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972 lines
28 KiB
972 lines
28 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "622dfcd6",
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"metadata": {},
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"source": [
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"# Group 09 - Automatic Sentence Completion for PubMed"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5e4cec3c",
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"metadata": {},
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"source": [
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"### Authors:\n",
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"- Eric Münzberg\n",
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"- Shahein Enjjar\t\n",
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"- Leonard Starke"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee9c1d92",
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"metadata": {},
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"source": [
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"### Firstly try to import the data modules"
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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": 29,
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"id": "e444b44c",
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" from Bio import Entrez, Medline \n",
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"except:\n",
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" !pip install Bio\n",
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" from Bio import Entrez, Medline \n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7bf15c30",
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"metadata": {},
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"source": [
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"### define function for loading the papers from PubMed database"
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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": 30,
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"id": "adfb256a",
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"metadata": {},
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"outputs": [],
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"source": [
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"def getPapers(myQuery, maxPapers, myEmail =\"leonard.starke@mailbox.tu-dresden.de\"):\n",
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" # Get articles from PubMed\n",
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" Entrez.email =myEmail\n",
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" record =Entrez.read(Entrez.esearch(db=\"pubmed\", term=myQuery, retmax=maxPapers))\n",
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" idlist = record[\"IdList\"]\n",
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" print(\"\\nThere are %d records for %s.\"%(len(idlist), myQuery.strip()))\n",
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" records = Medline.parse(Entrez.efetch(db=\"pubmed\", id=idlist, rettype=\"medline\", retmode=\"text\"))\n",
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" return list(records)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "46bc6298",
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"metadata": {},
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"source": [
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"### Verify that its working"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"id": "00481ec9",
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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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"\n",
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"There are 1600 records for Cancer[tiab].\n"
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]
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}
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],
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"source": [
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"myQuery =\"Cancer\"+\"[tiab]\" #query in title and abstract\n",
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"maxPapers = 1600 # thinkabout outsourcing params to seperate section\n",
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"records = getPapers(myQuery, maxPapers)\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": 32,
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"id": "56cf72de",
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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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"1600"
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]
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},
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"execution_count": 32,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(records)"
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]
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},
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|
{
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"cell_type": "markdown",
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"id": "b67747c6",
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"metadata": {},
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"source": [
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"### Now extract abstracts from records"
