uczenie_glebokie_projekt/T5Classification.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# T5 model classification training"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Colab link better looking output:\n",
        "https://colab.research.google.com/drive/1YwqsmdM4wKY3746mpmVH-zV_ABeiCpid?usp=sharing"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Links:\n",
        "- Tensorboard training: https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/#scalars\n",
        "- Huggingface dataset edited: https://huggingface.co/datasets/Zombely/sst2-project-dataset\n",
        "- Huggingface Trained model: https://huggingface.co/Zombely/t5-model\n",
        "- Huggingface Tokenizer: https://huggingface.co/Zombely/t5-tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "moS_Sk1kPztc",
        "outputId": "de22adf0-80a7-47f8-9df4-b6ac3732ded6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install datasets transformers sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Itn0ce_3P-Cv"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "import torch\n",
        "from transformers import T5ForConditionalGeneration, T5Tokenizer, TrainingArguments, Trainer\n",
        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
        "import tensorflow as tf\n",
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset\n",
        "import random\n",
        "import time\n",
        "import numpy as np\n",
        "import datetime\n",
        "import sklearn\n",
        "from tqdm.notebook import tqdm\n",
        "import os"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load data and transform dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "GQmeuSdNQkB7"
      },
      "outputs": [],
      "source": [
        "def load_and_process_dataset():\n",
        "    dataset = load_dataset(\"sst2\")\n",
        "    dataset.remove_columns('idx')\n",
        "    del dataset['test']\n",
        "    dataset['test'] = dataset['validation']\n",
        "    del dataset['validation']\n",
        "    split_dataset = dataset['train'].train_test_split(test_size=1600)\n",
        "    dataset['train'] = split_dataset['train']\n",
        "    dataset['validation'] = split_dataset['test']\n",
        "    return dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329,
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:datasets.builder:Found cached dataset sst2 (/root/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5)\n"
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          "metadata": {},
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        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['idx', 'sentence', 'label'],\n",
              "        num_rows: 65749\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['idx', 'sentence', 'label'],\n",
              "        num_rows: 872\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['idx', 'sentence', 'label'],\n",
              "        num_rows: 1600\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = load_and_process_dataset()\n",
        "dataset"
      ]
    },
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      "outputs": [
        {
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      ],
      "source": [
        "for key in dataset.keys():\n",
        "  dataset[key] = dataset[key].map(lambda x: {'source_text': f\"sentiment-analysis: {x['sentence']}\", 'target_text': 'positive' if x['label'] else 'negative'}).remove_columns(['idx', 'sentence', 'label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "wgEXht_UQpNp"
      },
      "outputs": [],
      "source": [
        "train = dataset['train']\n",
        "validation = dataset['validation']\n",
        "test = dataset['test']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3aTDCjDbfo3",
        "outputId": "b18fabac-8a24-42e3-c3be-831ab325f54e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'source_text': 'sentiment-analysis: smart , sassy and exceptionally charming ',\n",
              " 'target_text': 'positive'}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train'][0]"
      ]
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            "c5329f4e9694403883e0ce900a0790d4"
          ]
        },
        "id": "bD7AHpDqQsY_",
        "outputId": "0e600f59-60b5-4fcb-fef0-ed1f35213646"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5617871d8eac47db899bd41543f319be",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9be4a23e2ab345b2a6b825e4c8446822",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "384eb27bd40e4a4aa0886e3ea28182dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/242M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "60a7059acf914ce9913fa2f0ede019b4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenizer = T5Tokenizer.from_pretrained(\"t5-small\")\n",
        "model = T5ForConditionalGeneration.from_pretrained(\"t5-small\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "kNPSSfA-Ejn5"
      },
      "outputs": [],
      "source": [
        "model.cuda();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "2lbY87au4Ekf"
      },
      "outputs": [],
      "source": [
        "def tokenize(dataset):\n",
