T5/06-Text-to-sql/T5_text_to_sql.ipynb

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            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Install necessary dependencies"
      ],
      "metadata": {
        "id": "YzmSA5DGIW8B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U\n",
        "!pip install -q -U datasets\n",
        "!pip install scipy\n",
        "!pip install ipywidgets\n",
        "!pip install wandb\n",
        "!pip install transformers\n",
        "!pip install torch\n",
        "!pip install sentencepiece\n",
        "!pip install tqdm\n",
        "!pip install rouge_score\n",
        "!pip install evaluate"
      ],
      "metadata": {
        "id": "DcooKpr2wtSs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7eba6bb-b81f-4e8a-e68e-532d4e3228a9"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.26.1-py3-none-any.whl (270 kB)\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.26.1\n",
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            "Installing collected packages: jedi\n",
            "Successfully installed jedi-0.19.1\n",
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            "Building wheels for collected packages: rouge_score\n",
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            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.1 responses-0.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import dependencies\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "VAEh0LzrIbZh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data libraries\n",
        "from datasets import load_dataset, Dataset, concatenate_datasets, list_metrics, load_metric\n",
        "\n",
        "# preprocessing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "import nltk\n",
        "from nltk.tokenize import sent_tokenize\n",
        "nltk.download(\"punkt\")\n",
        "\n",
        "# Model libraries\n",
        "import transformers\n",
        "import ast\n",
        "import torch\n",
        "import sklearn\n",
        "from transformers import (\n",
        "    T5Tokenizer, T5TokenizerFast, T5ForConditionalGeneration,\n",
        "    AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM,\n",
        "    DefaultDataCollator, T5ForQuestionAnswering, DataCollatorForSeq2Seq, pipeline, AutoModelForQuestionAnswering, BertTokenizer, BertForQuestionAnswering\n",
        ")\n",
        "\n",
        "# Metrics library\n",
        "import evaluate\n",
        "from evaluate import evaluator\n",
        "\n",
        "# Visualization library\n",
        "import wandb\n",
        "\n",
        "# Warning libraries\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Utility functions\n",
        "import t5_utils as utils\n"
      ],
      "metadata": {
        "id": "TCMxGGaswvxk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182,
          "referenced_widgets": [
            "55dfb19c789c42098a41d8cad09964d1",
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            "41258fe70f964b51b8a4cfc342977cdd",
            "784d3ff0c8dc445dba3cb1b734273875",
            "418066704deb4497950a2dca64b74c94",
            "c4bd4a50afc34758a21c18e1a6e31cd4",
            "bca28370551f4e65be75827e33917e0d",
            "189702beaedc46cda61f86ce7ade3688",
            "ea1dfb29070f4a92b7a217dfbe9363a2",
            "cafc10e4dbd4455387586f12ad7c9b02",
            "4c19da1d2bb344648cc57833994f14ed",
            "776e5aa35ab64462a95d01987293ec01",
            "c85f3e0a783647099af89e4ab6986a8a",
            "7a581840e0ad4b7a8a2c817a30b81118",
            "31e8409f3e4042dc9a342072ec525185",
            "ef27e392eb974b669dfe689e4d1f2b8f",
            "b4842feef5574089813bc4c6f25de1b2",
            "a49d122fe747450b88f4ea6f930dcf7d",
            "f8602265b1cd4ed3935a3b990abd89d6",
            "0d6d164d27b14c33ba02750ebe188d85",
            "b0d2c0de24e449a59434e43b8d263820",
            "a63ba60151ca4c05b3d622fc182f01e8"
          ]
        },
        "outputId": "436541b2-8b76-4a27-c237-6d6e4a9fe435"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55dfb19c789c42098a41d8cad09964d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Global variables"
      ],
      "metadata": {
        "id": "mAdt8dLbko9B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DATASET = \"Clinton/texttosqlv2_25000_v2\"\n",
        "PREFIX = \"instruction\"\n",
        "INPUT_COL = \"input\"\n",
        "OUTPUT_COL = \"output\"\n",
        "MODEL = \"google/flan-t5-base\"\n",
        "TRAIN_SPLIT = \"train[:10000]\"\n",
        "VALIDATION_SPLIT = \"train[10000:12500]\"\n",
        "TEST_SPLIT = \"train[22500:25000]\"\n",
        "\n",
        "\n",
        "# Training args\n",
        "\n",
        "MAX_STEPS = 1000\n",
        "PER_DEVICE_TRAIN_BATCH_SIZE = 8\n",
        "PER_DEVICE_EVAL_BATCH_SIZE = 8\n",
        "SAVE_STEPS = 100\n",
        "EVAL_STEPS = 100\n",
        "LOGGING_STEPS = 100\n",
        "CHECKPOINT_SAVED = 1000\n",
        "OPTIMIZER = \"adamw_torch\"\n",
        "LEARNING_RATE = 2.5e-5\n",
        "COLLATOR = \"seq2seq\" # seq2seq or default\n",
