Train.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70SqtHZKsh5i"
      },
      "source": [
        "##Install requirements packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "m47GOgyfLOL1",
        "outputId": "c961cbc9-8dc5-4d11-aabc-3dce7c52bea9"
      },
      "source": [
        "!pip install transformers\n",
        "!pip install fastBPE\n",
        "!pip install fairseq\n",
        "# Install the vncorenlp python wrapper\n",
        "!pip install vncorenlp\n",
        "# Download VnCoreNLP-1.1.1.jar & its word segmentation component (i.e. RDRSegmenter) \n",
        "!mkdir -p vncorenlp/models/wordsegmenter\n",
        "!wget https://github.com/raw/vncorenlp/VnCoreNLP/master/VnCoreNLP-1.1.1.jar\n",
        "!wget https://github.com/raw/vncorenlp/VnCoreNLP/master/models/wordsegmenter/vi-vocab\n",
        "!wget https://github.com/raw/vncorenlp/VnCoreNLP/master/models/wordsegmenter/wordsegmenter.rdr\n",
        "!mv VnCoreNLP-1.1.1.jar vncorenlp/ \n",
        "!mv vi-vocab vncorenlp/models/wordsegmenter/\n",
        "!mv wordsegmenter.rdr vncorenlp/models/wordsegmenter/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fd/1a/41c644c963249fd7f3836d926afa1e3f1cc234a1c40d80c5f03ad8f6f1b2/transformers-4.8.2-py3-none-any.whl (2.5MB)\n",
            "\u001b[K     |████████████████████████████████| 2.5MB 7.3MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/e2/df3543e8ffdab68f5acc73f613de9c2b155ac47f162e725dcac87c521c11/tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3MB 45.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from transformers) (3.13)\n",
            "Collecting huggingface-hub==0.0.12\n",
            "  Downloading https://files.pythonhosted.org/packages/2f/ee/97e253668fda9b17e968b3f97b2f8e53aa0127e8807d24a547687423fe0b/huggingface_hub-0.0.12-py3-none-any.whl\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (4.6.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/ee/67241dc87f266093c533a2d4d3d69438e57d7a90abb216fa076e7d475d4a/sacremoses-0.0.45-py3-none-any.whl (895kB)\n",
            "\u001b[K     |████████████████████████████████| 901kB 44.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub==0.0.12->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
            "Installing collected packages: tokenizers, huggingface-hub, sacremoses, transformers\n",
            "Successfully installed huggingface-hub-0.0.12 sacremoses-0.0.45 tokenizers-0.10.3 transformers-4.8.2\n",
            "Collecting fastBPE\n",
            "  Downloading https://files.pythonhosted.org/packages/e1/37/f97181428a5d151501b90b2cebedf97c81b034ace753606a3cda5ad4e6e2/fastBPE-0.1.0.tar.gz\n",
            "Building wheels for collected packages: fastBPE\n",
            "  Building wheel for fastBPE (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fastBPE: filename=fastBPE-0.1.0-cp37-cp37m-linux_x86_64.whl size=483092 sha256=09c38223bad7521ea536e052d838a5d913537c4c535f254f1e1df8e3b7051d29\n",
            "  Stored in directory: /root/.cache/pip/wheels/f3/0c/9c/fc62058b4d473a5602bcd3d3edfece796f123875379ea82d79\n",
            "Successfully built fastBPE\n",
            "Installing collected packages: fastBPE\n",
            "Successfully installed fastBPE-0.1.0\n",
            "Collecting fairseq\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/ab/92c6efb05ffdfe16fbdc9e463229d9af8c3b74dc943ed4b4857a87b223c2/fairseq-0.10.2-cp37-cp37m-manylinux1_x86_64.whl (1.7MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7MB 8.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from fairseq) (1.9.0+cu102)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from fairseq) (2019.12.20)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from fairseq) (0.29.23)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from fairseq) (1.19.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from fairseq) (4.41.1)\n",
            "Requirement already satisfied: cffi in /usr/local/lib/python3.7/dist-packages (from fairseq) (1.14.5)\n",
