{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import lzma\n", "from itertools import islice\n", "import regex as re\n", "import sys\n", "from torchtext.vocab import build_vocab_from_iterator\n", "from torch import nn\n", "from torch.utils.data import IterableDataset\n", "import itertools" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"values,index =prob_dist.topk(20) \n", "for x,y in (values,index):\n", " print(x,y)\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# with lzma.open(\"train/in.tsv.xz\", encoding='utf8', mode=\"rt\") as fh:\n", "# for line in fh:\n", "# # print(line)\n", "# pattern = r'\\^\\^|\\n|\\\\|[<>]|[()]'\n", "# line = re.sub(pattern, '', line)\n", "# print(line)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def get_words_from_line(line):\n", " line = line.rstrip()\n", " yield ''\n", " for t in line.split():\n", " yield t\n", " yield ''\n", "\n", "\n", "def get_word_lines_from_file(file_name):\n", " with lzma.open(file_name, encoding='utf8', mode=\"rt\") as fh:\n", " for line in fh:\n", " pattern = r'\\^\\^|\\n|\\\\|[<>]|[()]'\n", " line = re.sub(pattern, '', line)\n", " yield get_words_from_line(line)\n", "\n", "vocab_size = 2500\n", "\n", "vocab = build_vocab_from_iterator(\n", " get_word_lines_from_file(\"train/in.tsv.xz\"),\n", " max_tokens = vocab_size,\n", " specials = [''])\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def look_ahead_iterator(gen):\n", " prev = None\n", " for item in gen:\n", " if prev is not None:\n", " yield (prev, item)\n", " prev = item\n", "\n", "class Bigrams(IterableDataset):\n", " def __init__(self, text_file, vocabulary_size):\n", " self.vocab = vocab\n", " self.vocab.set_default_index(self.vocab[''])\n", " self.vocabulary_size = vocabulary_size\n", " self.text_file = text_file\n", "\n", " def __iter__(self):\n", " return look_ahead_iterator(\n", " (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n", "\n", "train_dataset = Bigrams(\"train/in.tsv.xz\", vocab_size)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(24, 0)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from torch.utils.data import DataLoader\n", "\n", "next(iter(train_dataset))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[tensor([ 24, 0, 1021, 25, 0]),\n", " tensor([ 0, 1021, 25, 0, 0])]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "next(iter(DataLoader(train_dataset, batch_size=5)))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\User\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\container.py:141: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", " input = module(input)\n" ] }, { "data": { "text/plain": [ "tensor(0.0002, grad_fn=)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embed_size = 200\n", "\n", "class SimpleBigramNeuralLanguageModel(nn.Module):\n", " def __init__(self, vocabulary_size, embedding_size):\n", " super(SimpleBigramNeuralLanguageModel, self).__init__()\n", " self.model = nn.Sequential(\n", " nn.Embedding(vocabulary_size, embedding_size),\n", " nn.Linear(embedding_size, vocabulary_size),\n", " nn.Softmax()\n", " )\n", "\n", " def forward(self, x):\n", " return self.model(x)\n", "\n", "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size)\n", "\n", "vocab.set_default_index(vocab[''])\n", "ixs = torch.tensor(vocab.forward(['is']))\n", "out = model(ixs)\n", "out[0][vocab['is']]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 tensor(8.0177, grad_fn=)\n", "100 tensor(5.2132, grad_fn=)\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32mc:\\Users\\User\\VisualStudio\\challenging-america-word-gap-prediction\\lab7.ipynb Cell 8\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[39mprint\u001b[39m(step, loss)\n\u001b[0;32m 17\u001b[0m step \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m---> 18\u001b[0m loss\u001b[39m.\u001b[39;49mbackward()\n\u001b[0;32m 19\u001b[0m optimizer\u001b[39m.