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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Podejście softmax z embeddingami na przykładzie NER"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+ " warnings.warn(msg)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import gensim\n",
+ "import torch\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "from datasets import load_dataset\n",
+ "from torchtext.vocab import Vocab\n",
+ "from collections import Counter\n",
+ "\n",
+ "from sklearn.datasets import fetch_20newsgroups\n",
+ "# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
+ "\n",
+ "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "\n",
+ "from tqdm.notebook import tqdm\n",
+ "\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset conll2003 (/home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6)\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset = load_dataset(\"conll2003\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_vocab(dataset):\n",
+ " counter = Counter()\n",
+ " for document in dataset:\n",
+ " counter.update(document)\n",
+ " return Vocab(counter, specials=['', '', '', ''])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab = build_vocab(dataset['train']['tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "23627"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(vocab.itos)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "15"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vocab['on']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def data_process(dt):\n",
+ " return [ torch.tensor([vocab['']] +[vocab[token] for token in document ] + [vocab['']], dtype = torch.long) for document in dt]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def labels_process(dt):\n",
+ " return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_tokens_ids = data_process(dataset['train']['tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_tokens_ids = data_process(dataset['test']['tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "validation_tokens_ids = data_process(dataset['validation']['tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "train_labels = labels_process(dataset['train']['ner_tags'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "validation_labels = labels_process(dataset['validation']['ner_tags'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_labels = labels_process(dataset['test']['ner_tags'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ 2, 966, 22409, 238, 773, 9, 4588, 212, 7686, 4,\n",
+ " 3])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_tokens_ids[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n",
+ " 'id': '0',\n",
+ " 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0],\n",
+ " 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\n",
+ " 'tokens': ['EU',\n",
+ " 'rejects',\n",
+ " 'German',\n",
+ " 'call',\n",
+ " 'to',\n",
+ " 'boycott',\n",
+ " 'British',\n",
+ " 'lamb',\n",
+ " '.']}"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset['train'][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0])"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_labels[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_scores(y_true, y_pred):\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": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_tags = max([max(x) for x in dataset['train']['ner_tags'] ]) + 1 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "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(vocab.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": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lstm = LSTM()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "criterion = torch.nn.CrossEntropyLoss()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "optimizer = torch.optim.Adam(lstm.parameters())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "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": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NUM_EPOCHS = 5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
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+ "\n",
+ "(0.5068524970963996, 0.5072649075903755, 0.5070586184860281)\n"
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+ },
+ "metadata": {},
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+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.7140486069946651, 0.7001046146693014, 0.7070078647728607)\n"
+ ]
+ },
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
+ "version_minor": 0
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+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.756327964151629, 0.725909566430315, 0.7408066429418744)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "version_minor": 0
+ },
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+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2f78871f366f4fd1b7de6c4be5303906",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.7963248522230789, 0.7203301174009067, 0.7564235581324383)\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": "code",
+ "execution_count": 27,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5159f7a61c3a439bab45573f15ea55b2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(0.7963248522230789, 0.7203301174009067, 0.7564235581324383)"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_model(validation_tokens_ids, validation_labels, lstm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4b604bbb796f4d4cb99528fad98cfdff",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3453.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(0.7450810185185185, 0.6348619329388561, 0.685569755058573)"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_model(test_tokens_ids, test_labels, lstm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "14041"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(train_tokens_ids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## pytania\n",
+ "\n",
+ "- co zrobić z trenowaniem na batchach > 1 ?\n",
