add solution for 5gram with 173.58 perplexity
This commit is contained in:
kpierzynski
commit
c8496a4271
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README.md
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README.md
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## challenging-america-word-gap-prediction
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### using simple trigram nn
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calculated perplexity: 173.58
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1
config.txt
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config.txt
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--metric PerplexityHashed --precision 2 --in-header in-header.tsv --out-header out-header.tsv
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dev-0/.ipynb_checkpoints/out-checkpoint.tsv
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dev-0/.ipynb_checkpoints/out-checkpoint.tsv
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dev-0/expected.tsv
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dev-0/expected.tsv
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dev-0/hate-speech-info.tsv
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dev-0/hate-speech-info.tsv
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dev-0/in.tsv
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dev-0/in.tsv
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dev-0/out.tsv
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dev-0/out.tsv
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in-header.tsv
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in-header.tsv
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FileId Year LeftContext RightContext
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320
main.ipynb
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main.ipynb
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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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"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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out-header.tsv
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out-header.tsv
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Word
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run.py
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run.py
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#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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from torch.utils.data import IterableDataset, DataLoader
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from torchtext.vocab import build_vocab_from_iterator
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import regex as re
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import itertools
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from itertools import islice
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from torch import nn
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import torch
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from tqdm.notebook import tqdm
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embed_size = 300
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vocab_size = 30_000
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num_epochs = 1
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device = 'cuda'
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batch_size = 8192
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train_file_path = 'train/train.txt'
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# In[2]:
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# Function to extract words from a line of text
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def get_words_from_line(line):
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line = line.rstrip()
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yield '<s>'
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for m in re.finditer(r'[\p{L}0-9\*]+|\p{P}+', line):
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yield m.group(0).lower()
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yield '</s>'
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# Generator to read lines from a file
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def get_word_lines_from_file(file_name):
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with open(file_name, 'r', encoding='utf8') as fh:
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for line in fh:
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yield get_words_from_line(line)
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# Function to create 5-grams from a sequence
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def look_ahead_iterator(gen):
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prev2, prev1, next1, next2 = None, None, None, None
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for item in gen:
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if prev2 is not None and prev1 is not None and next1 is not None and next2 is not None:
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yield (prev2, prev1, next2, item, next1)
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prev2, prev1, next1, next2 = prev1, next1, next2, item
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# Dataset class for 5-grams
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class FiveGrams(IterableDataset):
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def __init__(self, text_file, vocabulary_size):
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self.vocab = build_vocab_from_iterator(
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get_word_lines_from_file(text_file),
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max_tokens=vocabulary_size,
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specials=['<unk>']
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)
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self.vocab.set_default_index(self.vocab['<unk>'])
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self.vocabulary_size = vocabulary_size
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self.text_file = text_file
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def __iter__(self):
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return look_ahead_iterator(
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(self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file)))
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)
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# Instantiate the dataset
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||||
train_dataset = FiveGrams(train_file_path, vocab_size)
|
||||
|
||||
|
||||
# In[3]:
|
||||
|
||||
|
||||
i = 0
|
||||
for x in train_dataset:
|
||||
print(train_dataset.vocab.lookup_tokens(x))
|
||||
if i >= 1:
|
||||
break
|
||||
i += 1
|
||||
|
||||
|
||||
# In[4]:
|
||||
|
||||
|
||||
class SimpleFiveGramNeuralLanguageModel(nn.Module):
|
||||
def __init__(self, vocabulary_size, embedding_size):
|
||||
super(SimpleFiveGramNeuralLanguageModel, self).__init__()
|
||||
self.embedding = nn.Embedding(vocabulary_size, embedding_size)
|
||||
self.linear1 = nn.Linear(embedding_size * 4, embedding_size)
|
||||
self.linear2 = nn.Linear(embedding_size, vocabulary_size)
|
||||
self.softmax = nn.Softmax(dim=1)
|
||||
self.embedding_size = embedding_size
|
||||
|
||||
def forward(self, x):
|
||||
embeds = self.embedding(x).view(x.size(0), -1)
|
||||
out = self.linear1(embeds)
|
||||
out = self.linear2(out)
|
||||
return self.softmax(out)
|
||||
|
||||
model = SimpleFiveGramNeuralLanguageModel(vocab_size, embed_size).to(device)
|
||||
|
||||
|
||||
# In[5]:
|
||||
|
||||
|
||||
data = DataLoader(train_dataset, batch_size=batch_size)
|
||||
optimizer = torch.optim.Adam(model.parameters())
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
|
||||
model.train()
|
||||
step = 0
|
||||
for _ in range(num_epochs):
|
||||
for x1, x2, x3, x4, y in tqdm(data, desc="Train loop"):
|
||||
y = y.to(device)
|
||||
x = torch.cat((x1.unsqueeze(1), x2.unsqueeze(1), x3.unsqueeze(1), x4.unsqueeze(1)), dim=1).to(device)
|
||||
optimizer.zero_grad()
|
||||
ypredicted = model(x)
|
||||
|
||||
loss = criterion(torch.log(ypredicted), y)
|
||||
if step % 5000 == 0:
|
||||
print(step, loss)
|
||||
step += 1
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
step = 0
|
||||
model.eval()
|
||||
|
||||
|
||||
# In[8]:
|
||||
|
||||
|
||||
def get_gap_candidates(words, n=20, vocab=train_dataset.vocab):
|
||||
ixs = vocab(words)
|
||||
ixs = torch.tensor(ixs).unsqueeze(0).to(device)
|
||||
|
||||
out = model(ixs)
|
||||
top = torch.topk(out[0], n)
|
||||
top_indices = top.indices.tolist()
|
||||
top_probs = top.values.tolist()
|
||||
top_words = vocab.lookup_tokens(top_indices)
|
||||
return list(zip(top_words, top_probs))
|
||||
|
||||
def clean(text):
|
||||
text = text.replace('-\\n', '').replace('\\n', ' ').replace('\\t', ' ')
|
||||
text = re.sub(r'\n', ' ', text)
|
||||
text = re.sub(r'(?<=\w)[,-](?=\w)', '', text)
|
||||
text = re.sub(r'\s+', ' ', text)
|
||||
text = re.sub(r'\p{P}', '', text)
|
||||
text = text.strip()
|
||||
return text
|
||||
|
||||
def predictor(prefix, suffix):
|
||||
prefix = clean(prefix)
|
||||
suffix = clean(suffix)
|
||||
words = prefix.split(' ')[-2:] + suffix.split(' ')[:2]
|
||||
candidates = get_gap_candidates(words)
|
||||
|
||||
probs_sum = 0
|
||||
output = ''
|
||||
for word, prob in candidates:
|
||||
if word == "<unk>":
|
||||
continue
|
||||
probs_sum += prob
|
||||
output += f"{word}:{prob} "
|
||||
output += f":{1-probs_sum}"
|
||||
|
||||
return output
|
||||
|
||||
|
||||
# In[9]:
|
||||
|
||||
|
||||
def generate_result(input_path, output_path='out.tsv'):
|
||||
lines = []
|
||||
with open(input_path, encoding='utf-8') as f:
|
||||
for line in f:
|
||||
columns = line.split('\t')
|
||||
prefix = columns[6]
|
||||
suffix = columns[7]
|
||||
lines.append((prefix, suffix))
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as output_file:
|
||||
for prefix, suffix in tqdm(lines):
|
||||
result = predictor(prefix, suffix)
|
||||
output_file.write(result + '\n')
|
||||
|
||||
generate_result('dev-0/in.tsv', output_path='dev-0/out.tsv')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user