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init repo
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{
"cells": [
{
"cell_type": "code",
solution part 1
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"execution_count": 1,
init repo
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"id": "d42ddd87",
"metadata": {},
"outputs": [],
"source": [
solution part 1
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"import torch\n",
neural trigram v1
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"from torch import nn\n",
"\n",
"torch.cuda.empty_cache()"
init repo
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]
},
{
"cell_type": "code",
solution part 1
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"execution_count": 2,
neural trigram v1
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"id": "37fa7d97",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import regex as re\n",
"import csv\n",
"\n",
"def clean_text(text):\n",
" text = text.lower().replace('-\\\\\\\\n', '').replace('\\\\\\\\n', ' ')\n",
" text = re.sub(r'\\p{P}', '', text)\n",
" text = text.replace(\"'t\", \" not\").replace(\"'s\", \" is\").replace(\"'ll\", \" will\").replace(\"'m\", \" am\").replace(\"'ve\", \" have\")\n",
"\n",
" return text"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "41e2f529",
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv('train/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
"train_labels = pd.read_csv('train/expected.tsv', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
"\n",
"train_data = train_data[[6, 7]]\n",
"train_data = pd.concat([train_data, train_labels], axis=1)\n",
"\n",
"train_data['text'] = train_data[6] + train_data[0] + train_data[7]\n",
"train_data = train_data[['text']]\n",
"\n",
"with open('processed_train.txt', 'w', encoding='utf-8') as file:\n",
" for _, row in train_data.iterrows():\n",
" text = clean_text(str(row['text']))\n",
" file.write(text + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 4,
solution part 1
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"id": "dc73124c",
init repo
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"metadata": {},
"outputs": [],
"source": [
neural trigram v1
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"vocab_size = 40000\n",
"embed_size = 300\n",
"hidden_size = 128\n",
solution part 1
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"\n",
"class SimpleTrigramNeuralLanguageModel(nn.Module):\n",
neural trigram v1
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" def __init__(self, vocabulary_size, embedding_size, hidden_size):\n",
solution part 1
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" super(SimpleTrigramNeuralLanguageModel, self).__init__()\n",
neural trigram v1
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" self.embedding = nn.Embedding(vocabulary_size * 2, embedding_size)\n",
" self.linear1 = nn.Linear(embedding_size, hidden_size)\n",
" self.linear2 = nn.Linear(hidden_size, vocabulary_size * 2)\n",
init repo
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"\n",
solution part 1
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" def forward(self, x):\n",
" x = self.embedding(x)\n",
neural trigram v1
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" x = self.linear1(x)\n",
" x = self.linear2(x)\n",
solution part 1
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" x = torch.softmax(x, dim=1)\n",
" return x"
init repo
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]
},
{
"cell_type": "code",
neural trigram v1
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"execution_count": 5,
solution part 1
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"id": "569b4c88",
init repo
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"metadata": {},
"outputs": [],
"source": [
solution part 1
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"import regex as re\n",
"from itertools import islice, chain\n",
"from torchtext.vocab import build_vocab_from_iterator\n",
"from torch.utils.data import IterableDataset\n",
init repo
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"\n",
solution part 1
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"def get_words_from_line(line):\n",
" line = line.rstrip()\n",
" yield '<s>'\n",
" for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
" yield m.group(0).lower()\n",
" yield '</s>'\n",
init repo
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"\n",
solution part 1
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"def get_word_lines_from_file(file_name):\n",
neural trigram v1
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" with open(file_name, 'r', encoding='utf-8') as fh:\n",
solution part 1
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" for line in fh:\n",
" yield get_words_from_line(line)\n",
" \n",
"def look_ahead_iterator(gen):\n",
neural trigram v1
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" prev_1 = None\n",
" prev_2 = None\n",
solution part 1
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" for item in gen:\n",
neural trigram v1
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" if prev_1 and prev_2:\n",
" yield (prev_2 + prev_1, item)\n",
" prev_2 = prev_1\n",
" prev_1 = item"
init repo
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]
},
{
"cell_type": "code",
neural trigram v1
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"execution_count": 6,
solution part 1
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"id": "f95cb913",
init repo
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"metadata": {},
solution part 1
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"outputs": [],
init repo
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"source": [
neural trigram v1
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"class Trigrams(IterableDataset):\n",
solution part 1
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" def __init__(self, text_file, vocabulary_size):\n",
" self.vocab = build_vocab_from_iterator(\n",
" get_word_lines_from_file(text_file),\n",
" max_tokens = vocabulary_size,\n",
" specials = ['<unk>']\n",
" )\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((self.vocab[t] for t in chain.from_iterable(get_word_lines_from_file(self.text_file))))"
init repo
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]
},
{
"cell_type": "code",
neural trigram v1
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"execution_count": 7,
solution part 1
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"id": "7a51f2b1",
init repo
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"metadata": {},
solution v2
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"outputs": [],
init repo
