Text_summarization_with_T5.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Taaniya/explore-T5-model/blob/main/Text_summarization_with_T5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook fine-tunes T5 model for text summarization task\n",
        "\n",
        "* Model - T5-base\n",
        "* Dataset - [Medium articles dataset on Kaggle](https://www.kaggle.com/datasets/fabiochiusano/medium-articles) (190K+ Medium articles)\n",
        "* In the interest of time, I have sampled train data from original dataset.\n",
        "Train data size - 17K, test - 851\n",
        "* Training - supervised training (no masking in inputs like it's done for denoising pre-training objective)\n",
        "* Evaluation metric used - ROUGE\n",
        "\n"
      ],
      "metadata": {
        "id": "64YnKC66sYue"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets transformers rouge_score nltk accelerate"
      ],
      "metadata": {
        "id": "uFiYxpm-uNng"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XXrKztuKt5Mq"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "from datasets import load_dataset, load_metric\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "import string\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "417WZkBXuT7x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"t5-base\"\n",
        "dataset_path = \"./drive/MyDrive/Colab Notebooks/NLP_exploration/exploring_T5/medium_articles_sample_50k.csv\"\n",
        "batch_size = 8\n",
        "model_name = \"t5-base-medium-title-generation\"\n",
        "model_dir = f\"./drive/MyDrive/Colab Notebooks/NLP_exploration/exploring_T5/Models/{model_name}\"\n"
      ],
      "metadata": {
        "id": "ZuP3e-oz1909"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Load dataset"
      ],
      "metadata": {
        "id": "ZrC6GotvvMZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "medium_datasets = load_dataset(\"csv\", data_files=dataset_path)"
      ],
      "metadata": {
        "colab": {
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        "outputId": "c71a2f2c-ee48-4388-8281-0274b8c6488b"
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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              "version_major": 2,
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              "model_id": "077932fceeb2478b8e45c8f00c6e7434"
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    },
    {
      "cell_type": "code",
      "source": [
        "medium_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v404p_u65Z0J",
        "outputId": "f636aee5-f4f1-418d-8572-9590adc083e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['title', 'text', 'url', 'authors', 'timestamp', 'tags'],\n",
              "        num_rows: 50000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasets_train_test = medium_datasets[\"train\"].train_test_split(test_size=3000)\n",
        "datasets_train_validation = datasets_train_test[\"train\"].train_test_split(test_size=3000)\n",
        "\n",
        "medium_datasets[\"train\"] = datasets_train_validation[\"train\"]\n",
        "medium_datasets[\"validation\"] = datasets_train_validation[\"test\"]\n",
        "medium_datasets[\"test\"] = datasets_train_test[\"test\"]\n"
      ],
      "metadata": {
        "id": "gRkCTVE55ed-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "medium_datasets[\"train\"] = medium_datasets[\"train\"].shuffle().select(range(20000))\n",
        "medium_datasets[\"validation\"] = medium_datasets[\"validation\"].shuffle().select(range(1000))\n",
        "medium_datasets[\"test\"] = medium_datasets[\"test\"].shuffle().select(range(1000))"
      ],
      "metadata": {
        "id": "dDg3tv7x5eaq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data preprocessing"
      ],
      "metadata": {
        "id": "mjsqOUt5DIC2"
      }
    },
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      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
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              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# prepend 'summarize' to each text\n",
        "prefix = \"summarize: \"\n",
        "\n",
        "max_input_length = 512\n",
        "max_target_length = 64\n",
        "\n",
        "def clean_text(text):\n",
        "  sentences = nltk.sent_tokenize(text.strip())\n",
        "  sentences_cleaned = [s for sent in sentences for s in sent.split(\"\\n\")]\n",
        "\n",
        "  # Include lines that end in punctuation\n",
        "  sentences_cleaned_no_titles = [sent for sent in sentences_cleaned\n",