        "\n",
        "# for compute metrics\n",
        "TASK = \"text2text-generation\"\n",
        "METRICS = \"rouge\""
      ],
      "metadata": {
        "id": "jMaLyb3ckola"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "utils.metric = evaluate.load(METRICS)\n",
        "utils.tokenizer = T5Tokenizer.from_pretrained(MODEL)"
      ],
      "metadata": {
        "id": "rjQ9z8gB-dRU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
          "referenced_widgets": [
            "bbd8341e0e084f53b25cd5374e42a246",
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        },
        "outputId": "8d4a922c-00ec-4a43-facf-dd1611d52318"
      },
      "execution_count": 4,
      "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,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "725ab8755e594353802d55a22c540bdb"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53e3ee46f7c341978ca641293d134770"
            }
          },
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data gathering and preparation"
      ],
      "metadata": {
        "id": "yTF0HsQuIeNx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This function takes data from HuggingFace or local data, split ( train, test, validation, train[:slicing] ), input_col, target_col, prefix(column or text)"
      ],
      "metadata": {
        "id": "_XGdlELJIikh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = utils.prepare_data(\n",
        "                                DATASET,\n",
        "                                TRAIN_SPLIT,\n",
        "                                INPUT_COL,\n",
        "                                OUTPUT_COL,\n",
        "                                PREFIX\n",
        "                                )\n",
        "\n",
        "eval_data = utils.prepare_data(\n",
        "                               DATASET,\n",
        "                               VALIDATION_SPLIT,\n",
        "                               INPUT_COL,\n",
        "                               OUTPUT_COL,\n",
        "                               PREFIX\n",
        "                               )\n",
        "\n",
        "test_data = utils.prepare_data(\n",
        "                               DATASET,\n",
        "                               TEST_SPLIT,\n",
        "                               INPUT_COL,\n",
        "                               OUTPUT_COL,\n",
        "                               PREFIX\n",
        "                               )"
      ],
      "metadata": {
        "id": "JYtfw4CHwwV8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "2f686a3377f1494ca66d3415b0dc353c",
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            "33fe23d648cd4e24bd15126ceb7a8199",
            "b4eae0ca6fa443f0b70dcbc1f0f6e7b7",
            "c95715ef7abe47fc81005f1f8cebecc4"
          ]
        },
        "outputId": "305fa5dc-aa7b-4d46-eff7-6270d3bfd0de"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f686a3377f1494ca66d3415b0dc353c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/56.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9353202da7944ed0a0b51c0e0d552b5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf179254c2fd4230a636c620fe7dd5ce"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data_first_train = train_data[0][\"input_text\"]\n",
        "targer_data_first_train = train_data[0][\"target_text\"]\n",
        "print(f\"input_text : {input_data_first_train}\")\n",
        "print(\"--------------------------------\")\n",
        "print(f\"target_text : {targer_data_first_train}\")"
      ],
      "metadata": {
        "id": "9e89tid45QFA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a33bfd8-03d0-463a-ea4d-bf8a7f0beac2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_text : tell me the number of times he placed 4th . : CREATE TABLE table_204_780 (\n",
            "    id number,\n",
            "    \"year\" number,\n",
            "    \"competition\" text,\n",
            "    \"venue\" text,\n",
            "    \"position\" text,\n",
            "    \"notes\" text\n",
            ")\n",
            "--------------------------------\n",
            "target_text : SELECT COUNT(*) FROM table_204_780 WHERE \"position\" = 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data_first_eval = eval_data[0][\"input_text\"]\n",
        "targer_data_first_eval = eval_data[0][\"target_text\"]\n",
        "print(f\"input_text : {input_data_first_eval}\")\n",
        "print(\"--------------------------------\")\n",
        "print(f\"target_text : {targer_data_first_eval}\")"
      ],
      "metadata": {
        "id": "HJgHCcLD5RPo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa0e0d3b-4f02-4533-96dd-6068dd333e88"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_text : What is the lead percentage when the socialist is at 35.5%? : CREATE TABLE table_1183 (\n",
            "    \"Date Released\" text,\n",
            "    \"Institute\" text,\n",
            "    \"Socialist\" text,\n",
            "    \"Social Democratic\" text,\n",
