challenging-america-word-ga.../lab7.ipynb
JulianZablonski 36942a5ba5 lab12_2
2023-06-15 11:49:01 +02:00

2855 lines
336 KiB
Plaintext

{
"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 '<s>'\n",
" for t in line.split():\n",
" yield t\n",
" yield '</s>'\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 = ['<unk>'])\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['<unk>'])\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=<SelectBackward0>)"
]
},
"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['<unk>'])\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=<NllLossBackward0>)\n",
"100 tensor(5.2132, grad_fn=<NllLossBackward0>)\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<cell line: 9>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X10sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m \u001b[39mprint\u001b[39m(step, loss)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X10sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m step \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X10sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m loss\u001b[39m.\u001b[39;49mbackward()\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X10sZmlsZQ%3D%3D?line=18'>19</a>\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 '<unk>' 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",
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"ChronAm:0.010227881371974945 is:0.008444960229098797 are:0.006903863977640867 to:0.005455975420773029 :0.015587559901177883\n",
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"TRIBUNE:0.006836355198174715 of:0.003062568139284849 Mr.:0.0025953517761081457 as:0.002292780438438058 :0.002262084512040019\n",
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"than:0.004401444923132658 side:0.0034171773586422205 TRIBUNE:0.0033840015530586243 his:0.00314173917286098 :0.0030922156292945147\n",
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"-90.184225:0.002001544926315546 Supreme:0.0019895844161510468 which:0.0018310900777578354 actual:0.0017272314289584756 :0.0016814458649605513\n",
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{
"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"
]
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"made:0.012885693460702896 not:0.012629787437617779 a:0.010603071190416813 in:0.00862963404506445 :0.005580437835305929\n",
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"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
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"which:0.0021849030163139105 two:0.002163633704185486 is:0.0020608343183994293 your:0.001900204923003912 :0.0023841660004109144\n",
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"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",
"to:0.00894448347389698 a:0.00495443120598793 the:0.004840463865548372 as:0.0046218521893024445 :0.01977972686290741\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"have:0.005044417455792427 will:0.0034630715381354094 were:0.002685091458261013 ChronAm:0.002601920161396265 :0.0025525556411594152\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"the:0.014330108650028706 to:0.004363406449556351 their:0.0036250457633286715 due:0.002878295024856925 :0.0029603049624711275\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"ChronAm:0.015831388533115387 and:0.006063428707420826 is:0.0036535137332975864 upon:0.0035333861596882343 :0.0025035769212991\n",
"ChronAm:0.011658554896712303 in:0.003950342535972595 who:0.003826563246548176 is:0.003179324558004737 :0.008605021983385086\n",
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"is:0.25620099902153015 was:0.05419865995645523 Is:0.043216150254011154 seems:0.005674568470567465 :0.004808856640011072\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"have:0.004256343003362417 was:0.003275996772572398 may:0.00246889959089458 .:0.002466937294229865 :0.002410076791420579\n",
"any:0.0173477903008461 three:0.011614865623414516 other:0.009230944328010082 more:0.008342713117599487 :0.2566910684108734\n",
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"the:0.00914869736880064 <s>:0.008486342616379261 has:0.005098903086036444 of:0.005064309574663639 :0.014235345646739006\n",
"own:0.02931779995560646 wife,:0.004907419439405203 years:0.004127251449972391 ChronAm:0.0038235432002693415 :0.01431277021765709\n",
"same:0.006965374108403921 said:0.006106619723141193 United:0.005009945016354322 first:0.00498473085463047 :0.6302009224891663\n",
"much:0.0032397201284766197 long:0.002930003684014082 an:0.002756246831268072 any:0.0025241521652787924 :0.0025146687403321266\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"have:0.003201050916686654 GAZETTE:0.0022110198624432087 two:0.0019693314097821712 now:0.0018942548194900155 :0.0019207235891371965\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",
"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",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\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",
"the:0.11162873357534409 be:0.05268840864300728 a:0.02626536600291729 have:0.010365024209022522 :0.4014780819416046\n",
"ChronAm:0.002726483391597867 fifty:0.0018652192084118724 -95.937873:0.0016142348758876324 REGISTER:0.0015589399263262749 :0.0015125360805541277\n",
"the:0.014333795756101608 few:0.011960748583078384 little:0.009230829775333405 great:0.008417336270213127 :0.6215581893920898\n",
"who:0.0032766531221568584 table:0.002007946604862809 -95.937873:0.0019584756810218096 -100.445882:0.0018828740576282144 :0.00478816544637084\n",
"the:0.042829595506191254 once:0.021265516057610512 least:0.017873402684926987 all:0.014264633879065514 :0.015432408079504967\n",
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"of:0.014755302108824253 .:0.005232805386185646 more:0.005168795119971037 on:0.003693794598802924 :0.0034033681731671095\n",
"-73.986614:0.0024099985603243113 know:0.0019803643226623535 WASHINGTON:0.0016368023352697492 .:0.0015905939508229494 :0.0014492485206574202\n",
