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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Uczenie głębokie – przetwarzanie tekstu – laboratoria\n",
+    "# 3. RNN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Podejście softmax z embeddingami na przykładzie NER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: torch in /home/pawel/.local/lib/python3.10/site-packages (2.3.0)\n",
+      "Collecting torchtext\n",
+      "  Downloading torchtext-0.18.0-cp310-cp310-manylinux1_x86_64.whl (2.0 MB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: filelock in /home/pawel/.local/lib/python3.10/site-packages (from torch) (3.13.1)\n",
+      "Requirement already satisfied: fsspec in /home/pawel/.local/lib/python3.10/site-packages (from torch) (2024.2.0)\n",
+      "Requirement already satisfied: sympy in /home/pawel/.local/lib/python3.10/site-packages (from torch) (1.12)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (12.1.105)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (4.10.0)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (8.9.2.26)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (10.3.2.106)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (12.1.0.106)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (12.1.105)\n",
+      "Requirement already satisfied: triton==2.3.0 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (2.3.0)\n",
+      "Requirement already satisfied: jinja2 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (3.1.3)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (2.20.5)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (12.1.3.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (11.0.2.54)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (11.4.5.107)\n",
+      "Requirement already satisfied: networkx in /home/pawel/.local/lib/python3.10/site-packages (from torch) (3.3)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/pawel/.local/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.4.127)\n",
+      "Requirement already satisfied: requests in /home/pawel/.local/lib/python3.10/site-packages (from torchtext) (2.31.0)\n",
+      "Requirement already satisfied: numpy in /home/pawel/.local/lib/python3.10/site-packages (from torchtext) (1.26.4)\n",
+      "Requirement already satisfied: tqdm in /home/pawel/.local/lib/python3.10/site-packages (from torchtext) (4.66.2)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /home/pawel/.local/lib/python3.10/site-packages (from jinja2->torch) (2.1.5)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/pawel/.local/lib/python3.10/site-packages (from requests->torchtext) (2024.2.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/pawel/.local/lib/python3.10/site-packages (from requests->torchtext) (3.6)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/pawel/.local/lib/python3.10/site-packages (from requests->torchtext) (3.3.2)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/pawel/.local/lib/python3.10/site-packages (from requests->torchtext) (2.2.1)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /home/pawel/.local/lib/python3.10/site-packages (from sympy->torch) (1.3.0)\n",
+      "Installing collected packages: torchtext\n",
+      "Successfully installed torchtext-0.18.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install torch torchtext"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "\n",
+    "import torch\n",
+    "from datasets import load_dataset\n",
+    "from torchtext.vocab import vocab\n",
+    "from tqdm.notebook import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wczytujemy zbiór danych `conll2003` (https://huggingface.co/datasets/conll2003), który zawiera teksty oznaczone znacznikami części mowy (*POS tags*): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset = load_dataset(\"conll2003\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Poiżej funkcja, która tworzy słownik (https://pytorch.org/text/stable/vocab.html).\n",
+    "\n",
+    "Parametr `special` określa symbole specjalne:\n",
+    "* `<unk>` – nieznany token\n",
+    "* `<pad>` – wypełnienie\n",
+    "* `<bos>` – początek zdania\n",
+    "* `<eos>` – koniec zdania"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "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": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = build_vocab(dataset['train']['tokens'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "itos = v.get_itos()  # mapowanie indeksów na tokeny"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23627"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(itos)  # liczba różnych tokenów w słowniku"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v['on']  # indeks tokenu `on`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v[\"<unk>\"]  # indeks nieznanego tokenu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "W przypadku, gdy w analizowanym tekście znajdzie się token, którego nie ma w słowniku, będzie reprezentowany przez indeks domyślny (*default index*). Ustawiamy, żeby był taki sam, jak indeks „nieznanego tokenu”:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v.set_default_index(v[\"<unk>\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def data_process(dt):\n",
+    "    # Wektoryzacja dokumentów tekstowych.\n",
+    "    return [ torch.tensor([v['<bos>']] +[v[token]  for token in  document ] + [v['<eos>']], dtype = torch.long) for document in dt]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def labels_process(dt):\n",
+    "    # Wektoryzacja etykiet (POS)\n",
+    "    return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Teraz wektoryzujemy wszystkie dane:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_tokens_ids = data_process(dataset['train']['tokens'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_tokens_ids = data_process(dataset['test']['tokens'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_tokens_ids =  data_process(dataset['validation']['tokens'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "train_labels = labels_process(dataset['train']['ner_tags'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_labels = labels_process(dataset['validation']['ner_tags'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_labels = labels_process(dataset['test']['ner_tags'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Przykład, jak wyglądają dane po zwektoryzowaniu:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 2,  4,  5,  6,  7,  8,  9, 10, 11, 12,  3])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_tokens_ids[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'id': '0',\n",
+       " 'tokens': ['EU',\n",
+       "  'rejects',\n",
+       "  'German',\n",
+       "  'call',\n",
+       "  'to',\n",
+       "  'boycott',\n",
+       "  'British',\n",
+       "  'lamb',\n",
+       "  '.'],\n",
+       " 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\n",
+       " 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n",
+       " 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset['train'][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_labels[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Funkcja, której użyjemy do ewaluacji:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_scores(y_true, y_pred):\n",
