t5_trainer_round_1.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/bibowskii/LLM-T5-small-summarizer/blob/main/t5_trainer_round_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSdV7PMQm2vI"
      },
      "source": [
        "preprocessing the text\n",
        "cleaning\n",
        "unused and unnecessary\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pePfILxVf-wX"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "\n",
        "def cleanhtml(raw_html):\n",
        "  cleanr = re.compile('<.*?>')\n",
        "  cleantext = re.sub(cleanr, '', raw_html)\n",
        "  return cleantext\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jegaCOQmm-J3"
      },
      "source": [
        "tokenizing using a pretrained model and loading our pretrained model\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "XbS7MvT8m9l2",
        "outputId": "03ae3439-2077-4123-9ec5-ad1f97a24330"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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": [
              "tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
            ],
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              "version_minor": 0,
              "model_id": "13f1669e8eb24bafb3a55c24d97c5957"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a12f9118c8140cbb661556f00fbf104"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "f75f644f7a7541828e7ee2acd8b486d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b15d62056374d3689f4e7efb0ff11d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7787892f02643638a49ac97c91e8f16"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59fade4fa49e40cf9b32dc4f48318fde"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
        "\n",
        "\n",
        "tokenizer = T5Tokenizer.from_pretrained('google-t5/t5-small')\n",
        "modelT5 = T5ForConditionalGeneration.from_pretrained('google-t5/t5-small')\n",
        "\n",
        "def tokenize(text):\n",
        "    cleaned_text = text\n",
        "\n",
        "    tokenized_text = tokenizer.encode(cleaned_text, return_tensors=\"pt\")\n",
        "    return tokenized_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3J8m4hLDo-Mm"
      },
      "source": [
        "loading the dataset and splitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eu_IkBUS0AEr",
        "outputId": "5069b5c0-9a42-4910-f8b9-e4343cd076cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
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          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
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            "900fc484d4bb47fead1cf465b1d6b2ad",
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            "caccb646b51242c5b7f46d034436692a",
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            "9fcbd340d7b241838db184cdbf6bb472",
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            "cfdb903673e64cc18057a6f22dc0a032",
            "0347a25619724ef1a0f256fa103ea266",
            "3f5a5df8a4c34e3084ee47ce30578b23"
          ]
        },
        "id": "wdIrj9gwpBfL",
        "outputId": "b502fcb8-91e1-4c82-8cf0-1d344b6110f3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "dataset_infos.json:   0%|          | 0.00/1.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c44e7e1a7dda4ce78491a7eb651e2d66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)-00000-of-00001-f0652266bed8b2c3.parquet:   0%|          | 0.00/14.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4866b8ef361482581ba0db56a50cc3b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)-00000-of-00001-ee1c3810c1ef3537.parquet:   0%|          | 0.00/16.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "32f3743465504e839be1205aae01bf12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)-00000-of-00001-ee7e01954dbd2a29.parquet:   0%|          | 0.00/16.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa12509e754a490282f865959f2a7770"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d47821973821496995feb18dc1f86cea"
            }
          },
          "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": "50f7c075e8254a55baf2638c229690ba"
            }
          },
          "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": "1d2967199a39415f842ccf6f01212ad5"
            }
          },
          "metadata": {}
        },
        {
          "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"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "\n",
        "trainingSet = load_dataset(\"iohadrubin/mini_xsum\", split=\"train\" )\n",
        "\n",
        "\n",
        "#checking the data\n",
        "print(trainingSet[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RrWRmN9Vpg5D"
      },
      "source": [
        "function to tokenize the dataset using the preloaded tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qk62DPFAqMA3",
        "outputId": "9637de67-5473-48e3-b7d7-15466a780935"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "modelT5.to(device)\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P19LU7WBmLU_"
      },
      "outputs": [],
      "source": [
        "def tokenize_dataset(dataset):\n",
        "    inputs = tokenizer(\n",
        "        dataset['document'],\n",
        "        return_tensors=\"pt\",\n",
        "        padding=\"max_length\",  # Pad to max_length (only specified once)\n",
        "        truncation=True,       # Truncate to max_length\n",
        "        max_length=1000        # Specify max_length for the article\n",
        "    )\n",
        "    targets = tokenizer(\n",
        "        dataset['summary'],\n",
        "        return_tensors=\"pt\",\n",
        "        padding=\"max_length\",  # Pad to max_length (only specified once)\n",
        "        truncation=True,       # Truncate to max_length\n",
        "        max_length=200         # Specify max_length for the highlights\n",
        "    )\n",
        "\n",
        "    inputs['labels'] = targets['input_ids']\n",
        "    inputs['decoder_attention_mask'] = targets['attention_mask']\n",
        "    return inputs\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dYBf2rLpoL0M"
      },
      "source": [
        "applying tokenization and mapping the input to the output\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "f948f75b88524e0e8cf075c11a366301",
            "e7d62773bcf44015a06d9f71ec14bec4",
            "1dedcba3bf90432c84c354679f181ec8",
            "c90cb3ed271d45048427fe3d4d44fbd6",
            "6a20124c5af3458ea2c1c194f02954ba",
            "981ed54fc0e84b5b917a5fda2a60123c",
            "9971e6f90a914e8c81045679aa0ac7ff",
