PyTorchT5Small_AbsSumm (2).ipynb

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        "%%capture\n",
        "!pip install transformers\n",
        "!pip install transformers[torch]\n",
        "!pip install datasets\n",
        "!pip install evaluate\n",
        "!pip install rouge_score\n",
        "!pip install nltk"
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        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
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      "source": [
        "from datasets import load_dataset\n",
        "#XSum\n",
        "train_data_xsum = load_dataset(\"xsum\", split=\"train[:20%]\")\n",
        "test_data_xsum = load_dataset(\"xsum\", split=\"test[:20%]\")\n",
        "\n",
        "#CNN\n",
        "train_data_cnn = load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train[:20%]\")\n",
        "test_data_cnn = load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"test[:20%]\")\n"
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4907b822ca0346d9855c1fd2e87d2880"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/11490 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "625b76d71a6d481da40fbff4682d8aa0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Untuk split train\n",
        "train_data_cnn = train_data_cnn.rename_column(\"article\", \"document\")\n",
        "train_data_cnn = train_data_cnn.rename_column(\"highlights\", \"summary\")\n",
        "\n",
        "#Untuk split test\n",
        "test_data_cnn = test_data_cnn.rename_column(\"article\", \"document\")\n",
        "test_data_cnn = test_data_cnn.rename_column(\"highlights\", \"summary\")"
      ],
      "metadata": {
        "id": "rMSya7dlCJi3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "train_data = concatenate_datasets([train_data_xsum, train_data_cnn])\n",
        "test_data = concatenate_datasets([test_data_xsum, test_data_cnn])"
      ],
      "metadata": {
        "id": "1GVRRWRaCJgm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")"
      ],
      "metadata": {
        "id": "KR3lZgjfCJed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "9efb1fc2c8de498195c9b2614f10a59b",
            "e979d66b0b7843f4b14116dd8aba6298",
            "79c76223d3084a37bef9e1b46dd3d921",
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            "230399c9132049f5b41c3d9ad9c350d1",
            "51cc0e73b66249ed8c2df9a05bbf15df",
            "ed09469fcf6f469187fb14ef663d9413",
            "aab503a175bc43f38f088067a9e94184",
            "e4e7f91358ed44beb5918f251643fcaa",
            "60c86fc0503b43c1b15bbb7b2293016f",
            "4bee5ad7738f4e50b0e977280d701236",
            "dd5918acff154cebb26d92eb148f521a",
            "34d36c5f47e94be2bed45a88c43e8f58",
            "b7b64b4264a64941a1986b687f38e344",
            "cbd7ba94febc4cf08a4553e9c68c16c2",
            "b078527cd3e6401c872374cad71120dc",
            "6df7d58e93d343ac9db22ffaf65b72d1",
            "0b4a9f9fc0244eb2ab088af3f9096981",
            "62ce245d1fdb439e879c5c8ce075edd4",
            "127d6d270c4f40808fe1090b18b0f7d0",
            "6ea4cf6ef92044a8964dc97cd7c78f55",
            "a16ec55fae36423093e606102c9a2eec"
          ]
        },
        "outputId": "6c1e76bf-82b5-4cf6-a202-e1976dcaa46a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9efb1fc2c8de498195c9b2614f10a59b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd5c4347ccf54ad3896fd0a006c589ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd5918acff154cebb26d92eb148f521a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prefix = \"summarize: \"\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = [prefix + doc for doc in examples[\"document\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=1024, truncation=True)\n",
        "    labels = tokenizer(text_target=examples[\"summary\"], max_length=128, truncation=True)\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "metadata": {
        "id": "kf74wjTcCJcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_train = train_data.map(preprocess_function, batched=True)\n",
        "tokenized_test = test_data.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "id": "Yb0_YL1wCJaH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "b3110ea0c0f443478ade6312fb9b22f7",
            "74d297a0b7b84f8f94112fe5c3b3475a",
            "90e458ecde684a7d87ebe73dcd8535db",
            "14bb7d168b54460e8a36e2295bff6828",
            "af90a744b9d342019a813cd34587c4ba",
            "dea9ba6a92b24e409ea36a74ba618408",
            "530d93705ead4c1d9e9accfb2a51af83",
            "a39c68002ecc4fc78e8ca4ae0b35bd74",
            "31948da67e6040d29f97f979f4955b08",
            "1ababa67a0ee4b679204d59d3d4f6751",
            "09c8a77c888244c297ef5ab58d291f5d",
            "b2a9ccac6c8d42788f8cdb87f3722200",
            "72d6028a5cce4baaa6fff3f55ffcc302",
            "fd409a40a84f433ab2ad860ac6f60176",
            "5e92fe539c8d4e818345c644770488d2",
            "916ddebc56a347019f3ba4857036460b",
            "4425e36cf9b1442699be4f7d843e7d76",
            "b281cdfc3df447eda6f25eb0cbac45b7",
            "1064ddd4fc254f5f9a21d4527bd32c61",
            "7f431c80fb3c4a6c8002c6ab42fdc6a8",
            "fdfd31332802410dbf4c33f8733e8256",
            "2a059ebfd885499d9e05c18f75238b9d"
          ]
        },
        "outputId": "65dcd4fe-2a19-485f-8d70-6afe3ebf7d00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/98232 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3110ea0c0f443478ade6312fb9b22f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4565 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2a9ccac6c8d42788f8cdb87f3722200"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=\"t5-small\")"
      ],
      "metadata": {
        "id": "FZBFmu8BCJX3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "rouge = evaluate.load(\"rouge\")"
      ],
      "metadata": {
        "id": "IM1SKuUBCJVU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7c4bf6a93bb1449f9f63dd4ee74205ca",
            "d67e75a985bc490d91aa1e276a0835b5",
            "49a6caf2ee934a3e94eb4fbc1aa979ca",
            "c5a0e908194e4249a6ea78138703dfe1",
            "fe87b18a514b4b96a26a28ba312aec11",
            "de03b52956884818a0586b165eab6c25",
