This commit is contained in:
Jakub Pokrywka
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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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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Ekstrakcja informacji </h1>\n",
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"<h2> 10. <i>CRF</i> [ćwiczenia]</h2> \n",
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"<h3> Jakub Pokrywka (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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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": "markdown",
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"metadata": {
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"scrolled": true
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},
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"source": [
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"https://pytorch-crf.readthedocs.io/en/stable/"
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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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"https://www.aclweb.org/anthology/W03-0419.pdf"
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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": 1,
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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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"Requirement already satisfied: pytorch-crf in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (0.7.2)\r\n"
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]
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}
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],
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"source": [
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"!pip install pytorch-crf"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import gensim\n",
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"import torch\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import torchtext\n",
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"from sklearn.model_selection import train_test_split\n",
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"\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 collections import Counter\n",
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"\n",
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"from sklearn.datasets import fetch_20newsgroups\n",
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"# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
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"\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"import torch\n",
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"from torchcrf import CRF"
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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": 2,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Reusing dataset conll2003 (/home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/63f4ebd1bcb7148b1644497336fd74643d4ce70123334431a3c053b7ee4e96ee)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "60fb8337cb5b4ab28969b9e1d60a851c",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/3 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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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": "code",
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"execution_count": 3,
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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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" vocab = torchtext.vocab.vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])\n",
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" vocab.set_default_index(0)\n",
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" return vocab"
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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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"outputs": [],
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"source": [
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"vocab = 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": 5,
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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": 5,
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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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"vocab['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": 6,
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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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" return [ torch.tensor([vocab['<bos>']] +[vocab[token] for token in document ] + [vocab['<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": 7,
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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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" 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": "code",
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"execution_count": 8,
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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": 9,
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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": 10,
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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": 11,
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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": 12,
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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": 13,
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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": "code",
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"execution_count": 14,
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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": 14,
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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": 15,
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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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" 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",
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" \n",
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" if precision + recall == 0.0 :\n",
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" f1 = 0.0\n",
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" else:\n",
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" f1 = 2* precision * recall / (precision + recall)\n",