            "    \"Green-Communist\" text,\n",
            "    \"Left Bloc\" text,\n",
            "    \"Peoples Party\" text,\n",
            "    \"Lead\" text\n",
            ")\n",
            "--------------------------------\n",
            "target_text : SELECT \"Lead\" FROM table_1183 WHERE \"Socialist\" = '35.5%'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data_first_test = test_data[0][\"input_text\"]\n",
        "targer_data_first_test = test_data[0][\"target_text\"]\n",
        "print(f\"input_text : {input_data_first_test}\")\n",
        "print(\"--------------------------------\")\n",
        "print(f\"target_text : {targer_data_first_test}\")"
      ],
      "metadata": {
        "id": "6Q2GZkgqmtaQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a66359a-6d66-4afa-a0b8-426033104db6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_text : how many times patient 004-65311 has been prescribed for 500 ml flex cont excel : sodium chloride 0.9 % iv soln until 2104? : CREATE TABLE lab (\n",
            "    labid number,\n",
            "    patientunitstayid number,\n",
            "    labname text,\n",
            "    labresult number,\n",
            "    labresulttime time\n",
            ")\n",
            "\n",
            "CREATE TABLE cost (\n",
            "    costid number,\n",
            "    uniquepid text,\n",
            "    patienthealthsystemstayid number,\n",
            "    eventtype text,\n",
            "    eventid number,\n",
            "    chargetime time,\n",
            "    cost number\n",
            ")\n",
            "\n",
            "CREATE TABLE medication (\n",
            "    medicationid number,\n",
            "    patientunitstayid number,\n",
            "    drugname text,\n",
            "    dosage text,\n",
            "    routeadmin text,\n",
            "    drugstarttime time,\n",
            "    drugstoptime time\n",
            ")\n",
            "\n",
            "CREATE TABLE treatment (\n",
            "    treatmentid number,\n",
            "    patientunitstayid number,\n",
            "    treatmentname text,\n",
            "    treatmenttime time\n",
            ")\n",
            "\n",
            "CREATE TABLE allergy (\n",
            "    allergyid number,\n",
            "    patientunitstayid number,\n",
            "    drugname text,\n",
            "    allergyname text,\n",
            "    allergytime time\n",
            ")\n",
            "\n",
            "CREATE TABLE microlab (\n",
            "    microlabid number,\n",
            "    patientunitstayid number,\n",
            "    culturesite text,\n",
            "    organism text,\n",
            "    culturetakentime time\n",
            ")\n",
            "\n",
            "CREATE TABLE patient (\n",
            "    uniquepid text,\n",
            "    patienthealthsystemstayid number,\n",
            "    patientunitstayid number,\n",
            "    gender text,\n",
            "    age text,\n",
            "    ethnicity text,\n",
            "    hospitalid number,\n",
            "    wardid number,\n",
            "    admissionheight number,\n",
            "    admissionweight number,\n",
            "    dischargeweight number,\n",
            "    hospitaladmittime time,\n",
            "    hospitaladmitsource text,\n",
            "    unitadmittime time,\n",
            "    unitdischargetime time,\n",
            "    hospitaldischargetime time,\n",
            "    hospitaldischargestatus text\n",
            ")\n",
            "\n",
            "CREATE TABLE vitalperiodic (\n",
            "    vitalperiodicid number,\n",
            "    patientunitstayid number,\n",
            "    temperature number,\n",
            "    sao2 number,\n",
            "    heartrate number,\n",
            "    respiration number,\n",
            "    systemicsystolic number,\n",
            "    systemicdiastolic number,\n",
            "    systemicmean number,\n",
            "    observationtime time\n",
            ")\n",
            "\n",
            "CREATE TABLE diagnosis (\n",
            "    diagnosisid number,\n",
            "    patientunitstayid number,\n",
            "    diagnosisname text,\n",
            "    diagnosistime time,\n",
            "    icd9code text\n",
            ")\n",
            "\n",
            "CREATE TABLE intakeoutput (\n",
            "    intakeoutputid number,\n",
            "    patientunitstayid number,\n",
            "    cellpath text,\n",
            "    celllabel text,\n",
            "    cellvaluenumeric number,\n",
            "    intakeoutputtime time\n",
            ")\n",
            "--------------------------------\n",
            "target_text : SELECT COUNT(*) FROM medication WHERE medication.patientunitstayid IN (SELECT patient.patientunitstayid FROM patient WHERE patient.patienthealthsystemstayid IN (SELECT patient.patienthealthsystemstayid FROM patient WHERE patient.uniquepid = '004-65311')) AND medication.drugname = '500 ml flex cont excel : sodium chloride 0.9 % iv soln' AND STRFTIME('%y', medication.drugstarttime) <= '2104'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_source_length, max_target_length = utils.identify_max_lengths(\n",