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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": 33,
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"id": "dcf5c217",
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"metadata": {},
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"outputs": [],
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"source": [
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"r_abstracts = []\n",
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"for r in records:\n",
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" if not (r.get('AB') is None):\n",
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" r_abstracts.append(r['AB'])"
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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": 34,
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"id": "eb1fb38b",
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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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"1532"
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(r_abstracts)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e309f6fe",
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"metadata": {},
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"source": [
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"### Now import torch modules needed to load the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "c3199444",
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" import torch\n",
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|
" from torch.utils.data import Dataset \n",
|
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" from torchtext.data import get_tokenizer\n",
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"except:\n",
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" !pip --default-timeout=1000 install torch\n",
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" !pip --default-timeout=1000 install torchtext\n",
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" import torch\n",
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" from torch.utils.data import Dataset \n",
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" from torchtext.data import get_tokenizer"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5b4007e8",
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"metadata": {},
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"source": [
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"### Import numpy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "daca9db6",
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" import numpy as np\n",
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"except:\n",
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" !pip install numpy\n",
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" import numpy as np\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4df1e449",
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"metadata": {},
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"source": [
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"### define token iterators"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "8a128d3c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def train_abstract_iter():\n",
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" for abstract in r_abstracts[:1000]:\n",
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" yield abstract"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "97e89986",
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"metadata": {},
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"outputs": [],
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"source": [
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"def val_abstract_iter():\n",
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" for abstract in r_abstracts[1001:1300]:\n",
|
|
" yield abstract"
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 39,
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"id": "0d6e89c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def test_abstract_iter():\n",
|
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" for abstract in r_abstracts[1301:1542]:\n",
|
|
" yield abstract"
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]
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},
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|
{
|
|
"cell_type": "markdown",
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|
"id": "e5e9c5a2",
|
|
"metadata": {},
|
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"source": [
|
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"### define Tokenize function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"id": "0bdbc40a",
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenizer = get_tokenizer(\"basic_english\")\n",
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"def tokenize_abstract_iter():\n",
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" for abstract in r_abstracts:\n",
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" yield tokenizer(abstract)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "37da40bb",
|
|
"metadata": {},
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"source": [
|
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"### Map every world to a id to store inside torch tensor"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"id": "a438ab1f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from torchtext.vocab import build_vocab_from_iterator\n",