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"\n",
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" return precision, recall, f1"
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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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_tags = max([max(x) for x in dataset['train']['ner_tags'] if x]) + 1 "
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"class FF(torch.nn.Module):\n",
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"\n",
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" def __init__(self,):\n",
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" super(FF, self).__init__()\n",
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" self.emb = torch.nn.Embedding(23627,200)\n",
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" self.fc1 = torch.nn.Linear(200,num_tags)\n",
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" \n",
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"\n",
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" def forward(self, x):\n",
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" x = self.emb(x)\n",
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" x = self.fc1(x)\n",
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" return x"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"ff = FF()"
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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": 19,
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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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"crf = CRF(num_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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"params = list(ff.parameters()) + list(crf.parameters())\n",
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"\n",
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"optimizer = torch.optim.Adam(params)"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"def eval_model(dataset_tokens, dataset_labels):\n",
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" Y_true = []\n",
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" Y_pred = []\n",
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" ff.eval()\n",
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" crf.eval()\n",
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" for i in tqdm(range(len(dataset_labels))):\n",
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" batch_tokens = dataset_tokens[i]\n",
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" tags = list(dataset_labels[i].numpy())\n",
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" emissions = ff(batch_tokens).unsqueeze(1)\n",
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" Y_pred += crf.decode(emissions)[0]\n",
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" Y_true += tags\n",
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"\n",
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" return get_scores(Y_true, Y_pred)\n",
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" "
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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": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"NUM_EPOCHS = 4"
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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": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "fc9748f7f63c47fea274592f4dba2c73",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/14042 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"> \u001b[0;32m/tmp/ipykernel_306568/4048919537.py\u001b[0m(12)\u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;32m 10 \u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mcrf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memissions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0m\u001b[0;32m 11 \u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0m\u001b[0;32m---> 12 \u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0m\u001b[0;32m 13 \u001b[0;31m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0m\u001b[0;32m 14 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0m\n",
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"ipdb> batch_tokens\n",
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"tensor([ 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 3])\n",
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"ipdb> tags.shape\n",
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"torch.Size([11, 1])\n",
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"ipdb> tags\n",
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"tensor([[0],\n",
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" [3],\n",
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" [0],\n",
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" [7],\n",
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" [0],\n",
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" [0],\n",
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" [0],\n",
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" [7],\n",
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" [0],\n",
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" [0],\n",
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" [0]])\n"
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]
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}
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],
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"source": [
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"for i in range(NUM_EPOCHS):\n",
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" ff.train()\n",
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" crf.train()\n",
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" for i in tqdm(range(len(train_labels))):\n",
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" batch_tokens = train_tokens_ids[i]\n",
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" tags = train_labels[i].unsqueeze(1)\n",
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" emissions = ff(batch_tokens).unsqueeze(1)\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss = -crf(emissions,tags)\n",
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" import pdb; pdb.set_trace()\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" \n",
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" ff.eval()\n",
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||||
" crf.eval()\n",
|
||||
" print(eval_model(validation_tokens_ids, validation_labels))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['T_destination',\n",
|
||||
" '__annotations__',\n",
|
||||
" '__call__',\n",
|
||||
" '__class__',\n",
|
||||
" '__delattr__',\n",
|
||||