"be:0.23808987438678741 not:0.025933699682354927 bo:0.018055440858006477 have:0.0130957355722785 :0.009518636390566826\n",
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"the:0.11162873357534409 be:0.05268840864300728 a:0.02626536600291729 have:0.010365024209022522 :0.4014780819416046\n",
"the:0.23462098836898804 a:0.026843780651688576 this:0.012386118993163109 said:0.010683613829314709 :0.45802026987075806\n",
"of:0.004518603440374136 on:0.0028673456981778145 is:0.002456100657582283 about:0.002324025146663189 :0.0022843696642667055\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"he:0.04289819300174713 it:0.039998434484004974 the:0.03839282691478729 they:0.025981521233916283 :0.01998070627450943\n",
"same:0.006965374108403921 said:0.006106619723141193 United:0.005009945016354322 first:0.00498473085463047 :0.6302009224891663\n",
".:0.01608576439321041 ChronAm:0.002864164300262928 <s>:0.002358792582526803 every:0.002111671958118677 :0.0024701848160475492\n",
"much:0.0610247440636158 far:0.05110550671815872 long:0.013353795744478703 that:0.011848034337162971 :0.13352146744728088\n",
"a:0.051128845661878586 it:0.03461145609617233 they:0.02874501794576645 to:0.027865994721651077 :0.11375875771045685\n",
"a:0.07568275183439255 the:0.045853495597839355 his:0.02007143944501877 all:0.013846635818481445 :0.04955475032329559\n",
"to:0.00299826730042696 Union:0.0028347144834697247 .:0.0027492670342326164 also:0.002017604187130928 :0.0019159052753821015\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\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",
"is:0.008556698448956013 GAZETTE:0.0063217091374099255 was:0.003894595894962549 be:0.0033266914542764425 :0.0028396551497280598\n",
"ChronAm:0.01092063169926405 -77.036646:0.0033764084801077843 two:0.002416850533336401 New:0.0022621648386120796 :0.002032480202615261\n",
"much:0.0019611928146332502 ChronAm:0.0018538435688242316 ba:0.0016861180774867535 seemed:0.0016696308739483356 :0.0016515844035893679\n",
"made:0.01681242510676384 and:0.008670934475958347 held:0.008592469617724419 taken:0.007940184324979782 :0.06627477705478668\n",
"the:0.23564565181732178 a:0.05724387615919113 tho:0.013461323454976082 his:0.011656158603727818 :0.15657949447631836\n",
"the:0.3011701703071594 a:0.03520622104406357 his:0.009904870763421059 account:0.007860109210014343 :0.08594886213541031\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
".:0.0042745815590023994 <s>:0.004117019474506378 as:0.0031554715242236853 for:0.0029572928324341774 :0.018431732431054115\n",
"was:0.028573885560035706 on:0.009485749527812004 in:0.008798889815807343 by:0.007207883056253195 :0.014137964695692062\n",
"the:0.2207714021205902 a:0.039634596556425095 this:0.0212148055434227 which:0.011685609817504883 :0.37857145071029663\n",
"been:0.14885377883911133 to:0.025148354470729828 no:0.012233811430633068 a:0.00745265232399106 :0.03908766806125641\n",
".:0.0061380136758089066 had:0.0032958851661533117 not:0.002725449623540044 2:0.002429395681247115 :0.0024026166647672653\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"that:0.0059100668877363205 it:0.0032330246176570654 make:0.0031369971111416817 not:0.002705535152927041 :0.00259611289948225\n",
"left:0.005139078479260206 but:0.0032761397305876017 day:0.00251993746496737 books:0.0022680400870740414 :0.0020116146188229322\n",
"to:0.006284847389906645 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 </s>: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 yet:0.009667816571891308 have:0.007443589624017477 :0.007218844722956419\n",
"the:0.11162873357534409 be:0.05268840864300728 a:0.02626536600291729 have:0.010365024209022522 :0.4014780819416046\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"on:0.0026515580248087645 any:0.0024402744602411985 persons:0.002341326791793108 given:0.002286721719428897 :0.01241431012749672\n",
"for:0.005025676917284727 of:0.004432681482285261 at:0.004391416907310486 to:0.00427007582038641 :0.0037302328273653984\n",
"of:0.05411530286073685 and:0.04940299689769745 the:0.0400581881403923 to:0.029938215389847755 :0.37532946467399597\n",
"the:0.0473141074180603 a:0.0060153985396027565 each:0.0025270506739616394 her:0.0023244984913617373 :0.002236206317320466\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",
"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",
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]
},
{
"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 14\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m create_outputs(\u001b[39m'\u001b[39;49m\u001b[39mdev-0\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=1'>2</a>\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 <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=5'>6</a>\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 <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=6'>7</a>\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----> <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m output_line \u001b[39m=\u001b[39m prediction(prefix)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=8'>9</a>\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 <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=0'>1</a>\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 <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=1'>2</a>\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----> <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m out \u001b[39m=\u001b[39m model(ixs)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=3'>4</a>\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 <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m top_indices \u001b[39m=\u001b[39m top\u001b[39m.\u001b[39mindices\u001b[39m.\u001b[39mtolist()\n",
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"\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 <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/User/VisualStudio/challenging-america-word-gap-prediction/lab7.ipynb#X15sZmlsZQ%3D%3D?line=12'>13</a>\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')"
]
}
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