929 lines
24 KiB
Plaintext
929 lines
24 KiB
Plaintext
{
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "6cb176b6c465408bad2c2e7fed25dcd0",
|
||
"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": "935c5d560d364b6b9930216f993caf2a",
|
||
"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": [
|
||
"(0.6289105835367207, 0.6589561780774148, 0.643582902877902)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "1827fd779e5c478ebc6b512788898c8e",
|
||
"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": "2a347fc594654853a6e6a2425a7a5c98",
|
||
"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": [
|
||
"(0.7031268719300348, 0.6822038823666163, 0.6925073746312684)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "788f646a32824270b7a9a5ef2b87662b",
|
||
"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": "8b44d34b37d844ac9d3075e9a3fd25d4",
|
||
"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": [
|
||
"(0.7354687113529558, 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
|
||
}
|