neural-trigram.ipynb
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neural-trigram.ipynb
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neural-trigram.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": null,
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"id": "dfeb7061",
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"metadata": {},
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"outputs": [],
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"source": [
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"from torchtext.vocab import build_vocab_from_iterator\n",
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"from torch.utils.data import DataLoader\n",
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"import torch\n",
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"from torch import nn\n",
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"import pandas as pd\n",
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"import nltk\n",
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"import regex as re\n",
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"import csv\n",
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"import itertools\n",
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"from nltk import word_tokenize\n",
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"from os.path import exists\n",
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"\n",
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"\n",
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"def clean(text):\n",
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" text = str(text).strip().lower()\n",
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" text = re.sub(\"’|>|<|\\.|\\\\\\\\|\\\"|”|-|,|\\*|:|\\/\", \"\", text)\n",
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" text = text.replace('\\\\\\\\n', \" \").replace(\"'t\", \" not\").replace(\"'s\", \" is\").replace(\"'ll\", \" will\").replace(\"'m\", \" am\").replace(\"'ve\", \" have\")\n",
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" text = text.replace(\"'\", \"\")\n",
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" return text\n",
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"\n",
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"def get_words_from_line(line, specials = True):\n",
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" line = line.rstrip()\n",
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" if specials:\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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" if specials:\n",
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" yield '</s>'\n",
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"\n",
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"def get_word_lines_from_data(d):\n",
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" for line in d:\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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"class SimpleBigramNeuralLanguageModel(torch.nn.Module):\n",
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" \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)\n",
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"\n",
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"def look_ahead_iterator(gen):\n",
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" w1 = None\n",
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" for item in gen:\n",
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" if w1 is not None:\n",
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" yield (w1, item)\n",
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" w1 = item\n",
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" \n",
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"class Bigrams(torch.utils.data.IterableDataset):\n",
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" def __init__(self, data, vocabulary_size):\n",
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" self.vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_data(data),\n",
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" max_tokens = vocabulary_size,\n",
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" specials = ['<unk>'])\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.data = data\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_data(self.data))))\n",
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"\n",
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"\n",
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"# ładowanie danych treningowych\n",
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"in_file = 'train/in.tsv.xz'\n",
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"out_file = 'train/expected.tsv'\n",
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"\n",
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"X_train = pd.read_csv(in_file, sep='\\t', header=None, quoting=csv.QUOTE_NONE, nrows=200000, on_bad_lines=\"skip\", encoding=\"UTF-8\")\n",
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"Y_train = pd.read_csv(out_file, sep='\\t', header=None, quoting=csv.QUOTE_NONE, nrows=200000, on_bad_lines=\"skip\", encoding=\"UTF-8\")\n",
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"\n",
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"X_train = X_train[[6, 7]]\n",
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"X_train = pd.concat([X_train, Y_train], axis=1)\n",
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"X_train = X_train[6] + X_train[0] + X_train[7]\n",
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"X_train = X_train.apply(clean)\n",
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"vocab_size = 30000\n",
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"embed_size = 150\n",
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"Dataset = Bigrams(X_train, 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": null,
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"id": "1cc73f1e",
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"metadata": {},
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"outputs": [],
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"source": [
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"\n",
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"if(not exists('nn_model2.bin')):\n",
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" data = DataLoader(Dataset, batch_size=8000)\n",
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" optimizer = torch.optim.Adam(model.parameters())\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 i in range(2):\n",
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" print(f\" Epoka {i}--------------------------------------------------------\")\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(), 'nn_model2.bin')\n",
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"else:\n",
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" model.load_state_dict(torch.load('nn_model2.bin')) \n",
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"\n",
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"\n",
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"vocab = Dataset.vocab\n",
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"\n",
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"\n",
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"# nltk.download('punkt')\n",
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"def predict_word(ws):\n",
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" ixs = torch.tensor(vocab.forward(ws)).to(device)\n",
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" out = model(ixs)\n",
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" top = torch.topk(out[0], 8)\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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" pred_str = \"\"\n",
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" for word, prob in list(zip(top_words, top_probs)):\n",
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" pred_str += f\"{word}:{prob} \"\n",
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"# pred_str += f':0.01'\n",
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" return pred_str\n",
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"\n",
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"\n",
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"def word_gap_prediction(file):\n",
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" X_test = pd.read_csv(f'{file}/in.tsv.xz', sep='\\t', header=None, quoting=csv.QUOTE_NONE, on_bad_lines='skip', encoding=\"UTF-8\")[6]\n",
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" X_test = X_test.apply(clean)\n",
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" with open(f'{file}/out.tsv', \"w+\", encoding=\"UTF-8\") as f:\n",
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" for row in X_test:\n",
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" result = {}\n",
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" before = None\n",
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" for before in get_words_from_line(clean(str(row)), False):\n",
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" pass\n",
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" before = [before]\n",
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" if(len(before) < 1):\n",
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" pred_str = \"a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1\"\n",
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" else:\n",
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" pred_str = predict_word(before)\n",
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" pred_str = pred_str.strip()\n",
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" f.write(pred_str + \"\\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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"id": "682d3528",
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"metadata": {},
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"outputs": [],
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"source": [
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"word_gap_prediction(\"dev-0/\")"
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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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"id": "74b9f66c",
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"metadata": {},
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"outputs": [],
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"source": [
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"word_gap_prediction(\"test-A/\")"
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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": "3.8.12"
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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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14828
test-A/out.tsv
14828
test-A/out.tsv
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