forked from kubapok/en-ner-conll-2003
cos tam
This commit is contained in:
parent
c6aaaf6544
commit
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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376
.ipynb_checkpoints/Zad11-checkpoint.ipynb
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376
.ipynb_checkpoints/Zad11-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "bce0cfa7",
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"metadata": {},
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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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"C:\\Users\\grzyb\\anaconda3\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
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" warnings.warn(msg)\n"
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]
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}
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],
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"source": [
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"from os import sep\n",
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"from nltk import word_tokenize\n",
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"import pandas as pd\n",
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"import torch\n",
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"from TorchCRF import CRF\n",
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"import gensim\n",
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"from torch._C import device\n",
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"from tqdm import tqdm\n",
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"from torchtext.vocab import Vocab\n",
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"from collections import Counter, OrderedDict\n",
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"import spacy\n",
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"\n",
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"\n",
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"from torch.utils.data import DataLoader\n",
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"import numpy as np\n",
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"from sklearn.metrics import accuracy_score, f1_score, classification_report\n",
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"import csv\n",
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"import pickle\n",
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"\n",
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"import lzma\n",
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"import re\n",
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"import itertools"
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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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"id": "67ace382",
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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 c:\\users\\grzyb\\anaconda3\\lib\\site-packages (0.7.2)\n"
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]
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}
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],
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"source": [
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"!pip3 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": 3,
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"id": "adc9a4de",
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'torchcrf'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-3-2a643b4fc1bb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchcrf\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCRF\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torchcrf'"
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]
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}
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],
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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 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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"id": "6695751c",
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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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"id": "d247e4fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"def data_process(dt, vocab):\n",
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" return [torch.tensor([vocab[token] for token in document], dtype=torch.long) for document in dt]\n",
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"\n",
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"\n",
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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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" 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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" if p > 0 and p == t:\n",
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" tp += 1\n",
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" if p > 0:\n",
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" selected_items += 1\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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" 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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" 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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" 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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"id": "b6061642",
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"metadata": {},
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"outputs": [],
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"source": [
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"def process_output(lines):\n",
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" result = []\n",
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" for line in lines:\n",
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" last_label = None\n",
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" new_line = []\n",