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"token_generator = tokenize_abstract_iter()\n",
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"vocab = build_vocab_from_iterator(token_generator, specials=['<unk>'])\n",
|
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"vocab.set_default_index(vocab['<unk>'])\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "221bdc48",
|
|
"metadata": {},
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"source": [
|
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"### now convert to tensor\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": 42,
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|
"id": "0e5bc361",
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"metadata": {},
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"outputs": [],
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"source": [
|
|
"def data_process(tokens_iter):\n",
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" \"\"\"Converts raw text into a flat Tensor.\"\"\"\n",
|
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" data = [torch.tensor(vocab(tokenizer(item)), dtype=torch.long) for item in tokens_iter]\n",
|
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" return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))"
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": 43,
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|
"id": "dfd7400d",
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|
"metadata": {},
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"outputs": [],
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"source": [
|
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"train_generator = train_abstract_iter()\n",
|
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"val_generator = val_abstract_iter()\n",
|
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"test_generator = test_abstract_iter()\n",
|
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"train_data = data_process(train_generator)\n",
|
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"val_data = data_process(val_generator)\n",
|
|
"test_data = data_process(test_generator)"
|
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]
|
|
},
|
|
{
|
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"cell_type": "markdown",
|
|
"id": "c49a2734",
|
|
"metadata": {},
|
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"source": [
|
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"### check gpu"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": 44,
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|
"id": "c155ee31",
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|
"metadata": {},
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"outputs": [],
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"source": [
|
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
|
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"id": "79b2d248",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"device(type='cuda')"
|
|
]
|
|
},
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"device"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
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|
"id": "2150ba71",
|
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"metadata": {},
|
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"source": [
|
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"### define model"
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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": 46,
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|
"id": "a33d722f",
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|
"metadata": {},
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|
"outputs": [],
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"source": [
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"import math\n",
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"from typing import Tuple\n",
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"\n",
|
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"import torch\n",
|
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"from torch import nn, Tensor\n",
|
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"import torch.nn.functional as F\n",
|
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"from torch.nn import TransformerEncoder, TransformerEncoderLayer\n",
|
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"from torch.utils.data import dataset\n",
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"\n",
|
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"class TransformerModel(nn.Module):\n",
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"\n",
|
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" def __init__(self, ntoken: int, d_model: int, nhead: int, d_hid: int,\n",
|
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" nlayers: int, dropout: float = 0.5):\n",
|
|
" super().__init__()\n",
|
|
" self.model_type = 'Transformer'\n",
|
|
" self.pos_encoder = PositionalEncoding(d_model, dropout)\n",
|
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" encoder_layers = TransformerEncoderLayer(d_model, nhead, d_hid, dropout)\n",
|
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" self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)\n",
|
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" self.encoder = nn.Embedding(ntoken, d_model)\n",
|
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" self.d_model = d_model\n",
|
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" self.decoder = nn.Linear(d_model, ntoken)\n",