        "  input_ids = []\n",
        "  target_ids = []\n",
        "  attention_masks = []\n",
        "  for row in dataset:\n",
        "    encoding = tokenizer.encode_plus(\n",
        "      row['source_text'],\n",
        "      padding=\"max_length\",\n",
        "      max_length=86,\n",
        "      truncation=True,\n",
        "      add_special_tokens = True,\n",
        "      return_attention_mask = True,\n",
        "      return_tensors=\"pt\"\n",
        "    )\n",
        "    target_encoding = tokenizer.encode_plus(\n",
        "      row['target_text'],\n",
        "      padding=\"max_length\",\n",
        "      max_length=2,\n",
        "      truncation=True,\n",
        "      add_special_tokens = True,\n",
        "      return_attention_mask = True,\n",
        "      return_tensors=\"pt\",\n",
        "    )\n",
        "    input_ids.append(encoding['input_ids'])\n",
        "    target_ids.append(target_encoding['input_ids'])\n",
        "    attention_masks.append(encoding['attention_mask'])\n",
        "  return TensorDataset(torch.cat(input_ids, dim=0), torch.cat(attention_masks, dim=0), torch.cat(target_ids, dim=0))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "qKGhmdBM6o3G"
      },
      "outputs": [],
      "source": [
        "train_tokenized = tokenize(train)\n",
        "validation_tokenized = tokenize(validation)\n",
        "test_tokenized = tokenize(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "wXkLoCQD_yIP"
      },
      "outputs": [],
      "source": [
        "batch_size = 32\n",
        "\n",
        "train_dataloader = DataLoader(\n",
        "            train_tokenized,\n",
        "            sampler = RandomSampler(train_tokenized),\n",
        "            batch_size = batch_size\n",
        "        )\n",
        "\n",
        "validation_dataloader = DataLoader(\n",
        "            validation_tokenized,\n",
        "            sampler = SequentialSampler(validation_tokenized),\n",
        "            batch_size = batch_size\n",
        "        )\n",
        "\n",
        "test_dataloader = DataLoader(\n",
        "            test_tokenized,\n",
        "            sampler = SequentialSampler(test_tokenized),\n",
        "            batch_size = batch_size\n",
        "        )"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Main model training class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "kInmDIcg4IWF"
      },
      "outputs": [],
      "source": [
        "class T5Model:\n",
        "  def __init__(self, model, tokenizer, train_dataloader, val_dataloader, logs_dir_path, seed=42, epochs=3, lr=1e-4, eps=1e-8):\n",
        "    self.model = model\n",
        "    self.tokenizer = tokenizer\n",
        "    self.train_dataloader = train_dataloader\n",
        "    self.val_dataloader = val_dataloader\n",
        "    self.seed=seed\n",
        "    self.epochs = epochs\n",
        "    self.learning_rate = lr\n",
        "    self.eps = eps\n",
        "    self.logs_dir_path = logs_dir_path\n",
        "    self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    self.optimizer = torch.optim.AdamW(model.parameters(), lr = lr, eps = eps)\n",
        "    self.tensorboard_writter()\n",
        "    self.init_seed()\n",
        "\n",
        "  def init_seed(self):\n",
        "    random.seed(self.seed)\n",
        "    np.random.seed(self.seed)\n",
        "    torch.manual_seed(self.seed)\n",
        "    torch.cuda.manual_seed_all(self.seed)\n",
        "\n",
        "  def tensorboard_writter(self):\n",
        "    if not os.path.exists(self.logs_dir_path):\n",
        "      os.mkdir(self.logs_dir_path)\n",
        "    self.summary_writer = tf.summary.create_file_writer(self.logs_dir_path)\n",
        "\n",
        "  def compute_metrics(self, target, preds):\n",
        "      return sklearn.metrics.accuracy_score(target, preds)\n",
        "\n",
        "  def _train(self, epoch_number):\n",
        "    print(f\"Training epoch: {epoch_number}\")\n",
        "    self.model.train()\n",
        "    train_accuracy_total = 0\n",
        "    pbar = tqdm(self.train_dataloader, total=len(self.train_dataloader))\n",
        "    for step, batch in enumerate(pbar):\n",
        "        b_input_ids = batch[0].to(self.device)\n",
        "        b_input_mask = batch[1].to(self.device)\n",
        "        b_labels = batch[2].to(self.device)\n",
        "\n",
        "        outputs = self.model(\n",
        "            input_ids=b_input_ids,\n",
        "            attention_mask=b_input_mask,\n",
        "            labels=b_labels\n",
        "        )\n",
        "\n",
        "        gen_output = self.model.generate(\n",
        "            input_ids = b_input_ids,\n",
        "            attention_mask = b_input_mask, \n",
        "            max_length=3, \n",
        "            num_beams=2,\n",
        "            repetition_penalty=2.5, \n",
        "            length_penalty=1.0, \n",
        "            early_stopping=True\n",
        "        )\n",
        "\n",
        "        preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]\n",
        "        target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]\n",