        "                                                                  train_data,\n",
        "                                                                  eval_data,\n",
        "                                                                  model = MODEL\n",
        "                                                                  )"
      ],
      "metadata": {
        "id": "54NjqYLerRpk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "bad38a2be81d46a78dac7b039dac71db",
            "2d00e6e380194230bc4abad30cbbee9a",
            "449343c3ff5c41798eae5d9542a159d3",
            "410d812846da4cfca601ee0265de8a2b",
            "cbff6b9011ca4ceab0b585b2228211c3",
            "436f94fe493a4374809d4221c1b70e2e",
            "5f0c0b3e339040758903a352cba3a372",
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            "b1e083e1bb774075827cafa5007e949f",
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            "a60074cc8661403fb4e76b051525fd1f",
            "8060544f77bc462792d5824431b55b6b",
            "93cd4f0732af45bb93ac809914e61202",
            "04346335f050476e9db4f69289480e3c",
            "496e0983f3fd472593067ff4cfdbd3ff",
            "3aebcadcea7647a8bfa94670986851f2",
            "4b85b999c66c400f9e8939cf5a36f1bd",
            "668973705f6c471eaeb9cc50b9f98cb3",
            "621a698cd4724a83909d417199650a03",
            "ad3d92f1351d471da57dff2d91b16073",
            "8e1e80b89405446797ad211e6c28fdc1",
            "3af14c9e88254ab087d459981b52820a"
          ]
        },
        "outputId": "32465dc3-8cd0-48ee-a72c-be2da85ddafe"
      },
      "execution_count": 9,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/12500 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bad38a2be81d46a78dac7b039dac71db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/12500 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8060544f77bc462792d5824431b55b6b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Input max length : {max_source_length}\")\n",
        "print(\"--------------------------------\")\n",
        "print(f\"Target max length : {max_target_length}\")\n"
      ],
      "metadata": {
        "id": "_rmWtBLdrb78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9939deb7-fe05-48fd-e012-64eca9f3faa2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input max length : 512\n",
            "--------------------------------\n",
            "Target max length : 512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_tokenized, eval_data_tokenized = utils.preprocessed_data(\n",
        "                                                                    train_data,\n",
        "                                                                    eval_data,\n",
        "                                                                    max_source_length,\n",
        "                                                                    max_target_length,\n",
        "                                                                    model = MODEL)\n"
      ],
      "metadata": {
        "id": "3YUfMTgTxC-c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307,
          "referenced_widgets": [
            "6534153c28fd41e2b0fd4f01953dd94f",
            "4299680993b3407eb2e03f18071e9792",
            "eae8ada211c845e1a5557b612a8ef52c",
            "d9e83959e0de4c8facd95202bcc17861",
            "e3a7ef1a51684004bb8c3513baa5f844",
            "19c1dfc95db943abaa1c0e09e0519e0d",
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            "f328a556bbda422893d6617d763e8214",
            "b2ab1150af9745e2903b2f4640b6352a",
            "f0b0a26ec1484caea2a0b183b68b5313",
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            "31c5d36296d24571a06f6d21e968bb8d",
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            "1b858888c0de4931899ddf97dbf62af3",
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            "dd1162ed72a4447a85f121f191bf68b0",
            "6f6f9a381dc04c679e9fdf9351b1e14f",
            "3959cb179efa42378ff2fdda821f76df"
          ]
        },
        "outputId": "af656ab7-6f91-4f33-93cf-8ad68e6e85a8"
      },
      "execution_count": 11,
      "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": "6534153c28fd41e2b0fd4f01953dd94f"
            }
          },
          "metadata": {}
        },
        {
          "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",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\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": [
              "Map:   0%|          | 0/2500 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31c5d36296d24571a06f6d21e968bb8d"
            }
          },
          "metadata": {}
        },
        {
          "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",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "yrSzkrbuJchg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Look at utility functions to get in-depth understanding of training function"