            "Collecting sacrebleu>=1.4.12\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7e/57/0c7ca4e31a126189dab99c19951910bd081dea5bbd25f24b77107750eae7/sacrebleu-1.5.1-py3-none-any.whl (54kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 10.5MB/s \n",
            "\u001b[?25hCollecting hydra-core\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c3/cd/85aa2e3a8babc36feac99df785e54abf99afbc4acc20488630f3ef46980a/hydra_core-1.1.0-py3-none-any.whl (144kB)\n",
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            "\u001b[?25hCollecting dataclasses\n",
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            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-cp37-none-any.whl size=141231 sha256=9a3bb04fa1b7811a05fd45c4c6ce18198aeeee608fe9cbb7e880965ea546f6ca\n",
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            "Installing collected packages: portalocker, sacrebleu, PyYAML, antlr4-python3-runtime, omegaconf, hydra-core, dataclasses, fairseq\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-5.4.1 antlr4-python3-runtime-4.8 dataclasses-0.6 fairseq-0.10.2 hydra-core-1.1.0 omegaconf-2.1.0 portalocker-2.0.0 sacrebleu-1.5.1\n"
          ],
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        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
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            }
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          "metadata": {
            "tags": []
          }
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          "output_type": "stream",
          "text": [
            "Collecting vncorenlp\n",
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            "  Building wheel for vncorenlp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for vncorenlp: filename=vncorenlp-1.0.3-cp37-none-any.whl size=2645952 sha256=931f1d02eb72da8c3a524880b917745deb39187d49a5cebe8cce6371d991b81a\n",
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            "Successfully built vncorenlp\n",
            "Installing collected packages: vncorenlp\n",
            "Successfully installed vncorenlp-1.0.3\n",
            "--2021-07-14 10:41:13--  https://github.com/raw/vncorenlp/VnCoreNLP/master/VnCoreNLP-1.1.1.jar\n",
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            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TkVhLdEPs6D_"
      },
      "source": [
        "**Install VnCoreNLP**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSVVNoRLLUbD"
      },
      "source": [
        "from vncorenlp import VnCoreNLP\n",
        "rdrsegmenter = VnCoreNLP(\"/content/vncorenlp/VnCoreNLP-1.1.1.jar\", annotators=\"wseg\", max_heap_size='-Xmx500m') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68qW7Yals5Zi"
      },
      "source": [
        "**Install pre-trained PhoBERT**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "te9-LZy7LXXN",
        "outputId": "264720bc-a7a6-49c6-8116-4c47b1826efd"
      },
      "source": [
        "!wget https://public.vinai.io/PhoBERT_base_transformers.tar.gz\n",
        "!tar -xzvf PhoBERT_base_transformers.tar.gz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-14 10:41:41--  https://public.vinai.io/PhoBERT_base_transformers.tar.gz\n",
            "Resolving public.vinai.io (public.vinai.io)... 52.84.169.60, 52.84.169.75, 52.84.169.37, ...\n",
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            "Saving to: ‘PhoBERT_base_transformers.tar.gz’\n",
            "\n",
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            "\n",
            "2021-07-14 10:41:45 (90.8 MB/s) - ‘PhoBERT_base_transformers.tar.gz’ saved [322405979/322405979]\n",
            "\n",
            "PhoBERT_base_transformers/\n",
            "PhoBERT_base_transformers/config.json\n",
            "PhoBERT_base_transformers/bpe.codes\n",
            "PhoBERT_base_transformers/model.bin\n",
            "PhoBERT_base_transformers/dict.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-JVOeR8LYL8",
        "outputId": "ffe4a64b-aeb0-4a37-a55d-3f4a854fb10d"
      },
      "source": [
        "!wget https://public.vinai.io/PhoBERT_base_fairseq.tar.gz\n",
