diff --git a/run_bigram.ipynb b/run_bigram.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from itertools import islice\n",
+ "import regex as re\n",
+ "import sys\n",
+ "from torchtext.vocab import build_vocab_from_iterator\n",
+ "from torch import nn\n",
+ "import torch\n",
+ "from torch.utils.data import IterableDataset\n",
+ "import itertools\n",
+ "import pandas as pd\n",
+ "from torch.utils.data import DataLoader\n",
+ "import csv\n",
+ "\n",
+ "def data_preprocessing(text):\n",
+ " return re.sub(r'\\p{P}', '', text.lower().replace('-\\\\n', '').replace('\\\\n', ' ').replace(\"'ll\", \" will\").replace(\"-\", \"\").replace(\"'ve\", \" have\").replace(\"'s\", \" is\"))\n",
+ "\n",
+ "def get_words_from_line(line):\n",
+ " line = line.rstrip()\n",
+ " yield ''\n",
+ " for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
+ " yield m.group(0).lower()\n",
+ " yield ''\n",
+ "\n",
+ "\n",
+ "def get_word_lines_from_file(data):\n",
+ " for line in data:\n",
+ " yield get_words_from_line(line)\n",
+ "\n",
+ "\n",
+ "class SimpleBigramNeuralLanguageModel(nn.Module):\n",
+ " def __init__(self, vocabulary_size, embedding_size):\n",
+ " super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
+ " self.model = nn.Sequential(\n",
+ " nn.Embedding(vocabulary_size, embedding_size),\n",
+ " nn.Linear(embedding_size, vocabulary_size),\n",
+ " nn.Softmax()\n",
+ " )\n",
+ "\n",
+ " def forward(self, x):\n",
+ " return self.model(x)\n",
+ "\n",
+ "\n",
+ "def look_ahead_iterator(gen):\n",
+ " prev = None\n",
+ " for item in gen:\n",
+ " if prev is not None:\n",
+ " yield (prev, item)\n",
+ " prev = item\n",
+ "\n",
+ "class Bigrams(IterableDataset):\n",
+ " def __init__(self, text_file, vocabulary_size):\n",
+ " self.vocab = build_vocab_from_iterator(\n",
+ " get_word_lines_from_file(text_file),\n",
+ " max_tokens = vocabulary_size,\n",
+ " specials = [''])\n",
+ " self.vocab.set_default_index(self.vocab[''])\n",
+ " self.vocabulary_size = vocabulary_size\n",
+ " self.text_file = text_file\n",
+ "\n",
+ " def __iter__(self):\n",
+ " return look_ahead_iterator(\n",
+ " (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
+ "\n",
+ "in_file = 'train/in.tsv.xz'\n",
+ "out_file = 'train/expected.tsv'\n",
+ "\n",
+ "train_set = pd.read_csv(\n",
+ " 'train/in.tsv.xz',\n",
+ " sep='\\t',\n",
+ " header=None,\n",
+ " quoting=csv.QUOTE_NONE,\n",
+ " nrows=35000)\n",
+ "\n",
+ "train_labels = pd.read_csv(\n",
+ " 'train/expected.tsv',\n",
+ " sep='\\t',\n",
+ " header=None,\n",
+ " quoting=csv.QUOTE_NONE,\n",
+ " nrows=35000)\n",
+ "\n",
+ "data = pd.concat([train_set, train_labels], axis=1)\n",
+ "data = train_set[6] + train_set[0] + train_set[7]\n",
+ "data = data.apply(data_preprocessing)\n",
+ "\n",
+ "vocab_size = 30000\n",
+ "embed_size = 150\n",
+ "\n",
+ "\n",
+ "bigram_data = Bigrams(data, vocab_size)\n",
+ "\n",
+ "device = 'cpu'\n",
+ "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
+ "data = DataLoader(bigram_data, batch_size=5000)\n",
+ "optimizer = torch.optim.Adam(model.parameters())\n",
+ "criterion = torch.nn.NLLLoss()\n",
+ "\n",
+ "model.train()\n",
+ "step = 0\n",
+ "for x, y in data:\n",
+ " x = x.to(device)\n",
+ " y = y.to(device)\n",
+ " optimizer.zero_grad()\n",
+ " ypredicted = model(x)\n",
+ " loss = criterion(torch.log(ypredicted), y)\n",
+ " if step % 100 == 0:\n",
+ " print(step, loss)\n",
+ " step += 1\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ "torch.save(model.state_dict(), 'model1.bin')\n",
+ "\n",
+ "vocab = bigram_data.vocab\n",
+ "prediction = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'\n",
+ "\n",
+ "def predict_word(w):\n",
+ " ixs = torch.tensor(vocab.forward(w)).to(device)\n",
+ " out = model(ixs)\n",
+ " top = torch.topk(out[0], 8)\n",
+ " top_indices = top.indices.tolist()\n",
+ " top_probs = top.values.tolist()\n",
+ " top_words = vocab.lookup_tokens(top_indices)\n",
+ " pred_str = \"\"\n",
+ " for word, prob in list(zip(top_words, top_probs)):\n",
+ " pred_str += f\"{word}:{prob} \"\n",
+ " return pred_str\n",
+ "\n",
+ "\n",
+ "def predict(f):\n",
+ " x = pd.read_csv(f'{f}/in.tsv.xz', sep='\\t', header=None, quoting=csv.QUOTE_NONE, on_bad_lines='skip', encoding=\"UTF-8\")[6]\n",
+ " x = x.apply(data_preprocessing)\n",
+ "\n",
+ " with open(f'{f}/out.tsv', \"w+\", encoding=\"UTF-8\") as f:\n",
+ " for row in x:\n",
+ " result = {}\n",
+ " before = None\n",
+ " for before in get_words_from_line(data_preprocessing(str(row)), False):\n",
+ " pass\n",
+ " before = [before]\n",
+ " if(len(before) < 1):\n",
+ " pred_str = prediction\n",
+ " else:\n",
+ " pred_str = predict_word(before)\n",
+ "\n",
+ " pred_str = pred_str.strip()\n",
+ " f.write(pred_str + \"\\n\")\n",
+ "\n",
+ "prediction(\"dev-0/\")\n",
+ "prediction(\"test-A/\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}