" '__dict__',\n",
|
||||
" '__dir__',\n",
|
||||
" '__doc__',\n",
|
||||
" '__eq__',\n",
|
||||
" '__format__',\n",
|
||||
" '__ge__',\n",
|
||||
" '__getattr__',\n",
|
||||
" '__getattribute__',\n",
|
||||
" '__gt__',\n",
|
||||
" '__hash__',\n",
|
||||
" '__init__',\n",
|
||||
" '__init_subclass__',\n",
|
||||
" '__le__',\n",
|
||||
" '__lt__',\n",
|
||||
" '__module__',\n",
|
||||
" '__ne__',\n",
|
||||
" '__new__',\n",
|
||||
" '__reduce__',\n",
|
||||
" '__reduce_ex__',\n",
|
||||
" '__repr__',\n",
|
||||
" '__setattr__',\n",
|
||||
" '__setstate__',\n",
|
||||
" '__sizeof__',\n",
|
||||
" '__str__',\n",
|
||||
" '__subclasshook__',\n",
|
||||
" '__weakref__',\n",
|
||||
" '_apply',\n",
|
||||
" '_backward_hooks',\n",
|
||||
" '_buffers',\n",
|
||||
" '_call_impl',\n",
|
||||
" '_compute_normalizer',\n",
|
||||
" '_compute_score',\n",
|
||||
" '_forward_hooks',\n",
|
||||
" '_forward_pre_hooks',\n",
|
||||
" '_get_backward_hooks',\n",
|
||||
" '_get_name',\n",
|
||||
" '_is_full_backward_hook',\n",
|
||||
" '_load_from_state_dict',\n",
|
||||
" '_load_state_dict_pre_hooks',\n",
|
||||
" '_maybe_warn_non_full_backward_hook',\n",
|
||||
" '_modules',\n",
|
||||
" '_named_members',\n",
|
||||
" '_non_persistent_buffers_set',\n",
|
||||
" '_parameters',\n",
|
||||
" '_register_load_state_dict_pre_hook',\n",
|
||||
" '_register_state_dict_hook',\n",
|
||||
" '_replicate_for_data_parallel',\n",
|
||||
" '_save_to_state_dict',\n",
|
||||
" '_slow_forward',\n",
|
||||
" '_state_dict_hooks',\n",
|
||||
" '_validate',\n",
|
||||
" '_version',\n",
|
||||
" '_viterbi_decode',\n",
|
||||
" 'add_module',\n",
|
||||
" 'apply',\n",
|
||||
" 'batch_first',\n",
|
||||
" 'bfloat16',\n",
|
||||
" 'buffers',\n",
|
||||
" 'children',\n",
|
||||
" 'cpu',\n",
|
||||
" 'cuda',\n",
|
||||
" 'decode',\n",
|
||||
" 'double',\n",
|
||||
" 'dump_patches',\n",
|
||||
" 'end_transitions',\n",
|
||||
" 'eval',\n",
|
||||
" 'extra_repr',\n",
|
||||
" 'float',\n",
|
||||
" 'forward',\n",
|
||||
" 'get_buffer',\n",
|
||||
" 'get_extra_state',\n",
|
||||
" 'get_parameter',\n",
|
||||
" 'get_submodule',\n",
|
||||
" 'half',\n",
|
||||
" 'load_state_dict',\n",
|
||||
" 'modules',\n",
|
||||
" 'named_buffers',\n",
|
||||
" 'named_children',\n",
|
||||
" 'named_modules',\n",
|
||||
" 'named_parameters',\n",
|
||||
" 'num_tags',\n",
|
||||
" 'parameters',\n",
|
||||
" 'register_backward_hook',\n",
|
||||
" 'register_buffer',\n",
|
||||
" 'register_forward_hook',\n",
|
||||
" 'register_forward_pre_hook',\n",
|
||||
" 'register_full_backward_hook',\n",
|
||||
" 'register_module',\n",
|
||||
" 'register_parameter',\n",
|
||||
" 'requires_grad_',\n",
|
||||
" 'reset_parameters',\n",
|
||||
" 'set_extra_state',\n",
|
||||
" 'share_memory',\n",
|
||||
" 'start_transitions',\n",
|
||||
" 'state_dict',\n",
|
||||
" 'to',\n",
|
||||
" 'to_empty',\n",
|
||||
" 'train',\n",
|
||||
" 'training',\n",
|
||||
" 'transitions',\n",
|
||||
" 'type',\n",
|
||||
" 'xpu',\n",
|
||||
" 'zero_grad']"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dir(crf)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Parameter containing:\n",
|
||||
"tensor([[ 0.1427, 0.0082, -0.0852, -0.0714, -0.0514, 0.0753, 0.0389, 0.0018,\n",
|
||||
" -0.0806],\n",
|
||||
" [-0.0809, -0.0508, 0.0520, -0.0619, 0.0181, -0.0729, -0.1430, -0.1055,\n",
|
||||
" 0.0384],\n",
|
||||
" [-0.0011, -0.1476, 0.0425, -0.0081, -0.1181, -0.0098, -0.0567, 0.0311,\n",
|
||||
" -0.0696],\n",
|
||||
" [-0.0443, -0.0741, 0.0463, -0.0967, -0.0403, -0.0243, 0.0098, -0.0063,\n",
|
||||
" -0.0811],\n",
|
||||
" [ 0.0632, -0.1175, -0.0992, 0.0198, 0.0310, -0.0059, 0.0191, -0.1303,\n",
|
||||
" -0.1423],\n",
|
||||
" [ 0.0029, 0.0296, 0.0152, -0.0418, -0.1068, -0.0920, -0.0380, 0.0461,\n",
|
||||
" 0.0167],\n",
|
||||
" [-0.1167, -0.0559, -0.0428, -0.0115, -0.1006, -0.1511, 0.0035, -0.0273,\n",
|
||||
" -0.1201],\n",
|
||||
" [-0.0378, 0.0481, -0.1474, -0.0154, 0.0347, -0.0392, -0.0755, -0.1227,\n",
|
||||
" 0.0448],\n",
|
||||
" [-0.0383, -0.0402, 0.0054, 0.0145, -0.1353, -0.0460, 0.0257, -0.0322,\n",
|
||||
" 0.0023]], requires_grad=True)"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"crf.transitions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Parameter containing:\n",
|
||||
" tensor([-0.0432, -0.1150, -0.1045, -0.0779, -0.0858, 0.0287, -0.1437, -0.1446,\n",
|
||||
" 0.0335], requires_grad=True),\n",
|
||||
" Parameter containing:\n",
|
||||
" tensor([ 0.0838, -0.0097, -0.1136, 0.0010, -0.1177, 0.0225, 0.0292, -0.0837,\n",
|
||||
" -0.1063], requires_grad=True),\n",
|
||||
" Parameter containing:\n",
|
||||
" tensor([[ 0.1427, 0.0082, -0.0852, -0.0714, -0.0514, 0.0753, 0.0389, 0.0018,\n",
|
||||
" -0.0806],\n",
|
||||
" [-0.0809, -0.0508, 0.0520, -0.0619, 0.0181, -0.0729, -0.1430, -0.1055,\n",
|
||||
" 0.0384],\n",
|
||||
" [-0.0011, -0.1476, 0.0425, -0.0081, -0.1181, -0.0098, -0.0567, 0.0311,\n",
|
||||
" -0.0696],\n",
|
||||
" [-0.0443, -0.0741, 0.0463, -0.0967, -0.0403, -0.0243, 0.0098, -0.0063,\n",
|
||||
" -0.0811],\n",
|
||||
" [ 0.0632, -0.1175, -0.0992, 0.0198, 0.0310, -0.0059, 0.0191, -0.1303,\n",
|
||||
" -0.1423],\n",
|
||||
" [ 0.0029, 0.0296, 0.0152, -0.0418, -0.1068, -0.0920, -0.0380, 0.0461,\n",
|
||||
" 0.0167],\n",
|
||||
" [-0.1167, -0.0559, -0.0428, -0.0115, -0.1006, -0.1511, 0.0035, -0.0273,\n",
|
||||
" -0.1201],\n",
|
||||
" [-0.0378, 0.0481, -0.1474, -0.0154, 0.0347, -0.0392, -0.0755, -0.1227,\n",
|
||||
" 0.0448],\n",
|
||||
" [-0.0383, -0.0402, 0.0054, 0.0145, -0.1353, -0.0460, 0.0257, -0.0322,\n",
|
||||
" 0.0023]], requires_grad=True)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"list(crf.parameters())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"eval_model(validation_tokens_ids, validation_labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"eval_model(test_tokens_ids, test_labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"len(train_tokens_ids)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Zadanie domowe\n",
|
||||
"\n",
|
||||
"- en-ner-conll-2003\n",
|
||||
"- stworzyć klasyfikator bazujący na sieci neuronowej feed forward w pytorchu + CRF (można bazować na tym jupyterze lub nie).\n",
|
||||
"- sieć feedforward powinna obejmować aktualne słowo, poprzednie i następne + dodatkowe cechy (np. długość wyrazu, czy wyraz zaczyna się od wielkiej litery, stemmming słowa, czy zawiera cyfrę)\n",
|
||||
"- 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",
|
||||
"- 60 punktów, za najlepszy wynik- 100 punktów\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"author": "Jakub Pokrywka",
|
||||
"email": "kubapok@wmi.amu.edu.pl",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.4"
|
||||
},
|
||||
"subtitle": "10.CRF[ćwiczenia]",
|
||||
"title": "Ekstrakcja informacji",
|
||||
"year": "2021"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue