FLAN_T5_Finetuning_QA_Yahoo_Data.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# FLAN-T5 Finetuning for QA on Yahoo Data\n",
        "\n",
        "Code inspired from Taughdata tutorial\n",
        "\n",
        "https://www.toughdata.net/blog/post/finetune-flan-t5-question-answer-quora-dataset"
      ],
      "metadata": {
        "id": "eyiZvex3MHe5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prerequisites"
      ],
      "metadata": {
        "id": "eJ-_-poaMPIy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "pip install nltk\n",
        "pip install datasets\n",
        "pip install transformers[torch]\n",
        "pip install tokenizers\n",
        "pip install evaluate\n",
        "pip install rouge_score\n",
        "pip install sentencepiece\n",
        "pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OnBUYgEhMOUq",
        "outputId": "a19cb92d-34ce-47f2-ef3c-d4b30bae2671"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import the libraries"
      ],
      "metadata": {
        "id": "-6onZgq8OfGk"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-1GjAUfpCUOm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "import evaluate\n",
        "import numpy as np\n",
        "from datasets import load_dataset\n",
        "from transformers import T5Tokenizer, DataCollatorForSeq2Seq\n",
        "from transformers import T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer"
      ],
      "metadata": {
        "id": "r5kWGZd2Oen6"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Loading"
      ],
      "metadata": {
        "id": "ssE-wFL1M8UX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset"
      ],
      "metadata": {
        "id": "1fm5svl5M2hZ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Acquire the training data from Hugging Face\n",
        "yahoo_answers_qa = load_dataset(\"yahoo_answers_qa\")\n",
        "\n",
        "yahoo_answers_qa = yahoo_answers_qa[\"train\"].train_test_split(test_size=0.3)"
      ],
      "metadata": {
        "id": "7w-r91o_NAcx"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the length of the data and its structure\n",
        "yahoo_answers_qa"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQOdjH-1NGj-",
        "outputId": "0306b077-b4e5-4c34-aeb1-552dd570a262"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'question', 'answer', 'nbestanswers', 'main_category'],\n",
              "        num_rows: 61153\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'question', 'answer', 'nbestanswers', 'main_category'],\n",
              "        num_rows: 26209\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"# Take a subset of the original dataset\n",
        "SAMPLE_SIZE = 3000\n",
        "\n",
        "subset_yahoo_answers_qa = yahoo_answers_qa.select(range(SAMPLE_SIZE))\n",
        "\n",
        "subset_yahoo_answers_qa\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "IEnbokgn_9ix",
        "outputId": "21253be6-5c16-48fc-9e25-0e073e1875f8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'# Take a subset of the original dataset\\nSAMPLE_SIZE = 3000\\n\\nsubset_yahoo_answers_qa = yahoo_answers_qa.select(range(SAMPLE_SIZE))\\n\\nsubset_yahoo_answers_qa'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the tokenizer, model, and data collator\n",
        "MODEL_NAME = \"google/flan-t5-base\"\n",
        "tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)\n",
        "model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296,
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        "id": "yIZmX8wY3jQ_",
        "outputId": "43b2052a-46b9-43a5-e94c-e789c4e519f7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
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        {
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "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 thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8d5yGMng3jO1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We prefix our tasks with \"answer the question\"\n",
        "prefix = \"Please answer this question: \"\n",
        "\n",
        "# Define the preprocessing function\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    \"\"\"Add prefix to the sentences, tokenize the text, and set the labels\"\"\"\n",
        "    # The \"inputs\" are the tokenized answer:\n",
        "    inputs = [prefix + doc for doc in examples[\"question\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=128, truncation=True)\n",
        "\n",
        "    # The \"labels\" are the tokenized outputs:\n",
        "    labels = tokenizer(text_target=examples[\"answer\"], max_length=512, truncation=True)\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "metadata": {
        "id": "a57c-VOf3jMh"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Map the preprocessing function across our dataset\n",
        "tokenized_dataset = yahoo_answers_qa.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
