forked from filipg/aitech-eks-pub
428 lines
9.9 KiB
Plaintext
428 lines
9.9 KiB
Plaintext
{
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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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"### 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": null,
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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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"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": 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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"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": null,
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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": null,
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"len(vocab.itos)"
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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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"outputs": [],
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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": null,
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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": null,
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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": null,
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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": null,
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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": null,
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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": null,
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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": null,
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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": null,
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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": null,
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"metadata": {},
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"outputs": [],
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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": null,
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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": null,
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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'] ]) + 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": null,
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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": null,
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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": null,
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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": null,
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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": null,
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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": null,
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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": true
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},
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"outputs": [],
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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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" 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",
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" print(eval_model(validation_tokens_ids, validation_labels))"
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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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"outputs": [],
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"source": [
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"eval_model(validation_tokens_ids, validation_labels)"
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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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"outputs": [],
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"source": [
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"eval_model(test_tokens_ids, test_labels)"
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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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"outputs": [],
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"source": [
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"len(train_tokens_ids)"
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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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"### Zadanie domowe\n",
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"\n",
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"- sklonować repozytorium https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003\n",
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"- stworzyć klasyfikator bazujący na sieci neuronowej feed forward w pytorchu + CRF (można bazować na tym jupyterze lub nie).\n",
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"- 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",
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"- stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
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"- wynik fscore sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.65\n",
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"- proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, a w MS TEAMS umieścić link do swojego repo\n",
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"termin 15.06, 60 punktów, za najlepszy wynik- 100 punktów\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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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