        "                                 if len(sent) > 0 and\n",
        "                                 sent[-1] in string.punctuation]\n",
        "  text_cleaned = \"\\n\".join(sentences_cleaned_no_titles)\n",
        "  return text_cleaned\n",
        "\n",
        "def preprocess_data(examples):\n",
        "  # create inputs for model by tokenizing article text\n",
        "  texts_cleaned = [clean_text(text) for text in examples[\"text\"]]\n",
        "  inputs = [prefix + text for text in texts_cleaned]\n",
        "  model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
        "\n",
        "  # Setup the tokenizer for targets\n",
        "  # create labels for model by tokenizing article titles. Since this is text\n",
        "  # summarization, where the vocabulary for input and labels is the same, the same\n",
        "  # tokenizer is used for both inputs to encoder and labels (inputs to decoder)\n",
        "\n",
        "  with tokenizer.as_target_tokenizer():\n",
        "    labels = tokenizer(examples[\"title\"], max_length=max_target_length,\n",
        "                       truncation=True)\n",
        "\n",
        "  model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "  return model_inputs"
      ],
      "metadata": {
        "id": "N6H99qOF_qpy"
      },
      "execution_count": null,
      "outputs": []
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        "medium_datasets_cleaned = medium_datasets.filter(lambda example: (len(example['text']) >= 500) and (len(example['title']) >= 20))\n",
        "tokenized_datasets = medium_datasets_cleaned.map(preprocess_data, batched=True)\n",
        "tokenized_datasets"
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        "id": "TzD0x869D8CR",
        "outputId": "4e745f9f-ecce-4c50-9fc6-20893c405f98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3635: 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",
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        },
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['title', 'text', 'url', 'authors', 'timestamp', 'tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 17054\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['title', 'text', 'url', 'authors', 'timestamp', 'tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 849\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['title', 'text', 'url', 'authors', 'timestamp', 'tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 851\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -r {model_dir}"
      ],
      "metadata": {
        "id": "NB7Ws4Ot5eXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "args = Seq2SeqTrainingArguments(\n",
        "    model_dir,\n",
        "    evaluation_strategy=\"steps\",       # do evaluation every eval_steps\n",
        "    eval_steps=100,\n",
        "    logging_strategy=\"steps\",          # do logging every eval_steps\n",
        "    logging_steps=100,\n",
        "    save_strategy=\"steps\",             # Save checkpoint every save_steps\n",
        "    save_steps=200,\n",
        "    learning_rate=4e-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,               # save N recent chkpts including best model\n",
        "    num_train_epochs=1,\n",
        "    predict_with_generate=True,      # compute generative metrics - ROUGE, BLEU\n",
        "    fp16=True,                       # Whether to use fp16 16-bit (mixed) precision training instead of 32-bit\n",
        "    load_best_model_at_end=True,     #  ensures best model chkpt is always saved\n",
        "    metric_for_best_model=\"rouge1\",  # metric to compare chkpts, otherwise use loss\n",
        "    report_to=\"tensorboard\"\n",
        ")"
      ],
      "metadata": {
        "id": "9hjbOFfTzk1u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Details on each training argument can be found in the [Huggingface documentation for Seq2SeqTrainer](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)\n",
        "\n",
        "There's a different data collator for sequence to sequence task[DataCollatorForSeq2Seq](https://huggingface.co/docs/transformers/main_classes/data_collator#transformers.DataCollatorForSeq2Seq), since the length of labels for sequence to sequence task are variable or different from input length.\n",
        "So, the inputs and labels are padded accordingly, where padding token id for input is 0 and that of labels is -100 since -100 is the ignore_index for loss function to ignore padding token_ids from labels."
