{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xoIgh792ONRc"
      },
      "source": [
        "# A full training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_itPOQhONRm"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vNjGFVb5ONRo",
        "outputId": "dcc3fa1e-e2e2-41b5-9a92-eb0fb682d10d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.14.4-py3-none-any.whl (519 kB)\n",
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            "\u001b[?25hCollecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting transformers[sentencepiece]\n",
            "  Downloading transformers-4.32.1-py3-none-any.whl (7.5 MB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
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            "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
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            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "Collecting huggingface-hub<1.0.0,>=0.14.0 (from datasets)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
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            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.12.2)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers[sentencepiece])\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers[sentencepiece])\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hCollecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.7.1)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.4 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.16.4 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.32.1 xxhash-3.3.0\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.22.0-py3-none-any.whl (251 kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
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            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.22.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]\n",
        "!pip install accelerate\n",
        "# To run the training on TPU, you will need to uncomment the following line:\n",
        "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "k864HfF_ONRr",
        "outputId": "2eed1ad1-5b62-4a46-9723-244b01827d5b",
        "colab": {
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          "height": 561,
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              "model_id": "8c51da6627674e0cb3e16159bcc1a4b3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
        "\n",
        "## Data Collator used for padding the length of the sequences to the same length so that there is no problem\n",
        "## Map is used to map the batches columns to the values corresponding to them. Tokenizer function is used to tokenize the input sequences by using the sub word technique\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "gUQu8JILONRt",
        "outputId": "14b1342b-c790-4bd1-b73d-283601d0f622",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['labels', 'input_ids', 'token_type_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
        "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
        "tokenized_datasets.set_format(\"torch\")\n",
        "tokenized_datasets[\"train\"].column_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "qwh2jhMiONRu",
        "outputId": "6342f7ec-ee9a-44ef-e2b6-4cded75f5f96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['attention_mask', 'input_ids', 'labels', 'token_type_ids']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "E02RHD5xONRv"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "## Shuffle the data into training and validation datatset. Batchsize of 8 so total 8 batches of data fed in.\n",
        "train_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
        ")\n",
        "eval_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "vckQTGsgONRv",
        "outputId": "f89eb877-8956-44cd-92e2-3f4069c4e993",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'labels': torch.Size([8]),\n",
              " 'input_ids': torch.Size([8, 70]),\n",
              " 'token_type_ids': torch.Size([8, 70]),\n",
              " 'attention_mask': torch.Size([8, 70])}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "for batch in train_dataloader:\n",
        "    break\n",
        "{k: v.shape for k, v in batch.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "DU-eibfUONRx",
        "outputId": "ddf0b628-e999-4f89-c73d-818857aa24d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "53823fcab4dc44818dc8a914afd8096f",
            "369049afd28142e1af50e8a61b567413",
            "bd7f6e8d247f44e8afcee954aad35ecb",
            "24d974164e7147c99c7120bf8e5471b4",
            "4cf53629a83c414a8bb143931564769d",
            "03e960551bf64ee7a18ccb14bab722d4",
            "3f44eb0e1f5140509077b5856c896b48",
            "237046d271a446b297f0cc7c689de3fd",
            "dfa3862f222549728e24b992e99f12ce",
            "36632505926842c18430da3839143621",
            "14a2a41b8fca4a26ae4c4f02387ed470"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53823fcab4dc44818dc8a914afd8096f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "## here num_labels are the classification classes. The model for Bert removed and we added our own model head Auto Model for Sequence Classification for model prediction of classification\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "WT4yDSb_ONR1",
        "outputId": "11907a10-66e3-4674-e8fc-e8624b744c55",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.4845, grad_fn=<NllLossBackward0>) torch.Size([8, 2])\n"
          ]
        }
      ],
      "source": [
        "outputs = model(**batch)\n",
        "print(outputs.loss, outputs.logits.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "xuvVm-dgONR3",
        "outputId": "ef5d02b7-2c52-471a-d91f-dc097f4a3e3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import AdamW\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "PNTxdvEWONR5",
        "outputId": "d86b6ed7-9d62-4f1e-912c-4423ef160a65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1377\n"
          ]
        }
      ],
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_epochs = 3\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps,\n",
        ")\n",
        "print(num_training_steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "sJyO1F-2ONR6",
        "outputId": "9ad5cdf1-da95-4930-deaf-e5176ef093f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "17LSxvNkONR7",
        "outputId": "b1f84542-8be6-45ff-e9c7-a407f15b5279",
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            "7eeb6f08bfd84443a6fd9639109ce683",
            "5c9f479a1ce24e9c89279b06887c89ec"
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "c29c33bd969c45349399e44939d1af7b"
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          "metadata": {}
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in train_dataloader:\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)"
      ]
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      "execution_count": 13,
      "metadata": {
        "id": "CmYtKP-1ONR7",
        "outputId": "aeee5137-0334-4657-fbd8-e7fd0932d2b7",
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            "a9085e271e6d4fcfa600643da863bce9",
            "e599bada78c2470abebe940c7a714938"
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.8480392156862745, 'f1': 0.89198606271777}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"glue\", \"mrpc\")\n",
        "model.eval()\n",
        "for batch in eval_dataloader:\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**batch)\n",
        "\n",
        "    logits = outputs.logits\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "\n",
        "metric.compute()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "hzD2MA8PONR8",
        "outputId": "7962a0b6-9ea3-45ea-c99e-185edf6339e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "40fe51050ea4449a8a285c6d0bc76f0d",
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1377 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40fe51050ea4449a8a285c6d0bc76f0d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
        "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "num_epochs = 3\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps,\n",
        ")\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in train_dataloader:\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accelerator Used"
      ],
      "metadata": {
        "id": "wdZSeoJaP1kG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "bMRZAw9SONR8",
        "outputId": "405a4c64-2238-4c73-80a9-9671896fa89a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
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            "c79840a9ef144204a0bd3899469eac05"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1377 [00:00<?, ?it/s]"
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              "model_id": "48a59ea1e54248748bfab616323d1cd1"
            }
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        }
      ],
      "source": [
        "from accelerate import Accelerator\n",
        "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
        "\n",
        "accelerator = Accelerator()\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
        "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
        "\n",
        "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
        "    train_dataloader, eval_dataloader, model, optimizer\n",
        ")\n",
        "\n",
        "num_epochs = 3\n",
        "num_training_steps = num_epochs * len(train_dl)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps,\n",
        ")\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in train_dl:\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        accelerator.backward(loss)\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"glue\", \"mrpc\")\n",
        "model.eval()\n",
        "for batch in eval_dataloader:\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**batch)\n",
        "\n",
        "    logits = outputs.logits\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "\n",
        "metric.compute()"
      ],
      "metadata": {
        "id": "_h0thSPtSdGJ",
        "outputId": "c8734ab3-f260-4893-fc15-dd794ed349e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.875, 'f1': 0.9116117850953207}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    }
  ],
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