+ "- co zrobić, żeby sieć uwzględniała następne tokeny, a nie tylko poprzednie?\n",
+ "- w jaki sposób wykorzystać taką sieć do zadania zwykłej klasyfikacji?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Zadanie na zajęcia ( 20 minut)\n",
+ "\n",
+ "zmodyfikować sieć tak, żeby była używała dwuwarstwowej, dwukierunkowej warstwy GRU oraz dropoutu. Dropout ma nałożony na embeddingi.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Zadanie domowe\n",
+ "\n",
+ "\n",
+ "- sklonować repozytorium https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003\n",
+ "- stworzyć model seq labelling bazujący na sieci neuronowej opisanej w punkcie niżej (można bazować na tym jupyterze lub nie).\n",
+ "- model sieci to GRU (o dowolnych parametrach) + CRF w pytorchu korzystając z modułu CRF z poprzednich zajęć- - stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
+ "- wynik fscore sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.65\n",
+ "- proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, a w MS TEAMS umieścić link do swojego repo\n",
+ "termin 22.06, 60 punktów, za najlepszy wynik- 100 punktów\n",
+ " "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/cw/11_NER_RNN_ODPOWIEDZI.ipynb b/cw/11_NER_RNN_ODPOWIEDZI.ipynb
new file mode 100644
index 0000000..03aceae
--- /dev/null
+++ b/cw/11_NER_RNN_ODPOWIEDZI.ipynb
@@ -0,0 +1,1137 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Podejście softmax z embeddingami na przykładzie NER"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
+ " warnings.warn(msg)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import gensim\n",
+ "import torch\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "from datasets import load_dataset\n",
+ "from torchtext.vocab import Vocab\n",
+ "from collections import Counter\n",
+ "\n",
+ "from sklearn.datasets import fetch_20newsgroups\n",
+ "# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
+ "\n",
+ "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "\n",
+ "from tqdm.notebook import tqdm\n",
+ "\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset conll2003 (/home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6)\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset = load_dataset(\"conll2003\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_vocab(dataset):\n",
+ " counter = Counter()\n",
+ " for document in dataset:\n",
+ " counter.update(document)\n",
+ " return Vocab(counter, specials=['', '', '', ''])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab = build_vocab(dataset['train']['tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "23627"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(vocab.itos)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "15"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vocab['on']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def data_process(dt):\n",
+ " return [ torch.tensor([vocab['']] +[vocab[token] for token in document ] + [vocab['']], dtype = torch.long) for document in dt]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def labels_process(dt):\n",
+ " return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_tokens_ids = data_process(dataset['train']['tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_tokens_ids = data_process(dataset['test']['tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "validation_tokens_ids = data_process(dataset['validation']['tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "train_labels = labels_process(dataset['train']['ner_tags'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "validation_labels = labels_process(dataset['validation']['ner_tags'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_labels = labels_process(dataset['test']['ner_tags'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ 2, 966, 22409, 238, 773, 9, 4588, 212, 7686, 4,\n",
+ " 3])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_tokens_ids[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n",
+ " 'id': '0',\n",
+ " 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0],\n",
+ " 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\n",
+ " 'tokens': ['EU',\n",
+ " 'rejects',\n",
+ " 'German',\n",
+ " 'call',\n",
+ " 'to',\n",
+ " 'boycott',\n",
+ " 'British',\n",
+ " 'lamb',\n",
+ " '.']}"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset['train'][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0])"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_labels[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_scores(y_true, y_pred):\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": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_tags = max([max(x) for x in dataset['train']['ner_tags'] ]) + 1 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "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(vocab.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": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lstm = LSTM()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "criterion = torch.nn.CrossEntropyLoss()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "optimizer = torch.optim.Adam(lstm.parameters())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "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": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NUM_EPOCHS = 5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "75184b632ce54ae690b3444778f44651",
+ "version_major": 2,
+ "version_minor": 0
+ },
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+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.5068524970963996, 0.5072649075903755, 0.5070586184860281)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
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+ "output_type": "display_data"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
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+ },
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+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.653649243957614, 0.6381494827385795, 0.6458063757205035)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ },