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" for label in line:\n",
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" if(label != \"O\" and label[0:2] == \"I-\"):\n",
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" if last_label == None or last_label == \"O\":\n",
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" label = label.replace('I-', 'B-')\n",
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" else:\n",
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" label = \"I-\" + last_label[2:]\n",
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" last_label = label\n",
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" new_line.append(label)\n",
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" x = (\" \".join(new_line))\n",
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" result.append(\" \".join(new_line))\n",
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" return result"
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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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"id": "3d7c4dd3",
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"metadata": {},
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"outputs": [],
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"source": [
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"class GRU(torch.nn.Module):\n",
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" def __init__(self):\n",
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" super(GRU, self).__init__()\n",
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" self.emb = torch.nn.Embedding(len(vocab_x.itos),100)\n",
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" self.dropout = torch.nn.Dropout(0.2)\n",
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" self.rec = torch.nn.GRU(100, 256, 2, batch_first = True, bidirectional = True)\n",
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" self.fc1 = torch.nn.Linear(2* 256 , 9)\n",
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" \n",
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" def forward(self, x):\n",
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" emb = torch.relu(self.emb(x))\n",
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" emb = self.dropout(emb) \n",
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" gru_output, h_n = self.rec(emb) \n",
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" out_weights = self.fc1(gru_output)\n",
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" return out_weights"
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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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"id": "cd5e419d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def dev_eval(model, crf, dev_tokens, dev_labels_tokens, vocab):\n",
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" Y_true = []\n",
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" Y_pred = []\n",
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" model.eval()\n",
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" crf.eval()\n",
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" for i in tqdm(range(len(dev_labels_tokens))):\n",
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" batch_tokens = dev_tokens[i].unsqueeze(0)\n",
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" tags = list(dev_labels_tokens[i].numpy())\n",
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" Y_true += tags\n",
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"\n",
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" Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
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" Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
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" Y_pred += [crf.decode(Y_batch_pred)[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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"id": "c808bbd5",
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"metadata": {},
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"outputs": [],
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"source": [
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"train = pd.read_csv('train/train.tsv', sep='\\t',\n",
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" names=['labels', 'document'])\n",
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"\n",
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"Y_train = [y.split(sep=\" \") for y in train['labels'].values]\n",
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"X_train = [x.split(sep=\" \") for x in train['document'].values]\n",
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"\n",
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"dev = pd.read_csv('dev-0/in.tsv', sep='\\t', names=['document'])\n",
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"exp = pd.read_csv('dev-0/expected.tsv', sep='\\t', names=['labels'])\n",
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"X_dev = [x.split(sep=\" \") for x in dev['document'].values]\n",
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"Y_dev = [y.split(sep=\" \") for y in exp['labels'].values]\n",
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"\n",
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"test = pd.read_csv('test-A/in.tsv', sep='\\t', names=['document'])\n",
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"X_test = test['document'].values"
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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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"id": "79485c9a",
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"metadata": {},
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"outputs": [],
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"source": [
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"vocab_x = build_vocab(X_train)\n",
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"vocab_y = build_vocab(Y_train)\n",
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"train_tokens = data_process(X_train, vocab_x)\n",
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"labels_tokens = data_process(Y_train, vocab_y)"