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        "outputId": "c7b4e372-f919-4de5-d46a-b26bee5b9596"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/61153 [00:00<?, ? examples/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5.py:303: UserWarning: This sequence already has </s>. In future versions this behavior may lead to duplicated eos tokens being added.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/26209 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98575b3c15e74e80a260eda598889bce"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ddaptRxF3jIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up Rouge score for evaluation\n",
        "nltk.download(\"punkt\", quiet=True)\n",
        "metric = evaluate.load(\"rouge\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6c8332fd750346c18bbdb917499ad65d",
            "d02c12960ad4482e8dff4974dfb9996c",
            "354010ccf2104989834cf3253896bacb",
            "efd3c5432025449398ada4dd3f0f388c",
            "85ee16bd3bce45c58408d8cb4278dee6",
            "3ded731bb3a749e197cc4113c4ca2b70",
            "8d5581c852ee42a7b701ec33f77f8295",
            "b1692c25dea34f77bcdaa1767baa2d5b",
            "c0a1aac01a434c11b303b30853c6ae44",
            "4396da2ca9c8439dae2c0422420f3fbb",
            "754fef29965f44b3833473bae6c24221"
          ]
        },
        "id": "SpqpqEEO3jGR",
        "outputId": "05a4229e-1078-418b-ac46-555ab561a022"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c8332fd750346c18bbdb917499ad65d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Em-PLkB63jEG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "\n",
        "    # decode preds and labels\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    # rougeLSum expects newline after each sentence\n",
        "    decoded_preds = [\"\\n\".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
        "    decoded_labels = [\"\\n\".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]\n",
        "\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "\n",
        "    return result"
      ],
      "metadata": {
        "id": "UOi8mqtk3jB8"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "51TlTbC-3jAM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IVBatkaj_PQ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Global Parameters\n",
        "L_RATE = 3e-4\n",
        "BATCH_SIZE = 8\n",
        "PER_DEVICE_EVAL_BATCH = 4\n",
        "WEIGHT_DECAY = 0.01\n",
        "SAVE_TOTAL_LIM = 3\n",
        "NUM_EPOCHS = 3\n",
        "\n",
        "# Set up training arguments\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=L_RATE,\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    per_device_eval_batch_size=PER_DEVICE_EVAL_BATCH,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    save_total_limit=SAVE_TOTAL_LIM,\n",
        "    num_train_epochs=NUM_EPOCHS,\n",
        "    predict_with_generate=True,\n",
        "    push_to_hub=False\n",
        ")"
      ],
      "metadata": {
        "id": "JPcXzlHb3i-H"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2I-pI3MD3i8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up trainer\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "Q8oP38pE3i6M"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oH6COIoM3i4H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Trigger the model training\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "K5vxWKX23i2F",
        "outputId": "9ae071ee-1a40-44b4-9ea9-b1b70c65d948"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='22935' max='22935' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [22935/22935 3:58:02, Epoch 3/3]\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",
              "      <th>Rouge1</th>\n",
              "      <th>Rouge2</th>\n",
              "      <th>Rougel</th>\n",
              "      <th>Rougelsum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.348200</td>\n",
              "      <td>3.157366</td>\n",
              "      <td>0.163463</td>\n",
              "      <td>0.029779</td>\n",
              "      <td>0.132157</td>\n",
              "      <td>0.145005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.148500</td>\n",
              "      <td>3.116664</td>\n",
              "      <td>0.166698</td>\n",
              "      <td>0.030155</td>\n",
              "      <td>0.133557</td>\n",
              "      <td>0.148603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.002400</td>\n",
              "      <td>3.112338</td>\n",
              "      <td>0.171596</td>\n",
              "      <td>0.031353</td>\n",
              "      <td>0.136349</td>\n",
              "      <td>0.152874</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1273: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1273: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1273: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=22935, training_loss=3.185420190126308, metrics={'train_runtime': 14285.5987, 'train_samples_per_second': 12.842, 'train_steps_per_second': 1.605, 'total_flos': 7040576129034240.0, 'train_loss': 3.185420190126308, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "fILXvACL6h_D"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r results.zip results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jVVqWOy3iz7",
        "outputId": "1f9c4929-e30a-4c47-89b2-0b0d79e65aa9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: results/ (stored 0%)\n",