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+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "eebed0407ba343e29cf8c2d607f631dc",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.7140486069946651, 0.7001046146693014, 0.7070078647728607)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "70792f22eea343c8916bcfcf9215c298",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5d400bf1b656433ba2091cf750ec2d78",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.756327964151629, 0.725909566430315, 0.7408066429418744)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "604c4fa13c03435d81bf68be37977d74",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2f78871f366f4fd1b7de6c4be5303906",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.7963248522230789, 0.7203301174009067, 0.7564235581324383)\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": "code",
+ "execution_count": 27,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5159f7a61c3a439bab45573f15ea55b2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(0.7963248522230789, 0.7203301174009067, 0.7564235581324383)"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_model(validation_tokens_ids, validation_labels, lstm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4b604bbb796f4d4cb99528fad98cfdff",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3453.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(0.7450810185185185, 0.6348619329388561, 0.685569755058573)"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_model(test_tokens_ids, test_labels, lstm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "14041"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(train_tokens_ids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## pytania\n",
+ "\n",
+ "- co zrobić z trenowaniem na batchach > 1 ?\n",
+ "- co zrobić, żeby sieć uwzględniała następne tokeny, a nie tylko poprzednie?\n",
+ "- w jaki sposób wykorzystać taką sieć do zadania zwykłej klasyfikacji?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Zadanie na zajęcia ( 20 minut)\n",
+ "\n",
+ "zmodyfikować sieć tak, żeby była używała dwuwarstwowej, dwukierunkowej warstwy GRU oraz dropoutu. Dropout ma nałożony na embeddingi.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "class GRU(torch.nn.Module):\n",
+ "\n",
+ " def __init__(self):\n",
+ " super(GRU, self).__init__()\n",
+ " self.emb = torch.nn.Embedding(len(vocab.itos),100)\n",
+ " self.dropout = torch.nn.Dropout(0.2)\n",
+ " self.rec = torch.nn.GRU(100, 256, 2, batch_first = True, bidirectional = True)\n",
+ " self.fc1 = torch.nn.Linear(2* 256 , 9)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " emb = torch.relu(self.emb(x))\n",
+ " emb = self.dropout(emb)\n",
+ " \n",
+ " gru_output, h_n = self.rec(emb)\n",
+ " \n",
+ " out_weights = self.fc1(gru_output)\n",
+ "\n",
+ " return out_weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gru = GRU()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "criterion = torch.nn.CrossEntropyLoss()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "optimizer = torch.optim.Adam(gru.parameters())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NUM_EPOCHS = 5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a0b15b129c294730ab2a6035b9a98b47",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b7b3bc93dc7349949b59f97751ebdec2",
+ "version_major": 2,
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+ },
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+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.6431379891406104, 0.39927932116703474, 0.49268502581755597)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "953a62a22eb44de4a33b0e1c53a472f0",
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+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.6638910917261432, 0.5838660932232942, 0.6213123879027769)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cb7cde559dd544b0961fa4c855b856d8",
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+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.6697936210131332, 0.7054515866558178, 0.6871603260869565)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d7211ba79faa4de3aeafff4ef39bbaf1",
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+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2a8dbd5393fa4ee2a8ee72738efa2a57",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.7091097308488613, 0.7166104847146344, 0.7128403769439787)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "84d0b4b0e1ec4c1a88aff0698836a362",
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+ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "31a2f0a285e44a55a7b583a81511dbc2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(0.7708963348252087, 0.740788097175404, 0.755542382928275)\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(NUM_EPOCHS):\n",
+ " gru.train()\n",
+ " #for i in tqdm(range(50)):\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 = gru(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",
+ " \n",
+ " gru.eval()\n",
+ " print(eval_model(validation_tokens_ids, validation_labels, gru))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Zadanie domowe\n",
+ "\n",
+ "\n",
+ "- sklonować repozytorium https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003\n",
+ "- stworzyć model seq labelling bazujący na sieci neuronowej opisanej w punkcie niżej (można bazować na tym jupyterze lub nie).\n",
+ "- model sieci to GRU (o dowolnych parametrach) + CRF w pytorchu korzystając z modułu CRF z poprzednich zajęć- - stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
+ "- wynik fscore sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.65\n",
+ "- proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, a w MS TEAMS umieścić link do swojego repo\n",
+ "termin 22.06, 60 punktów, za najlepszy wynik- 100 punktów\n",
+ " "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
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