News Summarization.ipynb

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            "\u001b[?25hInstalling collected packages: xxhash, requests, pyarrow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, fsspec, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.6.1\n",
            "    Uninstalling fsspec-2024.6.1:\n",
            "      Successfully uninstalled fsspec-2024.6.1\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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 fsspec-2024.5.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 pyarrow-17.0.0 requests-2.32.3 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset('xsum')\n",
        "train_data = dataset['train']\n",
        "validation_data = dataset['validation']\n",
        "test_data = dataset['test']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435,
          "referenced_widgets": [
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            "c601d2c753ab49d1abb302f63e64e507"
          ]
        },
        "id": "kNdY7BhfUutH",
        "outputId": "13fe4a21-c2c5-4993-dd1e-12128f075a1d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.76k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca9f791860344eaa9e6f77fc38158f33"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/6.24k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c905abee00d4bb4888605a906ade9e9"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for xsum contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/xsum.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/255M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2496eaf6dd946e096c871bc5ef44d23"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/1.00M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27409124134d466784dadfa738661cac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/204045 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7241231cbfe5412f8b14161eb35b36bb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/11332 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1aafe635a7fc47919a8add36c3355fea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/11334 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e30756dbedc14ccc97fa71143b7af945"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_data[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Ca8DhW7UwsA",
        "outputId": "00702412-9fd0-494e-d8ff-c40153a6bb50"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'document': 'The full cost of damage in Newton Stewart, one of the areas worst affected, is still being assessed.\\nRepair work is ongoing in Hawick and many roads in Peeblesshire remain badly affected by standing water.\\nTrains on the west coast mainline face disruption due to damage at the Lamington Viaduct.\\nMany businesses and householders were affected by flooding in Newton Stewart after the River Cree overflowed into the town.\\nFirst Minister Nicola Sturgeon visited the area to inspect the damage.\\nThe waters breached a retaining wall, flooding many commercial properties on Victoria Street - the main shopping thoroughfare.\\nJeanette Tate, who owns the Cinnamon Cafe which was badly affected, said she could not fault the multi-agency response once the flood hit.\\nHowever, she said more preventative work could have been carried out to ensure the retaining wall did not fail.\\n\"It is difficult but I do think there is so much publicity for Dumfries and the Nith - and I totally appreciate that - but it is almost like we\\'re neglected or forgotten,\" she said.\\n\"That may not be true but it is perhaps my perspective over the last few days.\\n\"Why were you not ready to help us a bit more when the warning and the alarm alerts had gone out?\"\\nMeanwhile, a flood alert remains in place across the Borders because of the constant rain.\\nPeebles was badly hit by problems, sparking calls to introduce more defences in the area.\\nScottish Borders Council has put a list on its website of the roads worst affected and drivers have been urged not to ignore closure signs.\\nThe Labour Party\\'s deputy Scottish leader Alex Rowley was in Hawick on Monday to see the situation first hand.\\nHe said it was important to get the flood protection plan right but backed calls to speed up the process.\\n\"I was quite taken aback by the amount of damage that has been done,\" he said.\\n\"Obviously it is heart-breaking for people who have been forced out of their homes and the impact on businesses.\"\\nHe said it was important that \"immediate steps\" were taken to protect the areas most vulnerable and a clear timetable put in place for flood prevention plans.\\nHave you been affected by flooding in Dumfries and Galloway or the Borders? Tell us about your experience of the situation and how it was handled. Email us on selkirk.news@bbc.co.uk or dumfries@bbc.co.uk.', 'summary': 'Clean-up operations are continuing across the Scottish Borders and Dumfries and Galloway after flooding caused by Storm Frank.', 'id': '35232142'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BartTokenizer, BartForConditionalGeneration, Trainer, TrainingArguments\n",
        "\n",
        "# Load the BART tokenizer and model\n",
        "model_name = \"facebook/bart-large-xsum\"\n",
        "tokenizer = BartTokenizer.from_pretrained(model_name)\n",
        "model = BartForConditionalGeneration.from_pretrained(model_name)\n",
        "\n",
        "# Preprocess the data\n",
        "def preprocess_function(examples):\n",
        "    inputs = examples[\"document\"]\n",
        "    model_inputs = tokenizer(inputs, max_length=1024, truncation=True, padding=\"max_length\")\n",
        "\n",
        "    # Tokenize the summaries as the labels\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(examples[\"summary\"], max_length=128, truncation=True, padding=\"max_length\")\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "# Apply the preprocessing to the datasets\n",
        "train_dataset = train_data.map(preprocess_function, batched=True)\n",
        "validation_dataset = validation_data.map(preprocess_function, batched=True)\n",
        "test_dataset = test_data.map(preprocess_function, batched=True)\n",
        "\n",
        "# Set the format for PyTorch\n",
        "train_dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n",
        "validation_dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n",
        "test_dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n",
        "\n",
        "# Define the training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=3e-5,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    logging_dir='./logs',\n",
        "    save_total_limit=1,\n",
        ")\n",
        "\n",
        "# Initialize the Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=validation_dataset,\n",
        ")\n",
        "\n",
        "# Fine-tune the model\n",
        "trainer.train()\n",
        "\n",
        "# Evaluate the model\n",
        "trainer.evaluate()\n",
        "\n",
        "# Generate a summary for a new document from the test set\n",
        "example_text = test_data[0][\"document\"]\n",
        "inputs = tokenizer([example_text], max_length=1024, return_tensors=\"pt\", truncation=True)\n",
        "summary_ids = model.generate(inputs[\"input_ids\"], max_length=128, num_beams=4, length_penalty=2.0, early_stopping=True)\n",
        "print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))\n",
        "\n",
        "# Save the model\n",
        "model.save_pretrained(\"./saved_model\")\n",
        "tokenizer.save_pretrained(\"./saved_model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293,
          "referenced_widgets": [
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        },
        "id": "6Mkb6QZOW08w",
        "outputId": "3aec8471-1119-4231-c3da-5d4c8d321029"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/309 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8046c8ab04147479445013908469463"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/204045 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70871ff32d4549418220775b19d569b1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:4016: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/11332 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "749ef64ddaa142668f4f0c262cd9f5c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/11334 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "904f76605f004a9e9a75a8b9e133a155"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='113' max='153036' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [   113/153036 03:20 < 76:32:17, 0.55 it/s, Epoch 0.00/3]\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>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}