        "!tar -xzvf PhoBERT_base_fairseq.tar.gz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-14 10:41:55--  https://public.vinai.io/PhoBERT_base_fairseq.tar.gz\n",
            "Resolving public.vinai.io (public.vinai.io)... 52.84.169.60, 52.84.169.75, 52.84.169.37, ...\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1243308020 (1.2G) [application/x-tar]\n",
            "Saving to: ‘PhoBERT_base_fairseq.tar.gz’\n",
            "\n",
            "PhoBERT_base_fairse 100%[===================>]   1.16G  88.8MB/s    in 13s     \n",
            "\n",
            "2021-07-14 10:42:09 (89.6 MB/s) - ‘PhoBERT_base_fairseq.tar.gz’ saved [1243308020/1243308020]\n",
            "\n",
            "PhoBERT_base_fairseq/\n",
            "PhoBERT_base_fairseq/bpe.codes\n",
            "PhoBERT_base_fairseq/model.pt\n",
            "PhoBERT_base_fairseq/dict.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLYn7Xzrtpsg"
      },
      "source": [
        "**Load Phobert**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oz_UGWFALZLF"
      },
      "source": [
        "from fairseq.data.encoders.fastbpe import fastBPE\n",
        "from fairseq.data import Dictionary\n",
        "import argparse\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "parser.add_argument('--bpe-codes', \n",
        "    default=\"/content/PhoBERT_base_transformers/bpe.codes\",\n",
        "    required=False,\n",
        "    type=str,\n",
        "    help='path to fastBPE BPE'\n",
        ")\n",
        "args, unknown = parser.parse_known_args()\n",
        "bpe = fastBPE(args)\n",
        "\n",
        "# Load the dictionary\n",
        "vocab = Dictionary()\n",
        "vocab.add_from_file(\"/content/PhoBERT_base_transformers/dict.txt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iinNzEt-t1vL"
      },
      "source": [
        "# Data preparing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVtfwcLHLcjK"
      },
      "source": [
        "import re\n",
        "\n",
        "train_path = '/content/test-3.txt'\n",
        "test_path = '/content/test-2.txt'\n",
        "\n",
        "train_id, train_text, train_labels = [], [], []\n",
        "test_id, test_text = [], []\n",
        "\n",
        "with open(train_path, 'r') as f_r:\n",
        "    data = f_r.read().strip()\n",
        "\n",
        "    data = re.findall('train_[\\s\\S]+?\\n.*\\n[1-3]\\n\\n', data)\n",
        "\n",
        "    for sample in data:\n",
        "        splits = sample.strip().split('\\n')\n",
        "\n",
        "        id = splits[0]\n",
        "        label = int(splits[-1])\n",
        "        text = ' '.join(splits[1:-1])\n",
        "        text = rdrsegmenter.tokenize(text)\n",
        "        text = ' '.join([' '.join(x) for x in text])\n",
        "\n",
        "        train_id.append(id)\n",
        "        train_text.append(text)\n",
        "        train_labels.append(label)\n",
        "\n",
        "\n",
        "with open(test_path, 'r') as f_r:\n",
        "    data = f_r.read().strip()\n",
        "    data = re.findall('test_[\\s\\S]+?\\n.*\\n\\n\\n', data)\n",
        "\n",
        "    for sample in data:\n",
        "        splits = sample.strip().split('\\n')\n",
        "\n",
        "        id = splits[0]\n",
        "        text = ' '.join(splits[1:])\n",
        "        text = rdrsegmenter.tokenize(text)\n",
        "        text = ' '.join([' '.join(x) for x in text])\n",
        "\n",
        "        test_id.append(id)\n",
        "        test_text.append(text)\n",
        "for i in range(len(train_labels)):\n",
        "  train_labels[i] -= 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KvCfuLbuHYj"
      },
      "source": [
        "**Train test split**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgkS6RjeLemu",
        "outputId": "057dd61a-06d1-49a6-b12f-7066c38e6404"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_sents, val_sents, train_labels, val_labels = train_test_split(train_text, train_labels, test_size=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'list'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXb8NDuYLhiA"
      },