      ],
      "metadata": {
        "id": "jY0xZWsxzoxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer)"
      ],
      "metadata": {
        "id": "uG9i3Gj_5eVL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metric = load_metric(\"rouge\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "\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()))\n",
        "                      for pred in decoded_preds]\n",
        "    decoded_labels = [\"\\n\".join(nltk.sent_tokenize(label.strip()))\n",
        "                      for label in decoded_labels]\n",
        "\n",
        "    # Compute ROUGE scores\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels,\n",
        "                            use_stemmer=True)\n",
        "\n",
        "    # Extract ROUGE f1 scores\n",
        "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
        "\n",
        "    # Add mean generated length to metrics\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id)\n",
        "                      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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
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            "fdf85fa170e64ad88b263aaa17f0cdc2",
            "c9a5e33ba6054fa18f89042b448f7771"
          ]
        },
        "id": "EM1caEizBjar",
        "outputId": "080f4be9-4614-4d61-c0bb-37a23311b6c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-77060938401b>:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"rouge\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3f23c5cecc64085a2f27aea93d8c5b2"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that returns an untrained model to be trained\n",
        "def model_init():\n",
        "    return AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model_init=model_init,\n",
        "    args=args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "id": "O9D53gCjBjXY",
        "outputId": "86e4ca9c-65a8-443b-e6d0-1c700522d8a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "347a4b6359b9411d83802a20273414dc"
            }
          },
          "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": "f5dbe17ee3a843d6a5028f898672f85b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Start TensorBoard before training to monitor it in progress\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir '{model_dir}'/runs"
      ],
      "metadata": {
        "id": "snxLacNZBjQS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "gVd1jvtdDYSb",
        "outputId": "39a2b28b-9a23-44ca-801a-4f3a6f284ec5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2132' max='2132' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2132/2132 56:48, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</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>100</td>\n",
              "      <td>3.161500</td>\n",
              "      <td>2.612373</td>\n",
              "      <td>25.395100</td>\n",
              "      <td>11.532100</td>\n",
              "      <td>22.799600</td>\n",
              "      <td>22.896500</td>\n",
              "      <td>12.305100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>2.823900</td>\n",
              "      <td>2.549661</td>\n",
              "      <td>28.468700</td>\n",
              "      <td>13.778300</td>\n",
              "      <td>25.859200</td>\n",
              "      <td>25.883900</td>\n",
              "      <td>11.458200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>2.725900</td>\n",
              "      <td>2.516123</td>\n",
              "      <td>29.177500</td>\n",
              "      <td>14.376700</td>\n",
              "      <td>26.842200</td>\n",
              "      <td>26.890600</td>\n",
              "      <td>11.561800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>2.655300</td>\n",
              "      <td>2.504573</td>\n",
              "      <td>29.116000</td>\n",
              "      <td>14.579600</td>\n",
              "      <td>26.702800</td>\n",
              "      <td>26.720500</td>\n",
              "      <td>11.633700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>2.669600</td>\n",
              "      <td>2.488245</td>\n",
              "      <td>29.058500</td>\n",
              "      <td>14.532600</td>\n",
              "      <td>26.490800</td>\n",
              "      <td>26.494600</td>\n",
              "      <td>11.994100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>2.685200</td>\n",
              "      <td>2.480134</td>\n",
              "      <td>29.324800</td>\n",
              "      <td>14.476000</td>\n",
              "      <td>26.877700</td>\n",
              "      <td>26.893600</td>\n",
              "      <td>11.422900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>2.667400</td>\n",
              "      <td>2.468766</td>\n",
              "      <td>29.452000</td>\n",
              "      <td>14.526900</td>\n",
              "      <td>27.113100</td>\n",
              "      <td>27.144900</td>\n",
              "      <td>11.340400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>2.652000</td>\n",
              "      <td>2.458986</td>\n",
              "      <td>29.630300</td>\n",
              "      <td>14.569900</td>\n",
              "      <td>27.148700</td>\n",
              "      <td>27.186500</td>\n",
              "      <td>11.449900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>2.653500</td>\n",
              "      <td>2.458302</td>\n",