      ],
      "metadata": {
        "id": "vwqWfWP_JeMg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "utils.train_model(\n",
        "                  train_data_tokenized,\n",
        "                  train_data_tokenized,\n",
        "                  project = \"Finetunning-t5\",\n",
        "                  model = MODEL,\n",
        "                  base_model_name = \"T5\",\n",
        "                  max_steps = MAX_STEPS,\n",
        "                  per_device_train_batch_size = PER_DEVICE_TRAIN_BATCH_SIZE,\n",
        "                  per_device_eval_batch_size = PER_DEVICE_EVAL_BATCH_SIZE,\n",
        "                  save_steps = SAVE_STEPS,\n",
        "                  eval_steps = EVAL_STEPS,\n",
        "                  logging_steps = LOGGING_STEPS,\n",
        "                  checkpoint_saved = CHECKPOINT_SAVED,\n",
        "                  optimizer = OPTIMIZER,\n",
        "                  learning_rate = LEARNING_RATE,\n",
        "                  collator = COLLATOR\n",
        "                  )"
      ],
      "metadata": {
        "id": "j1Gv0_vvU3oU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "7ff05395b3284408a219587e3e85452c",
            "a3906dce3ef24686bb52f17e05c31935",
            "712930d5e9b64ad8a60a75a7b22ebcbd",
            "d7e8dfc99a6e41d3aa1203cb7594b30c",
            "58117de6c5be4bb4a5e5c62d21ad17be",
            "8073ab53730c456cb0cb94b7c529eb4b",
            "cec3ff14b2c64e7292df0c9f8416211d",
            "3fe6e289f50f43e3af63cca022468db3",
            "7a3acafff5334d108b8c87017e7d1a43",
            "66bed8dc24134cc590584d8343c1adfe",
            "1122c0b4ba054b7f9abf3079dd491f42",
            "2dc786417e9c45579ea7c0aa7275f3e3",
            "8fce0d80b99e4f409cc9748825cc283e",
            "2d1e4ceb8e1f4d2d909050f00f04b127",
            "8675702a2f234bf0a8d30daa5a6eed79",
            "7a308a7683f44bbdbd5c923a6a9ee795",
            "97f7b66117b54af5ae7aee7f7dfff00b",
            "6e996de24f5f4477bbd619a56b43fa65",
            "3442234abcaa4684bd38dc6f00f7238c",
            "eed46f8a9eac43db81d396f94f19d6f4",
            "308bb1d0f86a4123baff680089af1fb3",
            "a6a052a6888b44df83044f5e8427b4f8",
            "f704c5db7a204b1f8eb42e743ce42f7c",
            "bc7d84f2bd444917986aff0dc6ff533d",
            "7aa9487850ae47a99d5b2e4b334a3343",
            "2088a25792ea43d596af0947d9598eb5",
            "05f01101f9cf4a74bf2b42949e2bec37",
            "96475de786e64efc8748e83cbd6ae76c",
            "efe571d38e874e738d027acb89fbdf34",
            "9111f347240046b8becf59f4099f43b6",
            "8105b17a7cfa48a898a872a178b41189",
            "65357a74b4784604b706af515b4183c2",
            "b3a3a03641894808b857ff9b625f3711"
          ]
        },
        "outputId": "106b6adb-55fa-4d59-85d1-b47f9cbea86b"
      },
      "execution_count": 12,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ff05395b3284408a219587e3e85452c"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2dc786417e9c45579ea7c0aa7275f3e3"
            }
          },
          "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": "f704c5db7a204b1f8eb42e743ce42f7c"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
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              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240131_182110-zl0f1he4</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/huzaifa-ali/huggingface/runs/zl0f1he4' target=\"_blank\">T5-Finetunning-t5-2024-01-31-18-21</a></strong> to <a href='https://wandb.ai/huzaifa-ali/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/huzaifa-ali/huggingface' target=\"_blank\">https://wandb.ai/huzaifa-ali/huggingface</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/huzaifa-ali/huggingface/runs/zl0f1he4' target=\"_blank\">https://wandb.ai/huzaifa-ali/huggingface/runs/zl0f1he4</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='701' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 701/1000 2:34:54 < 1:06:15, 0.08 it/s, Epoch 0.56/1]\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>1.595300</td>\n",
              "      <td>0.872786</td>\n",
              "      <td>74.140400</td>\n",
              "      <td>56.376300</td>\n",
              "      <td>72.877800</td>\n",
              "      <td>72.818200</td>\n",
              "      <td>512.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.000900</td>\n",
              "      <td>0.715405</td>\n",
              "      <td>78.333100</td>\n",
              "      <td>63.160800</td>\n",
              "      <td>77.217000</td>\n",
              "      <td>77.186800</td>\n",
              "      <td>512.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.894300</td>\n",
              "      <td>0.637364</td>\n",
              "      <td>80.547600</td>\n",
              "      <td>66.696800</td>\n",
              "      <td>79.490400</td>\n",
              "      <td>79.452300</td>\n",
              "      <td>512.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.883300</td>\n",
