From a2d183f2e33c86052ccce707e01e34c48ee5db4b Mon Sep 17 00:00:00 2001 From: Marcin Czerniak Date: Sat, 18 May 2024 22:57:13 +0200 Subject: [PATCH] Score: 6.04 --- dev-0/out.tsv | 21038 ++++++++++++++++++++-------------------- src/08_word2vec.ipynb | 643 ++ 2 files changed, 11162 insertions(+), 10519 deletions(-) create mode 100644 src/08_word2vec.ipynb diff --git a/dev-0/out.tsv b/dev-0/out.tsv index 72709b9..a1a3544 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,10519 +1,10519 @@ -the:0.21114222705364227 of:0.13852208852767944 and:0.11889281868934631 to:0.0977778509259224 a:0.06576600670814514 :0.36789900809526443 -the:0.2109917849302292 of:0.13878382742404938 and:0.1189134418964386 to:0.0985817238688469 a:0.06577884405851364 :0.3669503778219223 -the:0.21052104234695435 of:0.1389070451259613 and:0.11827906221151352 to:0.09824154525995255 a:0.06613366305828094 :0.36791764199733734 -the:0.21067284047603607 of:0.13851475715637207 and:0.11852195858955383 to:0.09843829274177551 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ride:0.05 plan:0.05 :0.44 +had:0.07 to:0.07 and:0.06 should:0.06 we:0.05 has:0.05 I:0.05 they:0.05 or:0.05 would:0.05 :0.44 diff --git a/src/08_word2vec.ipynb b/src/08_word2vec.ipynb new file mode 100644 index 0000000..2aa9954 --- /dev/null +++ b/src/08_word2vec.ipynb @@ -0,0 +1,643 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Trigram neural network model for gap fill task" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import required packages" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "import re\n", + "import nltk\n", + "import os\n", + "import csv\n", + "import pandas as pd\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import sys\n", + "import numpy as np\n", + "from torch.utils.data import DataLoader, TensorDataset\n", + "from bidict import bidict\n", + "import math\n", + "from sklearn.utils import shuffle\n", + "from collections import Counter\n", + "import random\n", + "from torchtext.vocab import build_vocab_from_iterator" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n", + "os.environ['TORCH_USE_CUDA_DSA'] = '1'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Global configuration variables" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "vocab_size = 40_000\n", + "batch_size = 256\n", + "embedding_dim = 128\n", + "hidden_dim = 1024\n", + "learning_rate = 0.001\n", + "epochs = 5\n", + "\n", + "output_size = vocab_size" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n" + ] + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "# device = torch.device(\"cpu\")\n", + "print(device)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build vocabulary" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def get_word_lines_from_dataset():\n", + " dataset_dir = os.path.join('..', 'train', 'in.tsv.xz')\n", + " expected_dir = os.path.join('..', 'train', 'expected.tsv')\n", + "\n", + " df = pd.read_csv(dataset_dir, sep='\\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)\n", + " expected_df = pd.read_csv(expected_dir, sep='\\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)\n", + "\n", + " for j, (df, expected_df) in tqdm(enumerate(zip(df, expected_df)), total=433):\n", + " df = df.replace(r'\\\\r+|\\\\n+|\\\\t+', ' ', regex=True)\n", + " \n", + " for left_context, word, right_context in zip(df['LeftContext'].to_list(), expected_df['Word'].to_list(), df['RightContext'].to_list()):\n", + " yield re.split(r\"\\s+\", left_context.strip()) + [str(word).strip()] + re.split(r\"\\s+\", right_context.strip())" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/433 [00:00'])" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "vocab.set_default_index(vocab[''])" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['', 'the', 'of', 'me.']" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocab.lookup_tokens([0, 1, 2, 1245])" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 433/433 [01:06<00:00, 6.50it/s]\n" + ] + } + ], + "source": [ + "dataset_dir = os.path.join('..', 'train', 'in.tsv.xz')\n", + "expected_dir = os.path.join('..', 'train', 'expected.tsv')\n", + "\n", + "df = pd.read_csv(dataset_dir, sep='\\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)\n", + "expected_df = pd.read_csv(expected_dir, sep='\\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)\n", + "\n", + "\n", + "input_corpus = []\n", + "target_corpus = []\n", + "\n", + "left_tokens = 2\n", + "right_tokens = 2\n", + "\n", + "for j, (df, expected_df) in tqdm(enumerate(zip(df, expected_df)), total=433):\n", + " df = df.replace(r'\\\\r+|\\\\n+|\\\\t+', ' ', regex=True)\n", + " \n", + " for left_context, word, right_context in zip(df['LeftContext'].to_list(), expected_df['Word'].to_list(), df['RightContext'].to_list()):\n", + " target_corpus.append([str(word).strip()])\n", + " input_corpus.append(re.split(r\"\\s+\", left_context.strip())[-left_tokens:] + re.split(r\"\\s+\", right_context.strip())[:right_tokens])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tokenize entire corpus" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 432022/432022 [00:02<00:00, 168428.47it/s]\n", + "100%|██████████| 432022/432022 [00:01<00:00, 332294.03it/s]\n" + ] + } + ], + "source": [ + "def