: 0.9419 - val_loss: 0.7072 - val_accuracy: 0.8285\n",
+      "Epoch 92/200\n",
+      "125/125 [==============================] - 14s 116ms/step - loss: 0.1989 - accuracy: 0.9425 - val_loss: 0.7139 - val_accuracy: 0.8289\n",
+      "Epoch 93/200\n",
+      "125/125 [==============================] - 14s 113ms/step - loss: 0.1966 - accuracy: 0.9432 - val_loss: 0.7173 - val_accuracy: 0.8288\n",
+      "Epoch 94/200\n",
+      "125/125 [==============================] - 21s 167ms/step - loss: 0.1940 - accuracy: 0.9440 - val_loss: 0.7222 - val_accuracy: 0.8288\n",
+      "Epoch 95/200\n",
+      "125/125 [==============================] - 18s 146ms/step - loss: 0.1911 - accuracy: 0.9449 - val_loss: 0.7298 - val_accuracy: 0.8283\n",
+      "Epoch 96/200\n",
+      "125/125 [==============================] - 18s 146ms/step - loss: 0.1882 - accuracy: 0.9455 - val_loss: 0.7287 - val_accuracy: 0.8279\n",
+      "Epoch 97/200\n",
+      "125/125 [==============================] - 18s 146ms/step - loss: 0.1856 - accuracy: 0.9463 - val_loss: 0.7367 - val_accuracy: 0.8277\n",
+      "Epoch 98/200\n",
+      "125/125 [==============================] - 19s 149ms/step - loss: 0.1827 - accuracy: 0.9476 - val_loss: 0.7370 - val_accuracy: 0.8283\n",
+      "Epoch 99/200\n",
+      "125/125 [==============================] - 19s 149ms/step - loss: 0.1802 - accuracy: 0.9484 - val_loss: 0.7434 - val_accuracy: 0.8282\n",
+      "Epoch 100/200\n",
+      "125/125 [==============================] - 18s 144ms/step - loss: 0.1778 - accuracy: 0.9486 - val_loss: 0.7473 - val_accuracy: 0.8274\n",
+      "Epoch 101/200\n",
+      "125/125 [==============================] - 18s 148ms/step - loss: 0.1751 - accuracy: 0.9497 - val_loss: 0.7547 - val_accuracy: 0.8266\n",
+      "Epoch 102/200\n",
+      "125/125 [==============================] - 18s 140ms/step - loss: 0.1723 - accuracy: 0.9507 - val_loss: 0.7575 - val_accuracy: 0.8266\n",
+      "Epoch 103/200\n",
+      "125/125 [==============================] - 17s 135ms/step - loss: 0.1701 - accuracy: 0.9511 - val_loss: 0.7621 - val_accuracy: 0.8269\n",
+      "Epoch 104/200\n",
+      "125/125 [==============================] - 18s 143ms/step - loss: 0.1677 - accuracy: 0.9519 - val_loss: 0.7708 - val_accuracy: 0.8263\n",
+      "Epoch 105/200\n",
+      "125/125 [==============================] - 18s 145ms/step - loss: 0.1659 - accuracy: 0.9522 - val_loss: 0.7669 - val_accuracy: 0.8277\n",
+      "Epoch 106/200\n",
+      "125/125 [==============================] - 20s 162ms/step - loss: 0.1628 - accuracy: 0.9533 - val_loss: 0.7766 - val_accuracy: 0.8267\n",
+      "Epoch 107/200\n",
+      "125/125 [==============================] - 18s 143ms/step - loss: 0.1606 - accuracy: 0.9538 - val_loss: 0.7791 - val_accuracy: 0.8275\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 108/200\n",
+      "125/125 [==============================] - 18s 145ms/step - loss: 0.1583 - accuracy: 0.9548 - val_loss: 0.7820 - val_accuracy: 0.8268\n",
+      "Epoch 109/200\n",
+      "125/125 [==============================] - 18s 143ms/step - loss: 0.1564 - accuracy: 0.9550 - val_loss: 0.7863 - val_accuracy: 0.8266\n",
+      "Epoch 110/200\n",
+      "125/125 [==============================] - 19s 149ms/step - loss: 0.1544 - accuracy: 0.9558 - val_loss: 0.7927 - val_accuracy: 0.8259\n",
+      "Epoch 111/200\n",
+      "125/125 [==============================] - 18s 146ms/step - loss: 0.1517 - accuracy: 0.9566 - val_loss: 0.7972 - val_accuracy: 0.8257\n",
+      "Epoch 112/200\n",
+      "125/125 [==============================] - 19s 150ms/step - loss: 0.1490 - accuracy: 0.9573 - val_loss: 0.8031 - val_accuracy: 0.8262\n",
+      "Epoch 113/200\n",
+      "125/125 [==============================] - 21s 172ms/step - loss: 0.1478 - accuracy: 0.9575 - val_loss: 0.8029 - val_accuracy: 0.8261\n",
+      "Epoch 114/200\n",
+      "125/125 [==============================] - 27s 215ms/step - loss: 0.1459 - accuracy: 0.9580 - val_loss: 0.8135 - val_accuracy: 0.8256\n",
+      "Epoch 115/200\n",
+      "125/125 [==============================] - 19s 154ms/step - loss: 0.1437 - accuracy: 0.9585 - val_loss: 0.8143 - val_accuracy: 0.8267\n",
+      "Epoch 116/200\n",
+      "125/125 [==============================] - 14s 113ms/step - loss: 0.1415 - accuracy: 0.9591 - val_loss: 0.8153 - val_accuracy: 0.8260\n",
+      "Epoch 117/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.1394 - accuracy: 0.9601 - val_loss: 0.8186 - val_accuracy: 0.8257\n",
+      "Epoch 118/200\n",
+      "125/125 [==============================] - 14s 108ms/step - loss: 0.1377 - accuracy: 0.9602 - val_loss: 0.8334 - val_accuracy: 0.8253\n",
+      "Epoch 119/200\n",
+      "125/125 [==============================] - 14s 109ms/step - loss: 0.1355 - accuracy: 0.9612 - val_loss: 0.8374 - val_accuracy: 0.8251\n",
+      "Epoch 120/200\n",
+      "125/125 [==============================] - 14s 110ms/step - loss: 0.1335 - accuracy: 0.9619 - val_loss: 0.8370 - val_accuracy: 0.8250\n",
+      "Epoch 121/200\n",
+      "125/125 [==============================] - 16s 132ms/step - loss: 0.1317 - accuracy: 0.9620 - val_loss: 0.8438 - val_accuracy: 0.8245\n",
+      "Epoch 122/200\n",
+      "125/125 [==============================] - 17s 135ms/step - loss: 0.1302 - accuracy: 0.9626 - val_loss: 0.8457 - val_accuracy: 0.8246\n",
+      "Epoch 123/200\n",
+      "125/125 [==============================] - 14s 116ms/step - loss: 0.1287 - accuracy: 0.9628 - val_loss: 0.8478 - val_accuracy: 0.8246\n",
+      "Epoch 124/200\n",
+      "125/125 [==============================] - 12s 100ms/step - loss: 0.1265 - accuracy: 0.9636 - val_loss: 0.8581 - val_accuracy: 0.8247\n",
+      "Epoch 125/200\n",
+      "125/125 [==============================] - 12s 97ms/step - loss: 0.1247 - accuracy: 0.9642 - val_loss: 0.8579 - val_accuracy: 0.8244\n",
+      "Epoch 126/200\n",
+      "125/125 [==============================] - 14s 112ms/step - loss: 0.1237 - accuracy: 0.9643 - val_loss: 0.8601 - val_accuracy: 0.8256\n",
+      "Epoch 127/200\n",
+      "125/125 [==============================] - 17s 136ms/step - loss: 0.1226 - accuracy: 0.9646 - val_loss: 0.8719 - val_accuracy: 0.8234\n",
+      "Epoch 128/200\n",
+      "125/125 [==============================] - 19s 149ms/step - loss: 0.1203 - accuracy: 0.9655 - val_loss: 0.8740 - val_accuracy: 0.8233\n",
+      "Epoch 129/200\n",
+      "125/125 [==============================] - 18s 142ms/step - loss: 0.1183 - accuracy: 0.9662 - val_loss: 0.8812 - val_accuracy: 0.8233\n",
+      "Epoch 130/200\n",
+      "125/125 [==============================] - 15s 122ms/step - loss: 0.1162 - accuracy: 0.9669 - val_loss: 0.8817 - val_accuracy: 0.8233\n",
+      "Epoch 131/200\n",
+      "125/125 [==============================] - 16s 131ms/step - loss: 0.1148 - accuracy: 0.9672 - val_loss: 0.8795 - val_accuracy: 0.8242\n",
+      "Epoch 132/200\n",
+      "125/125 [==============================] - 17s 139ms/step - loss: 0.1132 - accuracy: 0.9677 - val_loss: 0.8941 - val_accuracy: 0.8234\n",
+      "Epoch 133/200\n",
+      "125/125 [==============================] - 14s 116ms/step - loss: 0.1118 - accuracy: 0.9679 - val_loss: 0.8929 - val_accuracy: 0.8233\n",
+      "Epoch 134/200\n",
+      "125/125 [==============================] - 16s 126ms/step - loss: 0.1101 - accuracy: 0.9686 - val_loss: 0.8995 - val_accuracy: 0.8236\n",
+      "Epoch 135/200\n",
+      "125/125 [==============================] - 16s 126ms/step - loss: 0.1089 - accuracy: 0.9688 - val_loss: 0.9013 - val_accuracy: 0.8231\n",
+      "Epoch 136/200\n",
+      "125/125 [==============================] - 15s 116ms/step - loss: 0.1078 - accuracy: 0.9690 - val_loss: 0.9056 - val_accuracy: 0.8225\n",
+      "Epoch 137/200\n",
+      "125/125 [==============================] - 13s 106ms/step - loss: 0.1061 - accuracy: 0.9697 - val_loss: 0.9144 - val_accuracy: 0.8223\n",
+      "Epoch 138/200\n",
+      "125/125 [==============================] - 16s 128ms/step - loss: 0.1040 - accuracy: 0.9706 - val_loss: 0.9144 - val_accuracy: 0.8227\n",
+      "Epoch 139/200\n",
+      "125/125 [==============================] - 15s 120ms/step - loss: 0.1038 - accuracy: 0.9703 - val_loss: 0.9172 - val_accuracy: 0.8239\n",
+      "Epoch 140/200\n",
+      "125/125 [==============================] - 15s 117ms/step - loss: 0.1018 - accuracy: 0.9712 - val_loss: 0.9252 - val_accuracy: 0.8225\n",
+      "Epoch 141/200\n",
+      "125/125 [==============================] - 15s 116ms/step - loss: 0.1005 - accuracy: 0.9714 - val_loss: 0.9298 - val_accuracy: 0.8234\n",
+      "Epoch 142/200\n",
+      "125/125 [==============================] - 13s 105ms/step - loss: 0.0991 - accuracy: 0.9717 - val_loss: 0.9344 - val_accuracy: 0.8219\n",
+      "Epoch 143/200\n",
+      "125/125 [==============================] - 13s 102ms/step - loss: 0.0972 - accuracy: 0.9724 - val_loss: 0.9379 - val_accuracy: 0.8222\n",
+      "Epoch 144/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0964 - accuracy: 0.9727 - val_loss: 0.9478 - val_accuracy: 0.8226\n",
+      "Epoch 145/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0961 - accuracy: 0.9725 - val_loss: 0.9507 - val_accuracy: 0.8222\n",
+      "Epoch 146/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0942 - accuracy: 0.9731 - val_loss: 0.9532 - val_accuracy: 0.8219\n",
+      "Epoch 147/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0934 - accuracy: 0.9736 - val_loss: 0.9513 - val_accuracy: 0.8217\n",
+      "Epoch 148/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0917 - accuracy: 0.9740 - val_loss: 0.9572 - val_accuracy: 0.8225\n",
+      "Epoch 149/200\n",
+      "125/125 [==============================] - 13s 104ms/step - loss: 0.0900 - accuracy: 0.9744 - val_loss: 0.9587 - val_accuracy: 0.8223\n",
+      "Epoch 150/200\n",
+      "125/125 [==============================] - 13s 104ms/step - loss: 0.0892 - accuracy: 0.9749 - val_loss: 0.9657 - val_accuracy: 0.8225\n",
+      "Epoch 151/200\n",
+      "125/125 [==============================] - 13s 102ms/step - loss: 0.0876 - accuracy: 0.9750 - val_loss: 0.9735 - val_accuracy: 0.8203\n",
+      "Epoch 152/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0866 - accuracy: 0.9754 - val_loss: 0.9647 - val_accuracy: 0.8231\n",
+      "Epoch 153/200\n",
+      "125/125 [==============================] - 12s 100ms/step - loss: 0.0852 - accuracy: 0.9759 - val_loss: 0.9787 - val_accuracy: 0.8225\n",
