3_RNN.ipynb
This commit is contained in:
Paweł Skórzewski
parent
3e045c950b
commit
ccd30390b3
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3_RNN.ipynb
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928
3_RNN.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Uczenie głębokie – przetwarzanie tekstu – laboratoria\n",
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"# 3. RNN"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Podejście softmax z embeddingami na przykładzie NER"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Defaulting to user installation because normal site-packages is not writeable\n",
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"Requirement already satisfied: torch in /home/pawel/.local/lib/python3.10/site-packages (2.3.0)\n",
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"Collecting torchtext\n",
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" Downloading torchtext-0.18.0-cp310-cp310-manylinux1_x86_64.whl (2.0 MB)\n",
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"\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",
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"\u001b[?25hRequirement already satisfied: filelock in /home/pawel/.local/lib/python3.10/site-packages (from torch) (3.13.1)\n",
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"Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /home/pawel/.local/lib/python3.10/site-packages (from torch) (2.20.5)\n",
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"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",
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"Requirement already satisfied: numpy in /home/pawel/.local/lib/python3.10/site-packages (from torchtext) (1.26.4)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/pawel/.local/lib/python3.10/site-packages (from requests->torchtext) (2.2.1)\n",
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"Requirement already satisfied: mpmath>=0.19 in /home/pawel/.local/lib/python3.10/site-packages (from sympy->torch) (1.3.0)\n",
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"Installing collected packages: torchtext\n",
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"Successfully installed torchtext-0.18.0\n"
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]
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}
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],
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"source": [
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"!pip install torch torchtext"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import Counter\n",
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"\n",
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"import torch\n",
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"from datasets import load_dataset\n",
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"from torchtext.vocab import vocab\n",
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"from tqdm.notebook import tqdm"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wczytujemy zbiór danych `conll2003` (https://huggingface.co/datasets/conll2003), który zawiera teksty oznaczone znacznikami części mowy (*POS tags*): "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"dataset = load_dataset(\"conll2003\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Poiżej funkcja, która tworzy słownik (https://pytorch.org/text/stable/vocab.html).\n",
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"\n",
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"Parametr `special` określa symbole specjalne:\n",
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"* `<unk>` – nieznany token\n",
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"* `<pad>` – wypełnienie\n",
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"* `<bos>` – początek zdania\n",
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"* `<eos>` – koniec zdania"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"def build_vocab(dataset):\n",
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" counter = Counter()\n",
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" for document in dataset:\n",
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" counter.update(document)\n",
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" return vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"v = build_vocab(dataset['train']['tokens'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"metadata": {},
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"outputs": [],
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"source": [
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"itos = v.get_itos() # mapowanie indeksów na tokeny"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"23627"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(itos) # liczba różnych tokenów w słowniku"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"21"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"v['on'] # indeks tokenu `on`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"execution_count": 31,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"v[\"<unk>\"] # indeks nieznanego tokenu"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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”:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"v.set_default_index(v[\"<unk>\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [],
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"source": [
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"def data_process(dt):\n",
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" # Wektoryzacja dokumentów tekstowych.\n",
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" return [ torch.tensor([v['<bos>']] +[v[token] for token in document ] + [v['<eos>']], dtype = torch.long) for document in dt]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [],
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"source": [
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"def labels_process(dt):\n",
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" # Wektoryzacja etykiet (POS)\n",
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" return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Teraz wektoryzujemy wszystkie dane:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_tokens_ids = data_process(dataset['train']['tokens'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_tokens_ids = data_process(dataset['test']['tokens'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"validation_tokens_ids = data_process(dataset['validation']['tokens'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"train_labels = labels_process(dataset['train']['ner_tags'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"validation_labels = labels_process(dataset['validation']['ner_tags'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_labels = labels_process(dataset['test']['ner_tags'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Przykład, jak wyglądają dane po zwektoryzowaniu:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([ 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 3])"
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]
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},
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train_tokens_ids[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'id': '0',\n",
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" 'tokens': ['EU',\n",
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" 'rejects',\n",
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" 'German',\n",
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" 'call',\n",
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" 'to',\n",
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" 'boycott',\n",
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" 'British',\n",
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" 'lamb',\n",
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" '.'],\n",
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" 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\n",
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" 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n",
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" 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}"
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]
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},
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"execution_count": 42,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dataset['train'][0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([0, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0])"
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]
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},
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"execution_count": 43,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train_labels[0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Funkcja, której użyjemy do ewaluacji:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_scores(y_true, y_pred):\n",
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" # Funkcja zwraca precyzję, pokrycie i F1\n",
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" acc_score = 0\n",
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" tp = 0\n",
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" fp = 0\n",
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" selected_items = 0\n",
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" relevant_items = 0 \n",
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"\n",
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" for p,t in zip(y_pred, y_true):\n",
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" if p == t:\n",
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" acc_score +=1\n",
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"\n",
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" if p > 0 and p == t:\n",
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" tp +=1\n",
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"\n",
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" if p > 0:\n",
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" selected_items += 1\n",
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"\n",
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" if t > 0 :\n",
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" relevant_items +=1\n",
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"\n",
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" \n",
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" \n",
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" if selected_items == 0:\n",
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" precision = 1.0\n",
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" else:\n",
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" precision = tp / selected_items\n",
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" \n",
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" \n",
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" if relevant_items == 0:\n",
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" recall = 1.0\n",
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" else:\n",
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" recall = tp / relevant_items\n",
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" \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
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user