\u001b[39mstep()\n", "File \u001b[1;32mc:\\Users\\User\\anaconda3\\lib\\site-packages\\torch\\_tensor.py:363\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m 354\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39mself\u001b[39m):\n\u001b[0;32m 355\u001b[0m \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m 356\u001b[0m Tensor\u001b[39m.\u001b[39mbackward,\n\u001b[0;32m 357\u001b[0m (\u001b[39mself\u001b[39m,),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 361\u001b[0m create_graph\u001b[39m=\u001b[39mcreate_graph,\n\u001b[0;32m 362\u001b[0m inputs\u001b[39m=\u001b[39minputs)\n\u001b[1;32m--> 363\u001b[0m torch\u001b[39m.\u001b[39;49mautograd\u001b[39m.\u001b[39;49mbackward(\u001b[39mself\u001b[39;49m, gradient, retain_graph, create_graph, inputs\u001b[39m=\u001b[39;49minputs)\n", "File \u001b[1;32mc:\\Users\\User\\anaconda3\\lib\\site-packages\\torch\\autograd\\__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m 168\u001b[0m retain_graph \u001b[39m=\u001b[39m create_graph\n\u001b[0;32m 170\u001b[0m \u001b[39m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[0;32m 171\u001b[0m \u001b[39m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m 172\u001b[0m \u001b[39m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 173\u001b[0m Variable\u001b[39m.\u001b[39;49m_execution_engine\u001b[39m.\u001b[39;49mrun_backward( \u001b[39m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m 174\u001b[0m tensors, grad_tensors_, retain_graph, create_graph, inputs,\n\u001b[0;32m 175\u001b[0m allow_unreachable\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, accumulate_grad\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "device = 'cpu'\n", "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n", "data = DataLoader(train_dataset, batch_size=6000)\n", "optimizer = torch.optim.Adam(model.parameters())\n", "criterion = torch.nn.NLLLoss()\n", "\n", "model.train()\n", "step = 0\n", "for x, y in data:\n", " x = x.to(device)\n", " y = y.to(device)\n", " optimizer.zero_grad()\n", " ypredicted = model(x)\n", " loss = criterion(torch.log(ypredicted), y)\n", " if step % 100 == 0:\n", " print(step, loss)\n", " step += 1\n", " loss.backward()\n", " optimizer.step()\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "torch.save(model.state_dict(), 'model1.bin')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([19])\n", "['had', 'was', 'has', 'did', 'would', 'went', 'could', 'said,', 'be', 'must'] \n", " [36, 10, 37, 131, 49, 203, 99, 788, 11, 127] \n", " [0.052415624260902405, 0.0358351431787014, 0.027370842173695564, 0.01275723148137331, 0.010935396887362003, 0.009363135322928429, 0.009096388705074787, 0.0070375604555010796, 0.005759422201663256, 0.005631867330521345]\n" ] } ], "source": [ "device = 'cpu'\n", "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n", "model.load_state_dict(torch.load('model1.bin'))\n", "model.eval()\n", "\n", "ixs = torch.tensor(vocab.forward(['he'])).to(device)\n", "print(ixs)\n", "\n", "out = model(ixs)\n", "top = torch.topk(out[0], 10)\n", "top_indices = top.indices.tolist()\n", "top_probs = top.values.tolist()\n", "top_words = vocab.lookup_tokens(top_indices)\n", "print(top_words,'\\n',top_indices,'\\n',top_probs)\n", "# list(zip(top_words, top_indices, top_probs))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def prediction(word: str) -> str:\n", " ixs = torch.tensor(vocab.forward([word])).to(device)\n", " out = model(ixs)\n", " top = torch.topk(out[0], 5)\n", " top_indices = top.indices.tolist()\n", " top_probs = top.values.tolist()\n", " top_words = vocab.lookup_tokens(top_indices)\n", " zipped = list(zip(top_words, top_probs))\n", " for index, element in enumerate(zipped):\n", " unk = None\n", " if '' in element:\n", " unk = zipped.pop(index)\n", " zipped.append(('', unk[1]))\n", " break\n", " if unk is None:\n", " zipped[-1] = ('', zipped[-1][1])\n", " print(' '.join([f'{x[0]}:{x[1]}' for x in zipped]))\n", " return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prompt = 'Think about'\n", "max_seq_len = 30\n", "seed = 0" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def create_outputs(folder_name):\n", " print(f'Creating outputs in {folder_name}')\n", " with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n", " with open(f'{folder_name}/out.