+      "Epoch 154/200\n",
+      "125/125 [==============================] - 13s 100ms/step - loss: 0.0841 - accuracy: 0.9761 - val_loss: 0.9868 - val_accuracy: 0.8217\n",
+      "Epoch 155/200\n",
+      "125/125 [==============================] - 13s 100ms/step - loss: 0.0828 - accuracy: 0.9765 - val_loss: 0.9908 - val_accuracy: 0.8215\n",
+      "Epoch 156/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0820 - accuracy: 0.9769 - val_loss: 0.9904 - val_accuracy: 0.8228\n",
+      "Epoch 157/200\n",
+      "125/125 [==============================] - 13s 100ms/step - loss: 0.0813 - accuracy: 0.9769 - val_loss: 0.9992 - val_accuracy: 0.8212\n",
+      "Epoch 158/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0802 - accuracy: 0.9772 - val_loss: 1.0037 - val_accuracy: 0.8204\n",
+      "Epoch 159/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0792 - accuracy: 0.9776 - val_loss: 1.0000 - val_accuracy: 0.8209\n",
+      "Epoch 160/200\n",
+      "125/125 [==============================] - 13s 100ms/step - loss: 0.0789 - accuracy: 0.9777 - val_loss: 1.0039 - val_accuracy: 0.8212\n",
+      "Epoch 161/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0774 - accuracy: 0.9780 - val_loss: 1.0144 - val_accuracy: 0.8210\n",
+      "Epoch 162/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0767 - accuracy: 0.9782 - val_loss: 1.0230 - val_accuracy: 0.8202\n",
+      "Epoch 163/200\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "125/125 [==============================] - 12s 98ms/step - loss: 0.0751 - accuracy: 0.9787 - val_loss: 1.0155 - val_accuracy: 0.8201\n",
+      "Epoch 164/200\n",
+      "125/125 [==============================] - 12s 97ms/step - loss: 0.0749 - accuracy: 0.9786 - val_loss: 1.0207 - val_accuracy: 0.8214\n",
+      "Epoch 165/200\n",
+      "125/125 [==============================] - 13s 100ms/step - loss: 0.0734 - accuracy: 0.9792 - val_loss: 1.0302 - val_accuracy: 0.8214\n",
+      "Epoch 166/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0725 - accuracy: 0.9796 - val_loss: 1.0320 - val_accuracy: 0.8208\n",
+      "Epoch 167/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0716 - accuracy: 0.9797 - val_loss: 1.0349 - val_accuracy: 0.8209\n",
+      "Epoch 168/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0706 - accuracy: 0.9799 - val_loss: 1.0365 - val_accuracy: 0.8204\n",
+      "Epoch 169/200\n",
+      "125/125 [==============================] - 12s 100ms/step - loss: 0.0694 - accuracy: 0.9803 - val_loss: 1.0432 - val_accuracy: 0.8215\n",
+      "Epoch 170/200\n",
+      "125/125 [==============================] - 12s 98ms/step - loss: 0.0692 - accuracy: 0.9805 - val_loss: 1.0522 - val_accuracy: 0.8202\n",
+      "Epoch 171/200\n",
+      "125/125 [==============================] - 12s 100ms/step - loss: 0.0683 - accuracy: 0.9806 - val_loss: 1.0662 - val_accuracy: 0.8193\n",
+      "Epoch 172/200\n",
+      "125/125 [==============================] - 13s 102ms/step - loss: 0.0676 - accuracy: 0.9808 - val_loss: 1.0561 - val_accuracy: 0.8206\n",
+      "Epoch 173/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0670 - accuracy: 0.9808 - val_loss: 1.0581 - val_accuracy: 0.8207\n",
+      "Epoch 174/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0654 - accuracy: 0.9816 - val_loss: 1.0665 - val_accuracy: 0.8190\n",
+      "Epoch 175/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0643 - accuracy: 0.9816 - val_loss: 1.0628 - val_accuracy: 0.8205\n",
+      "Epoch 176/200\n",
+      "125/125 [==============================] - 12s 100ms/step - loss: 0.0638 - accuracy: 0.9819 - val_loss: 1.0731 - val_accuracy: 0.8192\n",
+      "Epoch 177/200\n",
+      "125/125 [==============================] - 12s 98ms/step - loss: 0.0630 - accuracy: 0.9822 - val_loss: 1.0767 - val_accuracy: 0.8195\n",
+      "Epoch 178/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0631 - accuracy: 0.9821 - val_loss: 1.0787 - val_accuracy: 0.8204\n",
+      "Epoch 179/200\n",
+      "125/125 [==============================] - 12s 100ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 1.0760 - val_accuracy: 0.8201\n",
+      "Epoch 180/200\n",
+      "125/125 [==============================] - 12s 100ms/step - loss: 0.0610 - accuracy: 0.9826 - val_loss: 1.0882 - val_accuracy: 0.8198\n",
+      "Epoch 181/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0601 - accuracy: 0.9832 - val_loss: 1.0883 - val_accuracy: 0.8196\n",
+      "Epoch 182/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0596 - accuracy: 0.9830 - val_loss: 1.0883 - val_accuracy: 0.8208\n",
+      "Epoch 183/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0589 - accuracy: 0.9834 - val_loss: 1.0956 - val_accuracy: 0.8199\n",
+      "Epoch 184/200\n",
+      "125/125 [==============================] - 13s 102ms/step - loss: 0.0582 - accuracy: 0.9836 - val_loss: 1.0977 - val_accuracy: 0.8194\n",
+      "Epoch 185/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0582 - accuracy: 0.9834 - val_loss: 1.0947 - val_accuracy: 0.8197\n",
+      "Epoch 186/200\n",
+      "125/125 [==============================] - 12s 99ms/step - loss: 0.0565 - accuracy: 0.9840 - val_loss: 1.1029 - val_accuracy: 0.8200\n",
+      "Epoch 187/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0560 - accuracy: 0.9842 - val_loss: 1.1064 - val_accuracy: 0.8202\n",
+      "Epoch 188/200\n",
+      "125/125 [==============================] - 13s 100ms/step - loss: 0.0552 - accuracy: 0.9844 - val_loss: 1.1135 - val_accuracy: 0.8193\n",
+      "Epoch 189/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0551 - accuracy: 0.9844 - val_loss: 1.1150 - val_accuracy: 0.8194\n",
+      "Epoch 190/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0538 - accuracy: 0.9847 - val_loss: 1.1186 - val_accuracy: 0.8200\n",
+      "Epoch 191/200\n",
+      "125/125 [==============================] - 13s 103ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 1.1152 - val_accuracy: 0.8191\n",
+      "Epoch 192/200\n",
+      "125/125 [==============================] - 12s 97ms/step - loss: 0.0530 - accuracy: 0.9850 - val_loss: 1.1191 - val_accuracy: 0.8188\n",
+      "Epoch 193/200\n",
+      "125/125 [==============================] - 12s 98ms/step - loss: 0.0530 - accuracy: 0.9849 - val_loss: 1.1290 - val_accuracy: 0.8188\n",
+      "Epoch 194/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0525 - accuracy: 0.9850 - val_loss: 1.1289 - val_accuracy: 0.8190\n",
+      "Epoch 195/200\n",
+      "125/125 [==============================] - 12s 97ms/step - loss: 0.0511 - accuracy: 0.9856 - val_loss: 1.1290 - val_accuracy: 0.8192\n",
+      "Epoch 196/200\n",
+      "125/125 [==============================] - 12s 98ms/step - loss: 0.0507 - accuracy: 0.9856 - val_loss: 1.1378 - val_accuracy: 0.8190\n",
+      "Epoch 197/200\n",
+      "125/125 [==============================] - 13s 101ms/step - loss: 0.0505 - accuracy: 0.9856 - val_loss: 1.1372 - val_accuracy: 0.8192\n",
+      "Epoch 198/200\n",
+      "125/125 [==============================] - 12s 97ms/step - loss: 0.0498 - accuracy: 0.9858 - val_loss: 1.1386 - val_accuracy: 0.8188\n",
+      "Epoch 199/200\n",
+      "125/125 [==============================] - 12s 98ms/step - loss: 0.0484 - accuracy: 0.9864 - val_loss: 1.1423 - val_accuracy: 0.8189\n",
+      "Epoch 200/200\n",
+      "125/125 [==============================] - 12s 100ms/step - loss: 0.0484 - accuracy: 0.9864 - val_loss: 1.1581 - val_accuracy: 0.8176\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(\n",
+    "    optimizer=\"rmsprop\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
+    ")\n",
+    "model.fit(\n",
+    "    [encoder_input_data, decoder_input_data],\n",
+    "    decoder_target_data,\n",
+    "    batch_size=batch_size,\n",
+    "    epochs=epochs,\n",
+    "    validation_split=0.2,\n",
+    ")\n",
+    "\n",
+    "model.save(\"s2s_model.keras\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4zhoiYDkISvy"
+   },
+   "source": [
+    "### Run interference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "cgNB5j6qIWdo"
+   },
+   "outputs": [],
+   "source": [
+    "encoder_inputs = model.input[0]  # input_1\n",
+    "encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output  # lstm_1\n",
+    "encoder_states = [state_h_enc, state_c_enc]\n",
+    "encoder_model = keras.Model(encoder_inputs, encoder_states)\n",
+    "\n",
+    "decoder_inputs = model.input[1]  # input_2\n",
+    "decoder_state_input_h = keras.Input(shape=(latent_dim,))\n",
+    "decoder_state_input_c = keras.Input(shape=(latent_dim,))\n",
+    "decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n",
+    "decoder_lstm = model.layers[3]\n",
+    "decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(\n",
+    "    decoder_inputs, initial_state=decoder_states_inputs\n",
+    ")\n",
+    "decoder_states = [state_h_dec, state_c_dec]\n",
+    "decoder_dense = model.layers[4]\n",
+    "decoder_outputs = decoder_dense(decoder_outputs)\n",
+    "decoder_model = keras.Model(\n",
+    "    [decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states\n",
+    ")\n",
+    "\n",
+    "# Reverse-lookup token index to decode sequences back to\n",
+    "# something readable.\n",
+    "reverse_input_char_index = dict((i, char) for char, i in input_token_index.items())\n",
+    "reverse_target_char_index = dict((i, char) for char, i in target_token_index.items())\n",
+    "\n",
+    "\n",
+    "def decode_sequence(input_seq):\n",
+    "    # Encode the input as state vectors.\n",
+    "    states_value = encoder_model.predict(input_seq, verbose=0)\n",
+    "\n",
+    "    # Generate empty target sequence of length 1.\n",
+    "    target_seq = np.zeros((1, 1, num_decoder_tokens))\n",
+    "    # Populate the first character of target sequence with the start character.\n",
+    "    target_seq[0, 0, target_token_index[\"\\t\"]] = 1.0\n",
+    "\n",
+    "    # Sampling loop for a batch of sequences\n",
+    "    # (to simplify, here we assume a batch of size 1).\n",
+    "    stop_condition = False\n",
+    "    decoded_sentence = \"\"\n",
+    "    while not stop_condition:\n",
+    "        output_tokens, h, c = decoder_model.predict(\n",
+    "            [target_seq] + states_value, verbose=0\n",
+    "        )\n",
+    "\n",
+    "        # Sample a token\n",
+    "        sampled_token_index = np.argmax(output_tokens[0, -1, :])\n",