        "\n",
        "        loss = outputs[0]\n",
        "        self.optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        self.optimizer.step()\n",
        "\n",
        "        accuracy = self.compute_metrics(target, preds)\n",
        "        train_accuracy_total += accuracy\n",
        "        pbar.set_description(f\"Epoch {epoch_number+1}, step {step}, train loss {loss}, accuracy: {accuracy}\")\n",
        "\n",
        "        with self.summary_writer.as_default():\n",
        "          tf.summary.scalar(name=f\"Epoch: {epoch_number+1}, step/train_accuracy\", data=accuracy ,step=step)\n",
        "          tf.summary.scalar(name=f\"Epoch: {epoch_number+1}, step/train_loss\", data=loss.cpu().detach().numpy(), step=step)\n",
        "        self.summary_writer.flush()\n",
        "    print(f\"Epoch: {epoch_number+1}, Average train accuracy: {train_accuracy_total/len(self.train_dataloader)}\")\n",
        "\n",
        "  def train(self):\n",
        "    print(f\"Starting training, epochs: {self.epochs}\")\n",
        "    for i in range(0, self.epochs):\n",
        "      self._train(i)\n",
        "      self.validate(i)\n",
        "      \n",
        "  def validate(self, epoch_number):\n",
        "    print(f\"Validation epoch: {epoch_number}\")\n",
        "    self.model.eval()\n",
        "    val_accuracy_total = 0\n",
        "    pbar_val = tqdm(self.val_dataloader, total=len(self.val_dataloader))\n",
        "    for step, batch in enumerate(pbar_val):\n",
        "        b_input_ids = batch[0].to(self.device)\n",
        "        b_input_mask = batch[1].to(self.device)\n",
        "        b_labels = batch[2].to(self.device)\n",
        "\n",
        "        with torch.no_grad():        \n",
        "\n",
        "          outputs = self.model(\n",
        "            input_ids=b_input_ids,\n",
        "            attention_mask=b_input_mask,\n",
        "            labels=b_labels\n",
        "          )\n",
        "\n",
        "          loss = outputs[0]\n",
        "\n",
        "          gen_output = self.model.generate(\n",
        "            input_ids = b_input_ids,\n",
        "            attention_mask = b_input_mask, \n",
        "            max_length=3, \n",
        "            num_beams=2,\n",
        "            repetition_penalty=2.5, \n",
        "            length_penalty=1.0, \n",
        "            early_stopping=True\n",
        "          )\n",
        "\n",
        "          preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]\n",
        "          target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]\n",
        "\n",
        "          accuracy_val = self.compute_metrics(target, preds)\n",
        "          val_accuracy_total += accuracy_val\n",
        "          pbar_val.set_description(f\"Epoch {epoch_number+1}, val loss {loss}, accuracy: {accuracy_val}\")\n",
        "\n",
        "          with self.summary_writer.as_default():\n",
        "            tf.summary.scalar(name=f\"Epoch: {epoch_number+1}, step/val_accuracy\", data=accuracy_val ,step=step)\n",
        "            tf.summary.scalar(name=f\"Epoch: {epoch_number+1}, step/val_loss\", data=loss.cpu().detach().numpy(), step=step)\n",
        "          self.summary_writer.flush()\n",
        "\n",
        "    print(f\"Epoch: {epoch_number+1}, Average validation accuracy: {val_accuracy_total/len(self.val_dataloader)}\")\n",
        "\n",
        "  def evaluate(self, test_dataloader):\n",
        "    self.model.eval()\n",
        "    total_test_acc = 0\n",
        "    for batch in tqdm(test_dataloader, total=len(test_dataloader)):\n",
        "        b_input_ids = batch[0].to(self.device)\n",
        "        b_input_mask = batch[1].to(self.device)\n",
        "        b_labels = batch[2].to(self.device)\n",
        "        \n",
        "        with torch.no_grad():        \n",
        "\n",
        "          gen_output = self.model.generate(\n",
        "            input_ids = b_input_ids,\n",
        "            attention_mask = b_input_mask, \n",
        "            max_length=3, \n",
        "            num_beams=2,\n",
        "            repetition_penalty=2.5, \n",
        "            length_penalty=1.0, \n",
        "            early_stopping=True\n",
        "          )\n",
        "            \n",
        "        preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]\n",
        "        target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]\n",
        "        total_test_acc += self.compute_metrics(preds, target)  \n",
        "    print(\"Accuracy: \",total_test_acc / len(test_dataloader))\n",
        "    "
      ]
    },
    {
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting training, epochs: 3\n",
            "Training epoch: 0\n"
          ]
        },
        {
          "data": {
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              "model_id": "f836ca46b8d64d718b4e3e39191cd91d",
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            "text/plain": [
              "  0%|          | 0/2055 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Average train accuracy: 0.8825947167188043\n",
