321 lines
10 KiB
Plaintext
321 lines
10 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f3452caf-df58-4394-b0d6-46459cb47045",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"S:\\WENV_TORCHTEXT\\Lib\\site-packages\\torchtext\\vocab\\__init__.py:4: UserWarning: \n",
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"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
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"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
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" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
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"S:\\WENV_TORCHTEXT\\Lib\\site-packages\\torchtext\\utils.py:4: UserWarning: \n",
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"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
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"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
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" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"
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]
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}
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],
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"source": [
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"from torch.utils.data import IterableDataset, DataLoader\n",
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"from torchtext.vocab import build_vocab_from_iterator\n",
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"\n",
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"import regex as re\n",
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"import itertools\n",
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"from itertools import islice\n",
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"\n",
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"from torch import nn\n",
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"import torch\n",
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"\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"embed_size = 300\n",
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"vocab_size = 30_000\n",
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"num_epochs = 1\n",
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"device = 'cuda'\n",
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"batch_size = 8192\n",
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"train_file_path = 'train/train.txt'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "93279277-0765-4f85-9666-095fc7808c81",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Function to extract words from a line of text\n",
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"def get_words_from_line(line):\n",
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" line = line.rstrip()\n",
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" yield '<s>'\n",
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" for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
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" yield m.group(0).lower()\n",
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" yield '</s>'\n",
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"\n",
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"# Generator to read lines from a file\n",
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"def get_word_lines_from_file(file_name):\n",
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" with open(file_name, 'r', encoding='utf8') as fh:\n",
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" for line in fh:\n",
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" yield get_words_from_line(line)\n",
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"\n",
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"# Function to create 5-grams from a sequence\n",
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"def look_ahead_iterator(gen):\n",
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" prev2, prev1, next1, next2 = None, None, None, None\n",
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" for item in gen:\n",
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" if prev2 is not None and prev1 is not None and next1 is not None and next2 is not None:\n",
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" yield (prev2, prev1, next2, item, next1)\n",
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" prev2, prev1, next1, next2 = prev1, next1, next2, item\n",
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"\n",
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"# Dataset class for 5-grams\n",
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"class FiveGrams(IterableDataset):\n",
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" def __init__(self, text_file, vocabulary_size):\n",
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" self.vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_file(text_file),\n",
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" max_tokens=vocabulary_size,\n",
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" specials=['<unk>']\n",
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" )\n",
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" self.vocab.set_default_index(self.vocab['<unk>'])\n",
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" self.vocabulary_size = vocabulary_size\n",
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" self.text_file = text_file\n",
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"\n",
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" def __iter__(self):\n",
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" return look_ahead_iterator(\n",
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" (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file)))\n",
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" )\n",
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"\n",
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"# Instantiate the dataset\n",
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"train_dataset = FiveGrams(train_file_path, vocab_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "980103d6-05a3-4b9a-a539-b59815f6a45d",
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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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"['<s>', 'came', 'the', 'last', 'fiom']\n",
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"['came', 'fiom', 'last', 'place', 'the']\n"
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]
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}
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],
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"source": [
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"i = 0\n",
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"for x in train_dataset:\n",
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" print(train_dataset.vocab.lookup_tokens(x))\n",
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" if i >= 1:\n",
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" break\n",
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" i += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "6eb5fbd9-bc0f-499d-85f4-3998a4a3f56e",
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"metadata": {},
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"outputs": [],
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"source": [
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"class SimpleFiveGramNeuralLanguageModel(nn.Module):\n",
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" def __init__(self, vocabulary_size, embedding_size):\n",
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" super(SimpleFiveGramNeuralLanguageModel, self).__init__()\n",
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" self.embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
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" self.linear1 = nn.Linear(embedding_size * 4, embedding_size)\n",
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" self.linear2 = nn.Linear(embedding_size, vocabulary_size)\n",
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" self.softmax = nn.Softmax(dim=1)\n",
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" self.embedding_size = embedding_size\n",
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"\n",
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" def forward(self, x):\n",
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" embeds = self.embedding(x).view(x.size(0), -1)\n",
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" out = self.linear1(embeds)\n",