+    "        sampled_char = reverse_target_char_index[sampled_token_index]\n",
+    "        decoded_sentence += sampled_char\n",
+    "\n",
+    "        # Exit condition: either hit max length\n",
+    "        # or find stop character.\n",
+    "        if sampled_char == \"\\n\" or len(decoded_sentence) > max_decoder_seq_length:\n",
+    "            stop_condition = True\n",
+    "\n",
+    "        # Update the target sequence (of length 1).\n",
+    "        target_seq = np.zeros((1, 1, num_decoder_tokens))\n",
+    "        target_seq[0, 0, sampled_token_index] = 1.0\n",
+    "\n",
+    "        # Update states\n",
+    "        states_value = [h, c]\n",
+    "    return decoded_sentence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1GhhZn_uIiVJ",
+    "outputId": "6930ea66-0a44-4975-b613-00464dc31179"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-\n",
+      "Input sentence: Go.\n",
+      "Decoded sentence: Idź.\n",
+      "\n",
+      "-\n",
+      "Input sentence: Hi.\n",
+      "Decoded sentence: Cześć.\n",
+      "\n",
+      "-\n",
+      "Input sentence: Run!\n",
+      "Decoded sentence: Uciekaj!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Run.\n",
+      "Decoded sentence: Uciekaj.\n",
+      "\n",
+      "-\n",
+      "Input sentence: Run.\n",
+      "Decoded sentence: Uciekaj.\n",
+      "\n",
+      "-\n",
+      "Input sentence: Who?\n",
+      "Decoded sentence: Kto?\n",
+      "\n",
+      "-\n",
+      "Input sentence: Wow!\n",
+      "Decoded sentence: O, dziama?\n",
+      "\n",
+      "-\n",
+      "Input sentence: Wow!\n",
+      "Decoded sentence: O, dziama?\n",
+      "\n",
+      "-\n",
+      "Input sentence: Duck!\n",
+      "Decoded sentence: Unik!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Fire!\n",
+      "Decoded sentence: Strzelaj!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Fire!\n",
+      "Decoded sentence: Strzelaj!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Fire!\n",
+      "Decoded sentence: Strzelaj!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Help!\n",
+      "Decoded sentence: Pomocy!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Hide.\n",
+      "Decoded sentence: Schowaj się.\n",
+      "\n",
+      "-\n",
+      "Input sentence: Jump!\n",
+      "Decoded sentence: Skacz!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Jump.\n",
+      "Decoded sentence: Skok.\n",
+      "\n",
+      "-\n",
+      "Input sentence: Stay.\n",
+      "Decoded sentence: Zostań.\n",
+      "\n",
+      "-\n",
+      "Input sentence: Stop!\n",
+      "Decoded sentence: Zatrzymaj się!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Stop!\n",
+      "Decoded sentence: Zatrzymaj się!\n",
+      "\n",
+      "-\n",
+      "Input sentence: Wait!\n",
+      "Decoded sentence: Zaczekajcie!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for seq_index in range(20):\n",
+    "    # Take one sequence (part of the training set)\n",
+    "    # for trying out decoding.\n",
+    "    input_seq = encoder_input_data[seq_index : seq_index + 1]\n",
+    "    decoded_sentence = decode_sequence(input_seq)\n",
+    "    print(\"-\")\n",
+    "    print(\"Input sentence:\", input_texts[seq_index])\n",
+    "    print(\"Decoded sentence:\", decoded_sentence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nLylna2WI5VY"
+   },
+   "source": [
+    "### Bleu evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "background_save": true,
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YOYWSVlpJe_M",
+    "outputId": "07ca0d9f-1729-42f5-939d-a9616fc127cc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5965545320892247\n",
+      "0.5981615012338071\n",
+      "0.5930356751147987\n",
+      "0.5942200927187505\n",
+      "0.5951351487924014\n",
+      "0.5919465093543784\n",
+      "0.590832790904886\n",
+      "0.5919919489002385\n",
+      "0.5452219773056319\n",
+      "BLEU score: 0.5095920718253371\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nltk.translate.bleu_score as bleu\n",
+    "smoothing_function = bleu.SmoothingFunction().method4\n",
+    "bleu_scores = 0\n",
+    "for seq_index in range(len(encoder_input_data)):\n",
+    "    input_seq = encoder_input_data[seq_index : seq_index + 1]\n",
+    "    decoded_sentence = decode_sequence(input_seq)\n",
+    "    reference = [list(target_texts[seq_index])]\n",
+    "    candidate = list(decoded_sentence.strip())\n",
+    "    bleu_score = bleu.sentence_bleu(reference, candidate, smoothing_function=smoothing_function)\n",
+    "    bleu_scores += bleu_score\n",
+    "    if seq_index % (len(encoder_input_data)//10) == 0 and seq_index != 0:\n",
+    "        print(bleu_scores/seq_index)\n",
+    "bleu_final = bleu_scores/len(encoder_input_data)\n",
+    "print(\"BLEU score:\", bleu_final)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/5. Transformer/out.tsv b/5. Transformer/out.tsv
new file mode 100644
index 0000000..ad05e05
--- /dev/null
+++ b/5. Transformer/out.tsv	
@@ -0,0 +1,230 @@
+O O O O O O O O O O O O O O O O O O B-LOC I-LOC I-LOC O O B-LOC O O O O O B-MISC I-MISC O O O O O O O B-LOC O O O B-MISC O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O B-LOC O O B-LOC O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC O O O O O B-MISC I-MISC O O O O O O O O O O O B-MISC I-MISC O O O O O B-LOC O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O B-MISC O O O B-PER O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-LOC O O O O O B-MISC O O O O O O O O O O O B-MISC O O O O O O O O O O O O O B-LOC O B-PER O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O B-MISC I-MISC O O O O O O O O O B-ORG O O O O O O O O O O O B-LOC O B-LOC O B-LOC I-LOC O O B-LOC O O O O B-MISC O O O B-LOC O O O O O O O O O O O O O
+O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O B-MISC I-MISC O O O O O O O O O O B-LOC O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC I-MISC O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER O B-PER I-PER O B-PER O O B-PER I-PER O B-PER O O B-PER I-PER O B-PER I-PER O B-PER I-PER O B-PER O O B-PER O O B-PER O O B-PER I-PER O B-PER I-PER O B-PER O O O B-PER O O B-PER O O B-PER O O B-PER O O B-PER O O B-PER O O B-PER O O O
+O O O O O O O O O O O O O B-LOC I-LOC I-LOC O O O O O O O O O O O B-LOC O O O B-MISC I-MISC O O B-LOC O O O O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O B-PER I-PER O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O B-PER O O O O B-PER I-PER O O O B-PER I-PER O O O B-PER O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O B-LOC O O O O O B-MISC I-MISC I-MISC O O O O O O O O O O O O O O B-LOC O O O O O B-PER I-PER O B-LOC O O O O B-PER I-PER O B-LOC O O O O O O O B-LOC O O O O O B-PER O B-LOC O O O O O O O B-LOC O O O O B-PER I-PER O O O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER I-PER O O O O O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O B-PER I-PER O O O O O O B-PER I-PER O O O O O O B-PER I-PER O O O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O
+O O O O O O O O O O O B-LOC I-LOC I-LOC O O O O B-MISC I-MISC O O O O O O O O B-LOC O B-LOC O O O O O O O O O B-LOC O B-PER I-PER O O O O O O O O O B-LOC O O O O O O O O O O B-LOC O B-LOC O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O B-LOC O O O O O O O O B-LOC O O O O O O O O B-LOC O O O O O O O O
+O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O B-LOC O B-LOC I-LOC O O O O O B-LOC O O O O O O O O O O O O O O O B-PER O O O O O O O O B-PER O O O B-PER O O O O O O O O O O O O O B-PER O O O O O B-PER O O O B-PER O O O B-PER O O O O O O O B-PER O O O O O B-PER O O O B-PER O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O B-PER I-PER O O O O O B-PER O O B-PER O O O O O O O O O O O O O B-LOC I-LOC O O O B-PER O O B-PER O O O O O O O O B-PER O O O O O O O O B-PER O O O O O B-PER O O O O O O O B-PER O O O O O O O O B-PER O O B-PER O O O B-PER O O O O O O O O O O O O O B-PER O O B-PER O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O B-PER I-PER O O O O B-PER O O O O O B-LOC O O O O O O O O O O O O O O B-LOC O O
+O O O O O O O O O O O O O O O O B-MISC I-MISC I-MISC I-MISC I-MISC O O O O O O O B-ORG O B-ORG O O
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+O O O O O O O O O O O B-LOC O O O O O O O O O B-MISC I-MISC I-MISC O O O O O O O O B-LOC O O B-PER I-PER O B-LOC O O O O O O O B-LOC O O B-PER I-PER O O O O O O O
+O O O O O O O O O O O O O B-LOC O O B-ORG O O O B-PER I-PER O O O O B-LOC O O B-MISC I-MISC O O O O O O O B-PER I-PER O O O O O O O O O O O O O O B-PER I-PER O O B-PER O O O O O O O O B-ORG I-ORG O O O O B-MISC I-MISC I-MISC I-MISC O O O O O O B-LOC O B-MISC O O O O O O O O O O O O O B-LOC O O O O B-LOC O O O O O O O O O B-MISC O B-LOC O O O O O O O O O O B-PER O O B-ORG O B-LOC O O O O O O O O O O O O B-PER O B-ORG O O B-PER I-PER O B-ORG O O B-PER I-PER O O B-ORG O O B-PER I-PER O B-ORG O O B-PER I-PER O B-ORG O O B-PER O O B-ORG I-ORG O B-LOC O O B-PER O O B-ORG O B-LOC O O B-PER O O B-ORG I-ORG O B-LOC O O O O O B-PER O O B-ORG O O B-PER O O B-ORG O O B-PER I-PER O B-ORG O O B-PER I-PER O O O B-LOC O O B-PER O O O O B-LOC O O O O O B-PER O O B-ORG O O O O O O O O O B-PER O O B-ORG O O B-PER I-PER O O O B-LOC O O B-PER I-PER O B-ORG I-ORG O B-LOC O O O
+O O O O O O O O O O O O B-ORG I-ORG O O O O O B-MISC O O O O B-ORG I-ORG O B-ORG O O O O O O B-ORG I-ORG O O B-ORG O O O O O O O O O B-ORG I-ORG O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG I-ORG I-ORG O O B-ORG I-ORG O O O B-ORG I-ORG I-ORG O O O B-ORG I-ORG O O O O O O O B-ORG O O B-ORG I-ORG I-ORG O O B-ORG O O O B-ORG I-ORG O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O O O O B-PER O O O O B-PER O B-PER O O O B-PER O O B-PER O O B-PER I-PER O B-PER O B-PER O O B-PER O O B-PER I-PER O O O O O O O O O O B-PER I-PER O O O O O B-PER O O B-PER I-PER O O O O B-ORG O O B-PER O B-PER O O B-PER O O O O O O B-PER O O O O O O B-PER O O B-PER O O O O O O O B-PER O O O O B-PER I-PER O
+O O O O O O O O O O O O O B-MISC O O O O O O O O O B-LOC I-LOC O O O O O O O O O B-ORG O O O O B-LOC O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O O O O B-LOC O B-PER I-PER O O O B-ORG I-ORG O B-PER I-PER O O O O O B-MISC I-MISC O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O B-PER I-PER O O O
