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3_RNN.ipynb
2024-05-10 14:56:49 +02:00
{
"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",
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"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",
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"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",
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"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",
NER, nie POS
2024-05-13 08:23:16 +02:00
" # Wektoryzacja etykiet (NER)\n",
3_RNN.ipynb
2024-05-10 14:56:49 +02:00
" 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": [
NER, nie POS
2024-05-13 08:23:16 +02:00
"Ile mamy różnych tagów NER?"
3_RNN.ipynb
2024-05-10 14:56:49 +02:00
]
},
{
"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": [
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" 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
}
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