+    "    # Funkcja zwraca precyzję, pokrycie i F1\n",
+    "    acc_score = 0\n",
+    "    tp = 0\n",
+    "    fp = 0\n",
+    "    selected_items = 0\n",
+    "    relevant_items = 0 \n",
+    "\n",
+    "    for p,t in zip(y_pred, y_true):\n",
+    "        if p == t:\n",
+    "            acc_score +=1\n",
+    "\n",
+    "        if p > 0 and p == t:\n",
+    "            tp +=1\n",
+    "\n",
+    "        if p > 0:\n",
+    "            selected_items += 1\n",
+    "\n",
+    "        if t > 0 :\n",
+    "            relevant_items +=1\n",
+    "\n",
+    "            \n",
+    "            \n",
+    "    if selected_items == 0:\n",
+    "        precision = 1.0\n",
+    "    else:\n",
+    "        precision = tp / selected_items\n",
+    "        \n",
+    "            \n",
+    "    if relevant_items == 0:\n",
+    "        recall = 1.0\n",
+    "    else:\n",
+    "        recall = tp / relevant_items\n",
+    "    \n",
+    "    \n",
+    "    if precision + recall == 0.0 :\n",
+    "        f1 = 0.0\n",
+    "    else:\n",
+    "        f1 = 2* precision * recall  / (precision + recall)\n",
+    "\n",
+    "    return precision, recall, f1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ile mamy różnych POS tagów?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_tags = max([max(x) for x in dataset['train']['ner_tags'] ]) + 1 \n",
+    "print(num_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Implementacja rekurencyjnej sieci neuronowej LSTM:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "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 , 9)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        emb = torch.relu(self.emb(x))\n",
+    "        \n",
+    "        lstm_output, (h_n, c_n) = self.rec(emb)\n",
+    "        \n",
+    "        out_weights = self.fc1(lstm_output)\n",
+    "\n",
+    "        return out_weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stworzenie modelu:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lstm = LSTM()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Definicja funkcji kosztu:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "criterion = torch.nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Definicja optymalizatora:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(lstm.parameters())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Funkcja do ewaluacji modelu:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "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",
+    "\n",
+    "    return get_scores(Y_true, Y_pred)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Uczenie modelu:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NUM_EPOCHS = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b88376fa1a6481b92da7e8308b581cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/14041 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ec3025ecf6a4ed69a5f1df4d2e8099d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3250 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.49656056896350703, 0.4950598628385447, 0.49580908032596044)\n"
+     ]
+    },
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+       "  0%|          | 0/14041 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
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+    },
+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6289105835367207, 0.6589561780774148, 0.643582902877902)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/14041 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a347fc594654853a6e6a2425a7a5c98",
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+      "text/plain": [
+       "  0%|          | 0/3250 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.7031268719300348, 0.6822038823666163, 0.6925073746312684)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/14041 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b44d34b37d844ac9d3075e9a3fd25d4",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3250 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.7354687113529558, 0.6912704870394049, 0.7126850020971898)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cab6377c9f54f3e8acf262273a4dbf0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/14041 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ae82f803d424b83b7abdc3319282131",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3250 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.7134837896666285, 0.7239335115657329, 0.7186706669743826)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(NUM_EPOCHS):\n",
+    "    lstm.train()\n",
+    "    #for i in tqdm(range(500)):\n",
+    "    for i in tqdm(range(len(train_labels))):\n",
+    "        batch_tokens = train_tokens_ids[i].unsqueeze(0)\n",
+    "        tags = train_labels[i].unsqueeze(1)\n",
+    "        \n",
+    "        \n",
+    "        predicted_tags = lstm(batch_tokens)\n",
+    "\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",
+    "    print(eval_model(validation_tokens_ids, validation_labels, lstm))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ewaluacja:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d494e8de77cc4597b07eb4bbaff1d241",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3250 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.7134837896666285, 0.7239335115657329, 0.7186706669743826)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_model(validation_tokens_ids, validation_labels, lstm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a221459483784b94bc2251b8dae6bbba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3453 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.6529463280370325, 0.6433678500986193, 0.6481217013349891)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_model(test_tokens_ids, test_labels, lstm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Zadanie 3\n",
+    "\n",
+    "Sklonuj repozytorium https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003\n",
+    "\n",
+    "Stwórz model *sequence labelling* oparty o dowolną rekurencyjną sieć neuronową (możesz wzorować się na przykładzie z zajęć).\n",
+    "\n",
+    "W plikach dev-0/out.tsv oraz test-A/out.tsv umieść wyniki predykcji dla dev-0/in.tsv i test-A/in.tsv odpowiednio.\n",
+    "Do ewaluacji wykorzystaj narzędzie GEval (https://gitlab.com/filipg/geval):\n",
+    "\n",
+    "    wget https://gonito.net/get/bin/geval\n",
+    "    chmod u+x geval\n",
+    "    ./geval --help\n",
+    "\n",
+    "Liczba punktów uzyskanych za zadanie zależy od uzyskanej wartości accuracy na zbiorze `test-A` (wynik zaokrąglony w górę):\n",
+    "\n",
+    "    points = math.ceil(accuracy * 7.0)\n",
+    "\n",
+    "⚠️ W systemie Moodle proszę załączyć plik `test-A/out.tsv` oraz link do repozytorium z rozwiązaniem zadania.\n",
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "author": "Jakub Pokrywka",
+  "email": "kubapok@wmi.amu.edu.pl",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "lang": "pl",
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "subtitle": "11.NER RNN[ćwiczenia]",
+  "title": "Ekstrakcja informacji",
+  "year": "2021"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}