+O O B-ORG O O O O O O O O O O B-ORG O O O O O O O O O O B-ORG I-ORG B-PER O O O O O O O O O O O O O O B-ORG O B-PER I-PER O O O O O O O B-MISC O B-ORG I-ORG I-ORG O O O O O O O O O O O O O O O B-PER O O O O O O O B-ORG O O O O O O O B-MISC I-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O B-ORG O B-MISC O O O O O O O O O O O O O O O O O O O O O O B-MISC I-MISC I-MISC I-MISC O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O B-PER O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O B-LOC O O O O O O O O B-MISC O O O B-MISC I-MISC O O
+O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O
+O O O O O O O O O O O O O B-MISC I-MISC I-MISC O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O B-ORG O O O O O O B-MISC O O O O O O O O O O O B-MISC B-PER O O O O B-ORG O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O B-MISC O B-PER I-PER O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O B-MISC O O O O O O O O O O B-LOC O O O O O O B-MISC O B-MISC I-MISC O O O O O O O O O O O B-MISC O B-PER I-PER O O B-PER I-PER O O B-ORG O O O O O O O B-MISC B-PER O O B-PER I-PER O B-PER O O O O O B-ORG I-ORG O O O O O O O O O O O B-ORG O O O O O O O O B-MISC I-MISC I-MISC O O B-MISC O O O O O O O O O O O O O B-PER I-PER O O O O O B-ORG O B-PER O O O B-MISC O O O O O O O O O B-ORG O O O O B-MISC O B-PER O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O B-ORG O O O O O O B-MISC O B-PER O O O B-ORG O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O B-ORG O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O B-MISC O B-PER I-PER O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O O O O O O O O O O O O B-MISC O O O O B-LOC O O O B-ORG O B-LOC O O O O O O O O O O O O B-PER I-PER O O B-PER I-PER O O B-PER I-PER O O O O B-PER I-PER O O B-PER I-PER O O B-PER I-PER O O O O O O B-LOC O O O B-LOC O B-LOC O O O B-ORG O B-LOC O O O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O O O B-LOC O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC I-MISC O O O O O B-MISC O O O B-MISC O O O O B-PER I-PER O O O O O O O O O O B-PER I-PER O B-LOC O O B-PER O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O B-LOC O O O O O O O B-MISC I-MISC O O O O O O B-PER I-PER O B-LOC O O B-PER I-PER O B-LOC O O O O B-PER I-PER O B-LOC O O B-PER I-PER O B-LOC O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O B-LOC I-LOC O O O O O O O O O O O O O O O O B-LOC I-LOC O O O O O O O O O O O O O O O O O O O O O O O B-PER O O B-ORG I-ORG I-ORG O B-ORG O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O
+O O O O O O O O O O O O O O O B-MISC O O O O O O O O O B-PER I-PER O O O O B-MISC O O O O B-LOC I-LOC O O O O O O O O O O O B-PER O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O B-LOC I-LOC O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O B-PER I-PER O O O O O O O O O O O O B-MISC O O B-LOC O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER O O B-MISC O O O O O O O O O O B-LOC I-LOC O O B-MISC O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O B-ORG I-ORG O O B-MISC O O O O O O O O O O O O O B-ORG O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O B-LOC O O O O B-PER I-PER O O O O O O O O O O O O O B-ORG O O O O O O O O O O B-MISC O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O B-ORG I-ORG O O O O O O B-ORG I-ORG O O O O O O O O O O O O O B-ORG I-ORG I-ORG O O B-ORG I-ORG I-ORG O B-ORG O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-ORG O O O O O O O O O O B-LOC O O O O O O O O O O O O O B-LOC O O O O O B-LOC O B-LOC O B-LOC O O O O O O O O B-LOC O O O O
+O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG I-ORG I-ORG O O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O B-MISC O O B-LOC O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O B-PER O O O O O O O O B-MISC O O B-PER I-PER O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG B-MISC O O O O O O O O B-LOC O O O
+O O O O O O O O O O O O O O O B-LOC O O O O B-MISC O O O O O O O O O B-MISC I-MISC O O O O B-MISC O O O O O O O O O O O O B-MISC I-MISC I-MISC O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O B-MISC O O O O O O O O O O B-LOC O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O B-LOC O O O O O B-MISC O O O O O O O O O B-MISC O O O O O O O O B-LOC O O O O O O O B-MISC O O O O O O B-MISC I-MISC O O O O B-LOC O O O B-MISC O O O O O O O O O O O O O O O O O O B-LOC O B-LOC O O O B-MISC O O O O O O O O O O O B-MISC I-MISC I-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC O O B-MISC O O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O B-MISC O O O O O O O O O O O O O B-MISC O O O O O O B-MISC O O O O O O O O O O O O O O B-MISC O O O O O O O B-MISC O O O O O B-MISC O O O O O O O O O O
+O O O O O O O O O B-PER O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O B-LOC O O O O O O B-LOC O B-LOC O O B-PER I-PER O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC I-MISC B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O B-LOC O O O O O B-ORG I-ORG O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC B-LOC O O O O O O O O B-MISC O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O
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+O O O O O O B-LOC O O O O O B-ORG I-ORG O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O B-LOC O O O O
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+O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG I-ORG I-ORG O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
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+O O B-MISC O O O O O O O B-LOC O O B-MISC O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O B-LOC O O O O O O O O O O O B-LOC I-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O B-MISC O O B-ORG O O O O O B-LOC O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O
+O O O O O O O O O O O O O B-LOC O O O O O O O O O O O B-MISC I-MISC O B-MISC O O O O O O O O O O O O O O B-LOC O O B-MISC I-MISC O O O O O O O O O O O O O O B-MISC O O O O B-MISC I-MISC O O O O O O O O O B-MISC I-MISC O O
+O O O O O O O O O O O O B-LOC O O O O O O O O O O B-MISC I-MISC O B-MISC O O O O O O O B-LOC O O O O O O O O O O B-LOC O O O O B-PER I-PER O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O O B-PER O B-LOC O O O O O B-PER O B-LOC O O O O B-PER I-PER O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O B-PER O O O O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER O O O O O O O B-PER O O O O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O O O O O O O
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+O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O B-MISC I-MISC O O B-LOC I-LOC I-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC O B-MISC I-MISC O O O O O O O O O O O O O O O O B-PER I-PER O B-PER I-PER O O O O O O O O O B-PER I-PER O B-PER I-PER O O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O B-PER I-PER O B-PER I-PER O O O O O O O O O O O O
+O O O O O O O O O O B-LOC O O O O O O O O O O O B-MISC I-MISC O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O B-LOC O O O O O O O O B-LOC O O O O O O O O O O O O O B-MISC I-MISC O O B-MISC I-MISC O O O O O O O O O O O O B-MISC I-MISC O O O
+O O O O O O O O O O B-LOC O O O O B-MISC I-MISC O O O O O O O O O O O O O O O O B-LOC O O O O O O O O B-LOC O O O O B-PER I-PER O B-LOC O O O O O O O B-LOC O O O O B-PER I-PER O B-LOC O O O O B-PER O O B-LOC O O O O B-PER I-PER O B-LOC O O O O O O O B-LOC O O O O O B-PER I-PER O B-LOC O O O O B-PER I-PER O O O O O O O O O B-LOC O O O O B-PER I-PER O B-LOC O O O O O B-PER O O O O O O O B-PER O B-LOC O O O O B-PER I-PER O B-LOC O O O O O B-PER O B-LOC O O O O O O O B-LOC O O O O B-PER I-PER O B-LOC O O O O B-PER I-PER I-PER O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O B-MISC I-MISC O O O O O O O O O B-MISC O O O O O O O B-LOC O O O O O O O O B-LOC O O O O O O O B-LOC O O O O B-PER I-PER O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER O O O O O O O O B-LOC O O O O B-PER O O B-LOC I-LOC O O O O O O O B-LOC O O O O B-PER I-PER O B-LOC O O O O O B-PER O B-LOC O O O O B-PER I-PER O B-LOC O O O O B-MISC O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O O O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER O O B-LOC O O O O B-PER O O B-LOC O O O O O O O B-LOC O O O O B-PER I-PER O B-LOC O O O O O O O B-LOC O O O O O B-PER O B-LOC O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
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+O O O O O O O O O B-LOC O O O O O O O B-MISC I-MISC I-MISC O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O B-LOC O O O O O O O B-PER O O O B-LOC O O O O O O O B-PER I-PER O B-LOC O O O O O O O O B-PER O B-LOC O O O O O O B-MISC I-MISC O O O B-PER O B-LOC O O O O O B-PER I-PER O B-LOC O O O O O O O O O O O O O O B-PER I-PER O B-LOC O O O O O O O O O O O O O O O O O O B-PER O O O B-LOC O O O O O O O B-PER I-PER O B-LOC O O O O O O O B-PER I-PER O B-LOC O O O O O O B-MISC I-MISC O O O O O O O O O O O O O O
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+O O B-LOC B-ORG O O O O O O O B-LOC O O B-PER I-PER O B-LOC B-ORG O O O O O O B-MISC I-MISC I-MISC O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O B-PER I-PER O O B-MISC I-MISC O O O O O O O O O B-MISC O O B-LOC O O O O B-PER I-PER O O B-PER I-PER O O O O B-MISC I-MISC O O O O O O O O O O O O B-MISC O O O B-MISC O O O O O O O O B-LOC O B-LOC O O B-LOC I-LOC O B-LOC O O O O O O B-MISC I-MISC O O O O O O O O O O O B-LOC O O B-PER I-PER O O B-PER O O O O O O O O O O
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+O O O O O O O O O O O O B-LOC O O B-LOC I-LOC O O O B-LOC O O O O O B-LOC O O O B-LOC O O B-PER I-PER O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O B-PER O O O B-MISC I-MISC O O O O O O B-PER I-PER O O O O O O O B-LOC O O O O O O O O O O O B-LOC O O O O O B-PER I-PER O B-LOC O O O O O O O B-PER I-PER O B-ORG I-ORG O O O B-LOC I-LOC O O B-MISC I-MISC I-MISC I-MISC O O O O O O O O O O O O O O O O O O O O O B-LOC I-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O
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+O O O O O O O O O O O B-LOC O O O O O O O B-MISC I-MISC O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O B-PER O O B-LOC O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-LOC O O O O O O O O O O O O B-LOC O O O O O O O O O B-MISC I-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O B-LOC O O O
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+O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O B-ORG O O B-PER O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O B-PER O O B-PER O O B-ORG O O B-PER O O O O O O O O O B-ORG O O B-PER O O B-PER O O O O O O O O O B-PER O O B-PER O O O O O O O O O O O O O