tokenize(w):\n", + " return vocab[w]\n", + " \n", + "def detokenize(t):\n", + " return vocab.lookup_tokens([t])[0]\n", + "\n", + "tokenized_input_corpus = []\n", + "tokenized_target_corpus = []\n", + "\n", + "for words in tqdm(input_corpus):\n", + " tokenized_input_corpus.append([tokenize(word) for word in words])\n", + "\n", + "for words in tqdm(target_corpus):\n", + " tokenized_target_corpus.append([tokenize(word) for word in words])" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "tokenized_input_corpus, tokenized_target_corpus = shuffle(tokenized_input_corpus, tokenized_target_corpus)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "indices = np.nonzero(np.array(tokenized_target_corpus).flatten())\n", + "\n", + "tokenized_input_corpus = np.take(tokenized_input_corpus, indices, axis=0)\n", + "tokenized_target_corpus = np.take(tokenized_target_corpus, indices, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "input_corpus_tensor = torch.flatten(torch.tensor(tokenized_input_corpus, dtype=torch.long, device=device), end_dim=-2)\n", + "target_corpus_tensor = torch.flatten(torch.tensor(tokenized_target_corpus, dtype=torch.long, device=device)).reshape(-1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([378666, 4])\n", + "torch.Size([378666, 1])\n" + ] + } + ], + "source": [ + "print(input_corpus_tensor.size())\n", + "print(target_corpus_tensor.size())" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['silver', 'republicans', 'silver', 'den']\n", + "['and']\n" + ] + } + ], + "source": [ + "random_index = random.randint(0, len(input_corpus_tensor) - 1)\n", + "\n", + "# Get random element from input corpus\n", + "random_input_element = input_corpus_tensor[random_index]\n", + "\n", + "# Get corresponding element from target corpus\n", + "random_target_element = target_corpus_tensor[random_index]\n", + "\n", + "print([detokenize(int(idx)) for idx in random_input_element])\n", + "print([detokenize(int(idx)) for idx in random_target_element])" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = TensorDataset(input_corpus_tensor, target_corpus_tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the trigram neural network model" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "class TrigramNN(nn.Module):\n", + " def __init__(self, vocab_size, embedding_dim, hidden_dim, output_size):\n", + " super(TrigramNN, self).__init__()\n", + " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n", + " self.linear = nn.Linear(embedding_dim * (left_tokens + right_tokens), output_size)\n", + " \n", + " def forward(self, inputs):\n", + " out = self.embedding(inputs)\n", + " out = out.view(inputs.size(0), -1)\n", + " out = self.linear(out)\n", + " # out = torch.softmax(out, dim=1)\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize the model, loss function, and optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "model = TrigramNN(vocab_size, embedding_dim, hidden_dim, output_size)\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training loop" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/1480 [00:00 0]\n", + " top_predicted_indices = top_predicted_indices[:len(top_predicted_scores)]\n", + "\n", + " total_score = np.sum([score for score in top_predicted_scores[:20]])\n", + "\n", + " predictions = list(zip(top_predicted_scores, top_predicted_indices))\n", + " predictions = [(round(float(score), 2), detokenize(idx)) for score, idx in predictions[:10]]\n", + " \n", + " words = [word for _, word in predictions]\n", + " scores = [round(score/total_score, 2) for score, _ in predictions]\n", + "\n", + " remaining_score = round(1.0 - np.sum(scores), 2)\n", + "\n", + " predictions = ' '.join([f\"{word}:{score}\" for score, word in zip(scores, words)]) + ' :' + str(remaining_score)\n", + "\n", + " return predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a:0.07 the:0.06 The:0.06 A:0.06 my:0.05 ,:0.05 to:0.05 and:0.05 -:0.05 an:0.05 :0.45\n", + "of:0.07 on:0.06 and:0.06 be:0.06 for:0.05 in:0.05 school,:0.05 ol:0.05 it:0.05 the:0.05 :0.45\n" + ] + } + ], + "source": [ + "print(predict([\"came\", \"fiom\"], [\"26th\", \"place\"]))\n", + "print(predict([\"will\", \"buy\"], [\"telephone\", \"and\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate result for dev dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_dir = os.path.join('..', 'dev-0', 'in.tsv.xz')\n", + "output_dir = os.path.join('..', 'dev-0', 'out.tsv')\n", + "\n", + "df = pd.read_csv(dataset_dir, sep='\\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE)\n", + "df = df.replace(r'\\\\r+|\\\\n+|\\\\t+', ' ', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10519/10519 [00:51<00:00, 203.44it/s]\n" + ] + } + ], + "source": [ + "final = \"\"\n", + "\n", + "for i, (_, row) in tqdm(enumerate(df.iterrows()), total=len(df)):\n", + " left_context = re.split(r\"\\s+\", row['LeftContext'].strip())[-left_tokens:]\n", + " right_context = re.split(r\"\\s+\", row['RightContext'].strip())[:right_tokens]\n", + "\n", + " final += predict(left_context, right_context) + '\\n'\n", + "\n", + "with open(output_dir, 'w', encoding=\"UTF-8\") as f:\n", + " f.write(final)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "p311-cu121", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}