BART_finetuning_amazonreviews.ipynb

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            "Collecting datasets\n",
            "  Downloading datasets-2.2.2-py3-none-any.whl (346 kB)\n",
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            "\u001b[?25hCollecting transformers\n",
            "  Downloading transformers-4.19.2-py3-none-any.whl (4.2 MB)\n",
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            "\u001b[?25hCollecting rouge-score\n",
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            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
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            "  Downloading huggingface_hub-0.6.0-py3-none-any.whl (84 kB)\n",
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            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
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            "Collecting pyyaml\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
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            "  Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
            "Collecting frozenlist>=1.1.1\n",
            "  Downloading frozenlist-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n",
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            "\u001b[?25hCollecting multidict<7.0,>=4.5\n",
            "  Downloading multidict-6.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (94 kB)\n",
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            "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
            "  Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
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            "\u001b[?25hCollecting async-timeout<5.0,>=4.0.0a3\n",
            "  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (2.0.12)\n",
            "Collecting aiosignal>=1.1.2\n",
            "  Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.8.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.1)\n",
            "Installing collected packages: multidict, frozenlist, yarl, urllib3, asynctest, async-timeout, aiosignal, pyyaml, fsspec, aiohttp, xxhash, tokenizers, responses, huggingface-hub, transformers, rouge-score, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-2.2.2 frozenlist-1.3.0 fsspec-2022.5.0 huggingface-hub-0.6.0 multidict-6.0.2 pyyaml-6.0 responses-0.18.0 rouge-score-0.0.4 tokenizers-0.12.1 transformers-4.19.2 urllib3-1.25.11 xxhash-3.0.0 yarl-1.7.2\n"
          ]
        }
      ],
      "source": [
        "! pip install datasets transformers rouge-score nltk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387,
          "referenced_widgets": [
            "31e730a0c77c470ab355448642807d32",
            "f22e7e45d1a141568ba25306d24fdba8",
            "c5573aa30db845e58ebd45cc4e2428f1",
            "4245cd8a5ec741eba39a0da181268e77",
            "fb7a46835ac94d5ebdbf73c18c2c69f6",
            "4ef99ae1b7074c34936984758071168b",
            "7ada83eebe4e4e74b7f8693249eb7404",
            "295ef38641d44ef7b2c5c319db844c53",
            "ed048636332f4adca7d4dd109f2b8907",
            "e817df750241408f95211413e616e9bd",
            "2fdfc966d11543688138b04768377037",
            "7c57d879190743d69168f72f6227674b",
            "37dab6a20a814477ad32e2d1c31b6776",
            "2c9261c5e58347f0a8bad1cd187fe8d9",
            "407cb8f3ba5e49f3ae47a46eeccb0769",
            "0eca6b9f69934f4db00ba5514e2e34cc",
            "6a2daa345365410595d97328fc80424f"
          ]
        },
        "id": "UNvlzQHHsVQm",
        "outputId": "828e1966-fc7b-4696-fb22-27e588cbc2eb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install git-lfs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdB1NwCjsmjI",
        "outputId": "382374c6-1eb6-4831-9386-f0df156d3d00"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (2.3.4-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 42 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "\n",
        "print(transformers.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuXJ8xaisOcx",
        "outputId": "c2908127-b365-400c-fd93-e17690643833"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.19.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"sshleifer/distilbart-cnn-12-6\""
      ],
      "metadata": {
        "id": "tKD3CVEzuSce"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive\n",
        "from datasets import load_dataset, Dataset\n",
        "import pandas as pd\n",
        "df= pd.read_csv('My Drive/amazon_summary_data.csv')\n",
        "df = df[['review_headline', 'review_body']]\n",
        "df = df.sample(frac=0.05)\n",
        "# df = df.sample()\n",
        "# df.columns\n",
        "df = df[['review_body','review_headline']]\n",
        "df = df.dropna()\n",
        "dataset = Dataset.from_pandas(df)\n",
        "dataset = dataset.train_test_split(test_size=0.1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_TVoBAss69T",
        "outputId": "9c7ebd72-62f1-4ff7-d346-df19cbe230f3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n",
            "/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "# raw_datasets = load_dataset(\"xsum\")\n",
        "metric = load_metric(\"rouge\")"
      ],
      "metadata": {
        "id": "YZBxiHtntGvw"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fPVV3bFtRI-",
        "outputId": "942dc07e-7a70-43f3-cc46-1deaf5ca68d3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'__index_level_0__': 22362,\n",
              " 'review_body': 'Terrible. Not sure if this is a fake wet brush but it repeatedly comes apart!',\n",