+O O O O O O O O O O O O O B-PER I-PER O O O O O O O O B-LOC O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O B-PER I-PER O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG I-ORG O O B-MISC I-MISC O O O O O O O O B-PER I-PER O O O O O O O B-LOC O O O O O O O O O O O O B-LOC O O O O O O B-PER I-PER O O O O O O O O O O O
+O O O O O O O O O O O O O B-MISC O O O O O O O O B-ORG O O B-PER O O O O O B-ORG O O O O O B-PER O O O O O O O O O O O O O B-ORG O O O O O O O O B-ORG O O B-PER O O O O O O O O O O O O O B-ORG O O O O O B-ORG O O O O O O O O O O O O O O O B-ORG O O B-PER O O B-ORG O O B-PER O O O O O O O O O B-ORG O B-ORG I-ORG O O O O O O O O O O O O B-ORG O B-ORG O O O O O B-ORG O B-ORG I-ORG O O B-PER O O O O O O O O O B-ORG O O B-PER O O B-ORG O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O O B-MISC O O O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O B-ORG O B-ORG O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O
+O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O O B-MISC O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O B-MISC O O O B-ORG O O O B-ORG O O O O O B-LOC O O O O O O O O O O B-PER I-PER O B-PER I-PER O O O O O O O O B-ORG O O O O O O O O O B-PER I-PER O O O O O O O B-ORG O O O O O O O O O O O O O O O B-PER I-PER O O B-ORG I-ORG O O B-ORG O O B-ORG O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER O O O B-ORG O O O O B-ORG O O O O O
+O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O B-ORG O O B-ORG O O O O O O B-ORG I-ORG O O O O B-ORG O O B-ORG O O O O O O O B-ORG O O O O O B-ORG O B-ORG I-ORG O O O O O O O O O O O O B-ORG O B-ORG O O O B-ORG O B-ORG O O O O O O O O O B-ORG O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O O B-ORG O O B-ORG I-ORG I-ORG O B-MISC I-MISC O O O O O O O O B-ORG I-ORG O O O O O O B-ORG O O O B-ORG O B-ORG O O O O O O O O O O
+O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O B-ORG O B-ORG O O B-ORG O B-ORG O O B-ORG O B-ORG O O B-ORG O B-ORG O O B-ORG O B-ORG I-ORG O O B-ORG O B-ORG O O B-ORG O B-ORG I-ORG O O B-ORG O B-ORG O O O O O O O O O O B-ORG O B-ORG O O B-ORG O B-ORG O O B-ORG I-ORG O B-ORG O O B-ORG O B-ORG O O B-ORG O B-ORG O O B-ORG I-ORG O B-ORG O O B-ORG O B-ORG I-ORG I-ORG O O B-ORG O B-ORG I-ORG O O B-ORG I-ORG O B-ORG O O B-ORG O O O O O O O B-ORG O O B-ORG O O B-MISC I-MISC O O O O O O O O B-ORG O O O O B-ORG I-ORG O B-ORG O O O B-ORG I-ORG O O O O B-ORG O B-ORG O O B-ORG O B-ORG O O B-ORG O B-ORG O O O O B-ORG O O B-ORG O B-ORG O O O B-ORG O O O O B-ORG O O B-ORG O O B-ORG O O O O O B-ORG O O O O B-ORG O B-ORG O O O B-ORG O O O O O O B-ORG O O B-ORG O O B-ORG O O O O O O O B-ORG O O O O
+O O O O O O O O O O O O O O O O B-LOC O O O B-PER I-PER O O O O O O O O O O O O O O B-MISC O O B-LOC O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O B-MISC O O O O O O O O B-LOC O O O B-PER I-PER O O O O B-PER I-PER O B-PER I-PER O B-PER I-PER O O O O B-PER O O O O O O O B-PER O O O O O O O O O B-PER I-PER O B-PER I-PER O O O O B-PER I-PER O O
+O O O O O O O O O O O O O O O O O O B-MISC I-MISC I-MISC O O O B-MISC I-MISC O O O O O O B-PER I-PER O B-LOC O O O O O O B-PER I-PER O O O O O B-PER I-PER O B-LOC O O O O O O B-PER I-PER O O O O B-PER I-PER O B-LOC O O O O O O B-PER I-PER O O O O O B-PER I-PER I-PER O O O O O B-PER I-PER O O O O O B-PER I-PER O O O O B-PER I-PER O O O O B-PER I-PER O O O O O O B-PER I-PER O B-LOC O O O O O B-PER I-PER I-PER I-PER O O B-LOC O O O O O B-PER I-PER O O O O B-PER I-PER I-PER O B-LOC O O O O O O B-PER I-PER O B-LOC O O O O O B-PER I-PER I-PER O B-LOC O O O O O B-PER I-PER O O O O B-PER I-PER O O O O O O B-PER I-PER O O O O B-PER I-PER O O O O O B-PER I-PER O O O O B-PER I-PER O O O O O
+O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O B-MISC I-MISC O O O O B-LOC I-LOC O O O O O O O O B-LOC O O O O B-LOC O O O O O O O O O O O B-ORG O O B-LOC O O O O O O O O O O B-ORG I-ORG I-ORG O O B-PER I-PER O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O B-LOC I-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O
+O O O O O O O O O O O O O B-LOC O O O B-LOC O O B-PER I-PER O O O O O O B-ORG O O O O B-MISC I-MISC O O B-ORG O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O B-LOC O B-LOC I-LOC O O B-PER O O O O O O O O O O O O B-MISC O B-PER I-PER O B-PER I-PER O B-PER I-PER O O B-PER O O O O O O O O O O O O O B-LOC O O B-PER I-PER O O O O O O O O O O B-PER I-PER O O B-PER O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O B-PER I-PER O O O O B-PER I-PER O O O O O O O O O O O O O O O B-PER I-PER O O O O O B-ORG O O
+O O O O O O O O O B-LOC O O O O O O O O O O O O O B-MISC I-MISC O O O B-ORG O B-ORG O B-LOC I-LOC O O O O B-ORG O O O O O B-PER I-PER O O B-PER I-PER O O B-PER I-PER O O O B-ORG O O O O B-PER I-PER O O O O O O
+O O O O O O O O O O O O O O O B-LOC I-LOC O O O O O B-MISC I-MISC I-MISC O O O O O O B-LOC O O O B-MISC O O O O O O O O O O O O O O B-LOC I-LOC O B-LOC O O O B-MISC I-MISC O O O O O O O O O O O O O O O O B-PER I-PER O O O O O B-LOC I-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O B-MISC O B-PER I-PER O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O B-LOC O O O O O O O O O O O B-MISC O O O O O O O O O O O B-MISC O O O O O O O O O O O O B-MISC O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER I-PER O O O B-PER O O O O O O O O O O B-LOC I-LOC O O O B-PER O B-PER O O O B-PER I-PER O O O O B-PER I-PER O O O O O B-PER I-PER O O O B-PER O B-PER O O O O O B-PER I-PER O O O O B-PER O O O O O B-PER O B-PER O O O O B-PER I-PER O O O B-PER I-PER I-PER O O O O B-PER O O O O B-PER O B-PER O O
+O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG I-ORG O O B-ORG I-ORG O B-ORG I-ORG O O B-ORG I-ORG I-ORG O O O O O B-ORG I-ORG I-ORG O B-ORG I-ORG O O B-ORG I-ORG O B-ORG I-ORG I-ORG O O O O O O B-ORG I-ORG O O B-ORG I-ORG I-ORG O B-ORG I-ORG O O B-ORG I-ORG I-ORG O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O
+O O O O O O O O O O O O O B-MISC I-MISC O B-MISC O O O O O B-LOC I-LOC I-LOC O B-LOC O O O O O O O O O B-LOC O B-PER I-PER O O O B-PER O O O O O O O B-LOC O O O O O O O O O O O B-LOC I-LOC O B-LOC O O O O O O O O B-LOC I-LOC O B-PER I-PER O O O O B-PER I-PER O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC I-LOC O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O
+B-ORG O O O O O O O O O O O O O O O B-ORG I-ORG I-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
+B-ORG B-MISC O O O O O O O O O O O O B-ORG I-ORG I-ORG I-ORG O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O B-ORG O O B-ORG I-ORG O O O O B-ORG O O O O B-ORG O O O O O O O B-ORG O O O O B-ORG O O B-ORG O O O O O B-ORG O O B-ORG O O
+B-ORG O O O O O O O O O O O O O O O O B-MISC I-MISC I-MISC I-MISC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
+B-ORG O O O O O O O O O O O O O O B-MISC I-MISC I-MISC I-MISC O O O O O O O O O O O B-ORG O O B-ORG O O O O B-ORG O O O O O B-ORG O O O O B-ORG O O O O B-ORG O O O O B-ORG I-ORG O O O O O O B-ORG O O
+B-ORG O O O O O O O O O O O O O O O O B-ORG O O O O B-PER I-PER O O O O O O O B-ORG I-ORG I-ORG O O O O O O O O O B-ORG O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O B-ORG O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O B-PER O O O O O O O O O O O O O O B-ORG O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O
+O O O O O O O O O O O O O O B-MISC B-PER O O B-LOC O B-PER O O O B-ORG I-ORG B-MISC I-MISC I-MISC O O O O O O
+O O O O O O O O O O O O O O O B-MISC O O O O O B-ORG I-ORG O O O O O O O O O O B-ORG O O O O O O O O O O O
+O O O O O O O O O O O O O O O O B-ORG I-ORG O B-LOC O O O O O O O O O O O O O O O O B-ORG O B-LOC O O O O O O O O O O O B-ORG O O O O O O O O O O B-ORG O O O O O O O O O O O B-MISC O O B-ORG O O O O O O O O O O O O O O O O B-ORG O O B-MISC O O O O O O O O O O B-MISC O O O O O O O O O B-PER O O O O O O O B-ORG O O O O O O O O O O O O O O O O B-MISC O O O O
+O O O O O O O O O O O O B-MISC O O O B-MISC O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O B-MISC O O O B-MISC O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O B-MISC I-MISC O O O O O O O O O O O O B-LOC I-LOC I-LOC O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O
+O O O O O O O O O O O O O O O O O O B-MISC O O O O O O B-ORG I-ORG O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O B-ORG O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O O O O O O O O O O O O O O O
diff --git a/5. Transformer/transformer.ipynb b/5. Transformer/transformer.ipynb
new file mode 100644
index 0000000..bb7be9b
--- /dev/null
+++ b/5. Transformer/transformer.ipynb	
@@ -0,0 +1,8793 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "am0MK_BNa4eB",
+   "metadata": {
+    "id": "am0MK_BNa4eB"
+   },
+   "outputs": [],
+   "source": [
+    "# !unzip -q /content/en-ner-conll-2003.zip -d /content/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb529423",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip uninstall torch\n",
+    "!pip install torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "59fb7b12",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: transformers in c:\\users\\pawel\\anaconda3\\lib\\site-packages (4.32.1)\n",
+      "Requirement already satisfied: filelock in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from transformers) (3.14.0)\n",
+      "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in c:\\users\\pawel\\anaconda3\\lib\\site-packages (from transformers) (0.15.1)\n",
+      "Requirement already satisfied: numpy>=1.17 in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from transformers) (1.26.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\pawel\\anaconda3\\lib\\site-packages (from transformers) (23.1)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\pawel\\anaconda3\\lib\\site-packages (from transformers) (6.0)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in c:\\users\\pawel\\anaconda3\\lib\\site-packages (from transformers) (2022.7.9)\n",
+      "Requirement already satisfied: requests in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from transformers) (2.32.2)\n",
+      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in c:\\users\\pawel\\anaconda3\\lib\\site-packages (from transformers) (0.13.2)\n",
+      "Requirement already satisfied: safetensors>=0.3.1 in c:\\users\\pawel\\anaconda3\\lib\\site-packages (from transformers) (0.3.2)\n",
+      "Requirement already satisfied: tqdm>=4.27 in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from transformers) (4.66.4)\n",