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"\n",
|
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" self.init_weights()\n",
|
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"\n",
|
|
" def init_weights(self) -> None:\n",
|
|
" initrange = 0.1\n",
|
|
" self.encoder.weight.data.uniform_(-initrange, initrange)\n",
|
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" self.decoder.bias.data.zero_()\n",
|
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" self.decoder.weight.data.uniform_(-initrange, initrange)\n",
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"\n",
|
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" def forward(self, src: Tensor, src_mask: Tensor) -> Tensor:\n",
|
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" \"\"\"\n",
|
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" Args:\n",
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" src: Tensor, shape [seq_len, batch_size]\n",
|
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" src_mask: Tensor, shape [seq_len, seq_len]\n",
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"\n",
|
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" Returns:\n",
|
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" output Tensor of shape [seq_len, batch_size, ntoken]\n",
|
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" \"\"\"\n",
|
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" src = self.encoder(src) * math.sqrt(self.d_model)\n",
|
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" src = self.pos_encoder(src)\n",
|
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" output = self.transformer_encoder(src, src_mask)\n",
|
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" output = self.decoder(output)\n",
|
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" return output\n",
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"\n",
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"\n",
|
|
"def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
|
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" \"\"\"Generates an upper-triangular matrix of -inf, with zeros on diag.\"\"\"\n",
|
|
" return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)"
|
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
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|
"id": "da8fb12b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "c2f6d33b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class PositionalEncoding(nn.Module):\n",
|
|
"\n",
|
|
" def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):\n",
|
|
" super().__init__()\n",
|
|
" self.dropout = nn.Dropout(p=dropout)\n",
|
|
"\n",
|
|
" position = torch.arange(max_len).unsqueeze(1)\n",
|
|
" div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))\n",
|
|
" pe = torch.zeros(max_len, 1, d_model)\n",
|
|
" pe[:, 0, 0::2] = torch.sin(position * div_term)\n",
|
|
" pe[:, 0, 1::2] = torch.cos(position * div_term)\n",
|
|
" self.register_buffer('pe', pe)\n",
|
|
"\n",
|
|
" def forward(self, x: Tensor) -> Tensor:\n",
|
|
" \"\"\"\n",
|
|
" Args:\n",
|
|
" x: Tensor, shape [seq_len, batch_size, embedding_dim]\n",
|
|
" \"\"\"\n",
|
|
" x = x + self.pe[:x.size(0)]\n",
|
|
" return self.dropout(x)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "9e184841",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def batchify(data: Tensor, bsz: int) -> Tensor:\n",
|
|
" \"\"\"Divides the data into bsz separate sequences, removing extra elements\n",
|
|
" that wouldn't cleanly fit.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" data: Tensor, shape [N]\n",
|
|
" bsz: int, batch size\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" Tensor of shape [N // bsz, bsz]\n",
|
|
" \"\"\"\n",
|
|
" seq_len = data.size(0) // bsz\n",
|
|
" data = data[:seq_len * bsz]\n",
|
|
" data = data.view(bsz, seq_len).t().contiguous()\n",
|
|
" return data.to(device)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"id": "a4def1ac",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"batch_size = 20\n",
|
|
"eval_batch_size = 10\n",
|
|
"train_data = batchify(train_data, batch_size) # shape [seq_len, batch_size]\n",
|
|
"val_data = batchify(val_data, eval_batch_size)\n",
|
|
"test_data = batchify(test_data, eval_batch_size)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"id": "4ab5b8fd",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bptt = 35\n",
|
|
"def get_batch(source: Tensor, i: int) -> Tuple[Tensor, Tensor]:\n",
|
|
" \"\"\"\n",
|
|
" Args:\n",
|
|
" source: Tensor, shape [full_seq_len, batch_size]\n",
|
|
" i: int\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" tuple (data, target), where data has shape [seq_len, batch_size] and\n",
|
|
" target has shape [seq_len * batch_size]\n",
|
|
" \"\"\"\n",
|
|
" seq_len = min(bptt, len(source) - 1 - i)\n",
|
|
" data = source[i:i+seq_len]\n",
|
|
" target = source[i+1:i+1+seq_len].reshape(-1)\n",
|
|
" return data, target"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"id": "c53764da",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ntokens = len(vocab) # size of vocabulary\n",
|
|
"emsize = 200 # embedding dimension\n",
|
|
"d_hid = 200 # dimension of the feedforward network model in nn.TransformerEncoder\n",
|
|
"nlayers = 2 # number of nn.TransformerEncoderLayer in nn.TransformerEncoder\n",
|
|
"nhead = 2 # number of heads in nn.MultiheadAttention\n",
|
|
"dropout = 0.2 # dropout probability\n",
|
|
"model = TransformerModel(ntokens, emsize, nhead, d_hid, nlayers, dropout).to(device)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"id": "50ab3fb6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import copy\n",