              " 'review_headline': 'One Star'}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "import random\n",
        "import pandas as pd\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "def show_random_elements(dataset, num_examples=5):\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "        picks.append(pick)\n",
        "    \n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    for column, typ in dataset.features.items():\n",
        "        if isinstance(typ, datasets.ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "    display(HTML(df.to_html()))"
      ],
      "metadata": {
        "id": "BAUsMZd8unYh"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_random_elements(dataset[\"train\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "_wLuratCuqYa",
        "outputId": "24371511-b3e3-40da-fc66-7f46c63e6736"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review_body</th>\n",
              "      <th>review_headline</th>\n",
              "      <th>__index_level_0__</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Goes on pretty easily with a light touch.  Doesn't make the mess my former product did - and looks better too!</td>\n",
              "      <td>I'm sold:-)</td>\n",
              "      <td>254592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>great!</td>\n",
              "      <td>Five Stars</td>\n",
              "      <td>344001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Best mouth wash ever</td>\n",
              "      <td>Five Stars</td>\n",
              "      <td>335799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>It's a good set of nail grooming tools. I like it.</td>\n",
              "      <td>Four Stars</td>\n",
              "      <td>11841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>So I am 15 years old and wanted a fresh sent that could be worn at dances and other party like atmospheres. I have never smelt this sent before and bought it off of YouTube reviews and other reviewers on Amazon. I bought the 100 ml bottle and was quite suprised at how big it was. THE SHIPPING TOOK ONLY 2 DAY!!!&lt;br /&gt;&lt;br /&gt;So I came across this fragrance choice when I typed in &amp;#34;cologne that smells like vanilla and mint&amp;#34;. I have seen reviews that say it smells like lemon... Well I smell no lemon in this cologne but rather a heavy mint with touches of vanilla. I have never smelled a cologne (out of my fathers, mine, and Dillard's, Macy's, ECT...) this is a unique smell that cools you. It reminds me of winter mint gum (not that it smells like gum but the cooling effect). It does smell fresh. This is defiantly more of a winter night cologne but could be worn in other seasons.&lt;br /&gt;&lt;br /&gt;The cologne is very strong; so much that the smell of the cologne you will notice after hours. It won't go full to your senses because you have became accustom to the smell. This cologne is a very manly smell to it while maintaining the sweet cooling smell. This is a smell I would say works best with age 14-28. Also it's projection is only a few feet with 1 spray; although it is VERY STRONG it doesn't project as good as it could. The longer you have it on the sweeter it gets. And it should last about 8 hours on skin. On cloths it will last days.&lt;br /&gt;&lt;br /&gt;The box is like a vault that holds gold at Fort Knox. The box is very difficult to open.&lt;br /&gt;&lt;br /&gt;O would recommend this cologne and I bought it directly from Amazon. It is the real deal- with a discount (about 30 dollars cheaper) YOU WONT BE DISAPPOINTED!!!</td>\n",
              "      <td>Blind buy</td>\n",
              "      <td>288664</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metric"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vWH9BQGus04",
        "outputId": "9bde5021-c464-4b50-d9d0-12f055cb6082"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metric(name: \"rouge\", features: {'predictions': Value(dtype='string', id='sequence'), 'references': Value(dtype='string', id='sequence')}, usage: \"\"\"\n",
              "Calculates average rouge scores for a list of hypotheses and references\n",
              "Args:\n",
              "    predictions: list of predictions to score. Each prediction\n",
              "        should be a string with tokens separated by spaces.\n",
              "    references: list of reference for each prediction. Each\n",
              "        reference should be a string with tokens separated by spaces.\n",
              "    rouge_types: A list of rouge types to calculate.\n",
              "        Valid names:\n",
              "        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n",
              "        `\"rougeL\"`: Longest common subsequence based scoring.\n",
              "        `\"rougeLSum\"`: rougeLsum splits text using `\"\n",
              "\"`.\n",
              "        See details in https://github.com/huggingface/datasets/issues/617\n",
              "    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n",
              "    use_aggregator: Return aggregates if this is set to True\n",
              "Returns:\n",
              "    rouge1: rouge_1 (precision, recall, f1),\n",
              "    rouge2: rouge_2 (precision, recall, f1),\n",
              "    rougeL: rouge_l (precision, recall, f1),\n",
              "    rougeLsum: rouge_lsum (precision, recall, f1)\n",
              "Examples:\n",
              "\n",
              "    >>> rouge = datasets.load_metric('rouge')\n",
              "    >>> predictions = [\"hello there\", \"general kenobi\"]\n",
              "    >>> references = [\"hello there\", \"general kenobi\"]\n",