+      "Requirement already satisfied: fsspec in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2024.5.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.12.0)\n",
+      "Requirement already satisfied: colorama in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from tqdm>=4.27->transformers) (0.4.6)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from requests->transformers) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from requests->transformers) (3.7)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from requests->transformers) (2.2.1)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\pawel\\appdata\\roaming\\python\\python311\\site-packages (from requests->transformers) (2024.2.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "36c02fac",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "36c02fac",
+    "outputId": "992e4fef-67b1-490e-adbb-22fba93a0dc4"
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Failed to import transformers.pipelines because of the following error (look up to see its traceback):\n[WinError 126] Nie można odnaleźć określonego modułu. Error loading \"C:\\Users\\Pawel\\AppData\\Roaming\\Python\\Python311\\site-packages\\torch\\lib\\shm.dll\" or one of its dependencies.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\utils\\import_utils.py:1130\u001b[0m, in \u001b[0;36m_LazyModule._get_module\u001b[1;34m(self, module_name)\u001b[0m\n\u001b[0;32m   1129\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m importlib\u001b[38;5;241m.\u001b[39mimport_module(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m module_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\importlib\\__init__.py:126\u001b[0m, in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m    125\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _bootstrap\u001b[38;5;241m.\u001b[39m_gcd_import(name[level:], package, level)\n",
+      "File \u001b[1;32m<frozen importlib._bootstrap>:1204\u001b[0m, in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
+      "File \u001b[1;32m<frozen importlib._bootstrap>:1176\u001b[0m, in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
+      "File \u001b[1;32m<frozen importlib._bootstrap>:1147\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
+      "File \u001b[1;32m<frozen importlib._bootstrap>:690\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n",
+      "File \u001b[1;32m<frozen importlib._bootstrap_external>:940\u001b[0m, in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n",
+      "File \u001b[1;32m<frozen importlib._bootstrap>:241\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[1;34m(f, *args, **kwds)\u001b[0m\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\pipelines\\__init__.py:28\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PreTrainedFeatureExtractor\n\u001b[1;32m---> 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage_processing_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseImageProcessor\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauto\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfiguration_auto\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoConfig\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\image_processing_utils.py:28\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BatchFeature \u001b[38;5;28;01mas\u001b[39;00m BaseBatchFeature\n\u001b[1;32m---> 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage_transforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m center_crop, normalize, rescale\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ChannelDimension\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\image_transforms.py:44\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_torch_available():\n\u001b[1;32m---> 44\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_tf_available():\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\torch\\__init__.py:141\u001b[0m\n\u001b[0;32m    140\u001b[0m             err\u001b[38;5;241m.\u001b[39mstrerror \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m Error loading \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdll\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m or one of its dependencies.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 141\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m    143\u001b[0m kernel32\u001b[38;5;241m.\u001b[39mSetErrorMode(prev_error_mode)\n",
+      "\u001b[1;31mOSError\u001b[0m: [WinError 126] Nie można odnaleźć określonego modułu. Error loading \"C:\\Users\\Pawel\\AppData\\Roaming\\Python\\Python311\\site-packages\\torch\\lib\\shm.dll\" or one of its dependencies.",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[6], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mregularizers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m l2\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Counter\n\u001b[1;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pipeline, AutoModelForTokenClassification, AutoTokenizer\n",
+      "File \u001b[1;32m<frozen importlib._bootstrap>:1229\u001b[0m, in \u001b[0;36m_handle_fromlist\u001b[1;34m(module, fromlist, import_, recursive)\u001b[0m\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\utils\\import_utils.py:1120\u001b[0m, in \u001b[0;36m_LazyModule.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   1118\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_module(name)\n\u001b[0;32m   1119\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_class_to_module\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m-> 1120\u001b[0m     module \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_module(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_class_to_module[name])\n\u001b[0;32m   1121\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(module, name)\n\u001b[0;32m   1122\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\utils\\import_utils.py:1132\u001b[0m, in \u001b[0;36m_LazyModule._get_module\u001b[1;34m(self, module_name)\u001b[0m\n\u001b[0;32m   1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m importlib\u001b[38;5;241m.\u001b[39mimport_module(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m module_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m-> 1132\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m   1133\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to import \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodule_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m because of the following error (look up to see its\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1134\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m traceback):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1135\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Failed to import transformers.pipelines because of the following error (look up to see its traceback):\n[WinError 126] Nie można odnaleźć określonego modułu. Error loading \"C:\\Users\\Pawel\\AppData\\Roaming\\Python\\Python311\\site-packages\\torch\\lib\\shm.dll\" or one of its dependencies."
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import gensim\n",
+    "from gensim.models import KeyedVectors\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "import matplotlib.pyplot as plt\n",
+    "from keras.regularizers import l2\n",
+    "from collections import Counter\n",
+    "from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db84429c",
+   "metadata": {
+    "id": "db84429c"
+   },
+   "source": [
+    "### Declare path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fc2539ae",
+   "metadata": {
+    "id": "fc2539ae"
+   },
+   "outputs": [],
+   "source": [
+    "data_dir_path = 'en-ner-conll-2003'\n",
+    "train_path = os.path.join(data_dir_path, 'train', 'train.tsv')\n",
+    "dev_texts_path = os.path.join(data_dir_path, 'dev-0', 'in.tsv')\n",
+    "dev_labels_path = os.path.join(data_dir_path, 'dev-0', 'expected.tsv')\n",
+    "dev_predicted_path = os.path.join(data_dir_path, 'dev-0', 'out.tsv')\n",
+    "test_texts_path = os.path.join(data_dir_path, 'test-A', 'in.tsv')\n",
+    "test_predicted_path = os.path.join(data_dir_path, 'test-A', 'out.tsv')\n",
+    "word2vec_file_path = 'word2vec_100_3_polish.bin'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4ea0458",
+   "metadata": {
+    "id": "e4ea0458"
+   },
+   "source": [
+    "### Load files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4e038df7",
+   "metadata": {
+    "id": "4e038df7"
+   },
+   "outputs": [],
+   "source": [
+    "train_data = pd.read_csv(train_path, sep='\\t', usecols=[0, 1], header=None, names=['label', 'text'])\n",
+    "dev_texts_data = pd.read_csv(dev_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])\n",
+    "dev_labels_data = pd.read_csv(dev_labels_path, sep='\\t', usecols=[0], header=None, names=['label'])\n",
+    "test_texts_data = pd.read_csv(test_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e00b6d56",
+   "metadata": {},
+   "source": [
+    "### Do transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "43c3c2d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'AutoModelForTokenClassification' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m AutoModelForTokenClassification\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdbmdz/bert-large-cased-finetuned-conll03-english\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgoogle-bert/bert-base-cased\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m recognizer \u001b[38;5;241m=\u001b[39m pipeline(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mner\u001b[39m\u001b[38;5;124m\"\u001b[39m, model\u001b[38;5;241m=\u001b[39mmodel, tokenizer\u001b[38;5;241m=\u001b[39mtokenizer)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'AutoModelForTokenClassification' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-cased\")\n",
+    "recognizer = pipeline(\"ner\", model=model, tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2bc73a6",
+   "metadata": {
+    "id": "f2bc73a6"
+   },
+   "source": [
+    "### Build Vocab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e0022f7b",
+   "metadata": {
+    "id": "e0022f7b"
+   },
+   "outputs": [],
+   "source": [
+    "def build_vocab(dataset):\n",
+    "    counter = Counter()\n",
+    "    for document in dataset:\n",
+    "        counter.update(document)\n",
+    "    return vocab(counter, specials=[\"<unk>\", \"<pad>\", \"<bos>\", \"<eos>\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7d9fb428",