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n", " for line in fid:\n", " separated = line.split('\\t')\n", " prefix = separated[6].replace(r'\\n', ' ').split()[-1]\n", " output_line = prediction(prefix)\n", " f.write(output_line + '\\n')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating outputs in dev-0\n", "the:0.01148879062384367 of:0.006031177006661892 a:0.005755534861236811 his:0.004050770308822393 :0.009219370782375336\n", "the:0.023308811709284782 a:0.02173895761370659 own:0.012746201828122139 that:0.007281431928277016 :0.02100181020796299\n", "the:0.02363738790154457 that:0.008539832197129726 a:0.008165501989424229 an:0.007165413349866867 :0.035898447036743164\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "a:0.07568275183439255 the:0.045853495597839355 his:0.02007143944501877 all:0.013846635818481445 :0.04955475032329559\n", "the:0.00316080660559237 all:0.002639115322381258 them.:0.0024603919591754675 as:0.002170901047065854 :0.0020744542125612497\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "as:0.0028613379690796137 purpose:0.0024742446839809418 the:0.002384566469117999 child:0.0021554669365286827 :0.0030256747268140316\n", "ChronAm:0.010227881371974945 is:0.008444960229098797 are:0.006903863977640867 to:0.005455975420773029 :0.015587559901177883\n", "the:0.11162873357534409 be:0.05268840864300728 a:0.02626536600291729 have:0.010365024209022522 :0.4014780819416046\n", "was:0.005478866398334503 that:0.0034455687273293734 in:0.003273994894698262 of:0.0031581225339323282 :0.015685532242059708\n", "same:0.006965374108403921 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Change the call to include dim=X as an argument.\n", " input = module(input)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "the:0.23462098836898804 a:0.026843780651688576 this:0.012386118993163109 said:0.010683613829314709 :0.45802026987075806\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "any:0.0173477903008461 three:0.011614865623414516 other:0.009230944328010082 more:0.008342713117599487 :0.2566910684108734\n", "a:0.007183287292718887 was:0.005211757030338049 not:0.004996122792363167 being:0.0044728415086865425 :0.02697567455470562\n", "the:0.23462098836898804 a:0.026843780651688576 this:0.012386118993163109 said:0.010683613829314709 :0.45802026987075806\n", "same:0.006965374108403921 said:0.006106619723141193 United:0.005009945016354322 first:0.00498473085463047 :0.6302009224891663\n", "an:0.004460567608475685 their:0.004396467469632626 the:0.0036188296508044004 will:0.0034394278191030025 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was:0.0050723375752568245 the:0.004311299417167902 a:0.004104405175894499 :0.003683132119476795\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "when:0.0022979723289608955 little:0.001996095757931471 line:0.0019944454543292522 ChronAm:0.0019684061408042908 :0.0016793470131233335\n", "the:0.23462098836898804 a:0.026843780651688576 this:0.012386118993163109 said:0.010683613829314709 :0.45802026987075806\n", "same:0.006965374108403921 said:0.006106619723141193 United:0.005009945016354322 first:0.00498473085463047 :0.6302009224891663\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "that:0.008999471552670002 of:0.008661560714244843 :0.006143592298030853 as:0.0038221918512135744 :0.005208002869039774\n", "made:0.01681242510676384 and:0.008670934475958347 held:0.008592469617724419 taken:0.007940184324979782 :0.06627477705478668\n", "a:0.07568275183439255 the:0.045853495597839355 his:0.02007143944501877 all:0.013846635818481445 :0.04955475032329559\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "the:0.23462098836898804 a:0.026843780651688576 this:0.012386118993163109 said:0.010683613829314709 :0.45802026987075806\n", "the:0.014333795756101608 few:0.011960748583078384 little:0.009230829775333405 great:0.008417336270213127 :0.6215581893920898\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "and:0.00876183807849884 of:0.005571023095399141 the:0.0035896694753319025 his:0.0034328557085245848 :0.013222860172390938\n", "-119.657178:0.004490300547331572 JOURNAL:0.0025691858027130365 taken:0.002350510098040104 said:0.0020159147679805756 :0.0018291013548150659\n", "have:0.05766370892524719 am:0.03934941813349724 was:0.03610505908727646 had:0.021183080971240997 :0.018509119749069214\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "therefore:0.0023102466948330402 recorded:0.0022191409952938557 was:0.0019702285062521696 -119.657178:0.0017024920089170337 :0.0016570868901908398\n", "same:0.006965374108403921 said:0.006106619723141193 United:0.005009945016354322 first:0.00498473085463047 :0.6302009224891663\n", "is:0.15743258595466614 was:0.08402499556541443 will:0.043737489730119705 has:0.019259853288531303 :0.04039118066430092\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "a:0.004209653940051794 the:0.0034594214521348476 that:0.0027430495247244835 city:0.0019319605780765414 :0.0064316014759242535\n", "same:0.006965374108403921 said:0.006106619723141193 United:0.005009945016354322 first:0.00498473085463047 :0.6302009224891663\n", "ChronAm:0.020030559971928596 to:0.0040446254424750805 who:0.0033765602856874466 the:0.0030685639940202236 :0.004845851566642523\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "only:0.03261493891477585 be:0.019187044352293015 yet:0.009667816571891308 have:0.007443589624017477 :0.007218844722956419\n", "the:0.2207714021205902 a:0.039634596556425095 this:0.0212148055434227 which:0.011685609817504883 :0.37857145071029663\n", "the:0.06398080289363861 a:0.01724325865507126 that:0.01525508239865303 to:0.012055670842528343 :0.4824085533618927\n", "is:0.0050055221654474735 ChronAm:0.0017839663196355104 great:0.0017726457444950938 years.:0.0017659531440585852 :0.0017521826084703207\n", "only:0.03261493891477585 be:0.019187044352293015 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the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "it:0.007032510358840227 as:0.004858741536736488 he:0.004815338179469109 they:0.0047087655402719975 :0.01022434327751398\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "a:0.03787093982100487 he:0.010212422348558903 an:0.009582641534507275 it:0.00794568657875061 :0.05852727219462395\n", "ChronAm:0.012679246254265308 who:0.008016659878194332 it:0.002872867975383997 have:0.0027014627121388912 :0.0026797896716743708\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "a:0.051128845661878586 it:0.03461145609617233 they:0.02874501794576645 to:0.027865994721651077 :0.11375875771045685\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "the:0.007453502621501684 a:0.007409723941236734 be:0.005380432587116957 as:0.003875732421875 :0.012674572877585888\n", "had:0.03700923174619675 have:0.026049671694636345 are:0.023810384795069695 has:0.012904404662549496 :0.10115291178226471\n", "the:0.014333795756101608 few:0.011960748583078384 little:0.009230829775333405 great:0.008417336270213127 :0.6215581893920898\n", "the:0.011728186160326004 a:0.007595855742692947 ChronAm:0.004566391929984093 they:0.004258312750607729 :0.008390507660806179\n", "been:0.14885377883911133 to:0.025148354470729828 no:0.012233811430633068 a:0.00745265232399106 :0.03908766806125641\n", "been:0.14885377883911133 to:0.025148354470729828 no:0.012233811430633068 a:0.00745265232399106 :0.03908766806125641\n", "the:0.014333795756101608 few:0.011960748583078384 little:0.009230829775333405 great:0.008417336270213127 :0.6215581893920898\n", "the:0.23462098836898804 a:0.026843780651688576 this:0.012386118993163109 said:0.010683613829314709 :0.45802026987075806\n", "the:0.2207714021205902 a:0.039634596556425095 this:0.0212148055434227 which:0.011685609817504883 :0.37857145071029663\n", "are:0.004368331748992205 given:0.0030210388358682394 per:0.002506023272871971 .:0.0020549260079860687 :0.0018529655644670129\n", "have:0.05766370892524719 