|
|
"import time\n",
|
|
"\n",
|
|
"criterion = nn.CrossEntropyLoss()\n",
|
|
"lr = 5.0 # learning rate\n",
|
|
"optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n",
|
|
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)\n",
|
|
"\n",
|
|
"def train(model: nn.Module) -> None:\n",
|
|
" model.train() # turn on train mode\n",
|
|
" total_loss = 0.\n",
|
|
" log_interval = 200\n",
|
|
" start_time = time.time()\n",
|
|
" src_mask = generate_square_subsequent_mask(bptt).to(device)\n",
|
|
"\n",
|
|
" num_batches = len(train_data) // bptt\n",
|
|
" for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):\n",
|
|
" data, targets = get_batch(train_data, i)\n",
|
|
" seq_len = data.size(0)\n",
|
|
" if seq_len != bptt: # only on last batch\n",
|
|
" src_mask = src_mask[:seq_len, :seq_len]\n",
|
|
" output = model(data, src_mask)\n",
|
|
" loss = criterion(output.view(-1, ntokens), targets)\n",
|
|
"\n",
|
|
" optimizer.zero_grad()\n",
|
|
" loss.backward()\n",
|
|
" torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)\n",
|
|
" optimizer.step()\n",
|
|
"\n",
|
|
" total_loss += loss.item()\n",
|
|
" if batch % log_interval == 0 and batch > 0:\n",
|
|
" lr = scheduler.get_last_lr()[0]\n",
|
|
" ms_per_batch = (time.time() - start_time) * 1000 / log_interval\n",
|
|
" cur_loss = total_loss / log_interval\n",
|
|
" ppl = math.exp(cur_loss)\n",
|
|
" print(f'| epoch {epoch:3d} | {batch:5d}/{num_batches:5d} batches | '\n",
|
|
" f'lr {lr:02.2f} | ms/batch {ms_per_batch:5.2f} | '\n",
|
|
" f'loss {cur_loss:5.2f} | ppl {ppl:8.2f}')\n",
|
|
" total_loss = 0\n",
|
|
" start_time = time.time()\n",
|
|
"\n",
|
|
"def evaluate(model: nn.Module, eval_data: Tensor) -> float:\n",
|
|
" model.eval() # turn on evaluation mode\n",
|
|
" total_loss = 0.\n",
|
|
" src_mask = generate_square_subsequent_mask(bptt).to(device)\n",
|
|
" with torch.no_grad():\n",
|
|
" for i in range(0, eval_data.size(0) - 1, bptt):\n",
|
|
" data, targets = get_batch(eval_data, i)\n",
|
|
" seq_len = data.size(0)\n",
|
|
" if seq_len != bptt:\n",
|
|
" src_mask = src_mask[:seq_len, :seq_len]\n",
|
|
" output = model(data, src_mask)\n",
|
|
" output_flat = output.view(-1, ntokens)\n",
|
|
" total_loss += seq_len * criterion(output_flat, targets).item()\n",
|
|
" return total_loss / (len(eval_data) - 1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"id": "09c4d4ce",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"| epoch 1 | 200/ 383 batches | lr 5.00 | ms/batch 63.06 | loss 8.09 | ppl 3258.10\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 1 | time: 17.05s | valid loss 6.34 | valid ppl 566.15\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 2 | 200/ 383 batches | lr 4.75 | ms/batch 19.83 | loss 6.14 | ppl 463.13\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 2 | time: 8.38s | valid loss 6.01 | valid ppl 406.56\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 3 | 200/ 383 batches | lr 4.51 | ms/batch 19.83 | loss 5.61 | ppl 273.67\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 3 | time: 8.38s | valid loss 5.95 | valid ppl 383.10\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 4 | 200/ 383 batches | lr 4.29 | ms/batch 19.89 | loss 5.25 | ppl 190.90\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 4 | time: 8.40s | valid loss 5.96 | valid ppl 386.38\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 5 | 200/ 383 batches | lr 4.07 | ms/batch 19.88 | loss 4.96 | ppl 142.55\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 5 | time: 8.40s | valid loss 5.99 | valid ppl 398.76\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 6 | 200/ 383 batches | lr 3.87 | ms/batch 19.89 | loss 4.71 | ppl 111.09\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 6 | time: 8.40s | valid loss 6.04 | valid ppl 421.64\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 7 | 200/ 383 batches | lr 3.68 | ms/batch 19.89 | loss 4.49 | ppl 89.44\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 7 | time: 8.40s | valid loss 6.11 | valid ppl 452.51\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 8 | 200/ 383 batches | lr 3.49 | ms/batch 19.92 | loss 4.30 | ppl 73.72\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 8 | time: 8.42s | valid loss 6.17 | valid ppl 479.04\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 9 | 200/ 383 batches | lr 3.32 | ms/batch 19.93 | loss 4.13 | ppl 62.43\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 9 | time: 8.42s | valid loss 6.26 | valid ppl 522.27\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| epoch 10 | 200/ 383 batches | lr 3.15 | ms/batch 19.95 | loss 3.99 | ppl 53.96\n",
|
|
"-----------------------------------------------------------------------------------------\n",
|
|
"| end of epoch 10 | time: 8.43s | valid loss 6.31 | valid ppl 548.35\n",
|
|
"-----------------------------------------------------------------------------------------\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"best_val_loss = float('inf')\n",
|
|
"epochs = 10\n",
|
|
"best_model = None\n",
|
|
"\n",
|
|
"for epoch in range(1, epochs + 1):\n",
|
|
" epoch_start_time = time.time()\n",
|
|
" train(model)\n",
|
|
" val_loss = evaluate(model, val_data)\n",
|
|
" val_ppl = math.exp(val_loss)\n",
|
|
" elapsed = time.time() - epoch_start_time\n",
|
|
" print('-' * 89)\n",
|
|
" print(f'| end of epoch {epoch:3d} | time: {elapsed:5.2f}s | '\n",
|
|
" f'valid loss {val_loss:5.2f} | valid ppl {val_ppl:8.2f}')\n",
|
|
" print('-' * 89)\n",
|
|
"\n",
|
|
" if val_loss < best_val_loss:\n",
|
|
