challenging-america-word-gap-prediction-neural/run.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d42ddd87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc73124c",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = 20000\n",
    "embed_size = 100\n",
    "\n",
    "class SimpleTrigramNeuralLanguageModel(nn.Module):\n",
    "    def __init__(self, vocabulary_size, embedding_size):\n",
    "        super(SimpleTrigramNeuralLanguageModel, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
    "        self.linear = nn.Linear(embedding_size, vocabulary_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.embedding(x)\n",
    "        x = self.linear(x)\n",
    "        x = torch.softmax(x, dim=1)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "569b4c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import regex as re\n",
    "from itertools import islice, chain\n",
    "from torchtext.vocab import build_vocab_from_iterator\n",
    "from torch.utils.data import IterableDataset\n",
    "\n",
    "def get_words_from_line(line):\n",
    "    line = line.rstrip()\n",
    "    yield '<s>'\n",
    "    for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
    "        yield m.group(0).lower()\n",
    "    yield '</s>'\n",
    "\n",
    "def get_word_lines_from_file(file_name):\n",
    "    with open(file_name, 'r') as fh:\n",
    "        for line in fh:\n",
    "            yield get_words_from_line(line)\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f95cb913",
   "metadata": {},
   "outputs": [],
   "source": [
    "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 = ['<unk>']\n",
    "        )\n",
    "        self.vocab.set_default_index(self.vocab['<unk>'])\n",
    "        self.vocabulary_size = vocabulary_size\n",
    "        self.text_file = text_file\n",
    "\n",
    "    def __iter__(self):\n",
    "        return look_ahead_iterator((self.vocab[t] for t in chain.from_iterable(get_word_lines_from_file(self.text_file))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a51f2b1",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'SimpleBigramNeuralLanguageModel' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_16179/3272155308.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'cuda'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtrain_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBigrams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'europarl.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocab_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSimpleTrigramNeuralLanguageModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_16179/1892442743.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vocabulary_size, embedding_size)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mSimpleTrigramNeuralLanguageModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocabulary_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membedding_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSimpleBigramNeuralLanguageModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEmbedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvocabulary_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membedding_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedding_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocabulary_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'SimpleBigramNeuralLanguageModel' is not defined"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "device = 'cuda'\n",
    "train_dataset = Bigrams('europarl.txt', vocab_size)\n",
    "model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
    "data = DataLoader(train_dataset, batch_size=2000)\n",
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "criterion = torch.nn.NLLLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "474194ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "for epoch in range(1):\n",
    "    model.train()\n",
    "    for x, y in data:\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(x)\n",
    "        loss = criterion(torch.log(outputs), 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(), 'model/model1.bin')"
   ]
  }
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