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "MAX_LEN = 195\n",
        "\n",
        "train_ids = []\n",
        "for sent in train_sents:\n",
        "    subwords = '<s> ' + bpe.encode(sent) + ' </s>'\n",
        "    encoded_sent = vocab.encode_line(subwords, append_eos=True, add_if_not_exist=False).long().tolist()\n",
        "    train_ids.append(encoded_sent)\n",
        "\n",
        "val_ids = []\n",
        "for sent in val_sents:\n",
        "    subwords = '<s> ' + bpe.encode(sent) + ' </s>'\n",
        "    encoded_sent = vocab.encode_line(subwords, append_eos=True, add_if_not_exist=False).long().tolist()\n",
        "    val_ids.append(encoded_sent)\n",
        "    \n",
        "train_ids = pad_sequences(train_ids, maxlen=MAX_LEN, dtype=\"long\", value=0, truncating=\"post\", padding=\"post\")\n",
        "val_ids = pad_sequences(val_ids, maxlen=MAX_LEN, dtype=\"long\", value=0, truncating=\"post\", padding=\"post\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEGzshdhLif9"
      },
      "source": [
        "train_masks = []\n",
        "for sent in train_ids:\n",
        "    mask = [int(token_id > 0) for token_id in sent]\n",
        "    train_masks.append(mask)\n",
        "\n",
        "val_masks = []\n",
        "for sent in val_ids:\n",
        "    mask = [int(token_id > 0) for token_id in sent]\n",
        "\n",
        "    val_masks.append(mask)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05p28hR_uXVm"
      },
      "source": [
        "**DataLoader**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrJPCUVSLjRk"
      },
      "source": [
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "import torch\n",
        "train_inputs = torch.tensor(train_ids)\n",
        "val_inputs = torch.tensor(val_ids)\n",
        "train_labels = torch.tensor(train_labels)\n",
        "val_labels = torch.tensor(val_labels)\n",
        "train_masks = torch.tensor(train_masks)\n",
        "val_masks = torch.tensor(val_masks)\n",
        "\n",
        "train_data = TensorDataset(train_inputs, train_masks, train_labels)\n",
        "train_sampler = SequentialSampler(train_data)\n",
        "train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=32)\n",
        "\n",
        "val_data = TensorDataset(val_inputs, val_masks, val_labels)\n",
        "val_sampler = SequentialSampler(val_data)\n",
        "val_dataloader = DataLoader(val_data, sampler=val_sampler, batch_size=32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4NkVuYLup6D"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBMcEJLGLj5P",
        "outputId": "eeb0720d-64bf-406f-a129-9ce853570ada"
      },
      "source": [
        "from transformers import RobertaForSequenceClassification, RobertaConfig, AdamW\n",
        "\n",
        "config = RobertaConfig.from_pretrained(\n",
        "    \"/content/PhoBERT_base_transformers/config.json\", from_tf=False, num_labels = 3, output_hidden_states=False,\n",
        ")\n",
        "BERT_SA = RobertaForSequenceClassification.from_pretrained(\n",
        "    \"/content/PhoBERT_base_transformers/model.bin\",\n",
        "    config=config\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You are using a model of type bert to instantiate a model of type roberta. This is not supported for all configurations of models and can yield errors.\n",
            "Some weights of the model checkpoint at /content/PhoBERT_base_transformers/model.bin were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.bias', 'roberta.pooler.dense.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.decoder.weight', 'lm_head.decoder.bias']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at /content/PhoBERT_base_transformers/model.bin and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_qU8rydLkpK",
        "outputId": "5ef7c12e-08e9-40d7-9d38-854056e1b9b2"
      },
      "source": [
        "BERT_SA.cuda()\n",
        "print('Done')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlIC1FWjLmj3"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "\n",
        "def flat_accuracy(preds, labels):\n",
        "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat = labels.flatten()\n",