              "    >>> results = rouge.compute(predictions=predictions, references=references)\n",
              "    >>> print(list(results.keys()))\n",
              "    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n",
              "    >>> print(results[\"rouge1\"])\n",
              "    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n",
              "    >>> print(results[\"rouge1\"].mid.fmeasure)\n",
              "    1.0\n",
              "\"\"\", stored examples: 0)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fake_preds = [\"hello there\", \"general kenobi\"]\n",
        "fake_labels = [\"hello there\", \"general kenobi\"]\n",
        "metric.compute(predictions=fake_preds, references=fake_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4IEUCIAKuyY0",
        "outputId": "ffc614c0-d770-41bf-dae1-05d70ff9f2ad"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0)),\n",
              " 'rouge2': AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0)),\n",
              " 'rougeL': AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0)),\n",
              " 'rougeLsum': AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "    \n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "id": "xQBplda1u1IH"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(\"Hello, this one sentence!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgbcyWhcu4lO",
        "outputId": "ed91bcf6-ccb7-4bb6-f991-6eca225daa30"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 31414, 6, 42, 65, 3645, 328, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with tokenizer.as_target_tokenizer():\n",
        "    print(tokenizer([\"Hello, this one sentence!\", \"This is another sentence.\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43wTqqUMvDRP",
        "outputId": "4402d25c-d57b-4cb0-d822-3caccd517f85"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [[0, 31414, 6, 42, 65, 3645, 328, 2], [0, 713, 16, 277, 3645, 4, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if model_checkpoint in [\"t5-small\", \"t5-base\", \"t5-larg\", \"t5-3b\", \"t5-11b\"]:\n",
        "    prefix = \"summarize: \"\n",
        "else:\n",
        "    prefix = \"\""
      ],
      "metadata": {
        "id": "RbmdKpXCvFU7"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_input_length = 1024\n",
        "max_target_length = 128\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = [prefix + doc for doc in examples[\"review_body\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
        "\n",
        "    # Setup the tokenizer for targets\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(examples[\"review_headline\"], max_length=max_target_length, truncation=True)\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "metadata": {
        "id": "gqlmiPx6vRJf"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocess_function(dataset['train'][:2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvHC-Qw4vRhD",
        "outputId": "95b9f622-4380-4777-8668-1415b4510d4f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[0, 32579, 24786, 4, 1491, 686, 114, 42, 16, 10, 4486, 7727, 11822, 53, 24, 3987, 606, 4102, 328, 2], [0, 713, 16, 127, 78, 86, 7, 860, 42, 1152, 8, 38, 101, 24, 182, 203, 4, 1437, 38, 2740, 5, 2233, 9, 231, 7728, 25587, 6, 142, 24, 21, 5, 21084, 169, 7, 213, 4, 85, 21, 540, 1152, 87, 38, 5207, 13, 4, 125, 24, 202, 851, 162, 10, 778, 7, 192, 141, 127, 652, 74, 2519, 4, 85, 2334, 157, 328, 2860, 6, 11, 754, 328, 38, 437, 416, 21090, 7, 5494, 179, 1168, 6, 98, 89, 21, 117, 6574, 50, 932, 4, 38, 33, 341, 6710, 9856, 132, 1178, 13, 107, 4, 125, 14, 21, 27251, 6, 98, 854, 2547, 328, 41552, 3809, 48709, 100, 120, 7, 465, 10, 5010, 328, 36, 7608, 109, 51, 109, 42, 7, 201, 28749, 43, 41552, 3809, 48709, 45273, 347, 242, 46803, 18, 9944, 179, 1168, 321, 4, 245, 888, 1411, 15, 55, 17359, 87, 6710, 9856, 4, 20, 319, 1499, 2653, 11052, 4122, 4, 85, 156, 127, 3024, 619, 11052, 4122, 4, 38, 101, 24, 10, 319, 328, 41552, 3809, 48709, 100, 40, 2299, 907, 42, 1152, 456, 4, 13088, 6, 11, 10, 2514, 1836, 4, 125, 5, 7728, 1836, 376, 11, 14732, 150, 5796, 49069, 3809, 48709, 49420, 2336, 2444, 35, 387, 612, 975, 1864, 2606, 6405, 975, 21814, 347, 242, 46803, 29, 34017, 2444, 3384, 321, 4, 245, 111, 112, 7803, 1525, 231, 1589, 155, 4, 406, 571, 255, 39749, 36, 401, 25587, 5457, 10, 410, 81, 155, 73, 306, 21388, 24521, 37183, 15450, 19218, 363, 1416, 7, 25185, 5, 2772, 9, 1046, 5284, 6, 25169, 6328, 6, 2051, 2301, 6, 8, 36045, 48392, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[0, 3762, 2141, 2], [0, 23719, 4122, 4, 38, 16923, 2011, 85, 328, 2]]}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = dataset.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "568cfab527964ff887e2c7e2196e76a1",
            "c1571572f86944d6a24208522f960570",
            "f1cac27a13c64ebfaf5f79a927ee922b",
            "a3fcf4c406a24f2c9f7e3ff376d9a391",
            "44e60fdf9ca040ea9dcfec8c2fffa8e5",
            "667fb6c4b7d04c38a0656a79b8620db8",
            "825310fdf2d843588130e956045c44f7",
            "ca37e215a293482cb4e35113a8ddf301",
            "fdb654a908a1484486d5981b15f77a5d",
            "9daee0af5abe4b6db50119bd8cb3a784",
            "1a4297d40bc7415182ed7d5f83451d95",
            "897b44ba9bf14b739b4c4bbfeb4008c7",
            "4b458d1ab2c54f60a4f13cfc283404fe",
            "1cc4d6a42ce248a4b7b16c2a771cd89d",
            "5099e516eb214f2d9ca5878d401323aa",
            "213cbe8cd9e34289b4039249a4c1b97f",
            "92e1d91950704e7399c5abbe5795d214",
            "fa34471348c94750b499af6d89c8a9b3",
