challenging-america-word-gap-prediction/cw8zad1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Imports"
   ],
   "metadata": {
    "collapsed": false,
    "id": "dKHaG5NrdzWS"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": true
    },
    "id": "bAOmtZk6dzWU"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import lzma\n",
    "import numpy as np\n",
    "import regex as re\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import IterableDataset, DataLoader\n",
    "from torchtext.vocab import build_vocab_from_iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from google.colab import drive"
   ],
   "metadata": {
    "id": "vBeRolsudzWV"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Definitions"
   ],
   "metadata": {
    "collapsed": false,
    "id": "BvKqUYBidzWV"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Functions"
   ],
   "metadata": {
    "collapsed": false,
    "id": "0e53LE2DdzWV"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def clean_line(line: str):\n",
    "    # Preprocessing\n",
    "    separated = line.split('\\t')\n",
    "    prefix = separated[6].replace(r'\\n', ' ')\n",
    "    suffix = separated[7].replace(r'\\n', ' ')\n",
    "    return prefix + ' ' + suffix"
   ],
   "metadata": {
    "id": "N35v7jfAdzWV"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def get_words_from_line(line):\n",
    "    line = clean_line(line)\n",
    "    for word in line.split():\n",
    "        yield word"
   ],
   "metadata": {
    "id": "wtSA8gBQdzWW"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def get_word_lines_from_file(file_name):\n",
    "    with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:\n",
    "        for line in fid:\n",
    "            yield get_words_from_line(line)"
   ],
   "metadata": {
    "id": "XHpb8LeVdzWW"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def double_look_ahead_iterator(gen):\n",
    "    prev_prev = None\n",
    "    prev = None\n",
    "    for item in gen:\n",
    "        if prev_prev is not None:\n",
    "            yield np.asarray((prev_prev, prev, item))\n",
    "        prev_prev = prev\n",
    "        prev = item"
   ],
   "metadata": {
    "id": "PXvqM1eXdzWW"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "def prediction(words, model) -> str:\n",
    "    words_tensor = [train_dataset.vocab.forward([word]) for word in words]\n",
    "    ixs = torch.tensor(words_tensor).view(-1).to(device)\n",
    "    out = model(ixs)\n",
    "    top = torch.topk(out[0], 5)\n",
    "    top_indices = top.indices.tolist()\n",
    "    top_probs = top.values.tolist()\n",
    "    top_words = vocab.lookup_tokens(top_indices)\n",
    "    zipped = list(zip(top_words, top_probs))\n",
    "    for index, element in enumerate(zipped):\n",
    "        unk = None\n",
    "        if '<unk>' in element:\n",
    "            unk = zipped.pop(index)\n",
    "            zipped.append(('', unk[1]))\n",
    "            break\n",
    "    if unk is None:\n",
    "        zipped[-1] = ('', zipped[-1][1])\n",
    "    return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])"
   ],
   "metadata": {
    "id": "BfjgLTuIdzWW"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "def create_outputs(folder_name, model):\n",
    "    print(f'Creating outputs in {folder_name}')\n",
    "    with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
    "        with open(f'{folder_name}/out-EMBED_SIZE={embed_size}.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
    "            for line in fid:\n",
    "                separated = line.split('\\t')\n",
    "                prefix = separated[6].replace(r'\\n', ' ').split()[-2:]\n",
    "                output_line = prediction(prefix, model)\n",
    "                f.write(output_line + '\\n')"
   ],
   "metadata": {
    "id": "olhkh30mdzWX"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "def train_model():\n",
    "    model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
    "    data = DataLoader(train_dataset, batch_size=batch_size)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "    criterion = torch.nn.NLLLoss()\n",
    "\n",
    "    model.train()\n",
    "    step = 0\n",
    "    for batch in data:\n",
    "        x = batch[:, :2]\n",
    "        y = batch[:, 2]\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",
    "\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 10)\n",
    "\n",
    "\n",
    "        optimizer.step()\n",
    "\n",
    "    torch.save(model.state_dict(), path_to_model)"
   ],
   "metadata": {
    "id": "C_l59tEudzWX"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "def with_hyperparams():\n",
    "    for e_size in [200, 300]:\n",
    "        global embed_size\n",
    "        embed_size = e_size\n",
    "        train_model()\n",
    "        model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
    "        model.load_state_dict(torch.load(path_to_model))\n",
    "        model.eval()\n",
    "\n",
    "        create_outputs('dev-0', model)\n",
    "        create_outputs('test-A', model)"
   ],
   "metadata": {
    "id": "kdjy-pX9dzWX"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Classes"
   ],
   "metadata": {
    "collapsed": false,
    "id": "j-cvkMIPdzWY"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "class Trigrams(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",
    "        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 double_look_ahead_iterator(\n",
    "            (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))"
   ],
   "metadata": {
    "id": "UoMnDsvAdzWY"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "class SimpleTrigramNeuralLanguageModel(nn.Module):\n",
    "    def __init__(self, vocabulary_size, embedding_size, hidden_size):\n",
    "        super(SimpleTrigramNeuralLanguageModel, self).__init__()\n",
    "        self.embedding_size = embedding_size\n",
    "        self.embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
    "        self.lin1 = nn.Linear(2 * embedding_size, hidden_size)\n",
    "        self.rel = nn.ReLU()\n",
    "        self.lin2 = nn.Linear(hidden_size, vocabulary_size)\n",
    "        self.sm = nn.Softmax()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.embedding(x).view((-1, 2 * self.embedding_size))\n",
    "        x = self.lin1(x)\n",
    "        x = self.rel(x)\n",
    "        x = self.lin2(x)\n",
    "        return self.sm(x)"
   ],
   "metadata": {
    "id": "FmTh8LyOdzWY"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Training"
   ],
   "metadata": {
    "collapsed": false,
    "id": "wO2ySrT9dzWY"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Params"
   ],
   "metadata": {
    "collapsed": false,
    "id": "8FPFxG4_dzWY"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "vocab_size = 30000\n",
    "embed_size = 200\n",
    "hidden_size = 500\n",
    "batch_size = 2000\n",
    "device = 'cuda'\n",
    "path_to_train = 'train/in.tsv.xz'\n",
    "path_to_model = 'model1.bin'"
   ],
   "metadata": {
    "id": "cPlf7WOldzWY"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Colab"
   ],
   "metadata": {
    "collapsed": false,
    "id": "PcgQND_LdzWZ"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/drive\n",