              "      <td>29.954700</td>\n",
              "      <td>14.816500</td>\n",
              "      <td>27.244000</td>\n",
              "      <td>27.260700</td>\n",
              "      <td>11.621900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>2.685800</td>\n",
              "      <td>2.448932</td>\n",
              "      <td>29.745500</td>\n",
              "      <td>14.846900</td>\n",
              "      <td>27.464300</td>\n",
              "      <td>27.518900</td>\n",
              "      <td>11.498200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>2.562300</td>\n",
              "      <td>2.448724</td>\n",
              "      <td>30.426600</td>\n",
              "      <td>15.323700</td>\n",
              "      <td>28.007900</td>\n",
              "      <td>28.010300</td>\n",
              "      <td>11.505300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>2.611500</td>\n",
              "      <td>2.446198</td>\n",
              "      <td>30.263600</td>\n",
              "      <td>15.234800</td>\n",
              "      <td>27.829400</td>\n",
              "      <td>27.817200</td>\n",
              "      <td>11.225000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>2.565200</td>\n",
              "      <td>2.443630</td>\n",
              "      <td>30.083600</td>\n",
              "      <td>15.173600</td>\n",
              "      <td>27.810500</td>\n",
              "      <td>27.773700</td>\n",
              "      <td>11.167300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>2.647200</td>\n",
              "      <td>2.437647</td>\n",
              "      <td>30.531000</td>\n",
              "      <td>15.321300</td>\n",
              "      <td>28.108200</td>\n",
              "      <td>28.165200</td>\n",
              "      <td>11.686700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>2.677500</td>\n",
              "      <td>2.429845</td>\n",
              "      <td>30.108400</td>\n",
              "      <td>15.039100</td>\n",
              "      <td>27.697700</td>\n",
              "      <td>27.697600</td>\n",
              "      <td>11.802100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>2.626300</td>\n",
              "      <td>2.424139</td>\n",
              "      <td>30.002600</td>\n",
              "      <td>14.654400</td>\n",
              "      <td>27.507900</td>\n",
              "      <td>27.553200</td>\n",
              "      <td>11.851600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>2.642200</td>\n",
              "      <td>2.429283</td>\n",
              "      <td>29.765100</td>\n",
              "      <td>14.344000</td>\n",
              "      <td>27.313800</td>\n",
              "      <td>27.346100</td>\n",
              "      <td>11.808000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>2.601200</td>\n",
              "      <td>2.439487</td>\n",
              "      <td>29.485200</td>\n",
              "      <td>14.367300</td>\n",
              "      <td>27.218900</td>\n",
              "      <td>27.293100</td>\n",
              "      <td>11.637200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>2.620400</td>\n",
              "      <td>2.449188</td>\n",
              "      <td>29.102600</td>\n",
              "      <td>14.219800</td>\n",
              "      <td>27.112500</td>\n",
              "      <td>27.139200</td>\n",
              "      <td>11.447600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>2.667700</td>\n",
              "      <td>2.451101</td>\n",
              "      <td>29.293300</td>\n",
              "      <td>14.286900</td>\n",
              "      <td>27.265700</td>\n",
              "      <td>27.284600</td>\n",
              "      <td>11.341600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>2.699300</td>\n",
              "      <td>2.451264</td>\n",
              "      <td>29.344400</td>\n",
              "      <td>14.342400</td>\n",
              "      <td>27.322400</td>\n",
              "      <td>27.334000</td>\n",
              "      <td>11.346300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='101' max='2132' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 101/2132 00:52 < 18:02, 1.88 it/s, Epoch 0.05/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</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='214' max='107' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [107/107 05:07]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2132, training_loss=2.680229229953902, metrics={'train_runtime': 3409.3796, 'train_samples_per_second': 5.002, 'train_steps_per_second': 0.625, 'total_flos': 1.038516786561024e+16, 'train_loss': 2.680229229953902, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model()"
      ],
      "metadata": {
        "id": "dFrdgkzoDYPI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Load model from Google drive"
      ],
      "metadata": {
        "id": "nGSFm5emDfvG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"t5-base-medium-title-generation/checkpoint-2000\"\n",
        "model_dir = f\"./drive/MyDrive/Colab Notebooks/NLP_exploration/exploring_T5/Models/{model_name}\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_dir)\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)\n",
        "\n",
        "max_input_length = 512"
      ],
      "metadata": {
        "id": "OmKwm6KmDYME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"\"\n",
        "Many financial institutions started building conversational AI, prior to the Covid19\n",