            "c2a5a9d0b9074e9abf76d8f18f75ddbf",
            "b998cc0775214541bd7f2910f56c5ea6",
            "c62f12efa0a04197ad327f14d49f8ceb",
            "9ca0770a4fb54fd1a50c01b0a0b7a674"
          ]
        },
        "id": "XKLyebmpvUAr",
        "outputId": "9ccb6df3-c019-4af0-ac05-cb64191dc9f6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/24 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "568cfab527964ff887e2c7e2196e76a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "897b44ba9bf14b739b4c4bbfeb4008c7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "id": "awZbYV7Bvg9n"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 4\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "args = Seq2SeqTrainingArguments(\n",
        "    f\"{model_name}-finetuned-xsum\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=1,\n",
        "    predict_with_generate=True,\n",
        "    fp16=True,\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "eifonddowOUZ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ],
      "metadata": {
        "id": "sNcVmFG_wSqF"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "    # Replace -100 in the labels as we can't decode them.\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "    \n",
        "    # Rouge expects a newline after each sentence\n",
        "    decoded_preds = [\"\\n\".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
        "    decoded_labels = [\"\\n\".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]\n",
        "    \n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "    # Extract a few results\n",
        "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
        "    \n",
        "    # Add mean generated length\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    \n",
        "    return {k: round(v, 4) for k, v in result.items()}"
      ],
      "metadata": {
        "id": "yDFQuG42w5Is"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /gdrive/MyDrive/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgtTfycbxWLM",
        "outputId": "4af16f1d-5fb7-495d-956d-ffea745bf97f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/gdrive/MyDrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwvMOgK1w5mV",
        "outputId": "f8ec7fa2-36a2-4720-c65f-17383a38480e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/gdrive/MyDrive/distilbart-cnn-12-6-finetuned-xsum is already a clone of https://huggingface.co/sumedh/distilbart-cnn-12-6-finetuned-xsum. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "Using amp half precision backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install wandb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTxGSgVux1Wi",
        "outputId": "8b90455e-3da5-47c5-92da-eb0325e5d656"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.12.16)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (6.0)\n",
            "Requirement already satisfied: Click!=8.0.0,>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n",
            "Requirement already satisfied: sentry-sdk>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.5.12)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n",
            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n",
            "Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.1.27)\n",
            "Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.9)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (1.25.11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wUg3ghTU5BOK",
        "outputId": "f6358d06-d286-43e4-92a5-793c1b78d88e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hYPXRIC5w7U_",
        "outputId": "aebabbe2-f46a-4812-8ea7-268ea8d003dc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `BartForConditionalGeneration.forward` and have been ignored: __index_level_0__, review_headline, review_body. If __index_level_0__, review_headline, review_body are not expected by `BartForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running training *****\n",
            "  Num examples = 23014\n",
            "  Num Epochs = 1\n",
            "  Instantaneous batch size per device = 4\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 4\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 5754\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5754' max='5754' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5754/5754 29:05, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rouge1</th>\n",
              "      <th>Rouge2</th>\n",
              "      <th>Rougel</th>\n",
              "      <th>Rougelsum</th>\n",
              "      <th>Gen Len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.287500</td>\n",
              "      <td>1.629388</td>\n",
              "      <td>11.009000</td>\n",
              "      <td>7.461800</td>\n",
              "      <td>10.557300</td>\n",
              "      <td>10.808700</td>\n",