      "/content/drive/MyDrive\n"
     ]
    }
   ],
   "source": [
    "drive.mount('/content/drive')\n",
    "%cd /content/drive/MyDrive/"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NkOOve5pdzWZ",
    "outputId": "5ceef3ef-9c38-47de-adf3-ced102d7a059"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Run"
   ],
   "metadata": {
    "collapsed": false,
    "id": "K4lTNNQRdzWZ"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "vocab = build_vocab_from_iterator(\n",
    "    get_word_lines_from_file(path_to_train),\n",
    "    max_tokens=vocab_size,\n",
    "    specials=['<unk>']\n",
    ")\n",
    "\n",
    "vocab.set_default_index(vocab['<unk>'])"
   ],
   "metadata": {
    "id": "4RYvjsWvdzWZ"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "train_dataset = Trigrams(path_to_train, vocab_size)"
   ],
   "metadata": {
    "id": "U-JdyELWdzWZ"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-12-cce599098537>:16: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  return self.sm(x)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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      "56600 tensor(5.2067, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56700 tensor(5.7416, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56800 tensor(5.4107, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56900 tensor(5.4789, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57000 tensor(5.5753, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57100 tensor(5.3689, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57200 tensor(5.6297, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57300 tensor(5.6960, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57400 tensor(5.3610, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57500 tensor(5.4340, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57600 tensor(5.8130, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57700 tensor(5.5437, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57800 tensor(5.4003, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57900 tensor(5.4354, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58000 tensor(5.3039, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58100 tensor(5.5298, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58200 tensor(5.4036, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58300 tensor(5.5035, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58400 tensor(5.4694, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58500 tensor(5.4644, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58600 tensor(5.3628, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58700 tensor(5.5305, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58800 tensor(5.5496, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58900 tensor(5.1605, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59000 tensor(5.4481, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59100 tensor(5.5008, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59200 tensor(5.5580, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59300 tensor(5.4181, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59400 tensor(5.1767, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59500 tensor(5.5949, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59600 tensor(5.1543, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59700 tensor(5.4442, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59800 tensor(5.2701, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59900 tensor(5.4101, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60000 tensor(5.3686, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60100 tensor(5.2843, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60200 tensor(5.5036, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60300 tensor(5.3552, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60400 tensor(5.5374, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60500 tensor(5.1537, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60600 tensor(5.4950, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60700 tensor(5.2628, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60800 tensor(5.5945, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60900 tensor(5.5902, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61000 tensor(5.4887, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61100 tensor(5.2792, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61200 tensor(5.5803, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61300 tensor(5.4461, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61400 tensor(5.0183, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61500 tensor(5.3240, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61600 tensor(5.4643, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61700 tensor(5.3920, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61800 tensor(5.5427, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61900 tensor(5.8412, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62000 tensor(5.4249, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62100 tensor(5.5865, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62200 tensor(5.3857, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62300 tensor(5.0211, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62400 tensor(5.2934, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62500 tensor(5.2083, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62600 tensor(5.2642, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62700 tensor(4.9303, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62800 tensor(5.1333, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62900 tensor(5.5126, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63000 tensor(4.8968, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63100 tensor(5.3211, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63200 tensor(5.4832, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63300 tensor(5.4616, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63400 tensor(5.3212, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63500 tensor(5.2929, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63600 tensor(5.4305, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63700 tensor(5.2080, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63800 tensor(5.4208, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63900 tensor(5.4145, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64000 tensor(5.3525, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64100 tensor(5.5111, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64200 tensor(5.1437, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64300 tensor(5.4269, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64400 tensor(5.5086, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64500 tensor(5.3559, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64600 tensor(5.3799, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64700 tensor(5.5940, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64800 tensor(5.1958, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64900 tensor(5.3498, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65000 