+   "metadata": {
+    "id": "7d9fb428"
+   },
+   "outputs": [],
+   "source": [
+    "train_X = train_data['text'].apply(lambda x: gensim.utils.simple_preprocess(x))\n",
+    "v = build_vocab(train_X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7e793633",
+   "metadata": {
+    "id": "7e793633"
+   },
+   "outputs": [],
+   "source": [
+    "itos = v.get_itos()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d3cca05a",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "d3cca05a",
+    "outputId": "053dc5b9-23f8-4f63-9352-68d1bd318fa6"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['<unk>', '<pad>', '<bos>', '<eos>', 'eu', 'rejects', 'german', 'call', 'to', 'boycott', 'british', 'lamb', 'peter', 'blackburn', 'brussels', 'the', 'european', 'commission', 'said', 'on', 'thursday', 'it', 'disagreed', 'with', 'advice', 'consumers', 'shun', 'until', 'scientists', 'determine', 'whether', 'mad', 'cow', 'disease', 'can', 'be', 'transmitted', 'sheep', 'germany', 'representative', 'union', 'veterinary', 'committee', 'werner', 'zwingmann', 'wednesday', 'should', 'buy', 'sheepmeat', 'from', 'countries', 'other', 'than', 'britain', 'scientific', 'was', 'clearer', 'we', 'do', 'support', 'any', 'such', 'recommendation', 'because', 'see', 'grounds', 'for', 'chief', 'spokesman', 'nikolaus', 'van', 'der', 'pas', 'told', 'news', 'briefing', 'he', 'further', 'study', 'required', 'and', 'if', 'found', 'that', 'action', 'needed', 'taken', 'by', 'proposal', 'last', 'month', 'farm', 'commissioner', 'franz', 'fischler', 'ban', 'brains', 'spleens', 'spinal', 'cords', 'human', 'animal', 'food', 'chains', 'highly', 'specific', 'precautionary', 'move', 'protect', 'health', 'proposed', 'wide', 'measures', 'after', 'reports', 'france', 'under', 'laboratory', 'conditions', 'could', 'contract', 'bovine', 'spongiform', 'encephalopathy', 'bse', 'but', 'agreed', 'review', 'his', 'standing', 'mational', 'officials', 'questioned', 'justified', 'as', 'there', 'only', 'slight', 'risk', 'spanish', 'minister', 'loyola', 'de', 'palacio', 'had', 'earlier', 'accused', 'at', 'an', 'ministers', 'meeting', 'of', 'causing', 'unjustified', 'alarm', 'through', 'dangerous', 'generalisation', 'backed', 'committees', 'are', 'due', 're', 'examine', 'issue', 'early', 'next', 'make', 'recommendations', 'senior', 'have', 'long', 'been', 'known', 'scrapie', 'brain', 'wasting', 'similar', 'which', 'is', 'believed', 'transferred', 'cattle', 'feed', 'containing', 'waste', 'farmers', 'denied', 'danger', 'their', 'expressed', 'concern', 'government', 'avoid', 'might', 'influence', 'across', 'europe', 'what', 'extremely', 'careful', 'how', 'going', 'take', 'lead', 'welsh', 'national', 'nfu', 'chairman', 'john', 'lloyd', 'jones', 'bbc', 'radio', 'bonn', 'has', 'led', 'efforts', 'public', 'consumer', 'confidence', 'collapsed', 'in', 'march', 'report', 'suggested', 'humans', 'illness', 'eating', 'contaminated', 'beef', 'imported', 'year', 'nearly', 'half', 'total', 'imports', 'brought', 'tonnes', 'mutton', 'some', 'percent', 'overall', 'rare', 'hendrix', 'song', 'draft', 'sells', 'almost', 'london', 'handwritten', 'guitar', 'legend', 'jimi', 'sold', 'auction', 'late', 'musician', 'favourite', 'possessions', 'florida', 'restaurant', 'paid', 'pounds', 'ai', 'no', 'telling', 'penned', 'piece', 'hotel', 'stationery', 'end', 'january', 'concert', 'english', 'city', 'nottingham', 'threw', 'sheet', 'paper', 'into', 'audience', 'where', 'retrieved', 'fan', 'buyers', 'also', 'snapped', 'up', 'items', 'were', 'put', 'former', 'girlfriend', 'kathy', 'etchingham', 'who', 'lived', 'him', 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'neves', 'bishops', 'flees', 'passport', 'consular', 'soliciting', 'icac', 'graft', 'bribe', 'scams', 'honduran', 'passports', 'thinks', 'solve', 'woes', 'rajapat', 'warnings', 'pullouts', 'chatichai', 'choonhavan', 'chavalit', 'yongchaiyudh', 'aspiration', 'insincere', 'slowdown', 'switched', 'monarchy', 'yassin', 'ramle', 'fundamentalist', 'wrecking', 'confined', 'freeing', 'skhirat', 'purely', 'vital', 'conlon', 'triumphal', 'abruptly', 'eavesdrop', 'conversations', 'campaigns', 'invaluable', 'captivated', 'worried', 'senator', 'dianne', 'feinstein', 'bump', 'dogged', 'wrongdoing', 'misconduct', 'questionable', 'judgment', 'selecting', 'advisers', 'revert', 'revelled', 'heartland', 'hoarse', 'voice', 'resolute', 'wit', 'stilted', 'glib', 'piper', 'jaffray', 'compiled', 'gen', 'muscular', 'dystrophy', 'technique', 'telethon', 'method', 'muscle', 'degenerates', 'adulthood', 'dystrophin', 'altered', 'minimise', 'susceptibility', 'injected', 'mice', 'bred', 'genes', 'experiment', 'fibers', 'diminishing', 'oilseeds', 'oilseed', 'assn', 'cargill', 'mitsui', 'dreyfus', 'dns', 'ww', 'donation', 'taichung', 'procurement', 'soybeans', 'bunge', 'pnw', 'pl', 'buyer', 'upland', 'sorghum', 'pima', 'poultry', 'fruits', 'gaining', 'quota', 'onic', 'panel', 'cutout', 'offal', 'moines', 'steer', 'uneven', 'goldstein', 'hiring', 'matched', 'heist', 'jewelry', 'showboat', 'capt', 'andrews', 'videotape', 'suitcase', 'resembling', 'wholesaler', 'schein', 'suitcases', 'closet', 'jewellers', 'twenties', 'distract', 'mcgilley', 'description', 'simpson', 'hints', 'financially', 'jury', 'gag', 'prohibits', 'acquittal', 'crying', 'blues', 'erroneous', 'jam', 'distanced', 'buffalo', 'wildly', 'supportive', 'gifts', 'supermarket', 'reprinted', 'editions', 'vitriol', 'journalism', 'dignify', 'surgeon', 'discarding', 'amputated', 'improperly', 'disposed', 'deformed', 'infant', 'prosthesis', 'orthopedic', 'educational', 'freezer', 'spoiled', 'crab', 'trap', 'flesh', 'cop', 'shrub', 'guys', 'bagged', 'checker', 'pines', 'toting', 'earl', 'feugill', 'camouflaged', 'shaggy', 'freeze', 'marines', 'attaching', 'strips', 'burlap', 'disguise', 'staking', 'robberies', 'stakeout', 'dla', 'logistics', 'nbc', 'consultant', 'reshaped', 'ire', 'repositioning', 'gluten', 'meal', 'pickup', 'pellets', 'decatur', 'il', 'cedar', 'rapids', 'ia', 'cereals', 'ecus', 'provisionally', 'optional', 'enjoys', 'subtracting', 'shipyard', 'cherbourg', 'yeard', 'cutback', 'indre', 'managements', 'curtail', 'estates', 'hub', 'gadoon', 'unearthed', 'cages', 'tando', 'allahyar', 'hyderabad', 'recorder', 'sui', 'technologists', 'sugarcane', 'pillaged', 'gujar', 'megawatt', 'mohib', 'textile', 'defaulted', 'leasing', 'modarabas', 'multiply', 'shahid', 'privatisation', 'sindh', 'restraining', 'javedan', 'dadabhoy', 'pvt', 'directions', 'occupying', 'rebelled', 'wrecked', 'mined', 'halo', 'roadsides', 'qotbuddin', 'hilal', 'officiated', 'sprinkling', 'petrol', 'andhra', 'pradesh', 'rarest', 'beings', 'roasted', 'appellants', 'plotted', 'elephant', 'tramples', 'rampaging', 'trampled', 'hari', 'maya', 'poudels', 'madhumalla', 'asleep', 'beast', 'kingdoms', 'elephants', 'overrun', 'kudapokuna', 'welikanda', 'vavuniya', 'undercover', 'vicorp', 'sabourin', 'cfo', 'denver', 'bestop', 'boulder', 'socal', 'edison', 'kilovolt', 'kv', 'wildfire', 'raging', 'conroy', 'crews', 'residues', 'megawatts', 'mw', 'containment', 'arid', 'windy', 'leong', 'munis', 'municipals', 'semiconductor', 'expectation', 'emerge', 'modem', 'networking', 'elias', 'moosa', 'roberston', 'pmc', 'chipsets', 'severance', 'randall', 'soundview', 'focusing', 'develops', 'shrink', 'hambrecht', 'quist', 'slowing', 'housecall', 'sink', 'underperform', 'hospice', 'limitation', 'subsidiaries', 'forma', 'oasis', 'gallagher', 'obscene', 'swore', 'hate', 'supermodel', 'noel', 'laryngitis', 'patsy', 'kensit', 'tickets', 'slough', 'upbeat', 'pretax', 'hammerson', 'warburg', 'stances', 'maintaining', 'rental', 'incomes', 'shaved', 'nigel', 'mobbs', 'birt', 'shrinks', 'seasonally', 'canadians', 'burea', 'shaken', 'backdrop', 'espionage', 'rings', 'jomhuri', 'eslami', 'photographing', 'pan', 'turkism', 'exiles', 'fallahiyan', 'distillates', 'kerosene', 'sort', 'noticeable', 'differential', 'ports', 'exceed', 'cargoes', 'lowest', 'suspicious', 'overhang', 'sulphur', 'manuscript', 'thieves', 'testament', 'akhbar', 'undated', 'pretended', 'wyche', 'invoked', 'recess', 'confirming', 'predecessor', 'mabus', 'shops', 'shutters', 'exporters', 'purchasing', 'destinations', 'southland', 'mainframe', 'delivering', 'constitutes', 'contends', 'nba', 'continually', 'baxter', 'immuno', 'miilion', 'secures', 'cos', 'chipset', 'hotels', 'doubletree', 'toxic', 'compuserve', 'impasse', 'authorised', 'trashed', 'nudge', 'gop', 'reporter', 'beachcomber', 'highlands', 'debris', 'bearing', 'markings', 'conducting', 'fireball', 'wallets', 'shoes', 'mechanical', 'leila', 'corcoran', 'writes', 'enslaved', 'pleads', 'abraham', 'maimed', 'poses', 'volunteers', 'preserved', 'microfilm', 'stowed', 'rarely', 'enlistment', 'discharge', 'certificates', 'unveiling', 'memorial', 'shall', 'oportunity', 'enjoyment', 'sic', 'slavery', 'oppreses', 'liberty', 'outpouring', 'curse', 'separated', 'matron', 'plead', 'dishonourable', 'prisoner', 'thoughtless', 'folly', 'notation', 'regiment', 'pardon', 'anguish', 'amputee', 'terrible', 'haunted', 'posed', 'chested', 'insight', 'rhythms', 'carbondale', 'ulysses', 'writings', 'poetic', 'linguistic', 'treasure', 'trove', 'sociology', 'seared', 'consciousness', 'confederacy', 'retirees', 'budge', 'weidman', 'shepherded', 'predicts', 'databases', 'battlefields', 'discharged', 'incapacity', 'inebetude', 'arise', 'masturbation', 'vow', 'sylvie', 'ajaccio', 'mediterranean', 'defused', 'debre', 'corse', 'lax', 'powerless', 'beauvau', 'nightly', 'figaro', 'shaky', 'racked', 'principally', 'backlash', 'om', 'gruppen', 'omlx', 'pulpex', 'pulp', 'timber', 'recycled', 'complements', 'softs', 'cocoa', 'unpredictable', 'risky', 'hedge', 'tankage', 'naphtha', 'kero', 'throughput', 'bunkering', 'blenkinsop', 'pantomime', 'enacting', 'brandished', 'theatre', 'prop', 'gorleben', 'depot', 'catania', 'sicily', 'slaying', 'nephew', 'puglisi', 'salvatore', 'botta', 'cemetery', 'sicilian', 'hardened', 'tip', 'consciences', 'nitto', 'santapaola', 'knelt', 'ambush', 'cemetary', 'canteen', 'limerick', 'warders', 'takings', 'italians', 'hiv', 'pensioner', 'harassing', 'hookers', 'aids', 'pietro', 'cruising', 'hurling', 'blanks', 'sighted', 'arctic', 'spitzbergen', 'rune', 'ntb', 'vnukovo', 'tupolev', 'longyearbyen', 'airstrip', 'rescuers', 'opera', 'mountainside', 'barentsburg', 'galeforce', 'meteorological', 'cellars', 'trees', 'uprooted', 'roofs', 'trains', 'cm', 'communes', 'rmt', 'turnhout', 'scouts', 'camping', 'meadow', 'hindered', 'excavations', 