335 lines
9.7 KiB
Plaintext
335 lines
9.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "PAM8swqfl3YC"
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},
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"outputs": [],
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"source": [
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"import itertools\n",
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"import lzma\n",
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"import regex as re\n",
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"import torch\n",
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"from torch import nn\n",
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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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"import pickle\n",
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"import os"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "52BQle50l92y",
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"outputId": "1f98398d-f385-4711-c2b7-3abe7418fbdb"
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},
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"outputs": [],
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"source": [
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "PNb3_zqUl3YD"
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},
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"source": [
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"### Definitions"
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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": null,
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"metadata": {
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"id": "b_6d7n2al3YE"
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},
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"outputs": [],
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"source": [
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"def clean_line(line: str):\n",
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" separated = line.split('\\t')\n",
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" prefix = separated[6].replace(r'\\n', ' ').strip()\n",
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" suffix = separated[7].replace(r'\\n', ' ').strip()\n",
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" return prefix + ' ' + suffix\n",
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"\n",
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"def get_words_from_line(line):\n",
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" line = clean_line(line)\n",
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" for word in line.split():\n",
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" yield word\n",
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"\n",
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"def get_word_lines_from_file(file_name):\n",
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" with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:\n",
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" for line in fid:\n",
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" yield get_words_from_line(line)\n",
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"\n",
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"\n",
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"def look_ahead_iterator(gen):\n",
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" prev = None\n",
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" for item in gen:\n",
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" if prev is not None:\n",
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" yield (prev, item)\n",
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" prev = item\n",
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"\n",
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"def predict(word: str, num_of_top: str) -> str:\n",
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" ixs = torch.tensor(vocab.forward([word])).to(device)\n",
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" out = model(ixs)\n",
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" top = torch.topk(out[0], num_of_top)\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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" zipped = list(zip(top_words, top_probs))\n",
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" if '<unk>' in [element[0] for element in zipped]:\n",
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" zipped = [(element[0] if element[0] != '<unk>' else '', element[1]) for element in zipped]\n",
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" zipped[-1] = ('', zipped[-1][1])\n",
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" else:\n",
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" zipped[-1] = ('', zipped[-1][1])\n",
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" return ' '.join([f'{element[0]}:{element[1]}' for element in zipped])\n",
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"\n",
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"def execute(path):\n",
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" with lzma.open(f'{path}/in.tsv.xz', 'rt', encoding='utf-8') as f, \\\n",
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" open(f'{path}/out.tsv', 'w', encoding='utf-8') as out:\n",
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" for line in f:\n",
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" prefix = line.split('\\t')[6]\n",
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" left = prefix.replace(r'\\n', ' ').split()[-1]\n",
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" result = predict(left, num_of_top)\n",
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" out.write(f\"{result}\\n\")\n"
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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": null,
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"metadata": {
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"id": "ZfV8fDhyl3YF"
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},
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"outputs": [],
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"source": [
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"class Bigrams(IterableDataset):\n",
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" def __init__(self, text_file, vocabulary_size):\n",
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" self.vocab = vocab\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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"class SimpleBigramNeuralLanguageModel(nn.Module):\n",
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" def __init__(self, vocabulary_size, embedding_size):\n",
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" super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
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" self.model = nn.Sequential(\n",
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" nn.Embedding(vocabulary_size, embedding_size),\n",
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" nn.Linear(embedding_size, vocabulary_size),\n",
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" nn.Softmax()\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" return self.model(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "W0O6U62El3YG"
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},
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"source": [
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"### Parameters"
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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": null,
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"metadata": {
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"id": "eUS-U3_6l3YG"
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},
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"outputs": [],
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"source": [
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"vocab_size = 10000\n",
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"embed_size = 250\n",
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"batch_size = 5000\n",
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"num_of_top = 10"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "CPeVRcYZl3YG"
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},
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"source": [
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"### Vocabulary building"
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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": null,
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"metadata": {
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"id": "4wBx0OTal3YH"
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},
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"outputs": [],
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"source": [
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"if os.path.exists('./vocabulary.pickle'):\n",
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" with open('vocabulary.pickle', 'rb') as handle:\n",
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" vocab = pickle.load(handle)\n",
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"else:\n",
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" vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_file('./drive/MyDrive/ColabNotebooks/america/train/in.tsv.xz'),\n",
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" max_tokens = vocab_size,\n",
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" specials = ['<unk>'])\n",
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"\n",
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" with open('vocabulary.pickle', 'wb') as handle:\n",
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" pickle.dump(vocab, handle, protocol=pickle.HIGHEST_PROTOCOL)"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "oVMVipnhl3YH",
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"outputId": "e588d083-be33-4dce-e61c-b26e664b2c5f"
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},
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"outputs": [],
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"source": [
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"vocab.lookup_tokens([0, 1, 2, 3, 4, 4500])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "1gJscHJUl3YI"
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},
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"source": [
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"### Training"
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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": null,
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"metadata": {
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"id": "W_OjBUInl3YI"
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},
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"outputs": [],
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"source": [
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size)\n",
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"vocab.set_default_index(vocab['<unk>'])"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "f373Kduzl3YI",
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"outputId": "0e6100df-d00e-4be3-83c4-cd2f671041e6"
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},
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"outputs": [],
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"source": [
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"#uczenie\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"device = 'cuda'\n",
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"train_dataset = Bigrams('./drive/MyDrive/ColabNotebooks/america/train/in.tsv.xz', vocab_size)\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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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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"\n",
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"#funkcja kosztu\n",
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"criterion = torch.nn.NLLLoss()\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 x, y in data:\n",
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" x = x.to(device)\n",
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" y = y.to(device)\n",
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" optimizer.zero_grad()\n",
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" ypredicted = model(x)\n",
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" loss = criterion(torch.log(ypredicted), y)\n",
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" if step % 100 == 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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"\n",
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"torch.save(model.state_dict(), 'model1.bin')\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "IHrqskyXl3YI"
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},
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"source": [
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"### Evaluation"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "yK3oK65fl3YI",
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"outputId": "ee3e64a6-c361-4e96-cad0-82f2950827ca"
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},
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"outputs": [],
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"source": [
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"model.load_state_dict(torch.load('model1.bin'))\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": null,
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"metadata": {
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"id": "ihz4Px0bl3YJ"
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},
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"outputs": [],
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"source": [
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"execute('./drive/MyDrive/ColabNotebooks/america/dev-0')\n",
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"execute('./drive/MyDrive/ColabNotebooks/america/test-A')"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"provenance": []
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},
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"gpuClass": "standard",
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.10.8"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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