tensor(5.3998, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65100 tensor(5.2237, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65200 tensor(5.0362, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65300 tensor(5.5109, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65400 tensor(5.2673, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65500 tensor(5.0693, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65600 tensor(5.4907, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65700 tensor(5.5288, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65800 tensor(5.3971, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65900 tensor(5.3500, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66000 tensor(5.7787, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66100 tensor(5.1555, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66200 tensor(5.4229, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66300 tensor(5.1499, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66400 tensor(5.5168, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66500 tensor(5.6282, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66600 tensor(5.3283, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66700 tensor(5.3960, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66800 tensor(5.3382, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66900 tensor(5.2665, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67000 tensor(5.3828, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67100 tensor(5.2455, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67200 tensor(5.7224, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67300 tensor(5.5869, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67400 tensor(5.4242, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67500 tensor(5.4228, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67600 tensor(5.3538, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67700 tensor(5.1782, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67800 tensor(5.3206, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67900 tensor(5.2828, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68000 tensor(5.3962, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68100 tensor(5.3605, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68200 tensor(5.1993, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68300 tensor(5.3261, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68400 tensor(5.8642, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68500 tensor(5.1566, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68600 tensor(5.3310, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68700 tensor(5.3318, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68800 tensor(5.5199, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68900 tensor(5.3169, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69000 tensor(5.2783, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69100 tensor(5.4604, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69200 tensor(5.3401, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69300 tensor(5.0342, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69400 tensor(5.3514, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69500 tensor(5.1504, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "Creating outputs in dev-0\n",
      "Creating outputs in test-A\n",
      "0 tensor(10.3829, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "100 tensor(8.0792, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "200 tensor(7.3059, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "300 tensor(6.8478, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "400 tensor(6.6292, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "500 tensor(6.6597, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "600 tensor(6.7076, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "700 tensor(6.4022, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "800 tensor(6.1865, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "900 tensor(6.3715, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1000 tensor(6.2953, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1100 tensor(5.8570, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1200 tensor(6.3739, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1300 tensor(6.4504, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1400 tensor(6.1518, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1500 tensor(5.9614, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1600 tensor(6.0159, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1700 tensor(6.3196, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1800 tensor(6.3034, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1900 tensor(6.1724, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2000 tensor(6.1985, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2100 tensor(6.0150, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2200 tensor(6.2215, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2300 tensor(6.1963, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2400 tensor(6.1551, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2500 tensor(6.1821, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2600 tensor(6.1207, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2700 tensor(6.2244, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2800 tensor(6.1407, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2900 tensor(6.0838, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3000 tensor(6.0838, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3100 tensor(5.8551, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3200 tensor(6.4406, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3300 tensor(6.1330, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3400 tensor(5.9802, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3500 tensor(5.9609, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3600 tensor(6.2390, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3700 tensor(6.0141, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3800 tensor(6.1221, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3900 tensor(6.0129, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4000 tensor(5.9146, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4100 tensor(6.0411, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4200 tensor(5.9824, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4300 tensor(5.8674, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4400 tensor(5.6331, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4500 tensor(5.9987, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4600 tensor(5.8823, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4700 tensor(5.7188, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4800 tensor(5.8505, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4900 tensor(5.9353, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5000 tensor(6.0726, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5100 tensor(5.9119, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5200 tensor(6.1238, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5300 tensor(6.0750, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5400 