am:0.03934941813349724 was:0.03610505908727646 had:0.021183080971240997 :0.018509119749069214\n", "said:0.003262424375861883 -77.047023:0.0029405367095023394 NEWS:0.0022632726468145847 table:0.00198462326079607 :0.0018401546403765678\n", "ChronAm:0.011204823851585388 the:0.010302089154720306 of:0.007154138293117285 It:0.004126411397010088 :0.010227753780782223\n", "number:0.016300620511174202 than:0.004105078522115946 a:0.0031880128663033247 more:0.003157192375510931 :0.015177523717284203\n", "be:0.26419901847839355 have:0.006759104784578085 well:0.005758954212069511 at:0.0042464653961360455 :0.014073442667722702\n", "of:0.009489885531365871 as:0.0054425024427473545 any:0.004404222592711449 been:0.004291977733373642 :0.004133882932364941\n", "not:0.007925108075141907 the:0.00710888858884573 a:0.00600279588252306 tho:0.00444074859842658 :0.004349788185209036\n", "the:0.014333795756101608 few:0.011960748583078384 little:0.009230829775333405 great:0.008417336270213127 :0.6215581893920898\n", "made:0.02501744218170643 it:0.005859693977981806 to:0.0041046347469091415 that:0.003942021168768406 :0.016590742394328117\n", "same:0.006965374108403921 said:0.006106619723141193 United:0.005009945016354322 first:0.00498473085463047 :0.6302009224891663\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "time:0.027251621708273888 other:0.00586853688582778 for:0.005746112205088139 the:0.005367009434849024 :0.00462797936052084\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "the:0.06398080289363861 a:0.01724325865507126 that:0.01525508239865303 to:0.012055670842528343 :0.4824085533618927\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "a:0.051128845661878586 it:0.03461145609617233 they:0.02874501794576645 to:0.027865994721651077 :0.11375875771045685\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "made:0.02501744218170643 it:0.005859693977981806 to:0.0041046347469091415 that:0.003942021168768406 :0.016590742394328117\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", ".:0.002714558271691203 man:0.002530201803892851 a:0.0024893630761653185 ChronAm:0.002468528924509883 :0.0023187785409390926\n", "not:0.05740215629339218 no:0.02295796573162079 a:0.020055823028087616 now:0.018580088391900063 :0.007114442531019449\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "the:0.23462098836898804 a:0.026843780651688576 this:0.012386118993163109 said:0.010683613829314709 :0.45802026987075806\n", "any:0.007761837914586067 the:0.00464628404006362 those:0.004130451940000057 at:0.0035480486694723368 :0.011344119906425476\n", "he:0.04289819300174713 it:0.039998434484004974 the:0.03839282691478729 they:0.025981521233916283 :0.01998070627450943\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "the:0.13920944929122925 Mr.:0.0098115811124444 a:0.009446715004742146 to:0.007693719584494829 :0.0508672334253788\n", "is:0.04121091961860657 are:0.02389271929860115 were:0.014991623349487782 was:0.009098860435187817 :0.0033286097459495068\n", "he:0.04289819300174713 it:0.039998434484004974 the:0.03839282691478729 they:0.025981521233916283 :0.01998070627450943\n", "the:0.008810002356767654 .:0.00781511515378952 this:0.0030308214481920004 many:0.00290261534973979 :0.036565180867910385\n", "of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n", "are:0.11246718466281891 were:0.05960462987422943 have:0.02738392911851406 will:0.0197915006428957 :0.01913267932832241\n", "the:0.014333795756101608 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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32mc:\\Users\\User\\VisualStudio\\challenging-america-word-gap-prediction\\lab7.ipynb Cell 14\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m create_outputs(\u001b[39m'\u001b[39;49m\u001b[39mdev-0\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m 2\u001b[0m create_outputs(\u001b[39m'\u001b[39m\u001b[39mtest-A\u001b[39m\u001b[39m'\u001b[39m)\n", "\u001b[1;32mc:\\Users\\User\\VisualStudio\\challenging-america-word-gap-prediction\\lab7.ipynb Cell 14\u001b[0m in \u001b[0;36mcreate_outputs\u001b[1;34m(folder_name)\u001b[0m\n\u001b[0;32m 6\u001b[0m separated \u001b[39m=\u001b[39m line\u001b[39m.