            "Validation epoch: 0\n"
          ]
        },
        {
          "data": {
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              "  0%|          | 0/50 [00:00<?, ?it/s]"
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          },
          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Average validation accuracy: 0.926875\n",
            "Training epoch: 1\n"
          ]
        },
        {
          "data": {
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              "model_id": "225d2a25d5874717820b1211d192c920",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Average train accuracy: 0.9252577916811494\n",
            "Validation epoch: 1\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "Epoch: 2, Average validation accuracy: 0.936875\n",
            "Training epoch: 2\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3cacafbfc6974fb88cebd4fd18c7ea76",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2055 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Average train accuracy: 0.9391720252577916\n",
            "Validation epoch: 2\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fbe579ef91614cdc856adc4b95f1a2af",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Average validation accuracy: 0.93875\n"
          ]
        }
      ],
      "source": [
        "t5model = T5Model(model, tokenizer, train_dataloader, validation_dataloader, \"./logs\")\n",
        "t5model.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "2c1910a4fc30451f8112f8ef9139ec96",
            "f9b30a6541e64808afaad39bc34c7545",
            "ce570046c07d4f9f97a715dc32a0cfdf",
            "b5c8f57e112249e0b5186c4ab2452d17",
            "ba8964c541e84a3fa562d0083ef5832b",
            "ece14b9b056445259cfb1bd8b8edf46a",
            "161ce1c991754ac0b2c7a20abb2ac653",
            "d272e67085d84532a1cfcec2f35199c5",
            "092b2a86efe947b39f5076c99938c8d0",
            "42441c4de5dd4fe8ad90be8e549addce",
            "990fb606bc40427a82320d79d9459a00"
          ]
        },
        "id": "pmmQgj1lUjNN",
        "outputId": "ee815579-3725-4b02-ffa0-abed2acb1788"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c1910a4fc30451f8112f8ef9139ec96",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/28 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.9129464285714286\n"
          ]
        }
      ],
      "source": [
        "t5model.evaluate(test_dataloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JIPplO9NELv",
        "outputId": "95d2aa60-5b6d-4102-d3a1-522a50f115df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-02-13 11:49:46.367397: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2023-02-13 11:49:46.367509: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2023-02-13 11:49:46.367529: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
            "\n",
            "***** TensorBoard Uploader *****\n",
            "\n",
            "This will upload your TensorBoard logs to https://tensorboard.dev/ from\n",
            "the following directory:\n",
            "\n",
            "logs\n",
            "\n",
            "This TensorBoard will be visible to everyone. Do not upload sensitive\n",
            "data.\n",
            "\n",
            "Your use of this service is subject to Google's Terms of Service\n",
            "<https://policies.google.com/terms> and Privacy Policy\n",
            "<https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service\n",
            "<https://tensorboard.dev/policy/terms/>.\n",
            "\n",
            "This notice will not be shown again while you are logged into the uploader.\n",
            "To log out, run `tensorboard dev auth revoke`.\n",
            "\n",
            "Continue? (yes/NO) yes\n",
            "\n",
            "To sign in with the TensorBoard uploader:\n",
            "\n",
            "1. On your computer or phone, visit:\n",
            "\n",
            "   https://www.google.com/device\n",
            "\n",
            "2. Sign in with your Google account, then enter:\n",
            "\n",
            "   ZBRH-SMMW\n",
            "\n",
            "\n",
            "Upload started and will continue reading any new data as it's added to the logdir.\n",
            "\n",
            "To stop uploading, press Ctrl-C.\n",
            "\n",
            "New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/\n",
            "\n",
            "\u001b[1m[2023-02-13T11:50:01]\u001b[0m Started scanning logdir.\n",
            "\u001b[1m[2023-02-13T11:50:04]\u001b[0m Total uploaded: 12630 scalars, 0 tensors, 0 binary objects\n",
            "\n",
            "\n",
            "Interrupted. View your TensorBoard at https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/tensorboard\", line 8, in <module>\n",
            "    sys.exit(run_main())\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorboard/main.py\", line 46, in run_main\n",
            "    app.run(tensorboard.main, flags_parser=tensorboard.configure)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 308, in run\n",