tensor(5.9476, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5500 tensor(5.9410, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5600 tensor(5.9437, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5700 tensor(6.0198, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5800 tensor(5.9568, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5900 tensor(5.9119, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6000 tensor(5.8231, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6100 tensor(5.8608, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6200 tensor(5.9699, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6300 tensor(5.8318, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6400 tensor(6.1696, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6500 tensor(5.7545, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6600 tensor(6.0710, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6700 tensor(5.9385, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6800 tensor(5.8292, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6900 tensor(5.7408, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7000 tensor(5.9290, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7100 tensor(5.7789, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7200 tensor(5.8751, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7300 tensor(6.0428, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7400 tensor(5.7333, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7500 tensor(5.9126, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7600 tensor(5.9192, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7700 tensor(5.7885, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7800 tensor(5.8290, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7900 tensor(5.9408, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8000 tensor(5.6841, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8100 tensor(6.0080, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8200 tensor(5.9377, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8300 tensor(5.8161, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8400 tensor(5.9945, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8500 tensor(5.2545, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8600 tensor(5.7752, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8700 tensor(5.4092, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8800 tensor(5.8751, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8900 tensor(5.8199, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9000 tensor(5.7431, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9100 tensor(5.7856, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9200 tensor(5.8164, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9300 tensor(5.7747, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9400 tensor(5.9959, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9500 tensor(5.6087, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9600 tensor(5.8762, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9700 tensor(5.7811, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9800 tensor(5.9176, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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      "56500 tensor(5.1546, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56600 tensor(5.1825, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56700 tensor(5.7089, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56800 tensor(5.3728, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56900 tensor(5.4364, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57000 tensor(5.5370, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57100 tensor(5.2860, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57200 tensor(5.5949, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57300 tensor(5.6466, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57400 tensor(5.3175, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57500 tensor(5.4093, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57600 tensor(5.7817, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57700 tensor(5.5003, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57800 tensor(5.3439, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57900 tensor(5.4006, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58000 tensor(5.2684, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58100 tensor(5.4849, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58200 tensor(5.3593, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58300 tensor(5.4589, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58400 tensor(5.4310, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58500 tensor(5.4389, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58600 tensor(5.3162, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58700 tensor(5.4941, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58800 tensor(5.5161, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58900 tensor(5.1200, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59000 tensor(5.4373, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59100 tensor(5.4599, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59200 tensor(5.4921, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59300 tensor(5.3883, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59400 tensor(5.1357, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59500 tensor(5.5514, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59600 tensor(5.1275, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59700 tensor(5.4095, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59800 tensor(5.2168, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "59900 tensor(5.3622, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60000 tensor(5.3232, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60100 tensor(5.2477, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60200 tensor(5.4876, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60300 tensor(5.3204, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60400 tensor(5.5030, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60500 tensor(5.1152, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60600 tensor(5.4408, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60700 tensor(5.2033, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60800 tensor(5.5601, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "60900 tensor(5.5461, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61000 tensor(5.4563, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61100 tensor(5.2254, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61200 tensor(5.5692, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61300 tensor(5.4247, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61400 tensor(4.9635, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61500 tensor(5.2972, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61600 tensor(5.4258, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61700 tensor(5.3653, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61800 tensor(5.5186, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "61900 tensor(5.8254, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62000 