            "  adding: results/runs/ (stored 0%)\n",
            "  adding: results/runs/Nov02_21-54-44_54c8c32c6436/ (stored 0%)\n",
            "  adding: results/runs/Nov02_21-54-44_54c8c32c6436/events.out.tfevents.1698962106.54c8c32c6436.631.0 (deflated 63%)\n",
            "  adding: results/checkpoint-21500/ (stored 0%)\n",
            "  adding: results/checkpoint-21500/special_tokens_map.json (deflated 85%)\n",
            "  adding: results/checkpoint-21500/model.safetensors (deflated 7%)\n",
            "  adding: results/checkpoint-21500/trainer_state.json (deflated 79%)\n",
            "  adding: results/checkpoint-21500/rng_state.pth (deflated 25%)\n",
            "  adding: results/checkpoint-21500/training_args.bin (deflated 51%)\n",
            "  adding: results/checkpoint-21500/config.json (deflated 62%)\n",
            "  adding: results/checkpoint-21500/scheduler.pt (deflated 55%)\n",
            "  adding: results/checkpoint-21500/optimizer.pt (deflated 12%)\n",
            "  adding: results/checkpoint-21500/generation_config.json (deflated 29%)\n",
            "  adding: results/checkpoint-21500/spiece.model (deflated 48%)\n",
            "  adding: results/checkpoint-21500/added_tokens.json (deflated 83%)\n",
            "  adding: results/checkpoint-21500/tokenizer_config.json (deflated 95%)\n",
            "  adding: results/checkpoint-22000/ (stored 0%)\n",
            "  adding: results/checkpoint-22000/special_tokens_map.json (deflated 85%)\n",
            "  adding: results/checkpoint-22000/model.safetensors (deflated 7%)\n",
            "  adding: results/checkpoint-22000/trainer_state.json (deflated 79%)\n",
            "  adding: results/checkpoint-22000/rng_state.pth (deflated 25%)\n",
            "  adding: results/checkpoint-22000/training_args.bin (deflated 51%)\n",
            "  adding: results/checkpoint-22000/config.json (deflated 62%)\n",
            "  adding: results/checkpoint-22000/scheduler.pt (deflated 55%)\n",
            "  adding: results/checkpoint-22000/optimizer.pt (deflated 12%)\n",
            "  adding: results/checkpoint-22000/generation_config.json (deflated 29%)\n",
            "  adding: results/checkpoint-22000/spiece.model (deflated 48%)\n",
            "  adding: results/checkpoint-22000/added_tokens.json (deflated 83%)\n",
            "  adding: results/checkpoint-22000/tokenizer_config.json (deflated 95%)\n",
            "  adding: results/checkpoint-22500/ (stored 0%)\n",
            "  adding: results/checkpoint-22500/special_tokens_map.json (deflated 85%)\n",
            "  adding: results/checkpoint-22500/model.safetensors (deflated 7%)\n",
            "  adding: results/checkpoint-22500/trainer_state.json (deflated 79%)\n",
            "  adding: results/checkpoint-22500/rng_state.pth (deflated 25%)\n",
            "  adding: results/checkpoint-22500/training_args.bin (deflated 51%)\n",
            "  adding: results/checkpoint-22500/config.json (deflated 62%)\n",
            "  adding: results/checkpoint-22500/scheduler.pt (deflated 55%)\n",
            "  adding: results/checkpoint-22500/optimizer.pt (deflated 12%)\n",
            "  adding: results/checkpoint-22500/generation_config.json (deflated 29%)\n",
            "  adding: results/checkpoint-22500/spiece.model (deflated 48%)\n",
            "  adding: results/checkpoint-22500/added_tokens.json (deflated 83%)\n",
            "  adding: results/checkpoint-22500/tokenizer_config.json (deflated 95%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "819gIU0L3ixf",
        "outputId": "0e2b83e4-6777-42a4-8f95-085b7e756f40"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "checkpoint-21500  checkpoint-22000  checkpoint-22500  runs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run in inference mode"
      ],
      "metadata": {
        "id": "0TIfRNStXU3j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
        "\n",
        "last_checkpoint = \"./results/checkpoint-22500\"\n",
        "\n",
        "finetuned_model = T5ForConditionalGeneration.from_pretrained(last_checkpoint)\n",
        "tokenizer = T5Tokenizer.from_pretrained(last_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3V3oPRMJ3iqG",
        "outputId": "390a361f-dfe8-4282-c279-df8484c1b8d0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_question = \"What do you think about the benefit of Artificial Intelligence?\"\n",
        "inputs = \"Please answer to this question: \" + my_question\n",
        "\n",
        "inputs = tokenizer(inputs, return_tensors=\"pt\")"
      ],
      "metadata": {
        "id": "4H5F6wHhYG9Y"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = finetuned_model.generate(**inputs)\n",
        "answer = tokenizer.decode(outputs[0])\n",
        "\n",
        "print(\"Answer:\", fill(answer, width=80))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBZM5CZhYG7g",
        "outputId": "0d7086e4-d037-4175-ea31-c526442fa839"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer: <pad>I think it's a great way to get people to think about things. It'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mynCPM6AjDHd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aXVdsfVGcrUg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}