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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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"id": "3726c82a",
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"metadata": {},
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"outputs": [],
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"source": [
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"torch.cuda.get_device_name(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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"id": "f29e3b63",
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"metadata": {},
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"outputs": [],
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"source": [
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"device_gpu = torch.device(\"cuda:0\")\n",
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"model = GRU()\n",
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"crf = CRF(9)\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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"id": "9c321d58",
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"metadata": {},
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"outputs": [],
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"source": [
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"mask = torch.ByteTensor([1, 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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"id": "05482a7c",
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"metadata": {},
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"outputs": [],
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"source": [
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"criterion = torch.nn.CrossEntropyLoss()\n",
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"params = list(model.parameters()) + list(crf.parameters())\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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"id": "21a5282e",
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(2):\n",
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" crf.train()\n",
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" model.train()\n",
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" for i in tqdm(range(len(labels_tokens))):\n",
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" batch_tokens = train_tokens[i].unsqueeze(0)\n",
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" tags = labels_tokens[i].unsqueeze(1)\n",
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"\n",
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" predicted_tags = model(batch_tokens).squeeze(0).unsqueeze(1)\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss = -crf(predicted_tags, tags)\n",
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"\n",
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" loss.backward()\n",
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" optimizer.step()"
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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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"id": "cec14c35",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip3 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": null,
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"id": "1ee634f7",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"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": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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"from gensim.models.word2vec import Word2Vec"
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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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"id": "b476f295",
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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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"Collecting gensim\n",
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" Downloading gensim-4.0.1-cp38-cp38-win_amd64.whl (23.9 MB)\n",
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"Requirement already satisfied: scipy>=0.18.1 in c:\\users\\grzyb\\anaconda3\\lib\\site-packages (from gensim) (1.6.2)\n",
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"Collecting Cython==0.29.21\n",
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" Downloading Cython-0.29.21-cp38-cp38-win_amd64.whl (1.7 MB)\n",
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"Requirement already satisfied: numpy>=1.11.3 in c:\\users\\grzyb\\anaconda3\\lib\\site-packages (from gensim) (1.20.1)\n",
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"Collecting smart-open>=1.8.1\n",
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" Downloading smart_open-5.1.0-py3-none-any.whl (57 kB)\n",
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"Installing collected packages: smart-open, Cython, gensim\n",
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" Attempting uninstall: Cython\n",
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" Found existing installation: Cython 0.29.23\n",
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" Uninstalling Cython-0.29.23:\n",
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" Successfully uninstalled Cython-0.29.23\n",
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"Successfully installed Cython-0.29.21 gensim-4.0.1 smart-open-5.1.0\n"
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]
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}
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],
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"source": [
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"!pip install gensim"
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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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@ -148,7 +117,7 @@
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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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"execution_count": 7,
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"id": "66bee163",
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"metadata": {},
