challenging-america-word-gap-prediction/lab12_2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8-j-5oV0o46",
        "outputId": "2b90f9b1-acb4-45e2-9a2f-2818e6fe03b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, multiprocess, aiosignal, aiohttp, datasets\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.13.0 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 xxhash-3.2.0 yarl-1.9.2\n"
          ]
        }
      ],
      "source": [
        "!pip install torch\n",
        "!pip install transformers\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6aa5mpE0s6H",
        "outputId": "43b0e006-1678-41f1-9b40-6964f9127a74"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'challenging-america-word-gap-prediction'...\n",
            "remote: Wymienianie obiektów: 27, gotowe.\u001b[K\n",
            "remote: Zliczanie obiektów: 100% (27/27), gotowe.\u001b[K\n",
            "remote: Kompresowanie obiektów: 100% (23/23), gotowe.\u001b[K\n",
            "remote: Razem 27 (delty 2), użyte ponownie 17 (delty 0), paczki użyte ponownie 0\u001b[K\n",
            "Receiving objects: 100% (27/27), 278.33 MiB | 21.80 MiB/s, done.\n",
            "Resolving deltas: 100% (2/2), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone --single-branch git://gonito.net/challenging-america-word-gap-prediction -b master"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "uHkXCRs-0iSr"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import sys\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "HyKM4zn41YvQ"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import sys\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "\n",
        "import lzma\n",
        "from itertools import islice\n",
        "import regex as re\n",
        "import math\n",
        "import sys\n",
        "from torchtext.vocab import build_vocab_from_iterator\n",
        "from torch import nn\n",
        "from torch.utils.data import IterableDataset\n",
        "import itertools"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-k8RhlmI06mQ",
        "outputId": "39d0fc5d-c6ea-4f4b-e75e-cbe199bd175a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/challenging-america-word-gap-prediction\n"
          ]
        }
      ],
      "source": [
        "%cd /content/challenging-america-word-gap-prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "PCA7Ank2dnwM"
      },
      "outputs": [],
      "source": [
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
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              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
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              "Downloading model.safetensors:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
            ]
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        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-large\")\n",
        "model = GPT2LMHeadModel.from_pretrained(\"gpt2-large\").to(device)"
      ]
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        "def create_outputs(folder_name):\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-{folder_name}.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', ' ')\n",
        "                new_val = []\n",
        "                for j in range(5):\n",
        "                  val = 1\n",
        "                  prefix = separated[6].replace(r'\\n', ' ')\n",
        "                  for i in range(5):\n",
        "                    left_context =tokenizer.encode(prefix, return_tensors=\"pt\").to(device)\n",
        "                    out = model(left_context)\n",
        "                    prob_dist=torch.softmax(out[0][0][-1],dim=0)\n",
        "                    values,index  =prob_dist.topk(5)\n",
        "                    token = []\n",
        "                    total = 0\n",
        "                    for x in index:\n",
        "                      token.append(tokenizer.decode(x))\n",
        "                    zipped = list(zip(values, token))\n",
        "                    val *=float(values[0])\n",
        "                    prefix+=' '+zipped[j][1].strip()\n",
        "                  new_val.append(math.sqrt(val))\n",
        "                prefix = separated[6].replace(r'\\n', ' ')\n",
        "                left_context =tokenizer.encode(prefix, return_tensors=\"pt\").to(device)\n",
        "                out = model(left_context)\n",
        "                prob_dist=torch.softmax(out[0][0][-1],dim=0)\n",
        "                values,index  =prob_dist.topk(5)\n",
        "                token = []\n",
        "                for x in index:\n",
        "                  token.append(tokenizer.decode(x))\n",
        "                output = new_val\n",
        "                zipped = list(zip(output, token))\n",
        "                res = sorted(zipped, key = lambda x: x[1])\n",
        "\n",
        "                output_line = ' '.join([f'{x[1].strip()}:{x[0]}' for x in res])\n",
        "                f.write(output_line + '\\n')"
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          "text": [
            "Creating outputs in dev-0\n",
            "Creating outputs in test-A\n"
          ]
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