        "pandemic, as part of a digital transformation initiative. These initial solutions\n",
        "were high profile, highly personalized virtual assistants — like the Erica chatbot\n",
        "from Bank of America. As the pandemic hit, the need changed as contact centers were\n",
        "under increased pressures. As Cathal McGloin of ServisBOT explains in “how it started,\n",
        "and how it is going,” financial institutions were looking for ways to automate\n",
        "solutions to help get back to “normal” levels of customer service. This resulted\n",
        "in a change from the “future of conversational AI” to a real tactical assistant\n",
        "that can help in customer service. Haritha Dev of Wells Fargo, saw a similar trend.\n",
        "Banks were originally looking to conversational AI as part of digital transformation\n",
        "to keep up with the times. However, with the pandemic, it has been more about\n",
        "customer retention and customer satisfaction. In addition, new use cases came about\n",
        "as a result of Covid-19 that accelerated adoption of conversational AI. As Vinita\n",
        "Kumar of Deloitte points out, banks were dealing with an influx of calls about new\n",
        "concerns, like questions around the Paycheck Protection Program (PPP) loans. This\n",
        "resulted in an increase in volume, without enough agents to assist customers, and\n",
        "tipped the scale to incorporate conversational AI. When choosing initial use cases\n",
        "to support, financial institutions often start with high volume, low complexity\n",
        "tasks. For example, password resets, checking account balances, or checking the\n",
        "status of a transaction, as Vinita points out. From there, the use cases can evolve\n",
        "as the banks get more mature in developing conversational AI, and as the customers\n",
        "become more engaged with the solutions. Cathal indicates another good way for banks\n",
        "to start is looking at use cases that are a pain point, and also do not require a\n",
        "lot of IT support. Some financial institutions may have a multi-year technology\n",
        "roadmap, which can make it harder to get a new service started. A simple chatbot\n",
        "for document collection in an onboarding process can result in high engagement,\n",
        "and a high return on investment. For example, Cathal has a banking customer that\n",
        "implemented a chatbot to capture a driver’s license to be used in the verification\n",
        "process of adding an additional user to an account — it has over 85% engagement\n",
        "with high satisfaction. An interesting use case Haritha discovered involved\n",
        "educating customers on financial matters. People feel more comfortable asking a\n",
        "chatbot what might be considered a “dumb” question, as the chatbot is less judgmental.\n",
        "Users can be more ambiguous with their questions as well, not knowing the right\n",
        "words to use, as chatbot can help narrow things down.\n",
        "\"\"\"\n",
        "\n",
        "inputs = [\"summarize: \" + text]\n",
        "\n",
        "inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors=\"pt\")\n",
        "output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)\n",
        "decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]\n",
        "predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]\n",
        "\n",
        "print(predicted_title)\n",
        "# Conversational AI: The Future of Customer Service"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o85EoAb2DYJI",
        "outputId": "76aa255d-8760-4e2c-b810-d0d3e4e9e463"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversational AI: The Future of Customer Service\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing above model output with base model without fine-tuned on summarization"
      ],
      "metadata": {
        "id": "HTnFhwr-oXLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "_HEk5ihRo1pd",
        "outputId": "b9b21422-1f57-4d87-ee7b-add23cbba999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'t5-base'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_tokenizer = AutoTokenizer.from_pretrained(\"t5-base\")\n",
        "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-base\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kF-gGnQoWmv",
        "outputId": "8ad931b3-0e4c-4162-dd6c-2c155b2511df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = [\"summarize: \" + text]\n",
        "\n",
        "inputs_v2 = base_tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors=\"pt\")\n",
        "output_v2 = base_model.generate(**inputs_v2, num_beams=8, do_sample=True, min_length=10, max_length=64)\n",
        "base_model_decoded_output = base_tokenizer.batch_decode(output_v2, skip_special_tokens=True)[0]\n",
        "base_model_predicted_title = nltk.sent_tokenize(base_model_decoded_output.strip())[0]\n",
        "\n",
        "print(base_model_predicted_title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuECnrBAp1eV",
        "outputId": "b38579c0-e7f9-4d0f-c2bd-7104abbdb47c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "many financial institutions started building conversational AI prior to the covid19 pandemic.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The base model simply reconstructed the beginning of the 1st line of input text."