            "174303de0e084bceaeffb3efd508ec8c",
            "d7a645a0c53045a485c6873a1c91dd73",
            "c30852de7da548a097c8b57164967281",
            "7d3e3fd854084968a39da24c3542ccce",
            "94b3c85c69ff43f08590282cc500b8ce"
          ]
        },
        "outputId": "f237d6c8-4a5c-4cb2-e859-6b2a7264b071"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c4bf6a93bb1449f9f63dd4ee74205ca"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "\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": "7mFQYQYwCJTN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")"
      ],
      "metadata": {
        "id": "6bpI2E2xCJQ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "3a8697957bc84cc4b15bc46012c3f237",
            "e186e0f1895b4dc6afc941267bc3b630",
            "9dec318a67f44846ad52918ac8148c5d",
            "c3ee956a4e884c3280b7a1d6d7f108c1",
            "7826125d4697411bafcd5d94357b6b1d",
            "2514d11467564b319fac8d01525ba1d3",
            "10f8a07b7b5c44ee8874f2543b6daae5",
            "adf6f8e31f8c4520923ac1324448ae73",
            "b1bd4ad52fcd481dbc5450b96c2a9bb3",
            "3d91a15cff65480aacb5869782e980de",
            "63490e4ce9994c7d847a515b012f7977",
            "0f009bcd94be4c6d9ef245ab905090de",
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            "f5019949418248b192b1cc7b1aae735a",
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            "0a32974781794e0cb64dc2edaa04b1d6",
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            "1b811c4aa914427aa0550da89f2c5296",
            "b6e3f582f43340d6b2aa2cfdfdeb2aae",
            "d8f31bd2235845cab523337e36ba199d",
            "d525f84dc7dc434380eea2be23bc1538",
            "c41e7118b24d40178d04d2b13dff8c8e"
          ]
        },
        "outputId": "33de21dd-0479-46c7-f0f0-13862c8079e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "3a8697957bc84cc4b15bc46012c3f237"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f009bcd94be4c6d9ef245ab905090de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fce4f4e7f59c4ba8bdaf90930fcd1552"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"T5-Small_AbsSumm_XSumCNN\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=4,\n",
        "    predict_with_generate=True,\n",
        "    fp16=True,\n",
        "    push_to_hub=True,\n",
        ")\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_train,\n",
        "    eval_dataset=tokenized_test,\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "IUYXiPzgCJIF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "3bff5ac5-a792-4ebe-ba71-7571aff45a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='24560' max='24560' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [24560/24560 5:58:17, Epoch 4/4]\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>2.178900</td>\n",
              "      <td>2.262195</td>\n",
              "      <td>0.250100</td>\n",
              "      <td>0.077700</td>\n",
              "      <td>0.196000</td>\n",
              "      <td>0.196000</td>\n",
              "      <td>18.862400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.149500</td>\n",
              "      <td>2.243245</td>\n",
              "      <td>0.254600</td>\n",
              "      <td>0.079300</td>\n",
              "      <td>0.199300</td>\n",
              "      <td>0.199300</td>\n",
              "      <td>18.868800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.128700</td>\n",
              "      <td>2.236404</td>\n",
              "      <td>0.255500</td>\n",
              "      <td>0.079500</td>\n",
              "      <td>0.199400</td>\n",
              "      <td>0.199400</td>\n",
              "      <td>18.874700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.148500</td>\n",
              "      <td>2.232910</td>\n",
              "      <td>0.256600</td>\n",
              "      <td>0.080400</td>\n",
              "      <td>0.200600</td>\n",
              "      <td>0.200700</td>\n",
              "      <td>18.890300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=24560, training_loss=2.1661259269093067, metrics={'train_runtime': 21500.6172, 'train_samples_per_second': 18.275, 'train_steps_per_second': 1.142, 'total_flos': 1.0627518714976666e+17, 'train_loss': 2.1661259269093067, 'epoch': 4.0})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "text = \"summarize: The Inflation Reduction Act lowers prescription drug costs, health care costs, and energy costs. It's the most aggressive action on tackling the climate crisis in American history, which will lift up American workers and create good-paying, union jobs across the country. It'll lower the deficit and ask the ultra-wealthy and corporations to pay their fair share. And no one making under $400,000 per year will pay a penny more in taxes.\"\n",
        "summarizer = pipeline(\"summarization\", model=\"DaviadiAF/T5-Small_AbsSumm_XSumCNN\")\n",
        "summarizer(text)"
      ],
      "metadata": {
        "id": "WjD7iDGFeQA2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
          "referenced_widgets": [
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          ]
        },
        "outputId": "fa678bb0-327e-45d1-d2ed-a58ee9fbf8b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.50k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d74b6a184de4439a85b0b7ac4229a5b6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/242M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f5c42cba35b4713b8b85ae9833a6825"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/20.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0677f99d61b4497eb40cfae75208b181"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2b3722946cd4602b314e3e43b227af6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d093f1778c644138a8b46ae5d8ab1147"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 200, but your input_length is only 103. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=51)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': \"No one making under $400,000 per year will pay a penny more in taxes than any other tax payer in the U.S. it's the most aggressive action on the climate crisis in America.\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    }
  ]
}