        "    \n",
        "    F1_score = f1_score(pred_flat, labels_flat, average='macro')\n",
        "    \n",
        "    return accuracy_score(pred_flat, labels_flat), F1_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "shGICIbwLnh5",
        "outputId": "f251c135-16a8-4b1d-9190-565c2369d1d5"
      },
      "source": [
        "import random\n",
        "from tqdm import tqdm_notebook\n",
        "device = 'cuda'\n",
        "epochs = 5\n",
        "max = 0\n",
        "\n",
        "param_optimizer = list(BERT_SA.named_parameters())\n",
        "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "]\n",
        "\n",
        "optimizer = AdamW(optimizer_grouped_parameters, lr=5e-7, correct_bias=False)\n",
        "\n",
        "\n",
        "for epoch_i in range(0, epochs):\n",
        "    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
        "    print('Training...')\n",
        "\n",
        "    total_loss = 0\n",
        "    BERT_SA.train()\n",
        "    train_accuracy = 0\n",
        "    nb_train_steps = 0\n",
        "    train_f1 = 0\n",
        "    \n",
        "    for step, batch in tqdm_notebook(enumerate(train_dataloader)):\n",
        "        b_input_ids = batch[0].to(device)\n",
        "        b_input_mask = batch[1].to(device)\n",
        "        b_labels = batch[2].to(device)\n",
        "\n",
        "        BERT_SA.zero_grad()\n",
        "        outputs = BERT_SA(b_input_ids, \n",
        "            token_type_ids=None, \n",
        "            attention_mask=b_input_mask, \n",
        "            labels=b_labels)\n",
        "        loss = outputs[0]\n",
        "        total_loss += loss.item()\n",
        "        \n",
        "        logits = outputs[1].detach().cpu().numpy()\n",
        "        label_ids = b_labels.to('cpu').numpy()\n",
        "        tmp_train_accuracy, tmp_train_f1 = flat_accuracy(logits, label_ids)\n",
        "        train_accuracy += tmp_train_accuracy\n",
        "        train_f1 += tmp_train_f1\n",
        "        nb_train_steps += 1\n",
        "        \n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(BERT_SA.parameters(), 1.0)\n",
        "        optimizer.step()\n",
        "        \n",
        "    avg_train_loss = total_loss / len(train_dataloader)\n",
        "    print(\" Accuracy: {0:.4f}\".format(train_accuracy/nb_train_steps))\n",
        "    print(\" F1 score: {0:.4f}\".format(train_f1/nb_train_steps))\n",
        "    print(\" Average training loss: {0:.4f}\".format(avg_train_loss))\n",
        "    BERT_SA.save_pretrained('/content/drive/MyDrive/Deep/modelbert/{}'.format(epoch_i+1))\n",
        "    print(\"Running Validation...\")\n",
        "    BERT_SA.eval()\n",
        "    eval_loss, eval_accuracy = 0, 0\n",
        "    nb_eval_steps, nb_eval_examples = 0, 0\n",
        "    eval_f1 = 0\n",
        "    for batch in tqdm_notebook(val_dataloader):\n",
        "\n",
        "        batch = tuple(t.to(device) for t in batch)\n",
        "\n",
        "        b_input_ids, b_input_mask, b_labels = batch\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = BERT_SA(b_input_ids, \n",
        "            token_type_ids=None, \n",
        "            attention_mask=b_input_mask)\n",
        "            logits = outputs[0]\n",
        "            logits = logits.detach().cpu().numpy()\n",
        "            label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "            tmp_eval_accuracy, tmp_eval_f1 = flat_accuracy(logits, label_ids)\n",
        "\n",
        "            eval_accuracy += tmp_eval_accuracy\n",
        "            eval_f1 += tmp_eval_f1\n",
        "            nb_eval_steps += 1\n",
        "    if max < eval_accuracy/nb_eval_steps:\n",
        "      max = eval_accuracy/nb_eval_steps\n",
        "      e = epoch_i\n",
        "    print('logits:    {}'.format(np.argmax(logits, axis=1).flatten()))\n",
        "    print('labels_id: {}'.format(label_ids))\n",
        "    print(\" Accuracy: {0:.4f}\".format(eval_accuracy/nb_eval_steps))\n",