              "      <td>0.586240</td>\n",
              "      <td>81.555400</td>\n",
              "      <td>68.326400</td>\n",
              "      <td>80.521000</td>\n",
              "      <td>80.482300</td>\n",
              "      <td>512.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.809900</td>\n",
              "      <td>0.552417</td>\n",
              "      <td>82.043000</td>\n",
              "      <td>69.216000</td>\n",
              "      <td>81.075900</td>\n",
              "      <td>81.038400</td>\n",
              "      <td>512.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.785100</td>\n",
              "      <td>0.527347</td>\n",
              "      <td>82.529900</td>\n",
              "      <td>70.057000</td>\n",
              "      <td>81.587900</td>\n",
              "      <td>81.558200</td>\n",
              "      <td>512.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1250' max='1250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1250/1250 05:22]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-12-9ed16be41bff>\", line 1, in <cell line: 1>\n",
            "    utils.train_model(\n",
            "  File \"/content/t5_utils.py\", line 225, in train_model\n",
            "    trainer.train()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\", line 1555, in train\n",
            "    return inner_training_loop(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\", line 1922, in _inner_training_loop\n",
            "    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\", line 2271, in _maybe_log_save_evaluate\n",
            "    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\", line 3011, in evaluate\n",
            "    output = eval_loop(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\", line 3304, in evaluation_loop\n",
            "    metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))\n",
            "  File \"/content/t5_utils.py\", line 145, in compute_metrics\n",
            "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\", line 3706, in batch_decode\n",
            "    return [\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\", line 3707, in <listcomp>\n",
            "    self.decode(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\", line 3746, in decode\n",
            "    return self._decode(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py\", line 1012, in _decode\n",
            "    if skip_special_tokens and token in self.all_special_ids:\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\", line 1349, in all_special_ids\n",
            "    all_ids = self.convert_tokens_to_ids(all_toks)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py\", line 649, in convert_tokens_to_ids\n",
            "    ids.append(self._convert_token_to_id_with_added_voc(token))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py\", line 652, in _convert_token_to_id_with_added_voc\n",
            "    def _convert_token_to_id_with_added_voc(self, token):\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n",
            "    module = getmodule(object, filename)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 878, in getmodule\n",
            "    os.path.realpath(f)] = module.__name__\n",
            "  File \"/usr/lib/python3.10/posixpath.py\", line 396, in realpath\n",
            "    path, ok = _joinrealpath(filename[:0], filename, strict, {})\n",
            "  File \"/usr/lib/python3.10/posixpath.py\", line 431, in _joinrealpath\n",
            "    st = os.lstat(newpath)\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "object of type 'NoneType' has no len()",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
            "\u001b[0;32m<ipython-input-12-9ed16be41bff>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m utils.train_model(\n\u001b[0m\u001b[1;32m      2\u001b[0m                   \u001b[0mtrain_data_tokenized\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                   \u001b[0mtrain_data_tokenized\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/t5_utils.py\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(train_data, eval_data, project, base_model_name, model, max_steps, per_device_train_batch_size, per_device_eval_batch_size, save_steps, eval_steps, logging_steps, checkpoint_saved, optimizer, learning_rate, collator)\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m   \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1554\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1555\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1556\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1921\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1922\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_log_save_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1923\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_maybe_log_save_evaluate\u001b[0;34m(self, tr_loss, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2270\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2271\u001b[0;31m                 \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mignore_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2272\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report_to_hp_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, eval_dataset, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m   3010\u001b[0m         \u001b[0meval_loop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprediction_loop\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_legacy_prediction_loop\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluation_loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3011\u001b[0;31m         output = eval_loop(\n\u001b[0m\u001b[1;32m   3012\u001b[0m             \u001b[0meval_dataloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mevaluation_loop\u001b[0;34m(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m   3303\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3304\u001b[0;31m                 \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvalPrediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_preds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3305\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/t5_utils.py\u001b[0m in \u001b[0;36mcompute_metrics\u001b[0;34m(eval_preds)\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m     \u001b[0mdecoded_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_special_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m     \u001b[0;31m# Replace -100 in the labels as we can't decode them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mbatch_decode\u001b[0;34m(self, sequences, skip_special_tokens, clean_up_tokenization_spaces, **kwargs)\u001b[0m\n\u001b[1;32m   3705\u001b[0m         \"\"\"\n\u001b[0;32m-> 3706\u001b[0;31m         return [\n\u001b[0m\u001b[1;32m   3707\u001b[0m             self.decode(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   3706\u001b[0m         return [\n\u001b[0;32m-> 3707\u001b[0;31m             self.decode(\n\u001b[0m\u001b[1;32m   3708\u001b[0m                 \u001b[0mseq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, token_ids, skip_special_tokens, clean_up_tokenization_spaces, **kwargs)\u001b[0m\n\u001b[1;32m   3745\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3746\u001b[0;31m         return self._decode(\n\u001b[0m\u001b[1;32m   3747\u001b[0m             \u001b[0mtoken_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py\u001b[0m in \u001b[0;36m_decode\u001b[0;34m(self, token_ids, skip_special_tokens, clean_up_tokenization_spaces, spaces_between_special_tokens, **kwargs)\u001b[0m\n\u001b[1;32m   1011\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfiltered_tokens\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1012\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mskip_special_tokens\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_special_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mall_special_ids\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1348\u001b[0m         \u001b[0mall_toks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_special_tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1349\u001b[0;31m         \u001b[0mall_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_tokens_to_ids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_toks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1350\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mall_ids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py\u001b[0m in \u001b[0;36mconvert_tokens_to_ids\u001b[0;34m(self, tokens)\u001b[0m\n\u001b[1;32m    648\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtokens\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\u001b[0;31m             \u001b[0mids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_token_to_id_with_added_voc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    650\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py\u001b[0m in \u001b[0;36m_convert_token_to_id_with_added_voc\u001b[0;34m(self, token)\u001b[0m\n\u001b[1;32m    651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 652\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m_convert_token_to_id_with_added_voc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    653\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2098\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2099\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2099\u001b[0m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2101\u001b[0;31m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0m\u001b[1;32m   2102\u001b[0m                                             value, tb, tb_offset=tb_offset)\n\u001b[1;32m   2103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1365\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m         return FormattedTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1368\u001b[0m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[1;32m   1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1265\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose_modes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1266\u001b[0m             \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m             return VerboseTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1268\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1269\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;34m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0m\u001b[1;32m   1125\u001b[0m                                                                tb_offset)\n\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1082\u001b[0;31m         \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m         \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m    380\u001b[0m     \u001b[0;31m# first frame (from in to out) that looks different.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m     \u001b[0;31m# Select filename, lineno, func_name to track frames with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate Model"
      ],
      "metadata": {
        "id": "krM3s0Ku0f6m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference"
      ],
      "metadata": {
        "id": "Sj_5ZUHVJnjI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This function takes 3 argumnets : task(summarization, classification, etc), text(data), model(pretrained, fine tuned)\n"
      ],
      "metadata": {
        "id": "kB0eQx27JpRw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "utils.inference(\"What is Years, when Nationality is United States, and when Position is PG / SG?\",\n",
        "                \"CREATE TABLE table_name_83 ( years VARCHAR, nationality VARCHAR, position VARCHAR )\",\n",
        "                fined_tuned_path = \"/content/T5-Finetunning-t5/checkpoint-100\")"
      ],
      "metadata": {
        "id": "KPBtiJZzGNOj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "7f39a196-7041-42d3-8e9a-880704cbbec9"
      },
      "execution_count": 13,
      "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'SELECT YEARS FROM table_name_83 WHERE nationality = \"United States\" AND position = \"PG/SG\"'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "utils.inference(\"What owner or owners have an operational description?\",\n",
        "                \"CREATE TABLE table_name_34 ( owner_s_ VARCHAR, description VARCHAR )\",\n",
        "                fined_tuned_path = \"/content/T5-Finetunning-t5/checkpoint-100\")"
      ],
      "metadata": {
        "id": "6h_fboK0zIkx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "e21a6867-eee9-4bea-c06b-8c93522ba9ac"
      },
      "execution_count": 14,
      "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'SELECT owner_s__description FROM table_name_34 WHERE owner_s__description = \"operational\"'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = T5Tokenizer.from_pretrained(\"/content/T5-Finetunning-t5/checkpoint-600\")\n",
        "model = T5ForConditionalGeneration.from_pretrained(\"/content/T5-Finetunning-t5/checkpoint-600\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GWjZcGeWjJC",
        "outputId": "4bb2d9b2-2851-46d8-d6b4-d39c481639b5"
      },
      "execution_count": 16,
      "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": [
        "tokenizer.push_to_hub(\"HuzaifaHPC/T5_Text_to_SQL\")\n",
        "model.push_to_hub(\"HuzaifaHPC/T5_Text_to_SQL\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "e781fdaeca734fe19a32ddd81d5da83e",
            "fa7d24427b8a442499e48895e7f75307",
            "31736d9b548542a58ca537754a0d2109",
            "454bdf7f1f5f465bb81a487f7632de18",
            "75405b8438414ff09afa0df9ae6f9440",
            "bb0aa51ff86f41fe9f65326539ea1f3c",
            "addfe0cf26bf4941bae9bf7a52540ad2",
            "f68f4e7044514151937cb469364e9831",
            "b3a6a4c5cb4d459bb0c496913c7f1162",
            "f024c5e584334f5abaa22703b8f943fb",
            "b3282c04cb6d4405ba57a078de082e50",
            "98b7f6fc716f4db8939216e05d2b43a6",
            "8830d09e0bea4607a3d42a6c5c12ca07",
            "7174998a07774dc5b42b46f349b39675",
            "8ef5e2df207445a4a0e4d9e58f3867b5",
            "c50d26f728f344ecacd9489e629bf39e",
            "60df759caead4756a8aefda832cfca40",
            "401d4f797b5f4c849e58349a63007355",
            "eee584170b9a4949b2fe57802cc5bf16",
            "ddb907479f23440187baa2783a4b2973",
            "a9bee9eb753a40389325423c3b86a3a5",
            "4281b48181fa4eec853f24f44296ed72"
          ]
        },
        "id": "7QZGpiN2WjFy",
        "outputId": "f576d652-51e4-4718-a252-5f1c41f76094"
      },
      "execution_count": 17,
      "outputs": [
        {
          "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": "e781fdaeca734fe19a32ddd81d5da83e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98b7f6fc716f4db8939216e05d2b43a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/HuzaifaHPC/T5_Text_to_SQL/commit/2eda43b44c9584f5582af2463b5a0c5e459d760e', commit_message='Upload T5ForConditionalGeneration', commit_description='', oid='2eda43b44c9584f5582af2463b5a0c5e459d760e', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    }
  ]
}