              "      <td>58.338200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-500\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-500/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-500/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-4500] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-1000\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-1000/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-1000/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-5000] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-1500\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-1500/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-1500/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-5500] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-2000\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-2000/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-2000/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-500] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-2500\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-2500/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-2500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-2500/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-2500/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-1000] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-3000\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-3000/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-3000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-3000/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-3000/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-1500] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-3500\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-3500/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-3500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-3500/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-3500/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-2000] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-4000\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-4000/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-4000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-4000/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-4000/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-2500] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-4500\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-4500/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-4500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-4500/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-4500/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-3000] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-5000\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-5000/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-5000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-5000/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-5000/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-3500] due to args.save_total_limit\n",
            "Saving model checkpoint to distilbart-cnn-12-6-finetuned-xsum/checkpoint-5500\n",
            "Configuration saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-5500/config.json\n",
            "Model weights saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-5500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-5500/tokenizer_config.json\n",
            "Special tokens file saved in distilbart-cnn-12-6-finetuned-xsum/checkpoint-5500/special_tokens_map.json\n",
            "Deleting older checkpoint [distilbart-cnn-12-6-finetuned-xsum/checkpoint-4000] due to args.save_total_limit\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BartForConditionalGeneration.forward` and have been ignored: __index_level_0__, review_headline, review_body. If __index_level_0__, review_headline, review_body are not expected by `BartForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2558\n",
            "  Batch size = 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5755' max='5754' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5754/5754 22:58, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1280' max='640' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [640/640 36:21]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=5754, training_loss=1.2821295946722524, metrics={'train_runtime': 1745.4432, 'train_samples_per_second': 13.185, 'train_steps_per_second': 3.297, 'total_flos': 3899674265911296.0, 'train_loss': 1.2821295946722524, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"sumedh/distilbart-cnn-12-6-amazonreviews\")\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"sumedh/distilbart-cnn-12-6-amazonreviews\")"
      ],
      "metadata": {
        "id": "aEFbiTcXoRX-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_text = dataset['test'][25]\n",
        "sample_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_1uN15FQt1T",
        "outputId": "1f1828a4-69e9-4a5f-88e5-42d80cbaa251"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'__index_level_0__': 248495,\n",
              " 'review_body': \"My facial trimmer broke so I bought this as a last minute resort, and it barely shaved any hairs off my face. I tried it with water, without water, with shaving cream, nothing. It didn't do its job at all. Waste of 10 dollars. I was very disappointed. I hope I received a bad unit.\",\n",
              " 'review_headline': 'Waste of 10 dollars'}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = pipeline(\"summarization\", model=model, tokenizer=tokenizer)\n",
        "pipe_out = pipe(sample_text['review_body'])\n",
        "pipe_out"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3F5CdR6oRZyr",
        "outputId": "e816e3de-215c-4b67-ab79-c8955c49556c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 142, but you input_length is only 69. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=34)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': \"Waste of 10 dollars. I was very disappointed. I hope I received a bad unit. I'm not a fan of this product. I tried it with water, without water, with shaving cream, etc. It didn't do its job at all.\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "N-fmbrNRScyA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}