        "    print(\" F1 score: {0:.4f}\".format(eval_f1/nb_eval_steps))\n",
        "print(\"Training complete!\")\n",
        "print('Best Valid acc : {}, epoch: {}'.format(max, e+1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "======== Epoch 1 / 5 ========\n",
            "Training...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:27: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "72c868621ac54e98a9c5762b35204661",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Accuracy: 0.9784\n",
            " F1 score: 0.9718\n",
            " Average training loss: 0.0868\n",
            "Running Validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:61: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a5a1a3caadc746ba96df8453ff1be7ed",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "logits:    [1 0 2 2]\n",
            "labels_id: [1 0 2 2]\n",
            " Accuracy: 0.9062\n",
            " F1 score: 0.8946\n",
            "======== Epoch 2 / 5 ========\n",
            "Training...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17d7caf0d8f044b79b5d47bd013d6bdd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Accuracy: 0.9763\n",
            " F1 score: 0.9705\n",
            " Average training loss: 0.0809\n",
            "Running Validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ec80f1b4a8d340f987fb4c1072509489",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "logits:    [1 0 2 2]\n",
            "labels_id: [1 0 2 2]\n",
            " Accuracy: 0.9062\n",
            " F1 score: 0.8946\n",
            "======== Epoch 3 / 5 ========\n",
            "Training...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a34f722e3bf451ba5d1dae672cd6dd7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Accuracy: 0.9774\n",
            " F1 score: 0.9689\n",
            " Average training loss: 0.0850\n",
            "Running Validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "676aa218233447d09ab766d502a7eefe",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "logits:    [1 0 2 2]\n",
            "labels_id: [1 0 2 2]\n",
            " Accuracy: 0.9141\n",
            " F1 score: 0.9022\n",
            "======== Epoch 4 / 5 ========\n",
            "Training...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5d44d7cdb3634ece84855094cf3af249",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Accuracy: 0.9806\n",
            " F1 score: 0.9764\n",
            " Average training loss: 0.0736\n",
            "Running Validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c523f4e19d0d471380ff9a1290d0e74d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "logits:    [1 0 2 2]\n",
            "labels_id: [1 0 2 2]\n",
            " Accuracy: 0.9141\n",
            " F1 score: 0.9022\n",
            "======== Epoch 5 / 5 ========\n",
            "Training...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62d44b5bc27242d0bb443d10179d83c6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Accuracy: 0.9849\n",
            " F1 score: 0.9816\n",
            " Average training loss: 0.0651\n",
            "Running Validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6f4e40b8b3040fb915c47f814a5173d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "logits:    [1 0 2 2]\n",
            "labels_id: [1 0 2 2]\n",
            " Accuracy: 0.9062\n",
            " F1 score: 0.8943\n",
            "Training complete!\n",
            "Best Valid acc : 0.9140625, epoch: 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eo_KfDUou76o"
      },
      "source": [
        "# Save model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNG7XZDOLoRG",
        "outputId": "9f1868cb-d6c7-4bdb-efa6-627251f931a5"
      },
      "source": [
        "!zip -r /content/model.zip/content/drive/MyDrive/Deep/modelbert/3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/drive/MyDrive/Deep/modelbert/3/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/Deep/modelbert/3/config.json (deflated 52%)\n",
            "  adding: content/drive/MyDrive/Deep/modelbert/3/pytorch_model.bin (deflated 18%)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}