tensor(5.3711, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62100 tensor(5.5506, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62200 tensor(5.3525, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62300 tensor(4.9781, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62400 tensor(5.2654, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62500 tensor(5.1860, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62600 tensor(5.2197, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62700 tensor(4.8901, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62800 tensor(5.0782, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "62900 tensor(5.4533, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63000 tensor(4.8650, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63100 tensor(5.2813, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63200 tensor(5.4397, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63300 tensor(5.4245, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63400 tensor(5.2748, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63500 tensor(5.2523, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63600 tensor(5.3960, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63700 tensor(5.1610, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63800 tensor(5.3532, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "63900 tensor(5.3806, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64000 tensor(5.3295, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64100 tensor(5.4567, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64200 tensor(5.1251, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64300 tensor(5.3982, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64400 tensor(5.4605, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64500 tensor(5.3091, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64600 tensor(5.3547, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64700 tensor(5.5553, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64800 tensor(5.1512, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "64900 tensor(5.3059, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65000 tensor(5.3715, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65100 tensor(5.1765, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65200 tensor(4.9975, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65300 tensor(5.4619, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65400 tensor(5.2211, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65500 tensor(5.0544, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65600 tensor(5.4778, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65700 tensor(5.4886, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65800 tensor(5.3707, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "65900 tensor(5.3304, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66000 tensor(5.7419, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66100 tensor(5.1063, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66200 tensor(5.3704, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66300 tensor(5.1073, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66400 tensor(5.4869, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66500 tensor(5.6025, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66600 tensor(5.3030, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66700 tensor(5.3760, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66800 tensor(5.3238, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "66900 tensor(5.2442, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67000 tensor(5.3488, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67100 tensor(5.2200, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67200 tensor(5.6754, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67300 tensor(5.5589, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67400 tensor(5.3765, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67500 tensor(5.3911, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67600 tensor(5.3410, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67700 tensor(5.1323, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67800 tensor(5.2726, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "67900 tensor(5.2314, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68000 tensor(5.3615, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68100 tensor(5.3275, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68200 tensor(5.1481, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68300 tensor(5.2834, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68400 tensor(5.8378, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68500 tensor(5.0982, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68600 tensor(5.2805, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68700 tensor(5.2916, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68800 tensor(5.4921, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "68900 tensor(5.2871, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69000 tensor(5.2191, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69100 tensor(5.4146, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69200 tensor(5.3098, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69300 tensor(4.9947, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69400 tensor(5.3038, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "69500 tensor(5.1063, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "Creating outputs in dev-0\n",
      "Creating outputs in test-A\n",
      "0 tensor(10.3276, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "100 tensor(7.9401, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "200 tensor(7.2381, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "300 tensor(6.8126, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "400 tensor(6.6045, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "500 tensor(6.6184, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "600 tensor(6.6869, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "700 tensor(6.3630, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "800 tensor(6.1966, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "900 tensor(6.3506, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1000 tensor(6.2652, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1100 tensor(5.8459, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1200 tensor(6.3685, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1300 tensor(6.4105, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1400 tensor(6.1318, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1500 tensor(5.9373, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1600 tensor(5.9996, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1700 tensor(6.2852, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1800 tensor(6.2778, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "1900 tensor(6.1339, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2000 tensor(6.1958, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2100 tensor(5.9972, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2200 tensor(6.2078, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2300 