            "    _run_main(main, args)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 254, in _run_main\n",
            "    sys.exit(main(argv))\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorboard/program.py\", line 276, in main\n",
            "    return runner(self.flags) or 0\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 691, in run\n",
            "    return _run(flags, self._experiment_url_callback)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 124, in _run\n",
            "    intent.execute(server_info, channel)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/grpc/_channel.py\", line 1564, in __exit__\n",
            "    self._close()\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/grpc/_channel.py\", line 1550, in _close\n",
            "    self._channel.close(cygrpc.StatusCode.cancelled, 'Channel closed!')\n",
            "  File \"src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi\", line 513, in grpc._cython.cygrpc.Channel.close\n",
            "  File \"src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi\", line 399, in grpc._cython.cygrpc._close\n",
            "  File \"src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi\", line 429, in grpc._cython.cygrpc._close\n",
            "  File \"/usr/lib/python3.8/threading.py\", line 364, in notify_all\n",
            "    def notify_all(self):\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ],
      "source": [
        "!tensorboard dev upload --logdir logs --name t5-sst2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHSu41ZvnaBB",
        "outputId": "2948e2b2-54a1-43aa-9d29-27a7b40538ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('./model/tokenizer_config.json',\n",
              " './model/special_tokens_map.json',\n",
              " './model/spiece.model',\n",
              " './model/added_tokens.json')"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "t5model.model.save_pretrained(\"./model\")\n",
        "t5model.tokenizer.save_pretrained(\"./model\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xja8cK6yoHcM",
        "outputId": "df987295-d175-4d17-928e-d9b03207169c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  adding: model/ (stored 0%)\n",
            "  adding: model/tokenizer_config.json (deflated 82%)\n",
            "  adding: model/config.json (deflated 62%)\n",
            "  adding: model/generation_config.json (deflated 29%)\n",
            "  adding: model/pytorch_model.bin (deflated 8%)\n",
            "  adding: model/special_tokens_map.json (deflated 86%)\n",
            "  adding: model/spiece.model (deflated 48%)\n"
          ]
        }
      ],
      "source": [
        "!zip -r /content/model model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16jB6CpRoslW",
        "outputId": "15561efe-747b-4f21-aefc-84fb913c3037"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) y\n",
            "Token is valid.\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133,
          "referenced_widgets": [
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            "256c1b04cfd94ec984e6a844c60e881c",
            "500de4e8a3c5444aa8395a6bb3fa90f3",
            "25b3a226518f44f983cd12cdbde7e434"
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        },
        "id": "DptjUZgypVN9",
        "outputId": "339f2bc3-0ccc-4e72-9bb9-f90d056a6044"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e8cfa36fed049e2a7b7d8666bd57cdf",
              "version_major": 2,
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            },
            "text/plain": [
              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
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        {
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            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/242M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Zombely/t5-model/commit/caab3e8d5523bc7309774f13f522fd6f06666b29', commit_message='Upload T5ForConditionalGeneration', commit_description='', oid='caab3e8d5523bc7309774f13f522fd6f06666b29', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "t5model.model.push_to_hub(\"Zombely/t5-model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133,
          "referenced_widgets": [
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        },
        "id": "OyhjXaK2pd3S",
        "outputId": "4aaa2128-d6c9-4c96-e38c-018b4a42dcda"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d90b46344c204bb7af2bedaf880dfd19",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
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          },
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          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Zombely/t5-tokenizer/commit/640998bd3d496cbd5266140fbeff873d07c0158e', commit_message='Upload tokenizer', commit_description='', oid='640998bd3d496cbd5266140fbeff873d07c0158e', pr_url=None, pr_revision=None, pr_num=None)"
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          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "t5model.tokenizer.push_to_hub(\"Zombely/t5-tokenizer\")\n"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
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