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" out = self.linear2(out)\n",
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" return self.softmax(out)\n",
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"\n",
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"model = SimpleFiveGramNeuralLanguageModel(vocab_size, embed_size).to(device)"
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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": 5,
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"id": "d0dc7c69-3f27-4f00-9b91-5f3a403df074",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "3064f4f089604c8c8d0d6a6a826876bc",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Train loop: 0it [00:00, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"0 tensor(10.3575, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5000 tensor(4.8030, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"10000 tensor(4.6310, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"15000 tensor(4.5446, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"SimpleFiveGramNeuralLanguageModel(\n",
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" (embedding): Embedding(30000, 300)\n",
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" (linear1): Linear(in_features=1200, out_features=300, bias=True)\n",
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" (linear2): Linear(in_features=300, out_features=30000, bias=True)\n",
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" (softmax): Softmax(dim=1)\n",
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")"
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]
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},
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"execution_count": 5,
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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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"data = DataLoader(train_dataset, batch_size=batch_size)\n",
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"optimizer = torch.optim.Adam(model.parameters())\n",
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"criterion = torch.nn.CrossEntropyLoss()\n",
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"\n",
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"model.train()\n",
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"step = 0\n",
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"for _ in range(num_epochs):\n",
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" for x1, x2, x3, x4, y in tqdm(data, desc=\"Train loop\"):\n",
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" y = y.to(device)\n",
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" x = torch.cat((x1.unsqueeze(1), x2.unsqueeze(1), x3.unsqueeze(1), x4.unsqueeze(1)), dim=1).to(device)\n",
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" optimizer.zero_grad()\n",
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" ypredicted = model(x)\n",
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" \n",
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" loss = criterion(torch.log(ypredicted), y)\n",
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" if step % 5000 == 0:\n",
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" print(step, loss)\n",
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" step += 1\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" step = 0\n",
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"model.eval()"
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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": 8,
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"id": "9a1b2240-d2ed-4c56-8443-12113e66b514",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_gap_candidates(words, n=20, vocab=train_dataset.vocab):\n",
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" ixs = vocab(words)\n",
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" ixs = torch.tensor(ixs).unsqueeze(0).to(device)\n",
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"\n",
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" out = model(ixs)\n",
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" top = torch.topk(out[0], n)\n",
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" top_indices = top.indices.tolist()\n",
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" top_probs = top.values.tolist()\n",
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" top_words = vocab.lookup_tokens(top_indices)\n",
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" return list(zip(top_words, top_probs))\n",
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"\n",
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"def clean(text):\n",
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" text = text.replace('-\\\\n', '').replace('\\\\n', ' ').replace('\\\\t', ' ')\n",
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" text = re.sub(r'\\n', ' ', text)\n",
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" text = re.sub(r'(?<=\\w)[,-](?=\\w)', '', text)\n",
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" text = re.sub(r'\\s+', ' ', text)\n",
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" text = re.sub(r'\\p{P}', '', text)\n",
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" text = text.strip()\n",
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" return text\n",
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" \n",
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"def predictor(prefix, suffix):\n",
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" prefix = clean(prefix)\n",
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" suffix = clean(suffix)\n",
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" words = prefix.split(' ')[-2:] + suffix.split(' ')[:2]\n",
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" candidates = get_gap_candidates(words)\n",
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"\n",
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" probs_sum = 0\n",
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" output = ''\n",
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" for word, prob in candidates:\n",
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" if word == \"<unk>\":\n",
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" continue\n",
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" probs_sum += prob\n",
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" output += f\"{word}:{prob} \"\n",
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" output += f\":{1-probs_sum}\"\n",
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"\n",
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" return output"
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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": 9,
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"id": "40af2781-3807-43e8-b6dd-3b70066e50c1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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||
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"model_id": "743a03c2e3064f9485d196e8eafe80e9",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/10519 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"def generate_result(input_path, output_path='out.tsv'):\n",
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" lines = []\n",
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" with open(input_path, encoding='utf-8') as f:\n",
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" for line in f:\n",
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" columns = line.split('\\t')\n",
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" prefix = columns[6]\n",
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" suffix = columns[7]\n",
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" lines.append((prefix, suffix))\n",
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"\n",
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" with open(output_path, 'w', encoding='utf-8') as output_file:\n",
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" for prefix, suffix in tqdm(lines):\n",
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" result = predictor(prefix, suffix)\n",
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" output_file.write(result + '\\n')\n",
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"\n",
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"generate_result('dev-0/in.tsv', output_path='dev-0/out.tsv')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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