" best_val_loss = val_loss\n",
|
|
" best_model = copy.deepcopy(model)\n",
|
|
"\n",
|
|
" scheduler.step()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 54,
|
|
"id": "12fdd0aa",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"=========================================================================================\n",
|
|
"| End of training | test loss 5.80 | test ppl 329.59\n",
|
|
"=========================================================================================\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"test_loss = evaluate(best_model, test_data)\n",
|
|
"test_ppl = math.exp(test_loss)\n",
|
|
"print('=' * 89)\n",
|
|
"print(f'| End of training | test loss {test_loss:5.2f} | '\n",
|
|
" f'test ppl {test_ppl:8.2f}')\n",
|
|
"print('=' * 89)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e685d3e1",
|
|
"metadata": {},
|
|
"source": [
|
|
"### define input batch "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 274,
|
|
"id": "cfb30fe0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"input_batch = [\n",
|
|
" \"The brain is\",\n",
|
|
" \"The lung is\"\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 275,
|
|
"id": "305853e8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bptt = 3\n",
|
|
"src_mask = generate_square_subsequent_mask(bptt).to(device)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 276,
|
|
"id": "afe585d6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def predict_abstract_iter():\n",
|
|
" for batch in input_batch:\n",
|
|
" yield tokenizer(batch)\n",
|
|
"predict_generator = predict_abstract_iter()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f7ac6188",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 278,
|
|
"id": "0788b045",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = [torch.tensor(vocab.lookup_indices(item)) for item in predict_generator]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 279,
|
|
"id": "8bfaa8bd",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[tensor([ 3, 555, 16]), tensor([ 3, 76, 16])]"
|
|
]
|
|
},
|
|
"execution_count": 279,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 280,
|
|
"id": "dd0e7310",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"device(type='cuda')"
|
|
]
|
|
},
|
|
"execution_count": 280,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
|
|
"device"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 281,
|
|
"id": "1728f0fd",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for d in data:\n",
|
|
" d.to(device)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 282,
|
|
"id": "49d27864",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"best_model.eval()\n",
|
|
"for batch in data:\n",
|
|
" output = best_model(batch.to(device), src_mask)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 283,
|
|
"id": "a3404169",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"result_np = []\n",
|
|
"pred_np = output.cpu().detach().numpy()\n",
|
|
"for el in pred_np:\n",
|
|
" result_np.append(el)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 284,
|
|
"id": "c7064c0c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[array([[-0.5258564 , 4.6108465 , 4.8358154 , ..., -0.46871045,\n",
|
|
" -0.04386039, -0.13068362],\n",
|
|
" [-0.35341978, 8.821181 , 9.57295 , ..., -1.2820313 ,\n",
|
|
" -0.989242 , -0.15542248],\n",
|
|
" [-0.39717233, 5.7111125 , 6.4295497 , ..., -0.27339834,\n",
|
|
" -1.5333815 , 0.16042188]], dtype=float32),\n",
|
|
" array([[-0.5291481 , 4.6452312 , 4.7958803 , ..., -0.4642661 ,\n",
|
|
" -0.04427804, -0.12225106],\n",
|
|
" [-0.35347146, 8.824585 , 9.5098095 , ..., -1.2693769 ,\n",
|
|
" -0.97772634, -0.13521233],\n",
|
|
" [-0.39733842, 5.693817 , 6.368334 , ..., -0.26423275,\n",
|
|
" -1.527182 , 0.16518843]], dtype=float32),\n",
|
|
" array([[-0.53349733, 4.6777644 , 4.7953978 , ..., -0.4346264 ,\n",
|
|
" -0.03433151, -0.11583059],\n",
|
|
" [-0.3695631 , 8.82613 , 9.477964 , ..., -1.2404828 ,\n",
|
|
" -0.9594678 , -0.11550146],\n",
|
|
" [-0.41203997, 5.699034 , 6.341655 , ..., -0.24370295,\n",
|
|
" -1.5179048 , 0.16230991]], dtype=float32)]"
|
|
]
|
|
},
|
|
"execution_count": 284,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"result_np"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 285,
|
|
"id": "679e2316",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def predict(input_line, n_predictions=1):\n",
|
|
" print('\\n> %s' % input_line)\n",
|
|
" with torch.no_grad():\n",
|
|
" output = best_model(input_line.to(device), src_mask)\n",
|
|
"\n",
|
|
" # Get top N categories\n",
|
|
" topv, topi = output.topk(n_predictions, 1, True)\n",
|
|
" #x, y = output.topk(n_predictions, 1, True)\n",
|
|
" #print(x.shape)\n",
|
|
" #print(topv.shape)\n",
|
|
" # print(topi.shape)\n",
|
|
" predictions = []\n",
|
|
" for i in range(n_predictions):\n",
|
|
" value = topv[0][i]\n",
|
|
" v1, v2 = value.topk(1)\n",
|
|
" predict_token_index = v2.cpu().detach().numpy()\n",
|
|
" print(vocab.lookup_token(predict_token_index))\n",
|
|
" #print(category_index)\n",
|
|
" #print('(%.2f) %s' % (value, all_categories[category_index]))\n",
|
|
" #predictions.append([value, all_categories[category_index]])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 286,
|
|
"id": "03389137",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"> tensor([ 3, 555, 16])\n",
|
|
"tumors\n",
|
|
"\n",
|
|
"> tensor([ 3, 76, 16])\n",
|
|
"cancer\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for d in data:\n",
|
|
" predict(d)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.10.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|