tensor(6.1827, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2400 tensor(6.1275, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2500 tensor(6.1562, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2600 tensor(6.0775, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2700 tensor(6.2004, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2800 tensor(6.1155, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "2900 tensor(6.0537, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3000 tensor(6.0540, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3100 tensor(5.8310, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3200 tensor(6.3952, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3300 tensor(6.1059, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3400 tensor(5.9665, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3500 tensor(5.9202, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3600 tensor(6.2096, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3700 tensor(5.9983, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3800 tensor(6.0919, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "3900 tensor(6.0015, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4000 tensor(5.8796, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4100 tensor(6.0101, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4200 tensor(5.9665, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4300 tensor(5.8365, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4400 tensor(5.6078, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4500 tensor(5.9602, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4600 tensor(5.8495, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4700 tensor(5.6834, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4800 tensor(5.8261, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "4900 tensor(5.9137, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5000 tensor(6.0360, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5100 tensor(5.8791, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5200 tensor(6.1084, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5300 tensor(6.0378, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5400 tensor(5.9057, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5500 tensor(5.9146, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5600 tensor(5.9022, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5700 tensor(5.9767, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5800 tensor(5.9410, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "5900 tensor(5.8609, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6000 tensor(5.8036, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6100 tensor(5.8270, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6200 tensor(5.9282, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6300 tensor(5.7968, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6400 tensor(6.1270, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6500 tensor(5.7318, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6600 tensor(6.0448, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6700 tensor(5.9031, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6800 tensor(5.7908, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "6900 tensor(5.7183, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7000 tensor(5.8839, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7100 tensor(5.7365, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7200 tensor(5.8651, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7300 tensor(6.0091, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7400 tensor(5.7031, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7500 tensor(5.8671, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7600 tensor(5.8997, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7700 tensor(5.7679, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7800 tensor(5.7867, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "7900 tensor(5.9113, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8000 tensor(5.6723, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8100 tensor(5.9791, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8200 tensor(5.8998, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8300 tensor(5.8103, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8400 tensor(5.9573, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8500 tensor(5.2251, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8600 tensor(5.7472, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8700 tensor(5.3789, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8800 tensor(5.8526, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "8900 tensor(5.7923, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9000 tensor(5.7036, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9100 tensor(5.7377, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9200 tensor(5.7688, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9300 tensor(5.7391, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9400 tensor(5.9497, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9500 tensor(5.5777, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9600 tensor(5.8298, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "9700 tensor(5.7534, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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      "56400 tensor(5.3978, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56500 tensor(5.1121, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56600 tensor(5.1803, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56700 tensor(5.6812, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56800 tensor(5.3351, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "56900 tensor(5.3909, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57000 tensor(5.5231, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57100 tensor(5.2635, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57200 tensor(5.5820, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57300 tensor(5.6190, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57400 tensor(5.3154, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57500 tensor(5.3776, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57600 tensor(5.7486, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57700 tensor(5.4974, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57800 tensor(5.3434, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "57900 tensor(5.3827, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58000 tensor(5.2429, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58100 tensor(5.4746, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58200 tensor(5.3179, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58300 tensor(5.4343, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58400 tensor(5.4178, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58500 tensor(5.4002, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
      "58600 tensor(5.2959, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
     ]
    }
   ],
   "source": [
    "with_hyperparams()"
   ],
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