      ],
      "metadata": {
        "id": "d2R0evhKqy55"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluate the model on test set"
      ],
      "metadata": {
        "id": "3RHLjW1GD3HD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# get test split\n",
        "test_tokenized_dataset = tokenized_datasets[\"test\"]\n",
        "\n",
        "# pad texts to the same length\n",
        "def preprocess_test(examples):\n",
        "  inputs = [prefix + text for text in examples[\"text\"]]\n",
        "  model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True,\n",
        "                           padding=\"max_length\")\n",
        "  return model_inputs\n",
        "\n",
        "test_tokenized_dataset = test_tokenized_dataset.map(preprocess_test, batched=True)\n",
        "\n",
        "# prepare dataloader\n",
        "test_tokenized_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\n",
        "dataloader = torch.utils.data.DataLoader(test_tokenized_dataset, batch_size=32)\n",
        "\n",
        "# generate text for each batch\n",
        "all_predictions = []\n",
        "for i,batch in enumerate(dataloader):\n",
        "  predictions = model.generate(**batch)\n",
        "  all_predictions.append(predictions)\n",
        "\n",
        "# flatten predictions\n",
        "all_predictions_flattened = [pred for preds in all_predictions for pred in preds]\n",
        "\n",
        "# tokenize and pad titles\n",
        "all_titles = tokenizer(test_tokenized_dataset[\"title\"], max_length=max_target_length,\n",
        "                       truncation=True, padding=\"max_length\")[\"input_ids\"]\n",
        "\n",
        "# compute metrics\n",
        "predictions_labels = [all_predictions_flattened, all_titles]\n",
        "compute_metrics(predictions_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190,
          "referenced_widgets": [
            "e9367e577cf64a29a458aebabfaf1bdc",
            "0666a05f8c0a44ada099fe2ca9f28525",
            "17127386c4a64650b9cb493f73c27a0f",
            "35adb75d5e7e4e6385c07143d53961a3",
            "621fee61d933492186aadc03e3dbbed5",
            "c105e75ba18f40c0976edd0fe0cc373f",
            "61063d3347804084b1e90ab7ca4a02ff",
            "2e2388c4877a4c5288243aa6f0e48abf",
            "548fc021defe48eb806281aacc348254",
            "89c72fd26b98403fa52fa2ee18738fe2",
            "e340d47dc12c4c86b6e8f87332714bbd"
          ]
        },
        "id": "HH3Ha4waD2T8",
        "outputId": "c39951a1-8a06-420b-c301-37adde8c81d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/851 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9367e577cf64a29a458aebabfaf1bdc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': 43.3828,\n",
              " 'rouge2': 30.9557,\n",
              " 'rougeL': 41.4247,\n",
              " 'rougeLsum': 41.4544,\n",
              " 'gen_len': 11.7368}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### References\n",
        "* [Original blog post on fine-tuning T5 for summarization](https://medium.com/nlplanet/a-full-guide-to-finetuning-t5-for-text2text-and-building-a-demo-with-streamlit-c72009631887)\n",
        "\n",
        "* [Medium articles Kaggle dataset](https://www.kaggle.com/datasets/fabiochiusano/medium-articles)\n",
        "\n",
        "* [Data collators - Huggingface](https://youtu.be/-RPeakdlHYo)\n"
      ],
      "metadata": {
        "id": "pvzJwz9mucxN"
      }
    }
  ]
}