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"outputs": [],
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@ -172,7 +141,7 @@
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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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"execution_count": 8,
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"id": "39046f3f",
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"metadata": {},
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"outputs": [],
|
||||
@ -182,7 +151,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"id": "9b40a8b6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -194,7 +163,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 10,
|
||||
"id": "02a12cbd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -204,7 +173,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 11,
|
||||
"id": "8cc6d19d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -222,7 +191,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 12,
|
||||
"id": "690085f6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -232,7 +201,7 @@
|
||||
"'NVIDIA GeForce RTX 2060'"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -243,7 +212,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 13,
|
||||
"id": "64b2d751",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -256,7 +225,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 14,
|
||||
"id": "094d7e69",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -267,7 +236,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 15,
|
||||
"id": "17291b41",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -277,7 +246,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 16,
|
||||
"id": "045b7186",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -286,20 +255,20 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"epoch: 0\n",
|
||||
"f1: 0.6373470953763748\n",
|
||||
"acc: 0.9116419913061858\n",
|
||||
"f1: 0.6310260230881535\n",
|
||||
"acc: 0.9099004714510215\n",
|
||||
"epoch: 1\n",
|
||||
"f1: 0.7973076923076923\n",
|
||||
"acc: 0.9540771782783307\n",
|
||||
"f1: 0.7977381727751791\n",
|
||||
"acc: 0.9539025667888947\n",
|
||||
"epoch: 2\n",
|
||||
"f1: 0.8640167364016735\n",
|
||||
"acc: 0.9702287410511612\n",
|
||||
"f1: 0.8635445687583837\n",
|
||||
"acc: 0.9699162783858546\n",
|
||||
"epoch: 3\n",
|
||||
"f1: 0.9038441719055962\n",
|
||||
"acc: 0.9793820591289644\n",
|
||||
"f1: 0.9047002002591589\n",
|
||||
"acc: 0.9794417946385082\n",
|
||||
"epoch: 4\n",
|
||||
"f1: 0.928903400400047\n",
|
||||
"acc: 0.9850890978100043\n"
|
||||
"f1: 0.9300697243387956\n",
|
||||
"acc: 0.9852774944170274\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -346,7 +315,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 17,
|
||||
"id": "f75aa5e2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -367,7 +336,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 18,
|
||||
"id": "49215802",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -391,7 +360,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 19,
|
||||
"id": "8c5b007e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -412,6 +381,17 @@
|
||||
" for line in results_processed:\n",
|
||||
" f.write(line + \"\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"id": "000dd425",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model_path = \"seq_labeling.model\"\n",
|
||||
"torch.save(ner_model.state_dict(), model_path)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
376
Zad11.ipynb
Normal file
376
Zad11.ipynb
Normal file
@ -0,0 +1,376 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "bce0cfa7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\grzyb\\anaconda3\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
|
||||
" warnings.warn(msg)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from os import sep\n",
|
||||
"from nltk import word_tokenize\n",
|
||||
"import pandas as pd\n",
|
||||
"import torch\n",
|
||||
"from TorchCRF import CRF\n",
|
||||
"import gensim\n",
|
||||
"from torch._C import device\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"from torchtext.vocab import Vocab\n",
|
||||
"from collections import Counter, OrderedDict\n",
|
||||
"import spacy\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from torch.utils.data import DataLoader\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.metrics import accuracy_score, f1_score, classification_report\n",
|
||||
"import csv\n",
|
||||
"import pickle\n",
|
||||
"\n",
|
||||
"import lzma\n",
|
||||
"import re\n",
|
||||
"import itertools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "67ace382",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Requirement already satisfied: pytorch-crf in c:\\users\\grzyb\\anaconda3\\lib\\site-packages (0.7.2)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pip3 install pytorch-crf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "adc9a4de",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'torchcrf'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-3-2a643b4fc1bb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchcrf\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCRF\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torchcrf'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import gensim\n",
|
||||
"import torch\n",
|
||||
"import pandas as pd\n",
|
||||
"import seaborn as sns\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"\n",
|
||||
"from torchtext.vocab import Vocab\n",
|
||||
"from collections import Counter\n",
|
||||
"\n",
|
||||
"from sklearn.datasets import fetch_20newsgroups\n",
|
||||
"# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
|
||||
"\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"\n",
|
||||
"from tqdm.notebook import tqdm\n",
|
||||
"\n",
|
||||
"import torch\n",
|
||||
"from torchcrf import CRF"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6695751c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def build_vocab(dataset):\n",
|
||||
" counter = Counter()\n",
|
||||
" for document in dataset:\n",
|
||||
" counter.update(document)\n",
|
||||
" return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d247e4fe",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def data_process(dt, vocab):\n",
|
||||