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"source": [
solution part 1
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"from torch.utils.data import DataLoader\n",
init repo
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"\n",
neural trigram v1
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"train_dataset = Trigrams('processed_train.txt', vocab_size)\n",
"model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
"data = DataLoader(train_dataset, batch_size=800)\n",
solution part 1
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"optimizer = torch.optim.Adam(model.parameters())\n",
"criterion = torch.nn.NLLLoss()"
init repo
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]
},
{
"cell_type": "code",
solution v2
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"execution_count": 8,
solution part 1
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"id": "474194ae",
init repo
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"metadata": {},
solution v2
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
neural trigram v1
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},
{
"name": "stdout",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"159600 tensor(6.3715, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"159700 tensor(6.4539, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"159900 tensor(6.0975, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160000 tensor(6.3995, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160100 tensor(6.1775, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160200 tensor(5.9064, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160300 tensor(6.2468, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160400 tensor(6.0242, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160500 tensor(6.1718, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160600 tensor(5.8328, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160700 tensor(5.9871, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160800 tensor(5.9541, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"160900 tensor(6.3061, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"161000 tensor(6.3106, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"161100 tensor(6.0869, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"161500 tensor(6.0799, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
solution v2
2022-05-01 12:26:53 +02:00
]
},
{
neural trigram v1
2022-05-02 13:38:28 +02:00
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"201400 tensor(6.2391, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"201500 tensor(6.3647, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"201600 tensor(5.6603, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"201700 tensor(6.1209, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"201800 tensor(6.3297, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"201900 tensor(6.3605, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202000 tensor(6.1686, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202100 tensor(6.3533, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202200 tensor(6.2875, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202300 tensor(6.6533, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202400 tensor(6.2482, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202500 tensor(6.4789, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202600 tensor(6.2016, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202700 tensor(6.2338, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202800 tensor(6.2872, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"202900 tensor(6.1065, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203000 tensor(6.4077, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203100 tensor(5.9212, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203200 tensor(5.9851, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203300 tensor(6.3276, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203400 tensor(6.2920, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203500 tensor(6.1845, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203600 tensor(6.1408, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203700 tensor(6.0589, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203800 tensor(6.4621, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"203900 tensor(6.0378, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204000 tensor(5.6533, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204100 tensor(5.9323, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204200 tensor(6.3041, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204300 tensor(6.3849, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204400 tensor(6.1515, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204500 tensor(6.7930, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204600 tensor(5.9025, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204700 tensor(6.0259, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204800 tensor(6.3013, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"204900 tensor(6.4514, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"205000 tensor(6.3252, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"205100 tensor(6.2335, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"205200 tensor(6.1989, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"205300 tensor(6.4334, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"205400 tensor(6.0725, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"205500 tensor(5.9396, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"205600 tensor(6.2119, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"205700 tensor(6.2683, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"205900 tensor(6.4856, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"206000 tensor(5.9640, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"206100 tensor(6.2160, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"206300 tensor(6.1862, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"206400 tensor(6.2911, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"206600 tensor(5.7706, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"206900 tensor(6.1206, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"207000 tensor(6.3212, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"207100 tensor(6.2621, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"207200 tensor(5.8658, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"207300 tensor(6.2654, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"208000 tensor(6.4104, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"208100 