'jumet', 'lightly', 'tides', 'pesetas', 'chemicals', 'madness', 'overreacted', 'liberte', 'leveilly', 'finns', 'captive', 'tampere', 'luxury', 'ilkka', 'laasonen', 'iltalehti', 'audi', 'ingolstadt', 'demel', 'hedged', 'kekkila', 'fertilisers', 'saplings', 'trend', 'fluctuations', 'untroubled', 'bumble', 'predict', 'echoing', 'automotive', 'equally', 'predicting', 'rationalised', 'destocked', 'destocking', 'relaxed', 'cycle', 'fraction', 'commenting', 'piston', 'kolbenschmidt', 'obstacles', 'huddart', 'jolts', 'zealands', 'waiau', 'hanmer', 'prone', 'earthquakes', 'smooth', 'trevelyan', 'yearly', 'absolutely', 'predicated', 'precepts', 'trough', 'draconian', 'rentals', 'reclamation', 'narrowly', 'downtrend', 'hover', 'touching', 'intraday', 'technically', 'doreen', 'siow', 'siam', 'banker', 'bishi', 'int', 'jcr', 'jbri', 'daewoo', 'dacom', 'commem', 'manufactures', 'ski', 'antara', 'evident', 'happening', 'motivated', 'utomo', 'indonesias', 'extinguished', 'injures', 'exaggerate', 'rents', 'haneda', 'itami', 'contesting', 'stymies', 'dismantle', 'considerable', 'directorate', 'bureaucrats', 'mandarin', 'secretaries', 'src', 'distillation', 'stuck', 'varied', 'exchanger', 'petrolem', 'chevron', 'texaco', 'loxley', 'reviewed', 'jobless', 'inem', 'cur', 'prod', 'adj', 'trln', 'fcast', 'def', 'qtr', 'epa', 'annualised', 'ine', 'variation', 'corresponds', 'relate', 'finances', 'obstructing', 'lasa', 'zabala', 'gal', 'prescriptions', 'gomez', 'liano', 'aznar', 'bch', 'hive', 'coopers', 'lybrand', 'emigrates', 'lenzing', 'viscose', 'fibre', 'preview', 'schillings', 'fibres', 'economies', 'fundamentalists', 'masterminds', 'ideologists', 'financers', 'clarifications', 'clarification', 'amending', 'donna', 'topic', 'pundits', 'debated', 'forgo', 'unflagging', 'sidestepped', 'bentsen', 'viewed', 'distinctively', 'widness', 'timely', 'instances', 'stands', 'expertise', 'faust', 'bailard', 'biehl', 'kaiser', 'awfully', 'greenspan', 'reappointment', 'perelstein', 'mainstay', 'amstetten', 'ewald', 'brenner', 'stieglmair', 'brunmayr', 'marino', 'hartson', 'samokhalova', 'spirited', 'quigley', 'numerical', 'superiority', 'goulnara', 'fatkoullina', 'snatching', 'lapped', 'ingrid', 'haringa', 'lancien', 'tatiana', 'stiajkina', 'arndt', 'vikstedt', 'boyden', 'godras', 'kombouare', 'abderdeen', 'aitken', 'affection', 'ginola', 'influential', 'influenced', 'imola', 'doohan', 'bayle', 'norifumi', 'abe', 'luca', 'cadalora', 'criville', 'tadayuki', 'okada', 'checa', 'shinichi', 'itoh', 'swe', 'bradley', 'hughes']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(itos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fa981cf6",
+   "metadata": {
+    "id": "fa981cf6"
+   },
+   "outputs": [],
+   "source": [
+    "v.set_default_index(v[\"<unk>\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed6fe85",
+   "metadata": {
+    "id": "4ed6fe85"
+   },
+   "source": [
+    "### Preprocess data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "q3jPiHDvdkEJ",
+   "metadata": {
+    "id": "q3jPiHDvdkEJ"
+   },
+   "outputs": [],
+   "source": [
+    "reformat_ner_dict = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "149c6b1f",
+   "metadata": {
+    "id": "149c6b1f"
+   },
+   "outputs": [],
+   "source": [
+    "def fit_data_Y(column):\n",
+    "    dt = [\n",
+    "        [reformat_ner_dict[token] for token in row.split()] for row in column\n",
+    "    ]\n",
+    "    return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]\n",
+    "\n",
+    "def fit_data_X(dt):\n",
+    "    return [\n",
+    "        torch.tensor(\n",
+    "            [v[\"<bos>\"]] + [v[token] for token in document.split()] + [v[\"<eos>\"]],\n",
+    "            dtype=torch.long,\n",
+    "        )\n",
+    "        for document in dt\n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "train_X = fit_data_X(train_data['text'])\n",
+    "train_Y = fit_data_Y(train_data['label'])\n",
+    "dev_X = fit_data_X(dev_texts_data['text'])\n",
+    "dev_Y = fit_data_Y(dev_labels_data['label'])\n",
+    "test_X = fit_data_X(test_texts_data['text'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fa44315",
+   "metadata": {
+    "id": "1fa44315"
+   },
+   "source": [
+    "### Create model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "jAUWEqOse936",
+   "metadata": {
+    "id": "jAUWEqOse936"
+   },
+   "outputs": [],
+   "source": [
+    "num_tags = len(reformat_ner_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1eeecf36",
+   "metadata": {
+    "id": "1eeecf36"
+   },
+   "outputs": [],
+   "source": [
+    "class LSTM(torch.nn.Module):\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        super(LSTM, self).__init__()\n",
+    "        self.emb = torch.nn.Embedding(len(v.get_itos()), 100)\n",
+    "        self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)\n",
+    "        self.fc1 = torch.nn.Linear(256, num_tags)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        emb = torch.relu(self.emb(x))\n",
+    "        lstm_output, (h_n, c_n) = self.rec(emb)\n",
+    "        out_weights = self.fc1(lstm_output)\n",
+    "        return out_weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "CIRuZkFjfaBc",
+   "metadata": {
+    "id": "CIRuZkFjfaBc"
+   },
+   "outputs": [],
+   "source": [
+    "lstm = LSTM()\n",
+    "criterion = torch.nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.Adam(lstm.parameters())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wJ4trgNPfcPF",
+   "metadata": {
+    "id": "wJ4trgNPfcPF"
+   },
+   "source": [
+    "### Evaluation model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8zkCdkWTf472",
+   "metadata": {
+    "id": "8zkCdkWTf472"
+   },
+   "outputs": [],
+   "source": [
+    "def get_accuracy(y_true, y_pred):\n",
+    "    hit = 0\n",
+    "    missed = 0\n",
+    "\n",
+    "    for p, t in zip(y_pred, y_true):\n",
+    "        if p == t:\n",
+    "            hit += 1\n",
+    "        else:\n",
+    "            missed += 1\n",
+    "\n",
+    "    accuracy = hit / (hit + missed)\n",
+    "    return accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ljxy_LC-feYD",
+   "metadata": {
+    "id": "ljxy_LC-feYD"
+   },
+   "outputs": [],
+   "source": [
+    "def eval_model(dataset_tokens, dataset_labels, model):\n",
+    "    Y_true = []\n",
+    "    Y_pred = []\n",
+    "    for i in tqdm(range(len(dataset_labels))):\n",
+    "        batch_tokens = dataset_tokens[i].unsqueeze(0)\n",
+    "        tags = list(dataset_labels[i].numpy())\n",
+    "        Y_true += tags\n",
+    "\n",
+    "        Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
+    "        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
+    "        Y_pred += list(Y_batch_pred.numpy())\n",
+    "\n",
+    "    return get_accuracy(Y_true, Y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec76b0f6",
+   "metadata": {
+    "id": "ec76b0f6"
+   },
+   "source": [
+    "### Train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e72a055c",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 839,
+     "referenced_widgets": [
+      "148e531ca4d246bab191b479727a590f",
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+      "e763556731d44e848d1ebdaa135a2f4f",
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+       "model_id": "808e1edf72374db08e0452bf9b875444",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/215 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dev_accuracy: 0.8345517857468414\n"
+     ]
+    }
+   ],
+   "source": [
+    "NUM_EPOCHS = 10\n",
+    "dev_accuracy_history = list()\n",
+    "for i in range(NUM_EPOCHS):\n",
+    "    lstm.train()\n",
+    "    for i in tqdm(range(len(train_Y))):\n",
+    "        batch_tokens = train_X[i].unsqueeze(0)\n",
+    "        tags = train_Y[i].unsqueeze(1)\n",
+    "\n",
+    "        predicted_tags = lstm(batch_tokens)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))\n",
+    "\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "    lstm.eval()\n",
+    "    dev_accuracy = eval_model(dev_X, dev_Y, lstm)\n",
+    "    dev_accuracy_history.append(dev_accuracy)\n",
+    "    print(\"dev_accuracy:\", dev_accuracy)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "561f4db3",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 472
+    },
+    "id": "561f4db3",
+    "outputId": "6a9cc103-0ce0-492b-9c11-6576f6df771d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(dev_accuracy_history, label='Validation Accuracy')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.title('Model Accuracy Over Epochs')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d0b9315",
+   "metadata": {
+    "id": "4d0b9315"
+   },
+   "source": [
+    "### Predict and save results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "T9rzUXtEpEbd",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "T9rzUXtEpEbd",
+    "outputId": "dca59368-894e-47f2-c47f-a57dd99ff1c0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{0: 'O', 1: 'B-PER', 2: 'I-PER', 3: 'B-ORG', 4: 'I-ORG', 5: 'B-LOC', 6: 'I-LOC', 7: 'B-MISC', 8: 'I-MISC'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "reversed_ner_dict = {v: k for k, v in reformat_ner_dict.items()}\n",
+    "print(reversed_ner_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "54f93a9b",
+   "metadata": {
+    "id": "54f93a9b"
+   },
+   "outputs": [],
+   "source": [
+    "def predict_and_save(X, filename):\n",
+    "    Y_predicted = []\n",
+    "    for i in tqdm(range(len(X))):\n",
+    "        batch_tokens = X[i].unsqueeze(0)\n",
+    "        Y_batch_pred_weights = lstm(batch_tokens).squeeze(0)\n",
+    "        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
+    "        Y_processed_pred = \" \".join(reversed_ner_dict[item] for item in Y_batch_pred.numpy()[1:-1])\n",
+    "        Y_predicted.append(Y_processed_pred)\n",
+    "    Y_predicted_df = pd.DataFrame(Y_predicted, columns=['predicted_label'])\n",
+    "    Y_predicted_df.to_csv(filename, sep='\\t', index=False, header=None)"
+   ]
+  },
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+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7a8863307a64d8f8b203cdb96760077",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/215 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+    "dev_predicted = predict_and_save(dev_X, dev_predicted_path)\n",
+    "test_predicted = predict_and_save(test_X, test_predicted_path)"
+   ]
+  }
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