" return [torch.tensor([vocab[token] for token in document], dtype=torch.long) for document in dt]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_scores(y_true, y_pred):\n",
|
||||
" acc_score = 0\n",
|
||||
" tp = 0\n",
|
||||
" fp = 0\n",
|
||||
" selected_items = 0\n",
|
||||
" relevant_items = 0\n",
|
||||
" for p, t in zip(y_pred, y_true):\n",
|
||||
" if p == t:\n",
|
||||
" acc_score += 1\n",
|
||||
" if p > 0 and p == t:\n",
|
||||
" tp += 1\n",
|
||||
" if p > 0:\n",
|
||||
" selected_items += 1\n",
|
||||
" if t > 0:\n",
|
||||
" relevant_items += 1\n",
|
||||
"\n",
|
||||
" if selected_items == 0:\n",
|
||||
" precision = 1.0\n",
|
||||
" else:\n",
|
||||
" precision = tp / selected_items\n",
|
||||
"\n",
|
||||
" if relevant_items == 0:\n",
|
||||
" recall = 1.0\n",
|
||||
" else:\n",
|
||||
" recall = tp / relevant_items\n",
|
||||
"\n",
|
||||
" if precision + recall == 0.0:\n",
|
||||
" f1 = 0.0\n",
|
||||
" else:\n",
|
||||
" f1 = 2 * precision * recall / (precision + recall)\n",
|
||||
"\n",
|
||||
" return precision, recall, f1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b6061642",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def process_output(lines):\n",
|
||||
" result = []\n",
|
||||
" for line in lines:\n",
|
||||
" last_label = None\n",
|
||||
" new_line = []\n",
|
||||
" for label in line:\n",
|
||||
" if(label != \"O\" and label[0:2] == \"I-\"):\n",
|
||||
" if last_label == None or last_label == \"O\":\n",
|
||||
" label = label.replace('I-', 'B-')\n",
|
||||
" else:\n",
|
||||
" label = \"I-\" + last_label[2:]\n",
|
||||
" last_label = label\n",
|
||||
" new_line.append(label)\n",
|
||||
" x = (\" \".join(new_line))\n",
|
||||
" result.append(\" \".join(new_line))\n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3d7c4dd3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class GRU(torch.nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(GRU, self).__init__()\n",
|
||||
" self.emb = torch.nn.Embedding(len(vocab_x.itos),100)\n",
|
||||
" self.dropout = torch.nn.Dropout(0.2)\n",
|
||||
" self.rec = torch.nn.GRU(100, 256, 2, batch_first = True, bidirectional = True)\n",
|
||||
" self.fc1 = torch.nn.Linear(2* 256 , 9)\n",
|
||||
" \n",
|
||||
" def forward(self, x):\n",
|
||||
" emb = torch.relu(self.emb(x))\n",
|
||||
" emb = self.dropout(emb) \n",
|
||||
" gru_output, h_n = self.rec(emb) \n",
|
||||
" out_weights = self.fc1(gru_output)\n",
|
||||
" return out_weights"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cd5e419d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def dev_eval(model, crf, dev_tokens, dev_labels_tokens, vocab):\n",
|
||||
" Y_true = []\n",
|
||||
" Y_pred = []\n",
|
||||
" model.eval()\n",
|
||||
" crf.eval()\n",
|
||||
" for i in tqdm(range(len(dev_labels_tokens))):\n",
|
||||
" batch_tokens = dev_tokens[i].unsqueeze(0)\n",
|
||||
" tags = list(dev_labels_tokens[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 += [crf.decode(Y_batch_pred)[0]]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c808bbd5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train = pd.read_csv('train/train.tsv', sep='\\t',\n",
|
||||
" names=['labels', 'document'])\n",
|
||||
"\n",
|
||||
"Y_train = [y.split(sep=\" \") for y in train['labels'].values]\n",
|
||||
"X_train = [x.split(sep=\" \") for x in train['document'].values]\n",
|
||||
"\n",
|
||||
"dev = pd.read_csv('dev-0/in.tsv', sep='\\t', names=['document'])\n",
|
||||
"exp = pd.read_csv('dev-0/expected.tsv', sep='\\t', names=['labels'])\n",
|
||||
"X_dev = [x.split(sep=\" \") for x in dev['document'].values]\n",
|
||||
"Y_dev = [y.split(sep=\" \") for y in exp['labels'].values]\n",
|
||||
"\n",
|
||||
"test = pd.read_csv('test-A/in.tsv', sep='\\t', names=['document'])\n",
|
||||
"X_test = test['document'].values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "79485c9a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vocab_x = build_vocab(X_train)\n",
|
||||
"vocab_y = build_vocab(Y_train)\n",
|
||||
"train_tokens = data_process(X_train, vocab_x)\n",
|
||||
"labels_tokens = data_process(Y_train, vocab_y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3726c82a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"torch.cuda.get_device_name(0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f29e3b63",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"device_gpu = torch.device(\"cuda:0\")\n",
|
||||
"model = GRU()\n",
|
||||
"crf = CRF(9)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9c321d58",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mask = torch.ByteTensor([1, 1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "05482a7c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"criterion = torch.nn.CrossEntropyLoss()\n",
|
||||
"params = list(model.parameters()) + list(crf.parameters())\n",
|
||||
"optimizer = torch.optim.Adam(params)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "21a5282e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in range(2):\n",
|
||||
" crf.train()\n",
|
||||
" model.train()\n",
|
||||
" for i in tqdm(range(len(labels_tokens))):\n",
|
||||
" batch_tokens = train_tokens[i].unsqueeze(0)\n",
|
||||
" tags = labels_tokens[i].unsqueeze(1)\n",
|
||||
"\n",
|
||||
" predicted_tags = model(batch_tokens).squeeze(0).unsqueeze(1)\n",
|
||||
"\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" loss = -crf(predicted_tags, tags)\n",
|
||||
"\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cec14c35",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip3 install pytorch-crf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1ee634f7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"from torchcrf import CRF"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
215
dev-0/out.tsv
215
dev-0/out.tsv
File diff suppressed because one or more lines are too long
BIN
seq_labeling.model
Normal file
BIN
seq_labeling.model
Normal file
Binary file not shown.
@ -183,4 +183,7 @@ results_processed = process_output(results)
|
||||
with open("test-A/out.tsv", "w") as f:
|
||||
for line in results_processed:
|
||||
f.write(line + "\n")
|
||||
|
||||
model_path = "seq_labeling.model"
|
||||
torch.save(ner_model.state_dict(), model_path)
|
||||
|
||||
230
test-A/out.tsv
230
test-A/out.tsv
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user