            "625dfbbfd20a45b4acf85f85c25687c6",
            "1cdde9e128584ddfa7d127e9f8c07085",
            "ebb2b9d6586f4af29456fa9100ce3518",
            "5730b8a6830a4948a08fb9fafe738c3b"
          ]
        },
        "id": "YxJS7Sk9oJnm",
        "outputId": "cce7e24b-6be0-4162-c37f-8ebb0e527b28"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f948f75b88524e0e8cf075c11a366301"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_trainingSet = trainingSet.map(tokenize_dataset, batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwbqsWxuoU1M"
      },
      "source": [
        "formatting the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "juVKir80oYtY"
      },
      "outputs": [],
      "source": [
        "tokenized_trainingSet.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels', 'decoder_attention_mask'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNrDydPmooCg"
      },
      "source": [
        "verifying the datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGFsPEkFo54o",
        "outputId": "56454de9-1582-496d-97cb-dc630a48b248"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['input_ids', 'attention_mask', 'labels', 'decoder_attention_mask'])\n",
            "The full cost of damage in Newton Stewart, one of the areas worst affected, is still being assessed. Repair work is ongoing in Hawick and many roads in Peeblesshire remain badly affected by standing water. Trains on the west coast mainline face disruption due to damage at the Lamington Viaduct. Many businesses and householders were affected by flooding in Newton Stewart after the River Cree overflowed into the town. First Minister Nicola Sturgeon visited the area to inspect the damage. The waters breached a retaining wall, flooding many commercial properties on Victoria Street - the main shopping thoroughfare. Jeanette Tate, who owns the Cinnamon Cafe which was badly affected, said she could not fault the multi-agency response once the flood hit. However, she said more preventative work could have been carried out to ensure the retaining wall did not fail. \"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. \"That may not be true but it is perhaps my perspective over the last few days. \"Why were you not ready to help us a bit more when the warning and the alarm alerts had gone out?\" Meanwhile, a flood alert remains in place across the Borders because of the constant rain. Peebles was badly hit by problems, sparking calls to introduce more defences in the area. Scottish Borders Council has put a list on its website of the roads worst affected and drivers have been urged not to ignore closure signs. The Labour Party's deputy Scottish leader Alex Rowley was in Hawick on Monday to see the situation first hand. He said it was important to get the flood protection plan right but backed calls to speed up the process. \"I was quite taken aback by the amount of damage that has been done,\" he said. \"Obviously it is heart-breaking for people who have been forced out of their homes and the impact on businesses.\" He 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. Have 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.</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>\n",
            "Clean-up operations are continuing across the Scottish Borders and Dumfries and Galloway after flooding caused by Storm Frank.</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>\n"
          ]
        }
      ],
      "source": [
        "example = tokenized_trainingSet[0]\n",
        "print(example.keys())\n",
        "print(tokenizer.decode(example['input_ids']))\n",
        "print(tokenizer.decode(example['labels']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3EAjBKApRs7"
      },
      "source": [
        "data loader to test in batches"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AwBRuQ6OpRQg"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "trainingLoader = DataLoader(tokenized_trainingSet, batch_size=4, shuffle= False, num_workers=2)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kb6TscEGqSgC"
      },
      "source": [
        "trainer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YnN2zKmxrl6G"
      },
      "outputs": [],
      "source": [
        "from transformers import AdamW\n",
        "from torch.nn import CrossEntropyLoss\n",
        "\n",
        "def train_model(model, trainLoader, optimizer, device):\n",
        "  model.train()\n",
        "  total_loss = 0\n",
        "  for batch in trainLoader:\n",
        "    input_ids = batch['input_ids'].to(device)\n",
        "    attention_mask = batch['attention_mask'].to(device)\n",
        "    labels = batch['labels'].to(device)\n",
        "    optimizer.zero_grad()\n",
        "    outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
        "    loss = outputs.loss\n",
        "    total_loss += loss.item()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "  return total_loss / len(trainLoader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4Nan0mlspel"
      },
      "source": [
        "epochs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQKVLTXms3QC",
        "outputId": "28bfb83a-f880-4fa5-d985-f66518dc4c3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Epoch 1/3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.6763982767939568\n",
            "time taken for this epoch:  15.69 minutes\n",
            "Epoch 2/3\n",
            "Train Loss: 0.4311529669106007\n",
            "time taken for this epoch:  15.73 minutes\n",
            "Epoch 3/3\n",
            "Train Loss: 0.41603048772215845\n",
            "time taken for this epoch:  15.75 minutes\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "import time\n",
        "drive.mount('/content/drive')\n",
        "checkpoint_path = \"/content/drive/MyDrive/LLMmodels/epoch={epoch}\"\n",
        "num_epochs = 3\n",
        "optimizer = AdamW(modelT5.parameters(), lr=5e-5)\n",
        "for epoch in range(num_epochs):\n",
        "  start_time = time.time()\n",
        "  print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
        "  train_loss = train_model(modelT5, trainingLoader, optimizer, device)\n",
        "  print(f\"Train Loss: {train_loss}\")\n",
        "  modelT5.save_pretrained(checkpoint_path.format(epoch=epoch+1))\n",
        "  end_time = time.time()\n",
        "  excution_time = (end_time - start_time)/60\n",
        "  print('time taken for this epoch:  {:.2f} minutes' .format(excution_time))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "eI1OwOjgpllt"
      }
    }
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