\u001b[39msplit(\u001b[39m'\u001b[39m\u001b[39m\\t\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m 7\u001b[0m prefix \u001b[39m=\u001b[39m separated[\u001b[39m6\u001b[39m]\u001b[39m.\u001b[39mreplace(\u001b[39mr\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\\\u001b[39m\u001b[39mn\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m)\u001b[39m.\u001b[39msplit()[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n\u001b[1;32m----> 8\u001b[0m output_line \u001b[39m=\u001b[39m prediction(prefix)\n\u001b[0;32m 9\u001b[0m f\u001b[39m.\u001b[39mwrite(output_line \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m)\n", "\u001b[1;32mc:\\Users\\User\\VisualStudio\\challenging-america-word-gap-prediction\\lab7.ipynb Cell 14\u001b[0m in \u001b[0;36mprediction\u001b[1;34m(word)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mprediction\u001b[39m(word: \u001b[39mstr\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mstr\u001b[39m:\n\u001b[0;32m 2\u001b[0m ixs \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mtensor(vocab\u001b[39m.\u001b[39mforward([word]))\u001b[39m.\u001b[39mto(device)\n\u001b[1;32m----> 3\u001b[0m out \u001b[39m=\u001b[39m model(ixs)\n\u001b[0;32m 4\u001b[0m top \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mtopk(out[\u001b[39m0\u001b[39m], \u001b[39m5\u001b[39m)\n\u001b[0;32m 5\u001b[0m top_indices \u001b[39m=\u001b[39m top\u001b[39m.\u001b[39mindices\u001b[39m.\u001b[39mtolist()\n", "File \u001b[1;32mc:\\Users\\User\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1106\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1107\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1109\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1110\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1111\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", "\u001b[1;32mc:\\Users\\User\\VisualStudio\\challenging-america-word-gap-prediction\\lab7.ipynb Cell 14\u001b[0m in \u001b[0;36mSimpleBigramNeuralLanguageModel.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m---> 13\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel(x)\n", "File \u001b[1;32mc:\\Users\\User\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1106\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1107\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1109\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1110\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1111\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", "File \u001b[1;32mc:\\Users\\User\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\container.py:141\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[0;32m 140\u001b[0m \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[1;32m--> 141\u001b[0m \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m module(\u001b[39minput\u001b[39;49m)\n\u001b[0;32m 142\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39minput\u001b[39m\n", "File \u001b[1;32mc:\\Users\\User\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1106\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1107\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1109\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1110\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1111\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", "File \u001b[1;32mc:\\Users\\User\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\linear.py:103\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 103\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mlinear(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "create_outputs('dev-0')\n", "create_outputs('test-A')" ] } ], "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": "3.9.7" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "1b132c2ed43285dcf39f6d01712959169a14a721cf314fe69015adab49bb1fd1" } } }, "nbformat": 4, "nbformat_minor": 2 }