tensor(6.1244, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"208300 tensor(6.3414, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"208500 tensor(6.3633, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"208600 tensor(6.3104, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"208700 tensor(6.3589, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"208800 tensor(6.3015, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"208900 tensor(6.4318, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209000 tensor(6.2347, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209100 tensor(6.2905, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209200 tensor(6.2379, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209300 tensor(6.4833, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209400 tensor(6.1210, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209500 tensor(6.3428, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209600 tensor(6.5130, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209700 tensor(6.3444, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209800 tensor(6.2653, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"209900 tensor(6.2726, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210000 tensor(6.2480, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210100 tensor(5.8864, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210200 tensor(6.4154, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210300 tensor(6.3754, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210400 tensor(6.3736, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210500 tensor(6.0709, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210600 tensor(6.4558, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210700 tensor(6.2008, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"210800 tensor(6.4275, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"210900 tensor(6.1214, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211000 tensor(6.0207, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211100 tensor(6.1209, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211200 tensor(6.2109, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211300 tensor(6.0009, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211400 tensor(6.2715, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211500 tensor(6.4340, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211600 tensor(6.4781, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211700 tensor(6.2207, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211800 tensor(6.2370, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"211900 tensor(5.9837, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"212000 tensor(6.2359, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"212100 tensor(6.4122, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
solution v2
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]
}
],
init repo
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"source": [
solution v2
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"step = 0\n",
"\n",
neural trigram v1
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"for epoch in range(2):\n",
solution part 1
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" model.train()\n",
" for x, y in data:\n",
" x = x.to(device)\n",
" y = y.to(device)\n",
" optimizer.zero_grad()\n",
" outputs = model(x)\n",
" loss = criterion(torch.log(outputs), y)\n",
" if step % 100 == 0:\n",
" print(step, loss)\n",
" step += 1\n",
" loss.backward()\n",
neural trigram v1
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" optimizer.step()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ec906796",
"metadata": {},
"outputs": [],
"source": [
solution part 1
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"torch.save(model.state_dict(), 'model/model1.bin')"
init repo
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]
solution v2
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},
{
"cell_type": "code",
neural trigram v1
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"execution_count": 23,
solution v2
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"id": "alpha-leonard",
"metadata": {},
"outputs": [],
neural trigram v1
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"source": [
"device = 'cuda'\n",
"model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
"model.load_state_dict(torch.load('model/model1.bin'))\n",
"model.eval()\n",
"\n",
"def predict(words):\n",
" ixs = torch.tensor(train_dataset.vocab.forward(['with'])).to(device)\n",
" predictions = model(ixs)\n",
" top = torch.topk(out[0], 30)\n",
" top_indices = top.indices.tolist()\n",
" top_probs = top.values.tolist()\n",
" top_words = train_dataset.vocab.lookup_tokens(top_indices)\n",
" top_preds = list(zip(top_words, top_indices, top_probs))\n",
" \n",
" total_prob = 0.0\n",
" pred_str = ''\n",
" for word, _, prob in top_preds:\n",
" if word != '<unk>':\n",
" pred_str += f'{word}:{prob} '\n",
" total_prob += prob\n",
" pred_str += f':{1 - total_prob}'\n",
" \n",
" return pred_str"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "43ba8f83",
"metadata": {},
"outputs": [],
"source": [
"dev_data = pd.read_csv('dev-0/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
"test_data = pd.read_csv('test-A/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "358a3d52",
"metadata": {},
"outputs": [],
"source": [
"from nltk import word_tokenize\n",
"\n",
"with open('dev-0/out.tsv', 'w') as file:\n",
" for index, row in dev_data.iterrows():\n",
" left_text = clean_text(str(row[6]))\n",
" left_words = word_tokenize(left_text)\n",
" if len(left_words) < 3:\n",
" prediction = ':1.0'\n",
" else:\n",
" prediction = predict(left_words[-2:])\n",
" file.write(prediction + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "abc35d78",
"metadata": {},
"outputs": [],
"source": [
"with open('test-A/out.tsv', 'w') as file:\n",
" for index, row in test_data.iterrows():\n",
" left_text = clean_text(str(row[6]))\n",
" left_words = word_tokenize(left_text)\n",
" if len(left_words) < 3:\n",
" prediction = ':1.0'\n",
" else:\n",
" prediction = predict(left_words[-2:])\n",
" file.write(prediction + '\\n')"
]
init repo
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}
],
"metadata": {
"kernelspec": {
neural trigram v1
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"display_name": "Python 3 (ipykernel)",
init repo
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"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",
neural trigram v1
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"version": "3.8.2"
init repo
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}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink